Search Results (211)

Many environmental risks, such as global warming and depletion of natural resources, force governments to achieve economic growth and financial development without causing environmental degradation. The dependency of countries’ dependence on fossil fuels also causes energy supply security problems due to the associated risks at regional and global levels. These reasons lead countries to diversify and increase their renewable energy investments. In this context, this study focuses on the most attractive countries in terms of renewable energy investments and analyzes the relationships between renewable energy consumption (REC), carbon dioxide emissions (CO₂), economic growth (EGRO), financial development (FD), and energy dependence (EDP) using the panel regression method. This research uses data from 38 countries between 1991 and 2021 within the scope of the “Renewable Energy Attractiveness Index” (RECAI) created by Ernst & Young. As a result of the heterogeneity and cross-sectional dependency tests, the data were analyzed using the Westerlund cointegration test, the Augmented Mean Group (AMG) estimator, and the Emirmahmutoglu and Kose causality test. The findings from this study show that FD and EGRO have a positive and significant effect on REC, whereas they have a negative and significant relationship with CO₂ emissions. Findings from the causality test show that FD has an impact on both CO₂ and EGRO. In addition, within the scope of this study, a causality was determined between EDP and REC, and a mutual relationship between energy demand and CO₂ was revealed. In light of these findings, governments should increase their investments in renewable energy to ensure sustainable economic growth and energy supply security while minimizing environmental degradation. Full article

Recreational fisheries increasingly intersect with the habitats of flagship species, i.e., species that attract public attention and drive conservation efforts, raising potential ecological conflicts. This study investigated the spatial coupling between recreational fisheries and three flagship species in the Yangtze River Basin: the Chinese alligator (Alligator sinensis), the Yangtze finless porpoise (Neophocaena phocaenoides), and the scaly-sided merganser (Mergus squamatus). Drawing on over 10,000 fishing Points of Interest recorded between 2015 and 2024 and over 300 verified species occurrences, this study applied a Random Forest model with spatial integration and a Maximum Entropy model to examine estimated current distributions and forecast interactions from 2025 to 2035. Flagship species habitat suitability was modeled and projected at a spatial resolution of 1 km, while recreational fishing density was resolved on a coarser grid of 1.875° × 1.25° in latitude–longitude dimensions. Results reveal a substantial increase in high-risk overlap zones. For example, high-density fishing areas within high-suitability habitats for the scaly-sided merganser expanded from 0 km² in 2015 to 85,359 km² in 2024. Projections indicate continued intensification of such overlaps, particularly in regions including Ma’anshan–Wuhu, the Taihu–Chaohu–Poyang lake system, and Yibin. These findings offer robust, model-driven evidence of growing spatial conflicts and offer actionable insights for ecosystem-based governance. The methodological framework is transferable and supports broader applications in other regions and species under ecological sustainability goals. Full article

As global energy consumption continues to rise, it is essential to adopt measures that regulate electricity use while still meeting the demands of modern society. These efforts align with the United Nations Sustainable Development Goals and are supported by various organizations. This study applies a methodology that combines the implementation of a Level 2 Energy Audit with the evaluation of the Electricity Consumption Index (ECI) at the Department of English of the Multidisciplinary Academic Unit of the Altiplano Region, Universidad Autónoma de San Luis Potosí. The study identifies strategies to reduce electricity consumption related to lighting systems and equipment operation throughout the department. Additionally, it assesses the percentage of users who promote and practice energy-saving habits. Key recommendations include transitioning the lighting system to LED technology, expected to reduce electricity consumption by 15, and implementing power factor correction measures, projected to yield an additional 6.17% in energy and cost savings. Together, these strategies could result in an estimated annual electricity savings of 21.17%, making them attractive to institutional decision-makers. Furthermore, by comparing the department’s ECI with a reference index established for educational institutions in temperate climate regions of Mexico, the study determines whether the proposed strategies should be implemented immediately or planned for the medium to long term. This decision-making framework represents the main contribution of the case study. Full article

