Search Results (6,000)

Marine plastic pollution has become a critical transboundary environmental issue, particularly affecting coastal regions with insufficient waste management infrastructure. This study applies a modified Lagrangian hydrodynamic model, TrackMPD v.1, to simulate the movement and accumulation of macroplastics in the West Africa Coastal Area. The research investigates three case studies: (1) the Liberia–Gulf of Guinea region, (2) the Mauritania–Gulf of Guinea coastal stretch, (3) the Cape Verde, Mauritania, and Senegal regions. Using both forward and backward simulations, macroplastics’ trajectories were tracked to identify key sources and accumulation hotspots. The findings highlight the cross-border nature of marine litter, with plastic debris transported far from its source due to ocean currents. The Gulf of Guinea emerges as a major accumulation zone, heavily impacted by plastic pollution originating from West African rivers. Interesting connections were found between velocities and directions of the plastic debris and some of the characteristics of the West African Monson climatic system (WAM) that dominates the area. Backward modelling reveals that macroplastics beached in Cape Verde largely originate from the Arguin Basin (Mauritania), an area influenced by fishing activities and offshore oil and gas operations. Results are visualized through point tracking, density, and beaching maps, providing insights into plastic distribution and accumulation patterns. The study underscores the need for regional cooperation and integrated monitoring approaches, including remote sensing and in situ surveys, to enhance mitigation strategies. Future work will explore 3D simulations, incorporating degradation processes, biofouling, and sinking dynamics to improve the representation of plastic behaviour in marine environments. This research is conducted within the Global Development Assistance (GDA) Agile Information Development (AID) Marine Environment and Blue Economy initiative, funded by the European Space Agency (ESA) in collaboration with the Asian. Development Bank and the World Bank. The outcomes provide actionable insights for policymakers, researchers, and environmental managers aiming to combat marine plastic pollution and safeguard marine biodiversity. Full article

Public building-attached green spaces are increasingly important urban carbon sinks, yet their carbon sequestration potential remains poorly understood and underutilized. This study quantified vegetation carbon storage across three attached green space typologies (green square, roof garden, and sunken courtyard) at a representative public building in Wuhan, China, using field surveys and species-specific allometric equations. Total carbon storage reached 19,873.43 kg C, dominated by the green square (84.98%), followed by a roof garden (12.29%) and sunken courtyard (2.72%). Regression analysis revealed strong correlations between carbon storage and morphological traits, with diameter at breast height (DBH) showing the highest predictive power for trees (r = 0.976 for evergreen, 0.821 for deciduous), while crown diameter (CD) best predicted shrub carbon storage (r = 0.833). Plant configuration optimization strategies were developed through correlation analysis and ecological principles, including replacing low carbon sequestering species with high carbon native species, enhancing vertical stratification, and implementing multi-layered planting. These strategies increased total carbon storage by 131.5% to 45,964.00 kg C, with carbon density rising from 2.00 kg C∙m⁻² to 4.63 kg C∙m⁻². The findings provide a quantitative framework and practical strategies for integrating carbon management into the design of building-attached green spaces, supporting climate-responsive urban planning and advancing sustainable development goals. Full article

The green landscapes of oasis cities play an important role in maintaining ecological security. However, these ecosystems face increasing threats from desertification and fragmentation, driven by intensifying climate change and rapid urbanization. Understanding the characteristics and driving mechanisms behind changes in green landscape patterns is crucial for advancing sustainable urban green space management. This study explores the spatio-temporal changes in the green landscape pattern in Urumqi during 1990–2020 using a random forest classifier. This study also applies geographical detectors and geographically weighted regression to comprehensively determine the driving mechanism and spatio-temporal nonstationarity. The results are as follows: (1) The landscape types are primarily dominated by unused land, urban green spaces, and construction land, accounting for more than 80%. The areas of urban green spaces, water bodies, cropland, and unused land decreased by 0.38%, 37.41%, 0.57%, and 4.58%, respectively, from 1990 to 2020. With rapid urbanization, construction land exhibited a significant expansion trend, and the degree of fragmentation of urban green spaces increased spatially over these 30 years. (2) From 1990 to 2020, each landscape index exhibited fluctuating characteristics. Overall, the Shannon’s diversity and evenness indices of the urban green landscapes exhibited an increasing trend. The contagion and connectivity indices exhibited a decreasing trend, decreasing from 50.894 and 99.311 in 1990 to 46.584 and 99.048 in 2020, respectively. (3) During these 30 years, the dynamics of urban greenery were affected by a combination of natural and social factors, with elevation determining the overall urban green distribution pattern. Precipitation and temperature dominate the urban green space changes in the north and south of Urumqi. Socioeconomic factors such as GDP, population, river distance, and town distance regulate the urban green space changes in the central built-up area. Full article

