Search Results (1,416)

IoT devices operate as integrated systems spanning hardware, firmware/software layers, and communication layers. In operational settings, many faults and performance degradations are emergent: they arise from cross-layer interactions, workload changes, and telemetry artifacts, rather than a single physics-of-failure mechanism. These realities make traditional supervised fault classification difficult because labeled fault data are rarely available during deployment, and the fault surface is unknown and a priori. This paper presents a practitioner-oriented, label-free fault detection and diagnosis (FDD) pattern based on Dynamic Time Warping (DTW) for rapid implementation in production IoT telemetry. The method represents a device as a sequence of overlapping episodes and organizes telemetry into interpretable layers (hardware sensors, communication health proxies, and software/firmware-derived KPIs). A reference library of regular episodes is built from an assumed-healthy training window; new episodes are scored using constrained DTW distances against this library, while retaining per-layer and per-channel contributions for attribution. We show that production performance depends strongly on operational parameterization, including episode length, DTW constraints, robust threshold learning, and temporal validation. Within a verified-healthy evaluation window, the tuned configuration achieves an AUROC of 0.97 for the temporally structured faults DTW is suited to (bias, drift, and interaction faults, with spikes detected at an AUROC of 0.93), detecting 100% of injected faults, with a mean delay under 25 min. We further show that constant-value (stuck-at) and missing-data (dropout) faults fall outside DTW’s shape-matching scope (AUROC about 0.66) and are better served by complementary variance- and missingness-based detectors, a consequence of DTW’s shape-matching scope rather than a parameter choice. This work contributes a system-level methodological framework for deploying DTW as an IoT fault-detection-and-diagnosis capability: an episode-and-layer architecture aligned with hardware, communication, and software/firmware ownership; a label-free reference library requiring only assumed-healthy data; per-layer and per-channel attribution for cross-domain triage; and a reproducible operational tuning procedure. Together, these deliver a fast-to-deploy, scalable, and accurate first-line detector for label-scarce IoT systems. Full article

This study examines the effect of values-based leadership on workplace engagement and explores the moderating role of sustainable social responsibility. The proposed study is based on the social learning theory, the leader–member exchange theory, and the social exchange theory, proposing a multidimensional model of values-based leadership, leadership qualities (LQ), ethical values (EV) and balance in achieving interests (BAI). The quantitative survey design was employed in the collection of data amongst 390 employees of the Ministry of Interior, Qatar. The measurement and the structural models were tested using the partial least squares structural equation modeling (PLS-SEM) using WarpPLS V. 7 Software. The findings show that the three dimensions of values-based leadership make important and positive contributions to engagement in the workplace. The results indicated that sustainable social responsibility had no significant moderating effect on the relationship between leadership qualities and workplace engagement, or on the relationship between achieving a balance of interests and workplace engagement. However, sustainable social responsibility significantly moderated the relationship between ethical values and workplace engagement. The study adds value to the literature on leadership and workplace engagement by separating the dimensions of values-based leadership and the contextualized enhancing role of sustainable social responsibility. Full article

