Search Results (5,221)

Aluminum alloys under long-term service or repetitive stress are prone to small fatigue cracks (FCs) with arbitrary orientations, necessitating eddy current probes with focused magnetic fields and directional selectivity for reliable detection. This study presents a flexible printed circuit board (FPCB) probe with a double-layer planar excitation coil and a double-layer differential receiving coil. The excitation coil employs a reverse-wound design to enhance magnetic field directionality and focusing, while the differential receiving coil improves sensitivity and suppresses common-mode noise. The probe is optimized by adjusting the excitation coil overlap and the excitation–receiving coil angles to maximize eddy current concentration and detection signals. Finite element simulations and experiments confirm the system’s effectiveness in detecting surface cracks of varying sizes and orientations. To further characterize these defects, two time-domain features are extracted: the peak-to-peak value ($\Delta$P), reflecting amplitude variations associated with defect size and orientation, and the signal width ($\Delta$W), primarily correlated with defect angle. However, substantial overlap in their value ranges for defects with different parameters means that these features alone cannot identify which specific parameter has changed, making prior defect classification using a Transformer-based approach necessary for accurate quantitative analysis. The proposed method demonstrates reliable performance and clear interpretability for defect evaluation in aluminum components. Full article

This paper presents a flutter analysis of the UHBR Open Fan Testcase ECL5 for an off-design point at part speed and focuses on the second eigenmode, which has a strong torsional character near the blade tip. Recent studies by Pagès et al., using a time-linearized solver, showed strong negative damping for an operating point at 80% speed close to the maximal pressure ratio. This was identified as a phenomenon of convective resonance; for a certain nodal diameter and frequency, the blade vibration is in resonance with convective disturbances that are linearly unstable. In this work, a nonlinear frequency domain method (harmonic balance) is applied to the problem of aerodynamic damping prediction for this off-design operating point. It is shown that, to obtain plausible results, it is necessary to treat the turbulence model as unsteady. The impact of spurious reflections due to numerical boundary conditions is estimated for this case. While strong negative damping is not predicted by the analysis presented here, we observe particularly high sensitivity of the aerodynamic response with respect to turbulence model formulation and the frequency for certain nodal diameters. The combination of nodal diameter and frequency of maximal sensitivities are interpreted as points near resonance. We recover from these near-resonance points convective speeds and compare them to studies of the onset of nonsynchronous vibrations of the ECL5 fan at part-speed conditions. Full article

Financial time-series prediction remains a significant challenge, driven by market volatility, nonlinear dynamic characteristics, and the complex interplay between quantitative indicators and investor sentiment. Traditional time-series models (e.g., ARIMA and GARCH) struggle to capture the nuanced sentiment in textual data, while static deep learning integration methods fail to adapt to market regime transitions (bull markets, bear markets, and consolidation). This study proposes a hybrid framework that integrates investor forum sentiment analysis with adaptive deep reinforcement learning (DRL) for dynamic model integration. By constructing a domain-specific financial sentiment dictionary (containing 16,673 entries) based on the sentiment analysis approach and word-embedding technique, we achieved up to 97.35% accuracy in forum title classification tasks. Historical price data and investor forum sentiment information were then fed into a Support Vector Regressor (SVR) and three Transformer variants (single-layer, multi-layer, and bidirectional variants) for predictions, with a Deep Q-Network (DQN) agent dynamically fusing the prediction results. Comprehensive experiments were conducted on diverse financial datasets, including China Unicom, the CSI 100 index, corn, and Amazon (AMZN). The experimental results demonstrate that our proposed approach, combining textual sentiment with adaptive DRL integration, significantly enhances prediction robustness in volatile markets, achieving the lowest RMSEs across diverse assets. It overcomes the limitations of static methods and multi-market generalization, outperforming both benchmark and state-of-the-art models. Full article

