Search Results (117,973)

A novel distributed-order Cattaneo–Christov model is proposed to effectively characterize non-classical heat conduction processes with memory effect and time–space relaxation behaviors originating from distributed-order fractional derivatives. A fractional physics-informed neural networks (fPINN) algorithm is employed to address both the forward and inverse problems of the distributed-order heat conduction model. For the forward problem, we propose an SfPINN algorithm that incorporates a squared loss term and employs an adaptive updating strategy for the loss-term weights. First, the boundary conditions are embedded into the network output such that they are automatically satisfied. In addition, we design a two-stage training strategy to enhance computational efficiency: in the first stage, the squared loss term associated with the initial condition is incorporated into the loss function; in the second stage, the squared residual term of the governing equation is introduced into the loss function. Numerical results show that the proposed algorithm outperforms the standard fPINN method in both solution accuracy and training iteration speed. For the inverse problem, the numerical results demonstrate that as the iteration number increases, the estimated parameter values progressively converge to their true values and finally stabilize. Full article

Fascia is increasingly recognized as a dynamic functional system. It can actively sense, transmit, and regulate mechanical, sensory, and metabolic signals. Why does fascia play such a critical role in chronic pain and movement disorders? Researchers are now rethinking the pathophysiological mechanisms underlying this role. Previous systematic reviews have typically focused primarily on specific mechanisms or interventions. In contrast, this study takes a holistic view of fascial function. It integrates multiple physiological functions of the fascia: mechanical integration, sensory modulation, cellular and matrix remodeling, as well as metabolic and immune regulation. From the perspective of functional imbalance, we further explore the pathological mechanisms associated with the fascia. Building on this, we then focus on how to assess fascial function from multiple dimensions and on specific targeted interventions. For assessment, we have systematically compiled a set of multi-stage quantitative techniques. These include clinical palpation, ultrasound, and elastography, tissue mechanics testing, microdialysis, omics approaches, electrophysiological testing, and digital modeling. For interventions, we have listed a range of modulating approaches, such as manual therapy, exercise rehabilitation, dry needling and acupuncture, fascial injections, targeted drugs, and biotechnological materials derived from tissue engineering. This review summarizes a clinical decision-making framework guided by the assessment of fascial functional status. It emphasizes a systematic approach and links quantitative diagnosis with precise interventions. Additionally, it provides a literature synthesis for understanding fascial mechanisms and related disorders and offers a reference foundation for the field’s transition from empirical treatment to measurable, reproducible, and individualized practice. Full article

In recent years, a sharp increase in coal-based power generation has been observed in a number of countries. Coal-fired thermal power plants remain the main source of harmful emissions in the energy sector of many countries, including Kazakhstan. This creates a strong need for the development of effective methods to reduce pollutant emissions at thermal power plants. The aim of the present study is to perform a numerical investigation of the effectiveness of staged combustion technology with secondary air injection (Over-Fire Air, OFA) applied to three boilers—PK-39, BKZ-160, and BKZ-75—which differ in design, capacity, and furnace configuration. CFD modeling was carried out using the FLOREAN package, adapted to the conditions of the Kazakh energy sector, which relies on high-ash coal (more than 40%) for coal-based power generation. Model validation was performed against experimental data obtained from operating thermal power plants. It was found that air injection through OFA injectors intensifies turbulent mixing, reduces peak temperatures in the main combustion zone, and ensures a more uniform distribution of heat release along the furnace height, thereby suppressing thermal NOx formation. It is shown that the spatial structure of NO concentration fields at the furnace outlet strongly depends on the design features of each boiler. The results demonstrate the high efficiency of staged combustion technology in reducing nitrogen oxide emissions and improving the environmental performance of pulverized-coal boiler units. The obtained results can be used in the design of new and the modernization of existing thermal power plants utilizing coal-based power generation. Full article

