Search Results (2,771)

Effective electrochemical CO₂ reduction to liquid fuels requires that the local catalytic environment facilitates the desired reactivity, yet a microscopic understanding of this environment is difficult to achieve from experiment alone. In this work, a 3D reaction-diffusion model was developed to explore the effects of electrode surface area and local geometry on the performance of a heterogeneous catalyst that performs a two-step CO₂ reduction cascade reaction to CO and then CH₃OH under aqueous conditions. Kinetic parameters for the model were inspired by experimental results using a cobalt phthalocyanine (CoPc) catalyst. Three-dimensional architectures composed of arrays of square pillars with varying dimensions and either smooth or periodically modulated surfaces were tested, revealing the extent to which geometry modulates the performance of the cascade reactions. Although structural variations modulate local concentration gradients, we find that electrochemically active surface area predominantly governs the overall cascade reaction. Moreover, the results suggest that supersaturation of CO, with concentrations up to ten-fold higher than the equilibrium solubility limit, might be critical for more efficient conversion to CH₃OH. For any given geometry, the spatially averaged ratio of [CO] to [CO₂] is dictated by the electrochemically active surface area and determines the yield of CH₃OH. For a fixed surface area, geometries that spatially confine the electrolyte yield moderate local [CO] to [CO₂] ratios within small volumes. In contrast, less confining geometries result in a broader distribution of local ratios spread over larger volumes, with both configurations yielding the same spatially averaged [CO] to [CO₂] ratio. These insights provide valuable design principles—highlighting the critical importance of surface area and possibly CO supersaturation—for engineering advanced electrode architectures that leverage intermediate trapping and CO supersaturation to enhance overall performance in tandem CO₂ reduction systems. Full article

In public safety scenarios, the precise detection and positioning of prohibited weapons such as firearms and knives along with the involved personnel are the core pre-requisite technologies for violent risk warning and emergency response. However, in security surveillance scenarios, there are common problems such as object occlusion, difficulty in capturing small-sized weapons, and complex background interference, which lead to the shortcomings of existing general object detection models in the tasks of detecting and locating security-related objects, including poor adaptability, low detection accuracy, and insufficient robustness in complex scenarios. Therefore, this paper proposes a threat object detection framework for security scenarios (PDGV-DETR) based on adaptive dynamic convolution and cross-scale semantic fusion, specifically optimized for the detection and positioning tasks of weapons and personnel objects in static security surveillance images. This research focuses on category recognition at the object level and pixel-level spatial positioning, and does not involve the classification and identification of violent behaviors based on temporal information. There are clear technical boundaries and scene limitations between the two. This framework is optimized through three core modules: designing a dynamic hierarchical channel interaction convolution module to reduce computational complexity while enhancing the ability to detect occluded and incomplete objects; constructing an improved bidirectional hybrid feature pyramid network, combining the cross-scale fusion module to strengthen multi-scale feature expression, and adapting to the simultaneous detection requirements of small weapon objects and large personnel objects; and introducing a global semantic weaving and elastic feature alignment network to solve the problem of low discrimination between objects and complex backgrounds. Under the same experimental configuration, the proposed model is verified against current mainstream models on typical datasets: on a dataset of 2421 conflict scene personnel violent images, the peak average precision mAP50 of PDGV-DETR reached 85.9%. Through statistical verification, compared with the baseline model RT-DETR with an average value ± standard deviation of 0.840 ± 0.007, the average value ± standard deviation of PDGV-DETR reached 0.858 ± 0.004, demonstrating statistically significant performance improvement, with a p-value less than 0.01. This model can accurately complete the task of locating the object area of personnel, and compared with the deformable DETR, the accuracy improvement rate reached 15.1%.; on the weapon-specific dataset OD-WeaponDetection, the mAP for gun and knife detection reached 93.0%, improving by 2.2% compared to RT-DETR. Compared to the performance fluctuations of other general object detection models in complex security scenarios, PDGV-DETR not only has better detection and positioning accuracy for security-related objects, but also significantly improves the generalization and stability of the model. The results show that PDGV-DETR effectively balances the accuracy of positioning, detection, and computational efficiency, accurately completing end-to-end detection and positioning of weapon and personnel objects in static security surveillance images, demonstrating highly competitive performance in the detection and positioning of security-related objects in security scenes, providing core object-level pre-processing technology support for scenarios such as public area monitoring, intelligent video monitoring, and early warning of violent risks, and providing basic data for subsequent violent behavior recognition based on temporal data. Full article