Deep learning algorithms have significantly reduced the computational time and improved the spatial resolution of particle image velocimetry (PIV). However, the models trained on synthetic datasets might have degraded performances on practical particle images due to domain gaps. As a result, special residual patterns are often observed for the vector fields of deep learning-based estimators. To reduce the special noise step by step, we employ a denoising diffusion model (FlowDiffuser) for PIV analysis. And a data-hungry iterative denoising diffusion model is trained via a transfer learning strategy, resulting in our PIV-FlowDiffuser method. Specifically, we carry out the following: (1) pre-training a FlowDiffuser model with multiple optical flow datasets of the computer vision community, such as Sintel and KITTI; (2) fine-tuning the pre-trained model on synthetic PIV datasets. Note that the PIV images are upsampled by a factor of two to resolve small-scale turbulent flow structures. The visualized results indicate that our PIV-FlowDiffuser effectively suppresses the noise patterns. Therefore, the denoising diffusion model reduces the average endpoint error ($AEE$) by $59.4\%$ over the RAFT256-PIV baseline on the classic Cai’s dataset. In addition, PIV-FlowDiffuser exhibits enhanced generalization performance on unseen particle images due to transfer learning. Overall, this study highlights transfer-learning-based denoising diffusion models for PIV. Full article

Artificial intelligence tools are quickly being applied in many areas of science, including learning sciences. Learning requires various types of thinking, sustained by distinct sets of neural networks in the brain. Labelling these systems gives us tools to manage them. This paper presents a pilot application of Large Language Models (LLMs) to physics textbook analysis, grounded in a well-developed neural network theory known as the Five Pillars of the Mind. The domain-specific networks, innate sense, and the five pillars provide a framework with which to examine how physics is learnt. For example, one can identify which pillars are active when discussing a physics concept. Identifying which pillars belong to which physics concept may be significantly influenced by the bias of the author and could be too time-consuming for longer, more complex texts involving physics concepts. Therefore, using LLMs to identify pillars could enhance the application of this framework to physics education. This article presents a case study in which we used selected Large Language Models to identify pillars within eight pages of learning material concerning forces aimed at 12- to 14-year-old pupils. We used GPT-4o and o4-mini, as well as MAXQDA AI Assist. Results from these models were compared with the authors’ manual analysis. Precision, recall, and F1-Score were used to evaluate the results quantitatively. MAXQDA AI Assist obtained the best results with 1.00 precision, 0.67 recall, and an F1-Score of 0.80. Both products by OpenAI hallucinated and falsely identified several concepts, resulting in low precision and, consequently, low F1-Score. As predicted, ChatGPT o4-mini scored twice as high as ChatGPT 4o. The method proved to be promising, and its future development has the potential to provide research teams with analysis not only of written learning material, but also of pupils’ written work and their video-recorded activities. Full article

Induction motors (IMs) are crucial in many industrial applications, offering a cost-effective and reliable source of power transmission and generation. However, their continuous operation imposes considerable stress on electrical and mechanical parts, leading to progressive wear that can cause unexpected system shutdowns. Bearings, which enable shaft motion and reduce friction under varying loads, are the most failure-prone components, with bearing ball defects representing most severe mechanical failures. Early and accurate fault diagnosis is therefore essential to prevent damage and ensure operational continuity. Recent advances in the Internet of Things (IoT) and machine learning (ML) have enabled timely and effective predictive maintenance strategies. Among various diagnostic parameters, vibration analysis has proven particularly effective for detecting bearing faults. This study proposes a hybrid diagnostic framework for induction motor bearings, combining vibration signal analysis with Support Vector Machines (SVMs) and Artificial Neural Networks (ANNs) in an IoT-enabled Industry 4.0 architecture. Statistical and frequency-domain features were extracted, reduced using Principal Component Analysis (PCA), and classified with SVMs and ANNs, achieving over 95% accuracy. The novelty of this work lies in the hybrid integration of interpretable and non-linear ML models within an IoT-based edge–cloud framework. Its main contribution is a scalable and accurate real-time predictive maintenance solution, ensuring high diagnostic reliability and seamless integration in Industry 4.0 environments. Full article

