Search Results (3,820)

Solar thermal concentrating solar power (CSP) plants have attracted growing interest in the field of renewable energy generation due to their capability for large-scale electricity generation, high photoelectric conversion efficiency, and enhanced reliability and flexibility. Meanwhile, driven by the rapid advancement of power electronics technology, extensive wind farms (WFs) and large-scale battery energy storage systems (BESSs) are being increasingly integrated into the power grid. From these points of view, grid-connected CSP–BESS–wind hybrid energy systems are expected to emerge in the future. Currently, most studies focus solely on the stability of renewable energy generation systems connected to the grid via power converters. In fact, within CSP–BESS–wind hybrid energy systems, interactions between the CSP, collection grid, and the converter controllers can also arise, potentially triggering system oscillations. To fill this gap, this paper investigated the interaction mechanism and oscillation characteristics of a grid-connected CSP–BESS–wind hybrid energy system. Firstly, by considering the dynamics of CSP, BESSs, and wind turbines, a comprehensive model of a grid-connected CSP–BESS–wind hybrid energy system was developed. With this model, the Nyquist stability criterion was utilized to analyze the potential interaction mechanism within the hybrid system. Subsequently, the oscillation characteristics were examined in detail, providing insights to inform the design of the damping controller. Finally, the analytical results were validated through MATLAB/Simulink simulations. Full article

This study addresses the critical gap between academic training and the competency demands of the maritime logistics and port management sector. Using a mixed-methods approach, it integrates benchmarking of postgraduate programs from leading universities, interviews with 15 stakeholders representing diverse industry profiles, and an in-depth curriculum analysis. The research identifies and categorizes essential technical, management, and interpersonal competencies, culminating in the development of a Competency Matrix to guide the alignment of academic curricula with industry requirements. Key competencies identified include strategic decision-making, operations management, data analysis, adaptability, teamwork, and customer engagement, all of which are critical to ensuring efficiency and competitiveness in the sector. This study introduces an innovative framework by combining benchmarking with qualitative insights, addressing a crucial gap in the literature while offering actionable strategies for academia to enhance training programs. The findings highlight the urgent need for universities to develop courses tailored to global challenges, such as digitalization, sustainability, and supply chain resilience. Although this study is exploratory and based on a limited sample size, it provides meaningful insights into the Portuguese maritime and port logistics sector, laying a solid foundation for future research. Further studies should investigate how innovation and emerging technologies, such as artificial intelligence and blockchain, are reshaping competency requirements in this dynamic and globalized industry. Full article

This study reviews research from 2010 to 2023 on the integration of airborne laser scanning (ALS) metrics with satellite and ground-based data for forest monitoring, highlighting the potential of the combined use of ALS and optical remote sensing data in improving the accuracy and the frequency. Following an in-depth screening process, 42 peer-reviewed scientific manuscripts were selected and comprehensively analyzed, identifying how the integration among different sources of information facilitate frequent, large-scale updates, crucial for monitoring forest ecosystems dynamics and changes, aiding in supporting sustainable management and climate smart forestry. The results showed how ALS metrics—especially those related to height and intensity—improved estimates precision of forest volume, biomass, biodiversity, and structural attributes, even in dense vegetation, with an R² up to 0.97. Furthermore, ALS data were particularly effective for monitoring urban forest variables (R² 0.83–0.92), and for species classification (overall accuracy up to 95%), especially when integrated with multispectral and hyperspectral imagery. However, our review also identified existing challenges in predicting biodiversity variables, highlighting the need for continued methodological improvements. Importantly, while some studies revealed great potential, novel applications aiming at improving ALS-derived information in spatial and temporal coverage through the integration of optical satellite data were still very few, revealing a critical research gap. Finally, the ALS studies’ distribution was extremely biased. Further research is needed to fully explore its potential for global forest monitoring, particularly in regions like the tropics, where its impact could be significant for ecosystem management and conservation. Full article

This study aims to investigate how innovation development drives green finance in the Visegrad countries by analyzing the role of R&D investments, high-tech trade, and patent activity in attracting greenfield investments. Using a vector autoregression (VAR) model with data from 2007 to 2022, this study employs forecasting techniques, impulse response functions, and variance decomposition analyses to assess the dynamic relationship between innovation and green financial flows. The findings reveal that R&D expenditures are the strongest driver of green investments, explaining over 93% of the variance in Poland and Hungary. High-tech trade significantly influences investment trends, contributing up to 84% of the variance in the Czech Republic, while patent applications initially boost greenfield investments but show diminishing returns over time. Although innovation-driven investments remain stable overall, the impact of trade and patents varies across countries, reflecting regional differences. This study identifies key challenges, such as commercialization gaps and policy disparities, highlighting the need for targeted financial and innovation policies. To sustain green finance growth, policymakers should expand R&D funding, strengthen trade infrastructure, and enhance intellectual property commercialization. Additionally, financial institutions and investors should play a more active role in developing green investment markets to support long-term economic resilience and sustainability. Full article

