In recent decades, organizational research has given special attention to the mechanisms promoting the health and wellbeing of nursing professionals. In this context, self-esteem is a personal resource which influences wellbeing at work and psychological wellbeing of nurses. The purpose of this study was to analyze the mediating role of eating in the effect of sleep quality on self-esteem in nursing professionals. The sample of 1073 nurses were administered the Rosenberg General Self-Esteem Scale, the Pittsburgh Sleep Quality Index and the Three-Factor Eating Questionnaire-R18. The results show that poor sleep quality and type of eating directly and indirectly affect self-esteem. More so, poor sleep quality deteriorated self-esteem through emotional eating, and even though emotional eating facilitated disinhibited eating, this relationship had no significant effects on self-esteem. The findings of this study suggest that hospital management should implement employee health awareness programs on the importance of healthy sleep and design educational interventions for improving the quality of their diet.

A Pediatric Sleep Questionnaire (PSQ) and a recording of the respiratory parameters with a noc-turnal home cardiorespiratory polygraphy (PG) are necessary to diagnose OSAS. Few researchers have studied how obesity could influence the respiratory parameters in OSAS children. The aim of the study was to investigate how obesity can influence sleep respiratory parameters in OSAS children. Methods: The study analyzes 56 Caucasian children and adolescents aged 11 ± 2.79 years with a BMI> 5th percentiles and PSQ value of 0.33. Children were divided into the Obesity Group (OG) with a BMI of  95th and the Control Group (CG) with a 5th<BMI> 95th percentile. All selected children underwent PG. Respiratory parameters were extracted to divide into levels of severity OSAS: snoring, mild, moderate, and severe. Results: The comparison analysis of AHI, SaO2, and Nadir between the OG and CG showed a statistical significance only for ODI (p = 0.02). The comparison analysis of PSQ between the OG and CG and of PSQ and respiratory parameters showed no significant result. A statistically significant correlation between BMI and AHI (r2=0.01), SaO2 (r2=0.09), and Nadir O2 (r2=0.00) was found. Conclusion: There was no strong correlation between Obesity and OSAS, but it was found between BMI increasing and AHI severity

Abstract: Rationale. Object tracking has significance in many applications ranging from control of unmanned vehicles to autonomous monitoring of specific situations and events, especially when providing safety for patients with certain adverse conditions such as epileptic seizures. Conventional tracking methods face many challenges, such as the need for dedicated attached devices or tags, influence by high image noise, complex object movements, and intensive com-putational requirements. We have developed earlier computationally efficient algorithms for global optical flow reconstruction of group velocities that provide means for convulsive seizure detection and have potential applications in fall and apnea detection. Here, we address the chal-lenge of using the same calculated group velocities for object tracking in parallel. Methods. We propose a novel optical flow-based method for object tracking. It utilizes real-time image se-quences from the camera and directly reconstructs global motion-group parameters of the con-tent. These parameters can steer a rectangular region of interest surrounding the moving object to follow the target. The method successfully applies to multi-spectral data, further improving its effectiveness. Results. Experimental results on simulated tests and complex real-world data demonstrate the method's capabilities. The proposed optical flow reconstruction can provide accurate, robust, and faster results compared to current state-of-the-art approaches.

This research addresses the formidable challenge of achieving precise trajectory tracking for autonomous vehicle formations in uncertain environments. It introduces an adaptive control system tailored for autonomous vehicles, with a primary focus on ensuring prescribed performance in trajectory tracking within a leader-follower formation paradigm. The system incorporates a guidance law that allows the leader vehicle to dynamically adjust desired yaw angles and speeds for follower vehicles based on the established reference trajectory, enhancing tracking accuracy and responsiveness to environmental changes. To address practical external disturbances, the study utilizes Radial Basis Function Neural Networks (RBFNN) in conjunction with second-order filters for error approximation. This approach is further strengthened by a carefully formulated adaptive law. The innovative integration of a barrier Lyapunov function with the backstepping method significantly enhances the system’s adaptability and robustness, ensuring trajectory tracking performance meets predetermined standards. Simulation results illustrate the control system’s adept handling of various external disturbances, consistently maintaining trajectory tracking errors within predefined limits. This underscores the system’s potential to markedly enhance the operational reliability and efficiency of autonomous vehicle formations in unpredictable environmental conditions.

In this paper, a framework for simultaneous tracking and recognizing drone targets using a low-cost and small-sized millimeter-wave radar is presented. The radar collects the reflected signals of multiple targets in the field of view including drone and non-drone targets. The analysis of the received signals allows multiple targets to be distinguished because of the different reflection patterns. The proposed framework consists of four processes including signal processing, cloud points clustering, target tracking, and target recognition. Signal processing translates the raw signals into spare cloud points. These points are merged into several clusters, each representing a single target. Target tracking estimates the new locations of each detected target. A novel convolutional neural network model is developed for drone and/or non-drone targets feature extraction and recognition. For performance evaluation, a dataset collected with an IWR6843ISK mmWave sensor by Texas Instruments is used for training and testing the convolutional neural network. The proposed recognition model achieves an accuracy of 98.4% for one target and 98.1% for two targets.

Cyclists are one of the main categories of road users particularly exposed to accident risk. The increasing use of this ecological means of transport requires a specific assessment of cyclist safety in terms of traffic flow and human factors. In this study particular visual tracking tool has been used in order to highlight not only the main critical points of the infrastructure, where a high level of distraction is recorded but also the various interactions with different road users (pedestrians, vehicles, buses, wheelchairs, cyclists). In order to confirm the critical points of the infrastructure and the trend of workload, a similar circuit was reproduced in a bicycle simulator, which also allowed a meaningful comparison of cycling behaviour. The cycling performance was also evaluated both from an objective point of view, with the count of frames related to each category of visualization, and a subjective one, through the questionnaires. The results show the crossing as a critical point because of only 4/3% fixation for both simulated and real tests in order to confirm the significance of the comparison between the two experiments. The high attention rate resulting from frame-by-frame analysis also points to a clear difference in the perception of users, who feel with a low workload.

Pedestrian multi-target tracking technology plays an important role in artificial intelligence, driverless, virtual reality and other fields. The pedestrian multi-target tracking algorithm DeepSORT based on detection is widely used in industry. It mainly tracks multiple pedestrian targets continuously and keeps their ID unchanged. In order to improve the applicability and tracking accuracy of DeepSORT algorithm, this paper improved the IOU distance measurement in the matching process. At the same time, ResNet50 is used as the feature extraction backbone network, and combined with FPN (Feature Pyramid Network), the appearance features of multi-layer pedestrians are fused to improve the tracking accuracy of DeepSORT algorithm. The proposed algorithm is verified on the public data set MOT-16 and it’s tracking accuracy is enhanced to 4.1%.

Currently, various industrial processes are carried out in fluidized bed reactors, such as fermentation, water treatment, and algae growth. Knowing its internal dynamics is fundamental for the intensification of these processes. This work assesses the motion of fluidized calcium alginate spheres under the influence of an upward fluid flow within a 1.2 m high and 0.1 m inner diameter acrylic column. The Radioactive Particle Tracking technique is a proper methodology to study the internal dynamics of these kinds of equipment. Liquid-Solid Fluidized Bed is compared with the Gas-Liquid-Solid Fluidized Bed operation mode in terms of mixing behavior. Data gathered is analyzed in view of Shannon entropy as a dynamic mixing measure. Mixing times are found to be of the same order of magnitude. However, the Liquid-Solid Fluidized Bed achieves a lower homogenization degree. Also, we found good agreement between two criteria for assessing stability for the mixed state: the Shannon Entropy time-series plateau and the Glansdorff-Prigogine criterion based on Fisher information.

This paper proposes a neural network which fuses the data received from a camera system on a gantry, to detect moving objects and calculate relative position and velocity of the vehicles traveling on a freeway, this information is used to estimate the traffic flow. To estimate the traffic flow at both microscopic and macroscopic view, this paper used YOLO v4 and DeepSORT for vehicle detection and tracking, then counting the number of vehicles pass through the freeway by drawing virtual lines and hot zones, also counting the velocity of each vehicles. The information is then pass to the traffic control center, in order to monitoring and control traffic flow on freeways, and analyzing freeway conditions.

(1) Background: There is robust evidence of an attention bias-anxiety relationship in children, but lack of appropriate methods has limited the number of studies with children younger than 8 years old. This study used eye-tracking as a measure of overt attention in young children. The aim of this study was to assess anxiety related attention bias in children aged 4 to 8 years. Age was considered as a moderator and the influence of effortful control was investigated. (2) Method: A community sample of 104 children were shown pairs of happy-neutral and angry-neutral faces. Growth curve analyses were used to examine patterns of gaze over time. (3) Results: Analyses revealed moderation by age and anxiety, with distinct patterns of anxiety-related biases seen in different age groups in the angry-neutral face trials. Effortful control did not account for age related effects. (4) Conclusions: Results support a moderation model of the development of anxiety in children.

Sleep is of vital necessity for health, it has a restorative and protective function for children and adolescents with chronic conditions and their families. The purpose was to identify the scientific production on sleep patterns in children and adolescents with chronic conditions and their families. This integrative review was developed in March and June 2022, of the MEDLINE, Web of Science, CINAHL and PSYCInfo. The articles included were original papers published be-tween January 2007 and mid-2022. Excluded were review studies that did not evaluate sleep and whose participants did not have chronic conditions or were not children, adolescents and/or their families. The searches returned 814 abstracts. After exclusions, 47 studies were selected to be read in full; of these, 29 were selected and were grouped empirically into four categories: major alterations in sleep patterns of children and adolescents with chronic conditions; the relationship between sleep disorders and symptoms in children and adolescents with chronic conditions; impaired sleep patterns of families of children and adolescents with chronic conditions; sleep alterations and their relationship with other problems in families of children and adolescents with chronic conditions. All studies showed sleep pattern impairment in children and adolescents with chronic conditions and their families.

