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Rudnytskyy, W., S. Svirko, A. Dykyi, O. Dyka, and T. Trosteniuk. "EVALUATION OF THE EFFICIENCY OF STATE INSTITUTIONS OF HIGHER EDUCATION IN UKRAINE: THEORETICAL PRINCIPLES AND GENERAL METHODOLOGICAL APPROACHES." Financial and credit activity: problems of theory and practice 2, no. 37 (2021): 161–70. http://dx.doi.org/10.18371/fcaptp.v2i37.229952.
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Abstract. The purpose of the article is to study the theoretical issues of determining the effectiveness of state higher educational institutions of Ukraine and the development of theoretical and methodological principles, as well as general methodological approaches to its evaluation. To implement it, the authors formed and proposed a supplement to the thesaurus of management of the concept of «key performance indicators» (Key Efficacy Indicators, KEI), which, in the context of the object of study, is proposed to understand a set of numerical indicators of public higher education. nature, which are the central element of the system of evaluation of its activities in terms of its effectiveness and efficiency in order to achieve certain goals of the state institution of higher education. In order to form a stable basis of the evaluation methodology, a classification of key performance indicators was formed according to 16 features, according to which the corresponding groups were identified and their characteristics were given. The outline of the methodology is based on the basic classification features of key performance indicators, in particular by: types (efficiency and effectiveness), degree of importance (major and minor) and areas of coverage (educational, research, financial, contingent, infrastructure, socio-ethical); it provides for the final definition of an integrated key performance indicator through the sum of complex indicators of efficiency and effectiveness or the sum of complex indicators of educational, research, financial, socio-ethical spheres, as well as areas of contingent and infrastructure. The final formation of the methodology for assessing the effectiveness of state institutions of higher education will optimally and deeply assess the activities of these entities, which will contribute to the actual implementation of all management functions in time and space to fulfill their mission, objectives and development goals. Keywords: key performance indicators, key еfficacy indicators, effectiveness, efficiency, public higher education institutions. JEL Classification M41, I23 Formulas: 11; fig.: 0; tabl.: 1; bibl.: 25.
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МАНДРЫКИН, А. В., and Ю. В. ПАХОМОВА. "EFFICACY EVALUATION METHODOLOGY FINANCIAL PROCESSES OF THE ENTERPRISE." Экономика и предпринимательство, no. 4(153) (June 15, 2023): 1014–18. http://dx.doi.org/10.34925/eip.2023.153.4.199.
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В статье разработан методический подход к оценке эффективности инвестиционных проектов в электроэнергетике с учетом рисков, учитываемых при расчете ставки дисконтирования для каждой фазы жизненного цикла проекта, что позволяет более точно рассчитывать основные показатели эффективности инвестиционного проекта. В современном быстроменяющемся мире применение достижений научно-технического прогресса, разработка и внедрение инвестиционных проектов становятся конкурентным преимуществом и залогом успешного развития регионов, кластеров, корпораций. В некоторых, наиболее динамичных отраслях, инвестирование становится вопросом не просто эффективной работы, но и обуславливает присутствие компаний на рынке. Прикладные исследования, а тем более фундаментальные требуют значительных вложений, отдача от которых на первых этапах разработки и реализации инвестиционных проектов трудно прогнозируема. Конечный результат так же заведомо не предсказуем, что делает инвестирование одной из наиболее рискованных сфер деятельности современных компаний. The article developed a methodological approach to assessing the effectiveness of investment projects in the electric power industry, taking into account the risks taken into account when calculating the discount rate for each phase of the project life cycle, which makes it possible to more accurately calculate the key performance indicators of the investment project.. The institutional and economic environment of developing countries may be the reason for the lack of positive effect expected from attracting investment from enterprises. These ambiguous results regarding the influence of investment form the motivation and problem of dissertation research. Identifying and improving methodological and economic parameters for increasing the efficiency of investment in the electric power industry will always be one of the main tasks for owners of enterprises and managers, which determines the relevance of the study. The article developed a methodological approach to assessing the effectiveness of investment projects in the electric power industry, taking into account the risks taken into account when calculating the discount rate for each phase of the project life cycle, which makes it possible to more accurately calculate the key performance.
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Khlaif, Murtadha Hasan, Raghad Mounir Farhan, and Asaad Wahhab. "Assessing the Efficacy of Internal Control and its Influence on Financial Performance: Evidence from Iraqi Telecommunications Corporation." Technium Social Sciences Journal 70 (April 9, 2025): 342–50. https://doi.org/10.47577/tssj.v70i1.12692.
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Effective internal control serves as a safeguard for global firm projects. It safeguards their assets from depletion and mismanagement while ensuring the enduring sustainability of enterprises through prudent strategic choices. This study seeks to evaluate the impact of adequate internal controls on the financial performance of Iraqi telecommunications companies. The survey includes three telecommunications companies: Zain Iraq, Asia Cell, and Korek. Due to Korek's closure in 2024, the study sample was picked from Zain Iraq and Asiacell. To achieve the study's objectives and answer its questions, we gathered data on the financial performance of the companies investigated from financial statements issued on the Iraq Stock Exchange for 2018-2023. Data on the most frequently utilized performance indicators in the strategic decision-making process were examined. Audited financial statements for the years 2018-2023 were analysed using the most prevalent financial analysis ratios to assess the efficacy of internal control. The study found that financial analysis indicators reflect the effectiveness of internal control in Iraqi telecommunications businesses in 2023. However, it failed frequently between 2019 and 2021, which might be attributed to the COVID-19 epidemic and the following economic impact on all enterprises in other countries worldwide.
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Chandrayani Rokde. "In-Depth Exploration of Technical Indicators for Stock Market Prediction Using Machine Learning and Reinforcement Learning." Communications on Applied Nonlinear Analysis 32, no. 6s (2025): 172–85. https://doi.org/10.52783/cana.v32.3285.
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Introduction: This research focuses on the comprehensive exploration of technical indicators for stock market prediction, leveraging machine learning and reinforcement learning methodologies. The study aims to examine these indicators in detail, evaluate their relevance and utility, and assess their integration into predictive models. The research also investigates the efficacy of machine learning algorithms and reinforcement learning agents in forecasting stock market trends. Accurate stock market prediction is crucial in financial markets, where informed decision-making, risk management, and capital allocation depend on precise and timely forecasts. The complexity of financial markets necessitates advanced computational techniques, positioning the application of machine learning and reinforcement learning as a vital area of study. The research involves a rigorous analysis of technical indicators, evaluating their historical performance and predictive capabilities. Machine learning models are empirically tested to determine their effectiveness in leveraging these indicators for enhanced forecast accuracy. Additionally, the study explores the innovative use of reinforcement learning agents, which autonomously navigate market complexities using historical data and reward-driven mechanisms. The findings contribute to a deeper understanding of technical indicators and provide empirical evidence for the effectiveness of machine learning and reinforcement learning models in stock market prediction. By emphasizing the empirical nature of the study, this research offers a valuable resource for financial market practitioners and researchers aiming to harness advanced technologies for strategic decision-making. It underscores the potential of these technologies to transform investment strategies in an increasingly data-driven financial environment, marking a significant contribution to the field of financial technology
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Phong Lan, Nguyen Ngoc. "IDENTIFYING DECEPTIVE FINANCIAL REPORTING VIA ANALYSIS OF FINANCIAL STATEMENTS: CASE IN VIETNAM." Economics, Finance and Management Review, no. 1(17) (March 30, 2024): 89–99. http://dx.doi.org/10.36690/2674-5208-2024-1-89.
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Deceptive financial reporting represents a significant worry for the main regulatory bodies overseeing Vietnam's capital market. Both regulatory bodies are continuously enhancing the criteria to ensure thorough monitoring of publicly listed companies. The objective of the current study is to investigate the link between financial statement analysis and fraudulent financial reporting. While numerous researchers have uncovered evidence suggesting the effectiveness of financial ratios in identifying fraudulent financial reporting, others have reached differing conclusions. The majority of these studies were conducted beyond the borders of Vietnam. The sample consists of companies listed in Vietnam, and the data utilized spans from 2011 to 2022. The findings revealed that various financial ratios, including total debt to total assets and receivables to revenue, emerged as significant indicators for identifying fraudulent financial reporting. This suggests that financial ratios could potentially aid in detecting fraudulent activities. These results contribute to the existing body of literature concerning the efficacy of financial ratios in fraud detection.
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Maldonado Román, Mary Beatriz, and Teresa Barrueto. "Influencia de la Normativa Contable en la Gestión Financiera Pública descentralizada ecuatoriana." Green World Journal 4, no. 2 (2021): 009. http://dx.doi.org/10.53313/gwj42011.
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Public Financial Management identifies the level of efficacy, efficiency, effectiveness and economy in the management of public finances with a view to obtaining levels of improvement. The objective of this work is to determine, through the analysis of case studies, whether the regulations governing the accounting system are an effective instrument in the financial management of public entities in southern Ecuador. The methodology used is descriptive, analytical-synthetic with a qualitative-quantitative correlational design that allowed interpreting the data in the Servperf matrix, where the information obtained was evaluated in five indicators or factors identification, efficiency, efficacy, effectiveness and development. The results show that compliance with regulations has a 4.67/5 impact on efficiency, since it allows for the fulfillment of institutional objectives. The 3.39/5 reflects that the functions of the institutions are in line with the regulations, since all staff members are aware of them and apply them. The effectiveness of the regulations reaches a 3.44/5, however, there are deficiencies that imply the low perception of the officials. The total average impact of the regulations on the effectiveness of these institutions is 3.30/5, which is significant. In conclusion, accounting regulations constitute an effective instrument in the financial management of public sector entities in southern Ecuador. Reform actions have a positive impact on administrative transparency.
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Carvalho, Fabio Santos Mariano, Patrick Onofre Chemp de Castro, and Ricardo César da Silva Guabiroba. "Eficiência e eficácia de clubes de futebol – uma análise comparativa." Revista Produção e Desenvolvimento 2, no. 2 (2016): 101–14. http://dx.doi.org/10.32358/rpd.2016.v2.124.
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Paper objective is to analyze the performance of football clubs, comparing Brazilian and European club’s. From this analysis, it is expected investigate three hypotheses: (1) the analyzed Brazilian clubs have a worse financial performance compared to the performance of European clubs; (2) the chosen management model is crucial for football clubs achieve good performance and (3) football clubs that have high financial performance, also have high efficacy regarding the amount of wins in games played in 2014. To achieve this goal, a procedure was structured in four steps: (1) defining the clubs, (2) development of indicators and performance measures, (3) aggregation of measures for each club and (4) sorting and analysis of clubs. As a result of this analysis, it was found that the three hypotheses under discussion were false. It was also found that most Brazilian clubs have low effectiveness and low/medium financial performance. In contrast, most European clubs have high effectiveness and medium/high financial performance.
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Terterashvili, Khatia. "Children's Welfare Program – One of the Indicators of Social Effectiveness of State Management." Works of Georgian Technical University, no. 2(532) (June 10, 2024): 279–85. http://dx.doi.org/10.36073/1512-0996-2024-2-279-285.
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For the last decade, various reforms in the direction of child protection and well-being have been intensively carried out in Georgia. Despite the fact that the current policy has definitely improved the legal status of children in Georgia, there are still a number of challenges in ensuring children's well-being. The Codex of Children's Rights ratified by the Parliament of Georgia is analyzed highlighting both its positive achievements and challenges. Additionally, the children's welfare program is explored as a pivotal benchmark for assessing the efficacy of governmental social management. Furthermore, the comprehensive metric for assessing the well-being of children – The Child Material and Social Deprivation Index – is discussed. This index signifies the presence of both material and social deficiencies in a child's life, indicating family's insufficient financial means to fulfill the child's fundamental needs for growth and development, encompassing essentials such as nourishment, clothing, age-appropriate literature, toys, and recreational activities. This approach facilitates a more comprehensive understanding of child well-being compared to solely focusing on monetary poverty. It encompasses not only access to fundamental necessities but also factors essential for the child's holistic bio-psychosocial development.
