Search Results (16,912)

As the development of the ocean extends to the deep and open seas, the application of multi-hull floating systems is becoming increasingly widespread, covering offshore oil and gas transfer and material replenishment operations. In multi-body floating systems, the hydrodynamic interactions between adjacent floating bodies significantly affect the overall motion response and load distribution. However, there is currently a lack of systematic experimental research on systems involving three or more units under the combined action of wind, waves, and currents. This study presents a 1:50 scale model experiment on a five-body offshore replenishment station, comprising a central transfer platform and four surrounding vessels. Absolute six-degree-of-freedom motions and relative displacements between the transfer platform and neighboring vessels were measured. The results indicate distinct differences among the units. The peripheral vessels have greater horizontal and yaw motions, while the central units are more restricted. The relative motions are substantially increased for beam and oblique wave conditions, implying increased interaction effects in the gaps between neighboring bodies. Moreover, the combined oblique environmental loading and asymmetric mooring stiffness result in increased global drift and yaw motions. These findings provide benchmark data for numerical validation and practical guidance for the design and operation of multi-body floating systems. Full article

Metaheuristic algorithms have become indispensable for solving high-dimensional, non-convex, and constrained optimization problems arising in science and engineering. However, no single method can simultaneously provide strong global exploration, accurate local exploitation, and robust performance across diverse problem classes. This paper proposes JADEFLO, a new hybrid nature-inspired metaheuristic that couples Adaptive Differential Evolution with Optional External Archive (JADE) and Frilled Lizard Optimization (FLO) in a two-stage search framework. In the first stage, JADE drives global exploration using p-best mutation, an external archive, and adaptive control of the mutation factor and crossover rate to maintain population diversity. In the second stage, FLO performs intensive local refinement by mimicking the hunting and tree-climbing behaviors of frilled lizards through dedicated exploration and exploitation moves. The resulting algorithm has linear time complexity with respect to the population size, dimensionality, and number of iterations. JADEFLO is evaluated on the IEEE CEC 2022 single-objective benchmark suite (F1–F12) and three constrained engineering design problems (Pressure Vessel, tension/compression spring, and speed reducer), using 30 independent runs and comparisons against more than thirty state-of-the-art metaheuristics, including GA, PSO, DE variants, GWO, WOA, MFO, and FLO. The results show that JADEFLO attains the best overall rank on the CEC functions, delivers faster convergence and higher accuracy on most test cases, and matches or improves the best-known designs with markedly reduced variance. These findings indicate that JADEFLO is a promising general-purpose optimizer and a flexible foundation for future extensions to multi-objective and large-scale optimization. Full article

Promoting irrigation efficiency is a central pillar of global water sustainability strategies but empirical evidence shows a counterintuitive outcome named the irrigation efficiency paradox or rebound effect. This occurs when on-farm water savings do not translate into basin-scale conservation and may even intensify water scarcity. This paper critically re-examines the rebound effect, moving beyond conventional hydrological and economic explanations toward an integrated socio-hydrological perspective. We argue that the paradox is not merely a technical accounting issue or a form of the Jevons Paradox, but a systemic problem arising from interactions among behavior, institutions, and political economy. The review traces the concept’s evolution and synthesizes global evidence on its main drivers and controversies. It critically evaluates dominant research paradigms, emphasizing the need for greater methodological pluralism. Significant gaps remain, particularly regarding behavioral economics, political economy, and social and environmental externalities. We conclude that overcoming the efficiency paradox requires a policy shift from technological fixes to transformative governance. Full article

Heterogeneous change detection (HCD) using satellite and Unmanned Aerial Vehicle (UAV) imagery is a pivotal task in remote sensing and Earth observation. However, the effective utilization of such multi-source data is significantly hindered by extreme spatial resolution disparities and distinct radiometric characteristics. Existing deep learning methods, often based on weight-sharing Siamese architectures, struggle to bridge these domain gaps, leading to spectral pseudo-changes and blurred detection boundaries. To address these challenges, we propose a novel Dual-Branch Cross-Scale Fusion Network (DBCF-Net) specifically tailored for heterogeneous satellite–UAV change detection. We introduce a Difference-Aware Attention Module (DAAM) to explicitly align cross-modal feature spaces and suppress domain-related noise through a hybrid local–global attention mechanism. Furthermore, an Adaptive Gated Fusion Module (AGFM) is designed to dynamically weight multi-scale interactions, ensuring the preservation of high-frequency spatial details from UAV imagery while maintaining the semantic consistency of satellite data. Extensive experiments on the Heterogeneous Satellite–UAV Dataset (HSUD) demonstrate that DBCF-Net achieves state-of-the-art performance, reaching an F1-score of 88.75% and an IoU of 80.58%. This study provides a robust technical framework for heterogeneous sensor fusion and high-precision monitoring in complex remote sensing scenarios. Full article

