Search Results (18,364)

Generative artificial intelligence (AI) is increasingly used to support decision-making in manufacturing quality assurance (QA), but its adoption raises concerns regarding governance, traceability, and auditability. This paper proposes a proactive framework that integrates generative AI into quality management and auditing while preserving standards alignment and human oversight. The framework structures quality activities across supplier, in-process, and post-market domains and across three hierarchical levels—product, process, and operation—to link quality outcomes with documentary evidence requirements. A proof-of-concept (PoC) study in electronics manufacturing focused on New Product Introduction (NPI) planning and compared two parallel workflows: an expert QA team and a generative AI-assisted chatbot workflow. Within a fixed time window, both workflows produced an aligned Process Failure Mode and Effects Analysis (PFMEA), Control Plan, supplier Production Part Approval Process (PPAP) request package, and internal audit evidence pack. Three independent experts evaluated the integrated deliverable package using five indices covering documentation quality and audit readiness, detection and containment logic, process capability and stability, governance and provenance safeguards, and execution (time) efficiency. Compared with the expert package, the generative AI–assisted workflow produced more traceable, governance-rich documentation (ownership, versioning, clause-to-evidence links) and reduced manual audit-evidence consolidation, supporting quality planning and change-control readiness. Full article

Aiming at solving the detection problems caused by weak partial discharge signals of underground cable joints and random and variable spatial electromagnetic wave interference, a non-contact detection technology based on the dynamic multi-notch method is proposed. This technology synchronously collects pure interference signals and mixed signals containing partial discharge through a dual-position detection antenna. After converting to the frequency domain via Fast Fourier Transform (FFT), the notch frequency bands are dynamically determined based on the real-time interference spectrum, and interference suppression is achieved by frequency domain zeroing filtering. Finally, the partial discharge pulse signal is restored through Inverse Fast Fourier Transform (IFFT). A simulation experiment platform for 10 kV XLPE cable joints was built to verify the detection of typical defects such as metal debris, insulation scratches, and conductor burrs. Experimental results show that the average extraction success rate of this method for weak partial discharge signals reaches 94.7%, and the detection accuracy is ≥92.3% in a normal environment without strong interference, which is significantly better than the traditional ultra-high frequency (UHF) detection method (45.8%) and the fixed notch method (68.3%). This technology realizes the accurate detection of weak partial discharge signals in complex environments, provides a reliable solution for the early warning of insulation defects in underground cable intermediate joints, and has important engineering application value. Full article

Offshore wind energy is an important source of clean energy. Single-post platforms, due to their simple structure and strong stability, can adapt to deep water environments through buoyancy and ballast systems, have small motion responses, and have low construction and maintenance costs. They are suitable for offshore wind energy development in deep-sea areas and help expand the application of offshore wind power. This paper conducts a coupled response analysis of offshore wind turbine foundations and mooring systems, as well as an optimization study on the form and number of mooring lines. Under the premise of considering the safety and economy of floating wind turbines, the mooring lines have been optimally arranged. The study calculates frequency-domain responses, time-domain responses, and mooring line forces under the constraints of the original three-line mooring system. Based on this benchmark, the study further optimizes the mooring forms and numbers for the same platform, analyzing four, six, and eight single mooring lines, as well as three groups of single-line, double-line, and triple-line mooring configurations. Finally, using AQWA software (2024 R1), the responses and mooring line forces of different mooring configurations were calculated, and the preferred mooring arrangement for this stepped single-post platform was determined to be a three-group, three-line system (a total of nine mooring lines). The mooring line tension decreased substantially from the original 3.2 × 10⁶ N to 1.8 × 10⁶ N, while the dynamic response was reduced to one-sixth of its original level. Meanwhile, this study provides strong support for the utilization of offshore wind energy and the construction of offshore wind turbine platforms and mooring systems. Full article

The mathematical characterization of non-stationary signals remains a significant challenge, particularly when impulsive components are obscured by high-dimensional noise and structural coupling. This paper proposes an application-driven mathematical methodology for a learnable discrete wavelet transform (LDWT) that combines classical multi-resolution analysis with task-optimized data-driven adaptivity. Rather than introducing entirely new foundational theory, our approach strategically relaxes constraints from orthogonal wavelet theory within the non-perfect reconstruction filter bank framework, enabling controlled spectral decomposition optimized for supervised fault diagnosis. We introduce a specialized regularization term based on the half-band property to ensure spectral complementarity and minimize cross-band correlation, while a Jacobian-based stabilization approach is formulated to ensure the convergence of filter coefficients during optimization. The proposed algorithmic architecture, LDBRFnet, features a dual-branch encoder system designed to capture the mathematical synergy between sub-band-level global statistics and time-domain local morphology. This dual-view representation effectively mitigates feature leakage and overconfidence in classification. Theoretical analysis and numerical experiments demonstrate that the learned filters satisfy the frequency-shift property and maintain robust spectral partitioning even under low signal-to-noise ratios. Validation on complex vibration datasets confirms that the framework achieves superior diagnostic accuracy (over 95.5%) and computational efficiency, reducing model parameters by 96.7% compared to state-of-the-art baselines. This work provides a generalizable mathematical approach for adaptive signal decomposition and robust pattern recognition in interdisciplinary applications. Full article

