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Gladun, Anatoly, Julia Rogushina, and Martin Lesage. "Hierarchical Aggregate Assessment of Multi-Level Teams Using Competency Ontologies." Acadlore Transactions on AI and Machine Learning 2, no. 2 (2023): 55–65. http://dx.doi.org/10.56578/ataiml020201.
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Lesage, Martin. "Hierarchical Aggregate Assessment (HAA): An Assessment Process of Teams with Several Levels of Hierarchy in Education." Creative Education 07, no. 14 (2016): 1974–94. http://dx.doi.org/10.4236/ce.2016.714200.
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Moolchandani, Sanjay. "Exploring Bayesian Hierarchical Models for Multi-Level Credit Risk Assessment: Detailed Insights." International Journal of Computer Science and Information Technology 16, no. 3 (2024): 67–74. http://dx.doi.org/10.5121/ijcsit.2024.16306.
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In this paper, we examine the use of Bayesian Hierarchical Models (BHMs) for multi-level credit risk assessment while focusing on their advantages compared to conventional valuation approaches of singlelevel models. Unlike most traditional methodologies, which consider events either separately or condition on an aggregate measure, each of the BHMs systematically incorporates data from different levels — loan or obligor level and institution level — to provide a more holistic view of credit risk under numerous uncertainties and dependencies. The paper reviews basic theoretical underpinnings of BHMs, such as Bayesian inference and hierarchical Modeling, while giving examples on how these mechanisms work in practice within the context of estimating default risk. In addition, the paper outlines computational challenges, highlights the role of prior distributions, and explains that BHMs could potentially be combined with machine learning for dynamic risk assessments. The paper highlights a real-world application, and provides detailed insights into how BHMs can help improve both the accuracy and interpretability of credit risk assessments.
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Hrytsiuk, Yu I. "SOFTWARE QUALITY MANAGEMENT SYSTEM." Ukrainian Journal of Information Technology 4, no. 1 (2022): 1–20. http://dx.doi.org/10.23939/ujit2022.01.001.
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The Software quality management system has been developed. The system allows you to determine the state of software quality at each hierarchical level of the management system, taking into account the cost of ensuring the required quality. The system also allows the IT company manager to set the potential costs for the transition of software quality indicators from one state to another. It was found out that software quality is a multifaceted concept that can be adequately expressed by some hierarchical structure of characteristics and attributes, which is called the model of software product quality. To assess the quality of the software, a set of criteria and aggregate indicators were used, which maximally characterise it in terms of functionality and usage options. It is established that the software quality model is the main attribute of the complex system of its assessment. The quality model determines which characteristics of the software product must be taken into account when transiting the control system from one state to another. The level of software quality that meets the stated and indirect needs of various stakeholders and is relevant to them is directly reflected in the quality model of the software product in the form of its properties. The software quality model classifies product properties into characteristics and sub-characteristics. A method for selecting stressful variants of software quality state according to input criteria or aggregate indicators of its current quality has been developed. The method allows to determine the current state of software quality at each hierarchical level according to the corresponding aggregate indicator, taking into account its development cost. A method for selecting the optimal version of the software quality management system from the set of permissible has been developed. The method takes into account the structure of criteria and aggregated indicators of current software quality at each hierarchical level of the management system. It is established that the task of choosing the optimal version of the software quality management system belongs to the tasks of multicriteria optimization. The task takes into account the following criteria: portability of the product and its ease of maintenance, product safety and compatibility, ease of use of the product and reliability of its work, as well as the functional suitability of the product and its efficiency of use. An example of implementation of the software quality management system in both quantitative and cost ratio is given. The example allows us to understand the essence of the method of choosing the optimal variant for the management system, as well as the features of the method of selection of stressful variants of the system state according to two criteria or aggregate indicators.
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Deshpande, Sameer K., and Abraham Wyner. "A hierarchical Bayesian model of pitch framing." Journal of Quantitative Analysis in Sports 13, no. 3 (2017): 95–112. http://dx.doi.org/10.1515/jqas-2017-0027.
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Abstract Since the advent of high-resolution pitch tracking data (PITCHf/x), many in the sabermetrics community have attempted to quantify a Major League Baseball catcher’s ability to “frame” a pitch (i.e. increase the chance that a pitch is a called as a strike). Especially in the last 3 years, there has been an explosion of interest in the “art of pitch framing” in the popular press as well as signs that teams are considering framing when making roster decisions. We introduce a Bayesian hierarchical model to estimate each umpire’s probability of calling a strike, adjusting for the pitch participants, pitch location, and contextual information like the count. Using our model, we can estimate each catcher’s effect on an umpire’s chance of calling a strike. We are then able translate these estimated effects into average runs saved across a season. We also introduce a new metric, analogous to Jensen, Shirley, and Wyner’s Spatially Aggregate Fielding Evaluation metric, which provides a more honest assessment of the impact of framing.
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Shivakumar, Abhishek, Thomas Alfstad, and Taco Niet. "A clustering approach to improve spatial representation in water-energy-food models." Environmental Research Letters 16, no. 11 (2021): 114027. http://dx.doi.org/10.1088/1748-9326/ac2ce9.
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Abstract Currently available water-energy-food (WEF) modelling frameworks to analyse cross-sectoral interactions often share one or more of the following gaps: (a) lack of integration between sectors, (b) coarse spatial representation, and (c) lack of reproducible methods of nexus assessment. In this paper, we present a novel clustering tool as an expansion to the Climate-Land-Energy-Water-Systems modelling framework used to quantify inter-sectoral linkages between water, energy, and food systems. The clustering tool uses Agglomerative Hierarchical clustering to aggregate spatial data related to the land and water sectors. Using clusters of aggregated data reconciles the need for a spatially resolved representation of the land-use and water sectors with the computational and data requirements to efficiently solve such a model. The aggregated clusters, combined together with energy system components, form an integrated resource planning structure. The modelling framework is underpinned by an open-source energy system modelling tool—OSeMOSYS—and uses publicly available data with global coverage. By doing so, the modelling framework allows for reproducible WEF nexus assessments. The approach is used to explore the inter-sectoral linkages between the energy, land-use, and water sectors of Viet Nam out to 2030. A validation of the clustering approach confirms that underlying trends actual crop yield data are preserved in the resultant clusters. Finally, changes in cultivated area of selected crops are observed and differences in levels of crop migration are identified.
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Sii, H. S., J. Wang, A. G. Eleye-Datubo, J. B. Yang, and J. Liu. "Safety Assessment of FPSO Turret-Mooring System Using Approximate Reasoning and Evidential Reasoning." Marine Technology and SNAME News 42, no. 02 (2005): 88–102. http://dx.doi.org/10.5957/mt1.2005.42.2.88.
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Numerous acts of Parliament and statutory instruments that apply to floating production, storage, and offloading (FPSO) developments in the United Kingdom Continental Shelf, covering a wide range of issues, including health, technical safety, work place safety, lifting operations, environmental protection, and pollution prevention and control, are described. A comprehensive study of system safety evaluation of a typical turret-mooring system used on FPSOs is described in this paper. A safety assessment method suggested using approximate reasoning and evidential reasoning approaches is proposed in this study. Subjective safety modeling at the bottom level in a hierarchical framework is carried out using an approximate reasoning approach. The evidential reasoning method is used to combine or aggregate safety estimates at lower levels to produce the safety estimate at the system level. The four main subsystems(turret, fluid transfer system, turret transfer system, and interfacing system) are thoroughly examined in order to perform a subjective safety assessment of the turret-mooring system.
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Sorokin, A. A. "Investigation of Hierarchical Fuzzy Inference Systems, when Obtaining Integral Estimates of the Analyzed Objects." Informacionnye Tehnologii 27, no. 11 (2021): 582–91. http://dx.doi.org/10.17587/it.27.582-591.
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The purpose of this paper is to study the patterns of the formation of output values in hierarchical systems offuzzy inference. Hierarchical fuzzy inference systems (HFIS) are used to aggregate heterogeneous parameters during the assessment of the state of various elements of complex systems. The use of HFIS allows avoiding the "curse" of the dimension associated with a strong increase in the number and complication of the structure of the production rule, which is characteristic of conventional fuzzy inference systems (FIS), which aggregate the results of interaction of different values of input variables in one knowledge base. As part of the research, numerical experiments were carried out to study the features of the formation of output patterns in HFIS, based on FIS using the Mamdani and Takagi-Sugeno algorithms. As a result of the experiment, it was shown that the output values of the studied HFIS tend to be grouped in the region of fixed values, and the output pattern itself acquires a stepwise character. The revealed property allows using HFIS to distribute the objects of the analyzed sample into groups of states. This property can be used to solve problems of distributing objects into groups in conditions when it is difficult to form a training sample for machine learning methods, but at the same time there is knowledge of the expert group about the features of the functioning of the object of research. Additionally, the paper investigates the features of the formation of output patterns depending on the parameters of the membership functions describing the input variables in HFIS, which are based on FIS using the Mamdani algorithm and HFIS, which are based on FIS using the Takagi-Sugeno algorithm.
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Made, Felix, Ngianga-Bakwin Kandala, and Derk Brouwer. "Bayesian Hierarchical Framework from Expert Elicitation in the South African Coal Mining Industry for Compliance Testing." International Journal of Environmental Research and Public Health 20, no. 3 (2023): 2496. http://dx.doi.org/10.3390/ijerph20032496.
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Occupational exposure assessment is important in preventing occupational coal worker’s diseases. Methods have been proposed to assess compliance with exposure limits which aim to protect workers from developing diseases. A Bayesian framework with informative prior distribution obtained from historical or expert judgements has been highly recommended for compliance testing. The compliance testing is assessed against the occupational exposure limits (OEL) and categorization of the exposure, ranging from very highly controlled to very poorly controlled exposure groups. This study used a Bayesian framework from historical and expert elicitation data to compare the posterior probabilities of the 95th percentile (P95) of the coal dust exposures to improve compliance assessment and decision-making. A total of 10 job titles were included in this study. Bayesian framework with Markov chain Monte Carlo (MCMC) simulation was used to draw a full posterior probability of finding a job title to an exposure category. A modified IDEA (“Investigate”, “Discuss”, “Estimate”, and “Aggregate”) technique was used to conduct expert elicitation. The experts were asked to give their subjective probabilities of finding coal dust exposure of a job title in each of the exposure categories. Sensitivity analysis was done for parameter space to check for misclassification of exposures. There were more than 98% probabilities of the P95 exposure being found in the poorly controlled exposure group when using expert judgments. Historical data and non-informative prior tend to show a lower probability of finding the P95 in higher exposure categories in some titles unlike expert judgments. Expert judgements tend to show some similarity in findings with historical data. We recommend the use of expert judgements in occupational risk assessment as prior information before a decision is made on current exposure when historical data are unavailable or scarce.