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Roy, Priyanka, and Binoti Patro. "Effect of women's self‐help group participation on their financial inclusion measured through a women‐centric index: A study in North‐East India." Annals of Public and Cooperative Economics 96, no. 1 (2025): 65–97. https://doi.org/10.1111/apce.12496.
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AbstractThis study attempts to examine the effect of women's participation in self‐help groups (SHGs) on their financial inclusion levels. A comprehensive financial inclusion index is created specifically suited to women by incorporating the concept of ‘autonomy’ in the usage of financial services. Adding this indicator with access and usage improves the practical effectiveness of women's financial inclusion as a means to a larger end. Further, objective weights have been assigned to the financial inclusion indicators through the application of the Criteria Importance through Inter‐criteria Correlation (CRITIC) method. The effect estimation is examined using propensity score matching (PSM) to gauge a true effect assessment free from self‐selection bias. The results are also checked for robustness using inverse probability weighted regression adjustment (IPWRA). Drawing primary data from 450 respondents from the Barak Valley region of Assam, the study establishes the importance of ‘autonomy’ in determining women's financial inclusion and finds SHG participation to be effective in enhancing the holistic financial inclusion of women. Underscoring the potential of SHGs in the upliftment of women, the study recommends regular monitoring of existing SHGs, besides expansion policies, to prevent group inactivity. To enhance women's financial autonomy, it is also suggested to conduct frequent training programmes on rights awareness, boosting self‐efficacy for financial management and self‐sufficiency in addressing financial challenges.
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Kristanti, Farida Titik, Mochamad Yudha Febrianta, Dwi Fitrizal Salim, Hosam Alden Riyadh, Yoga Sagama, and Baligh Ali Hasan Beshr. "Advancing financial analytics: Integrating XGBoost, LSTM, and Random Forest Algorithms for precision forecasting of corporate financial distress." Journal of Infrastructure, Policy and Development 8, no. 8 (2024): 4972. http://dx.doi.org/10.24294/jipd.v8i8.4972.
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This study thoroughly examined the use of different machine learning models to predict financial distress in Indonesian companies by utilizing the Financial Ratio dataset collected from the Indonesia Stock Exchange (IDX), which includes financial indicators from various companies across multiple industries spanning a decade. By partitioning the data into training and test sets and utilizing SMOTE and RUS approaches, the issue of class imbalances was effectively managed, guaranteeing the dependability and impartiality of the model’s training and assessment. Creating first models was crucial in establishing a benchmark for performance measurements. Various models, including Decision Trees, XGBoost, Random Forest, LSTM, and Support Vector Machine (SVM) were assessed. The ensemble models, including XGBoost and Random Forest, showed better performance when combined with SMOTE. The findings of this research validate the efficacy of ensemble methods in forecasting financial distress. Specifically, the XGBClassifier and Random Forest Classifier demonstrate dependable and resilient performance. The feature importance analysis revealed the significance of financial indicators. Interest_coverage and operating_margin, for instance, were crucial for the predictive capabilities of the models. Both companies and regulators can utilize the findings of this investigation. To forecast financial distress, the XGB classifier and the Random Forest classifier could be employed. In addition, it is important for them to take into account the interest coverage ratio and operating margin ratio, as these finansial ratios play a critical role in assessing their performance. The findings of this research confirm the effectiveness of ensemble methods in financial distress prediction. The XGBClassifier and RandomForestClassifier demonstrate reliable and robust performance. Feature importance analysis highlights the significance of financial indicators, such as interest coverage ratio and operating margin ratio, which are crucial to the predictive ability of the models. These findings can be utilized by companies and regulators to predict financial distress.
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Saif-Allah, Haider Ali, and Salah Mahdi Abbas. "Analyzing the Use of the (PATROL) Early Warning Model in Assessing the Financial Performance of The Banking Sector in Iraq." Journal of Economics and Administrative Sciences 31, no. 145 (2025): 66–79. https://doi.org/10.33095/98k25521.
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This paper describes the financial performance of the banking sector in Iraq using the PATROL early warning model. The research found the strengths and weaknesses in bank operations and forecasted the financial crises using data from 2018 to 2022 to enhance performance. The indicators included capital adequacy, credit risk, profitability, organizational efficiency, and liquidity, with performance rated on a scale from "very good" to "poor." Related findings proved that the Iraqi banks would be entirely successful in financial performance regarding most indicators and scaling-high in capital adequacy, profitability, organization, and liquidity, in accordance with sound management and robust operations. However, credit risk posed challenges because the assets demonstrated lower quality compared to other indicators. Findings from the research suggest the importance of the PATROL model in guiding regulatory authorities in monitoring financial health, correcting imbalances, and improving stability. Overall, the empirical study tends to validate the effectiveness of the model as a strategy for improving and assessing financial outcomes in the banking sector. The research study has been geared towards the analysis of the financial performance of the banking sector in Iraq using the PATROL early warning model. The research found strengths and weaknesses in bank operations and predicted the financial crises using data from the years 2018 to 2022 to enhance performance. The indicators included capital adequacy, credit risk, profitability, organizational efficiency, and liquidity which were rated with respect to performance on a scale from "very good" to "poor." Related findings proved that the Iraqi banks would be entirely successful in financial performance regarding most indicators and scaling-high: capital adequacy, profitability, organization, and liquidity according to sound management and robust operations. However, credit risk posed challenges, which showed lower quality assets compared to other metrics. The research points out the usefulness of the PATROL model in guiding regulatory authorities to monitor the financial health, correct imbalances, and improve stability. In general, the empirical study seems to affirm the efficacy of the model as a scheme for assessing and improving financial outcomes not just for the banking sector, but for all sectors.
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Andreyev, M. Y. "Effectiveness of the stabilization fiscal rule for resource-rich countries." Voprosy Ekonomiki, no. 12 (December 2, 2022): 72–97. http://dx.doi.org/10.32609/0042-8736-2022-12-72-97.
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This paper explores the efficacy of the fiscal rule enacted by regulators in exporting countries, which implies the smoothing of tax revenue from the sale of natural resources. Under “efficacy” we understand its stabilization effect that is lowering volatility (countercyclicality) of key macroeconomic variables following its imposition. The approach to the operation of the fiscal rule is based on two types of assumptions: those about the type of the fiscal rule and about the structure of the economy. The first ones take into account imperfections in the workings of the revenue smoothing mechanism; the second relate to the duration of price and wage contracts, the cost of investing in foreign assets, government demand structures, household habits and the share of Ricardian households. We use a DSGE model of a small open economy with a strong reliance on commodity exports. The paper analyzes the assumptions under which various modifications of the fiscal rule lose their efficiency in relation to inflation, output and the exchange rate. The study emphasizes the importance of accurate identifying of fiscal rule workings, as well as the need for correct measurement of economic indicators in order to determine the nature of the fiscal rule effect related to revenue smoothing. The conclusion is made regarding the effectiveness of the future fiscal rule in Russia in the absence of an external wealth fund and a closed financial account.
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Supriyadi, Supriyadi, Kazi Sohag, Dani Yuniawan, Diyah Sukanti Cahyaningsih, Grahita Chandrarin, and Dewi Astutty Mochtar. "Role of Institutional Quality and Financial Developments in Realizing Clean Energy Legislation in Indonesia." International Journal of Energy Economics and Policy 14, no. 6 (2024): 161–73. http://dx.doi.org/10.32479/ijeep.16892.
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This study examines the intersection of financial markets and institutional quality (IQ) in enhancing clean energy production and access in Indonesia, a country that has placed a strong emphasis on renewable energy (RE) through its legislative framework. Utilizing various econometric methods, including Dynamic and fully-modified ordinary least squares (OLS), and dynamic simulated autoregressive distributed lag (ARDL) models, we analyze time series data from 1996 to 2022. The outcomes underscore the pivotal role of IQ (encompassing indicators like control of corruption, rule of law, regulatory quality, government effectiveness, political stability, and voice & accountability) in fostering the adoption of RE sources and broadening the reach of clean cooking energy. Although the impact of financial markets in isolation yields inconclusive findings, their intersection with IQ consistently bolsters Indonesia’s transition towards clean energy. This study puts forth various practical implications for policymakers seeking to bolster the efficacy of clean energy policies.
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Shafana, M. A. C. N. "Evaluating the Efficacy of Risk Management Practices and Their Necessity for Enhancing the Profitability of Financial Institutions in Sri Lanka." Asian Journal of Economics, Business and Accounting 24, no. 8 (2024): 21–34. http://dx.doi.org/10.9734/ajeba/2024/v24i81439.
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Aims: This study aims to assess the effectiveness of credit and liquidity risk management practices employed by banking and non-banking financial institutions in Sri Lanka prior to the economic crisis that began in 2022, and to determine whether these practices contribute to enhancing profitability. Study Design: The investigation employed quantitative research utilizing time series data. Place and Duration of the Study: The study collected annualised quarterly data between 2014 and 2021 from the Central Bank reports of Sri Lanka. Methodology: The study utilised a multiple regression model using EViews software. Results: The findings revealed that banking and non-banking financial institutions in Sri Lanka demonstrated effective credit risk management practices, which significantly contributed to enhancing profitability. Conversely, inadequate liquidity risk management practices in both types of financial institutions significantly contributed to reducing profitability. Moreover, the financial institutions yielded very low profits relative to total assets during the study period. Practical Implications: The findings offer practical insights for financial institutions, underscoring the importance of maintaining effective credit and liquidity risk management practices to enhance profitability. Research Limitations: Data collection began in the first quarter of 2014, coinciding with the Central Bank's publication of comprehensive indicators for non-banking financial institutions from the fourth quarter of 2013 onwards. The data collection extended until the end of 2021. This timeframe was selected due to the adoption of more liberal credit policies by financial companies and the onset of liquidity challenges in 2022 resulting from the economic crisis in Sri Lanka. Originality: This study is the first to compare the impact of credit and liquidity risk management practices on profitability between banking and non-banking financial institutions in Sri Lanka.
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Rodrigues, Gustavo Fernandes, Samuel Captein Viana da Silva, Heber Tormentino de Sousa, and Gláucio Lopes Ramos. "SISTEMA DE TOMADA DE DECISÃO OBJETIVA PARA A IDENTIFICAÇÃO DE CICLOS DO MERCADO FINANCEIRO." Revista ft 29, no. 145 (2025): 51–52. https://doi.org/10.69849/revistaft/pa10202504211151.
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How can one identify trading market cycles? Patience, when combined with technical analysis, offers valuable insights into market cycles, providing investors with an effective framework for decision- making. Understanding the current phase of a market cycle is essential for identifying optimal opportunities to enter or exit a specific asset. While it is nearly impossible to perfectly predict market bottoms or peaks, it is feasible to identify regions that approximate these critical points. The development and implementation of systems that leverage strategies based on technical indicators to track market trends and automatically determine entry and exit points offer a promising alternative to passive management or decisions influenced by emotions and market news. This paper proposes an objective decision-making system designed to eliminate emotional bias and reliance on external market information. The proposed system utilizes the stochastic oscillator indicator, with its performance evaluated on stocks listed on the São Paulo Stock Exchange (Bovespa). The findings demonstrate the method’s effectiveness when compared to benchmark indicators, such as the Interbank Deposit Certificate (CDI) and the traditional “buy-and-hold” strategy. This study contributes to the ongoing discourse on the efficacy of automated trading systems designed to identify and capitalize on market cycle trends in financial markets.
