Search Results (7,510)

Accurate, real-time temperature monitoring of traction motors, battery packs, and power electronic modules is a critical requirement for the safety, efficiency, and long-term reliability of electric vehicles (EVs), particularly on the curved, space-constrained, and vibration-prone surfaces found on motor end-windings, battery-module casings, and busbar assemblies. To meet the needs of such automotive curved-surface applications, as well as wearable devices and flexible electronic skin, this paper presents complementary metal-oxide-semiconductor (CMOS) temperature sensor. Its core includes a CMOS sensor chip with an integrated bandgap reference circuit and dual-path electrostatic discharge (ESD) protection, combined with flexible printed circuit board (FPCB/FPC) for conformal mounting on curved surfaces. Circuit-level simulation predicts a typical reference-voltage temperature coefficient of 20 ppm/°C over −40 to 125 °C, while experimental temperature characterization yields a temperature output (TEMP) sensitivity of approximately 5.0 mV/°C over the reported temperature range. Absolute temperature error and linearity are not claimed as independently verified performance metrics in the present revision because the currently available experimental documentation does not preserve the reference-temperature calibration traceability, repeated-measurement information, measurement-uncertainty analysis, or calculation definitions required to substantiate the previously reported ±1 °C and 0.99% values. After flexible integration and 100 bending cycles at a 10 mm radius, the reference-voltage variation remains below 0.1%, while the reported temperature-equivalent TEMP-output shift remains within ±0.5 °C under the tested laboratory conditions. These results demonstrate short-term laboratory bending stability and temperature-sensing performance under the tested conditions rather than long-term fatigue or automotive vibration qualification. The proposed sensor therefore demonstrates potential for curved and space-constrained thermal-monitoring applications, including permanent magnet synchronous motor (PMSM) stator windings and battery-module surfaces, while validation on actual EV components and formal automotive qualification remain necessary for production deployment. Full article

Sustainable human development is a key component of sustainable economic development. It involves a combination of knowledge, a high standard of living and a healthy life. This research utilized the Human Development Index developed by the United Nations Development Programme in examining the main drivers of sustainable human development. The index measures development based on three dimensions of human development: knowledge, health and living standard. In this research, data from 15 ECOWAS countries covering the period of 2004 to 2022 were used. Methods of Moments Quantile Regression, Two-Stage Least Squares and Panel Correlation Standard Errors were employed to examine the effects of various determinants of sustainable human development. The results showed that digitalization, foreign direct investment, health expenditure and financial development are important determinants of sustainable human development. Employment presents asymmetric effects—negative influence in lower quantiles and positive influence in upper quantiles—showing the existence of thresholds in generating employment’s benefits for human development. Carbon emissions are also positively linked with human development, though the connection is strong in the lower quantiles and weak in the upper quantiles—validating the Environmental Kuznets Curve relationship. Nonetheless, renewable energy is counterproductive in advancing human development. These findings inform key policy implications for sustainable human development in ECOWAS. Full article

Welded structures are widely used in ships and marine equipment. The ultrasonic longitudinal critically refracted (LCR) wave method is a reliable technique for measuring residual stress in welded structures. Conventional ultrasonic measurement systems have high power consumption and are difficult to operate under the limited power supply conditions of marine environments. Reducing the excitation voltage can lower power consumption, but as the excitation voltage decreases, ultrasonic echo energy weakens and noise interference increases, thereby affecting the accuracy of stress measurement. This paper proposes a complexity-mutation-based adaptive singular spectrum analysis–variational mode decomposition algorithm (CM-SSA-VMD). The algorithm constructs a complexity index using the spectral centroid, waveform roughness, and zero-crossing rate. By identifying abrupt changes in complexity between adjacent singular spectrum analysis (SSA) components, it adaptively determines which components to retain for reconstruction. Then, variational mode decomposition (VMD) is used to further separate the residual high-frequency noise. Finally, the ultrasonic time of flight (TOF) is estimated using the cross-correlation algorithm, and the stress is calculated. The stress measurement accuracy of the proposed algorithm was evaluated at different excitation voltages: 5.2 V, 3.3 V, 1.2 V, and 0.5 V. The results show that when the excitation voltage drops to 0.5 V, after being processed by CM-SSA-VMD, the average relative error of stress measurement remains below 10%, while the average relative errors of a new adaptive denoising method, Grey Wolf Optimization–Variational Mode Decomposition–Wavelet Transform (GWO-VMD-WT), traditional FIR filtering and VMD are approximately 14%, 18% and 16% respectively. Compared with GWO-VMD-WT, FIR filtering and VMD, the measurement accuracy of this method is improved by 33.35%, 46.16% and 41.82% respectively. The CM-SSA-VMD algorithm can effectively suppress noise in ultrasonic signals at low excitation voltages and improve the reliability of stress monitoring. It provides a feasible method for low-power ultrasonic residual stress monitoring in marine engineering. Full article