Data Spaces appear to offer a solution to data sovereignty concerns in public cloud environments, which are managed by third parties and must therefore be considered potentially untrusted. The IDS Connector, a key component of Data Space architecture, acts as a secure gateway, enforcing data sovereignty by controlling data usage and ensuring that data processing occurs within a trusted and verifiable environment. This study compares the performance of cloud-native data sharing services offered by major cloud providers—Amazon, Microsoft, and Google—with Data Spaces services delivered via two connector implementations: the Dataspace Connector and the Prometheus-X Dataspace Connector. An extensive set of experiments reveals significant differences in the performance of cloud-native managed services, as well as between connector implementations and hosting methods. The results indicate that the differences in the performance of data sharing services are unexpectedly substantial between providers, reaching up to 187%, and that the performance of different connector implementations also varies considerably, with an average difference of 56%. This indicates that the choice of cloud provider and data space Connector implementation has a major impact on the performance of the designed solution. Full article

Radionuclide tracer technology, as a state-of-the-art tool for quantifying and monitoring soil erosion processes, has attracted much attention in global sustainable land management research in recent years. However, existing studies are fragmented in methodological applications, lack systematic knowledge integration and interdisciplinary perspectives, and lack global research trends and dynamic evolution of key themes. This study integrates Bibliometrix, VOSviewer, and CiteSpace to conduct bibliometric and knowledge mapping analysis of 1692 documents (2000–2023) in the Web of Science Core Collection, focusing on the overall developmental trends, thematic evolution, and progress of convergence and innovation. The main findings of the study are as follows: (1) China, the United States, and the United Kingdom are in a “three-legged race” at the national level, with China focusing on technological application innovation, the United States on theoretical breakthroughs, and the United Kingdom contributing significantly to methodological research; (2) “soil erosion” and “¹³⁷Cs” continue to be the core themes, while “climate change” and “human impact” on soil erosion and its reflection in radionuclide tracing became the focus of attention; and (3) multi-scale radionuclide tracing (watershed, slope), multi-method synergy (radionuclide tracing combined with RS, GIS, AI), and the integration of advanced measurement and control technologies (PGS, ARS) have become cutting-edge trends in soil erosion monitoring and control. This study provides three prospective research directions—the construction of a global soil erosion database, the policy transformation mechanism of the SDG interface, and the iterative optimization of multi-radionuclide tracer technology, which will provide scientific guidance for the realization of the sustainable management of soil erosion and the goal of zero growth of land degradation globally. Full article

The article presents Modified Scaled Adaptive Daqrouq Wavelet (MSADW) as an autonomous wavelet framework to overcome the analysis obstacles of traditional wavelets (Morlet and Daubechies) for signals with non-stationary characteristics. MSADW adjusts its waveform shape and frequency in real time based on the specific characteristics of the signal, allowing it to outperform conventional wavelet methods. The system reaches adaptability through three core methods featuring gradient-dependent scale adjustments for fast transient detection and smooth regions, and instantaneous frequency monitoring achieved by a combination of STFT and Hilbert transforms and an iterative error reduction process using gradient descent and genetic algorithms. Continuous Wavelet Transform (CWT) combined with Discrete Wavelet Transform (DWT) extracts features from ECG and speech signals. Throughout this process, MSADW maintains great time precision to detect transients as well as maintain sensitivity for the audio’s base stability. Testing MSADW in practical use reveals its superior performance because it detects R-peaks accurately within 0.01 s through zero-crossing methods, which combine P/T-wave detection with effective ECG signal segmentation and noise-free reconstructed speech ($\mathit{MSE:} 1.17\times {10}^{-31}$). The localized parameterization framework of MSADW, enabled by feedback refinement, fulfills missing aspects in biomedical signal evaluation and creates space for low-cost real-time evaluation methods for medical devices and arrhythmia and ischemic detection platforms. The theoretical backbone for MSADW establishes itself because this work shows how wavelet analysis can transition toward managing non-stationary and noise-prone domains. Full article

