Search Results (3,638)

In recent years, the sustainable use of marine space has become increasingly important due to the growing number of competing activities. To minimize conflicts and environmental impacts, the co-location of these activities in multi-use marine areas is essential. Several approaches have been proposed to evaluate synergies and incompatibilities among marine uses, but most of them are either complex, case-specific, or lack full automation, which can limit their broader applicability. In this context, the paper presents an enhanced version of a Decision Support Tool for identifying optimal combinations of co-located activities. The tool is based on a multi-criteria analysis integrating technological, environmental, social, and economic factors, and it automatically provides an optimal configuration through a guided, user-friendly procedure. Experts select options for each activity and criterion from drop-down menus, and the tool automatically assigns scores and combines them to rank the different activity combinations. Implemented in an Excel sheet with a wizard interface, it can be easily completed by experts from different fields, who can assign weights to each criterion through discussion. The tool’s general structure also allows its use by policy-makers and consultants, supporting informed decision-making and facilitating science–policy interaction. Full article

This study evaluated the effects of high doses of phytase on the growth performance, carcass characteristics, and serum chemistry of late-finishing pigs housed under space-restricted conditions. Pigs (n = 375; 94.63 ± 0.61 kg) were randomly assigned to 48 pens, with 7 to 8 pigs per pen, balanced for gilts and barrows. Two phytase doses (control of 2500 FTU/kg or hyper-dose of 5000 FTU/kg) and two space allocation dimensions (adequate with 0.85 m²/pig or restricted with 0.66 m²/pig) were combined to create a 2 × 2 factorial arrangement (12 replicates per treatment). The three heaviest pigs per pen were marketed on day 28, and the remaining pigs were marketed on day 42. No interactions (p > 0.10) were observed between the floor space allowance and phytase supplementation. The body weight, daily gain, and feed intake at the first marketing and for all pigs marketed were reduced (p ≤ 0.009) by space restriction, without affecting the gain-to-feed ratio. Space restriction increased serum protein and decreased urea N, and hyper-dosing phytase increased plasma inositol and serum glucose and decreased serum aspartate aminotransferase (p < 0.05). The results indicate that space restriction reduced the growth rate, feed intake, and body weight of late-finishing pigs, and that hyper-dosing phytase was not beneficial in improving growth performance regardless of space allowance. Full article

Rainfall stations in small and medium-sized river basins in China are sparsely distributed and unevenly spaced, resulting in insufficient spatial representativeness of precipitation data and posing challenges to the accuracy of flood forecasting. Spatial interpolation methods for rainfall data are a key tool for bridging the gap between discrete rainfall station data and continuous surface rainfall data; however, their applicability in flood forecasting for small and medium-sized river basins with sparse rainfall stations requires further investigation. Taking the Hezikou basin as the study area and focusing on the Liuxihe model, this study analyzes the distribution characteristics of the seven rainfall stations in the basin and the interpolation effectiveness of the original Thiessen Polygon Interpolation (THI) method in the model. It compares and discusses the applicability of the THI, the Inverse Distance Weighting (IDW) method, and the Trend Surface Interpolation (TSI) method in flood forecasting for this basin. Different rainfall station distribution scenarios (full coverage, upstream only, downstream only, single rainfall station) were set up to study the performance differences in each method under extremely sparse conditions. The results indicate that, under the sparse condition of only 0.0068 rainfall stations per square kilometer in the Hezikou basin, IDW interpolation yields the best flood forecasting results, with model Nash–Sutcliffe Efficiency (NSE) values all above 0.85, Kling–Gupta Efficiency (KGE) values exceeded 0.78, and the Peak Relative Error (PRE) was controlled within 0.09, significantly outperforming THI and TSI. Additionally, as rainfall station sparsity increased, IDW exhibited the smallest decline in performance, showing a weak negative correlation (p ≤ 0.05) between prediction performance and rainfall station sparsity, demonstrating stronger adaptability to sparse scenarios. When station information is extremely limited, IDW performs more stably than THI and TSI in terms of certainty coefficients (NSE, KGE) and flood peak error control. The Inverse Distance Weighting method (IDW) can provide reliable rainfall spatial interpolation results for flood forecasting in small and medium-sized basins with sparse rainfall stations. Full article

