Search

Financial sustainability is one of the major issues in the development process, particularly in developing countries. Microfinance started with two basic objectives of poverty reduction and women empowerment. However, unsustainable microfinance providers cannot assist the poor for a longer period of time, due to uncertainty about their existence. This study investigates that how financial sustainability of Pakistan’s microfinance sector is affected by various factors. A new financial sustainability index has been developed to measure the financial sustainability. Generalized Method of Moments (GMM) approach is employed to analyze the panel data from 34 Pakistani MFPs, yielding 344 MFI-years of observations from 2006 to 2018. The findings reveal that increase in loan size, female borrowers, liquidity, and leverage significantly enhances the financial viability of Microfinance Providers in Pakistan. However, the total borrowers and the operating cost per borrower negatively affect the financial sustainability of MFPs in Pakistan. MFIs needs to be financially independent, with less or no support from government or donor agencies. Financially sustainable MFPs contributes in the achievement of the 8 out of 17 Sustainable Development of Goals (2030 SDGs) of the United Nation. The data for 2018 onwards is seriously affected by COVID-19, which cannot be included in the current data set. Hence, researchers in future analyze the data from the pre-COVID-19 and post-COVID-19 periods to compare the analysis and examine the pandemic’s impact.

This study aims to determine the dynamics of local politics in the 2021 Election of Peaceful Village Heads in Sidenreng Rappang Indonesia. This research uses a qualitative descriptive method. Data collection techniques used were observation, interviews, field notes, and documentation. Resource persons were selected by purposive sampling. Data analysis namely data reduction, data presentation, and conclusions. Data analysis, namely data reduction, data presentation and conclusions displayed through Nvivo 12 Plus Software visualization. NVivo 12 Plus was chosen because it is capable of producing qualitative text data in the form of attractive data visualizations. The results of this study indicate that the local political dynamics that occurred in the Peace Village head election led to polarization in the village community structure. The election of the village head still leaves a mark in which there has been a rift in relations between the people who still have close family ties. The dynamics occur in three phases, namely pre-election, implementation of the election, and post-election. The behavior of the voting community in Damai Village is still dominated by the behavior of traditional voters. The occurrence of community polarization in Damai Village is the result of the political behavior of the people who are still traditional.

To allow the original publication to be better understood [...]

To allow the original publication to be better understood [...]

Abstract Background Vaccines are effective and reliable public health interventions against viral outbreaks and pandemics. However, hesitancy regarding the Coronavirus disease (COVID-19) vaccine is evident worldwide. Therefore, understanding vaccination-related behavior is critical in expanding the vaccine coverage to flatten the infection curve. This study explores the public perception regarding COVID-19 vaccination and identifies factors associated with vaccine hesitancy among the general adult populations in six Southeast Asian countries. Methods Using a snowball sampling approach, we conducted a descriptive cross-sectional study among 5260 participants in Indonesia, Malaysia, Myanmar, Philippines, Thailand, and Vietnam between February and May 2021. Binary logistic regression analysis with a backward conditional approach was applied to identify factors associated with COVID-19 vaccine hesitancy. Results Of the total, 50.6% were female, and the median age was 30 years (range: 15–83 years). The majority of the participants believed that vaccination effectively prevents and controls COVID-19 (81.2%), and 84.0% would accept COVID-19 vaccines when they become available. They agreed that health providers’ advice (83.0%), vaccination convenience (75.6%), and vaccine costs (62.8%) are essential for people to decide whether to accept COVID-19 vaccines. About half (49.3%) expressed their hesitancy to receive the COVID-19 vaccines. After adjustment for other covariates, COVID-19 vaccine hesitancy was significantly associated with age, residential area, education levels, employment status, and family economic status. Participants from Indonesia, Myanmar, Thailand, and Vietnam were significantly more likely to express hesitancy in receiving COVID-19 vaccines than those from Philippines. Conclusions In general, participants in this multi-country study showed their optimistic perception of COVID-19 vaccines’ effectiveness and willingness to receive them. However, about half of them still expressed their hesitancy in getting vaccinated. The hesitation was associated with several socioeconomic factors and varied by country. Therefore, COVID-19 vaccination programs should consider these factors essential for increasing vaccine uptake in the populations.

Due to the globalization of competition in various fields, suppliers must have the best performance by creating different competitive advantages. In addition, the oil and gas industry has lots of sustainability issues in the supply phase. Accordingly, this study provides a comprehensive framework for ranking and selecting sustainable suppliers in the oil and gas industry. In this research, first, using the fuzzy analytical network process method, the indicators of selecting stable suppliers are ranked. Next, by applying the Electre method, the most appropriate suppliers are chosen. Numerical results show that the quality indicators of materials purchased from suppliers and commitment to contracts were ranked first and second among all criteria. Moreover, the efficiency of the proposed framework is approved by comparing with the real-world situation.

In this study, a new optimal allocation and sizing have been proposed for an Electric Vehicle Charging Station (EVCS) on a Distribution System in Allahabad, India. The main idea is to optimize the EVCS configuration by considering Voltage Profile Improvement Index (VPII), Reactive Power Loss Reduction Index (QLRI), Real Power Loss Reduction Index (PLRI), and the preliminary development cost to get the minimum value of the installation cost and to provide higher quality of parameters for the power grid. For solving the studied nonlinear mixed-integer optimization problem, a new improved metaheuristic, called Balanced Mayfly Algorithm (BMA) is proposed. The modification is established to improve the accuracy and to resolve the exploration issue of the algorithm. The BMA used two modifications including elite mayfly couples and chaos mechanism to resolve these issues as it is possible. After validating the algorithm, it is applied to 30-bus distribution system in Allahabad, India and its results are compared with GAIPSO and basic MA. The results indicated that the voltage shape is smoothened and a reasonable balance between voltage profile and network losses is obtained. The results also show that the suggested method with 18.358 MW active power loss, 73.826 MVar reactive power loss, 10961 s computational burden, and 415 number of charging ports gives superior performance with lesser power losses. The number of CS allocated through GAIPSO and MA does not satisfy the demand of the city’s consumers.