How does artificial intelligence (AI) adoption shape administrative efficiency and e-citizen integration in local governments, and what role does professional development play in mediating these relationships? Drawing on a survey of 500 municipal employees across Spanish municipalities, this study employs partial least squares structural equation modelling (PLS-SEM), with formal non-linearity testing via Warp3 algorithms, to test a theoretically grounded model. The conceptual framework integrates Digital Transformation Theory and Public Value Theory as primary explanatory lenses, while drawing on the Technology Acceptance Model (TAM) and Total Factor Productivity (TFP) logic as complementary background perspectives that contextualise rather than directly operationalise the micro-level findings. Structural results reveal that AI adoption exerts a strong direct (and statistically linear) effect on perceived administrative efficiency (β = 1.04, p < 0.001; the standardised coefficient exceeding 1.0 and R² > 1 are a legitimate WarpPLS warp-model fit index rather than evidence of model misspecification: the Warp3 warp functions inflate the variance of predicted efficiency and break the additive identity SST = SSM + SSE, with the high AI–PD collinearity (r ≈ 0.84) as the contributing mechanism (RSCR = 1.000, SSR = 1.000); a comparative re-estimation without the moderation term yields β = 0.87 and R² = 0.76; we adopt this parsimonious specification (β ≈ 0.87, R² = 0.76) as the substantively interpretable estimate, with predictive relevance confirmed by a high Stone–Geisser Q² = 0.685, indicating that the model fits and predicts well rather than overfitting, while simultaneously stimulating professional development (β = 0.84, p < 0.001, R² = 0.70). Professional development positively predicted both efficiency (β = 0.27, p < 0.001) and e-citizen integration (β = 0.26, p < 0.01). Efficiency is the primary driver of e-citizen integration (β = 0.54, p < 0.001, R² = 0.53). The proposed moderation of AI adoption by professional development on efficiency was not supported (β = −0.01, p = 0.44), suggesting additive rather than synergistic effects. Model fit was robust (GoF = 0.701; ARS = 0.749; APC = 0.495); convergent and discriminant validity were confirmed by composite reliability, average variance extracted, Fornell–Larcker, and HTMT criteria; and common method bias diagnostics (Harman’s single-factor test, full-collinearity AFVIF, and marker-variable analysis) indicated that systematic method variance was not a material threat. These findings offer micro-empirical evidence of the mechanisms linking AI adoption to citizen service outcomes via a professional development pathway and provide actionable recommendations for Spanish and European municipalities navigating AI-driven governance reform. Full article

Epileptic seizure prediction using an Electroencephalogram (EEG) can improve patient safety by enabling early intervention, yet most existing approaches focus on short prediction horizons with limited personalization or computational efficiency. This study presents a unified deep learning framework evaluated across ten pre-ictal prediction windows up to 300 min before seizure onset, using recordings from 161 patients and 1023 seizure events. At the 5 min horizon, the generalized model achieved 96.30% accuracy and 91.62% sensitivity. Two complementary personalization strategies are introduced: incremental transfer learning, which progressively fine-tunes the generalized model using patient-specific data, and Dynamic Time Warping (DTW)-based similarity personalization, which constructs a morphology-aware training cohort from a single reference seizure. Personalized models consistently outperform generalized baselines, particularly at longer horizons, with the DTW-based approach achieving 89.68% accuracy using only 70 similar patients. Reliable prediction is demonstrated up to 60 min prior to onset, while model optimization reduces computational complexity with minimal performance loss, supporting deployment in resource-constrained clinical environments. Full article

Synthetic-materials-induced environmental burdens have shifted the focus of scientists towards sustainable packaging solutions. Three-dimensional (3D) woven fabrics offering superior mechanical durability are a promising solution to the problem. However, this area has remained unattended by researchers in the field of packaging technology. Hence this study focuses on development of warp, weft, and bidirectional interlock 3D woven fabrics for packaging applications. Aiming at mechanical durability, tensile and creep characterization have been carried out, depicting the strong influence of interlacement patterns on mechanical properties. Increasing the number of interlacements decreased tensile and creep strength, such as the lower weftwise tensile strength offered by weft interlock 3D, and vice versa for warp interlock. While elongations were found higher in interlocking directions, creep loadings carried out at 30% and 60% of breaking loads revealed unique after tensile creep behaviors. Weftwise tensile strength decreased after creep; warp interlock 3D entailed 42% decrease in tensile strength after creep. However, warpwise tensile strength was noticed to be higher for weft interlock 3D, owing to alignment of yarns during applied creep, while a decrease was noticed in elongation percentages. In a nutshell, the engineered 3D interlacements entailed successful tailoring of mechanical properties, paving a pathway towards high-strength sustainable packaging. Full article