Artificial light at night (ALAN) data is widely used in urban function analysis and socio-economic activity monitoring, but its application at the micro-scale of cities still faces challenges. This study utilizes high spatial resolution SDGSAT-1 nighttime light data to explore the spatial heterogeneity of ALAN at the street scale in two representative Chinese cities—Beijing and Guangzhou. By integrating multi-source data (such as building vector data, road networks, and point of interest data), a multi-dimensional indicator system covering urban morphology, functional structure, and transportation accessibility is constructed. Based on this, the study employs a Geographically Weighted Random Forest (GWRF) model combined with the Shapley Additive Explanations (SHAP) method to deeply analyze the non-linear relationships between ALAN intensity and multiple driving factors, as well as their spatial variability. Results demonstrate the superiority of the GWRF model over global models in capturing spatial non-stationarity, with R² values of 0.67 for Beijing and 0.74 for Guangzhou, compared to 0.62 and 0.71 for the random forest models, respectively. Road density is the dominant factor influencing nighttime light intensity in both Beijing and Guangzhou. However, the relationship between ALAN and its driving factors varies across these cities. In Beijing, a balanced multi-factor model is observed, whereas in Guangzhou, ALAN intensity is primarily driven by road density, with secondary influences from other factors like sky view factor. This study validates SDGSAT-1 for micro-scale analysis, offering a scientific basis for differentiated urban lighting planning. Full article

Epiphytic lichens are highly sensitive components of forest ecosystems, increasingly threatened by habitat disturbance and climate change. While habitat protection remains central to lichen conservation, translocation has emerged as a promising tool to address population decline, although its global effectiveness remains poorly evaluated. This scoping review, conducted under PRISMA-ScR guidelines, analyzes 30 taxa across 12 countries to evaluate current methodologies and outcomes. The reviewed literature is largely characterized by small-scale, method-oriented interventions, with a strong predominance of thallus fragment translocation over diaspore-based approaches. Success is most often evaluated through short-term survival and persistence of transplanted material, whereas indicators of long-term population self-maintenance and reproductive viability are rarely considered. Major limitations emerge from technical constraints, including early sample loss due to inadequate fixation, as well as from mismatches between donor requirements and recipient-site microhabitat conditions. Although high initial survival is frequently reported, evidence for long-term population stability, secondary colonization, and genetic resilience remains scarce. Overall, translocation may support short-term establishment under favorable environmental conditions, mainly at local scales, but its reliability as a long-term conservation strategy requires further validation. This review identifies a critical gap in long-term monitoring and highlights the need for research priorities that enhance the effectiveness, conceptual clarity, and technical precision of future translocation efforts to ensure the persistence of epiphytic lichen populations within changing forest landscapes. Full article

It is shown that transient trapped surfaces form in a class of emerging globally hyperbolic spacetimes, within punctured Planck-scale neighbourhoods of the geon supported on intersecting singular supports whose intersection forms a characteristic core in a non-strongly causal setting. These neighbourhoods shrink towards the intersecting singular support in the distributional geometry. In particular, the trapped surfaces occur near the characteristic limit corresponding to the unstable equilibrium of the self-gravitating geon. They act as an effective classical barrier for descriptions formulated purely within smooth differential geometry. The area of these trapped-surface configurations, computed on Planck-referenced neighbourhoods, is shown to tend to zero both in the asymptotically flat limit of the emerging spacetime and in the geon limit. Thus, transient trapped surfaces evaporate in the sense that their area vanishes as classical and asymptotically flat spacetime emerges within the quantum foam framework. A state-counting generating function for the transient trapped surfaces is constructed from the coherent-state density operator. This generating function maps microscopic occupation-number sectors to macroscopic data and thereby allows a definition of Boltzmann entropy (not to be confused with the von Neumann entropy, which is zero for any pure coherent state). Since the coherent state is constructed to implement the correspondence principle, expectation values of the relevant quantised observables reproduce their classical values. In particular, the expectation value of the bosonic occupation-number operator serves as a microstate-counting variable in the coherent sector. The generating function takes the form of an exponential of this expectation value, leading to an entropy–area relation consistent with the Hawking–Bekenstein scaling. Full article