Background/Objectives: Wilson’s disease (WD) is an autosomal recessive disorder characterized by pathological copper accumulation, primarily in the liver and brain. Severe hepatic involvement can be effectively treated with liver transplantation (LT). Geographic variation in ATP7B mutations suggests the presence of regional patterns that may impact disease presentation and management. This study aims to investigate the genetic basis of WD in patients from a major LT center in Iran. Methods: A retrospective analysis was conducted on clinical, biochemical, and pathological data from patients suspected of WD who underwent evaluation for LT between May 2020 and June 2025 at Shiraz University of Medical Sciences. Genetic testing was carried out on 20 patients at the Shiraz Transplant Research Center (STRC). Direct mutation analysis of ATP7B was performed for all patients, and the results correlated with clinical and demographic information. Results: In total, 20 WD patients who underwent liver transplantation (15 males, 5 females) carried 25 pathogenic or likely pathogenic ATP7B variants, 21 of which were previously unreported. Fifteen patients were homozygous, and five were compound-heterozygous; all heterozygous combinations occurred in the offspring of second-degree consanguineous unions. Recurrent changes included p.L549V, p.V872E, and p.P992S/L, while two nonsense variants (p.E1293X, p.R1319X) predicted truncated proteins. Variants were distributed across copper-binding, transmembrane, phosphorylation, and ATP-binding domains, and in silico AlphaMissense scores indicate damaging effects for most novel substitutions. Post-LT follow-up showed biochemical normalization in the majority of recipients, with five deaths recorded during the study period. Conclusions: This single-center Iranian study reveals a highly heterogeneous ATP7B mutational landscape with a large proportion of novel population-specific variants and underscores the benefit of comprehensive gene sequencing for timely WD diagnosis and family counseling, particularly in regions with prevalent consanguinity. Full article

Smartphone addiction has emerged as a significant public health concern, particularly among adolescents and young adults. This narrative review, conducted in line with the ANDJ checklist, synthesizes evidence from 25 systematic reviews and meta-analyses, complemented by randomized controlled trials and clinical studies, to provide a structured overview of the field. The study selection flow and publication trends reveal a rapidly expanding research landscape, with most evidence produced in the last decade, reflecting both the ubiquity of smartphones and increasing awareness of their health impacts. The synthesis highlights converging findings across reviews: excessive smartphone use is consistently associated with psychosocial, behavioral, and academic challenges, alongside sleep disturbances and mental health symptoms. Common messages include the recognition of smartphone addiction as a multidimensional phenomenon, while emerging themes point to heterogeneity in definitions, tools, and methodological approaches. Comparative analysis of reviews underscores both shared risk factors—such as emotional dysregulation and social isolation—and differences in study designs and target populations. Importantly, this review identifies critical gaps, including the lack of standardized definitions, limited longitudinal evidence, and scarce cross-cultural validation. At the same time, promising opportunities are noted, from lifestyle-based interventions (e.g., physical activity) to educational and policy-level strategies fostering digital literacy and self-regulation. The post-pandemic context further emphasizes the need for sustained monitoring and adaptive responses. Overall, this review calls for youth-centered, multi-sector interventions aligned with WHO recommendations, supporting coordinated, evidence-based action across health, education, and policy domains. Full article

The diverse usage of Unmanned Aerial Vehicles (UAVs) across commercial, military, and civil domains has significantly heightened the need for robust cybersecurity mechanisms. Given their reliance on wireless communications, real-time control systems, and sensor integration, UAVs are highly susceptible to cyber intrusions that can disrupt missions, compromise data integrity, or cause physical harm. This paper presents a comprehensive literature review of Intrusion Detection Systems (IDSs) that leverage artificial intelligence (AI) to enhance the security of UAV and UAV swarm environments. Through rigorous analysis of recent peer-reviewed publications, we have examined the studies in terms of AI model algorithm, dataset origin, deployment mode: centralized, distributed or federated. The classification also includes the detection strategy: online versus offline. Results show a dominant preference for centralized, supervised learning using standard datasets such as CICIDS2017, NSL-KDD, and KDDCup99, limiting applicability to real UAV operations. Deep learning (DL) methods, particularly Convolutional Neural Networks (CNNs), Long Short-term Memory (LSTM), and Autoencoders (AEs), demonstrate strong detection accuracy, but often under ideal conditions, lacking resilience to zero-day attacks and real-time constraints. Notably, emerging trends point to lightweight IDS models and federated learning frameworks for scalable, privacy-preserving solutions in UAV swarms. This review underscores key research gaps, including the scarcity of real UAV datasets, the absence of standardized benchmarks, and minimal exploration of lightweight detection schemes, offering a foundation for advancing secure UAV systems. Full article

