Search Results (687)

Rural landscapes are changing rapidly, yet many assessments remain descriptive and weakly connected to planning instruments. This study connects rural landscape analysis with planning and management by examining post-earthquake transformations in Topaktaş (Tuşba, Van), a village redesigned and relocated after the 2011 events. Using ArcGIS 10.8 and the Analytic Hierarchy Process (AHP), we integrate DEM, slope, aspect, CORINE land cover Plus, surface-water presence/seasonality, and proximity to hazards (active and surface-rupture faults) and infrastructure (Karasu Stream, highways, village roads). A risk overlay is treated as a hard constraint. We produce suitability maps for settlement, agriculture, recreation, and industry; derive a composite optimum land-use surface; and translate outputs into decision rules (e.g., a 0–100 m fault no-build setback, riparian buffers, and slope thresholds) with an outline for implementation and monitoring. Key findings show legacy footprints at lower elevations, while new footprints cluster near the upper elevation band (DEM range 1642–1735 m). Most of the area exhibits 0–3% slopes, supporting low-impact access where hazards are manageable; however, several newly designated settlement tracts conflict with risk and water-service conditions. Although limited to a single case and available data resolutions, the workflow is transferable: it moves beyond mapping to actionable planning instruments—zoning overlays, buffers, thresholds, and phased management—supporting sustainable, culturally informed post-earthquake reconstruction. Full article

Background and Clinical Significance: Palatal swellings may originate from various pathological disorders. These swellings may include congenital or acquired factors. The posterior hard palate, which contains many minor salivary glands, is a common site for such swellings. Case Presentation: We present a rare case of intraoral palatal myoepithelioma in a 45-year-old Egyptian male with a significant history of smoking. Detailed clinical, radiographic, and operative findings are discussed alongside histopathological evaluation, surgical management, and postoperative outcomes. This case highlights the importance of considering myoepithelioma lesions in the differential diagnosis of posterior palatal swelling. Conclusions: Palatal myoepithelioma is a rare but important benign salivary gland tumor that may resemble multiple other intraoral lesions. A complete clinical, radiographic, and histological investigation is required for a definitive diagnosis. Complete surgical excision achieved a favorable outcome. Increased awareness and reporting of this unusual pathology are critical for deepening knowledge and guiding clinical decisions. Full article

Advances in geroscience suggest that aging is modulated by molecular pathways that are amenable to dietary and pharmacological intervention. We conducted an integrative critical review of caloric restriction (CR), intermittent fasting (IF), and caloric restriction mimetics (CR-mimetics) to compare shared mechanisms, clinical evidence, limitations, and translational potential. Across modalities, CR and IF consistently activate AMP-activated protein kinase and sirtuins, inhibit mTOR (mechanistic target of rapamycin) signaling, and enhance autophagy, aligning with improvements in insulin sensitivity, lipid profile, low-grade inflammation, and selected epigenetic aging measures in humans. CR-mimetics, such as metformin, resveratrol, rapamycin, and spermidine, partially reproduce these effects; however, long-term safety and efficacy in healthy populations remain incompletely defined. Methodological constraints—short trial duration, selective samples, intermediate (nonclinical) endpoints, and limited adherence monitoring—impede definitive conclusions on hard outcomes (frailty, disability, hospitalization, mortality). We propose the Active Management of Aging and Longevity (AMAL) model, a three-level biomarker-guided framework that integrates personalized diet, chrono-nutrition, exercise, and the selective use of CR-mimetics, along with digital monitoring and decision support. AMAL emphasizes epigenetic clocks, multi-omics profiling, inflammatory and microbiome metrics, and adaptive protocols to enhance adherence and clinical relevance. Overall, CR, IF, and CR mimetics constitute promising, complementary strategies to modulate biological aging; rigorous long-term trials with standardized biomarkers and clinically meaningful endpoints are needed to enable their scalable implementation. Full article

