Search Results (11,992)

In networked control systems, power line communication technique is used to transfer data over existing energy cables. A soft fault degrades the integrity of the signal without impacting the system behaviour. This work develops a transferometry-based method for detecting, localising, and estimating the severity of two simultaneous soft faults in such cables. A soft fault is modelled as a series impedance, and the transmission coefficient (TC) is computed from the ABCD cascade model of the cable. We prove that, under unmatched terminations, the time-domain TC exhibits a five-pulse signature whose peak positions and amplitudes map directly to the two fault positions and their individual severities. A residual signal constructed from this signature yields closed-form estimators for the fault positions and their combined severities; individual fault severities require a bounded nonlinear least-square fit, valid for approximately symmetric, known terminations. We further show that the method extends to n simultaneous soft faults under a combined soft-fault condition, with the $(2n+1)$-pulse pattern verified in simulation for $n\in \{1,2,3,4\}$. A Monte Carlo study using correct localisation probability as the detection criterion establishes a practical SNR threshold of 25 dB; fault-separation resolvability shows intermittent, sidelobe-driven degradation rather than a single threshold. Simulations on a measured 24 AWG cable, extrapolated beyond its characterised band, confirm reliable two-fault diagnosis under additive noise, with reliable multi-fault performance demonstrated for $n=1,2$, presented as a numerical proof of concept on this extrapolated cable model rather than a characterisation confirmed by measurement over the full simulated band. Full article

Background: Diabetes distress is a key mental health dimension of type 2 diabetes (T2D) and a growing concern as populations age, yet its determinants in older adults remain insufficiently characterized. The independent contributions of demographic and metabolic factors to severe distress and their implications for multidisciplinary screening across the lifespan remain unclear. Methods: In this cross-sectional study, we assessed 453 adults with T2D using the 17-item Diabetes Distress Scale (DDS-17). We categorized participants as experiencing low (<2), moderate (2–2.9), or severe (≥3) distress. We compared categories using the Kruskal–Wallis H test and chi-square tests. Logistic regression models were constructed with severe distress (DDS ≥ 3) as the outcome, adjusting for sex, educational level, and HbA1c. Results: Severe distress was identified in 19 participants (4.2%). Age differed significantly across DDS categories (Kruskal–Wallis H test, p = 0.003), with the severe group being significantly older (72.95 ± 4.38 years). Emotional burden was the dominant subscale across all groups. In multivariable logistic regression, increasing age (aOR = 1.163 per year; 95% CI: 1.055–1.282; p = 0.002) and lower educational level (high school vs. basic: aOR = 0.187; p = 0.020) were independently associated with severe distress, whereas metabolic variables were not independently associated with severe distress. ROC analysis identified age ≥ 70 years as an exploratory, sample-derived cut-off (AUC = 0.726, 95% CI 0.602–0.827; sensitivity 84%, specificity 55%). Results were robust in sensitivity analyses that excluded patients with diabetes duration < 5 years. Conclusions: Severe diabetes distress is more strongly associated with demographic vulnerability than with metabolic burden. Age ≥ 70 years and lower educational attainment may help identify older adults at increased risk of diabetes-related distress, supporting targeted psychological screening strategies in this lifespan stage. Full article

The reduction in embodied environmental impacts in residential construction is increasingly important as operational energy performance improves and the relative contribution of materials, replacements, and end-of-life processes grows. This study presents a building-level life cycle assessment of a block of flats in Czechia over a 50-year reference study period. The inventory was derived from the bill of quantities and technical documentation and modelled in LCA for Experts using the integrated Sphera database. The assessed modules comprise A1–A3, A4, B4, B6, C1–C4, and D. Operational energy use in B6 was the largest positive contributor to global warming potential, reaching 16.31 million kg CO₂ eq. (1111 kg CO₂ eq./m²). Product-stage impacts in A1–A3 amounted to 7.15 million kg CO₂ eq. (488 kg CO₂ eq./m²), while replacements in B4 generated 3.32 million kg CO₂ eq. (227 kg CO₂ eq./m²), equivalent to approximately 46% of initial product-stage impacts. Replacement hotspots occurred mainly in non-load-bearing elements, equipment and facilities, façade and roof components, and technical systems. Including B4 changes hotspot prioritisation: products with limited A1–A3 impacts may become significant because of shorter service lives and repeated replacement. Linking building parts, material categories, and individual datasets supports durability, maintainability, and replacement planning in life-cycle GWP reduction. Full article

