Search Results (13,041)

Agrivoltaic systems integrate solar electricity generation with agricultural production on the same land and have emerged as a promising strategy to address land-use conflicts between food and energy systems. This PRISMA-based systematic review synthesizes evidence from 249 peer-reviewed studies published between 2010 and 2025, applying an integrated three-dimensional framework that simultaneously examines technical efficiency, environmental sustainability, and institutional governance. The results show that agrivoltaic systems consistently achieve superior land-use performance, with Land Equivalent Ratio values typically ranging between 1.2 and 1.8, indicating 20–80% greater territorial efficiency than separate agricultural and photovoltaic systems. In water-stressed regions, reported improvements in water-use efficiency commonly reach 15–30%, while life-cycle assessments indicate substantial reductions in greenhouse gas emissions and other environmental impacts. The integrated analysis also reveals important design-dependent trade-offs related to panel density, crop selection, and local agroclimatic conditions. Despite their demonstrated technical and environmental maturity, the large-scale deployment of agrivoltaic systems remains constrained by institutional barriers, including the lack of dedicated regulatory frameworks, fragmented agricultural and energy policies, and the strong geographical concentration of research in the Global North, with limited evidence from Latin America and other regions of the Global South. Overall, the findings indicate that agrivoltaic systems represent a credible component of integrated land-use and energy transition strategies, but their responsible scaling will depend primarily on advances in governance, policy alignment, and context-specific system design. Full article

To address the tendency of the traditional Children’s Drawing Development Optimization (CDDO) algorithm to fall into local optima and converge slowly in global optimization and fire-field robot path planning, this study proposes a Multi-Strategy Enhanced Children’s Drawing Development Optimization (MECDDO) algorithm. The algorithm achieves performance improvements through three core strategies: (1) an adaptive cooperative search strategy that integrates information from the global best, worst, and random individuals and guides updates via dynamic weighting, expanding the exploration of the solution space; (2) a multi-strategy adaptive selection mechanism that constructs a pool of four differentiated strategies and dynamically adjusts selection probabilities based on strategy success rates, balancing exploration and exploitation; and (3) a global-optimum guided boundary repair strategy that reduces the loss of high-quality information from out-of-bounds solutions, enhancing local exploitation efficiency. Experiments on the CEC2017 benchmark suite demonstrate that MECDDO achieves outstanding performance across 30-, 50-, and 100-dimensional spaces. Statistical significance was evaluated using the Friedman test and Wilcoxon signed-rank test at a 0.05 significance level. The Friedman test mean rankings (M.R.) are 1.63, 2.20, and 2.70, respectively, consistently outperforming traditional CDDO (M.R. = 9.83, 9.93, 9.73, ranked 10th). Applied to mobile robot path planning, MECDDO achieves an average path length of 27.95483 in 20 × 20 grid environments (rank 1), shortening paths by 8.83% compared with CDDO (30.66212, rank 10), and 61.15516 in 40 × 40 grids (rank 1), reducing paths by 37.19% versus CDDO (97.20336, rank 9), providing trajectories free of redundant turns and convergence speeds 2–3 times faster than competing algorithms. These results validate MECDDO’s significant advantages in numerical optimization accuracy and practical robot path planning. Full article

Aiming at the problem that the multi-scale feature interaction ability of the traditional deep learning-based line selection algorithm is insufficient, resulting in the decline of line selection accuracy, a multi-scale feature fusion line selection method based on transfer learning is proposed, abbreviated as TLM-Net. Firstly, to address the issue of the insufficient generalization ability of the line selection network in small-sample scenarios, a simulation data pre-training framework is constructed, and a robust feature representation basis is established through a cross-domain knowledge transfer mechanism. Secondly, aiming at the problem of insufficient extraction of feature information by traditional algorithms, a multi-scale feature fusion network (MFFN) is designed to integrate global context information and local detail features, achieving cross-level semantic complementarity and spatial alignment optimization. Then, to enhance the representation ability of weak fault feature information, an EKA mechanism integrating variable kernel convolution is designed. The background interference is reduced through adaptive multi-region feature focusing, and the edge recognition accuracy of the model for irregular targets is improved. Finally, the pre-trained model is transferred to the target domain by adopting the transfer learning strategy, and the network parameters are fine-tuned in combination with the on-site data to achieve cross-domain adaptation of the feature space. The experimental results show that the TLM-Net algorithm’s mAP@0.5 reaches 98.5%, the accuracy rate and recall rate reach 98.3% and 96.5%, respectively, and the accuracy is improved by 37.5% compared with the original model. Full article