Adaptive radiations are characterized by increases in rates of lineage and trait evolution, typically due to the opening of new ecological opportunities such as may follow from dispersal to a new region or the evolution of a trait that allows exploitation of new niches. This results in clades that have accumulated unusually high biological diversity within a relatively short evolutionary timespan and hence the phenomenon has attracted longstanding interest amongst evolutionary biologists. Natricidae is a family of snakes with a primarily Old World distribution but which have colonized the New World on a single occasion. This dispersal event coincides with an increased speciation rate that has led to a species-rich New World clade. Herein, we take a phylogenetic comparative approach to investigate a likely adaptive radiation of New World natricids. We first confirmed previously reported findings of a single origin (providing new ecological opportunity) coinciding with a burst of lineage diversification. We then estimate the rates of evolution for three ecologically important traits (body size and broad categories of diet and habitat) separately for New World and Old World natricids. Of these three traits, our results provide evidence that only transition rates between terrestrial and (semi-)aquatic habitats are higher in the New World clade. Taken together, this supports a scenario of an adaptive radiation in natricids primarily associated with differentiation by habitat as the clade spread across the New World following its arrival there. Considering other adaptive radiations alongside our evidence for Natricidae, we propose the hypothesis that there is a common distinction between spatially constrained ‘island’ adaptive radiations (which often diverge along trophic axes) and continental adaptive radiations, which diverge as the clade spreads across a larger spatial scale and adapts to different habitats. Full article

As the global energy structure transitions towards cleaner sources, large-scale integration of wind power has become a trend for modern power systems. However, the impact of low-inertia power electronic converters and the fault propagation effects at critical nodes pose significant challenges to power system stability. To this end, a Kuramoto-like model analysis method, considering node importance, is proposed in this paper. First, virtual node technology is utilized to optimize the power grid topology model. Then an improved PageRank algorithm embedded by a critical node identification method is proposed, which simultaneously considers transmission efficiency, coupling transmission probability, and voltage influence among nodes. On this basis, the traditional uniform coupling assumption is eliminated, thereby reallocating the coupling strength between critical nodes. In addition, the Kron method is applied to simplify the power grid model, constructing a hybrid Kuramoto-like model that integrates second-order synchronous machine oscillators and first-order wind power oscillators. Based on this model, the transient stability of the wind power integrated power system is analyzed. Finally, through estimating the attraction region range of the stable equilibrium point, a transient stability criterion is proposed for fault limit removal time assessment. The simulation results of the improved IEEE 39-bus system show that coupling strength optimization based on node importance reduces the system’s average critical coupling strength by 17%, significantly improving synchronization robustness. Time-domain simulations validate the accuracy of the method, with the relative error of fault removal time estimation controlled within 10%. This research provides a new analytical tool for transient stability analysis of wind power integration. Full article

High-completeness ship trajectory data are critical for analyzing navigation behavior characteristics and enhancing effective maritime management. To address the common issues of prolonged AIS data loss for small coastal vessels in nearshore waters, an intelligent trajectory repairing model based on the artificial potential field-enhanced A* algorithm (APF-A*) has been proposed. Kernel density estimation was utilized to quantify the distribution characteristics of vessels, thereby constructing an attractive potential field based on historical trajectories and a repulsive potential field based on coastal terrain. Speed distribution characteristics were extracted from historical trajectory points in different regions; on the basis of this, the A* algorithm, integrated with attractive and repulsive fields, was proposed to repair missing trajectory segments. Based on the speed distribution characteristics, time intervals, and distance information, the temporal information of the vessel trajectories was effectively reconstructed. The present study fills the research gap in AIS data reconstruction for small coastal vessels in complex coastal waters. A case study has been conducted in Luoyuan Bay, Fujian Province, China, to further validate the proposed model. The results demonstrate that the trajectory repairing model based on the artificial potential field-enhanced A* algorithm outperformed other models. More specifically, the Hausdorff Distance and Dynamic Time Warping (DTW) metrics decreased by 81.67% and 91.56%, respectively. The present study shares useful insights into intelligent maritime management and further supports accident prevention in coastal waters. Full article