Rice brown spot, caused by Bipolaris oryzae, is an important constraint for rice production and requires timely field-scale monitoring. This study evaluated the use of multispectral bands acquired with a UAV-mounted sensor, together with vegetation indices, combined with machine-learning models to estimate rice brown spot severity under field conditions in Lambayeque and Cajamarca, Peru. A total of 37 sampling observations were collected across the vegetative, flowering, and milk-ripening stages. Spectral variables were extracted from UAV orthomosaics and related to field-based disease severity assessments. The strongest correlations with severity were observed for NDRE (r = −0.83) and NPCI (r = 0.77). Three regression models were evaluated using leave-one-out cross-validation (LOOCV): support vector regression with radial basis function kernel (SVR-rbf), support vector regression with linear kernel (SVR-linear), and Random Forest (RF). The SVR-linear model showed the lowest prediction error using NDRE, GREEN, and BLUE as predictors (R²_CV = 0.76; RMSE_CV = 1.31), although its performance was very similar to that of SVR-rbf and RF. These results indicate that UAV-derived multispectral information can support plot-level estimation of rice brown spot severity. However, model performance should be interpreted cautiously because of the small dataset, heterogeneous disease conditions, and moderate prediction accuracy. Further studies with larger and independent datasets are needed to improve robustness and transferability. Full article

Urbanization underscores the critical role of the built environment in shaping human health outcomes. Recently, technology-driven assessment enables a more precise, dynamic, and objective evaluation of individuals’ biobehavioral responses to built environments and their health. However, existing reviews are limited to single technologies, single health outcomes, or specific environmental features. As a result, this narrative review summarizes 269 studies (2003–2025) to examine how such technology-driven methodologies capture the effects of built environments on psychophysiological well-being. Findings reveal a four-stage evolution in methodology from subjective evaluations and single-device monitoring to integrated subjective-objective measures and, more recently, multimodal synergistic frameworks. Accordingly, based on a technology-driven assessment of biobehavioral responses, this review synthesizes a dual-pathway framework linking the built environment to health: (1) psychological responses are mediated through emotion-arousal mechanisms, encompassing 22 key emotions across both positive and negative valences; and (2) physiological outcomes are influenced by behavioral–psychological mediation and direct environmental exposure, encompassing six categories that span from subclinical dysfunction to clinical disease risk. This review thereby provides a framework derived from the reviewed evidence that connects built environments to health through measurable biobehavioral pathways, directly supporting human-centered urban design and assessment. Full article

Steel plate defect detection is confronted with problems such as weak features of small defects, disconnection between physical priors and detection tasks, and semantic inconsistency of multi-scale fusion, which can easily lead to the misdetection of small defects. To solve these problems, this paper proposes a detection method named PG-SalDETR. Firstly, this paper proposes a physics-guided saliency perception mechanism (PGSPM), which transforms physical priors into learnable guidance signals and directly embeds them into the detection network for joint optimization. Secondly, this paper proposes the token sequence saliency perception network (TSSP-Net), which is designed to help improve the perception and representation of small defect features through an asymmetric dual-branch architecture, adaptive fusion, and residual fusion. Thirdly, a two-stage query refinement mechanism (TSQRM) is proposed. Through physically guided offset correction and adaptive multi-scale feature aggregation, it optimizes the query while preserving fine-grained defect details. Finally, the dynamic cross-scale fusion module (LCASF) is proposed. Through the dynamic cross-scale fusion strategy, the semantic inconsistency problem of small defect features in multi-scale fusion is alleviated. Experimental results demonstrate improvements. Compared to Salience DETR, PG-SalDETR achieves an AP increase of 3.8% and 2.6%, and an AP_S increase of 2.8% and 3.9% on the NEU-DET and GC10-DET datasets, respectively. These results indicate the effectiveness of the proposed method for small defect detection on steel plate surfaces. Full article

