Search Results (31,828)

The increasing adoption of electric vehicles (EVs) has created a growing need for battery systems that can deliver high efficiency, reliable operation, and effective thermal management. This study develops an EV battery model using MATLAB/Simulink and Simscape, with the BYD ATTO 3 specifications used as a reference, to investigate the influence of battery voltage, coolant flow, and battery capacity on four key performance indicators: battery temperature, power loss, state of charge (SOC), and state of health (SOH). Response surface methodology (RSM), combined with a Box–Behnken design (BBD) was employed to develop a quadratic regression model, evaluate the significance of the selected factors through analysis of variance (ANOVA), and identify the optimal operating conditions using the desirability function. The simulation results show that battery voltage and battery capacity have a greater influence on electrical performance, whereas coolant flow was more influential in thermal regulation. Under the optimal operating conditions, the battery achieved a temperature of 21.63 °C, a power loss of 3966.30 W, an SOC of 91.37%, and an SOH of 91.34%. The developed regression models demonstrated excellent predictive capability for all response variables, confirming the suitability of the proposed optimization approach. These findings demonstrate that the proposed optimization framework can balance thermal behavior, energy efficiency, and battery health. The developed methodology also provides a practical foundation for battery management and lookup-table-based control strategies in future EV applications. Full article

Reliable state identification is essential for intelligent flotation control because recovery, concentrate grade, entrainment, and mineral losses are only partially observable online. This systematic review examines field instrumentation, online analyzers, froth imaging, temporal synchronization, machine-vision methods, multimodal soft sensing, and the engineering requirements that determine whether a predictive model can operate as an industrial sensor. Scopus and Web of Science publications from 2021 to June 2026 were screened using a PRISMA-based protocol. The systematic evidence base includes 98 peer-reviewed technical studies published between 2021 and June 2026, and two PRISMA methodological publications are used to ensure the methodology for presenting the review. Additional methodological and contextual sources cited outside the systematic body of evidence are not included in the number of studies reflected in PRISMA. The evidence shows that machine vision is the most mature non-contact sensing approach, supporting bubble-size measurement, froth-velocity estimation, operating-state recognition, grade prediction, and visual monitoring. Current research is shifting from handcrafted descriptors toward convolutional, transformer, self-supervised, graph-based, temporal, and multimodal models. However, predictive accuracy alone does not demonstrate industrial readiness when camera geometry, illumination, contamination, delay compensation, temporal leakage, domain shift, uncertainty, inference latency, and SCADA/PLC integration are not evaluated. A five-dimensional Sensor Readiness Index is proposed to assess metrological validity, temporal integrity, validation rigor, operational robustness, and automation integration. The review defines the principal requirements for reliable industrial deployment of flotation sensing systems. Full article

Background/Objectives: This study aimed to identify a characteristic miRNA expression profile in the CSF of patients diagnosed with vestibular schwannoma and evaluate its potential for tumor assessment. Methods: In this prospective study, 17 CSF and corresponding tumor samples (seven small tumors—SVS and 10 large tumors—LVS) were collected from patients operated on for VS in a Tertiary Academic Center. The miRNA expression was analyzed using high-throughput RNA sequencing (NovaSeq 6000 Illumina). Data were normalized, and a comparative analysis of miRNA expression rankings was performed between VS patients and a public healthy donor dataset. Functional implications were explored using KEGG pathway enrichment analysis. Results: A total of 1633 miRNAs were identified in all CSF samples derived from VS patients. Comparison with healthy donors revealed a moderate ranking correlation (ρ = 0.39), with significant shifts for specific molecules like hsa-miR-766-3p and hsa-miR-182-5p. Only six miRNAs were found to correlate between CSF and tumor tissue, while 16 exhibited a negative correlation. No statistical correlation was found between tumor size and the CSF miRNA profile. KEGG analysis highlighted enriched pathways, including neurotrophin signaling and focal adhesion. Conclusions: The results of our study support the feasibility of miRNA-based CSF liquid biopsy for VS assessment. However, the results of miRNA expression profiling conducted in tumor tissue cannot be directly transferred into CSF sample analyses. Further studies are warranted to explain this phenomenon and to search for reliable miRNA markers of VS progression in the CSF liquid biopsy specimens. Full article