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Uddin, Muhammed Sameer, Omaima Eltahir Babikir Mohamed, and John Ebert. "Predictive Analytics for Accounting Fraud Detection: A Study Based on Integrating Corporate Governance and Underpinning Theories." Asian Journal of Economics, Business and Accounting 25, no. 3 (2025): 122–35. https://doi.org/10.9734/ajeba/2025/v25i31699.
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Accounting fraud is a major problem in today's dynamic financial world, particularly for stock exchange listed companies in Bangladesh. Accounting fraud undermines investor faith in the market, affects financial stability, and deteriorates market integrity, posing a major threat to the nation's economic growth. Traditional methods of detecting fraud, which depend primarily on hand audits, have proved ineffective in detecting rapid fraudulent transactions. This paper argues for the use of predictive analytics as a forward-thinking approach to detect accounting fraud before it occurs. Predictive analytics use statistical models and data mining techniques to discern patterns and anomalies in financial data, facilitating the early detection and prevention of fraudulent activity. This study aims to create a predictive analysis model that employs essential financial indicators—namely profitability ratios, liquidity ratios, leverage ratios, and cash flow metrics—and assess their efficacy in identifying probable fraud in publicly listed companies in Bangladesh. The study also examines the mediating function of corporate governance disclosures, such as audit committee effectiveness and board independence, in improving fraud detection. This study uses a quantitative research method to turn fraud detection practices from simple compliance requirements into a strategic advantage, which improves financial transparency and strengthens investor confidence in Bangladesh's financial markets.
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Fang Liu, Fang Liu. "Financial Statement Analysis Based on RNN-RBM Model." Journal of Electrical Systems 20, no. 1 (2024): 106–23. http://dx.doi.org/10.52783/jes.670.
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Financial statement analysis is a critical component of decision-making for businesses, investors, and financial professionals. To enhance the accuracy and effectiveness of such analysis, this paper introduces the application of an innovative approach known as the Intelligent Swarm Regression ARIMA Model. This advanced model combines the power of swarm intelligence with ARIMA (AutoRegressive Integrated Moving Average) time series forecasting, offering a robust methodology for predicting and analyzing key financial metrics. The study begins by providing an overview of the Intelligent Swarm Regression ARIMA model and its application to financial data. Through a comprehensive analysis of financial statements, including market capitalization, revenue, net income, and other crucial indicators, the model's efficacy in predicting future values is evaluated. Additionally, the paper examines the deviations between predicted and actual financial values, offering insights into the model's accuracy and areas for potential improvement. The findings of this research are invaluable for investors, financial analysts, and companies seeking to optimize their financial performance and strategic decision-making. By leveraging the Intelligent Swarm Regression ARIMA Model, stakeholders can make well-informed choices that lead to better financial outcomes and a competitive advantage in a dynamic economic landscape. This paper represents a significant step forward in the financial analysis, providing a practical methodology and a pathway to enhanced financial decision-making. As the importance of financial data continues to grow, this research offers a promising avenue for achieving financial success and stability.
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Cangul, Ozcel, Roberto Rocchetta, Murat Fahrioglu, and Edoardo Patelli. "Optimal Allocation and Sizing of Decentralized Solar Photovoltaic Generators Using Unit Financial Impact Indicator." Sustainability 15, no. 15 (2023): 11715. http://dx.doi.org/10.3390/su151511715.
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A novel financial metric denominated unit financial impact indicator (UFII) is proposed to minimize the payback period for solar photovoltaic (PV) systems investments and quantify the financial efficiency of allocation and sizing strategies. However, uncontrollable environmental conditions and operational uncertainties, such as variable power demands, component failures, and weather conditions, can threaten the robustness of the investment, and their effect needs to be accounted for. Therefore, a new probabilistic framework is proposed for the robust and optimal positioning and sizing of utility-scale PV systems in a transmission network. The probabilistic framework includes a new cloud intensity simulator to model solar photovoltaic power production based on historical data and quantified using an efficient Monte Carlo method. The optimized solution obtained using weighted sums of expected UFII and its variance is compared against those obtained by using well-established economic metrics from literature. The efficiency and usefulness of the proposed approach are tested on the 14-bus IEEE power grid case study. The results prove the applicability and efficacy of the new probabilistic metric to quantify the financial effectiveness of solar photovoltaic investments on different nodes and geographical regions in a power grid, considering the unavoidable conditional and operational uncertainty.
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Fazlurahman, Faesal, Chandra Fatriananda, and Rifqie Jauhari. "Analisis Kinerja Keuangan Pemerintah Kota Bandung Sebelum dan Setelah Mendapatkan Opini WTP dari BPK." Jurnal Ilmiah Ekonomi Global Masa Kini 11, no. 1 (2020): 56. http://dx.doi.org/10.36982/jiegmk.v11i1.1062.
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<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p><span>ABSTRACT</span></p><p><span>The purpose of this research is to find out the difference on the financial performance of Bandung before and after obtaining an unqualified opinion in terms of regional financial independence, effectiveness of regional income, efficiency of regional income, and growth in regional income. This study uses secondary data in the form of Bandung City government financial reports from 2014 to 2018. The financial performance of the City of Bandung is measured by financial ratios then compared with performance indicators. The results showed that the ratio of independent regional finance, the ratio of effectiveness of regional income, and growth of regional income. Financial performance of Bandung City before and after obtaining a fair opinion without exception has shown a significant improvement, this is evidenced by the increasing ratio of regional independence, and increasing the effectiveness of regional income.</span></p><p><span>Keywords: </span><span>Financial Performance, Unqualified Opinion, Regional Financial Independence, Regional Income Efficacy, Regional Income Efficiency</span></p><p><span>ABSTRAK</span></p><p><span>Tujuan penelitian ini adalah untuk mengetahui apakah terdapat perbedaan dalam kinerja Pemerintah Kota Bandung sebelum dan sesudah mendapatkan opini wajar tanpa pengecualian dalam hal kemandirian keuangan daerah, efektivitas pendapatan daerah, efisiensi pendapatan daerah, dan pertumbuhan pendapatan daerah. Penelitian ini menggunakan data sekunder berupa laporan keuangan pemerintah Kota Bandung dari tahun 2014 sampai 2018. Kinerja keuangan Kota Bandung diukur dengan rasio keuangan kemudian dibandingkan dengan indikator kinerja. Hasil penelitian menunjukkan bahwa rasio independen keuangan daerah, rasio efektivitas pendapatan daerah, dan pertumbuhan pendapatan daerah kinerja keuangan kota bandung sebelum dan sesudah memperoleh opini wajar tanpa pengecualian telah menunjukkan perbaikan yang berarti, hal ini dibuktikan dengan meningkatnya rasio kemandirian daerah, serta meningkatnya Efektivitas pendapatan daerah.</span></p><p><span>Kata Kunci: </span><span>Kinerja Keuangan, Opini Wajar Tanpa Pengecualian, Kemandirian Keuangan Daerah, Efektivitas Penerimaan Daerah, Efisiensi Penerimaan Daerah</span></p></div></div></div>
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Delgadillo, Josiel, Johnson Kinyua, and Charles Mutigwe. "FinSoSent: Advancing Financial Market Sentiment Analysis through Pretrained Large Language Models." Big Data and Cognitive Computing 8, no. 8 (2024): 87. http://dx.doi.org/10.3390/bdcc8080087.
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Predicting the directions of financial markets has been performed using a variety of approaches, and the large volume of unstructured data generated by traders and other stakeholders on social media microblog platforms provides unique opportunities for analyzing financial markets using additional perspectives. Pretrained large language models (LLMs) have demonstrated very good performance on a variety of sentiment analysis tasks in different domains. However, it is known that sentiment analysis is a very domain-dependent NLP task that requires knowledge of the domain ontology, and this is particularly the case with the financial domain, which uses its own unique vocabulary. Recent developments in NLP and deep learning including LLMs have made it possible to generate actionable financial sentiments using multiple sources including financial news, company fundamentals, technical indicators, as well social media microblogs posted on platforms such as StockTwits and X (formerly Twitter). We developed a financial social media sentiment analyzer (FinSoSent), which is a domain-specific large language model for the financial domain that was pretrained on financial news articles and fine-tuned and tested using several financial social media corpora. We conducted a large number of experiments using different learning rates, epochs, and batch sizes to yield the best performing model. Our model outperforms current state-of-the-art FSA models based on over 860 experiments, demonstrating the efficacy and effectiveness of FinSoSent. We also conducted experiments using ensemble models comprising FinSoSent and the other current state-of-the-art FSA models used in this research, and a slight performance improvement was obtained based on majority voting. Based on the results obtained across all models in these experiments, the significance of this study is that it highlights the fact that, despite the recent advances of LLMs, sentiment analysis even in domain-specific contexts remains a difficult research problem.
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Nadege, Niyongabo, Robert Turyamureeba, and Prize Tayebwa. "The Efficacy of Women Empowerment Initiatives in Agricultural Development of Mugamba Region, Burundi." East African Journal of Environment and Natural Resources 8, no. 1 (2025): 346–59. https://doi.org/10.37284/eajenr.8.1.2938.
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The global catastrophe of hunger is getting worse every day. According to recent figures, severe nutritional deprivation affects almost 9.2% of the global population. Recent statistics suggest that more than a billion people in Asia, 676 million people in Africa, and 188 million people in Latin America suffer from starvation. Despite dominating the agricultural sector, women suffer disproportionately from hunger. The effectiveness of programs aimed at empowering women in the agricultural development of Burundi's Mugamba region is investigated in this study. We adopted a cross-sectional study using mixed methods to enable concurrent triangulation. The results show that women's agricultural productivity is highly impacted by limited access to land, with a mean score of 3.2007 and a standard deviation of 1.37476. Just 41.4% of women in the Mugamba region are empowered across five domains, indicating a lack of meaningful empowerment. We recommend strategic adjustments to indicators that lead to disempowerment, such as non-farm earning options and educational initiatives, development of the infrastructure as well as close oversight and assessment of empowerment. Meaningful involvement of more women in agriculture can lead to increased child nutrition and food security as well as other positive socio-economic impacts such as increased household income. Furthermore, there is a need for targeted legislation and financial assistance to women in Burundi's agricultural industry. To fully comprehend the significance of women's empowerment in this particular setting, more research is required
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Zhang, Xueer. "Stock price prediction of PayPal by Linear Regression, SVM, Random Forest and LSTM." Applied and Computational Engineering 52, no. 1 (2024): 201–7. http://dx.doi.org/10.54254/2755-2721/52/20241568.
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This research paper undertakes an extensive and detailed examination of four distinct machine learning models, specifically Linear Regression, Support Vector Machine (SVM), Random Forest, and Long Short-Term Memory (LSTM). The stock values of PayPal are forecast employing these techniques, and Python serves as the fundamental tool in facilitating a comprehensive and thorough evaluation of their efficacy in predicting trends in the financial market. In the realm of methodology, this study encompasses a multifaceted approach. Python forms the cornerstone for data analysis, model development, and testing. The data collection process encompasses historical stock price data for PayPal, alongside an array of relevant economic indicators. The core of the study lies in the comparative analysis of the four machine learning models. Each model is rigorously tested against historical data, allowing for a nuanced understanding of their strengths and weaknesses. Linear Regression, remarkably, emerges as the standout performer in terms of predictive accuracy and consistency. This firmly establishes Linear Regression as the optimal choice for forecasting stock prices. The significance distinction of this research extends beyond its findings. It advances the discipline of financial forecasting by illuminating the comparative effectiveness of different models, offering valuable insights for guiding future research initiatives within this domain. The integration of advanced methodologies and the clear-cut conclusion regarding Linear Regression's superiority underscore the pivotal role of this study in enhancing the precision of financial market trend forecasting.
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Kolosnitsyna, Marina, Maria Makarova, Anastasia Vladimirskaya, et al. "A MODEL FOR ASSESSING MEDICAL EFFICACY AND COST EFFECTIVENESS OF EARLY DETECTION OF HEREDITARY CANCER USING WHOLEGENOME AND TARGETED GENETIC TESTING." Social Aspects of Population Health 69, no. 5 (2023): 14. http://dx.doi.org/10.21045/2071-5021-2023-69-5-14.