This work presents the system-level design, fabrication, and experimental validation of a 1-bit reconfigurable reflectarray with a $12\times 12$ aperture operating at 15 GHz in the Ku-band. Each unit cell consists of a circular microstrip patch with four symmetrically arranged PIN diodes that are driven collectively to realize two reflection states with an approximately ${180}^{\circ }$ phase difference near the operating frequency. The fourfold-symmetric unit cell exhibits similar reflection characteristics for x- and y-polarized incidence. The complete prototype combines four reusable $6\times 6$ subarrays, a modular bias network, and an MCU-based controller that independently addresses all 144 unit cells. Measurements demonstrate beam steering from $-{50}^{\circ }$ to $+{50}^{\circ }$, a maximum measured gain of 17.9 dBi, an aperture efficiency of 17.8%, a cross-polarized level below $-25$ dB, and a 7.9% 1-dB gain bandwidth. The demonstrated combination of wide-angle beam steering, similar unit-cell characteristics for x- and y-polarized incidence, low cross-polarization, and reusable subarray construction provides a practical and scalable hardware approach for Ku-band wireless links, satellite communications, and radar systems. Full article

Flexible perovskite solar cells (F-PSCs) exhibit broad application prospects due to their lightweight and bendable properties. However, uneven substrates and thermal–mechanical deformation during bending hinder the growth of high-quality perovskite films. Meanwhile, stress accumulation at the interfaces of flexible devices aggravates carrier recombination, resulting in deteriorated device performance and stability. Thus, we construct a poly(methyl methacrylate) (PMMA)/[1,1′-biphenyl]-4-carboxamidine hydrochloride (BPhADCl) composite modification layer to synergistically optimize the performance of F-PSCs. Specifically, PMMA can passivate interfacial defects and buffer bending stress. BPhADCl enables the in situ formation of 2D perovskite as nucleation sites to induce the growth of high-quality films, and the formed 2D/3D perovskite heterojunction can block moisture erosion and improve device stability. The optimized F-PSC delivers a champion power conversion efficiency (PCE) of 24.33%, remarkably higher than 20.71% of the control device. After 5000 bending cycles at a bending radius of 5 mm, the device retains 83% of its initial PCE. Moreover, the unencapsulated device maintains 91% of its original efficiency after 1100 h storage under ambient conditions. Full article