Sleep disorder is a disease that can be categorized as both an emotional and physical problem. It imposes several difficulties and problems, such as distress during the day, sleep-wake disorders, anxiety, and several other problems. Hence, the main objective of this research is to utilize the strong capabilities of machine learning in the prediction of sleep disorders. In specific, this research aims to meet three main objectives. These objectives are to identify the best regression model, the best classification model, and the best learning strategy that highly suits sleep disorder datasets. Considering two related datasets and several evaluation metrics that are related to the tasks of regression and classification, the results revealed the superiority of the MultilayerPerceptron, SMOreg, and KStar regression models compared with the other twenty-three regression models. Also, IBK, RandomForest, and RandomizableFilteredClassifier showed superior performance compared with other classification models that belong to several learning strategies. Finally, the Function learning strategy showed the best predictive performance among the six considered strategies in both datasets and with respect to the most evaluation metrics.

Keywords: Parkinson’s disease; sleep disorders; GBA; Parkin

The study aimed to evaluate the after-effects of the COVID-19 pandemic on the sleep quality and patterns of women across different cultural settings within Iraq, spanning the period of 2022. A cross-sectional survey was administered to a diverse cohort of women from multiple provinces in Iraq. The survey measured various parameters, including sleep duration, onset latency, and disturbances. The post-pandemic period has seen a notable decline in sleep quality among the female population. Contributing factors include psychological stress, lifestyle changes due to the pandemic, and ongoing socio-economic challenges. The impact on sleep quality exhibited variation across different cultural groups, indicating a significant cultural influence on the pandemic's psychological aftermath. The study concludes that the post-pandemic phase has adversely affected the sleep health of women in Iraq, with cultural factors playing a critical role in these changes. There is a pressing need for culturally sensitive public health strategies to address sleep disturbances and mitigate their long-term effects on women's health. Public health initiatives should be tailored to address the specific cultural contexts within Iraq to improve sleep health among women. Further research is warranted to explore the long-term consequences of disturbed sleep patterns and the effectiveness of different intervention strategies.

Healthcare workers have atypical working schedule and are exposed to experienced stress that led to poor sleep health and frequent mental complaints, particularly since the Covid-19 crisis. We conducted a cross-sectional study to determine the prevalence of poor sleep hygiene and mental complaints and their associations among healthcare workers. Usual sleep-wake timings were ex-plored during workdays and free days and used to compute sleep duration, sleep efficiency and social jetlag. Insomnia, sleepiness, anxiety and depressive symptoms, and fatigue were calculated using validated scales. A total of 1,562 participants were included (80.5% of women and mean age of 40.0). Among them, 25.9% slept less than 6 hours, 24.3% had a poor sleep efficiency, and 11.5% reported a social jetlag. A total of 33.9% of participants reported insomnia, 45.1% reported EDS, 13.1% reported fatigue, 16.5% reported depression and 35.7% reported anxiety. After ad-justment, sleep duration and sleep efficiency were associated with mental complaints. Social jetlag was associated with significant insomnia but not with anxiety or depression symptoms. Healthcare workers have a high prevalence of poor sleep hygiene and mental complaints. The promotion of sleep health through behavioral sleep strategies should be encouraged to ensure good health for these professionals.

The objective of this literature review was to provide an up-to-date overview, and an analysis of the current knowledge on narcolepsy. A systematic search was conducted in different databases to identify relevant studies on various aspects of narcolepsy. The search terms included "narcolepsy," "excessive daytime sleepiness," "cataplexy," and related terms. The search was limited to studies published up until 2022. The initial screening of studies was performed based on titles and abstracts to identify potentially relevant articles. Full-text assessment was then conducted to determine the eligibility of each study for inclusion in the review. Studies were included if they provided information on the symptoms, classification, genetic aspects, impact on daily life, and gaps in knowledge regarding narcolepsy. The review reveals several important findings regarding narcolepsy: 1. the classification of narcolepsy - Type 1 narcolepsy, previously known as narcolepsy with cataplexy, and Type 2 narcolepsy, also referred to as narcolepsy without cataplexy. 2. the genetic component of narcolepsy and the complex nature of the disorder, which is characterized by excessive daytime sleepiness, disrupted sleep patterns, and potential impacts on daily life activities and social functioning. 3. the important implications for clinical practice in the management of narcolepsy. Healthcare professionals should be aware of the different types of narcolepsies and their associated symptoms, as this can aid in accurate diagnosis and treatment planning. The review underscores the need for a multidisciplinary approach to narcolepsy management, involving specialists in sleep medicine, neurology, psychiatry, and psychology. Clinicians should consider the impact of narcolepsy on a person's daily life, including their ability to work, study, and participate in social activities, and provide appropriate support and interventions. There are several gaps in knowledge regarding narcolepsy. Future research should focus on further elucidating the genetic causes of narcolepsy and exploring potential biomarkers for early detection and diagnosis. Long-term studies assessing the effectiveness of different treatment approaches, including pharmacological interventions and behavioural therapies, are needed. Additionally, there is a need for research on strategies to improve the overall well-being and quality of life of individuals living with narcolepsy, including the development of tailored support programs and interventions.

Abstract Background: Parkinson's disease (PD) is a neurodegenerative disorder featured with motor and non-motor deficits. Using 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) induced dopamine neuron degeneration has been widely used to produce reliable animal models of the PD. However, most of previous preclinical studies focused on the motor dysfunctions, few non-motor symptoms were evaluated. So far there is a lack of comprehensive investigation of the non-motor symptoms in animal models. Objectives: In this study, we aim to use a battery of behavioral methods to evaluate the non-motor symptoms in MPTP-induced non-human primate PD models. Methods: Cognitive functions, sleep and psychiatric behaviors were evaluated in MPTP-treated cynomolgus monkeys. The tests consisted of delayed matching-to-sample (DMTS), physical activity monitor (PAM), apathy feeding task (AFT), human intruder test (HIT), novel fruit test (NFT) and predator confrontation test (PCT). In addition, we tested whether the dopamine receptor agonist pramipexole (PPX) will improve these non-motor symptoms. Results: The present results show that the MPTP-treated monkeys exhibited cognitive deficits, abnormal sleep, and anxiety-like behaviors as compared to the control monkeys. These symptoms were relieved partially by PPX. Conclusions: These results suggest that MPTP-induced PD monkeys displayed non-motor symptoms that were similar to those found in PD patients. PPX treatment showed moderate therapeutic effects on these non-motor symptoms. This battery of behavioral tests may provide a valuable model for future preclinical research.

Gout is not only associated with obstructive sleep apnea (OSA), but the intermittent episodes of hypoxia that occur with OSA may also have a role in causing gout. Epidemiological studies have documented a higher incidence and prevalence of gout in individuals diagnosed with OSA than in individuals never diagnosed with OSA. The pathophysiology of OSA’s chronic episodes of hypoxia leading to hyperuricemia and gout involves boththe overproduction and underexcretion of uric acid. Treating OSA may be an additional way to control gout and its life-threatening comorbidities. Clinicians are urged to evaluate their patients with hyperuricemia/gout for OSA as it may lead to alternative ways to control gout with superior outcomes that simply pharmacologic treatment alone.

This study examined the prevalence of illegal drug use in UK students and motivators behind such behaviour. Additionally, we explored possible relationships between substance use, psychosocial motivators, and psychiatric distress. N=543 students completed online measures of substance use, anxiety, depression, perceived stress, insomnia. A series of reasons behind their use were ranked based on importance. Reported cannabis, cocaine, nitrous oxide, ketamine and MDMA use were most prevalent based on lifetime, past year, and month assessments. The experience of anxiety, depression, perceived stress, and insomnia were related to increased reports of substance use. Poor self-confidence and self-medication were key motivators of illicit drug use in those presenting greater psychiatric distress. These outcomes add to the sparse body of literature concerning illicit substance use in relation to psychiatric distress amongst UK students. Furthermore, we provided novel insight into the psychosocial motivators of such use.

Background: Medical comorbidities increase the risk of severe COVID-19 infection. In some studies, obstructive sleep apnea (OSA) has been identified as a comorbid condition that is associated with an increased prevalence of COVID-19 infection and hospitalization, but few have investigated this association in a general population. Research Question: In a general population, is OSA associated with increased odds of COVID-19 infection and hospitalization and are these altered with COVID-19 vaccination? Study Design: Cross-sectional survey of a diverse sample of 15,057 U.S. adults Results: COVID-19 infection and hospitalization rates were 38.9% and 2.9% respectively. OSA or OSA symptoms were reported in 19.4%. In logistic regression models adjusted for demographic, socio-economic and comorbid medical conditions, OSA was positively associated with COVID-19 infection (aOR: 1.58, 95%CI: 1.39-1.79) and COVID-19 hospitalization (aOR: 1.55, 95% CI: 1.17-2.05). In fully adjusted models, boosted vaccination status was protective against both infection and hospitalization. Boosted vaccination status attenuated the association between OSA and COVID-19 related hospitalization, but not infection. Participants with untreated or symptomatic OSA were at greater risk for COVID-19 infection; those with untreated, but not symptomatic OSA were more likely to be hospitalized. Interpretation: In a general population sample, OSA is associated with a greater likelihood of having had a COVID-19 infection and a COVID-19 hospitalization with the greatest impact observed among persons experiencing OSA symptoms or who were untreated for their OSA. Boosted vaccination status attenuated the association between OSA and COVID-19 related hospitalization.

