Search Results (132)

(1) The beach areas of Galveston, Texas, USA are heavily used for recreational activities and often experience elevated fecal indicator bacteria levels, representing a potential threat to ecosystem services, human health, and tourism-based economies that rely on suitable water quality. (2) During the span of 15 months (March 2022–May 2023), water samples that exceeded the U.S. Environmental Protection Agency-accepted alternative Beach Action Value (BAV) for enterococci of 104 MPN/100 mL were analyzed via microbial source tracking (MST) through quantitative polymerase chain reaction (qPCR) assays. The Bacteroides HF183 and DogBact as well as the Catellicoccus LeeSeaGull markers were used to detect human, dog, and gull fecal sources, respectively. The qPCR MST data were then utilized in a quantitative microbial risk assessment (QMRA) to assess human health risks. Additionally, samples collected in July and August 2022 were sequenced for 16S rRNA and matched with fecal sources through the Bayesian SourceTracker2 program. (3) Overall, 26% of the 110 samples with enterococci exceedances were positive for at least one of the MST markers. Gull was revealed to be the primary source of identified fecal contamination through qPCR and SourceTracker2. Human contamination was detected at very low levels (<1%), whereas dog contamination was found to co-occur with human contamination through qPCR. QMRA identified Campylobacter from canine sources as being the primary driver for human health risks for contact recreation for both adults and children. (4) These MST results coupled with QMRA provide important insight into water quality in Galveston that can inform future water quality and beach management decisions that prioritize public health risks. Full article

Effective monitoring of fish passage through river barriers is essential for evaluating fishway performance and supporting adaptive river management. Traditional methods are often invasive, labor-intensive, or too costly to enable widespread implementation across most fishways. Infrared (IR) beam-break counters offer a promising alternative, but their adoption has been limited by high costs and a lack of flexibility. We developed and tested a novel, low-cost infrared beam-break counter—FishTracker—based on open-source Raspberry Pi and Arduino platforms. The system detects fish passages by analyzing interruptions in an IR curtain and reconstructing fish silhouettes to estimate movement, direction, speed, and morphometrics under a wide range of turbidity conditions. It also offers remote access capabilities for easy management. Field validation involved controlled tests with dummy fish, experiments with small-bodied live specimens (bleak) under varying turbidity conditions, and verification against synchronized video of free-swimming fish (koi carp). This first version of FishTracker achieved detection rates of 95–100% under controlled conditions and approximately 70% in semi-natural conditions, comparable to commercial counters. Most errors were due to surface distortion caused by partial submersion during the experimental setup, which could be avoided by fully submerging the device. Body length estimation based on passage speed and beam-interruption duration proved consistent, aligning with published allometric models for carps. FishTracker offers a promising and affordable solution for non-invasive fish monitoring in multispecies contexts. Its design, based primarily on open technology, allows for flexible adaptation and broad deployment, particularly in locations where commercial technologies are economically unfeasible. Full article

For unmanned aerial vehicles with long-duration autonomous missions, efficient energy management is critically important. One of the most promising solutions is solar power, the implementation of which requires the continuous orientation tracking of the Sun’s position. This study presents a three-axis active solar tracking system based on a gimbal mount, providing full kinematic control of the panel in space. A mathematical model of orientation is developed using the Earth-Centered Inertial, local geographic frame, and unmanned aerial vehicle body coordinate systems. An aerodynamic analysis is conducted, including a quantitative assessment of drag, lift, and torque on the panel. Based on the obtained characteristics, limiting conditions for the safe operation of the tracker are formulated. An adaptive control algorithm is introduced, minimizing a generalized objective function that accounts for angular deviation, aerodynamic loads, and current energy balance. Numerical simulations are described, demonstrating system stability under various scenarios: turbulence, maneuvers, power limitations, and sensor errors. The results confirm the effectiveness of the proposed approach under real-world operating conditions. Full article

Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by motor symptoms such as tremors, rigidity, and bradykinesia. The accurate and continuous monitoring of these symptoms is essential for optimizing treatment strategies and improving patient outcomes. Traditionally, clinical assessments have relied on scales and methods that often lack the ability for continuous, real-time monitoring and can be subject to interpretation bias. Recent advancements in wearable technologies, such as smartphones, smartwatches, and activity trackers (ATs), present a promising alternative for more consistent and objective monitoring. This review aims to evaluate the use of smartphones and smart wrist devices, like smartwatches and activity trackers, in the management of PD, assessing their effectiveness in symptom evaluation and monitoring and physical performance improvement. Studies were identified by searching in PubMed, Scopus, Web of Science, and Cochrane Library. Only 13 studies of 1027 were included in our review. Smartphones, smartwatches, and activity trackers showed a growing potential in the assessment, monitoring, and improvement of motor symptoms in people with PD, compared to clinical scales and research-grade sensors. Their relatively low cost, accessibility, and usability support their integration into real-world clinical practice and exhibit validity to support PD management. Full article

Multiple object tracking (MOT) is a critical and active research topic in computer vision, serving as a fundamental technique across various application domains such as human–robot interaction, autonomous driving, and surveillance. MOT typically consists of two key components: detection, which produces bounding boxes around objects, and association, which links current detections to existing tracks. Two main approaches have been proposed: one-shot and two-shot methods. While previous works have improved MOT systems in terms of both speed and accuracy, most works have focused primarily on enhancing association performance, often overlooking the impact of accelerating detection. Thus, we propose a high-speed MOT system that balances real-time performance, tracking accuracy, and robustness across diverse environments. Our system comprises two main components: (1) a hybrid tracking framework that integrates low-frequency deep learning-based detection with classical high-speed tracking, and (2) a detection label-based tracker management strategy. We evaluated our system in six scenarios using a high-speed camera and compared its performance against seven state-of-the-art (SOTA) two-shot MOT methods. Our system achieved up to 470 fps when tracking two objects, 243 fps with three objects, and 178 fps with four objects. In terms of tracking accuracy, our system achieved the highest MOTA, IDF1, and HOTA scores with high-accuracy detection. Even with low detection accuracy, it demonstrated the potential of long-term association for high-speed tracking, achieving comparable or better IDF1 scores. We hope that our multi-processing architecture contributes to the advancement of MOT research and serves as a practical and efficient baseline for systems involving multiple asynchronous modules. Full article

The article emphasises both the advantages and disadvantages of photovoltaic power plant deployment, assessing the current stage of development as well as the deficient characteristic criteria, such as the occupied specific surface area or the associated unpredictability. The authors consider that current technologies related to photovoltaic plants provide a maximum efficiency of approximately 28%. Consequently, management methods must be applied in order to improve efficiency and eliminate the reported deficiencies. When assessing a medium- to high-power PV plant, the initial investment, projected efficiency, and parameters of the desired plant are correlated, and sometimes, a cheaper and less efficient power plant can be recommended. Although solar trackers may represent a viable solution in certain scenarios, their effectiveness is strongly influenced by various factors, including panel orientation, climatic conditions, installed capacity, and the specific technologies. These variables can significantly affect such systems’ overall efficiency and suitability. The present study proposes a statistical approach to assessing the economic efficiency of photovoltaic systems equipped with solar trackers, aiming to enhance energy production performance. The results are correlated and validated using field data obtained from existing literature studies to ensure the reliability and accuracy of the analysis. For a better analysis, the paper presents two methods, ANOVA and STEM, which are derived from quality control. The novelty aspect of this proposal consists of the combination of specific data obtained from the PVGIS platform with a new approach for optimisation of energy production in photovoltaic systems based on geographical coordinates. The STEM statistical method provides a high degree of novelty because, although it is a well-known method, it has not yet been applied to analyse the technical and economic efficiency of photovoltaic systems. One of the main advantages of this method is its ability to incorporate a wide range of technical and economic performance parameters. A case study is provided to evaluate the benefits of implementing the STEM method. Full article