Background/Objectives: With an estimated 70% of new cancer diagnoses expected to be in older adults within the next decade, cancer care for this population has attracted increasing global attention. Additionally, older patients are less likely to receive optimal cancer treatments. Methods: This retrospective cohort study utilized data from the Samsung Medical Center Cancer Registry, which includes patients diagnosed with cancer between 2008 and 2022. A 15-year cohort analysis was conducted to examine trends and survival outcomes by cancer type and stage in patients aged 80 years and older. Results: Among 301,055 patients with cancer, 13,111 (4.4%) were aged 80 years or older at diagnosis. The proportion of patients in this age group increased from 2.4% in 2008 to 5.8% in 2022. The most prevalent cancers in patients aged ≥80 years were lung (18.9%), stomach (15.3%), and colorectal cancer (13.8%). Among individuals with localized or regional-stage disease, the 5-year survival rate was 49.66% in those aged ≥80 years compared to 81.46% in younger patients (HR = 1.41; 95% CI = 1.35, 1.46). For distant-stage disease, survival was lower, at 10.53% in patients aged ≥80 years versus 27.61% in those aged <80 (HR = 1.14; 95% CI = 1.10, 1.19). Among patients aged 80 years and older, 55% received anti-cancer treatment, with the proportion increasing from 54.5% in 2008 to 60.3% in 2021. This increase was particularly notable in individuals with distant-stage disease. Additionally, the proportion of clinical trial participants aged ≥80 years exhibited an upward trend. Patients in this age group who underwent treatment had significantly improved survival compared to those who did not, in both localized or regional disease (HR = 0.45; 95% CI = 0.42, 0.49) and distant disease (HR = 0.58; 95% CI = 0.53, 0.62). Conclusions: The findings from this cohort of the SMC Cancer Registry highlight key trends, including a rising number of patients aged ≥80 years and an increasing proportion receiving treatment, particularly after 2020, when more than 60% received therapy. Furthermore, survival benefits associated with treatment were comparable to those observed in younger patients across all cancer types. Full article

This paper proposes hybrid LSTM-DES models that combine traditional forecasting methods with recurrent neural network techniques. We experimented with these proposed models using four passenger datasets from different regions of Thailand. Additionally, we compared their performance with several individual forecasting models, including the Double Moving Average (DMA), Double Exponential Smoothing (DES), and Holt–Winters methods (both additive and multiplicative trends), as well as long short-term memory (LSTM) recurrent neural networks. Our proposed hybrid model builds upon previous work with improvements in hyperparameter tuning using the GRG nonlinear optimization method. The results demonstrate that the hybrid LSTM-DES models outperformed all individual models in terms of both accuracy and demand variation. The reason behind the success of the hybrid model is that it works well with both linear and nonlinear trends, as well as the seasonality of certain periods. Furthermore, the forecast results for train passengers will serve as input variables to estimate the future revenue of train travel programs in various regions, including rail tourism. This information will help identify which regions should receive increased focus and investment by the train tourism program. For example, if the forecasted number of passengers in the northern region is high, the State Railway of Thailand will promote and improve infrastructure at the train station and nearby tourist attractions. Full article

In recent years, finite-control-set model predictive control (FCS-MPC) has attracted significant attention in power electronic converter control, resulting in substantial research advancements. However, no formal method currently exists to prove the stability of FCS-MPC systems. Additionally, many application studies have yet to adequately address the relationship between the selection of design parameters and system performance. To address the lack of stability and performance guarantees in FCS-MPC system design, this paper investigates a class of single-step FCS-MPC systems. The analysis is based on regional input-to-state stability (ISS) theory. Sufficient conditions for ensuring regional stability are derived, and a method for estimating the system’s domain of attraction and ultimate bounded region is developed. Simulation experiments validated the analytical results and revealed the relationships between the domain of attraction and system stability, as well as between the ultimate bounded region and steady-state performance. The results indicate that appropriate parameter design can ensure system stability. Furthermore, the proposed method elucidates how changes in design parameters affect system stability and steady-state performance, providing a theoretical foundation for designing a class of FCS-MPC systems. Full article