To address the issues of negative transfer and reduced stability in transfer learning models for rolling bearing fault diagnosis under variable working conditions, an unsupervised multi-adversarial transfer learning fault diagnosis algorithm based on bearing dynamics simulation data is proposed. Firstly, the algorithm constructs both a global domain classifier and a subdomain classifier. In the subdomain classifier, the simulated vibration signal, which contains rich bearing fault label information, is generated by constructing dynamic equations to replace the label prediction of target domain data, thereby achieving alignment of marginal and conditional distributions. Simultaneously, an improved loss function with embedded maximum mean discrepancy is designed to reduce the feature distribution gap between source and target domain data. Finally, a weight allocation mechanism for source domain and simulation domain samples is developed to promote positive transfer and suppress negative transfer. Experiments were conducted using the Paderborn University dataset and the Huazhong University of Science and Technology dataset, achieving accuracy rates of 89.457% and 96.436%, respectively. The results show that, in comparison with existing unsupervised cross-domain fault diagnosis methods, the proposed method demonstrates significant improvements in diagnostic accuracy and stability, demonstrating its superiority in rolling bearing fault diagnosis under variable operational conditions. Full article

This study aims to analyze the shifting focus and emerging themes in contemporary energy entrepreneurship research, alongside the challenges and opportunities faced by select energy entrepreneurs in a rapidly transforming global landscape that is driven by sustainability imperatives, resilience, and systemic energy transitions. Employing a semi-systematic and critical literature review, 238 relevant scientific articles from the Web of Science database were identified and analyzed. We then conducted focused case studies of energy entrepreneurs in a less-developed regional ecosystem. The findings reveal two distinct “generations” of energy entrepreneurship research, marked by a clear shift towards sustainability themes, innovative energy business models, and corporate responsibility. Additionally, we introduce the “Energy Innovation Scorecard” (EN.I.SCORE) framework—a comprehensive macro–meso–micro guideline designed to support energy entrepreneurs. Applying this framework to a sample of 89 surveyed and 8 interviewed firms reveals that, especially among microfirms, there is weak integration of strategy–technology–management, limited energy innovation, and poor financial performance prevail—challenges typical of underdeveloped ecosystems. By examining the origins, evolution, and holistic transformation of energy entrepreneurship, and by investigating the selected regional case, this research potentially helps bridge critical gaps in understanding the dynamics of this field. Full article

China has made significant progress in environmental protection. As the country advances towards modernizing its environmental governance, environmental sociology plays an increasingly crucial role. This study employs a bibliometric analysis of 3867 publications from the Web of Science Core Collection (1972–2023) and CNKI (1990–2023) to reveal the disparities between Chinese and international environmental sociology research, with a particular focus on assessing the contributions of environmental sociology to environmental governance in China. The findings reveal several key insights. The results show a steady increase in global research output, with the United States (42.79%) and the United Kingdom (11.15%) leading in publication volume. While international research has expanded interdisciplinary collaboration, Chinese studies remain highly concentrated. The findings also reveal a growing tension between internationalization and localization in Chinese environmental sociology. Since 2017, publications in international journals have surged, while domestic publications have declined, reflecting scholars’ prioritization of global recognition over local policy engagement. However, language barriers and limited interdisciplinary integration—with over 80% of scholars rooted in philosophy and sociology—restrict the discipline’s ability to address complex governance challenges. Institutional influence remains imbalanced. Renmin University, Hohai University, and the Ocean University of China contribute 42.72% of domestic publications, yet no Chinese institution ranks among the global top 10, and citation impact lags behind leading Western institutions. This contrasts with international research, which tends to focus on global environmental issues, whereas Chinese research emphasizes localized case studies. Our analysis identifies a notable gap in Chinese research’s understanding and study of environmental governance experiences. It is recommended to strengthen the role of environmental sociology throughout the governance process from public opinion collection to policy formulation, policy implementation, dynamic feedback, and post-implementation evaluation. Full article

This study examines the application of Geographic Information Systems (GIS) in tourism planning and sustainable destination management, using Gelnica, Slovakia, as a case study. The research highlights a key challenge—the absence of systematic visitor data collection—which hinders tourism market analysis, demand assessment, and strategic decision-making. The study integrates alternative data sources, including the Google Places API, to address this gap to analyse Points of Interest (POIs) based on user-generated reviews, ratings, and spatial attributes. The methodological framework combines data acquisition, spatial analysis, and GIS-based visualisation, employing thematic and heat maps to assess tourism resources and visitor behaviour. The findings reveal critical spatial patterns and tourism dynamics, identifying high-demand zones and underutilised locations. Results underscore the potential of GIS to optimise tourism infrastructure, enhance visitor management, and inform evidence-based decision-making. This study advocates for systematically integrating GIS technologies with visitor monitoring and digital tools to improve destination competitiveness and sustainability. The proposed GIS-driven approach offers a scalable and transferable model for data-informed tourism planning in similar historic and environmentally sensitive regions. Full article