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Significance. Malignant neoplasms can be associated with genetic predisposition. Identification of clinically significant mutations enables to determine the risk of neoplasms of other localizations in patients and their relatives. Besides, diagnosis of oncological diseases at an early stage increases the treatment success. One of the objectives of the genetic diagnostics is to reduce the malignant neoplasms associated incidence and mortality in families with a high hereditary risk, thus reducing financial burden of patients and the state. The purpose of the study is to develop a model for assessing medical efficacy and cost effectiveness of early diagnosis of malignant neoplasms based on whole-genome and targeted testing. Material and Methods. To develop a model for assessing medical efficacy and cost effectiveness, the authors used data of the study "Development of a personalized approach in providing medical care to persons with hereditary forms of malignant neoplasms in the Yamalo-Nenets Autonomous Okrug" as well as official data of the Federal State Statistics Service, Pension Fund of Russia, Territorial Compulsory Health Insurance Fund of the Yamalo-Nenets Autonomous Okrug and the Ministry of Economic Development of the Russian Federation. To assess medical efficacy and cost effectiveness, a standard method of cost-benefit analysis was applied to compare social costs in case of genetic testing (whole genome and targeted sequencing) of patients and their relatives and social costs without genetic testing. Results. The use of whole genome and targeted genetic testing in the sample of 200 patients with diagnosed malignant neoplasms, provided the survival of the study participants and their relatives up to retirement age with due regard to demographic indicators, makes it possible to save 1 billion rubles (the cumulative social effect over a 20–year period with the average age of 40 years for patients with pathogenic genetic variants), primarily by reducing mortality. Conclusions. Despite high initial costs of genetic testing, it allows to detect malignant neoplasms at an early stage, which is especially effective for relatives of patients. In the long term, the expenses are more than covered by both saving costs associated with cancer treatment at a late stage, social payments, and other disability benefits, and preserving human capital, especially the labor function of patients.
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Alena, Marushchak, and Shorikov Evgeniy. "Pharmacoeconomic analysis of the use of ramipril and candesartan in patients with arterial hypertension." ScienceRise: Pharmaceutical Science, no. 3(13) (June 29, 2018): 8–13. https://doi.org/10.15587/2519-4852.2018.135771.
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Arterial hypertension (AH) is the most common cardiovascular disease in the world and is the cause of disability of the population of the developed countries of the world. A feature of hypertension pharmacotherapy is a lifelong intake of medications. Indicators of economic evaluation of the effectiveness of different methods of treatment contribute to the selection of pharmacotherapy, which will provide the patient with the highest effectiveness of treatment, taking into account the financial capabilities of a particular patient. The main objectives of antihypertensive therapy is to achieve the target level of blood pressure (BP), the protection of target organs and a positive impact on risk factors for cardiovascular complications. The "cost-effectiveness" method makes it possible to compare costs with the same therapeutic effectiveness, which was the purpose of our study. Materials and methods. 100 protocols of medical cards of inpatients were studied. Two groups of patients were isolated: the first group for the treatment of AH received candesartan, and the second group – ramipril. The parameters were taken into account: achievement of target blood pressure, as well as possible complications of drug therapy - hypotension, cough, angioedema. To assess the cost of treatment against the background of the use of ramipril and candesartan, a "cost-effectiveness" pharmaco-economic analysis was carried out. Results of the study. Using the "cost-effectiveness" method, an analysis of ramipril and candesartan was carried out. The minimum, average and maximum costs of a single, daily, and course dose were calculated and the cost of BP normalization was calculated. The data obtained by us indicate a lower cost of treatment with ramipril. Analysis of the effectiveness of the drugs showed that ramipril is more effective in achieving the target blood pressure, but the percentage of side effects among which heart failure and cough were significantly higher. The data obtained indicate a high therapeutic efficacy of candesartan and ramipril, which allows us to draw conclusions: Conclusions. The most effective in achieving target BP was ramipril than candesartan. Analysis of the efficacy of drugs has established that ramipril has side effects and causes cough and heart failure, and candesartan can cause angioedema and hypotension. In analyzing the direct costs of treating hypertension, based on current standards of medical care for patients with AH, it was found that the least expensive is the treatment with ramipril. The analysis "cost-effectiveness", showed that ramipril possesses the greatest pharmaco-economic advantage
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Pyadushkina, E. A., A. M. Lila, E. E. Yagnenkova, V. I. Ignatyeva, and E. V. Derkach. "New possibilities for the treatment of systemic lupus erythematosus. How much does the effect cost?" Modern Rheumatology Journal 19, no. 1 (2025): 35–43. https://doi.org/10.14412/1996-7012-2025-1-35-43.
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Our previous study has shown that the new biologic disease-modifying antirheumatic drug (bDMARD) anifrolumab (AFM) can be considered as a cost-effective alternative to belimumab (BLM) in the treatment of systemic lupus erythematosus (SLE). Following a review by the Commission of the Ministry of Health of Russia, AFM was recommended for inclusion in the List of vital and Essential Drugs (VED).Objective: to conduct a clinical and economic analysis (CEA) of the use of bDMARD for the treatment of adult patients with moderate to severe SLE, to update the previously conducted CEA considering changing circumstances, including populational changes, price characteristics and law in the context of the Russian healthcare system, and to assess the impact of changes.Material and methods. The CEA was conducted from a healthcare system perspective and was based on the results of an indirect comparison of the efficacy of AFM and BLM drugs using a cost-effectiveness analysis. The costs of drug therapy with the compared bDMARDs for one year, the cost-effectiveness ratio and the incremental indicator of cost-effectiveness growth were calculated. Drug prices were calculated based on the register of maximum retail prices for drugs from the VED. A comparison of the main results of the analysis is presented.Results and discussion. The results of the indirect comparison of efficacy showed statistically significant advantages of AFM over BLM according to the SRI-4 criterion: odds ratio – 2.61 (95% confidence interval 1.22–5.58). The cost of therapy for one patient for 52 weeks, recalculated considering the changed indicators, for AFM in combination with standard therapy was 655.5 thousand rubles, for BLM – 622 thousand rubles, the difference in cost was 5.1%. When prescribing AFM, the cost of the effect (achieving a response to therapy according to the SRI-4 criterion) in one patient was 1181 thousand rubles, which is 38.6% lower than when using BLM (1924 thousand rubles). The additional cost of achieving a response according to SRI-4 in one additional patient for AFM compared to BLM amounted to 144 thousand rubles, which is significantly less than the previously calculated similar indicator – 3.4 million rubles.Conclusion. The CEA, considering the current data allows us to conclude that the use of AFM in patients with moderate to severe SLE is economically feasible within the framework of the Russian healthcare system. The expansion of the VED list by including new, more effective and economically justifiable options, and registration of maximum retail prices will increase the availability of SLE therapy, reduce the financial burden on the budgets of regions of the Russian Federation and enable more patients to receive the necessary treatment.
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Suprun, Nataliia, Tetiana Petrishyna, Olha Smyrna, Maryna Sadovenko та Nataliia Yatsenko. "Управління ефективністю діяльності вітчизняних страхових компаній в умовах військового стану". Economics and technical engineering 1, № 2 (2023): 33–47. http://dx.doi.org/10.62911/ete.2023.01.02.03.
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The article examines the factors contributing to the efficiency of participants in Ukraine's insurance market amidst the backdrop of war. Differences in the functioning of banking and insurance systems during wartime are examined. The paper identifies the key factors that impede the operations of Ukrainian insurers and directly impact their effectiveness. It is noted that issues stemming from the war have led to a reduction in the number of market participants, and this trend continues. The analysis showed that the majority of leading insurance companies in the Ukrainian insurance market managed to overcome the initial adverse effects of the war and reestablish profitable operations by the end of 2022. The experience gained during the COVID-19 pandemic aided insurers in maintaining control and preserving their client base in unoccupied territories. Calculations based on reported data from well-known insurance companies indicate that acceptable return on equity indicators were achieved by the end of 2022, primarily due to the use of financial leverage (considering the specific nature of insurance). However, the high level of inflation in the country during 2022 hindered insurers from attaining a satisfactory rate of return on their assets, thus constraining their overall effectiveness. Stabilization and gradual market growth in 2023, coupled with official forecasts of reduced inflation in 2024-2025, provide grounds for optimism regarding the attainment of the necessary operational and financial efficiency in the upcoming years. The research demonstrates the efficacy of anti-crisis measures while also shedding light on their limitations in terms of future success. To achieve strategic transformations and ensure effective long-term operation, enterprises must increase capitalization through external investments, develop and implement new insurance services aligned with contemporary needs, design, deepen, and execute digital transformation programs for the insurance sector, refine investment policies, and collaborate with industry associations to work towards the elimination of discriminatory tax legislation.
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Asif Chouhan, Dr Mohammad. "THE EFFECTIVENESS OF AI IN PREDICTING STOCK MARKET TRENDS: A COMPARATIVE STUDY OF THE LAST FEW YEARS OF INDIAN MARKETS." International Journal of Engineering Applied Sciences and Technology 09, no. 11 (2025): 100–108. https://doi.org/10.33564/ijeast.2025.v09i11.016.
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The integration of Artificial Intelligence (AI) into financial forecasting has transformed traditional stock market prediction methods. This research paper explores the effectiveness of AI techniques in forecasting stock trends within the Indian stock market over the last few years. We examine AI methodologies, including machine learning (ML), deep learning (DL), and hybrid models applied to the Bombay Stock Exchange (BSE) and the National Stock Exchange (NSE). By comparing historical data with AI-predicted trends, this study evaluates prediction accuracy and market relevance (Patel et al., 2015). Furthermore, the research outlines existing study gaps and proposes a future scope of integrating AI with behavioral finance and real-time analytics (Chen et al., 2022). The prediction of stock market trends remains a significant challenge due to the stochastic and non-linear nature of financial time series data (Zhang & Zhou, 2020). With the proliferation of Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL) techniques, there has been a paradigm shift in the modeling and forecasting of stock price movements (Fischer & Krauss, 2018). This paper presents a comprehensive study on the effectiveness of AI in predicting stock market trends within the Indian financial ecosystem, focusing on a comparative analysis of AI models implemented over the last five years (2018–2023) on major indices and stocks listed on the NSE and BSE. This research evaluates the performance of multiple AI algorithms—including Support Vector Machines (SVM), Random Forests (RF), Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRU), and hybrid ensemble models—in forecasting short- and medium-term price trends using historical stock data (Krauss et al., 2017; Chen & He, 2021). Standard evaluation metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and classification accuracy are employed to assess model efficacy. The results indicate that while traditional machine learning algorithms offer moderate predictive power, advanced deep learning and hybrid models significantly outperform them, particularly during periods of high market volatility, such as the COVID-19 pandemic and post-lockdown recovery phases (Weng et al., 2018; Jain & Jain, 2021). The study identifies key research gaps, including limited sectoral diversification in datasets, inadequate integration of sentiment and behavioral data, overfitting issues in complex models, and the lack of real-time prediction systems in the Indian context (Gupta & Pathak, 2022). Furthermore, current models often ignore nonquantitative factors such as investor sentiment, macroeconomic indicators, and global events, which can critically impact prediction accuracy (Nassirtoussi et al., 2014). These limitations suggest the need for a more holistic and interdisciplinary approach to AI-driven financial forecasting. The scope for future research includes the development of real-time, adaptive AI systems using high-frequency trading data, the incorporation of behavioral finance through social media and news analytics, and the exploration of quantum computing-based AI models (Liu et al., 2023). From a practical standpoint, the findings of this study offer valuable insights for institutional investors, financial analysts, regulatory bodies, and developers of AIpowered trading platforms. The study concludes that while AI is not a definitive solution for market prediction, it provides a powerful augmentative tool that, when designed with robustness, transparency, and adaptability, can significantly enhance decision-making in India’s fastevolving financial markets.