Hypertension is a leading global health risk and requires accurate and continuous monitoring for effective management. Although photoplethysmography (PPG) is a promising non-invasive modality for cuffless blood pressure (BP) assessment, many existing approaches (especially raw-signal deep learning) are vulnerable to data leakage, overfitting on small datasets, limited interpretability, and poor performance on minority BP stages. To address these limitations, we propose a robust and physiologically grounded framework for multi-class BP stage classification based on interpretable PPG features. Our approach centers on a comprehensive multi-domain feature engineering pipeline that extracts 124 PPG features, including demographic, morphological, functional decomposition, spectral, nonlinear dynamics, and clinical composite indices. We apply rigorous preprocessing and feature selection prior to model training. We validate the framework on two datasets: PPG-BP dataset (657 segments, 4 classes) for benchmarking and PulseDB (283,773 segments, 3 classes) to assess scalability. We evaluate the proposed framework using a segment-level train/test split, appropriate for assessing intra-subject BP tracking after initial personalization. For the PulseDB dataset, this follows the protocol established by the dataset creators, while for the PPG-BP dataset, it enables direct comparison with prior work given practical dataset constraints. On PPG-BP, LightGBM trained on the selected features achieved macro-F1 = 0.78 and accuracy = 0.74, outperforming comparable deep-learning models. On the PulseDB, a custom Residual MLP achieved accuracy = 0.81 and macro-F1 = 0.79, supporting generalization at scale. These results show that the proposed feature-based approach can outperform complex end-to-end deep-learning models on small datasets while providing improved interpretability. This work establishes a reliable and transparent pathway toward clinically viable continuous BP staging, moving beyond black-box models toward physiologically grounded decision support. Ablation analysis reveals that engineered features provide most of the predictive power (F1 = 0.911), while raw PPG features alone achieve modest performance (F1 = 0.384). For the minority hypertension stage 2 (HT-2) class, a bootstrap 95% confidence interval of [0.762, 1.000] is reported, reflecting uncertainty due to limited sample size. Full article

Arsenic (As) is a ubiquitous and highly toxic metalloid with well-established carcinogenicity. Its accumulation and secondary release from lake sediments pose potential risks to lake ecosystem integrity and human health. Meanwhile, the ongoing intensification of lake eutrophication at the global scale has altered the sources, composition, and environmental behavior of internally derived dissolved organic matter (DOM). These changes have profoundly influenced As mobilization and transformation at the sediment-water interface (SWI). To advance understanding of the regulatory roles and underlying mechanisms of algal dissolved organic matter (ADOM) and submerged macrophyte dissolved organic matter (SMDOM) in As biogeochemical cycling under lake ecosystem regime shifts, extensive findings from the international literature were synthesized. The characteristic properties and environmental behaviors of ADOM and SMDOM were systematically compared, and their distinct regulatory pathways in lacustrine systems were further summarized. Results indicate that ADOM is typically characterized by low molecular weight, weak aromaticity, and high bioavailability. It can enhance As dissolution and mobilization from sediments through direct complexation, competition for adsorption sites, and stimulation of microbial metabolism and Fe(III) reduction. In contrast, SMDOM exhibits higher molecular weight, greater aromaticity, and a higher degree of humification. It tends to form stable complexes with mineral phases. Under the influence of radial oxygen loss (ROL) from submerged macrophyte roots during the growth phase, its capacity to promote mineral reduction is relatively limited. This process favors stable As retention in sediments. The regulatory effects of ADOM and SMDOM on As behavior are strongly modulated by environmental factors such as pH, redox potential (Eh), temperature, and light conditions, as well as by microbial communities. ADOM is more sensitive to reducing environments and photochemical processes. SMDOM, in contrast, exerts more persistent control under oxidizing conditions and at mineral-water interfaces. In addition, ADOM more readily drives microbial community shifts toward assemblages with enhanced capacities for Fe(III) reduction and As reduction or methylation. SMDOM is less likely to trigger strongly reducing processes. Based on these mechanisms, the outbreak and decay phases in algal-dominated lakes often correspond to critical periods of enhanced As mobilization and elevated ecological risk. In submerged macrophyte-dominated lakes, the decay phase may represent an important window for sedimentary As release. Finally, a conceptual framework describing the differential regulation of As biogeochemical cycling by ADOM and SMDOM is proposed. This framework provides a theoretical basis for As risk identification, the determination of critical risk periods, and the development of management strategies across lakes with different trophic states. Full article