Abstract: Background: Recently, cardiorespiratory fitness (CRF) has been postulated as an adverse health outcome related to poor sleep quality. However, studies investigating the relationship between CRF with subjective sleep quality index are scarce. Thus, the current study aimed to investigate the association between CRF and Pittsburgh sleep quality index (PSQI) in apparently healthy people. The secondary aim was to investigate the association between reported physical activities (PA) and PSQI. Methods: 33 apparently healthy male participants volunteered to participate. CRF (VO2 peak) was measured via cardiopulmonary exercise testing on a treadmill. A short form of the International physical activity questionnaire (IPAQ) was used to measure PA, and PSQI was used for sleep quality index. Results: There was no correlation between CRF and PSQI total score or any component of the PSQI. Also, there was no correlation between IPAQ and PSQI total score. Categorical data analysis of the two questionnaires revealed that 45.5% of the participants reported low physical activity and poor sleep quality. Conclusions: There was no association between CRF, reported PA with subjective sleep quality index. The use of objective tools for assessing the quality and quantity of sleep should be recommended for future studies as it may clarify the association between CRF and sleep quality.

Sleep quality has been directly linked to cognitive function, quality of life, and a variety of serious diseases across many clinical domains such as psychiatry and cardiology. Standard methods for assessing sleep involve overnight studies in hospital settings, which are uncomfortable, expensive, not representative of real sleep, and difficult to conduct on a large scale. Recently, a number of commercial digital devices have been developed that record physiological data which can act as a proxy for sleep quality in lieu of standard electroencephalogram recording equipment. Each device company makes different claims of accuracy and measures different features of sleep quality, and it is still unknown how well these devices correlate with one another and perform in a research setting. In this pilot study of 21 participants, we investigated whether outputs from four sensors, specifically FitBit, Withings Aura, Hexoskin, and Oura Ring, were related to known cognitive and psychological metrics, including the PSQI and N-back test. We found that sleep metrics extracted from these devices did not predict cognitive and psychological metrics well in our pilot data. However, we did identify certain signification associations, specifically the Oura Ring’s total sleep duration and efficiency in relation to the PSQI measure with p=0.004 and p=0.033, respectively. Additionally, correlation of various sleep features among the devices across the sleep cycle was almost uniformly low. These findings can hopefully be used to guide future sensor-based sleep research.

The Internet use has become an integral part of daily life, adolescents are especially at a higher risk to develop problematic Internet use (PIU). Although one of the most well-known comorbid conditions of PIU is sleep disturbance, little is known about the sex disparity in this association. This school-based survey in students of grades 7-9 was conducted to estimate the prevalence of PIU and sleep disturbance among Chinese adolescents, to test the association between PIU and sleep disturbance, and to investigate the role of the child’s sex in this association. A two-stage stratified cluster sampling method was used to recruit participants, and a two-level logistic regression models were fitted. The mean Internet addiction test scores was 37.2 (SD: 13.2), and 15.5% (736) met the criteria for PIU. After adjusting for control variables, problematic Internet users were at a higher risk of sleep disturbance (adjusted odds ratio=2.41, 95% CI=2.07-3.19). Sex-stratified analyses also demonstrated that association was greater in girls than boys. In this respect, paying more attention to the sleep patterns of adolescents who report excessive Internet use is recommended, and this early identification may be of practical importance for schools, parents, and adolescents themselves.

This paper introduces PatchFusion, an innovative approach for nonrigid tracking and reconstruction of deformable objects using a single RGB-D sensor. Existing methods face challenges in accurately capturing the rapid deformations of soft and flexible objects, thereby limiting their utility in diverse scenarios. Our approach overcomes this challenge by employing a dynamic patch-based framework that adapts to rapid inter-frame motions. Firstly, patch-wise rigid transformation fields for non-overlapping patches are solved via Iterative Closest Point (ICP) by incorporating geometric features as additional similarity constraints, thereby enhancing robustness and accuracy. Secondly, deformation optimization based on a nonrigid solver is applied to refine the coarse transformation fields. In order to enable simultaneous tracking and reconstruction of deformable objects, the patch-based rigid solver is designed to run in parallel with the nonrigid solver, serving as a plug-and-play module requiring minimal modifications for integration while enabling real-time performance. Following a comprehensive evaluation, PatchFusion showcases superior performance in effectively dealing with rapid inter-frame deformations when compared to existing techniques, rendering it a promising solution with broad applicability across domains such as robotics, computer vision, and human-computer interaction.

Managing a portfolio whose value closely tracks an index by trading only in a subset of the index constituents involves an NP-hard optimisation problem. In the prior literature, it has been suggested to solve this problem using sequential Monte Carlo (SMC, a.k.a. particle filter) methods. However, this literature does not take transaction costs into account, although transaction costs are the primary motivation for attempting to replicate the index by trading in a subset, rather than the full set of index constituents. This paper extends the SMC approach to index tracking to allow for proportional transaction costs and implements this extended method on empirical data for a variety stock indices. In addition to providing a more practically useful tracking strategy by allowing for transaction costs, we find that including a penalty for transaction costs in the optimisation objective can actually lead to better tracking performance.

: In this paper, we examine potential methods for autonomously tracking and landing multi-rotor unmanned aerial vehicles (UAVs), a complex yet essential problem. Autonomous tracking and landing control technology utilizes visual navigation, relying solely on vision and landmarks to track targets and achieve autonomous landing. This technology improves the UAV's environment perception and autonomous flight capabilities in GPS-free scenarios. In particular, we are researching tracking and landing as a cohesive unit, devising a switching plan for various UAV tracking and landing modes, and creating a flight controller that has an inner and outer loop structure based on relative position estimation. The inner and outer nested markers aid in the independent monitoring and touchdown of UAV. Optimal parameters are determined via optimized experiments on the measurements of the inner and outer markers. An indoor experimental platform for tracking and landing UAV was established. Tracking performance was verified by tracking three trajectories of unmanned ground vehicle (UGV) at varying speeds, and landing accuracy was confirmed through static and dynamic landing experiments. The experimental results show that the proposed scheme has good dynamic tracking and landing performance.

This study focuses on the recently emerged Internet of Vehicles (IoV) concept to provide an integrated agricultural vehicle/machinery tracking system through two leading low power wide area network (LPWAN) technologies, namely LoRa and NB-IoT. The main aim is to investigate the theoretical coverage limits by considering the urban, suburban, and rural environments. Two vehicle tracking units (VTUs) have been designed for LoRa and NB-IoT connectivity technologies that can be used as reference hardware in coverage analysis. On this basis, the closed-form explicit analytical expressions of the maximum transmission range have been derived using the Hata path loss model. Besides, the computer simulation results have been validated via the maps from XIRIO online radio planning tool. In light of the obtained findings, several evaluations have been made to enhance the LPWAN-based agricultural vehicle tracking feasibility in smart farms.

Real-time tracking is one of the most challenging problems in computer vision. Most Transformer-based trackers usually require expensive computational and storage power, which leads to the inability of these robust trackers to achieve real-time performance in resource-constrained devices. In this work, we propose a lightweight tracker, AnteaTrack. To localize the target more precisely, we develop a scaling-invariant max-filtering operator employing a sliding window combined with local max-pooling, which filters out the suspected target from the feature and performs an augmented representation while suppressing the background. For a more tight bounding box, we employ Pixel-Shuffle to increase the resolution of the feature map and get a more fine-grained representation of the target. In addition, AnteaTrack can run in real-time at 47 frames per second(FPS) on the CPU. We tested AnteaTrack on 5 datasets, and a large number of experiments have shown that AnteaTrack provides the most efficient solution compared to the same type of CPU real-time trackers. The code will be available at https://github.com/cnchange/AnteaTrack.

We propose a deep reinforcement learning based manipulator path tracking method to solve the computationally difficult and non-unique problem of manipulator path tracking methods based on inverse kinematics. By transforming the path tracking task into a sequence decision problem, our method adopts an end-to-end learning method for closed-loop control and avoids the process of finding the inverse solution. We first explored the feasibility of the deep reinforcement learning method in the path tracking of the manipulator. After verifying the feasibility, the path tracking of the multi-degree-of-freedom(multi-DOF) manipulator was realized by combining the maximum entropy deep reinforcement learning algorithm. The experimental results show that our method has a good effect on the path tracking of the manipulator, which not only avoids the process of finding the inverse kinematics solution, but also requires no dynamic model. Therefore, we believe that our method has great significance in the study of manipulator path tracking.

Oculomotor Dysfunctions (OMDs) are problems with eye-musculature relating to coordination and accuracy of eye movements. Eye-tracking (ET) technologies show great promise in the identification of OMDs. Current computer technologies for vision screening are specialized devices or laptop based technologies with limited by screen size and the inability to measure depth. In this experimental study, we examine the possibilities of immersive virtual reality (VR) technologies for increased user experiences, presence, immersiveness, and the use of serious games. Further results of an experimental study present increased interest in VR-based screening, its ability to focus better, and aspects motivating for its use, despite the actual limitations of current technologies. These limitations currently include lower performance and confidence in results of the used HMDs. Some users also describe being more focused when screening in VR, free from outside distractions. Using serious games for screening in VR is also estimated to have great potential for developing a more robust vision screening tool, especially for younger children.

