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Krupali Dhawale. "Hybrid VGG19 with Combined XGBoost Deep Neural Network for Improved Papulosquamous Skin Diseases classification." Advances in Nonlinear Variational Inequalities 28, no. 6s (2025): 64–80. https://doi.org/10.52783/anvi.v28.4122.
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This Skin condition classification is crucial for early diagnosis and treatment in dermatology, yet it poses challenges due to the complexity and variability of dermatological images. Traditional classification methods often struggle to balance performance across multiple classes, especially when dealing with rare conditions or those with overlapping features, hindering their reliability in diverse applications. This study aims to evaluate multiple classification models to determine their effectiveness in accurately classifying skin conditions, focusing on identifying the most suitable model for achieving consistent and precise results across all classes. This study compares the performance of five models: Support Vector Machine (SVM), Random Forest, ResNet50, XGBoost machine learnig algorithim and VGG19+XGBoost proposed deep learning model. The models were evaluated based on their accuracy, precision, recall, and consistency across classes. The models were trained and tested on a DermNet image dataset containing samples representing multiple skin conditions, including Lichen Planus and Pityriasis Rubra Pilaris. Proposed VGG19+XGBoost hybrid model achieved the highest accuracy of 96% by leveraging its ability to analyze image data directly, capturing intricate spatial details crucial for image based classification tasks. SVM delivered the most balanced performance with 72% accuracy and consistent metrics across all classes, making it ideal for applications requiring uniform class emphasis. Random Forest achieved 70% accuracy and exhibited high recall for Pityriasis Rubra Pilaris, but its lower precision suggested challenges with false positives. In summary, while SVM offers balanced performance and Random Forest, ResNet50 may be advantageous for targeted recall, VGG19+XGBoost hybrid model demonstrates superior accuracy and precision for dermatological image classification.
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Wu, Qingcong, Xingsong Wang, and Fengpo Du. "Development and analysis of a gravity-balanced exoskeleton for active rehabilitation training of upper limb." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 230, no. 20 (2016): 3777–90. http://dx.doi.org/10.1177/0954406215616415.
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Robot-assisted therapy has become an important technology applied in rehabilitation engineering, allowing patients with motion impairment problems to perform training programs without continuous supervision from physiotherapists. The goal of this paper is to develop a gravity balanced exoskeleton for active rehabilitation training of upper limb. The mechanical structure and kinematics of the exoskeleton are described and optimized to enable natural interaction with user and avoid singular configurations within the desired workspace. The gravity balancing of the human arm and mechanism is achieved through a hybrid strategy making use of auxiliary links and zero-free-length springs to balance the effect of gravity over the range of motion. The balance errors resulting from the variation of anthropometric parameters are analyzed and discussed. Further experiments involving trajectories tracking tasks with and without gravity balancing are conducted to evaluate the improvement of the control performance and energetic efficiency made by the developed balanced mechanism. The experimental results indicate that the proposed balance strategy can achieve a reduction of 34.56% in overall power consumption compared with the cost in unbalanced condition.
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Chilakala, Ramireddy, K. Suresh, P. Anusha, Sk Najma, B. I. Rajkumar, and B. Prasanna Lakshmi. "A passive islanding detection method for hybrid distributed generation system under balanced islanding." Indonesian Journal of Electrical Engineering and Computer Science 14, no. 1 (2019): 9. http://dx.doi.org/10.11591/ijeecs.v14.i1.pp9-19.
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<p>To solve the energy consumption demand of the world and environmental problems in the future the distributed generation is considered an alternative approach. In this paper a new passive islanding detection technique was proposed for the Hybrid distributed generation (HDG) system based on changes in negative sequence voltage (NSV) and currents during an unintentional islanding. Islanding is caused in the distributed generation system due to failures in the power grid. As per distribution energy resources interconnection standards, it should be detected within 2 sec with the equipments connected to it. It is difficult to detect islanding during zero power imbalance condition. Sequence analyzer will separate the positive, negative and zero sequence components of voltages and currents from the voltages and currents obtained at the point of common coupling (PCC). During a wide range of power imbalance conditions the change in negative sequence voltage and currents are examined for islanding detection. The simulation shows that this method is free from Non detection zone, even at zero power imbalances between load and distribution generation. The computer simulations made in Matlab/ Simulink laboratory show the effectiveness of this method</p>
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Suresh, K., P. Anusha, Sk. Najma, B. I. Rajkumar, Reddy Ch. Rami, and B. Prasanna Lakshmi. "A passive islanding detection method for hybrid distributed generation system under balanced islanding." Indonesian Journal of Electrical Engineering and Computer Science 14, no. 1 (2019): 9–19. https://doi.org/10.11591/ijeecs.v14.i1.pp9-19.
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To solve the energy consumption demand of the world and environmental problems in the future the distributed generation is considered an alternative approach. In this paper a new passive islanding detection technique was proposed for the Hybrid distributed generation (HDG) system based on changes in negative sequence voltage (NSV) and currents during an unintentional islanding. Islanding is caused in the distributed generation system due to failures in the power grid. As per distribution energy resources interconnection standards, it should be detected within 2 sec with the equipments connected to it. It is difficult to detect islanding during zero power imbalance condition. Sequence analyzer will separate the positive, negative and zero sequence components of voltages and currents from the voltages and currents obtained at the point of common coupling (PCC). During a wide range of power imbalance conditions the change in negative sequence voltage and currents are examined for islanding detection. The simulation shows that this method is free from Non detection zone, even at zero power imbalances between load and distribution generation. The computer simulations made in Matlab/ Simulink laboratory show the effectiveness of this method
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Hua, Yuyang, Xin Jin, and Sen Xie. "Outlet Liquid Material Concentration Prediction of an Evaporation Process Based on Knowledge and Data Information." Processes 10, no. 12 (2022): 2525. http://dx.doi.org/10.3390/pr10122525.
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The outlet liquid material concentration is a key production indicator to evaluate the evaporation quality and an important basis to adjust the evaporation operation parameters. However, the online concentration analyzer has strict installation conditions and high prices, and it is difficult to obtain the liquid material concentration in time. Usually, the field works perform imprecise operations according to the time delay information. In addition, the process data contain errors, which affects the accuracy and timeliness of process optimization and control. Therefore, a hybrid prediction model of concentration based on data reconciliation is presented in this paper. First, to obtain the high-quality process data, the data reconciliation method is applied for preprocessing. Moreover, the process mechanistic model is constructed by utilizing the process knowledge and the balance principle. Taking into account the volatility and nonlinearity characteristics, a data-driven model based on autoregressive integrated moving average integrated generalized autoregressive conditional heteroscedasticity is established, and then the support vector regression model is built for prediction residual optimization. Furthermore, the prediction results of the mechanistic model and the data-driven model are balanced comprehensively. Finally, an evaporation process is selected for simulation verification. The results demonstrate that the proposed hybrid prediction model has improved the prediction condition and performance.
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Ren, Yaxing, Saqib Jamshed Rind, and Lin Jiang. "A coordinated control strategy for battery/supercapacitor hybrid energy storage system to eliminate unbalanced voltage in a standalone AC microgrid." Journal of Intelligent Manufacturing and Special Equipment 1, no. 1 (2020): 3–23. http://dx.doi.org/10.1108/jimse-08-2020-0007.
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PurposeA standalone microgrid (MG) is able to use local renewable resources and reduce the loss in long distance transmission. But the single-phase device in a standalone MG can cause the voltage unbalance condition and additional power loss that reduces the cycle life of battery. This paper proposes an energy management strategy for the battery/supercapacitor (SC) hybrid energy storage system (HESS) to improve the transient performance of bus voltage under unbalanced load condition in a standalone AC microgrid (MG).Design/methodology/approachThe SC has high power density and much more cycling times than battery and thus to be controlled to absorb the transient and unbalanced active power as well as the reactive power under unbalanced condition. Under the proposed energy management design, the battery only needs to generate balanced power to balance the steady state power demand. The energy management strategy for battery/SC HESS in a standalone AC MG is validated in simulation study using PSCAD/EMTDC.FindingsThe results show that the energy management strategy of HESS maintains the bus voltage and eliminates the unbalance condition under single-phase load. In addition, with the SC to absorb the reactive power and unbalanced active power, the unnecessary power loss in battery is reduced with shown less accumulate depth of discharge and higher average efficiency.Originality/valueWith this technology, the service life of the HESS can be extended and the total cost can be reduced.
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Hartono, Hartono, and Erianto Ongko. "Avoiding Overfitting dan Overlapping in Handling Class Imbalanced Using Hybrid Approach with Smoothed Bootstrap Resampling and Feature Selection." JOIV : International Journal on Informatics Visualization 6, no. 2 (2022): 343. http://dx.doi.org/10.30630/joiv.6.2.985.
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The dataset tends to have the possibility to experience imbalance as indicated by the presence of a class with a much larger number (majority) compared to other classes(minority). This condition results in the possibility of failing to obtain a minority class even though the accuracy obtained is high. In handling class imbalance, the problems of diversity and classifier performance must be considered. Hence, the Hybrid Approach method that combines the sampling method and classifier ensembles presents satisfactory results. The Hybrid Approach generally uses the oversampling method, which is prone to overfitting problems. The overfitting condition is indicated by high accuracy in the training data, but the testing data can show differences in accuracy. Therefore, in this study, Smoothed Bootstrap Resampling is the oversampling method used in the Hybrid Approach, which can prevent overfitting. However, it is not only the class imbalance that contributes to the decline in classifier performance. There are also overlapping issues that need to be considered. The approach that can be used to overcome overlapping is Feature Selection. Feature selection can reduce overlap by minimizing the overlap degree. This research combined the application of Feature Selection with Hybrid Approach Redefinition, which modifies the use of Smoothed Bootstrap Resampling in handling class imbalance in medical datasets. The preprocessing stage in the proposed method was carried out using Smoothed Bootstrap Resampling and Feature Selection. The Feature Selection method used is Feature Assessment by Sliding Thresholds (FAST). While the processing is done using Random Under Sampling and SMOTE. The overlapping measurement parameters use Augmented R-Value, and Classifier Performance uses the Balanced Error Rate, Precision, Recall, and F-Value parameters. The Balanced Error Rate states the combined error of the majority and minority classes in the 10-Fold Validation test, allowing each subset to become training data. The results showed that the proposed method provides better performance when compared to the comparison method
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Nireekshana, Turaka, Bhavani J., Sudha Rani K., and Naveen Kumar G. "Comparative Analysis on Power Transmission Congestion Management using Evolutionary Algorithms, Hybrid Model under Overload and Contingency Conditions." Journal of Recent Trends in Electrical Power System 6, no. 3 (2023): 1–18. https://doi.org/10.5281/zenodo.8245538.