The increasing global demand for sustainable water purification technologies has accelerated research on catalytic degradation and advanced oxidation processes for the removal of refractory pollutants. This study provides a comprehensive bibliometric analysis of global research trends in catalytic water and wastewater treatment from 2010 to 2025, combining quantitative mapping with a qualitative synthesis of emerging technological directions. Bibliographic data were retrieved from the Scopus database and screened using the PRISMA framework, followed by analysis using VOSviewer (v1.6.20) and OriginPro (version 2023, OriginLab Corporation, Northampton, MA, USA) to examine publication growth, citation patterns, international collaboration networks, and thematic evolution. A total of 1550 publications, including 1265 research articles and 285 review papers, were analyzed. The results show a significant increase in research output after 2015, reflecting growing global attention to water sustainability and environmental remediation. China, the United States, and India were identified as the leading contributors, with strong international collaboration networks. Keyword co-occurrence analysis revealed three dominant research themes: photocatalytic degradation and semiconductor engineering, Fenton and Fenton-like advanced oxidation processes, and emerging hybrid catalytic systems involving carbon-based materials and metal–organic frameworks. The analysis also indicates a recent shift toward multifunctional hybrid catalysts designed to improve efficiency, stability, and performance in complex wastewater systems. These findings highlight key scientific developments and suggest future research priorities, including green catalyst synthesis, reactor and process scale-up, AI-assisted catalyst design, and life-cycle sustainability assessment to support the transition from laboratory research to practical water treatment applications. Full article

Glass fiber-reinforced epoxy composites were modified with carbon nanotubes (CNTs), Al₂O₃, and TiO₂ nanoparticles to comparatively evaluate their influence on tensile, flexural, and low-velocity impact performance within an integrated experimental–numerical framework. Nanoparticles were incorporated at controlled weight fractions to identify dispersion-controlled reinforcement regimes and the onset of heterogeneity-driven mechanical transitions. Among all formulations, 0.5 wt% CNTs provided the most pronounced static mechanical enhancement, increasing tensile strength to 419.50 MPa (≈21% improvement over the reference GF laminate) and flexural strength to 230.23 MPa (≈26% increase). In contrast, impact performance exhibited a non-monotonic evolution; the highest absorbed energy (9.64 J) was observed at 2 wt% CNTs, indicating that dynamic energy dissipation mechanisms do not necessarily scale proportionally with static strength gains. Oxide-filled systems demonstrated stiffness-dominated behavior, where increasing filler content amplified elastic mismatch and progressively reduced strength despite modulus enhancement. Finite element simulations conducted in ANSYS LS-DYNA (MAT_022) reproduced global stiffness trends within the dispersion-controlled regime. Tensile strength predictions agreed within 0–9% at optimal CNT loading, whereas larger deviations (up to ~33%) emerged under bending-dominated loading in oxide-rich systems, reflecting amplified sensitivity to microstructural heterogeneity. The coupled evolution of stiffness–strength decoupling (SSDI) and FEM deviation (η) enabled identification of a Composite Heterogeneity Threshold (CHT), defined as the nanoparticle concentration beyond which stiffness enhancement no longer translates into proportional strength or toughness improvement. Beyond this threshold, dispersion-induced heterogeneity not only reduces mechanical efficiency but also marks the boundary of homogenized continuum model adequacy across static and dynamic loading conditions. Full article

Climatic niche breadth is defined as the range of climatic conditions (e.g., temperature and precipitation) under which a species occurs. However, the relationship between niche breadth variation and climatic factors remains poorly studied, and existing results require more general testing. We studied spatial patterns of variation in climatic niche breadths in lizards of the family Agamidae and compared patterns within and across regions to see if they parallel or differ from each other using geo-referenced occurrence records, climatic data and phylogenetic comparative methods. We found that (1) species in warmer environments have narrower temperature niche breadths; (2) precipitation niche breadths are positively correlated with precipitation niche position, and also with temperature niche breadths; and (3) most of the variation in temperature niche breadths is explained by within-locality variation in climatic conditions, whereas most of the variation in precipitation niche breadths is explained by among-locality variation. The patterns of climatic niche breadth in agamids are consistent across regional and global scales, similar to those in other amphibians and reptiles. This suggests that this is a widespread phenomenon among ectothermic vertebrates. Full article