Cyber-physical infrastructures such as hospitals and smart campuses face hybrid threats that target both digital and physical domains. Traditional security solutions separate surveillance from network monitoring, leaving blind spots when attackers combine these vectors. This paper introduces ARGUS, an autonomous robotic platform designed to close this gap by correlating cyber and physical anomalies in real time. ARGUS integrates computer vision for facial and weapon detection with intrusion detection systems (Snort, Suricata) for monitoring malicious network activity. Operating through an edge-first microservice architecture, it ensures low latency and resilience without reliance on cloud services. Our evaluation covered five scenarios—access control, unauthorized entry, weapon detection, port scanning, and denial-of-service attacks—with each repeated ten times under varied conditions such as low light, occlusion, and crowding. Results show face recognition accuracy of 92.7% (500 samples), weapon detection accuracy of 89.3% (450 samples), and intrusion detection latency below one second, with minimal false positives. Audio analysis of high-risk sounds further enhanced situational awareness. Beyond performance, ARGUS addresses GDPR and ISO 27001 compliance and anticipates adversarial robustness. By unifying cyber and physical detection, ARGUS advances beyond state-of-the-art patrol robots, delivering comprehensive situational awareness and a practical path toward resilient, ethical robotic security. Full article

TCP transcription factors represent a crucial family of plant regulators that contribute significantly to growth and developmental processes. Although the TCP gene family has been extensively studied in various plant species, research on Chimonanthus praecox (wintersweet) remains limited. Here, we performed genome-wide identification and analysis of the TCP gene family in C. praecox and identified 22 CpTCP genes. We further systematically examined the associated physicochemical properties, evolutionary relationships, gene structures, and regulatory features. Analysis revealed that all CpTCP proteins possess a conserved TCP domain, and subcellular localization prediction indicated their localization in the nucleus. Promoter analysis revealed that multiple cis-elements are associated with abiotic stress responses and plant growth regulation. Further analysis revealed high CpTCP2 expression in the leaves and stamen, with significantly increased levels during flower senescence. CpTCP2 expression was upregulated in response to methyl jasmonate (MeJA), salicylic acid, abscisic acid, and shade. CpTCP2 overexpression in Arabidopsis thaliana resulted in a reduced leaf area, delayed flowering, and increased rosette leaf numbers. Moreover, MeJA treatment accelerated leaf senescence in CpTCP2 transgenic Arabidopsis. These findings provide insights into the evolutionary characteristics of the TCP family in C. praecox, highlighting the functional role of CpTCP2 in regulating leaf development and flowering time in Arabidopsis, thereby offering valuable genetic resources for wintersweet molecular breeding. Full article

Advancements in electroencephalography (EEG) technology and feature extraction methods have paved the way for wearable, non-invasive systems that enable continuous sleep monitoring outside clinical environments. This study presents the development and evaluation of an EEG-based acquisition system for sleep staging, which can be adapted for wearable applications. The system utilizes a custom experimental setup with the ADS1299EEG-FE-PDK evaluation board to acquire EEG signals from the forehead and in-ear regions under various conditions, including visual and auditory stimuli. Afterward, the acquired signals were processed to extract a wide range of features in time, frequency, and non-linear domains, selected based on their physiological relevance to sleep stages and disorders. The feature set was reduced using the Minimum Redundancy Maximum Relevance (mRMR) algorithm and Principal Component Analysis (PCA), resulting in a compact and informative subset of principal components. Experiments were conducted on the Bitbrain Open Access Sleep (BOAS) dataset to validate the selected features and assess their robustness across subjects. The feature set extracted from a single EEG frontal derivation (F4-F3) was then used to train and test a two-step deep learning model that combines Long Short-Term Memory (LSTM) and dense layers for 5-class sleep stage classification, utilizing attention and augmentation mechanisms to mitigate the natural imbalance of the feature set. The results—overall accuracies of 93.5% and 94.7% using the reduced feature sets (94% and 98% cumulative explained variance, respectively) and 97.9% using the complete feature set—demonstrate the feasibility of obtaining a reliable classification using a single EEG derivation, mainly for unobtrusive, home-based sleep monitoring systems. Full article

