Search Results (7,476)

Community water systems in the United States provide drinking water to more than 300 million people annually, making their reliability fundamental to public health. In regions with long histories of racial segregation and unequal infrastructure maintenance, water system failures can deepen existing environmental injustices. This study examines water quality conditions in the Jackson, Mississippi, metropolitan area following the 2022 distribution system collapse and a decade of repeated noncompliance with the Safe Drinking Water Act’s Lead and Copper Rule (LCR). Using the U.S. Environmental Protection Agency’s 2024 updated LCR tap sampling protocol, water samples from 29 sites were collected. Samples were analyzed for lead, copper, iron, zinc, chlorine, sulfate, pH, and total dissolved solids concentrations. Chlorine-to-sulfate mass ratios (CSMR) were also calculated to evaluate corrosion potential. Demographic surveys, statistical analyses, and geospatial visualizations were used to interpret neighborhood-level patterns. Our findings show that all sites met primary drinking water standards and complied with LCR action levels but exceeded secondary drinking water standards at 100% of study sites. Seven sites exhibited CSMR values above the threshold, indicating increased susceptibility to corrosion. These results highlight the need for targeted corrosion control, treatment optimization, and ongoing monitoring, particularly in historically marginalized communities. Full article

To reduce water consumption and potential formation damage associated with conventional water-based fracturing fluids while improving the proppant-carrying and flow adaptability of CO₂-based systems without relying on specialized CO₂ thickeners, a CO₂–water polymer hybrid fracturing fluid was developed using an AM/AA copolymer (poly(acrylamide-co-acrylic acid), P(AM-co-AA)) as the thickening agent for the aqueous phase. Systematic experimental investigations were conducted under high-temperature and high-pressure conditions. Fluid-loss tests at different CO₂ volume fractions show that the CO₂–water polymer hybrid fracturing fluid system achieves a favorable balance between low fluid loss and structural continuity within the range of 30–50% CO₂, with the most stable fluid-loss behavior observed at 40% CO₂. Based on this ratio window, static proppant-carrying experiments indicate controllable settling behavior over a temperature range of 20–80 °C, leading to the selection of 60% polymer-based aqueous phase + 40% CO₂ as the optimal mixing ratio. Rheological results demonstrate pronounced shear-thinning behavior across a wide thermo-pressure range, with viscosity decreasing systematically with increasing shear rate and temperature while maintaining continuous and reproducible flow responses. Pipe-flow tests further reveal that flow resistance decreases monotonically with increasing flow velocity and temperature, indicating stable transport characteristics. Phase visualization observations show that the CO₂–water polymer hybrid fracturing fluid system exhibits a uniform milky dispersed appearance under moderate temperature or elevated pressure, whereas bubble-dominated structures and spatial phase separation gradually emerge under high-temperature and relatively low-pressure static conditions, highlighting the sensitivity of phase stability to thermo-pressure conditions. True triaxial hydraulic fracturing experiments confirm that the CO₂–water polymer hybrid fracturing fluid enables stable fracture initiation and sustained propagation under complex stress conditions. Overall, the results demonstrate that the AM/AA copolymer-based aqueous phase can provide effective viscosity support, proppant-carrying capacity, and flow adaptability for CO₂–water polymer hybrid fracturing fluid over a wide thermo-pressure range, confirming the feasibility of this approach without the use of specialized CO₂ thickeners. Full article