Satellite altimetry technology provides along-track sea level anomaly (SLA) data for studying mesoscale eddies. However, accurately reconstructing their spatial structures from discrete and non-uniform along-track observations remains a significant challenge. This study systematically evaluates the performance of bi-quadratic, bi-cubic, and bi-quartic quasi-uniform B-spline surface fitting methods for mesoscale eddy reconstruction in the South Indian Ocean (60°S–30°S, 75°E–105°E). By combining idealized experiments with real satellite data, a comprehensive comparison is conducted across several dimensions, including fitting accuracy, computational efficiency, parameter robustness, error distribution, and the physical plausibility of derived vorticity fields. For SLA surface fitting, all three methods achieve comparable accuracy, but the bi-quadratic B-spline demonstrates marked advantages in computational efficiency. Its single-fit time is only 53% and 27% of that of the bi-cubic and bi-quartic methods, respectively, and it shows insensitivity to node configuration, highlighting its practicality. In contrast, vorticity field inversion, which relies on the second derivative of the fitted surface, requires higher-order continuity. Only the bi-quartic B-spline, with C³ continuity, produces physically credible and smooth vorticity fields, whereas lower-degree methods result in discontinuous or non-smooth fields. Based on these findings, this study proposes an application-oriented selection principle: the bi-quadratic B-spline is recommended for efficiency-focused tasks, such as eddy detection, while the bi-quartic B-spline is necessary for dynamic analyses involving vorticity. Full article

Cultivated land spatial layout optimization is of great significance for enhancing comprehensive agricultural productivity and safeguarding food security. However, existing studies primarily focus on production suitability as the optimization objective, while rarely incorporating improvements in cultivated land use resilience and stable use as explicit objectives, which may leave optimized layouts difficult to sustain. To fill this gap, this study takes Meizhou City as a case and conceptualizes cultivated land use resilience under non-grain conversion of the agricultural production structure as a key proxy for stable use. Based on 2019 data, a resistance–reconversion capacity assessment framework is developed, and a 2035-oriented cultivated land layout is generated under a transfer-in–transfer-out area-balance constraint by integrating XGBoost–PVI, the InVEST model, and particle swarm optimization (PSO). The optimized configuration is evaluated using a 2019–2024 observation window. The results show that, after optimization, the mean and minimum cultivated land use resilience increase by 1.72% and 15.16%, respectively, and the share of cultivated land in medium-to-high resilience classes rises by approximately 11.06%. Validation further indicates that parcels selected for transfer-out and transfer-in in the optimized scheme are more likely to undergo transfer-out and restoration in practice. Incorporating cultivated land use resilience into multi-objective layout optimization can simultaneously enhance stable-use potential and spatial integration efficiency, providing decision support for cultivated land layout optimization and sustainable use. Full article

Down-sampling-based video compression frameworks have shown great potential in improving compression efficiency in modern sensing and imaging systems. However, existing methods ignore critical spatial and temporal redundancy, and treat all frames uniformly during down-sampling. This leads to the loss of important information and impacts compression efficiency. To address these limitations, this paper proposes a temporal down-sampling system, in which only intermediate frames are down-sampled while preserving key frames with high quality for reference. On the decoding side, we employ a frame-recurrent enhancement mechanism to maximize the use of temporal redundancy information. In the fusion of enhancement stage, we design a Multi-scale Temporal-Spatial Attention (MTSA) module. MTSA consists of two components: Multi-Temporal Attention (MTA) and Pyramid Spatial Attention (PSA). MTA performs multi-scale temporal correlation modeling, expanding the receptive field and providing stable cues in compressed regions. PSA integrates local spatial saliency and contextual structure in a progressive and multi-stage manner. Extensive experiments show that our approach achieves consistent BD-rate reductions. Under All-Intra, Low-Delay-P, and Random Access configurations, we observe BD-rate reductions of I, P, and B frames ranging from 14% to 39% compared to VVC, and outperform prior approaches anchored by the standard HEVC. Full article