Wildfires pose a regularly increasing threat to ecosystems and critical infrastructure. The severity of this threat is steadily increasing. The growing threat necessitates the development of technologies for rapid and accurate early detection. However, the prevailing fire point detection algorithms, including several deep learning models, are generally constrained by the inherent hard threshold limitations in their decision-making logic. As a result, these methods lack adaptability and robustness in complex and dynamic real-world scenarios. To address this challenge, the present paper proposes an innovative two-stage, semi-supervised anomaly detection framework. The framework initially employs a Transformer-based autoencoder, which serves to transform raw fire-free time-series data derived from satellite imagery into a multidimensional deep anomaly feature vector. Self-supervised learning achieves this transformation by incorporating both reconstruction error and latent space distance. In the subsequent stage, a semi-supervised XGBoost classifier, trained using an iterative pseudo-labeling strategy, learns and constructs an adaptive nonlinear decision boundary in this high-dimensional anomaly feature space to achieve the final fire point judgment. In a thorough validation process involving multiple real-world fire cases in Yunnan Province, China, the framework attained an F1 score of 0.88, signifying a performance enhancement exceeding 30% in comparison to conventional deep learning baseline models that employ fixed thresholds. The experimental results demonstrate that by decoupling feature learning from classification decision-making and introducing an adaptive decision mechanism, this framework provides a more robust and scalable new paradigm for constructing next-generation high-precision, high-efficiency wildfire monitoring and early warning systems. Full article

Construction site layout planning (CSLP) plays a pivotal role in determining the overall efficiency and cost-effectiveness of construction projects. Material handling operations, which constitute a significant portion of indirect project costs, heavily depend on the spatial arrangement of temporary facilities such as site offices, storage yards, and equipment zones. Poorly planned layouts can lead to excessive travel distances, increased material handling times, and operational delays, all of which contribute to inflated costs and reduced productivity. Therefore, optimizing the layout of construction sites to minimize transportation distances and enhance workflow is a critical task for project managers, contractors, and other stakeholders. The challenge in CSLP lies in the complexity of simultaneously satisfying multiple, often conflicting, requirements such as space constraints, safety regulations, and functional proximities. This complexity is compounded by the dynamic nature of construction activities and the presence of numerous facilities to be allocated within limited and irregularly shaped site boundaries. Mathematically, this problem can be formulated as a Quadratic Assignment Problem (QAP), a well-known NP-hard combinatorial optimization problem. The QAP seeks to assign a set of facilities to specific locations in a manner that minimizes the total cost, typically modeled as the sum of products of flows (e.g., material movement) and distances between assigned locations. However, due to the computational complexity of QAP, exact solutions become impractical for medium to large-scale site layouts. In recent years, metaheuristic algorithms have gained traction for effectively tackling such complex optimization problems. Among these, the Advanced Jaya Algorithm (A-JA), a recent population-based metaheuristic, stands out for its simplicity, parameter-free nature, and robust search capabilities. This study applies the A-JA to solve the CSLP modeled as a QAP, aiming to minimize the total weighted travel distance of material handling within the site. The algorithm’s performance is validated through two realistic case studies, showcasing its strong search capabilities and competitive results compared to traditional optimization methods. This promising approach offers a valuable decision-support tool for construction managers seeking to enhance site operational efficiency. Full article

This study investigated how Ti6Al4V substrate topography affects the performance of diamond-like carbon (DLC) coatings. Substrates with four finishes (unpolished, #100, #400, #800 grit) were coated, and their morphology, wettability, bonding structure, mechanical properties, and biological response were examined. Characterization was performed using AFM, SEM, contact angle tests, Raman spectroscopy, and nanoindentation. Biocompatibility was evaluated with A549 epithelial cells. DLC deposition reduced roughness while partly preserving surface features. Increasing Ra was associated with lower surface free energy and ID/IG ratios. It also correlated with higher hardness and modulus, reflecting greater sp³ bonding. Biological results, however, indicated that surface organization was more decisive than Ra magnitude. The #100-grit surface, with aligned anisotropic grooves, supported uniform wetting, protein adsorption, and sustained proliferation. In contrast, the unpolished and smoother surfaces did not maintain long-term growth. These findings suggest that anisotropy, rather than Ra alone, plays a key role in optimizing DLC-coated Ti6Al4V implants. Full article

