Search Results (3,208)

Inverters are widely applied in aviation, distributed power grids, and vehicles, where their health status directly impacts the stable operation of entire systems. Existing health assessment methods suffer from poor real-time performance, require additional measurement circuits, and are prone to misjudgment, while failing to adequately address slow degradation behaviors during inverter operation. To address these challenges, this study proposes an inverter health assessment method based on an improved D-S evidence theory. First, based on the practical requirements of subway auxiliary inverters, 13 key evaluation indicators were selected. Subjective weights were obtained using the Analytic Hierarchy Process (AHP), while objective weights were derived through the Critic method, credibility, and falsity weighting. These were then fused using game theory to obtain composite weights. Next, after data normalization, a ridge-type membership function was employed to describe health state uncertainty. Finally, the improved D-S evidence theory integrates multi-source information to achieve online health status assessment. Experimental validation demonstrates that this method effectively evaluates the impact of IGBT failures, sensor malfunctions, and capacitor–inductor degradation on the inverter. It exhibits strong robustness under DC voltage fluctuations and load variations, enabling real-time output of health scores and grades to provide a reliable basis for maintenance decisions. Full article

For low-speed heavy-load steering of electric forklifts, conventional three-loop proportional–integral (PI) control employs a fixed saturation limit on the position-loop output. Consequently, the maximum allowable speed cannot be adjusted according to load variations. Under light-load conditions, the steering motor speed is excessively constrained, which wastes the available voltage margin. Under heavy-load conditions, the allowable speed may exceed the voltage limit, thereby causing voltage saturation. Moreover, load-torque feedforward compensation is commonly adopted to improve load-carrying capability. However, at medium and high speeds, excessive feedforward action may cause voltage saturation and current-vector offset. This can lead to loss of control of the steering motor. To address these issues, a voltage-limit-constrained dynamic saturation and load-torque feedforward control strategy is proposed for electric forklift steering systems. First, fuzzy PI control is adopted in the position loop. Then, considering the nearly identical direct-axis and quadrature-axis inductances of a surface-mounted permanent magnet synchronous motor (PMSM), the direct-axis current is set to zero. An analytical expression of the maximum safe speed is derived with the quadrature-axis current as the only independent variable. Based on this expression, a dynamic saturation limit is designed for the position-loop output. Finally, a reduced-order disturbance observer (DOB) is utilized to estimate the equivalent load torque in real time. The current feedforward gain is dynamically regulated according to the voltage margin. This compensates for torque limitation caused by speed-loop saturation while preventing voltage saturation. A Simulink simulation platform is developed using a forklift as the case study. The results demonstrate that, compared with the conventional three-loop PI controller, the proposed strategy reduces the no-load 180° step-response time by 30%. Under heavy-load and large-angle steering conditions, the voltage margin is maintained at approximately 10%. Full article

Human pose estimation (HPE) has advanced rapidly with deep learning, enabling a transition from specialized sensing and multi-view systems toward monocular RGB-based approaches. These developments have expanded applications in healthcare, robotics, sports analytics, and human–computer interaction. However, the growing diversity of deep learning paradigms, ranging from convolutional and recurrent models to graph-based and Transformer-based approaches, has resulted in a fragmented literature, making it difficult to systematically compare methods and guide system design. This paper addresses this challenge by providing a comprehensive survey of deep learning-based monocular HPE methods published over the past decade and introducing a unified modular framework. The proposed framework organizes HPE systems into six modular estimation paradigms, including single-image-based estimation, multi-frame-based estimation, Top-Down and Bottom-Up pose estimation strategies, 2D-to-3D pose reconstruction, and direct 3D estimation. Each module is analyzed in terms of representative approaches, design trade-offs, and practical considerations, supported by algorithmic formulations that outline the computational pipeline at each stage. Unlike prior surveys that primarily catalog methods or report benchmark results in isolation, this work emphasizes how component-level design choices relate to overall system performance. The paper summarizes performance trends on benchmarks including Human3.6M, COCO, and MPII, highlighting persistent challenges such as occlusion and viewpoint variation, and outlines future research directions including interaction-aware modeling, efficient deployment, and improved robustness under real-world conditions. Full article