Large language models (LLMs) are increasingly proposed for clinical summarization, yet evaluations relying on textual similarity overlook clinically consequential failure modes such as fabrication, omission, contradiction, and negation inversion. We treat medical summarization as controlled clinical compression, operationalizing a multidimensional framework of seven quality dimensions, an eight-category error taxonomy, and fourteen replicability components. The prototype runs on $N=511$ de-identified Portuguese-language Picture Archiving and Communication System (PACS)-derived multi-modality imaging reports, generating English impressions via local gemma4:latest under three prompt variants (R03) and temperature control. Factuality was scored claim-by-claim by an LLM judge with a two-pass protocol yielding 100% label coverage on 6190 claims, cross-checked by three external judges and two non-radiologist physicians. Factuality reached $S=99.38$% (P1, 95% confidence interval (CI) 98.94–99.64), $99.54$% (P2, 99.12–99.76), and $92.77$% (P3, 91.60–93.79); critical errors were $0.50$% (95% CI 0.35–0.71); critical omissions (source-to-summary) are outside the claim-level scheme. Pairwise Cohen’s $\kappa$ among external judges ranged $0.677$–$0.908$. A controlled Portuguese (PT) → PT re-run ($n=50$) yielded significantly lower factuality than PT → EN ($\Delta =-7$ to $-15$ pp per variant, $p<0.0001$ paired), restricting claims to PT → EN. Human annotation elicited a factuality–completeness criterion gap; BERTScore F1 correlated weakly with factuality ($r=0.047$); temperature had no significant effect. Full article

Cold Metal Transfer wire arc additive manufacturing (CMT-WAAM) efficiently fabricates medium and large metallic components, yet deposited wall cumulative height varies spatially along the build direction and deposition path due to heat accumulation, inter-layer cooling, travel direction and inherited layer geometry. This study establishes a multi-factor prediction framework for single-bead multi-layer walls and evaluates CAFi-TCN, a temporal convolutional network enhanced with feature-wise linear modulation and causal attention. Height profiles were extracted from registered point clouds under 2–4 mininter-layerr cooling; the model uses deposition position, layer number, cooling time, travel direction, prior height increment and cumulative height to predict current-layer cumulative height. On the tenth-layer test set, CAFi-TCN achieved the lowest mean absolute error (MAE = 0.1836 mm) among the evaluated direct-height models, reducing MAE by 74.3%, 40.4% and 53.9% versus standard TCN, polynomial ridge regression and MLP, respectively. A Random Forest model trained on the height-increment target (RF-Δ) produced slightly higher MAE but lower RMSE and maximum absolute error, showing a trade-off between average-error control and extreme-error suppression. Additional no-PreDH, simple increment-baseline, path-block bootstrap and rolling-layer analyses show that prediction performance depends on both process-state variables and inherited geometry rather than simple copying of the previous layer. The results support bounded, layer-wise height forecasting for single-bead WAAM walls and provide a basis for pre-adjustment error identification. Full article