Amid the global urbanization process, addressing the spatial carrying capacity constraints of historic urban districts and enhancing sustainable tourism vitality has become a critical issue in urban renewal research. This study takes Suzhou Old City as a case study and innovatively constructs a dynamic spatiotemporal analytical framework to examine the relationship between tourism facility attractiveness and tourism vitality in historic districts. This study integrates multi-source spatiotemporal data and applies factor analysis, weighted kernel density estimation (KDE), spatial autocorrelation analysis, and multiscale geographically weighted regression (MGWR) to systematically investigate the spatial distribution patterns of tourism facilities and elucidate their multidimensional driving mechanisms on tourism vitality. The findings reveal a generally positive correlation between tourism attractiveness and tourism vitality. However, significant temporal and spatial variations exist, with different types of tourism facilities demonstrating distinct attractiveness patterns at different times of the day. These variations underscore the intrinsic link between visitor behavior and regional functionality as well as the structural contradictions within historic districts. This study not only advances theoretical insights into the spatial optimization of tourism facilities and tourism vitality enhancement but also provides scientific evidence and policy recommendations for improving facility distribution, revitalizing historic districts, and promoting sustainable urban development. Full article

Rural tourism has become a key driver of rural revitalization in China, contributing to poverty alleviation while also irreversibly altering the spatial evolution of rural settlements. In the ecologically fragile regions of northwest China, the rapid expansion of rural tourism destinations has raised ecological concerns, particularly regarding land resource utilization. Therefore, it is crucial to examine the phenomenon of industrial agglomeration in the evolution of rural tourism within the context of tourism-driven poverty alleviation. This study uses Lanzhou City as a case study and employs nearest neighbor analysis and kernel density estimation to analyze the spatial agglomeration patterns of rural tourism destinations, focusing on agglomeration forms, scales, and patterns. Additionally, it explores the spatial coupling distribution between agglomeration levels and influencing factors. The results show that from 1987 to 2022, the development of rural tourism destinations in Lanzhou City has progressed through several stages, from initial emergence to rapid growth. The form of industrial agglomeration has shifted from a dispersed to a clustered distribution, gradually expanding from urban centers to peripheral areas. The spatial agglomeration follows a multi-core hierarchical point-axial diffusion model, forming multiple core and sub-core agglomeration zones of varying scales. This transformation is primarily driven by geographical factors, transportation accessibility, and the presence of high-quality tourist attractions. However, a comparison of land use changes and ecological vulnerability indices over multiple periods indicates that the industrial agglomeration of rural tourism has led to irregular land use patterns and ecosystem instability. Finally, based on the complex relationship between rural tourism development, industrial agglomeration, and ecological sustainability, this study proposes strategies for the development of rural tourism in Lanzhou City, with the aim of providing valuable insights for the development of rural tourism in ecologically fragile regions of China. Full article

In this study, we propose an optimized shield gate trench 4H-SiC structure with effective gate oxide protection. The proposed device has a split trench with a P+ shield region, and the P+ shield is grounded by the middle deep trench. Our simulation results show that the peak electric field near the gate oxide is almost completely suppressed. Compared with a conventional P+ shield device, our proposed structure achieves a 78% reduction in the Q_gd and a 108% increase in the FoM (figure of merit) simultaneously. Additionally, it is estimated that the device cell pitch can be reduced to 1.8 μm with a R_on below 0.94 mΩ·cm², in theory. These demonstrated device performance metrics, as well as its simple structure and good compatibility, make our proposed SiC MOSFET highly attractive for future high-performance applications. Full article

