Search Results (28,695)

In order to meet the urgent needs of refined geological disaster risk assessment at a county scale, and in view of the shortcomings of existing methods in the aspects of sample dependence, rainfall time-varying differences, and vulnerability quantification, this study takes Linxiang City as an example, integrates multi-source data such as geology, geography, meteorology, remote sensing, and field survey, and explores practical methods. A random forest (RF) model was implemented for geological hazard susceptibility mapping, and its hyper-parameters were tuned using Bayesian optimization. Based on a statistical analysis of the frequency of historical disaster events, a risk classification of rainfall in the flood season and non-flood season was evaluated. A vulnerability simplification method based on the value and exposure of disaster-bearing bodies was proposed. Finally, rapid risk assessment was achieved by matrix superposition. The results showed that the model had high accuracy (AUC = 0.903). The use of field survey risk types effectively enhanced the susceptibility sample set and verified the accuracy of risk assessment. The risk factor in the flood season and non-flood season was significantly different, and the very-high- and high-risk areas in the flood season were mainly distributed in the shallow metamorphic rock mountainous area in the east of Yanglousi Town and the granite residual soil area in the south of Zhanqiao Town, the latter of which was highly consistent with the field survey results. This study demonstrated value in terms of sample enhancement, model optimization, consideration of time-varying rainfall, and vulnerability simplification. The evaluation results can provide direct support for the construction of a “point–area dual control” system for geological disasters in Linxiang City, and the methodological framework can also provide a practical reference for risk evaluation in other counties. Full article

Background: Phylogenetic relationships within Eulipotyphla have long been debated due to their complex evolutionary history and the frequent inconsistency among phylogenetic trees inferred from different data sources. This order comprises both above-ground and subterranean mammals, providing an opportunity to investigate their adaptation to hypoxic, hypercapnic, and dark environments. Methods: In this study, we reconstructed the phylogeny of Eulipotyphla based on whole-genome comparisons and explored the causes of phylogenetic incongruence as well as the genetic basis of underground adaptation. We analyzed the genomes of ten species, including four above-ground species and six subterranean species. We also identified homologous coding sequences through whole-genome alignment and inferred phylogenetic trees based on genome-wide windows of 1000 bases. Divergence times among major lineages were estimated using MCMCtree, and the causes of inconsistent tree topologies were examined using QuIBL to distinguish incomplete lineage sorting from introgression. Finally, we designated the six subterranean species as foreground branches and applied branch-site models to identify genes under positive and negative selection. Results: Whole-genome analyses recovered a clear clustering pattern, in which the six subterranean species formed a monophyletic group, whereas the four above-ground species clustered into a distinct clade. Divergence time estimation suggested that the split between above-ground and subterranean lineages occurred approximately 53.51 to 68.78 million years ago. Gene tree analyses revealed substantial variation in tree topologies at several internal nodes, and QuIBL results indicated that introgression contributed to phylogenetic discordance in addition to incomplete lineage sorting. Positive selection analyses identified genes associated with heart regulation, blood circulation, oxidative stress response, and erythrocyte differentiation, while negatively selected genes were linked to cardiac septum and chamber development. Conclusions: These results clarify the phylogenetic relationships within Eulipotyphla and provide insights into the genomic basis of adaptation to underground environments. Full article

In new distribution systems with high penetration of renewable energy, inverter-based sources exhibit significant differences in fault characteristics compared to traditional power sources due to the absence of a constant electromotive force and their operation under nonlinear control links, rendering conventional fault current calculation methods inadequate. To address these challenges, a full-time-domain analysis-based method for modelling and calculating fault transient characteristics is proposed. First, a dynamic model of inverter-based sources accounting for current loop saturation effects is established, and phase plane analysis is employed to resolve nonlinear control regions. On this basis, a full-time-domain fault current calculation method is proposed, wherein the steady-state operating point after a fault is determined by iteratively solving the network node voltage equations. By integrating control strategies and derived transient differential equations, the fault current expression across the full-time-domain scope is formulated. Furthermore, a multi-objective optimization control strategy is proposed to achieve effective fault current suppression, and an improved Simulated Annealing-Particle Swarm Optimization (SA-IPSO) hybrid algorithm is adopted for efficient solution. Finally, SIMULINK-based simulation experiments validate the accuracy and effectiveness of the proposed method in transient characteristic analysis and current suppression. Full article

