Search Results (206)

Background: Investigating human exposure to toxic contaminants through dietary consumption is critical to identify disease risk factors and health guidelines. Methods: In this study, we developed a cross-sectional online survey to collect information about dietary patterns and related food consumption habits among adults (age ≥ 18) and adolescents (ages 13–17) in Southern California, focusing on popular staple foods and/or those targeted most commonly under California’s Proposition 65 law for lead and acrylamide exposure. Results: Results identified root vegetables, rice, leafy greens, pasta/noodles, tea, juice, and seafood to be among the most heavily consumed foods by mass, while the daily intake of many foods such as stuffed grape leaves, tamarind/chili candy and herbs/spices varied by age and race/ethnicity, suggesting that many of Proposition 65’s pollution allowances may be exacerbating issues of health inequity and environmental injustice. Moreover, findings from this study indicate that the methods of exposure assessment often applied under Prop 65, especially relating to herbs/spices, are likely to underestimate single-day exposures, thus allowing unsafe products on the market without warning labels. Conclusions: Study outcomes are broadly relevant to environmental health and nutrition science, with particular relevance to public health practitioners and California’s Prop 65 regulators and other stakeholders. Full article

Wildfires pose a regularly increasing threat to ecosystems and critical infrastructure. The severity of this threat is steadily increasing. The growing threat necessitates the development of technologies for rapid and accurate early detection. However, the prevailing fire point detection algorithms, including several deep learning models, are generally constrained by the inherent hard threshold limitations in their decision-making logic. As a result, these methods lack adaptability and robustness in complex and dynamic real-world scenarios. To address this challenge, the present paper proposes an innovative two-stage, semi-supervised anomaly detection framework. The framework initially employs a Transformer-based autoencoder, which serves to transform raw fire-free time-series data derived from satellite imagery into a multidimensional deep anomaly feature vector. Self-supervised learning achieves this transformation by incorporating both reconstruction error and latent space distance. In the subsequent stage, a semi-supervised XGBoost classifier, trained using an iterative pseudo-labeling strategy, learns and constructs an adaptive nonlinear decision boundary in this high-dimensional anomaly feature space to achieve the final fire point judgment. In a thorough validation process involving multiple real-world fire cases in Yunnan Province, China, the framework attained an F1 score of 0.88, signifying a performance enhancement exceeding 30% in comparison to conventional deep learning baseline models that employ fixed thresholds. The experimental results demonstrate that by decoupling feature learning from classification decision-making and introducing an adaptive decision mechanism, this framework provides a more robust and scalable new paradigm for constructing next-generation high-precision, high-efficiency wildfire monitoring and early warning systems. Full article

Aeration systems are among the most energy-intensive components of wastewater treatment plants (WWTPs), consuming up to 75% of total electricity while being prone to performance degradation caused by diffuser fouling and pressure losses. Traditional maintenance strategies are largely reactive or preventive, leading to inefficient interventions, higher operational costs, and limited fault anticipation. This study addresses the need for an advanced predictive maintenance framework capable of early detection and differentiation of multiple aeration system faults. Using the Benchmark Simulation Model No. 2 (BSM2), two representative degradation scenarios—acute airflow pressure loss and chronic diffuser fouling—were simulated to generate a labeled dataset. A hybrid machine learning approach was developed, combining Random Forest-based feature selection with Long Short-Term Memory (LSTM) neural networks for temporal, multi-label fault classification. To enhance interpretability and operator trust, SHapley Additive exPlanations (SHAP) were applied to quantify feature contributions and provide transparent model predictions. The results show that the proposed framework achieves over 94% detection accuracy and provides early warnings compared to static threshold-based methods. The integration of explainable AI ensures actionable insights for maintenance planning. This approach supports more energy-efficient, reliable, and sustainable operation of WWTP aeration systems and offers a benchmark methodology for future predictive maintenance research. Full article