Short-term typhoon wind-field prediction across multiple lead times is challenging because forecast error grows with lead time and inappropriate sample construction may leak future storm-position information. We adopt STL-Net, a lightweight spatiotemporal framework integrating convolutional encoding, long short-term memory (LSTM) temporal modeling, squeeze-and-excitation recalibration, and multi-branch fusion for 10 m wind-field prediction at 6, 12, and 24 h lead times. A fixed-t₀ protocol anchors the target patch at the initialization-time storm position, avoiding future best-track leakage. Trained in 2020–2021, validated in 2022, and tested in 2023, STL-Net achieves a root mean square error (RMSE) of 3.11, 4.06, and 5.31 m·s⁻¹ at 6, 12, and 24 h. Among deep-learning baselines, U-Net achieves the lowest errors at 6 and 12 h and convolutional neural network (CNN) the lowest 24 h RMSE and mean absolute error (MAE), while STL-Net remains competitive with 1.673 million parameters and low inference latency. Ten-seed ablation identifies multi-resolution fusion as the most consistently beneficial component. An out-of-year evaluation with 2020 as the test year reproduces the error-growth pattern, supporting cross-year robustness. Models are deterministic without uncertainty quantification; error growth is deterministic, not calibrated uncertainty. Diagnostic interpretation links the error structure to the multi-scale organization of typhoon winds, storm translation, and wind-field asymmetry. Results constitute an offline proof-of-concept, not an operational system. Full article

Three-dimensional optical ranging has become an effective tool for railway infrastructure monitoring, enabling accurate geometric characterization of tracks and the detection of structural anomalies. Beyond track inspection, this work investigates the potential of a low-cost optical ranging approach for indirect monitoring of railway suspension components. Two low-cost 1D laser ranging sensors arranged in quadrature were used to observe the lateral profile of a helical bogie spring under loading conditions. Owing to the 18° field of view of each sensor, the measured responses exhibited a quasi-sinusoidal dependence on spring height. By applying phase unwrapping to the quadrature signals, the spring height was reconstructed in real time. Two independent vertical displacement tests were performed, achieving mean errors of 0.66 mm and −1.47 mm, with standard deviations of 2.35 mm and 2.16 mm, respectively, demonstrating consistent accuracy and repeatability of the method. The proposed quadrature sensing approach provides a simple, non-contact, and cost-effective solution for indirect estimation of the bogie spring height. It is particularly suitable for installations where direct vertical measurements are impractical and offers a promising method for monitoring spring compression and suspension loading in railway condition monitoring systems. Full article

This paper presents the closed-loop integration of three neural estimators within a physically based guidance, navigation, and control (GNC) architecture for a canard-controlled rocket. The estimators support Global Navigation Satellite System (GNSS) carrier-phase ambiguity processing, reconstruct the gravity vector in body axes, and correct the terminal line-of-sight (LOS) estimate obtained by fusing GNSS, inertial measurement unit (IMU), and semi-active laser (SAL) quadrant-detector information. Building on earlier specialized or partially integrated studies, the contribution is the simultaneous embedding of the three estimators in a common nonlinear six-degree-of-freedom closed loop and their evaluation, against a matched model-based baseline and an eight-configuration ablation study, under nominal, wind, high-angular-rate, GNSS-degradation, and combined-disturbance scenarios. By combining complementary attitude, gravity, and target-relative information while retaining physical validation and model-based fallback, the architecture is designed to improve navigation resilience and functional coverage across multiple sensor-degradation modes. All 500 closed-loop evaluation trajectories completed without numerical divergence. The overall trajectory-averaged squared vector errors were $4.13\times {10}^{-3}{\mathrm{m}}^2/{\mathrm{s}}^4$ for gravity and $1.59\times {10}^{-3}$(dimensionless, corresponding to a $2.3^{\circ }$ RMS-equivalent angular error) for LOS; under combined disturbances, their mean errors were 3.08 and 2.99 times the corresponding nominal values. Relative to the matched model-based baseline, the integrated architecture improved the GNSS ambiguity fix-success rate from $96.4\%$ to $97.9\%$ and reduced terminal-guidance circular error probable (CEP50) from $0.49$ m to $0.35$ m (an absolute reduction of $0.140$ m; paired-bootstrap $95\%$ CI $[0.118,0.156]$ m), corresponding to a $28.6\%$ relative reduction, and the ablation study indicated a near-additive, monotonically improving contribution from each of the three modules. The results demonstrate simultaneous operation of the three modules as active components of the propagated GNC loop over the tested conditions, with a quantified improvement over the matched conventional configuration. Full article