Accurate predictions of Initial Public Offerings (IPOs) aftermarket performance are essential for making informed investment decisions in the financial sector. This paper attempts to predict IPO short-term underperformance during a month post-listing. The current research landscape lacks modern models that address the needs of small and imbalanced datasets relevant to emerging markets, as well as the risk preferences of investors. To fill this gap, we present a practical framework utilizing tree-based ensemble learning, including Bagging Classifier (BC), Random Forest (RF), AdaBoost (Ada), Gradient Boosting (GB), XGBoost (XG), Stacking Classifier (SC), and Extra Trees (ET), with Decision Tree (DT) as a base estimator. The framework leverages data-driven methodologies to optimize decision-making in complex financial systems, integrating ANOVA F-value for feature selection, Randomized Search for hyperparameter optimization, and SMOTE for class balance. The framework’s effectiveness is assessed using a hand-collected dataset that includes features from both pre-IPO prospectus and firm-specific financial data. We thoroughly evaluate the results using single-split evaluation and 10-fold cross-validation analysis. For the single-split validation, ET achieves the highest accuracy of 86%, while for the 10-fold validation, BC achieves the highest accuracy of 70%. Additionally, we compare the results of the proposed framework with deep-learning models such as MLP, TabNet, and ANN to assess their effectiveness in handling IPO underperformance predictions. These results demonstrate the framework’s capability to enable robust data-driven decision-making processes in complex and dynamic financial environments, even with limited and imbalanced datasets. The framework also proposes a dynamic methodology named Investor Preference Prediction Framework (IPPF) to match tree-based ensemble models to investors’ risk preferences when predicting IPO underperformance. It concludes that different models may be suitable for various risk profiles. For the dataset at hand, ET and Ada are more appropriate for risk-averse investors, while BC is suitable for risk-tolerant investors. The results underscore the framework’s importance in improving IPO underperformance predictions, which can better inform investment strategies and decision-making processes. Full article

Fever visit management (FVM) played a critical role in reducing the risk of local outbreaks caused by positive cases during the coronavirus disease 2019 (COVID-19) pandemic under the dynamic zero-COVID-19 policy. Fever clinics were established to satisfy the healthcare needs of citizens with fever symptoms, including those with and without COVID-19. Learning how FVM affects fever medical care accessibility for citizens in different places can support decision making in establishing fever clinics more equitably. However, the dynamic nature of the population at different times has rarely been considered in evaluating healthcare facility accessibility. To fill this gap, we adjusted the Gaussian-based two-step floating catchment area method (G2SFCA) by considering the hourly dynamics of the population distribution derived from mobile phone location data. The results generated from Xining city, China, showed that (1) the accessibility of fever clinics explicitly exhibited spatial distribution patterns, being high in the center and low in surrounding areas; (2) the accessibility reduction in suburban areas caused by FVM was approximately 2.8 times greater than that in the central city for the 15 min drive conditions; and (3) the accessibility of fever clinics based on the nighttime anchor point was overestimated in central areas, but underestimated in suburban areas. Full article

Zoonotic diseases pose a significant global health threat, driven by factors such as globalization, climate change, urbanization, antimicrobial resistance (AMR), and intensified human–animal interactions. The increasing interconnectedness of human, animal, and environmental health underscores the importance of the OH paradigm in addressing zoonotic threats in a globalized world. This review explores the complex epidemiology of zoonotic diseases, the challenges associated with their management, and the necessity for cross-sector collaboration to enhance prevention and control efforts. Key public health strategies, including surveillance systems, infection control measures, and community education programs, play crucial roles in mitigating outbreaks. However, gaps in governance, resource allocation, and interdisciplinary cooperation hinder effective disease management, particularly in low- and middle-income countries (LMICs). To illustrate the effectiveness of the OH approach, this review highlights successful programs, such as the PREDICT project, Rwanda’s National One Health Program, the EcoHealth Alliance, and the Rabies Elimination Program in the Philippines. These initiatives demonstrate how integrating human, animal, and environmental health efforts can enhance early detection, improve outbreak responses, and reduce public health burdens. Strengthening global health governance, enhancing surveillance infrastructure, regulating antimicrobial use, and investing in research and technological innovations are essential steps toward mitigating zoonotic risks. Ultimately, a coordinated, multidisciplinary approach is vital for addressing the dynamic challenges posed by zoonotic diseases and ensuring global health security in an increasingly interconnected world. Full article

