Search Results (69)

The growing demand for safe and reliable weaponry has heightened performance requirements for explosives. Cocrystal systems, offering a balance between high energy density and safety, have become key targets in advanced energetic material research. However, the influence of molar ratios and crystal facets on thermal sensitivity, mechanical strength, and detonation properties remains underexplored. This study investigates cocrystals of hexanitrohexaazaisowurtzitane (CL-20) and 1,1-diamino-2,2-dinitroethylene (FOX-7) with molar ratios of 3:1, 5:1, and 8:1 on the (1 0 1) crystal facet, using the Forcite module in Materials Studio. Comparative analysis with (0 1 1) facet and pure explosives revealed that the 5:1 cocrystal achieved the highest cohesive energy density (0.773 kJ/cm³) and theoretical crystal density (1.953 g/cm³), driven by strong electrostatic and non-bonded interactions—indicating superior detonation performance. In contrast, the 3:1 cocrystal displayed optimal mechanical strength, with an elastic modulus of 8.562 GPa and shear modulus of 3.365 GPa, suitable for practical applications. The results suggest increasing CL-20 content enhances energy performance up to a point, beyond which structural loosening occurs (8:1 ratio) due to steric hindrance weakening van der Waals forces. This work clarifies how molar ratio regulates the influence between sensitivity, strength, and energy, providing guidance for designing application-specific high-energy cocrystals. Full article

This study evaluates the estimation of Weibull distribution parameters (shape, k; scale, c) for wind speed modeling in wind energy potential assessments. Traditional empirical methods—Justus Moment Method (JEM), Power Density Method (PDM), Energy Pattern Factor Method (EPFM), Lysen Moment Method (LAM), and Standard Deviation Empirical Method (SEM)—are compared with advanced artificial intelligence optimization algorithms (AIOAs), including Genetic Algorithm (GA), Gravitational Search Algorithm (GSA), Sine Cosine Algorithm (SCA), Teaching-Learning-Based Optimization (TLBA), Grey Wolf Optimizer (GWA), Red Fox Algorithm (RFA), and Red Panda Optimization Algorithm (RPA). Using hourly wind speed data from Foça, Urla, Karaburun, and Çeşme in Turkey, the analysis demonstrates that AIOAs, particularly GA, GSA, SCA, TLBA, and GWA, outperform empirical methods, achieving low RMSE (0.0071) and high R² (0.9755). SEM and LAM perform competitively among empirical methods, while PDM and EPFM show higher errors, highlighting their limitations in complex wind speed distributions. The study also conducts a techno-economic analysis, assessing capacity factors, unit energy costs, and payback periods. Foça and Urla are identified as optimal investment sites due to high energy yields and economic efficiency, whereas Çeşme is unviable due to low production and long payback periods. This research provides a robust framework for Weibull parameter estimation, demonstrating AIOAs’ superior accuracy and offering a decision-support tool for sustainable wind energy investments. Full article

Pediatric type 1 diabetes (T1D) disrupts musculoskeletal development during critical windows of growth, puberty, and peak bone mass accrual. Beyond classic micro- and macrovascular complications, accumulating evidence shows a dual burden of diabetic bone disease—reduced bone mineral density, microarchitectural deterioration, and higher fracture risk—and diabetic myopathy, characterized by loss of muscle mass, diminished strength, and metabolic dysfunction. Mechanistically, chronic hyperglycemia, absolute or functional insulin deficiency, and glycemic variability converge to suppress PI3K–AKT–mTOR signaling, activate FoxO-driven atrogenes (atrogin-1, MuRF1), and impair satellite-cell biology; advanced glycation end-products (AGEs) and RAGE signaling stiffen extracellular matrix and promote low-grade inflammation (IL-6, TNF-α/IKK/NF-κB), while oxidative stress and mitochondrial dysfunction further compromise the bone–muscle unit. In vitro, ex vivo, and human studies consistently link these pathways to lower BMD and trabecular/cortical quality, reduced muscle performance, and increased fractures—associations magnified by poor metabolic control and longer disease duration. Prevention prioritizes tight, stable glycemia, daily physical activity with weight-bearing and progressive resistance training, and optimized nutrition (adequate protein, calcium, vitamin D). Treatment is individualized: supervised exercise-based rehabilitation (including neuromuscular and flexibility training) is the cornerstone of skeletal muscle health. This review provides a comprehensive analysis of the mechanisms underlying the impact of type 1 diabetes on musculoskeletal system. It critically appraises evidence from in vitro studies, animal models, and clinical research in children, it also explores the effects of prevention and treatment. Full article