Aero-engine performance prediction provides an essential basis for condition assessment and health management during ground testing. Conventional component-level models require costly calibration, whereas purely data-driven models are prone to overfitting under data-scarce conditions and generally offer limited physical interpretability. To address these limitations, this study proposes a turbofan engine performance prediction method that integrates a component-topology prior with season-aware multi-source transfer learning. Based on the main gas-path connections and the mechanical coupling between the high- and low-pressure spools, a component-topology-informed fusion prediction model (Engine-BLT) is developed. It characterizes the dynamic coupling behavior of the entire engine through component-specific feature extraction, feature propagation along the gas-flow direction, and cross-component feature fusion. In addition, multiple source domains are constructed according to seasonal information, including ambient temperature, ambient pressure, and relative humidity. A similarity-based weighting strategy is then employed to improve model adaptability under small-sample transfer-learning conditions. Finally, a component-level GasTurb model is employed to provide an independent aerothermodynamic reference under representative steady-state operating conditions. The results show that Engine-BLT achieves R² values of 0.9650, 0.9991, and 0.9962 for corrected low-pressure-turbine outlet total temperature (T_5,corr), corrected net thrust (F_N,corr), and corrected high-pressure-compressor outlet total pressure (P_t3,corr), respectively, yielding the best overall prediction accuracy among the evaluated models. The season-aware weighted multi-source transfer learning (SWMT) strategy also provides the best overall performance among the investigated transfer-learning schemes. Under ground idle, maximum continuous thrust, and maximum takeoff thrust conditions, its mean absolute relative errors for P_t3,corr, T_5,corr, and F_N,corr are 1.17%, 0.99%, and 0.33%, respectively, which are comparable in magnitude to the steady-state aerothermodynamic reference obtained from the GasTurb model. The proposed method improves prediction accuracy under small-sample acceptance-test conditions while maintaining target-domain adaptability and physical interpretability. Full article

Frequency-shift (FRESH) filtering is a low-compute technique that is deemed an attractive alternative to successive interference cancellation (SIC) algorithms in communication systems that involve resource-constrained devices. FRESH filters exploit the cyclostationary properties of interfering signals by linearly combining the spectrally redundant components of a signal such that they destructively add. Therefore, the performance in terms of bit error rate or mean squared error achievable by a FRESH filter is dependent on the cyclostationarity features exhibited by a signal. Their computationally simple architecture makes FRESH filters well-suited for low-power wireless sensors, whereas their protocol-agnostic operation is appealing to all manners of cognitive radio, making them an excellent component of ad hoc or infrastructure-less networks. This paper surveys the published FRESH filter designs for communication systems and provides empirical data on their performance under a variety of signal-of-interest and interferer signal properties. We contrast various FRESH filter configurations and ways to determine filter coefficients, comparing against a baseline SIC algorithm in terms of cancellation performance. Full article