Surface-enhanced Raman scattering (SERS) offers a powerful route for biomolecular detection because it combines molecular specificity with high sensitivity, rapid optical readout, and multiplexing capability. In real biological samples, however, analytical performance is rarely determined by signal enhancement alone. Biofluids such as serum, plasma, saliva, urine, and interstitial fluid contain complex biomolecular mixtures that interfere with target capture, spectral response, and data interpretation. A practical SERS biosensor must therefore localize targets, stabilize spectral responses, tolerate matrix-induced variation, and convert complex spectra into reliable analytical information. This review discusses recent progress in SERS biosensing from an integrated system perspective, with particular focus on artificial intelligence/machine learning (AI/ML)-assisted interpretation. Direct label-free SERS provides chemically transparent readouts but is limited by stochastic adsorption, hotspot heterogeneity, and spectral variation in complex samples. Bio-recognition interfaces improve target localization, while signal-transduction strategies based on nanotags, immunoassays, clustered regularly interspaced short palindromic repeats (CRISPR) systems, nanozymes, and lateral-flow formats decouple molecular recognition from spectral generation. Digital SERS further improves measurement robustness by converting fluctuating intensities into countable, event-based outputs. AI/ML-assisted analysis can support full-spectrum classification, calibration transfer, explainability, and patient-level decision-making. We frame AI/ML-assisted SERS biosensing as an integrated architecture connecting substrate design, interface engineering, signal transduction, digital measurement, and clinical validation. Future progress will depend as much on validation-ready workflows as on plasmonic enhancement itself, especially for systems intended to operate across different samples, instruments, and clinical settings. Full article

Artificial intelligence (AI) has reshaped higher education over six decades, evolving from drill-and-practice programs to adaptive cognitive tutors and, most recently, transformer-based generative models. This article presents a critical review of publisher-built generative AI assistants, adopting an explicitly socio-technical perspective that combines a technological lens with a pedagogical one. It makes three contributions. First, it synthesizes the technical and algorithmic evolution of educational AI, from rule-based and expert systems through knowledge tracing and learning analytics to large language models and retrieval-augmented generation, and organizes these mechanisms into a taxonomy. Second, it introduces a reproducible evaluation framework that couples the TRIAD rubric (Trust, Relevance, Impact, Adoption, and Design) with a Jobs-to-Be-Done (JTBD) lens, complete with anchored scoring criteria, an evidence-and-confidence grading scheme, and reported inter-rater reliability. Third, it applies the framework to eleven assistants released by U.S. publishers, distinguishing peer-reviewed evidence from institutional reports and commercial claims. The analysis reflects a mid-2025 snapshot and is presented as a reusable template rather than a static ranking. Findings reveal substantial variation in privacy safeguards, curricular alignment, documented impact, adoption, and usability. The review identifies application scenarios and recommendations for researchers and institutional leaders seeking to guide the responsible integration of AI in higher education. Full article

Arthropathies are a major global health challenge because of their high prevalence, chronic progression, and significant impact on quality of life and health systems. Therefore, prompt and accurate diagnosis is critical for slowing disease progression and improving outcomes. Traditional imaging modalities, such as ultrasound and magnetic resonance imaging, suffer from significant limitations, including operator dependence, limited accessibility, high cost, and limited reproducibility. Infrared thermography has become a promising non-invasive imaging technique for identifying thermal variations linked to inflammatory and metabolic processes. Advances in quantitative thermography, automated segmentation, and artificial intelligence have greatly enhanced its clinical applicability. This review summarizes recent advances in thermography-based biomarkers, including region-of-interest-derived metrics, asymmetry indices, hotspot burden, spatial and texture descriptors, and composite thermographic scores. It discusses the role of machine learning and deep learning in prediction, phenotyping, and multimodal integration with clinical, laboratory, and imaging data. Heterogeneity of protocols, variability in measurements, domain shift, validation design, overfitting, and reporting quality are also addressed. Overall, thermography combined with AI is highly promising as an adjunct to early diagnosis, assessment of disease activity, and follow-up in arthropathies. However, clinical application at a large scale requires strict standardization, external validation, transparent reporting, and well-elucidated, reproducible analytical processes. Full article

Endemic human coronaviruses (HCoVs) are important contributors to respiratory infections, yet genomic data from sub-Saharan Africa remain limited. We analyzed 13,530 nasopharyngeal samples collected through the national influenza sentinel surveillance network in Côte d’Ivoire between 2022 and 2024 to characterize the circulation and genomic diversity of endemic HCoVs. A subset of 52 RT-qPCR-positive samples with Ct values ≤ 28 was selected for targeted hybrid-capture sequencing using the Twist Bioscience Respiratory Virus Research Panel. Genome recovery metrics were available for 28 samples, including HCoV-NL63 (n = 9), HCoV-229E (n = 8), HCoV-OC43 (n = 9), and HCoV-HKU1 (n = 2). Endemic HCoVs circulated throughout the study period, with temporal variation across species and increased detections during several rainy-season months. No co-presence of multiple endemic HCoV species was identified in the final analytical dataset. Genome recovery differed by species, with broader and more consistent coverage for HCoV-OC43 and HCoV-NL63 than for HCoV-229E and HCoV-HKU1. Phylogenetic analysis showed that all recovered HCoV-229E genomes clustered within genotype L6 and all recovered HCoV-HKU1 genomes within genotype A, whereas HCoV-OC43 and HCoV-NL63 were distributed across multiple genotypes among recovered genomes. To our knowledge, these findings provide the first genomic data on endemic HCoVs from Côte d’Ivoire and support the feasibility and further targeted integration of targeted hybrid-capture sequencing into routine genomic surveillance of respiratory viruses. Full article