Surface roughness of woven fabrics plays a key role in tactile comfort and skin–textile interaction, particularly in medical applications involving prolonged contact with human skin. This study focuses on the surface roughness of woven fabrics in plain and twill (1/3 S) weaves intended for hospital bed sheets and bedding applications. Plain weave represents a structurally symmetric system, while twill weave exhibits a pronounced diagonal structure. Roughness was evaluated using the Fabric Touch Tester (FTT) and further analyzed through amplitude (Rq), height distribution (Rku), and frequency-related parameters (linear peak density) obtained by signal processing and peak analysis in OriginPro 2026. The results showed that weave structure is the dominant factor influencing surface topography. Plain weave fabrics exhibited higher amplitude roughness and more uniform height distribution, while twill fabrics showed lower global roughness but stronger directional dependence, particularly in diagonal directions. Linear peak density was not significantly affected by laundering cycles, fiber composition, or finishing, but was strongly dependent on weave type. The findings demonstrate that due to the orthotropic nature of woven fabrics, surface roughness, derived from surface topography, cannot be adequately described by a single parameter, and that a combined analysis of amplitude and spatial descriptors is required, with the surface being evaluated not only along the principal symmetry directions (warp and weft) but also in off-axis directions. These results provide valuable insight for the design of hospital textiles with improved tactile comfort and reduced risk of skin irritation. Full article

The deployment of CubeSats requires reliable, lightweight, and space-efficient launch mechanisms. Traditional spring-based deployers often rely on standard off-the-shelf components, limiting the design flexibility. This study presents a pilot design-to-verification workflow for a CubeSat deployment mechanism manufactured by Laser Powder Bed Fusion from 316L stainless steel. The workflow integrates analytical sizing, kinematic and numerical force assessment, FEM-based LPBF process simulation employed as a design-support tool to predict thermal displacements and residual stress that occur during manufacturing, prototype manufacturing and optical inspection. Optical scanning indicated that the main envelope dimensions remained close to the nominal CAD values, while the support-plate warping was localized at the plate corners due to the residual thermal stress after the support removal. The study validates the manufacturability of a single LPBF orbital-deployer lunch mechanism and assesses its dimensional accuracy and workflow feasibility, rather than its functional mechanical performance. It also includes mitigation strategies for deployer distortions. Full article

Microsatellites operate in highly dynamic thermal environments due to severe physical constraints, making temperature telemetry a critical onboard health indicator. Conventional threshold-based monitoring fails to distinguish normal operational mode transitions from genuine faults, causing excessive false alarms. To address this, we propose a two-stage framework integrating unsupervised thermal mode discovery with mode-specific deep learning prediction. Raw temperature telemetry is downsampled and segmented into orbital cycles. Unsupervised clustering identifies two nominal thermal regimes and four canonical fault-type libraries (step, spike, drift, and noise), each corresponding to distinct in-orbit failure mechanisms. For each nominal mode, a Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) is trained on 7-day historical windows to forecast 3-day temperature evolution. Post-downlink, incoming cycle mode is inferred via nearest-neighbor DTW classification; anomalies are flagged when prediction residuals exceed mode-adaptive thresholds. Validation on Macau Science Satellite-1B (MSS-1B, COSPAR 2023-069-B, NORAD 56732) in-orbit telemetry from a 41° inclination low-Earth orbit—where solar illumination dominates external thermal loading and internal heat from the data-communication module and scientific payload constitutes the primary internal thermal source—shows the method reduces anomaly flags by 96.6% and improves prediction mean absolute error by 51.3% compared to a non-classified global baseline under nominal operating conditions, correctly detecting a known operational transient while suppressing spurious alarms. A synthetic fault injection experiment with four anomaly types and five baseline methods further confirms the framework’s detection capability, achieving an overall F1 score of 0.725 vs. 0.258 for the global baseline—a 2.8× improvement driven primarily by a 4× precision gain. Sensitivity analysis reveals that the two-stage advantage is most pronounced for low-magnitude and short-duration faults, where mode-specific context is essential. This work advances microsatellite autonomous health management by providing reliable anomaly detection with quantified fault detection performance. Full article

Semiconductor manufacturing demands exceptional precision, as even minor process deviations can result in significant yield degradation. The increasing deployment of sensors generates extensive time-series data. However, such data are often affected by temporal misalignments, nonlinear distortions, and inter-wafer variability, complicating direct comparison and automated anomaly detection. To address these challenges, this paper proposes a robust framework that employs a Dynamic Time Warping (DTW)-based two-stage alignment strategy with Sakoe–Chiba constraint followed by a bidirectional polyline distance measure to identify subtle anomalies. This approach effectively handles scarce anomaly labels and high variability in sensor data, enabling reliable process health monitoring. Experimental results on real semiconductor production data demonstrate that the framework enhances detection accuracy, contributing to early fault identification and reduced wafer scrap in manufacturing environments. Full article