A CCHP is modeled here, which is operated based on thermal energy provision because the target consumer demands more heat and cooling than electricity. The main components of the CCHP system are a PEMFC as the main generator, absorption chiller, electric chiller, and auxiliary boiler. The fuel consumption, cost, and carbon release improvements compared to a separated generation system (SGS) are maximized in this system using the proposed modified mayfly algorithm. The system design and operation are optimized in two different stages. A hotel building has been chosen as the case study to demonstrate the effectiveness of the proposed CCHP system and optimization in comparison to the traditional mayfly algorithm (MA) and genetic algorithm (GA). The proposed optimization algorithm has performed 8.15% and 10.06% better compared to MA and GA, respectively. It also reached its solution in far shorter time. The optimum size of PEMDC and share of auxiliary chiller (AC) in cooling provision is determined to be 548 kW and 43.1%, respectively. It is shown that improvement achieved by using the AC in the CCHP is increased by 23.21% compared to a CCHP without one. Furthermore, the AC make the improvement of CCHP more constant and robust to load variation. Moreover, the sensitivity of the optimum operation condition of the CCHP against the electricity and fuel price changes and share of the AC in supplying the cooling demand are studied to provide the system operators with the necessary tools needed to optimally handle price changes.

Industrial development, increased demand for energy, limited fossil fuels, and the prevention of environmental damage have led to the use of renewable energy to produce power. The combined system includes renewable wind and solar energy systems or other renewable energy. The hybrid system has been widely used in recent decades to provide energy. In this research, an optimal integrated system has been used to generate electricity. The proposed system includes wind, hydropower, and photovoltaic systems. to estimate the maximum power generation and reduce fluctuations in electricity generation, three proposed algorithms have been used and three common solutions have been applied to solve the optimization problem. The performance of the combined system in different weather conditions has also been assessed. The results showed that among the three proposed algorithms, the model improved the Barnacles Mating Optimizer Algorithm (IBMO) has the best distribution and convergence. Among the three common solutions, a common solution has the most logical solution to the optimization problem. And among the different hydrological conditions, the highest electricity generation is related to the wet season. the most important role in the hybrid system related the hydropower so that despite the reduction of electricity produced in the wind and photovoltaic power plants, the hydropower plant compensates for the drop in production of these systems and causes the production flow to continue.

IntroductionIt is clear that medical science has advanced much in the past few decades with the development of vaccines and this is even true for the novel coronavirus outbreak. By late 2020, COVID-19 vaccines were starting to be approved by national and global regulators, and across 2021, there was a global rollout of several vaccines. Despite rolling out vaccination programs successfully, there has been a cause of concern regarding uptake of vaccine due to vaccine hesitancy. In tackling the vaccine hesitancy and improving the overall vaccination rates, digital health literacy (DHL) could play a major role. Therefore, the aim of this study is to assess the digital health literacy and its relevance to the COVID-19 vaccination.MethodsAn internet-based cross-sectional survey was conducted from April to August 2021 using convenience sampling among people from different countries. Participants were asked about their level of intention to the COVID-19 vaccine. Participants completed the Digital Health Literacy Instrument (DHLI), which was adapted in the context of the COVID Health Literacy Network. Cross-tabulation and logistic regression were used for analysis purpose.ResultsOverall, the mean DHL score was 35.1 (SD = 6.9, Range = 12–48). The mean DHL score for those who answered “Yes” for “support for national vaccination schedule” was 36.1 (SD 6.7) compared to 32.5 (SD 6.8) for those who either answered “No” or “Don't know”. Factors including country, place of residence, education, employment, and income were associated with the intention for vaccination. Odds of vaccine intention were higher in urban respondents (OR-1.46; C.I.-1.30–1.64) than in rural respondents. Further, higher competency in assessing the relevance of online information resulted in significantly higher intention for vaccine uptake.ConclusionPriority should be given to improving DHL and vaccination awareness programs targeting rural areas, lower education level, lower income, and unemployed groups.

IntroductionSeveral studies exhibited varying reports of perception toward vaccine effectiveness, vaccine hesitancy, and acceptance of COVID-19 vaccines. As this fluctuated with evidence generation, this study explored the perception toward vaccine effectiveness in rural and urban communities among various countries.MethodsA cross-sectional study was conducted online from April to August 2021 using convenience sampling among people from different countries approved by the Asia Metropolitan University Medical Research and Ethics. We adapted the questionnaire from the World Health Organization's (WHO) survey tool and guidance on COVID-19. The logistic regression models were performed to show perception toward vaccine effectiveness.ResultsA total of 5,673 participants responded to the online survey. Overall, 64% of participants agreed that the vaccine effectively controlled viral spread, and 23% agreed that there was no need for vaccination if others were vaccinated. Males had 14% higher odds of believing that there was no need for vaccination. Less social media users had 39% higher odds of developing the belief that there is no need for vaccination than all other people vaccinated.ConclusionPeople's perceptions toward vaccine acceptance have fluctuated with the information flow in various social media and the severity of COVID-19 cases. Therefore, it is important that the current scenario of peoples' perception toward vaccine acceptance and determinants affecting the acceptance are explored to promote the vaccination approach against COVID-19 prevention and transmission effectively.