Accurate pressure measurement under high-temperature conditions is challenging for silica-diaphragm-based fiber-optic Fabry–Perot (F-P) sensors because temperature causes both optical cavity length (OCL) baseline drift and pressure-sensitivity variation. In this work, a structurally simple and readily fabricated silica-diaphragm-based fiber-optic F-P pressure sensor was developed, and a neural-network-assisted compensation strategy was proposed to suppress the residual errors of conventional analytical compensation. A temperature-dependent response model was established to describe OCL drift and sensitivity variation. The OCL was demodulated from reflection spectra using an FFT-assisted dual-peak and MMSE refinement method, and static pressure measurements were performed over 25–400 °C and 0–2.4 MPa. Based on the experimentally verified response characteristics, a fitting-based compensation method considering both OCL drift and sensitivity variation was first implemented. A lightweight neural network was then constructed using the OCL variation, $\Delta \mathrm{O}\mathrm{C}\mathrm{L}$, and ambient temperature as physically meaningful input features. Compared with fixed-sensitivity compensation and drift-and-sensitivity fitting compensation, whose maximum full-scale errors were 7.10% F.S. and 2.74% F.S., respectively, the proposed method reduced the maximum error to 0.90% F.S. with an RMSE of 0.0045 MPa. Additional validation at the independent intermediate temperatures of 150, 250, and 350 °C further confirmed the generalization capability of the proposed NNC model between calibrated temperature gradients, achieving an overall RMSE of 0.0055 MPa and a maximum full-scale error below 0.77% F.S. The proposed approach provides a high-accuracy and practical solution for high-temperature pressure monitoring using simple fabricated silica-diaphragm F-P sensors. Full article

This paper develops a generalized kinematic model for a lever-link-type flat pressing mechanism used in food processing applications for compacting the coagulate. The study aims to highlight the influence of the geometric parameter that defines the position of the intermediate coupling on the driving element on the mechanism’s configuration and on the main kinematic variables of the active pressing point. Under an idealized representation—assuming rigid links, perfect joints, and a vertical constraint acting on the active element—general analytical expressions for displacement, velocity, and acceleration were established using the vector-kinematic method. The results show that modifying the position of the intermediate coupling produces nonlinear variations in the length of the connecting element, its spatial orientation, and the vertical motion of the active point. Increased values of this parameter are associated with a greater effective stroke and higher vertical velocities toward the end of the motion, while the calculated accelerations remain relatively low, indicating a smooth kinematic evolution. The model establishes analytical relationships that describe the influence of geometric parameters on the kinematic behavior of the mechanism and can serve as a basis for further developments involving dynamic analysis and experimental validation. Full article

Background/Objectives: Nipah virus (NiV) is a highly pathogenic zoonotic virus with fatality rates exceeding 70% and causes recurring outbreaks in South and Southeast Asia. Reliable serological assays are critical for outbreak surveillance, diagnosis, and evaluation of vaccine-induced immune responses. This study aimed to develop and qualify an indirect enzyme-linked immunosorbent assay (ELISA) based on recombinant NiV glycoprotein G for the detection of virus-specific IgG antibodies in human serum. Methods: An indirect ELISA was developed and optimized for antigen concentration, blocking conditions, and serum dilution. The assay performance was evaluated using convalescent human sera from Bangladesh, along with the World Health Organization (WHO) International Standard for anti-Nipah virus antibodies, maintained and distributed by the National Institute for Biological Standards and Control (NIBSC). Analytical validation was conducted in accordance with ICH Q2 (R2) guidelines, including assessments of sensitivity, specificity, Precision, Linearity, and detection limits. Results: The assay demonstrated 100% sensitivity and specificity relative to reference sera. Intra-assay coefficients of variation ranged from 0.36% to 5.73%, and inter-assay variation was 4.16%, indicating high precision. The ELISA showed excellent Linearity (R² > 0.995). The lower limit of detection was 0.51 IU/mL, and the lower limit of quantification was 0.98 IU/mL. Conclusions: The developed ELISA is a BSL-2-compatible, robust, and scalable platform suitable for serosurveillance and the assessment of vaccine-induced immunity in endemic regions. Calibration against an international standard supports its applicability for standardized antibody measurement. This assay provides a practical tool for NiV outbreak response and vaccine evaluation. Full article