Autonomous driving has emerged as a rapidly advancing field in both industry and academia over the past decade. Among the enabling technologies, computer vision (CV) has demonstrated high accuracy across various domains, making it a critical component of autonomous vehicle systems. However, CV tasks are computationally intensive and often require hardware accelerators to achieve real-time performance. Field Programmable Gate Arrays (FPGAs) have gained popularity in this context due to their reconfigurability and high energy efficiency. Numerous researchers have explored FPGA-accelerated CV solutions for autonomous driving, addressing key tasks such as lane detection, pedestrian recognition, traffic sign and signal classification, vehicle detection, object detection, environmental variability sensing, and fault analysis. Despite this growing body of work, the field remains fragmented, with significant variability in implementation approaches, evaluation metrics, and hardware platforms. Crucial performance factors, including latency, throughput, power consumption, energy efficiency, detection accuracy, datasets, and FPGA architectures, are often assessed inconsistently. To address this gap, this paper presents a comprehensive literature review of FPGA-accelerated, vision-based autonomous driving systems. It systematically examines existing solutions across sub-domains, categorizes key performance factors and synthesizes the current state of research. This study aims to provide a consolidated reference for researchers, supporting the development of more efficient and reliable next generation autonomous driving systems by highlighting trends, challenges, and opportunities in the field. Full article

Background and Objectives: Boosting parasympathetic activity may enhance both physical and mental functions. In this study, we introduced the Lin Nasal Airflow Reducer (L.NAR), a silicone device designed to reduce nasal airflow. This pilot and exploratory study aimed to investigate the effect of L.NAR on parasympathetic activity in adults. Materials and Methods: The test protocol consisted of two 16 min ECG sessions. In the first session, participants did not wear the L.NAR for the initial 8 min (Test 1) but wore it for the remaining 8 min (Test 2). Following a 30 min rest, the second session reversed the sequence, with participants wearing the L.NAR for the first 8 min (Test 3) and removing it for the final 8 min (Test 4). Time- and frequency-domain analyses and non-linear analyses were used to assess heart rate variability (HRV) for every 300 s moving by 10 s. Repeated measurement ANOVA was conducted to compare the means across the four tests. Results: A total of 49 participants were enrolled in the analysis, with a mean age of 40.3 ± 10.7 years. Male participants had a higher body mass index (BMI) than female participants (24.0 ± 3.3 vs. 21.3 ± 2.9 kg/m², p = 0.014). Participants in Test 3 and Test 4 had significantly lower heart rate values than those in Test 1. Participants wearing the L.NAR (Test 2 and Test 3) had significantly higher RMSSD values compared to those not using the L.NAR. Among the participants, 33 (67.3%) who wore the L.NAR showed significantly higher RMSSD levels compared to their pre-L.NAR levels during the first practice. This improvement was achieved after an average of 2.5 ± 2.9 sessions. Conclusions: In this study, we introduced a novel approach using the L.NAR to increase RMSSD, a key indicator of parasympathetic activity. Full article

Classical spectral analysis characterizes linear systems effectively but often fails to reveal the nonlinear temporal structure of chaotic dynamics. We introduce the ordinal spectrum, a frequency-domain characterization derived from the ordinal-pattern representation of a time series. Applied to both synthetic and real-world datasets—including periodic, stochastic, and chaotic signals from physical, biological, and astronomical sources—the ordinal spectrum identifies the temporal scales implied in a possible chaotic behavior. By providing an interpretable, data-driven view of symbolic dynamics in the frequency domain, this approach complements state–space reconstructions and enhances the detection of nonlinear temporal organization that classical spectra may obscure. Its ability to distinguish between qualitatively different dynamics make it a useful tool for exploring complex time series across diverse scientific domains. Full article