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<em>The power system is said to be in balanced state if generated power is equal to demand ideally. Here demand comprises of industrial loads, commercial loads, infrastructure, domestic loads, critical loads, etc. Due to increase in population the demand of electricity is also increasing drastically. Due to increase in demand, there is a considerable effect on power carrier (transmission lines) connecting generating stations and loads in the form of congestion. Since, if there is increase in demand the transmission lines must carry the power sometimes it can be more than its maximum power transfer limit and may cause protective system to operate to protect the transmission line this may divert the power in adjacent transmission lines and may lead to cascade outages. Similar situation can be experienced under contingency conditions as well. However, these effects can be overcome if series FACTS device (Thyristor Controlled Series Capacitor) is installed in the transmission system. In this paper a control algorithm is proposed to change the reactance of transmission line based on power carrying capacity at that instant using TCSC. This algorithm has been integrated with Evolutionary Algorithms like PSO and GA separately for optimal design of TCSC. In this paper a comparative analysis is also done on the performance of PSO, GA and Hybrid Model on 14 bus system under overloading condition, contingency condition. Initially, in this work the control algorithm is tested in 5 bus test system and then control algorithm along with EA technologies (PSO, GA and Hybrid Model) is tested on 14 bus test system.</em>
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Murugan, R., Rajagopal Ramesh, and K. Padmanabhan. "Investigation on Vibration Behaviour of Cantilever Type Glass/Carbon Hybrid Composite Beams at Higher Frequency Range Using Finite Element Method." Advanced Materials Research 984-985 (July 2014): 257–65. http://dx.doi.org/10.4028/www.scientific.net/amr.984-985.257.
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Woven fabric composites are now being increasingly used in aircraft and automobile structures due to balanced properties in the fabric plane. In the present work, woven fabric glass beam is modified and strengthened by interplying high modulus carbon fabric plies for improving the strength to weight ratio and thereby to achieve better performance in various dynamic conditions. The objective of the present work is to investigate the vibration behavior of cantilever type glass/carbon hybrid composite beams subjected to higher frequency of operation using finite element method. Unit plied woven fabric glass, carbon and hybrid of glass/carbon laminates were fabricated using hand layup technique. Experimental modal analysis of unit plied composite beams was carried out by impulse excitation technique under fixed free boundary condition. Theoretical modal analysis was done by finite element method using elastic constants derived from rule of mixture equations. The experimental and theoretical frequency results were compared and analyzed for finding the degree of deviation using regression analysis. The coefficients of regression analysis were used to find effective elastic constants of composite laminates. Further these effective elastic constants were applied for modal analysis of hybrid composite beams under higher frequency range. The results of mode shape, modal frequency of hybrid beams were reported and discussed. The effect of stacking sequence and effect of beam size on vibration characteristics at higher frequency range was also discussed.
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Ekong, Victor. "SOFT COMPUTING SYSTEM FOR THE DIAGNOSIS OF HORMONAL IMBALANCE." Transactions on Machine Learning and Artificial Intelligence 7, no. 6 (2020): 30–42. http://dx.doi.org/10.14738/tmlai.76.7507.
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Soft computing, as a science of modelling systems, applies techniques such as evolutionary computing, fuzzy logic, and their hybrids to solve real life problems. Soft computing techniques are quite tolerant to incomplete, imprecise, and uncertainty when dealing with complex situations. This study adopts a hybrid of genetic algorithm and fuzzy logic in diagnosing hormonal imbalance. Hormones are chemical messengers that are vital for growth, reproduction, and are essential for human existence. Hormones may sometimes not be balanced which is a medical condition that often go unnoticed and it’s quite difficult to be diagnosed by medical experts. Hormonal imbalance has several symptoms that could also be confused for other ailments. This proposed system serves as support for medical experts to improve the precision of diagnosis of hormonal imbalance. The study further demonstrates the effective hybridization of genetic algorithm and fuzzy logic in resolving human problems.
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HASSAN, Mohamed K., Muneef H. ALAMERI, Sufyan AZAM, Somia M. S. ALFATIH, and Mohammed Y. ABDELLAH. "Sustainable Innovations: Mechanical and Tribological Advancements in Carbon and Kevlar Reinforced Epoxy Composites." Acta Mechanica et Automatica 19, no. 2 (2025): 309–17. https://doi.org/10.2478/ama-2025-0038.
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Abstract The study of technical textiles and their composites is crucial for material selection in performance-driven applications. This research investigates the mechanical and abrasion behavior of plain-woven Kevlar and carbon fiber fabrics and their epoxy-based composites under various environmental conditions. Tensile tests and Martindale abrasion tests were performed in warp and weft directions, following ASTM D3039, ISO 105-E04:2013, and ISO 12947-3:1998 standards. Samples were tested in dry conditions, after immersion in water, and in a salt solution simulating human sweat. Dry fabrics exhibited the highest tensile strength, with Kevlar fabric outperforming carbon fabric due to its denser weave and resistance to moisture-induced degradation. However, carbon/epoxy composites showed superior mechanical properties, owing to better fiber–matrix adhesion and stiffness. Hybrid Kevlar-carbon composites offered a balanced mechanical response, particularly in the warp direction. Abrasion tests revealed lower mass loss in Kevlar fabrics compared to carbon, with damage intensifying under wet conditions especially in sweat simulations due to salt-induced weakening. This behavior is linked to fiber structure, fiber–matrix bonding, and abrasive wear mechanisms. Sweat simulation testing reflects realistic service conditions found in protective clothing and aerospace applications. The results support the development of durable, lightweight composites for environments involving moisture or salt exposure. While statistical consistency was ensured using sample averaging, future studies will include detailed statistical analyses. To mitigate wet-condition degradation, future work will explore the use of surface treatments or coatings. This study contributes to sustainable material design by enabling longer service life, reduced material waste, and optimized hybrid fiber configurations. Further research will explore bio-based matrices and nano-enhanced hybrids to expand eco-friendly performance solutions.
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Karnehm, Dominic, Wolfgang Bliemetsrieder, Sebastian Pohlmann, and Antje Neve. "Controlling Algorithm of Reconfigurable Battery for State of Charge Balancing Using Amortized Q-Learning." Batteries 10, no. 4 (2024): 131. http://dx.doi.org/10.3390/batteries10040131.
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In the context of the electrification of the mobility sector, smart algorithms have to be developed to control battery packs. Smart and reconfigurable batteries are a promising alternative to conventional battery packs and offer new possibilities for operation and condition monitoring. This work proposes a reinforcement learning (RL) algorithm to balance the State of Charge (SoC) of reconfigurable batteries based on the topologies half-bridge and battery modular multilevel management (BM3). As an RL algorithm, Amortized Q-learning (AQL) is implemented, which enables the control of enormous numbers of possible configurations of the reconfigurable battery as well as the combination of classical controlling approaches and machine learning methods. This enhances the safety mechanisms during control. As a neural network of the AQL, a Feedforward Neuronal Network (FNN) is implemented consisting of three hidden layers. The experimental evaluation using a 12-cell hybrid cascaded multilevel converter illustrates the applicability of the method to balance the SoC and maintain the balanced state during discharge. The evaluation shows a 20.3% slower balancing process compared to a conventional approach. Nevertheless, AQL shows great potential for multiobjective optimizations and can be applied as an RL algorithm for control in power electronics.
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Mishra, Pawan, Nitesh Singh, Sudhanshu Ranjan, and Vishal Patel. "Automated Cardiovascular Disease Detection with Hybrid Machine Approach." International Journal of Innovative Research in Advanced Engineering 11, no. 11 (2024): 834–40. https://doi.org/10.26562/ijirae.2024.v1111.07.
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Cardiovascular ailment (CVD) is a main reason of death and disability globally, posing tremendous challenges for early detection and prevention. A heart condition or the accumulation of fatty deposits inside the arteries, is frequently linked to CVD and raises the risk of blood clots and other consequences. Predicting and diagnosing CVD is critical to mitigating its effect and enhancing patient results. With the appearance of massive data in healthcare, large volumes of patient records are available that may assist in identifying early warning symptoms of coronary heart disorder. This project aims to generate a hybrid machine learning technique which leverages deep learning and traditional machine learning algorithms to automatically locate cardiovascular illnesses. By integrating more than one dataset, we intend to upgrade the model's overall performance in diagnosing coronary heart situations. Numerous machine learning classification models, consisting of Random Forest and Gradient Boosting, might be employed and in comparison, to evaluate their effectiveness in detecting CVD. Each model can be evaluated using overall performance indicators like precision, accuracy, recall, and F1-Score. In previous research, the Random Forest model did 94% accuracy in coronary heart disorder detection, at the same time as the Gradient Boosting model established a balanced overall performance through accuracy, precision, recall, and F1-score with 73% each in detecting cardiovascular diseases via this hybrid technique. We aim to enhance prediction accuracy and offer a greater reliable tool for early CVD detection, in the long run enhancing affected person care and saving lives.
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Sharma, Ashwani Kumar. "Performance Analysis of Mutual Funds: A Comparative Study of the Select Hybrid Mutual Fund Schemes in India." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem50492.
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Abstract- The paper will do a thorough performance analysis of some hybrid mutual fund schemes in India, in terms of their risk adjusted returns as well as perception by the investors so that it will be beneficial to the investors as well as the fund managers. A balanced investment strategy that tactfully allocates equity and debt securities, is the hybrid mutual funds that offer high returns with reduced risk. By using the standard measures of performance including the Sharpe ratio, Treynor ratio, and Jensen alpha, this study takes a select sample of funds and studies their performance over an identified time frame to gauge their efficiency in the Indian financial market condition. The quantitative analysis is complemented by survey data of investors which can show the levels of satisfaction, the risk perception and the decision-making factors. The results demonstrate a heterogeneity of preferences among investors, the essentiality of risk-adjusted performance measurement and transparency and education as the means of enhancing investment outcomes. The study has value in terms of empirical data and practical guidelines even though it has shortcomings associated with small sample size and the use of secondary data. Keywords- Hybrid Mutual Funds, Performance Analysis, Risk-Adjusted Returns, Sharpe Ratio, Treynor Ratio, Jensen’s Alpha, Investor Satisfaction, India
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Yenila, Firna, and Suci Wahyuni. "Expert System Diagnosing Gastric Disease Using Hybrid Method." Knowbase : International Journal of Knowledge in Database 2, no. 1 (2022): 46. http://dx.doi.org/10.30983/ijokid.v2i1.5662.