Although small and medium enterprises (SMEs) anchor employment and output across Sub-Saharan Africa, their uptake of artificial intelligence (AI) lags global benchmarks, and prevailing explanations dwell on capital, infrastructure, and institutional voids while overlooking the leadership competencies that determine whether available resources are mobilised at all. Addressing this gap, the present study asks how the digital leadership capabilities of SME owner-managers shape their intention to adopt AI in Nigeria, and through what organisational mechanisms and under what boundary conditions this influence operates. Anchored in the Diffusion of Innovation Theory and the Tigre–Henriques–Curado model of digital leadership, a cross-sectional survey was administered to owner-managers of registered SMEs drawn from six states; a sample of 390 was derived from a population of 23,290 firms using the Taro Yamane formula with proportionate allocation, and 306 valid responses were retained. Partial Least Squares Structural Equation Modelling (WarpPLS 8.0) was applied after confirming reliability (Cronbach’s $\alpha$: 0.69–0.84; composite reliability: 0.83–0.88), convergent validity (AVE: 0.56–0.67), and common method bias control. Strategic ($\beta$ = 0.298), interpersonal ($\beta$ = 0.245), and personal attribute ($\beta$ = 0.129) capabilities each significantly raised AI adoption intention. In contrast, delivery-related capabilities ($\beta$ = 0.090, p = 0.057) did not, indicating that pre-adoption intention is governed by cognitive-strategic and relational competencies rather than execution skills. Organisational innovation climate partially transmitted the effects of strategic and interpersonal capabilities, and firm size amplified the interpersonal pathway in medium-sized firms. The study contributes a leadership-centred account of AI adoption in an under-researched African setting and, by estimating mediation and moderation within a single framework, clarifies both why and when digital leadership translates into AI readiness, yielding capability-specific guidance for owner-managers and SME support policy. Full article

Road network extraction and updating are crucial for urban development, map updating, and mobility applications. Existing trajectory-based methods often underutilize grid-level semantic information and neighborhood context, thereby limiting their robustness to noisy, heterogeneous, and cross-city trajectory conditions. This study proposes a supervised framework for trajectory-driven road network extraction by coupling intra-grid movement semantics with inter-grid neighborhood context. Multi-level features, including convex-hull shape descriptors, directional clustering, DTW-based (Dynamic Time Warping) heterogeneity, and neighborhood density differences, are used to train a Random Forest classifier for key-grid detection. The detected key grids are further processed through morphology-aware thinning and Kalman smoothing to generate a topology-preserving and vectorization-ready road skeleton. The model is trained on pedestrian trajectories from Shenzhen and directly transferred to vehicle trajectories in Wuhan and Changsha under a zero-shot setting. Experimental results show that the proposed method achieves longer correctly extracted road length and competitive length-based precision compared with raster-based reference methods, while feature-importance and ablation analyses confirm the complementary role of neighborhood context. The proposed pipeline is scalable, interpretable, and transferable, supporting trajectory-based road map updating and urban network analysis. Full article

This article proposes a double-pre-warped Tustin bi-quad filter (DPT-BQF) to suppress mechanical resonance for non-contact integrated permanent magnet vernier motors (NIPMVMs). First, this article analyzes how conventional notch filters suffer from distortion in notch frequency, bandwidth, and depth when discretized with low-precision methods, which degrades filtering performance and weakens resonance suppression. To address this issue, the inherent limitations of the pre-warped Tustin discretization for BQFs are discussed. On this basis, a double pre-warped Tustin method is proposed by compensating for the bandwidth distortion, and its discretization performance is comprehensively evaluated. Furthermore, the principle, parameter design, and robustness range of the filter are deeply analyzed in the discrete domain. Finally, experimental validation on an NIPMVM test platform demonstrates the effectiveness and feasibility of the proposed method. Full article