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Bolsunovskaya, Marina V., Aleksei M. Gintciak, Zhanna V. Burlutskaya, Daria A. Zubkova, Alexandra A. Petryaeva, and Darya E. Fedyaevskaya. "Complex Method of the Consumer Value Estimation on the Way to Risk-Free and Sustainable Production." Sustainability 15, no. 2 (2023): 1273. http://dx.doi.org/10.3390/su15021273.
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Sustainable consumption and production strive for the rational management of natural resources, which implies a transition to the production of fewer goods with the greatest consumer value. Consequently, the consumer value assessment is a key task in the product and service design. However, a large number of applied practices for assessing consumer value is a challenge for researchers. Multiple heterogeneous solutions without a common classification and structure do not allow comparing methods with each other. Thus, there is a demand for some universal algorithm for assessing consumer value, which would be a model for the development of individual industry practices. Therefore, the present research aims to develop a universal algorithm for assessing consumer value, which is a unified sample. The work analyzes the current expertise in assessing consumer value. The paper provides a comparison of mathematical tools for aggregate indicators in order to develop a general formula for assessing consumer value. As a result, an algorithm for assessing consumer value has been developed, which includes the following stages: market segmentation by consumer groups, taking into account their personal characteristics and needs; product hierarchical division into groups according to indicators valuable to the consumer; selection of a scale for evaluating indicators; hierarchical convolution, calculation of the consumer value of selected indicators and their aggregation into a final assessment in accordance with coefficients obtained as a result of the initial data analysis. As part of the algorithm verification, an example of the implementation of the algorithm steps based on expert assessment of the tourist product characteristics is proposed. At the next stage of the study, a register of mathematical tools will be specified to ensure the implementation of the algorithm steps, and practical testing on real data on several products from different industries.
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He, Gengsheng, Yu Huang, Guori Huang, Xi Liu, Pei Li, and Yan Zhang. "Assessment of Low-Carbon Flexibility in Self-Organized Virtual Power Plants Using Multi-Agent Reinforcement Learning." Energies 17, no. 15 (2024): 3688. http://dx.doi.org/10.3390/en17153688.
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Virtual power plants (VPPs) aggregate a large number of distributed energy resources (DERs) through IoT technology to provide flexibility to the grid. It is an effective means to promote the utilization of renewable energy, and enable carbon neutrality for future power systems. This paper addresses the evaluation issue of DERs‘ low-carbon benefits, proposes a flexibility assessment model for self-organized VPP to quantify the low-carbon value of DERs’ response behavior in different time periods. Firstly, we introduce the definition of zero-carbon index based on the curve simultaneous rate of renewable energy and load demand. Then, we establish a multi-level self-organized aggregation method for virtual power plants, define the basic rules of DER, and characterize its self-organized aggregation as a Markov game process. Moreover, we use QMIX to achieve a bottom-up, hierarchical construction of VPP from simple to complex. Experimental results show that when users track the zero-carbon curve, they can achieve zero carbon emissions without reducing the overall load, significantly enhancing the grid’s regulation capabilities and the consumption of renewable energy. Additionally, self-organized algorithms can optimize the combinations of DERs to improve the coordination efficiency of VPPs in complex environments.
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Fong, Charmie, Simon Alesi, Aya Mousa, et al. "Efficacy and Safety of Nutrient Supplements for Glycaemic Control and Insulin Resistance in Type 2 Diabetes: An Umbrella Review and Hierarchical Evidence Synthesis." Nutrients 14, no. 11 (2022): 2295. http://dx.doi.org/10.3390/nu14112295.
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Background: Nutrient supplements are widely used for type 2 diabetes (T2D), yet evidence-based guidance for clinicians is lacking. Methods: We searched the four electronic databases from November 2015–December 2021. The most recent, most comprehensive, high-ranked systematic reviews, meta-analyses, and/or umbrella reviews of randomised controlled trials in adults with T2D were included. Data were extracted on study characteristics, aggregate outcome measures per group (glycaemic control, measures of insulin sensitivity and secretion), adverse events, and Grading of Recommendations Assessment, Development, and Evaluation (GRADE) assessments. Quality was assessed using A Measurement Tool to Assess Systematic Reviews Version 2.0 (AMSTAR 2). Results: Twelve meta-analyses and one umbrella review were included. There was very low certainty evidence that chromium, Vitamin C, and omega-3 polyunsaturated fatty acids (Ω-3 PUFAs) were superior to placebo for the primary outcome of glycated hemoglobin (HbA1c) (Mean Difference/MD −0.54, −0.54 and ES −0.27, respectively). Probiotics were superior to placebo for HbA1c (Weighted Mean Difference/WMD −0.43%). There was very low certainty evidence that Vitamin D was superior to placebo for lowering HbA1c in trials of <6 months (MD −0.17%). Magnesium, zinc, Vitamin C, probiotics, and polyphenols were superior to placebo for FBG. Vitamin D was superior to placebo for insulin resistance. Data on safety was limited. Conclusions: Future research should identify who may benefit from nutrient supplementation, safety, and optimal regimens and formulations.
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Mikheev, P. N. "Facet-Hierarchical Approach to the Classification of Climate Risks in the Context of the Economic Activity of the Enterprise". Issues of Risk Analysis 19, № 6 (2022): 34–49. http://dx.doi.org/10.32686/1812-5220-2022-19-6-34-49.
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The article presents a facet-hierarchical approach to the classification of climate risks in the context of the economic activity of an enterprise. The relevance of climate issues is due to the unprecedented pace of global warming in previous decades and the increased climate impact on the economy, not only at the macroeconomic level, but also at the level of individual organizations. An important task for the enterprise is to create such a risk classification system that will allow not only to identify and aggregate information about the risks associated with climate change in the context of economic activity, but also to ensure a rapid operative response to existing and emerging hazards. The purpose of this work is to study the possibilities of using the facet-hierarchical method of climate risk classification in application to risk management in the context of the economic activity of an enterprise. The structure of the classifier is built on the basis of the principle of integration of the enterprise activity, presented in the form of four main subsystems: object, process, design and environment. An important addition to the facet approach is the hierarchical structure of each facet. The hierarchy of subordinate features is built within each facet and allows taking into account, on the one hand, the multilateral nature of the climate impact, on the other hand, the complexity of the economic structure of the enterprise. The proposed method can be modified depending on specific practical tasks and integrated into the overall risk management system of the organization. The information obtained based on logically proven accounting, classification and risk assessment can contribute to the adoption of effective management decisions and increase the value of the company.
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Bachev, H., and G. Kharlamova. "Eco-System Approach for Assessing Agrarian Sustainability in Bulgaria." Bulletin of Taras Shevchenko National University of Kyiv. Economics, no. 203 (2019): 17–30. http://dx.doi.org/10.17721/1728-2667.2019/203-2/3.
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Ecosystem approach has been increasingly incorporated in the management and evaluation of sustainability levels in general and in agriculture in particular. Despite enormous progress in the theory and practice of this new area, still there is no consensus on how to assess the sustainability of agro-ecosystems due to diverse understandings, approaches, methods, employed data, etc. In Bulgaria there are practically no in-depth studies on sustainability level of diverse agro-eco-systems. This articles assesses the sustainability level of agro-ecosystems of different type in Bulgaria. A holistic hierarchical framework for assessing integral, economic, social and ecological sustainability of agro-ecosystems is suggested including 17 principles, 35 criteria, and 46 indicators and reference values. Assessment is made on overall and aspects sustainability of large (agro)ecosystems in four geographic regions and particular main and specific types of agro-ecosystems of the country. The assessment is based on first- hand information collected though in-depth interviews with the managers of “typical” farms in the respective ago-ecosystems. The study has found out that there is considerable differentiation in the level of integral sustainability in agricultural ecosystems of different types. There are also substantial variations in the levels of economic, social and ecological sustainability of agro- ecosystems of different type, and the critical indicators enhancing or deterring overall and particular sustainability of individual agro-ecosystems. Results of the integral agrarian sustainability level based on the micro agro-ecosystem (farm) data, are similar to the previous assessment based on the aggregate sectoral (statistical, etc.) data. Having in mind the importance of holistic assessments of this kind for improving agrarian sustainability, farm management and agrarian policies, they are to be expended and their precision and representation increased.
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Akinwole, Isreal Oluwatimileyin, Ayodele Christianah Adeboye, Isa Elabor, Gibson Alaiya, and Abdulganiyu A. Akangbe. "Heavy Metal Pollution Risk Assessment and Source Analysis in Warri River Sediments." Environmental Research, Engineering and Management 80, no. 4 (2024): 101–17. https://doi.org/10.5755/j01.erem.80.4.35739.
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Contaminated river sediment poses a threat to aquatic life and public health. The focus of this study was to investigate possible contamination of sediments from the Warri River. Triplicate samples were collected from ten sites of intense industrial activities. The sediments were analysed for heavy metals (HMs), anions, and physicochemical properties, including total hydrocarbon content (THC), an indicator of hydrocarbon contamination. The contamination level was evaluated using the following indices: contamination factor index (CF), geo-accumulation index (Igeo), enrichment factor (EF), ecological risk index (ERI), modified degree of contamination (mCd), pollution load index (PLI), risk index (RI) and sediment quality guidelines (SQG). Principal component analysis (PCA) and hierarchical clustering analysis (HCA) were used to identify the sources of HM contamination. The aggregate contamination indices (mCd, PLI and RI) indicate no contamination/pollution or ecological risk. Similarly, the single contamination indices (CF, Igeo, and ERI) showed low contamination and ecological risk for almost all heavy metals except for Cd, which was high in many sample locations. Most of the heavy metals exhibited values below both the threshold-effects level (TEL) and the probable-effects level (PEL), except for Cd, where 80% of locations recorded levels between TEL and PEL, suggesting possible ecological risk for Cd. PCA suggests that PC1, loaded with Fe, Zn, Ni, Cd and Cr, indicates anthropogenic activity. PC2, loaded with Mn and Pb, suggests both anthropogenic and geogenic origins. In conclusion, the possible HM contamination necessitates urgent government intervention to protect aquatic ecosystems and public health.
