Search Results (11,660)

Quantifying the extent and distribution of aquaculture ponds has become the key to management in the aquaculture industry, thereby contributing to the sustainable development of the region. However, accurate extraction of individual aquaculture pond boundaries from mesoscale remote sensing images remains a significant challenge. In this work, we developed the Multi-source Object-oriented Framework for extracting Aquaculture ponds (MOFA) to address mapping challenges in the Chaohu Lake basin, China. The MOFA combined Sentinel-1 synthetic aperture radar (SAR) with Sentinel-2 data, applying an object-oriented approach with adaptive threshold segmentation for robust and automated aquaculture pond delineation. Our performance evaluation results showed that the overall accuracy is as high as 90.75%. The MOFA is thus capable of distinguishing seasonal water bodies, lakes, reservoirs, and rivers from individual (non-centralized, contiguous) aquaculture ponds. Our results showed that the central and south sections of the Chaohu Lake basin are characterized by denser aquaculture pond distributions, relative to those in the western basin. The total area of aquaculture ponds across the entire basin decreased from 19,297.86 hm² in 2016 to 18,262.77 hm² in 2023, which is likely attributed to local policy adjustments, resource optimization, shifting market demands, or natural environmental changes. The abandonment and unregulated expansion of aquaculture ponds threaten sustainable development. Local governments must implement adaptive governance strategies to balance ecological preservation with economic growth. Overall, the MOFA can quickly and accurately extract and map aquaculture ponds, and further support the scientific planning of sustainable aquaculture development. Full article

This work proposes a power-efficient framework for adaptive video streaming in UAV-assisted wireless networks specially designed for disaster-hit areas where existing base stations are nonfunctional. Delivering high-quality videos requires higher video rates and more resources, which leads to increased power consumption. With the increasing demand of mobile video, efficient bandwidth allocation becomes essential. In shared networks, users with lower bitrates experience poor video quality when high-bitrate users occupy most of the bandwidth, leading to a degraded and unfair user experience. Additionally, frequent video rate switching can significantly impact user experience, making the video rates’ smooth transition essential. The aim of this research is to maximize the overall users’ quality of experience in terms of power-efficient adaptive video streaming by fair distribution and smooth transition of video rates. The joint optimization includes power minimization, efficient resource allocation, i.e., transmit power and bandwidth, and efficient two-dimensional positioning of the UAV while meeting system constraints. The formulated problem is non-convex and difficult to solve with conventional methods. Therefore, to avoid the curse of complexity, the block coordinate descent method, successive convex approximation technique, and efficient iterative algorithm are applied. Extensive simulations are performed to verify the effectiveness of the proposed solution method. The simulation results reveal that the proposed method outperforms 95–97% over equal allocation, 77–89% over random allocation, and 17–40% over joint allocation schemes. Full article

Managing risk, matching resources efficiently, and ensuring fair allocation are fundamental challenges in both finance and decision-making processes. In many scenarios, participants contribute unequally to collective outcomes, raising the question of how to distribute costs, benefits, or opportunities in a justifiable and optimal manner. This paper applies the Shapley value—a solution concept from cooperative game theory—as a principled tool in the following two specific financial settings: first, in tax cooperation games; and second, in assignment markets. In tax cooperation games, we use the Shapley value to determine the equitable tax burden distribution among three firms, A, B, and C, which operate in two countries, Italy and Poland. Our model ensures that countries participating in coalitions face a lower degree of tax evasion compared to non-members, and that cooperating firms benefit from discounted tax liabilities. This structure incentivizes coalition formation and reveals the economic advantage of joint participation. In assignment markets, we use the Shapley value to find the optimal pairing in a four-buyers and four-sellers housing market. Our findings show that the Shapley value provides a rigorous framework for capturing the relative importance of participants in the coalition, leading to more balanced tax allocations and fairer market transactions. Our theoretical insights with computational techniques highlights the Shapley value’s effectiveness in addressing complex allocation challenges across financial management domains. Full article

