Search Results (2,940)

Background: Age-related macular degeneration (AMD) is the leading cause of irreversible vision loss in older adults. An important challenge is the recognition of its early asymptomatic stages and the monitoring of its progression, which requires reliable biomarkers. Growing evidence indicates that AMD-related biochemical changes are reflected in the proteome of tear fluid (TF). Although TF is a non-invasive and easily collectable diagnostic material, its proteomic analysis is complex and costly and therefore has limited clinical value. Methods: In this pilot single-center retrospective cross-sectional study, we developed a new method for dry AMD screening based on analysis of nano-differential scanning fluorimetry (nanoDSF) tear protein denaturation profiles (TDPs) within 15 min. The TDPs were recorded in representative groups of dry AMD patients (37% early, 48% intermediate, 15% geographic atrophy), and in control groups, including patients with refractive abnormalities (basic control), other retinal degenerative diseases (diabetic retinopathy, peripheral retinal dystrophy), or TF-affecting conditions (dry eye syndrome). High-dimensional TDP data were processed using unsupervised machine learning followed by k-means cluster analysis. Results: The presented pipeline distinguished AMD from the basic control with 74% accuracy and a sensitivity of 0.81 without relying on prior labels. The specificity of AMD detection was confirmed by its effective differentiation from diabetic retinopathy (72%; 0.74), peripheral retinal dystrophy (79%; 0.76) and dry eye disease (76%; 0.81). Classifying the AMD group from the entire population of other patients yielded an accuracy of 71% and a sensitivity of 85%, with a false-negative rate of only 15%. Conclusions: This study is a proof of concept for the nanoDSF-based approach, which can be considered a fast, cost-effective, and convenient tool for population screening for dry AMD, suitable for use in preventive medicine and public health. Full article

To improve the efficiency of TIG(Tungsten Inert Gas) welding, our team developed a novel fast-frequency pulsed twin-TIG welding power source and matched welding procedures to overcome the drawbacks of conventional high-efficiency TIG welding. After parameter optimization, stable, high-efficiency and high-quality welding of 316L stainless steel can be realized. Compared with traditional DC TIG welding, the mechanical properties of joints are greatly improved: the weld grain size is refined by 38% under moderate current, while tensile strength, elongation and microhardness rise by 13.6%, 26% and 10% respectively, which achieves simultaneous improvement in strength and ductility. Numerical simulations were carried out to analyze the evolution of molten pool temperature field and velocity vector flow field. The simulation results are highly consistent with experimental data, which verifies the reliability of the model and lays a foundation for the study of molten pool behavior. Combined with molten pool flow characteristics and weld microstructure, the evolution mechanism of microstructure and texture as well as grain refinement in this welding process is revealed. Full article

Anomaly detection models trained exclusively on healthy data are widely used in wind turbine condition monitoring because failure data are scarce, heterogeneous, and often unavailable. However, these models produce anomaly indicators that are sensitive not only to fault-related degradation but also to normal operational variability, transient disturbances, and changes in loading conditions. As a result, the practical behavior of an alarm system depends not only on the anomaly detection model but also on the decision rule used to activate and maintain alarm states. This study presents a decision-oriented evaluation of persistence-based alarm confirmation in wind turbine anomaly detection. Four representative techniques are analyzed within a unified framework: Isolation Forest, One-Class Support Vector Machine, Referenced Moving Window Principal Component Analysis using Q-statistic and percentage component weight indicators, and Autoencoder-based reconstruction error. The evaluation combines controlled OpenFAST simulations of rotor unbalance under different severity and noise conditions with an industrial SCADA case study involving a documented main bearing fault. Results show that temporal persistence strongly shapes alarm outcomes across methods and datasets. Low persistence values favor early detection but promote alarms from isolated threshold exceedances, whereas moderate persistence substantially reduces false positives while preserving detection capability in severe and well-observable faults. Excessive persistence increases detection latency and missed detections, particularly for weak, intermittent, or slowly evolving fault signatures. These findings indicate that persistence-based alarm confirmation should be treated as an explicit decision-level configuration variable, rather than as a fixed post-processing or alarm-state heuristic, when designing anomaly detection systems for wind turbine condition monitoring. Full article

