Search Results (12,764)

Global energy demand relies heavily on fossil fuels, which produce greenhouse gas emissions. Additionally, municipal solid waste, driven by population growth, represents another source of emissions. In Mexico, organic waste contributes 61 million tons of CO₂eq annually due to inadequate disposal. In Mérida, Yucatan, over 231,000 tons of organic waste are generated yearly, including Urban Tree Pruning (UTP) from 760 public spaces—a significant, undervalued lignocellulosic resource. This study presents a comprehensive, year-round compositional characterization of Mérida’s UTP to establish its chemical profile and assess its seasonal stability as a precursor for bio-based products (i.e., bioethanol). Characterizing local and stable feedstocks, such as UTP, is a fundamental step to enabling Mexico’s compliance with biofuel policies like the 5.8% gasoline blend mandate (NOM-016-CRE) and the Alcohol-to-Jet strategy, supporting progress toward SDGs 7, 11, and 13. Based on a stratified random sampling, monthly analysis (May 2024–April 2025) revealed a consistent biochemical profile with mean annual contents of 23.32% lignin and 62.46% holocellulose. Statistical analysis (Tukey’s test) confirmed its structural homogeneity throughout the year. This uniformity is a key operational attribute, as it allows for the use of standardized industrial pretreatment parameters. Furthermore, the characterized composition supports a theoretical ethanol yield of 170 g/kg of dry biomass, a value competitive with traditional feedstocks like sugarcane bagasse. Consequently, Mérida’s UTP is characterized as a reliable and consistent biomass resource, supporting a transition from linear waste disposal to a circular bioeconomy model. Full article

With the scale of new energy access expanding, the proportion of nonlinear loads in the power grid has increased, leading to frequent impact disturbance events. The types of power quality disturbances (PQDs) are becoming increasingly complex, placing greater demands on the accurate identification of disturbance signals. Therefore, this paper proposes a PQD recognition method based on adaptive KS transform and a Multimodal Feature Fusion Network (MFNet). Firstly, using an improved red-billed blue magpie optimization algorithm, the traditional KS transform window function parameters are adaptively optimized to achieve accurate time–frequency localization of PQD. Secondly, considering the differential characteristics of PQDs in different modes, combined with the proposed adaptive KS transform, a parallel MFNet with three branches in the time domain, frequency domain, and time–frequency domain is constructed; to further enhance feature extraction capability and reduce information loss, residual structures are introduced in the network. Multiple comparative experimental results show that the proposed method achieves an average classification accuracy of 99.52% at 20 dB of noise and demonstrates good noise resistance. Full article

Conventional buckling-restrained braces (BRBs) with rectangular steel tube confinement suffer from stress concentration and inefficient material utilization, limiting their seismic performance. To address these limitations, this study proposes a novel non-rectangular concrete-filled steel tube BRB system incorporating elliptical and corrugated cross-sections. Comprehensive finite element simulations using ABAQUS are conducted to systematically investigate the influence of key geometric parameters—wall thickness (1–14 mm), corner radius (40–55 mm), and corrugation angle (30–75°)—on hysteretic behavior, load-bearing capacity, and failure modes. The results demonstrate that optimized non-rectangular sections achieve load-bearing capacity comparable to conventional rectangular designs (e.g., elliptical section with 12 mm wall thickness reaches 10.02 MN, a 75% increase over 1 mm thickness) while significantly improving material efficiency. Corrugated sections exhibit enhanced weak-axis performance, with equivalent viscous damping ratios exceeding the NIST-recommended threshold of 0.25. Parametric analyses reveal that wall thickness above 12 mm yields diminishing returns; corner radius reduction to 40 mm triggers local buckling yet increases peak capacity; and corrugation angles exceeding 50° induce instability. All non-buckling models satisfy AISC compression strength adjustment factor requirements (β ≤ 1.3). This study systematically evaluates non-rectangular BRB geometries, filling a critical gap in the literature and providing design guidelines that leverage shape optimization to enhance both seismic resilience and material economy. Full article