As higher education institutions respond to societal transformation and institutional reform, academic staff wellbeing is increasingly relevant to the conditions that sustain teaching quality, research productivity, and service performance. This study, the second phase of a research project, evaluated Academic Staff Wellbeing (ASW) as a reflective–reflective second-order construct and examined its relationships with Job Satisfaction (JS) and Academic Performance (AP), including JS mediation. A cross-sectional questionnaire with closed- and open-ended items was completed by 119 academic staff at a single Saudi university. Partial Least Squares Structural Equation Modelling (PLS-SEM) with 5000 bootstrap resamples was complemented by structured interviews with eight faculty members and a focus group with eight academic leaders; qualitative evidence was analyzed thematically and integrated at the interpretation stage. The measurement model demonstrated satisfactory reliability, convergent validity, and discriminant validity. ASW explained 74.3% of the variance in JS and, together with JS, 36.1% of the variance in AP. ASW was strongly associated with JS and significantly associated with AP, whereas the JS–AP relationship and the indirect ASW–JS–AP relationship were not significant (β = 0.125, 95% bias-corrected confidence interval [−0.185, 0.474]). Qualitative findings highlighted leadership, institutional support, workload, resources, professional development, workplace quality, and recognition as organizational mechanisms. Within the limitations of a cross-sectional, single-institution design and self-reported academic performance, these findings suggest that ASW may function as a multidimensional institutional condition supporting teaching, research, and service performance through multiple organizational pathways rather than primarily through job satisfaction alone. Full article

The rapid development of biodegradable polymers has intensified the need for reliable, standardized methodologies that can accurately assess their environmental fate. However, biodegradability is not an intrinsic property of materials, but rather the result of complex interactions between polymer chemistry, physicochemical characteristics, environmental conditions, and microbial activity. Consequently, biodegradation performance determined under one set of conditions cannot be directly extrapolated to other environments, highlighting the importance of selecting appropriate testing methodologies. This review provides a comprehensive and critical evaluation of the main international standards used to assess polymer biodegradability, including ISO, ASTM, EN, and OECD methods applicable to soil, industrial composting, freshwater, and marine environments. It discusses the fundamental mechanisms of polymer biodegradation, together with the major factors governing degradation kinetics, and comparatively analyzes the biodegradation behavior of polyesters, polysaccharides, polyamides, and polyesteramides across different environmental compartments. Unlike previous reviews, this work critically compares the applicability, strengths, limitations, and biodegradation endpoints of standardized methodologies, identifies current methodological gaps, and proposes a practical decision-making framework for selecting appropriate tests according to the intended end-of-life scenario of polymeric materials. Finally, it highlights the need for more harmonized and environmentally relevant testing approaches to better distinguish compostability from true environmental biodegradability. Full article

Non-contact inclination detection based on LiDAR point clouds has become an effective approach for structural condition assessment. However, due to measurement noise, lattice structural characteristics, and discontinuous point distributions, cross-sectional point clouds of lattice steel towers often contain outliers, local missing regions, and irregular boundaries, which may affect the reliability of extracted geometric features. This study establishes a comparative framework to investigate the influence of cross-sectional feature extraction algorithms on LiDAR-based inclination detection of lattice steel towers. The universality and performance of the RANSAC and Marching Square algorithms were systematically evaluated using a 110 kV overhead transmission line operating tower. Terrestrial laser scanning was employed to acquire the point cloud data. The initial registration results were subsequently further optimized through initial point cloud registration and multi-station adjustment. Four cross-sectional slicing schemes were designed, and the two algorithms were independently applied to extract cross-sectional geometric features and calculate centroid coordinates. The tower inclination was then determined by fitting the spatial distribution of centroid points. Experimental results demonstrated that both algorithms successfully extracted cross-sectional features and achieved reliable inclination detection results, with all inclination ratios satisfying the requirement specified in DL/T 741—2019 (Code of Practice for Operation of Overhead Transmission Lines). The RANSAC-based method produced inclination ratios ranging from 8.52‰ to 8.82‰, with a variation range of 0.30‰ and a mean deviation of 0.12‰. In comparison, the Marching Square-based method showed a larger variation range of 1.00‰ and a mean deviation of 0.41‰. The results indicate that RANSAC provides better robustness against point cloud noise, local data gaps, and boundary irregularities due to its inlier–outlier discrimination capability, whereas Marching Square exhibits advantages in preserving continuous contour representations when point cloud distributions are relatively complete. This study provides practical insights into the selection and optimization of cross-sectional feature extraction algorithms for LiDAR-based inclination assessment of lattice steel towers. Full article

