Search Results (44,032)

Distribution networks are being transformed by the growing penetration of distributed generation (DG), which changes power flows, voltage profiles, and the optimal operating point of the feeder. This study proposes a hybrid technique that combines the Gray Wolf Optimizer (GWO) with a neural network (NN) surrogate model to solve the distribution network reconfiguration (DNR) problem. The method minimizes active power losses while improving voltage regulation and preserving radial operation under operational constraints. The GWO performs global exploration of discrete switch configurations, whereas the NN accelerates local refinement by screening candidates before exact AC power flow validation. This manuscript presents benchmark results for the IEEE 33-bus and IEEE 69-bus distribution test systems. For the IEEE 33-bus benchmark, DG units are installed at buses 14, 25, and 30. For the IEEE 33-bus case, losses are reduced from 282.94 kW in the base case to 120.65 kW with DG and to 87.08 kW after hybrid reconfiguration, while the minimum voltage magnitude improves from 0.8829 p.u. to 0.9587 p.u. For the IEEE 69-bus case, total active losses decrease from 224.95 kW to 82.22 kW with DG and to 29.92 kW after reconfiguration while concurrently improving the voltage profile and line loading. From a sustainability perspective, the main contribution of the proposed workflow is to reduce technical losses at the distribution level, thereby improving energy efficiency for a given demand. Overall, the results show that the combined use of DG and surrogate-assisted reconfiguration can yield substantial efficiency gains across benchmark feeders of varying sizes, while broader multi-feeder validation and more detailed surrogate error quantification remain necessary before claiming general applicability. Full article

With the continuous advancement in reproductive biology, oocyte vitrification has become a critical technology for preserving female germplasm and protecting it from environmental disruptions. This technique also eliminates temporal and spatial constraints in animal embryo engineering research. However, during the vitrification of animal oocytes, exposure to low temperatures and high concentrations of cryoprotectants can cause various forms of damage, including cytoskeletal disruption, spindle abnormalities, mitochondrial dysfunction, apoptosis, oxidative stress and epigenetic modifications. These issues are now understood to severely restrict the subsequent developmental competence of oocytes, resulting in lower cleavage and blastocyst formation rates than those of fresh oocytes. Currently, the mechanisms of cryodamage in vitrified oocytes remain poorly understood, and standardized strategies to enhance vitrification efficiency have yet to be firmly established. This review provides a formal overview of the physiological factors underlying oocyte sensitivity to vitrification, alongside the mechanisms of cryodamage and the variables influencing post-thaw survival and reproductive success. It evaluates strategies for mitigating vitrification-induced stress, compares interspecies differences, and addresses current research limitations. By identifying future directions, this review offers new insights for optimizing mammalian oocyte cryopreservation techniques. Full article

This systematic review and meta-analysis evaluate the comparative diagnostic efficacy and safety of endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) and transbronchial mediastinal cryobiopsy (EBUS-TBMC) for sampling mediastinal and hilar lymph nodes. Following the PRISMA 2020 guidelines, 20 studies published between January 2020 and July 2025 were analysed to provide a comprehensive performance overview. The results demonstrate that EBUS-TBMC offers a significantly higher overall diagnostic efficacy compared to EBUS-TBNA, with a pooled risk difference (RD) of 0.30 (95% CI: 0.17–0.44, p < 0.001). The subgroup analyses revealed a trend toward a superior yield for EBUS-TBMC in lymphoma (RD 0.11, p = 0.05) and sarcoidosis (RD 0.03, p = 0.077), while no significant differences were found for lung cancer subtypes. Safety profiles remained comparable, with no significant differences in the risk of pneumothorax (RD 0.00, p = 1.00) or bleeding (RD 0.00, p = 0.965). In conclusion, these findings support integrating EBUS-TBMC into diagnostic algorithms when preserved tissue architecture is critical, such as for lymphoproliferative disorders, granulomatous diseases, and advanced molecular profiling, providing a safe and more effective alternative to conventional needle aspiration. Full article