The present study investigates the effect of whey powder incorporation on the nutritional composition, structural characteristics, and functional properties of rice flour-based gluten-free systems. Composite flours and biscuits were formulated by substituting rice flour with 5%, 10%, and 15% whey powder. Proximate composition, mineral profile, and structural modifications were evaluated using standard analytical methods, complemented by Fourier Transform Infrared Spectroscopy (FTIR) and Small-Angle X-ray Scattering (SAXS). The results showed that whey addition significantly improved the protein content of both flours and biscuits, increasing from 8.45% in the control to 15.06% at the highest enrichment level. Whey powder showed elevated phosphorus (912 mg/kg), sodium (434.65 mg/kg), and calcium (526.49 mg/kg) contents compared to rice flour. Consequently, mineral levels increased progressively in the composite flours, with phosphorus rising from 528 mg/kg to 647 mg/kg, sodium from 105.66 mg/kg to 132.81 mg/kg, and calcium from 102.15 mg/kg to 137.33 mg/kg as the whey incorporation level increased. Iron content showed minor variations among the gluten-free biscuit formulations (76.01–95.16 mg/kg). Whey incorporation led to a progressive increase in copper content, from 8.91 mg/kg in the control biscuits to 15.50 mg/kg, while zinc levels decreased from 27.47 mg/kg to 18.47 mg/kg with increasing whey addition. FTIR analysis revealed clear structural changes associated with whey addition, including the progressive intensification of amide I and II bands and a reduction in starch-specific signals, confirming the incorporation of whey proteins into the starch matrix and the formation of protein–starch interactions. These findings were supported by SAXS analysis, which indicated modifications in the internal structural organization of the systems. Sensory evaluation indicated good overall acceptability of the fortified biscuits at moderate whey incorporation levels, while higher whey addition slightly reduced taste scores due to the characteristic salty flavor associated with acid whey. Overall, the study demonstrates that whey powder is an effective functional ingredient for enhancing the nutritional and structural properties of gluten-free products. However, achieving an optimal balance between improved nutritional quality, technological performance, and mineral composition remains essential for the development of high-quality gluten-free formulations. Full article

Background/Objectives: Single-case studies represent a sophisticated and rigorous methodological framework, widely established in clinical research for providing high-resolution data on individual functional responses. This study evaluates the clinical utility of integrating immersive Virtual Reality (VR) gaming as a novel “functional ingredient” within the Video Game Therapy (VGT) protocol. Given the exploratory single-case nature of this intervention, clinical state-modulations cannot be rigorously validated using standard aggregated group statistics. Therefore, the core objective of this paper is to investigate the therapeutic potential of the VR session on psychological state-modulation, introducing the Single-Case Normative Analytics (SCNA) framework as the mandatory statistical vehicle required to validate individual longitudinal shifts against normative data. Methods: The study treats individual VR exposures as independent, short-term clinical probes embedded within a real-world clinical journey. The SCNA framework was deployed by integrating Crawford’s modified t-tests with longitudinal percentile tracking against an empirical normative reference group (n = 20). Acute state-anxiety variations (STAI-Y1), psychological well-being (PGWBI), and flow dynamics were tracked across three distinct sessions to monitor the patient’s relative repositioning within the normative distribution. Results: The inferential analysis indicates that the immersive 20-min environment facilitated reliable, statistically significant changes in acute state anxiety and flow dimensions, systematically exceeding standard measurement error boundaries and successfully moving the patient’s psychometric profile toward healthy normative ranges. Conclusions: While these findings focus on individual, idiographic reactivity, they demonstrate the utility of the SCNA framework in providing clinicians with objective, evidence-based feedback on the clinical viability of specific VR-based functional units. This approach allows for a rigorous evaluation of standalone digital tools independently of a full, holistic VGT protocol, offering a structured alternative to traditional designs focused on identifying general patterns across groups. Full article