Optimal scheduling of reconfigurable interconnected microgrids is a precious and critical task for the residential consumers especially with the integration of renewable energy sources, dispatchable units and energy storage systems. In this regard, not only the optimal scheduling of the microgrids in a realistic and correlated environment is a necessity, but also the guarantied security and the prevention of cyber-attacks are mandatory tasks for the operators. This article first addresses these issues by developing a novel framework based on blockchain for secured data transaction from the individual microgrids’ components to the central control unit and then tries to find the optimal scheduling plan using stochastic programming based on point estimate method (PEM). Through such a hybrid PEM-blockchain based framework, the interconnected microgrids can supply the residential loads in a fully reliable, economic and secured structure. We also consider a social-economic framework to not only minimize the total operating cost of the microgrids, but also benefit the customers by enhancing the social factors through the optimal switching. Considering the complex and nonlinear nature of the problem, an effective corrected crow search (CCS) algorithm is deployed to find the most optimal operating point for the microgrids. The quality and capabilities of the proposed model are investigated using a practical residential interconnected microgrid. The results show that the optimal switching could reduce the total operation cost from $22,716 to $21,935 (3.56% reduction). Also, the average energy not supplied (AENS) has reduced from 1.4115 to 1.352 kWh/customer.yr (4.40% reduction), which are notable values. The results advocate the quality and functionality of the proposed framework.

This paper presents a novel optimal real-time controller for inverter-based distributed generation units in an islanded microgrid. With respect to the fact that the microgrid has a completely nonlinear structure and its dynamics is constantly changing, the linear controllers with constant and inflexible coefficients cannot maintain proper response in a wide range of operation conditions. Hence, an optimal nonlinear controller that theirs coefficient are adjusted in a real-time manner based on fuzzy logic is presented. To improve the performance of the proposed real-time controller, its fuzzy system parameters are determined using an offline particle swarm optimization algorithm for various operation conditions. In the proposed real-time controller, proportional-resonant controllers are used due to their advantage in the stationary reference frame for controlling the voltage and current of distributed generation units in the microgrid. Capability and efficiency of the proposed real-time controller are evaluated in different operation scenarios in MATLAB/Simulink environment. The simulation results shows that changing the control coefficients online with respect to operation condition leads to achievement of an optimum answer for voltage during the occurrence of islanding condition and also in case of load variation in the islanded microgrid.

One of the best ways to optimally consuming fossil fuel thermal energy is to utilize combined cooling, heating, and power (CCHP) systems. In such systems, by recycling heat wasted from hot gases simultaneously generating power, heat, and refrigeration from combustion, as well as water and cooling oil in electricity production systems, the overall system energy utilization efficiency can be increased to more than 85%. Analysis and optimization of the CCHP systems are mostly in accordance to the operating cost reduction without considering the emissions reduction and actual energy utilization. The present study proposes an energy flow for CCHP system to decrease the main power consumption based on a building thermal demand in Kerman area, Iran. The method introduced a developed version of the owl search algorithm to increase the efficiency of the CCHP system in comparison with the separation production system. Final simulations declare well efficient results for the presented method.

Model-identification and parameter extraction of the proton exchange membrane fuel cell (PEMFC) is a well-defined procedure for improving the PEMFC efficiency for designing and control purposes. This paper presents a new version of the improved fluid search optimization algorithm for optimal parameter identification of the undetermined parameters of the PEMFCs. The total of square deviations between the experimentally measured values and the optimal achieved values from the algorithm is considered the cost function. Two empirical PEMFC models including BCS 500-W and NedStack PS6 are employed and analyzed to present the capability of the proposed procedure under different conditions. Simulation results are compared with different optimizers under the same conditions to demonstrate the system efficiency. The final results showed that the proposed chaos-based fluid search optimization algorithm is successfully used to extract the parameters of a PEMFC model precisely.

Recently, the application of the proton exchange membrane fuel cells (PEMFCs) is extensively increasing as a popular renewable energy source. PEMFCs need low temperature for the operation along with high power density and easy implementation ability. These characteristics turned them into the most interesting type of fuel cells. However, PEMFCs need a technique for keeping them in a desired operating point. This is an essential item, especially during the current variation. In this study, a new configuration is proposed for designing an optimal PEMFC system. Two-phase interleaved boost DC/DC converter is also utilized for increasing the output voltage terminal. LQR optimal strategy is used for regulating the PEMFC. For improving the efficiency of the proposed LQR controller, a newly developed version of the whale optimization algorithm, called improved chaotic whale optimization algorithm is proposed. Simulation results of the proposed system are compared with different methods and the results showed that the proposed system has higher efficiency from the viewpoint of the current ripple and overshoot.

A new methodology has been proposed for optimal size selection of a hybrid energy system (HES) including lithium-ion battery and polymer electrolyte membrane (PEM) fuel cell to supply the driving force of a locomotive. The main purpose is to minimize the total cost of HES with different constraints including the capacity constraint of the battery and the fuel cell state-of-charge limit The optimization problem has been solved based on a new improved model of the Krill Herd (KH) algorithm, converged krill herd optimization algorithm (CKH). Simulation results are analyzed based on the average power demand, speed demand of the locomotive, and the locomotive slope. The results of the presented CKH algorithm have been compared with the standard KH and PSO algorithm and the results declared that the total cost for HES based on CKH has the minimum value such that the value for the CKH for 0%, 1%, and 2% slope are 3.15×106, 3.56×106, and 3.93×106toward KH with 3.47×106, 4.01×106, and 4.56×106and PSO with 3.74×106, 4.27×106, and 4.72×106HES, respectively.