Macedonian is a low-resource language for automatic speech recognition: annotated speech data are scarce, dialectal variation is substantial, and existing evaluations focus almost entirely on read speech in Standard Macedonian. This paper presents a unified pipeline that converts Macedonian speech, including regional dialects, into accurate transcripts and structured, queryable knowledge, a capability required for applications such as searchable parliamentary archives, broadcast transcription and subtitling, and dialectological documentation. Parameter-efficient adaptation of Whisper large-v3-turbo via low-rank adaptation is evaluated against strong zero-shot and language-specific baselines on four newly curated dialect corpora (Ohrid, Veles, Tikvesh, and Gostivar) and three Standard Macedonian corpora, two of which were collected for this work. The adapted model reduces word error rate by 57–70% relative to the strongest zero-shot baseline and by 33–66% relative to the language-specific BUKI Whisper 2.0 model on dialectal speech, with comparable improvements over zero-shot baselines on standard Macedonian speech, while updating only about 0.7% of parameters. Beyond transcription, the pipeline adds speaker diarization with cross-recording speaker linking and a graph-based retrieval-augmented generation component that enables speaker-, topic-, and time-aware querying of diarized transcripts, evaluated on long-form Macedonian parliamentary recordings. Together, these results establish parameter-efficient adaptation, speaker-aware processing, and graph-based retrieval as a practical and transferable framework for transforming under-resourced speech into accessible, structured knowledge. Full article

Blast-induced ground vibration is traditionally predicted using empirical scaled-distance equations or numerical simulations driven by simplified analytical loading functions. Although dynamic finite element modeling has significantly advanced the understanding of stress-wave propagation, existing approaches remain unable to reproduce the complete seismic response of actual production blasting because the true blast source is generally unknown and is therefore replaced by simplified pressure–time functions. Consequently, a field-calibrated physics-informed Digital Twin framework for production blasting remains insufficiently established. This study presents a PI-DDT framework for production blasting based on field-derived seismic source reconstruction. The proposed methodology consists of two complementary innovations. First, the three-component near-field pilot-blast record was analyzed through deconvolution-based inverse wave propagation, and the transverse, longitudinal, and vertical components were independently deconvolved to reconstruct three orthogonal equivalent single-hole seismic source histories at the pilot blast hole. Second, the three reconstructed source histories were incorporated into the PLAXIS 3D dynamic finite element model through component-specific dynamic multiplier functions together with the actual production-blast geometry, blast-hole coordinates, electronic initiation sequence, site-specific rock-mass properties, and attenuation characteristics to establish a field-calibrated PI-DDT framework for a full-scale production blast. The proposed framework was applied to a full-scale production blast comprising 83 blast holes in an operating open-pit mine. Model performance was evaluated through a multi-domain performance assessment including PPV, amplitude-envelope development, frequency-spectrum agreement, and cumulative-energy evolution. The simulated responses showed practically meaningful agreement with field measurements across multiple monitoring locations, with a median component PPV error of 11.8%, a mean three-dimensional resultant PPV error of 14.2%, a mean resultant spectral similarity of 84.0%, and a mean three-dimensional cumulative-energy MAE of 4.7%. Unlike conventional blasting simulations that rely on simplified analytical loading functions, the proposed methodology reconstructs a field-derived equivalent seismic source signature and integrates it directly into a physics-based numerical model. The principal scientific contribution of the study lies in the field-calibrated integration of equivalent seismic source reconstruction, actual production-blast geometry and initiation timing, three-dimensional dynamic FEM, and multi-domain model-performance evaluation within a unified physics-informed framework. The developed framework provides a physics-based computational foundation for blast-design evaluation, vibration-control planning, digital mining applications, and future AI-assisted blast-design optimization and adaptive vibration-control workflows. Full article

Reduced biquaternion (RB) matrices provide a commutative algebraic framework for coupled multicomponent data, but their factorizations are usually formulated through complex or real representation matrices. The aim of this study is to develop and validate direct Householder-based QR and singular value decomposition (SVD) algorithms for RB matrices that operate in the native $e_1$–$e_2$ idempotent coordinates. The formulation treats vanishing idempotent components explicitly so that zero divisors require no invertibility assumption, keeps all factors in native RB form, and exposes two independent complex branches that execute concurrently. We distinguish implicit Householder reference constructions from accelerated LAPACK backends, establish their exact arithmetic equivalence, and derive normwise error bounds inherited from the complex kernels. An optional one-step residual-monotone correction lowers the measured reconstruction residual without altering the factorization invariants. Controlled benchmarks against real quaternion and commutative quaternion implementations, together with zero-divisor, rank-deficient, and ill-conditioned stress tests, confirm the roundoff-level residuals and competitive runtimes. The framework is then applied to three-component seismic denoising through a Hankel embedding and truncated RB-SVD, with parameters selected based on training noise realizations and evaluated on disjoint held-out realizations, including a reference-free rank selection variant and a noise level sensitivity study. The results establish a zero-divisor-safe and computationally efficient RB factorization framework for coupled multichannel low-rank processing. Full article