The existing research on Android malware detection using graph neural networks (GNNs) has largely focused on architectural improvements, while input node feature representations have received less systematic attention. This study adopts a representation-centric approach to enhance function call graph (FCG)-based malware classification through interpretability-driven feature engineering. We propose a dual-level structural feature framework integrating local topological patterns with global graph-level properties. The initial feature set comprises 13 dimensions: five local degree profile (LDP) features and eight global structural features capturing community structure, execution flow, and connectivity patterns. To mitigate the curse of dimensionality, we apply an interpretability-driven selection using integrated gradients (IG), gradient-weighted class activation mapping (GradCAM), and Shapley additive explanations (SHAP), yielding an optimized seven-dimensional subset. Experiments on the MalNet-Tiny benchmark demonstrate that the proposed approach achieves 94.47 ± 0.25% accuracy with jumping knowledge GraphSAGE (JK-GraphSAGE), improving the LDP-only baseline by 0.32 percentage points while reducing feature dimensionality by 46%. The selected features exhibit consistent importance across four GNN architectures and multiple message-passing layers, demonstrating model-agnostic effectiveness. The results reveal that aggregation mechanisms critically influence feature utility, highlighting the necessity of interpretability-guided design for robust malware detection. This work provides a systematic methodology for feature engineering in graph-based security applications. Full article

Marine ranching, as a pivotal strategy for enhancing the ocean’s carbon sequestration potential, offers significant potential to mitigate nearshore fishery depletion and restore marine ecosystems amid the global carbon neutrality agenda. However, the mechanistic pathways linking sediment total organic carbon (TOC) to various environmental factors in tropical marine ranches remain insufficiently quantified. This study selected the Wuzhizhou Island Marine Ranch in Hainan Province—a representative tropical marine ranch—as the research site. Field investigations and sampling were conducted during the dry (March 2024) and wet (September 2024) seasons to quantify TOC in surface sediments and associated environmental variables. A two-step analytical framework, integrating Principal Component Analysis (PCA) and Generalized Additive Models (GAM), was employed to elucidate the environmental drivers governing the spatiotemporal dynamics of TOC. The results show that the surface sediment TOC at Wuzhizhou Island Marine Ranch exhibits a distinct spatial gradient—Core Reef > Atoll > Control > Estuarine, and a pronounced seasonal pattern with elevated concentrations in the dry season relative to the wet season. The spatiotemporal differentiation of TOC is mainly driven by a gradient (explaining 52.1% of variation) that encompasses processes related to carbon accumulation from terrestrial inputs and primary production, as well as organic matter degradation promoted by nutrients and higher water temperatures. Sediment total nitrogen (TN) emerges as the primary environmental driver of TOC distribution, contributing up to 46.9% of the variance at an extremely significant level (p < 0.001). Furthermore, total phosphorus (TP), pH, and water temperature (WT) have relatively minor influences on the distribution of sedimentary TOC. Our study offers a crucial reference for elucidating the key processes governing the carbon cycle in tropical marine ranches and provides essential theoretical support for optimizing ocean carbon sink strategies in the context of global climate change. Full article

Over the last decade, prefabrication has emerged as a strategic alternative to address the global construction industry’s challenges concerning sustainability, productivity, and the housing deficit. This study analyzes the advances, benefits, limitations, and research gaps associated with its application in real estate projects between 2015 and 2025. A systematic literature review was conducted under the PRISMA protocol, which allowed for the selection of 58 high-quality articles sourced from Scopus, Web of Science, SciELO, and Redalyc. The findings highlight Asia as the leader in innovation and industrialization, while Latin America is identified as an emerging region with applications in social housing, education, and modular infrastructure. Reported benefits include reduced time and costs, improved environmental performance, and the integration of digital technologies such as BIM, 3D printing, and digital twins. Nevertheless, regulatory gaps, cultural resistance, and limited coordination among industry, government, and academia persist. The study concludes that prefabrication constitutes a transformative engine for the real estate sector, but its consolidation requires stronger regulatory frameworks, broader empirical research in Latin America, and the adoption of circular economy and digitalization strategies to ensure a sustainable and socially accepted impact. Full article