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Saruarov, Yerbolat, Gulnaz Nuskabayeva, Mehmet Ziya Gencer та ін. "Associations of Clusters of Cardiovascular Risk Factors with Insulin Resistance and Β-Cell Functioning in a Working-Age Diabetic-Free Population in Kazakhstan". International Journal of Environmental Research and Public Health 20, № 5 (2023): 3918. http://dx.doi.org/10.3390/ijerph20053918.
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Cardiovascular risk factors aggregate in determined individuals. Patients with Type 2 diabetes mellitus (T2DM) have higher cardiovascular This study aimed to investigate insulinresistance (IR) and β-cell function using the homeostasis model assessment (HOMA) indexes in a general Kazakh population and determine the effect he effect that cardiovascular factors may have on those indexes. We conducted a cross-sectional study among employees of the Khoja Akhmet Yassawi International Kazakh-Turkish University (Turkistan, Kazakhstan) aged between 27 and 69 years. Sociodemographic variables, anthropometric measurements (body mass, height, waist circumference, hip circumference), and blood pressure were obtained. Fasting blood samples were collected to measure insulin, glucose, total cholesterol (TC), triglycerides (TG), and high- (HDL) andlow-density lipoprotein (LDL) levels. Oral glucose tolerance tests were performed. Hierarchical and K-means cluster analyses were obtained. The final sample was composed of 427 participants. Spearmen correlation analysis showed that cardiovascular parameters were statistically associated with HOMA-β (p < 0.001) and not with HOMA IR. Participants were aggregated into the three clusters where the cluster with a higher age and cardiovascular risk revealed deficient β-cell functioning, but not IR (p < 0.000 and p = 0.982). Common and easy to obtain biochemical and anthropometric measurements capturing relevant cardiovascular risk factors have been demonstrated to be associated with significant deficiency in insulin secretion. Although further longitudinal studies of the incidence of T2DM are needed, this study highlights that cardiovascular profiling has a significant role not just for risk stratification of patients for cardiovascular prevention but also for targeted vigilant glucose monitoring.
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PUSHAK, Yaroslav, Natalia TURLENKO, and Olena ALEKSEYCHUK. "THEORETICAL-METHODICAL ASPECTS OF EVALUATION OF INVESTMENT MECHANISMS IN THE CONDITIONS OF SUSTAINABLE DEVELOPMENT OF PRODUCTION AREAS OF ECONOMY OF THE REGION." Ukrainian Journal of Applied Economics, no. 2 (2019): 104–13. http://dx.doi.org/10.36887/2415-8453-2019-2-12.
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Introduction. The study considers the theoretical and methodological aspects to determine the economic evaluation of investment mechanisms in the conditions of industrial spheres sustainable development of the region's economy. The purpose of the article is to study the theoretical and methodological aspects to the investment mechanisms assessment in the conditions of industrial spheres sustainable development of the region's economy. Results. The indicators groups for the investment attractiveness evaluation of the region by I. Blanco's method are considered. It is established that a significant drawback of the methodology is the lack of risk consideration. The significance of each synthesized indicator in the aggregate assessment of the region's investment attractiveness is calculated. A combination of investment risk assessment and investment potential is proposed in evaluating the region's investment attractiveness rating. An algorithm for calculating the integral index of region investment attractiveness in accordance with the content of the hierarchical scheme is proposed. The advantage of this technique is the ability to uniquely interpret the values obtained. A comprehensive approach to regions investment activity assessing based on integrated components of profitability and liquidity, including a number of smaller indicators, is considered. The estimation methodology involves taking into account the weights of the selected indicators, which are calculated on the basis of expert judgment. Conclusions. When analyzing the factors that affect the investment potential of the region, it should be taken into account that the investment climate in the region is affected by both internal, regional and external factors – from the state and other countries. Only a comprehensive approach to the region investment attractiveness assessing gives the opportunity to introduce an integrated characteristic of a particular region in terms of investment attractiveness taking into account all the factors and conditions of regional development, based on existing methods. Keywords: assessment, investments, projects, investing, regional development, investment attraction, methodology, methodical approach.
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Zhang, Ting, Mohammad R. Haider, Yehia Massoud, and J. Iwan D. Alexander. "An Oscillatory Neural Network Based Local Processing Unit for Pattern Recognition Applications." Electronics 8, no. 1 (2019): 64. http://dx.doi.org/10.3390/electronics8010064.
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Prolific growth of sensors and sensor technology has resulted various applications in sensing, monitoring, assessment and control operations. Owing to the large number of sensing units the the aggregate data volume creates a burden to the central data processing unit. This paper demonstrates an analog computational platform using weakly coupled oscillator neural network for pattern recognition applications. The oscillator neural network (ONN) has been studied over the last couple of decades for it’s increasing computational efficiency. The coupled ONN can realize the classification and pattern recognition functionalities based on its synchronization phenomenon. The convergence time and frequency of synchronization are considered as the indicator of recognition. For hierarchical sensing, the synchronization is detected in the first layer, and then the classification is accomplished in the second layer. In this work, a Kuramoto model based frequency synchronization approach is utilized, and simulation results indicate less than 160 ms convergence time and close frequency match for a simplified pattern recognition application. An array of 10 sensors is considered to affect the coupling weights of the oscillating nodes, and demonstrate network level computation. Based on MATLAB simulations, the proposed ONN architecture can successfully detect the close-in-match pattern through synchronization, and differentiate the far-out-match pattern through loss of synchronization in the oscillating nodes.
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Sorokin, Aleksandr Aleksandrovich, and Natalia Sergeevna Maltseva. "Comprehensive assessment of environmental, social and managerial efficiency of enterprises in the fishing and fish processing industry." Vestnik of Astrakhan State Technical University. Series: Fishing industry 2024, no. 1 (2024): 43–50. http://dx.doi.org/10.24143/2073-5529-2024-1-43-50.
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The purpose of the work is to form provisions for determining a comprehensive assessment of the effectiveness of the activities of a fishing and fish processing company in the field of ecology, social responsibility and in matters of management activities. When forming this assessment, indicators are taken into account that characterize the effectiveness of environmental protection measures; improvement of working conditions of employees, organization of corporate governance. To aggregate these characteristics, methods based on the position of the theory of fuzzy sets are proposed. The computing complex itself for processing information about the state of controlled parameters is implemented in the form of a hierarchical fuzzy inference system and an additional block that is used to distribute objects of analysis by state classes. The computing complex includes fuzzy inference systems based on the Sugeno algorithm. As a result of aggregation of the estimated parameters, two estimates are formed: one allows you to rank the analyzed objects, the second – to correlate the object of analysis with a certain class of state. The simulation showed that the computing complex has a high sensitivity: if at least one of the input variables is in the range of low (bad) values, this leads to a decrease in the value of the final score. This property makes it possible to exclude the occurrence of massaging of “bad” values of individual variables with “good” values of other variables. The results of the study confirmed the ability of the complex to distribute the objects of analysis by state classes. The results can be used for the development of corporate governance systems that are oriented for use in enterprises of the fisheries industry.
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Thella, Prabhat Kumar, and Ulagamuthalvi Venugopal. "A Group Labelled Classification Model for Accurate Medical Plant Detection Used in Drug Preparation." Revue d'Intelligence Artificielle 35, no. 2 (2021): 159–65. http://dx.doi.org/10.18280/ria.350208.
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The use of medical plants in the preparation of medicines has been increased in recent years. Medical plants are an essential component in the production of medicinal products. Medicines are made from root powder or plant leaves. When the herbal medicine is reduced to powder, more experience is required to determine the medicinal product through pharmacognoses. Inaccurate medical plants can cause patients serious health problems. For standardization and quality control of medical drugs the correct identification of the powder shape of medical plants is important. Medical plants are currently classified using a chemical leaf-based assessment, physical assessment and biological assessment. In medicine industry it is extremely necessary to identify the right medicinal plants for the preparation of a medicine. Its leaves form, color and texture are the key features needed to recognize a medicinal plant. In hierarchical clustering technology, the coefficient of inconsistency is used to generate natural clusters. Intra class differences can be seen with the amount of clusters obtained for plant organisms. The aggregate of the corresponding vectors of each sample of a cluster is calculated for one cluster representation. In terms of its leaf samples, therefore, the multiple members of the valued interval type are used to represent the plants in an effective way. The proposed model performs classification of leaf features using group labelled clustering model and then perform locking of labelling. This paper considers a Group Labelled Classification (GLC) Model that examines feature on the front and back of a green leaf, along with morphological characteristics, to achieve a specific optimal combination of features that optimize the recognition rate. The proposed model efficiently extracts the relevant features only from the medical leaf for accurate medical leaf detection. The proposed model is compared with the traditional methods and the results show that the proposed model performance is better.
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Alexandris, Panos, Samantha Quaife, Christine D. Berg, et al. "Protocol for a systematic review and individual participant data meta-analysis for risk factors for lung cancer in individuals with lung nodules identified by low-dose CT screening." BMJ Open 15, no. 1 (2025): e085118. https://doi.org/10.1136/bmjopen-2024-085118.