Temperature has always been an important environmental factor, and changes in water temperature are closely related to the entire life process of fish. Investigating the impact of thermal stress on fish physiology is critical for optimizing aquaculture. This study employed transcriptomic and metabolomic approaches to investigate temperature-induced variations in the gene expression and metabolic profiles of turbot. The results showed that thermal stress could induce abnormal genes transcription, and functional annotation demonstrated predominant associations of these genes with key pathways including PI3K-Akt signaling, PPAR regulation, steroid biosynthesis, fatty acid metabolism, and FoxO signaling cascade. Metabolomic analysis revealed that amino acid metabolism was dysregulated, such as valine, leucine, and isoleucine. Joint analysis revealed significant positive associations between CDH1, Col9a1, and ECT2 genes and leucine/isoleucine metabolism. The expression levels of Plch2 and Col9a1 genes exhibited significant regulatory effects on valine metabolism. Moreover, the gene cluster comprising DNAJB6, Gcnt1 and trim71 was significantly involved in the metabolic regulation of galactonic acid. Collectively, these findings demonstrate that thermal stress induces significant alterations in gene expression, metabolic profiles, and signaling pathways in turbot, offering new perspectives for thermal stress mitigation strategies. Full article

Although extracellular vesicles (EVs) from mesenchymal stem cells have shown potential in skin wound repair, the diversity of EV sources and the optimization of delivery systems still need further exploration. This study is the first to demonstrate that extracellular vesicles from chemically induced mammary epithelial cells (CiMECs-EVs) possess distinct skin wound repair activity. To enhance the therapeutic efficacy of CiMECs-EVs and optimize their delivery efficiency, we innovatively combined them with a chitosan hydrogel to construct a composite repair system (CiMECs-EVs-chitosan hydrogel, CMECG). This system was then applied to a rat skin wound model. The results showed that CMECG significantly promoted the proliferation and migration of fibroblasts and mammary epithelial cells (MECs). In animal experiments, the relative wound closure efficiency of the control group was approximately 70% on day 14, while that of the CMECG group (loaded with 200 μg CiMECs-Exo) was enhanced to 90%, markedly accelerating the wound healing process. Histological analysis indicated that this system could effectively restore the structural continuity of various skin layers and significantly promote the synthesis and remodeling of collagen at the wound site. Mechanistically, the wound healing effect of CiMECs-EVs is closely associated with the endogenous miRNAs they encapsulate. These miRNAs can coordinately regulate cell proliferation, migration, and angiogenesis, modulate the inflammatory microenvironment, and inhibit excessive scar formation—thus regulating the entire repair process. This process involves multiple wound healing-related signaling pathways, including MAPK, PI3K-Akt, FoxO, TGF-β, and JAK-STAT. In summary, this study successfully constructed a novel EV-chitosan hydrogel repair system. This system is expected to provide an effective and innovative EV-based therapeutic strategy for the clinical treatment of skin wound repair. Full article

Educational data mining (EDM) plays a crucial role in developing intelligent early warning systems that enable timely interventions to improve student outcomes. This study presents a novel approach to student performance prediction by integrating metaheuristic hyperparameter optimization with explainable artificial intelligence for enhanced learning analytics. While Light Gradient Boosting Machine (LightGBM) demonstrates efficiency in educational prediction tasks, achieving optimal performance requires sophisticated hyperparameter tuning, particularly for complex educational datasets where accuracy, interpretability, and actionable insights are paramount. This research addressed these challenges by implementing and evaluating five nature-inspired metaheuristic algorithms: Fox Algorithm (FOX), Giant Trevally Optimizer (GTO), Particle Swarm Optimization (PSO), Sand Cat Swarm Optimization (SCSO), and Salp Swarm Algorithm (SSA) for automated hyperparameter optimization. Using rigorous experimental methodology with 5-fold cross-validation and 20 independent runs, we assessed predictive performance through comprehensive metrics including Coefficient of Determination (R²), Root Mean Squared Error (RMSE), Mean Squared Error (MSE), Relative Absolute Error (RAE), and Mean Error (ME). Results demonstrate that metaheuristic optimization significantly enhances educational prediction accuracy, with SCSO-LightGBM achieving superior performance with R² of 0.941. SHapley Additive exPlanations (SHAP) analysis provides crucial interpretability, identifying Attendance, Hours Studied, Previous Scores, and Parental Involvement as dominant predictive factors, offering evidence-based insights for educational stakeholders. The proposed SCSO-LightGBM framework establishes an intelligent, interpretable system that supports data-driven decision-making in educational environments, enabling proactive interventions to enhance student success. Full article