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<p class="Abstract">The gastric is a vital organ that must be maintained by maintaining a balanced life. The lack of time to discuss with patients, the causes of stomach disease is frequently overlooked. Need a new way to consult with patients without seeing the time. Expert consul is an application based on knowledge such as an expert system. This research was proposed to provide education to system users who have problems with the stomach. So that treatment can be obtained quickly and the stomach's condition can be better. This research includes a system designed by incorporating an expert's expertise into a system by combining the forward chaining method by describing the conditions experienced by patients in the form of sequential questions and the certainty of factors that provide some of the patient's belief in experiencing these conditions, which are used as desires in decision making. The result of the expert system using the hybrid method obtained consultation results with a 76 % certainty level, indicating that the patient was quite certain of having the disease that was conveyed in accordance with the knowledge that had been adopted by the expert so that the patient received the right therapy early before further consultation with experts.</p><p class="Abstract"> </p><p class="Abstract">Lambung merupakan organ vital yang harus dijaga dengan menjaga hidup seimbang. Singkatnya waktu untuk berdiskusi dengan pasien menyebabkan penyakit lambung sering terabaikan. Dibutuhkan cara baru untuk berkonsultasi dengan pasien tanpa melihat waktu konsultasi pakar berupa sebuah aplikasi berbasis pengetahuan seperti system pakar. Penelitian ini diajukan untuk memberikan edukasi kepada pengguna system yang memiliki masalah dengan lambung berupa informasi persentase menderita penyakit tersebut. Sehingga penanganan cepat didapatkan dan kondisi lambung bisa kembali seperti sediakala. Penelitian ini mengusung aplikasi system pakar yang dirancang dengan menyadurkan kepakaran seorang ahli kedalam sebuah system dengan menggabungkan metode forward chaining dengan memaparkan kondisi yang dialami oleh pasien dalam bentuk pertanyaan secara berurutan dan certainty factor yang memberikan persentase tingkat keyakinan pasien mengalami kondisi tersebut yang dijadikan acuan dalam pengambilan keputusan. Hasil yang didapatkan dalam system pakar yang melakukan pengujian terhadap metode hybrid tersebut didapatkan hasil konsultasi dengan tingkat kepastian 76% yang menyatakan bahwa pasien dinyatakan cukup yakin mengidap penyakit yang disampaikan sesuai dengan pengetahuan yang sudah disadurkan oleh pakar, sehingga pasien mendapatkan terapi yang tepat secara dini sebelum berkonsultasi lanjut dengan pakar.</p>
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Wang, Xiran, Ke Sun, Xuan Yang, Kai Yang, and Jiaxi Chen. "Optimal allocation method of hybrid energy storage capacity of multi-energy system under low-carbon background." International Journal of Low-Carbon Technologies 18 (2023): 820–28. http://dx.doi.org/10.1093/ijlct/ctad070.
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Abstract The energy dispatching and distribution ability is improved by optimizing the configuration of hybrid energy storage capacity of multi-energy system in low-carbon background, and an optimal configuration method of hybrid energy storage capacity of multi-energy system in low-carbon background based on equilibrium control and dynamic optimization algorithm is proposed. The data structure model of hybrid energy storage capacity distribution of multi-energy system is constructed. Under the condition that the energy storage optimal allocation model based on cost analysis meets the system performance index, the energy storage optimal allocation model is established with the objective function of minimizing the cost of configuring energy storage system, and with the objective of minimizing the fluctuation of active power of distributed power sources, such as wind and light. Taking the penalty cost of wind energy storage combined output power exceeding the fluctuation limit as the objective function, low-pass filtering method is adopted to stabilize the fluctuation of new energy power, and the optimal configuration capacity of energy storage system is determined according to the allowable frequency deviation and voltage stability of the system. Balanced control and dynamic optimization algorithm are adopted to realize the optimal configuration of hybrid energy storage capacity of multi-energy system under low-carbon background by combining different wind and solar energy combinations, different sampling intervals and different number of power stations. The simulation results show that the hybrid energy storage capacity allocation of multi-energy system has strong adaptability and high environmental adaptability, which effectively improves the transient stability of power grid system and further promotes the safe and stable operation of power grid system.
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Alzahrani, Ahmed, Muhammad Ali Raza, and Muhammad Zubair Asghar. "Demystifying diagnosis: an efficient deep learning technique with explainable AI to improve breast cancer detection." PeerJ Computer Science 11 (April 16, 2025): e2806. https://doi.org/10.7717/peerj-cs.2806.
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As per a WHO survey conducted in 2023, more than 2.3 million breast cancer (BC) cases are reported every year. In nearly 95% of countries, the second leading cause of death for females is BC. Breast and cervical cancers cause 80% of reported deaths in middle-income countries. Early detection of breast cancer can help patients better manage their condition and increase their chances of survival. However, traditional AI models frequently conceal their decision-making processes and are mainly tailored for classification tasks. Our approach combines composite deep learning techniques with explainable artificial intelligence (XAI) to enhance interpretability and predictive accuracy. By utilizing XAI to examine features and provide insights into its classifications, the model clarifies the rationale behind its decisions, resulting in an understanding of concealed patterns linked to breast cancer detection. The XAI strengthens practitioners’ and health researchers’ confidence and understanding of artificial intelligence (AI)-based models. In this work, we introduce a hybrid deep learning bi-directional long short-term memory-convolutional neural network (BiLSTM-CNN) model to identify breast cancer using patient data effectively. We first balanced the dataset before using the BiLSTM-CNN model. The hybrid deep learning (DL) model presented here performed well in comparison to other studies, with 0.993 accuracy, precision 0.99, recall 0.99, and F1-score 0.99.
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Airlangga, Gregorius. "Anemia Classification Using Hybrid Machine Learning Models: A Comparative Study of Ensemble Techniques on CBC Data." Journal of Computer System and Informatics (JoSYC) 5, no. 4 (2024): 1108–17. https://doi.org/10.47065/josyc.v5i4.5848.
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Anemia is a prevalent and potentially serious medical condition characterized by a deficiency in the number or quality of red blood cells. Accurate classification of anemia types is crucial for ensuring appropriate treatment, as different types of anemia require distinct therapeutic approaches. However, the classification of anemia presents specific challenges due to the complexity of the condition, the variability in CBC data, and the need to differentiate between multiple anemia types that may present with overlapping symptoms. In this study, we explore the application of hybrid machine learning models to classify anemia types using Complete Blood Count (CBC) data. We evaluated the performance of various models, including DecisionTree, RandomForest, XGBoost, LightGBM, CatBoost, and ensemble methods such as Stacking and Voting. The ensemble models, particularly Stacking and Voting, demonstrated superior performance with balanced accuracy reaching 0.9976 and F1 scores of 0.9964, significantly outperforming individual classifiers. These results underscore the efficacy of ensemble techniques in handling the complex and imbalanced datasets commonly encountered in medical diagnostics. Despite their high accuracy, we identified challenges related to model interpretability, computational demands, and data quality. The complexity and resource requirements of these models may limit their practical application in resource-constrained environments. This study provides a comprehensive analysis of the trade-offs between model complexity, accuracy, and interpretability, offering valuable insights for the deployment of machine learning models in clinical settings. Our findings highlight the potential of hybrid models to improve anemia diagnosis, suggesting their integration into healthcare systems could enhance diagnostic accuracy and patient outcomes. Future work will focus on expanding the dataset, refining model interpretability, and addressing ethical considerations in the use of AI in healthcare.
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Metsaranta, Juha, Suzanne Beauchemin, Sean Langley, Bryan Tisch, and Phyllis Dale. "Assessing the Long-Term Ecosystem Productivity Benefits and Potential Impacts of Forests Re-Established on a Mine Tailings Site." Forests 9, no. 11 (2018): 707. http://dx.doi.org/10.3390/f9110707.
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Restoring sites disturbed by industrial activity to a forested condition can ensure the continued provision of economic and ecosystem services from these areas. Impounded mine tailings are particularly challenging sites, and positive benefits of establishing trees must be balanced against risks associated with metal contamination, ongoing tailings stability, and the possibility of acid mine drainage. We used a hybrid biometric modelling approach based on dendrochronological reconstruction to retrospectively (1980–2015) quantify productivity and carbon dynamics of pine plantations growing on impounded mine tailings at the Vale waste management facility near Sudbury, Canada. Historical reclamation practices had remediated conditions sufficiently to allow conifer plantation establishment in the late 1970s. The revegetated sites were highly productive, when compared to reference conditions based on site index, wood volume growth, and ecosystem production, congruent with other studies showing that forests on revegetated post mining sites can be highly productive. However, metal concentrations in the forest floor were high, and further research is warranted to evaluate ecosystem impacts. Due to the requirement for energy-intensive inputs, we estimated that it took 12 years or more to recover the emissions associated with the revegetation process through C accumulated in biomass and soil at the revegetated sites.
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Nebagiri, Manjula Hulagappa, and Latha Pillappa Hanumanthappa. "Hybrid trust-based optimized virtual machine migration for dynamic load balancing and replica management in heterogeneous cloud." Multiagent and Grid Systems 19, no. 3 (2023): 231–52. http://dx.doi.org/10.3233/mgs-230025.
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Cloud computing is an upcoming technology that has garnered interest from academic as well as commercial domains. Cloud offers the advantage of providing huge computing capability as well as resources that are positioned at multiple locations irrespective of time or location of the user. Cloud utilizes the concept of virtualization to dispatch the multiple tasks encountered simultaneously to the server. However, allocation of tasks to the heterogeneous servers requires that the load is balanced among the servers. To address this issue, a trust based dynamic load balancing algorithm in distributed file system is proposed. Load balancing is performed by predicting the loads in the physical machine with the help of the Rider optimization algorithm-based Neural Network (RideNN). Further, load balancing is carried out using the proposed Fractional Social Deer Optimization (FSDO) algorithm, where the virtual machine migration is performed based on the load condition in the physical machine. Later, replica management is accomplished for managing the replica in distributed file system with the help of the devised FSDO algorithm. Moreover, the proposed FSDO based dynamic load balancing algorithm is evaluated for its performance based on parameters, like predicted load, prediction error, trust, cost and energy consumption with values 0.051, 0.723, 0.390 and 0.431J correspondingly.
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Zhao, Chengbi, and Ming Ma. "A Hybrid 2.5-Dimensional High-Speed Strip Theory Method and Its Application to Apply Pressure Loads to 3-Dimensional Full Ship Finite Element Models." Journal of Ship Production and Design 32, no. 04 (2016): 216–25. http://dx.doi.org/10.5957/jspd.2016.32.4.216.
