Search Results (2,929)

Directed Energy Deposition (DED), as a core branch of additive manufacturing, encompasses two typical processes: laser directed energy deposition (LDED) and wire and arc additive manufacturing (WAAM), which are widely used in manufacturing aerospace engine blades and core components of high-end equipment. In recent years, with the increasing adoption of deep learning (DL) technologies, the research focus in DED has gradually shifted from traditional “process parameter optimization” to “AI-driven process optimization” and “online real-time monitoring”. Given the complex and distinct influence mechanisms of key parameters (such as laser power/arc current, scanning/travel speed) on melt pool behavior and forming quality in the two processes, the introduction of artificial intelligence to address both common and specific issues has become particularly necessary. This review systematically summarizes the application of DL techniques in both types of DED processes. It begins by outlining DL frameworks, such as artificial neural networks (ANNs), recurrent neural networks (RNNs), convolutional neural networks (CNNs), and reinforcement learning (RL), and their compatibility with DED data. Subsequently, it compares the application scenarios, monitoring accuracy, and applicability of AI in DED process monitoring across multiple dimensions, including process parameters, optical, thermal fields, acoustic signals, and multi-sensor fusion. The review further explores the potential and value of DL in closed-loop parameter adjustment and reinforcement learning control. Finally, it addresses current bottlenecks such as data quality and model interpretability, and outlines future research directions, aiming to provide theoretical and engineering references for the intelligent upgrade and quality improvement of both DED processes. Full article

Endoscopic nasobiliary drainage (ENBD) is a well-established technique for biliary decompression in both benign and malignant conditions. Over the past decades, its role has been extensively evaluated in comparison with endoscopic biliary stenting and percutaneous transhepatic biliary drainage. ENBD provides distinct clinical advantages, including real-time monitoring of bile output, the possibility to perform irrigation, and the ability to collect bile samples for cytological analysis. However, it also presents specific challenges such as patient discomfort, tube dislodgement, and the need for careful maintenance. This narrative review synthesizes current evidence from randomized controlled trials, retrospective cohorts, systematic reviews, and meta-analyses, highlighting the main indications, technical innovations, comparative outcomes with alternative drainage techniques, and strategies to prevent complications. Furthermore, it discusses emerging approaches aimed at improving patient tolerance, procedural efficiency, and environmental sustainability, offering an updated framework for optimizing patient management in both benign and malignant biliary obstruction. Full article

Mental fatigue significantly impairs student performance and learning outcomes, yet reliable neurophysiological assessment methods remain elusive in educational research. This systematic review examines electroencephalography (EEG) as an objective monitoring tool for mental fatigue in student populations, with particular focus on portable and wearable device applications. Following PRISMA guidelines, we systematically analysed 18 empirical studies (2012–2024, N = 595 participants, ages 10–32) employing continuous EEG during educational tasks. We evaluated frequency band definitions, EEG hardware configurations (from 4-channel portable devices to 64-channel research systems), electrode placements, preprocessing pipelines, and analytical approaches, including machine learning methods. Most studies identified increased frontal theta (4–8 Hz) and decreased beta (13–30 Hz) power as primary fatigue markers across diverse EEG systems. However, substantial methodological heterogeneity emerged: frequency band definitions varied considerably, preprocessing techniques differed, and small sample sizes (median N = 20) limited statistical power. While portable EEG systems demonstrate promise for objective, non-invasive cognitive state monitoring in naturalistic educational settings, current methodological inconsistencies constrain reliability and validity. This review identifies critical standardisation gaps and provides evidence-based recommendations for wearable EEG device development and implementation, including standardised protocols, automated artifact removal strategies, and validation linking EEG measures to educational outcomes. Full article

Drying is a crucial postharvest preservation step, particularly for fruits and vegetables, due to their high moisture content. Physical, sensory, and storage qualities after drying are of interest to food engineers; however, for medicinal purposes, such as nutraceuticals or functional foods, the retention of pharmacological or bioactive compounds is of great interest. This review discusses conventional novel/modern drying technologies and their impact on pharmacological compounds of MEFVs. Conventional drying techniques (sun drying and hot air drying) are cost-effective but slow and usually induce significant losses of thermolabile pharmacological compounds. In contrast, novel/modern drying techniques (solar drying, vacuum drying, freeze drying, microwave drying, infrared drying, heat pump, refractance window, and electrohydrodynamic drying) can accelerate faster moisture removal, but their impact on the pharmacological compounds varies. Current trends in drying research emphasize process optimization, technology hybridization, pretreatment methods, real-time monitoring, and green energy integration to enhance pharmacological compound retention while ensuring sustainability. Full article