Long-tailed image classification remains challenging for vision–language models. Head classes dominate training while tail classes are underrepresented and noisy, and short prompts with weak text supervision further amplify head bias. This paper presents TASA, an end-to-end framework that stabilizes textual supervision and enhances cross-modal fusion. A Semantic Distribution Modulation (SDM) module constructs class-specific text prototypes by cosine-weighted fusion of multiple LLM-generated descriptions with a canonical template, providing stable and diverse semantic anchors without training text parameters. Dual-Space Cross-Modal Fusion (DCF) module incorporates selective-scan state–space blocks into both image and text branches, enabling bidirectional conditioning and efficient feature fusion through a lightweight multilayer perceptron. Together with a margin-aware alignment loss, TASA aligns images with class prototypes for classification without requiring paired image–text data or per-class prompt tuning. Experiments on CIFAR-10/100-LT, ImageNet-LT, and Places-LT demonstrate consistent improvements across many-, medium-, and few-shot groups. Ablation studies confirm that DCF yields the largest single-module gain, while SDM and DCF combined provide the most robust and balanced performance. These results highlight the effectiveness of integrating text-driven prototypes with state–space fusion for long-tailed classification. Full article

Multi-exposure image fusion (MEF) aims to generate a well-exposed image by combining multiple photographs captured at different exposure levels. However, deep learning-based approaches are often highly dependent on the quality of the training data, which can lead to inconsistent color reproduction and loss of fine details. To address this issue, this study proposes a variance-driven hybrid MEF framework based on a U-Net architecture, which adaptively balances structural and chromatic information. In the proposed method, the variance of randomly cropped patches is used as a training weight, allowing the model to emphasize structurally informative regions and thereby preserve local details during the fusion process. Furthermore, a fusion strategy based on the geometric color distance, referred to as the Chroma scale, in the LAB color space is applied to preserve the original chroma characteristics of the input images and improve color fidelity. Visual gamma compensation is also employed to maintain perceptual luminance consistency and synthesize a natural fine image with balanced tone and smooth contrast transitions. Experiments conducted on 86 exposure pairs demonstrate that the proposed model achieves superior fusion quality compared with conventional and deep-learning-based methods, obtaining high JNBM (17.91) and HyperIQA (70.37) scores. Overall, the proposed variance-driven U-Net effectively mitigates dataset dependency and color distortion, providing a reliable and computationally efficient solution for robust MEF applications. Full article

Fiber-reinforced polymer (FRP) composites, particularly glass fiber-reinforced polymer (GFRP), are increasingly utilized in civil engineering due to their high strength-to-weight ratio, corrosion resistance, and environmental sustainability compared to steel. Shear connectors in FRP–concrete hybrid beams are critical for effective load transfer, yet their behavior under static loads remains underexplored. This study aims to investigate the shear strength, stiffness, and failure modes of GFRP, CFRP, AFRP, and stainless-steel shear connectors in FRP–concrete hybrid beams through a comprehensive parametric analysis, addressing gaps in material optimization, bolt configuration, and design guidelines. A validated finite element model in Abaqus was employed to simulate push-out tests based on experimental data. The parameters analyzed included shear connector material (GFRP, CFRP, AFRP, and stainless steel), bolt diameter (16–30 mm), number of bolts (1–6), longitudinal spacing (60–120 mm), embedment length (40–70 mm), and concrete compressive strength (30–70 MPa). Shear load–slip (P-S) curves, ultimate shear load (P), secant stiffness (K₁), and failure modes were evaluated. CFRP bolts exhibited the highest shear capacity, 26.50% greater than stainless steel, with failure dominated by flange bearing, like AFRP and stainless steel, while GFRP bolts failed by shear failure of bolt shanks. Shear capacity increased by 90.60%, with bolt diameter from 16 mm to 30 mm, shifting failure from bolt shank to concrete splitting. Multi-bolt configurations reduced per-bolt shear capacity by up to 15.00% due to uneven load distribution. Larger bolt spacing improved per-bolt shear capacity by 9.48% from 60 mm (3d) to 120 mm (6d). However, in beams, larger spacing reduced the total number of bolts, decreasing overall shear resistance and the degree of shear connection. Higher embedment lengths (he/d ≥ 3.0) mitigated pry-out failure, with shear capacity increasing by 33.59% from 40 mm to 70 mm embedment. Increasing concrete strength from 30 MPa to 70 MPa enhanced shear capacity by 22.07%, shifting the failure mode from concrete splitting to bolt shank shear. The study highlights the critical influence of bolt material, diameter, number, spacing, embedment length, and concrete strength on shear behavior. These findings support the development of FRP-specific design models, enhancing the reliability and sustainability of FRP–concrete hybrid systems. Full article