When process operators anticipate an alarm prior to its annunciation, that alarm loses information value and becomes a nuisance. This study investigated using eye trackers to measure and adjust the salience of alarms with three methods of gaze-based acknowledgement (GBA) of alarms that estimate operator anticipation. When these methods detected possible alarm anticipation, the alarm’s audio and visual salience was reduced. A total of 24 engineering students (male = 14, female = 10) aged between 18 and 45 were recruited to predict alarms and control a process parameter in three scenario types (parameter near threshold, trending, or fluctuating). The study evaluated whether behaviors of the monitored parameter affected how frequently the three GBA methods were utilized and whether reducing alarm salience improved control task performance. The results did not show significant task improvement with any GBA methods (F(3,69) = 1.357, p = 0.263, partial η² = 0.056). However, the scenario type affected which GBA method was more utilized (X² (2, N = 432) = 30.147, p < 0.001). Alarm prediction hits with gaze-based acknowledgements coincided more frequently than alarm prediction hits without gaze-based acknowledgements (X² (1, N = 432) = 23.802, p < 0.001, OR = 3.877, 95% CI 2.25–6.68, p < 0.05). Participant ratings indicated an overall preference for the three GBA methods over a standard alarm design (F(3,63) = 3.745, p = 0.015, partial η² = 0.151). This study provides empirical evidence for the potential of eye tracking in alarm management but highlights the need for additional research to increase validity for inferring alarm anticipation. Full article

This study evaluates the performance of You Only Look Once version 8 (YOLOv8) and a SAM-based unified and robust zero-shot visual tracker with motion-aware instance-level memory (SAMURAI) for worker detection in masonry construction environments under varying occlusion conditions. Computer vision-based monitoring systems are widely used in construction, but traditional object detection models struggle with occlusion, limiting their effectiveness in real-world applications. The research employed a structured experimental framework to assess both models in brick transportation and brick laying tasks across three occlusion levels: non-occlusion, partial occlusion, and severe occlusion. Results demonstrate that while YOLOv8 processes frames 2.5 to 3.5 times faster (28–32 FPS versus 9–12 FPS), SAMURAI maintains significantly higher detection accuracy, particularly under severe occlusion conditions (92.67% versus 52.67%). YOLOv8’s frame-by-frame processing results in substantial performance degradation as occlusion severity increases, whereas SAMURAI’s memory-based tracking mechanism enables persistent worker identification across frames. This comparative analysis provides valuable insights for selecting appropriate monitoring technologies based on specific construction site requirements. YOLOv8 is suitable for construction environments characterized by minimal occlusions and a high demand for real-time detection, whereas SAMURAI is more applicable to scenarios with frequent and severe occlusions that require the sustained tracking of worker activity. The selection of an appropriate model should be based on an initial assessment of environmental factors such as layout complexity, object density, and expected occlusion frequency. The findings contribute to the advancement of more reliable vision-based monitoring systems for enhancing productivity assessment and safety management in dynamic construction settings. Full article

In commercial poultry farming, automated behavioral monitoring systems hold significant potential for optimizing production efficiency and improving welfare outcomes at scale. The behavioral detection of free-range broilers matters for precision farming and animal welfare. Current research often focuses on either behavior detection or individual tracking, with few studies exploring their connection. To continuously track broiler behaviors, the Only Detect Broilers Once (ODBO) method is proposed by linking behaviors with identity information. This method has a behavior detector, an individual Tracker, and a Connector. First, by integrating SimAM, WIOU, and DIOU-NMS into YOLOv8m, the high-performance YOLOv8-BeCS detector is created. It boosts P by 6.3% and AP by 3.4% compared to the original detector. Second, the designed Connector, based on the tracking-by-detection structure, transforms the tracking task, combining broiler tracking and behavior recognition. Tests on sort-series trackers show HOTA, MOTA, and IDF1 increase by 27.66%, 28%, and 27.96%, respectively, after adding the Connector. Fine-tuning experiments verify the model’s generalization. The results show this method outperforms others in accuracy, generalization, and convergence speed, providing an effective method for monitoring individual broiler behaviors. In addition, the system’s ability to simultaneously monitor individual bird welfare indicators and group dynamics could enable data-driven decisions in commercial poultry farming management. Full article