Dust accumulation on photovoltaic (PV) modules is a major factor contributing to reduced power output, lower efficiency, and accelerated material degradation, particularly in arid and industrialized regions. This study presents a comprehensive review and analysis of the influence of dust deposition on PV performance, covering its optical, thermal, and electrical impacts. Findings from global literature indicate that dust-induced efficiency losses typically range from 10% to 70%, depending on particle characteristics, environmental conditions, and surface orientation. Experimental and modeled I–V and P–V characteristics further reveal significant declines in current and power output as soiling levels increase. Through an extensive literature assessment, this paper identifies Machine Learning (ML)-based approaches as emerging and highly effective techniques for dust detection and mitigation. Recent studies demonstrate the integration of image processing, drone-assisted monitoring, and convolutional neural networks (CNNs) to enable automated, real-time soiling assessment. These intelligent methods outperform conventional manual and time-based cleaning strategies in accuracy, scalability, and cost efficiency. By synthesizing current research trends, this review highlights the growing role of ML and data-driven technologies in enhancing PV system reliability, informing predictive maintenance, and supporting sustainable solar energy generation. Full article

Electroencephalography (EEG) has emerged as a powerful, non-invasive modality for investigating neurological and oculomotor disorders, particularly when combined with advances in artificial intelligence (AI). This systematic review examines recent progress in machine learning (ML) and deep learning (DL) techniques applied to EEG-based analysis for the diagnosis, classification, and monitoring of neurological conditions, including oculomotor-related disorders. Following the PRISMA guidelines, a structured literature search was conducted across major scientific databases, resulting in the inclusion of 15 peer-reviewed studies published over the last decade. The reviewed works encompass a range of neurological and ocular-related disorders and employ diverse AI models, from conventional ML algorithms to advanced DL architectures capable of learning complex spatiotemporal representations of neural signals. Key trends in feature extraction, signal representation, model design, and validation strategies are synthesized here to highlight methodological advancements and common challenges. While the reviewed studies demonstrate the growing potential of AI-enhanced EEG analysis for supporting clinical decision-making, limitations such as small sample sizes, heterogeneous datasets, and limited external validation remain prevalent. Addressing these challenges through standardized methodologies, larger multi-center datasets, and robust validation frameworks will be essential for translating EEG-driven AI approaches into reliable clinical applications. Overall, this review provides a comprehensive overview of current methodologies and future directions for AI-driven EEG analysis in neurological and oculomotor disorder assessment. Full article

Mini-LC systems, including Cap-LC, Nano-LC and Chip-LC, offer a sustainable alternative to conventional LC methods thanks to their reduced solvent consumption, enhanced separation efficiency and environmentally friendly operation. Integrating micro-scale sample preparation techniques, such as µ-SPE, IT-SPME, LPME and QuEChERS, with Mini-LC significantly improving analytical sensitivity and selectivity. Mini-LC coupled with mass spectrometry has demonstrated excellent performance in the detection of trace levels of pesticides, pharmaceuticals, veterinary drug residues, perfluoroalkyl substances (PFASs), and mycotoxins. Despite current challenges relating to matrix effects, instrument stability and method standardization, Mini-LC represents a promising analytical platform for the cost-effective, high-sensitivity, green monitoring of contaminants in food safety and environmental analysis. This review summarizes recent advances in the application of Mini-LC techniques for analyzing emerging organic contaminants (EOCs) in food and environmental samples. This paper also provides a critical review of this topic, covering works published in the last four years (early 2022–mid 2025). Additionally, it discusses the use of these techniques in combination with mass spectrometry (e.g., low-resolution MS or high-resolution MS) for the detection of EOCs in food and environmental samples. Full article

In this research, a novel hybrid methodology is proposed for predicting the structural response of high concrete arch dams, combining the Discrete Element Method (DEM) with the Locally Estimated Scatterplot Smoothing (LOESS) technique. A structured calibration strategy is employed during the numerical model preparation to enable the generation of a wide range of reliable output variables for training and prediction. The methodology is then applied to the El Atazar arch dam to demonstrate its capability to forecast displacement and stress responses. The study reveals that using the current air temperature as an input variable is not adequate for representing the thermal behavior of the dam body; instead, the mean air temperature over a specified period yields significantly better results. Additionally, the findings highlight the importance of the loading path and the dam’s initial state in determining its structural response. The developed model shows a strong agreement between predicted and observed data, demonstrating its effectiveness in capturing the nonlinear behavior of high concrete arch dams. Compared to traditional parametric models commonly used for dam deformation analysis, the proposed framework offers greater flexibility in representing nonlinearity while requiring less training data, making it ideal for dams with limited monitoring records, such as older dams or newly operated ones. Full article