Automated visual inspection of safety-critical metal assemblies such as automotive door lock strikes remains challenging due to their complex three-dimensional geometry, highly reflective surfaces, and scarcity of defect samples. While 3D sensing technologies are often constrained by cost and speed, traditional 2D optical methods struggle with severe imaging artifacts and poor generalization under few-shot conditions. This work constructs a complete system integrating defect imaging, generation, and detection. It proposes an integrated framework through the co-design of an image acquisition system and deep generative models to holistically enhance defect perception capability. First, we develop an imaging system using dome illumination and a small-aperture lens to acquire high-quality images of non-planar metal surfaces. Subsequently, we introduce a dual-stage generation strategy: stage one employs an improved FastGAN with Dynamic Multi-Granularity Fusion Skip-Layer Excitation (DMGF-SLE) and perceptual loss to efficiently generate high-quality local defect patches; stage two utilizes Poisson image editing and an optimized loss function to seamlessly fuse defect patches into specified locations of normal images. This strategy avoids modeling the complete complex background, concentrating computational resources on creating realistic defects. Experiments on a dedicated dataset demonstrate that our method can efficiently generate realistic defect samples under few-shot conditions, achieving 11–24% improvement in Fréchet Inception Distance (FID) scores over baseline models. The generated synthetic data significantly enhances downstream detection performance, increasing YOLOv8’s mAP@50:95 from 50.4% to 60.5%. Beyond proposing individual technical improvements, this research provides a complete, synergistic, and deployable system solution—from physical imaging to algorithmic generation—delivering a computationally efficient and practically viable technical pathway for defect detection in highly reflective, non-planar metal components. Full article

Increased global energy consumption contributes to higher operational costs in the energy sector and results in environmental deterioration. This study evaluates the effectiveness of integrating Internet of Things (IoT) sensors and machine learning techniques to predict adaptive thermostat setpoints to support behavior-aware Heating, Ventilation, and Air Conditioning (HVAC) operation in residential buildings. The dataset was collected over two years from 2080 IoT devices installed in 370 zones in two buildings in Halifax, Canada. Specific categories of real-time information, including indoor and outdoor temperature, humidity, thermostat setpoints, and window/door status, shaped the dataset of the study. Data preprocessing included retrieving data from the MySQL database and converting the data into an analytical format suitable for visualization and processing. In the machine learning phase, deep learning (DL) was employed to predict adaptive threshold settings (“from” and “to”) for the thermostats, and a gradient boosted trees (GBT) approach was used to predict heating and cooling thresholds. Standard metrics (RMSE, MAE, and R²) were used to evaluate effective prediction for adaptive thermostat setpoints. A comparative analysis between GBT ”from” and “to” models and the deep learning (DL) model was performed to assess the accuracy of prediction. Deep learning achieved the highest performance, reducing the MAE value by about 9% in comparison to the strongest GBT model (1.12 vs. 1.23) and reaching an R² value of up to 0.60, indicating improved predictive accuracy under real-world building conditions. The results indicate that IoT-driven setpoint prediction provides a practical foundation for behavior-aware thermostat modeling and future adaptive HVAC control strategies in smart buildings. This study focuses on setpoint prediction under real operational conditions and does not evaluate automated HVAC control or assess actual energy savings. Full article

In modern cybersecurity and deep learning, conditional password guessing plays a critical role in improving password-cracking efficiency by leveraging known patterns and constraints. In contrast with traditional brute-force or dictionary-based attacks, we developed an approach that adopts a latent diffusion model to simulate human password selection behavior, generating more realistic password candidates. We incorporated masked character inputs as conditions and applied advanced feature extraction to capture common patterns such as character substitutions and typing habits. Furthermore, we employed visualization techniques, including autoencoders and principal component analysis, to analyze password distributions, enhancing model interpretability and aiding both offensive and defensive security strategies. Full article

Accurate spatiotemporal alignment of multi-view video streams is essential for a wide range of dynamic-scene applications such as multi-view 3D reconstruction, pose estimation, and scene understanding. However, synchronizing multiple cameras remains a significant challenge, especially in heterogeneous setups combining professional- and consumer-grade devices, visible and infrared sensors, or systems with and without audio, where common hardware synchronization capabilities are often unavailable. This limitation is particularly evident in real-world environments, where controlled capture conditions are not feasible. In this work, we present a low-cost, general-purpose synchronization method that achieves millisecond-level temporal alignment across diverse camera systems while supporting both visible (RGB) and infrared (IR) modalities. The proposed solution employs a custom-built LED Clock that encodes time through red and infrared LEDs, allowing visual decoding of the exposure window (start and end times) from recorded frames for millisecond-level synchronization. We benchmark our method against hardware synchronization and achieve a residual error of 1.34 ms RMSE across multiple recordings. In further experiments, our method outperforms light-, audio-, and timecode-based synchronization approaches and directly improves downstream computer vision tasks, including multi-view pose estimation and 3D reconstruction. Finally, we validate the system in large-scale surgical recordings involving over 25 heterogeneous cameras spanning both IR and RGB modalities. This solution simplifies and streamlines the synchronization pipeline and expands access to advanced vision-based sensing in unconstrained environments, including industrial and clinical applications. Full article