Background/Objectives: Rubidium-82 (⁸²Rb) PET myocardial perfusion imaging (MPI) yields, in a single study, quantitative absolute myocardial blood flow (MBF, mL/min/g), myocardial flow reserve (MFR), and gated left ventricular (LV) function (LVEF, EDV, ESV, SV). These quantitative values depend on the tracer, scanner, kinetic model, software, and underlying population and have been characterised almost exclusively in North American and European cohorts. The Arabian Gulf, where Kuwait has among the highest age-standardised diabetes prevalence worldwide (25.6%), is essentially unstudied, so the distribution and behaviour of these parameters in such a real-world cardiometabolic population are unknown. To evaluate the distribution of the quantitative ⁸²Rb PET parameter set in a real-world Arabian Gulf cohort and, within a small clinically defined normal subgroup, to describe sex- and age-related patterns in absolute MBF, MFR, and LV function. Given the limited size of the normal subgroup, these values are presented as exploratory, hypothesis-generating observations rather than definitive population reference norms. Methods: Retrospective single-centre study of 330 consecutive ⁸²Rb PET/CT studies (analytic cohort n = 292 after exclusion of repeat studies and those with incomplete quantitative output; mean age 63.1 ± 12.1 years) who underwent adenosine-stress ⁸²Rb PET/CT MPI. The clinically defined normal subgroup, normal perfusion (C1), normal global MFR (≥2.0), normal resting LVEF, and no documented cardiac history, comprised 44 patients (25 female, 19 male). Reference values are reported as sex- and age-stratified centiles (5th, 25th, median, 95th percentiles), with the 5th percentile reported for each parameter. Sex differences used Mann–Whitney U with rank-biserial r and Cohen’s d (95% CI); age was examined across broad bands. All analyses followed APA 7 standards with Bonferroni correction. Results: In the clinically defined normal subgroup, median global stress MBF was 2.94 mL/min/g (5th percentile 2.03) and median global MFR was 2.72 (5th percentile 2.06); every value satisfied the C1 definition (MFR ≥ 2.0) by construction, so these limits describe the preselected subgroup and cannot independently validate the 2.0 threshold. Women had higher resting MBF than men (median 1.07 vs. 0.90 mL/min/g; p = 0.007), with numerically lower global MFR that did not survive correction for multiple comparisons (2.57 vs. 2.98; p = 0.022); stress MBF did not differ by sex (3.00 vs. 2.91; p = 0.522). LV volumes were smaller in women, while LVEF was similar between sexes. The 5th percentile for stress LVEF was 54% (women) and 53% (men). These absolute-flow reference values are lower than those reported for Western low-risk cohorts (stress MBF ~3.25 mL/min/g; MFR ~3.18), consistent with the higher cardiometabolic burden of this population. Conclusions: In this exploratory single-centre evaluation, quantitative ⁸²Rb PET flow values in a small clinically defined normal Arabian Gulf subgroup were lower than Caucasian-derived values, while sex-related differences in resting flow were evident and age-related differences were numerically consistent with previously reported trends. Because the normal subgroup is small, these findings are hypothesis-generating and require confirmation in larger, prospectively screened cohorts before use as population reference values; they nonetheless indicate that population- and pipeline-specific calibration is needed when quantitative ⁸²Rb thresholds derived elsewhere are applied locally. Full article

Matrix information geometry (MIG) has recently demonstrated distinct advantages in detecting radar targets in heterogeneous clutter backgrounds. However, existing MIG detectors still adopt a single geometric distance to quantify the dissimilarity between target signals and clutter, overlooking the availability of target prior information. This paper proposes a geometric adaptive matched filter (GAMF) for target detection on the Hermitian positive definite (HPD) manifolds. By exploiting target prior information, a steering-vector-guided trajectory is constructed, imposing an additional geometric constraint on the cell under test (CUT). The detection decision is then made by jointly measuring the deviation of the CUT from the clutter centroid and its proximity to the steering-vector-guided trajectory. GAMF realizes a dual mechanism of adaptive clutter characterization and steering-vector-guided target matching on the HPD manifold. Experimental results on simulated and measured data demonstrate that GAMF achieves superior detection performance and robustness, with an average detection performance gain of 4.6 dB compared with conventional methods, while exhibiting improved tolerance to steering-vector mismatch. Full article

Artificial intelligence (AI) is increasingly proposed for diagnosing superficial mycoses, yet whether this literature covers the diagnostic pathway evenly, and is mature enough for clinical translation, remains unclear. We mapped AI applications onto an operational partition of the diagnostic pathway (D1–D4), a construct of this review: pre-analytical clinician-facing imaging (D1), analytical microscopy and histopathology (D2), analytical molecular identification (D3) and integrative post-analytical interpretation (D4). Thirty-three primary studies (2007 to 2026) were included, 85% published since 2021 and predominantly from Asia. Applications concentrated on D2 (n = 16) and D1 (n = 13), with few in D3 (n = 4) and none in D4. Reported accuracy was frequently high but rested on internal, single-centre validation: complete external validation and prospective preregistration were each present in a single study, only a handful shared both public code and public data, about half reported no demographic data and no D1 study stratified performance by Fitzpatrick phototype. A substantial minority carried conflicts of interest tied to the evaluated tool, concentrated in the molecular domain where independent verification was scarcest; where such re-evaluation was possible, reported accuracy fell markedly (one area under the curve (AUC) from 0.98 to 0.75). This corpus is skewed toward the early diagnostic steps, leaves the integrative post-analytical step unaddressed and shows methodological maturity insufficient for routine clinical translation. Institutionalised external validation, reproducibility through open code and data, declaration of dataset overlap and conflicts of interest, and clinically meaningful outcomes are the priorities for future work. Full article