Objectives: The aim of this study was to evaluate the stability of azacitidine (AZA) under clinical storage conditions (room temperature vs. refrigeration) to identify practical protocols that minimize waste and improve cost-effectiveness. Methods: AZA solutions (1 mg/mL) were stored at 23 ± 2 °C or 4 °C. Stability was assessed using a validated high-performance liquid chromatography (HPLC) method. Chromatographic separation was achieved on a Hypersil ODS C₁₈ column (250 mm × 4.6 mm, 5 μm) using an isocratic mobile phase of 50 mM potassium phosphate buffer (pH 7.0)-acetonitrile (98:2, v/v) at a flow rate of 1.0 mL/min, with UV detection at 245 nm and a 20 μL injection volume. The method demonstrated specificity for AZA and its main degradation product (DP), with LOD and LOQ of 12.56 μg/mL and 62.8 μg/mL, respectively. Linearity (R² = 0.9928), precision (RSD% < 5 for mid/high levels), and accuracy (mean recovery 96%) were established. Results: Azacitidine degraded rapidly at room temperature, with >85% loss within 24 h. In contrast, refrigeration at 4 °C significantly delayed degradation, with only ~26% loss observed over the same 24 h period. Chromatographic analysis confirmed the formation of a primary degradation product (tentatively identified as the open-ring hydrolytic species N-(formylamidino)-N′-β-D-ribofuranosylurea based on its chromatographic behavior and literature data), consistent with the known hydrolytic pathway. The applied HPLC-UV method offered an optimal balance of specificity and practicality for monitoring this main degradation trend under clinical storage conditions, distinguishing it from more complex techniques used primarily for structural elucidation. Conclusions: The pronounced instability of reconstituted AZA underscores the critical importance of strict adherence to immediate-use protocols. Refrigeration provides only a limited stability window. Based on our kinetic data, maintaining the reconstituted solution within an acceptable degradation limit (e.g., ≤10% loss) at 4 °C would require administration within a very short timeframe, supporting current handling guidelines to ensure therapeutic efficacy and minimize economic waste. Full article

Wire Arc Additive Manufacturing (WAAM), also known as Wire Arc Directed Energy Deposition, is used for fabricating large metallic components with high deposition rates. However, the process often leads to residual stress, distortion, defects, undesirable microstructure, and inconsistent bead geometry. These challenges necessitate reliable in-situ monitoring for process understanding, quality assurance, and control. While several reviews exist on in-situ monitoring in other additive manufacturing processes, systematic coverage of sensing methods specifically tailored for WAAM remains limited. This review fills that gap by providing a comprehensive analysis of existing in-situ monitoring approaches in WAAM, including thermal, optical, acoustic, electrical, force, and geometric sensing. It compares the relative maturity and applicability of each technique, highlights the challenges posed by arc light, spatter, and large melt pool dynamics, and discusses recent advances in real-time defect detection and control, process monitoring, microstructure and property prediction, and minimization of residual stress and distortion. Apart from providing a synthesis of the existing literature, the review also provides research needs, including the standardization of monitoring methodologies, the development of scalable sensing systems, integration of advanced AI-driven data analytics, coupling of real-time monitoring with multi-physics modeling, exploration of quantum sensing, and the transition of current research from laboratory demonstrations to industrial-scale WAAM implementation. Full article

Oxidative stability plays an important role in determining the quality of oxidation-sensitive foods and beverages such as beer, wine, and edible oils. Oxidation occurs through radical chain reactions producing off-flavors and leading to deterioration and decrease in the quality and nutritional value of food and beverages. In this context, electron paramagnetic resonance (EPR) spectroscopy has emerged as a powerful and selective technique for investigating reactions involving paramagnetic species, particularly free radicals and transition metal ions. This review provides a critical overview of the applications of EPR spectroscopy in the study of the oxidative stability and antioxidant activity of the above-mentioned matrices. It highlights the main methodological approaches that this technique can offer to gain insight into oxidative processes. Furthermore, current advances in low-cost and portable EPR instrumentation are discussed, along with their implications for broader adoption in both research and industry settings. The aim is to provide an up-to-date literature survey on the application of EPR spectroscopy for studying the oxidative stability and antioxidant activity of beer, wine, and edible oils, providing a methodological tool for academic and food industry researchers interested in monitoring, improving, and extending food shelf life through reliable analytical tools. Full article