This article examines the governance of Global Value Chains (GVCs) through the lens of economic competence based on a systematic literature review of 32 selected studies. The findings show that economic competence functions as a governance-contingent construct whose effects vary across hierarchical, captive, relational, and modular governance structures. Rather than directly determining upgrading outcomes, competence dimensions operate through governance repositioning and shifts in dependence asymmetries within value chains. The review identifies recurring mechanisms—such as substitutability reduction, coordination cost mitigation, and institutional alignment—that explain how competence and governance interact. The analysis further demonstrates that economic competence is multidimensional, encompassing innovation-oriented, market-oriented, decision-making, relational, and systemic components. These dimensions operate differently depending on coordination complexity and power distribution within the chain. By advancing a contingency-based framework, the study refines GVC governance theory through a micro-foundational explanation of upgrading dynamics. From a managerial perspective, the framework offers a structured tool for aligning competence development strategies with specific governance configurations, supporting informed capability investments and improved strategic positioning. Overall, the study contributes by systematically integrating competence theory with governance typologies and power asymmetries, providing a coherent analytical model for future empirical research. Full article

Background: Drug-induced sleep endoscopy (DISE) with a jaw-thrust maneuver is used to simulate mandibular advancement in obstructive sleep apnea (OSA), yet determinants of functional airway improvement remain incompletely defined. Objective: To identify clinical, polysomnographic, and baseline DISE anatomic factors associated with jaw-thrust responsiveness. Methods: We conducted a single-center retrospective observational study of adults with polysomnography-confirmed OSA who underwent DISE with paired baseline and jaw-thrust VOTE assessments between 1 January 2015 and 31 December 2025 (n = 355). Jaw-thrust responsiveness was defined a priori as a within-subject reduction in the number of obstructed VOTE sites (grade ≥ 1). Multivariable logistic regression was used to identify independent correlates within a prespecified explanatory modeling framework. The study was approved by the Institutional Review Board of Taipei Tzu Chi Hospital (protocol 14-IRB079), with the need for informed consent being waived. Results: Jaw thrust reduced overall obstruction burden from two (two to three) to one (one to two) sites (Wilcoxon p < 0.001). Hypopharyngeal levels demonstrated the greatest improvement, particularly at the tongue base (39.2% to 7.6%) and epiglottis (23.9% to 5.4%) (both p < 0.001). Overall, 62.8% met responder criteria and 18.9% achieved complete normalization. In multivariable analysis (n = 272), baseline tongue-base collapse (adjusted odds ratio [aOR] 2.46, 95% CI 1.20–5.04) and greater baseline multilevel obstruction burden (aOR 1.85 per SD, 95% CI 1.19–2.85) were independently associated with responsiveness, whereas conventional PSG severity metrics were not. Conclusions: In adults with OSA, jaw-thrust responsiveness during DISE is more strongly associated with baseline anatomic phenotype than with global PSG severity. Standardized DISE functional assessment may provide complementary information to support phenotype-informed selection of non-CPAP therapies, pending prospective validation. Full article

This study aimed to analyze changes in vegetation, built-up areas, and population growth in Lahore city from 1990 to 2023. The data was acquired from Google Earth Engine, and the spectral bands were retrieved from Landsat 5 and Landsat 8. The decadal analysis of the landscape was conducted from 1993 to 2001, 2001 to 2012, and from 2013 to 2023. Further analysis was conducted in ArcGIS version 10.3 to evaluate the Normalized Difference Vegetation Index and the Normalized Difference Built-up Index to assess vegetation and built-up areas, respectively. To analyze the urban population of Lahore, data were obtained from the Global Human Settlement Layer for 1990, 2000, 2010, and 2020. Results revealed that the total vegetated area of Lahore city decreased from 1453.0 km² in 1993–2001 to 788.2 km² in 2013–2023. Moreover, the urban built-up area expanded from 319.6 km² in 1993–2001 to 966.8 km² in 2013–2023. Sub-district-level analysis indicated that Model Town and Raiwind areas of Lahore depicted better vegetation recovery in this decade. The population of Lahore has been increasing steadily, with the 2010s being a particularly rapid period of growth. The projections for 2030 also depict a continuous growth pattern. This study was further developed by integrating multi-decadal averaging coupled with selected-year analysis to distinguish gradual land transformation from relatively accelerated phases of urban expansion of Lahore. Also, by combining NDVI and NDBI values on both Lahore and its tehsil level, the research provides a collective sub-district- and district-level perspective into the spatial heterogeneity of peri-urban transformations. The findings of the study explain how major infrastructural projects shape the urban growth patterns of cities like Lahore and cause a decline in the green areas of fast-growing cities in South Asia. This study further highlights the consequences of unplanned urban expansion in regions where high population growth has compromised green infrastructure and threatened ecological balance. In addition, it supports several Sustainable Development Goals (SDGs), particularly SDG 11 (Sustainable Cities and Communities), SDG 13 (Climate Action), and SDG 15 (Life on Land) by providing spatial evidence of urban expansion of the city and losses of its green spaces. The findings offer empirical insights to support climate-resilient developments. The study also demonstrates the necessity of integrating green infrastructure and providing robust strategies for forthcoming urban planning projects and policy development regarding urban expansion. Full article