Radar space target recognition is faced with inherent challenges due to complex electromagnetic scattering properties and limited training samples. Conventional single-modality approaches cannot fully characterize targets due to information incompleteness, and existing multi-modal fusion methods often neglect deep exploration of cross-modal feature correlations. To address this issue, this paper presents a novel multi-modal feature fusion network with fully connected self-attention (MFFN-FCSA) for robust radar space target recognition. The proposed framework innovatively integrates multi-modal radar data, including high-resolution range profiles (HRRPs) and inverse synthetic aperture radar (ISAR) images, to exploit the complementary characteristics comprehensively. Our MFFN-FCSA consists of three modules: the parallel convolutional branches for modality-specific feature extraction of HRRPs and ISAR images, an FCSA-based fusion module for cross-modal feature fusion, and a classification head. Specially, the designed FCSA fusion module simultaneously learns spatial and channel-wise dependencies via a fully connected self-attention mechanism, which enables learning dynamic weights of discriminative features across modalities. Furthermore, our end-to-end MFFN-FCSA model incorporates a composite loss function that combines a focal cross-entropy loss to address class imbalance and a triplet margin loss for enhanced metric learning. Experimental results based on a space target dataset with 10 categories show the high recognition accuracy of our model compared to related single-modality and existing fusion approaches, particularly showing promising generalization capabilities on few-shot and polarization variation scenarios. Full article

Crack segmentation in cluttered scenes with slender and irregular patterns remains difficult, and practical systems must balance accuracy and efficiency. We present CONTI-CrackNet, which is a lightweight visual state-space network that integrates a Multi-Directional Selective Scanning Strategy (MD3S). MD3S performs bidirectional scanning along the horizontal, vertical, and diagonal directions, and it fuses the complementary paths with a Bidirectional Gated Fusion (BiGF) module to strengthen global continuity. To preserve fine details while completing global texture, we propose a Dual-Branch Pixel-Level Global–Local Fusion (DBPGL) module that incorporates a Pixel-Adaptive Pooling (PAP) mechanism to dynamically weight max-pooled responses and average-pooled responses. Evaluated on two public benchmarks, the proposed method achieves an F1 score (F1) of 0.8332 and a mean Intersection over Union (mIoU) of 0.8436 on the TUT dataset, and it achieves an mIoU of 0.7760 on the CRACK500 dataset, surpassing competitive Convolutional Neural Network (CNN), Transformer, and Mamba baselines. With 512 × 512 input, the model requires 24.22 G floating point operations (GFLOPs), 6.01 M parameters (Params), and operates at 42 frames per second (FPS) on an RTX 3090 GPU, delivering a favorable accuracy–efficiency balance. These results show that CONTI-CrackNet improves continuity and edge recovery for thin cracks while keeping computational cost low, and it is lightweight in terms of parameter count and computational cost. Full article

Integrated urban–rural development is of great significance in promoting coordinated development in underdeveloped areas across provinces and advancing common prosperity. Previous studies have mostly focused on typical counties in single or developed areas, with insufficient exploration of integrated urban–rural development in underdeveloped areas. A total of 71 counties in Wuling Mountain area were taken as the research object, and a conceptual model of “element–structure–function” was constructed based on the theory of the urban–rural integration system. The entropy weight ideal point method, variation coefficient method, coupling coordination model, and obstacle model were employed to analyze the integrated urban–rural development in counties of the Wuling Mountain area during 2010 and 2023 from the five dimensions of population, economy, space, society, and ecology, and to explore their coupling coordination relationship and key obstacle factors. The research results indicate the following: (1) During the study period, the average annual growth rate of integrated urban–rural development was only 1.213%, showing a relatively low level. The spatial evolution exhibited a trend of “overall optimization–gap convergence–multipolar linkage–hot in the south and cold in the north”. (2) The comprehensive coupling coordination increased from 0.6380 in 2010 to 0.7016 in 2023, and the coupling coordination of “population–space” became the dominant mode. Nearly 60% of counties achieved a level upgrade from the transition stage to the coordination stage, and the multidimensional coordination relationship was mainly affected by the dual effects of spatial polarization and ecological constraints. (3) The obstacle of spatial integration ranked first and the mismatch of factors was severe. Land urbanization and population distribution imbalance were key obstacles, and their core contradictions were concentrated in the tripartite dilemma of “extensive land utilization–factor blockage–ecological antagonism”. It is urgent to achieve coordinated and sustainable development of urban and rural integration through market-oriented reforms of two-way factor flow. The conceptual model of “element–structure–function” constructed by the research results can provide a theoretical tool for analyzing the integrated development of urban and rural areas in counties, and can provide decision support for solving the dilemma of element mismatch. Full article

