Search Results (74,930)

Selenium (Se) is an antioxidant essential trace element influencing inflammatory and immune pathways. This systematic review aimed to evaluate the role of maternal Se status during pregnancy in miscarriage risk. A systematic search of PubMed and Embase up to July 2024 was conducted to identify relevant original research studies in English. Available evidence was qualitatively synthesized and predefined sources of bias were assessed. Of 2345 studies identified, 421 full texts were assessed and 14 were included, encompassing 2309 pregnancies. Despite notable methodological limitations across several studies, current evidence indicates that maternal blood Se concentrations are lower among women who experience miscarriage compared to those with uncomplicated pregnancies. Findings regarding placental Se levels were inconsistent, but important methodological issues were noted. Environmental Se exposure was investigated in a single low-powered study, which did not demonstrate a statistically significant association. Potential interactions between Se status, co-exposure to other environmental or lifestyle factors, and effect modification remain insufficiently explored. Adequate maternal Se status during early gestation may reduce miscarriage risk by mitigating oxidative stress and ferroptosis, supporting immune regulation, and modulating thyroid autoimmunity and function. However, causal inference cannot be established due to the absence of randomized interventional evidence. Full article

The rapid and reliable geometric documentation of historic built heritage is a key requirement for a wide range of conservation, analysis, and risk assessment activities. In recent years, portable and wearable Simultaneous Localization and Mapping (SLAM)-based systems have emerged as efficient tools for fast 3D data acquisition, offering significant advantages in terms of operational speed, accessibility, and flexibility. This paper presents an experimental performance assessment of three portable SLAM-based mobile mapping systems applied to the 3D documentation of historic religious buildings. Two historic parish churches in the Lunigiana region (Italy) are used as case studies to evaluate the systems under real-world conditions. The analysis focuses on key performance indicators relevant to metric documentation, including georeferencing accuracy, 3D model accuracy, point cloud density and resolution, and model completeness. The results highlight the capabilities and limitations of the tested systems, showing that all instruments can efficiently capture the primary geometries of complex historic buildings, while differences emerge in terms of accuracy, data consistency, and readability of architectural details. Although the work is framed within a broader research project addressing seismic vulnerability of historic structures, this contribution specifically focuses on the experimental evaluation of SLAM-based surveying performance. The results demonstrate that portable SLAM systems provide reliable geometric datasets suitable for preliminary documentation tasks and for supporting further multidisciplinary analyses, representing a valuable resource for the rapid 3D documentation of historic built heritage. Full article

Background and Objectives: Transcatheter aortic valve implantation (TAVI) has become the mainstay of treatment for symptomatic aortic stenosis (AS) in patients over 70 years of age. It is also indicated for younger patients with significant comorbidities, for valve-in-valve interventions, and in selected patients with severe aortic insufficiency. We aimed to evaluate procedural and clinical outcomes of transfemoral TAVI performed over the course of 12 years by the same operators using different bioprosthetic valves. Materials and Methods: Between 2012 and 2023, 375 patients underwent TAVI in our clinic, with six types of bioprosthetic valves (Edwards Sapien XT, Medtronic Valves [CoreValve and Evolut R], Portico, Myval, Acurate Neo, and Direct Flow Medical). A transfemoral approach was used in all patients. The procedural and clinical outcomes were defined according to Valve Academic Research Consortium-3 (VARC-3) criteria. Results: The mean age of the patients was 78.4 ± 7.3, and their median STS score was 4.2 (2.9–5.9). Of the 375 patients, 361 had severe AS, 4 had severe aortic insufficiency, 5 were valve-in-valve, and 5 were valve-in-TAVI. Seven patients required a second valve implantation: four due to embolization of the prosthetic valve and three due to deep implantation of the prosthetic valve. Based on the VARC-3 criteria, the rates of technical success and device success were 90.4% and 85.3%, respectively. Major vascular complications were observed in 18 (4.8%) patients. Also, 42 (11.2%) patients required permanent pacemaker implantation. The incidence of moderate or worse paravalvular leak was 2.9%. The peri-procedural, 30-day, 1-year, and 5-year mortality rates were 5.1%, 4.3%, 15.2%, and 45.6%, respectively. STS scores (HR:1.129, 95%CI: 1.068–1.192, p < 0.001) and post-TAVI acute kidney injury (HR:3.993, 95%CI:1.629–9.785, p = 0.002) were detected as independent predictors of mortality in Cox regression analysis. Conclusions: This registry demonstrated the evolution of TAVI procedures at a single center over 12 years. A high level of collaboration between experienced operators and innovations in devices seem to be the key features for achieving high procedural success and low complication rates. Full article