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BackgroundWorldwide, lung cancer (LC) is the second most frequent cancer and the leading cause of cancer related mortality. Low-dose CT (LDCT) screening reduced LC mortality by 20–24% in randomised trials of high-risk populations. A significant proportion of those screened have nodules detected that are found to be benign. Consequently, many individuals receive extra imaging and/or unnecessary procedures, which can have a negative physical and psychological impact, as well as placing a financial burden on health systems. Therefore, there is a need to identify individuals who need no interval CT between screening rounds.Methods and analysisThe aim of this study is to identify risk factors predictive of LC, which are known at the time of the scan, in patients with LDCT screen-detected lung nodules. The MEDLINE and EMBASE databases will be searched and articles that are on cohorts or mention cohorts of screenees with nodules will be identified. A data extraction framework will ensure consistent extraction across studies. Individual participant data (IPD) will be collected to perform a one-stage IPD meta-analysis using hierarchical univariate models. Clustering will be accounted for by having separate intercept terms for each cohort. Where IPD is not available, the effects of risk factors will be extracted from publications, if possible. Effects from IPD cohorts and aggregate data will be reported and compared. The PROBAST (Prediction model Risk Of Bias ASsessment Tool) will be used for assessment of quality of the studies.Ethics and disseminationEthical approval was not required as this study is a secondary analysis. The results will be disseminated through publication in peer-reviewed journals and presentations at relevant conferences.PROSPERO registration numberCRD42022309515
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Maheshwari Munigala. "Novel Applications of Statistical and Machine Learning Methods to Analyze Trial Level Data from Congnitive Measures." Journal of Information Systems Engineering and Management 10, no. 45s (2025): 1359–70. https://doi.org/10.52783/jisem.v10i45s.9445.
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Traditional analytical methods use aggregate metrics, which fail to show the fine-grained patterns that emerge from trial-level fluctuations of cognitive performance due to dynamic internal states like attention, fatigue, and learning processes. This research examines how statistical and machine learning (ML) methods can analyse trial-level behavioural and physiological data to improve understanding of cognitive dynamics. A total of 9,276 trials were obtained from 112 participants who completed Stroop, N-back, and Go/No-Go tasks. The annotation of each trial included reaction time measurements alongside accuracy data, task condition information, and EEG-derived alpha power measurements. Our analysis incorporated Bayesian hierarchical models, generalized linear mixed models, state-space models, Random Forest, XGBoost, deep neural networks, and Long Short-Term Memory (LSTM) networks to forecast both reaction times and task accuracy. The LSTM model demonstrated the best predictive power by achieving R² = 0.862 for RT prediction and AUC-ROC = 0.925 for accuracy classification. The AUC-ROC score reached 0.925 for classification, while R² reached 0.862 for reaction time prediction, which proved superior to all other techniques. The predictive features of trial number, task congruency, and EEG alpha power emerged through Shapley Additive Explanations (SHAP) and LSTM saliency maps. The research demonstrates how combining statistical transparency with ML flexibility helps reveal personalized and time-dependent cognitive patterns. The proposed method provides a powerful structure for modelling trials while creating potential applications for individualized cognitive assessment systems in educational and mental health settings.
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Alexandre, Duy Anh, Chiranjib Chaudhuri, and Jasmin Gill-Fortin. "Continental Scale Regional Flood Frequency Analysis: Combining Enhanced Datasets and a Bayesian Framework." Hydrology 11, no. 8 (2024): 119. http://dx.doi.org/10.3390/hydrology11080119.
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Flood frequency analysis at large scales, essential for the development of flood risk maps, is hindered by the scarcity of gauge flow data. Suitable methods are thus required to predict flooding in ungauged basins, a notoriously complex problem in hydrology. We develop a Bayesian hierarchical model (BHM) based on the generalized extreme value (GEV) and the generalized Pareto distribution for regional flood frequency analysis at high resolution across a large part of North America. Our model leverages annual maximum flow data from ≈20,000 gauged stations and a dataset of 130 static catchment-specific covariates to predict extreme flows at all catchments over the continent as well as their associated statistical uncertainty. Additionally, a modification is made to the data layer of the BHM to include peaks over threshold flow data when available, which improves the precision of the discharge level estimates. We validated the model using a hold-out approach and found that its predictive power is very good for the GEV distribution location and scale parameters and improvable for the shape parameter, which is notoriously hard to estimate. The resulting discharge return levels yield a satisfying agreement when compared with the available design peak discharge from various government sources. The assessment of the covariates’ contributions to the model is also informative with regard to the most relevant underlying factors influencing flood-inducing peak flows. According to the developed aggregate importance score, the key covariates in our model are temperature-related bioindicators, the catchment drainage area and the geographical location.
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Nasca, Ludovica, Salvatore Giuffrida, and Maria Rosa Trovato. "Value and Quality in the Dialectics between Human and Urban Capital of the City Networks on the Land District Scale." Land 11, no. 1 (2021): 34. http://dx.doi.org/10.3390/land11010034.
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This contribution proposes an inter-scalar and multi-polar analysis evaluation model of the territory of the Enna district, aimed at providing a robust axiological representation of the salient aspects of the general issue of internal areas, and therefore of the set of criticalities affecting them from the perspective of the human and urban capital they express. In the prospect of investigating the relations between urban and life quality—corresponding to the “city effect”—in the territorial context of each of the 20 municipalities of the Enna district, a hierarchical descriptive-valuation model was created, which coordinates a relevant amount of information units (data) and the corresponding attributes, indicators and indices that have been turned in aggregate value judgments attributed to each administrative land unit, from the perspectives of the criteria referred to as the main forms of the territorial capital. This is a multi-dimensional valuation model based on the Multi-Attribute Value Theory. Each survey and processing is mapped with different levels of detail at the scale of municipalities, census sections and cadastral land units. The outcome of this complex process of analysis and assessment provides multiple comparisons, revealing unexpected and sometimes counter-intuitive aspects in several municipalities, some of which are characterised by innovative prospects and opportunities for redevelopment of their historic centers. Correlations between information units at the different levels of the dendrogram have also indicated interesting trends and attitudes, whose comparisons can address territorial policies on both a local and provincial scale. Furthermore, the focus on the “cities network” is here assumed and proposed as the privileged point of observation of territory and the related aspects of the quality of life.
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Vashai, Y. V., and A. Y. Zhemba. "International Systems for Assessing the Economic Security of a State: Some Integrated Approaches." Problems of Economy 3, no. 45 (2020): 4–10. http://dx.doi.org/10.32983/2222-0712-2020-3-4-10.
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The article is dedicated to analyzing the international approaches to assessing the state of economic security at the state level and countries’ place in the world economic ratings. The urgency of the topic lies in the fact that the methods of world rating agencies, which rank countries in certain economic spheres based on a set of economic indices, are becoming more and more widely used in macroeconomic analysis. This affects their investment attractiveness and requires scientific analysis to carry out economic security management. The article aims at studying the complex global methods of assessing economic security in order to see, whether it is possible to apply them for macroeconomic analysis in Ukraine. The article systematizes the approaches to the economic security analysis in terms of their main schools and fundamental provisions. Peculiarities of measuring global economic indices have been determined, their advantages and disadvantages have been analyzed. The authors have come to conclusion that narrow assessment of the state economic security, by a single aggregate index only, does not provide valuable macroeconomic analysis. International practice shows that using indices, which are based solely on financial analytical data, is only appropriate for assessing a country’s creditworthiness and financial stability, but is insufficient for internal management. If we consider economic security as a part of national security, then the connection between economic and national security becomes one of a hierarchical nature. However, nowadays, when the social and political ties become more and more complicated, economic and national security can be considered as identical concepts, given that economic context is present in almost all areas of national security.
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Wang Boya, Yang Xiaochun, Lu Shengrong, Tang Yongping, Hong Shuquan, and Jiang Huiyuan. "A Multi-Dimensional Node Importance Evaluation Method Based on Graph Convolutional Networks." Acta Physica Sinica 73, no. 22 (2024): 0. http://dx.doi.org/10.7498/aps.73.20240937.
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This paper addresses the problem of identifying, evaluating, and ranking key nodes in complex networks by introducing a novel Multi-Parameter Control Graph Convolutional Network (MPC-GCN) for assessing node importance. Drawing inspiration from the multidimensional and hierarchical interactions between nodes in physical systems, this approach integrates the automatic feature learning capabilities of Graph Convolutional Networks (GCNs) with a comprehensive analysis of nodes’ intrinsic properties, their interactions with neighbors, and their roles within the broader network. The MPC-GCN model offers an innovative framework for key node identification, leveraging GCNs to iteratively aggregate node and neighbor features across layers. This process captures and combines local, global, and positional characteristics, enabling a more nuanced, multidimensional assessment of node importance. Moreover, the model incorporates a flexible parameter adjustment mechanism, allowing the relative weights of different dimensions to be tuned, thereby adapting the evaluation process to various network structures. To validate the model’s effectiveness, we first tested the influence of model parameters on randomly generated small networks. We then conducted extensive simulations on eight large-scale networks using the Susceptible-Infected-Recovered (SIR) model. Evaluation metrics, including the M(R) score, Kendall’s tau correlation, the proportion of infected nodes, and the relative size of the largest connected component, were used to assess the model’s performance. The results demonstrate that MPC-GCN outperforms existing methods in terms of monotonicity, accuracy, applicability, and robustness, providing more precise differentiation of node importance. By addressing the limitations of current methods—such as their reliance on single-dimensional perspectives and lack of adaptability—MPC-GCN offers a more comprehensive and flexible approach to node importance evaluation. This method significantly improves the breadth and applicability of node ranking in complex networks.
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Holyk, H. M., and I. V. Goncharenko. "Syntaxonomy, synphytoindication analysis and anthropogenic transformation of forest vegetation in Kyiv city." Ecology and Noospherology 28, no. 1-2 (2017): 49–63. http://dx.doi.org/10.15421/031705.