To address bounding-box merging, missed detections, and class confusion in complex document layouts, this study proposes YOLO-GFD, a lightweight document layout detection algorithm that balances global layout modeling and fine-grained feature representation. Built upon YOLO11n, the proposed method introduces an RMSNorm-optimized AIFI-Lite module at the high-semantic stage to enhance long-range dependency modeling with improved stability and parameter efficiency, incorporates an enhanced upsampling and reconstruction mechanism in the feature pyramid to better preserve edge and texture details, and employs a hybrid convolution–attention structure in the mid-scale branch to improve discrimination of adjacent regions. Experimental results show that, on the self-constructed ExamDoc-CN dataset, YOLO-GFD improves mAP@0.5 and mAP@0.5:0.95 by 1.3 and 2.8 percentage points over YOLO11n, respectively. On the CDLA and IIIT-AR-13K datasets, mAP@0.5 increases by 1.0 and 0.8 points, while mAP@0.5:0.95 improves by 1.8 and 0.4 points, respectively. These results demonstrate that YOLO-GFD achieves consistent performance gains across different document layout scenarios with only marginal computational overhead, indicating an effective trade-off between detection accuracy and efficiency. Full article

Bamboo is a rapidly renewable lignocellulosic resource widely used in construction, composites, and bio-based materials. However, its practical applications are often limited by high hygroscopicity, biological degradation, and dimensional instability under humid conditions. This review synthesizes current research on bamboo structure, microbial interactions, and material modification strategies to better understand how bamboo-associated microbiomes influence both deterioration and potential material enhancement. We summarize conventional chemical and thermal modification approaches that improve hydrophobicity, durability, and mechanical stability while also discussing their technical limitations. Emerging studies on bamboo-associated microbial communities reveal complex interactions between fungi, bacteria, and lignocellulosic substrates, including enzymatic degradation, nutrient cycling, and potential bioprotective functions. Advances in multi-omics technologies have further provided insights into the functional gene pools and metabolic pathways involved in bamboo–microbe interactions. Recent conceptual developments in microbiome engineering and engineered living materials (ELMs) suggest possible future directions for integrating microbial functionality into bamboo-based materials. However, direct experimental evidence for microbial enhancement of bamboo structural performance remains limited. Future interdisciplinary research integrating material science, microbial ecology, and synthetic biology will be essential to evaluate the feasibility and safety of such biohybrid systems. Full article

Wireless sensor network (WSN) coverage optimization is a challenging high-dimensional and nonlinear problem that directly affects network performance, including sensing quality, energy efficiency, and system reliability. Although metaheuristic algorithms have been widely applied to this problem, many existing methods still suffer from premature convergence, insufficient population diversity, and an imbalance between exploration and exploitation. To address these issues, this paper proposes a multi-strategy enhanced enterprise development optimization algorithm (MEEDOA) inspired by business management mechanisms. The proposed method integrates a hybrid population initialization strategy, an adaptive activity switching mechanism based on performance feedback, a multi-elite collaborative learning strategy, and a Lévy flight-based stagnation escape mechanism. These strategies are tightly coupled within a unified adaptive framework to improve global search capability, convergence speed, and robustness. Furthermore, a mathematical model for WSN deployment is constructed based on a binary sensing model and discrete coverage evaluation. From the perspective of symmetry, the sensing regions of sensor nodes exhibit significant geometric symmetry in both two-dimensional and three-dimensional deployment spaces. In the two-dimensional case, the sensing and communication regions are modeled as concentric circular structures, while in the three-dimensional case, the sensing regions are represented by isotropic spheres with symmetric spatial distributions. Such symmetry properties provide an effective basis for describing coverage behavior, reducing redundant overlap, and improving the uniformity of node deployment. Meanwhile, the proposed MEEDOA preserves population diversity and enhances search balance, enabling the algorithm to better capture symmetric coverage patterns and more effectively explore complex spatial deployment configurations. Extensive experiments on CEC2014, CEC2017, CEC2020, and CEC2022 benchmark functions demonstrate that MEEDOA achieves superior convergence accuracy, faster convergence speed, and stronger robustness compared with several state-of-the-art algorithms. Additional simulation results in WSN deployment applications verify its effectiveness in improving coverage performance under both symmetric and irregular spatial deployment scenarios. The results indicate that the proposed MEEDOA provides a reliable and efficient solution for complex global optimization problems and practical engineering applications. Full article