Macro-fibre composite (MFC) actuators offer improved flexibility and damage tolerance over monolithic piezoceramics, which have been widely utilised recently. However, direct bonding to a heritage substrate of MFC actuators may cause local stress concentration and complicate removal. To overcome this, a protective aluminium–concrete sandwich configuration is proposed, where the MFC patch is attached to a replaceable thin aluminium carrier layer that transfers a controlled deformation field to the concrete substrate through the bonded aluminium–concrete interface, enabling reversible, low-intrusion deformation control without direct modification of the protected substrate. A coupled electromechanical finite-element formulation is derived from the linear piezoelectric constitutive equations and Hamilton’s principle. The model is first is validated against benchmark deflection and modal data for a traditional MFC-bonded aluminium beam, achieving a maximum deflection error below 1% (0.9142 mm vs. 0.9141 mm at the free end) and excellent frequency agreement (19.7 Hz and 112.0 Hz). Laboratory cantilever tests under 400 V show a systematic amplitude reduction relative to the perfect-bond model: the measured free-end displacement is approximately 0.598 mm compared with 0.9142 mm numerically. A one-parameter effective actuation-transfer coefficient of 0.656 reduces the displacement-profile RMSE to 0.0072 mm (NRMSE 1.23%, R² = 0.9987), indicating that the dominant discrepancy is an amplitude loss associated with non-ideal strain transfer and boundary/electric-field effects rather than a change in deformation mode. For the proposed sandwich beam, simulations reveal a monotonic, voltage-dependent response: tip deflection rises from approximately 0.03 mm at 200 V to 0.115 mm at 800 V over a 200 mm span. Actuator placement near the fixed end yields higher bending authority, consistent with classical placement theory. The sandwich concept demonstrates that a replaceable protective layer can generate controllable curvature while maintaining moderate stress levels in the protected substrate, making it suitable for micro-crack suppression and temporary stabilisation of fragile components such as cultural relics or aged concrete. The validated model provides a foundation for future experimental calibration and distributed actuator optimisation. Full article

Personalized learning pathways are difficult to support in distributed educational systems because learner records cannot always be centralized and one shared federated predictor may not represent heterogeneity across institutions, learning tasks, and individual learners. We propose FedPath-MTL, a federated multi-task framework with global, task-specific, and learner-specific parameters, a bounded future-error proxy, a dynamic task graph, reliability-aware aggregation, and a constrained primal–dual beam-search planner. We evaluate only the next-outcome predictive component on four public educational releases: ASSISTments 2009–2010 Combined, Junyi Academy 2018–2019, EdNet-KT1, and OULAD. Every method is evaluated both before and after the same validation-only temperature-scaling protocol, and AUC, negative log-likelihood (NLL), Brier score, and 15-bin expected calibration error (ECE) are reported. The pooled central comparator has the highest mean AUC on all four releases. FedPath-MTL AUC ranges from 0.531 to 0.580, and identically calibrated ECE ranges from 0.098 to 0.127; matched comparisons with FedAvg-style training do not show a uniform AUC or calibration advantage. Observed school identifiers define clients only for ASSISTments; the other releases use explicitly labeled simulation partitions. The public logs do not jointly identify actions, propensities, subsequent-learning rewards, resource catalogs, and institutional constraints, so no pathway-effectiveness or causal learning-gain claim is made. Software tests verify equation-level implementation of the planner but do not validate educational benefit or real-institution constraint satisfaction. Full article