The following study presents the results obtained from a comparative analysis of dry (powder and hard snow) and wet snow based on satellite data and in situ data methods for monitoring in the high mountain belt of Bulgaria. The aim of the study is to analyze the effectiveness of different spectral indices based on satellite data from Synthetic Aperture Radar (SAR), high-resolution (HR) imagery, and spectrometer data for assessing the state and dynamics of the snow cover. The methods studied and the results obtained were validated by instrument-based field observations, with instruments using thermal imaging cameras, spectrometer measurements, ground control points, and HR imagery. Satellite data offer an ever-widening view of trends in snow distribution over time. All these data combined provide a detailed picture of surface temperature and snow properties, which are crucial for understanding snowmelt processes and the energy balance in the high-altitude belt. The findings suggest that a multi-method approach, utilizing the combined advantages of SAR satellite data, offers the most comprehensive and accurate framework for satellite-based snow cover monitoring in the high mountain regions of Bulgaria, such as Rila Mountain. This integrative strategy not only improves the precision of snow cover estimates but can also support many water resource-related studies, such as snowmelt runoff studies, snow avalanche modeling, and better-informed decisions in the management and maintenance of winter tourism resorts. Full article

Heavy metals (HMs), characterized by their non-biodegradable nature, are prone to enrichment in river sediments, thereby severely jeopardizing the equilibrium of ecosystems and human health. Given the critical importance of safeguarding valuable water resources, it is of utmost urgency to initiate research on HMs within the Yellow River Basin (YRB). This study collected river sediment samples from the Yellow River Basin and analyzed the distribution characteristics, health risks, and pollution sources of HMs utilizing the pollution index method, health risk assessment, and positive matrix factorization (PMF) model. The results demonstrate that arsenic (As), zinc (Zn), and cadmium (Cd) are the primary elements contributing to heavy metal (HM) pollution in the sediments of the YRB. The proportions of sediment samples with low HM pollution in the upstream, midstream, and downstream are 36.48%, 71.43%, and 72.73%, respectively, whereas the proportions of samples with moderate pollution are 63.16%, 28.57%, and 27.27%, respectively. The health risk assessment reveals that the non-carcinogenic risks posed by HM pollution in the sediments to adults are negligible, whereas those to children are not. Regarding carcinogenic risks, the carcinogenic risk index of As is significantly higher than that of the other HMs. The primary sources of HM pollution in the sediments are identified as traffic–industrial sources, agricultural–industrial sources, and industrial sources, with respective contribution rates of 32.47%, 44.87%, and 22.66%. As and Zn are prioritized as elements for health risk control, while agricultural–industrial sources are highlighted as the priority sources for pollution control. Full article

The distribution of in situ stress fields in reservoirs is critical for the accurate exploration and efficient exploitation of hydrocarbon resources, especially in deep, fault-developed strata where tectonic activities significantly complicate stress field patterns. To clarify the perturbation effects of faults on in situ stress fields in deep reservoirs, this study combines dynamic–static parameter conversion models derived from laboratory experiments (acoustic emission Kaiser effect and triaxial compression tests) with a coupled “continuous matrix–discontinuous fault” numerical framework implemented in FLAC^3D6.0. Focusing on the BKQ Formation reservoir in the MH area, China, we developed a multivariate regression-based inversion model integrating gravitational and bidirectional tectonic stress fields, validated against field measurements with errors of −2.96% to 9.07%. The key findings of this study include the following: (1) fault slip induces stress reductions up to 22.3 MPa near fault zones, with perturbation ranges quantified via exponential decay functions (184.91–317.74 m); (2) the “continuous matrix–discontinuous fault” coupling method resolves limitations of traditional continuum models by simulating fault slip through interface contact elements; and (3) stress redistribution exhibits NW-SE gradients, aligning with regional tectonic compression. These results provide quantitative guidelines for optimizing hydrocarbon development boundaries and hydraulic fracturing designs in faulted reservoirs. Full article

