Search Results (13,559)

The polymorphism of the bovine β-casein gene (CSN2) is of increasing interest due to its relevance for A2 milk production. This study genotyped 2773 Holstein-Friesian cows for five CSN2 alleles (A1, A2, A3, B, I) using both conventional DNA-based PCR and a newly evaluated direct PCR protocol. Eleven genotypes were detected, with A2/A2 (33.9%) and A1/A2 (30.3%) being the most common, resulting in an A2 allele frequency of 59.0%. Genetic diversity indices indicated moderate polymorphism and a significant deviation from Hardy–Weinberg equilibrium, consistent with ongoing selection for the A2 allele. Associations between CSN2 genotype and milk traits (305-day milk, fat, and protein yield; fat% and protein%) were evaluated using linear mixed-effects models including lactation number, age at calving, and calving year as covariates, and cow ID as a random intercept. Several genotype effects reached statistical significance (p < 0.05); however, all effect sizes were very small (partial η² < 0.01), indicating that any influence of CSN2 on production traits is negligible within this population and management context. These findings suggest that A2-oriented selection is unlikely to compromise productivity. The direct PCR genotyping method achieved 96–100% success and enabled substantially faster and more cost-efficient processing (approximately 80–90% reduction in reagent costs), providing a rapid and scalable approach for large herds. Full article

With the rapid advancement of deepfake generation technologies, forged videos have become increasingly realistic in visual quality and temporal consistency, posing serious threats to multimedia security. Existing detection methods often struggle to effectively model temporal dynamics and capture subtle inter-frame anomalies. To address these challenges, we propose a Stochastic Differential Equation and Quantum Uncertainty Network (SDEQ-Net), a novel deepfake video anomaly detection framework that integrates continuous time stochastic modeling with quantum uncertainty mechanisms. First, a Continuous Time Neural Stochastic Differential Filtering Module (CNSDFM) is introduced to characterize the continuous evolution of latent inter-frame states using neural stochastic differential equations, enabling robust temporal filtering and uncertainty estimation. Second, a Quantum Uncertainty Aware Fusion Module (QUAFM) incorporates Hermitian-symmetric density matrix representations and von Neumann entropy to enhance feature fusion under uncertainty, leveraging the mathematical symmetry properties of quantum state representations for principled uncertainty quantification. Third, a Fractional Order Temporal Anomaly Detection Module (FOTADM) is proposed to generate fine grained temporal anomaly scores based on fractional order residuals, which are used as dynamic weights to guide attention toward anomalous frames. Extensive experiments on three benchmark datasets, including FaceForensics++, Celeb-DF, and DFDC, demonstrate the effectiveness of the proposed method. SDEQ-Net achieves AUC scores of 99.81% on FF++ (c23) and 97.91% on FF++ (c40). In cross dataset evaluations, it obtains 89.55% AUC on Celeb-DF and 86.21% AUC on DFDC, consistently outperforming existing state-of-the-art methods in both detection accuracy and generalization capability. Full article

Background: The efficacy of real-time fMRI neurofeedback (NFB) depends critically on how feedback is presented and perceived by the participant. Although various visual feedback designs are used in practice, there is limited evidence on the impact of modality on learning and performance. We conducted a feasibility study to compare the effectiveness of different feedback modalities, and to evaluate the technical performance of NFB across two scanner field strengths. Methods: In a single-session study, nine healthy adults (6 men, 3 women) voluntarily adapted the activation level of the primary sensorimotor cortex (SMC) to reach three predefined activation levels. We contrasted a continuous, signal-proportional feedback (cFB; a thermometer-style bar) with an affect-based, categorical feedback (aFB; a smiling face). A no-feedback transfer condition (noFB) was included to probe regulation based on internal representations alone. To assess technical feasibility, three participants were scanned at 7T and six at 3T. Results: Participants achieved successful regulation in 44.4% of trials overall (cFB 46.9%, aFB 43.8%, noFB 42.6%). Overall success rates did not differ significantly between modalities and field strengths when averaged across the session; given the small feasibility sample, this null result is inconclusive and does not establish equivalence. Learning effects were modality-specific. Only cFB showed a significant within-session improvement (+14.8 percentage points from RUN1 to RUN2; p = 0.031; d_z = 0.94), whereas aFB and noFB showed no evidence of learning. Exploratory whole-brain contrasts (uncorrected) suggested increased recruitment of ipsilateral motor regions during noFB. The real-time pipeline demonstrated robust technical performance: transfer/reconstruction latency averaged 497.8 ms and workstation processing averaged 296.8 ms (≈795 ms end-to-end), with rare stochastic outliers occurring predominantly during 7T sessions. Conclusions: In this single-session motor rt-fMRI NFB paradigm, continuous signal-proportional feedback supported rapid within-session learning, whereas affect-based categorical cues did not yield comparable learning benefits. Stable low-latency operation was achievable at both 3T and 7T. Larger, balanced studies are warranted to confirm modality-by-learning effects and to better characterize transfer to feedback-free self-regulation. Full article