In the industrial edge-to-cloud continuum, data is often privacy-sensitive and spans multiple organizations that do not fully trust one another, making central aggregation of raw data undesirable and often non-compliant with regulations such as the General Data Protection Regulation (GDPR). Federated Learning (FL) addresses this by sharing model updates rather than raw data, but conventional FL assumes a central coordinator, leaving it exposed to poisoning and inference attacks and to a single point of trust and failure. Decentralized Federated Learning (DFL) couples FL with Distributed Ledger Technologies (DLTs), removing the coordinator and enabling verifiable aggregation in trustless, cross-organizational environments. In this work, we assess the applicability of DFL to on-device spoken-command recognition—a representative edge audio task underpinning voice-driven industrial interfaces—by comparing decentralized and centralized training under idealized and adversarial conditions. Using a Convolutional Neural Network (CNN) replicated across edge nodes, we evaluate resilience to inter-node data imbalance, to poisoning attacks, and to a privacy-preserving noise-injection defense against inference attacks, together with model compression for resource-constrained edge devices. The system pairs this comparison with a validation-based poisoning defense in which each node scores its peers’ updates on its own held-out data, and an update is aggregated only if a majority of nodes report a weighted F1-score above a threshold—requiring neither a shared validation set nor a trusted validator. Our results indicate that the DFL system achieves accuracy comparable to centralized baselines in most scenarios (weighted F1-score 0.762 across nine nodes, against 0.896 centralized), and that a cross-node validation mechanism reliably excludes poisoned updates as long as fewer than half of the nodes are compromised (within 3.54% of the unpoisoned model). Noise-based inference defenses reduce accuracy substantially (44.7% on average at a noise standard deviation of 1.0), exposing a sharp privacy–utility trade-off, whereas model compression preserves performance (0.765 against 0.762 for pruning and format conversion, with 8-bit quantization costing up to a further 13.3%). These findings clarify both the promise and the current limitations of decentralized, privacy-preserving learning for the industrial edge-to-cloud continuum. Full article

Air compressors are among the most energy-intensive cross-cutting technologies used in the industry, making accurate performance evaluation essential for improving energy efficiency. ISO 1217, the internationally recognized standard for displacement compressor acceptance testing, has remained largely unchanged since its last revision in 2009. The standard specifies that flow-rate measurement should be performed as indicated in ISO 5167-1 and ISO 9300. However, it does not prescribe a specific flow-meter technology, allowing different technologies to be selected for compressor performance measurements. Since flow-rate measurement is one of the most critical parameters for compressor performance assessment, the choice of flow-meter technology can directly influence the evaluated compressor performance. Furthermore, the importance of energy efficiency has increased, while flow-measurement technologies have continued to develop since the last revision of ISO 1217 in 2009. Therefore, the influence of flow-meter technology and its measurement accuracy should be considered more explicitly in the ongoing revision of the standard to support reliable performance evaluation and the development of more energy-efficient displacement compressors. To investigate the influence of flow-meter technology, this paper reviews the physical operating principles of commonly used flow-meter technologies for compressed-air applications and discusses their respective advantages and limitations. In addition, four flow meters from different brands, utilizing different technologies, were experimentally compared on a dedicated laboratory test bench under controlled conditions at compressor discharge pressures between 5 and 8 ${\mathrm{bar}}_{\mathrm{e}}$, representing typical industrial operating pressures. To ensure comparability, all measured flow rates were normalized to common reference conditions according to ISO 2533. The results reveal substantial differences among the investigated technologies. The rotary displacement meter showed the closest agreement with the Venturi reference meter, with deviations generally below 3%. In contrast, thermal and ultrasonic flow meters exhibited larger systematic deviations, particularly when operated at low load factors relative to their measurement ranges. Based on the test results obtained with different flow-meter brands, the choice of flow-meter technology was found to significantly influence compressor performance calculations according to ISO 1217, resulting in calculated isentropic efficiencies ranging from approximately 58% to 72% under the same compressor operating conditions. These findings demonstrate that both flow-meter technology and sensor sizing can substantially affect the result of compressor performance evaluations. The results highlight the importance of appropriate flow-meter selection and indicate a need for clearer guidance regarding flow-measurement instrumentation in revisions of ISO 1217. Full article

