Search Results (3,545)

Drought presents significant challenges in arid regions, influencing local climate and environmental dynamics. While the large-scale climatic phenomena in Xinjiang, northwest China, are well-documented, the finer-scale climatic variability in subregions such as the Ili River Valley (IRV) remains insufficiently studied. This knowledge gap impedes effective regional planning and environmental management in this ecologically sensitive area. In this study, we analyze the spatiotemporal evolution of drought in the IRV from 1961 to 2023, using data from ten meteorological stations. The SPEI drought index, along with Sen’s trend analysis, the Mann–Kendall test, the cumulative departure method, and wavelet analysis, were employed to assess drought patterns. Results show a significant drying trend in the IRV, starting in 1995, with frequent drought events from 2018 onwards, and no notable transition year observed from wet to dry conditions. The overall drought rate was −0.09 per decade, indicating milder drought severity in the IRV compared to broader Xinjiang. Seasonally, the IRV experiences drier summers and wetter winters compared to regional averages, with negligible changes in autumn and milder drought conditions in spring. Abrupt changes in the drying seasons occurred later in the IRV than in Xinjiang, with delays of 21 years for summer, and over 17 and 35 years for spring and autumn, respectively, indicating a lagged response. Spatially, the western plains are more prone to aridification than the central and eastern mountainous regions. The study also reveals significant differences in drought cycles, which are longer than those in Xinjiang, with distinct wet–dry phases observed across multiple time scales and seasons, emphasizing the complexity of drought variability in the IRV. In conclusion, the valley exhibits unique drought characteristics, including milder intensity, pronounced seasonal variation, spatial heterogeneity, and notable resilience to climate change. These findings underscore the need for region-specific drought management strategies, as broader approaches may not be effective at the subregional scale. Full article

Most existing low-light image enhancement (LIE) methods are primarily designed for human-vision-friendly image formats, such as sRGB, due to their convenient storage and smaller file sizes. In addition, raw images provide greater detail and a wider dynamic range, which makes them more suitable for LIE tasks. Despite these advantages, raw images, the original format captured by cameras, are larger and less accessible and are hard to use in methods of LIE with mobile devices. In order to leverage both the advantages of sRGB and raw domains while avoiding the direct use of raw images as training data, this paper introduces sRrsR-Net, a novel framework with the image translation process of sRGB–raw–sRGB for LIE task. In our approach, firstly, the RGB-to-iRGB module is designed to convert sRGB images into intermediate RGB feature maps. Then, with these intermediate feature maps, to bridge the domain gap between sRGB and raw pixels, the RAWFormer module is proposed to employ global attention to effectively align features between the two domains to generate reconstructed raw images. For enhancing the raw images and restoring them back to normal-light sRGB, unlike traditional Image Signal Processing (ISP) pipelines, which are often bulky and integrate numerous processing steps, we propose the RRAW-to-sRGB module. This module simplifies the process by focusing only on color correction and white balance, while still delivering competitive results. Extensive experiments on four benchmark datasets referring to both domains demonstrate the effectiveness of our approach. Full article

The digital transformation of museums has elevated their websites from mere informational tools to dynamic platforms that foster cultural engagement, inclusivity, and preservation. This study evaluates the performance of 234 museum websites worldwide, focusing on critical dimensions such as accessibility, usability, SEO, and speed. By employing a comprehensive diagnostic framework of evaluation metrics, the research reveals disparities between mobile and desktop versions, highlights regional variations, and identifies key performance drivers. Generally, desktop sites outperform their mobile counterparts, underscoring the necessity for tailored optimization strategies that strike a balance between fast-loading, visually stable mobile pages and content-rich desktop experiences. A key contribution of this study is the development of an easy-to-adopt and inclusive evaluation framework that unites fragmented approaches, enabling museums of all sizes to enhance their digital presence. Furthermore, the research provides actionable insights for administrators, particularly those in resource-constrained institutions, through a cost-free, user-friendly toolkit that simplifies technical metrics and promotes internal staff capacity building in digital analytics. Ultimately, the findings help empower museums to bridge digital performance gaps while ensuring they continue to function as vibrant cultural hubs in a rapidly changing digital landscape. Full article