New energy vehicles (NEVs) rely heavily on Insulated-Gate Bipolar Transistors (IGBTs) to perform frequent battery voltage conversions for operations such as acceleration, deceleration, and hill climbing. Consequently, effective thermal management of the IGBT junction temperature is critically important. This study investigates the junction temperature of IGBT modules equipped with pin-fin heat sinks of varying spacings under diverse operating conditions. The effects of the coolant inlet flow velocity and temperature on the junction temperature were examined. Furthermore, the pin-fin heat sink structure was optimized to enhance temperature uniformity across the IGBT chips. The results indicate that (1) IGBT modules with small-spacing pin-fin heat sinks exhibit improved thermal performance and enhanced temperature uniformity under specific conditions; (2) coolant inlet flow velocity is positively correlated with both module cooling efficiency and temperature uniformity; (3) coolant inlet temperature is inversely correlated with module junction temperature and chip junction temperature uniformity; and (4) among the three optimization schemes evaluated, the dual-channel, non-uniformly spaced pin-fin heat sink delivered the optimal performance, reducing the maximum junction temperature difference between IGBT chips to approximately 0.5 °C and that between diode chips to approximately 1.0 °C. Full article

The growing demand for electricity and the urgent need to reduce environmental impact have made sustainable energy utilization a global priority. Street lighting, as a significant consumer of urban electricity, requires innovative solutions to enhance efficiency and reliability. This study presents an off-grid smart street lighting system that combines solar photovoltaic generation with battery storage and Internet of Things (IoT)-based control to ensure continuous and efficient operation. The system integrates Long Range Wide Area Network (LoRaWAN) communication technology for remote monitoring and control without internet connectivity and employs the Perturb and Observe (P&O) maximum power point tracking (MPPT) algorithm to maximize energy extraction from solar sources. Data transmission from the LoRaWAN gateway to the cloud is facilitated through the Message Queuing Telemetry Transport (MQTT) protocol, enabling real-time access and management via a graphical user interface. Experimental results demonstrate that the proposed system achieves a maximum MPPT efficiency of 97.96%, supports reliable communication over distances of up to 10 km, and successfully operates four LED streetlights, each spaced 400 m apart, across an open area of approximately 1.2 km—delivering a practical, energy-efficient, and internet-independent solution for smart urban infrastructure. Full article

With the rapid expansion of urban green spaces and the increasing amount of domestic waste, efficient and sustainable treatment of green waste (GW) and kitchen waste (KW) has become a pressing issue. Co-composting offers a green and low-carbon solution, yet a systematic understanding of its greenhouse gas (GHG) emission dynamics remains lacking. This study aims to investigate the impact of varying GW:KW ratios on GHG emissions during composting, in order to identify optimal mixing strategies and sup-port the development of low-carbon urban waste management systems. Six treatments with different GW:KW ratios (10:0, 9:1, 8:2, 7:3, 6:4, and 5:5) were evaluated under continuous aeration for 42 days. Results showed: (1) All treatments exhibited a typical composting temperature profile (mesophilic, thermophilic, cooling, maturation), with final seed germination index (GI) > 95% and significantly reduced E4/E6 ratios, indicating maturity. (2) When kitchen waste (KW) was ≤20%, cumulative GHG emissions slightly increased; KW ≥ 30% led to net reductions, with the 6:4 treatment (A4) achieving the highest decrease (17.44%) in total CO₂-equivalent emissions. In conclusion, maintaining KW at 40–50% optimally balances compost maturity and emission reduction, providing a viable strategy for the high-value utilization of urban organic waste and carbon mitigation. Full article