Facial expression recognition (FER) for assistive and telepresence robotics remains challenging under resource-constrained conditions because landmark normalization is often unstable, many datasets have limited variability, and full facial landmark sets introduce redundancy. This paper proposes a lightweight, privacy-preserving FER framework for assistive healthcare robotics based on geometric facial landmarks rather than raw RGB images. The objective is to improve recognition robustness and deployment suitability on low-power edge devices through two complementary contributions: a revised nose-centered landmark normalization method and an optimized Facial Feature Mapping, FFM-L03. The proposed normalization replaces the expression-sensitive upper-lip reference with a geometrically stable nose-center anchor, while FFM-L03 combines FACS-guided anatomical priors with ANOVA F-score, LASSO, PCA, and t-SNE/UMAP to retain 60 informative landmarks. In addition, a heterogeneous Freepik dataset was constructed to increase variability in lighting, background, resolution, and subject appearance. Experimental evaluation across 15 landmark groups, four datasets, and four classifiers shows that the proposed method consistently improves performance over prior landmark configurations, achieving gains of up to 22.4 percentage points over the Ciraolo baseline and 22.1 percentage points over the full-landmark baseline in accuracy, precision, recall, and F1-score, while maintaining lightweight operation. These results demonstrate that principled normalization and targeted landmark selection can substantially improve FER for real-time, privacy-aware assistive robotic systems. Full article

The integrating sphere with sample inside (ISSI) method is useful for absorption spectroscopy of scattering samples, but the measured absorbance (${\mathrm{A}}_{\mathrm{meas}}$) becomes nonlinear with dye concentration (c) because the sample is placed inside the sphere. This study modeled the ${\mathrm{A}}_{\mathrm{meas}}-c$ relationship for ISSI using a cylindrical cell (CC) and a Brewster cell (BC) with simple analytical expressions based on the fraction of light not passing through the sample and the effective weights of light passing through it multiple times. Four aqueous dye solutions—Trypan Blue, Brilliant Blue FCF, Tartrazine, and New Coccine—were used as non-scattering samples. For CC, a single-pass model reproduced the measured relationship well for all dyes, and linearity was maintained in the low-absorbance region (up to approximately half of the saturation absorbance, $A_{\mathrm{m}\mathrm{a}\mathrm{x}}/2\approx 0.67$ Abs). For BC, the same low-absorbance region (up to approximately $A_{\mathrm{m}\mathrm{a}\mathrm{x}}/2\approx 1.21$ Abs) also exhibited practical linearity, but the full relationship including saturation required a multiple-pass model. Model selection based on adjusted RMSE and AICc identified the 3-pass model as the minimum sufficient model for BC. The saturation absorbance ${\mathrm{A}}_{\max }$ was on average 1.81 times higher for BC than for CC (corresponding to an approximately 12-fold expansion in linear intensity ratio), and the upper concentration limit of the linear approximation was on average 1.85 times higher. These results demonstrate that BC extends the measurable concentration range while preserving practical low-absorbance linearity. In addition, the wavelength dependence of ${\mathrm{A}}_{\max }$ observed at short wavelengths is attributed primarily to the reduced reflectance of the BaSO₄ integrating-sphere wall rather than to the refractive-index dispersion of the quartz cell. Full article

This paper introduced the Enhanced Puzzle Optimization Algorithm (EPOA), a hybrid metaheuristic that augmented the original Puzzle Optimization Algorithm (POA) with uniform crossover, random-resetting mutation, and explicit elitism. The contribution does not lie in inventing these operators individually, since they are classical search components, but in integrating them into POA’s two-phase search dynamics to address premature convergence, diversity loss, and best-solution preservation in a targeted manner. This paper formalized EPOA’s update rules, provided pseudocode and flow diagrams, and enforced bound handling for box-constrained problems. Comprehensive tests on the CEC2022 single-objective benchmark suite (F₁–F₁₂) showed that EPOA attained rank 1 on 11 of 12 functions and rank 3 on the remaining case, with large error reductions relative to baseline POA (e.g., on F₁, the mean error dropped from 62.836 to 0.004; on F₆, the mean error dropped from 2370.962 to 7.239). The method was further evaluated on six classical constrained engineering design problems (welded beam, tension/compression spring, speed reducer, pressure vessel, three-bar truss, and cantilever beam). Statistical indicators such as the mean and standard deviation were used to assess robustness. The results showed that EPOA delivered a strong exploration–exploitation balance and robust solution quality across rugged landscapes and real-world constraints. Full article