Access to clean drinking water is crucial for any region’s social and economic growth. However, rapid urbanization and industrialization have significantly deteriorated water quality, posing severe pollution threats from domestic, agricultural, and industrial sources. This study presents an innovative framework for assessing water quality in North Bhubaneswar, integrating the Water Quality Index (WQI) with statistical analysis, geospatial technologies, and machine learning models. The WQI, calculated using the Weighted Arithmetic Index method, provides a single composite value representing overall water quality based on several key physicochemical parameters. To evaluate potable water quality across 21 wards in the northern zone, several key parameters were monitored, including pH, electrical conductivity (EC), dissolved oxygen (DO), hardness, chloride, total dissolved solids (TDSs), and biochemical oxygen demand (BOD). The Weighted Arithmetic WQI method was employed to determine overall water quality, which ranged from excellent to good. Furthermore, Principal Component Analysis (PCA) revealed a strong positive correlation (r > 0.6) between pH, conductivity, hardness, and alkalinity. To enhance the accuracy and reliability of water quality assessment, multiple machine learning models Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naïve Bayes (NB) were applied to classify water quality based on these parameters. Among them, the Decision Tree (DT) and Random Forest (RF) models demonstrated the highest precision (91.8% and 92.7%, respectively) and overall accuracy (91.7%), making them the most effective in predicting water quality and integrating WQI, machine learning, and statistics to analyze water quality. The study emphasizes the importance of continuous water quality monitoring and offers data-driven recommendations to ensure sustainable access to clean drinking water in North Bhubaneswar. Full article

Diabetic Retinopathy (DR) is a leading cause of vision impairment globally, underscoring the need for accurate and early diagnosis to prevent disease progression. Although fundus imaging serves as a cornerstone of Computer-Aided Diagnosis (CAD) systems, several challenges persist, including lesion scale variability, blurry morphological patterns, inter-class imbalance, limited labeled datasets, and computational inefficiencies. To address these issues, this study proposes an end-to-end diagnostic framework that integrates an enhanced preprocessing pipeline with a novel deep learning architecture, Hierarchical-Inception-Residual-Dense Network (HIRD-Net). The preprocessing stage combines Contrast Limited Adaptive Histogram Equalization (CLAHE) with Dilated Difference of Gaussian (D-DoG) filtering to improve image contrast and highlight fine-grained retinal structures. HIRD-Net features a hierarchical feature fusion stem alongside multiscale, multilevel inception-residual-dense blocks for robust representation learning. The Squeeze-and-Excitation Channel Attention (SECA) is introduced before each Global Average Pooling (GAP) layer to refine the Feature Maps (FMs). It further incorporates four GAP layers for multi-scale semantic aggregation, employs the Hard-Swish activation to enhance gradient flow, and utilizes the Focal Loss function to mitigate class imbalance issues. Experimental results on the IDRiD-APTOS2019, DDR, and EyePACS datasets demonstrate that the proposed framework achieves 93.46%, 82.45% and 79.94% overall classification accuracy using only 4.8 million parameters, highlighting its strong generalization capability and computational efficiency. Furthermore, to ensure transparent predictions, an Explainable AI (XAI) approach known as Gradient-weighted Class Activation Mapping (Grad-CAM) is employed to visualize HIRD-Net’s decision-making process. Full article

In this study, a fuzzy-logic-based decision support model is developed to predict the fatigue life of load-bearing system elements such as torque arm. Traditional methods for fatigue life prediction are mostly based on certain mathematical expressions and fixed parameters and do not adequately take into account the uncertainties caused by many factors such as material structure, surface condition, loading pattern and heat treatment. In order to overcome these deficiencies, the fuzzy logic method is preferred. The model is based on a fuzzy logic system and predicts outputs according to specific input conditions using rules derived from expert knowledge and experience. The input parameters of the model are material type, surface hardness, maximum applied stress level, and type of heat treatment. Although these parameters can be expressed numerically in the classical sense, the relationship between them is often imprecise and based on experience and engineering interpretation. Therefore, a more realistic and flexible prediction model has been created with the linguistic variables and rule-based approach of fuzzy logic. Full article