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The aim of this work was to determine the floristic composition, phytocoenotic diversity of woody vegetation, degree of anthropogenic transformation and ecological analysis using the synphytoindication method. The objects of our study were forest communities, forest-parks and green area of Kyiv. In 2015–2015, we fulfilled 323 relevés that were made by the standard procedure. All plant species were recorded. For each relevés geographic coordinates are indicated. The floristic analysis of the woody vegetation of Kyiv was conducted using the Braun-Blanquet approach and the phytosociological table was constructed with non-hierarchical clustering algorithm for vegetation classification so called DRSA («distance-ranked sorting assembling»). Phytoindication method was used for environmental analysis. We assessed amplitudes of syntaxa by phytoindication method for moisture (Hd), acidity (Rc), soil nitrogen content (Nt), total salt regime (Tr), light in community (Lc) and we calculated hemeroby index. Synecological amplitudes are characterized by the values of the minimum, maximum and average values ecofactors of the aggregate description of each community. Systematic, biomorphological, geographical structures were analyzed as well. We used hierarchical agglomeration cluster analysis of syntaxons to estimate the similarity of the species composition of the cenoflor. We identified that woody vegetation is divided into two classes Querco-Fagetea and Robinietea which include 11 communities types of rank association-variant. A total number consist of 169 species 133 genera 59 families. Species of alien origin constituted 20-40% of the cenofloras, which indicate increasing anthropogenic impact. Recreational load is a leading factor in anthropogenic impact. Biomorphological analysis indicates the predominance of herbal perennial plants – hemicryptophytes in the species composition of plants. And we determined that the nitrogen content in soil, acidity and moisture are the environmental factors leading to differentiation of vegetation. For assessment of anthropogenic transformation we used hemeroby index. Main purpose of the hemeroby index is to determine the degree and dynamics of human impact on plant communities. Hemeroby index is ecologically well founded, plausible and easy to interpret. The lowest level is represented in phytocoenoses order Fagetalia, followed by the communities of order Quercetalia and Chelidonio-Robinietalia from class Robinietea. Robinia pseudoacacia + Chaerophyllum temulum communities were most disordered. To determine the degree of anthropogenic transformation of vegetation under the influence of recreational activity we used eco-cenological spectrum changing analysis. It was found that increasing homogenization of the vegetation and the occurrence of ruderal communities dominated by alien invasive species are symptomatic of the synanthropization process. The proportion of terophytes, the ratio of the proportion of adventitious species to aboriginal, and apophytes to the natural species in disturbed and homogenized forests are increasing.
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UMANETS, T. V., and V. V. LUKASHCHUK. "METHODOLOGICAL FOUNDATIONS FOR BUILDING A MODEL OF COORDINATION FLOWS OF SYNERGY EFFECT FROM INTERACTIONS BETWEEN SUBJECTS OF THE MARKET OF INNOVATIVE TECHNOLOGIES." Economic innovations 23, no. 3(80) (2021): 342–51. http://dx.doi.org/10.31520/ei.2021.23.3(80).342-351.
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Topicality. Transformational changes in the era of building an intelligent society - Society 5.0, the emergence of "digital capitalism" in the world and the course taken by Ukraine towards digitalization in aggregate lead to a new organizational practice of entrepreneurship and economic management, where innovations in the field of science and technology will play a leading role in ensuring balanced economic development and solving social problems. The above has led to the intensification of activities related to the processing and dissemination of information in management structures, which has become an important area of work in economic research in Ukraine. This explains the need to take into account in practice the informational relationships between the subjects of management structures in the context of the digitalization of the Ukrainian economy and the coordination of their actions.Aim and tasks. The purpose of the article is to develop and substantiate the methodological foundations for constructing a model of coordination flows of synergistic effect from interaction between the subjects of the market of innovative technologies.Research results. The component base of construction of model of coordination streams of synergetic effect from interaction between subjects of the market of innovative technologies on the basis of economic synergetics and the theory of "soft" modeling on the following components is offered and substantiated; conceptual and categorical apparatus of modeling; conceptual approach to building a model of coordination flows to calculate the synergetic effect of the interaction between the subjects of the market of innovative technologies; elements of information interaction of participants of managerial relations; organizational and hierarchical structure of the model of coordination flows to calculate the synergetic effect of the interaction between the subjects of the market of innovative technologies.Conclusion. The model of coordination flows to calculate the synergetic effect of the interaction between the subjects of the market of innovative technologies is a three-tier management system, which has the main characteristics of multidimensional hierarchical systems. The range of information change varies from "0" to "1" and allows the selection of the information base to use the method of integrated assessment and expert observation. The main result of the decision according to this model is to obtain sufficient (optimal) quantity and quality of information by each management entity in the market of innovative technologies, which allows to formulate a general quasi-optimal solution facing the market of innovative technologies of Ukraine.This model is universal and can be used to analyze any information flows, as well as it is advisable to use in the construction of hybrid models of decision-making in the management of the processes of the market of innovative technologies.
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Li, Bao, Zhenyu Liu, Yang Du, and Jie Tian. "Abstract PO4-07-04: Breast Cancer HER2 Status Prediction from Hematoxylin-Eosin Stained Images Using Point Cloud Transformer." Cancer Research 84, no. 9_Supplement (2024): PO4–07–04—PO4–07–04. http://dx.doi.org/10.1158/1538-7445.sabcs23-po4-07-04.
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Abstract Background: Human epidermal growth factor receptor-2 (HER2) status is a key factor in determining the treatment strategy for breast cancer patients. Patients with HER2-positive status are more likely to benefit from HER2-targeted therapy, leading to improved prognosis. In current routine diagnostic practice, pathologists utilize Hematoxylin-Eosin (HE) stained tumor tissues for histopathological assessment. Subsequently, IHC assessment and/or FISH test are performed to evaluate the HER2 status. However, manual assessment results may be affected by tissue usability and observer-subjectivity. Therefore, there is a necessity to predict the HER2 status directly from HE images to minimize time and cost while ensuring enhanced consistency. Methods: We identified 608 HE diagnostic slides with HER2 status from The Cancer Genome Atlas in breast cancer (TCGA-BRCA). It contains 474 HER2-positive slides (IHC 3+, IHC 2+ and FISH positive) and 134 HER2-negative slides (IHC 0, IHC 1+, IHC 2+ and FISH negative). To analyze these slides, we first tiled the HE images into patches with a fixed size of 256 × 256 at 20 × magnification. Then the patch-level feature was derived from a self-supervised pretrained transformer. Meanwhile, artificial intelligence (AI) methods are adopted to predict HER2 status from HE images. To capture the long-distance patch relations within a slide, we represented the patches as distinct points and utilized the Point Cloud Transformer model for HER2 status prediction. Specifically, 1024 patches (points) in each slide were randomly selected and input into the Point Cloud Transformer. This process yielded a slide-level prediction result for the HER2 status. Furthermore, Graph Attention Network (GAT), Graph Sample and Aggregate Network (Graph SAGE), and hierarchical Point Cloud Network (PointNet++) were adopted to compare the effectiveness of HER2 status prediction using Point Cloud Transformer. Of note that Point Cloud Transformer incorporated attention mechanisms for point aggregation compared to PointNet++. Results: The Point Cloud Transformer was trained with 5-fold cross-validation, and the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was reported. The performance of point-based models outperformed the graph-based models. And the Point Cloud Transformer achieved the highest AUC of 0.7496 among all AI models. The detailed AUC for each AI model was shown in Table 1. Conclusion: Our study revealed that the HER2 status can be predicted directly from HE images without using IHC images. Furthermore, point-based models have demonstrated the ability to capture long-distance relations among patches, surpassing graph-based models in terms of prediction performance. To further enhance performance, we adopted a better point aggregation method, such as the Point Cloud Transformer, which held promise for further improving the accuracy of predictions in the future. Table 1: AUC of HER2 status prediction from HE images in the TCGA-BRCA dataset Method GAT Graph SAGE PointNet++ Point Cloud Transformer AUC 0.6640 0.6737 0.7201 0.7496 Citation Format: Bao Li, Zhenyu Liu, Yang Du, Jie Tian. Breast Cancer HER2 Status Prediction from Hematoxylin-Eosin Stained Images Using Point Cloud Transformer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-07-04.
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Bulygin, S. Yu, S. V. Vitvitskiy, and M. E. Bulygina. "Regulation of technological loading on soils: a concept for solving the problem." Mehanization and electrification of agricultural, no. 13(112) (2021): 20–36. http://dx.doi.org/10.37204/0131-2189-2021-13-2.
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Аnnotation Purpose. Justify the possibility and feasibility of using the technological standard (Tn) to regulate the technological load on soils to eliminate the conditions of their degradation using the energy assessment of the action of agricultural machinery systems and agronomic measures in terms of the structural-aggregate and humus state, as well as biological activity. Methods. General scientific (field, morphological-genetic, comparative-analytical, agroanalytical, laboratory) and special (determination of the content of elementary soil particles (ESP) and microaggregation by the microscopic method according to Bulygin S. Yu.). Results. Under the conditions of stationary experiments of the Right-Bank Forest-Steppe of Ukraine, it was revealed that more than 60 years of use of alkaline chernozem soil in a 10-field crop rotation with a field of clover with a traditional tillage system using moldboard plowing against the background of different fertilization options did not significantly worsen any of the parameters of agrophysical and humus state, biological activity, quantitative and species composition of the microbiological pool. More than 40 years of use of dark gray podzolized soil at a full rate of fertilization, applying lime to neutralize hydrolytic acidity and growing green manures after cereal early ripening crops, leaving the non-marketable part of the crop on the field under the conditions of a traditional processing system with moldboard plowing in a 7-field crop rotation , also with a field of clover, did not worsen the main parameters of its fertility. A conceptual mathematical model has been developed for the processing soil conservation agriculture systems based on the studies. Conclusions 1. Agrophysical parameters of soil have different information content and different sensibility. Each hierarchical level of the soil body system is characterized by a certain set of indicators, including agrophysical ones. It has been established that a scientifically grounded crop rotation against the background of moderate doses of mineral fertilizers, necessary chemical ameliorants with the abandonment of the non-commercial part of the crop and the use of green manure after early grain crops guaranteed to hold the agro physical condition of soils from deterioration. 2. The guarantor of a quasi-stable humus state is the stability of the agrophysical framework of the soil system and the harmony of the microbiological pool, the quantitative and species composition of which determines the balance of the synthesis and mineralization of humus and other organic substances. 3. Changing the effective influence of the living soil phase is determined by its agrophysical state, which leads to a change in the humus state – the first iteration passed. Next – the next and next chain of the spiral of the “life” of the soil (thesis-antithesis synthesis by Hegel), until the bottom or the top of the dynamic equilibrium is achieved, which is determined by the level of agriculture culture. 4. The direction (vector and trend) of the soil formation is determined by the energy ratio of the energy of the Sun in the form of a fresh vegetable residue (the second factor of the genesis of soils according to Dokuchaev) to anthropogenic energy, which is consumed in the implementation of crop technologies, which determines the value of the technological regulation (Tn). 5. The energy assessment of technologies for growing crops under the conditions of long-term stationary experiments made it possible to determine technological standards (Tn): for meadow-chernozem soil – 8.6, for dark gray podzolized soil – 7.2. Keywords: technological standard of energy load, elementary soil particles (ESP), coefficient of microaggregation, humus state, microbiological activity, energy assessment of plant technologies.
