Search Results (148,062)

Abstract: The integration of photovoltaic and electric vehicles in distribution networks is rapidly increasing due to the shortage of fossil fuels and the need for environmental protection. However, the randomness of photovoltaic and the disordered charging loads of electric vehicles cause imbalances in power flow within the distribution system. These imbalances complicate voltage management and cause economic inefficiencies in power dispatching. This study proposes an innovative economic strategy utilizing battery energy storage system and electric vehicles cooperation to achieve voltage regulation in photovoltaic-connected distribution system. Firstly, a novel pelican optimization algorithm-XGBoost is introduced to enhance the accuracy of photovoltaic power prediction. To address the challenge of disordered electric vehicles charging loads, a wide-local area scheduling method is implemented using Monte Carlo simulations. Additionally, a scheme for the allocation of battery energy storage system and a novel slack management method are proposed to optimize both the available capacity and the economic efficiency of battery energy storage system. Finally, we recommend a day-ahead real-time control strategy for battery energy storage system and electric vehicles to regulate voltage. This strategy utilizes a multi-particle swarm algorithm to optimize economic power dispatching between battery energy storage system on the distribution side and electric vehicles on the user side during the day-ahead stage. At the real-time stage, the superior control capabilities of the battery energy storage system address photovoltaic power prediction errors and electric vehicle reservation defaults. This study models an IEEE 33 system that incorporates high-penetration photovoltaics, electric vehicles, and battery storage energy systems. A comparative analysis of four scenarios revealed significant financial benefits. This approach ensures economic cooperation between devices on both the user and distribution system sides for effective voltage management. Additionally, it encourages trading activities of these devices in the power market and establishes a foundation for economic cooperation between devices on both the user and distribution system sides. Full article

Background: Aging is a natural biological process influenced by multiple factors and is a significant contributor to various chronic diseases. Slowing down the aging process and extending health span have been pursuits of the scientific field. Methods: Examination of the effects of dietary polyphenols on hallmarks of aging such as genomic instability, telomere attrition, epigenetic alterations, loss of proteostasis, disabled macroautophagy, deregulated nutrient-sensing, mitochondrial dysfunction, cellular senescence, stem cell exhaustion, altered intercellular communication, chronic inflammation, and dysbiosis. Results: Polyphenols, abundant in nature, exhibit numerous biological activities, including antioxidant effects, free radical scavenging, neuroprotection, and anti-aging properties. These compounds are generally safe and effective in potentially slowing aging and preventing age-related disorders. Conclusions: The review encourages the development of novel therapeutic strategies using dietary polyphenols to create holistic anti-aging therapies and nutritional supplements. Full article

Taking into consideration the moderating role of perceived organizational support within the framework of the natural resource-based view (RBV) theory, the purpose of this study is to investigate the connection between corporate social responsibility (CSR) and sustainable environmental performance. Specifically, this study focuses on the roles that green capability and green transformational leadership play as mediators in this relationship. Through the use of a survey questionnaire, information was collected from 420 employees working for small- and medium-sized enterprises (SMEs) in Bangladesh. The data were analyzed with the help of AMOS and SPSS. The findings indicate that the level of CSR has a significant impact on the performance of sustainable environmental practices. To a large extent, green capability and green transformational leadership serve as mediators in the relationship between CSR and sustainable environmental performance. Furthermore, perceived organizational support plays a significant role in moderating the relationship between CSR and sustainable environmental performance. The relationship between green transformational leadership and sustainable environmental performance is also significantly moderated by perceived organizational support. This is a significant contributor to the connection. This multidimensional corporate social responsibility model can be used to assess sustainable environmental performance in both industrialized and developing countries, and it can also be extended to other service sectors, according to the theoretical conclusion that can be drawn from the research. This research demonstrates that there is a direct connection between corporate social responsibility and sustainable environmental performance. As a result, practitioners are able to develop strategies that are effective in terms of corporate social responsibility. These findings should be taken into consideration by policymakers and managers who are dedicated to promoting equitable development of the country. Full article

In order to express the energy flow, motion flow, and control flow in wireless rechargeable sensor networks accurately and intuitively, and to maximize the charging benefit of MVs (mobile vehicles), a type of MTS-HACO (Mobile Transition Sequence Hybrid Ant Colony Optimization) is proposed. Firstly, node places are grouped according to the firing time of node’s energy consumption transition to ensure that in each time slot, MV places only enable charging transitions for the node places with lower remaining lifetimes. Then, the FSOMCT (Firing Sequence Optimization of Mobile Charging Transition) problem is formulated under the constraints of MV places capacity, travelling arc weight, charging arc weight, and so on. The elite strategy and the Max–Min Ant Colony system are further introduced to improve the ant colony algorithm, while the improved FWA (fireworks algorithm) optimizes the path constructed by each ant. Finally, the optimal mobile charging transition firing sequence and charging times are obtained, ensuring that MVs have sufficient energy to return to the base station. Simulation results indicate that, compared with the periodic algorithm and the PE-FWA algorithm, the proposed method can improve charging benefit by approximately 48.7% and 26.3%, respectively. Full article

