Search Results (7,455)

Post-war reconstruction requires effective decision-support tools capable of integrating technical, economic, and organizational criteria under conditions of high uncertainty. The evaluation and prioritization of damaged buildings for recovery interventions are critical challenges for reconstruction project management. This study proposes a hybrid decision-support framework for assessing the strategic feasibility of building recovery using a novel Strategic Revitalization Index (SRI). The proposed methodology integrates a hierarchical fuzzy inference system, simulation techniques, and an artificial neural network surrogate model. The fuzzy model aggregates four key evaluation dimensions: technical condition of the building, economic feasibility of recovery actions, project implementation capability, and environmental and social impact. To analyze the model’s behavior and generate training data, a synthetic dataset was created using Latin Hypercube Sampling, covering a wide range of possible reconstruction conditions. The generated dataset was subsequently used to train an artificial neural network capable of approximating the nonlinear mapping implemented by the fuzzy decision model. The obtained results demonstrate high predictive performance of the surrogate model, with R² = 0.976, RMSE = 0.0266, MAE = 0.0133, and MAPE = 4.95%. Scenario analysis further illustrates how different recovery strategies influence SRI values and enables comparison of alternative reconstruction approaches. The proposed framework provides a flexible analytical tool for supporting strategic decision-making in post-war reconstruction projects. By combining fuzzy logic, simulation techniques, and machine learning, the model enables systematic prioritization of recovery strategies and may support large-scale reconstruction planning in post-conflict environments. Full article

Lithology identification in complex heterogeneous formations faces the challenges of overlapping well-log responses and long-tailed data distributions, causing traditional flat models to misclassify fine-grained lithologies. To address this, we propose a hierarchical routing method integrating geological priors and rock mechanics. A joint resampling strategy combining stratified undersampling and SMOTE first rectifies data bias. Next, conventional well logs are inverted into mechanical parameters, such as uniaxial compressive strength and abrasiveness, constructing a high-dimensional physical discriminative space. Finally, a two-stage network is deployed: an XGBoost gating model performs macroscopic rock classification to block cross-facies noise, followed by a dynamic routing mechanism distributing samples to deep neural network (DNN) sub-experts for fine-grained decoupling. Tested on actual multi-block logging data, the XGBoost-DNN architecture achieved a global accuracy of 0.85 and improved the macro F1-score from 0.55 under the baseline model to 0.84. Integrating physical mechanisms with a hierarchical topology effectively overcomes bottlenecks in fine-grained lithology identification, offering robust geological interpretability and engineering value. Full article

Background/Objective: Sesame (Sesamum indicum L.) is a nutritionally and economically important oilseed crop that is grown predominantly by smallholder farmers in Mozambique. However, its breeding process is constrained by a limited understanding of the genetic diversity in sesame germplasm. Therefore, this study determined the genetic diversity and population structure of a panel of 109 sesame accessions from Instituto de Investigação Agrária de Mocambique (IIAM) using DArTseq SNPs. Methods: The generated 14,763 SNPs were filtered, retaining 11,502 high-quality SNPs for this study. Results: Overall genetic diversity was moderate (mean He = 0.30, Ho = 0.30, MAF = 0.21, PIC = 0.25). Population structure analysis using sparse non-negative matrix factorization identified eight subpopulations, consistent with principal component analysis implemented via the Latent factor mixed model. Discriminant analysis of principal components (DAPC) and Ward’s hierarchical clustering based on Nei’s distance resolved the same eight clusters, although DAPC revealed overlap among clusters, consistent with extensive admixture. Analysis of molecular variance showed that 85.85% of total molecular variation was within subpopulations and 14.15% among the subpopulations. Pairwise fixation indices (ranging from 0.02 to 0.10) identified divergent subpopulations 7 and 1 as suitable candidates for hybridization. Within subpopulations, observed heterozygosity exceeded expected heterozygosity, likely reflecting residual heterozygosity in sesame landraces, admixture, reverse Wahlund effect and scoring of paralogs as heterozygous SNPs. Conclusions: Overall, this study provided insights into sesame’s genetic diversity in Mozambique, contributing to germplasm conservation and informed parental selection. Full article