Signals generated during tool wear are nonlinear, non-stationary, and easily affected by machining noise, which makes reliable tool condition monitoring difficult in intelligent manufacturing. To address this issue, this study proposes a tool wear degree classification framework, FCTrans-CA, that fuses time-domain and frequency-domain information through a lightweight cross-attention (CA) bridge. Fast Fourier transform (FFT) is first used to obtain frequency-domain representations. The raw time-domain signals are processed by a multi-scale one-dimensional convolutional neural network (MS-CNN) to extract temporal wear features, while the FFT-derived representations provide complementary spectral cues. These two feature streams are fused by an asymmetric CA module in which frequency-domain features guide the selection of wear-sensitive temporal features. K-means clustering is used to divide the measured flank wear (VB) trajectory of each tool into initial-, normal-, and severe-wear stages, thereby reducing subjectivity in label generation. Experiments on the PHM2010 milling dataset show that FCTrans-CA achieves 99.43% classification accuracy on 40,648 test samples. The results indicate that cross-domain feature interaction improves the separability of wear states and provides a reproducible data-driven route for tool wear monitoring. Full article

Background: Prolonged intermittent fasting is associated with metabolic and immune adaptation; however, the extent to which transcriptional immune responses translate into systemic inflammatory changes, and how these processes relate to autophagy, senescence-associated signaling, and inflammasome regulation, remains incompletely understood. Methods: This study represents a secondary integrative analysis of a previously characterized cohort of healthy young men undergoing Ramadan fasting. Longitudinal data across four time points (T1–T4) were re-analyzed, integrating targeted mRNA profiling of autophagy-, senescence-, and inflammasome-related genes with circulating cytokines and clinical parameters. Baseline-stratified regression and exploratory clustering were applied to assess inter-individual variability. Results: Fasting was associated with modest reductions in body weight (−1.78 ± 1.44 kg, FDR < 0.001) and BMI (−0.56 ± 0.47 kg/m², FDR < 0.001), without hemodynamic instability. Autophagy-related transcripts (ULK1, ATG5) were upregulated, while senescence markers showed divergent regulation (p53↑, p21↓). Inflammasome-related genes (NLRP3, IL1B) increased at the transcriptional level; however, circulating IL-1β and IL-6 remained stable and TNFα decreased (FDR < 0.001), indicating dissociation between transcriptional priming and systemic cytokine output. ΔNLRP3 was inversely associated with baseline expression (β = −1.88, R² = 0.31, p = 0.0056), suggesting baseline-dependent transcriptional responsiveness. Responses followed a continuous spectrum rather than discrete subtypes. Conclusions: Prolonged intermittent fasting is associated with coordinated immunometabolic remodeling characterized by transcriptional changes in autophagy-, senescence-, and inflammasome-related pathways, without systemic inflammatory escalation. Inflammasome-related responses appear baseline-dependent, suggesting graded immunological responsiveness rather than a uniform activation. These findings are hypothesis-generating and support the interpretation of fasting as a graded immunometabolic modulator rather than a uniform pro-inflammatory stimulus within the limitations of a secondary exploratory analysis. Full article

Phenomenological crystal plasticity (CP) models are widely used in Integrated Computational Materials Engineering (ICME) to bridge microstructural features with engineering-scale mechanical behaviour. However, their practical application is hindered by two major challenges: high computational costs of physics-based simulations, and the labour-intensive, trial-and-error nature of parameter calibration. These challenges are amplified in additively manufactured (AM) materials, where location-dependent properties require calibration to be repeated at multiple points to produce a detailed property map. Additionally, a limited understanding of how individual parameters of the CP models influence stress–strain predictions across the strain spectrum compounds these issues, making it challenging to utilise CP models for efficient materials design. To address these limitations, we developed an integrated framework that combines deep neural network (DNN) surrogates, optimisation algorithms (OAs), and explainable AI (XAI) techniques. We also utilised experimental tensile data from AM AlSi10Mg alloy as ground truth since AM materials are expected to benefit the most from our investigation. We demonstrate that, by using OAs such as a Natural Evolutionary Strategy or a Genetic Algorithm, the calibration process can be made more accurate and significantly accelerated. We also investigated the utility of employing deep neural network (DNN) surrogates of CP simulations in the calibration process. The fast-solving DNN surrogates achieved substantial time savings in the absence of OAs, i.e., during exhaustive parameter searches mandated by trial-and-error strategies. However, their effectiveness in parameter discovery was context-dependent when used in conjunction with OAs, since OAs can sometimes converge with fewer simulations than required for DNN training. Furthermore, we applied Shapley Additive exPlanations (SHAP), an XAI method, which revealed intricate interactions among some CP parameters, offering insight into why conventional trial-and-error calibration approaches often prove challenging. Our study contributes to strengthening the practical relevance of CP models for modelling-informed materials engineering and optimisation applications. Finally, our integrated framework offers broad applicability beyond materials modelling, enabling accelerated discovery of tuneable parameters in phenomenological models and providing deeper insight into their contributions to predictions. Full article