Sulfamethazine (SMZ) is widely used in livestock production, and its residues can enter water and soil environments, posing potential risks to human health and ecosystems. This study focuses on environmental samples and constructs an AuNP-based colorimetric aptasensor using the SMZ1S aptamer for the rapid visual detection of SMZ. Under optimized conditions, the aptasensor showed a wide linear range from 0.05 to 0.4 µg/mL and a limit of detection of 0.039 µg/mL. Molecular dynamics simulations have demonstrated that the aptamer’s binding to SMZ is stable, providing a theoretical basis for the high selectivity of the aptasensor. Spike-and-recovery experiments yielded recoveries of 87.3–105.5%, 88.6–102.8%, and 87.5–103.4% for SMZ in lake water, tap water, and soil samples, respectively, with relative standard deviations of 5.9–8.3%, 8.0–10.6%, and 4.8–9.6%, showing good agreement with high-performance liquid chromatography (HPLC) results (R² ≥ 0.981). Overall, the proposed aptasensor provides a simple and effective approach for rapid detection of SMZ in environmental samples. Full article

Table tennis has increasingly been adopted as a tool to promote physical and mental health, yet evidence on its outcomes and implementation remains scattered. This study conducted a rapid scoping review to summarise available research on the health outcomes of table tennis within recreational or non-elite settings and identify how table tennis-for-health activities are structured and delivered. Peer-reviewed articles in English were included when they focused the outcomes of table tennis participation on health in community or social settings. Searches across two multidisciplinary databases, complemented by reference screening, led to 17 studies published between 2010 and 2025 being included. Studies were then charted for their methodological, intervention and outcome characteristics. Most studies employed quantitative methods, with experimental or controlled designs predominating, and targeted children, adolescents, older adults, and individuals with conditions such as ADHD or Parkinson’s disease. Across various settings, table tennis was associated with improvements in physical fitness, balance, agility, and body composition, alongside cognitive benefits such as enhanced executive functioning and visual–perceptual skills. Psychological and social outcomes, including improved self-efficacy, emotional regulation, cooperation and social interaction, were also reported. Though no formal quality assessment was conducted, there are clear methodological limitations, including small sample sizes, geographic and gender imbalances, and limited reporting on intervention characteristics that restrict the strength and generalisability of the findings. Overall, this review provides a starting point for trainers and health professionals in the area, presenting promising but preliminary evidence for table tennis as a health-enhancing activity and highlighting the need for more rigorous and comprehensive evaluation. Full article

White noise has been proposed to enhance cognitive performance in children with ADHD, but findings are inconsistent, and benefits vary across tasks and individuals. Such variability suggests that diagnostic comparisons may overlook meaningful developmental differences. This exploratory study examined whether developmental characteristics and subjective evaluations of auditory and visual white noise predicted performance changes in two eye-movement tasks: Prolonged Fixation (PF) and Memory-Guided Saccades (MGS). Children with varying degrees of ADHD symptoms completed both tasks under noise and no-noise conditions, and noise benefit scores were calculated as the performance difference between conditions. Overall, white-noise effects were small and dependent on noise modality and task. In the PF task, large parent-rated perceptual difficulties and high visual noise discomfort were associated with improved performance under noise. In the MGS task, poor motor skills predicted visual noise benefit, whereas large visual noise discomfort predicted reduced noise benefit. These findings suggest that beneficial effects of white noise are influenced by developmental characteristics and subjective perception in task-dependent ways. The results highlight the need for individualized, transdiagnostic approaches in future noise research and challenge the notion of white noise as categorically beneficial for ADHD. Full article