Understanding mold flux crystallization is essential for assessing heat transfer during steel casting. The complexity of the mold gap presents challenges in identifying the optimal testing method and nucleation type. This study investigates how variations in wetting properties influence nucleation dynamics, in particular the wetting behaviors of mold flux in platinum and graphite crucibles and how they affect crystallization temperatures and solidification mechanisms. Advanced analytical techniques, including confocal laser scanning microscopy (CLSM), and differential scanning calorimetry (DSC) were employed to analyze nucleation under different conditions, with calibration using synthetic slag, Li₂SO₄, and thermodynamic equilibrium simulations. The findings highlight the crucial role of crucible materials in modifying nucleation energy barriers and undercooling requirements. These insights enhance the understanding of mold flux behavior, contributing to the refinement of testing methodologies and the optimization of heat transfer and solidification processes in continuous casting. Full article

This study addresses a specialized variant of the full-truckload delivery problem inspired by a Turkish logistics firm that operates in the liquid transportation sector. An exact algorithm is proposed for the relevant problem, to which no exact approach has been applied before. Multiple customer and trailer types, as well as washing operations, are introduced simultaneously during the exact solution process, bringing new aspects to the exact algorithm approach among full-truckload systems in the literature. The objective is to minimize transportation costs while addressing constraints related to multiple time windows, trailer types, customer types, product types, a heterogeneous fleet with limited capacity, multiple departure points, and various actions such as loading, unloading, and washing. Additionally, the elimination or reduction of waiting times is provided along transportation routes. In order to achieve optimal solutions, an exact algorithm based on the column generation method is proposed. A route-based insertion algorithm is also employed for initial routes/columns. Regarding the acquisition of integral solutions in the exact algorithm, both dynamic and static sets of valid inequalities are incorporated. A label-setting algorithm is used to generate columns within the exact algorithm by being accelerated through bi-directional search, ng-route relaxation, subproblem selection, and heuristic column generation. Due to the problem-dependent structure of the column generation method and acceleration techniques, a tailored version of them is included in the solution process. Performance analysis, which was conducted using artificial input sets based on the real-life operations of the logistics firm, demonstrates that optimality gaps of less than 1% can be attained within reasonable times even for large-scale instances relevant to the industry, such as 120 customers, 8 product and 8 trailer types, 4 daily time windows, and 40 departure points. Full article

The shift to sustainable energy requires a thorough understanding of the elements affecting policy adoption, especially regarding political and economic dynamics. Current approaches, such as the technology acceptance model (TAM), theory of planned behavior (TPB), and unified theory of acceptance and use of technology (UTAUT), mainly emphasize individual behavioral aspects, often neglecting macro-level implications. This research uses the hybrid model for energy policy adoption (HMEPA) to bridge this gap, including economic and political factors with behavioral theories to evaluate energy policy acceptability. We propose that social impact, attitudes toward the policy, and financial and political considerations substantially affect stakeholders’ acceptance intentions. We gathered 421 valid answers from people in Taiwan using a questionnaire survey and analyzed the data using structural equation modeling (SEM). The findings demonstrate that whereas effort expectation and enabling circumstances have little impact, social influence and attitude are the most significant determinants of policy adoption intention. Moreover, political variables influence attitudes and social dynamics, while economic policy impacts performance expectations, perceived behavioral control, and enabling circumstances. These results underscore the need to synchronize policy plans with political and economic realities. Policymakers may use these findings to formulate stakeholder-oriented policies that promote sustainable energy transitions. Full article

To address the challenges of carbon emission reduction in the global shipping industry and the requirements of the International Maritime Organization (IMO)’s Carbon Intensity Indicator (CII) rating, this paper takes China’s commuter ships as an example to study the dynamic optimization of ship routes based on CII implementation requirements. In response to the existing research gap in the collaborative optimization of routes and carbon emissions under CII constraints, this paper constructs a mixed-integer programming model that comprehensively considers CII limits, port throughput capacity, channel capacity, and the stochastic demand for spot cargo. The objective is to minimize the operating costs of shipping companies, and an adaptive genetic algorithm is designed to solve the dynamic route scheduling problem. Numerical experiments demonstrate that the model can reasonably plan routes under different sequences of spot cargo arrivals, ensuring compliance with CII ratings while reducing total costs and carbon emissions. The results indicate that the proposed method provides efficient decision-making support for dynamic ship scheduling under CII constraints, contributing to the green transformation of the shipping industry. Future work will extend the model to scenarios involving multiple ship types and complex maritime conditions, further enhancing its applicability. Full article