Lawns have evolved from medieval European grasslands into globally accepted urban green surfaces, serving recreational, aesthetic and cultural purposes. Today lawn surfaces are essential components of public urban green space (PUGS), fulfilling ecosystem services such as urban heat mitigation, carbon sequestration and social well-being. However, their ecological and resource-intensive disservices, particularly in dry climates, have prompted growing concerns among environmental scientists, urban planners and landscape designers. In water-scarce regions like Perth, Western Australia, traditional lawns face increasing scrutiny due to their high irrigation demands and limited ecological diversity. This study contributed to the transdisciplinary LAWN as Cultural and Ecological Phenomenon project, focusing on the perspectives of professionals, landscape architects, park managers, turf producers and researchers responsible for the planning, design and management of urban lawn in PUGS. Using qualitative methods (semi-structured in-depth interviews), the research explores expert insights on the values, challenges and future trajectories of lawn use in a warming, drying climate. The interviews included 21 participants. Findings indicate that while professionals acknowledge lawns’ continued relevance for sports and active recreation, water scarcity is a major concern influencing design and species selection. Alternatives such as drought-tolerant plants, hard landscaping and multifunctional green spaces are increasingly considered for non-sporting areas. Despite growing concerns, the ideal lawn is still envisioned as an expansive, green, soft surface, mirroring entrenched public preferences. This study underscores the need to balance environmental sustainability with public preference and cultural expectations of green lawns. Balancing expert insights with public attitudes is vital for developing adaptive, water-conscious landscape design strategies suited to future urban planning and environmental conditions in Mediterranean climates. Full article

Hydrological modeling is essential for the sustainable management of watershed systems. Physically based models like MOHID-Land simulate soil water dynamics using Richards’ equation, parameterized through the van Genuchten–Mualem (VGM) model. Although the sensitivity of individual VGM parameters—residual water content (${\theta }_r$), saturated water content (${\theta }_s$), pore size distribution ($n$), inverse of air entry pressure ($\alpha$), and saturated hydraulic conductivity ($K_{sat}$)—is well documented, their combined effects remain underexplored. This study assessed both isolated and joint impacts of these parameters through a deterministic ±10% perturbation scheme, resulting in 31 unique parameter combinations. Model performance was evaluated using the Nash–Sutcliffe Efficiency (NSE) and Percent Bias (PBIAS). Full-parameter interaction achieved the best results (NSE = 0.50, PBIAS = 25.32), compared to the uncalibrated baseline (NSE = 0.01, PBIAS = 34.06). The pair ${\theta }_s$ and $n$ emerged as the most influential. Adding secondary parameters to this core pair yielded only marginal performance gains, while removing them from the full set caused similarly marginal declines. These findings reveal a hierarchical sensitivity structure, emphasizing ${\theta }_s$ and $n$ as key targets for calibration. Prioritizing this pair enables a more efficient soil calibration process, preserving model accuracy while reducing computational cost by limiting parameter space exploration. Full article

Olive groves (Olea europaea L.) are highly susceptible to soil degradation, particularly water erosion, due to sparse canopy cover and wide inter-row spacing. This study evaluated the effect of different vegetation cover management practices on soil quality and erosion control in a tropical olive grove in southeastern Brazil. The experiment followed a randomized block design with five treatments: exposed soil (BS), olive trees on exposed soil (OB), olive trees with spontaneous vegetation managed with herbicide (OVH), with mowing (OVM), and with mowing + localized weeding (OVMC). Physical, chemical, and biological indicators and losses due to water erosion were analyzed. The OVM and OVMC treatments promoted an increase in soil organic matter (up to 39 g kg⁻¹), microbial biomass carbon (40% higher than BS), enzymatic activity, and glomalin, improving aggregate stability (WMD of 4.9 mm) and reducing soil and water losses by more than 99% compared to exposed soil. The BS and OB treatments, on the other hand, showed higher acidity, lower microbial activity, and greater susceptibility to erosion. The study reinforces that maintaining vegetation cover improves soil quality, mitigates erosion, and promotes the sustainability of olive groves in tropical regions. Full article