To address the challenges of low manual harvesting efficiency and high difficulty in automated picking of safflower filaments in the unstructured field environments of Xinjiang, this study proposes an intelligent harvesting method that integrates lightweight visual detection and adaptive localization. Firstly, a safflower image dataset covering multiple scenarios and growth stages was constructed. An improved lightweight detection model, named SSO-YOLO, was proposed based on the YOLOv11n model. By introducing the StarNet backbone network, the SEAttention mechanism, and structural optimization, this model achieves a high detection accuracy (mAP@0.5 of 97.4%) while reducing the model size by 29.4% to 3.94 MB, significantly enhancing its deployment feasibility on mobile devices. Secondly, based on the detection results, an adaptive localization algorithm for picking points was developed. This algorithm achieves precise localization of picking points at the filament–flower head junction by integrating geometric analysis of filament growth posture, dynamic judgment of connection conditions, and intersection calculation of rotated bounding boxes. Experimental results demonstrate that this algorithm achieves an average localization success rate of 87.3% across various unstructured scenarios such as occlusion and backlighting, representing an improvement of approximately 10.7 percentage points over traditional methods. The estimation error for filament posture angle is merely 0.6°, and the localization success rate remains above 90% across the entire growth cycle. This study provides an efficient and robust visual solution for the automated harvesting of safflower filaments and offers valuable insights for advancing intelligent harvesting technologies for specialty cash crops. Full article

Certified flight simulation training devices support pilot training and standardized instruction. However, high acquisition costs and vendor constraints on high-resolution operational/flight data can hinder academic research. This paper describes a low-cost, academically accessible simulator research infrastructure for systematic flight data logging, traceability, and post-flight visualization/analysis. The platform combines a two-station architecture (pilot and instructor) with a modular cockpit layout and physical interfaces (control column, rudder pedals, and switch panels), visual/auditory feedback, and software for scenario management and monitoring. A key contribution is a high-resolution (≥60 Hz) end-to-end data logging and traceability workflow that captures relevant telemetry, stores it in purpose-oriented formats (replay, .csv/.xlsx for analysis, and .log for maintenance), and enables time-aligned debriefing via the IOS/Pilot Log. As a proof of concept, a single-sample illustrative demonstration uses landing-phase data to generate representative diagnostic plots (approach profile, pitch–roll behavior, heading–track relationships), demonstrating the types of post-flight diagnostic visualizations that the infrastructure can generate. Since no baseline/control conditions, repeated trials, or benchmarks are included, the demonstration does not support generalized performance claims. Overall, the system is designed to provide an experimental infrastructure for researchers seeking to collect and analyze flight data using a simulator. Full article

Background: Peripherally inserted central catheters (PICCs) are widely used for medium- and long-term intravenous therapies but remain associated with mechanical and thrombotic complications, particularly during venipuncture and guidewire insertion. The growing use of Non-Operating Room Anesthesia (NORA) environments, where anesthesiologists frequently perform ultrasound-guided vascular access under conditions of limited resources and support, underscores the need for simple, reproducible, and inherently safe techniques. The objective of this technical note is to describe a modified ultrasound-guided venipuncture technique for non-tunneled PICC insertion, specifically developed for NORA settings, aimed at reducing procedure-related complications and preserving patient safety in routine clinical practice. Methods: The proposed technique consists of controlled intraluminal advancement of the needle tip (approximately 0.3–0.5 cm) under continuous ultrasound visualization, combined with progressive reduction in the insertion angle to achieve stable central intraluminal alignment before guidewire insertion. The technique has been applied in routine clinical practice across multiple Italian centers over the last two years, within a large multicenter real-world experience exceeding 5000 non-tunneled PICC procedures. Results: Based on real-world clinical observations, the systematic application of the technique was associated with a low incidence of early mechanical complications, including failed guidewire advancement, multiple venipuncture attempts, local pain, and hematoma formation. During standardized post-procedural ultrasound follow-up of the catheterized upper-extremity veins, no cases of catheter-related deep vein thrombosis were detected. Conclusions: This modified ultrasound-guided venipuncture technique represents a feasible and reproducible procedural refinement for non-tunneled PICC insertion in NORA environments. By enhancing intraluminal needle stability during guidewire advancement, it may contribute to improving procedural reliability and supporting patient safety in routine clinical practice. Further prospective and comparative studies are warranted to confirm these findings and define the generalizability of this approach. Full article