Respiratory diseases remain one of the most significant health challenges in modern swine production, leading to substantial economic losses, compromised animal welfare, and increased antimicrobial use. In recent years, advances in artificial intelligence (AI), particularly machine learning and deep learning, have enabled the development of non-invasive, continuous monitoring systems based on pig vocalizations. Among these, audio-based technologies have emerged as especially promising tools for early detection and monitoring of respiratory disorders under real farm conditions. This review provides a comprehensive synthesis of AI-driven audio classification approaches applied to pig farming, with focus on respiratory health and welfare monitoring. First, the biological and acoustic foundations of pig vocalizations and their relevance to health and welfare assessment are outlined. The review then systematically examines sound acquisition technologies, feature engineering strategies, machine learning and deep learning models, and evaluation methodologies reported in the literature. Commercially available systems and recent advances in real-time, edge, and on-farm deployment are also discussed. Finally, key challenges related to data scarcity, generalization, environmental noise, and practical deployment are identified, and emerging opportunities for future research including multimodal sensing, standardized datasets, and explainable AI are highlighted. This review aims to provide researchers, engineers, and industry stakeholders with a consolidated reference to guide the development and adoption of robust AI-based acoustic monitoring systems for respiratory health management in swine. Full article

This paper aims to address the problem of the low success rate of seedling picking and throwing, and the high damage rate of pot seedling, caused by the unclear interaction and parameter mismatch between the seedling picking end effector and the pot seedling during the seedling picking and throwing process of automatic transplanters. An EDEM–RecurDyn coupled simulation was conducted, through which the disturbance of substrate particles in the bowl body during the inserting, extracting, and transporting processes by the seedling picking end effector was visualized and analyzed. The force and motion responses of the particles during their interaction with the seedling picking end effector were explored, and the working parameters of the seedling picking end effector were optimized. A seedling picking end effector using two fingers and four needles is taken as the research object, a kinematic mathematical model of the seedling picking end effector is established, and the dimensional parameters of each component of the end effector are determined. Physical characteristic tests are conducted on Shanghai bok choy pot seedlings to obtain relevant parameters. A discrete element model of the pot seedling is established in EDEM 2022 software, and a virtual prototype model of the seedling picking end effector is established in Recurdyn 2024 software. Through EDEM-Recurdyn coupled simulation, the force and movement of the substrate particles in the bowl body during the inserting, extracting, and transporting processes of the seedling picking end effector under different operating parameters were explored, providing a theoretical basis for optimizing the working parameters of the end effector. The inserting and extracting velocity, transporting velocity, and inserting depth of the seedling picking end effector were used as experimental factors, and the success rate of seedling picking and throwing, and the loss rate of substrate, were used as evaluation indicators; single-factor tests and three-factor, three-level Box–Behnken bench tests were conducted. Variance analysis, response surface methodology, and multi-objective optimization were performed using Design-Expert 13 software to obtain the optimal parameter combination: when the inserting and extracting velocity was 228 mm/s, the transporting velocity was 264 mm/s, the inserting depth was 37 mm, the success rate of seedling picking and throwing was 97.48%, and the loss rate of substrate was 2.12%. A verification experiment was conducted on the bench, and the success rate of seedling picking and throwing was 97.35%, and the loss rate of substrate was 2.34%, which was largely consistent with the optimized results, thereby confirming the rationality of the established model and optimized parameters. Field trial showed the success rate of seedling picking and throwing was 97.04%, and the loss rate of substrate was 2.41%. The error between the success rate of seedling picking and throwing and the optimized result was 0.45%, indicating that the seedling picking end effector has strong anti-interference ability, and verifying the feasibility and practicality of the established model and optimized parameters. Full article

The accurate short-term forecasting of global horizontal irradiance (GHI) is essential to optimizing the operation and integration of solar energy systems into the power grid. This study evaluates the performance of the Weather Research and Forecasting (WRF) model in predicting GHI over a 48 h forecast horizon at an Italian site: the ENEA Casaccia Research Center, near Rome (central Italy). The instantaneous GHI provided by WRF at model output frequency was post-processed to derive the mean GHI over the preceding hour, consistent with typical energy forecasting requirements. Furthermore, a decomposition model was applied to estimate direct normal irradiance (DNI) and diffuse horizontal irradiance (DHI) from the forecasted GHI. These derived components enable the estimation of solar energy yield for both concentrating solar power (CSP) and photovoltaic (PV) technologies (on tilted surfaces) by accounting for direct, diffuse, and reflected components of solar radiation. Model performance was evaluated against ground-based pyranometer and pyrheliometer measurements by using standard statistical indicators, including RMSE, MBE, and correlation coefficient (r). Results demonstrate that WRF-based forecasts, combined with suitable post-processing and decomposition techniques, can provide reliable 48 h predictions of GHI and DNI at the study site, highlighting the potential of the WRF framework for operational solar energy forecasting in the Mediterranean region. Full article