Background/Objectives: Symptoms of autonomic dysfunction are common in infection-associated chronic conditions and illnesses (IACCIs), including myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). This study aimed to evaluate autonomic symptoms and their impact on ME/CFS illness severity. Methods: Data came from a multi-site study conducted in seven ME/CFS specialty clinics during 2012–2020. Autonomic dysfunction was assessed using the Composite Autonomic Symptom Scale 31 (COMPASS-31), medical history, and a lean test originally described by the National Aeronautics and Space Administration (NASA). Illness severity was assessed using Patient-Reported Outcomes Measurement Information System measures, the 36-item short-form, as well as the CDC Symptom Inventory. This analysis included 442 participants who completed the baseline COMPASS-31 assessment, comprising 301 individuals with ME/CFS and 141 healthy controls (HC). Results: ME/CFS participants reported higher autonomic symptom burden than HC across three assessment tools (all p < 0.0001), including the COMPASS-31 total score (34.1 vs. 6.8) and medical history indicators [dizziness or vertigo (42.6% vs. 2.8%), cold extremities (38.6% vs. 5.7%), and orthostatic intolerance (OI, 33.9% vs. 0.7%)]. Among ME/CFS participants, 97% had at least one autonomic symptom. Those with symptoms in the OI, gastrointestinal, and pupillomotor domains had significantly higher illness severity than those without these symptoms. Conclusions: ME/CFS patients exhibit a substantial autonomic symptom burden that correlates with greater illness severity. Individualized care strategies targeting dysautonomia assessment and intervention may offer meaningful improvements in symptom management and quality of life for those with ME/CFS and similar chronic conditions. Full article

Hybrid Renewable Energy Systems are pivotal for the sustainable energy transition, yet their design and operation present complex optimization challenges due to diverse components, stochastic resources, and multifaceted objectives. This systematic review formalizes the HRES optimization problem space and identifies critical research gaps. Employing the PRISMA 2020 guidelines, it comprehensively analyzes the literature (2015–2025) from Scopus, IEEE Xplore, and Web of Science, focusing on architectures, mathematical formulations, objectives, and solution methodologies. The results reveal a decisive shift from single-objective to multi-objective optimization (MOO), increasingly incorporating environmental and emerging social criteria alongside traditional economic and technical goals. Metaheuristic algorithms (e.g., NSGA-II, MOPSO) and AI techniques dominate solution strategies, though challenges persist in scalability, uncertainty management, and real-time control. The integration of hydrogen storage, vehicle-to-grid (V2G) technology, and multi-vector energy systems expands system boundaries. Key gaps include the lack of holistic frameworks co-optimizing techno-economic, environmental, social, and resilience objectives; disconnect between long-term planning and short-term operation; computational limitations for large-scale or real-time applications; explainability of AI-based controllers; high-fidelity degradation modeling for emerging technologies; and bridging the “valley of death” between simulation and bankable deployment. Future research must prioritize interdisciplinary collaboration, standardized social/resilience metrics, scalable and trustworthy AI, and validation frameworks to unlock HRESs’ potential. Full article

The issue of abandoned cropland poses a significant threat to national food security and the sustainable use of land resources, highlighting the urgent need for an efficient and interpretable remote sensing identification framework. This study integrates three authoritative land cover datasets—the European Space Agency WorldCover (ESA), the Environmental Systems Research Institute Land Cover (ESRI), and the China Resource and Environment Data Cloud Platform (CRLC). Multi-source remote sensing features were extracted using the Google Earth Engine platform, and high-quality training samples were constructed by randomly selecting sample points based on these features in ArcGIS. A recursive feature cross-validation method is employed to eliminate redundant variables, thereby optimizing the feature structure without compromising classification accuracy. In terms of model construction, a multi-objective optimization strategy combining the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and eXtreme Gradient Boosting (XGBoost) is proposed. By incorporating a pruning mechanism, computational efficiency is significantly improved—accelerating the identification speed by up to 75%—while maintaining model accuracy (OA: 0.9817; Kappa: 0.9633; F1-score: 0.9817; recall: 0.9866). For result interpretation, the SHapley Additive exPlanations (SHAP) method is used to evaluate global feature importance, revealing that variables such as SAVG, B3_p25, Road, DEM, and Population contribute most significantly to the identification of abandoned cropland. Meanwhile, the Local Interpretable Model-Agnostic Explanations (LIME) method is applied to conduct local interpretability analysis on typical samples. The results show that, while some samples share consistent dominant features with the global results, others exhibit stronger local influences from features such as slope and SAVG. The combination of SHAP and LIME for global–local interpretability provides insight into the heterogeneous drivers of cropland abandonment and enhances the transparency of the classification model. This study presents a practical, scalable framework for the rapid identification and management of abandoned cropland, balancing precision, interpretability, and efficiency. Full article