Human amniotic epithelial cell-derived exosomes (hAECs-Exos) are nanoscale extracellular vesicles with neuroprotective, regenerative, and anti-inflammatory properties, presenting a promising cell-free therapeutic approach for ischemic stroke. This study investigated the protective effects of hAECs-Exos against ischemic injury and explored the underlying molecular mechanisms. An optimized oxygen-glucose deprivation/reoxygenation (OGD/R) model was established in murine hippocampal HT22 neurons and BV2 microglial cells to simulate ischemic conditions. hAECs-Exos were successfully isolated and characterized via transmission electron microscopy, nanoparticle tracking analysis, and Western blotting. Confocal microscopy confirmed efficient exosome uptake by both cell types. Functional analyses revealed that hAECs-Exos significantly improved cell viability, suppressed pro-inflammatory cytokine release, alleviated oxidative stress, and modulated apoptosis-related proteins. RNA sequencing identified Forkhead box protein M1 (FoxM1) as a significantly upregulated transcription factor following hAECs-Exos treatment. Further experiments demonstrated that knockdown of FoxM1 in hAECs abolished the beneficial effects of exosomes on the viability of HT22 and BV2 cells and on the suppression of inflammation, oxidative stress, and apoptosis. These findings indicate that hAECs-Exos confer neuroprotection through FoxM1-dependent mechanisms. Together, our results highlight the therapeutic potential of hAECs-Exos as a safe, effective, and clinically translatable strategy for ischemic stroke treatment, warranting future validation in vivo and rescue experiments to fully elucidate FoxM1’s causal role. Full article

In this study, we propose an architectural model for path optimization in cluster order picking within warehouse robotics, utilizing a hybrid approach that combines symbolic control and metaheuristic techniques. Among the optimization strategies, we incorporate bio-inspired metaheuristic algorithms such as the Walrus Optimization Algorithm (WOA), Puma Optimization Algorithm (POA), and Flying Foxes Algorithm (FFA), which are grounded in behavioral models observed in nature. We consider large-scale warehouse robotic systems, partitioned into clusters. To manage shared resources between clusters, the set of clusters is first formulated as a symbolic control design task within a discrete synthesis framework. Subsequently, the desired control goals are integrated into the model, encoded using parallel synchronous dataflow languages; the resulting controller, derived using our safety-focused and optimization-based synthesis approach, serves as the manager for the cluster. Safety objectives address the rigid system behaviors, while optimization objectives focus on minimizing the traveled path of the warehouse robots through the constructed cost function. The metaheuristic algorithms contribute at this stage, drawing inspiration from real-world animal behaviors, such as walruses’ cooperative movement and foraging, pumas’ territorial hunting strategies, and flying foxes’ echolocation-based navigation. These nature-inspired processes allow for effective solution space exploration and contribute to improving the quality of cluster-level path optimization. Our hybrid approach, integrating symbolic control and metaheuristic techniques, demonstrates significantly higher performance advantage over existing solutions, with experimental data verifying the practical effectiveness of our approach. Our proposed algorithm achieves up to 3.01% shorter intra-cluster paths compared to the metaheuristic algorithms, with an average improvement of 1.2%. For the entire warehouse, it provides up to 2.05% shorter paths on average, and even in the worst case, outperforms competing metaheuristic methods by 0.28%, demonstrating its consistent effectiveness in path optimization. Full article