Heliopsis longipes, commonly known as chilcuague, is a Mexican medicinal plant recognized for its diverse biological activities, largely attributed to its alkamide content, particularly affinin. However, metabolic variations associated with plant development remain poorly understood. This study evaluated the influence of phenological stage on the phytochemical profile and vasodilatory activity of H. longipes roots. Dichloromethane root extracts from plants at different developmental stages were analyzed using metabolomics based on ¹H NMR spectroscopy, complemented by GC–MS profiling. Major alkamides were isolated and structurally characterized as analytical standards. Notably, three alkamides, N-isobutylundeca-2(E)-en-8,10-diynamide, N-isobutylundeca-3(E)-en-8,10-diynamide, and N-isobutyl-2(E),4(Z)-undecadiene-8,10-diynamide, are reported for the first time in H. longipes roots. Multivariate analyses (PCA and OPLS-DA) revealed significant stage-dependent metabolic variation, particularly in affinin. The lack of correlation between valine decarboxylase activity and affinin levels suggests additional regulatory steps in its biosynthesis. Vasodilatory activity increased during development, reaching maximum effect during fructification and defoliation stages, with no significant differences between them. These findings highlight the impact of phenological stage on alkamide production and bioactivity, providing a basis for optimizing cultivation and harvest timing. Full article

Background/Objectives: Nanopore sequencing is increasingly used in mycobacterial diagnostics, where clinical microbiologists and diagnostic laboratories must decide when broad metagenomic next-generation sequencing (mNGS) or focused targeted next-generation sequencing (tNGS) is most appropriate. This review examined reported clinical and laboratory roles of nanopore mNGS and tNGS in tuberculosis (TB) and nontuberculous mycobacterial (NTM) settings. Methods: Targeted searches of PubMed/MEDLINE, Embase, Web of Science Core Collection, and Scopus were refreshed on 4 April 2026. Thirty-five records spanning original clinical studies, evidence syntheses, and guideline-context documents were included. Results: Nanopore mNGS is most useful for broad organism detection and diagnostic rescue in unresolved pulmonary and extrapulmonary presentations, particularly when first-line testing is negative, discordant, low-yield, or when mixed infection is suspected. Nanopore tNGS appears better aligned with predefined TB confirmation and resistance-focused workflows because targeted regions allow more standardized interpretation. Agreement is strongest for rifampicin- and isoniazid-related resistance targets. In NTM settings, evidence is stronger for detection and species identification than for disease-level diagnosis. Common implementation constraints include pre-analytical variation, contamination control, host-background interference, inconsistent bioinformatics, and limited workforce capacity. Conclusions: A practical tiered approach is supported in which mNGS is positioned mainly for diagnostic rescue and discovery, whereas tNGS is considered for predefined workflows requiring standardized target interrogation and resistance-associated mutation reporting under local validation and quality systems. Full article

Background: Instant Cascara (IC) beverages, derived from dried coffee cherry pulp, represent an upcycled plant-based ingredient rich in phenolic compounds and methylxanthines. Although spray-drying enables the production of soluble cascara powders without carriers, previous sensory evaluation highlighted limitations in palatability, supporting the need for formulation strategies. Objective: To evaluate how the incorporation of inulin-type carriers with different degrees of polymerization modulates production yield, the apparent recovery of bioactive compounds, and formulation-dependent in vitro biological and receptor-level responses of Instant Cascara beverages. Methods: Formulations without carrier (IC 0.0) and with long-chain inulin (IC 1.0) or oligofructose-enriched inulin (IC 2.0) were prepared and characterized. Production yield, phytochemical composition, and in vitro antioxidant, anti-inflammatory, antiproliferative, and receptor-mediated responses were assessed using analytical tools, cell-based assays, and receptor-based platforms. Results: Carrier incorporation improved production yield, particularly for IC 1.0. Although differences in apparent recovery of bioactive compounds were observed, all formulations preserved relevant in vitro biological activities. IC 2.0 showed stronger nitric oxide inhibition and apoptosis induction in colorectal cancer cell models. Receptor-based assays revealed formulation-dependent differences, including reduced activation of bitter taste receptors (TAS2Rs), absence of sweet receptor (TAS1R2/TAS1R3) activation, and modulation of muscarinic (M3) and dopaminergic (D3/D4) receptor responses. These effects are consistent with variations in the composition and effective concentration of bioactive compounds between formulations, particularly caffeine. Conclusions: The incorporation of inulin-type carriers influences production yield and modulates in vitro biological responses and receptor-level responses of Instant Cascara beverages. IC 2.0 represents a formulation with a favorable balance between technological performance and functional responses, associated with a distinct receptor-level profile. This balance may be related to a reduced contribution of bitterness-associated compounds, such as caffeine, together with the preservation of other bioactive components contributing to the observed biological responses. These findings provide a mechanistic in vitro basis for future sensory and in vivo studies evaluating how formulation-dependent differences in bioactive composition may influence physiological responses and consumer perception. Full article