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Essien, Ettah Bassey, Ernest Asuquo Collins, and Abraham Onyeka Okoloegbo. "Economic Policies in Nigeria 2000 – 2015: An Evaluation of their Efficacy." International Journal of Contemporary Issues and Trends in Research 1, no. 1 (2023): 13–31. https://doi.org/10.5281/zenodo.10794039.
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<strong>Abstract</strong> This study evaluates the success of economic policies in Nigeria between the year 2000 and 2015. Since Nigeria’s independence in 1960, several economic policies and programmes have been formulated and implemented to achieve specified macroeconomic objectives. However, the persistence of macroeconomic ills such as the rising cost of living, worrisome unemployment situation and staggering economic growth cast doubts on the effectiveness of these policies; which therefore calls for their evaluation. Specifically, the research evaluates the National Economic Empowerment and Development Strategy (NEEDS) and the Transformation Agenda. The objectives of the study include the assessment of the extent to which NEEDS was successful with respect to wealth creation, employment generation, and poverty reduction, and evaluation of the success of Transformation Agenda with reference to growth and development indicators – GDP growth, inflation rate, unemployment rate and growth in capital formation. Method of analysis adopted is purely descriptive. Empirical results reveal that NEEDS programme failed to achieve its objectives of wealth creation, employment generation and poverty reduction in Nigeria. The study also reveals that Transformation Agenda failed to stimulate growth in the economy; unemployment and cost of living measured by inflation rate were observed to be higher during the period of Transformation Agenda than in the period before; this was judged to be disappointing given the huge amount of money that has been allocated for its implementation. On the whole, it is concluded that both NEEDS and the Transformation Agenda failed to achieve their objectives. Based on the findings and for the purpose of achieving success in future economic policies and programmes in Nigeria, it is recommended among others, that the problem of poor financial intermediation be addressed through provision of reliable information between borrowers and lenders; ensuring continuity in implementation of government programmes; and holding public office holders accountable on the outcome of government programmes in their trust.
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Orbuda, Vincent Bob H., and Dr Aprell L. Abellana. "Well – Being and Organizational Commitment on Teaching Competence in Public Secondary and Integrated Schools in Rural Areas." International Journal of Research and Innovation in Social Science IX, no. V (2025): 5011–20. https://doi.org/10.47772/ijriss.2025.905000387.
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The study investigated the relationship of well-being, organizational commitment, and teaching competence among 250 public secondary and integrated school teachers in rural areas of the Municipality of Kitaotao, Bukidnon, during the school year 2024 – 2025. The study used a survey questionnaire to examine the well-being and organizational commitment of these teachers. For the total survey, respondents rated mental, subjective and financial well-being highly. Organizational commitment was also rated high level in terms of loyalty and perceived obligations to stay with the organization in rural area schools. Teaching competence was rated overall as “excellent” among all of the indicators assessed, including content knowledge, pedagogical skills, interpersonal communication, classroom organization, and student assessment. A statistical analysis using the SPSS confirmed the presence of a significant positive correlation among well-being and teaching competence. On the other hand, there was no significant relationship between organizational commitment and teaching competence. In addition, out of the sub-variables of well-being, both financial well-being and mental well-being showed substantial association with teaching competence. However, organizational commitment including sub variables: emotional attachment, perceived obligation to stay and sense of loyalty did not give any sign of influence on the teacher’s teaching performance. Definitely, provide assistance for teachers’ mental health is essential for their effectiveness in the classroom settings. In general, teachers have a rich financial and organizational support system in many ways. However, more needs related to emergency funds, long-term planning, and self-efficacy may further contribute to teacher well-being. Educational institutions need to work with support system and mental health measures to support and enhance the competence of teachers, so that their students experience better outcomes in rural schools.
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Kabir, Md Mohsin, Mohammad Saddam Hosen, Dr Mohammad Thoufiqul Islam, and Shamal Chandra Hawlader. "Practices of Managerial Analytics in IoT-based Sustainable Employee Training and Organizational Performance at the Bank and Financial Institutes." International Journal of Management and Humanities 10, no. 11 (2024): 13–24. http://dx.doi.org/10.35940/ijmh.l1732.10110724.
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The study has analyzed managerial analytics integrated with the Internet of Things (IoT) that has mobilized sustainable employee training and organizational performance in the banking sector. The intention is to evaluate the managerial analytics practiced by Bangladeshi banks and financial institutes (FIs) and their impact on employees' training and performance. The present research investigates the implementation of sustainable employee training initiatives and effectiveness in working fields using IoT, the historical extant training practices of the organization, and the relationship between managerial analytics factors that affect the banking system. Here in this study, a scenario-based approach was used to demonstrate the integration of smart training for employees with IoT using managerial analytics tools, and a cross-sectional research strategy was also experienced among the related employees of Bangladesh in Dhaka city. And 143 purposive sampling metadata were analyzed. We offer a model for evaluating the efficacy of managerial analytics on employees, which enhances operational and learning outcomes. The study's results confirmed the validity of the proposed model for evaluating the training of employees. The findings have identified the indicators- training content and attitude as analytical patterns, and IoT technology and monitoring as technological that significantly impacts the employees' performance. It emphasizes the managerial analytics concept that facilitates training and development for employees with newly required competencies in the banking sector through IoT. Managerial analytics integrated into IoT-based employee training is significantly effective among operations and promotes smart performance observation in the banking sector. These insights offer valuable guidance to bankers, policymakers, and managerial analysts striving to incorporate sustainable practices into their operations to foster long-term growth in the banking sector.
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Md., Mohsin Kabir. "Practices of Managerial Analytics in IoT-based Sustainable Employee Training and Organizational Performance at the Bank and Financial Institutes." International Journal of Management and Humanities (IJMH) 10, no. 11 (2024): 13–24. https://doi.org/10.35940/ijmh.L1732.10110724.
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<strong>Abstract:</strong> The study has analyzed managerial analytics integrated with the Internet of Things (IoT) that has mobilized sustainable employee training and organizational performance in the banking sector. The intention is to evaluate the managerial analytics practiced by Bangladeshi banks and financial institutes (FIs) and their impact on employees' training and performance. The present research investigates the implementation of sustainable employee training initiatives and effectiveness in working fields using IoT, the historical extant training practices of the organization, and the relationship between managerial analytics factors that affect the banking system. Here in this study, a scenario-based approach was used to demonstrate the integration of smart training for employees with IoT using managerial analytics tools, and a cross-sectional research strategy was also experienced among the related employees of Bangladesh in Dhaka city. And 143 purposive sampling metadata were analyzed. We offer a model for evaluating the efficacy of managerial analytics on employees, which enhances operational and learning outcomes. The study's results confirmed the validity of the proposed model for evaluating the training of employees. The findings have identified the indicators- training content and attitude as analytical patterns, and IoT technology and monitoring as technological that significantly impacts the employees' performance. It emphasizes the managerial analytics concept that facilitates training and development for employees with newly required competencies in the banking sector through IoT. Managerial analytics integrated into IoT-based employee training is significantly effective among operations and promotes smart performance observation in the banking sector. These insights offer valuable guidance to bankers, policymakers, and managerial analysts striving to incorporate sustainable practices into their operations to foster long-term growth in the banking sector.
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Mungklachaiya, Sujitranan, and Anongporn Salaiwarakul. "Machine Learning Model for Predicting the Suitability of Cultivating Alternative Crops in Lower Northern Thailand." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 19, no. 2 (2025): 195–206. https://doi.org/10.37936/ecti-cit.2025192.257686.
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Intensive rice cultivation presents significant environmental and economic challenges. While crop diversification offers potential benefits for agricultural sustainability and financial resilience, farmers face considerable uncertainty when transitioning to alternative crops. This study assessed the prediction efficacy of machine learning (ML) models in identifying suitable crops for cultivation in a specific geographical area considering various factors influencing agricultural viability. Through comprehensive experimentation, a decision tree model, an artificial neural network (ANN), and a Naïve Bayes model were used for predictions and rigorously evaluated for various crops, including rubber, coconut, longan, durian, rambutan, and mangosteen. Various hyperparameter configurations were tested, and multiple evaluation indicators were employed to assess the prediction performance of the models. The results consistently demonstrated the superiority of the decision tree model, which exhibited high accuracy, precision, recall, and F-measure across most crops. Its ability to capture intricate patterns and relationships between crop attributes and suitability levels underscores its value as a decision-support tool in agriculture. While the ANN model performed well for coconut, its effectiveness varied across the other crops, highlighting the need for tailored model selection. This study provides valuable insights into the application of ML in agricultural decision-making processes, suggesting potential avenues for future optimization and enhancement of prediction accuracy.
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Subodh Nath Pushpak. "Quantum Machine Learning Technique for Insurance Claim Fraud Detection with Quantum Feature Selection." Journal of Information Systems Engineering and Management 10, no. 8s (2025): 750–56. https://doi.org/10.52783/jisem.v10i8s.1193.
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This paper demonstrates a novel use of quantum machine learning (QML) algorithms for detecting fraudulent activities in the home insurance sector. Utilizing actual data and IBM Quantum processors through the Qiskit software stack, the study introduces an innovative method for selecting quantum features that are specifically designed to accommodate the limitations of Near Intermediate Scale Quantum (NISQ) technology by using the Quantum Support Vector Machine (QSVM) in conjunction with traditional machine learning techniques. A comprehensive comparison was conducted to evaluate their effectiveness in detecting fraud. The indicators such as accuracy, recall, and false positive rate are carefully analyzed. Despite the constraints of current quantum technology, QSVM shows excellent accuracy, especially on limited datasets, indicating its potential to enhance insurance fraud detection. The research emphasizes the crucial feature selection in optimizing QML algorithms for fraud detection tasks. It investigates the capacities of hybrid quantum/classical machine learning ensembles. Future research directions include expanding this study to actual hardware implementations to verify its practical feasibility. The work enhances financial security in the insurance business by using quantum computing technology in fraud detection approaches. It establishes the feasibility and efficacy of using quantum resources to solve difficult real-world issues, setting a foundation for or the broader application of QML in fraud detection and other fields.
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Olubiyi, Timilehin Olasoji, Olumuyiwa Oluseun Adeoye, Bolaji Jubril, Omolade Sunday Adeyemi, and Julius Paul Eyanuku. "Measuring Inequality in Sub-Saharan Africa Post-Pandemic: Correlation Results for Workplace Inequalities and Implication for Sustainable Development Goal ten." International Journal of Professional Business Review 8, no. 4 (2023): e01405. http://dx.doi.org/10.26668/businessreview/2023.v8i4.1405.