Background and Objectives: Rapid diagnosis is fundamental to acute ischemic stroke management; however, access to neuroradiological expertise remains limited. This scoping review maps the diagnostic accuracy, workflow impact, and cost-effectiveness of leading AI platforms (Brainomix, Aidoc, RapidAI, and Viz.ai), characterizing industry and peer-reviewed metrics. Materials and Methods: Following PRISMA-ScR guidelines, we searched PubMed, Cochrane Library, and HTA repositories for studies (2019–2025). Using a PICO-based framework, 29 studies were included for thematic mapping of the technological landscape. Results: Twenty-nine studies were included. Platforms show high proximal LVO sensitivity (78–97%), while performance for distal/MVO and posterior circulation occlusions was more variable. RapidAI is frequently mapped using historical perfusion trial parameters; however, volumetric discrepancies with platforms like Viz.ai indicate outputs are not interchangeable. Brainomix shows extensive validation for automated NCCT ASPECTS in triage. Aidoc demonstrates operational advantages via worklist prioritization, while. Viz.ai is associated with door-to-puncture time reductions (11–25 min). Economically, cost-effectiveness is driven by improved functional outcomes and expanded access to thrombectomy, rather than labor substitution. Conclusions: AI platforms function as diagnostic safety nets and workflow optimizers. Reported roles, such as perfusion-centric analysis (RapidAI) or workflow coordination (Viz.ai), reflect current research trends rather than definitive technological superiority. Institutional selection should consider these evidence clusters alongside local validation and specific clinical priorities. Full article

Light is a critical factor influencing fish behavior, yet the low-light conditions in deep-sea cages may impair feeding in visual species like rainbow trout Oncorhynchus mykiss (Walbaum, 1792). This study investigated the effects of light intensity and photoperiod on the feeding behavior of rainbow trout. Using green light, a factorial design tested three light intensities (10, 100, and 1000 lx) and three photoperiods (8L:16D, 16L:8D, and 24L:0D), alongside a complete darkness control (0 lx and 0L:24D). Key behavioral parameters during feeding were quantified via video analysis. The results showed significant main and interactive effects of light intensity and photoperiod on feeding behaviors. Feeding activity was substantially suppressed under continuous darkness. On the initial experimental day, exploratory movement was greatest under 10 lx and 8L:16D. Following 50 days of exposure, fish in light groups exhibited more focused swimming trajectories near the feeding point, indicating behavioral adaptation and spatial learning. Correlation analyses suggested a strategic shift from broad exploration to precise, efficient localization over time. In conclusion, specific lighting conditions, notably low intensity under a regular photoperiod, promote efficient feeding behavior in rainbow trout, whereas darkness or extreme light regimens are inhibitory. These findings reveal adaptive behavioral plasticity in this species and provide a scientific basis for optimizing light management in offshore salmonid aquaculture. Full article

Background/Objectives: Wide Awake Local Anesthesia No Tourniquet (WALANT) and ultrasound-guided peripheral nerve blocks (PNBs) are increasingly used alternatives to general anesthesia for hand and forearm surgery. While WALANT is commonly perceived as a time-efficient and resource-sparing technique, comparative data regarding patient satisfaction, perioperative pain, and time-related outcomes remain inconsistent. This study aimed to compare WALANT and ultrasound-guided supraclavicular peripheral nerve block techniques with respect to patient satisfaction, perioperative pain, time-related parameters, and surgeon-related outcomes in elective hand and forearm extremity surgery. Methods: This prospective comparative observational study included 80 adult patients undergoing elective hand or forearm surgery. Patients received either WALANT or ultrasound-guided supraclavicular brachial plexus block according to patient preference. The primary outcome was overall patient satisfaction assessed within 24 h postoperatively. Secondary outcomes included block performance time, waiting time, total anesthesia-related time, intraoperative and postoperative pain scores, additional sedation requirements, postoperative numbness, willingness to choose the same anesthetic technique again, safety outcomes and surgeon satisfaction. Results: Overall patient satisfaction was significantly higher in the peripheral nerve block group compared with the WALANT group (median [IQR]: 90 [85–100] vs. 80 [70–90], p < 0.0001). Intraoperative and postoperative pain scores were also significantly lower in the peripheral nerve block group. Although block performance time was longer with the peripheral nerve block, waiting time and total anesthesia-related time were significantly shorter compared with WALANT. Surgeon satisfaction and the need for additional intraoperative sedation did not differ significantly between groups. Conclusions: In elective hand and forearm surgery, ultrasound-guided supraclavicular peripheral nerve block was associated with higher patient satisfaction, lower pain scores, and shorter total anesthesia-related time compared with WALANT. Surgical satisfaction scores were similar with both anesthetic techniques. Considering the heterogeneity of clinical settings and procedural requirements, as well as cost and resource utilization considerations, anesthetic technique selection should be individualized. Full article