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As the three-dimensional (3D) finite element model (FEM) has become the de facto standard for ship structural design, interest in accurately transferring seakeeping loads to panel-based structural models has increased dramatically in recent years. In today's design practices, panel-based hydrodynamic analyses are often used for mapping seakeeping loads to 3D FEM structural models. However, 3D panel-based hydrodynamic analyses are computationally expensive. For monohull ships, methods based on strip theories have been successfully used in the industry for many years. They are computationally efficient, and provide good predictions for motions and hull girder loads. However, many strip theory methods provide only hull girder sectional forces and moments, such as vertical bending moment and vertical shear force, which are difficult to apply to 3D finite element structural models. Previously, the authors have proposed a hybrid strip theory method to transfer 2D strip theory-based seakeeping loads to 3D FEM. In the hybrid approach, the velocity potentials of strip sections are first calculated based on the ordinary 2D strip theories. The velocity potentials of a finite element panel are obtained from the interpolation of the velocity potentials of the strip sections. The panel pressures are then computed based on Bernoulli's equation. Integration of the pressure over the FEM wetted panels yields the hydrodynamic forces and moments. The equations of motion are then formulated based on the FEM. The method not only produces excellent ship motion results, but also results in a perfectly balanced structural model. In this article, the hybrid approach is extended to the 2.5D high-speed strip theory. The simple Rankine source function is used to compute velocity potentials. The original linearized free surface condition, where the forward speed term is not ignored, is used to formulate boundary integral equations. A model based on the Series-64 hull form was used for validating the proposed hybrid method. The motion response amplitude operators are in good agreement with VERES's 2.5D strip theory and with experimental results. Finally, an example is provided for transferring seakeeping loads obtained by the 2.5D hybrid strip theory to a 3D FEM.
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Rao, Moram Vishnu Vardhana, and Aparna Chaparala. "An Efficient Data Mining Technique for Structural Strength Monitoring System." Ingénierie des systèmes d information 26, no. 2 (2021): 237–43. http://dx.doi.org/10.18280/isi.260211.
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A fundamental target of strength monitoring frameworks for different structures is to analyze the condition of the structure and to assess its conceivable danger and furthermore to investigation, identification, and characterization of danger in complex structures is a critical part of auxiliary strength checking. The capacities are browsed as lexicon of time-recurrence movement and scaled variants of a basic Gaussian hypothesis work. This word reference is likewise adjusted to utilize genuine estimated information. Characterization is then accomplished by coordinating the removed damage includes in the time-frequency. In this paper, we utilize our model to assess our information mining approach for the fault checking. The balanced scratch-off and high-pass sifting strategies are consolidated adequately to take care of basic issues in numerical reconciliation signs gathered from sensors are disintegrated into direct blends of very confined Gaussian capacities utilizing the coordinating significance decay calculation. The combination exactness is enhanced and contrasted with former numerical integrators. Rough set analysis uses only internal knowledge and does not rely on prior model assumption as fuzzy set methods or probabilistic models do. In this manuscript a novel hybrid algorithm combining the features of Rough set Support vector machine (Rs-SVM) classified structures and Rough set Artificial Neural Network (Rs-ANN) classified structures are used. At long last the vertices of the structure of different types are connected and analysed by the Hybrid algorithm and furthermore to additionally enhance order execution, the data gathered from numerous sensors is incorporated utilizing a Bayesian sensor combination approach.
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Lalli, K., and M. Senbagavalli. "Enhancing Deep Learning for Autism Spectrum Disorder Detection with Dual-Encoder GAN-based Augmentation of Electroencephalogram Data." Salud, Ciencia y Tecnología - Serie de Conferencias 3 (January 1, 2024): 958. http://dx.doi.org/10.56294/sctconf2024958.
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Autism Spectrum Disorder (ASD) is a general neurodevelopmental condition that requires early and accurate diagnosis. Electroencephalography (EEG) signals are reliable biomarkers for ASD detection and diagnosis. A recent Deep Learning (DL) model called Resting-state EEG-based Hybrid Graph Convolutional Network (Rest-HGCN) has been developed for this purpose. However, a challenge in ASD diagnosis is the limited availability of EEG data, leading to imbalanced classes and ineffective model training. To address this issue, a new approach is proposed in this paper, which involves a generative model for EEG data augmentation. A novel Dual Encoder-Balanced Conditional Wasserstein Generative Adversarial Network (DEBCWGAN) is designed to produce fine synthetic minority-class EEG examples and augment the original training dataset. This model integrates the Variational Auto-Encoder (VAE) and balanced conditional Wasserstein GAN. Initially, EEG signals for ASD in the training dataset are pre-processed as Differential Entropy (DE) features and split into different segments. Each feature segment is processed in the temporal and the spatial domain depending on the electrode place. Then, twin encoders are trained to capture both spatial and temporal information from these features, concatenate them as Latent Variables (LVs), and provide them to the decoder to produce synthetic EEG examples. Additionally, gradient penalty and L2 regularization are used to speed up convergence and prevent overfitting effectively. Further, the augmented dataset is used to train the Rest-HGCN for ASD detection, enhancing its robustness and generalizability. Finally, test outcomes demonstrate that the DEBWGAN-GP-Rest-HGCN on the EEG Dataset for ASD and ABC-CT dataset achieves 91,6 % and 88,1 % accuracy, respectively compared to the Rest-HGCN, AlexNet, K-Nearest Neighbor (KNN) and Support Vector Machine (SVM)
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Kumar, Rakesh, and Mr Anurage Khare. "Designing of SPVWM Inverter Controlled System By Using Suitable Architecture Of Power Quality Enhancement Device For Driving Mixed Loads." SMART MOVES JOURNAL IJOSCIENCE 6, no. 1 (2020): 9. http://dx.doi.org/10.24113/ijoscience.v6i1.256.
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The stability of a power system is the ability of a power system to restore an operating state of equilibrium for a given initial operating condition after it has been subjected to a physical disturbance, most of the variables of the system being limited so that almost the entire system remains intact. Designing of a SPVWM controlled inverter that has to be made available for efficient power supply to the load of different types. The control of inverter has to be designed in order to enhance the system power supply. To achieve this a space vector pulse width modulation technique has been designed which is then used to provide pulses to the three leg inverter. To design a power quality enhancement device with a simplified architecture such that it will accommodate for the reactive power supply along with an improvement in the active power available at the load terminals. Thus it can be drawn from this work that while designing an inverter with SPVWM control strategy the proposed power quality enhancement device can serve the purpose with better results in terms of power and efficiency. This architecture can also be used in hybrid systems thus making it more reliable controlling method. The system designed is also fitted to feed different types of load like nonlinear load, balanced load and unbalanced load.
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Mudretsov, Anatoly F., Aleksandr N. Pavlov, and Аnna А. Prudnikova. "Challenges and prospects of green energy development in the context of transition to sustainable development." Market economy problems, no. 4 (2023): 85–97. http://dx.doi.org/10.33051/2500-2325-2023-4-85-97.
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Subject/topic. This article explores the features of green energy development, which is a crucial condition for achieving low-carbon economic development, thereby reducing the impact on the planet's climate system. Goals/Objectives. The aim of this research is to analyze changes in greenhouse gas emissions by countries and economic sectors, and to determine the trajectory of green energy development. Methodology. In the process of working on the article, various methods were employed, including analysis, synthesis, systematization, economic and logical analysis, content analysis, as well as comparisons and groupings. Results. Over the past few decades, there has been significant growth in the use of green energy in many countries. The expansion of green energy is closely linked to reduced costs for renewable energy, increased energy efficiency, ongoing technological progress, informed decision-making at the business level, global collaboration among countries, and effective national policies. To meet the growing energy demand in the near future while ensuring sustainable societal development, the application of hybrid energy supply models is necessary, with renewable energy sources as their foundation. Conclusions/Significance. Green energy will contribute to addressing Earth's environmental issues by reducing greenhouse gas emissions and will help avoid energy crises. The use of renewable energy sources requires a balanced assessment of resources, appropriate technologies, and systems that can support sustainable and low-carbon economic development.
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Angelova, Violina T., Borislav Georgiev, Tania Pencheva, et al. "Design, Synthesis, In Silico Studies and In Vitro Evaluation of New Indole- and/or Donepezil-like Hybrids as Multitarget-Directed Agents for Alzheimer’s Disease." Pharmaceuticals 16, no. 9 (2023): 1194. http://dx.doi.org/10.3390/ph16091194.
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Alzheimer’s disease (AD) is considered a complex neurodegenerative condition which warrants the development of multitargeted drugs to tackle the key pathogenetic mechanisms of the disease. In this study, two novel series of melatonin- and donepezil-based hybrid molecules with hydrazone (3a–r) or sulfonyl hydrazone (5a–l) fragments were designed, synthesized, and evaluated as multifunctional ligands against AD-related neurodegenerative mechanisms. Two lead compounds (3c and 3d) exhibited a well-balanced multifunctional profile, demonstrating intriguing acetylcholinesterase (AChE) inhibition, promising antioxidant activity assessed by DPPH, ABTS, and FRAP methods, as well as the inhibition of lipid peroxidation in the linoleic acid system. Compound 3n, possessing two indole scaffolds, showed the highest activity against butyrylcholinesterase (BChE) and a high selectivity index (SI = 47.34), as well as a pronounced protective effect in H2O2-induced oxidative stress in SH-SY5Y cells. Moreover, compounds 3c, 3d, and 3n showed low neurotoxicity against malignant neuroblastoma cell lines of human (SH-SY5Y) and murine (Neuro-2a) origin, as well as normal murine fibroblast cells (CCL-1) that indicate the in vitro biocompatibility of the experimental compounds. Furthermore, compounds 3c, 3d, and 3n were capable of penetrating the blood–brain barrier (BBB) in the experimental PAMPA-BBB study. The molecular docking showed that compound 3c could act as a ligand to both MT1 and MT2 receptors, as well as to AchE and BchE enzymes. Taken together, those results outline compounds 3c, 3d, and 3n as promising prototypes in the search of innovative compounds for the treatment of AD-associated neurodegeneration with oxidative stress. This study demonstrates that hydrazone derivatives with melatonin and donepezil are appropriate for further development of new AChE/BChE inhibitory agents.
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Korol, V. G. "Tomato hybrids recommended for growing in photoculture conditions." Vegetable crops of Russia, no. 4 (September 4, 2021): 71–77. http://dx.doi.org/10.18619/2072-9146-2021-4-71-77.