Single-walled carbon nanotubes (SWNTs) exhibit distinct electronic properties, categorized as metallic or semiconducting, determined by their chirality. The precise and selective separation of these electronic types is pivotal for advancing nanotechnology applications. While conventional gel chromatography has been widely employed for large-scale separations, its limitations in addressing microscale dynamics and electronic-type differentiation have persisted. Here, we present a polydimethylsiloxane (PDMS)-based microfluidic gel chromatography platform, coupled with real-time in situ Raman spectroscopy, designed to achieve the high-resolution electronic-type separation of SWNTs. This platform systematically isolates metallic- and semiconducting-enriched fractions (M1–M3 and S1–S3) while quantitatively analyzing separation dynamics through G-band spectral shifts and G⁻/G⁺ intensity ratios. By normalizing the SDS concentration and calculating rate constants, we reveal the intrinsic elution kinetics of SWNTs, with metallic fractions exhibiting faster elution dynamics compared to their semiconducting counterparts. Our approach bridges the gap between microscale precision and industrial scalability, emphasizing the critical role of dispersant concentration in fine-tuning separation outcomes. This advancement not only resolves the challenges of electronic-type differentiation but also demonstrates the versatility of PDMS microfluidic systems in delivering real-time insights into nanomaterial purification processes. By integrating continuous dynamic analysis with gel chromatography, this study establishes a transformative framework for scaling nanomaterial separations and unlocking new potential in chirality-specific applications. Full article

Rapid economic growth in China has brought about a significant challenge: the widening gap in regional development. Addressing this disparity is crucial for ensuring sustainable development. However, existing studies have largely overlooked the intrinsic spatial and temporal dynamics of regional disparities on various levels. This study thus employed five advanced multiscale geographically and temporally weighted regression models—GWR, MGWR, GTWR, MGTWR, and STWR—to analyze the spatio-temporal relationships between ten key conventional socio-economic indicators and per capita GDP across different administrative levels in China from 2000 to 2019. The findings highlight a consistent increase in regional disparities, with secondary industry emerging as a dominant driver of long-term economic inequality among the indicators analyzed. While a clear inland-to-coastal gradient underscores the persistence of regional disparity determinants, areas with greater economic disparities exhibit pronounced spatio-temporal heterogeneity. Among the models, STWR outperforms others in capturing and interpreting local variations in spatio-temporal disparities, demonstrating its utility in understanding complex regional dynamics. This study provides novel insights into the spatio-temporal determinants of regional economic disparities, offering a robust analytical framework for policymakers to address region-specific variables driving inequality over time and space. These insights contribute to the development of targeted and dynamic policies for promoting balanced and sustainable regional growth. Full article

Electric vehicles (EVs) are gaining significant attention as an environmentally friendly transportation solution. However, limitations in battery technology continue to restrict EV range and charging speed, resulting in range anxiety, which hampers widespread adoption. While there has been increasing research on EV route optimization, personalized path planning that caters to individual user needs remains underexplored. To bridge this gap, we propose the electric vehicle charging route planning based on user requirements (EVCRP-UR) problem, which aims to integrate user preferences and multiple constraints. Our approach utilizes topology optimization to reduce computational complexity and improve path planning efficiency. Furthermore, we introduce an improved ant colony optimization (IACO) algorithm incorporating novel heuristic functions and refined probability distribution models to select optimal paths and charging stations. To further enhance charging strategies, we develop a discrete electricity dynamic programming (DE-DP) algorithm to determine charging times at efficiently chosen stations. By combining these methods, the proposed IACO algorithm leverages the strengths of each approach, overcoming their individual limitations and delivering superior performance in EV routing and charging optimization. Full article

Cancer occurrence rates exhibit diverse age-related patterns, and understanding them may shed new and important light on the drivers of cancer evolution. This study systematically analyzes the age-dependent occurrence rates of 23 carcinoma types, focusing on their age-dependent distribution patterns, the determinants of peak occurrence ages, and the significant difference between the two genders. According to the SEER reports, these cancer types have two types of age-dependent occurrence rate (ADOR) distributions, with most having a unimodal distribution and a few having a bimodal distribution. Our modeling analyses have revealed that (1) the first type can be naturally and simply explained using two age-dependent parameters: the total number of stem cell divisions in an organ from birth to the current age and the availability levels of bloodborne growth factors specifically needed by the cancer (sub)type, and (2) for the second type, the first peak is due to viral infection, while the second peak can be explained as in (1) for each cancer type. Further analyses indicate that (i) the iron level in an organ makes the difference between the male and female cancer occurrence rates, and (ii) the levels of sex hormones are the key determinants in the onset age of multiple cancer types. This analysis deepens our understanding of the dynamics of cancer evolution shared by diverse cancer types and provides new insights that are useful for cancer prevention and therapeutic strategies, thereby addressing critical gaps in the current paradigm of oncological research. Full article