Feline food palatability is fundamental to ensure nutritional intake and the commercial success of pet foods, yet the field has several gaps, unresolved contradictions, and methodological inconsistencies. A literature search identified 141 relevant publications. Critical evaluation of this body of work revealed four recurring areas of tension: the inconsistent definition of palatability, where the same term evaluates different constructs; contradictory behavioral indicators, where the same behaviors (e.g., nose-licking) are variably interpreted as positive or negative across studies; methodological fragmentation arising from overarching test selection, study design implementation, and inconsistent or selective reporting; and the disconnect between owners’ perception of palatability and objectively measured feline responses. This critical narrative review discusses the origins of these inconsistencies, their implications for cross-study comparison, and their combined effect on the reliability of existing evidence. Resolving these contradictions may benefit from a proposed integrative framework for defining palatability, a standardized ethogram for behavioral indicators, and a decision framework for selecting assessment methodologies. These approaches could improve the comparability and reliability of future studies, but their effectiveness requires validation. Full article

Smart textile interfaces hold promise for battery-free wearable interaction, yet their adoption is limited by complex fabrication and insufficient on-body evaluation. We present TriboTex, a low-cost spray-patterning workflow that forms nylon–Cu–nylon triboelectric stacks on cotton textiles using laser-cut PET stencils and commercially available materials. The core consumables cost approximately USD 0.003/cm², and sensor geometry can be rapidly iterated by modifying only the digital stencil. Controlled characterization across nine devices from three fabrication batches showed a peak open-circuit voltage of 52.3 V and a maximum power density of 1870 µW/m² at 4 GΩ. The output retained 96.1% of its initial voltage after 1000 bending cycles and 94.2% after 24 h of simplified saline immersion. Three-sample environmental sweeps showed voltage amplitudes of 41.9–43.7 V from 15 to 45 °C, with a decrease to 27.7 V at 0 °C; the humidity response remained within 92.7–104.5% of the 20% RH value over 20–60% RH but decreased to 19.9% at 70% RH. Two wearable prototypes were developed: a single-electrode garment sleeve recognized tap, double-tap, and swipe gestures with 95.0% accuracy across 1200 trials from 12 participants; a single-electrode insole generated action-dependent peak voltages up to 123 V under repeated foot loading and was connected through a rectification and voltage-regulation module to charge a battery. Across the two 12-participant studies, attachment and fit stability emerged as shared integration requirements, while participant feedback and controlled humidity measurements highlighted moisture management as a priority for reliable on-body sensing and energy capture. The primary contribution is an accessible, low-cost, and geometry-flexible route for early-stage wearable sensing experiments and application demonstrations, supported by documented fabrication, electrical characterization, and human-centered evaluation. Full article

Rapid seismic intensity estimation supports situational awareness, emergency response, and the assessment of earthquake-induced structural risk. Instrument-based systems provide authoritative measurements, but limited deployment density and installation cost can restrict spatial coverage. Video sensing offers a complementary route by converting the visible motion of a calibrated target into physical motion indicators; its reliability, however, depends on stable frame-wise localisation under illumination variation, blur, and target deformation. This paper presents FAMTrack, a vision-based seismic intensity prediction framework whose tracking module augments a one-stream Vision Transformer with a parallel Frequency-Aware Matching branch that encodes patch-wise Fourier amplitude and phase. A Cross-Domain Fusion Module exchanges spatial and frequency evidence through bidirectional cross-attention without modifying the detection head or tracking loss. On LaSOT, FAMTrack-256 attains a 75.1% AUC, 4.0 percentage points above OSTrack-256. On GOT-10K, it reaches 77.9% AO, a gain of 4.2 points. In the held-out seismic-video evaluation, FAMTrack reduces the displacement RMSE from 0.523 to 0.332 mm and improves window-level intensity prediction accuracy from 80.0% to 86.7% relative to OSTrack-256. Under the fixed calibrated-video pipeline and the evaluated controlled conditions, these results indicate that frequency-aware localisation is associated with improved public-benchmark tracking, motion recovery, and seismic intensity estimation. Full article