This paper evaluates a dense, total-variation-based optical flow method for retrieving wildfire smoke plume motion vectors from geostationary, deep-space, and airborne remote sensing imagery. Using multiple major fire events, we assess the robustness of the approach across a range of spatial resolutions and time intervals. The test cases include Geostationary Operational Environmental Satellite (GOES) observations of the 2025 Los Angeles Fires and the 2024 Park Fire, imagery from NASA’s Enhanced MODIS Airborne Simulator (eMAS) for the 2019 Sheridan and Williams Flats Fires, and a complementary Park Fire image pair from the Earth Polychromatic Imaging Camera (EPIC) aboard the Deep Space Climate Observatory (DSCOVR). Optical flow is computed directly on radiance fields, and smoke plumes are isolated using smoke masks derived from the Segmentation, Instance Tracking, and data Fusion Using multi-SEnsor imagery (SIT-FUSE) framework where available. Performance is evaluated by comparing the root mean square error (RMSE) between original image pairs and between the first image and the second image after warping with the retrieved motion field. RMSE is computed both globally and over smoke-only regions. Across GOES and eMAS cases, optical flow systematically reduces RMSE, often by more than a factor of two within smoke regions, indicating substantially improved frame-to-frame alignment of plume structures after motion correction. The DSCOVR/EPIC case, despite its coarser spatial resolution and longer temporal separation, also shows a marked reduction in global RMSE, demonstrating that the method remains informative under a broader range of observational conditions. For a selected subset of 10 consecutive GOES Park Fire pairs, we additionally compare the retrieved smoke motion vectors with collocated winds from the High-Resolution Rapid Refresh (HRRR) model and find the closest agreement in a broad lower-tropospheric layer centered near 875 hPa. These results show that dense optical flow can capture fine-scale plume evolution in high-temporal-resolution datasets while also providing useful motion estimates in coarser, global-view imagery. RMSE reduction is interpreted here as evidence of improved motion-compensated alignment, while the HRRR comparison provides initial physical context rather than independent validation. The resulting smoke motion vector fields provide a foundation for future comparison with model winds and for applications in plume analysis, fire hazard monitoring, and air quality studies. Full article

The China Containerized Freight Index exhibits multi-scale periodicity and nonlinear responses to uncertainty, which challenge traditional forecasting methods. This study proposes a dynamic multi-stage deep learning framework with COVID-19 as an interval node to construct event windows. Breakpoint detection identifies shipping-related events. A three-stage procedure, including Maximal Information Coefficient, Boruta, and Granger causality, selects uncertainty risk indicators as core features, while K-shape clustering groups the exogenous variables. The proposed hybrid model integrates a Temporal Convolution Kolmogorov–Arnold Network with a Warped Fourier and Shock Kernel. Prophet decomposition supplies baseline and residual terms. Temporal Convolution Kolmogorov–Arnold Network unifies local temporal feature extraction and universal nonlinear approximation under sparse samples. The Warped Fourier component adapts to drifting and superimposed seasonality, and the Shock Kernel quantifies uncertainty shock intensity and decay. A gating fusion mechanism suppresses noise and enhances information efficiency. Comparative experiments demonstrate competitive accuracy and robustness, with statistically significant gains in several benchmark comparisons; ablation studies confirm incremental contributions of each component. Empirical analysis shows that under event-driven uncertainty, demand-side policy variables show stronger predictive relevance to China Containerized Freight Index fluctuations, while simultaneously transmitting effects to the carbon market and accelerating the green energy cost transition. These findings provide insights for freight rate forecasting and shipping market risk management. Full article

To address uneven surface hardness distribution in 65Mn external tooth friction plates after furnace quenching and disc mold tempering, we adopted an integrated quenching and forming process, using an internal-circulation mold. By simultaneously implementing pressure forming and quenching within the internal-circulation mold, the hardness uniformity of the friction plate during forming was improved, effectively suppressing warping deformation. A multi-field coupled model of the friction plate quenching in the internal-circulation mold was established to simulate the dynamic evolution of the temperature field, the microstructural transformation, and the stress field, thus obtaining the complete heat treatment response of the martensitic transformation. The experimentally observed microstructure agreed well with the simulation results. Data analysis showed that after quenching in the internal-circulation mold, the surface hardness difference of a single friction plate was reduced from 3 HRC to 0.9 HRC, and the end face runout decreased from 0.1–0.15 mm to no more than 0.06 mm, significantly improving the product’s dimensional accuracy and performance consistency. Full article