Ground-based remote sensing of seismic and geophysical displacements remains a major challenge due to environmental hazards, signal attenuation, and practical deployment limitations of traditional seismometers. In this study, we present a detailed design, implementation, and performance evaluation of a Moiré-based apparatus for remote ground displacement measurement. The system operates by detecting fringe shifts formed between a fixed and a displaced grating, with displacement magnified through controlled angular superposition. We systematically assess each component of the system, including telescope optics, imaging sensors, and grating configurations, to optimize spatial resolution, contrast, and robustness under varying environmental conditions. A digital approach for fringe generation was employed, allowing controlled magnification and improved sensitivity without the need for physical alignment of dual gratings. Indoor experiments under low-turbulence conditions validated the system’s capability to detect displacements as small as $50\mathsf{\mu }\mathrm{m}$. Subsequent outdoor trials at different distances demonstrated successful measurement of both square-wave and seismic-like displacements despite increased atmospheric turbulence and wind. The results confirm the system’s ability to perform real-time, long-range, non-contact displacement monitoring with high accuracy and resilience to environmental variability. This study establishes a foundation for the application of Moiré-based sensing in challenging field conditions, including volcanic and seismic zones. Full article

The objective of this study was to evaluate the accuracy of detecting crop damage caused by wild boar in rapeseed fields using UAV (unmanned aerial vehicle)-derived RGB (red, green and blue) imagery and deep learning segmentation models. The experiments were conducted on rapeseed crops at full maturity shortly before harvest in central-western Poland in 2021. Four convolutional neural network architectures—U-Net (U-shaped network), U-Net++, DeepLabV3+ (deep learning + labelling), and PSPNet (Pyramid Scene Parsing Network)—were benchmarked using two input configurations: RGB imagery alone and RGB combined with the topographic position index (TPI) derived from a digital surface model (DSM). Model performance was assessed using overall accuracy, F1-score (harmonic mean of precision and recall), and Intersection over Union (IoU), with class-specific metrics reported to provide a realistic evaluation of damaged-area detection. For RGB-only data, overall accuracy ranged from 0.957 to 0.972, while damaged-class F1 and IoU reached 0.752 and 0.603, respectively, for the best-performing model (U-Net). When RGB data were supplemented with TPI, overall accuracy and damaged-class metrics changed only slightly, indicating limited benefit from the topographic feature under these field conditions. Non-damaged crop areas were consistently well-classified (F1 > 0.977, IoU > 0.955). These results confirm that UAV-based RGB imagery enables reliable late-season assessment of wildlife-induced crop damage, and that reporting class-specific metrics in spatially independent test sets is essential for realistic performance evaluation. Full article