Unmanned Aerial Vehicles (UAVs) play a crucial role in the continuous monitoring and coordination of disaster relief operations, providing real-time information that aids in decision-making and resource allocation. However, optimizing the deployment of multi-UAV systems in disaster-stricken areas presents a significant challenge. This challenge arises due to conflicting objectives, such as maximizing coverage while minimizing energy consumption, critical to ensuring prolonged operational capability in dynamic and unpredictable environments. To address these challenges, this paper proposes a novel successive deployment method specifically designed for optimizing UAV placements in complex disaster relief scenarios. The overall optimization problem is decomposed into two NP-hard subproblems: the coverage problem and the Energy Consumption (EC) problem. To achieve maximum coverage of the affected area, we employ the Strong Weiszfeld (SW) algorithm to determine optimal UAV placement. Simultaneously, to minimize energy consumption while maintaining optimal coverage performance, we utilize the Steepest Descent with Goldstein (SDG) algorithm. This dual-algorithmic approach is tailored to balance the trade-offs between wide-area coverage and energy efficiency. We validate the effectiveness of the proposed SW + SDG method by comparing its performance against traditional deployment strategies across multiple scenarios. Experimental results demonstrate that our approach significantly reduces energy consumption while maintaining extensive coverage, and outperforms conventional algorithms. This not only ensures a more sustainable and long-lasting operational network but also enhances deployment efficiency and stability. These findings suggest that the SW + SDG algorithm is a robust and versatile solution for optimizing multi-UAV deployments in dynamic, resource-constrained environments, providing a balanced approach to coverage and energy efficiency. Full article

This paper aims to study a distribution decision support system (DSS) conceptual framework for textile logistics, combining the operational requirements of logistics enterprises in textile markets to optimize vehicle surplus tonnage usage and distribution flexibility, using the integrated computer-aided manufacturing definition (IDEF) method and developing a comprehensive conceptual framework for textile logistics distribution decisions, complemented by an in-depth analysis of its underlying database structure. Further, this paper constructs the model base and proposes two vehicle-loading models and their solving algorithms, including one model with constraints on the maximum loading rate and the other with constraints on the smallest vehicle numbers, with these algorithms implemented by linear programming in operational research and performed by programming techniques. This paper also constructs the method base and designs some methods, such as the method of vehicle surplus tonnage utilization, the method of vehicle-loading priority order selection, and the simultaneous loading method of multi-freight cargo and multiple vehicles; these methods are implemented by the database principle and technological or programming techniques. We use a test distribution DSS conceptual framework to run the data example and obtain a good test result. The findings indicate that the DSS conceptual framework can integrate the model and method bases and can also solve the hard problems of the use of surplus tonnage vehicles and simultaneous loading. Full article

This study proposes a novel non-destructive approach to assessing and predicting the quality of stored date fruits using a composite quality index (Qi) modeled via visible–near-infrared (VIS–NIR) spectroscopy and artificial neural networks (ANNs). Two leading cultivars, Sukkary and Khlass, were stored for 12 months using three temperature regimes (25 °C, 5 °C, and −18 °C) and five types of packaging. The samples were grouped into six moisture content categories (4.36–36.70% d.b.), and key physicochemical traits, namely moisture, pH, hardness, total soluble solids (TSSs), density, color, and microbial load, were used to construct a normalized, dimensionless Qi. Spectral data (410–990 nm) were preprocessed using second-derivative transformation and modeled using partial least squares regression (PLSR) and the ANNs. The ANNs outperformed PLSR, achieving the correlation coefficient (R²) values of up to 0.944 (Sukkary) and 0.927 (Khlass), with corresponding root mean square error of prediction (RMSEP) values of 0.042 and 0.049, and the relative error of prediction (REP < 5%). The best quality retention was observed in the dates stored at −18 °C in pressed semi-rigid plastic containers (PSSPCs), with minimal microbial growth and superior sensory scores. The second-order Qi model showed a significantly better fit (p < 0.05, AIC-reduced) over that of linear alternatives, capturing the nonlinear degradation patterns during storage. The proposed system enables real-time, non-invasive quality monitoring and could support automated decision-making in postharvest management, packaging selection, and shelf-life prediction. Full article