To investigate the hydraulic instability mechanisms of low-specific-speed pump–turbines operating in turbine mode, this study experimentally characterized the pressure distribution and pulsation evolution on the guide vanes of a model unit (ns = 28) using an embedded sensor technique. By overcoming the accessibility limitations of traditional measurement methods, this research reveals the distinct pressure response mechanisms on the guide vane Front Side (upstream-facing) and Back Side (runner-facing). The results demonstrate that the time-averaged pressure distribution is highly sensitive to the Guide Vane Opening (GVO). Specifically, pressure on the Front Side increases with GVO, dominated by the improvement of flow pattern and stagnation effect, whereas pressure on the Back Side decreases monotonically, governed by the Bernoulli effect. Increasing the GVO significantly improves pressure uniformity, reducing the surface pressure gradient by 55%. Regarding dynamic characteristics, pressure fluctuation intensity on the Back Side is significantly higher than that on the Front Side. Furthermore, fluctuations are notably amplified near the tongue, confirming that flow distortion induced by the tongue is a key factor driving circumferential non-uniformity. Spectral analysis identifies the Blade Passing Frequency (BPF) as the dominant frequency, verifying Rotor–Stator Interaction (RSI) as the primary excitation source, while the guide vane channel exhibits a significant low-pass filtering effect on high-order harmonics. These findings provide a solid theoretical foundation and data support for the optimal design and stability control of pump–turbine guide vanes. Full article

Dogs are deeply social, built to stay in touch with others of their kind. In cities, though, most now live as single dogs. Housing rules, work schedules, and constant supervision have constrained their social environment. They still meet other dogs, but the meetings are short, managed, and rarely turn into real bonds. This review tries to pull together what is known about how such limited contact affects canine welfare and emotional balance. The sources come mostly from ethology, psychology, and urban studies, published between 2010 and 2025, and include comparisons between urban pets and free-ranging dogs that still organise their own social lives. Across studies, the pattern is similar: when dogs lose steady companions, they also lose the kind of social buffering that once helped them recover from stress. Over time, this does not always look like distress—more often it shows up as quiet tension, watchfulness, or an overdependence on human cues. The evidence points to social deprivation as a slow, structural welfare issue rather than an occasional problem. Meaningful improvement may therefore require moving beyond control and training alone, toward conditions that allow dogs to form small, stable circles of familiar peers that support lower arousal and more reliable recovery. Full article

The growing interest in using edible flowers as functional ingredients has increased the demand for reliable and sustainable strategies to recover and characterize their bioactive compounds. Torch ginger is a tropical species rich in anthocyanins. In this study, an ultrasound-assisted extraction (UAE) method was developed, optimized, and validated for the efficient recovery of anthocyanins from torch ginger flowers, with a clear focus on food-related applications. A Box–Behnken experimental design was applied to evaluate the influence of solvent composition, temperature, solvent-to-sample ratio, and pH on anthocyanin yield, using chromatographic responses. Solvent composition and solvent-to-sample ratio were identified as the most influential parameters, and effective extraction was achieved under mild temperature and pH conditions. The optimized conditions consisted of 84% methanol in water as the extraction solvent, a temperature of 30 °C, a solvent-to-sample ratio of 20:1 (mL g⁻¹), and a pH of 5.6. Kinetic studies revealed that a 5 min extraction time maximized recovery while preventing compound degradation. The method was successfully applied to different torch ginger varieties, revealing a strong correlation between flower color and anthocyanin concentration. This research provides a fast, reliable, and environmentally friendly approach for assessing anthocyanin content in torch ginger flowers. The results support the valorization of this edible flower as a potential source of natural colorants and bioactive ingredients, contributing to ingredient selection, quality control, and the future development of functional foods and clean-label products. Full article

