Inertia is a critical factor in maintaining the frequency stability of power systems. However, the growing integration of power electronics-based renewable energy sources (RESs) has significantly reduced system inertia. AC and dc microgrids have emerged as key solutions for integrating RESs. Unlike traditional synchronous generators, power electronic converters interfacing RESs lack inherent inertia and damping, posing challenges to the control and stability of these microgrids. To address these challenges, virtual inertia control strategies, which emulate the behavior of synchronous generators, have been widely adopted to enhance the stability of ac microgrids. Drawing on the analogies between ac and dc systems, similar virtual inertia concepts have been extended to dc microgrids, demonstrating their potential to improve system stability. This article provides a comprehensive review of inertia enhancement strategies for dc microgrids, examining their key features, benefits, and limitations. The analogy between synchronous generators/dc machines and energy storage systems is explored, with a particular focus on the implementation of virtual inertia and damping control in energy storage converters as a promising solution to mitigate power fluctuations. In addition, this article investigates the grid-forming and grid-following converter analogies in ac and dc microgrids. Various stability analysis methods applied to inertia enhancement strategies are also reviewed, offering readers a comprehensive understanding of the current state of research. By addressing the conceptual and technical analogies between ac and dc systems, this review aims to provide valuable insights for developing advanced control strategies for next-generation microgrids.
The need for efficient approaches to track and assess fish behavior in rivers impacted by hydropeaking is increasing. Nonetheless, employing an automated camera system for underwater monitoring requires that the algorithms function under highly variable environmental conditions, which affect the ability to detect and assess fish size. Additionally, there is a lack of openly accessible freshwater fish classification and size estimation datasets. To address these limitations, we propose a binocular underwater fish monitoring system capable of real-time fish detection and size estimation. The system was deployed and tested over one week in two Portuguese rivers affected by hydropeaking. The week-long analysis also provided new insights regarding wild fish behavior in rivers affected by hydropeaking. Results indicate that hydropeaking strongly influences how fish may use instream flow refuges during hydropeaking. Fish were less frequently detected in the flow refuge during peak flow events, suggesting that the flow conditions created habitat instability and difficulty accessing the flow refuge. In contrast, fish in the non-hydropeaking river consistently used refuge areas, reinforcing their importance as shelter during natural flow variations. This study demonstrates the potential of a computer vision-based pipeline for real-time, fully automated fish monitoring of hydropeaking’s impacts on riverine fish. Additionally, we provide PTFish, an open dataset with 18,523 manually annotated frames featuring infrared and color video frames. These findings emphasize that automated, camera-based solutions for hydropeaking monitoring can be used to develop evidence-based mitigation strategies to sustain fish populations in rivers impacted by hydropeaking.
<p>A hybrid method for the numerical solution of the system of delayed linear fuzzy mixed VolterraFredholm integral equations (FMDVFIES) is introduced. Using the hybrid of Bernstein polynomials and blockpulse functions (HBBFs), an approximate solution for the equations system is provided. Firstly, the HBBFs and their operational matrices are introduced, and some of their characteristics are described. Then by applying the operational matrices on FMDVFIES convert it to the algebraic equations system. The numerical solution is obtained by solving this algebraic system. Then the convergence is investigated and some numerical examples are presented to show the effectiveness of the method.</p>
Ognjen Radišić-Aberger, Peter Burggräf, Fabian Steinberg
et al.
By applying machine learning algorithms, predictive business process monitoring (PBPM) techniques provide an opportunity to counteract undesired outcomes of processes. An especially complex variation of business processes is the engineering change (EC) process. Here, failing to adhere to planned implementation dates can have severe impacts on assembly lines, and it is paramount that potential negative cases are identified as early as possible. Current PBPM research, however, has seldomly investigated the predictive performance of machine learning approaches and their applicability at early process steps, let alone for the EC process. In our research, we show that given adequate feature encoding, shallow learners can accurately predict schedule adherence after process initialisation. Based on EC data from an automotive manufacturer, we provide a case sensitive performance overview on algorithm-encoding combinations. For that, three algorithms (XGBoost, Random Forest, LSTM) were combined with four encoding techniques. The encoding techniques used were the two common aggregation-based and index-based last state encoding, and two new combinations of these, which we term advanced aggregation-based and complex aggregation-based encoding. The study indicates that XGBoost-index-encoded approaches outclass regarding average predictive performance, whereas Random-Forest-aggregation-encoded approaches perform better regarding temporal stability due to reduced influence by dynamic features. Our research provides a case-based reasoning approach for deciding on which algorithm-encoding combination and evaluation metrics to apply. In doing so, we provide a blueprint for an early warning and monitoring method within the EC process and other similarly complex processes.
