Search Results (7,244)

Changes in modal order alter the predictor dimension, pole-label swaps disrupt frequency continuity, and time-varying envelopes affect phasor magnitude and phase. Estimating these quantities independently can propagate errors across successive processing stages. A coordinated estimator is developed for the modal subspace, pole states, and regression parameters. An order confidence index combines the spectral gap, cumulative energy, and noise separation to select the model order and reconstruct the signal in one low-rank subspace. Variable-order recursive prediction and frequency–damping state association then form continuous pole trajectories, followed by adaptive smoothing and class-dependent unit-circle projection. The associated oscillatory and decaying direct-current (DC) poles update the Maclaurin regression atoms. Finite-window coupling is handled by either modal initialization followed by Gram iteration or a direct joint regularized solution, avoiding repeated leakage compensation. Tests with modal-order changes, frequency dynamics, modal crossings, amplitude modulation, and decaying DC show that the coordinated parameter chain preserves pole identity and improves dynamic phasor measurement. In the main dynamic test case, the mean and 95th-percentile total vector errors (TVEs) are 3.2082% and 6.0672%; the 95% paired confidence interval for the mean-TVE difference between the proposed method and estimation of signal parameters via rotational invariance techniques (ESPRIT) remains below zero. A separate RK3568 bare-metal test of the standalone three-tone Prony kernel completed 800 frames without a processing failure. Its mean processing time was 18.621 ms per frame, with observed values from 18.537 to 19.070 ms. Full article

Reliable textile sensors must maintain a stable electrical response during repeated bending and across different bending speeds encountered in wearable use. Here, a multi-walled carbon nanotube (MWCNT)-coated cotton fabric fabricated by the drop-casting, sonication, and thermal treatment (DSTT) method was evaluated as a piezoresistive bending sensor against a room-temperature-dried (RT) control at the same coating cycle. The DSTT sensor retained 89.2% of its peak response after 1000 continuous bending cycles, compared with 20.0% for the RT control. Across sequential tests at 1, 2, 4, and 8 mm/s, the DSTT mean peak response varied by 11.1% of the grand mean while maintaining regular waveforms and baseline recovery. Carriage-separation testing gave an average regression sensitivity of approximately 0.58%/mm (R² = 0.982–0.986) and a maximum loading-unloading difference of 8.7% of full scale. A glove-mounted DSTT sensor also produced distinct response levels at manually assigned finger positions of 0°, 45°, and 90°. For the selected specimens, these results demonstrate greater dynamic response stability for DSTT than for RT under the tested protocols, together with discrimination among manually assigned finger positions in the glove demonstration. The speeds were applied in a fixed order; therefore, the effects of bending speed and cumulative cycle history cannot be separated. Full article

Existing top-down carbon emission estimation studies often rely on single models or conventional ensemble approaches, which may limit their ability to capture complex nonlinear relationships under small-sample conditions. To address this limitation, this paper proposes a heterogeneous ensemble stacking network (HESTNet) for city-level monthly CO₂ emission estimation using electricity-centered multi-source data. A candidate feature set integrating sector-specific electricity consumption, socioeconomic statistics, nighttime light data, and environmental and climatic variables is first constructed, from which 28 core features are selected using autoencoder reconstruction errors. A heterogeneous stacking ensemble comprising CatBoost, TabPFN v2, and TabM is then developed, with five-fold out-of-fold predictions fused by an L2-regularized Ridge meta-learner. Because reliable ground-truth city-level monthly CO₂ observations are generally unavailable, the model is trained and evaluated using 360 province-year samples from 30 provincial-level regions in mainland China during 2013–2024, with provincial annual CO₂ emissions used as supervised labels. At the supervised province-year evaluation scale, HESTNet achieves an R² of 0.914, RMSE of 0.0781, MAE of 0.0580, and MAPE of 9.38%. Compared with the conventional stacking baseline, HESTNet yields numerical improvements of 0.028 in R² and approximately 12.6%, 13.2%, and 14.0% in RMSE, MAE, and MAPE, respectively; however, the paired RMSE difference does not reach statistical significance after Holm–Bonferroni correction (adjusted p = 0.061). The trained model is subsequently applied to city-month-scale predictors to generate relative Emission Proxy Indices (EPIs). Under the constraint of official city-level annual CO₂ emissions, the EPIs are normalized into monthly allocation weights to derive model-derived, annual-constrained monthly CO₂ estimates. The proposed framework integrates electricity-centered multi-source proxies, heterogeneous ensemble learning, and annual-constrained temporal disaggregation, providing a data-driven approach for characterizing intra-annual variations in city-level CO₂ emissions. Full article