Trust evaluation in Industrial Internet of Things (IIoT) edge networks must account for both device behavior and the operational importance of industrial assets. Existing models often apply uniform scoring rules, which may limit their response to semantic attacks and whitewashing behavior while increasing the processing burden on edge devices. This paper presents an Asset-Aware Resilient Trust (ART) framework. ART separates dynamic behavioral credibility from physical asset criticality through a dual-plane architecture. Cross-layer evidence is collected from communication, identity, physical, and semantic interactions. A Fuzzy Triggered-Entropy Weight Method (Fuzzy T-EWM) recalculates evidence weights only when the observed fluctuation exceeds a preset threshold. Trust scores are updated using a Fast-Drop Slow-Rise rule, together with a tolerance margin for routine network jitter. The simulation results show that ART detects stealthy False Data Injection attacks, limits trust recovery after whitewashing behavior, and reduces accumulated computational overhead by 76.4% compared with the Standard EWM baseline. The credibility-weighted aggregation mechanism also limits collusive recommendation manipulation during cold-start evaluation. These results support differentiated trust regulation for IIoT edge networks. Full article

Background: Biodegradable implants manufactured from Mg-based alloys are one of the most commonly used in orthopedics. However, their overall clinical acceptance is influenced by their fast corrosion speed and hydrogen emission. Based on an innovative manufacturing route previously described, this study introduces a preliminary proof-of-concept for a Gemini-assisted Digital Twin (Gemini-DT),which is an AI-augmented in silico framework designed to consider a MgF₂ conversion coating on the implant surface and to model the synchronization of the degradation process with new bone formation. Methods: Based on the integration of experimental data for Mg-Nd and Mg-Zn alloys and by considering the implant geometry and coating formation, we developed, in collaborative work with LLM Gemini 1.5 Flash (Google), a four-module cognitive framework (surface thermodynamic synergy (Module 1), degradation analysis and alloy extract concentration management (Module 2), micro-channel fluidics and mechanical stability (Module 3), and bio-mechanical synchronization and regenerative evaluation (Module 4)) to evaluate simulated implant behaviors). Results: Using a 10,000 iteration Monte Carlo stability simulation, the model demonstrated a potential 12% reduction in false-negative design screening errors compared to rigid rule-based systems, achieving strong internal decision consistency in sustaining the mandated parametric compliance window. Computational verification supports the projected biocompatibility trends of Mg-Zn alloys, as previously demonstrated in our in vivo studies. Conclusions: Our research leads to a consistent computational architecture dedicated to Mg-based implants and offers a robust platform for virtual design and optimization. These observations suggest that the developed model can recover viable designs, whereas traditional linear models may reject them. Full article

Arabic natural language processing (NLP) faces major difficulties due to the language’s rich morphological structure and the scarcity of high-quality datasets, especially for educational material distributed across diverse online platforms. Many existing large-scale corpus construction methods depend on extensive web crawling followed by substantial post-processing. This process may introduce irrelevant or low-quality data and often fails to represent the target domain adequately. As a result, a robust approach to developing corpora tailored for domain-sensitive educational NLP systems and linguistic depth is critical, as most current resources are inadequate. This paper presents ArabicEduCrawler, an AI-assisted focused crawling framework designed to improve the acquisition, discovery, and organization of Arabic educational web content. The framework integrates domain-aware source selection, in-crawl Arabic language detection using FastText, large language model (LLM)-assisted XPath extraction, and metadata retrieval to support corpus quality and traceability. Its two-layer architecture combines dynamic web crawling using Scrapy-Playwright with advanced NLP processing, including automatic linguistic annotation with GateNLP and Stanza and a sentence-aware chunking strategy designed for transformer-compatible token limits. Experiments across four major Arabic educational domains resulted in the creation of the Arabic Educational Web Corpus (AraEdu-WC), which consists of 101,770 documents segmented into approximately 286 k text chunks, with more than 50 million tokens, 289,778 sentences, and nearly 3.5 million named entities. The system achieved a harvest ratio of 95.25%, indicating its effectiveness in filtering and retaining relevant content. The sentence-aware chunking evaluation showed consistent improvements in top-ranked retrieval, achieving the highest Hit Rate@10 and MRR@10 across all four embedding models. In particular, the multilingual-E5-large model achieved a Hit Rate@10 of 70%, Precision@10 of 18%, and MRR@10 of 57%. These findings demonstrate that the proposed approach provides an effective balance between crawl efficiency, language purity, and content richness, offering a high-quality Arabic educational corpus for downstream NLP and retrieval research. Full article