Saline–alkali stress is a major abiotic constraint limiting crop production worldwide. To address the lack of a systematic evaluation system for oat seedling saline–alkali tolerance adapted to soda saline–alkali soil in the Hetao Plain, 80 oat accessions with diverse genetic backgrounds were used as materials in this study. Seedlings were subjected to stress induced by a 75 mM composite saline–alkali solution (NaCl:Na₂SO₄:NaHCO₃:Na₂CO₃ = 1:9:9:1), which simulates the typical salt composition of local soda saline–alkali soils. Six seedling-stage traits, including seedling emergence rate, chlorophyll content, relative electrolyte leakage, malondialdehyde content, free proline content, and soluble sugar content, were determined, and the saline–alkali tolerance coefficient of each trait was calculated. Principal component analysis, the subordinate function method, and hierarchical cluster analysis were integrated to construct a comprehensive evaluation system for saline–alkali tolerance. The results showed that the coefficients of variation of all tested traits ranged from 21.15% to 113.66%, indicating abundant genotypic variation among different germplasms. Three principal components with eigenvalues greater than 1 were extracted, with a cumulative contribution rate of 65.79%; these components mainly reflected cell membrane status, osmotic substance accumulation, and growth performance, respectively. The comprehensive evaluation value (D-value) ranged from 0.290 to 0.884, and stepwise regression analysis identified soluble sugar, relative electrolyte leakage, and proline as core screening indicators. Combined with hierarchical cluster analysis, the 80 oat germplasms were classified into five saline–alkali tolerance grades, and significant differences in key traits were observed among grades. Germplasms with high saline–alkali tolerance performed outstandingly in maintaining membrane integrity and accumulating osmotic substances. This study established a practical multi-index comprehensive evaluation system for seedling-stage saline–alkali tolerance in oats adapted to soda saline–alkali environments and identified three candidate tolerant germplasm resources. These results provide technical methods and preliminary candidate materials for oat saline–alkali tolerance breeding and saline–alkali land utilization in the Hetao Plain, while the tolerance performance of the selected germplasms still needs to be verified under multi-concentration stress and field conditions. Full article

Reliable structural monitoring for smart buildings and construction sites requires more than sensor acquisition; it requires sensing and communication paths that remain traceable, recoverable, and compatible with platform-side data processing under heterogeneous field constraints. This study presents a dual-path IoT sensing and communication framework that deliberately separates high-data-rate vibration monitoring from low-data-rate inclination-status monitoring while maintaining common requirements for preservation of available time information, data-source identification, and backend interpretability. The smart-building path integrates an ADXL355 triaxial accelerometer, ESP32-S3, Power over Ethernet (PoE), and Message Queuing Telemetry Transport (MQTT) for 200 Hz vibration acquisition, together with a second-order 10 Hz low-pass filter, 40-record batching, and a Flash LittleFS-based store-and-recovery mechanism that interleaves live and replayed records after reconnection. The construction-site path combines an SCL3300-D01 inclinometer with LoRaWAN, baseline-referenced relative-angle estimation, and a hysteresis state machine with distinct alarm and recovery thresholds. In a 24 h validation, four vibration nodes delivered all 69,120,000 expected records, and four forced-outage trials recovered all offline records while live transmission continued. Frequency-domain analysis confirmed attenuation of high-frequency components while retaining the dominant low-frequency response. The inclination path demonstrated quantifiable angle accuracy, correct alarm/recovery transitions, continuous LoRaWAN frame delivery over the observed interval, and correct backend decoding. The results show that path-specific communication design, combined with a common traceability concept, supports prototype functionality under the reported test conditions, not immediate construction-site deployment. Full 3D visual synchronization, BIM/GIS asset mapping, and digital-twin platform interfacing were not implemented and remain future development tasks. Full article