Marketing. Distribution of products, Management. Industrial management
Fly ash, a by-product of coal combustion in thermal power plants is utilized as a substitute for Portland Cement in concrete due to its pozzolanic properties. Mainly, class F fly ash—produced from the combustion of anthracite coal at approximately 1560˚C (according to SK SNI S15-1990-F)—contains less than 10% lime (CaO) and exhibits significant pozzolanic or filler properties. This research investigates the impact of varying fly ash proportions (0%, 10%, 15%, and 20%) on the compressive strength of concrete. The experimental setup included 108 cylindrical test specimens (10 cm in diameter and 20 cm in height), representing different brands of cement and fly ash mixtures, tested at 7, 14, and 28 days. The study was conducted at PT. Waskita Beton Precast Plant in Sadang, Purwakarta, West Java, targeting a concrete compressive strength of 40 MPa. Results indicate that at 0% fly ash, the compressive strength using Garuda Cement reached 76.66 MPa after 28 days. However, this strength decreased with increasing fly ash content, measuring 72.35 MPa, 69.07 MPa, and 68.13 MPa for 10%, 15%, and 20% fly ash, respectively. These findings highlight the influence of fly ash content on the structural integrity of concrete, suggesting a potential trade-off between sustainability and mechanical performance.
Keyword: Fly Ash, Compressive Strength of Concrete, Portland Cement
Preserving Balinese cultural heritage is crucial for sustaining community identity. In Bali, temples (pura) are central to spiritual and cultural life. However, younger generations, especially temple caretakers of Pemerajan Agung Sakti Padangsambian, are increasingly losing knowledge of these sacred spaces, weakening their sense of belonging, to preserve cultural traditions. Current media efforts has failed to engage this demographic. This research addresses this challenge by developing an application-integrated images compiled into books and Android-based AR technology. The application employed a user-centered design approach involving analysis, design, development, testing, and evaluation phases. Results show AR effectively bridges the knowledge gap, with usability scores and a significant increase in user knowledge of 42.43%. This research demonstrates AR's potential for preserving and transmitting cultural heritage, including the reconstruction of damaged historical objects through 3D modeling with the marker detection technology, to ensure seamless integration between the real and virtual worlds.
Extant research addressing the relations between TikTok videos and sustainable apparel consumption behavior is limited. This study explores these relations by testing the following theories and constructs: social consciousness, prior sustainable apparel purchasing, attitude toward TikTok videos (featuring sustainable apparel content), and theory of planned behavior. Results from an online survey supported the proposed conceptual framework, suggesting that cognitive, affective, and behavioral factors relevant to sustainable apparel consumption had a positive influence on sustainable apparel purchase intention.
In the present paper, the online valet driving problem (OVDP) is studied. In this problem, customers request a valet driving service through the platform, then the valets arrive on e-bikes at the designated pickup location and drive the vehicle to the destination. The key task is to assign the valets effectively for driving orders to minimize the overall cost. To serve that purpose, we first propose a new online scheduling strategy that divides the planning horizon into several rounds with fixed length of time, and each round consists of pooling time and scheduling time. By including the features of online scheduling and the power level of e-bikes, this OVDP becomes more practical but nevertheless challenging. To solve the OVDP, we formulate it into a set partitioning model and design a branch-and-price (B&P) algorithm. To improve the computation efficiency, a label setting algorithm is incorporated to address the pricing subproblem, which is accelerated via a heuristic pricing method. As an essential part of the algorithm design, an artificial column technique and a greedy-based constructive heuristic are implemented to obtain the initial solution. Based on the numerical analysis of various scaled instances, it is verified that the proposed B&P algorithm is not only effective in optimum seeking, but also shows a high level of efficiency in comparison with the off-the-shelf commercial solvers. Furthermore, we also explore the impact of pooling and scheduling time on the OVDP and discover a bowl-shaped trend of the objective value with respect to the two time lengths.
Sameh Alsaqoor, Ahmad Alqatamin, Ali Alahmer
et al.