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YETİŞİR, Mehmet İkbal. "The Effect of School and Student-Related Factors on PISA 2015 Science Performances in Turkey." International Journal of Psychology and Educational Studies 8, no. 2 (2021): 170–86. http://dx.doi.org/10.52380/ijpes.2021.8.2.433.
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The Program for International Student Assessment (PISA) is a research project conducted by the Organization for Economic Co-operation and Development, which evaluates the knowledge and skills gained by 15-year-old students over three-year terms. Within this study’s' scope, the PISA 2015 data were analysed to determine whether school-related factors [including the schools’ economic, social, and cultural status (ESCS)] were related to Turkish students’ science performances. Due to its nested structure, the released PISA 2015 data were analysed using the hierarchical linear model (HLM). Two models were considered to examine how Aggregated ESCS at the school level makes a difference. Thereby in model 1 shortage of educational material, staff shortage, student behaviours, and teacher behaviours were included in the analysis; in addition to these variables listed, aggregated ESCS was also added to the analysis in Model 2. The results of the analysis revealed that school-related factors - in particular, staff shortage, student behaviours, and aggregated ESCS indexes - were statistically related to students’ science performances. When the aggregated ESCS was controlled, it is observed that the school-level variables had a higher effect on students’ science performances.
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Giri, Smith, Madan R. Aryal, Han Yu, et al. "Evaluation of Efficacy and Safety of Front-Line Regimens for the Treatment of Transplant Ineligible Patients with Multiple Myeloma: A Network Meta-Analysis of Phase 2/3 Randomized Controlled Trials." Blood 134, Supplement_1 (2019): 2188. http://dx.doi.org/10.1182/blood-2019-130389.
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Background: Several options exist for front-line treatment of previously untreated patients with Multiple Myeloma (MM) ineligible for hematopoietic cell transplant (HCT). Many of these have not been evaluated in head to head clinical trials. Since these patients are often old and frail, treatment regimens should have dual goals: a) maximize clinical efficacy b) minimize therapy related toxicity. However, published network meta-analysis (NMA) till date have ignored the equally important safety outcome. In this systematic review, we provide an updated comparative efficacy and safety data of various front-line treatment regimens for HCT ineligible MM patients, using a Bayesian framework. Methods: We searched Medline, Embase, Web of Science Core Collection and the Cochrane library for published phase II/phase III RCTs from inception to October 2018 comparing at least two frontline regimens for the treatment of HCT ineligible MM patients. The primary efficacy outcome was progression free survival (PFS) whereas secondary outcomes included overall response rate (ORR) and overall Survival (OS). We defined safety as rate of all-cause and common grade 3 or higher adverse events (AE) in each arm. Risk of bias was assessed using Cochrane Risk of Bias Assessment Tool. Using aggregate data, a Bayesian random-effect hierarchical model was fit with non-informative priors and adjusting for correlation between effects in multi-arm trials. Assessment of inconsistency was performed by node-splitting approach. For toxicity, a multivariate Bayesian probit-normal regression model was fitted to the observed number of adverse events for each toxicity in an arm based meta-analytic approach. The relative ranking of agents was assessed using surface under the cumulative ranking (SUCRA) probabilities. (PROSPERO # CRD42018115364) Results: After screening 5587 publications, we selected 27 clinical trials involving 12,194 patients comparing 25 different regimens for final analysis (table 1). 26 of these studies were incorporated into one network geometry. The median age of study participants ranged from 63 to 79 years and 47% were females (table 1). Our NMA showed that for PFS, the three most effective regimens were: Daratumumab, Bortezomib, Melphalan and Prednisone (Dara-VMP, SUCRA 0.960) followed by Daratumumab, lenalidomide and dexamethasone (Dara-RD, SUCRA 0.847) and Bortezomib, melphalan, prednisone, thalidomide followed by maintenance with bortezomib-thalidomide (VMPT-VT, SUCRA 0.834), with results consistent for ORR and OS (table 2). For safety, we extracted data on 33 different grade ¾ AEs, 7 of which were reported by >50% of studies (anemia, neutropenia, thrombocytopenia, infections, neuropathy, thrombosis and gastrointestinal AEs) and used for further analyses. Among the three most efficacious regimens, the toxicity profile was most favorable for Dara-RD (median additional AEs per patient vs dexamethasone=0.74; 95% CrI 0.51-1.17; SUCRA 0.430) followed by Dara-VMP (median additional AE 0.91; 95% CrI 0.65-1.34; SUCRA 0.279) and VMPT-VT (median additional AE 0.98; 95% CrI 0.79-1.24; SUCRA 0.224) (figure 3). Conclusion: We provide direct/indirect comparison of efficacy and safety of various frontline regimens for HCT ineligible MM patients. While Dara-VMP was most likely to be the most efficacious regimen, Dara-RD provided the best balance between efficacy and safety. The results of our analysis should provide guidance to clinicians and patients in choosing the right regimen tailored to individual patient needs. Disclosures Huntington: Celgene: Consultancy, Research Funding; Genentech: Consultancy; Bayer: Consultancy, Honoraria; DTRM Biopharm: Research Funding; Pharmacyclics: Honoraria; AbbVie: Consultancy. Dhakal:Amgen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Honoraria; Sanofi: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees.
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Cadigan, Noel G., and Steven E. Campana. "Hierarchical model-based estimation of population growth curves for redfish (Sebastes mentella and Sebastes fasciatus) off the Eastern coast of Canada." ICES Journal of Marine Science 74, no. 3 (2016): 687–97. http://dx.doi.org/10.1093/icesjms/fsw195.
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Northwest Atlantic (NWA) redfish (Sebastes mentella and Sebastes fasciatus) stocks are currently assessed using survey indicators and age-aggregated production models rather than age-based models because routine age readings are not available due to the difficulty in obtaining reliable measurements for these fish. However, recruitment is highly variable for redfish species so age-aggregated production models are not a good approach to provide short-term harvest advice. Recently a relatively large dataset of validated age readings was published that provide a good basis to model growth and its variability [i.e. population growth curve (PGC)]. In this article we propose a hierarchical random effects growth model that includes between-individual variation to estimate PGCs for 10 NWA redfish stocks and for males and females separately. These growth curves are required to develop age-based stock assessment models. External estimates of measurement error in length and age are included in our model to separate these sources of variation from the PGC variability. The hierarchical approach leads to more realistic growth curves than if each stock and sex are modelled separately. Model results indicated that S. mentella usually grow to larger sizes than S. fasciatus and that females of both these species grow to larger sizes than males. There was little evidence of a change in growth rates over time.
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Marsili, Francesca, and Jörg Bödefeld. "Integrating Cluster Analysis into Multi-Criteria Decision Making for Maintenance Management of Aging Culverts." Mathematics 9, no. 20 (2021): 2549. http://dx.doi.org/10.3390/math9202549.
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Negligence in relation to aging infrastructure systems could have unintended consequences and is therefore associated with a risk. The assessment of the risk of neglecting maintenance provides valuable information for decision making in maintenance management. However, infrastructure systems are interdependent and interconnected systems of systems characterized by hierarchical levels and a multiplicity of failure scenarios. Assessment methodologies are needed that can capture the multidimensional aspect of risk and simplify the risk assessment, while also improving the understanding and interpretation of the results. This paper proposes to integrate the multi-criteria decision analysis with data mining techniques to perform the risk assessment of aging infrastructures. The analysis is characterized by two phases. First, an intra failure scenario risk assessment is performed. Then, the results are aggregated to carry out an inter failure scenario risk assessment. A cluster analysis based on the k-medoids algorithm is applied to reduce the number of alternatives and identify those which dominate the decision problem. The proposed approach is applied to a system of aging culverts of the German waterways network. Results show that the procedure allows to simplify the analysis and improve communication with infrastructure stakeholders.
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Castellani, Rudy J. "The Significance of Tau Aggregates in the Human Brain." Brain Sciences 10, no. 12 (2020): 972. http://dx.doi.org/10.3390/brainsci10120972.
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Neurofibrillary degeneration has attracted the attention of neuroscientists as both a hallmark of the disease and a subject for experimentation for more than a century. Recent studies implicate phosphorylated tau (p-tau) directly in neurodegenerative disease pathogenesis, although the human data continue to raise questions. P-tau accumulates with age in a roughly hierarchical manner, but avoids abundance in the neocortex unless co-occurring with amyloid-β. Neurodegenerative tauopathies tend to have p-tau morphologies that differ from aging and Alzheimer’s disease. Tau isoforms (3R vs. 4R) have a tendency to vary with tauopathy phenotype for unknown reasons. Selective vulnerability to p-tau and spatial-temporal disconnect from amyloid-β are evident in aging. P-tau assessment at autopsy involves tissue decomposition, which may skew microanatomical observations toward limited biological meaning. Two major consensus guidelines for interpreting p-tau at autopsy emphasize the challenges of clinicopathologic correlation, and reinforce the observation that regional neurodegeneration is a better correlate of clinical signs than is proteinopathy. Despite the proliferation of interesting and novel theories related to tau-mediated pathogenesis, the weight of the human observations suggests that neurofibrillary degeneration is an epiphenomenal hallmark of aging and disease rather than an epicenter of neurotoxicity. This is consistent with numerous tau-targeted therapeutic strategies that have been unsuccessful to date.
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Verstina, Natalia G., and Natalia M. Fomenko. "Specifics of managerial decisions made by the heat-supplying organizations management during the selection of the partner organizations." E3S Web of Conferences 220 (2020): 01101. http://dx.doi.org/10.1051/e3sconf/202022001101.