The modeling and control of photovoltaic and other engineering systems highly depend on the accuracy of parameter identification. However, parameter extraction for photovoltaic equivalent models typically presents a high-dimensional, strongly nonlinear, and multimodal global optimization problem. Traditional analytical or gradient-based methods are sensitive to initial values and easily fall into local optima. To address this issue, this paper proposes a multi-strategy improvement teaching–learning-based optimization algorithm (SNTLBO). A social learning network structure with symmetric interaction topology is introduced into the classical TLBO framework to characterize the knowledge propagation relationships among individuals. Through this symmetric and balanced information exchange mechanism, learners can be guided not only by the teacher but also by multiple neighbors within the network, enabling more diverse and symmetric exploration of the search space and enhancing population diversity and global search capability. Furthermore, a teacher reputation mechanism is constructed, where historical performance is used to weight teacher influence, strengthening the guidance of high-quality solutions and accelerating convergence. Meanwhile, an adaptive teaching factor is designed to dynamically adjust the teaching intensity based on the distance between the teacher and students in the solution space, maintaining a dynamic balance (symmetry) between exploration and exploitation. To evaluate the performance of the proposed algorithm, SNTLBO is systematically compared with 11 advanced optimization algorithms on two benchmark test suites, CEC2017 (30D, 50D) and CEC2022 (10D, 20D). Non-parametric statistical tests are conducted to assess significance. The results demonstrate that SNTLBO shows competitive advantages in terms of convergence speed, solution accuracy, and stability. Finally, SNTLBO is applied to the parameter estimation of single-diode, double-diode, triple-diode, quadruple-diode, and photovoltaic module models. Experimental results show that the proposed algorithm achieves higher identification accuracy and robustness in terms of RMSE, IAE, and I–V/P–V curve fitting, verifying its effectiveness and practical value for complex global optimization and practical engineering applications. Full article

Background: Psoriasis is a chronic immune-mediated inflammatory disease characterized by systemic inflammation and complex immune dysregulation that extends beyond the skin and may affect the oral environment. Increasing evidence suggests that saliva may serve as a non-invasive diagnostic medium reflecting both local and systemic pathological processes. This systematic review aimed to critically evaluate current evidence on salivary biomarkers in psoriasis, focusing on inflammatory mediators, oxidative stress parameters, immune-related factors, and oral microbiota alterations, and to assess their potential clinical and diagnostic relevance. Methods: A systematic literature search was performed according to PRISMA guidelines using PubMed, Scopus, and Web of Science databases, covering studies published between 1994 and October 2024. Original human studies evaluating salivary biomarkers in patients with psoriasis were included based on predefined PECOS criteria. Studies involving confounding inflammatory oral diseases without separate analysis were excluded. Eleven eligible studies were included in a qualitative synthesis. Results: The analyzed studies consistently demonstrated multidimensional alterations in salivary composition in psoriasis patients compared with healthy controls. Increased levels of pro-inflammatory cytokines (TNF-α, IFN-γ, IL-2) and reduced anti-inflammatory IL-10 indicated persistent immune activation. Elevated oxidative stress markers, including total oxidant status and oxidative stress index, supported the role of redox imbalance in disease pathogenesis. Alterations in innate immune components, such as salivary α-amylase, immunoglobulin A, and lysozyme, suggested impaired oral immune regulation. Moreover, emerging microbiome data revealed shifts toward pro-inflammatory bacterial taxa, including Prevotella and Porphyromonas. Some studies indicated that biologic therapy may modulate salivary biomarker profiles. Conclusions: Salivary biomarkers reflect systemic inflammatory and immunological alterations associated with psoriasis and represent promising non-invasive tools for disease monitoring and clinical assessment. Nevertheless, substantial methodological heterogeneity and limited sample sizes highlight the need for large-scale, standardized, and longitudinal studies to validate their diagnostic applicability. Full article