The Kidney Disease: Improving Global Outcomes (KDIGO) CGA framework remains the essential basis for chronic kidney disease (CKD) classification, risk stratification, and guideline-based therapy. However, eGFR and albuminuria do not always explain the physiological mechanism maintaining the current filtration level or the heterogeneity of treatment responses. This narrative review proposes a hypothesis-generating functional–hemodynamic extension of KDIGO CGA that incorporates renal functional reserve (RFR), blood pressure, volume status, proteinuria phenotype, and selected tubular markers. RFR is discussed as a dynamic stress test of nephron reserve rather than as a replacement for eGFR or albuminuria. A low, zero, or negative RFR may suggest reserve exhaustion or relative hyperfiltration, but its interpretation depends on standardized testing conditions and clinical context. We distinguish established evidence-based therapy—RAAS blockade in albuminuric or hypertensive CKD, SGLT2 inhibition for kidney and cardiorenal protection, and non-steroidal MRA therapy in selected patients—from conceptual sequencing hypotheses such as RAASi-prioritized, SGLT2i-prioritized, early dual, or staged triple renoprotection. The review also summarizes albuminuria as a two-compartment phenomenon involving both glomerular passage and proximal tubular handling of filtered proteins. The proposed framework is not a validated treatment algorithm. It is intended to support physiological phenotyping, interpretation of early eGFR changes, and the design of prospective studies that test whether RFR adds independent prognostic or therapeutic value beyond KDIGO CGA. Full article

Background: Foliar fertilization is a crucial practice in modern viticulture to enhance grape yield and fruit quality. Micronized calcite is a fine-particle mineral fertilizer that potentially improves vine performance; however, its treatment timing and optimal dosage require further scientific validation under field conditions. Methods: This study investigated the effects of the use of micronized calcite as a foliar biostimulant on the Red Globe grape variety. Control and three treatments were compared: a control (no treatment), a single treatment (pre-bloom, 0.5%), two treatments (pre- and post-bloom, 0.5% + 0.5%), and three treatments (pre-bloom, post-bloom, and véraison, 0.5% + 0.5% + 0.5%). The evaluation encompassed phenological stages, total effective temperature, yield components, cluster and berry characteristics, and must composition. This was a single-season trial (2022) conducted at a single location with three replicates per treatment (n = 3); multi-year, multi-location validation is therefore required before the findings can be generalized. Results: All micronized calcite treatments slightly shortened the vegetation period compared to the control. Under the conditions of this single-season trial, the single pre-bloom treatment was associated with the highest yield parameters, with the average number of clusters per vine, cluster weight, and total grape yield per vine being higher than the control by 48.67%, 51.16%, and 121%, respectively. For must composition, only must yield differed significantly (between the 1st and 3rd treatments; p < 0.05); soluble solids content (+5.64%) and ripening index (+16.99%) were numerically higher but not statistically significant (p ≥ 0.05) and are therefore reported as trends rather than improvements. By contrast, the two-treatment (pre- and post-bloom) showed the highest values for physical berry traits, with cluster width and 100-berry weight exceeding the control by 35.03% and 11.11%, respectively. Conclusion: Under the semi-arid conditions of this single-season trial, foliar treatments of micronized calcite, particularly a single pre-bloom application, were associated with notable improvements in yield and must quality of Red Globe grapevines. These preliminary findings suggest that finely milled calcite may serve as a promising supplementary foliar fertilizer in viticulture; however, the results are context-specific, and multi-year, multi-location trials are required before broader recommendations can be made. Full article