Tantalum pentoxide (Ta₂O₅) films deposited on fused silica substrates are critical components of high-power laser systems. Ion-assisted electron beam evaporation (IAD-EBE) is the mainstream technique for fabricating Ta₂O₅ films. However, it commonly requires extensive experimental efforts for deposition quality optimization, while each coating cycle is extremely time-consuming. To solve this issue, this work establishes a dataset targeting the surface roughness (Rq) and refractive index (n) of Ta₂O₅ films using atomic force microscopy, as well as ellipsometer and deposition experiments. Influence of assisting ion source beam voltage (V)/current (I) and Ar (Q₁)/O₂ (Q₂) flow rate on the n and Rq of Ta₂O₅ films are analyzed. Combining energy-field mechanism analysis with a Bayesian optimization approach (PI-BO), both deposition quality prediction and feature analysis of process parameters are achieved. The determination coefficient/mean absolute error for the prediction models of n and Rq reach 0.927/0.013 nm and 0.821/0.049 nm, respectively. Based on sensitivity analysis, the weight factors of V, I, Q₁, and Q₂ affecting n/Rq of Ta₂O₅ films are determined to be 0.616/0.274, 0.199/0.144, 0.113/0.582, and 0.072/0.000. V and Q₂ are identified as the core factors for regulating deposition quality. The optimal ranges for V and Q₂ are 600~700 V and 70~80 sccm, respectively. This study proposes a PI-BO method for predicting Rq and n of Ta₂O₅ films under small-data conditions, while determining the preferred parameter ranges and their sensitivity weight factors. These findings provide effective theoretical support and technical guidance for IAD-EBE strategy design and optimization of optical films in high-power laser systems. Full article

Background/Objectives: First row transition metal ions have recently regained attention in coordination chemistry as alternatives to gadolinium-based paramagnetic contrast agents, motivated by emerging safety concerns associated with certain Gd³⁺-based contrast agents. In this study, we report the development of a novel homoleptic diketonate Fe³⁺ complex functionalized with biocompatible indole moieties. We investigate its potential as a paramagnetic relaxation agent by evaluating its ability to modulate the T₁ and T₂ relaxation times of water proton. Methods: Iron(III) tris-1,3-(1-methylindol-3-yl)propanedionate [Fe(BIP)₃] was synthesized via a thermal method from bis(1-methylindol-3-yl)-1,3-propanedione (HBIP) using Fe(ClO₄)₃∙6 H₂O as the metal source. The complex was characterized by UV-Vis, IR and NMR spectroscopy, differential scanning calorimetry–thermogravimetric analysis, and single-crystal X-ray diffraction. Fe(BIP)₃ aggregation behavior in aqueous environment, including size and morphology of aggregates, was investigated using dynamic light scattering and scanning electron microscopy. Incorporation of the aggregates into phospholipid vesicles was evaluated by fluorescence resonance energy transfer and fluorescence correlation spectroscopy. The paramagnetic properties of monomeric Fe(BIP)₃ were probed in solution by nuclear magnetic resonance recurring to the Evans bulk magnetization method. Results: The designed synthetic procedure successfully afforded Fe(BIP)₃, which was fully characterized by UV-Vis and IR spectroscopy, as well as single-crystal X-ray diffraction. Aqueous solutions of Fe(BIP)₃ spontaneously formed rice-grain-shaped nanoscale aggregates of hydrodynamic radius ≈ 30 nm. Incorporation of these aggregates into phospholipid vesicles enhanced their stability. The longitudinal r₁ and transverse r₂ relaxivities of Fe(BIP)₃ aggregates were assessed to be 1.92 and 52.3 mM⁻¹s⁻¹, respectively, revealing their potential as paramagnetic relaxation agents. Conclusions: Fe(BIP)₃ aggregates, stabilized through incorporation into phospholipid vesicles, demonstrate promising potential as novel paramagnetic relaxation agents in aqueous environments. Full article

This paper presents, for the first time, the structure-dependent parameter trade-off optimization on figure-of-merit (R_onC_off) and power compression of AlGaN/GaN high electron mobility transistors (HEMTs) for radio frequency (RF) switch applications. For GaN HEMTs operating in switching mode, it was demonstrated that R_onC_off can be effectively reduced by increasing the gate foot length (L_{g_foot}), decreasing the gate cap length (L_{g_cap}), reducing the gate bias resistance (r_g), and adopting a high work function metal for the gate electrode (Φ_g). However, these parameter adjustments affect power compression and R_onC_off in opposing manners. This paper also presents supplementary research on the effects of source-drain spacing (L_ds) and gate width (W_g) on switching performance. This research achieves a dynamic balancing method for structural parameters, delivering application-specific design rules for different scenarios ranging from high-frequency to high-power applications. Full article