Power-line work-at-height monitoring requires the simultaneous detection of visually heterogeneous evidence: supervisory markers that may occupy only a few pixels after resizing, and worker-state cues that depend on body posture, equipment, and surrounding scene geometry. In a compact single-stage detector, this scale gap affects feature preservation, training assignment, and prediction stability. Shallow downsampling can weaken the high-frequency traces needed by tiny targets, standard matching may allocate too few positives to small categories, and different detection heads may produce inconsistent predictions for the same physical instance. We view these effects through the lens of asymmetric treatment at three stages of the detector, and introduce three targeted modifications to YOLO11n: a shallow wavelet detail preservation module that enhances low- and high-frequency sub-bands before resolution is lost; a class- and head-aware TinyAssign strategy that adjusts positive-sample allocation by category scale; and a ground-truth-aligned multi-scale consistency regularizer (GT-MSCR) that anchors cross-head agreement to ground-truth indices during training without inference overhead. On a self-collected four-class power-line dataset, and averaged over five independent runs, the model raises mAP@0.5 from 68.10% to 69.45% and recall from 60.84% to 65.32%. The largest per-class gain is obtained by the category most prone to scale-induced detection failure. External validation on the Pictor-v3 PPE and SH17 benchmarks further shows consistent gains on public data, with a parameter increase of only 0.005 M and an additional 2.66 ms of latency over the baseline. Full article

Artificial intelligence is increasingly viewed as both a catalyst for clean-energy development and a potential constraint on green innovation. This study examines the quantile-specific relationships between artificial intelligence, clean-energy market performance, and green innovation using daily market data from 15 June 2018 to 29 June 2026. The analysis employs Quantile Kernel Regularized Least Squares (QKRLS), which captures nonlinear and heterogeneous associations across different market conditions. The findings reveal a positive and statistically significant association between artificial intelligence and clean-energy-market performance across all examined quantiles, with the strongest estimated marginal associations occurring in the lower quantiles. Conversely, artificial intelligence is negatively and significantly associated with green innovation throughout the distribution, with the strongest negative associations observed at the 0.10 and 0.90 quantiles. The study therefore highlights the need for policies that align artificial-intelligence growth with the sustained development of green innovation. Full article

Publicly available bearing vibration datasets are widely used as benchmarks for developing condition monitoring and prognostic algorithms, yet the extent to which they represent the signal characteristics of real operational machinery has not been systematically investigated. This study presents a systematic comparison of five public benchmark datasets with vibration data acquired from industrial and maritime machinery operating under real service conditions. A unified signal-processing framework was applied across all datasets, including standardised segmentation, normalisation, and feature extraction in the time, frequency, and time–frequency domains. Degradation behaviour was further characterised using geometric descriptors of trajectory shape capturing trajectory regularity and smoothness. The analysis revealed consistent differences between laboratory-generated and operational vibration data, with public benchmark datasets generally exhibiting lower operating-condition variability and smoother degradation trajectories. Importantly, dataset realism emerged as a continuous characteristic rather than a binary property, with individual datasets occupying different positions along a realism continuum. An unexpected finding was that non-stationarity during nominal operation discriminated strongly between the two groups but in the direction opposite to that hypothesised, an effect attributed to latent degradation drift during accelerated laboratory testing. To quantify these aspects of realism along this continuum, a composite Realism Index (RI) was developed by combining two physically motivated and empirically complementary signal dimensions: operating-condition variability and degradation irregularity. Across 41 laboratory and 10 operational bearing runs, the RI separated the two groups with a large effect size (Cliff’s δ = 0.61, 95% CI [0.43, 1.00]), providing a quantitative framework for comparatively assessing the signal and operating-condition representativeness of benchmark datasets, with potential relevance to benchmark selection and evaluation practice in condition monitoring research. Full article