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Relevance. The most important influence on the yield, the timing of growing vegetables in greenhouses and the cost of the resulting crop, in addition to the arrival of solar radiation, is exerted by the presence of a system of artificial lighting in greenhouses and its capacity.It is no exaggeration to say that today artificial lighting in greenhouses is more effective than natural lighting. So, to obtain one kilogram of vegetables using artificial lighting, it is necessary to spend 4.5-5.0 thousand J/cm2, and in the case of natural lighting: 5.0-6.5 thousand J/cm2. This is due to the fact that when using artificial lighting in the greenhouse, we create the most comfortable growing conditions for the plants. Of course, every culture, every hybrid requires its own illumination parameters, so you should not try to formulate what the ideal artificial lighting will be. Proper lighting means getting the maximum yield from your crops at the lowest cost. An increase in the productivity of plants when grown under photoculture conditions occurs not only due to an increase in the lighting power per unit area of greenhouses, but also due to correctly selected hybrids. The cultivated hybrid is one of the most important criteria for both increasing its productivity and improving the quality of the fruit. But not all tomato hybrids recommended by breeding companies for growing under artificial lighting are ideal for such conditions.Methods. The methods used in agronomic science were used. The information base of the research was made up of reference materials from specialized publications on the subject under study (catalogs of breeding companies); materials received from participants in the greenhouse vegetable market (breeding companies, greenhouse plants); own research, articles and reviews in specialized journals.Results. Correctly selected tomato hybrids provide a significant increase in productivity in greenhouses, an improvement in fruit quality and a balanced cultivation technology under these conditions. An assortment of recommended tomato hybrids for growing in photoculture conditions is presented, an analysis of their advantages and disadvantages is made, and the main requirements for hybrids for these conditions are formulated.
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Teodoro-Cerna, Elin, Eroncio Mendoza-Nieto, and Sergio Eduardo Contreras-Liza. "Grain Yield of Maize Hybrids in Response to Inoculation with Azospirillum sp. under Nitrogen Limiting Conditions in Huaura, Peru." Sustainable Agriculture Research 10, no. 1 (2020): 1. http://dx.doi.org/10.5539/sar.v10n1p1.
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This experiment was conducted to evaluate the effect of Azospirillum sp. inoculated with different nitrogen levels (0, 60, 90, 180 kg ha-1) on grain yield, yield components and agronomic traits of hybrid maize at the arid conditions of the central coast of Peru. The experimental design used was randomized complete block design with four replications per treatment, under the arrangement of subdivided plots, in which three corn hybrids were assigned to complete plots and four inoculant-N fertilization treatments, to the subplots. The results showed that for dose fertilization of 180 kg ha-1 of nitrogen (control), the grain yield of hybrid corn was similar as compared to 60-120 kg ha-1 N fertilization inoculated at 15 and 45 days with a native strain of Azospirillum sp., suggesting that the response in grain yield for hybrid corn can be balanced with nitrogen fertilization and inoculation with Azospirillum sp. 1x108 CFU mL-1 in conditions of arid soils, with possible impact on the use of this microorganism in the maize production system
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Starina, A., N. Dmitrenko, and N. Moskaleva. "RACIAL COMPOSITION OF THE CAUSATIVE AGENT OF SUNFLOWER DOWNY MILDEW UNDER THE CONDITIONS OF THE CENTRAL EXPERIMENTAL BASE OF VNIIMK." Scientific heritage, no. 97 (September 23, 2022): 7–10. https://doi.org/10.5281/zenodo.7106907.
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Iintensive cultivation of sunflower in the last twenty years with violation of scientifically based crop rotations and attraction of foreign hybrids has upset the existing balance in the parasite-host system and stimulated the raceforming process of the downy mildew pathogen. It is possible to increase the yield of high quality sunflower with the placement of a variety or hybrid in a crop rotation, taking into account the stability of the biotype of the pathogen in the area, which requires monitoring the process of race formation.
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Nurmawati, Widya Putri, Indahwati Indahwati, and Farit Mochamad Afendi. "Improving Stroke Detection with Hybrid Sampling and Cascade Generalization." JUITA : Jurnal Informatika 12, no. 1 (2024): 9. http://dx.doi.org/10.30595/juita.v12i1.19386.
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The prevalence of stroke in Indonesia has increased. One survey in Indonesia that contains information about the health conditions of the Indonesian people is the Indonesian Family Life Survey (IFLS). The proportion of respondents who had a stroke and non-stroke in IFLS5 showed an imbalance with an extreme level of imbalance; hence, this research aims to overcome this problem with SMOTE, SMOTE-Tomek Link, and SMOTE-ENN; then, the balanced dataset is classified using the ensemble and cascade approaches to improve the detection of stroke risk and to identify the important variables. However, the stroke respondents were still challenging to classify after imbalance class handling, presumably because of the large amount of data before and after balancing. The solution is to balance the training data with various percentages. The results showed the best percentage is applied to 5% of the training data, balanced by the SMOTE-ENN, and the ensemble method with the cascade approach increases the sensitivity and balanced accuracy values. Random forest and logistic regression combine models that produce the best performance, with a classification tree as the final model. The important variables obtained from this combination are the addition of probability from random forest, logistic regression, history of hypertension, age, and physical activity.
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Orlyanskaya, N. A., N. A. Orlyansky, and D. S. Chebotarev. "Comparative indexing of early-maturing corn hybrids in multi-environment trial." Agricultural Science Euro-North-East 24, no. 4 (2023): 581–91. http://dx.doi.org/10.30766/2072-9081.2023.24.4.581-591.
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The article shows the results of a multi-environment trial which was conducted in 2022 to determine the adaptability of corn hybrids and identify genotypes which are better to choose for a short growing season. Ten new early-maturing (FAO 130-150) corn hybrids were studied in 6 plant breeding organisations – ecological sites in 4 soil-climatic zones of Russia such as the Central Black Earth region, North Caucasus, Urals, West Siberia. The corn hybrids were evaluated by plasticity (bi), stability (σd2) and selection indices: the breeding value of the cultivar (Svc), the selection index (Si) and the selection value index of the cultivar (Ssvi). Nalchik (Ij = +1.51), Belgorod (Ij = +0.18) and Voronezh (Ij = +0.04) had favourable environmental conditions, unfavourable environments were in Chelyabinsk (Ij = -1.09), Omsk (Ij = -0.56) and Pyatigorsk (Ij = -0.08). Hybrid 140/26 had the highest grain yield (6.50 t/ha) on average, it exceeded the best standard by 1.15 t/ha. The hybrids 140-28 had the highest mean yield (7.66 t/ha) and 140/24 (7.65 t/ha) under favourable conditions, hybrid 100/28 was the best (5.42 t/ha) in unfavourable environments. Hybrids 100/24 (18.7 %), 140/25 (19.0 %), 100/27 (20.5 %) had the lowest grain moisture at harvest. Hybrids 140/24 (bi = 1.82, σd2 = 0.08), 140/28 (bi = 1.54, σd2 = 0.09), 100/26 (bi = 1.45, σd2 = 0.17) with high ecological plasticity and stability were recommended to grow under intensive growing conditions. It was shown that the application of the technique for evaluating corn hybrids by selection indices makes it possible to identify regionally oriented genotypes. It is better to grow hybrids 100/28 (Svic = 8.44) и 100/27 (Svic = 8.26), which had an optimal balance of environmental stability, grain yield and grain moisture at harvest, in unfavourable conditions with a short growing season.
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Siwale, Juliana, Jonathan Kimmitt, and Joseph Amankwah-Amoah. "The Failure of Hybrid Organizations: A Legitimation Perspective." Management and Organization Review 17, no. 3 (2021): 452–85. http://dx.doi.org/10.1017/mor.2020.70.
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ABSTRACTOrganizational hybridity refers to the combination of multiple institutional logics and identities that, within an organizational setting, do not conventionally complement one another. In such conditions, organizations must develop strategies to combine logics and sustain their hybrid forms. Success, however, is not inevitable. In this article, we take a legitimacy-as-process perspective to focus on a failed Microfinance Organization (MFO) in the African context of Zambia. MFOs represent a fascinating context because of their hybrid nature and need to balance several competing institutional demands. We utilise field interviews to analyse the process through which MFOs fail, analysing actor legitimation responses to emerging hybridity demands. We identify three phases associated with these changes: 1) dependent coupling, (2) misaligning legitimation, and (3) circumnavigating over conformity. Our findings emphasise that legitimation efforts in a failed hybrid are not simply the reverse of those that succeed. We observe adaptive processes consistent with successful hybrids but that ultimately sow the seeds of eventual failure. This demonstrates the need to re-think the role of legitimation strategies in hybrids alongside their potential deleterious consequences.
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Prokopchuk, Valentyna, and Inna Palamarchuk. "ESTIMATION OF YIELD OF TABLE BEET FOR USE OF BIOLOGICAL PREPARATIONS IN THE CONDITIONS OF THE FOREST-STEPPE OF THE RIGHT BANK OF UKRAINE." Agriculture and Forestry, no. 1 (April 29, 2022): 143–54. http://dx.doi.org/10.37128/2707-5826-2022-1-10.
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The results of researches of influence of biological preparations on biometric parameters of plants and productivity of table beet are resulted. The influence of biological preparations on the phases of growth and development of table beet plants, its yield and biometric parameters of products is established. Treatment of plants with the biological complex Organic Balance + Azotophyte + Liposomes accelerated the phenological phases of growth and development of table beet plants. As a result of measuring the height of plants, an increase in control over the treatment of plants with biological products was detected, in particular in the variety Red Ball Organic Balance + Azotophyte + Liposam provided an increase of 1.0 cm relative to control. The increase in plant height in the Pablo F1 hybrid was 2.2 cm with the use of Organic Balance + Azotophyte + Liposam. The largest number of leaves was observed for the treatment of plants Organic Balance + Nitrogen + Liposomes: in the variety Red Ball growth relative to the variant without treatment was 2.3 pcs / plant, in the hybrid Pablo F1 - 2.8 pcs / plant. Treatment of plants Organic Balance + Azotophyte + Liposam contributed to an increase in leaf area by 0.09 dm2 / plant in the variety Red Ball, 0.22 dm2 / plant in the hybrid Pablo F1. The use of Humifrend + Azotofit + Liposam also contributed to the increase of this indicator with an increase relative to the controls of 0.06 and 0.15 dm2 / plant, respectively. The influence of biological preparations on the biometric parameters of plants was established in the phase of intensive root growth. The largest biometric parameters in the phase of intensive root growth were recorded during the treatment of plants Organic Balance + Azotophyte + Liposam. In particular, the height of plants treated with Organic Balance + Azotophyte + Liposam increased by 2.2 and 3.1 cm compared to the untreated options. A slightly smaller increase in this indicator was recorded for treatments of plants Humifrend + Azotofit + Liposam - 0.8 and 1.0 cm, respectively. The number of leaves also increased during the treatment of plants with biological products. In the Pablo F1 hybrid, this figure was higher than the Red Ball variety - 16.0 - 18.7 units / plant. The use of Organic Balance + Azotophyte + Liposam provided an increase in the number of leaves by 2.8 pcs / plant relative to control, for the treatment of plants Humifrend + Azotophyte + Liposam increase was 1.8 pcs / plant. The positive effect of the use of biologicals was observed taking into account the area of leaves. With the use of Organic Balance + Azotophyte + Liposam, this figure in the studied varieties and hybrids increased by 0.8-1.3 thousand m2 / ha, respectively, compared with the control. Slightly less growth of this indicator was observed for the treatment of plants Humifrend + Azotofit + Liposam 0.3-0.7 thousand m2 / ha, respectively. The highest indicators of biometric parameters of plants were observed in the hybrid Pablo F1 for plant treatment Organic Balance + Azotophyte + Liposam, where the increase in plant height was 0.7-1.2 cm, the number of leaves - 1.8-2.1 pcs / plant, leaf area - 0.8-1.1 thousand m2 / ha, respectively. The highest yields were recorded with the application of Organic Balance + Nitrogen + Liposomes: in the Red Ball variety the increase was 7.1 t / ha, in the Pablo F1 hybrid - 10.3 t / ha relative to control. A positive effect was also obtained with the application of Humifrend + Azotofit + Liposam, where the increase was 4.9 and 8.4 t / ha, respectively. Treatment of plants with a complex of biological preparations Organic Balance + Azotophyte + Liposam provided an increase in root mass in the variety Red Ball - 45 g, in the hybrid Pablo F1 - 30 g, respectively. During the treatment of plants Humifrend + Azotofit + Liposam the increase was 30 g - variety Red Ball and 25 g - hybrid Pablo F1. Indicators, the diameter of the root crop was in the range of 8.4 and 9.0 cm. 5 cm. The increase in root diameter with the use of Humifrend + Azotofit + Liposam was slightly smaller and amounted to 0.1-0.3 cm. plants Humifrend + Azotofit + Liposam contributed to an increase in this indicator relative to control by 0.3-0.2 cm
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Videira, Arthur M., Willians R. Mendes, Vicente A. Ventrella, and Irene Calliari. "Increasing the Corrosion Resistance in the UNS S32750 Super Duplex Steel Welded Joints through Hybrid GTAW-Laser Welding and Nitrogen." Materials 16, no. 2 (2023): 543. http://dx.doi.org/10.3390/ma16020543.