When control room operators encounter mental fatigue, the accuracy of their work will decline. Accurately predicting the mental fatigue of industrial control room operators is of great significance for preventing operational mistakes. In this study, facial data of experimental participants were collected via cameras, and fatigue levels were evaluated using an improved Karolinska Sleepiness Scale (KSS). Subsequently, a dataset of fatigue samples based on facial features was established. A novel early-warning framework was put forward, framing fatigue prediction as a time series prediction task. Two innovative data processing techniques were introduced. Reverse data binning transforms discrete fatigue labels into continuous values through a random perturbation of ≤0.3, enabling precise temporal modeling. A fatigue-aware data screening method uses the 6 s rule and a sliding window to filter out transient states and preserve key transition patterns. Five prediction models, namely Light Gradient Boosting Machine (LightGBM), Gated Recurrent Unit (GRU), Temporal Convolutional Network (TCN), Transformer, and Attention-based Temporal Convolutional Network (Attention-based TCN), were evaluated using the collected dataset of fatigue samples based on facial features. The results indicated that LightGBM demonstrated outstanding performance, with an accuracy rate reaching 93.33% and an average absolute error of 0.067. It significantly outperformed deep learning models. Moreover, its computational efficiency further verified its suitability for real-time deployment. This research integrates predictive modeling with industrial safety applications, providing evidence for the feasibility of machine learning in proactive fatigue management. Full article

Wind turbine radar echoes exhibit significant scattering power and Doppler spectrum broadening effects, which can interfere with the detection of meteorological targets and subsequently impact weather prediction and disaster warning decisions. In operational weather radar applications, the influence of wind farm on radar observations must be fully considered by meteorological departments and related institutions. In this paper, a Wind Turbine Clutter Classification Algorithm based on Random Forest (WTCDA-RF) classification is proposed. The level-II radar data is processed in blocks, and the spatial position invariance of wind farm clutter is leveraged for feature extraction. Samples are labeled based on position information, and valid samples are screened and saved to construct a vector sample set of wind farm clutter. Through training and optimization, the proposed WTCDA-RF model achieves an ACC of 90.92%, a PRE of 89.37%, a POD of 92.89%, and an F1-score of 91.10%, with a CSI of 83.65% and a FAR of only 10.63%. This not only enhances the accuracy of weather forecasts and ensures the reliability of radar data but also provides operational conditions for subsequent clutter removal, improves disaster warning capabilities, and ensures timely and accurate warning information under extreme weather conditions. Full article

Early detection of physiological dysregulation is critical for timely intervention and effective health management. Traditional monitoring systems often rely on labeled data and predefined thresholds, limiting their adaptability and generalization to unseen conditions. To address this, we propose a framework for label-free classification of physiological states using Heart Rate Variability (HRV), combined with unsupervised machine learning techniques. This approach is particularly valuable when annotated datasets are scarce or unavailable—as is often the case in real-world wearable and IoT-based health monitoring. In this study, data were collected from participants under controlled conditions representing rest, stress, and physical exertion. Core HRV parameters such as the SDNN (Standard Deviation of all Normal-to-Normal intervals), RMSSD (Root Mean Square of the Successive Differences), DFA (Detrended Fluctuation Analysis) were extracted. Principal Component Analysis was applied for dimensionality reduction. K-Means, hierarchical clustering, and Density-based spatial clustering of applications with noise (DBSCAN) were used to uncover natural groupings within the data. DBSCAN identified outliers associated with atypical responses, suggesting potential for early anomaly detection. The combination of HRV descriptors enabled unsupervised classification with over 90% consistency between clusters and physiological conditions. The proposed approach successfully differentiated the three physiological conditions based on HRV and fractal features, with a clear separation between clusters in terms of DFA α1, α2, LF/HF, and RMSSD (with high agreement to physiological labels (Purity ≈ 0.93; ARI = 0.89; NMI = 0.92)). Furthermore, DBSCAN identified three outliers with atypical autonomic profiles, highlighting the potential of the method for early warning detection in real-time monitoring systems. Full article