Groundwater is a critical lifeline for ecosystems and human settlements in arid and semi-arid regions, yet it is increasingly vulnerable to the dual pressures of extreme climatic conditions and intensifying anthropogenic activities. This study investigated 24 groundwater and 4 river water samples to discuss the hydrogeochemical evolution and water quality suitability in the Tianjun Basin, a typical high-altitude arid basin on the northeastern Tibetan Plateau. The results indicate that groundwater is mildly alkaline (pH: 7.65–8.35) and predominantly fresh (TDS: 233.77–1061.42 mg/L). Hydrochemical facies evolve from HCO₃-Ca type in upstream areas to Mixed HCO₃-Na·Ca and Cl-Na types. Hydrochemical analysis suggests that silicate weathering and carbonate dissolution are the dominant natural processes, while cation exchange further modifies the ionic composition. Notably, anthropogenic nitrogen (NO₃⁻ and NH₄⁺) contamination, primarily from domestic sewage in the Tianjun Basin, has significantly impacted groundwater quality. Health risk assessment shows that infants are the most vulnerable group, with 16.67% of samples posing a non-carcinogenic risk via the oral pathway. Regarding irrigation suitability, while sodium hazards are generally low, a significant salinity hazard is identified due to elevated electrical conductivity in the arid environment. This poses a substantial risk of secondary soil salinization, necessitating strict salt management strategies to preserve long-term land productivity. These findings provide critical insights for the sustainable management of fragile groundwater resources in extreme arid environments. Full article

Promoting equitable and sustainable urban mobility requires accessibility assessment approaches that extend beyond purely geometric proximity measures toward service-sensitive and behavior-informed evaluation. This study develops an open-source GIS workflow that integrates General Transit Feed Specification (GTFS) datasets, demographic grid data, and spatial proximity modelling to construct three complementary accessibility-related indicators. Transit operational data are processed to derive service-strength indicators representing temporal service intensity at the stop level. Spatial proximity is evaluated through distance-based measurements between population grid centroids and the nearest public transport stops, subsequently transformed into a normalized proximity score reflecting perceived spatial effort. Demographic attributes, specifically age and gender structure, are also translated into a behavior potential index representing relative travel sensitivity across the urban grid. Then, rather than aggregating these components into a single composite accessibility indicator, the study analyses the spatial distribution of service strength (Sg), behavior potential (Bg), and proximity score (Pg) independently. Its empirical application to the metropolitan area of Valencia, Spain, reveals notable spatial disparities across these dimensions and highlights zones where demographic demand potential diverges from operational service provision. By relying exclusively on standardized open datasets and non-proprietary GIS tools, the proposed framework enhances methodological transparency, reproducibility, and transferability. The workflow provides a diagnostic foundation for future integrated accessibility modelling while preserving interpretative clarity at the indicator level. Full article

Mining-induced subsidence monitoring is essential for safe coal production and ecological protection in mining areas. UAV photogrammetry has become a widely adopted technique for constructing Digital Subsidence Models (DSuM); however, multi-scale composite noise significantly limits model accuracy and parameter extraction reliability. Taking the 2S201 working face of Wangjiata Coal Mine in a western arid–semi-arid region as the study area, this study systematically investigates DSuM noise characteristics and proposes a hierarchical multi-scale denoising framework. First, subsidence value interval stratification is employed to analyze the spatial distribution of noise. Based on this analysis, a two-stage strategy is developed. In the first stage, large-scale outliers are identified and removed using an improved DBSCAN algorithm with empirically calibrated and density-adaptive parameter computation. In the second stage, small-scale mixed noise is suppressed through a curvature-adaptive multi-stage denoising method. Validation using 20 ground monitoring points demonstrates that the RMSE decreases from 154 mm to 86 mm after large-scale denoising and further to 59 mm, achieving a 61.5% overall accuracy improvement. The denoised model exhibits enhanced surface continuity, smoother deformation profiles, and clearer subsidence boundaries while preserving overall deformation trends. The proposed framework effectively improves DSuM geometric accuracy and spatial consistency, providing reliable technical support for subsidence monitoring with improved accuracy in complex mining environments. Full article