As distributed energy resources and nonlinear loads are integrated into power grids on a large scale, power quality issues have grown increasingly prominent, triggering a substantial rise in distribution transformer losses. Traditional approaches struggle to accurately forecast transformer losses under complex power quality conditions and lack quantitative analysis of the influence of various power quality indicators on losses. This study presents a data-driven methodology for transformer loss prediction and sensitivity analysis in such environments. First, an experimental platform is designed and built to measure transformer losses under composite power quality conditions, enabling the collection of actual measurement data when multi-source disturbances exist. Second, a high-precision loss prediction model—dubbed Newton-Raphson-Based Optimizer-Transformer-Bidirectional Long Short-Term Memory (NRBO-Transformer-BiLSTM)—is developed on the basis of an enhanced deep neural network. Finally, global sensitivity analysis methods are utilized to quantitatively evaluate the impact of different power quality indicators on transformer losses. Experimental results reveal that the proposed prediction model achieves an average error rate of less than 0.18% and a similarity coefficient of over 0.9989. Among all power quality indicators, voltage deviation has the most significant impact on transformer losses (with a sensitivity of 0.3268), followed by three-phase unbalance (sensitivity: 0.0109) and third harmonics (sensitivity: 0.0075). This research offers a theoretical foundation and technical support for enhancing the energy efficiency of distribution transformers and implementing effective power quality management. Full article

Protected areas (PAs) constitute a fundamental strategy for mitigating biodiversity loss. The land–sparing approach has expanded in response to international agreements, but expansion of PAs does not guarantee conservation objectives. The objective was to assess PA effectiveness in conserving Nothofagus antarctica forests in Santa Cruz (Argentina), evaluating human impacts associated with fires, animal uses, and harvesting. The research was conducted within pure native forests in Santa Cruz, Argentina. This province encompasses 52 protected areas, representing the highest concentration of conservation units within the forested landscapes across Argentina. At least eight PAs included N. antarctica forests. Three land tenure categories were evaluated: protected areas (PAs), a buffer of 15 km from PA boundaries on private lands (BL), and private lands (PL) outside the buffer. In total, 103 stands were sampled, where 38 variables were assessed (impacts, soil, forest structure, understory, and animal use). Three indices were developed to analyze ecosystem integrity: forest structure (FI), soil (SI), and animal use (AI). PAs presented the highest FI (0.64 for PA, 0.44 for BL, and 0.30 for PL) and AI (0.60 for PA, 0.55 for BL, and 0.52 for PL), and together with buffer areas, the highest SI (0.43 for PA, 0.47 for BL, and 0.32 for PL). PAs were clearly distinct from private lands; however, sustained actions for livestock exclusion, harvest regulation, and fire management remain necessary for future sustainable planning at the landscape level. Full article

This critical review explores the transformative impact of artificial intelligence (AI), particularly machine learning (ML) and computer vision (CV), on scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS) for metallic microstructure analysis, spanning research from 2010 to 2025. It critically evaluates how AI techniques balance automation, accuracy, and scalability, analysing why certain methods (e.g., Vision Transformers for complex microstructures) excel in specific contexts and how trade-offs in data availability, computational resources, and interpretability shape their adoption. The review examines AI-driven techniques, including semantic segmentation, object detection, and instance segmentation, which automate the identification and characterisation of microstructural features, defects, and inclusions, achieving enhanced accuracy, efficiency, and reproducibility compared to traditional manual methods. It introduces the Microstructure Analysis Spectrum, a novel framework categorising techniques by task complexity and scalability, providing a new lens to understand AI’s role in materials science. The paper also evaluates AI’s role in chemical composition analysis and predictive modelling, facilitating rapid forecasts of mechanical properties such as hardness and fracture strain. Practical applications in steelmaking (e.g., automated inclusion characterisation) and case studies on high-entropy alloys and additively manufactured metals underscore AI’s benefits, including reduced analysis time and improved quality control. Extending prior reviews, this work incorporates recent advancements like Vision Transformers, 3D Convolutional Neural Networks (CNNs), and Generative Adversarial Networks (GANs). Key challenges—data scarcity, model interpretability, and computational demands—are critically analysed, with representative trade-offs from the literature highlighted (e.g., GANs can substantially augment effective dataset size through synthetic data generation, typically at the cost of significantly increased training time). Full article