The need for creative solutions in the real-time monitoring of health is rapidly increasing, especially in light of health incidents in relation to drivers of motor vehicles. A sensor-based health monitoring system provides an integrated mechanism for diagnosing and managing in real time, enabling the detection, prediction, and recommendation of treatment and the prevention of disease onset. The real-time monitoring of driver’s health represents a significant advancement in the assurance of driver safety and well-being. From fitness trackers to advanced biosensors, these devices have not only made healthcare more accessible but have also transformed how people interact with their health data. The purpose of this scoping review is to systematically collect and evaluate information from publications on driver health monitoring systems to provide a comprehensive overview of the current state of research on wearable or remote sensor technologies for driver health monitoring. It aims to identify knowledge gaps that need to be addressed and suggest future research directions that will help to fill these gaps. This approach involves the topic of vehicle safety and healthcare and will contribute to the advancement of this field. By focusing on the real-time monitoring of health parameters in an automotive context, this review highlights the potential of different types of technologies to bridge the gap between health monitoring and driver safety. Full article

Agrivoltaics have emerged as a promising solution to mitigate climate change effects as well as competition for land use between food and energy production. While previous studies have demonstrated the potential of agrivoltaic systems to enhance land productivity, limited research has focused on their impact on specific crops, particularly in organic processing tomatoes. In the present study, a two-year experiment was conducted in northwest Italy to assess the suitability of the agrivoltaic system on processing tomato yield and quality in the organic farming system. In the first growing season, the transplanting of tomato was carried out under the following light conditions: internal control (A1)—inside the tracker rows obtained by removing PV panels; extended agrivoltaic panels—shaded condition with an increased ground coverage ratio (GCR) of 41% (A2); and external control (FL)—full-light conditions outside the tracker rows. The second year of experimentation involved the transplanting of tomato under the following light conditions: internal control (B1); dynamic shading conditions that consist of solar panels in a vertical position until full fruit set (B2); standard agrivoltaic trackers (GCR = 13%, shaded conditions) (B3); and external control (FL). In 2023, the results showed that A2 achieved a total yield of only 24.5% lower than FL, with a marketable yield reduction of just 6.5%, indicating its potential to maintain productivity under shaded conditions. In 2024, B2 management increased marketable yield by 80.6% compared to FL, although it also led to a 46.2% increase in fruit affected by blossom end rot. Moreover, B2 improved nitrogen agronomic efficiency and fruit water productivity by 6.4% while also reducing the incidence of rotten fruit. Our findings highlight that moderate coverage (A2 and B2) can sustain high marketable yields and improve nitrogen use efficiency in different growing seasons. Full article

Background: Attention Deficit Hyperactivity Disorder (ADHD) has been described as a neurodevelopmental disorder characterized by inattention, hyperactivity, and impulsivity affecting cognitive, emotional, and social functioning. While pharmacological and behavioral treatments remain primary, physical activity (PA) (digital and non-digital versions) has emerged as a great complementary intervention due to its potential impact on executive functions, emotional regulation, and neurobiological markers. Objectives: This study aimed to assess the effects of PA on ADHD symptoms, executive function, and emotional regulation, exploring its potential impact and new practical applications in digital and non-digital treatment. Methods: This narrative review assessed 132 studies published between 1 January 2010 and January 2025, ensuring the inclusion of the most recent and relevant findings. The review was conducted in Scopus, PubMed, and Web of Science, using a predefined combination of terms related to ADHD, physical activity, executive function, neuroplasticity, and emotional regulation. Results: Regular PA improves executive functions, attention, inhibitory control, and cognitive flexibility in ADHD. Aerobic exercise enhances sustained attention, high-intensity training improves impulse control, and coordinative activities boost cognitive flexibility. Non-digital and digital innovations, such as exergaming and wearable fitness trackers, offer promising solutions to improve adherence to PA regimens, reinforcing their role as a key intervention in ADHD management. Conclusions: PA could be a valuable complementary intervention for ADHD through a hybrid approach that may improve cognitive and emotional functioning while addressing comorbidities. Full article