The rapid growth of e-commerce has intensified operational and sustainability challenges in urban last-mile delivery, necessitating routing methods that perform reliably under realistic traffic and spatial conditions. This study evaluates three routing algorithms, Nearest Neighbor (NN), Clarke–WrightSavings (CWS), and Ant Colony Optimization (ACO), using 1764 real-world Amazon delivery stops grouped into ten operational clusters in the Nashville metropolitan area. Travel distances and times were obtained through the Google Maps Distance Matrix API in driving mode to reflect actual road network structure and typical traffic conditions. Substantial performance differences were observed across algorithms and cluster configurations. NN achieved a strong performance in compact clusters (18.43 miles and 58.48 min in Cluster 4) but performed poorly in dispersed clusters (82.44 miles and 196.48 min in Cluster 9), reflecting high sensitivity to spatial dispersion. In contrast, CWS consistently reduced travel distance and time across clusters, achieving the shortest observed route (18.50 miles and 47.82 min in Cluster 10). Relative to ACO, CWS reduced travel distance by up to 42% (Cluster 9) and reduced travel time by over 45% in high-dispersion clusters. ACO exhibited the highest variability, with distances reaching 98.77 miles and travel times exceeding 218 min. Multi-criteria evaluation using efficiency ratios, distributional analysis, performance quadrant visualization, and a Composite Performance Index (CPI) confirmed the dominance of CWS. CPI scores of 1.00 (CWS), 0.78 (NN), and 0.00 (ACO) reflected balanced spatial and temporal efficiency under identical traffic-informed inputs. The results demonstrate that deterministic savings-based routing provides superior stability, efficiency, and scalability in semi-static urban delivery systems. However, the present study did not benchmark the evaluated algorithms against state-of-the-art exact TSP solvers (e.g., Concorde, LKH) or more recent metaheuristics such as Genetic Algorithms or Variable Neighborhood Search. The objective was to provide a controlled empirical comparison under consistent traffic-informed cost matrices rather than to establish global optimality bounds. Consequently, while the findings strongly support the relative superiority of the Clarke–Wright Savings approach within the evaluated framework, future research incorporating advanced exact and hybrid optimization methods would further contextualize algorithmic performance. Full article

This study applies an integrated analytical framework combining GeoDetector and multiscale geographically weighted regression (MGWR) to examine how the spatial distribution of cultural heritage values in the Chungcheong region of South Korea (Chungcheongnam-do and Chungcheongbuk-do) relates to regional socio-spatial contexts. Using the Korea Heritage Service’s heritage basic survey data (coordinates, attributes, and value assessments), we aggregated heritage value scores to a 1 km grid and modeled six value dimensions—historical, artistic, academic, social, rarity, and conservation—as separate dependent variables. We then integrated socio-spatial indicators derived from statistical grid maps published by the National Geographic Information Institute (official land price, building density, green space, road accessibility, total population, working-age population share, and aging rate). GeoDetector was first used to identify key determinants and interaction effects by value dimension, and MGWR was then used to estimate local effect heterogeneity and variable-specific operating scales. Results show that heritage values are better explained by multi-factor configurations—urbanization, land value, green space, accessibility, and demographic structure—whose importance varies by value dimension, and that the same factor can exert different directions and strengths across local contexts. By linking “what matters” (key determinants) with “where and at what scale it matters” (local effects and bandwidths), this study provides quantitative evidence to support place-based conservation and utilization strategies. The proposed GeoDetector–MGWR framework is transferable to other regions where spatial heritage inventories and comparable socio-spatial indicators are available. Full article

Remote sensing-based Earth observation provides essential spatial data for analyzing and monitoring both natural and urban environments. Precise characterization of objects in these scenes is vital for environmental management, land-use planning, and monitoring global change. Semantic segmentation of remote sensing imagery (RSI) is a fundamental yet complex task due to significant variability in object shape, scale, and distribution, as well as the complexity of multiscale landscapes captured by advanced sensors. Convolutional neural networks, especially the U-Net architecture, have achieved notable success in segmentation tasks. However, their application in remote sensing is often impeded by persistent issues such as loss of spatial detail, substantial intra- and inter-class variability, and high sensitivity to hyperparameter settings. Manual tuning of hyperparameters is typically inefficient and error-prone, which highlights the importance of heuristic methods for automated optimization. Genetic Algorithms (GAs), Differential Evolution (DE), and Particle Swarm Optimization (PSO) are metaheuristics that provide systematic approaches for exploring large hyperparameter spaces. This study investigates an evolutionary framework for the automated optimization of four critical U-Net hyperparameters—learning rate, number of training epochs, optimizer, and loss function—using micro-evolutionary algorithms. Specifically, micro Genetic Algorithms (micro-GAs), micro Differential Evolution (micro-DE), and micro Particle Swarm Optimization (micro-PSO) are employed to efficiently explore the hyperparameter search space under reduced population settings. The experimental results demonstrate that the proposed micro-evolutionary optimization framework consistently enhances segmentation performance, achieving improvements in Mean Intersection over Union (MIoU) ranging from 3% to 35%, along with systematic gains in overall accuracy across different datasets and configurations. Full article