This paper explores contemporary approaches to preserving and promoting cultural heritage by integrating game elements and advanced technologies, such as Virtual Reality (VR) and Augmented Reality (AR). In an era increasingly shaped by digital innovation, preserving cultural heritage demands new strategies to sustain engagement with historical narratives and artifacts. Emerging technologies like VR and AR offer immersive, interactive experiences that appeal to modern audiences, especially younger generations accustomed to digital environments (Bekele and Champion). Gamification—the use of game design principles in non-game contexts—has gained significant traction in education and cultural heritage, providing new methods for increasing user engagement and retention (Werbach and Hunter). By incorporating gamified features, heritage can be made more accessible, fostering emotional connections and deeper understanding (Huotari and Hamari; Zichermann and Cunningham). This aligns with the shift toward interactive digital storytelling as a tool to transform static heritage presentations into dynamic, participatory experiences (Champion and Rahaman). Central to this research is the conceptualization and development of a mobile application leveraging VR and AR to enhance user engagement and education around cultural heritage. Drawing on the principles of self-determination theory (Deci and Ryan) and empirical findings on gamified learning (Landers and Landers), the application combines educational content with interactive elements, creating an immersive learning environment. By addressing both content accessibility and interactive immersion, this application bridges the gap between traditional heritage preservation and the expectations of a digitally native audience. The recent literature underscores the potential of VR and AR in cultural preservation, emphasizing their ability to transcend physical boundaries, simulate historical environments, and promote active participation (Milgram and Kishino, Addison; Azuma). As virtual environments evolve, platforms like the metaverse expand possibilities for experiencing cultural heritage in spaces free of geographical limitations (Cipresso et al.; Radianti et al.). Such advancements have already demonstrated significant educational and experiential benefits (Wu et al.; Akçayır and Akçayır). This study employs both quantitative and qualitative methods to examine the target group’s attitudes toward gamified technologies for cultural heritage preservation. The initial results indicate substantial interest and willingness among users to engage with applications employing VR and AR. This aligns with findings in the literature that suggest immersive experiences can enhance learning outcomes and foster long-term engagement (Merchant et al.; Speicher et al.). The project has garnered significant recognition, receiving the Rector’s Award for the best scientific paper in the technical field at the University of Zagreb and earning bronze medals at the ARCA Innovation Fair and the INOVA Fair. These accolades underscore the project’s innovative approach and its potential for real-world application. By presenting a robust framework for integrating gamification and immersive technologies into cultural heritage preservation, this paper contributes to the growing discourse on utilizing advanced digital tools to ensure the sustainability and relevance of cultural heritage for future generations. Full article

The expansion of renewable energy sources (RES) is essential to achieving regional sustainability in alignment with global climate goals. This study investigates the dynamics and projected growth of RES in West Pomerania, Poland, a region with significant potential due to its geographical characteristics and supportive policy frameworks. Historical data from 2010 to 2023 were used to perform a time series analysis that evaluated the annual growth rate (AGR) of various RES technologies, including wind, solar, biomass, and biogas. The analysis revealed a consistent upward trend in RES capacity, particularly in wind and solar energy, demonstrating effective resource mobilisation in the region. Subsequently, a forecasting model was employed to project the growth of the RES capacity through 2033 based on historical trends and technological advancements. The results indicate significant anticipated increases in RES capacity, highlighting West Pomerania’s potential to reduce its reliance on fossil fuels. This growth supports increased energy security and environmental sustainability. This study addresses a notable gap in the literature by linking regional renewable energy development with broader policy frameworks, such as the European Green Deal, and exploring the specific challenges of grid integration and economic disparities in the context of local energy transitions. These findings highlight the importance of sustained investment and policy support to scale renewable infrastructure while aligning regional initiatives with international sustainability goals. By bridging this gap, this study concludes that the West Pomerania strategy can serve as a model for other regions aiming to enhance their renewable energy portfolios and effectively meet the climate goals of the EU. Full article