While prior work has explored learner behavior using learning management systems (LMS) data, few studies provide week-level clustering validated against external engagement labels. To understand and assist students in online learning platforms and environments, this study presents a week-level engagement profiling framework for e-learning environments, utilizing K-means clustering and label-based validation. Leveraging log data from 127 students over a 13-week course, 44 activity-based features were engineered to classify student engagement into high, moderate, and low levels. The optimal number of clusters (k = 3) was identified using the elbow method and assessed through internal metrics, including a silhouette score of 0.493 and R² of 0.80. External validation confirmed strong alignment with pre-labeled engagement levels based on activity frequency and weighting. The clustering approach successfully revealed distinct behavioral patterns across engagement tiers, enabling a nuanced understanding of student interaction dynamics over time. Regression analysis further demonstrated a significant association between engagement levels and academic performance, underscoring the model’s potential as an early warning system for identifying at-risk learners. These findings suggest that clustering based on LMS behavior offers a scalable, data-driven strategy for improving learner support, personalizing instruction, and enhancing retention and academic outcomes in digital education settings such as MOOCs. Full article

Mango, as an important economic crop in tropical and subtropical regions, suffers from chilling injuries caused by postharvest low-temperature storage, which seriously affect its quality and economic benefits. Traditional detection methods have limitations such as low efficiency and strong destructiveness. This study designs and implements a flexible visible light spectral sensing system based on visible light spectral sensing technology and low-cost environmentally friendly flexible circuit technology. The system is structured based on a perception-analysis-warning-processing framework, utilizing laser-induced graphene electroplated copper integrated with laser etching technology for hardware fabrication, and developing corresponding data acquisition and processing functionalities. Taking Yunnan Yumang as the research object, a three-level chilling injury label dataset was established. After Z-Score standardization processing, the prediction accuracy of the SVM (Support Vector Machine) model reached 95.5%. The system has a power consumption of 230 mW at 4.5 V power supply, a battery life of more than 130 days, stable signal transmission, and a monitoring interface integrating multiple functions, which can provide real-time warning and intervention, thus offering an efficient and intelligent solution for chilling injury monitoring in mango cold chain storage. Full article

The increasing deployment of wind energy systems, particularly offshore wind farms, necessitates advanced monitoring and maintenance strategies to ensure optimal performance and minimize downtime. Supervisory Control And Data Acquisition (SCADA) systems have become indispensable tools for monitoring the operational health of wind turbines, generating vast quantities of time series data from various sensors. Anomaly detection techniques applied to this data offer the potential to proactively identify deviations from normal behavior, providing early warning signals of potential component failures. Traditional model-based approaches for fault detection often struggle to capture the complexity and non-linear dynamics of wind turbine systems. This has led to a growing interest in data-driven methods, particularly those leveraging machine learning and deep learning, to address anomaly detection in wind energy applications. This study focuses on the development and application of a semi-supervised, multivariate anomaly detection model for horizontal axis wind turbines. The core of this study lies in Bidirectional Long Short-Term Memory (BI-LSTM) networks, specifically a BI-LSTM autoencoder architecture, to analyze time series data from a SCADA system and automatically detect anomalous behavior that could indicate potential component failures. Moreover, the approach is reinforced by the integration of the Isolation Forest algorithm, which operates in an unsupervised manner to further refine normal behavior by identifying and excluding additional anomalous points in the training set, beyond those already labeled by the data provider. The research utilizes a real-world dataset provided by EDP Renewables, encompassing two years of comprehensive SCADA records collected from a single offshore wind turbine operating in the Gulf of Guinea. Furthermore, the dataset contains the logs of failure events and recorded alarms triggered by the SCADA system across a wide range of subsystems. The paper proposes a multi-modal anomaly detection framework orchestrating an unsupervised module (i.e., decision tree method) with a supervised one (i.e., BI-LSTM AE). The results highlight the efficacy of the BI-LSTM autoencoder in accurately identifying anomalies within the SCADA data that exhibit strong temporal correlation with logged warnings and the actual failure events. The model’s performance is rigorously evaluated using standard machine learning metrics, including precision, recall, F1 Score, and accuracy, all of which demonstrate favorable results. Further analysis is conducted using Cumulative Sum (CUSUM) control charts to gain a deeper understanding of the identified anomalies’ behavior, particularly their persistence and timing leading up to the failures. Full article