Self-collision detection has become the dominant computational bottleneck in GPU-accelerated cloth simulation, as modern parallel solvers such as XPBD have drastically reduced the cost of position updates while leaving collision resolution largely unoptimized. Existing spatial partitioning methods treat all cloth regions uniformly, saturating GPU memory bandwidth despite the fact that the vast majority of the mesh surface remains geometrically flat and collision-free at any given frame. We propose a hierarchical self-collision culling framework built upon a resolution-independent discrete curvature metric derived from the $h^2$-normalized Laplace-Beltrami operator, integrated with a discrete Kirchhoff–Love shell model combining distance and dihedral bending constraints within XPBD. Unlike prior cache-dependent acceleration strategies, our method tightly couples curvature-driven geometric pruning with a fused GPU kernel design and shows that this stateless formulation is both faster and physically more reliable. Evaluated on meshes of $512\times 512$ and $1024\times 1024$ particles, our method achieves a $5.5\%$ and $9.7\%$ FPS improvement alongside a $34.9\%$ and $28.4\%$ reduction in active collision pairs, respectively, with qualitative validation via high-fidelity rendering confirming artifact-free self-contact and strict ground-plane non-penetration. Ablation results further reveal that temporal coherence, conventionally regarded as an optimization standard, strictly degrades both performance (FPS decrease of $1.4\%p$ to $1.9\%p$) and physical accuracy (penetration depth increase of $36.1\%$ to $100.0\%$ relative to the curvature-only stage) on RTX 3070 GPU, advocating for stateless per-frame geometric evaluation as the preferred design paradigm. Full article

Radar echo extrapolation under severe convective conditions remains challenging because efficient prediction models still tend to suffer from strong-echo attenuation, boundary blurring, and performance degradation at longer lead times. To address these issues, this study proposes SMG-Net, a SimVP-based radar echo extrapolation model with a collaborative multistage design. The proposed framework integrates multiscale spatial enhancement, trend–disturbance differentiated temporal modeling, and gated hierarchical feature fusion to improve structural preservation and temporal stability. Experiments on a regional radar dataset show that SMG-Net achieves the lowest MSE (0.032) and the highest SSIM (0.830) among the compared models. At the 30 dBZ threshold, CSI, POD, and FAR reach 0.042, 0.045, and 0.250, respectively, indicating improved strong-echo detectability and reduced false alarms. The results further show that SMG-Net is particularly effective in preserving the morphology, boundary structure, and intensity distribution of medium- and strong-echo regions at longer lead times, while introducing only limited additional computational cost over the baseline SimVP. These findings indicate that SMG-Net improves the preservation of medium- and strong-echo structures in efficient radar echo extrapolation and has practical value for short-term precipitation nowcasting in severe convective scenarios. Full article

Short-term wind power forecasting is considered a critical challenge in smart grid management due to the nonlinear, unstable, and multi-scale noise characteristics of wind signals. Although recent advances in hybrid deep learning have improved the accuracy of short-term wind power forecasting, many state-of-the-art models usually consider signal denoising, residual decomposition, and attention mechanisms as independent modules without providing a unified solution. This paper proposes an end-to-end solution, W-HiTS-Attention (Wavelet Transform, N-stacked Hierarchical Interpolation for Time Series, Attention), which coherently integrates wavelet denoising, hierarchical residual learning from N-HiTS (Neural Hierarchical Interpolation), and an in-block self-attention mechanism. The proposed solution outperforms 21 benchmarks in accuracy, including state-of-the-art baselines such as N-BEATS, N-HiTS, TCN, Informer, Autoformer, LSTM, BiLSTM, GRU, and Prophet, achieving an RMSE of 55.56 W and an R² of 0.9918. Furthermore, the results show that the proposed solution is efficient in terms of parameter count (0.033M), latency (0.0036 ms/sample), and training time, making it promising for low-latency inference in resource-constrained environments. The results show that the coherent integration of frequency preprocessing, hierarchical residual forecasting, and attention-based temporal refinement provides a robust, explainable, and deployable solution for short-term wind power forecasting. Full article