Soil release appearance grading evaluates residual stains after standardized laundering, but visual assessment is subjective and adjacent half grades are difficult to distinguish. This study proposes a lightweight Tri-Semantic Injection Network (TSI-Net) for nine-grade assessment. From one red–green–blue (RGB) image, a fixed CIE L*a*b* (CIELAB) branch constructs a mean-background image B, a pixel-wise color-difference image D, and a stain-appearance image S. The trainable backbone progressively injects S, D, and B and predicts grades from 1.0 to 5.0 at 0.5-grade intervals. Gaussian soft targets represent the ordering of these grades. The dataset contains 325 images, and TSI-Net has 1,403,336 trainable parameters. In a 33-image evaluation, Gaussian-trained TSI-Net achieved exact-grade and within-half-grade accuracies of 87.88% and 96.97%, compared with 84.85% and 93.94% for one-hot training. Both training methods achieved 100.00% accuracy within one grade. Gaussian training therefore showed a 3.03-percentage-point advantage for each of the two stricter metrics in this comparison. This method requires no manual stain segmentation and provides a compact framework for standardized fabric appearance assessment under controlled acquisition conditions. Full article

To address the challenges of susceptibility to interference and limited accuracy inherent in conventional gas flow rate measurement methods, this paper proposes and investigates a laser Doppler gas flow rate measurement method based on synchronous three-point velocity measurement. This method simultaneously measures the flow velocities at three characteristic points within the pipeline cross-section, subsequently fits and reconstructs the velocity distribution across the entire profile, and ultimately achieves high-precision flow measurement. The feasibility of selecting the center point, the quarter-width point, and the near-wall point as the three characteristic measurement positions is analyzed through computational fluid dynamics simulations, and the full-profile velocity distribution is fitted accordingly. A three-point synchronous velocity-flow rate measurement system is designed and constructed, employing a transmitting optical path based on the “three beam splitters and two mirrors” scheme and a receiving optical path based on the “multi-lens independent reception” scheme, and experimental validation is conducted. Experimental results demonstrate that the system operates stably with good repeatability. In contrast to the flow calculation method using a single-point Pitot tube combined with an empirical formula, the proposed system, which directly fits the velocity profile and integrates it for flow calculation, effectively avoids the significant model errors caused by using fixed empirical coefficients in non-circular pipe flows, and is inherently more universally applicable in principle. Through comparison with the TSI reference standard (3D LDV), the measurement results of the proposed system are in close agreement with the reference values, with relative errors all below −0.8%. This paper provides an effective solution for high-precision gas flow measurement in square pipelines. Full article

University–industry research collaboration is an important pathway through which firms integrate external scientific knowledge, strengthen innovation capabilities, and develop sustainability-oriented technological solutions. Public procurement, as a demand-side policy instrument, may provide market demand, resource expectations, and policy signals that encourage firms to cooperate with universities and research institutes. However, whether public procurement is associated with firms’ university–industry research collaboration remains insufficiently examined. Based on Chinese A-share listed firms from 2013 to 2024, this study matches public procurement contract data with listed firms and their subsidiaries and constructs firm-year measures of public procurement. University–industry research collaboration is measured by co-applied patents between firms and universities or research institutes. The baseline two-way fixed-effects results show a positive and statistically significant association between public procurement scale and university–industry research collaboration, although the estimated magnitude is economically small. Additional count-data and selection-adjusted analyses provide partial support for this relationship, but the results are not fully consistent across all specifications. The mechanism analysis provides suggestive evidence that R&D investment scale may serve as a channel through which public procurement is related to collaborative research activities. Further analysis shows that financing constraints positively moderate this relationship, and ownership heterogeneity indicates that the association is stronger among state-owned enterprises. These findings suggest that public procurement should not be understood as a uniformly effective driver of university–industry collaboration. Rather, its role appears to depend on firms’ financial conditions and institutional characteristics. This study contributes to research on demand-side innovation policy, university–industry collaboration, and sustainable innovation systems by highlighting the conditional nature of the procurement–collaboration relationship. Full article