As the global cumulative capacity of photovoltaic (PV) systems continues its rapid expansion, reaching approximately 2.2 TW by the end of 2024, the frequent occurrence of various facility failures has become a critical challenge, leading to generation losses and increased maintenance costs. While previous studies have predominantly focused on binary anomaly detection, they often lack the capacity for root cause analysis and maintenance prioritization. To address these limitations, this study defines nine major anomaly types based on actual PV operational data and evaluates the performance of binary classification for each specific anomaly type to detect its individual occurrence. To optimize classification performance, we conducted a comparative analysis of various machine learning and deep learning models—including tree-based, distance-based, and Transformer-based algorithms—across four data representation methods: raw data and three distinct binning strategies (domain knowledge-based, K-means clustering-based, and decision tree-based). Experimental results demonstrate that deep learning models capable of processing raw time-series data, specifically CNN and Transformer models, achieved the highest performance. These findings provide a more robust framework for multi-anomaly diagnosis in large-scale PV plants and suggest a strategic direction for future research in machine learning models. Full article

Musculoskeletal disorders remain a persistent concern in automotive repair, yet empirical evidence on task-specific ergonomic risks in Middle Eastern contexts is limited. This study provides a detailed ergonomic risk profile of auto body specialists in Jeddah, Saudi Arabia, using a mixed-method approach that integrates the Rapid Upper Limb Assessment (RULA), Rapid Entire Body Assessment (REBA), and a validated Nordic Musculoskeletal Questionnaire. Twenty-five specialists across diverse tasks including installation, weighing, painting, cutting, and lifting were systematically evaluated to identify both postural and self-reported risk patterns. Results showed a high prevalence of discomfort in the lower back (64%), shoulders (52%), and wrists (48%). Ergonomic assessment revealed that the evaluated tasks were predominantly classified as moderate-to-high-risk, with RULA scores ranging from 6 to 7 and REBA scores ranging from 8 to 11. Beyond confirming the physical strain inherent to auto body work, the study highlights contextual factors such as prolonged static postures, limited use of mechanical aids, and constrained workshop layouts that exacerbate ergonomic risks. Importantly, the findings inform multi-level recommendations ranging from workshop practices to industry standards and policy considerations ensuring that interventions are both practical and scalable. By situating locally grounded results within the broader discourse on musculoskeletal risk prevention, the study offers region-specific evidence while providing globally relevant lessons for labor-intensive industries. Full article