Early diagnosis of Diabetic Retinopathy (DR) is critical for preventing irreversible vision loss, but precise lesion annotation by ophthalmologists is the dominant cost in building any clinical-grade DR detection model. The structural problem in real hospital settings is not labeling cost per se, but expert availability: ophthalmologists’ time is bounded by clinical duties, so the active-learning (AL) cycle can iterate only a handful of times in practice. We frame this constraint explicitly and ask which AL designs work best under a tight expert budget. We propose Virtuous Cycle, a Human-in-the-Loop (HITL) pipeline that integrates (i) a YOLOv8x-based object detector for microaneurysms, hemorrhages, and exudates, (ii) four AL sampling strategies (Average Confidence, Random, Hybrid-Diversity, Monte Carlo Dropout), and (iii) an in-hospital annotation platform (Diavision Studio) in which clinicians refine AI pre-labels rather than draw from scratch. We evaluate Virtuous Cycle on a real-world fundus dataset from the National Medical Center (NMC) across eight AL rounds, expanding the labeled pool from 81 images (R0) to 481 images (R8) within the actual expert-time budget of two ophthalmologists. Across three independent random seeds, random sampling dominates at cold start (mean mAP@50 $0.14\to 0.25$ over R0–R1), whereas Hybrid-Diversity converges to the highest mAP@50, Precision, and Recall by R7 (431 images; mAP@50 $0.40$, Precision $0.55$, Recall $0.41$), with MC Dropout close behind; by R8, the labeled pool is exhausted and all strategies converge to the same final model. A clinician crossover analysis of 36 paired clinical images, controlling for per-clinician speed bias and per-image difficulty bias, shows no statistically significant difference in overall per-image labeling time between AI-assisted and manual annotation ($p=0.52$), but a statistically significant increase in confirmed lesion detections under AI assistance ($p=0.0058$), driven predominantly (84–$100\%$ of the net increase) by microaneurysms, the lesion type most prone to being missed unaided. The results indicate that, under expert-budget constraints, AL strategy choice should be staged: random sampling for cold start, uncertainty-and-diversity sampling once the model has matured, and that AI assistance trades a modest, lesion-burden-dependent time cost for a measurable gain in the sensitivity of microaneurysm detection. Full article

The determination of optimal geometric characteristics of a catamaran that minimize vessel motion responses under prescribed design and operational conditions remains insufficiently addressed in existing engineering practice. This study presents a systematic methodology for the evaluation of catamaran seakeeping performance through the structured parametric comparison of principal geometric parameters. The proposed methodology comprises the identification of relevant geometric variables, the specification of their admissible variation ranges in accordance with design constraints, the selection of appropriate numerical evaluation tools, and the quantitative analysis of resulting motion responses. The objective is to determine parameter combinations that yield minimum motion amplitudes. The methodology presented in this article is partly a complex methodology for evaluation of seakeeping and total resistance, and partly selection of the most favorable combinations of geometrical parameters satisfying the design task parameters across both above-mentioned hydrodynamic qualities. The resistance part of the methodology is presented in previous works with links and description provided in this article. A graphical system for presenting simulation results is developed, allowing arrangement of the calculation results on one horizontal axis, representing catamaran length variations, grouped by the speed and demihull separation values and including catamaran demihull symmetry considerations. Aligned under each other, the graphs provide an intuitive interpretation of total resistance trends and seakeeping across various geometric configurations and operational speeds. This method, the seakeeping part of which is illustrated in the results paragraph, enables a comprehensive comparison of multiple design variants within a clear visual framework. The methodology is applied to a representative catamaran configuration by parametrically varying key geometric characteristics, including vessel length, demihull separation, and hull symmetry. The corresponding seakeeping responses are evaluated using the Maxsurf Motions computational framework. The results demonstrate that systematic variation and analysis of geometric parameters enable the identification of configurations with significantly reduced motion amplitudes. Pitching RAO amplitudes for different catamaran lengths can vary 45–50%, for demihull separation—25–50% and for asymmetry 27–50%. Heaving RAO amplitudes for different catamaran lengths can vary 45–50%, for demihull separation—32–65% and for asymmetry 30–60%. The findings indicate that demihull separation, hull-form symmetry, and overall vessel length each play a significant role in determining catamaran seakeeping performance. The proposed approach provides a robust basis for the early-stage design structured parametric comparison of catamarans, facilitating the selection of geometric configurations that minimize projected vessel motions and improve overall seakeeping performance. Full article