Listeria monocytogenes biofilms on surfaces that come into contact with food create ongoing challenges in produce-processing environments, highlighting the necessity for effective surface sanitation. This research examined the effectiveness of chlorine (200 ppm), quaternary ammonium compound (QAC, 400 ppm), and UV-C light (0.85 J/cm²) against L. monocytogenes biofilms developed on stainless steel, polyethylene terephthalate (PET), and silicone rubber materials frequently used in apple packing settings. Biofilms were cultivated using a mixture of LCDC and V7 strains in diluted apple juice and evaluated after 1 and 7 days of growth. The type of surface material and the age of the biofilm had a significant impact on the performance of the sanitizing agents (p < 0.05). Chlorine achieved a reduction of 2.84 ± 0.06 log CFU/coupon on 1-day-old biofilms on stainless steel, although its effectiveness dropped to 1.90 ± 0.07 log CFU/coupon on biofilms aged 7 days. Similar trends were noted for QAC (2.42 ± 0.05 to 1.73 ± 0.06 log CFU/coupon) and UV-C (2.71 ± 0.05 to 1.57 ± 0.08 log CFU/coupon) over time. PET and silicone rubber consistently exhibited lower log reductions than stainless steel for all treatments. The presence of organic matter from apple juice reduced the efficacy of sanitizers on all surfaces. These results emphasize the significant role of surface material, biofilm age, and organic load on sanitation effectiveness, offering practical recommendations for enhancing the control of L. monocytogenes in produce-processing facilities. Full article

This research aimed to assess the effect of chitosan nanoparticles (ChNPs) during in vitro shoot proliferation of vanilla using temporary immersion bioreactors (TIB). TIB culture is a biotechnological process that uses semiautomated containers for the production of explants exposed in liquid culture medium. Concentrations of control, 25, 50, 100, 200, and 400 mg/L ChNPs were evaluated in Murashige and Skoog culture medium. Morphological characterization of ChNPs was performed using scanning electron microscopy. At 60 days of culture, survival (%), development variables, photosynthetic pigment content, lipid peroxidation expressed in malondialdehyde, total phenolic content (TPC), hydrogen peroxide (H₂O₂) content, and total antioxidant capacity (TAC) expressed in trolox equivalents were evaluated. The data were analyzed with analysis of variance, with a Tukey test (p ≤ 0.05) using SPSS statistics software, version 29. The results revealed that the greatest survival (%) was obtained at concentrations of control, 25, and 50 mg/L ChNPs, while the lowest survival (%) was observed at concentrations of 400 mg/L ChNPs. Growth stimulation was found, as well as an increase in chlorophyll and β-carotene at concentrations of 25 and 50 mg/L ChNPs. The level of H₂O₂ increased at 25 and 50 mg/L ChNPs. Lipid peroxidation showed no differences among treatments. TPC increased at 100 and 200 mg/L ChNPs, while TAC increased at 200 and 400 mg/L ChNPs. In conclusion, the administration of ChNPs at low concentrations can stimulate growth, while at high concentrations they can inhibit it, a response known as hormesis or hormetic effect. Full article

The aim of this study is to systematically capture, synthesize, and evaluate current research trends related to Automated Multiple-Choice Question Generation as they emerge within the broader landscape of natural language processing (NLP) and large language model (LLM)-based educational and assessment research. A systematic search and selection process was conducted following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, using predefined inclusion and exclusion criteria. A total of 240 eligible publications indexed in the Scopus database were identified and analyzed. To provide a comprehensive overview of this evolving research landscape, a bibliometric analysis was performed utilizing performance analysis and scientific mapping methods, supported by the Bibliometrix (version 4.2.2) R package and VOSviewer (version 1.6.19) software. The findings of the performance analysis indicate a steady upward trend in publications and citations, with significant contributions from leading academic institutions—primarily from the United States—and a strong presence in high quality academic journals. Scientific mapping through co-authorship analysis reveals that, despite the increasing research activity, there remains a need for enhanced collaborative efforts. Bibliographic coupling organizes the analyzed literature into seven thematic clusters, highlighting the main research axes and their diachronic evolution. Furthermore, co-word analysis identifies emerging research trends and underexplored directions, indicating substantial opportunities for future investigation. To the best of our knowledge, this study represents the first systematic bibliometric analysis that examines Automated Multiple-Choice Question Generation research within the context of the broader LLM-driven educational assessment literature. By mapping the relevant scientific production and identifying research gaps and future directions, this work contributes to a more coherent understanding of the field and supports the ongoing development of research at the intersection of generative AI and educational assessment. Full article

