Search Results (5,990)

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

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

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

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

High-entropy alloys (HEAs) are typically produced using high-temperature metallurgical routes; however, alternative synthesis approaches based on wet-chemical processing remain relatively unexplored. In this study, a compositionally complex two-phase AgCuCoNiFe high-entropy alloy was synthesized using a precipitation–reduction strategy involving co-precipitation of mixed metal carbonates followed by thermal reduction in a reducing atmosphere. The objective of the work was to evaluate the feasibility of this hydrometallurgical route for preparing compositionally complex alloys and to investigate the structural evolution of the material as a function of reduction time. Quantitative MP-AES analysis confirmed efficient co-precipitation of all five elements, enabling the preparation of a precursor with near-equimolar metal composition. Structural characterization using SEM, EDS, and XRD revealed the presence of surface compositional heterogeneity in the as-reduced state, characterized by Ag-enriched domains. After controlled surface abrasion, the internal material exhibited significantly more uniform elemental distribution, although the obtained composition was not equimolar. X-ray diffraction patterns showed a transition from multiple sharp reflections at the surface to broadened peaks in the bulk, consistent with enhanced alloying within the bulk compared to the surface, while still revealing a two-phase character. Microhardness measurements indicated moderate hardness with mean values in the range of 187–221 HV with no significant dependence on reduction time, while wettability analysis revealed moderately hydrophilic behavior with contact angles in the range of approximately 75–83°. The results suggest that precipitation–reduction can be a viable alternative route for the synthesis of multicomponent HEAs, enabling the formation of chemically mixed alloy structures without the use of conventional melting-based processing. However, the obtained alloy exhibits incomplete chemical homogeneity, indicating that further optimization of the synthesis conditions is required to achieve a fully uniform composition. Full article

To address the difficulty of detecting demagnetization severity in permanent magnet synchronous motors (PMSMs), this paper proposes a demagnetization severity detection method based on temperature signal and Convolutional Neural Network (CNN). First, the differences between local demagnetization and eccentricity fault in stator current harmonics are analyzed from an electromagnetic perspective, and fast Fourier transform (FFT) is used for frequency-domain analysis of the stator current to identify local demagnetization faults. On this basis, an electromagnetic–thermal coupling model is established by considering motor losses and heat dissipation boundary conditions to obtain the winding temperatures under different demagnetization severities and operating conditions. Furthermore, the temperature time series, together with speed and load torque, is constructed into a three-dimensional state space, and the proposed Conditionally Modulated Multi-Scale Convolutional Neural Network (CMSCNN) is introduced for feature learning to achieve demagnetization severity detection. Experimental results show that the proposed method achieves an average detection accuracy of 98.06% on the simulation test set and outperforms the baseline CNN model. On measured data collected from the faulty prototype, the average detection accuracy reaches 93.34%, verifying the effectiveness of the proposed method for demagnetization severity detection. Full article

Traffic accident detection is a critical component of intelligent transportation systems (ITS), enabling timely incident response and traffic management. While most existing approaches rely on temporal information from video sequences, such methods are not always applicable in resource-constrained surveillance environments. This study investigates the feasibility of detecting traffic accidents from single static images by formulating the task as a binary classification problem. Representative architectures, including Vision Transformer (ViT), Swin Transformer, and ResNet-50, are systematically evaluated on the Car Crash Dataset (CCD) under multiple training configurations. To assess generalization capability, cross-domain evaluation is conducted using an external crash video dataset (ECVD) constructed to approximate real-world deployment conditions. Experimental results show that all models achieve strong performance under in-domain evaluation. However, cross-domain testing reveals substantial performance degradation, particularly in recall, indicating limited generalization capability under domain shift. Qualitative analysis further shows that missed detections are associated with weak visual cues, occlusion, and complex traffic environments, while false positives are caused by visually ambiguous patterns resembling accident scenarios. Unlike prior studies that primarily report performance improvements, this work provides empirical evidence that model behavior in static-image-based accident detection is governed by dataset composition rather than architectural design. Therefore, static-image-based accident detection should be interpreted as a coarse-level screening tool rather than a fully reliable decision-making system. This study highlights the importance of data-centric design and cross-domain evaluation for improving real-world applicability. Full article