In this paper, a multi-objective optimization method has been established on a hybrid PV, wind, fuel cell, and battery system. The optimization is based on three models including energy supply reliability, electricity efficiency, and capital cost of the hybrid system. A new model of the Elephant Herding Optimization (BEHO) Algorithm is utilized to solve the multi-objective optimization problem and is validated based on different algorithms and benchmark functions. The main purpose is to determine the Pareto surface including a set of possible design solutions to help the decision-makers obtaining the global optimum solution. The final results indicated that the proposed method is an applicable approach for designing of the proposed hybrid system.

One important part of designing and manufacturing of the fuel cells is their model identification. The present study proposes an optimal method for optimal parameter estimation of the undetermined parameters in Proton Exchange Membrane Fuel Cells (PEMFCs). The method uses a novel modified version of the Moth Search Algorithm, called Converged Moth Search Algorithm (CMSA) to minimize the total of the squared deviations (TSD) between the output voltage and the experimental data. The method is then applied to two different test cases including BCS 500-W PS6 and NedStack PS6. The results show that the suggested CMSA has a TSD and running time equal to 0.012 and 2.96 for BCS and 2.15 and 3.19 for the NedStack that are the minimum values for both case studies toward the other compared algorithms. therefore, the results showed that the suggested method has a good data agreement with the experimental data.

The present paper proposes a new optimal method for modeling and simulating of a proton exchange membrane fuel cell (PEMFC) system. The main idea is to minimize the Sum of Squared Error (SSE) between the experimental and the estimated output voltages to achieve the maximum agreement between them. To minimize the error value, a modified metaheuristic, called Balanced Manta-Ray Foraging Optimization (BMRFO) algorithm has been designed. The designed algorithm is proposed for resolving the algorithm premature convergence and to improve the algorithm diversity. By performing 30 independent runs for the suggested BMRFO algorithm and comparing it with some other algorithms from the literature, it is observed that the proposed method gives better convergency in speed and accuracy.

The use of Combined Cooling, Heating, and Power (CHP) system, as one of the possible solutions to increase efficiency and to reduce environmental pollution, is rapidly increasing in residential units. The primary mover of combined CHP-based buildings can be based on gas-burning engine, micro-turbine, fuel cell (FC), or a sterling engine. Characteristics such as higher effectiveness, very lower pollution, without moving parts, modularity, and extensive suffering for FCs have led many developed countries to support FC development at the top of their budget plans. In the present study, to reduce the power demand, a new optimal arrangement has been suggested for the combined CHP system in a residential building in Yazd, Iran. The research gives a Developed version of Pathfinder Optimizer (DPF) to optimize the combined CHP configuration by considering the highest unified operation for the suggested combined CHP system. A comparison of the outcomes of the suggested method with the SMS algorithm and basic PF algorithm is carried out to indicate the algorithm superiority. The final results show that using the considered method decreases the ratio of carbon dioxide emission ratio (RCDE). The results also showed that based on the proposed method, yearly overall cost-saving (YOCS) has the minimum effect on the combined effectiveness.

BackgroundMass vaccination campaigns have significantly reduced the COVID-19 burden. However, vaccine hesitancy has posed significant global concerns. The purpose of this study was to determine the characteristics that influence perceptions of COVID-19 vaccine efficacy, acceptability, hesitancy and decision making to take vaccine among general adult populations in a variety of socioeconomic and cultural contexts.MethodsUsing a snowball sampling approach, we conducted an online cross-sectional study in 20 countries across four continents from February to May 2021.ResultsA total of 10,477 participants were included in the analyses with a mean age of 36±14.3 years. The findings revealed the prevalence of perceptions towards COVID-19 vaccine's effectiveness (78.8%), acceptance (81.8%), hesitancy (47.2%), and drivers of vaccination decision-making (convenience [73.3%], health providers' advice [81.8%], and costs [57.0%]). The county-wise distribution included effectiveness (67.8-95.9%; 67.8% in Egypt to 95.9% in Malaysia), acceptance (64.7-96.0%; 64.7% in Australia to 96.0% in Malaysia), hesitancy (31.5-86.0%; 31.5% in Egypt to 86.0% in Vietnam), convenience (49.7-95.7%; 49.7% in Austria to 95.7% in Malaysia), advice (66.1-97.3%; 66.1% in Austria to 97.3% in Malaysia), and costs (16.0-91.3%; 16.0% in Vietnam to 91.3% in Malaysia). In multivariable regression analysis, several socio-demographic characteristics were identified as associated factors of outcome variables including, i) vaccine effectiveness: younger age, male, urban residence, higher education, and higher income; ii) acceptance: younger age, male, urban residence, higher education, married, and higher income; and iii) hesitancy: male, higher education, employed, unmarried, and lower income. Likewise, the factors associated with vaccination decision-making including i) convenience: younger age, urban residence, higher education, married, and lower income; ii) advice: younger age, urban residence, higher education, unemployed/student, married, and medium income; and iii) costs: younger age, higher education, unemployed/student, and lower income.ConclusionsMost participants believed that vaccination would effectively control and prevent COVID-19, and they would take vaccinations upon availability. Determinant factors found in this study are critical and should be considered as essential elements in developing COVID-19 vaccination campaigns to boost vaccination uptake in the populations.