Sarcopenia, defined as the progressive decline of muscle mass, strength, and function, severely compromises autonomy and quality of life in older adults. This systematic review evaluated synergistic effects of protein supplementation combined with resistance exercise on biochemical and functional biomarkers of sarcopenia. The search for scientific evidence was conducted in PubMed, Scopus, ScienceDirect, and Cochrane databases (2019–2025), applying explicit inclusion and exclusion criteria, like only randomized controlled trials in humans, published in English, Spanish, or French, were included to ensure high-quality evidence. After selection, the risk of bias of the articles was assessed according to the Cochrane Handbook for Systematic Reviews of Interventions. Seven randomized controlled trials, with a total of 260 participants, met the eligibility criteria. Interventions combining resistance exercise three times per week at 60–80% of one-repetition maximum with daily protein supplementation of at least 15 g, mainly from dairy sources, showed synergistic effects. Improvements were observed in inflammatory and anabolic biomarkers, with reductions in myostatin, activin, and IL-6, and increases in IGF-1, follistatin, and IL-10. Functional outcomes included gains in muscle strength, fat-free mass, and muscle fiber cross-sectional area. Despite heterogeneity in duration and sample size, findings support this combined approach as a promising and clinically applicable strategy to prevent and treat sarcopenia. No external funding was received, and the review is registered in PROSPERO (CRD42025640989). Full article

The article reports on a bilingual and interpretable book recommendation platform for schoolchildren. This platform uses a lightweight K-Nearest Neighbors algorithm combined with gamification and learning analytics. This application has been designed for a bilingual learning environment in Kazakhstan, supporting learning in Kazakh and Russian languages, and is intended to improve reading engagement through culturally adjusted personalization. The recommendation engine combines content and collaborative filtering in that it leverages structured book data (genres, target age ranges, authors, languages, and semantics) and learner attributes (language of instruction, preferences, and learner history). A hybrid ranking function combines the similarity to the user and the item similarity to produce top-N recommendations, whereas gamification elements (points, achievements, and reading challenges) are used to foster sustained activity.Teacher dashboards show learners’ overall reading activity and progress through real-time data visualization. The initial calibration of the model was carried out using an open-source book collection consisting of 5197 items. Thereafter, the model was modified for a curated bilingual collection of 600 books intended for use in educational institutions in the Kazakh and Russian languages. The validation experiment was carried out on a pilot test involving 156 children. The experimental outcome suggests a stable level of recommendation in terms of the Precision@10 and Recall@10 values of 0.71 and 0.63 respectively. The computational complexity remained low. Moreover, the bilingual normalization technique increased the relevance of recommendations of non-majority language items by 12.4%. In conclusion, the proposed approach presents a scalable and transparent framework for AI-assisted reading personalization in bilingual e-learning systems. Future research will focus on transparent recommendation interfaces and more adaptive learner modeling. Full article

Background: Emergency rescuers frequently carry heavy equipment for extended periods, making musculoskeletal disorders a major occupational concern. Loading type and road slope play important roles in inducing physical fatigue; however, the assessment of physical fatigue under these conditions remains limited. Aim: This study aims to investigate physical fatigue under different loading types and road slope conditions using both electromyography (EMG) and eye movement metrics. In particular, this work focuses on eye movement metrics as a non-contact data source in comparison with EMG, which remains largely unexplored for physical fatigue assessment. Method: Prolonged load-bearing walking was simulated to replicate the physical demands experienced by emergency rescuers. Eighteen male participants completed experimental trials incorporating four loading types and three road slope conditions. Results: (1) Loading type and road slope significantly affected EMG activity, eye movement metrics, and perceptual responses. (2) Saccade time (ST), saccade speed (SS), and saccade amplitude (SA) exhibited significant differences in their rates of change across three stages defined by perceptual fatigue. ST, SS, and SA showed strong correlations with subjective fatigue throughout the entire load-bearing walking process, whereas pupil diameter demonstrated only a moderate correlation with subjective ratings. (3) Eye movement metrics were incorporated into multivariate quadratic regression models to quantify physical fatigue under different loading types and road slope conditions. Conclusions: These findings enhance the understanding of physical fatigue mechanisms by demonstrating the potential of eye movement metrics as non-invasive indicators for multidimensional fatigue monitoring in work environments involving varying loading types and road slopes. Full article