Dengue and chikungunya are endemic arboviral diseases in many low- and middle-income countries, often co-circulating and presenting with overlapping symptoms that hinder early diagnosis. Timely differentiation is critical, especially in resource-limited settings where laboratory testing is unavailable. We developed and evaluated machine-learning (ML)- and deep-learning (DL) models to classify dengue, chikungunya, and discarded cases using a large-scale, real-world dataset of over 6.7 million entries from Brazil (2013–2020). After applying the Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance, we trained six ML models and one artificial neural network (ANN) using only demographic, clinical, and comorbidity features. The Random Forest model achieved strong multi-class classification performance (Recall: 0.9288, the Area Under the Curve (AUC): 0.9865). The ANN model excelled in identifying chikungunya cases (Recall: 0.9986, AUC: 0.9283), suggesting its suitability for rapid screening. External validation confirmed the generalizability of our models, particularly for distinguishing discarded cases. Our models demonstrate high-accuracy in differentiating dengue and chikungunya using routinely collected clinical and epidemiological data. This work supports the development of Artificial Intelligence-powered decision-support tools to assist frontline healthcare workers in under-resourced settings and aligns with the One Health approach to improving surveillance and diagnosis of neglected tropical diseases. Full article

The rapid development of large language models (LLMs), including ChatGPT, Gemini, and Copilot, has led to their increasing use in health communication and patient education. However, their growing popularity raises important concerns about whether the language they generate aligns with recommended readability standards and patient health literacy levels. This review synthesizes evidence on the readability of medical information generated by chatbots using established linguistic readability indices. A comprehensive search of PubMed, Scopus, Web of Science, and Cochrane Library identified 4209 records, from which 140 studies met the eligibility criteria. Across the included publications, 21 chatbots and 14 readability scales were examined, with the Flesch–Kincaid Grade Level and Flesch Reading Ease being the most frequently applied metrics. The results demonstrated substantial variability in readability across chatbot models; however, most texts corresponded to a secondary or early tertiary reading level, exceeding the commonly recommended 8th-grade level for patient-facing materials. ChatGPT-4, Gemini, and Copilot exhibited more consistent readability patterns, whereas ChatGPT-3.5 and Perplexity produced more linguistically complex content. Notably, DeepSeek-V3 and DeepSeek-R1 generated the most accessible responses. The findings suggest that, despite technological advances, AI-generated medical content remains insufficiently readable for general audiences, posing a potential barrier to equitable health communication. These results underscore the need for readability-aware AI design, standardized evaluation frameworks, and future research integrating quantitative readability metrics with patient-level comprehension outcomes. Full article

In the face of mounting complexity in container terminal operations, the selection of an effective information system is paramount. The TOS (Terminal Operating System) is the most significant of all the information systems in existence for terminals. The objective of this study is to establish a set of criteria for selecting container TOS, determine the priority weights of these criteria and investigate their interactions. To the author’s knowledge, this is the first study to address this topic in such a detailed context. The hybrid FAHP (Fuzzy Analytic Hierarchy Process) and F-DEMATEL (Fuzzy Decision-Making Trial and Evaluation Laboratory) methodology was employed for the 18 criteria that were identified through the academic literature and expert views. The findings demonstrated that container terminal operators have expressed an expectation for a TOS structure that integrates complex business processes, provides effective decision support, increases traceability, works in harmony with advanced technologies, supports smart port transformation processes, enhances digital maturity and enables rapid intervention in bottlenecks. Furthermore, the fact that TOSs should support integration with external stakeholders is also critical in terms of collaboration and transparency, which are of great importance in supply chain management. It is hoped that the present study will contribute to the relevant literature and also provide a structural framework for terminal operators to select the most suitable TOS and for providers to design the most effective product. Full article

The rapid development of viticulture in subtropical regions represents a significant achievement in China’s table grape industry over the last two decades. However, insufficient winter chilling in these areas often leads to inadequate dormancy, which compromises nutrient translocation and storage in grapevines. Insufficient chilling accumulation results in asynchronous budbreak and reduced cane quality. In this study, ‘Shine Muscat’ grapevines were used to systematically evaluate how different defoliant agents affect budbreak characteristics from the perspective of nutrient translocation and storage. The results indicated that applications of ethephon or urea alone, as well as their combinations with boric acid, yielded unstable effects, often causing primary bud necrosis, decreased flower formation rates, and phytotoxicity. In contrast, the combination of lime sulfur and boric acid exhibited a remarkable synergistic effect, significantly promoting dry matter and starch accumulation in canes while enhancing the budbreak speed, uniformity, and flower cluster formation rate. Further experiments with varying concentrations of lime sulfur combined with 0.2% boric acid revealed that utilizing 2% lime sulfur in this combination produced the most pronounced effects, achieving the highest dormancy-breaking efficacy under conventional cultivation conditions. This treatment was used for the first time to produce a second crop during off-season cultivation. The dual effects of dormancy release and bud promotion achieved via this approach represent a reliable solution in high-quality and efficient grape production in subtropical regions. Full article