Combined Cooling, Heating, and Electrical (CCHP)-based microgrids meet various energy demands at the same time. Risk analysis of such CCHP-based microgrids under environmental and economic constraints is a challenging issue. Furthermore, high penetration of various renewable energy sources in such CCHP-based microgrids clouds the issue. Thus, Downside risk constraints technique (DRCT) is employed in this paper to manage and minimize the risk associated with various uncertainties in the studied sample CCHP-based microgrid under environmental and economic constraints. The uncertainties of solar radiation, wind speed, market tariff and cooling, heating, and electrical demands are considered, and the stochastic optimization approach is used to model the mentioned uncertainties. The effect of the Real-Time Pricing rate of the Demand Response Program (RTP-DRP) has been studied in this paper. By analyzing the obtained results, it can be found that with considering DRCT, the risk-in-cost (RC) level can plunge to 0 respectively by paying 2.61% and 1.68% without and with RTP-DRP.

Risk analysis and scheduling of a Generation Company (GenCo) under uncertain environments are challenging issues. So, stochastic optimization and downside risk constraints approaches are used in this paper to model and manage the risk associated with various uncertainties. The presented GenCo model comprised five thermal units, photovoltaic systems, and wind farms. It is assumed that all thermal units, photovoltaic systems, and wind farms can participate in the energy market. In contrast, only thermal units can participate in the reserve market. The uncertainty of electricity and reserve market prices and output power of photovoltaic systems and wind farms are modeled via a stochastic optimization approach. Afterward, the downside risk constraints method is used to manage the risks associated with various uncertainties. By analyzing the obtained results, it can be seen that the level of the average risk can plunge to 0 by gaining 4.68% less average profit. So, the GenCo can be immune against considered uncertainties with gaining a little bit less profit. Furthermore, the offering strategy is studied in two risk-neutral and risk-averse strategies. Finally, the CPLEX solver of GAMS software is used to optimize the studied linear-based model of GenCo.

Electricity retailer is the most critical player in the restructured electricity market. Retailer's electricity procurement problem is a big challenge for them. Electricity retailer can purchase their energy from various options such as pool market, forward contracts, and etc. A relatively new way with a lesser investigation is the demand response exchange market. In this paper, the risk-constrained stochastic power procurement problem of electricity retailers is formulated by modeling the uncertainty of pool-market price and consumers' electricity demand. The Downside risk constraints (DRC) risk-evaluation method is used to obtain risk-based power procurement scheduling of electricity retailers. By using the proposed method, conservative electricity retailer can experience a scenario-independent strategy over the stochastic optimization. In other words, the proposed risk strategy is such that impose more cost for electricity retailer while having an equal cost in all scenarios. Based on the obtained results, the operation cost of electricity retailers in all scenarios is closed to about $4827570, which is a relatively zero-risk strategy due to equality overall scenarios. Besides, results are compared in two cases to demonstrate the advantages of the proposed risk-evaluation method. Also, the Pareto front between risk-in-cost and expected cost can introduce an optimal risk-strategy for electricity retailers in the presence of uncertainties. Finally, the risk-averse strategy of electricity retailer is proposed to obtain the retailer's optimal conservative schedule during power procurement in the presence of uncertainties.

The optimal performance of a multi-chiller system (MCS) is the crucial factor in managing the expected power consumption (EPC). Also, the uncertainty of cooling demand in the industrial or residential sector plays a crucial role, which should be modeled and managed. So, the stochastic risk-constrained performance of the optimal chiller loading is studied in an uncertain environment in this paper. Scenario-based stochastic programming is applied to the provided case study to model the cooling demand uncertainty (CDU), and the downside risk constraints (DRC) are implemented to model the associated risks. The risk-averse performance of the MCS is compared with the risk-neutral one to show the positive effects of the DRC. The proposed model is implemented under the DICOPT solver in GAMS software. The comparison results show that the expected power consumption of MCS is increased slowly, while the expected risk-in-power consumption (ERIPC) is decreased promptly.

In this paper, to get optimal scheduling of microgrids (MGs) in short-term, info-gap decision-theory (IGDT) is applied to assess load uncertainty. In order to provide a comprehensive load uncertainty study, best and worst possible conditions are evaluated using IGDT's functions of opportunity and robustness, respectively, in which risk-taker and risk-averse strategies are obtained while deterministic case study is also carried-out under the risk-neutral strategy. Real-time pricing (RTP) and time-of-use pricing (TOU) of demand response program (DRP) are applied to peak-load management. For each strategy, three cases as without DRP, TOU-DRP, and RTP-DRP are investigated. According to the results in the risk-neutral strategy, by applying TOU-DRP and RTP-DRP, operating cost is reduced about 2.5% and 6.6%, respectively. In the risk-averse strategy, by considering MG operating cost equal to $6,000, the MG will robust against load uncertainty up to 18.37%, 21.52%, and 24.82% for without DRP, TOU-DRP, and RTP-DRP cases, respectively. In the risk-taker strategy, MG operating cost for 28% of load reduction, operating costs will be equal to $3,698.12, $3,605.21, and $3,338.87 for without DRP, TOU-DRP, and RTP-DRP, respectively, in comparison with the risk-neutral strategy.

The development of mathematical reasoning ability of students affect the process of settlement of problems exiting in the process og learning matheatics. This study has the objective to determine the learning model that is effective between the Problem Based Learning models and the Learning Cycle 5E models. This study using quantitative study, data analysis techniques using T-test. The study results obtained it can be concluded that, mathematical reasoning ability of students has increased more effectively when the learning proces apply the Problem Based Learning models.