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The issue of the selection of business partners in the conditions of the uniform heat-supplying organizations functioning is considered in the article. The need of quality and timely evaluation of the potential partners for the purpose of the increase in the level of quality of generation, transportation and distribution of heat energy is proved. The author’s approach of holding the procedure of selection of the most reliable partner was used with the help of the method of the hierarchical analysis. The method of pair comparisons was also applied within the research, the blocks and indicators of the assessment were defined. The model of additive convolution was chosen as the method of the aggregated criterion creation.
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Mata, Nitinan, and Sakchai Tangwannawit. "Kinematic Skeleton Extraction from 3D Model Based on Hierarchical Segmentation." Symmetry 17, no. 6 (2025): 879. https://doi.org/10.3390/sym17060879.
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A new approach for skeleton extraction has been designed to work directly with 3D point cloud data. It blends hierarchical segmentation with a multi-scale ensemble built on top of modified PointNet models. Outputs from three network variants trained at different spatial resolutions are aggregated using majority voting, unweighted averaging, and adaptive weighting, with the latter yielding the best performance. Each joint is set at the center of its part. A radius-based filter is used to remove any outliers, specifically, points that fall too far from where the joints are expected to be. When evaluated on benchmark datasets such as DFaust, CMU, Kids, and EHF, the model demonstrated strong segmentation accuracy (mIoU = 0.8938) and low joint localization error (MPJPE = 22.82 mm). The method generalizes well to an unseen dataset (DanceDB), maintaining strong performance across diverse body types and poses. Compared to benchmark methods such as L1-Medial, Pinocchio, and MediaPipe, our approach offers greater anatomical symmetry, joint completeness, and robustness in occluded or overlapping regions. Structural integrity is maintained by working directly with 3D data, without the need for 2D projections or medial-axis approximations. The visual assessment of DanceDB results indicates improved anatomical accuracy, even in the absence of quantitative comparison. The outcome supports practical applications in animation, motion tracking, and biomechanics.
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Gao, Caroline X., Jonathan C. Broder, Sam Brilleman, et al. "Evaluating the impact of Hazelwood mine fire event on students’ educational development with Bayesian interrupted time-series hierarchical meta-regression." PLOS ONE 18, no. 3 (2023): e0281655. http://dx.doi.org/10.1371/journal.pone.0281655.
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Background Environmental disasters such as wildfires, floods and droughts can introduce significant interruptions and trauma to impacted communities. Children and young people can be disproportionately affected with additional educational disruptions. However, evaluating the impact of disasters is challenging due to difficulties in establishing studies and recruitment post-disasters. Objectives We aimed to (1) develop a Bayesian model using aggregated school-level data to evaluate the impact of environmental disasters on academic achievement and (2) evaluate the impact of the 2014 Hazelwood mine fire (a six-week fire event in Australia). Methods Bayesian hierarchical meta-regression was developed to evaluate the impact of the mine fire using easily accessible aggregated school-level data from the standardised National Assessment Program-Literacy and Numeracy (NAPLAN) test. NAPLAN results and school characteristics (2008–2018) from 69 primary/secondary schools with different levels of mine fire-related smoke exposure were used to estimate the impact of the event. Using an interrupted time series design, the model estimated immediate effects and post-interruption trend differences with full Bayesian statistical inference. Results Major academic interruptions across NAPLAN domains were evident in high exposure schools in the year post-mine fire (greatest interruption in Writing: 11.09 [95%CI: 3.16–18.93], lowest interruption in Reading: 8.34 [95%CI: 1.07–15.51]). The interruption was comparable to a four to a five-month delay in educational attainment and had not fully recovered after several years. Conclusion Considerable academic delays were found as a result of a mine fire, highlighting the need to provide educational and community-based supports in response to future events. Importantly, this work provides a statistical method using readily available aggregated data to assess the educational impacts in response to other environmental disasters.
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Dubiec, Gabriela. "Wykorzystanie wybranych metod wielowymiarowej analizy porównawczej do oceny poziomu zjawiska przestępczości w powiatach Polski w 2012 roku." Ekonomia 26, no. 1 (2020): 87–108. http://dx.doi.org/10.19195/2658-1310.26.1.6.
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The article attempts to identify particularly vulnerable counties which are prone to the phenomenon of crime and to create crime maps using multi-dimensional comparative analysis (MCA). As a tool for comparing Polish counties in terms of crime and identifying factors determining it, the article uses two MCA methods, i.e. the pattern and anti-pattern method, and the Ward method as an ex-ample of a hierarchical grouping method. Therefore, a synthetic measure of crime was determined, which replaces the set of many features of the examined objects (in this case they are variables characterising crime) into one aggregated normalised variable. This approach not only enables the assessment of counties using one quantity, but also allows them to be ordered in terms of the phe-nomenon under consideration.
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Zhu, Enya, Ellen McCreedy, Laura Dionne, and Vincent Mor. "RELATING THE ORGANIZATIONAL CULTURE ASSESSMENT INSTRUMENT TO ADHERENCE IN A PRAGMATIC TRIAL OF A MUSIC INTERVENTION." Innovation in Aging 7, Supplement_1 (2023): 656–57. http://dx.doi.org/10.1093/geroni/igad104.2135.
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Abstract Embedded pragmatic trials encourage the translation of evidence-based interventions to “real-world” settings. Most pragmatic trials of behavioral interventions for people with dementia suffer from low adherence. Understanding how organizational values and structure may increase adherence is important. We report findings from an embedded, pragmatic trial (ePCT) of a personalized music intervention for managing behaviors in residents with dementia, conducted in 54 nursing homes (NHs) from four corporations between June 2019 and February 2020. Before the trial began, the administrator and a nursing staff member from each NH completed the Organizational Culture Assessment Instrument (OCAI). Using the OCAI, respondents rated their organizational culture by allocating a total of 100 points across four competing domains: Clan, Adhocracy, Hierarchy, and Market. Results were aggregated to understand how differences in culture impacted corporate-level adoption of the intervention. All four corporations allocated the majority of their points to Clan culture, which is focused on collaboration and staff engagement. However, corporations differed in their scoring of the secondary culture type. The two corporations that rated Hierarchical culture, which prioritizes consistency and efficiency, highly were more likely to adhere to the intervention protocols. The corporation with Market highly rated had the lowest adherence to the protocols. After controlling for other corporate characteristics, including for-profit status, size, and overall quality, hierarchical culture was associated with greater numbers of exposed residents and a higher dose of the music, compared to other culture types. Understanding the role of organizational culture on pragmatic implementation is an understudied area for research.
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Chowdhury, Shakhawat. "Decision making with uncertainty: an example of water treatment approach selection." Water Quality Research Journal 47, no. 2 (2012): 153–65. http://dx.doi.org/10.2166/wqrjc.2012.107.
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Decision makers often encounter uncertainty in selecting the best available option. Fuzzy multicriteria decision making aggregates different basic criteria through a hierarchy structure. This aggregation combines fuzzy assessment and priority matrices. When available data are imprecise, the assessment and priority matrices for different hierarchy level criteria are developed from expert judgments. In fuzzy aggregation, uncertainties in the assessment matrix are captured with fuzzy membership functions. The priority matrix is developed through pairwise comparison, in which the relative importance of the criteria is represented in crisp values; thus uncertainties associated with priority assignments are not incorporated in traditional fuzzy aggregation. This paper presents the application of a methodology to incorporate fuzziness in developing a priority matrix for environmental decision making. Fuzzy α-cut technique for different confidence intervals has been incorporated. The gradient eigenvector method has been followed to obtain consistency in constructing the priority matrix for different hierarchy level criteria. The max-min paired elimination method for hierarchical aggregation has been used to obtain the final fuzzy set. A case study for drinking water treatment technology evaluation is performed to present the potential environmental application of this approach, leading to the identification of the best treatment technology.
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Tikunov, Vladimir, and Olga Chereshnya. "Integrated Multi-Level Assessment of Regional Development Sustainability and Mapping." Kartografija i geoinformacije 17, no. 29 (2018): 20–30. http://dx.doi.org/10.32909/kg.17.29.2.
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This article describes a method for the complex assessment of the sustainable development of territories based on social development, economic development and ecological situation indices. The method allows an analysis of the comprehensive, sustainable development of territories, and at the same time details elements of the problem detected by the successive hierarchical decomposition of the aggregated indices. It takes into account the peculiarities of non-homogeneous regions and those which are difficult to compare. Their evaluation using conventional methods of identification and typology does not produce the most rapid, effective, or objective results. The Russian Federation was used an example to test the methodology. The potential analysis of sustainable development was supplemented with a map created according to the colour triangle method, which allows the balance of components to be visualized at each level analysed. The theoretical principles considered and their practical use helped complete an assessment of the basic parameters of sustainable development in the Russian regions. Based on this, it was possible to form a unified list of criteria which might become the national standard for assessing sustainable development at the local, regional and federal levels, and recommended for practical use in the development and adoption of new regional solutions.
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Gomes, V., O. O. C. Zara, G. M. Colleto, and M. G. da Silva. "Clustering Strategies for Defining Archetypes to Support Integrated Simulations of Environmental Impacts." IOP Conference Series: Earth and Environmental Science 1078, no. 1 (2022): 012045. http://dx.doi.org/10.1088/1755-1315/1078/1/012045.
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Abstract Life cycle assessment (LCA)’s inherent data-intensiveness hampers application to neighbourhood environmental assessments, particularly for built stock modelling. Data collection can be reduced to a manageable amount by grouping a large number of buildings into a limited set of aggregates with similar characteristics and defining exemplars (archetypes) that represent each group. LCAs would be performed for the archetypes only and their results extended to the represented buildings. As archetype definition is seldom detailed in the literature, this paper tests, and details different procedures that could enable neighbourhood LCAs. K-medoids and CLARA partition algorithms, as well as agglomerative hierarchical clustering techniques, were applied to group over 300 buildings into a limited number of clusters. A building representative of each cluster was identified to proceed to bottom-up LCA. K-medoids clustering stands out for the quality of clusters and their representatives. Restraining the maximum number of clusters to keep subsequent LCA work manageable imposes some quality loss yet allows for achieving satisfactory division results. Regardless of the clustering technique used, data was the best divided the larger the number of clusters used, for the various factors in the database depicting the studied area resulted in several possible data combinations. Although detailed representation is desirable in LCA modelling, limiting the number of variables facilitates data pre-treatment and an optimal balance should be pursued in future studies.