In recent years, Serbia has become increasingly popular as a tourism destination, attracting travelers from the surrounding region, as well as Europe and even distant locations. The environmental impact linked with tourism activities, specifically their carbon footprints, has gained growing attention as sustainability becomes an important factor when discussing the future of tourism. This research, which is based on the DEFRA and ADEME methodologies using the Greentripper tool, examines the significance of carbon footprint estimations for incoming tourism in Serbia from a scientific standpoint. By considering the emissions produced from transportation, accommodation, and on-site activities, the results of 1,431,394,511 kg CO2e offer valuable information about the extent of carbon emissions linked to tourism movements. The primary driver of this carbon footprint is transportation (80.2%), on-site activities (15.2%), and housing (4.6%). The per capita tourism carbon footprint is 670 kg CO2e, which is lower compared to the values derived using general data and carbon emissions on a worldwide basis, which amount to 759 kg CO2e. These findings are essential for comprehending the environmental sustainability of tourism operations. Furthermore, carbon footprint assessments play a crucial role as a tool for making informed decisions and implementing initiatives to reduce carbon emissions in the tourism business. This could involve selecting environmentally friendly modes of travel, advocating for sustainable hotel choices, or integrating carbon offsetting activities into vacation packages. In addition, carbon footprint assessments promote transparency and responsibility in the tourism industry. Full article

A cornerstone of climate action plans around the world, wind power is increasingly recognised as a primary source of clean, sustainable energy. Amidst the escalating challenges of global climate change, wind energy provides an essential balance, enabling environmental progress without compromising economic resilience. However, the significant investment costs associated with wind turbines require careful evaluation alongside the projected energy output to ensure both financial viability and operational efficiency. Given the localised nature of wind resources, it is essential that analysis and feasibility studies are carried out on a regional scale to take account of geographical and climatic variations, thereby maximising the effectiveness of wind energy deployment. This study presents a comprehensive analysis of wind turbine deployment in the Eastern Thrace region, using region-specific energy data and wind characteristics together with performance data from twenty comparable installations in the area. A Monte Carlo-based numerical simulation approach using probabilistic models was applied to provide valuable insights into the financial viability of wind energy investment in the region. The results show a strong potential for cost-effective wind power generation in Eastern Thrace, with an estimated 90% probability of achieving payback within five years. These results underline the economic and environmental benefits of wind energy, confirming its attractiveness to investors and its role as a key driver of sustainable development in the region. Full article

Investigating the spatio-temporal trends in agricultural green production level and proposing pathways to improve it can offer valuable insights for promoting the green, low-carbon, and sustainable development of China’s agriculture, as well as contributing to the achievement of the United Nations’ Sustainable Development Goals by 2030. Therefore, in order to investigate the spatio-temporal variations in agricultural green production level and its driving factors, and explore pathways to improve it in the major grain-producing areas of China, a new multi-dimensional framework for estimating the agricultural green production level was proposed, and based on the OLS regression and scenario prediction, the agricultural green production levels from 2012 to 2030 were estimated. The findings indicate that from 2012 to 2021, the agricultural green production level in the major grain-producing areas experienced a consistent annual increase. The average annual value for the agricultural green production level was recorded at 0.443. At a spatial scale, the agricultural green production level exhibited a pronounced regional pattern, showing higher levels in the central and eastern areas, while lower levels were noted in the northeastern and western regions. The actual utilization of foreign capital and the per capita disposable income of farmers positively influenced the agricultural green production level. In contrast, factors such as the proportion of the secondary industry, the proportion of the tertiary industry, and the urbanization rate negatively affected this level. From 2022 to 2030, the agricultural green production level is expected to demonstrate a gradual growth trend under the baseline scenario, although the rate of growth is expected to decrease over time. Conversely, under the green and sustainable development scenario, a notably significant growth trend in agricultural green production level is projected. However, under the rapid economic development scenario, it is estimated that the agricultural green production level will initially increase slowly before peaking in 2026 and then experiencing a decline. With the aim of ensuring the ongoing enhancement of agricultural green production level objectives, the major grain-producing areas should proactively encourage inter-provincial collaboration in agricultural green production, vigorously attract foreign investment to facilitate the advancement of green production technologies, promote the harmonious integration of primary, secondary, and tertiary industries in rural regions, and improve farmers’ income. Full article