Manual material handling tasks in industry cause work-related musculoskeletal disorders. Exoskeletons are being introduced to reduce the risk of musculoskeletal injuries. This study investigated the effect of using a passive lumbar exoskeleton in terms of moderate ergonomic risk. Eight participants were monitored by electromyogram (EMG) and motion capture (MoCap) while performing tasks with and without the lumbar exoskeleton. The results showed a significant reduction in the root mean square (VRMS) for all muscles tracked: erector spinae (8%), semitendinosus (14%), gluteus (5%), and quadriceps (10.2%). The classic fatigue parameters showed a significant reduction in the case of the semitendinosus: 1.7% zero-crossing rate, 0.9% mean frequency, and 1.12% median frequency. In addition, the logarithm of the normalized Dimitrov’s index showed reductions of 11.5, 8, and 14% in erector spinae, semitendinosus, and gluteus, respectively. The calculation of range of motion in the relevant joints demonstrated significant differences, but in almost all cases, the differences were smaller than 10. The findings of the study indicate that the passive exoskeleton reduces muscle activity and introduces a minor change of strategies for motion. Thus, EMG and MoCap appear to be appropriate measurements for designing an exoskeleton assessment procedure.

The fields of electronics and information technology have witnessed rapid development during the last decades, providing significant technical support to the field of biological sciences. Radio-Frequency Identification (RFID) technology has been used to automate the monitoring of animal location and behaviors in a wide range of vertebrate and invertebrate species, including social insects such as ants and honey bees (Apis mellifera L.) This technology relies on electromagnetic fields to identify and track transponders attached to objects automatically. Implementing new technologies to serve research purposes could be time-consuming and require technical expertise from entomologists and researchers. Herein, we present a detailed description on how to harness RFID technology to serve honey bee research effectively. We describe how to build and operate a 32-antenna RFID system used to monitor various honey bee behaviors such as foraging, robbing, queen and drone mating, which can be used in other social insects as well. Preliminary data related to queen nuptial flights were obtained using this unit and presented in this study. Virgin queens labeled with ~5mg transponders performed multiple (1-4) nuptial/orientation flights a day (9 am to 5 pm) ranging from 8 to 145 seconds each. Contrary to virgin queens, no hive exit was recorded for mated-queens. At full capacity, this unit can monitor up to 32 honey bee colonies concurrently and is self-sustained by a solar panel to work in remote areas. All materials, hardware and software needed to build and operate this unit are detailed in this study, offering researchers and beekeepers a practical solution and a comprehensive source of information enabling the implementation of RFID technology in their research perspective.

With the depletion of shallow surface resources, the future mining work will develop towards the deep surface, and the objective conditions such as the mining environment will be more complex and dangerous than now, and the requirements for personnel and equipment will be higher and higher. The efficient exploitation of deep space cannot be separated from such mobile and flexible production and transportation equipment as scraper. In the new era, intelligence is the development trend of the LHD, and path tracking control technology is the key to the intelligent LHD, and it is also an urgent problem to be solved for its unmanned driving. This paper describes the realization of the automatic operation function of articulated LHD from two aspects of mathematical model and trajectory tracking control method, and focuses on the research of the path tracking control scheme in the field of unmanned driving, that is, LQR controller. On this basis, the parameters of the LQR controller are optimized by combining different intelligent cluster algorithms to find the optimal solution of the LQR controller.

The safety of people is an important phenomenon nowadays. This importance arises due to the crowded places including subway station, universities, colleges, airport, shopping mall and square, and city squares. Therefore, the development of an effective system based on physical characteristics of crowd layout is of significant demand. In this paper, we proposed a novel automated and intelligent systems for crowd event analysis based on a set of physical elements. For this purpose, we take into account optical flow and spatial-time gradient, contour features, and Gaussian processes. Our method combine these characteristics into a unique model to deal with the challenging problem of crowd event analysis. For evaluating our proposed method, we consider a benchmark dataset and a number of different performance metrics. These analysis demonstrate the robustness and effectiveness of our proposed method.

In tracking-by-detection paradigm for multi-target tracking, target association is modeled as an optimization problem that is usually solved through network flow formulation. In this paper, we proposed combinatorial optimization formulation and used a bipartite graph matching for associating the targets in the consecutive frames. Usually, the target of interest is represented in a bounding box and track the whole box as a single entity. However, in the case of humans, the body goes through complex articulation and occlusion that severely deteriorate the tracking performance. To partially tackle the problem of occlusion, we argue that tracking the rigid body organ could lead to better tracking performance compared to the whole body tracking. Based on this assumption, we generated the target hypothesis of only the spatial locations of person’s heads in every frame. After the localization of head location, a constant velocity motion model is used for the temporal evolution of the targets in the visual scene. Qualitative results are evaluated on four challenging video surveillance dataset and promising results has been achieved.

Accurate detection of players and teams in ice hockey games is crucial to the tracking of individual players on court and team tactical decisions, which is therefore becoming an important task for coaches and other analysts. However, hockey is a fluid sport due to complex dynamics and frequent substitutions by both teams, resulting in various body positions of players. Few traditional player detection models from other team sports take these characteristics into account, especially for the detection of teams without prior annotations. Here, we design a two-phase cascaded Convolutional Neural Network (CNN) model coupling between individual players position information and team uniform colors to hierarchically detect players in ice hockey games. Our model filters most of disturbing information, such as audience and sideline advertising bars, in Phase I, and refines the detection of targeted players in Phase II, which is efficient and accurate with a precision rate of 91.30% and a recall rate of 95.60% for individual players detection, and an average accuracy of 93.05% in team classification from a self-built dataset of collected images in the 2018 Winter Olympics. Meanwhile, we also present results based on the images and real-time detection from broadcasting videos of 2019-20 NHL regular games covering all 31 teams to show the robustness of our model.

Music acoustics is an interdisciplinary field and mathematics is the basis in the music art form. Music and mathematics correlation exist since the inception of music. Various philosophers, scientists, mathematicians and musicians have expressed their views about this relationship. This paper attempts to explore this association with focus on melodic pattern identification. Mathematics in Indian Classical music with raga as the basis and just intonation tuning system is discussed. Indian vocal music clips are used for different pitch estimation algorithms in the experimentation. Harmonic product spectrum and auto-correlation algorithms are tested for accurate pitch estimation. Enhanced auto-correlation function using audio segmentation is compared with other approaches for effective pitch extraction. Results indicate pitch extraction with enhanced auto-correlation function provides accurate results as compared with other approaches tested.

When a rotary machine is running, from which the acquired vibro-acoustic signals enable to reveal its operation status and health condition. The study proposed a DSP-based adaptive angular-velocity Vold-Kalman filtering order tracking (AV2KF_OT) algorithm with an online real-time nature for signal interpretation and machine condition monitoring. Theoretical derivation and numerical implementation of computation schemes are briefly introduced. An online real-time monitoring system based on the AV2KF_OT algorithm, which was implemented through both a digital signal processor and a user interface coded by using LabVIEW, was developed. Two experimental tasks were applied to justify the proposed technique, including (i) the detection of startup on the fluid-induced whirl performed through a journal-bearing rotor rig, and (ii) the separation of close orders from the measured signals of a multifunction transmission-element ball-bearing bench.

This paper presents a new adaptive and optimal algorithm for the trajectory tracking control of a quadrotor using iterative learning algorithm (ILA) and enumerative learning algorithm. Ordinarily the ILA, as an adaptive method, can perform well with PID control to improve the controller’s performance for a nonlinear system. Quadrotors are considered as non-linear and unstable systems which the use of an adaptive and optimal controller can increase its stability and decrease error level. In this method, a PID controller is proposed for the outer and inner control loops of a quadrotor and the ILA is used to adapt PID control gains. Subsequently, an enumerative learning algorithm is used to optimize the learning rates of the ILA. For this purpose, at first, the dynamic model of the quadrotor is acquired. After that, the structure of the inner and outer control loops is defined. In the end, the simulation results for the trajectory tracking control of a quadrotor are demonstrated. Through simulation, it is concluded that as time increases, the performance of the suggested control method in trajectory tracking control becomes better and better and error signals convergence to zero.

Sports video analysis is an interesting and emerging field in the domain of computer vision. It has become a very popular subject among scholars all over the world in the last decade due to the need of automated classification of events such as offsides, in/out situations, goals and so on. The most important problem in sports video analysis is detection and tracking of ball and players. Extraction of court lines is also another important problem since it is necessary in the classification of aforementioned events. In this study, considering some of the previous work on the subject, we develop a method for tracking the ball and players in tennis videos where the camera position and orientation is supposed fixed.

Continuous object tracking in WSNs, such as monitoring of mud-rock flows, forest fires etc., is a challenging task due to characteristic nature of continuous objects. They can appear randomly in the network field, move continuously, and can change in size and shape. Monitoring such objects in real-time generally require tremendous amount of messaging between sensor nodes to synergistically estimate object’s movement and track its location. In this paper, we propose a novel twofold-sink mechanism, comprising of a mobile and a static sink node. Both sink nodes gather information about boundary sensor nodes, which is then used to uniformly distribute energy consumption across all network nodes, thus helping in saving residual energy of network nodes. Numerous object tracking schemes, using mobile sink, have been proposed in the literature. However, existing schemes employing mobile sink cannot be applied to track continuous objects, because of momentous variation of network topology. Therefore, we present in this paper a mechanism, transformed from K-means algorithm, to find the best sensing location of the mobile sink node. It helps to reduce transmission load on the intermediate network nodes situated between static sink node and the ordinary network sensing nodes. The simulation results show that the proposed scheme can distinctly improve life time of the network, compared to one-sink protocol employed in continuous object tracking.

We develop an interactive likelihood (ILH) for sequential Monte-Carlo (SMC) methods for image-based multiple target tracking applications. The purpose of the ILH is to improve tracking accuracy by reducing the need for data association. In addition, we integrate a recently developed deep neural network for pedestrian detection along with the ILH with a multi-Bernoulli filter. We evaluate the performance of the multi-Bernoulli filter with the ILH and the pedestrian detector in a number of publicly available datasets (2003 PETS INMOVE, AFL, and TUD-Stadtmitte) using standard, well-known multi-target tracking metrics (OSPA and CLEAR MOT). In all datasets, the ILH term increases the tracking accuracy of the multi-Bernoulli filter.