Accurate estimation of vehicle sideslip angle and lateral velocity is essential for the stability control of Advanced Driver Assistance Systems (ADASs). Traditional physics-based observers often exhibit dynamic response distortions under stability-limit conditions due to unmodeled tire relaxation effects, while data-driven methods lack physical interpretability. This paper proposes a Physics-Driven Hybrid Estimation Framework (PD-HEF) to bridge this gap. First, a nonlinear nominal model is constructed as a physical skeleton, and dynamic residual equations are derived to define learning targets. Second, a Spatio-Temporal Feature Coupled Residual Network is designed to capture time-domain phase lag and compensate for spatial nonlinear deviations. Furthermore, a hybrid unscented Kalman filter is developed to inject predicted residuals into the sigma-point evolution. A Dual-Layer Adaptive Mechanism is also introduced to regulate trust weights based on innovation statistics. Joint simulations demonstrate that the proposed framework reduces the root mean square error by over 60% compared to traditional observers while satisfying real-time constraints. Full article

Traumatic injuries are a major cause of disability in adults, with long-term consequences that extend beyond acute survival. Understanding the longitudinal trajectory of quality of life (QoL) following trauma is essential for optimising recovery pathways. This study aimed to evaluate changes in QoL over a six-month period after injury and to characterise the most affected health domains. Methods: A longitudinal observational study was conducted including 136 adult trauma patients. QoL was assessed using the EQ-5D-5L at three time points: retrospectively for the pre-trauma state, and prospectively at one and six months post-injury. Statistical analysis included Paired T-Tests and Cohen’s d to evaluate the significance and magnitude of changes across five dimensions: mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. Results: The sample was predominantly male (57.4%), and falls were the most common mechanism of injury (57.4%). One month after trauma, a significant decline was observed across all EQ-5D dimensions (p < 0.001), with large effect sizes particularly in usual activities (d = 0.89) and self-care (d = 0.86). At six months, significant improvement was noted in all domains compared to the one-month assessment (p < 0.001). However, only mobility returned to pre-trauma levels (p = 0.137), while persistent impairments remained in pain/discomfort and anxiety/depression. The EQ-VAS score declined from a pre-trauma mean of 82.74 to 69.00 at one month and partially recovered to 77.29 at six months. Notably, only 15.4% of patients received specialized rehabilitation services. Conclusions: Trauma results in a profound immediate reduction in QoL. Although physical mobility tends to recover by six months, functional autonomy and psychological well-being remain compromised. The findings highlight the need for multidisciplinary post-discharge interventions, focusing on pain management and psychological support to bridge the gap in long-term recovery. Full article

High-frequency transformer (HFT) noise is a pivotal indicator of equipment performance. To conduct a comprehensive evaluation, this study systematically performed testing and evaluation on the noise generated by a 70 kW HFT under no-load conditions. Acoustic data were collected using acoustic sensors and a head-and-torso simulator, followed by an analysis of noise characteristics focusing on the impacts of voltage levels and operating frequencies. A multi-dimensional evaluation of HFT noise was carried out using sound quality parameters to unravel its intrinsic attributes under electrical parameter excitation. The key findings are as follows: HFT noise exhibits steady-state time-domain behavior and distinct tonal frequency-domain features; the dominant frequency is twice the operating frequency, with prominent harmonics. The noise intensity increases with the voltage levels (~47.0 dB (A) at 200 V to ~72.0 dB (A) at 750 V at 5 kHz) but decreases with the operating frequencies (~82.0 dB (A) at 4 kHz to ~47.0 dB (A) at 10 kHz at 750 V). This study establishes correlations between the electrical parameters and sound quality metrics; the loudness, sharpness, tone-to-noise ratio and prominence ratio are sensitive to the electrical parameters of HFT. Single-frequency noise from HFT exhibits remarkable perceptual salience, exacerbating the perceived annoyance. Thus, HFT design should prioritize reducing single-frequency noise to alleviate such issues. Full article