Low-voltage AC series arc faults are a leading cause of electrical fires, posing significant risks to life and property. While artificial intelligence-based detection methods have achieved high accuracy, they often suffer from limited interpretability and are typically tailored to specific loads, thus struggling to adapt to the diverse and dynamic load conditions in residential environments. To address these limitations, this paper proposes a novel interpretable arc fault detection model based on prototype learning with a hybrid attention mechanism. Specifically, we design a Tri-Domain Dynamic Attention (TDDA) module that integrates time-domain, frequency-domain, and temporal derivative information, and embed it into a Convolutional Neural Network (CNN) for enhanced feature extraction. Visual prototypes are constructed from sample characteristics, forming a tri-domain arc fault prototype set. A dedicated non-arc prototype set is further introduced to refine the decision boundary and improve accuracy. The proposed model is validated through comprehensive experiments and hardware implementation on a dedicated test platform. Results demonstrate that our model achieves an accuracy of 99.65%, maintains over 99% accuracy across various single-load conditions, and exhibits high detection performance under complex multi-load scenarios. Full article

Background/Objectives: Non-invasive samples offer an attractive alternative to logistically challenging invasive approaches in wildlife genetic studies but often contain low-quality host DNA that limits downstream analyses. Here, we assessed the applicability of moulted Eurasian goshawk feathers as a DNA source for whole-genome re-sequencing. Methods: We analysed 75 moulted feathers collected opportunistically from breeding territories. Each feather was measured from tip to tip, and its condition was visually assessed. Whole-genome re-sequencing was performed with a target coverage of 13× using 150 bp paired-end reads. Results: Feathers yielded an average of 7.19 ± 10.93 ng/μL DNA. DNA yield was positively correlated with feather size and the presence of blood traces in the calamus. On average, feather samples performed well, producing 208.7 ± 59.82 million reads, of which 82.69 ± 27.15% aligned to the reference genome, resulting in 83.58 ± 19.02% of the genome being covered at least once. After quality filtering, 10.34 ± 3.11 million biallelic single-nucleotide variants remained, of which 457,745 were common variants (MAF > 0.05). Larger feathers in good condition, with higher DNA yields and blood traces in the calamus, tended to perform better throughout the re-sequencing workflow. Nevertheless, approximately 22.7% of samples failed due to high missing data or poor genotype quality. Conclusions: Performance varied substantially even among samples with similar characteristics, indicating that improved sample selection incorporating direct measures of host DNA quality may be beneficial. Despite these challenges, moulted feathers represent a readily available DNA source for genome-wide re-sequencing of medium- to large-sized raptor species. Full article

In practical underwater object detection tasks, imbalanced sample distribution and the scarcity of samples for certain classes often lead to insufficient model training and limited generalization capability. To address these challenges, this paper proposes FS2-DETR (Few-Shot Detection Transformer for Sonar Images), a transformer-based few-shot object detection network tailored for sonar imagery. Considering that sonar images generally contain weak, small, and blurred object features, and that data scarcity in some classes can hinder effective feature learning, the proposed FS2-DETR introduces the following improvements over the baseline DETR model. (1) Feature Enhancement Compensation Mechanism: A decoder-prediction-guided feature resampling module (DPGFRM) is designed to process the multi-scale features and subsequently enhance the memory representations, thereby strengthening the exploitation of key features and improving detection performance for weak and small objects. (2) Visual Prompt Enhancement Mechanism: Discriminative visual prompts are generated to jointly enhance object queries and memory, thereby highlighting distinctive image features and enabling more effective feature capture for few-shot objects. (3) Multi-Stage Training Strategy: Adopting a progressive training strategy to strengthen the learning of class-specific layers, effectively mitigating misclassification in few-shot scenarios and enhancing overall detection accuracy. Extensive experiments conducted on the improved UATD sonar image dataset demonstrate that the proposed FS2-DETR achieves superior detection accuracy and robustness under few-shot conditions, outperforming existing state-of-the-art detection algorithms. Full article