Side-scan sonar (SSS) serves as the primary perceptual instrument for Autonomous Underwater Vehicles (AUVs) in large-scale marine search and rescue (SAR) operations. However, the detection of critical targets is frequently hindered by severe hydro-acoustic noise, the spatial discontinuity of wreckage, and the weak visual signatures of small targets. To surmount these challenges, this paper presents WPG-DetNet. First, we introduce a Wavelet-Embedded Residual Backbone (WERB) to reconstruct the conventional downsampling paradigm. By substituting standard pooling with the Discrete Wavelet Transform (DWT), this architecture explicitly disentangles high-frequency noise from structural information in the frequency domain, thereby achieving the adaptive preservation of edge fidelity for large human-made targets while filtering out speckle interference. Then, addressing the distinct challenge of discontinuous aircraft wreckage, the framework further incorporates a Debris Graph Reasoning Module (D-GRM). This module models scattered fragments as nodes in a topological graph to capture long-range semantic dependencies, transforming isolated instance recognition into context-aware scene understanding. Finally, to bridge the gap between AI and underwater physics, we design a Shadow-Aided Decoupling Head (SADH) equipped with a physics-informed geometric loss. By enforcing mathematical consistency between target height and acoustic shadow length, this mechanism establishes a rigorous discriminative criterion capable of distinguishing weak-echo human bodies from seabed rocks based on shadow geometry. Experiments on the SCTD dataset demonstrate that WPG-DetNet achieves a mean Average Precision ($mA{P}_{50}$) of 97.5% and a Recall of 96.9%. Quantitative analysis reveals that our framework outperforms the classic Faster R-CNN by a margin of 12.8% in $mA{P}_{50}$ and surpasses the Transformer-based RT-DETR-R18 by 5.6% in high-precision localization metrics ($mA{P}_{50:95}$). Simultaneously, WPG-DetNet maintains superior efficiency with an inference speed of 62.5 FPS and a lightweight parameter count of 16.8 M, striking an optimal balance between robust perception and the real-time constraints of AUV operations. Full article

Unsupervised change detection (UCD) from heterogeneous bitemporal optical–SAR imagery is challenging due to modality discrepancy, speckle/illumination variations, and the absence of change annotations. We propose MV-S2CD, a vision foundation model (VFM)-based framework that learns a modality-bridged latent space and produces dense change maps in a fully unsupervised manner. To robustly adapt pretrained VFM priors to heterogeneous inputs with minimal task-specific parameters, MV-S2CD incorporates lightweight modality-specific adapters and parameter-efficient low-rank adaptation (LoRA) in high-level layers. A shared projector embeds the two observations into a common geometry, enabling consistent cross-modal comparison and reducing sensor-induced domain shift. Building on the bridged representation, we design a dual-branch change reasoning module that decouples structure-sensitive cues from semantic-consistency cues: a structure pathway preserves fine boundaries and local variations, while a semantic-consistency pathway employs reliability gating and multi-scale context aggregation to suppress pseudo-changes caused by modality-specific nuisances and residual misregistration. For label-free optimization, we develop a difference-centric self-supervision scheme with two perturbation views and reliability-guided pseudo-partitioning, jointly enforcing pseudo-unchanged invariance, pseudo-changed/unchanged separability, and sparsity and edge-preserving regularization. Experiments on three heterogeneous optical–SAR benchmarks demonstrate that MV-S2CD consistently improves the Precision–Recall trade-off and achieves state-of-the-art performance among unsupervised baselines, while remaining backbone-flexible and efficient. Full article