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McElroy, E., P. Casey, G. Adamson, P. Filippopoulos, and M. Shevlin. "A comprehensive analysis of the factor structure of the Beck Depression Inventory-II in a sample of outpatients with adjustment disorder and depressive episode." Irish Journal of Psychological Medicine 35, no. 1 (2017): 53–61. http://dx.doi.org/10.1017/ipm.2017.52.
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ObjectivesDespite being commonly used in research and clinical practice, the evidence regarding the factor structure of the Beck Depression Inventory-II (BDI-II) remains equivocal and this has implications on how the scale scores should be aggregated. Researchers continue to debate whether the BDI-II is best viewed as a unidimensional scale, or whether specific subscales have utility. The present study sought to test a comprehensive range of competing factor analytic models of the BDI-II, including traditional non-hierarchical multidimensional models and confirmatory bifactor models.MethodParticipants (n=370) were clinical outpatients diagnosed with either depressive episode or adjustment disorder. Confirmatory factor analysis and confirmatory bifactor modelling were used to test 15 competing models. The unidimensionality of the best fitting model was assessed using three strength indices (explained common variance, percentage of uncontaminated correlations and ω hierarchical).ResultsOverall, bifactor solutions provided superior fit than both unidimensional and non-hierarchical multidimensional models. The best fitting model consisted of a general depression factor and three specific factors: cognitive, somatic and affective. High factor loadings and strength indices for the general depression factor supported the view that the BDI-II measures a single latent construct.ConclusionsThe BDI-II should primarily be viewed as a unidimensional scale, and should be scored as such. Although it is not recommended that scores on individual subscales are used in isolation, they may prove useful in clinical assessment and/or treatment planning if used in conjunction with total scores.
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dela Torre, D. M. G., P. K. A. dela Cruz, R. P. Jose, N. B. Gatdula, and A. C. Blanco. "GEOSPATIAL ASSESSMENT OF VULNERABILITIES OF CROPLANDS TO FLOODING RISKS: A CASE STUDY OF PHILIPPINE RIVER BASINS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W19 (December 23, 2019): 173–80. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w19-173-2019.
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Abstract. This study utilized the Analytic Hierarchical Process and spatial analysis using various datasets to produce sub-provincial vulnerability maps with 20 km resolution. Five (5) indicators for exposure, four (4) for sensitivity and seven (7) for adaptive capacity were selected and weighted using aggregated rankings from twenty-three (23) experts. Based on these indicators, gridded maps of exposure, sensitivity, adaptive capacity, and vulnerability were produced. Using river basins as the unit of analysis, the Pampanga River Basin was determined to be the most vulnerable, followed by the Agus River Basin, having the highest sensitivity, and Buayan River Basin, having the lowest adaptive capacity. These areas have large agricultural regions and river systems with high flooding risk. Coastal regions in southern Mindanao and eastern Visayas were also highly vulnerable to flooding. High poverty rates with high dependence on agricultural incomes and low adaptive capacities characterize these areas. Vulnerability hotspots can easily be identified through these maps, which have value in planning initiative to reduce potential damages of floods to agricultural areas.
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Shi, Pengfei, Tao Yang, Bin Yong, et al. "A New Uncertainty Measure for Assessing the Uncertainty Existing in Hydrological Simulation." Water 11, no. 4 (2019): 812. http://dx.doi.org/10.3390/w11040812.
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The absence of aggregated uncertainty measures restricts the assessment of uncertainty in hydrological simulation. In this work, a new composite uncertainty measure is developed to evaluate the complex behaviors of uncertainty existing in hydrological simulation. The composite uncertainty measure is constructed based on a framework, which includes three steps: (1) identification of behavioral measures by analyzing the pairwise correlations among different measures and removing high correlations; (2) weight assignment by means of a new hierarchical weight assembly (HWA) approach incorporating the intra-class and inter-class weights; (3) construction of a composite uncertainty measure through incorporating multiple properties of the measure matrix. The framework and the composite uncertainty measure are demonstrated by case studies in uncertainty assessment for hydrological simulation. Results indicate that the framework is efficient to generate a composite uncertainty index (denoted as CUI) and the new measure CUI is competent for uncertainty evaluation. Besides, the HWA approach performs well in weighting, which can characterize subjective and objective properties of the information matrix. The achievement of this work provides promising insights into the performance comparison of uncertainty analysis approaches, the selection of proper cut-off threshold in the GLUE method, and the guidance of reasonable uncertainty assessment in a range of environmental modelling.
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Nirav PravinSinh Rana. "From forecasting to trust: Engineering interpretability and accuracy metrics in predictive platforms." World Journal of Advanced Engineering Technology and Sciences 15, no. 3 (2025): 292–98. https://doi.org/10.30574/wjaets.2025.15.3.0923.
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This article presents a framework for engineering interpretability and accuracy metrics into predictive forecasting platforms, addressing the trust deficit that emerges when stakeholders must make high-stakes decisions based on opaque predictions. The architecture implements origin tracking through a multi-dimensional data model that distinguishes between machine learning-generated, user-adjusted, and hierarchically aggregated forecasts. A historical accuracy tracking framework captures temporal snapshots, enabling assessment of predictive reliability across different timeframes and organizational levels. The user experience design employs layered information disclosure and structured feedback mechanisms that transform individual domain expertise into institutional knowledge. Empirical assessment reveals a non-linear trust development trajectory as users progress from initial skepticism to collaborative engagement with the system. While the framework successfully enhances transparency and decision confidence, limitations exist in capturing complex collaborative adjustments and addressing qualitative aspects of forecast quality. Potential applications extend to healthcare resource planning, supply chain optimization, financial risk assessment, and public sector planning, with future directions focusing on uncertainty visualization and rhetorical dimensions of forecast presentation.
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Reisig, Michael D., and Roger B. Parks. "Can Community Policing Help the Truly Disadvantaged?" Crime & Delinquency 50, no. 2 (2004): 139–67. http://dx.doi.org/10.1177/0011128703253157.
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Community policing advocates argue that reforms designed to break down barriers between police and citizens can produce favorable outcomes. The authors test a series of related hypotheses in a multivariate context by using four independent data sources— community surveys, patrol officer interviews, Census Bureau, and police crime records— to estimate hierarchical linear models. The results show that citizens who perceive police partnerships favorably report fewer problems related to incivilities and also express higher levels of safety. Findings from models including cross-level interaction terms indicate that the positive outcomes associated with police partnerships are not restricted to citizens residing in affluent neighborhoods. In our ecological analysis, we find that police-community collaboration is associated with higher aggregate quality of life assessments and that community policing as a form of public social control mediates the adverse effects of concentrated disadvantage. The findings support social-psychological and ecological theories on which community policing practices are partially based.
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GORELSKIY, I. Ye. "STATE INSTITUTIONAL CAPACITIES IN A COMPARATIVE PERSPECTIVE: AN EXPERIENCE OF BAYESIAN AGGREGATION OF STATE CAPACITY INDEX." Comparative Politics Russia 13, no. 3 (2024): 53–73. http://dx.doi.org/10.46272/2221-3279-2022-3-13-53-73.
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This article endeavors to construct a composite indicator designed to facilitate the comparative assessment of institutional capacities across diverse political systems. The focal point of analysis resides within the domain of state capacity, a pivotal determinant for a myriad of inquiries that seek to evaluate the efficacy of public policy implementation across varying spheres. The attainment of the research objective materialized through the adoption of Bayesian methodologies tailored for the scrutiny of latent variables, a prevalent recourse within contemporary social sciences to address analogous empirical puzzles. Drawing upon antecedent advancements collaboratively undertaken with co-authors and a minimal selection of variables, ranging from the comprehensive gauge of control over violence to formal economic activity (reciprocally aligned with the gauge of informal economy), two distinct models were subjected to empirical examination: namely, the linear and hierarchical ones. The ensuing analysis of 150 countries across three temporal junctures (1996, 2005, and 2015) distinctly advocates for the adoption of the latter model after accommodating convergence testing. The hierarchical model, oriented towards encapsulating the tripartite dimensions of state capacity, namely administrative, coercive, and extractive facets, gains preference. This predilection aligns with the contribution of J. Hanson and R. Sigman, whose conceptual underpinnings are frequently espoused by scholars when delineating the contours of the observed construct. The hierarchical model affirms the efficacy and discriminatory acumen of the proposed approach in engendering pertinent state rankings. These empirical revelations, in turn, constitute an indispensable stride towards interrogating assorted hypotheses concerning trajectories of institutional development and the construction of classificatory frameworks delimiting state capacity. Moreover, they extend utility in the realm of comparative studies by furnishing an aggregated indicator conducive to cross-national analysis.
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Dharmendra Yadav, Et al. "Mathematical Modeling and Comparative Assessment of Centroid Based Leach Routing Protocol for Wireless Sensor Network." Advances in Nonlinear Variational Inequalities 27, no. 1 (2024): 188–202. http://dx.doi.org/10.52783/anvi.v27.355.
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This research evaluates the performance of two key routing protocols, LEACH and an enhanced version of Centralized LEACH, in Wireless Sensor Networks (WSNs). The study sets initial simulation parameters, gathers data over numerous rounds, and presents results through diagrams. Additionally, it conducts a comparative analysis with existing research. In WSNs, nodes within transmission range communicate directly, while distant nodes rely on intermediaries. The study introduces hierarchical clustering with cluster heads for network efficiency. The number of cluster heads significantly impacts energy consumption .LEACH is explored, emphasizing its two phases: setup and steady-state. The setup phase involves autonomous cluster formation without central control. Cluster heads are selected based on thresholds and advertise their presence. Nodes then choose clusters based on signal strength. TDMA scheduling optimizes data transmission. In the steady-state phase, nodes follow a timetable to send data to cluster heads. Uneven node distribution among cluster heads may lead to varying data loads. Power control minimizes energy usage, and TDMA scheduling maximizes bandwidth use. Cluster heads forward aggregated data to the base station. The paper includes an energy model detailing communication components. Findings offer insights into LEACH and its enhanced variant, contributing to energy-efficient routing protocols in WSNs.