Background: Prediabetes is frequently underdiagnosed and undertreated, yet it poses significant cardiovascular risks. This study investigates the impact of prediabetes on short- and long-term survival outcomes in patients who experienced ST-elevation myocardial infarction (STEMI). Methods: In this retrospective, single-center cohort study, we evaluated 725 STEMI patients stratified into non-diabetic, prediabetic, and diabetic groups based on HbA1c levels at presentation. A Kaplan–Meier survival analysis was employed to compare long-term outcomes over a three-year follow-up period. Cardiovascular risk factors, including hypertension and dyslipidemia, were analyzed across the groups. The discriminatory power of HbA1c for predicting all-cause mortality was assessed using an Area Under the Receiver Operating Characteristic (AUROC) analysis. Results: Of the 725 patients, 407 (56.1%) were non-diabetic, 184 (25.4%) were prediabetic, and 134 (18.5%) were diabetic. Prediabetic patients exhibited significant additional cardiovascular risk factors, such as arterial hypertension (67.4%) and dyslipidemia (78.3%), with prevalence rates between those of non-diabetic and diabetic patients. The Kaplan–Meier analysis revealed that at a three-year follow-up, prediabetic patients faced a survival disadvantage, with a significant decrease in survival rates compared to non-diabetic patients (log-rank p = 0.016); their survival outcomes approached those of diabetic patients (p = 0.125). The AUROC analysis demonstrated that HbA1c was a significant predictor of short- and long-term mortality, with a cut-off value of 5.75% and an Area Under the Curve (AUC) of 0.580–0.617 (95% CI: 0.458–0.721), indicating a moderate ability to predict survival in patients with STEMI. Conclusions: Prediabetes significantly worsens survival outcomes following STEMI, nearly approaching the risk level of diabetes. Integrating rigorous cardiovascular risk management strategies for prediabetic individuals, including lifestyle interventions and potentially pharmacological treatments, could prevent the progression to diabetes and mitigate associated cardiovascular risks. Full article

Localization is one of the most challenging problems in wireless sensor networks (WSNs), primarily driven by the need to develop an accurate and cost-effective localization system for Internet of Things (IoT) applications. While machine learning (ML) algorithms have been widely applied in various WSN-based tasks, their effectiveness is often compromised by limited training data, leading to issues such as overfitting and reduced accuracy, especially when the number of sensor nodes is low. A key strategy to mitigate overfitting involves increasing both the quantity and diversity of the training data. To address the limitations posed by small datasets, this paper proposes an intelligent data augmentation strategy (DAS)-based deep neural network (DNN) that enhances the localization accuracy of WSNs. The proposed DAS replicates the estimated positions of unknown nodes generated by the Dv-hop algorithm and introduces Gaussian noise to these replicated positions, creating multiple modified datasets. By combining the modified datasets with the original training data, we significantly increase the dataset size, which leads to a substantial reduction in normalized root mean square error (NRMSE). The experimental results demonstrate that this data augmentation technique significantly improves the performance of DNNs compared to the traditional Dv-hop algorithm at a low number of nodes while maintaining an efficient computational cost for data augmentation. Therefore, the proposed method provides a scalable and effective solution for enhancing the localization accuracy of WSNs. Full article

This Special Issue contains a selection of papers dealing with Fusarium wilt of banana (FWB), with a special focus on the Fusarium strain Tropical Race 4 (TR4), and explores (1) options for effective integrated management strategies, (2) the detection and development of disease-resistant cultivars, and (3) the distribution and diversity of the pathogen [...] Full article