There is an ongoing need to evaluate whether commonly used educational software effectively supports inquiry-based learning and computational thinking skills development, which are key objectives in secondary STEM curricula. This research establishes criteria for characterising digital technologies, such as modelling and simulation software, virtual laboratories, and microcosms, to ensure their suitability in supporting students’ computational thinking through inquiry-based activities in STEM courses. The main criteria focus on six key areas: (a) production of meaning, (b) support in problem formulation, (c) ability to manage processes easily, (d) support in expressing solutions, (e) support in executing and evaluating solutions, and (f) ability to articulate and reflect on processes and solutions. Using this evaluation framework, two widely used software tools, Tracker 6.1.3 and GeoGebra 5, commonly employed in high school physics and mathematics, were assessed. The trial evaluation results are discussed, with recommendations for improving the software to support these educational objectives. Full article

Background/Objectives: Neurodegenerative diseases such as Parkinson’s disease (PD) are becoming increasingly prevalent, necessitating diverse treatment options to manage symptoms. The effectiveness of these treatments depends on accurate and sensitive diagnostic methods. This exploratory pilot study explores the use of eye tracking and compares it to neuropsychological tests on patients treated with deep transcranial magnetic stimulation (dTMS). Methods: We used the HTC Vive Pro Eye VR headset with Tobii eye tracker to measure eye movements in 10 Parkinson syndrome patients while viewing three 360-degree scenes. Eye movements were recorded pre- and post-dTMS, focusing on Fixation Duration, Longest Fixation Period, Saccade Rate, and Total Fixations. Neuropsychological assessments (MoCA, TUG, BDI) were conducted before and after stimulation. dTMS was performed using the Brainsway device with the H5 helmet, targeting the motor cortex (1 Hz) and the prefrontal cortex (10 Hz) for 7–12 sessions. Results: ROC analysis indicated a moderate ability to differentiate between states using eye movement parameters. Significant correlations were found between changes in the longest fixation period and MoCA scores (r = 0.65, p = 0.025), and between fixation durations and BDI scores (r = −0.55, p = 0.043). Paired t-tests showed no significant differences in eye movement parameters, but BDI scores significantly reduced post-dTMS (t(5) = 2.57, p = 0.049). Conclusions: Eye-tracking parameters, particularly the Longest Fixation Duration and Saccade Rate, could serve as sensitive and feasible biomarkers for cognitive changes in Parkinson’s Syndrome, offering a quick alternative to traditional methods. Traditional neuropsychological tests showed a significant improvement in depressive symptoms after dTMS. Further research with larger sample sizes is necessary to validate these findings and explore the diagnostic utility of eye tracking. Full article

Between 2018 and 2024, the global market has experienced significant advancements in sensor technologies for monitoring patients’ health conditions, which have demonstrated a pivotal role in diagnostics, treatment monitoring, and healthcare optimization. Progress in microelectronics, device miniaturization, and wireless communication technologies has facilitated the development of sophisticated sensors, including wearable devices such as smartwatches and fitness trackers, enabling the real-time monitoring of key health parameters. These devices are widely employed across clinical settings, nursing care, and daily life to collect critical data on vital signs, including heart rate, blood pressure, oxygen saturation, and respiratory rate. A systematic review of the developments within this period highlights the transformative potential of AI and IoT-based technologies in healthcare personalization, particularly in disease symptom prediction and public health management. Furthermore, innovative techniques such as respiratory inductive plethysmography (RIP) and millimeter-wave radar systems (mmTAA) have emerged as precise, non-contact solutions for respiratory monitoring, with applications spanning diagnostics, therapeutic interventions, and enhanced safety in daily life. Full article