Superhydrophobic coatings are regarded as a promising passive anti-icing strategy; however, their practical engineering application, particularly in electrical insulation, is severely hindered by the performance deterioration caused by mechanical damage and a lack of theoretical understanding of microscopic ice adhesion mechanisms. In this study, a layered polymer composite coating was designed to resolve the trade-off between abrasion resistance and low ice adhesion. The chemistry of the coating relies on a synergistic “primer–topcoat” design: the primer consists of an epoxy resin matrix chemically modified by amino silicone oil to lower its surface energy and improve toughness, while the topcoat features hierarchical SiO₂ clusters functionalized with hexamethyldisilazane (HMDS) and silane coupling agents. This architecture was fabricated via a controllable layer-by-layer spraying method. Systematic investigations revealed that the hierarchical micro/nanostructure, composed of microscale protrusions and nanoscale SiO₂ clusters, provides excellent superhydrophobicity (contact angle of 155.2°, sliding angle of 2°). Crucially, the crosslinked polymer network and stable siloxane (Si-O-Si) covalent bonding ensure that the coating maintains its functionality after a cumulative sand impact of 3 kg, demonstrating superior mechanical durability. Furthermore, differentiated theoretical models for ice adhesion in Cassie–Baxter and Wenzel states were established based on intermolecular interactions, identifying that maintaining a stable Cassie–Baxter state is key to reducing adhesion. This study offers a robust approach to balancing functionality and durability in polymer composites through synergistic structural design, providing both a scalable fabrication strategy and a quantitative theoretical framework for understanding interfacial ice adhesion. Full article

The objective of this study was to assess and model the condition and resilience of the Cunas River using integrated indices and multivariate statistics in order to determine the impact of anthropogenic pressure and enhance water security in the Peruvian Andes. Stations in the upper, middle, and lower reaches of the river were monitored during the rainy and dry seasons, applying quality indices (NSF-WQI, WA-WQI, CCME-WQI, and I-WQI), principal component analysis (PCA), hierarchical cluster analysis (HCA), and Spearman’s rank correlation (ρ) to assess the intensity and direction of associations between physical–chemical parameters. The results reveal severe degradation in the lower section of the river, with critical hypoxia and extreme coliform levels during the dry season, drastically exceeding the levels in the upper reach. The I-WQI demonstrated superior performance (322.24; Unfit) by being more sensitive than the NSF-WQI (53.15–59.87). PCA confirmed that low flow explains the greatest variance in pollution (PC1 71.55%), while HCA identified maximum synergy (rescaling distance < 1) between biochemical oxygen demand (BOD₅) and total phosphorus, indicating the collapse of self-purification capacity. The HCA identified a maximum synergy between BOD₅ and total phosphorus during the low-flow season, while the PCA confirmed that low discharge intensifies pollutant concentrations. These findings support the need for resilience-based governance that prioritizes the protection of natural infrastructure. Full article

Mathematics achievement in South African schools continues to be limited by identifiable barriers to instructional improvement, including inadequate technological infrastructure, excessive teacher workloads, and inconsistent institutional support for professional learning. Although data-driven instruction is widely promoted, little is known about how psychological constructs such as instrumental attitudes, perceived control, social norms, and self-efficacy influence teachers’ use of data. This study, therefore, explored mathematics teachers’ perspectives on data use, guided by the Theory of Planned Behaviour (TPB). TPB was selected because, unlike purely cognitive or socio-cultural models, it integrates individual psychological factors, attitudes, perceived control, and self-efficacy with social and contextual influences on behaviour, making it particularly well suited to examining data use within complex, resource-constrained school environments. A qualitative design was employed, involving focus-group discussions with senior-phase mathematics teachers. Data were thematically analysed using NVivo 14, with iterative coding aligned with TPB constructs. Findings revealed that while teachers valued data for diagnosing learning gaps, they perceived data tasks as administratively demanding. Collegial collaboration fostered authentic engagement, whereas hierarchical accountability and limited technological capacity reduced motivation and autonomy. The interaction among attitudes, social norms, and perceived control showed that both belief systems and institutional conditions shape teachers’ behavioural intentions. The study concludes that professional development should strengthen teachers’ data literacy, encourage collaborative learning cultures, and improve infrastructural support to promote effective data-driven mathematics instruction in resource-constrained contexts. Full article