Consuming fast food draws consumers’ attention to emerging issues related to such consumption. Namely, the consumption of fast food affects environmental sustainability, healthy living, and other sustainable activities. The main objective of this study is to explore how environmental awareness, healthy living, and sustainability-oriented fast-food stimuli may influence neurophysiological response patterns during food-related cognitive processing. Eighteen voluntary subjects, aged 19 to 53 years, who frequently consume fast food and have no physical or mental disorders, took part in the experiment. An experiment was conducted in which data were collected using Electroencephalography (EEG) and analyzed with WinEEG. The waves detected from brain activity signals were digitally converted to data using WinEEG. The resulting digital data was further analyzed using Detrended Fluctuation Analysis, Neural Networks (NN) algorithms, and K Nearest Neighbors (k-NN) algorithms. Herewith, the findings suggest that fast-food-related visuals associated with healthy living may elicit stronger patterns of cognitive engagement among participants. The findings provide exploratory insights into implicit cognitive engagement associated with healthy-living and sustainability-related fast-food stimuli. Additionally, the discussion helps in understanding sustainability-oriented food perception and consumer neuroscience research. Full article

Lack of fusion (LOF) is a dominant defect in Laser Powder-Bed Fusion (PBF-LB/M) caused by insufficient overlapping between adjacent melt pools. This study introduces a rapid, first-principles model based on Rosenthal’s analytical solution for a moving point heat source to predict melt pool geometry. Using geometric criteria, the model evaluates whether the melt pool width exceeds the hatching distance and whether the melt pool depth exceeds the layer thickness. Based on these conditions, LOF-based process windows are constructed by plotting laser power against scanning speed and classifying each parameter combination as either LOF or no LOF. The process developed here for constructing LOF process windows can be applied to metallic PBF-LB/M systems. As PBF-LB/M of copper is commonly associated with LOF defects, the approach is examined for pure copper by evaluating a range of laser powers and scanning speeds for both near-infrared (NIR) (1064 nm) and green (515 nm) lasers using copper-specific absorptivity values. The resulting process windows are validated against literature-reported relative density data for pure copper, using high relative density values as indicators of full fusion and lower relative density values reported with LOF characteristics as indicators of lack of fusion. For a 30 µm layer thickness, the predicted LOF boundary agreed with 43 of 46 literature-reported copper PBF-LB/M data points when the data were classified using relative density and reported defect morphology. Sensitivity analysis showed that the agreement changed modestly when the relative-density threshold was reduced from 99% to 98.5% and 98% and that near-boundary classifications were sensitive to the selected absorptivity within the reported NIR range. The agreement supports the use of the framework as a preliminary screening tool for identifying LOF-prone parameter regions. By providing a fast, physics-based screening tool for LOF-limited process windows, this framework offers a computationally efficient alternative to high-fidelity numerical simulations commonly used in PBF-LB/M process development. Full article

High-resolution remote sensing is essential for information acquisition in smart agriculture, yet real-time processing remains a critical challenge. Although high-resolution imagery provides comprehensive data, its massive volume complicates efficient handling. Existing techniques are predominantly restricted to offline scenarios, conflicting with the practical requirements for online acquisition and transmission. To address these challenges, we propose an adaptive sparse coding method for agricultural remote sensing images that dynamically selects compression strategies based on image content. Using this approach, we developed an embedded terminal system for real-time agricultural data transmission over 5G networks. Experimental results show that at a 95% compression ratio, transmission time is reduced by over 90% compared with uncompressed images. The method also achieves high-fidelity reconstruction; the deviation rates for the normalized difference vegetation index (NDVI) and normalized difference red edge index (NDRE) remain below 5% even at a 97% compression ratio. This approach offers fast transmission, high compression efficiency, and strong reconstruction quality, making it suitable for field equipment such as unmanned aerial vehicles in real-time monitoring networks. Full article