Fatigue crack propagation in friction stir welded joints significantly affects aircraft structural integrity. This study investigates the influence of welding speed, rotational speed, specimen thickness, loading frequency, and stress ratio on the fatigue crack growth rate. Four classical machine learning models with different structures—Deep Back-Propagation Network, Random Forest, Support Vector Regression, and K-Nearest Neighbors—were employed to predict fatigue crack growth behavior. The results show that all models achieve strong predictive performance. For FSWed joints, Deep BP and KNN exhibit comparable performance (R² > 0.98) on the training data, indicating similar learning capabilities with sufficient data coverage. Notably, KNN achieves the fastest training time (<0.3 s), while all models require less than 5 s of computation time. These results demonstrate that machine learning-based models provide an efficient and reliable alternative for rapid fatigue crack growth evaluation, supporting damage-tolerant design and structural integrity assessment in aircraft engineering. Full article

The Xinding Basin is located in the high-heat-flow geothermal anomaly zone in the north-central part of China. Revealing the geothermal origin mechanism of the basin is of great significance for filling the measurement gap in heat flow values in China and providing a scientific basis for the evaluation and utilization of regional geothermal resources. Based on the hydrogeochemical characteristics of thermal reservoirs and borehole data in the Xinding Basin, this paper analyzes water–rock interaction process between geothermal water and heat reservoirs and discusses the types of geothermal systems in the basin. The results indicate that the fault structures in the basin are well-developed. The hydrochemical type of typical geothermal fields is dominated by the Cl·SO₄-Na type. Geothermal water is mainly immature water and receives recharge from shallow cold water with relatively rapid circulation. The discovered magma intrusion residues in the basin indicate that sections of the upper mantle with a shallow burial depth serve as the dynamic heat sources for regional thermal reservoirs. Intense extensional stretching in the Cenozoic Era resulted in high terrestrial heat flow values and an upward arching phenomenon of the Curie isothermal surface in the basin. Neotectonic movement is active in the basin. The regional geothermal reservoirs in the Xinding Basin occur in the glutenite beds of the Cenozoic Erathem and the rock formations of the New Archaean Erathem. The thick-layered Cenozoic loose sediments serve as the thermal cap rocks in this area. An efficient heat-convergent geothermal system integrating a heat source, heat channel, thermal reservoir, and cap rock (the “four-in-one” system) has promoted the formation of geothermal resources in the Xinding Basin. Full article

The purpose of this study was to examine the use of portable spectroscopy technologies for rapid milk composition and hygiene quality assessment in ovine milk. Two portable analyzers, namely SmartAnalysis (UV/Vis absorbance) and SpectraPod (NIR transmittance), were used to obtain spectral data of raw milk samples. Additionally, reference values of the milk’s compositional, physical, and hygienic traits were measured. Machine learning algorithms were used to explore the correlations between spectral data and milk traits. The initial results indicated a promising potential of utilizing spectral technologies to predict milk quality and hygienic parameters. Regression models presented a moderate predictive accuracy, with R² values between 0.55 and 0.34, respectively, regarding fat (RF-NIR) and protein (LR-UV/Vis). Classification models indicated high accuracy for hygienic parameters, with the highest accuracy and AUC values up to 0.87 and 0.83, respectively, predicting increased levels of total bacterial count (TBC), while somatic cell count (SCC) level was less accurately predicted by the model, with AUC values lower than 0.70. The results demonstrate the applicability potential of UV/Vis and NIR portable devices in milk quality assessment, enabling its rapid evaluation, including milk composition and hygiene parameters at the point of service. Full article