The objective of this study is to describe the influence of principals' leadership, work environment, and affiliation motivation on improving the performance of certified teachers at Private Vocational Schools in Tegal City, Indonesia. To achieve this objective, the current study carried out a research survey in Private Vocational Schools of Tegal City. Therefore, data collection was achieved by using survey questionnaire and respondents of the study was school teachers. Thus, this study used quantitative research approach with cross-sectional research design. Data were analyzed by using latest technique, namely; Structural Equation Modeling (SEM) based on Partial Least Square (PLS). The results of the study showed that the leadership of the principal, the work environment, and the motivation to affiliate were well per-ceived by the teachers. Both principal leadership and work environment had direct effects on improving the certified teacher’s performance. The teacher’s performance can be explained by independent head leadership and work environment variables, however, for independent variables namely; motivation to affiliate did not have enough significant influence. Hence, teacher’s performance can be improved through the leadership of principals and a conducive work environment.

This study proposes an optimal model to design and simulate the proton exchange membrane fuel cell (PEMFC) systems. The purpose of this paper is to present an improved version of seagull optimization algorithm for optimal parameter identification of the PEMFC stacks. The new algorithm uses the Lévy flight mechanism to give faster convergence rates. The sum of the squared error between the empirical values and achieved optimal model is analyzed based on two empirical PEMFC models including BCS 500-W and NedStack PS6. This analysis is performed to show the potential of the presented method by considering different conditions. Simulation results are compared with several optimization algorithms and show the algorithm’s superiority in terms of the solutions quality and the convergence speed. Keywords: Seagull optimization algorithm, Lévy flight, Proton exchange membrane fuel cells, Modeling

This paper presents a multi-criteria economics, environment, and thermodynamics assessment for a combined cooling, heating, and power (CCHP) system consist of a heat recovery system, a small absorption chiller, a 5kW PEMFC stack, gas compressor, and a humidifier. This study presents an improved version of a new optimization technique called improved emperor penguin optimization (IEPO) algorithm for optimizing the system efficiency. After simulations, the system is analyzed in terms of energy and exergy efficiencies, annual cost, and pollutant emission reduction. Simulation results declare low operating temperature develop GHG emission reduction, high relative humidity, pressure of inlet gases, and system exergy performance. The results of the IEPO algorithm are the compared with the standard EPO and also NSGA-II as a widely used optimization algorithm for showing the superiority of the algorithm. Keywords: Multi-criteria assessment, Combined cooling, heat and power system, Proton exchange membrane, Improved, Emperor penguin optimization algorithm

Parameter identification of the proton exchange membrane fuel cell (PEMFC) is a good way of increasing their efficiency in the next designs. In this study, an optimized improved Elman neural network based on a new hybrid optimization algorithm is proposed for this purpose. The proposed algorithm is a hybrid algorithm based on a combination of two newly algorithms, the world cup optimization (WCO) and the fluid Search Optimization (FSO) algorithms. The proposed method is applied to improve the method efficiency for estimating the PEMFC model parameters. The method is then validated by four different operational conditions. The optimization algorithm efficiency is also analyzed by comparison with some popular algorithms. Simulation results showed that using the designed method gives higher accuracy forecast for the PEMFC model parameters. Keywords: Proton exchange membrane fuel cell, Parameter identification, Optimization, WCO, FSO, Improved Elman neural network

Students' comprehension of the theory is often implemented as an effort to improve their achievements. However, other learning processes are rarely carried out by teachers, such as the practice or tools uses as the lesson application. The purpose of this study is to analyze the learning effectiveness by applying the geophysical surveys practice method or the use of environmental geophysics tools. The assessment of students' abilities is based on the interpretation results of environmental geophysical survey data and the presentations and discussions about the results of interpretation. The TGT Cooperative learning model was used with a quasi-experimental method and quantitative design. Data collection was carried out using a series of research instruments in the form of questionnaires. The results of the study were based on comparisons between the experimental class and the control class. The results show that the experimental class has excellent findings. 1) Based on the results of the post-test, the TGT cooperative learning method is very effective in this study, because teamwork can maintain students' enthusiasm and understanding during the learning process. 2) Retrieval of field data makes students better understand the application of geoelectric concepts and determination of rock resistivity.

The primary objective of this study is associated with the management of educational system in schools using Three Law Auguste Comte at Pesantren Tebuireng Jombang, East Java, Indonesia. Since June 2006 the Tebuireng Islamic Boarding School has been providing the Islamic education. However, in these schools, the students coming from other schools do not have Islamic education and face difficulty while taking courses. Additionally, the students of Tebuireng Islamic Boarding Schools are not familiar with traditional education, such as science-based subjects; mathematics, chemistry, physics, biology and management. Therefore, man-agement of educational system is required in Tebuireng Islamic Boarding Schools. For this purpose, data were collected from the teachers of these schools by using a questionnaire survey. Partial Least Square (PLS)-Structural Equation Modeling (SEM) was used to analyze the data. Findings of the study revealed that all levels of education in Islamic schools (Pesantren) should provide science-based subjects rational, among others: mathematics, chemistry, physics, biology, management, etc. Moreover, there should be a balance between Islamic education and science-based education.

The use of technology in business in the era of 4.0 is a difficult task for Small and Medium Enterprises (SMEs) due to lack of resources and other market problems. To attract customers to buy a product, the marketing of a product is usually influenced by various factors, such as price, product quality, and brand. In this study, the researchers analyzed the impact of price, product quality, and product brand on customer’s buying interest. This research is a case study conducted in Fahmi Mandiri SMEs aiming to increase the income. The method used is multiple regression analysis and the data is collected through questionnaires distributed to the customers. The results show that the biggest partial correlation influencing customer buying interest are price of a product and product brand. The quality of a product does not affect customers' buying interest significantly, because consumers will trust the quality of a product when the product brand is known and has the right price.