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The development of techniques to improve the welding of super duplex steels is necessary in order to ensure that the phase balance and properties of the material are not affected during this process. Hybrid arc-laser welding is a perfect combination of the advantages of both processes, producing deeper weld beads with more balanced phases than the pulsed laser process. Here, the objective was to improve the corrosion resistance of UNS S32750 weld beads by increasing the volumetric austenite percentage in the fusion zone (FZ) with a hybrid process of GTAW (gas tungsten arc welding) and pulsed laser Nd-YAG (neodymium-doped yttrium aluminum garnet). Welds were performed in bead on plate conditions with fixed laser parameters and a varying heat input introduced through the GTAW process. Additionally, welds within a nitrogen atmosphere were performed. After base metal characterization, an analysis of the FZ and heat affected zone were performed with optical microscopy, scanning electron microscopy and critical pitting tests (CPT). The synergy between the thermal input provided by the hybrid process and austenite-promoting characteristic of nitrogen led to a balanced volumetric austenite/ferrite fraction. Consequently, the results obtained in CPT tests were better than conventional welding processes, such as laser or GTAW solely.
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S. Hamisu, H., S. G. Ado, M. Y. Yeye, et al. "Heterosis for Fruit Yield and Heat Tolerance Traits in Tomato (Lycopersicon Lycopersicum Mill.) Under Field Conditions." Journal of Agricultural Studies 5, no. 4 (2018): 48. http://dx.doi.org/10.5296/jas.v6i2.13047.
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Field experiments were conducted at the National Horticultural Research Institute, Bagauda in the Sudan Savannah and Institute for Agricultural Research, Samaru in the Northern Guinea Savanna ecological zones of Nigeria between July-October, 2014 rainy season to estimates heterosis for fruit yield and heat tolerance traits of tomato under field conditions. The experiment comprised two heat tolerant (Icrixina and Rio Grande) and four heat susceptible tomato (Tima, Tropimech, Petomech and Roma Savana) which were crossed using half diallel mating design in the screen house. The resultant 15 hybrids, their parental lines along with four checks were laid out in partially balanced lattice design with three replications. Analysis of variance revealed significant variation among the genotypes for all traits except fruit diameter and cell membrane thermostability, indicating sufficient variability existed among the genotypes. The cross combinations Icrixina × Rio Grande, Icrixina × Tima, Icrixina × Roma Savana and Icrixina × Petomech were found heterotic over better parent for fruit yield and heat tolerance traits (Number of branches per plant, number of clusters per plant, number of flowers per cluster, number of fruits per cluster, number of fruits per plant, percentage fruit set and chlorophyll content) among the hybrids. These hybrids were superior over better parent have the potentiality to be exploited for developing commercial heat tolerant tomato hybrid under field conditions.
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Li, Xiaoxiao, Yongjin Zhou, Peng Shuai, Xinyu Wang, Shaobing Peng, and Fei Wang. "Source–Sink Balance Optimization Depends on Soil Nitrogen Condition So as to Increase Rice Yield and N Use Efficiency." Agronomy 13, no. 3 (2023): 907. http://dx.doi.org/10.3390/agronomy13030907.
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Genetic improvement has been devoted to increasing rice yield by increasing the spikelet number per panicle and the spikelet/leaf ratio. As a result, indica-japonica hybrid rice “Yongyou” varieties with large panicles and superhigh yield potential have been developed. These varieties exhibit significantly higher grain yield and nitrogen use efficiency for grain (NUEg) under moderate and high N supply conditions due to their large sink size, but their yield performance remains obscure under low N input and low soil fertility conditions. In the present study, we investigated four varieties including Yongyou2640 (YY2640, large-panicle india-japonica hybrid variety), Yangliangyou6 (YLY6, two-line indica hybrid variety), Quanyou6 (QY6, three-line indica hybrid variety), and Huanghuazhan (HHZ, indica inbred variety) under two low soil fertility treatments [LF (removing half of soil depth) and CK] and two N fertilizer rates (0 and 100 kg N ha−1) in Central China. The results showed that the grain yield of YY2640 was more responsive to fertility than that of other varieties, which was 19.4–42.3% higher than that of the other three varieties under CK N100 treatment, but it was 14.5–19.4% lower than that of YLY6 and QY6 under LF N0 and LF N100. A higher spikelet/leaf ratio resulted in more biomass and N partition to panicles rather than to leaves under LF N0 and N100. Slightly more post-flowering dry matter obtained from higher leaf N content and crop growth rate failed to compensate for the adverse effects of reduced pre-flowering dry matter accumulation and stem-to-grain translocation during grain filling. This led to the lower NUEg of YY2640 than YLY6 and QY6 under low soil fertility conditions. Based on these findings, the present study suggested that the source–sink relationship of the super hybrid varieties should be optimized according to the soil N supply condition.
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Rochefort, Coralie, Janie Cournoyer, Andrew Post, T. Blaine Hoshizaki, Roger Zemek, and Heidi Sveistrup. "Brain tissue strain and balance impairments in children following a concussion: An exploratory study." Journal of Concussion 3 (January 2019): 205970021988923. http://dx.doi.org/10.1177/2059700219889233.
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Background Balance impairments present in approximately 30% of concussion cases. Biomechanical reconstructions model the degree and location of brain tissue strain associated with injury. The objective was to examine the relationship between the magnitude and location of brain tissue strain and balance impairment following a concussion. Methods Children one month post-concussion (n = 33) and non-injured children (n = 33) completed two balance conditions while standing on a Wii Balance Board that recorded the centre of pressure during (i) double-leg stance with eyes closed (EC) and (ii) dual-task (DT) combining double-leg stance while completing a cognitive task. Injury reconstructions were performed for 10 of the concussed participants. A 5th percentile Hybrid III headform was used to obtain linear and rotational acceleration time-curves of the head impact. These data were input in the University College Dublin Brain Trauma Model (UCDBTM) to calculate maximum principal strains and cumulative strain damage values at 10% (CSDM-10) and 20% (CSDM-20) for different brain regions. Correlations between balance and reconstruction variables were calculated. Results Out of the 10 reconstructed cases, six participants had impaired balance on the EC condition, six had impaired balance on the DT condition and four had impaired balance on both the EC and DT conditions. For maximum principal strain values, correlations with balance variables ranged from −0.0190 to 0.394 for the DT condition and from −0.225 and 0.152 for the EC condition. For CSDM-10 values, correlations with balance variables ranged from 0.280 to 0.386 for the DT condition and from −0.103 to 0.252 for the EC condition. For CSDM-20 values, correlations with balance variables ranged from 0.0629 to 0.289 for the DT condition and from −0.353 to −0.155 for the EC condition. Conclusions Although a subset of the concussed participants continued to show balance impairments, no association was established between the presence of balance impairment and the magnitude and/or location of brain tissue strain. Maintaining balance is a complex process integrated into multiple subcortical regions, white matter tracts and cranial nerves, which were not represented in the brain model, and as a result the UCDBTM may not be sensitive to damage in these areas.
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Sai Kumar, K., Sayanti Chatterjee, P. Siva Kumar, Ranjith Kumar Gatla, and A. Naresh Kumar. "Mitigation of Power Quality Problems Using Fuzzy Logic-Based Unified Power Quality Conditioner (UPQC)." Journal Européen des Systèmes Automatisés 56, no. 2 (2023): 195–200. http://dx.doi.org/10.18280/jesa.560203.
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This suggested research work provided a distributed generator-based unified power quality conditioner with a decreased rating and a star-connected transformer as a means of enhancing power quality. The unified power quality conditioner (UPQC), a Y-connected transformer, and an LC filter are all included in this work. When the source voltage is out of balance, the hybrids of the approach greatly enhance UPQC performance. The quality conditioner that is suggested here is intended to correct issues with the voltage and current quality of delicate loads and to reduce load current harmonics when the supply is distorted. In this case, the DC link control method has been implemented using a fuzzy logic-based controller. A 500 kVA grid model is taken into consideration, along with an analysis and description of the suggested solution. The results were produced using the power system block set toolboxes in the MATLAB/Simulink environment. According to the thorough simulation results, hybrid UPQC with distributed generation has a greater ability to reduce the voltage sag effects and swell and to suppress the load current harmonics, phase current harmonics, and neutral current when the supply is distorted. To validate the results generated by the proposed method, the hybrid methodology yields better results when compared to the traditional UPQC method.
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Černý, Ivan, Alexandra Veverková, Marek Kovár, and Martin Mátyás. "The variability of sunflower (Helianthus annuus L.) yield and quality influenced by wheater conditions." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 61, no. 3 (2013): 595–600. http://dx.doi.org/10.11118/actaun201361030595.