Aquatic ecological issues have garnered significant attention in recent years, driving the demand for convenient, effective, and systematic assessment methods in environmental risk evaluation. This review provides a comprehensive introduction to methodologies for assessing the toxicity of chemicals toward aquatic microorganisms, which include viruses, bacteria, fungi, protozoa, and algae. Among these, microalgae are commonly used as model organisms due to their relative simplicity. The article details conventional biological methods, general chemical techniques, modern instrumental analyses, and informatics approaches, with a particular focus on algae and bacteria as model organisms for toxicity assessment. The principles, advantages, and limitations of each method are discussed, along with examples of their application in various contexts. Biological methods offer direct visualization, convenience, and rapid results, while modern instrumental techniques enable mechanistic insights at molecular and biochemical levels. Informatics methods facilitate toxicity evaluation in complex systems. While aquatic microorganisms encompass viruses, fungi, protozoa, bacteria, and algae, this review primarily focuses on bacteria and algae as model organisms due to their ecological relevance, sensitivity, and widespread use in standardized assays. Full article

Bongkrekic acid (BKA), a highly lethal toxin, has been implicated in frequent poisoning incidents in recent years, posing a serious threat to global food safety and creating an urgent need for rapid and sensitive detection methods. This review provides a systematic analysis of the entire BKA detection technologies, covering sample pretreatment techniques, instrumental analysis, immunoassays, and biosensing methods. It assesses the merits of key methods and also explores the strategic cross-application of detection paradigms developed for analogous toxins. This review delivers a comprehensive and critical evaluation of BKA detection technologies. First, it discusses sample pretreatment strategies, notably solid-phase extraction (SPE) and QuEChERS. Subsequently, it analyzes the principles, performance, and applications of core detection methods, including high-performance liquid chromatography–tandem mass spectrometry (HPLC-MS/MS), high-resolution mass spectrometry (HRMS), time-resolved fluorescence immunoassay (TRFIA), dual-mode immunosensors and nanomaterial-based sensors. Instrumental methods (e.g., HRMS) offer unmatched sensitivity [with a limit of detection (LOD) as low as 0.01 μg/kg], yet remain costly and laboratory-dependent. Immunoassay and biosensor approaches (TRFIA and dual-mode sensors) enable rapid on-site detection with high sensitivity (ng/mL to pg/mL), though challenges in stability and specificity remain. Looking forward, the development of next-generation BKA detection could be accelerated by cross-applying cutting-edge strategies proven for toxins—such as Fumonisin B1 (FB1), Ochratoxin A (OTA), and Aflatoxin B1 (AFB1)—including nanobody technology, CRISPR-Cas-mediated signal amplification, and multimodal integrated platforms. To translate this potential into practical tools, future research should prioritize the synthesis of high-specificity recognition elements, innovative signal amplification strategies, and integrated portable devices, aiming to establish end-to-end biosensing systems capable of on-site rapid detection through multitechnology integration. Full article

Rock matrices, as low-permeability media, play a critical role in controlling the persistence and fate of groundwater contaminants. Accurately quantifying contaminant mass stored in these matrices is therefore essential for understanding contamination transport processes. In this study, a microwave-assisted extraction (MAE) method was developed to accelerate the complete extraction of trichloroethylene (TCE) from rock samples. Because microwave–sample interactions depend on multiple factors, extraction conditions, including solvent type, temperature, and extraction time, were optimized using dolostone samples collected from industrial sites with decades-old contamination in Guelph, Canada. Method performance was evaluated through extensive comparison of the newly developed MAE procedure with a conventional shake-flask extraction method used as a reference. In addition, the necessity of field preservation was assessed, given its importance in the overall analytical workflow and accuracy of total mass concentrations and mass stored. The MAE method provided recoveries comparable to or greater than those obtained with the reference method, while avoiding several drawbacks of the shake-flask approach, such as sample cross-contamination during prolonged extraction times over several weeks. Its shorter processing time and faster turnaround support rapid, field-based decision-making. Field preservation was determined to be essential, as non-preserved samples consistently yielded lower measured concentrations than preserved samples. Full article