Swiss banks are at a pivotal moment as digital assets gain traction, presenting both challenges and opportunities. This study examines how Swiss banks can leverage their internal resources and capabilities to establish a competitive advantage in the digital asset ecosystem. Using the Resource-Based View and the VRIO (Value, Rarity, Imitability, and Organization) framework, this study investigates the strategic importance of key services such as custody, staking, and tokenization. Drawing on expert interviews with Swiss banking leaders, this research identifies these services as vital for maintaining Switzerland’s financial leadership. Findings suggest that Swiss banks’ established reputation for trust, combined with regulatory clarity under the Distributed Ledger Technology Act, creates a strong foundation for digital asset adoption. While digital asset custody services address the growing demand for security, tokenization presents significant growth potential, particularly in real-world asset markets. This study concludes that Swiss banks can sustain their competitive edge by investing in blockchain expertise, fostering fintech partnerships, and enhancing educational initiatives. By combining traditional banking strengths with innovative digital asset services, Swiss banks are well positioned to capitalize on this evolving financial landscape. Full article

The Shuixie Cu–Co polymetallic orefield, located in western Yunnan Province (southeastern margin of the Qinghai–Tibet Plateau), is renowned for its Cu–Co mineralization. A recent resource reassessment identified Sb–Au and Cu–Co–Bi (Sb–Au) orebodies as genetically associated with primary Cu–Co mineralization. The mineralization characteristics and microscopic observations indicate that gold mineralization in the Sb–Au orebodies follow a pulsating fluid injection model. The model includes four pulses: (1) euhedral gold-poor pyrite (PyI₁) precipitation; (2) margin-parallel growth of gold-rich pyrite (PyI₂) on PyI₁; (3) continued growth of gold-rich pyrite (PyI₃) along PyI₂; and (4) outermost concentric gold-rich pyrite (PyI₄) formation. This study examined gold-bearing pyrite in orebodies and host rocks. In situ laser ablation inductively coupled plasma mass spectrometry (LA–ICP–MS) analysis of pyrite and inductively coupled plasma mass spectrometry (ICP–MS) whole-rock trace element analysis were conducted to track the ore-forming fluid evolution. Compared with CI chondrite, pyrites from all pulses were enriched in LREEs over HREEs. The pyrite REE distribution curves exhibited right-skewed patterns, reflecting LREE enrichment. The Hf/Sm, Nb/La, and Th/La ratios were generally below 1, indicating high-field-strength element depletion. These results suggest a Cl-rich, F-poor ore-forming fluid. The pyrite trace elements showed enrichment in the chalcophile elements (e.g., Cu and Pb) and exceptionally high Bi levels compared with the continental crust. The chalcophile elements (e.g., Zn and Cd) were depleted, whereas iron-group elements (e.g., Co) were enriched and Ni was depleted. The pyrite δCe values (0.87–1.28, mean = 1.01) showed weak anomalies, indicating a reducing ore-forming environment. The δEu values of pyrite during pulses 1 to 4 ranged widely, from 0.2–3.01 (mean of 1.17), 0.27–1.39 (0.6), and 0.41–1.40 (0.96) to 0.4–1.36 (0.84), respectively, suggesting an initial temperature decline and subsequent increase in the ore-forming fluid. Significant variations were found in the Y/Ho, Zr/Hf, and Nb/Ta ratios across pulses, indicating the potential involvement of high-temperature hydrothermal fluids or late-stage alteration during ore formation. The Y/Ho ratio of pyrite overlapped most closely with that of the continental crust of China, indicating a close relationship between the ore-forming fluids and the crust. Full article

Precision agriculture and advanced sowing technologies, including variable sowing rates, can be used to optimise sunflower yields by ensuring a uniform plant distribution, efficient resource utilisation, and adaptation to soil variability. These agronomic and technological innovations help mitigate field heterogeneity effects, enhancing sunflower establishment, growth, and overall yield stability. The main goal of this research was to analyse the interactions among management, soil, and environmental variables and their effects on the sowing quality and yield in the case of precision sunflower production. A sowing field experiment was set up in the period between 2021 and 2023 to identify these effects and their complex interactions, which were evaluated with the aim of improving the sowing and yield parameters, while also understanding the importance of each different parameter. As a key outcome for precision sowing, this research demonstrates that the variability in sowing parameters—such as double and missing sowing rates, as well as sowing uniformity—was significantly influenced by the field conditions, productivity zones, and nominal crop density. These findings underscore the importance of implementing site-specific management strategies to optimise sunflower production and maximise yields. Overall, of the various factors influencing sunflower production, the crop year proved to be more significant than the soil parameters due to the strong influence of annual climatic variability. The field zone was also identified as a more critical determinant of sowing and yield variability than crop density, highlighting the importance of spatial management within fields, and also marking possible directions for future research. Full article