The rapid development of new energy has caused a sharp increase in the stochasticity on the source side of the new power system (NPS), and extreme weather along with climate variability have also led to increased stochasticity in power demand on the load side; thus, how to achieve source-load matching and enable the load to track the source under the new situation is the key to the efficient operation of the power system. Aiming at the problem that existing load regulation potential evaluation mainly focuses on physical capacity, making it difficult to reflect users’ subjective willingness to participate as well as the dynamic changes in regulation capability under different operating scenarios, this paper proposes a two-stage dynamic profiling classification method for multi-type power user loads considering regulation willingness. First, an evaluation index system is constructed from three dimensions, physical reliability, execution reliability, and behavioral willingness, to achieve the unified characterization of the regulation capabilities of heterogeneous resources such as industrial loads and electric vehicle (EV) aggregators. Second, the DBSCAN algorithm is adopted to identify typical annual operating scenarios. Finally, the Dynamic Time Warping (DTW) distance is introduced to improve the K-Means++ algorithm, achieving the profiling classification of user regulation potential. This paper takes a certain NPS demonstration park as an example for verification, and the results show that the annual operating scenarios can be divided into 4 types of typical days; the proposed DTW-K-Means++ method has better classification performance compared with traditional Euclidean distance clustering, can effectively identify the differences and dynamic migration characteristics of user regulation potential under different operating scenarios, and stably classifies users into three types of profiles: deep regulation type, agile response type, and rigid constraint type. The research results aim to provide reliable data support for the refined dispatch of the power grid by effectively quantifying the dynamic migration patterns of heterogeneous resources under variable scenarios. Full article

This paper presents the optimization-oriented structural design of aircraft empennages developed within the HERFUSE project, funded by the Clean Aviation Joint Undertaking. The study focuses on the horizontal and vertical tailplane of a hybrid-electric regional aircraft, considering the structural challenges introduced by distributed propulsion and novel integration requirements. The distributed propulsion layout influences the tail load envelope through powered-on maneuver/gust and asymmetric-thrust conditions, which contribute to the laminate redistribution in regions subjected to high bending–torsional demand. Starting from a conceptual structural configuration, the empennage is refined through a multi-objective optimization process. The structure is discretized into multiple regions, each characterized by independent laminate definitions selected from a predefined set of stacking sequences. The optimization aims to minimize the overall structural mass while ensuring adequate structural integrity under the project complete set of load cases. Structural performance is evaluated using a failure criterion suitable for composite materials, allowing the identification of critical regions and the redistribution of material accordingly. The results highlight the effectiveness of the adopted optimization strategy in improving structural efficiency, providing a refined configuration with a final mass of 433 kg, corresponding to an approximately 27.23% reduction with respect to the reference configuration coming from project specifications, while maintaining controlled failure index levels. The proposed approach demonstrates its suitability for supporting early-stage design decisions in next-generation hybrid-electric aircraft. Full article

Background/Objectives: Previous studies have measured a limited number of biomechanical parameters during medical rehabilitation of an anterior cruciate ligament (ACL) rupture. This study aimed to quantitatively assess changes in gait biomechanics, knee function, and lower-extremity muscle activity during after ACL reconstruction. Methods: The study included 32 patients after arthroscopic ACL reconstruction. The patients were divided into three groups based on postoperative time points: 0.5 year (12 men), 1 year (7), and over 1 year (9). Gait analysis at both self-selected and fast speeds was performed using an inertial system. Statistical analysis was performed using rank models and full-factorial orthogonal designs. Results: After 0.5 year, the timing of the gait cycle at self-selected speed was within the control group’s range and showed no significant asymmetry. With increasing speed, a decrease in knee joint range of motion was observed in the 0.5 year and 1-year groups, without achieving a full physiological increase in range of motion at long-term follow-up. Multivariate analysis revealed the greatest biomechanical imbalance during fast walking at one year and a phase-dependent effect of time after surgery, speed, and limb status on kinematics and EMG, particularly in the quadriceps. Conclusions: Basic temporal gait parameters during self-selected walking were within the control range by 0.5 year, but load-dependent knee kinematic and EMG abnormalities persisted. The knee joint’s response to increased loads remained impaired for at least one year. The persistence of phase-specific compensatory changes in kinematics and muscle activity at later stages can be assessed using exercise testing. Full article