This study presents a comparative analysis of two bio-inspired optimization techniques: the Dragonfly Algorithm (DA) and Cuckoo Search (CS). The DA models the collective behavior of dragonflies, replicating dynamic processes such as foraging, evasion, and synchronized movement to effectively explore and exploit the solution space. In contrast, the CS algorithm draws inspiration from the brood parasitism strategy observed in certain Cuckoo species, where eggs are laid in the nests of other birds, thereby leveraging randomization and selection mechanisms for optimization. To enhance the performance of both algorithms, Type-2 fuzzy logic systems were integrated into their structures. Specifically, the DA was fine-tuned through the adjustment of its inertia weight (W) and attraction coefficient (Beta), while the CS algorithm was optimized by calibrating the Lévy flight distribution parameter. A comprehensive set of benchmark functions, F1 through F10, was employed to evaluate and compare the effectiveness and convergence behavior of each method under fuzzy-enhanced configurations. Results indicate that the fuzzy-based adaptations consistently improved convergence stability and accuracy, demonstrating the advantage of integrating Type-2 fuzzy parameter control into swarm-based optimization frameworks. Full article

Cities around the world are facing growing challenges related to climate change, urban sprawl, infrastructure strain, and digital transformation. In response, smart and sustainable urban development has become a global focus, aiming to integrate technology and environmental stewardship to improve the quality of life. The smart and sustainable city concept is typically applied at the city scale; however, its impact is most tangible at the neighborhood level, where residents interact directly with infrastructure, services, and community spaces. A variety of global frameworks have been developed to assess sustainability and technological integration. However, these models often fall short in addressing localized needs, particularly in regions with distinct environmental and cultural contexts. In Saudi Arabia, Vision 2030 emphasizes livability, sustainability, and digital transformation, yet there remains a lack of tailored tools to evaluate smart and sustainable progress at the neighborhood scale. This study develops HayyScore, a localized evaluation framework and prototype digital platform developed to assess neighborhood performance across five core categories: (i) Environment and Urban Resilience, (ii) Smart Infrastructure and Governance, (iii) Mobility and Accessibility, (iv) Quality of Life and Social Inclusion, and (v) Economy and Innovation. The HayyScore platform operationalizes this framework through an interactive web-based tool that allows users to input data through structured forms, calculate scores, receive category-based and overall certification levels, and view results through visual dashboards. The methodology involved a comprehensive review of global frameworks, expert input to define localized indicators, and iterative prototyping of the platform using Python 3.13.5 and Streamlit 1.45.1. To demonstrate its practical application, the prototype was tested on two Saudi neighborhoods: King Abdullah Petroleum Studies and Research Center (KAPSARC) and King Fahd University of Petroleum and Minerals (KFUPM). Key platform features include automated scoring logic, category weighting, certification generation, dynamic performance charts, and a rankings page for comparing multiple neighborhoods. The platform is designed to be scalable, with the ability to add new indicators, support multilingual access, and integrate with real-time data systems in future iterations. Full article