The scientific evaluation of cropland resource utilization efficiency is crucial for ensuring food security and promoting sustainable agricultural development. At present, the research on the utilization of cropland resources primarily focuses on the multiple cropping index and cropping intensity, but these data are insufficient to reveal long-term trends and potential future changes in crop production. To fill this knowledge gap, this study took Zhenjiang City, Jiangsu Province, as a case study and proposed a method to determine the distribution and spatiotemporal change frequency of single- and double-season cropping patterns using spatiotemporal fusion and crop phenological rhythm. By combining Sentinel-2 NDVI and MOD13Q1 satellite data, a dataset with 10 m resolution was developed to show the interannual distribution frequency of the three cropping patterns in the study area. The accuracy evaluation revealed that the interannual cropping intensity distribution frequency of the three cropping patterns exhibited good verification accuracy, with an average overall accuracy and Kappa coefficient of 81.53% and 0.68, respectively. This study provides essential support for government agencies to assess future food production potential and develop policies for improving cropland use efficiency. Full article

To optimize the management of groundwater resources in the Bourgogne-Franche-Comté (BFC, France) region, data from the Size-Eaux database were cross-referenced with the French Reference Framework for Groundwater Bodies (GWB). The information contained in this dataset was synthesized using Principal Component Analysis (PCA), followed by Agglomerative Hierarchical Clustering (AHC) of GWBs based on their average coordinates along the main factorial axes. The results reveal 11 distinct GWB groups, each internally homogeneous in terms of chemical composition and ongoing processes responsible for intra-group variability. The distribution of the groups aligns with the region’s structural geology, lithology, and agricultural activity patterns. Livestock farming areas, prone to fecal contamination, and cereal-growing areas, characterized by high nitrate concentrations, stand out distinctly. Furthermore, the analysis of GWB groups highlights regional processes such as denitrification, confirming the existence of spatial structuring of these mechanisms beyond local specificities. The major physicochemical and bacteriological zones show strong contrasts between groups while maintaining significant internal homogeneity. Despite the region’s vast size and diversity, spanning three major watersheds, further subdivision was not necessary to obtain applicable results. These findings confirm observations made in other regions and pave the way for an optimized monitoring and surveillance strategy. Full article

Background/Objectives: This study analyzed the stability of individual preferences for the allocation of expenditure in the healthcare system using an experimental setting. Understanding these preferences can support policy decisions aimed at achieving a more needs-based allocation of scarce resources in healthcare systems. Stability in preferences might be essential in order to avoid frequent legislative changes and can potentially enhance public satisfaction with the healthcare system. Methods: Individual preferences were assessed through two questionnaire-based experimental studies conducted before and after the COVID-19 pandemic, each with about 160 participants, in the context of a healthcare seminar in the MaxLab of the Otto-von-Guericke-University Magdeburg, Germany. This study was intended as a preliminary study for a larger follow-up panel study. In particular, the questionnaire contained questions regarding satisfaction with the healthcare system, optimization options, possible maximum contributions, and preferences for the allocation of notional healthcare budget and research funds in order to provide initial evidence regarding the stability of such preferences. As the data were collected both before and after the COVID-19 pandemic, this significant change in the situation helps to provide clear indications of stability. The preferences collected were compared to the actual allocation of expenditure derived from official statistics in order to identify potential areas for policy adjustment. Results: Preferences for the allocation of healthcare expenditure appear to be relatively stable despite the effects of the pandemic. However, noticeable discrepancies exist between individual preferences and actual healthcare spending. Satisfaction with the healthcare system also remains relatively stable at a high level. Conclusions: Overall, the scientific measurement of public preferences could support more informed political decision-making and contribute to sustained satisfaction with the healthcare system. In particular, the distribution of funds to different disease categories should be adjusted on the basis of such preferences, taking into account the respective medical indications after representative regular surveys have been carried out. Full article