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The field polyfactorial trials were carried out on experimental fields in Nitra - Dolná Malanta in two experimental years 2010 and 2011. Experimental locality is situated in the maize production area (climatic region: warm; climatic sub-region dry; climatic zone: warm, dry with mild winter and long sunshine), in altitude 250 m above sea level, with brown soil. In the trials was observed the influence of both temperature and moisture conditions of experimental area on sunflower yield of achenes and fat content (conventional hybrids NK Kondi and NK Tristan). Technological system of sunflower cultivation was realized in accordance with conventional technology of cultivation. The basic fertilization was made by balance method on the base of soil agrochemical analysis for expected yield 3 t ha−1. The results show, in the range of achieved both achenes yield and fat content, that the year 2011 was statistically high significantly more favorable for yield formation than 2010. The variability of hybrids was statistically high significant, where the most productive was hybrid NK Kondi in comparison with NK Tristan in yield and quality the point of view. In the 2010 was found small negative correlation between yield of achenes and fat content. The correlation was slightly positive in the 2011, when the lower amount of precipitation and higher temperature influenced positively the yield formation and achene quality.
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Tayebi, Safiyeh, Saeed Esfandi, Sajedeh Bahraini Moqadam, and Ayyoob Sharifi. "Investigating the Role of Neighborhood Development Offices (NDOs) in the Resilience of Deteriorated Urban Neighborhoods against the COVID-19 Pandemic: An Empirical Study of Tehran, Using a Hybrid Balanced-Based Assessment Framework." Urban Science 6, no. 4 (2022): 77. http://dx.doi.org/10.3390/urbansci6040077.
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This study aimed to develop a balanced-based assessment framework to evaluate the effectiveness of Neighborhood Development Offices’ (NDOs) actions in improving the resilience of Tehran’s deteriorated neighborhoods against the COVID-19 pandemic. For this purpose, considering the main missions of NDOs, 20 indicators were extracted from the literature and delivered to the offices and residents of target neighborhoods to prioritize them. Next, using a combination of the K-means clustering method and the balance-based conceptual model, the degree of balance between the measures taken by NDOs and residents’ needs in each neighborhood was determined. Finally, short-term actions (such as teaching health protocols, providing neighborhood services, and providing walking and cycling infrastructures) and long-term actions (developing public spaces, facilitating access to healthcare, and reducing social inequality) are suggested, which simultaneously promote balanced resilience against the COVID-19 pandemic and possible future pandemics in all aspects of NDOs’ missions. The framework presented in this research can also be used to evaluate and boost the resilience of other deteriorated neighborhoods with similar conditions.
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Tayebi, Safiyeh, Saeed Esfandi, Moqadam Sajedeh Bahraini, and Ayyoob Sharifi. "Investigating the Role of Neighborhood Development Offices (NDOs) in the Resilience of Deteriorated Urban Neighborhoods against the COVID-19 Pandemic: An Empirical Study of Tehran, Using a Hybrid Balanced-Based Assessment Framework." Urban Science 6, no. 4 (2024): 77. https://doi.org/10.3390/urbansci6040077.
Full textAbstract:
This study aimed to develop a balanced-based assessment framework to evaluate the effectiveness of Neighborhood Development Offices’ (NDOs) actions in improving the resilience of Tehran’s deteriorated neighborhoods against the COVID-19 pandemic. For this purpose, considering the main missions of NDOs, 20 indicators were extracted from the literature and delivered to the offices and residents of target neighborhoods to prioritize them. Next, using a combination of the K-means clustering method and the balance-based conceptual model, the degree of balance between the measures taken by NDOs and residents’ needs in each neighborhood was determined. Finally, short-term actions (such as teaching health protocols, providing neighborhood services, and providing walking and cycling infrastructures) and long-term actions (developing public spaces, facilitating access to healthcare, and reducing social inequality) are suggested, which simultaneously promote balanced resilience against the COVID-19 pandemic and possible future pandemics in all aspects of NDOs’ missions. The framework presented in this research can also be used to evaluate and boost the resilience of other deteriorated neighborhoods with similar conditions.
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Domaratskiy, Yevhenii, Larysa Potravka, and Vitalii Pichura. "Economic Efficiency of Applying Biological Growth Regulators for Growing Sunflower in the Zone of Steppe Soils." Global Journal of Agricultural Innovation, Research & Development 10 (October 19, 2023): 92–101. http://dx.doi.org/10.15377/2409-9813.2023.10.5.
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In the conditions of climate change, the selection and adjustment of crop cultivation systems in the moisture deficit zone and the management of risky agriculture to ensure the profitability of production remain an urgent issues. In particular, the Steppe of Ukraine is a zone of increased risk for agricultural production, which is associated with difficult climatic conditions characterized by droughts and low rainfall. An effective measure to ensure stable harvests is the use of growth-regulating biological preparations. Therefore, the purpose of the research was to establish the regularities of the influence of growth-regulating biological preparations on the formation of productivity and the economic efficiency of sunflower cultivation in the zone of risky agriculture. The study was conducted in 2021–2022 in the Mykolaiv region of Ukraine. A three-factor field experiment was set up to study the influence of various biological preparations (Helafit Combi, Organic Balance, and Biocomplex-BTU) and plant stand density (30, 40, and 50 thousand pcs/ha) on the productivity of sunflower hybrids Vyrii, Yarylo, Blysk, Yaskravyi, and Epikur. It was found that the hybrids Yarylo, Epikur, and Yaskravyi had considerably lower levels of productivity. However, foliar fertilization had a positive effect and contributed to an increase in their productivity. A low level of productivity in 2022 (1.51 t/ha) was observed in the hybrid Epikur under the plant density of 30 thousand pcs/ha. The results of the field experiments allowed establishing that foliar fertilization with different biological preparations is an efficient and effective method for improving plant growth conditions, and can increase the level of agrocenosis genetic potential realization. It was found that the hybrid Vyrii with a seeding rate of 40 thousand pcs/ha and plant treatment with the biological preparation Helafit Combi is the most economically efficient hybrid, with a profitability of 25.59%, and a net profit of $127.20 per ha.
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Bin Wan Aziz, Wan Muhd Fauzi, and Noor Azzah Said. "Hybrid Working and Employee Job Performance in Felcra Berhad: An Examination of The Moderating Role of Job Satisfaction." Information Management and Business Review 16, no. 3S(I)a (2024): 417–29. http://dx.doi.org/10.22610/imbr.v16i3s(i)a.4215.
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In today's dynamic work environment, understanding the impact of hybrid working on job performance is crucial. Many organizations, including FELCRA Berhad, adopted hybrid working during the COVID-19 pandemic to enhance employee efficiency and flexibility. This study explores FELCRA Berhad's job performance and satisfaction during the pandemic and evaluates the potential success of hybrid working post-pandemic. Using a quantitative research design and purposive sampling, data were collected from FELCRA Berhad employees through online and physical questionnaires. The data were analyzed with IBM SPSS Version 28 to examine the relationship between hybrid working (working conditions, work-life balance, and recognition) and job performance, with job satisfaction as a moderating factor. The results indicated a significant positive relationship between working conditions and job performance. While work-life balance and recognition showed moderate positive relationships with job performance, they were not statistically significant. Job satisfaction did not significantly moderate these relationships. However, the group effect for hybrid working remained significant across all dimensions. In conclusion, the study confirms a significant positive relationship between hybrid work conditions and job performance, providing a foundation for FELCRA Berhad to consider implementing hybrid work practices based on their direct effects on job performance.
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Tynykulov, Marat, Natalya Malitskaya, Aigul Tleppayeva, and Mariya Auzhanova. "PRODUCTIVITY OF FORAGE CROPS IN THE STEPPE ZONE OF THE NORTHERN KAZAKHSTAN." 3i intellect idea innovation - интеллект идея инновация 3 (2024): 99–107. http://dx.doi.org/10.52269/22266070_2024_3_99.
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The article presents the results of a comparative evaluation of unconventional annual forage crops based on their productivity in the steppe zone of the Northern Kazakhstan. The subjects of the study were varieties and hybrids of Sorghum-sudan grass hybrids, Zea mays, Echinochloa frumentacea and Pennisétum gláucum. The relevance of the topic lies in the selection and introduction of new, more productive and drought-resistant forage crops for the steppe zone of the Northern Kazakhstan. The objectives of the study are to examine the features of growing forage crops in the steppe zone of the Northern Kazakhstan; conduct a comparative assessment of the productivity of forage crops; and identify the most promising forage crops for cultivation in the steppe zone of the Northern Kazakhstan. The scientific significance of the paper lies in obtaining new data on the comparative productivity of various forage crops in the conditions of the steppe zone of the Northern Kazakhstan. The study used the following research methods: the experimental method by B.A. Dospekhov, the vegetation accounting method by D. Braun, and the soil moisture determination method by N.M. Bakayev; laboratory analyses; and mathematical processing of yield data. The study results showed that the sorghum-sudan grass hybrid had the highest yield: 209.1 c/ha of green mass and 32.6 c/ha of dry matter. Corn and the sorghum-sudan grass hybrid showed a balanced content of crude protein and crude fiber in the forage. The yield of Echinochloa frumentacea and Pennisétum gláucum is lower but still sufficiently high compared to corn and the sorghum-sudan grass hybrid.
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Kovalev, A. A. "Значение ментальной безопасности в современном мире". Вестник Вятского государственного университета, № 3(149) (12 січня 2024): 61–70. http://dx.doi.org/10.25730/vsu.7606.23.036.