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Purpose: This paper aims to investigate the relationship between workplace inequalities and business effectiveness in selected and listed businesses in Nigeria. It includes an analysis of four independent sub-variables (gender segregation, incentive disparity, ethnic factors and professional disparity) for the variable workplace inequalities and four dependent sub-variables (creativity, service quality,productivity and business growth)for the variable business effectiveness. Methodology: This study used a survey research approach to investigate the relationship between workplace inequalities and business effectiveness in selected and listed businesses in Nigeria. The survey research design was cross-sectional in this paper, and the technique was quantitative. The adoption of this design was influenced by the research problem and its related research questions. The unit of analysis for this study was the staff of consumer goods companies of Seven listed consumer goods companies in Nigeria that controlled 93.08% of the total market capitalization of the entire consumer goods sector as of October 31, 2022. The population comprised 491 employees of the selected consumer goods companies in Nigeria. A structured questionnaire with Cronbach’s alpha reliability coefficients for the constructs ranging from 0.77 to 0.88 was used for data collection. Findings: The findings of this study are in consonance with the view of Bandura (2012), who insists that entrepreneurial self-efficacy is the level of confidence shown by an individual towards the undertaking of a task or position which favourably influences small business performance. The findings of this study are consistent with those of Ngek (2015); Dessyana and Riyanti (2017); Adolfina and Lumintang (2018); Budiman and Pangestu (2018); Cumberland, Meek, & Germain, (2015): Yusuff et al. (2019); Oyeku et al. (2020); Islam et al. (2020); Kale, (2020); Khalil et al. (2021); McGee & Peterson, (2019); Torres and Watson, (2013) which demonstrate that entrepreneurial self-efficacy positively and significantly affects business performance Research limitation and implication: It recognized that the findings and implications of this paper are situated within Lagos, Nigeria, and primarily small businesses as the focus of its attention. The research can be expanded with similar studies conducted across large businesses in Nigeria or other climes. To achieve more generalizability and trustworthiness, the sample size can be raised by considering more respondents with innovative data gathering techniques. In this research, the absence of data in the majority of small businesses in Lagos was the most significant challenge to obtaining objective performance measurements, for this reason, the non-financial performance indicators were adopted. According to Khalil, et al. (2021), subjective measurements may be produced without objective measures. Practical implication: For every business to scale or survive there is a need to have a reliable configuration of strategies of which managerial capability is key, as such the findings of this study are expected to help create a reminder of the need to improve skill sets in the owner-managers of small businesses to beat the competition and remain viable Social implication: This research will help society comprehend the need for managerial capabilities and entrepreneurial self-efficacy for harmonious business growth and performance in operations. Consequently, generating employment and boosting firm income. Originality/value: The paper provides evidence of the performance of small business in Lagos State Nigeria. This paper extends understanding of entrepreneurial self-efficacy and managerial capabilities among owner-managers of small businesses, and should be of particular interest to entrepreneurs, policy-makers and academia. The paper provides a new interpretation of existing sources particularly the key indicators used for the purpose of determining managerial capabilities and performance of small businesses. This research contributes to the need for greater clarity and knowledge small business sector
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Meghani, Ankita, Manjula Sharma, Tanya Singh, et al. "Enhancing COVID-19 Vaccine Uptake among Tribal Communities: A Case Study on Program Implementation Experiences from Jharkhand and Chhattisgarh States, India." Vaccines 12, no. 5 (2024): 463. http://dx.doi.org/10.3390/vaccines12050463.
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Tribal populations in India have health care challenges marked by limited access due to geographical distance, historical isolation, cultural differences, and low social stratification, and that result in weaker health indicators compared to the general population. During the pandemic, Tribal districts consistently reported lower COVID-19 vaccination coverage than non-Tribal districts. We assessed the MOMENTUM Routine Immunization Transformation and Equity (the project) strategy, which aimed to increase access to and uptake of COVID-19 vaccines among Tribal populations in Chhattisgarh and Jharkhand using the reach, effectiveness, adoption, implementation, and maintenance framework. We designed a qualitative explanatory case study and conducted 90 focus group discussions and in-depth interviews with Tribal populations, community-based nongovernmental organizations that worked with district health authorities to implement the interventions, and other stakeholders such as government and community groups. The active involvement of community leaders, targeted counseling, community gatherings, and door-to-door visits appeared to increase vaccine awareness and assuage concerns about its safety and efficacy. Key adaptations such as conducting evening vaccine awareness activities, holding vaccine sessions at flexible times and sites, and modifying messaging for booster doses appeared to encourage vaccine uptake among Tribal populations. While we used project resources to mitigate financial and supply constraints where they arose, sustaining long-term uptake of project interventions appears dependent on continued funding and ongoing political support.
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Teja, Krishna. "COMPARATIVE ANALYSIS OF AI- DRIVEN AND TRADITIONAL FINANCIAL CREDIT RISK MODEL IN REAL ESTATE SUPPLY CHAINS." International Scientific Journal of Engineering and Management 04, no. 05 (2025): 1–7. https://doi.org/10.55041/isjem03323.
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Abstract: The assessment of credit risk in the real estate supply chain is an essential part of financial risk management that influences investment decisions, financial stability, and the health of the overall real estate segment. Traditional financial credit risk models have long been used for the assessment of borrower credibility and potential default prediction with historical financial data, credit score, and some various financial ratios, while other methods could complement this approach. Although these conventional approaches have some merit, they frequently fail in capturing real-time market fluctuations, new emerging risks, and complex interdependencies that build creditworthiness. The introduction of artificial intelligence (AI) and machine-learning technologies has planted the seeds of change in the credit risk analysis horizon. AI-based models have given way to advanced analytical techniques that use big data, predictive analytics, and real-time insights to assess risk dynamically and more accurately. This particular paper gives a thorough comparison between the AI-driven and the traditional financial credit risk models alongside their methodologies and performance on prediction, adaptability, and limitation. Credit risk assessment is AI-driven because it utilizes machine learning algorithms to process both structured and unstructured data of large sizes to identify so-called hidden behaviours that conventional models are not able to detect. Real-time market conditions as well as transaction behaviours and macroeconomic indicators are incorporated in AI risk models to improve accuracy and timeliness of risk evaluation. Such models also help financial institutions, lenders, and investors of the real estate sector in decision-making, thus reducing possible financial losses and improving total risk management strategies. On the contrary, traditional models remain relevant since they are regulatory-compliant, transparent, and rely on well-documented financial indicators. They might be slower in reacting to changing market conditions, yet they maintain an aspect of interpretability that is usually absent in AI models. The regulatory authorities and financial institutions are sceptical of the black box of AI models within which lies the accountability, ethical considerations, and potential biases woven into machine-learning algorithms. Data privacy issues and regulatory frameworks concerning AI adoption in financial risk assessment remain reverse challenges that require immediate attention. By systematically comparing AI techniques with the classic credit risk models, the study delineates some of the parameters of distinction, including accuracy, scalability, cost-effectiveness, and applicability in the real world for the real estate sector. Two comparison tables depict the efficiency and application of the two approaches, along with usefulness in contrasting their efficacy. The results, though, suggest that AI-based credit risk models possess superior predictive accuracy, adaptability, and risk mitigation when weighed against traditional methods; yet, those features need to be balanced against regulatory oversight and ethical viewpoints to allow for successful implementation. Ultimately, the aforementioned study shows that innovation and regulatory compliance should be seen as two sides of the same coin for credit risk evaluation. The application of AI for the financial risk evaluation process reconstructively resembles giving an identity to the rehabilitation of the entire real estate supply chain by making decision-making more proactive and also helping in mitigating defaults. However, the transition phase from conventional models to AI-driven models needs a holistic understanding of both these approaches, along with their relative pros and cons. With an active evolution of AI technologies, future works may focus on developing transparent, non-biased, and interpretable AI systems that comply with available industry regulations and ethical principles, so that their adoption in real estate credit risk management can be considered responsible. Keywords: Risk of Credit, Supply Chain in the Real Estate sector, Financial Stability, Conventional Templates of Credit, Models for Credit Powered by AI, Machine Learning, Big Data Analytics, Predictive Analytics, Risk Evaluation Recurrently, Default Risk Mitigation, Decision making in Investments, Credit Scoring, Financial Ratios, Risk Management Strategies, Efficiency of Models, Ethics in AI, Regulatory Compliance.
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Simon Kaggwa, Tochukwu Onunka, Prisca Ugomma Uwaoma, Okeoma Onunka, Andrew Ifesinachi Daraojimba, and Nsisong Louis Eyo-Udo. "EVALUATING THE EFFICACY OF TECHNOLOGY INCUBATION CENTRES IN FOSTERING ENTREPRENEURSHIP: CASE STUDIES FROM THE GLOBAL SOUT." International Journal of Management & Entrepreneurship Research 6, no. 1 (2024): 46–68. http://dx.doi.org/10.51594/ijmer.v6i1.695.
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This study conducts a comprehensive evaluation of the effectiveness of Technology Incubation Centres (TICs) in nurturing entrepreneurship, focusing on case studies from diverse regions of the Global South. With the growing emphasis on innovation and technology-driven economic development, TICs have emerged as vital entities in fostering entrepreneurial ecosystems. The research employs a mixed-methods approach, combining quantitative analysis and qualitative case studies. The quantitative aspect involves assessing key performance indicators, including startup success rates, job creation, and financial sustainability of TICs. Qualitative investigations delve into the nuanced experiences of entrepreneurs, exploring the support mechanisms provided by TICs, challenges faced, and the overall impact on their ventures. The case studies span various contexts within the Global South, encompassing regions with differing economic, social, and cultural landscapes. By examining TICs in diverse settings, the study aims to draw insights into the contextual factors influencing the efficacy of incubation programs and their relevance in different entrepreneurial ecosystems. Preliminary findings suggest a correlation between the success of TICs and factors such as effective mentorship, access to funding, and collaborative networks. However, challenges such as resource constraints, regulatory hurdles, and variations in local market dynamics pose significant barriers to the optimal functioning of TICs. The study contributes to the existing literature by offering a nuanced understanding of the role and impact of TICs in the Global South. Findings will inform policymakers, entrepreneurs, and stakeholders about the specific contextual considerations that influence the efficacy of TICs, ultimately aiding in the refinement of strategies to enhance entrepreneurship and innovation in emerging economies. As the Global South increasingly positions itself as a hub for technological innovation, this research provides timely insights into the evolving landscape of entrepreneurial support structures, offering practical implications for the future development of TICs and fostering a conducive environment for sustained entrepreneurial growth.
 Keywords: Technology Incubation Centres, Entrepreneurship, Global South, Case Studies, Innovation, Economic Development.
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Guo, Pingping, Yin Jin, Zhenzhen Xiang, et al. "Web-based interventions for pregnant women with gestational diabetes mellitus: a systematic review and meta-analysis protocol." BMJ Open 12, no. 6 (2022): e061151. http://dx.doi.org/10.1136/bmjopen-2022-061151.
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IntroductionGestational diabetes mellitus (GDM) is one of the most prevalent diseases during pregnancy, which is closely associated with many short-term and long-term maternal and neonatal complications and can incur heavy financial burden on both families and society. Web-based interventions have been used to manage GDM because of the advantages of high accessibility and flexibility, but their effectiveness has remained inconclusive. This systematic review and meta-analysis aims to comprehensively investigate the multidimensional effectiveness of web-based interventions for pregnant women with GDM, thereby aiding implementation decisions in clinical settings.Methods and analysisThis systematic review protocol strictly adheres to the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols guidelines. Six electronic databases (PubMed, Web of Science, Cochrane Central Register of Controlled Trials, Embase, CINAHL and PsycINFO) will be comprehensively searched from their inception to 26 January 2022 to identify randomised controlled trials and controlled clinical trials regarding the efficacy of web-based interventions for pregnant women with GDM on glycaemic control, behavioural outcomes, cognitive and attitudinal outcomes, mental health, maternal and neonatal clinical outcomes, and medical service utilisation and costs. Two reviewers will independently conduct the study selection, data extraction and quality assessment. The methodological quality of included studies will be assessed using the Effective Public Health Practice Project assessment tool. The overall meta-analyses for each of the interested outcomes will be performed if the outcome data are sufficient and provides similar effect measures, as well as subgroup analyses for glycaemic control indicators based on the different types of intervention format, interactivity and technology. We will conduct a qualitative synthesis for studies that cannot be quantitatively synthesised.Ethics and disseminationEthics approval is not required for this review as no human participants will be involved. The results will be disseminated via a peer-reviewed journal or an academic conference.PROSPERO registration numberCRD42022296625.
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Oren, Ohad. "Cost-Effectiveness Analyses of FDA-Approved Drugs for Cancer Indications, 2015-2017." Blood 132, Supplement 1 (2018): 5833. http://dx.doi.org/10.1182/blood-2018-99-109707.