Rice is one of the three primary staple crops worldwide. The accurate monitoring of its key growth stages is crucial for agricultural management, disaster early warning, and ensuring food security. The effective collection of ground reference data is a critical step for monitoring rice growth stages using satellite imagery, traditionally achieved through labor-intensive field surveys. Here, we propose utilizing UAVs as an alternative means to collect spatially continuous ground reference data across larger areas, thereby enhancing the efficiency and scalability of training and validation processes for rice growth stage mapping products. The UAV data collection involved the Nanchuan, Yongchuan, Tongnan, and Kaizhou districts of Chongqing City, encompassing a total area of 377.5 hectares. After visual interpretation, centimeter-level high-resolution labels of the key rice growth stages were constructed. These labels were then mapped to Sentinel-2 imagery through spatiotemporal matching and scale conversion, resulting in a reference dataset of Sentinel 2 data that covered growth stages such as jointing and heading. Furthermore, we employed 30 vegetation index calculation methods to explore 48,600 spectral band combinations derived from 10 Sentinel-2 spectral bands, thereby constructing a series of novel vegetation indices. Based on the maximum relevance minimum redundancy (mRMR) algorithm, we identified an optimal subset of features that were both highly correlated with rice growth stages and mutually complementary. The results demonstrate that multi-feature modeling significantly enhanced classification performance. The optimal model, incorporating 300 features, achieved an F1 score of 0.864, representing a 2.5% improvement over models based on original spectral bands and a 38.8% improvement over models using a single feature. Notably, a model utilizing only 12 features maintained a high classification accuracy (F1 = 0.855) while substantially reducing computational costs. Compared with existing methods, this study constructed a large-scale ground-truth reference dataset for satellite imagery based on UAV observations, demonstrating its potential as an effective technical framework and providing an effective technical framework for the large-scale mapping of rice growth stages using satellite data. Full article

Digital food-ordering apps make it simple to buy indulgent drinks yet hard to judge their health risks. We conducted five online experiments (N = 1048) to compare two messages for sugary beverages: self-promotion that stresses taste and self-disclosure that plainly warns “high sugar/high calories”. Brands that chose self-disclosure were seen as more socially responsible and transparent, which in turn raised trust and lifted purchase intent. These gains were strongest for users who care deeply about the category or the brand and remained robust even among highly health-conscious shoppers. The results show that, for “vice” foods, honest warnings can outperform glossy claims. Our study extends signaling and attribution theories to digital food markets and offers managers a straightforward playbook for complying with new labeling rules while still driving sales. Full article

The exotic pet trade is a major driver of alien species invasions. Improper introductions or a lack of management can result in severe ecological consequences. Therefore, accurate identification of exotic pets is essential for the prevention and early warning of species invasions. This paper proposes a novel recognition method for fine-grained images of small-scale exotic pet snakes in complex backgrounds based on an improved SimCLR framework. A hierarchical window attention mechanism is introduced into the encoder network to enhance feature extraction. In the loss function, a supervised contrastive mechanism is introduced to exclude false negative samples using label information, which helps reduce representation noise and enhance training stability. The training strategy incorporates random erasing and random grayscale data augmentation techniques to improve performance further. The projection head is constructed using a two-layer multilayer perceptron (MLP), and the cosine annealing schedule combined with the AdamW optimizer is adopted for learning rate adjustment. Experimental results on a self-constructed dataset demonstrate that the proposed model achieves a recognition accuracy of 97.5%, outperforming existing baseline models. This study fills a gap in exotic pet snake classification and provides a practical tool for species invasion prevention and early detection. Full article