Annona muricata (soursop) is a tropical medicinal plant widely used in traditional medicine across Africa, the Caribbean, and parts of South America. While its ethnomedicinal applications span a range of conditions, including infections, inflammation, and anemia-related symptoms, its potential relevance to hematopoiesis has not been systematically examined. This narrative review synthesizes ethnomedicinal knowledge, phytochemical composition, and experimental evidence to explore the biological plausibility by which A. muricata may indirectly influence hematopoietic processes. Bioactive constituents of A. muricata, including flavonoids, polyphenols, and acetogenins, have demonstrated antioxidant, anti-inflammatory, and immunomodulatory properties in preclinical models. These effects are particularly relevant given the established roles of oxidative stress and chronic inflammation in disrupting hematopoietic stem and progenitor cell function and bone marrow homeostasis. Rather than proposing direct erythropoietic activity, this review emphasizes indirect, marrow-supportive mechanisms through which A. muricata may contribute to the preservation of hematopoietic function under conditions of physiological or inflammatory stress. The limitations of the current evidence base, including the predominance of in vitro and animal studies and the absence of direct hematopoietic endpoints in humans, are critically discussed. Overall, this review provides a cautious, integrative framework linking A. muricata bioactivity to hematopoietic regulation and highlights key gaps that must be addressed before any translational or clinical relevance can be established. Full article

This work presents the results of an archaeometric research study of the geomaterials used in the construction of the Late Antique wall of Emerita Augusta (currently Mérida, Spain). Dated to the 5th century C.E., this structure belongs to one of the best-preserved historical ensembles in Europe. In-depth knowledge of the geomaterials used in this ancient wall is essential for ensuring reliable restoration strategies and the successful long-term conservation of this monument. To this end, a rigorous sampling procedure was conducted in areas containing original archaeological remains. Samples were characterized using optical microscopy, X-ray diffraction (XRD), X-ray fluorescence (XRF), inductively coupled plasma–mass spectrometry (ICP-MS), thermogravimetry and differential thermal analyses (TGA-DTA), and scanning electron microscopy (SEM). This integrated multi-analytical approach is highly effective for the study of built heritage. The mineralogical, textural, and geochemical properties of the granites allowed for the identification of the granite types used in the wall, while the results obtained for the mortars indicated that lime, fully carbonated and transformed into calcite, was used as the binding agent. Furthermore, the binder/aggregate ratios were found to be consistent with traditional Roman mortar formulations. These findings provide a comprehensive understanding of the material provenance and construction techniques used in this landmark of late antiquity. Full article

Background/Objectives: The composition of atherosclerotic plaques is increasingly recognized as a key factor determining cardiovascular risk. Features such as intraplaque hemorrhage, a necrotic lipid core, and the integrity of the fibrous cap are strongly associated with plaque instability and the occurrence of adverse clinical events. Magnetic resonance imaging (MRI) allows for non-invasive characterization of plaque microstructure through quantitative mapping of T1 and T2 relaxation times; however, image noise may limit the accuracy of these measurements. Methods: In this experimental study, a total of 15 ex vivo atherosclerotic plaque samples were imaged using a 1.5T scanner with a fast spin-echo sequence featuring variable repetition times (TR: 200–12,000 ms) and echo times (TE: 21–240 ms) to obtain T1 and T2 maps. An Attention–Residual–Dense U-Net neural network was trained on pairs of noisy and reference images to reduce Rician noise while preserving structural details. Results: The 15 samples examined exhibited T1 values ranging from 1768 to 3294 ms and T2 values ranging from 138 to 202 ms, which were shorter than those for water (T1: 3323 ms; T2: 114 ms), which is consistent with the presence of collagen, lipids, and mineral deposits. Variability among samples reflected differences in composition, with the shortest relaxation times suggesting advanced calcifications. The application of deep learning methods allowed for a threefold improvement in the signal-to-noise ratio (SNR) while preserving the microarchitecture of the lamina. Conclusions: Quantitative T1/T2 mapping combined with deep learning-based image enhancement methods constitutes a robust tool for high-resolution characterization of atherosclerotic plaque composition under ex vivo conditions. The results obtained indicate the potential for translating this method to in vivo studies to better detect tissue heterogeneity and features associated with plaque instability. Full article