Cervical cancer causes 350,000 deaths annually, with 90% occurring in low- and middle-income countries (LMICs), despite being largely preventable through vaccination and screening. This review examines innovative approaches to address screening coverage gaps worldwide, analysing both established programmes in high-income countries and implementation strategies for LMICs. Self-sampling technologies demonstrate significant potential to improve the uptake of cervical screening, thereby improving cervical cancer prevention compared to traditional methods, particularly benefiting underserved populations across all healthcare settings. Among self-collection devices, vaginal brushes achieve sensitivity of 94.6% (95% CI: 92.4–96.8) for HPV detection, while novel approaches like the tampon show promising results (sensitivity 82.9–100%, specificity 91.6–96.8%) with high user acceptability. Implementation strategies vary by healthcare context, with high-income countries achieving success through integrated screening programmes and digital solutions, while LMICs demonstrate effective adaptation through community-based distribution (20–35% uptake) and innovative delivery methods. In resource-limited settings, self-sampling increases participation through enhanced patient comfort and cultural acceptability, while reducing costs by 32–48%. Progress toward WHO’s cervical cancer elimination goals require careful consideration of local healthcare infrastructure, cultural contexts and sustainable financing mechanisms. Future research priorities include optimising self-sampling technologies for sustainability and scalability, developing context-specific implementation strategies and validating artificial intelligence applications to enhance screening efficiency across diverse healthcare settings. Full article

With the rapid development of the internet of things (IoT) and smart cities, the risk of network attacks, particularly distributed denial of service (DDoS) attacks, has significantly increased. Traditional centralized security systems struggle to address large-scale attacks while simultaneously safeguarding privacy. In this study, we created a decentralized security framework that integrates federated learning (FL) with blockchain technology for DDoS attack detection and prevention. Federated learning enables devices to collaboratively learn without sharing raw data and ensures data privacy, while blockchain provides immutable event logging and distributed monitoring to enhance the overall security of the system. The created framework leverages multi-layer encryption and Hashgraph technology for event recording, ensuring data integrity and efficiency. Additionally, software-defined networking (SDN) was employed for dynamic resource management and rapid responses to attacks. This system improves the accuracy of DDoS detection and effectively reduces communication costs and resource consumption. It has significant potential for large-scale attack defense in IoT and smart city environments. Full article

Xinjiang is located in an inland arid area, and it faces significant challenges in water resource supply and demand, with a fragile ecological environment. Exploring the internal relationship between the time–space distribution of agricultural water–soil matching and the evolution of the ecosystem service value (ESV) in the Tailan River Irrigation District of Xinjiang from 2000 to 2020, this study provides theoretical guidance for the balance of agricultural water–soil resources and the healthy and sustainable development of the ecological environment in the irrigation district. By integrating the water–soil matching coefficient and the equivalent factor method, the spatiotemporal distribution of agricultural water–soil matching and the spatiotemporal evolution of the ESV under the change of land use (LU) in the irrigation district are analyzed. Based on the Pearson correlation, the trade-off synergy between the two is explored. The results show that the following occurred in the past 20 years: (1) Grassland and dryland are the two categories of land with the biggest transfer-out and transfer-in areas in the Tailan River Irrigation District, and the conversion areas are mostly in Jiamu Town and Guleawati Township. (2) The area and reclamation rate of the irrigation district increased gradually, among which the highest reclamation rate was 85.93% in Kezile Town and the lowest was 76.37% in Guleawati Township. The average Gini coefficient of agricultural water–soil in the irrigation district is 0.118, which is absolutely fair. (3) Kezile Town has the highest agricultural water consumption, but the matching of agricultural water–soil always fluctuates between the best and the worst. The agricultural water consumption in Communist Youth League Town is the lowest, but the matching of agricultural water–soil has remained the best for many years. (4) The ESV of the irrigation district showed an overall increasing trend, from CNY 243 million in 2000 to CNY 678 million in 2020; in addition, soil conservation, hydrological regulation, grassland, and dryland contributed the most to ESV in each period. (5) There was a significant trade-off relationship between agricultural water–soil matching and ecosystem services in the Tailan River Irrigation District, while there was a significant synergistic relationship between ecosystem services. Full article