This study examines the impact of incorporating phase change material (PCM) in photovoltaic thermal (PVT) systems on their electrical and thermal performance. Although PVT systems have shown effectiveness in converting solar energy into both electricity and heat, there is a necessity for studies to investigate how integrating PCMs can further enhance performance. The study also aims to explore the effect of solar irradiation and coolant mass flow rate on the electrical and thermal output of both PVT and PVT-PCM systems. A graphical user interface was developed within the MATLAB Simulink under the weather conditions of Amman, Jordan. The results show that the incorporation of PCM in PVT systems significantly reduces solar cell temperature and increases electrical efficiency. The highest electrical efficiency of a PVT system with PCM was found to be 14%, compared to 13.75% in a PVT system without PCM. Furthermore, the maximum achievable electrical power in a PVT system with PCM was 21 kW, while in the PVT system without PCM it was 18 kW. The study also found that increasing the coolant mass flow rate in a PVT system with PCM further reduced PV cell temperature and increased electrical efficiency, while the electrical efficiency of both the PVT and PVT-PCM systems decreases as solar incident radiation flux increases, resulting in a significant rise in cell temperature. At an increased solar radiation level from 500 W/m2 to 1000 W/m2, the electrical efficiency of the PVT configuration decreases from 13.75% to 11.1%, while the electrical efficiency of the PVT-PCM configuration falls from 14% to 12%. The findings of this study indicate that the use of PCM in PVT systems can lead to significant improvements in energy production and cooling processes. The results provide valuable information for designing and optimizing PVT-PCM systems.
Nowadays, the society faces with financial and economic risks which play a special role in the diversity of risks. In the most general form, they affect the amount of available financial resources that can meet the current needs of the population and spread new living standards. The purpose of the study is to analyze the affection of financial and economic risks on social growth and to develop recommendations for creating a mechanism for managing them in the Russian health care system. For this goal achievement, the author has identified several tasks clarifying the approach to determining financial and economic risks in this paradigm, as well as identifying measures to change financing Russian health care. The methodological base: systemic; comparative analysis; synthesis; socio-economic and statistical methods of data analysis. The theoretical and practical significance of the study lies in an integrated system growth for managing financial and economic risks, which unites different economic entities, as well as in determining measures to change the financing mechanisms of the Russian health care system. The specialists can use the results obtained in subsequent work on the problems of risk management at the level of corporate organizations, state agencies, and society.
Alejandro Bermúdez Cifuentes, Paula Alejandra Ramírez Lugo, Yeison Camilo Herrera Mosquera
et al.
This project seeks to propose a drone system to improve security in Bogotá, given that insecurity rates have increasedsignificantly in recent years and even more so in the COVID-19 pandemic. The proposal is based on having an integratedsystem with special characteristics to face different situations, the drones would be equipped with non-lethal weapons,photo captures for facial and vehicle scanning, real-time location sending, aerial patrolling in more frequent areas ofcriminal acts, among other functions.
Engineering (General). Civil engineering (General), Information technology
The community and home-based elderly care service system has been proved an effective pattern to mitigate the elderly care dilemma under the background of accelerating aging in China. In particular, the participation of social organizations in community and home-based elderly care service has powerfully fueled the multi-supply of elderly care. As the industry of the elderly care service is in the ascendant, the management lags behind, resulting in the waste of significant social resources. Therefore, performance evaluation is proposed to resolve this problem. However, a systematic framework for evaluating performance of community and home-based elderly care service centers (CECSCs) is absent. To overcome this limitation, the SBM-DEA model is introduced in this paper to evaluate the performance of CECSCs. 186 social organizations in Nanjing were employed as an empirical study to develop the systematic framework for performance evaluation. Through holistic analysis of previous studies and interviews with experts, a systematic framework with 33 indicators of six dimensions (i.e., financial management, hardware facilities, team building, service management, service object and organization construction) was developed. Then, Sensitivity Analysis is used to screen the direction of performance optimization and specific suggestions were put forward for government, industrial associations and CECSCs to implement. The empirical study shows the proposed framework using SBM-DEA and sensitivity analysis is viable for conducting performance evaluation and improvement of CECSCs, which is conducive to the sustainable development of CECSCs.