Spinning tethered satellite systems represent a promising advancement in the design of spaceborne architectures for Earth and planetary observation. Leveraging the unique advantages of tether technology, such as mass efficiency in deploying large structures and fuel-free formation control, this study explores the feasibility and performance potential of CubeSat-scale spinning tethered formations. These systems consist of multiple spacecrafts connected by a tether, enabling easy dynamic adjustment of inter-satellite spacing and rotational velocity through conservation of angular momentum. Such flexibility facilitates precise, stable formations suitable for a range of remote sensing applications. In this paper, the authors present an overview of the dynamical modelling, deployment strategy, and operational advantages of spinning tether systems, focusing in particular on some key use cases: Earth, Moon and Mars surface observation. Three representative sensing modalities are analysed: (1) stereo imaging, where tethered platforms allow synchronized capture with tuneable baselines; (2) distributed radar sounding, which benefits from mechanically stabilized, spatially dispersed sensors to enhance resolution; and (3) Synthetic Aperture Radar (SAR) interferometry, where tether-induced baseline control improves accuracy and simplifies phase unwrapping. A performance assessment is provided for multiple orbital configurations around the Earth and the Moon. The results demonstrate that, while some issues still need to be explored in more detail, spinning tethered systems can offer competitive or superior observational performance in different mission scenarios compared to current technologies. The main challenges posed by this kind of architecture are discussed, alongside future research directions and development prospects. Full article

This study proposes a novel redesign of a solar water heater prototype by integrating a stationary compound parabolic concentrator (CPC) internally within a standard collector housing. Unlike conventional flat-plate systems or external trough collectors, this design aims to enhance thermal efficiency while maintaining a compact footprint suitable for residential retrofitting in tropical climates. The system’s thermal performance was analyzed using a 3D finite element method (FEM) based on the convection-diffusion equation, with a specific focus on a 2 cm focal length configuration designed to fit spatial constraints. The simulation results indicated a maximum water temperature of 62.9 °C under concentrated solar flux, while the experimental prototype achieved a maximum temperature of 55.0 °C under corresponding field conditions. The comparative analysis reveals a temperature discrepancy of approximately 8 °C (12.5%), which is attributed to the simplified boundary conditions neglecting radiative losses in the model. Despite this deviation, the proposed parabolic design demonstrated a distinct thermal enhancement compared to the conventional baseline. These findings validate the technical feasibility of the compact internal concentrator, offering a low-cost, high-performance alternative for domestic water heating applications. Full article

Module- and pack-level mechanical design of lithium-ion batteries in electric vehicles is a primary driver of swelling-induced stack pressure and spatially varying ageing. Current practice remains largely empirical or data-driven and configuration-specific, limiting the ability to predict how design changes translate into local pressure heterogeneity and state-of-health (SOH) loss. This motivates a compact chemo-mechanical model that maps packaging boundary conditions to pressure, swelling, and SOH evolution with few interpretable parameters. This study introduces finite-element-ready constitutive laws that couple reversible and irreversible swelling to SOH and through-thickness pressure, covering three boundary cases reported in literature: constant pressure, thickness clamp after an initial preload, and flexible support. Parameters are identified from different published datasets, and the model is validated against independent constraint scenarios. Good quantitative agreement is shown with averaged RMSE of 1.16% for SOH and 0.16 [MPa] for pressure evolution. Variance-based sensitivity analysis shows SOH uncertainty dominated by the damage-law parameters of the proposed constitutive relationship, whereas pressure evolution is primarily controlled by irreversible swelling and the non-linear through-thickness stiffness, indicating calibration priorities for engineering design studies. The framework is intended for fast comparative analyses of individual cells under a controlled environment. Further extensions, including SOC-dependent mechanics, refined hysteresis, temperature, and C-rate variations require dedicated datasets and are left for future work. Full article