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The article is devoted to the study of new challenges and risks of our time, which encroach on mental security as an important component of the national security of the state. The reorientation of mankind in the 1990s to the humanistic paradigm expanded the list of fundamental human rights, among which mental security took a special place. Mental warfare, so popular in the 21st century, as an integral element of the "hybrid wars" of modernity, has also exacerbated the problem of mental security of an individual nation and of all mankind. The dissonance of opportunities and risks creates a problem that cannot be solved by outdated and formulaic methods. Thus, modern technologies make people's lives comfortable, convenient, more systematic and understandable, but at the same time more risky, unpredictable and even dangerous under certain conditions. In such circumstances, mental health becomes very vulnerable and needs targeted maintenance and support. The purpose of the study is to consider the phenomenon of mental security in the context of national security and national health. The research used a network approach, categorization and a dialectical method. The paradigm shift and the dominance of the "risk society" bring the topic of mental security to the forefront of the well-being of modern society and the state, distributing responsibility between them in this matter. At the same time, health and safety are recognized as equally important indicators of the balanced existence of a "risk society". And the main condition for achieving such a balance is the flexibility and adaptability of the person himself, and not the desire to eliminate all real and potential threats and risks from the inherently unpredictable process of life. Thus, mental security is a strategically important task of the modern state in the overall national security system. The results obtained in the course of the study can form the basis of materials for scientific disciplines specializing in safety. The data obtained can also be useful in the development of specific programs, recommendations, and measures aimed at improving the mental security of the nation. Статья посвящена изучению новых вызовов и рисков современности, которые посягают на ментальную безопасность как важную составную часть национальной безопасности государства. Переориентация человечества в 1990-е гг. на гуманистическую парадигму расширила перечень фундаментальных прав человека, среди которых особое место заняла ментальная безопасность. Также обострила проблему ментальной безопасности отдельной нации и всего человечества столь популярная в XXI в. ментальная война как неотъемлемый элемент «гибридных войн» современности. Диссонанс возможностей и рисков порождает проблему, решение которой невозможно устаревшими и шаблонными методами. Так, современные технологии делают жизнь людей комфортной, удобной, более систематизированной и понятной, однако при этом и более рискованной, при определенных условиях непредсказуемой и даже опасной. В таких обстоятельствах ментальное здоровье становится очень уязвимым и нуждается в целенаправленном поддержании и поддержке. Цель исследования – рассмотрение феномена ментальной безопасности в контексте национальной безопасности и здоровья нации. В исследовании были использованы сетевой подход, категоризация и диалектический метод. Смена парадигмы и доминирование «общества риска» выводят тему ментальной безопасности на авансцену благополучия современных общества и государства, распределяя между ними ответственность в этом вопросе. При этом здоровье и безопасность признаются равными по значимости показателями сбалансированного существования «общества риска». И главным условием достижения такого баланса является гибкость и адаптивность самого человека, а не стремление устранить все реальные и потенциальные угрозы и риски из непредсказуемого по своей сути процесса жизнедеятельности. Таким образом, ментальная безопасность является стратегически важной задачей современного государства в общей системе национальной безопасности. Полученные в ходе исследования результаты могут лечь в основу материалов для научных дисциплин, специализирующихся на безопасности. Также полученные данные могут быть полезны при разработке конкретных программ, рекомендаций, мероприятий, направленных на повышение ментальной безопасности нации.
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Palamarchuk, I.I., O.I. Tsyhanska, M.V. Matusyak, and V.I Tsyhanskyi. "Growing table beets with the use of biological preparations in conditions of the rightbank forest-steppe of Ukraine." Modern Phytomorphology 16, no. 2 (2022): 21–27. https://doi.org/10.5281/zenodo.7731651.
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Research data on the influence of biological preparations on the cultivation of table beets are highlighted. Priority related studies were phenological observations, yield and biometric measurements of beetroots. It is proved that biological preparations and varietal features influence the phenological phases of growth and development of table beets. In the initial stages of table beet plants growth and development, there were no significant changes between phases. Further study of the phenological phases of growth and development of table beet plants showed the effect of biological preparations on their occurrence. The highest yields were recorded with the application of Organic Balance+Azotofit+Liposam: in the Chervona Kulia variety, the increase was 7.1 t/ha, in the Pablo F1 hybrid-10.3 t/ha relative to the control. The use of the Organic Balance+Azotofit+Liposam complex provided a 45 g root mass increase in the Chervona Kulia variety, in the Pablo F1 hybrid-30 g compared to the control. With the use of the Humifrend+Azotofit+Liposam complex, the increase made up 30 g in the Chervona Kulia variety and 25 g in Pablo F1 hybrid. Indicator of root diameter was in the range of 8.4 cm and 9.0 cm. The use of the Organic Balance+Azotofit+Liposam biological preparations complex contributed to the increase in this indicator, in particular in the Chervona Kulia variety by 0.4 cm, in the Pablo F1 hybrid-by 0.5 cm.
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Ivanina, V. V., and M. S. Daniuk. "Bioenergy productivity of sugar beet and the balance of nutrients in the soil under biological cultivation." Bioenergy, no. 1-2 (December 28, 2022): 43–45. http://dx.doi.org/10.47414/be.1-2.2022.271356.
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It has been established that sugar beet, when grown in conditions of unstable moisture on podzoliс chernozem, form a high energy productivity of biomass under the use of alternative organic-mineral fertilization systems and require the application of increased doses of nitrogen and potassium fertilizers to form their balanced balance in the soil. The maximum energy productivity of the ‘Bulava’ hybrid was achieved with the application of nitrogen fertilizers in the spring in the pre-sowing cultivation at a dose of 90 kg/ha on the background of 5 t/ha of straw + Filazonite, 10 l/ha + P90K90 under deep ploughing in autumn: bioethanol energy output — 118 GJ/ha, biogas — 54 GJ/ha, total energy output — 172 GJ/ha, exceeding control without fertilizers — by 37, 16 and 53 GJ/ha, respectively.
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Dudka, M. I., O. V. Kovtun, and A. M. Dudka. "The formation of maize grain yield depending on sowing dates in the Northern Steppe of Ukraine." Scientific Journal Grain Crops 8, no. 1 (2024): 67–76. https://doi.org/10.31867/2523-4544/0313.
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Topicality. The productivity potential of maize determines the prospects of this crop in the grain balance of Ukraine. In the context of the crisis caused by the war and global climate change, which has a significant effect on crop production in the Steppe zone, the productivity of grain crops, including maize, is becoming increasingly unstable over the years, leading to a decline in the gross harvest of the crop. Given the changing weather conditions, in particular climate warming, substantiation, development and implementation of agrotechnical practices to mitigate the negative effects of drought, heat and soil moisture deficit is relevant. In the complex of zonal agrotechnical practices, the sowing dates of crops play an important role. Seed germination, timely emergence of seedlings, and optimal plant density depend on the sowing date. Early sowing in insufficiently warmed soil prolongs the sowing – germination period, or late sowing in excessively dry soil results in sparseness of seedlings and low grain productivity. Therefore, there is a requirement to create the most favourable conditions for the growth of maize plants, considering the biological characteristics and environmental requirements of hybrids, soil, climate and weather conditions. Purpose. Identification of the peculiarities of growth and development of maize plants and formation of maize grain yield depending on the sowing date of biotypes belonging to different maturity groups in the Northern Steppe of Ukraine. Materials and Methods. New maize hybrids of different ripeness groups DN Pulsatsiia, DN Atlant, DN Drah, DN Nazar were sown on the experimental plot of the Laboratory of Agricultural Resources of Maize and Sorghum at the SE Institute of Grain Crops of NAAS in three dates. The following research methods were used: laboratory and field methods for determining plant height, number of leaves per plant, individual plant productivity, grain moisture content; statistical method for performing correlation, analysis of variance and statistical evaluation of research results. Results. It was found that the highest grain yields were formed by the early ripening hybrid DN Pulsatsiia (10.27 t/ha), the mid-early hybrid DN Atlant (10.96 t/ha), the mid-ripening hybrid DN Drah (11.61 t/ha) and the mid-late hybrid DN Nazar (11.49 t/ha) at the early sowing date. Conclusions. In the growing season of 2021–2023, the highest grain yield was formed by maize hybrids of all maturity groups at early sowing date. Sowing corn both at the optimal and late time resulted in a decrease in grain yield in corn hybrids by an average of 4.5 and 13.1%, respectively, compared to the early time. Based on the experimental data, it was found that new maize hybrids DN Pulsatsiia, DN Atlant, DN Drah, DN Nazar form the highest grain yield in the Northern Steppe of Ukraine when they are sown on 14–25 April. Key words: maize, hybrid, sowing date, yield attributes, individual productivity of plants, grain yield
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Tsytsiura, Yaroslav, and Ihor Didur. "OPTIMIZATION OF SUNFLOWER FERTILIZATION ACCORDING TO THE APPLICATION OF BIOLOGICAL PREPARATIONS IN THE CONDITIONS OF THE RIGHT BANK FOREST-STEPPE." Agriculture and Forestry, no. 4 (December 24, 2021): 36–51. http://dx.doi.org/10.37128/2707-5826-2021-4-4.
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The article evaluates and outlines the main aspects and vision of optimization of sunflower fertilizer through the use of modern biological products of complex bioorganic and biomineral nature through their use in the form of foliar fertilization. The relevance and importance of such technological solutions and the search for optimal options for maximizing the yield potential of modern sunflower hybrids are emphasized given the importance of this crop for the formation of agricultural potential of Ukraine. The peculiarities of sunflower mineral nutrition for a long cycle of its study are generalized and aspects of its nutrition optimization and expediency of application of complex and growth-regulating biologicals are determined. The hydrothermal conditions of sunflower vegetation for the two-year cycle of application of biological products for vegetation were evaluated, which allowed to evaluate the role of their components on the growth processes and crop formation of sunflowers. The effectiveness of different variants of BTU-Center biological preparations recommended for growing sunflower for foliar feeding - Organic Balance and BTU Biocomplex was studied in view of the regulations of their use in terms of critical phases of growth and development of culture with a dose interval of 0.5 l / ha (single application) up to 1.0 l / ha (double application) in phases 6-8 and 12-14 leaves in combination with application of bioadhesive Liposam (0.5 l / ha) and urea (7 kg / ha) to enhance the physiological action of biological products. The efficiency and expediency of using both studied drugs with technological significant advantage in the use of high-yielding sunflower hybrids on gray forest soils in the Forest-Steppe zone of the right-bank biological product Organic Balance in the variant of double use. %, an increase in the area of the assimilation surface by 19.6%. As a result, it was found that the highest yield of sunflower hybrid LG 5638 on average during the study period was formed in the variant of double use of biological product Organic Balance - 2.84 t / ha with an increase to control of 0.39 t / ha (15.9%).
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Cabrera-Ariza, Antonio M., Cristiano Tozzini, Sergio E. Espinoza-Meza, Rómulo E. Santelices-Moya, Carlos R. Magni-Díaz, and Máximo F. Alonso-Valdés. "Effect of crop management intensity on energy and carbon dioxide balance of two bioenergy Sorghum bicolor hybrids." Italian Journal of Agronomy 14, no. 1 (2018): 26–33. http://dx.doi.org/10.4081/ija.2018.1316.
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Although bioenergy sorghum has many traits that make it ideal for biofuel production, management conditions that can affect the productivity and sustainability of these systems are still poorly understood. This paper estimated the energy and CO2 balance of two bioenergy sorghum (Sorghum bicolor L. Moench.) hybrids (H128 and H133) cultivated during two growing seasons and under two different levels of crop management, high and low input. At the end of both growing season, sorghum was harvested for biomass yield determination. Calorific value and net energy production were also estimated. Crop management had important effects on sorghum CO2 and energy balance. The energy produced varied between 126 and 365 GJ ha–1 depending on crop management, hybrid and growing season. Regarding of the CO2 balance, the high level of crop management had a superior CO2 emission. However, the energy produced per kg of CO2 emitted was higher (>300%) than the energy produced with the use of fossil fuels. The use of bioenergy sorghum can contribute to better energy sustainability and reduced CO2 emission in Mediterranean ecosystems.