This study evaluated the spatiotemporal variability of water quality in the Han River Basin (HRB) as well as the contributions of potential pollution sources using multivariate statistical and absolute principal component score-multiple linear regression (APCS-MLR) modeling techniques. From 2011 to 2020, data on water quality parameters were collected from 14 sites in the Ministry of Environment’s water quality monitoring network. Using spatiotemporal cluster analysis, these sites were classified into two periods over the year (dry and wet seasons) and into three regions: low pollution region (LPR), moderate pollution region (MPR), and high pollution region (HPR). Through principal component analysis, we identified four potential factors accounting for 80.1% and 74.1% of the total variance in the LPR and MPR, respectively, and three that accounted for 72.7% of the total variance in the HPR. APCS-MLR results indicated domestic sewage and phytoplankton growth (25%), domestic sewage and seasonal influence (29%), and point pollution sources caused by domestic sewage and industrial wastewater discharge (31%) as potential factors for the LPR, MPR, and HPR. These results demonstrate that the multivariate statistical techniques and the APCS-MLR model can be effectively used to monitor network design, quantitatively evaluate potential pollution sources, and establish efficient water quality management policies.
Khaerani Suci Lestari, Aditya Fadilah Muhamad, Arif Susanto
et al.
Introduction: In the era of the Covid-19 pandemic, psychosocial issues as part of health and safety have become a popular mental health issue, particularly among gold and copper miners. This situation has led some companies to fire their unskilled and unproductive workers to minimize the production costs. However, there is no specific regulation for the workers that could be a barrier of this unpredicted situation. This condition mostly becomes a negative stressor for the workers at the jobsite. At some point, it develops to a critical health and safety condition known as risky and unhealthy behavior that brings to fatality. The aim of this research is to analyze the psychosocial risk factors that adversely affect the psychology of copper and gold mining workers during the pandemic. Method: This is a cross-sectional study with COPSOQ III, used to identify factors contributing to workers’ psychology, while Dolan & Arsenault’s questionnaire was used to analyze stress symptoms, with a Cronbach α value of 0.83-0.84. Results: Multivariate tests were conducted on the variables of work experience, body mass index, marital status, emotional demands, vertical trust, and organizational justice. Significance values were obtained for the variables of work experience (0.590 body mass index (0.517), marital status (0.122), emotional demands (0.187), vertical trust (0.000), and organizational justice (0.119). Of the six variables, only vertical trusthad a significant value. Conclusion: Psychological risk factors on copper and gold mining workers during the pandemic are only influenced by a vertical trust.
Keywords: COPSOQ III, leadership, pandemic, psychosocial, vertical trust
این پژوهش، با هدف تعیین ویژگیهای عناصر چهارگانۀ برنامۀ درسی دانشگاه و پژوهشگاه عالی دفاع، براساس نظریۀ ابتنائی انجام شد. پژوهش حاضر، ازنظر نوع تحقیق، کاربردی- توسعهای است و با رویکرد آمیختۀ اکتشافی و روش نظریۀ دادهبنیاد (نظریۀ مبنایی) صورت گرفته است. جامعۀ آماری این پژوهش، اعضای هیأتعلمی دانشگاههای تهران بهتعداد 124 نفر است. نمونه در بخش مطالعات کتابخانهای، براساس روش نمونۀ تمامشماری، شامل تمام منابع دردسترس است و در بخش متخصصان، بهروش نمونهگیری هدفمند و براساس قاعدۀ رسیدن به حد اشباع، 17 نفر متخصص انتخاب شدند. بهمنظور تحلیل دادهها، روش رمزگذاری، بازمحوری و انتخابی اتخاذ شد. نتایج نشان داد که در عنصر هدف، همکاری با مراکز آموزشـی و پژوهشی، علمی و نظامی در عنصر راهبردهای یاددهی-یادگیری، استفاده از راهبردهای مشارکتی در عنصر محتوا، استفاده از مباحث چالشبرانگیز و نیازمند گفتمان و درنهایت در عنصر ارزشیابی، استفاده از ارزشیابی گروهی و خودارزشیابی مورد تأکید قرار گرفت.
Social sciences (General), Organizational behaviour, change and effectiveness. Corporate culture
The Inequality of production efficiency is one of the main factors contributing to inequalities in economic growth among different regions. This study examines the factors affecting production efficiency by using province-level data for the period 2006-2015 by applying spatial Tobit model and translog production function approach. Results show that Chaharmahal and Bakhtiari and Khorasan Razavi Provinces, have the highest and lowest efficiency 0.63 and 0.09 respectively. Also, the results of spatial Tobit model estimation show that industrialization with a coefficient of 0.0018 has a positive and significant effect and increase of ratio of credits to production with a coefficient of 0.0013 has a negative and significant effect on the production efficiency of provinces. In addition, the spillover effects of industrialization and the ratio of credits are -0.0104 and 0.0042 respectively. Therefore basic development of industry and allocation of resource on the relative advantage is one of the most important factors for improving the production efficiency and increasing economic growth.