Soil salinity is a major constraint to wheat production worldwide. Efficient screening of salt-tolerant cultivars is essential for breeding programs, yet a rapid and reliable evaluation system based on full-life-cycle salt stress treatment is lacking. To address this, we conducted a hydroponic experiment encompassing the entire growth cycle of 37 wheat cultivars under control and salt stress (85.5 mM NaCl). Using principal component and stepwise regression analyses on 15 agronomic and yield-related traits, we identified five key indicators—total dry weight, root dry weight, plant height, thousand-grain weight, and number of grains per spike—that effectively represent overall salt tolerance. Based on a comprehensive evaluation value (D-value), the cultivars were classified into five distinct categories: highly salt-tolerant, salt-tolerant, moderately salt-tolerant, weakly salt-tolerant, and salt-sensitive. Notably, the highly salt-tolerant cultivar ‘Yangfumai 8′ and the salt-sensitive cultivar ‘Yangmai 22’ were selected as representative extremes. A subsequent pot experiment confirmed significant physiological differences between them in antioxidant enzyme activities (SOD, POD, CAT) and proline accumulation under salt stress. This study establishes a practical and efficient screening framework, providing breeders with a simplified index set for high-throughput evaluation and offering ideal contrasting materials for in-depth physiological research on salt tolerance mechanisms in wheat. Full article

The cotton bollworm (Helicoverpa armigera, Lepidoptera: Noctuidae) is a globally distributed agricultural pest. When conducting expression analysis of its functional genes, appropriate reference genes should be selected to ensure the reliability of the results. In this study, five algorithms including Delta Ct, GeNorm, Normfinder, BestKeeper, and RefFinder were used to evaluate the expression stability of eleven candidate reference genes under different developmental stages, larval tissues, adult sexes, plant secondary substance stresses, and insecticide treatments in H. armigera. The candidate genes included Actin, Tubulin, EF-1α, RPS3, RPS15, RPL27, RPL32, 28S, GAPDH, SOD, and TRX. The reliability of the recommended reference gene combinations was validated using the growth arrest and DNA-damage-inducible gene 45 (GADD45). The results showed that normalizing relative expression of the target gene with the combination of the two most stable reference genes is recommended. Specifically, the combination of RPS3 + RPL27 is recommended for developmental stage comparisons; RPL32 + RPL27 for larval tissues; RPS3 + RPL27 for adult sex comparisons; GAPDH + RPL32 under tannic acid stress; RPL32 + RPS3 under quercetin stress; RPS15 + RPL32 under 2-tridecanone stress; RPS3 + RPL32 under ZQ-8 stress; RPL27 + TRX following chlorantraniliprole treatment; and RPL27 + RPL32 following indoxacarb treatment. Moreover, larvae exposed to three concentrations of plant secondary substances and to sublethal doses of insecticides exhibited significant upregulation of GADD45: after 4 h of exposure to 1% tannic acid, 0.1% and 1% quercetin, 1% 2-tridecanone, and 0.05% ZQ-8; after 15 h of chlorantraniliprole treatment; and after 24 h of indoxacarb treatment. Thus, GADD45 was overexpressed in response to various plant secondary substances and insecticide treatments, indicating its involvement in the detoxification and metabolism of H. armigera. This study proves to be helpful for selecting reference genes in H. armigera under plant secondary substance and insecticide stress and lays the foundation for further research utilizing GADD45 as a molecular target for pest control. Full article

Education and post-licencing training programmes for novice drivers are widely implemented to improve road safety, yet their effectiveness remains debated. This study evaluates short-term attitudinal changes relating to participation in a mandatory post-licencing training programme for novice drivers in Slovenia. A within-subject pre–post survey methodology was used to evaluate self-reported driving attitudes across six safety-related domains among 225 novice drivers at a Slovenian driving training centre in 2024. Paired t-tests revealed minor yet statistically significant improvement following the programme in perceived support for the additional driver training, lowered overconfidence, heightened care in speeding and intersection behaviour, and enhanced attitudes towards vehicle operation and utilization of safety equipment. Attitudes regarding attention and adherence to traffic regulations showed negligible shifts, indicating a strong baseline attitude towards safe driving. The findings indicate a modest but fairly consistent short-term change in attitudes after programme participation. Due to the lack of a control group and dependence on self-reported data, the findings should be seen as evaluative rather than causative, necessitating more longitudinal and behavioural research to evaluate long-term and behavioural effects. Full article