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Abstract Background: In addition to a drug's anti-tumor efficacy and tissue toxicity, it is now apparent that affordability is a pivotal factor determining the net therapeutic value of an anti-cancer compound. Cost-Effectiveness Analyses (CEA) are therefore key to appreciating the overall balance between the clinical and economical repercussions of a pharmaceutical agent. We aimed to investigate the cost-effectiveness data of newly approved drugs in cancer care. Methods: The FDA website was reviewed for all drugs approved for any cancer indication between the years 2015-2017. A systematic search of Pubmed and Google Scholar was conducted for Cost-Effectiveness Analyses (CEA) of each of these medications. Average Wholesale Prices were collected from Uptodate.com. Results: 30 drugs were approved for cancer indications in 2015-2017. Each of the approved drugs had an average of 1.27 CEA studies (range 0-6). 4 of the 38 (10.5%) available CEAs were done before approval of an index drug. 18 (60%) of the approved medications had an available published CEA at the time of our search (March, 2018). Partition survival modelling (31.5%) and Markov modelling (26.3%) were the most common methods of analysis. 47.3% (18/38) of the CEAs were thought to reflect a favorable ICER per the analysis's investigators. The nature of the malignancy (solid versus hematologic) did not distinguish between the mean number of a drug's CEAs (1.25; 1.2) but drugs for solid cancer indications were less likely to be associated with a favorable CE assessment compared with hematological cancers (47.8% versus 58.3%). "Expensive" drugs (defined as monthly cost greater than 8,618 dollars) had a lower mean number of CEA studies than "less expensive" drugs (1.09 versus 1.36) and were less likely to be associated with a favorable cost-effectiveness profile (27.2% versus 73.6%). 47.3% (18/38) of the economic evaluations were published as conference proceeding/abstract, 36.8% (14/38) were in the form of a full-text article, and the remainder as journal letters or conference posters (6/38; 15.7%). Conclusion: Insufficient data on the cost-effectiveness profile of novel anti-cancer medications jeopardises our ability to determine their real value. Although these drugs are being routinely used in a large-scale fashion, gaps persist as to their financial harm relative to the associated (potential) clinical benefit. Our study shows that recently-approved anti-cancer drugs have a very small number of CEAs to back their clinical-societal merit. Paradoxically, more costly drugs have fewer CEAs compared with cheaper drugs. Also of note, the majority of the analyses followed the approval of the drug, were published in a non full-text format, and were associated with non-sustainable ICER values. We argue that publication of rigorous, peer-reviewed CEAs should be a mandatory pre-approval step for academia/industry and serve as a pre-requisite to the routine distribution and usage of new cancer-directed medications. Disclosures No relevant conflicts of interest to declare.
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Ribeiro, Fernanda Antunes, Lidiane Soares Sodré da Costa, Aline Cristina Pedroso, et al. "Evaluating multifaceted strategies to prevent nasoenteral tube complications and achieve significant cost savings in critically ill patients: the ENHANCE-CRIT trial." BMJ Open Quality 14, no. 1 (2025): e003177. https://doi.org/10.1136/bmjoq-2024-003177.
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BackgroundNasoenteral tube (NET) use is common in critically ill patients but is associated with significant complications, including accidental dislodgement, malpositioning in the bronchial tree or mechanical failures, which can impede nutritional therapy. These complications often lead to adverse events that increase hospital stay, costs, and patient morbidity.ObjectiveThis study aimed to reduce complications related to the placement and maintenance of NETs in critically ill patients using multifaceted strategies.MethodsWe conducted an observational cohort study between February 2022 and March 2023 using the Institute for Healthcare Improvement’s (IHI) Model for Improvement. Data were collected from electronic forms, and process indicators were analysed for adequacy of tube fixation and complication rates. The study compared preintervention data to outcomes following five strategies: nasal bridle fixation, staff training, integration of a decision-support tool, restructuring of the NET care plan and practical simulation-based training.ResultsAfter implementing the interventions, complications related to NET decreased from 41% to 28%, preventing 200 adverse events. This reduction led to an improvement in patient outcomes, including a shorter hospital stay by 10 days on average, freeing up a total of 6520 bed days. The intervention not only reduced complications but also optimised hospital resource utilisation. An economic analysis revealed total savings of US$95 208, demonstrating both clinical efficacy and financial benefits.ConclusionThe multifaceted strategies significantly reduced NET-related complications, improved patient outcomes, and enhanced resource efficiency in critical care settings. These interventions demonstrated substantial cost-effectiveness, underscoring the value of structured, preventive measures in improving patient safety and reducing healthcare costs.
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Dutko, Н. "EVALUATION OF THE EFFECTIVENESS OF PREVENTION AND TREATMENT OF CHRONIC CATARRHAL GINGIVITIS IN CHILDREN WITH OLIGOPHRENIA." Актуальні проблеми сучасної медицини: Вісник Української медичної стоматологічної академії 19, no. 4 (2019): 49–53. http://dx.doi.org/10.31718/2077-1096.19.4.49.
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Prevention of hard dental tissue diseases and periodontal diseases in children is one of the urgent issues of pediatric dentistry. This is especially true for the category of children with psychoneurological pathology. Due to the high prevalence of dental diseases, decreased immunity, and sometimes difficult assess to dental care for children with mental retardation, dental disease prevention is the most effective measure to maintain the normal functioning of the dentofacial apparatus. The results obtained by examining the children with oligophrenia allowed us to work up an algorithm for therapeutic and preventive measures aimed at preventing the development of oral tissue diseases in these children. The algorithm included measures for primary and secondary prevention of periodontal diseases. To assess the efficacy of the algorithm, the study involved a group of 55 mentally impaired children. The control group consisted of 50 children with oligophrenia, who were treated by traditional methods.
 Taking into account the burdened mental status of the children when developing the individual hygiene regimens, a motivational approach was worked out in co-operation with a psychiatrist and a paediatrician. Evaluation of treatment outcomes in the groups was performed in 1 month after the completion of the full course of treatment and prevention and in long-term intervals, in 6 and 12 months. Clinical evaluation of treatment efficacy was performed according to the following criteria: "normalization", "improvement", "progression". In one month following the regimen proposed, the "normalization" of the affected tissues was diagnosed in 40 children of the main group (72,73 ± 6,06%), p <0,01. The "improvement" of periodontal tissue condition was observed in 10 children (18.18 ± 5.25%), p <0.05. Only 5 children in the main group (9.09 ± 3.91%) failed to demonstrate any positive changes.
 In 6 months following the application of the treatment and prevention algorithm, the children of the main group demonstrate the "normalization" of periodontal tissue in 65,45 ± 6,47% of cases, while in the children of the control group this indicator was twice lower, 32,00 ± 6.66%, p <0.01. The "improvements" were found in 21.82 ± 5.62% of the children in the main group and in 14.00 ± 4.96% of the children in the control group, p <0.05. No effect of treatment and progression of inflammatory process were observed in 7 children of the main group that was in 4.2 times less than in children of the control group. In long-term follow-up, in 1 year, 61.82 ± 6.61% of children with oligophrenia, who received the treatment and prophylactic measures proposed demonstrated the "normalization" of the periodontal tissue health. In the children treated with standard methods, this indicator was 2.2 times lower (28.00 ± 6.41%), p <0.01. The "improvement" of periodontal condition was recorded in 27.27 ± 6.06% of children in the main group versus 10.00 ± 4.28% of the children in the control group, p <0.01. Treatment for 10.91 ± 4.24% of the children of the main group failed to be effective, but for the children of the control group this indicator was in 5.7 times higher during the long-term observation.
 The data obtained indicate the lack of effectiveness of standard methods of treatment for chronic catarrhal gingivitis and confirm the high efficiency of the developed algorithm for treatment and prevention of dental diseases in children with oligophrenia.
 The authors of this article confirm that the research and publication of the results were not associated with any conflicts regarding commercial or financial relations, relations with organizations and/or individuals who may have been related to the study, and interrelations of co-authors of the article.
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Ahmar, Ansari Saleh. "BetaSutte: Applying Novelty in Data Forecasting with the Modified Trend-Augmented α-Sutte Indicator, A Case Study on Bank Mandiri (BMRI) Stock Prices". Journal of Applied Science, Engineering, Technology, and Education 6, № 2 (2024): 211–24. https://doi.org/10.35877/454ri.asci3826.
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This study assesses the effectiveness of the BetaSutte forecasting model, an enhanced version of the α-Sutte Indicator, dubbed the Modified Trend-Augmented α-Sutte Indicator, in forecasting the stock prices of Bank Mandiri (BMRI). Data from investing.com, spanning January 2018 to December 2023, was divided into training and testing subsets to both develop and validate the forecasting model, ensuring it performs well across unseen data. BetaSutte builds on the foundational α-Sutte by integrating advanced trend analysis, mitigating the influence of outliers, and utilizing automatic parameter optimization to boost forecasting precision. The efficacy of BetaSutte is evaluated against well-established models such as SVR, XGBoost, and ARIMA. ARIMA was chosen for its detailed management of time-series data via autoregressive, differencing, and moving average components. In contrast, SVR and XGBoost are recognized for their strong predictive performance. The performance of these models was gauged using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). These metrics shed light on the extent of forecasting errors and the percentage of relative errors, respectively, providing a comprehensive view of each model’s predictive accuracy for BMRI stock prices. The results demonstrated that BetaSutte outstripped the other models in terms of RMSE and MAPE, highlighting its enhanced ability to accurately reflect the dynamics of BMRI’s stock prices with greater precision and dependability. This establishes BetaSutte as a formidable tool in financial forecasting, particularly valuable in environments characterized by volatile market conditions and unpredictable data patterns.
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Buroker, T. R., M. J. O'Connell, H. S. Wieand, et al. "Randomized comparison of two schedules of fluorouracil and leucovorin in the treatment of advanced colorectal cancer." Journal of Clinical Oncology 12, no. 1 (1994): 14–20. http://dx.doi.org/10.1200/jco.1994.12.1.14.
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PURPOSE To compare two commonly used schedules of fluorouracil (5FU) and leucovorin in the treatment of patients with advanced metastatic colorectal cancer. Each of these dosage administration schedules has been demonstrated to be superior to single-agent bolus 5FU in previous controlled trials. PATIENTS AND METHODS Three hundred seventy-two ambulatory patients with metastatic colorectal cancer were stratified according to performance status, and presence and location of any measurable indicator lesion(s). They were then randomized to receive chemotherapy with one of the following regimens: (1) intensive-course 5FU plus low-dose leucovorin (5FU 425 mg/m2 plus leucovorin 20 mg/m2 intravenous [IV] push daily for 5 days with courses repeated at 4- to 5-week intervals); (2) weekly 5FU plus high-dose leucovorin (5FU 600 mg/m2 IV push plus leucovorin 500 mg/m2 as a 2-hour infusion weekly for 6 weeks with courses repeated every 8 weeks). RESULTS Three hundred sixty-two of 372 patients randomized (97.3%) were eligible and included in the analysis. Three hundred forty-six patients (95.6%) have died. There were no significant differences in therapeutic efficacy between the two 5FU/leucovorin regimens tested with respect to the following parameters: objective tumor response (35% v 31%), survival (median, 9.3 v 10.7 months), and palliative effects (as assessed by relief of symptoms, improved performance status, and weight gain). There were significant (P < .05) differences in toxicity, with more leukopenia and stomatitis seen with the intensive-course regimen, and more diarrhea and requirement for hospitalization to manage toxicity with the weekly regimen. Financial cost was also higher with the weekly regimen. CONCLUSION Intensive-course 5FU plus low-dose leucovorin appears to have a superior therapeutic index compared with weekly 5FU plus high-dose leucovorin using the dosage administration schedules applied in this study based on similar therapeutic effectiveness, but lower financial cost, and less need for hospitalization to manage chemotherapy toxicity.