Oxidative stress appears to be implicated in both the initiation and progression of autoimmune thyroiditis. Selenomethionine, which exhibits antioxidant properties, has been shown to reduce thyroid antibody titers in patients with autoimmune thyroiditis. Recent evidence suggests that vitamin E, a fat-soluble antioxidant, may protect against the development of autoimmune thyroiditis, and that its supplementation has been associated with improvements in female sexual function. The objective of the present pilot study was to determine whether vitamin E intake modulates the effects of selenomethionine on female sexual function and depressive symptoms in individuals with thyroid autoimmunity. The study enrolled three groups of reproductive-age women with euthyroid autoimmune thyroiditis, with 26 participants in each group. The groups were matched for age, thyroid peroxidase antibody titers, and TSH levels and differed according to vitamin E intake: adequate intake (group A), low intake (group B), and high intake (group C). All participants received selenomethionine supplementation (200 µg/day) for six months. Antibody titers and hormone levels were measured, and participants completed questionnaires assessing female sexual function (FSFI) and depressive symptoms (BDI-II). At baseline, no differences in biochemical outcomes were observed between the groups, except for testosterone levels. The study groups differed in sexual desire and arousal domain scores, which were higher in group A than in the other two groups. Total FSFI scores, the remaining FSFI domain scores, and BDI-II scores did not differ between groups at baseline. Across all groups, selenomethionine reduced thyroid peroxidase and thyroglobulin antibody titers and increased SPINA-GD and the ratio of free triiodothyronine to free thyroxine; however, the effects on antibody titers were most pronounced in group A. An increase in SPINA-GT and testosterone levels following selenomethionine supplementation was observed only in group A. In this group, selenomethionine also led to significant improvements in total FSFI scores and all individual domain scores. In contrast, in the remaining groups, the effects of supplementation were limited to increases in domain scores for lubrication, sexual satisfaction, and pain. A treatment-related reduction in total BDI-II scores was observed exclusively in women with adequate vitamin E intake. These findings suggest, for the first time, that dietary intake of a natural antioxidant may influence the effects of exogenous selenomethionine on sexual function and depressive symptoms in reproductive-age women with euthyroid autoimmune thyroiditis. Full article

Steel moment-resisting frames exhibiting vertical geometric irregularities, particularly those with setback configurations, experience increased seismic demands due to stiffness discontinuities and complex dynamic interactions. These conditions present significant challenges for conventional vibration control strategies. This study introduces a performance-based optimization framework that utilizes the Circle-Inspired Optimization Algorithm (CIOA) to enhance the design of tuned liquid dampers (TLDs) in irregular steel structures. Structural responses are simulated in OpenSees, with a rheological model based on the Housner method employed to accurately capture fluid–structure interaction. Seismic performance is evaluated using a suite of real subduction-type ground motions, selected to represent the seismic hazard level of Armenia, Colombia, in accordance with the Conditional Scenario Spectra (CSS) methodology and the National Seismic Risk Model for Colombia. The optimization process considers the mean response across multiple ground-motion records to ensure robustness against seismic variability. Multiple time-domain objective functions are examined, including peak interstory drift, maximum displacement, and peak acceleration. The results indicate that objective functions related to interstory drift and displacement provide the most effective, stable, and consistent reductions in seismic demand across all scenarios, while acceleration-based objectives display greater sensitivity to record-to-record variability. These outcomes underscore the importance of objective function selection in determining both optimization stability and control effectiveness. The CIOA demonstrates rapid convergence, numerical robustness, and reliable performance, confirming its suitability as a computationally efficient and resilient optimization tool for the design of passive control systems in irregular steel structures exposed to high seismic hazard. Full article

The scope of actual pro-environmental initiatives, programs, interventions, and campaigns is limited. Therefore, spillover effects from these activities to other domains of economy, the private sphere, and society are crucial to achieve a transformation of society towards sustainability. Starting from the known literature and using Google Scholar as a platform for searching additional studies, this explorative, traditional narrative review analyses behavioural spillover effects, where either one behaviour influences the likelihood of another behaviour, or an intervention shows an impact on an environmentally significant behaviour, which it did not primarily address. In the scientific literature, spillover is classified by direction (environmentally positive versus negative), involved behaviours (similar or cross-behavioural), timing (short or long term), context (e.g., work to private life), and social scope (personal, interpersonal, intra- and inter-organisational, intergroup, or international). Positive spillover can result from cognitive dissonance reduction, consistent self-perception, pro-environmental values, norms, self-identity, action-based learning, and habit formation. Negative spillover emerges through rebound effects, moral licensing, and psychological reactance. Stronger spillover is observed between similar behaviours, while cross-domain spillover is generally weaker. According to previous research, a facilitated participatory approach with strong pro-environmental orientation appears recommendable for practitioners to foster the value change required for effective and sustained positive spillover. Full article