Globally, freshwater scarcity is driving the urgent demand for advanced and new desalination technologies to overcome the shortage of clean water. Reverse osmosis (RO) membranes dominate seawater and brackish water treatment but are limited by the permeability–selectivity trade-off, fouling, and structural instability. To overcome these challenges, we employed a phase inversion process to fabricate polysulfone (PSF) supports embedded with a graphene oxide–poly(amidoamine) (GO-PAMAM) nanocomposite at three concentrations (0.03, 0.06, and 0.10 wt%), alongside a pristine control membrane with no GO-PAMAM. Systematic variation in GO-PAMAM loading revealed that a 0.06 wt% nanoparticle helps in producing a more uniform polyamide layer that achieves a high NaCl rejection (95.88%) and higher water flux (42.6 L m⁻² h⁻¹). The performance was evaluated at an operating pressure of 20 bar with a feed flow rate of 4 L min⁻¹. The optimized membrane also demonstrated an improved fouling resistance, retaining 93% of its initial flux after fouling. This scalable approach highlights substrate-level modification as an effective strategy for next-generation RO membranes, advancing sustainable and energy-efficient desalination to meet escalating global water demands. Full article

This study evaluates performance data and genetic merit of the main horse populations competing in Olympic disciplines in Spain and examines their implications for the optimization of official Breeding Programs. Performance records from 2004–2023 were analyzed, including 101,093 participations in Dressage, 319,000 in Show Jumping, and 17,535 in Eventing. These records were combined with pedigree information from 35,589 horses in Dressage, 33,935 in Show Jumping, and 12,102 in Eventing and evaluated using BLUP animal models to obtain standardized Estimated Breeding Values (EBV; mean 100 ± 20) and a Genetic Global Index (GGI). A single unified evaluation model was implemented for all studbooks, enabling a direct comparison of genetic quality across different breeds. Results revealed marked differences in genetic merit and genetic progress among breeds. Similar mean EBVs were obtained for the three analyzed breeds in Dressage in both the complete and the top 10% populations, with positive genetic trends in Caballo de Deporte Español (CDE) and Pura Raza Española (PRE), while the slope of EBV on birth year was not significantly different from zero in Spanish Anglo-Árabe (AA). CDE showed the highest mean EBVs and accuracies in Show Jumping (EBV up to 109.27; R up to 0.72), with a clear positive genetic trend. In Eventing, CDE and AA showed similar EBVs, while PRE consistently exhibited lower ones, although with a comparatively more favorable genetic trend. Analysis of selection intensity indicated that PRE breeders applied the most consistent genetic criteria, preferentially using animals with GGI > 100, whereas CDE and AA showed discrepancies between genetic merit and reproductive use. Overall, the unified Spanish genetic evaluation system provides reliable comparative information across breeds and has enabled measurable genetic progress, although improvements in breeders’ decision-making and in the use of genetic information are needed to maximize selection response. Full article

We study a rigidity problem for functions $F:{\mathbb{R}}_{>0}\to {\mathbb{R}}_{\ge 0}$ that penalize deviation of a positive ratio from equilibrium $x=1$. Assuming (i) a d’Alembert-type composition law on ${\mathbb{R}}_{>0}$, and (ii) a single quadratic calibration at the identity (in logarithmic coordinates), we prove that F is uniquely determined. The composition law implies the normalization $F\left(1\right)=0.$ The unique solution is called the canonical reciprocal cost, namely the difference between the arithmetic and geometric means of x and its reciprocal. Our proof uses the logarithmic coordinates $H\left(t\right)=F\left(e^t\right)+1$, where the composition law becomes d’Alembert’s functional equation on $\mathbb{R}$. The calibration provides the minimal regularity needed to invoke the classical classification of continuous solutions and fixes the remaining scaling freedom, selecting the hyperbolic-cosine branch. We also establish the necessity of each assumption: without calibration the composition law admits a continuous one-parameter family; without the composition law the calibration does not determine the global form; and without regularity the composition law admits pathological non-measurable solutions. Finally, we establish a stability estimate for approximate solutions under bounded defect and characterize some properties of the canonical cost. Full article