The primary objective of the present study was to examine the impact of total quality management (TQM) practices on the performance of organization performance of Arab American University Palestine. The study was also interested in examining the role of organizational excellence in organizational performance and also investigated the impact of organizational excellence in the relationship between TQM practices and organizational performance. To achieve the objective of the current study, we employed the structural equation modeling using statistical package of smart PLS-3. The questionnaires were distributed among the administrative staff of the university. Prior to establishing direct and indirect links between variables, we accessed the measurement of model and after es-tablishment of the model fit, the structural model was used to examine the hypothesized relationships and the results confirmed the hypotheses of the survey. In other words, TQM practices had significant relationship with organizational performance and organizational performance had significant relationship with organizational performance. Moreover, TQM had a positive and significant effect on the organizational Excellence. Finally, organizational excellence also mediated the relationship between TQM and organizational performance.

Energy expenditures are now the main cost for two businesses that generate huge incomes each year for Thailand, which are agribusiness and community tourism. As entrepreneurs have to share a portion of their income as energy utility bills each month. This is a factor which results in them getting a low net return. Recognizing the need for energy management for sustainable use in agriculture focusing on durian cultivation in Kantharalak district and community tourism in Sisaket province, this research used a newly developed optimization algorithm called Kouprey-inspired optimization (KIO) to assist energy management in smart agriculture to support community-based tourism. This was initiated with a smart energy node to reduce the energy and labor costs for volcanic durian planting and accommodation in community-based tourist attractions in Sisaket province. The results showed that the combination of the KIO algorithm and smart energy node allowed for efficient management of the volcanic durian orchards and the use of clean energy in combination with traditional electric power for volcanic durian cultivation and community-based tourism. As the research area in Sisaket province had eight hours of solar power per day, this was sufficient for smart agriculture and community-based tourism in the daytime and in the evening. Furthermore, this allowed operators in both the agricultural and tourism sectors to reduce the labor costs of the durian orchard business and community-based tourism by about 30%, and in the energy sector, the costs could be reduced by 50%. As a consequence, this prototype would lead to the expansion and trial in durian orchards in the Eastern Economic Corridor area, which is an important economic area producing durian for export of the country.

Autonomous energy management is becoming a significant mechanism for attaining sustainability in energy management. This resulted in this research paper, which aimed to apply deep reinforcement learning algorithms for an autonomous energy management system of a microgrid. This paper proposed a novel microgrid model that consisted of a combustion set of a household load, renewable energy, an energy storage system, and a generator, which were connected to the main grid. The proposed autonomous energy management system was designed to cooperate with the various flexible sources and loads by defining the priority resources, loads, and electricity prices. The system was implemented by using deep reinforcement learning algorithms that worked effectively in order to control the power storage, solar panels, generator, and main grid. The system model could achieve the optimal performance with near-optimal policies. As a result, this method could save 13.19% in the cost compared to conducting manual control of energy management. In this study, there was a focus on applying Q-learning for the microgrid in the tourism industry in remote areas which can produce and store energy. Therefore, we proposed an autonomous energy management system for effective energy management. In future work, the system could be improved by applying deep learning to use energy price data to predict the future energy price, when the system could produce more energy than the demand and store it for selling at the most appropriate price; this would make the autonomous energy management system smarter and provide better benefits for the tourism industry. This proposed autonomous energy management could be applied to other industries, for example businesses or factories which need effective energy management to maintain microgrid stability and also save energy.

Abstract Introduction The Human Development Index (HDI), as one of the more complex composite indicators of the level of human potential and quality of life, is a combination of three dimensions (indicators, factors): life expectancy at birth, the middle number of years of education and the expected number of years of schooling combined into a single education index and economic benefits expressed by production, or GDP (gross domestic product) according to purchasing power (PPP US $). Methods The same measures and average achievements in the field of health, education, and living standards are presented. The HDI was first developed in 1990 under the United Nations Development Program (UNDP) and is published as Human Development Reports (HDR). At present, it has become the most widely used complex indicator suitable for international comparisons and assessments of the achieved development level of a particular country or region. Results The paper deals specifically with the more perspective view of human development in the Western Balkans, with a series of socio‐economic implications for the development policy of the countries under observation. Conclusion The particular significance of the conducted research stems from the fact that in the countries of the Western Balkans are identified factors at the beginning of the transition period were often marginalized in the creation of macroeconomic policies, but in recent years there have been more positive developments in that regard.