Background/Objectives: The Japanese Veterinary Antimicrobial Resistance Monitoring System (JVARM) conducts longitudinal monitoring of antimicrobial resistance (AMR) in indicator bacteria from food-producing animals. For Escherichia coli from healthy pigs, slaughterhouse-based sampling has been conducted for approximately a decade, yielding a substantial accumulation of MIC data. While JVARM reporting has traditionally focused on annual resistance proportions by drug, the availability of long-term data enables investigation of cross-drug relationships, including MIC similarity and co-resistance patterns. This study aimed to (i) identify the co-resistance structure among antimicrobial agents using MIC- and phenotype-based similarity measures and (ii) identify drug resistances most strongly associated with multidrug resistance (MDR). Methods: We analyzed broth microdilution MIC data obtained annually for E. coli isolates from healthy pigs in the JVARM program in Japan between 2012 and 2023. Antimicrobial resistance was classified from MIC results and annual resistance prevalence was calculated for each antimicrobial. For the co-resistance and MDR analyses, isolate-level data were pooled across the full study period. To identify co-resistance structure, we performed hierarchical clustering using (i) correlation-based similarity of MIC profiles and (ii) Jaccard similarity of binary resistance profiles (resistant/susceptible classification). Multidrug resistance (MDR; ≥3 antimicrobial classes) was further modeled using XGBoost with each drug resistance as a predictive feature, and feature contributions were evaluated using gain, permutation importance, and SHAP values. We also examined how SHAP-based attributions varied when the outcome definition was set to ≥1-, ≥2-, or ≥3-class resistance. Results: Within the study period, resistance remained highest for tetracycline and moderate for streptomycin, ampicillin, sulfamethoxazole–trimethoprim, and chloramphenicol, whereas resistance to other agents was low. MIC-based correlation analysis revealed coordinated variation among ampicillin, sulfamethoxazole–trimethoprim, streptomycin, chloramphenicol, and tetracycline. Separately, Jaccard similarity of binary resistance profiles identified two closely positioned co-resistance groupings (Ampicillin/Streptomycin/Tetracycline and chloramphenicol/sulfamethoxazole–trimethoprim). Ampicillin was identified as the medoid in both MIC-based and resistance-profile similarity spaces, with streptomycin also positioned near the center in both structures. In the XGBoost model for MDR (≥3 classes), ampicillin resistance was consistently the highest-contributing feature when evaluated by gain, permutation importance, and SHAP. When we examined how SHAP-based attributions varied across outcome definitions (≥1-, ≥2-, and ≥3-class resistance), feature importance largely followed resistance prevalence at ≥1–≥2 classes (tetracycline highest) but shifted at ≥3 classes to ampicillin as the top feature. Conclusions: Both MIC-based and phenotype-based analyses revealed co-resistance structures. Under the MDR definition used in this study, explainable machine-learning analyses showed that ampicillin resistance emerged as a leading resistance feature associated with MDR. Because these findings are associative rather than causal, further work will be needed to clarify mechanisms. These findings have important implications for antimicrobial resistance control in the Japanese pig sector, indicating that stewardship strategies may need to be tailored according to antimicrobial class and underlying co-resistance structure. Full article

Bone is a hierarchically organized composite material with unique mechanical properties and an intrinsic regenerative capacity that conventional repair strategies, including autografts, allografts, xenografts, and metallic or ceramic implants, fail to fully replicate due to donor scarcity, immunogenicity, mechanical mismatch, and poor long-term integration. Bone tissue engineering (TE) offers a biologically informed alternative by integrating osteoconductive scaffolds, osteogenic progenitor cells, and osteoinductive signaling molecules into a unified regenerative framework. Unlike existing reviews that evaluate these components in isolation, this review provides a mechanistically integrated analysis that repositions scaffold design as a biologically instructive platform whose topography, stiffness, porosity, and surface chemistry collectively govern cell adhesion, mechanotransduction, osteogenic differentiation, and extracellular matrix remodeling. Critically, it moves beyond cataloging materials and fabrication approaches to evaluate how specific scaffold features drive biological outcomes and to identify frequently understated limitations, including polymer-ceramic degradation kinetics and the inadequacy of small-animal models for clinical translation. By synthesizing advances in biomaterials, additive manufacturing, and smart scaffold technologies within this integrative framework, this review provides researchers and clinicians with a structured framework for evaluating emerging strategies and prioritizing future directions in functional bone regeneration. Full article

Driving behavior primitives serve as fundamental building blocks for modeling and semantically interpreting time-series driving behavior. Extracting behavior primitives is challenging due to the high dimensionality and complex interdependencies among behavioral variables, as well as the rich temporal dynamics of real-world driving maneuvers. This paper proposes an unsupervised two-stage framework that optimizes time-series segmentation and segment clustering to yield interpretable and context-aware behavior primitives. First, a Hierarchical Bayesian Model-based Agglomerative Sequence Segmentation (H-BMASS) method is introduced that decouples longitudinal and lateral driving behaviors and performs hierarchical segmentation. This design mitigates under-segmentation by ensuring that change points reflect genuine behavioral transitions. Second, to cluster driving segments of varying durations into a finite set of primitive types, an Integrating Numerical and Trend Discretization Latent Dirichlet Allocation (INT-LDA) model is developed. The model combines variables’ temporal trend discretization with numerical discretization to create symbolic representations of driving data, thereby preserving the essential time dependency of driving behavior and improving segment clustering accuracy. Evaluated on naturalistic driving data collected from a high-fidelity simulator, the proposed framework identifies five distinct behavior primitives with clear physical interpretations. The resulting primitives provide a compact, semantically rich representation of driving behavior, facilitating driver modeling, decision prediction, and scenario-based testing for autonomous vehicles. Full article