Pedestrian safety at urban intersections remains a major challenge in Intelligent Transportation Systems (ITSs). This study investigates whether crossing intention can be reliably inferred directly from temporal body-pose dynamics to drive real-time collision warnings on embedded edge platforms. Existing vision-based approaches that rely primarily on bounding-box proximity or scene-level spatial grids are often prone to false alarms in complex urban environments with motorcycles, stationary pedestrians, and background clutter. To overcome these limitations, we propose an end-to-end framework consisting of four sequential processing stages: (1) a perception layer integrating YOLOv8s, ByteTrack, a displacement filter, and rider suppression to generate reliable pedestrian trajectories; (2) a skeleton extraction layer utilizing YOLOv8s-pose to construct temporal sequences of 17 anatomical keypoints; (3) an ultra-lightweight Skeleton Graph Convolutional Network (SkeletonGCN, comprising 33.8 K parameters, <0.2 MB) that models body-joint kinematics and temporal motion dynamics; and (4) an image-space Time-to-Collision (TTC) risk-fusion module. While this fusion approach avoids explicit geometric camera calibration, it still relies on predefined scene-profile parameters and image-space motion assumptions. Furthermore, while the intention classifier is quantitatively evaluated, the risk-fusion module is procedurally defined, and its resulting four-level collision warnings are demonstrated operationally rather than validated against ground-truth hazard annotations. Evaluated on 49,948 valid sequences from the JAAD and PIE benchmark datasets under a strict video-level partitioning protocol, the unified SkeletonGCN achieves a macro-F1 score of 0.717 (with per-scene subset macro-F1 scores of 0.761 on JAAD/PIE urban and 0.895 on intersections), significantly outperforming baseline models. When deployed on an NVIDIA Jetson Orin NX edge device using TensorRT FP16, the full pipeline achieves an instrumented latency of 70.7 ms per frame (~14 fps) and a sustained wall-clock throughput of 7.4 fps on real-world urban dashcam video. System limitations include sensitivity to 2D printed human imagery and reduced prediction reliability under low-light nighttime conditions. Full article

The end-winding insulation structure of stator windings in pumped-storage generator units is complex, with pronounced electric field concentration under out-of-phase conditions, making them critical concerns in insulation design and condition-based maintenance. Although the finite element method (FEM) offers reliable accuracy, the strong nonlinearity of the anti-corona layer results in a computation time exceeding 10⁴ seconds per single solution, rendering it impractical for parameter optimization and rapid on-site assessment. This paper proposes a fast prediction method for end-region potential distribution based on a deep neural network (DNN). Taking a 334 MW unit as the research object, a three-dimensional electroquasistatic finite element model with six stator coils is established and validated through power-frequency withstand voltage and ultraviolet imaging experiments. Training samples are generated via design of experiments (DoE), and a multilayer DNN surrogate model with a 7-dimensional input (comprising 3D spatial coordinates and four physical parameters) and a 1-dimensional output is constructed to directly reconstruct the spatial potential field at the end region. The results demonstrate that the surrogate model achieves a maximum relative error of less than 2% along the entire path compared with the high-fidelity FEM solutions, with a single prediction time of approximately 38 s—representing a speedup factor of approximately 272—while also exhibiting good generalization capability. This method provides a feasible technical approach for rapid reconstruction of end-region field distribution and optimization of insulation structures. Full article