Objective To determine the impact of artificial intelligence (AI) on the selected economies in the Asia-Pacific region. Methods This secondary research collected data from macroeconomic and AI-specific data sets. The sources of data from which insights were gained included digital technology sectors and corporations and their functions. The focus was on the need to assess the capability of AI on business operations The macroeconomic data was collected from data resources of international organizations' including the World Economic Forum, the Organization for Economic Co-operation and Development (OECD), the World Intellectual Property Organization (WIPO), and the International Telecommunication Union (ITU). In addition, this study has considered 19 economic indicators to analyze the economic outcome of AI in selected economies of Asia-Pacific. Results From the results, the period between 2014 and 2016 witnessed China leading with over 25,000 citable documents on the AI topic. Regarding institutions that were observed to publish over 500 times on the AI topic, the countries in the ascending order include China (600), Hong Kong (1,100), and Singapore (2,000). As such, this study established that Asia-Pacific economies such as Hong Kong and Singapore though have smaller populations, but the majority of their higher education institutions have made a significant contribution to AI research; with the small economies also having a relatively higher number of computer scientists among the top 1,000 individuals. Additionally, through empirically analyses, during 1998–2016 with annual observations, it is found that various economic outcomes of AI were presented in 8 economies of targeted region. Limitations At first, the future outlook of AI is just discussed in conceptual meaning while empirical context still needs to be examined in upcoming studies. At second, covering the overall South Asian region provides better findings with more generalization which is missing in current research. At third, other dimensions of AI and economy like implication of AI impact index and its relationship with macroeconomic variables is also missing in current research which could be reconsidered in coming studies. Conclusion It is evident that AI exhibits the potential to be the main driver of Asia-Pacific's economic growth. Relative to the net and gross effect of AI on labor markets and the gross domestic product (GDP) of the top Asia-Pacific economies demonstrate that by 2030, AI might yield a 16-percent increase in output, translating into an estimated amount of $13 trillion. Overall, it is concluded that Asia-Pacific, when compared to developed regions such as North America, is lagging but the availability of a large pool of user data implies that the region can move ahead—given better resource and talent allocation.

Renewable microgrids are new solutions for enhanced security, improved reliability and boosted power quality and operation in power systems. By deploying different sources of renewables such as solar panels and wind units, renewable microgrids can enhance reducing the greenhouse gasses and improve the efficiency. This paper proposes a machine learning based approach for energy management in renewable microgrids considering a reconfigurable structure based on remote switching of tie and sectionalizing. The suggested method considers the advanced support vector machine for modeling and estimating the charging demand of hybrid electric vehicles (HEVs). In order to mitigate the charging effects of HEVs on the system, two different scenarios are deployed; one coordinated and the other one intelligent charging. Due to the complex structure of the problem formulation, a new modified optimization method based on dragonfly is suggested. Moreover, a self-adaptive modification is suggested, which helps the solutions pick the modification method that best fits their situation. Simulation results on an IEEE microgrid test system show its appropriate and efficient quality in both scenarios. According to the prediction results for the total charging demand of the HEVs, the mean absolute percentage error is 0.978, which is very low. Moreover, the results show a 2.5% reduction in the total operation cost of the microgrid in the intelligent charging compared to the coordinated scheme.

Given the significance of energy conservation as a prime objective of environmental sustainability, countries all around the world are keen to identify significant factors that lead to the augmentation of energy utilization. Considering the rising emphasis of economies in utilizing natural resources to attain higher levels of globalization, the current research was aimed at investigating how the returns of natural resources and globalization affect energy consumption in top Asian economies. In doing so, the study emphasized the nonlinear relationship among the variables and applied the novel nonparametric method of causality in quantile to identify the quantile-based causal connection of natural resources and globalization on the returns and volatility of energy utilization in selected Asian countries. Moreover, the presence of nonlinearity in the variables was tested by the Brock- Dechert-Scheinkman test (BDS test), which confirmed that all variables showed nonlinear behavior. Furthermore, the findings of quantile cointegration confirmed a nonlinear long-run relationship of natural resources and globalization with energy utilization. The prime findings of causality in quantile revealed that the returns of natural resources and globalization had a significant causal effect on the returns of energy consumption in all countries. On the other hand, the volatility in energy consumption concluded no causal association with the returns of natural resources and globalization in any of the studied Asian countries. The findings are beneficial for the policymakers to formulate policies that will help to reduce the level of energy consumption.

The purpose of the current study was to examine the determinants of R&D expenditure and health expenditure of ASEAN countries. The research objectives were developed to analyze the short-term as well as the long-term impact of economic growth, environmental pollution, and energy consumption on health and R&D expenditures. The data was collected for ten years for ASEAN countries using the Auto-Regressive Distributed Lag (ARDL) approach, which helped the researcher to assess long-run as well as the short-run association of these variables. The findings revealed that environmental pollution, energy consumption, and economic growth had a significant positive impact on health expenditure as well as on R&D expenditure of ASEAN countries in long-run. The findings further revealed that environmental pollution and economic growth had a significant impact on R&D expenditure in short-run; however, there was no significant impact of energy consumption on R&D expenditure in short-run. It was also found that there was no significant impact of any of the independent variables, i.e., energy consumption, economic growth, and environmental pollution, on health expenditure in short-run. The current study and findings have significant implications in theory and practice.

Technology adoption is always a difficult task for Small and Medium-sized Enterprises (SMEs) due to lack of resources and other market issues. Many technology challenges adversely affect the sustainable business performance of SMEs. However, the incorporation of Industry 4.0 can overcome various technology issues. The goal of Industry 4.0 is to attain an advanced level of operational effectiveness and productivity, as well as a higher level of automatization. Thus, the objective of this study is to identify the role of Industry 4.0 to promote sustainable business performance in SMEs in Thailand. A survey has been prepared to collect the data from managers of SMEs and analyzed with the help of Partial Least Square. The questionnaire was used to collect the data and questionnaires were distributed by using simple random sampling. A total of 500 questionnaires were distributed amongst the managerial staff of SMEs located in Thailand. From these distributed questionnaires, 280 were returned and 270 valid responses were found. Data were analyzed by using Partial Least Square (PLS)-Structural Equation Modeling (SEM). Findings reveal that Industry 4.0 is a key to the growth of sustainable business performance among SMEs. Elements of Industry 4.0 such as big data, Internet of Things and smart factory have a positive role in promoting information technology (IT) implementation, which contributes to sustainable business performance. Moreover, organization structure and process strengthen the positive relationship between Industry 4.0 and IT implementation.