Structural semiotics, as developed by A. J. Greimas and the Paris School, provides a powerful framework for analyzing narrative meaning through actantial roles, modalities, and hierarchical narrative structures. Despite its longstanding engagement with formal reasoning and diagrammatic tools, it has seen relatively few explicit mathematical formalizations. This article proposes a diagrammatic reconstruction of key Greimassian concepts using the language of symmetric monoidal and hypergraph categories. We treat the actantial model as a typing schema and introduce wiring diagrams as a formal semantics for representing narrative configurations, modal transformations, and actantial redistribution. Modal operations such as knowing-how-to-do, wanting-to-do, and causing-to-do are modeled as typed morphisms, while Frobenius structures account for duplication, erasure, and persistence of actants across narrative time. We show how operadic nesting captures hypotaxis, and how diagrammatic factorization yields higher-level abstractions corresponding to the hypotactical clusters of the canonical narrative schema. The approach is illustrated through a detailed analysis of Aesop’s The Fox & the Crow, culminating in a formal account of discoursivization via actorialization, spatialization, and temporalization. Rather than replacing structural semiotics, this work provides it with a compositional and mathematically explicit toolkit that clarifies existing concepts and opens new possibilities for comparative, computational, and interdisciplinary analysis. Full article

Accurate high-throughput fruit detection is the core prerequisite for precision citrus management. Existing models face a critical trade-off between accuracy for small fruits and computational efficiency, restricting large-scale industry transformation. To resolve this, we propose Citrus-SGYOLOv2, an optimized deep learning architecture specifically engineered for high-throughput phenotypic monitoring. The primary contribution of this work lies in three synergistic innovations: a novel Symmetric Ghost Backbone that prunes architectural redundancy while maintaining hierarchical feature depth; a Citrus Color Prior Calibration Attention Mechanism (Citrus_SE) that embeds physiological chromaticity priors to suppress complex spectral noise from foliage; and a P2-layer-based full-scale fusion strategy designed to recover fine-grained spatial details lost during downsampling. Experiments on our self-built dataset show that Citrus-SGYOLOv2 achieves 95.54% mAP@50 and 77.13% mAP@50–95, outperforming YOLOv11s by 5.03 and 9.90 percentage points respectively. Notably, the model achieves a 48.8% reduction in parameters (4.84 M) while sustaining a high-throughput inference speed of 139.00 FPS. This research provides a robust and efficient foundational framework for intelligent yield estimation and precision orchard management. Full article

The construction sector in Saudi Arabia is under increasing pressure to enhance productivity and technological capability in line with Vision 2030, yet the adoption of artificial intelligence (AI) remains uneven. This study investigates the multi-level barriers affecting AI adoption in the Saudi construction industry using a sequential explanatory design that combines large-scale survey analysis with Interpretive Structural Modeling (ISM) and MICMAC classification. Data were collected from 181 construction professionals through a structured questionnaire covering eight constructs and 50 measurement items. Descriptive statistics reveal moderate AI utilization with a clear preference for analytics-driven applications over physical automation technologies. Perceptual rankings identify trust deficits and workforce capability gaps as prominent concerns. However, the ISM hierarchy uncovers a different structural reality: limited government support emerges as the root driver, cascading through cost and leadership constraints into workforce deficiencies, attitudinal resistance, and ultimately data ecosystem challenges. This perception–structure divergence highlights the risk of prioritizing visible symptoms over foundational causes. The MICMAC analysis further confirms the dominance of policy and strategic drivers within the adoption system. The study contributes by providing one of the first hierarchical mappings of AI adoption barriers in the Saudi construction context and offers a phased intervention roadmap for policymakers and industry leaders. The findings emphasize that sustainable AI diffusion in government-influenced construction ecosystems requires coordinated action across regulatory, organizational, and human capital dimensions rather than isolated technical investments. Full article