Against the backdrop of global economic digital transformation and the rapid flow of creative factors, innovation spaces, as the key carriers of inventive activities, drive high-quality development in urban agglomerations. This study develops a three-dimensional framework of “Spatial Structure–Factor Synergy–Institutional Drivers” to uncover the evolution of innovation spaces and industrial shifts in the Beijing–Tianjin–Hebei urban agglomeration, China. Methodologically, spatial econometric techniques were applied to capture both the overall concentration and spatial disparities of innovation. Spatial Gini and variation coefficients measured innovation clustering, while standard deviation ellipses and location entropy identified spatial linkages among high-tech innovation clusters. Geographically weighted regression models explored spatial heterogeneity in influencing factors, and a policy intensity index was constructed to assess the effectiveness of differentiated policy interventions in optimizing innovation resources. Key findings include the following: (1) Innovation spaces are spatially polarized in a “core–periphery” pattern, yet require cross-regional collaboration. Concurrently, high-tech industries demonstrate a gradient structure: central cities leading in R&D, sub-central cities driving industrial applications, and node cities achieving specialized development through industrial transfer. (2) The driving mechanisms exhibit significant spatial heterogeneity: economic density shows diminishing returns in core areas, whereas R&D investment and ecological quality demonstrate increasingly positive effects, with foreign investment’s role evolving positively post-institutional reforms. (3) Regional innovation synergy has formed a preliminary framework, but strengthening sustainable policy mechanisms remains pivotal to advancing market-driven coordination and dismantling administrative barriers. These findings underscore the importance of integrated policy reforms for achieving balanced and high-quality innovation development in administratively coordinated urban agglomerations like BTH. Full article

Space technology, a frontier of global scientific innovation, is crucial for competitive edges and national tech innovation. Amid intensified international competition and rapid technological change, scientifically evaluating a country’s Scientific and Technological Strength in Space Technology (STSST) is vital. A model is innovatively proposed in this study called “Analytic Hierarchy Process-Maximum Entropy-Induced Ordered Weighted Average (AHP-ME-IOWA)” for the assessment of STSST. First, an STSST assessment indicator system is developed with four sub-dimensions: scientific research, industrial operation, innovation output, and policy resources. Second, the AHP model is used to convert experts’ qualitative judgments on indicator importance into initial individual weight vectors. Subsequently, the IOWA operator is employed to aggregate these individual weight vectors, thereby mitigating the impact of outliers and enhancing the robustness of the weights. Specifically, the weights are reordered using the cosine similarity between each expert’s weight vector and the temporary group mean as the induced value. Position weights are then determined via the ME method, and consensus weights are derived through re-aggregation. A systematic evaluation of the United States’ STSST was conducted using this method. The results show that the United States achieved a comprehensive STSST score of 8.73 (out of 10), which is in line with the actual situation, thereby providing empirical validation for the proposed method. Full article

Accurate abdominal multi-organ segmentation is essential for disease diagnosis and treatment planning. Although numerous deep-learning models have been proposed, current methods still struggle to balance segmentation accuracy with computational efficiency, particularly for images exhibiting inhomogeneous intensity distributions and complex anatomical structures. To address these challenges, we present a hybrid framework that integrates an Efficient Vision Mamba (EViM) module into a Transformer-based encoder. The EViM module leverages hidden-state mixer-based state-space duality to enable efficient global context modelling and channel-wise interactions. In addition, a weighted combination of cross-entropy and Jaccard loss is employed to improve boundary delineation. Experimental results on the Synapse dataset demonstrate that the proposed model achieves an average Dice score of 82.67% and an HD95 of 16.36 mm, outperforming current state-of-the-art methods. Further validation on the ACDC cardiac MR dataset confirms the generalizability of our approach across imaging modalities. The results indicate that the proposed framework achieves high segmentation accuracy while effectively integrating global and local information, offering a practical and robust solution for clinical abdominal multi-organ segmentation. Full article

The article considers the description of a macrosystem in terms that do not depend on the nature of the macrosystem. The results obtained can be used to describe macrosystem models of thermodynamic processes, and to create interdisciplinary models that take into account interactions of various natures. The macrosystem model is based on its representation in the form of a self-similar oriented weighted graph where the equation of state is fulfilled for each node, which connects extensive variables. One of the extensive variables is entropy, the maximum of which corresponds to the state of equilibrium. For processes in which fluxes are linearly dependent on driving forces, Onsager’s relations are shown to be true, which makes it possible to prove that in the space of stationary processes, entropy production in a closed macrosystem is a metric similar to the Mahalanobis metric, which determines the distance between processes. Zero in such a space indicates reversible processes, and thus the production of entropy shows the degree of irreversibility as the distance from a researched process to a reversible one. Full article