Germanium (Ge) is a high-tech critical metal typically hosted at trace levels in sphalerite, making its detection and characterization challenging in both primary ores and mine residues. This study presents a multi-scale analytical workflow combining laser-induced breakdown spectroscopy (LIBS), electron probe micro-analysis (EPMA), and multivariate statistics to detect, map and quantify Ge distribution in a representative Pb-Zn sample from the Les Malines deposit (France). µ-LIBS mapping enables rapid centimeter-scale screening at 15 µm resolution and identifies Ge-bearing domains over large areas, which are subsequently investigated at micrometer scale using EPMA chemical mapping and quantitative analyses. Results reveal a strong µm-scale heterogeneity of Ge distribution within sphalerite, with Ge systematically concentrated in an Fe-rich intermediate zonation associated with prismatic growth textures, while Cu/Cd/Ag are enriched in distinct collomorph domains. Multivariate statistical analyses (correlation matrices and PCA) confirm a strong geochemical structuring opposing an Fe/Ge association against a Cu/Cd/Ag pole. These findings demonstrate that Ge incorporation is controlled by localized growth conditions rather than bulk composition. The proposed workflow provides an efficient and scalable framework for exploration, enabling rapid targeting of critical metal enrichments and supporting their extension to multiple mineralization stages, Pb-Zn deposits, and other high-tech critical metals (HTCMs) such as Ga and In. Full article

Leaf litter decomposition is a key pathway for carbon transfer from forest ecosystems to soils and surface waters. Dissolved organic matter (DOM) released during early-stage leaching represents a potentially reactive fraction of this carbon pool; however, its molecular composition and short-term reactivity remain insufficiently characterised. This study provides a comparative characterisation of DOM released from composite leaf litter samples representing five common deciduous tree species (Betula pendula, Carpinus betulus, Alnus glutinosa, Populus tremula, and Quercus robur) under controlled laboratory conditions. Leaf material collected from multiple trees per species was pooled to obtain a single composite sample; therefore, replicate leaching experiments represent procedural rather than biological replication. DOM was isolated using solid-phase extraction (SPE) and analysed by gas chromatography–mass spectrometry (GC–MS) following trimethylsilyl (TMS) derivatisation, while chemical oxygen demand (COD) and biochemical oxygen demand (BOD₅) were used as indicators of oxidative reactivity and short-term biodegradability. The applied analytical approach captures a selective and operationally defined fraction of DOM, primarily low-molecular-weight and derivatisable compounds; therefore, the results are interpreted as semi-quantitative compositional fingerprints. Carbohydrates, phenolic compounds, and low-molecular-weight organic acids dominated the detected fraction of DOM, with differences observed among composite samples. The composite samples representing A. glutinosa and P. tremula contained higher relative proportions of carbohydrate-related compounds, whereas the composite samples representing B. pendula and C. betulus showed higher relative contributions of aromatic compounds. Apparent differences in BOD₅ were observed among composite samples; however, these observations likely reflect procedural variability rather than independent biological effects. The results indicate variability in DOM composition and apparent reactivity among composite litter samples under controlled laboratory conditions. Due to the lack of biological replication and the selective nature of the analytical approach, the findings should be interpreted as exploratory and not as evidence of generalised tree-species effects. Full article