Background: Quality of life (QoL) after transradial access in diagnostic cerebral angiography may be shaped by procedural demands as well as by the ambulatory setting itself. This study, for the first time, prospectively explored this dimension through follow-up assessments of QoL after the procedure. Methods: In this prospective study, QoL was assessed using the 36-Item Short Form Survey (SF-36), including the Physical and Mental Component Summary (PCS and MCS) as well as eight domain-specific subscales. After right transradial cerebral angiography, the SF-36 questionnaire was administered at baseline (pre-procedure), as well as at 1-month and 3-month follow-up visits. Mean PCS and MCS values were analyzed over time using linear mixed-effects regression models. In post hoc analyses, univariate and multivariable models were used to assess the influence of potential confounders. For subgroup analysis, patients were classified as transient deteriorators if PCS and/or MCS worsened by more than 0.5 SD at 1 month compared with baseline but not at 3 months. Permanent deteriorators were defined as worsening by more than 0.5 SD at both 1 month and 3 months compared with baseline. Results: A total of 35 patients (62.9% female) were recruited over the 12-month study period, with a mean age of 59.1 ± 10.1 years. No significant overall time effect was observed for mean PCS and MCS (p = 0.970 and p = 0.076). MCS showed a significant increase at 1 month compared with baseline (p = 0.046), with a trend toward significance at 3 months (p = 0.053). In post hoc analyses, sex, neurosurgical status, and dose area product were associated with MCS in univariate analyses (p < 0.05), but these associations did not persist after multivariable adjustment. For PCS, only age showed a significant association in univariate analysis (p < 0.05). In subgroup analyses, transient deterioration was more frequent in PCS than in MCS (11.4% [95% CI 3.2–26.7%] vs. 5.7% [95% CI 0.7–19.2%]), and permanent deterioration was also more common in PCS at 1- and 3-month follow-up (14.3% [95% CI 4.8–30.3%] vs. 8.6% [95% CI 1.8–23.1%]). Impairment predominantly involved the bodily pain subscale (88.9% [95% CI 51.8–99.7%]) within PCS and the vitality (80.0% [95% CI 28.4–99.5%]) and mental health sub-scales (80.0% [95% CI 28.4–99.5%]) within MCS. Conclusions: This short-term follow-up assessment demonstrated preserved QoL following transradial diagnostic cerebral angiography. Transient or permanent deterioration occurred in no more than five patients per subgroup (14%). These findings support the notion that a radial-first approach can be safely considered for diagnostic cerebral angiography without compromising patient-reported outcomes. Full article

Introduction: Distress during pediatric magnetic resonance imaging (MRI) neuroimaging can compromise scan quality and negatively impact children’s experiences. This review aimed to systematically synthesize biomarkers and psychological factors associated with distress in children, adolescents, and young adults undergoing neuroimaging. Methods: This systematic review was conducted according to PRISMA and AMSTAR-2 guidelines and preregistered in OSF. A systematic search was performed in six electronic databases, including observational articles published between 2000 and 2025 that assessed distress during MRI and functional MRI (fMRI). Data extraction and risk of bias assessment (QUIPS tool) were performed independently by two reviewers. Results: Ten studies (n = 558) examining distress during neuroimaging were included in this review. Distress was assessed through subjective self- and parent-reports, objective physiological measures, and qualitative interviews. Overall, distress levels were low to moderate; most participants tolerated scans well, though younger age, male sex, parental anxiety, procedure length, and chronic illness were associated with greater discomfort. Noise, immobility, and boredom emerged as the most frequent triggers, while strategies such as distraction, age-appropriate information, and reducing waiting times were perceived as helpful. Among participants with cancer, scan-related anxiety was closely linked to fear of recurrence and perceived stress. Risk of bias across studies was moderate to high, particularly in domains of attrition and statistical reporting. Conclusions: Distress during scanning is driven by anticipatory and parental anxiety, procedure length, and chronic illness. Biomarkers (e.g., cortisol, blood pressure) showed inconsistent links with subjective distress, highlighting the need for integrated measures. Full article