To address challenges related to complex background interference and insufficient multi-scale target feature extraction in lettuce growth stage detection. The lightweight YOLOv10 detection model and the specific characteristics of lettuce field data were used. The CNCM channel non-local mixture mechanism and ASF adaptive spatial frequency attention mechanism were incorporated to optimize lightweight modules, including DownSample, Zoom_cat, and ScalSeq, within the original model. Consequently, an improved CCASF-YOLOv10 model was constructed, integrating multi-scale feature fusion and enhanced target feature extraction. Experimental results demonstrate that, in an NVIDIA A40 GPU testing environment, the model achieves an accuracy rate of 91.9%, a recall rate of 91.6%, mAP@0.5 of 95.3%, and mAP@0.5:0.95 of 72.9%. The parameter size is 11.9 M, and the single-frame inference speed is 24.76 ms, indicating a favorable balance between detection precision, model efficiency, and real-time inference. Furthermore, an intelligent machine vision detection system for lettuce growth-stage monitoring and precise field control was developed using the CCASF-YOLOv10 model. This system facilitates the industrial advancement of lettuce cultivation. Full article

This paper introduces a new explicit integration method for second-order ordinary differential equations (ODEs) commonly encountered in engineering applications. Traditionally, these problems are solved either by reformulating them as first-order systems to apply one-step methods such as Runge–Kutta schemes, or by using direct second-order approaches widely adopted in linear dynamics, including the generalized-α, central difference, and Newmark methods. The proposed method is derived from a Taylor series expansion truncated at the third derivative, resulting in a fully explicit algorithm that requires only one function evaluation per time step. Similar to Newmark’s formulation, it includes adjustable parameters that allow the user to balance accuracy and stability. For a specific parameter choice, the method exhibits convergence and stability properties comparable to those of the central difference scheme. An important advantage is that it remains explicit even when nonlinearities depend on first-derivative terms. The paper presents a theoretical analysis covering stability, local truncation error, spectral properties, numerical damping, and period elongation. The method is validated through four test cases from multibody dynamics, including linear and nonlinear problems. Results demonstrate that the Explicit Integration Grade 3 (EIG-3) method achieves accuracy comparable to existing explicit second-order integrators while significantly reducing computational cost, particularly in nonlinear applications. Full article

Early and reliable detection of P-wave arrivals is critical for seismic monitoring and earthquake early warning, particularly under low signal-to-noise ratio (SNR) and non-stationary noise conditions. This study presents an automatic detection method based on singular value decomposition (SVD) entropy computed in sliding time windows with local signal filtering. Within this framework, the P-wave onset is interpreted as a local structural change in the signal rather than a simple energy increase. SVD entropy captures the redistribution of energy among dominant signal components, providing high sensitivity to the initial P-wave arrival even at moderate and low noise levels ($SNR\ge 2$). The method was validated using real seismic data from four regional stations operating under different noise conditions. Analysis of detection parameters revealed strong station dependence. For stations affected by low-frequency drift, polynomial detrending was identified as a necessary preprocessing step to ensure a stable entropy response and reliable detection. The proposed approach achieves detection accuracies of up to 93–98% at $SNR\ge 2$, significantly outperforming the classical STA/LTA algorithm and demonstrating performance comparable to modern deep learning methods. Since the method does not require model training or labeled datasets, it provides an interpretable and computationally efficient solution for automatic seismic monitoring. These properties make the proposed approach particularly suitable for real-time seismic monitoring systems and distributed sensor networks operating under limited computational resources. All computational stages were performed at the Farabi Supercomputer Centre of Al-Farabi Kazakh National University. The method requires no model training or labeled data, making it an interpretable, robust, and computationally efficient solution for automatic seismic monitoring and early warning systems. Full article

The study evaluated the drinking water quality of Rouxville (RX) in Mohokare Local Municipality in the Free State, using chemical, physical, and microbiological parameters in comparison with South African National Standard 241 (SANS 241:2015). Drinking water samples were collected monthly from five sample sites, including the water treatment plant (WTP) and four end-user points, over a period of three years (2021–2023). Microbiological parameters revealed persistent non-compliance, with total coliforms and Escherichia coli (E. coli) frequently exceeding recommended limits by SANS 241 at multiple sites. The highest total coliform concentration of 201 CFU was recorded at the Rouxville Water Treatment Plant during the third year (2023) of sampling, while E. coli reached a maximum of 11 CFU at an end-user point, indicating the presence of possible pathogens in the water system. Colour exceeded the recommended limit (15 Pt-Co mg/L) at all sampling sites, with the highest value of 133 Pt-Co mg/L recorded at Rolelethunya Library. Chemical parameters mostly complied with SANS 241 limits, elevated values of total alkalinity and aluminium were observed at certain sites, particularly during the third year (2023) of sampling. The Canadian Council of Ministers of the Environment Water Quality Index (CCME-WQI) was also used to determine the overall water quality of the sample sites. The findings revealed that several sample sites had non-compliant parameters. The CCME-WQI revealed that the drinking water quality of Rouxville was either in the marginal or fair category, indicating that the water quality may be occasionally or frequently threatened, posing public health risks. These findings highlight the urgent need to ensure regular maintenance of WTP and ensuring continuous microbial monitoring. Full article