In recent years, the integration of flexible resources into active distribution networks (ADNs) has been significantly enhanced. By coordinating a variety of such resources, the economic efficiency, operational security, and overall stability of ADNs can be improved. In this study, a bi-level planning model is proposed for active distribution networks. The upper-level model aims to minimize the annual comprehensive cost, while the lower-level model focuses on reducing network losses. To solve the upper-level problem, a hybrid whale optimization algorithm (HWOA) is developed. The algorithm integrates adaptive mutation based on Gaussian–Cauchy distributions, a nonlinear cosine-based control strategy, and a dual-population co-evolution mechanism. These enhancements allow HWOA to achieve faster convergence, higher accuracy, and stronger global search capabilities, thereby reducing the risk of falling into local optima. The lower-level problem is addressed using the interior point method due to its nonlinear and continuous nature. The proposed model and algorithm are validated through simulations on the IEEE 33-bus system. The results show that DG consumption increases by 88.77 MWh, network losses decrease by 6.8 MWh, and the total system cost is reduced by CNY 3.62 million over the entire project lifecycle. These improvements contribute to both the economic and operational performance of the ADN. Compared with the polar fox optimization algorithm (PFA), HWOA improves algorithmic efficiency by 18.92%, lowers network loss costs by 6.22%, and reduces the total system costs by 0.71%, demonstrating its superior effectiveness in solving complex bi-level optimization problems in active distribution networks. These findings not only demonstrate the technical efficiency of the proposed method but also contribute to the long-term goals of sustainable energy systems by improving renewable energy utilization, reducing operational losses, and supporting carbon reduction targets in active distribution networks. Full article

Dengue fever is a mosquito-borne viral disease that annually affects 100–400 million people worldwide. Early detection of dengue enables easy treatment planning and helps reduce mortality rates. This study proposes three Swarm-based Metaheuristic Algorithms, Golden Jackal Optimization, Fox Optimizer, and Sea Lion Optimization, for feature selection and hyperparameter tuning, and an Extreme Gradient Boost classifier to forecast dengue fever using the Predictive Clinical Dengue dataset. Several existing models have been proposed for dengue fever classification, with some achieving high predictive performance. However, most of these studies have overlooked the importance of feature reduction, which is crucial to building efficient and interpretable models. Furthermore, prior research has lacked in-depth analysis of model behavior, particularly regarding the underlying causes of misclassification. Addressing these limitations, this study achieved a 10-fold cross-validation mean accuracy of 99.89%, an F-score of 99.92%, a precision of 99.84%, and a perfect recall of 100% by using only two features: WBC Count and Platelet Count. Notably, FOX-XGBoost and SLO-XGBoost achieved the same performance while utilizing only four and three features, respectively, demonstrating the effectiveness of feature reduction without compromising accuracy. Among these, GJO-XGBoost demonstrated the most efficient feature utilization while maintaining superior performance, emphasizing its potential for practical deployment in dengue fever diagnosis. SHAP analysis identified WBC Count as the most influential feature driving model predictions. Furthermore, DiCE explanations support this finding by showing that lower WBC Counts are associated with dengue-positive cases, whereas higher WBC Counts are indicative of dengue-negative individuals. SHAP interpreted the reasons behind misclassifications, while DiCE provided a correction mechanism by suggesting the minimal changes needed to convert incorrect predictions into correct ones. Full article

This study was conducted to investigate the effects of a Chinese herbal medicine compound microecological agent (C-MEA) on the egg production performance, ovarian follicle development, ovary transcriptome, and cecal microbiota of caged laying ducks. A total of 108 black Muscovy ducks (150 days old) were randomly divided into three groups for 30 days in a formal feeding trial. Compared with the control basic diet (Group C) and 16 g/kg C-MEA dosage (Group B), the 8 g/kg C-MEA dosage (Group A) increased egg production (average laying rate 69.35%) and follicle development (5~7 Fs, 6~7 LYFs, 11~13 SYFs) mass (p < 0.05). According to RNA-Seq, the ovaries’ transcriptome among different dietary groups enriched six key pathways, including neuroactive ligand–receptor interaction, the PPAR signaling pathway, ECM–receptor interaction, focal adhesion, the adherens junction, and the FoxO signaling pathway, as well as 46 candidate key genes. According to 16S-Seq, the microbial diversity was significantly increased in Group A, and the genus abundances of Sphaerochaeta and UCG-004 were significantly changed among different dietary groups (p < 0.05). Supplementation with C-MEA may optimize the cecal microflora and the interactions between the intestinal microflora and the host. The results from combining RNA-Seq and 16S-Seq demonstrated that the relationship between Sphaerochaeta and the hub gene cluster (F2, KNG1, C5, PLG, F2RL1, FABP1, and GCG) is the most prominent. In conclusion, the egg performance of caged laying ducks can be modulated through the microbiota–gut–ovary axis. Our findings provide new insights for improving gut health and reproductive performance of caged laying ducks. Full article