The aging population and increasing prevalence of oxidative stress-related diseases underscore the need for functional food and pharmaceutical formulations enriched with bioactive compounds. This study aimed to design sustainable emulsion systems incorporating enzymatically modified fats with enhanced functional and bioactive properties. Enzymatic interesterification was employed as an environmentally friendly alternative to chemical catalysis, enabling the transformation of natural lipids without generating undesirable trans isomers. The lipid phase was formulated from blends of hemp oil, a plant-derived source rich in polyunsaturated fatty acids with documented antioxidant potential, and mutton tallow, in an effort to valorize meat industry by-products. Systematic evaluation of emulsion stability, viscosity, and textural properties was conducted using Turbiscan analysis and texture profile analysis. The results demonstrated that xanthan gum concentration was the primary determinant of structural stability, physicochemical stability, and structural integrity of the emulsion systems. Formulation no. 38 (0.8% w/w xanthan gum) was identified as the statistically most stable system based on Turbiscan Stability Index values (TSI = 1.4). Although emulsions containing 1.0% w/w xanthan gum exhibited similarly low TSI values and slightly smaller final droplet diameters, formulation E38 showed the smallest increase in droplet size during storage (<1 µm), indicating superior resistance to structural changes over time. Fat composition showed minimal influence on emulsion behavior, suggesting that lipid selection should prioritize nutritional and bioactive value. These findings indicate that emulsions based on enzymatically modified fats and stabilized with natural polysaccharides can serve as physically stable systems with potential applicability in food, cosmeceutical, and pharmaceutical formulations intended for bioactive compound delivery. Full article

With the increasing frequency extreme weather events associated with climate change, real-time monitoring of rainfall intensity is critical for water resource management, disaster warning, and other applications. Traditional methods, such as ground-based rain gauges, radar, and satellites, face challenges like high costs, low resolution, and monitoring gaps. This study proposes a novel real-time rainfall intensity monitoring method based on deep learning and audio signal processing, using acoustic features from rainfall to predict intensity. Conducted in the semi-arid region of Northwest China, the study employed a custom-designed sound collection device to capture acoustic signals from raindrop-surface interactions. The method, combining multi-feature extraction and regression modeling, accurately predicted rainfall intensity. Experimental results revealed a strong linear relationship between sound pressure and rainfall intensity (r = 0.916, R² = 0.838), with clear nonlinear enhancement of acoustic energy during heavy rainfall. Compared to traditional methods like CML and radio link techniques, the acoustic approach offers advantages in cost, high-density deployment, and adaptability to complex terrain. Despite some limitations, including regional and seasonal biases, the study lays the foundation for future improvements, such as expanding sample coverage, optimizing sensor design, and incorporating multi-source data. This method holds significant potential for applications in urban drainage, agricultural irrigation, and disaster early warning. Full article

The emergence of low-cost edge devices has enabled the integration of automatic speech recognition (ASR) into IoT environments, creating new opportunities for real-time language assessment. However, achieving reliable performance on resource-constrained hardware remains a significant challenge, especially on the Artificial Internet of Things (AIoT). This study presents an AIoT-based framework for automated English-speaking assessment that integrates architecture and system design, ASR benchmarking, and reliability analysis on edge devices. The proposed AIoT-oriented architecture incorporates a lightweight scoring framework capable of analyzing pronunciation, fluency, prosody, and CEFR-aligned speaking proficiency within an automated assessment system. Seven open-source ASR models—four Whisper variants (tiny, base, small, and medium) and three Vosk models—were systematically benchmarked in terms of recognition accuracy, inference latency, and computational efficiency. Experimental results indicate that Whisper-medium deployed on the Raspberry Pi 5 achieved the strongest overall performance, reducing inference latency by 42–48% compared with the Raspberry Pi 4 and attaining the lowest Word Error Rate (WER) of 6.8%. In contrast, smaller models such as Whisper-tiny, with a WER of 26.7%, exhibited two- to threefold higher scoring variability, demonstrating how recognition errors propagate into automated assessment reliability. System-level testing revealed that the Raspberry Pi 5 can sustain near real-time processing with approximately 58% CPU utilization and around 1.2 GB of memory, whereas the Raspberry Pi 4 frequently approaches practical operational limits under comparable workloads. Validation using real learner speech data (approximately 100 sessions) confirmed that the proposed system delivers accurate, portable, and privacy-preserving speaking assessment using low-power edge hardware. Overall, this work introduces a practical AIoT-based assessment framework, provides a comprehensive benchmark of open-source ASR models on edge platforms, and offers empirical insights into the trade-offs among recognition accuracy, inference latency, and scoring stability in edge-based ASR deployments. Full article