Reducing disaster risk is a precondition for sustainable urban development, and residents’ risk perception determines whether protective policy is accepted and acted upon. Risk objectivism, subjectivism, and constructivism each explain this perception but make competing predictions and are rarely tested against one another. This study tests them jointly through a multilevel comparison of Seoul and Busan, which share a national disaster-management framework but diverge in socioeconomic composition and physical risk. Individual data come from two government household surveys conducted in 2020 (Seoul, n = 20,911; Busan, n = 17,828); hazard, exposure, and vulnerability indices were built for all 41 districts from 14 indicators standardized over the pooled district set. Ordered logistic multilevel models show that the socioeconomic gradient reverses sign between the cities: lower income and education accompany higher perceived risk in Busan, the reverse in Seoul. The operative form of social capital also differs, institutional trust in Seoul and interpersonal trust in Busan, and area-level vulnerability predicts perception only in Busan. Individual-level regularities are therefore conditional on place. Because uniform risk communication will reach the most exposed residents in one city and miss them in the other, equitable and locally calibrated strategies are a precondition for sustainable urban resilience. Full article

Background/Objectives: Studies evaluating the association between chocolate consumption and cardiometabolic health have reported inconsistent findings, and whether these associations differ according to underlying metabolic status remains unclear. This cross-sectional study analysed whether metabolic profile modifies the associations between chocolate consumption and cardiometabolic risk markers in 1960 adults from the SALMANTICOR population-based cohort. Methods: Chocolate consumption was classified by chocolate type (dark or milk) and frequency of intake (occasional: 1–3 days/week; regular: ≥4 days/week). Multivariable linear regression models were used to evaluate associations with fasting glucose, glycated haemoglobin (HbA1c), high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, triglycerides, body mass index (BMI), waist circumference, and systolic blood pressure (SBP), with additional interaction analyses for diabetes, hypertension, and dyslipidaemia. Models were adjusted for age, sex, BMI (except when BMI was the outcome), smoking, alcohol consumption, physical activity, olive oil intake, and commercial pastry consumption. Results: Overall, 30.1% of participants reported habitual chocolate consumption. In multivariable models without interaction terms, both dark and milk chocolate consumption were associated with lower BMI, whereas dark chocolate consumption was associated with higher LDL cholesterol. After Bonferroni correction for multiple comparisons, diabetes modified the associations of dark chocolate consumption with fasting glucose and HbA1c and of milk chocolate consumption with HbA1c. Among individuals with diabetes, dark chocolate consumption was associated with lower fasting glucose and HbA1c, whereas milk chocolate consumption was associated with lower HbA1c compared with non-consumption. No such associations were observed among individuals without diabetes. Conclusions: These findings suggest that the association between chocolate consumption and cardiometabolic health is not uniform across the population but differs according to the underlying metabolic profile. In particular, the associations with glycaemic markers were confined to individuals with diabetes, suggesting that metabolic status may modify the relationship between habitual chocolate consumption and glycaemic control. Further prospective population-based studies and intervention trials are needed to confirm these findings. Full article

With the growing complexity of electromagnetic environments, electronic systems suffer from prominent strong electromagnetic interference in practical service. As a key component implementing power amplification and transmission in communication systems, interference and damage effects of a GaN HEMT power amplifier under High-altitude Electromagnetic Pulse (HEMP) directly affect the regular operation of systems. In this paper, a physical device model and an injection source model are built first; injection simulations of different HEMP pulses are adopted to analyze internal temperature and current density distributions, predicting vulnerable positions of the device under gate injection. A GaN HEMT power amplifier based on CGH40010F is then established to investigate the failure mechanism under HEMP injection and the damage effect of different pulse parameters. An injection experiment system is conducted according to HEMP pulse standard; results indicate that power amplifier failure stems from GaN HEMT device destruction. The damage evolution is tightly associated with injected energy accumulation, and the gate–source channel is the susceptible region for GaN HEMT under gate injection. These conclusions can provide important references for the protective design of GaN HEMT power amplifiers. Full article