Jump dynamics in financial markets exhibit significant complexity, often resulting in increased probabilities of subsequent jumps, akin to earthquake aftershocks. This study aims to understand these complexities within a multifractal framework. To do this, we employed the high-frequency intraday data from six major cryptocurrencies (Bitcoin, Ethereum, Litecoin, Dashcoin, EOS, and Ripple) and six major forex markets (Euro, British pound, Canadian dollar, Australian dollar, Swiss franc, and Japanese yen) between 4 August 2019 and 4 October 2023, at 5 min intervals. We began by extracting daily jumps from realized volatility using a MinRV-based approach and then applying Multifractal Detrended Fluctuation Analysis (MFDFA) to those jumps to explore their multifractal characteristics. The results of the MFDFA—especially the fluctuation function, the varying Hurst exponent, and the Renyi exponent—confirm that all of these jump series exhibit significant multifractal properties. However, the range of the Hurst exponent values indicates that Dashcoin has the highest and Litecoin has the lowest multifractal strength. Moreover, all of the jump series show significant persistent behavior and a positive autocorrelation, indicating a higher probability of a positive/negative jump being followed by another positive/negative jump. Additionally, the findings of rolling-window MFDFA with a window length of 250 days reveal persistent behavior most of the time. These findings are useful for market participants, investors, and policymakers in developing portfolio diversification strategies and making important investment decisions, and they could enhance market efficiency and stability. Full article

Hibernation is an energy-saving and adaptive strategy adopted by a diverse array of animals, rarely including warm-climate species, to survive in the harsh winter environment. Here, we collected large-intestinal microbial samples from two species of warm-climate lizards, one (the Reeves’ butterfly lizard Leiolepis reevesii) hibernating in the winter months and one (the many-lined sun skink Eutropis multifasciata) not doing so, in summer and winter to analyze and compare their microbiota using 16S rRNA gene amplicon sequencing technology. Gut microbiota were seasonally variable in L. reevesii but not in E. multifasciata. The decreased Firmicutes/Bacteroidetes ratio and increased relative abundance of Verrucomicrobia in hibernating butterfly lizards in a state of long-term fasting should help them live through the winter months, as bacteria of the phyla Bacteroidetes and Verrucomicrobia can use host-derived mucin glycans in the absence of dietary substrates. Facultative plant feeding by omnivorous butterfly lizards resulted in a significant increase in the relative abundance of bacteria of the phylum Firmicutes (e.g., Lachnospiraceae) with the ability to degrade plant fibers. This study not only validates the role of gut microbiota in dietary adaptation in lizards but also shows that gut microbial communities are seasonally variable in warm-climate lizards hibernating in the winter months. Full article

Foundation Models (FMs) are gaining increasing attention in the biomedical artificial intelligence (AI) ecosystem due to their ability to represent and contextualize multimodal biomedical data. These capabilities make FMs a valuable tool for a variety of tasks, including biomedical reasoning, hypothesis generation, and interpreting complex imaging data. In this review paper, we address the unique challenges associated with establishing an ethical and trustworthy biomedical AI ecosystem, with a particular focus on the development of FMs and their downstream applications. We explore strategies that can be implemented throughout the biomedical AI pipeline to effectively tackle these challenges, ensuring that these FMs are translated responsibly into clinical and translational settings. Additionally, we emphasize the importance of key stewardship and co-design principles that not only ensure robust regulation but also guarantee that the interests of all stakeholders—especially those involved in or affected by these clinical and translational applications—are adequately represented. We aim to empower the biomedical AI community to harness these models responsibly and effectively. As we navigate this exciting frontier, our collective commitment to ethical stewardship, co-design, and responsible translation will be instrumental in ensuring that the evolution of FMs truly enhances patient care and medical decision-making, ultimately leading to a more equitable and trustworthy biomedical AI ecosystem. Full article

The current source inverter (CSI) has become the main grid-connected interface of distributed generation systems due to its advantages, such as boost capability, current controllability, and short-circuit protection capability. However, in weak grids, the grid-connected CSI that uses a phase-locked loop to achieve grid voltage synchronization has problems, such as instability in the fundamental positive-sequence voltage phase detection at the point of common coupling and instability in the current loop control, which seriously hamper the promotion and application of the CSI and its interconnected systems. For this reason, this paper proposes a sliding mode observer-based phase-locking strategy for the CSI. The strategy proposes a sliding mode observer for grid voltage phase detection, so that the grid current can directly follow the grid voltage, solving the problem of inconsistency or distortion between the voltage phase of the point of common coupling and the grid voltage phase in weak grids. On this basis, the grid impedance is regarded as part of the CL filter, and a robust parameter design method is proposed for the grid current closed-loop control in weak grids, which achieves robust operation of a CSI in weak grids. Finally, an experimental platform for a single-phase grid-connected CSI is built to verify the effectiveness and feasibility of the proposed scheme. Full article