This study aimed to establish a fast and efficient method for the determination of N-nitrosamines (NAs) in meat products by integrating two sample preparation techniques—QuEChERS (Quick, Easy, Cheap, Effective, Rugged, and Safe) and FaPEx (Fast Pesticide Extraction)—with liquid chromatography–tandem mass spectrometry (LC–MS/MS). Chromatographic separation was performed on a Poroshell 120 Phenyl Hexyl column using a gradient elution of acetonitrile and 0.01% formic acid at a flow rate of 0.3 mL/min and a column temperature of 25 °C. Under these conditions, nine NAs and one internal standard were completely separated within 11 min with selective reaction monitoring mode (SRM) for detection. Samples were first extracted with QuEChERS powder using acetonitrile containing 0.1% formic acid, followed by purification with a FaPEx-Chl cartridge. This combined approach demonstrated superior performance compared with traditional solvent extraction or QuEChERS extraction alone. The recoveries of the developed method ranged from 76% to 111% and 52% to 103% at spiking levels of 50 ng/g and 20 ng/g, respectively. The limits of detection (LOD) and quantification (LOQ) were 0.002–0.3 ng/g and 0.006–1.00 ng/g, respectively. The inter-day and intra-day precisions (RSD%) ranged from 2.7% to 17% and 2.9% to 17%, respectively. These results indicate that the proposed method is among the most time-efficient and effective analytical approaches currently available for the determination of NAs in meat products. Full article

Rice lodging, a common physiological issue that occurs during rice growth and development, is a major factor contributing to a decline in rice production. Current techniques for the extraction of rice lodging are subjective and require considerable time and labor. In this paper, we propose a fully automated method in an end-to-end format to objectively calculate the rice-lodging rate based on remote sensing data captured by a drone under field conditions. An image post-processing method was applied to enhance the semantic-segmentation results of an operable lightweight model on an embedded board. The area of interest within the parcel was preserved based on these results, and the lodging occurrence rate was calculated in a fully automated manner without external intervention. Five models were compared based on the U-Net and lite-reduced atrous spatial pyramid pooling (LR-ASPP) models with MobileNet versions 1–3 as the backbones. The final model, MobileNetV1_U-Net, performed the best with an RMSE of 11.75 and R² of 0.875, and MobileNetV3_LR-ASPP (small) achieved the shortest processing time of 4.9844 s. This study provides an effective method for monitoring large-scale rice lodging, accurate extraction of areas of interest, and calculating lodging occurrence rates. Full article

Approaches, methods, and corresponding ground-based and air/space-borne instrumentation currently utilized for detecting, studying, and monitoring fogs (including in situ and remote sensing techniques) are summarized. Special attention is paid to the existing and some emerging methods enabling reliable assessments and quantification of basic fog parameters, such as visibility, liquid water content, droplet number/volume concentration, effective radius, and size distribution. Along with purely natural fogs and those resulting directly or indirectly from industrial, combustive, or other human activities (smog, chemical fogs), entirely artificially created fogs are also subject to consideration in this study. Systems and apparatuses for the generation and control of artificial fogs are presented and discussed in terms of operational principles, design, and applicability. Methods and devices for fog water collection/harvesting are presented in view of their importance for solving the lack of water problem in dry and desert regions. Some other actual and potential applications of natural and artificial fogs are summarized and discussed related to air freshening or cleaning from chemicals and radioactive aerosols, fire extinguishing, nebulized therapies in medicine, spray coating of tablets or material surfaces, aeroponic agriculture, dust-proof coatings, etc. Full article

Despite the growing interest in physiological stress monitoring, an objective measure of stress is currently lacking, especially in clinical and rehabilitation contexts. With the emerging integration of artificial intelligence (AI) in data analytics, heart rate variability (HRV) has gained attention as an effective biomarker; however, the literature remains fragmented across disciplines, stress types, and methodological approaches. This scoping review aims to investigate how AI techniques are applied to HRV analysis for stress detection and prediction in adult populations. Although this review does not focus on a specific subtype of stress, its primary objective is to explore the current methodological state of the art as reported in the literature, without restrictions on stress typology. Following PRISMA-ScR guidelines, a systematic search was conducted across PubMed, Scopus, and Google Scholar for studies published between 2005 and 2025, using MeSH terms including “HRV”, “Rehabilitation”, “SCI” (for Spinal Cord Injury), “Stress”, “Sympathetic”, “Parasympathetic”, “Non-linear”, “Gamification”, “AI” and “Machine Learning”. Inclusion criteria targeted adult human populations and studies employing HRV features as input for AI and machine learning techniques for psychophysical stress assessment. Of the 566 records identified, 15 studies met the eligibility criteria. The reviewed studies exhibit substantial heterogeneity in terms of settings, populations, sensors, and algorithms with most employing supervised methods (e.g., random forest, support vector machine), alongside several applications of deep learning and explainable AI. Only one study focused specifically on physiological stress, none focused on SCI populations, and rehabilitation-related research was scarce, thereby underscoring important gaps in the current literature. Overall, HR variability analysis, especially when combined with artificial intelligence techniques, represents a promising approach for stress assessment; however, the field is methodologically fragmented and clinically underdeveloped in critical areas, underscoring the need for a multidisciplinary methodological framework. Full article