Earthquake-induced landslide reactivation poses a sustained hazard for years following major seismic events, yet operational prediction remains constrained by heterogeneous multi-modal data, sparse supervision, and the absence of uncertainty-aware frameworks. This paper presents PIMSEL, a physically guided multi-modal semi-supervised framework for post-seismic landslide reactivation risk assessment. PIMSEL integrates satellite-derived morphological features, precipitation time series, and seismic hazard attributes through four components: entropy-regularized optimal transport for cross-modal semantic alignment without paired supervision; causally constrained hierarchical fusion enforcing domain-consistent modal weighting; scenario-based prototype mutation for semi-supervised learning from sparse expert annotations; and prototype-anchored variational graph clustering that simultaneously stratifies landslides into HIGH, MEDIUM, and LOW risk tiers and produces decomposed aleatoric and epistemic uncertainty estimates for operational triage. The HIGH risk tier operationally corresponds to predicted reactivation, validated against 598 documented reactivation events across 7482 co-seismic landslides from three Sichuan Province earthquake sequences: the 2013 Lushan (Mw 7.0), 2017 Jiuzhaigou (Mw 7.0), and 2022 Luding (Mw 6.8) events. PIMSEL achieves 82.5% reactivation recall and 66.4% precision, outperforming twelve baselines across clustering quality, classification, and uncertainty calibration metrics. Ablation studies confirm that optimal transport alignment contributes the largest individual performance gain. Current limitations include quarterly assessment frequency and dependence on optical imagery under cloud cover, which future integration of real-time meteorological triggers and SAR data should address. Full article

Piezo-enhanced photocatalysis is progressively considered an eco-friendly technology for contaminant removal, harvesting not only solar energy but also mechanical vibrations found in nature. Multiferroic materials present a coupled effect of various properties and can potentially increase the applicability of this process. In this study, Cr- doped bismuth ferrite thin film was deposited on SrTiO₃ substrate by HiPIMS, and its photo-, piezo-, and piezo-photocatalytic efficiencies in Rhodamine B (RhB) degradation were analyzed. The highest removal percentage was found under the simultaneous exposure of visible light and mechanical vibrations, reaching 86.2% after 180 min. The calculated efficiencies for photo- and piezocatalysis were 12.2% and 83.7%, respectively. The rate constant (k) for piezo-photocatalysis was 16.1 times higher than that found during photocatalytic experiments. To assess the contribution of each reactive species to the decomposition process, different reagents were added to the Rhodamine B contaminated solution. The results revealed that when p-benzoquinone was used, the degradation efficiency declined significantly from 86.2% to 37.6%, suggesting that superoxide radicals (O₂^•−) play a key role in decomposing RhB molecules. The structural, chemical, optical, and ferroelectric changes caused by the catalytic processes were analyzed and linked to the proposed degradation mechanisms. The poor photocatalytic efficiency was linked to an improper band structure and an improper polarization orientation of the ferroelectric domains in the as-deposited film. The degradation mechanisms in piezo-photocatalysis were driven partly by the band bending caused by mechanical vibrations and partly by the reorientation of the induced polarization of the domains in the unstrained film. Full article

Most neural architectures model time as a one-dimensional real-valued variable, constraining temporal reasoning to sequential propagation along a single axis. We introduce Complex-Time Neural Networks (CTNN), a new class of architectures in which temporal coordinates are elements of the complex plane T = t + iτ ∈ ℂ, where Re(T) preserves chronological ordering and Im(T) encodes an orthogonal experiential dimension. Within this geometry, Im(T) < 0 defines a memory domain enabling retrospective retrieval, Im(T) = 0 corresponds to present-moment computation, and Im(T) > 0 defines an imagination domain for prospective projection. We prove the Expressive Separation Theorem (Theorem 1), establishing that, within the temporally coupled function class G_TCP and under explicit Assumptions A1–A4 (in particular the bounded projection Assumption A3), CTNN accesses temporally coupled functions at O(1) cost with respect to temporal distance Δ₁, Δ₂, while real-time architectures incur Ω(Δ₁ + Δ₂) sequential steps. For layered compositions, this yields an exponential composition gap within G_TCP under A1–A4. These advantages hold under the stated assumptions and may not directly generalize to broader function classes or large-scale settings where A3 cannot be maintained. Therefore, Theorem 1 provides a formal separation result for G_TCP, while CTNN more broadly defines a geometric framework for temporal computation. As the first concrete instantiation of this framework, we develop Complex-Time Convolutional Neural Networks (CTCNN). CTCNN achieves state-of-the-art performance on Something-Something V2 (70.2 ± 0.4%, +1.1% over VideoMAE v2, p < 0.01), strong performance on Kinetics-400 (78.4 ± 0.3%), and substantial gains on Long Range Arena Path-X (87.3% vs. 79.6%, +7.7%), using 3.4× fewer parameters than VideoMAE v2. Learnable angular parameters α and β provide computationally interpretable parameters related to memory-access span and prospection breadth, with values varying systematically across task families. Full article