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Pratama, Alfian Wahyu, and Kusuma Ratnawati. "Pengaruh Financial Knowledge, Financial Experience, Dan Financial Self-Efficacy Terhadap Financial Behavior." Jurnal Management Risiko dan Keuangan 3, no. 1 (2024): 37–44. http://dx.doi.org/10.21776/jmrk.2024.03.1.04.
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The objective of this research is to identify and understand the influence of financial knowledge, financial experience, and financial self-efficacy on financial behavior. The population of this quantitative replication study is Shopee PayLater users in Malang City. Referring to Hair et al. (2010), the sample number of this research is ten times the number of its indicators. Because this research uses eleven indicators, 110 people were involved as the sample, selected using a purposive sampling technique. The research data was harvested from 5-point Likert scale questionnaires and was analyzed using PLS-SEM in SmartPLS 3.3.3. This research finds that financial knowledge, financial experience, and financial self-efficacy have a significant effect on the financial behavior of Shopee PayLater users. This indicates that they understand the policies and procedures for calculating interest or additional fees charged by Shopee PayLater. This understanding indirectly makes them more careful in using this service. In addition, users who have experience in using alternative financial services, such as consumer credit services, tend to have a better understanding about how such services work and what their financial impact is. Furthermore, Shopee PayLater users who have good financial self-efficacy are also more aware of the financial consequences of their decisions. This awareness leads them to better financial management. Abstrak Penelitian ini betujuan untuk mengetahui dan memahami pengaruh financial knowledge; financial experience; financial self-efficacy terhadap Financial Behavior. Kategori penelitian yang digunakan dalam penelitian ini merupakan penelitian kuantitatif dengan sifat penelitian replikasi. Jumlah sampel yang digunakan dalam penelitian ini sebanyak 110 (11 indikator dikali faktor pengali 10). Tehnik pengambilan sampel penelitian menggunakan purposive sampling, dengan kuisoner skala likert 1-5 sebagai metode pengambilan data. Tehnik analisis data yang digunakan yaitu PLS-SEM yang dibantu dengan software SmartPLS 3.3.3. Hasil penelitian ini menjelaskan bahwa financial knowledge, financial experience, dan financial self-efficacy berpengaruh secara signifikan pada financial behavior dari pengguna Shopee PayLater. Hasil ini mengindikasin bahwa pengguna Shopee PayLater dalam penelitian ini memamahi kebijakan dan prosedur perhitungan bunga atau biaya tambahan dari Shopee PayLater, sehingga secara tidak langsung dapat pengarahkan pada perilaku pengguna Shopee untuk lebih berhati-hati dalam menggunakan layanan tersebut. Tidak hanya itu, individu yang memiliki pengalaman dalam menggunakan layanan keuangan alternatif, seperti layanan kredit konsumen cenderung memiliki pemahaman yang lebih baik tentang bagaimana cara kerja layanan tersebut dan dampak keuangan dari penggunaannya. Lebih jauh, pengguna Shopee PayLater yang memiliki financial self-efficacy yang baik juga akan lebih sadar akan konsekuensi keuangan dari setiap keputusan, sehingga hal tersebut mengarahkan pada perilaku individu dalam manajemen keuangan yang lebih baik.
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Merezhko, Nina, Volodymyr Indutnyi, and Kateryna Pirkovich. "Indexes of effectiveness of antique internet auctions." InterConf, no. 42(189) (February 20, 2024): 109–23. http://dx.doi.org/10.51582/interconf.19-20.02.2024.011.
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Algorithms for calculating indicators of liquidity, the fall-increase of the financial efficiency of the auction, the duration of waiting for the sale of goods and the activity of traders, as well as some others, necessary for evaluating the work of Internet auctions of cultural values and forecasting indicators of the financial efficiency of participation in them, are described.
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Bilous, Andrii. "Theatre effectiveness calculation." Scientific journal “Library Science Record Studies Informology” 26, no. 1 (2024): 49–57. http://dx.doi.org/10.63009/cac/1.2024.49.
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With its origins dating back to ancient times, theatre faces competition from more modern and technological forms of entertainment in the 21st century. Consequently, the question of genre demand and its effectiveness – both mental and economic – is quite relevant. For Ukrainian theatre, which has faced the challenges of a full-scale invasion, the issue of effectiveness is even more significant. This study aims to determine the main indicators for assessing the effectiveness of theatres by analysing key performance indicators and financial aspects of institutional management. Using such methods as statistical analysis, comparison, synthesis, and forecasting, the research examined the current state of the industry and its economic indicators. As a result of the study, an increase in indicators of gross income, the number of employees, and personnel costs in Ukrainian theatres during 2015-2018 was proven, and the impact of COVID-19 on the activities of institutions was assessed using the example of Lviv. Separately, the impact of the war on the industry was studied, the mistakes made by Crimean theatres during the temporary occupation were assessed, and an analysis of the repertoire of relocated institutions on new stages was conducted. A unified system for comparing the economic indicators of various objects of performing arts was also developed, which is based on such a universal unit of measurement of goods as one spectator seat. The practical significance of the study lies in the creation of a set of economic effectiveness indicators – such as sales, cost, and troupe occupancy – which can be useful for relevant ministries and departments in calculating actual financial indicators and planning
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Kalyuzhnaya, E. S., V. Yu Rogozhkina, E. L. Dmitrieva, and E. V. Bykovskaya. "Financial Stability and Competitiveness as Indicators of Company Performance Efficacy." Voprosy sovremennoj nauki i praktiki. Universitet imeni V.I. Vernadskogo, no. 3(77) (2020): 066–72. http://dx.doi.org/10.17277/voprosy.2020.03.pp.066-072.
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It is shown that the economic assessment of the company performance efficacy is of great importance, both in order to make the right management decisions on emerging problems, and to maximize profits and ensure a stable financial position of the enterprise in the future, and the need to assess the financial condition of both your enterprise and existing potential competitors. Some indicators that characterize the efficacy of the enterprise are considered: financial stability and competitiveness as the most relevant indicators that can reflect the real picture in the activities of any enterprise.
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Rumyantseva, Olga I., Natalia A. Kostenko, and Svetlana S. Zemlyakova. "Evaluation of the pharmacoeconomical effectiveness of triple therapy of chronic obstructive pulmonary disease in employees at the large transport company." Russian Journal of Occupational Health and Industrial Ecology 63, no. 6 (2024): 399–407. http://dx.doi.org/10.31089/1026-9428-2024-64-6-399-407.
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Introduction. According to the literature, exacerbation of chronic obstructive pulmonary disease (COPD) is a key factor that leads to an increase in the severity of the symptoms of the disease, deterioration of lung function, decreased exercise tolerance and, as a result, contribute to an increase in mortality. The prevention of exacerbations of COPD has social and economic significance and should be a primary task in the treatment of this disease. It is known that the use of fixed combinations improves patients' adherence to therapy by about 2 times, which, in turn, leads to an increase in the effectiveness of treatment. The study aims to evaluate the pharmacoeconomical effectiveness of triple therapy for chronic obstructive pulmonary disease using the example of a fixed triple combination of budesonide+glycopyrronium bromide+formoterol (BUD/GLI/FORM). Materials and methods. 698,359 employees of a large transportation company participated in the study. 2,154 of whom had an updated diagnosis of COPD, and 193 cases of COPD were registered for the first time. The authors conducted a clinical and economic analysis taking into account the current recommendations of the Federal State Budgetary Institution "Center for Expertise and Quality Control of Medical Care" of the Ministry of Health, Russian Federation, published in December 2016. The authors have used methods such as cost estimation, budget impact analysis, and cost–effectiveness analysis. To conduct a pharmacoeconomical analysis, the researchers used data from clinical studies comparing the effectiveness of three types of therapy in patients with moderate and severe COPD on an outpatient basis. The cost of comparison drugs is taken from the official website of the state register of maximum selling prices. The researchers analyzed the costs of the employer in accordance with the Recommendations for heads of medical institutions and attending physicians, specialist doctors of the executive bodies of the Social Insurance Fund of the Russian Federation "Approximate terms of temporary disability in the most common diseases and injuries" (in accordance with ICD-10) (approved by the Ministry of Health of the Russian Federation and the Social Insurance Fund of the Russian Federation dated August 21 2000 No. 2510/9362-34, 02-08/10‑1977P). The authors determined the cost–effectiveness ratio (CER) using the formula: CER=DC/Ef, where DC (direct costs) is the direct cost of medical technology (cost of treatment, rubles); Ef is an indicator of the effectiveness of therapy, which is expressed in the percentage of patients who successfully completed the course, which allows us to evaluate the amount of cost to ensure stable remission in 1 patient. Results. The results clearly indicate a significant reduction in financial losses of the enterprise for the absence of an employee due to temporary disability (TD) against the background of using a triple combination of BUD/GLI/FORM compared with alternative schemes. The economic effect amounted to more than 124 million rubles compared with the appointment of dual therapy DAX/DBA and more than 61 million rubles compared with the appointment of dual therapy IGCS/DDBA. The assessment of indirect costs in case of potential loss of working capacity and labor productivity as a result of exacerbation of COPD from the point of view of the macroeconomics of the region demonstrated a decrease in gross domestic product (GDP) losses in case of exacerbation and death among patients receiving BUD/GLI/FORM. Conclusion. BUD/GLI/FORM therapy in a comparative study is dominant, characterized by excellent clinical efficacy and reduced overall costs associated with a decrease in the frequency of moderate and severe exacerbations of COPD. Ethics. This study was performed in accordance with the rules of good clinical practice and the Helsinki Declaration, and did not require the conclusion of the Ethics Committee.
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Ivanova, T. "Methodological foundations of evaluation of effectiveness indicators of small-scale business activities." Bulletin of Taras Shevchenko National University of Kyiv. Economics, no. 134 (2013): 27–29. http://dx.doi.org/10.17721/1728-2667.2013/134-1/7.
Full textAbstract:
The methodological approach to the measurement of financial indicators of small-scale enterprises has been developed. It enables to secure the comparability of financial condition indicators and the results of small-scale enterprise activities, and also to develop the methods of vertical integral estimate calculation at separate aspects of financial condition and the results of smallscale enterprise activities.
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Pashkova, T. A., I. P. Geraschenko, and N. L. Seitakhmetova. "MODEL FOR EVALUATING THE EFFECTIVENESS OF EXTERNAL MUNICIPAL FINANCIAL CONTROL BODIES." BULLETIN 2, no. 390 (2021): 106–12. http://dx.doi.org/10.32014/2021.2518-1467.57.
Full textAbstract:
External municipal financial control bodies based on the existing models of the evaluating the effec-tiveness of their activities. The authors of this article have developed the model of the effectiveness of the external municipal financial control bodies, composed of complex criteria for control and expert-analytical activities, including the system of indicators for comprehensive assessment of performance, efficiency, intensity and dynamism of the activities of external municipal financial control bodies. The features of the activities of the external municipal financial control bodies of the different subjects of the Russian Federation, their powers, scale and differences in the level of economic development, quantitative results of the activities, the quality of external municipal financial cont-rol, as well as scientific approaches of Russian and foreign authors were taken into account. In order to exclude the impact on the comprehensive assessment of the effectiveness of the external municipal financial control bodies of the dimension of indicators, the method was chosen for normalizing the values of indicators relative to the highest and lowest values, with the conversion of the values of indicators to the point scale in the range from 1 to 10. The convo-lution of point values of indicators into the complex criterion is carried out on the basis of normalization of the sum of actual point values relative to the sum of the maximum possible point value of the indicator. On the basis of complex criteria, generalized criterion for the effectiveness of the external municipal financial control bodies is determined; it is calculated as the geometric weighted average of complex criteria. For the purpose of analyzing the effectiveness of the external municipal financial control bodies, the discriminant function divides them into more or less effective ones.