Accidents and mishaps in industrial environments like construction, mining, and transport are rampant - mainly due to human negligence and improper monitoring of the workplace. In this paper, we address the safety of workers operating in dangerous environments by improving their situational awareness. According to Occupational health and safety rules, everyone must wear hard hats while on site. Our main idea is to make the hard hats smart by incorporating miniature-sized Doppler radars sensing the users’ surroundings. These Doppler radars are lightweight, rugged, and consume low-power compared to vision-based solutions. This paper discusses the observability of range from Doppler frequency measurements and the magnitude of estimation errors introduced by the human head, walking, and working motions. We present the framework to estimate the position of walls and targets surrounding the worker. For testing, we simulated an indoor environment with randomly moving workers. Experiments showed that once observability conditions are met, human head and walking movements can be handled through added noise in the system. We also present an innovative idea of using two Doppler radars to obtain the estimators’ initial estimates, reducing the estimation error to less than 5cm and convergence time by more than 80

In the contemporary accelerated pace of life, emphasizing the importance of sleep quality is essential for enhancing overall well-being and health. This study investigates the link between poor sleep quality and key factors affecting wellness, such as mental health and eating disorders. Through a cross-sectional analysis involving 407 participants, utilizing established measures including the Depression Anxiety Stress Scale (DASS-21), the Eating Disorder Examination Questionnaire Short (EDE-QS), and the single-item Sleep Quality Scale (SQS), data was collected and analyzed using SPSS v28 and R-Statistics. The findings reveal a significant correlation (p&lt;0.05) between DASS-21, EDE-QS, and SQS, indicating that individuals experiencing poor sleep quality exhibit higher levels of depression, anxiety, and stress. Furthermore, multinomial logistic regression analysis highlights low sleep quality as a risk factor for both mental health (OR: 1.071, 95% CI: 1.042, 1.102, p&lt;0.05, low vs. high sleep quality), and eating disorders (OR: 1.047, 95% CI: 1.004, 1.092, p&lt;0.05, low vs. high sleep quality). Overall, these results underscore the critical role of sleep quality in mental health and suggest insomnia as a predictive factor for both poor mental well-being and disordered eating habits. Further research through randomized controlled trials is warranted to validate the findings of this cross-sectional study.

Sleep and stress significantly affect emotions, productivity, and overall well-being, crucial for optimal health and performance. However, limited research examines how Nepalese nurses' quality of life relates to their sleep quality and stress levels. This study aims to assess this relationship among nurses in private hospitals in Kathmandu. Three private hospitals were selected for this cross-sectional analytical study. Eligible nurses were chosen through purposive sampling, and data were analysed using IBM SPSS v25, employing Chi-square tests and logistic regression analysis. Research revealed a high rate of poor sleep quality (83.9%) among individuals with a mean age of 24.33 ± 3.08 years. Initially, significant associations were found with the factors like bachelor's degree, no tobacco use, absence of insomnia, good sleep quality, and low stress with good quality of life. After adjustment, the association with bachelor's degree [AOR:2.025, CI at 95%: 1.021-4.016, p

We investigated if the abnormal sleep-wake rhythms in infants are related to the future development of autism spectrum disorder (ASD), using questionnaire from September to October 2016. The parents of a total 160 children with ASD (male, n=123; female, n=37) recruited from the two hospitals in K and H cities and as a control group, 145 children (male, n=75; female, n=70) recruited from four nursery schools in T city. Fisher’s exact test was used for the comparison of categorical variables. The associations of bedtime and waking time on weekdays and holidays in infancy, at 1-3≦ (1-3) years of age and at 3-5 ≦ (3-5)years of age with the prevalence of ASD were analyzed using a multivariable logistic regression analysis. Especially by 3 years of age, the following factors were associated with an increased risk of developing ASD in the future: 1) short sleep (<8h), 2) long time to fall asleep (>60min), 3) sleep onset time after 22:00h, 4) wake-up time after 07:00h and/or 08:00h on weekdays and holidays, respectively, 5) frequent (>3 times) and long-term awakening (>60min). Misalignment and/or shift of the sleep-wake circadian rhythm in infant may be one of the precursors and/or risk factor to the future development of ASD.

Sleep is a fundamental human need: sleep disruption, in fact, causes an increase in the activity of the sympathetic nervous system and the hypothalamic-pituitary-adrenal axis, metabolic effects, changes in circadian rhythms and pro-inflammatory responses. Sci-entific literature is finally starting to pay attention to the central role of sleep-wake rhythm alterations for patients health. Oxaliplatin is used extensively for the treatment of gastrointestinal cancer and other malignancies, with increased frequency of use in recent years. This study aims to understand the effects of sleep-wake rhythm alterations on health and quality of life on cancer patients treated with oxaliplatin. Study has been conducted through the creation and distribution of questionnaires to patients to inves-tigate the potential alteration of the sleep-wake quality. Evaluating the influence of the collected variables on sleep quality, only liver and biliary tract and pancreas tumor lo-calization, in males, and previous difficulty sleeping, and anxiety, worries and stress, in females, resulted statistically significant. The results obtained may serve as foundation for designing awareness campaigns and updating of patients, healthcare personnel and caregivers, to improve health and quality of patients life.

The coronavirus (COVID-19) pandemic limitations may negatively affect children and youth in terms of health behaviors, and it might be especially challenging for children who are diagnosed with neurodevelopmental disorders such as children and youth diagnosed with Autism Spectrum Disorder (ASD). The present study assessed alterations in physical activity, screen time, and sleep duration of ASD children and the prevalence of meeting the 24-hour movement guidelines during the COVID-19 outbreak. Forty-six Arab Israeli mothers of children with ASD were surveyed by an online cross-sectional survey. As reported by the mothers, the results show a significant decrease in physical activity, a significant increase in screen time, and a significant increase in sleep duration during the COVID-19 pandemic. Moreover, the proportion of the sample who gained the physical activity and screen time recommendations lessened while the percentage of children who met the sleep duration guidelines increased. The prevalence of ASD children who achieved the overall 24-hour movement guidelines was very small during the COVID-19 outbreak. The outcomes extend the body of knowledge regarding the negative effects of the COVID-19 pandemic on children diagnosed with neurodevelopmental disorders and highlight the need for instant healthcare and interventions and programs for children with ASD.

Sleep timing is one of the dimensions of sleep that refers to the time of day when sleep occurs. It was included in sleep-related research because of the potential associations between overweight and consumption of meals and snacks. This cross-sectional study aimed to investigate associations between sleep timing, meal and snack consumption, and weight status in 1333 schoolchildren aged 7-14 years. The midpoint of sleep was used as a sleep timing measure obtained by the midpoint between bedtime and wake-up time and classify as Early, intermediate, and Late. Schoolchildren in the Early group were less likely to be overweight (OR: 0.83, 95% CI 0.69; 0.99), had higher odds of mid-morning snack consumption (OR: 1.95, 95%CI 1.56; 2.44) and lower probability to consume the evening snack (OR: 0.75, 95%CI 0.59; 0.94) compared with the Intermediate group. The Late group had lower odds of mid-morning snack consumption (OR: 0.67, 95%CI 0.55, 0.80) than the Intermediate group. The consumption of mid-morning and evening snacks was associated with the Early and the Late midpoint of sleep. These results suggest that bedtime and wake-up time are relevant to consuming meals and snacks and may also be related to a greater probability of being overweight in children and adolescents.

Abstract: Introduction: Covid-19 lockdown has caused important changes in children's routines, especially in terms of nutrition, physical activity, screen time, social activity, and school time. Regarding these changes, recent studies show that Covid-19 lockdown was associated with higher levels of anxiety and depression in children. The objective of this study was to assess changes in sleep quality in Spanish children during lockdown decreed by the Spanish government between March and June 2020. Methodology: We compared the BEAR score of 478 participants in the SENDO project at the periods before, during and after lockdown. We used hierarchical models with two levels of clustering to account for the intra-cluster correlation between siblings. Interaction of time with and a set of a priori selected variables was assessed by introducing the interaction term into the model and calculating the likelihood ratio test. Results: Mean scores in the BEAR questionnaire referred to the periods before, during, and after lockdown were 0.52 (sd 1.25), 1.43 (sd 1.99) and 1.07 (sd 1.55), respectively, showing a worsening of sleep quality as a result of confinement. Parental level of education was found to be an effect modifier (p for interaction=0.004). Children whose parents had higher education (university graduates or higher) showed a smaller worsening than those without. Conclusion: We found that the mean score in the BEAR questionnaire significantly worsened during lockdown and significantly improved after it. However, it did not reach the initial level. The mean score in the BEAR questionnaire referred to the period after lockdown was significantly higher than before.

A modern definition of “deep sleep” is elusive despite being ubiquitously appreciated as an important physiological state supporting health and homeostasis. In modern times, human deep sleep is identified by specific bioelectric signatures in the electroencephalogram (EEG) emerging somewhere between periods of wakefulness. However, deep sleep has been used to describe states of quiescence well before the first electrical brain recordings in the late 1800s, highlighting its own evolution in both lay and medical literature. Furthermore, EEG states are not only ill-defined in most mammals outside of humans and laboratory rodents, but non-existent in some invertebrates. Given that all organisms rest and do so with seemingly well-defined utility, it remains a challenge linguistically, scientifically, and comparatively define what “deep sleep” means—or what it should—in a research context. Here, I explore standard definitions of deep sleep from a modern, comparative perspective, and discuss potential problems of using a strict and narrow definition of such a fleeting concept that has historically undergone significant updates. Finally, I suggest a path towards resolving inconsistencies around the meaning of “deep sleep” and consider whether it is truly reflected by any one measure.