Urban logistics face complexity due to traffic congestion, fleet heterogeneity, warehouse constraints, and driver workload balancing, especially in the Heterogeneous Multi-Trip Vehicle Routing Problem with Time Windows and Time-Varying Networks (HMTVRPTW-TVN). We develop a mixed-integer linear programming (MILP) model with dual-peak time discretization and exact linearization for heterogeneous fleet coordination. Given the NP-hard nature, we propose a Hyper-Heuristic based on Cumulative Reward Q-Learning (HHCRQL), integrating reinforcement learning with heuristic operators in a Markov Decision Process (MDP). The algorithm dynamically selects operators using a four-dimensional state space and a cumulative reward function combining timestep and fitness. Experiments show that, for small instances, HHCRQL achieves solutions within 3% of Gurobi’s optimum when customer nodes exceed 15, outperforming Large Neighborhood Search (LNS) and LNS with Simulated Annealing (LNSSA) with stable, shorter runtime. For large-scale instances, HHCRQL reduces gaps by up to 9.17% versus Iterated Local Search (ILS), 6.74% versus LNS, and 5.95% versus LNSSA, while maintaining relatively stable runtime. Real-world validation using Shanghai logistics data reduces waiting times by 35.36% and total transportation times by 24.68%, confirming HHCRQL’s effectiveness, robustness, and scalability. Full article

Background: Detailed assessments, documentation, and evaluation of the wound characteristics in hard-to-heal wounds are essential for optimizing and individualizing wound care. However, the remaining challenge in clinical care includes the lack of high accuracy and precision tools for automated wound size (surface area and depth assessment) and a wound bed evaluation, i.e., a qualitative and quantification assessment of slough and necrosis. Objective/Methods: This study evaluates the accuracy and precision of the AI-powered technique, SeeWound© 2, compared to digital planimetry for a wound surface area and a wound bed characterization (slough and necrosis) in “in vitro” models and in patients, and a probe for depth, including diabetic foot ulcers, venous ulcers, pressure ulcers, and ischemic ulcers. Results: The data show that accuracy and precision (SeeWound© 2) for the wound surface area, the depth, and the wound bed characterization (slough and necrosis) were accuracy 96.28% and 90.00%, (CV 5.56%), respectively (wound size); 90.75% and 89.55%, (CV 3.07%), respectively (wound depth); 80.30% (slough) and 84.73% (necrosis) and 93.51% (slough) (CV 4.15%) and 82.35% (CV 8.34%) (necrosis). The precision for the digital planimetry was 88.61% (CV 7.00%) (slough) 85.74% (CV 7.54%) (necrosis). Conclusions: The overall accuracy and precision of the AI model in identifying wound size and depth were close to 90%, except for the accuracy and precision for slough and necrosis, where levels around 80% were achieved when compared to digital planimetry. The findings for the wound surface area and depth assessments, together with quantification of slough and necrosis, suggest that the SeeWound© 2 model can offer significant clinical benefits by improving documentation and supporting decision-making in wound management. Full article

Under the framework of the “dual-carbon” goals of China (“carbon peak” by 2030 and “carbon neutrality” by 2060), the escalating complexity of emerging power systems presents significant challenges to safety governance. Traditional management models are now confronting bottlenecks, notably in knowledge inheritance breakdown and lagging risk prevention and control. This paper explores the application of large AI models in safety and emergency management in the power industry. Through core capabilities—such as natural language processing (NLP), knowledge reasoning, multimodal interaction, and auxiliary decision making—it achieves full-process optimization from data fusion to intelligent decision making. The study, anchored by 18 cases across five core scenarios, identifies three-dimensional challenges (including “soft”—dimension computing power, algorithm, and data bottlenecks; “hard”—dimension inspection equipment and wearable device constraints; and “risk”—dimension responsibility ambiguity, data bias accumulation, and model “hallucination” risks). It further outlines future directions for large-AI-model application innovation in power industry safety and management from a four-pronged outlook, covering technology, computing power, management, and macro-level perspectives. This work aims to provide theoretical and practical guidance for the industry’s shift from “passive response” to “intelligent proactive prevention”, leveraging quantified scenario-case analysis. Full article