Small decentralized water supply systems are often sensitive to local pollution and require a clear understanding of recharge conditions and the hydrodynamics within the water resource catchment. This study develops a conceptual hydrogeological model for the Šmarna Gora area based on geological mapping, long-term monitoring of chemical parameters, and stable isotope analyses (δ¹⁸O, δ²H) of precipitation and groundwater. The study was initiated in response to rising pollutant concentrations in the drinking water. Estimates of transit time (TT) and mean residence time (MRT) were used to characterize recharge, mixing processes, and differences between the SG and ZAVRH wells, the existing and alternative water supply wells. Isotope data show that the aquifer is predominantly recharged during colder periods and that Mediterranean air masses have become an increasingly important source of precipitation, suggesting a shift in precipitation patterns. The results indicate that SG has longer TT (6–8 months) and MRT (up to 1–2 years). In contrast, ZAVRH shows shorter TT and MRT (4–6 months), and lower pollutant concentrations. The hydrogeological regime in the catchment of the ZAVRH well is characterized by a dynamic, fast-flowing system with limited storage and more intensive dilution of contaminants by infiltrating water, whereas the catchment of the SG well functions as a deeper and more buffered aquifer with prolonged groundwater residence and a more direct hydraulic linkage to the contaminant source. The findings distinguish two hydrogeological regimes and provide a basis for planning water supply solutions and protection measures. Full article

An artificial neural network (ANN) prediction model based on the Levenberg–Marquardt (LM) algorithm has been developed to predict the nonlinear heat and mass transfer characteristics of Cattaneo–Christov Carreau–Yasuda tri-hybrid nanofluid (CCHMF–THNF) flow over a porous stretching sheet. A mathematical model of the phenomenon was developed based on a number of elements, including the combined effect of magnetohydrodynamic forces, thermal and solutal relaxation and the influence of viscoelastic fluid behavior and is numerically analyzed utilizing MATLAB bvp4c software. A set of standard data was generated as a reference for developing the ANN-LM model with one hidden layer containing 10 neurons and log-sigmoid activation function, to achieve rapid predictions of velocity, temperature and concentration profiles from the identified data set. This study introduces a novel methodology to provide fast prediction capabilities for transport characteristics through integration of the ANN–LM model with the non-linear CCHMF-THNF model, producing computational savings by providing prediction accuracy of transport characteristics with MSE values on the order of $1.0\times {10}^{-10}$ using ANN–LM in place of repeated bvp4c solutions. Furthermore, the predictive capability of the developed ANN–LM framework may be beneficial in the areas of thermal management systems, polymer processing, energy transport applications, and magnetically controlled cooling technologies since they all share a need for fast access to transportation characteristic evaluation data. Full article

Large-scale atmospheric dispersion model for emergency response to nuclear accidents requires high computational efficiency and numerical reliability. A GPU-oriented Lagrangian particle dispersion model was developed within FLEXPART framework to address these demands. Core transport processes—including advection, turbulent diffusion, convective mixing, and dry/wet deposition—were restructured for GPU parallel execution. Further incorporation of fast arithmetic operators and multi-level parallelization strategies substantially improved overall computational performance while preserving physical accuracy. Additional MPI-based parallel meteorological data decoupling and preprocessing tool has been developed, which alleviates data-handling bottlenecks. Meanwhile, multi-GPU execution and a load-balancing strategy enable efficient scaling in heterogeneous computing environments. Using the first release of European Tracer Experiment (ETEX-I) as a benchmark, the GPU program’s accuracy and acceleration were rigorously evaluated. Results show that, while maintaining nearly comparable accuracy (with relative errors on the order of ${10}^{-2}$), the program achieves an overall speedup of approximately 40.45 on a single-GPU platform, which can be further increased to about 52.05 in high-performance application scenarios where meteorological background fields are reusable. Moreover, multi-GPU experiments reveal favorable parallel scalability across configurations ranging from one to four GPUs, and confirm that the proposed load-balancing strategy effectively enhances computational efficiency in heterogeneous GPU environments. Full article