Although microbial communities in rice agroecosystems regulate nitrogen transformations, methane dynamics, crop residue decomposition, and pathogen suppression, their integration into agronomic decision-making remains limited. Existing rice microbiome reviews largely describe taxonomic diversity without critically linking microbial processes to management trade-offs, greenhouse gas mitigation, and productivity outcomes. This review synthesizes current knowledge through a process-based and management-oriented framework, emphasizing how water and crop residue management, fertilization, tillage, and genotype selection shape microbial functionality rather than merely community composition. Advances in stable isotope probing (SIP), metatranscriptomics, and multi-omics have improved functional inference, yet a persistent gap remains between genetic potential and in situ process rates. By integrating microbiome science within a One Health perspective, we propose a conceptual framework linking microbial network structure to interconnected dimensions of ecosystem, plant, and human health. This framework addresses not only agronomic outcomes but also food safety concerns, including mycotoxin contamination by fungal pathogens, microbial contributions to nutritional quality, and pathways through which soil and plant microbiomes influence human health via the food chain. We critically examine how microbiome management can simultaneously target productivity, environmental sustainability, and health risk mitigation. We identify priority research needs in predictive microbial ecology, activity-based validation, and microbiome-informed management strategies. Rather than framing microbiomes as a universal solution to global food security, this review critically examines their realistic and context-dependent contribution to improving sustainability, resilience, and resource-use efficiency in rice production under climatic and environmental constraints, while safeguarding food safety and public health. Full article

The recent revision of the European Energy Performance of Buildings Directive (EPBD) introduces mandatory whole-life global warming potential (GWP) reporting, creating practical challenges for building life-cycle assessment due to incomplete life-cycle phase coverage in conventional Environmental Product Declarations (EPDs). This study develops and validates an integrated BIM–LCA framework for structured whole-building GWP evaluation through harmonized life-cycle module alignment and cross-tool comparison, with emphasis on the early design stages. The workflow combines rapid BIM-based screening with detailed external LCA validation, establishing a tiered assessment strategy that enables iterative material optimization within the BIM environment prior to expert review. The methodology is applied to two residential construction systems (masonry and timber), and three functional units are evaluated: total whole-building GWP, area-normalized GWP, and material-level contributions. Five comparative scenarios are analyzed, including reference, nationally representative, optimized low-carbon, and European benchmark configurations. The results show progressive GWP reductions ranging from 5% to 30% across scenarios. Although substantial absolute deviations are observed between BIM-integrated and professional LCA tools, scenario-level rankings remain fully consistent across all functional units, confirming the robustness of the screening approach for comparative decision-making. Cross-tool validation focuses on an aligned embodied-carbon scope (A1–A3 plus selected end-of-life modules) to ensure screening robustness, while full whole-life LC-GWP (including B-modules and services) is positioned as the regulatory context for subsequent expert-stage assessment. The framework provides an efficient and transferable decision-support methodology that supports early-stage carbon optimization while preserving methodological transparency for regulatory reporting. Full article

The rapid spread of antibiotic resistance through plasmid-mediated conjugation remains a primary global health concern. Despite its critical role in horizontal gene transfer, no approved drugs currently target this process, leaving a critical therapeutic gap. Integration Host Factor (IHF), a DNA-binding protein essential for plasmid replication and mobilization, emerges as a promising yet underexplored target for anti-conjugation strategies. This work aimed to develop a predictive computational model and identify small molecules that disrupt IHF function, thereby reducing plasmid transfer and limiting resistance gene dissemination. A curated dataset of 65 compounds with reported anti-plasmid activity was analyzed using a 3D-QSAR model based on algebraic descriptors computed with QuBiLS-MIDAS. The model was validated through leave-one-out cross-validation (Q² = 0.82), Tropsha’s criteria, and Y-scrambling. Representative compounds were selected via pharmacophore clustering and evaluated through molecular docking at both the DNA-binding site and a predicted allosteric pocket of IHF. The most promising complexes underwent 200 ns molecular dynamics simulations to assess stability and interaction patterns. The QSAR model demonstrated strong predictive performance (R² = 0.90). Docking simulations revealed more favorable binding energies at the allosteric site (up to −12.15 kcal/mol) compared to the DNA-binding site. Molecular dynamics confirmed the stability of these interactions, with allosteric complexes showing lower RMSD fluctuations and consistent binding energy profiles. Dynamic cross-correlation analysis revealed that allosteric ligand binding induces conformational changes in key catalytic residues, including Pro65, Pro61, and Leu66. These alterations may compromise DNA recognition and disrupt the initiation of replication. To our knowledge, this is the first computational study proposing allosteric inhibition of IHF as an anti-conjugation strategy. These findings provide a foundation for experimental validation and the development of novel agents to prevent horizontal gene transfer, offering a promising approach to restoring antibiotic efficacy against multidrug-resistant pathogens. Full article