This retrospective study evaluated changes in the pharyngeal portion of the upper airway in pa-tients with constricted and normal airway treated with clear aligners (Invisalign, Align). Additionally, the paper has assessed the change of tongue position in the oral cavity from lateral view. Evaluation was performed with specialized software (Invivo 6.0, Anatomage) on pre-treatment and posttreatment pairs of cone beam computed tomography imaging (CBCT) data. The level of airway constriction, volume, cross-section minimal area, and tongue profile were evaluated. Patients with malocclusion, with pair or initial and finishing CBCT and without sig-nificant weight change between the scans, treated with Invisalign clear aligners were distributed in two groups. Group A consisted of fifty-five patients with orthodontic malocclusion and con-stricted upper airway. Control group B consisted of thirty-one patients with orthodontic malocclusions without any airway constriction. In the group with airway constriction, there was a statistically significant increase in volume during therapy (p<0.001). The surface of the most con-stricted cross-section of airway did not change significantly after treatment in any of the groups. The airway constriction was most frequently localized at the level of 2nd cervical vertebra. The final tongue position was different from initial in 62.2% of all clear aligner treatments.

The coronavirus COVID-19 epidemic has wreaked havoc on inhabitants of earth killing thousands of humans from more than 150 countries. The epidemic has put a number of countries under complete lockdown and the deadly situation is still prevailing around the globe. Vaccines have been long known as the most effective means of preventing viral infections. However, the lack of vaccines against COVID-19 has further worsened the situation. In this time of health crisis, it is the duty of scientific research community to provide alternative, effective and affordable strategies to vaccinate human bodies against viral infections-COVID-19 based on focused experimental approaches. Growing evidence suggests that certain natural foods and lifestyle changes have potential to optimize immune functions against viral infections including improving defense function, resistance towards invading pathogens, while maintaining self-tolerance. Boosting immune system gives an edge in fending off viruses and staying healthy. This review presents the six smart steps to add to your to-do list which let the inner work of immunity take place against viral infections-COVID-19 by dissolving the powers of disease and illness. Many of these factors are associated in their functions to improve or properly maintain the immune function such as promoting anti-inflammatory functions, inhibiting pro-inflammatory mediators, modulating cell-mediated immunity, altering the antigen-presenting cellular functions as well as promoting communication between the innate and adaptive immune responses. Thus, a scientific illustration of boosting the immune system by proper sleep, moderate exercise, avoiding stress, utilizing vitamins enriched foods, intake of more water and use of fruits and vegetables will hopefully help the community to deal with the coronavirus by vaccinating the human systems naturally.

Sleep postures monitoring systems in the hospital aim at transforming sensing signals into quantitative data to characterize the sleep behaviors of the patient. However, a home-care sleep posture monitoring system needs to be user friendly. In this paper, we present iSleePost - a user-friendly home-care intelligent sleep posture monitoring system. We address the labor-intensive labeling issue of traditional machine learning approaches in the training phase. Our proposed mobile health (mHealth) system leverages the communications and computation capabilities of mobile phones for provisioning a continuous sleep posture monitoring service. Our experiments show that iSleePost can achieve 90 percent accuracy in recognizing sleep postures. More importantly, iSleePost demonstrates that an easily-wear wrist sensor can accurately quantify sleep postures.

The Munich ChronoType Questionnaire (MCTQ) has now been available for more than 15 years; its original publication has been cited 1,240 times (Google Scholar, May 2019); its online version, which was available until July 2017, has produced almost 300,000 entries from all over the world (MCTQ database). The MCTQ has gone through several versions, has been translated into 13 languages and has been validated against other more objective measures of daily timing in several independent studies. Besides being used as a method to correlate circadian features of human biology with other factors – ranging from health issues to geographical factors – the MCTQ gave rise to quantifying old wisdoms, like “teenagers are late” and has produced new concepts, like social jetlag. Some like the MCTQ’s simplicity and some view it critically; it is time to have a self-critical view on the MCTQ, to address some misunderstandings and give some definitions about MCTQ-derived chronotype and the concept of social jetlag.

The function of sleep in mammal and other vertebrates is one of the great mysteries of biology. Many hypotheses have been proposed, but few of these have made even the slightest attempt to explain the essence of sleep - the uncompromising need for reversible unconsciousness. During sleep, epiphenomena - often of a somatic character - occur, but these cannot explain the core function of sleep. One answer could be hidden in the observations made for long periods of time of the function of the central nervous system (CNS). The CNS is faced with conflicting requirements on stability and excitability. A high level of excitability is desirable, and is also a prerequisite for sensitivity and quick reaction times; however, it can also lead to instability and the risk of feedback, with life-threatening epileptic seizures. Activity-dependent negative feedback in neuronal excitability improves stability in the short term, but not to the degree that is required. A hypothesis is presented here demonstrating how calibration of individual neurons - an activity which occurs only during sleep - can establish the balanced and highest possible excitability while also preserving stability in the CNS. One example of a possible mechanism is the observation of slow oscillations in EEGs made on birds and mammals during slow wave sleep. Calibration to a genetically determined level of excitability could take place in individual neurons during the slow oscillation, so that action potentials are generated during the oscillations “up-phase”. This can only take place offline, which explains the need for sleep. The hypothesis can explain phenomena such as the need for unconsciousness during sleep, with the disconnection of sensory stimuli, slow EEG oscillations, the relationship of sleep and epilepsy, age, the effects of sleep on neuronal firing rate and the effects of sleep deprivation and sleep homeostasis. This is with regard primarily to mammals, including humans, but also all other vertebrates.

Background: The aim of this study was to investigate the risk of obstructive sleep apnea (OSA) among ankylosing spondylitis (AS) patients in a nationwide population. Methods: We conducted a nationwide cohort study between 2003 and 2013 using the Taiwan National Health Insurance Research Database. The AS cohort included 2210 patients who were newly diagnosed between 2003 and 2013. Randomly selected non-AS controls were matched at a 1:4 ratio based on age, sex and index date. The endpoint of OSA was occurrence or the end of 2013. Cumulative incidences, hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated after adjusting for age, gender, comorbidities and co-medications. Multivariate analyses were performed using Cox proportional hazards model. Due to violation of the proportionality assumption, landmark analysis was conducted to explore the risk of OSA during specific follow-up periods. Results: The adjusted HR (aHR) of OSA for the AS group was 2.826 (95% C.I. = 1.727–4.625) compared to the control group. On landmark analysis, aHR was 7.919 (95% C.I. = 3.169–19.792) for AS group 0–24 months from index date, and decreased to 1.816 (95% C.I. = 0.944–3.494) at ≥ 24 months from index date. On subgroup analyses increased risks of OSA in AS group compared to the control group were found for both males and females (aHRs were 4.533 (95% C.I. = 1.441–14.262) and 2.672 (95% C.I. = 1.522–4.692) for females and males, respectively). On age stratified analysis, there was significant risk only for the 40–59 age group with aHR of 3.913 (95% C.I. = 1.890–8.102). Conclusions: A higher risk of developing OSA was found among newly diagnosed AS cohort during the 12-year follow-up period, especially within 2 years after AS index date and in the 40–59 age group.

Background: The variability of the obtained results of manual tests assessing the angle of strabismus depends on the experience, skills, and training of the examiner. The authors hope that the new measuring device will provide a more sensitive and repeatable method for detecting small strabismus angles compared to the gold standard – PCT. The purpose of this article is to present an innovative strabismus angle demonstration device, called Strabiscan, to present automated measurements of eye deviation, and to compare the obtained results of these measurements to the traditional manual method. Methods: In patients with the manifest strabismic disease (n = 30) and a group of healthy subjects (n = 30), a detailed history was taken and routine ophthalmologic examinations were performed, including best-corrected distance visual acuity, assessment of refractive error using an autorefractometer after cycloplegia, biomicroscopic evaluation of the anterior segment of the eye, and evaluation of the eye fundus by indirect ophthalmoscopy. Subsequently, each patient and healthy subjects were subjected to a prismatic cover-uncover test using a manual method, and then the presence of strabismus was detected and its angle assessed using demonstration Strabiscan device. Results: In the control group, using the Strabiscan demonstration device, small-angle latent strabismus ≤3DP was diagnosed in 83% of patients, and >3DP in 13%. In contrast, using the prismatic cover-uncover test, latent strabismus ≤3DP was diagnosed in only 13% of patients, and latent strabismus with an angle >3DP in 13% of patients. No statistically significant differences were noted in the measurements of strabismus angles made by the different methods. Conclusions: The Strabiscan demonstration device allows quick and accurate assessment of the strabismus angle. Compared to the prismatic cover-uncover test, it has a higher sensitivity for detecting low-angle latent strabismus. Measurements with the Strabiscan do not require the presence of additional co-assistants for the test.

: The radar multi target tracking (MTT) technique requires prior knowledge of a number of parameters about the sensor, the target and backgrounds. The Integrated Track Splitting (ITS) is a fully automatic track-while-scan (TWS) target tracking algorithm capable of extracting and tracking a target in a dense clutter environment using quality false track discrimination (FTD) methodology. The computational complexity in ITS algorithm is limited, compared to other algorithms they use statistical methods to discriminate between false and true tracks, such as multiple hypothesis tracking (MHT), mainly due to the FTD performed. The paper provides an analysis of tracking parameters that allows determining the limit of the possibility of successful target tracking. Extensive experiments have confirmed that the recursive determination of the probability of the existence of a track during tracking can confirm a true track and reject a false track. The clutter density, number of random occurred targets, targets load during the maneuver and the target detection probability were varied. The results of experiments, carried out via Monte Carlo simulations, shown over representative confirmed true tracks (CTT) diagrams, root mean square error position and normalized tracking efficiency parametric diagrams allow the user to select optimal multi-target tracking parameters for different scenarios and clutter densities.