This study examines the impact of the spatial flow of new quality productive forces (NQPFS) and the optimal combination of new quality productive forces (NQPFC) on the high-quality economic development (HQMED) of China’s coastal regions. Based on panel data from 53 coastal cities (2004–2023), the research constructs comprehensive evaluation systems and employs a two-way fixed effects model for empirical analysis. The main findings are as follows: First, Spatial Evolution: The HQMED level of coastal areas shows a continuous upward trend with marked regional disparities, forming a spatial pattern of “one core, two wings” characterized by “Eastern leadership with Northern and Southern regions following.” The inter-city development gap has widened, with the overall spatial structure evolving from a “core-periphery” model toward a clustered stage of “one core, multiple poles, and networked linkage.” Correspondingly, New Quality Productive Forces have transitioned from initial single-point agglomeration to a multi-polar and ultimately networked distribution. Second, both the spatial flow and optimal combination of New Quality Productive Forces exert stable positive effects on coastal HQMED. The marginal contribution of the factor optimal combination is significantly greater than that of spatial flow. Third, two complete mediation pathways are identified: NQPFS promotes HQMED primarily by enhancing the resilience of the marine industrial chain, while NQPFC drives HQMED mainly through cultivating new-quality marine business forms. Fourth, resource misallocation exerts a significant negative moderating effect on the relationship between NQPFS and HQMED. Conversely, a sound innovation ecosystem positively moderates the impact of NQPFC on HQMED. Fifth, the effects exhibit significant regional and institutional variation. Geographically, the impact follows a pattern of “strong in the East, suppressed in the North, and insignificant in the South.” Administratively, core cities demonstrate stronger factor capture and configuration efficiency compared to ordinary cities. The study confirms that facilitating the cross-regional flow and efficient internal recombination of the New Quality Productive Force is crucial for driving coastal HQMED. Policy should focus on reducing resource misallocation to remove barriers to factor mobility, optimizing regional innovation ecosystems to enhance factor synergy, and implementing differentiated strategies that balance the radiating role of core cities with the distinctive development of ordinary cities, thereby fostering a new, coordinated pattern of high-quality development across coastal regions. Full article

Assessing ecosystem health in rapidly urbanizing watersheds requires policy-relevant and empirically grounded indicator systems. Focusing on the Luxi River Basin in Chengdu’s Tianfu New Area, this study develops an ecosystem health evaluation and restoration zoning scheme based on the Pressure–State–Response framework (PSR). Utilizing remote sensing land use maps for 2004, 2014, and 2024 with overall accuracy and Kappa above 85% and 0.80, respectively, a 13-indicator PSR health index with entropy-based weighting was constructed at the township and subdistrict scales. Aiming to support objective indicator selection and interpretation, multiscale landscape dynamics were further quantified using FRAGSTATS and moving window analysis, including mean patch area, patch density, landscape shape index, largest patch index, Shannon diversity index, Shannon evenness index, contagion index, and splitting index, and sensitive landscape descriptors and major driving factors were identified. Results show a shift in landscape patterns, from relatively aggregated configurations toward highly complex and fragmented ones. Largest patch dominance, measured by the largest patch index, declined from 66.71 to 22.79, while connectivity, measured by the contagion index, decreased from 59.74 to 45.10. Subdivision, measured by the splitting index, increased from 2.24 to 12.88, and compositional heterogeneity, measured by the Shannon diversity index, increased from 0.86 to 1.26. The PSR assessment indicates that demographic pressure intensified over time, whereas improvements in water resource supply, technological progress, and industrial upgrading partially alleviated overall pressure in some subregions. Ecosystem state exhibited strong spatial heterogeneity, with sustained high health in the eastern Longquan Mountain area and substantial improvement around Xinglong Lake, while northern urbanized and southern agricultural subregions lagged behind. Environmental governance responses strengthened, with the response index increasing from 0.2297 to 0.9885. Overall ecosystem health demonstrated a modest but stable improvement from 2004 to 2024, with 65.48% of the area revealing slight improvement, 1.14% experiencing substantial improvement, 29.62% remaining stable, and 3.76% experiencing slight degradation. Finally, restoration priority zones were delineated, and targeted strategies were introduced to inform basin-scale ecological management in the Luxi River Basin. Full article