Background: Gamma Knife radiosurgery (GKS) delivers highly conformal intracranial irradiation, yet clinical decision-making still relies predominantly on physical dose metrics that do not account for fractionation, dose rate, treatment time, or DNA repair. Classical radiobiological models—including the linear–quadratic (LQ) formula and the Jones–Hopewell single-session repair model—do not extend naturally to 3- and 5-fraction GKS. Meanwhile, growing evidence suggests that biologically effective dose (BED) may better capture radiosurgical response in selected pathologies. A unified, biologically grounded, multi-fraction GKS framework has been lacking. Methods: We developed AI-BED-Fx, the first multi-fraction extension of the Jones–Hopewell radiobiological model capable of computing fraction-resolved BED for 1-, 3-, and 5-fraction GKS. The framework incorporates α/β ratio, dual-component repair kinetics, isocentre geometry, beam-on–time structure, and lesion-specific biological parameters. Four synthetic pathology-specific cohorts—arteriovenous malformation (AVM), meningioma (MEN), vestibular schwannoma (VS), and brain metastasis (BM)—were generated using distinct radiobiological signatures. Machine-learning models were trained to quantify the predictive value of physical dose versus BED for local control or obliteration. Additional experiments included Bayesian estimation of α/β and a neural-network surrogate for fast BED prediction. An exploratory comparison with a 60-lesion clinical brain–metastasis dataset was performed to assess whether key trends observed in the synthetic BM cohort were consistent with real radiosurgical outcomes. Results: AI-BED-Fx produced realistic pathology-specific BED distributions (AVM 60–210 Gy_2.47; MEN 41–85 Gy_3.5; VS 46–68 Gy₃; BM 37–75 Gy₁₀) and biologically coherent dose–response relationships. Predictive modeling demonstrated strong pathology dependence. In AVM, the three models achieved AUCs of 0.921 (Model A), 0.922 (Model B), and 0.924 (Model C), with corresponding Brier scores of 0.054, 0.051, and 0.051, with BED-based models performing best. In meningioma, BED was the dominant predictor, with AUCs of 0.642 (Model A), 0.660 (Model B), and 0.661 (Model C) and Brier scores of 0.181, 0.177, and 0.179, respectively. In vestibular schwannoma, the narrow BED range resulted in minimal BED contribution, with AUCs of 0.812, 0.827, and 0.830 and Brier scores of 0.165, 0.160, and 0.162, with physical dose and tumor volume determining performance. In brain metastases, outcomes were driven primarily by volume and physical dose, with AUCs of 0.614, 0.630, and 0.629 and Brier scores of 0.254, 0.250, and 0.253, showing negligible improvement from BED. AI-BED-Fx also accurately recovered the true α/β from synthetic outcomes (posterior mean 2.54 vs. true 2.47), and a neural-network surrogate reproduced full radiobiological BED calculations with near-perfect fidelity (R² = 0.9991). Conclusions: AI-BED-Fx provides the first unified, biologically explicit framework for modeling single- and multi-fraction Gamma Knife radiosurgery. The findings show that the predictive usefulness of BED is pathology-specific rather than universal, and that radiobiological dose provides additional predictive value only when repair kinetics and dose–response biology support it. By integrating mechanistic radiobiology with machine learning, AI-BED-Fx establishes the conceptual and computational foundations for biologically adaptive, AI-guided radiosurgery, and cross-pathology comparison of treatment response. This work uses large radiobiologically grounded synthetic cohorts for methodological validation; limited real-patient data are included only for exploratory consistency checks, and full clinical validation is planned. Full article