Spring and autumn phenology jointly regulate terrestrial carbon, water, and energy exchanges, yet the mechanisms linking seasonal transitions remain debated under increasing hydroclimatic stress. Here, we integrate satellite-derived phenology with reanalysis-based indicators of land–atmosphere coupling to examine how spring onset interacts with growing season controlling factors and how these interactions shape autumn senescence at the global scale. Globally, start-of-season (SOS) and end-of-season (EOS) timings are positively coupled, with later SOS generally followed by later EOS, and this relationship becomes stronger when only later-SOS years are considered. However, SOS does not induce coherent global shifts in growing season climate limitation. Piecewise structural equation modeling reveals that SOS influences EOS primarily through a direct phenological pathway, with a mean path coefficient of ~0.4 day·day⁻¹ explaining approximately 26% of global EOS variability. In contrast, energy and water-mediated pathways contribute smaller but spatially heterogeneous effects, together accounting for ~5% of explained variance on average. SOS–EOS coupling is strongest in water-limited regimes, particularly in grasslands and shrublands. Managed croplands exhibit distinct and more heterogeneous responses, reflecting partial decoupling of phenology from natural hydroclimatic constraints. Collectively, our results indicate that spring phenology exerts a robust but spatially variable influence on autumn timing, dominated by direct effects rather than indirect mediation through growing season climate limitations, with regional modulation imposed by background hydroclimatic conditions. Full article

Accurate crop classification provides fundamental data for agricultural resource management and ecological research. Hyperspectral image (HSI) classification is the core technique for achieving precise crop mapping. However, existing models often suffer from excessive parameters, limited robustness under few-shot conditions, and a trade-off between efficiency and robustness. To address these issues, this paper proposes a Multi-Scale Wavelet Kolmogorov–Arnold Network (MSWKN). The model employs a Two-Branch Feature Extractor (TBFE) to capture both spectral correlations and spatial textures. a Channel Cross-Spatial (CCS) module to suppress background clutter and highlight discriminative regions. A group convolution-based Fixed Wavelet Multi-Scale Convolutional Layer (FW-MSCL) that leverages the time–frequency localization of wavelets and learnable linear combinations to enhance robustness against spectral distortion while reducing parameters. And a Fourier-based Transformer encoder to enable global frequency–space modeling. Experiments on the WHU-Hi-HanChuan and WHU-Hi-HongHu hyperspectral crop datasets show that MSWKN achieves high overall accuracy and performs favorably on few-shot categories. Under lower parameter counts and fast inference conditions, the model demonstrates a reasonable trade-off between accuracy and computational efficiency. Ablation studies and wavelet kernel comparisons further confirm the contribution of each module and the advantage of the wavelet. The proposed framework provides an efficient and robust solution for fine-grained hyperspectral crop classification. Full article

Background: Home-based digital healthcare interventions are increasingly used to support people living with dementia (PLWD) and their family caregivers. However, evidence regarding their effectiveness across patient and caregiver outcomes remains heterogeneous. Methods: This systematic review followed PRISMA 2020 guidelines and was prospectively registered in PROSPERO (CRD420261302166). Six databases (PubMed, Embase, CINAHL, PsycINFO, Web of Science, and Scopus) were searched from January 2000 to October 2025. Randomized and quasi-experimental quantitative studies evaluating home-based or remotely delivered digital interventions for PLWD and/or informal caregivers were included. Risk of bias was assessed using RoB 2 and ROBINS-I. Due to heterogeneity, findings were synthesized narratively. Results: Fourteen studies met the inclusion criteria. Interventions included web-based psychoeducation, telecoaching, digital cognitive training, assistive technologies, and multicomponent programs. Caregiver outcomes demonstrated the most consistent benefits, including reduced burden and stress, improved self-efficacy, and improved sleep efficiency in technology-supported trials. For PLWD, small-to-moderate improvements were observed in global cognition and selected neuropsychiatric symptoms, particularly in interactive and personalized programs. Multicomponent interventions combining caregiver education with patient activation and professional feedback showed more durable effects. Conclusions: Home-based digital interventions appear feasible and beneficial, particularly for caregiver outcomes. Future large-scale trials with longer follow-up and standardized outcome measures are needed to confirm durability, equity, and cost-effectiveness. Full article