The rapid development of technology has led to the implementation of data-driven systems whose performance heavily relies on the amount and type of the data itself. In the latest decades, in the fields of bioengineering data management, among others, eye-tracking data has become one of the most interesting and essential components for many medical, psychological, and engineering research applications. However, despite the large usage of eye-tracking data in many studies and applications, a strong gap is still present in the literature regarding real-time data collection and management, which led to strong constraints for the reliability and accuracy of on-time results. To address this gap, this study aims to introduce a system that enables the collection, processing, real-time streaming, and storage of eye-tracking data. The system is developed by using Java programming language, WebSocket protocol, and Representational State Transfer (REST), improving the efficiency in transferring and managing eye-tracking data. Results were computed in two test conditions, i.e., local and online scenarios, within a time window of 100 seconds. The experiments conducted for this study were carried out by comparing the time delay between two different scenarios. Even if preliminary, results showed a significantly improved performance of data management systems in managing real-time data transfer. Overall, this system can significantly benefit the research community by providing real-time data transfer and storing the data, enabling more extensive studies using eye-tracking data.

The objective of the present work is to investigate the performance of flat-plate solar panels in Greece that continuously follow the daily motion of the Sun. To that end, the annual energy sums are estimated on such surfaces from hourly solar horizontal radiation values at 43 locations covering all of Greece. The solar horizontal radiation values are embedded in the typical meteorological years of the sites obtained from the PV-GIS tool. All calculations use a near-real surface albedo; an isotropic and an anisotropic model are used to estimate the diffuse-inclined radiation. The analysis provides regression equations for the energy sums as a function of time (month, season). The annual energy sums are found to vary between 2247 kWhm−2 and 2878 kWhm−2 under all-sky conditions with the anisotropic transposition model. Finally, maps of Greece showing the distribution of the annual and seasonal solar energy sums under all- and clear-sky conditions are derived for the first time.

The environmental impact and associated health issues have been seen as a significant global challenge. Efforts have been made to make solar cookers efficient to reduce the health risk and environmental impact of traditional cooking energy supplies used in rural areas. The solar cookers effectiveness for cooking purposes must be assessed in a specific area before installation. In essence, this paper assesses the experimental efficiency of German Scheffler, small mirror pieces, and aluminum foil based solar cookers. The solar cookers performances are evaluated at international standards by analyzing the figure of merits such as standard cooking power, sensible heating time, and exergy efficiency by using 1.7652 kg water and 1-liter oil as a heating material. The regression analysis is also performed to observe experimental data compliance with regression line. The time required to attain a water temperature of 95˚C by German Scheffler and small mirror pieces is 25 min and 1 hour with their exergy efficiency of 48.51% and 19.16% respectively, while the aluminum foil solar cooker achieves maximum water temperature of 74˚C within 2 hours and 5 minutes with 13.47% exergy efficiency. Similarly, the highest oil temperature of 275˚C is observed for the German Scheffler solar cooker. From experimental and regression results, it is revealed that German Scheffler possesses more cooking power relative to other solar cookers and their performances are highly dependent on following factors such as solar radiations, ambient temperature, environment (dust particles concentration and wind speed), radiation reflective material, aperture area, and heat losses respectively.

Beach sand may act as a reservoir for numerous micro-organisms, including enteric pathogens. Several of these pathogens originate in human or animal feces, which may pose a public health risk. In August 2019, high levels of fecal indicator bacteria (FIB) were detected in the sand of the Azorean beach Prainha, Terceira Island, Portugal. Remediation measures were promptly implemented, including sand removal and the spraying of chlorine to restore the beach sand quality. To determine the biological source of the contamination, during the first campaign, supratidal sand samples were collected from several sites along the beach, followed by microbial source tracking (MST) analyses of Bacteroides markers for five animal species, including humans. Some of the sampling sites revealed the presence of marker genes from dogs, seagulls, and ruminants. Making use of the information on biological sources originating partially from dogs, the municipality enforced restrictive measures for dog-walking at the beach. Subsequent sampling campaigns detected low FIB contamination due to the mitigation and remediation measures that were undertaken, thereby no longer requiring MST marker-gene analysis. This is the first case study where the MST approach was used to determine the contamination sources in the supratidal sand of a coastal beach. Our results show that MST can be an essential approach to determine sources of fecal contamination in the sand. This study shows the importance of holistic management of beaches that should go beyond water quality monitoring for FIB, putting forth evidence for the need for sands also to be monitored.

The ability to efficiently recognize the emotions on others’ faces is something that most of us take for granted. Children with callous-unemotional (CU) traits and impulsivity/conduct problems (ICP), such as attention-deficit hyperactivity disorder, have been previously described as being “fear blind”. This is also associated with looking less at the eye regions of fearful faces, which are highly diagnostic. Previous attempts to intervene into emotion recognition strategies have not had lasting effects on participants’ fear recognition abilities. Here we present both (a) additional evidence that there is a two-part causal chain, from personality traits to face recognition strategies using the eyes, then from strategies to rates of recognizing fear in others; and (b) a pilot intervention that had persistent effects for weeks after the end of instruction. Further, the intervention led to more change in those with the highest CU traits. This both clarifies the specific mechanisms linking personality to emotion recognition and shows that the process is fundamentally malleable. It is possible that such training could promote empathy and reduce the rates of antisocial behavior in specific populations in the future.

Background: global longitudinal strain (GLS) measures myocardial deformation and is a sensitive modality for detecting subclinical myocardial dysfunction and predicting cardiac outcomes. The accuracy of speckle-tracking echocardiography (STE) is dependent on temporal resolution. A novel software enables relatively high frame rate (Hi-FR) (~200 fps) echocardiographic images acquisition which empowers us to investigate the impact of Hi-FR imaging on GLS analysis. The goal of this pilot study was to demonstrate the feasibility of Hi-FR for STE. Methods: In this prospective study, we acquired echocardiographic images using clinical scanners on patients with normal left ventricular systolic function using Hi-FR and conventional frame rate (Reg-FR) (~50 FPS). GLS values were evaluated on apical 4-, 2- and 3-chamber images acquired in both Hi-FR and Reg-FR. Inter-observer and intra-observer variabilities were assessed in Hi-FR and Reg-FR. Results: There were 143 resting echocardiograms with normal LVEF included in this study. The frame rate of Hi-FR was 190 ± 25 and Reg-FR was 50 ± 3, and the heart rate was 71 ± 13. Strain values measured in Hi-FR were significantly higher than those measured in Reg-FR (all p < 0.001). Inter-observer and intra-observer correlations were strong in both Hi-FR and Reg-FR. Conclusions: We demonstrated that strain values were significantly higher using Hi-FR when compared with Reg-FR in patients with normal LVEF. It is plausible that higher temporal resolution enabled the measurement of myocardial strain at desired time point. The result of this study may inform clinical adoption of the novel technology. Further investigations are necessary to evaluate the value of Hi-FR to assess myocardial strain in stress echocardiography in the setting of tachycardia.

In the last two decades, extracellular vesicles (EVs) from the three domains of life, archae, bacteria and eukaryota, have gained increasing scientific attention. As such, the role of EVs in host-pathogen communication and immune modulation are being intensely investigated. Pivotal to EV research is the determination of how and where EVs are taken up by recipient cells and organs in vivo, which requires suitable tracking strategies including labelling. Labelling of EVs is often performed post-isolation which increases risks of non-specific labelling and the introduction of labelling artefacts. Here we exploited the inability of helminths to de novo synthesise fatty acids to enable labelling of EVs by whole organism uptake of fluorescent lipid analogues and the subsequent incorporation in EVs. We showed uptake of DOPE-Rhodamine in Anisakis spp. and Trichuris suis larvae. EVs isolated from supernatant of Anisakis spp. labelled with DOPE-Rhodamine were characterised to assess effects of labelling on size, structure and fluorescence of EVs. Fluorescent EVs were successfully taken up by the human macrophage cell line THP-1. This study therefore presents a novel staining method that can be utilized by the EV field in parasitology and potentially across multiple species.

Muon radiography is an imaging technique based on the measurement of the absorption of cosmic ray muons. This technique has recently been used successfully to investigate the presence of unknown cavities in the Bourbon Gallery in Naples and in the Cheops Pyramid at Cairo. The MIMA detector (Muon Imaging for Mining and Archaeology) is a muon tracker prototype for the application of muon radiography in the Archaeological and Mining fields. It is made of three couples of X-Y planes each consisting of 21 scintillator bars with silicon photomultiplier read-out. The detector is compact, robust, easily transportable and has a low power consumption: all of that makes the detector ideal for measurements in narrow and isolated environments. With this detector we have performed a measurement from inside the Temperino mine in the San Silvestro archaeo-mining park in Tuscany. The park includes about 25 km of mining tunnels arranged on several levels that have been excavated since the Etruscan time. The measured muon absorption was compared to the simulated one, obtained from the information provided by 3D laser scanner measurements and the cartographic maps of the mountain above the mine, in order to obtain information on the average density of the rock. This allowed to confirm the presence of a partially accessible exploitation opening and gave some hints on the presence of a high density body within the rock.