Leaks in pipe systems result in significant economic losses, environmental hazards, and public health risks. Transient-based leak detection methods, which exploit the dynamics of pressure waves in response to system anomalies, have emerged as efficient techniques for identifying and characterizing leaks in pressurized pipelines. These methods offer distinct advantages, including minimal data requirements, high sensitivity to low-pressure anomalies, and resilience to the ill-posed conditions often affecting steady-state models. This paper reviews transient-based leak detection, synthesizing findings from over 139 peer-reviewed publications spanning the past three decades. The review categorizes transient-based methods into transient damping, transient reflection, system response, and inverse transient methods, analyzing the prevalence, evolution, and research rate of each category over time. By structuring the review around key aspects such as simulation domain type, analysis approach, system response, solver strategies, adaptability to noise, viscoelasticity, and network complexity, this paper identifies significant trends and shifts in research focus. A comprehensive tabular dataset of 139 studies captures how research activity in various areas has accelerated, slowed, or reached stability, offering insights into the evolving priorities within the field. This review highlights areas for further development, particularly in addressing AI-enhanced applications, transient excitation and measurement sites design, noise resilience, comprehensive leak characterization, validation approaches, and scalability for complex network applications, providing a resource to guide future research in transient-based leak detection. Full article

Objective: This study developed the Uncertain Drop Stress Test (UDST), an uncertain stress induction paradigm based on the high survival-relevant threat of fear of falling, wherein neither the occurrence nor the timing of the fall is predictable. The aim was to compare its stress induction efficacy and neural oscillatory changes with those of the Socially Evaluated Cold Pressor Test (SECPT), a certain stress paradigm, and to examine gender differences. Methods: Forty-eight participants (24 males; 24 females) were recruited. Psychological indicators (subjective stress, negative affect, and state anxiety) and physiological indicators (heart rate, heart rate variability, galvanic skin response, and salivary cortisol) were measured before and after stress to compare induction efficacy. Resting-state EEG was collected for frequency domain analysis to explore neural oscillatory changes. Results: UDST induced more pronounced psychophysiological changes. Notably, only UDST significantly decreased heart rate variability and increased galvanic skin response. UDST triggered an “exogenous vigilance mode” characterized by enhanced high-frequency (Beta/Gamma) activity, whereas SECPT elicited an “interoceptive focusing mode” characterized by suppressed low-frequency (Theta/Alpha) activity. Females exhibited higher heart rate and Beta activity than males under both stress conditions. Conclusions: UDST elicits stronger psychophysiological responses and distinct neural oscillatory patterns, with females showing greater stress reactivity. Full article

We propose a monolithically integrated optical frequency comb (OFC) generation platform on thin-film lithium niobate (TFLN), featuring cascaded dual-drive Mach–Zehnder modulators (DDMZM) and a ${\mathrm{S}\mathrm{i}}_3{\mathrm{N}}_4$-assisted spot size converter (SSC). To capture microscopic mode mismatches and spatial phase accumulation often overlooked in idealized scalar simulations, we establish a multi-physics co-simulation framework integrating finite-difference time-domain (FDTD) analysis with macroscopic transmission modeling. Based on this framework, the cascaded modulator architecture generates 25 highly stable comb lines with a dense 2 GHz spacing and an envelope flatness within 2 dB. Tolerance analysis indicates that the comb generation is highly resilient to typical manufacturing and environmental variations, including thermal bias drift, RF phase mismatch, and half-wave voltage ($V_{\pi }$) dispersion. Furthermore, physical-layer modeling shows that the integrated SSC reduces fiber-to-chip coupling loss to 0.55 dB per facet, preserving the necessary optical power budget. To validate the platform’s viability as a multi-wavelength continuous-wave source for spatial-division multiplexed (SDM) interconnects, a parallel transmission over a 20 km standard single-mode fiber is modeled. Using a digital signal processing (DSP)-free 10 Gb/s non-return-to-zero (NRZ) scheme, the 25-channel system maintains a worst-case bit error rate strictly below the forward error correction (FEC) threshold. This work offers a practical, physics-based evaluation framework for high-density co-packaged optics (CPO). Full article