Ensuring that artificial intelligence contributes to sustainable, equitable education requires more than aggregate accuracy—it requires verifying that predictive systems serve all learners fairly, including across geographic regions. We audit a dropout-prediction pipeline built on the Open University Learning Analytics Dataset (OULAD) for disparities across gender, disability, and geographic region, using a student-level train/test partition to prevent the same student’s records from contaminating both sets. Logistic regression and random forest classifiers attain approximately 0.85 accuracy and 0.91–0.92 AUC overall, yet region-stratified recall (true-positive rate) ranges from 0.55 in Wales to 0.80 in the West Midlands Region, an equal-opportunity gap of 0.25 that is corroborated by region-specific AUC, by a random forest classifier, and, for actual withdrawals, by a likelihood-ratio test showing region predicts being missed by the classifier beyond what the Index of Multiple Deprivation (IMD) explains. A region-isolation test shows that excluding region as a model predictor is associated with a significantly narrower gap in both model families (0.13–0.19 without region versus 0.25–0.28 with region), an association not explained by IMD band alone; because the bootstrap 95% confidence interval on this difference ($[-0.003,0.173]$) narrowly includes zero, we treat the attribution to region specifically as suggestive rather than conclusively established. A naive region-specific decision-threshold mitigation, evaluated correctly on a held-out validation set, does not improve the gap; a shrinkage-regularized version recovers a modest, observed reduction (0.24 to 0.17) on the held-out test set, without a formal uncertainty interval for this difference, at the cost of a near-doubling of regional false-positive rates. Because our analysis is retrospective and several predictors are computed over the full module presentation, these findings support methodological lessons for the design and auditing of future systems rather than direct claims about the performance or fairness of an operational, real-time early-warning intervention; we report them, including the mitigation failure, as evidence that dropout-prediction systems audited only for aggregate accuracy, without a properly validated regional fairness assessment, risk under-serving or unevenly burdening students in specific regions, working against rather than for the aims of SDG 4. Full article

Mine reclamation is a process in which a mine pit is back-filled with overburden, covered with topsoil, and revegetated. It is essential to monitor barren areas that require revegetation for long-term reclamation monitoring. The study introduces a Modified Barren Index (MBaI) to classify barren areas in a coal mining region. The MbaI is a computationally efficient and effective approach for differentiating barren areas from active mines, overburden dumps, built-up areas and vegetation to support mine reclamation and monitoring. The new proposed index utilizes near- and shortwave infrared to distinguish between bare soil and other surfaces. Using Landsat 8 and Sentinel 2, the study was carried out in the Jharia Coal Field region in India and compared with commonly used indices, i.e., the Biophysical Composition Index, Modified Bare Soil Index and Normalized Difference Bare Soil Index. The comparison between actual reflectance data and laboratory ECOSTRESS data attests that MbaI can effectively differentiate between barren and non-barren areas while other indices struggled. The index was tested in coastal, snow and desert regions for assessing efficiency, with accuracies of 98%, 97% and 91% using Landsat 8 and 94%, 94% and 96% using Sentinel 2 data, respectively. The extracted barren areas using MbaI exhibited lower NDVI and NDMI values compared to areas extracted from other indices, suggesting better efficiency. The study can be useful for achieving faster and accurate classification of barren areas from non-barren areas in mining and non-mining regions in the Indian subcontinent using multi-satellite data. However, wet soil can limit the accuracy of barren area extraction by MbaI, which can become a major limitation with coal mining regions experiencing regular rainfall. Full article

Nonstationary convolution models are widely used as the forward formula of nonstationary seismic reflectivity inversion (NSRI). This forward formula is determined by the convolution of a time-varying wavelet and reflectivity. Previous NSRI algorithms just focus on the estimation of reflectivity according to different sparse regularization strategies. The forward-operator, time-varying wavelet matrix is usually generated using a user-defined constant Q-value. However, the Q-value changes with time and space; therefore, inaccurate reflectivity inversion results will be yielded by the traditional NSRI method. In order to improve the accuracy of the inverted reflectivity, we propose a new forward-operator construction method based on the monotone relationship between the equivalent Q-value (Q_e) and centroid-frequency (CF). We first extract CF from the Gabor time-frequency amplitude spectrum of the seismic signal. Then, we can obtain Q_e through the CF-Q_e template. Hence, the blindness of selecting a Q-value is avoided and a more accurate forward operator can be constructed. Synthetic and field-data examples demonstrate that the proposed method provides a more accurate reflectivity estimation by constructing a more reliable forward operator. Furthermore, the enhanced reflectivity sections enable a clearer delineation of top and bottom interfaces of coalbed methane reservoirs, providing high-resolution seismic support for coal seam interpretation and subsequent mining planning. Full article