The COVID-19 pandemic has severely impacted global tourism, raising the need for sustainable recovery strategies. This study examines how tourists’ confidence in tourism recovery (CTR) influences travel intentions across different pandemic phases—outbreak, stabilization, and conclusion. Positioned within the Theory of Planned Behavior (TPB), the research explores the mediating role of CTR in the relationships between attitude, subjective norms, perceived behavioral control, and travel intention. Using structural equation modeling (SEM), multi-group analysis (MGA), and Importance–Performance Matrix Analysis (IPMA), this study assesses changes in travel behavior during each pandemic phase. Data were collected through three surveys conducted in major Chinese cities between late 2022 and early 2023. The findings reveal that CTR significantly mediates the influence of behavioral factors on travel intention, especially during the stabilization and conclusion phases. The IPMA results highlight key areas for intervention, with CTR, perceived behavioral control, and subjective norms varying in importance across phases. This research underscores the need for adaptive and sustainable strategies to strengthen traveler confidence, offering practical insights for supporting long-term resilience and growth in the tourism industry during and beyond health-related crises. Full article

Literature on African urbanism has generally lacked insight into the significant roles of hunters and fishers as city founders. This has resulted in a knowledge gap regarding the cultural foundation of the cities that could enhance policy frameworks for sustainable urban governance. This article examines corollaries related to the complementarities of hunting and urbanism with case studies from the ethno-linguistic Yoruba region in southwestern Nigeria. Through qualitative methodologies involving ethnography and the (oral) history of landscapes of hunting from the pre-colonial and (British) colonial periods, as well as tracing the current cultural significance of hunting in selected Yoruba cities, the article reveals data that identify hunters and fishers as city founders. It shows that hunting, as a lived heritage, continues to be interlaced with cultural urban practices and Yoruba cosmology and that within this cultural imagery and belief, hunters remain key actors in nature conservation, contributing to socio-cultural capital, economic sustainability, and urban security structures. The article concludes with recommendations for strategies to reconnect with these value systems in rapidly westernizing urban Africa. These reconnections include the re-sacralization of desacralized landscapes of hunting, revival of cultural ideologies, decolonization from occidental conceptions, and re-definition of urbanism and place-making in light of African perspectives despite globalization. In doing so, the article contributes to a deeper understanding of the interconnections between the environmental and societal components of sustainability theory, agenda, and practice in urban contexts; underscores the societal value of lived heritage, cultural heritage, and cultural capital within the growing literature on urban social sustainability; and sheds more light on southern geographies within the social sustainability discourse, a field of study that still disproportionately reflects the global northwest. Full article

The purpose of the qualitative multiple case study was to explore the strategies some supply chain managers of the United Nations (UN) use to leverage operational efficiencies in the UN humanitarian programs. As a result, communities supporting sustainability and peace or those fostering economic development will be able to respond effectively to humanitarian crises. Moreover, the UN can remain operational and engage in political and conflict-reduction interventions integral to economic and social recovery and sustainability. This study applied the qualitative multiple case study through semi-structured interviews with nine supply chain managers in the United Nations, direct observations, document analysis, and artifacts. The research is grounded in the dynamic capability theory (DCT). The research revealed several strategies that supply chain managers of the UN use to ensure operational efficiencies grouped around three themes: (a) analytical, innovation, and knowledge management strategies; (b) effective supply chain management leadership strategies; and (c) risk management strategies. This study is one of the first to apply generic findings of humanitarian supply chain studies to the United Nations, the global organization with diverse mandates that continuously strives to achieve efficiencies required by donors providing financial support, thus remaining operational. The study’s results could help leaders in the various humanitarian organizations who operate in vulnerable environments and under strict scrutiny from donors to deliver their aid programs most efficiently by understanding dynamic capabilities. Previous studies indicate the lack of strategic frameworks applicable to the United Nations that could improve decision-making at the strategic, tactical, and operational levels, facilitate collaboration among supply chain stakeholders, and reduce the costs of the operational performance of the supply chain system in the UN. Full article

This review paper presents the current progress in the development of resistance welding techniques for thermoplastic composites, with a particular emphasis on their application in hybrid joints, such as those involving thermosetting composites and metals. Resistance welding, a fusion bonding method, offers significant advantages over adhesive bonding and mechanical joining by eliminating the need for additional adhesive materials and enabling integration into automated manufacturing processes. The study highlights the unique benefits of resistance welding, including lower energy consumption compared to other methods and its compatibility with automated manufacturing, which can reduce production costs by up to 40%. Key findings from the literature indicate that resistance welding is particularly effective in achieving strong, durable joints for complex and large structures, such as those used in the aerospace industry. The review also identifies the main challenges associated with resistance welding, including temperature control, current leakage in carbon-fiber-reinforced polymers, and potential corrosion when using metal meshes. To address these challenges, various strategies are discussed, including surface treatments, the use of nanocomposites, and the integration of carbon nanotubes. The review concludes by emphasizing the need for further research to optimize welding parameters and to develop non-destructive testing methods for industrial applications, ensuring the reliability and long-term performance of welded joints. Full article