Systemic risk remains a key concern for financial authorities, especially in emerging economies where traditional, low-frequency balance sheet indicators often lag rapidly changing market conditions. This study develops a high-frequency Systemic Risk Sentiment Index (SRSI) for the Philippines using news headlines from 2011 to 2025 and an ensemble of domain-specific financial sentiment models. Results show that negative sentiment is mainly driven by external-sector developments, market volatility, and equity-related news, with surges aligning with global and domestic stress episodes. Event study analysis demonstrates that the SRSI captures sharp deteriorations in sentiment several weeks before major financial stress events, while Granger causality results indicate modest predictive power for domestic equity market movements. Overall, the SRSI is best viewed as a responsive, real-time barometer that complements conventional systemic risk measures. This study represents one of the initial efforts to construct a sentiment-based systemic risk indicator tailored to the Philippine financial system and offers a scalable, low-cost framework that other central banks may adopt to enhance real-time macro-financial surveillance. Full article

Artificial intelligence (AI) has become an indispensable tool in orthopedic surgery. It provides new methods to increase surgical precision, improve patient safety, and support personalized treatment plans. This review presents a comprehensive analysis of AI-assisted orthopedic surgery across three core domains. Based on 89 recent studies, this review organizes findings around a perception–decision–execution framework. It groups diverse AI applications into certain categories while highlighting the mutuality across domains. Perception systems have progressed from basic CNN-based segmentation models to advanced transformer architectures. They support multi-modal data fusion and enable uncertainty quantification. Decision systems have moved far beyond rigid rule-based methods and evolve into data-driven models that support surgical planning, accurate risk prediction and continuous outcome optimization. And execution systems have advanced from passive navigation tools to active robotic assistance systems with real-time adaptive capabilities. Beyond mapping technological advances, this review also identifies pivotal challenges that hinder clinical translation and concludes with a clear roadmap for future research, which marks closed-loop surgical assistance systems as the next key development direction. Building on these findings, this review illuminates the potential of AI-assisted orthopedic surgery and guides future research toward innovations that can be translated into clinical practice. Full article

Aiming to address the problems of low accuracy in coal–rock identification during coal mining, which lead to energy waste and safety hazards, a high-precision coal–rock medium identification method combining terahertz time-domain spectroscopy technology and multiple machine learning algorithms is proposed. By preparing coal–rock samples with a gradient change in coal content, terahertz time-domain spectroscopy data of coal–rock mixed media are collected, and optical parameters such as the refractive index and absorption coefficient are extracted. Principal component analysis is used to reduce the dimensionality of the terahertz data, and machine learning algorithms such as support vector machine, least squares support vector machine, artificial neural networks, and random forests are adopted for classification and identification. The study found that terahertz waves are more sensitive to coal–rock media in the 0.7–1.3 THz frequency band, and that the refractive index and absorption coefficient of coal–rock mixed media are significantly positively correlated with coal content within the range of 0–30%. After feature extraction and K-fold cross-validation, the random forest model achieved a coal–rock classification accuracy of over 96% on the test set, significantly outperforming other comparison algorithms. The research verifies the efficiency and practicality of terahertz technology combined with multiple machine learning algorithms in coal–rock identification, providing a new method for fields such as mineral separation. This method has, to a certain extent, broken through the accuracy bottleneck of traditional coal–rock identification technologies within its applicable range, providing a new solution for real-time detection of coal–rock interfaces and is expected to further reduce the risks of ineffective mining and roof accidents in the future. Full article