Renewable energy sources are being progressively incorporated into modern power grids to increase sustainability, stability, and resilience. To ensure that residential, commercial, and industrial customers have a dependable and efficient power supply, the transmission system must deliver electricity to end-users via the distribution network. To improve the performance of the distribution system, this study employs distributed generator (DG) units and focuses on determining their optimal placement, sizing, and power factor. A novel metaheuristic algorithm, referred to as Rüppell’s fox optimizer (RFO), is proposed to address this optimization problem under various scenarios. In the first scenario, where the DG operates at unity power factor, it is modeled as a photovoltaic system. In the second and third scenarios, the DG is modeled as a wind turbine system with fixed and optimal power factors, respectively. The performance of the proposed RFO algorithm is benchmarked against five well-known metaheuristic techniques to validate its effectiveness and competitiveness. Simulations are conducted on the IEEE 33-bus and IEEE 69-bus radial distribution test systems to demonstrate the applicability and robustness of the proposed approach. Full article

The Pyrenean capercaillie (Tetrao urogallus aquitanicus) is a forest obligate grouse that has experienced a marked population decline in recent decades owing to the lack of optimal habitats. However, the effect of forest structure on potential predators and habitat competitors has not been well-studied. We conducted a camera-trapping study at three conservation areas in Huesca province (northeastern Spain), which were classified as ‘optimal’, ‘favorable’, and ‘unfavorable’ based on habitat suitability for the capercaillie. This study was conducted for 3417 days at a total of 130 camera locations in autumn–winter and spring–summer, capturing 8757 valid photos. In total, 36 different species were recorded. The most frequently detected species were Southern chamois (Rupicapra pyrenaica pyrenaica; 32.6%), roe deer (Capreolus capreolus; 18%), wild boar (Sus scrofa; 9.6%), red squirrel (Sciurus vulgaris; 6.1%), mustelids (5.6%), and red fox (Vulpes vulpes; 4.8%). Capercaillies were photographed in the optimal and favorable habitat areas. Nest predators, such as mustelids and red fox, were more frequently detected in the favorable area during autumn–winter and in the optimal area in spring–summer, while corvids were more frequently detected in the unfavorable habitat area during both periods. No clear pattern was found for wild boar (nest predator and habitat competitor) or cervids (competitors). As capercaillie coexist with a wide range of predators and competitors, and habitat structure may not always explain species relative abundance, factors such as disturbance and food resources should be also taken into account when aiming to develop targeted management for the benefit of the capercaillie. Full article

Pavement markings, as a crucial component of traffic guidance and safety facilities, are subject to degradation and abrasion after a period of service. To ensure traffic safety, retroreflectivity and diffuse illumination should be above the minimum thresholds and required to undergo inspection periodically. Therefore, an onboard light detection and ranging (LiDAR) and camera deployment optimization method is proposed for pavement marking distress detection to adapt to complex traffic conditions, such as shadows and changing light. First, LiDAR and camera sensors’ detection capability was assessed based on the sensors’ built-in features. Then, the LiDAR–camera deployment problem was mathematically formulated for pavement marking distress fusion detection. Finally, an improved red fox optimization (RFO) algorithm was developed to solve the deployment optimization problem by incorporating a multi-dimensional trap mechanism and an improved prey position update strategy. The experimental results illustrate that the proposed method achieves 5217 LiDAR points, which fall on a 0.58 m pavement marking per data frame for distress fusion detection, with a relative error of less than 7% between the mathematical calculation and the field test measurements. This empirical accuracy underscores the proposed method’s robustness in real-world scenarios, effectively mitigating the challenges posed by environmental interference. Full article

This work aims to constitute a framework dataflow based on the prediction, optimization, and characterization of optimal solutions. To this purpose, a metaheuristic optimization method is used to obtain the optimal design solutions for discrete plane frame structures considering as objective function the minimization of their maximum resultant displacement, subjected to side and behavioral constraints. The design variables that lead to the optimal solutions are constituted into datasets which are subsequently submitted to a clustering analysis. The results obtained provide pertinent insights about the optimal solutions clusters’ ranges, giving effective support to a specific solution selection. Full article