High Friction Surface Treatment (HFST) relies heavily on wear-resistant aggregates to ensure roadway safety, yet the conventional evaluation of aggregate abrasion resistance is time-consuming and resource-intensive. In this study, a machine learning framework was developed to predict the abrasion-induced angularity evolution of recycled high-alumina aggregates from their initial morphological characteristics, thereby enabling rapid abrasion-resistance screening. Six regression models were compared under leave-one-group-out cross-validation, and an improved whale optimization algorithm (IWOA) was proposed to tune the Gaussian process regression (GPR) model, incorporating five enhancements and a regularized fitness function to restrain overfitting. The models were trained on 42 samples from six aggregates, whose angularity, Form 2D, micro-texture, sphericity, and F:E ratio were measured with the AIMS II device before and after successive abrasion cycles. The IWOA-GPR model achieved the best performance, with an R² of 0.8909, an RMSE of 150.98, an MAE of 120.55, and a MAPE of 4.60%. The SHAP analysis identified the abrasion revolutions, the initial Form 2D, and the initial angularity as the dominant contributors to the worn angularity. Moreover, the early angularity loss after the first 500 revolutions correlated strongly with the measured Los Angeles abrasion value (r = 0.935), which allows the LAA of a candidate aggregate to be estimated after a single abrasion cycle. The proposed framework therefore provides a rapid and reliable tool for screening wear-resistant aggregates for HFST applications and supports the clean utilization of recycled solid wastes in anti-skid pavements. Full article

Distributed multiple-input multiple-output (MIMO) is a promising architecture for future wireless systems because cooperation among geographically separated base stations (BSs) improves coverage, spectral efficiency, and link reliability. However, the large effective aperture formed by distributed BSs makes near-field effects non-negligible and complicates accurate channel state information acquisition. Existing near-field estimators often suffer from modeling errors caused by approximate angle–range decoupling or from the high storage and computational costs of dense two-dimensional sparse representations. This article proposes an off-grid variational Bayesian channel-estimation framework for the considered distributed near-field MIMO geometry, which comprises equally spaced, collinear BS reference points and aligned uniform linear arrays (ULAs) with common inter-element spacing. We establish a geometry-coupled model based on the exact geometric spherical-wave phase response and map the local direction–range parameters observed by different BSs into a common reference coordinate system, yielding a two-dimensional jointly sparse representation. An independent-vector variational Bayesian inference algorithm then decomposes the high-dimensional multiuser recovery problem into user-specific posterior subproblems. It operates directly on the received pilot matrices, avoiding pilot–matrix inversion and the resulting distortion of noise statistics. A two-dimensional skewed off-grid update is further embedded in an expectation-maximization procedure to jointly refine angle and range offsets, mitigating basis mismatch while permitting a coarser initial dictionary. Simulation results support the effectiveness of the proposed method in the evaluated scenarios. Full article

This study aimed to evaluate the ability of different categories of direct restorative materials to withstand artificial dentin alterations. A total of 360 human dentin specimens were allocated into 18 groups (n = 20). An ion-releasing bulk-fill resin-based composite (Cention Forte/CF), a resin-modified glass ionomer cement (Fuji II LC/FJLC), and an experimental conventional glass ionomer cement (EXP) were applied on sound, artificially hypermineralized, and demineralized dentin. Shear bond strength (SBS) was performed after 1 week and 6 months of storage, followed by fractographic analysis. Statistical analysis was performed using one- and three-way ANOVA, Games–Howell post hoc test, independent t-tests (α = 0.05), and Weibull analysis. SBS and bond reliability of CF on sound and hypermineralized dentin were higher than on other materials after both aging periods. On these two substrates, the SBS values of FJLC were higher than those of EXP, except for sound dentin after 6 months. Demineralized dentin significantly reduced the SBS of all materials under both aging conditions, with a high number of pre-test failures. The impact of aging was significant only in demineralized dentin for all materials. Ion-releasing resin-based composite performed best in terms of bond strength and reliability on sound and hypermineralized dentin. Artificially demineralized dentin severely compromised the bonding performance and maturation of all materials. Full article

Manual soldering requires phase-dependent coordination of posture, hand movement, tool position, and visual attention. We propose a depth-camera framework that estimates the work phase and detects deviations using three-dimensional upper-body and hand features, task-related object positions, and gaze-related approximation features derived from facial orientation and head posture. The system estimates three predefined phases: preparation, active soldering, and cleanup. Windows with insufficient phase confidence are assigned to Uncertain Phase and routed to review rather than treated as deviation labels. In the evaluation, normal trials showed stable process sequences, whereas trials with scripted simulated unsafe-like movements produced local increases in the deviation score and review-required intervals associated with reduced phase-estimation reliability. These findings suggest that the framework may support retrospective safety-related assessment and the identification of process-inconsistent operations in seated manual soldering under controlled laboratory conditions. The bio-inspired contribution is a functional abstraction of phase-dependent perceptual-motor coordination into context-dependent engineering reference patterns and an uncertainty-aware review mechanism. Full article