The rapid expansion of civil infrastructure in China underscores the critical need for advanced solutions to ensure the structural health of aging bridges. This study introduces a novel data and knowledge-driven digital twin modeling (DK-DTM) framework designed to enhance the safe and efficient operation and maintenance (O&M) of bridges. Such a system should be capable of (1) monitoring structural dynamics in real time, (2) capturing spatiotemporal details and changes (e.g., defects and deformations), (3) analyzing structure deterioration patterns, (4) predicting structure failure risks, and (5) generating optimal maintenance and repair actions for ensuring structural safety. Previous studies have developed advanced sensing techniques and robust artificial intelligence algorithms for capturing and analyzing bridge health conditions. However, most existing techniques and algorithms heavily rely on high-quality data, which are difficult to obtain during bridge O&M. This raises the critical question of how to incorporate expert knowledge together with data-driven tools to establish a trustworthy DT for bridge O&M. This study presents the DK-DTM framework, which uniquely integrates multi-source data collection, spatiotemporal modeling, and expert knowledge reasoning. By combining these components, the framework supports smart structural health assessments of bridges, enabling comprehensive monitoring, prediction, and decision-making for efficient maintenance. The spatial and temporal models provide real-time data, while the expert knowledge model functions as an automated evaluation tool for structural health assessment. The results demonstrate that the proposed DK-DTM framework significantly enhances the accuracy and efficiency of O&M processes for aging bridges, addressing key gaps in existing digital twin methodologies. Full article

Exploring the economic value of carbon sinks in agricultural systems can improve the development of sustainable agriculture. However, there are few studies on the economic value of farmland carbon sinks from a systemic perspective. This study takes Zhejiang, China’s first common wealth demonstration zone, as an example, and quantifies the carbon sinks in farmland and their economic value. The driving mechanism is analyzed by using a system dynamics model. The potential value and management of farmland carbon sinks are discussed. The results show that from 2007 to 2021, the average annual carbon sinks in farmland of Zhejiang were 5.84 million tons, a downward trend. The annual economic value was CNY 149.80 million, a marked upward trend. A rational fertilization project is a win-win ecological and economical measure to enhance the carbon sinks in farmland. Artificially increasing the carbon price to 32% will help Zhejiang achieve the core goal of the common prosperity plan, bringing the urban–rural income gap below 1.9 in 2025. Achieving the economic value of farmland carbon sinks is a green way to narrow the urban–rural income gap. Our study indicates that the marketization of carbon sinks in agricultural land systems may be a very promising path to promote green agriculture. Full article

The residential sector is one of the main sectors responsible for the atmospheric emission of CO₂. Hence, a significant effort is required to develop technological solutions to enable decarbonization. The integration of Organic Rankine Cycle (ORC)-based units with renewable sources at a micro-scale of cogeneration units is commonly believed to be one of the most important technological alternatives. Indeed, an ORC-based unit allows the exploitation of low-temperature heat sources in the production of electricity. The low power scale of this application (1–5 kW) and the severe operating conditions call for the reliable and proper design of components. Particularly critical is the pump, as the experimental analyses available in the literature show its efficiency rarely exceeds values of 0.3. The most suitable technology is volumetric, and among those available, the sliding vane types are interesting candidates. However, low efficiency leads to a significant erosion of the power produced by the expander, limiting the achievement of high-efficiency values. What is more, in the literature, there is a lack of development of optimization strategies to improve the performance of this machine. To fill this knowledge gap, in this present paper an optimized sliding vane rotary pump was designed. Thanks to a comprehensive experimentally validated model, the pump performance was assessed for a wide range of operating conditions. Results confirmed that a disk-shaped configuration also ensures the best efficiency is achieved for small-scale pumps. Moreover, the model allowed for a detailed analysis of efficiency, evaluating the volumetric, fluid dynamic and mechanical behaviors. Results demonstrated that the weakest point was the mechanical efficiency, which was between 0.45 and 0.55. The best configuration was that involving four blades, the adoption of graphite and a clearance gap between the rotor face and casing of 10 μm. These design solutions improved efficiency by up to 25%, with a maximum value equal to 0.50, which is close to double with respect to the usual values. A final remark concerns the operating robustness of the machine, as the efficiency demonstrated weak variations even when wide operating conditions were considered. Full article