Introduction: This study examined whether severity-defined profiles of internalizing and externalizing problems among Chinese adolescents differed in their symptom connectivity. Method: Participants were 1200 Chinese adolescents aged 9 to 15 years who were assessed in 2018 and 2022. Fourteen internalizing and externalizing problem items were analyzed. Latent profile analysis was used to obtain a coarse severity-based grouping at each wave, after which Gaussian graphical models were estimated within profiles. Profile networks were formally compared using permutation-based Network Comparison Tests, and robustness to the EBIC hyperparameter was examined across γ = 0, 0.25, and 0.50. Results: A two-profile solution was retained at each wave as an interpretable lower- versus higher-symptom partition with adequate group sizes. Formal comparisons showed greater global strength in the lower-symptom profile at both waves. Overall network structure differed significantly between profiles at Time 1 but not at Time 2. Several node strength differences were detected, although the exact ordering of central nodes was not uniformly robust across regularization settings. Conclusions: Severity-defined profiles differed reliably in overall symptom connectivity, whereas evidence for broader profile-specific network configurations was wave-specific. These cross-sectional network findings describe conditional association patterns and should be treated as hypothesis-generating rather than as evidence of causal mechanisms or optimal intervention targets. Full article

Background/Objectives: Occupational pesticide exposure is a recognized hepatotoxic hazard; however, biomonitoring data characterizing liver function among Vietnamese agricultural workers remain scarce. This study compared serum hepatic biomarkers between pesticide-exposed fruit farmers (exposed group) and a non-exposed reference group (unexposed group) in a northern province in Vietnam, and evaluated whether biomarker alteration varied with cumulative exposure duration. Methods: A cross-sectional study using convenience sampling was conducted from April to October 2022 among 150 adults, comprising 100 fruit farmers with ≥1 year of direct occupational pesticide exposure and 50 unexposed controls. Serum aspartate aminotransferase (AST), alanine aminotransferase (ALT), total and indirect bilirubin, alpha-fetoprotein (AFP), and glucose were quantified using clinical laboratory assays. Multivariable linear regression models, adjusting for age, sex, and body mass index (BMI), were used to evaluate associations between pesticide exposure duration and biomarker levels. Results: Pesticide-exposed farmers had significantly higher mean concentrations of ALT (28.88 vs. 22.36 U/L, p = 0.002), AFP (3.06 vs. 1.16 ng/mL, p < 0.001), total bilirubin (9.93 vs. 7.74 μmol/L, p < 0.001), and indirect bilirubin (8.40 vs. 5.26 μmol/L, p < 0.001) than unexposed controls. While mean AST did not differ (p = 0.105), the proportion of abnormal AST cases was significantly higher among exposed individuals (23% vs. 8%, p = 0.043). After adjustment for age, sex, and BMI in multivariable linear regression models, prolonged exposure (>10 years) remained modestly associated with higher AFP and bilirubin levels (p < 0.05), while associations with ALT and AST were not statistically evident (p > 0.05). Conclusions: These findings suggest a possible association between occupational pesticide exposure and subclinical hepatic alterations, supporting calls for regular health surveillance and enhanced protective measures among fruit farmers. Full article

Free-wake vortex-ring models are the simplest way of describing a uniformly loaded actuator disc consistently with the Euler equations, i.e., including the radial velocity that accompanies slipstream contraction or expansion. This paper examines the numerical behavior of this model class using an independent open-source FORTRAN 90/95 implementation and a 1:1 Python (v3.12) replica, for the propeller case ($c_T=1$) and the Betz case ($c_T=-8/9$). Three results are reported. First, the two convergence measures in common use—the residual of the wake (sheet) equations and the deviation of the power coefficient $c_P$ from momentum theory—are shown not to be equivalent: the latter has a discretization floor and is not monotone, so it is unsuitable as a stopping criterion, and accuracy figures obtained with it are sometimes misleading. Second, the discrete Kelvin–Helmholtz saw-tooth mode is stabilized by a damping factor proportional to $\sqrt{z}$, which reaches the residual floor within a few hundred instead of ${10}^4$ iterations; the remaining $c_P$ fluctuation band reflects the unresolved disc-edge region and is removed by node collocation with a spacing-proportional vortex kernel, which converges to a unique fixed point with machine-level residuals and $c_P-(-16/27)=+4.7\times {10}^{-5}$. Third, the converged solutions show a bounded edge strength with fitted exponent $a=0.00\pm 0.04$ in $\gamma \propto s^a$, differing from both the $s^{-1/2}$ spiral and the constant-$\gamma$ proposals in the literature; it is explained why single-valued, regularized sheet discretizations cannot decide this question. Full article