Background: The majority of Australian children exceed the World Health Organization’s recommended dietary intake of free sugar, particularly through the consumption of sugar-sweetened beverages. Front-of-pack nutrition labels increase perceived risk and deter the consumption of sugar-sweetened beverages. However, past studies of young children have focused almost exclusively on a parent’s choice of beverage for children. This study investigated the influence of four label designs (text-based warning, tooth decay pictorial, teaspoons of sugar, and Health Star Rating) on the beverage choices of N = 1229 Australian children (aged 4–11 years) and their parents. Methods: In an online vending machine scenario, parent–child dyads were separately asked to select which beverage they would choose for themselves before and after being randomised to one label condition. The beverages displayed included 100% fruit juice, soft drink, soft drink with a non-nutritive sweetener, flavoured milk, plain milk and bottled water. Beverage healthiness was determined by a 1–10 rating based on a review by a panel of experts (10 dietitians and nutritionists). Results: Mixed-model ANOVAs showed that for parents, each label design performed comparably; however, for children, small but significant differences were seen in the effectiveness of different label designs, with the teaspoons of sugar label, text-based warning, and tooth decay pictorial found to be more impactful in promoting healthier drink choices than the Health Star Rating. Conclusions: These findings can inform public health advocacy efforts to improve food labelling and could be incorporated into educational resources to help children understand the nutritional profiles of different sugary drinks. Full article

Moderate wine consumption is, generally, the focus of various medical studies, while consumer behaviour research does not specifically centres on moderation in wine consumption. Wine consumption in moderation is an important part of various healthy diets; still, consumers need to make informed choices when purchasing wine and the information printed on wine labels partially contributes to this process. Therefore, the main aims of this paper were to develop a scale for measuring perceptions of the health benefits associated with moderate wine consumption, and to test the effect of dietary habits and non-obligatory wine label information on the perception of the health benefits associated with moderate wine consumption. The data were collected on a sample of wine consumers who participated in an interdisciplinary experiment regarding the impact of moderate wine consumption on human health. Univariate, bivariate, and multivariate statistics were used. The consumers’ socio-demographic characteristics were used as a starting point in the analysis because they influence wine consumption. Gender was identified as a consistently important variable in predicting the perception of health benefits associated with moderate wine consumption. Health behaviour was a significant predictor along with gender, but after introducing non-obligatory wine label information, its significance in explaining the dependent variable was diminished. The results suggest that a consumer’s perception of the scale of moderate wine consumption is a unidimensional construct. Furthermore, the non-obligatory information on wine labels was identified and classified as either wine-related warnings or wine-related health benefits. Full article

Complementary medicines (CMs) are widely used worldwide, with usage rates ranging from 24% to 71.3%. Despite their popularity, many CMs lack robust scientific support and can potentially lead to adverse health effects. Limited research exists on CMs-related adverse events and the role of CMs’ labels in conveying crucial information to consumers. This cross-sectional study investigated the usage, consumer perspectives, and influence of labels specifically on product-based CMs, including nutritional supplements, vitamins, minerals, probiotics, prebiotics, and herbal medicines. Practitioner-led therapies and mind-body practices were outside the scope of this research. Data were collected through an online questionnaire and analyzed using descriptive statistics and correlation analysis. The study enrolled 125 participants who were current CMs users. Pharmacies and supermarkets were the primary sources for CMs procurement. Participants’ perceptions of CMs effectiveness and safety were positively correlated. Label warnings prompted participants to seek additional information, but consultation with healthcare professionals was infrequent. Adverse reactions were reported by 18.5% of participants, with self-management approaches being common. Label warnings play a significant role in prompting consumers to seek more information about CMs. However, the limited engagement of healthcare professionals, especially pharmacists, suggests an opportunity for improved consumer education and pharmacist involvement in CMs-related discussions. Addressing these aspects can lead to safer CMs practices and informed decision-making among consumers. Full article