This study examines the flow behavior as well as the cuttings transport of non-Newtonian drilling fluid in the geometry of an eccentric annulus, accounting for what impacts drill pipe rotation might have on fluid velocity, as well as annular eccentricity on axial and tangential distributions of velocity. A two-phase Eulerian–Eulerian model was developed by using computational fluid dynamics to simulate drilling fluid flow and cuttings transport. The kinetic theory of granular flow was used to study the dynamics and interactions of cuttings transport. Non-Newtonian fluid properties were modeled using power law and Bingham plastic formulations. The simulation results demonstrated a marked improvement in efficiency, as much as 45%, in transport by increasing the fluid inlet velocity from 0.54 m/s to 2.76 m/s, reducing the amount of particle accumulation and changing axial and tangential velocity profiles dramatically, particularly at narrow annular gaps. At a 300 rpm rotation, the drill pipe brought on a spiral flow pattern, which penetrated tangential velocities in the narrow gap that had increased transport efficiency to almost 30% more. Shear-thinning behavior characterizes fluid of which the viscosity, at nearly 50% that of the central core low-shear regions, was closer to the wall high-shear regions. Fluid velocity and drill pipe rotation play a crucial role in optimizing cuttings transport. Higher fluid velocities with controlled drill pipe rotation enhance cuttings removal and prevent particle build-up, thereby giving very useful guidance on how to clean the wellbore efficiently in drilling operations. Full article

Riverbed morphology is in dynamic change due to the influences of both natural and human-induced factors. However, there is a knowledge gap in distinguishing the components caused by human activities as well as extreme flooding from the total riverbed evolution. The current study evaluated the water depth variation in the Nandu River (NR) and Wanquan River (WR) in Hainan Island in response to diverse driven factors. The results showed that the average water depth of both rivers significantly increased, but the spatial-temporal variation patterns were different. In the NR, the dominant spatial-temporal water depth variation was driven by extreme flooding, which contributed 59% to the total variance. Then, water–sediment conditions accounted for 30%, followed by direct human activities for 3.6%. However, the main spatial-temporal water depth variation patterns in the WR were 77%, driven by water–sediment conditions, 10% driven by extreme flooding, and 3.9% driven by direct human interventions, respectively. Considering the indirect effects of human activities on the water–sediment process, the total contributions of human activities on the water depth variation were 6.9% and 42.9% in the NR and WR, respectively. Due to the poor riverbed stability and worse resistance, island rivers are more fragile to extreme floods and human interventions. Our findings suggest that extreme floods usually lead to a significant increase in sediment carrying capacity, followed by severe erosion of the riverbed. In addition, combining with the decrease in sediment concentration and grainsize caused by human activities, the rebuilding effect on riverbeds would be magnified. These results highlight the important role of human activities and extreme floods in the evolution of island rivers, which can provide new insights and recommendations for river management and restoration engineering. Full article

As tourism demand continues to grow and fluctuate, the problems of increasing empty capacity and high operating costs for tourist shuttle buses have become more acute. Modular vehicles, an emerging transport technology, offer flexible length adjustments and provide innovative solutions to address these challenges. This paper develops a data-driven method to address the problem of scheduling modular vehicles in scenic areas with dynamic passenger demand. The aim is to minimize operating costs and maximize vehicle utilization by exploiting the adjustable capacity of modular vehicles. This approach is applied to tourist shuttle scenarios, and a sensitivity analysis is conducted by varying parameters such as individual vehicle capacity and waiting penalties. Then, we investigate the optimization performance gap between the proposed model and the theoretical global optimum model. The results show that increasing vehicle capacity and varying penalties improve the performance of the data-driven model, and the optimization rate of this model can reach 70.2% of the theoretical optimum, quantifying the effectiveness of the model. The method proposed in this study can effectively reduce the operating cost of shuttle vehicles for scenic areas and meet the challenge of unpredictable passenger demand, which serves as a good reference for fleet management in scenic areas. Full article