Multi-object tracking (MOT) in airborne videos is a challenging problem due to the uncertain airborne vehicle motion, vibrations of the mounted camera, unreliable detections, size, appearance and motion of the moving objects as well as occlusions due to the interaction between the moving objects and with other static objects in the scene.To deal with these problems, this work proposes a four-stage Hierarchical Association framework for multiple object Tracking in Airborne video (HATA). The proposed framework combines data association-based tracking (DAT) methods and target tracking using a Compressive Tracking approach, to robustly track objects in complex airborne surveillance scenes. In each association stage, different sets of tracklets and detections are associated to efficiently handle local tracklet generation, local trajectory construction, global drifting tracklet correction and global fragmented tracklet linking. Experiments with challenging airborne video datasets show significant tracking improvement compared to existing state-of-art methods.

A single-center, randomized, double-blind, placebo-controlled clinical trial with two arms was conducted in 71 healthy subjects suffering from sleep disturbances, which were treated with a dietary supplement based on an extract of Aloysia citrodora (or Lippia citriodora, lemon verbena) (n = 33) for 90 days. Subjects in the placebo group (n = 38) received capsules of microcrystalline and maltodextrin with the same organoleptic properties. Between-group significant differences in favor of the experimental group were found in the visual analogue scale (VAS) score rating sleep quality; overall score, sleep latency and sleep efficiency of the Pittsburg Sleep Quality Index (PSQI); and sleep latency, sleep efficiency, wakefulness after sleep onset and duration of awakening assessed by actigraphy. Plasma nocturnal melatonin levels also increased significantly in the experimental group. Changes in anthropometric parameters and physical activity levels were not found. This study shows that the administration of a dietary supplement comprised lemon verbena administered for 3 months was associated with a significant improvement of sleep quality as compared with placebo in a population of healthy subjects with sleep problems. Improvements were also apparent at the mid-study visit after 45 days of supplementation.

Sleep fragmentation (SF) can increase inflammation and production of reactive oxygen species (ROS), leading to metabolic dysfunction. SF is associated with inflammation of adipose tissue and insulin resistance. Several studies suggested that melatonin may have beneficial metabolic effects by activating AMP-activated protein kinase (AMPK). However, it is unclear whether melatonin affects the AMPK signaling pathway in SF-induced metabolic dysfunction. Therefore, we hypothesize that SF induces metabolic impairment and inflammation in white adipose tissue (WAT), as well as altered intracellular homeostasis. We further hypothesize that these conditions could be improved by melatonin treatment. We conducted an experiment using adult male C57BL/6 mice, which were divided into three groups: control, SF, and SF with melatonin treatment (SF+Mel). The SF mice were housed in SF chambers, while the SF+Mel mice received daily oral melatonin. After 12 weeks, glucose tolerance test, insulin tolerance test, adipose tissue inflammation, and AMPK assessments were performed. The SF mice showed increased weight gain, impaired glucose regulation, inflammation, and decreased AMPK in WAT compared to the controls. Melatonin significantly improved these outcomes by mitigating SF-induced metabolic dysfunction, inflammation, and AMPK downregulation in adipose tissue. The therapeutic efficacy of melatonin against cardiometabolic impairments in SF may be due to its ability to restore adipose tissue homeostatic pathways.

A significant percentage of children suffer from neurodevelopmental aberrations, which have long-term effects on both individuals and society. Since the developmental timelines of different brain regions vary, the type, severity and timing of harmful exposures are crucial for understanding the specific pathological processes and consequences involved. During development, the cerebellum is one of the first brain structures to begin to differentiate, but one of the last to achieve maturity. This relatively long period underscores its vulnerability to detrimental environmental exposures throughout gestation. Moreover, as the postnatal functionality of the cerebellum is multifaceted, enveloping sensorimotor, cognitive, and emotional domains, prenatal disruptions in cerebellar development can result in a variety of neurological and mental health disorders. Here, we review major intrauterine insults that affect cerebellar development in both humans and rodents, ranging from abuse of toxic chemical agents such as alcohol and nicotine, to stress and sleep, malnutrition as well as infections. Understanding these pathological mechanisms in the context of the different stages of cerebellar development in humans and rodents may help us to identify critical and vulnerable periods and thereby to prevent the risk of associated prenatal and early postnatal damage that can lead to lifelong neurological and cognitive disabilities.

Background: Health management is gaining importance as a business resource to enhance employee well-being. The rise of remote work during COVID-19 impacted lifestyle habits. This study explores the impact of tele-exercise intervention on remote workers' lifestyle habits, aiming to improve health and productivity. Methods: This study divided into remote (G1) and face-to-face (G2) groups. Over three months, G1 followed a 30-minute weekly exercise program. Measurements and questionnaires were collected at three time points covering areas like presenteeism, weight, sleep, stress, job satisfaction, and happiness. Data underwent Two-way repeated measures ANOVA, Autocorrelation, Correlation Analysis, and Correspondence Analysis to assess differences and relationships between G1 and G2. Results: ANOVA showed that a significant difference was observed in Daily Stress (DS) between G1 and G2, with G1 exhibiting higher DS. Autocorrelation Analyses showed for G1 that strong relationships each of BMI, Presenteeism (PST), Daily Stress (DS), Stress Relief (SR), Sleep Quality (SQ), Purpose of Work (PW), and Satisfaction of Life (SL) in the time frame. Correlation Analysis showed for G1 that the pairs of PRS and SQ, and DS and SL had strong positive relationships, and the pairs of BMI and PRS, and BMI and SQ had strong negative relationships. Autocorrelation Analyses showed for G2 that strong relationships each of SR, Stress due to Remote Work (SRW), ST, SQ, and PW in the time frame. Correspondence Analysis showed that that G1 was encompassed in a job and life satisfaction area, and G2 was encompassed in a sleep and stress for weight control area.

Background: Current obstructive sleep apnea treatment relies on manual PAP titration, but it has limitations. Complex interactions during titration and variations in SpO2 data accuracy pose challenges. Patients with co-occurring chronic hypercapnia may require precise oxygen titration. To address these issues, we propose a Clinical Decision Support System using Markov Decision Processes. Methods: This study, compliant with data protection laws, focused on adults with OSA-induced hypoxemia utilizing supplemental oxygen and CPAP/BiPAP therapy. PAP titration, conducted over one night, involved vigilant monitoring of vital signs and physiological parameters. Adjustments to CPAP pressure, potential BiLevel transitions, and supplemental oxygen were precisely guided by patient metrics. Markov Decision Processes outlined three distinct phases for sleep disorder management, incorporating expert medical insights. Results: In our study involving 14 OSA patients (average age: 63 years, 27% females, BMI 41 kg m−2), significant improvements were observed in key health parameters. The initial AHI of 61.8 events per hour significantly decreased to an average of 18.0 events per hour after PAP and oxygen titration (p < 0.0001), indicating a substantial reduction in sleep-disordered breathing severity. Concurrently, SpO2 levels increased significantly from an average of 79.7% before titration to 89.1% after titration (p < 0.0003). Pearson correlation coefficients demonstrated aggravation of hypercapnia in 50% of patients (N=5) with initial hypercapnia during the increase of CPAP pressure. However, transitioning to BiPAP exhibited a reduction in pCO2 levels, showcasing its efficacy in addressing hypercapnia. Simultaneously, BiPAP therapy correlated with a substantial increase in SpO2, underscoring its positive impact on oxygenation in OSA patients. Markov dynamic process analysis demonstrated realistic patient behavior during stable night conditions, emphasizing minimal apnea and good toleration to high CPAP pressure. Conclusions: the development of a framework for Markov Decision Processes of PAP and oxygen titration algorithms holds promise for providing algorithms for deviation in pCO2 and SpO2 markers. While challenges remain, including the need for high-quality data, the potential benefits in terms of patient management and care optimization are substantial, and this approach represents an exciting frontier in the realm of telemedicine and respiratory healthcare. Obstructive sleep apnea, PAP titration, hypoxia, hypercapnia, Markov decision processes

Autonomic dysregulation is associated with cardiovascular consequences in obstructive sleep apnea (OSA). This study aimed to investigate the effect of acute continuous positive airway pressure (CPAP) treatment on autonomic activity and to identify factors contributing to the heart rate variability (HRV) changes in OSA. Frequency domain HRV parameters were calculated and compared between the baseline polysomnography and during the CPAP titration in 402 patients with moderate to severe OSA. There were significant reductions in total power, very low-frequency band power, low-frequency band power, and high-frequency band power during the CPAP titration as compared to the baseline polysomnography. This tendency was more pronounced in men than in women, and in patients with severe OSA than those with moderate OSA. Multivariate analysis found that changes in apnea-hypopnea index and oxygen saturation were significantly associated with changes in sympathetic and parasympathetic activity, respectively. This study demonstrated that HRV parameters significantly changed during the CPAP titration, indicating a beneficial effect of CPAP in restoration of sympathetic and parasympathetic hyperactivity in OSA. Prospective longitudinal studies should determine whether long-term CPAP treatment aids in maintaining the long-lasting improvement of the autonomic functions, thereby contributing to the prevention of cardiovascular and cerebrovascular diseases in patients with OSA.

Obesity, characterized by the excessive accumulation of body fat, contributes to a multitude of physiological dysfunctions. This paper explores the complex relationship between obesity and various risk factors and complications, shedding light on critical health implications. Obesity triggers alterations in insulin, leptin, adiponectin, cytokines, and insulin-like growth factors, fostering conditions conducive to cancer initiation. Aberrations in nutrient-dependent intracellular signaling pathways, driven by the excess nutrition characteristic of obesity, contribute to the neoplastic transformation of cells. The Body Mass Index (BMI) correlates directly with adiposity, underlining its significance in cancer risk. Furthermore, insulin and insulin-like growth factors, notably IGF-2, play pivotal roles in this relationship. The expression of IR-A receptors is elevated in cancer. Obesity and cardiovascular diseases share a strong association. Obesity elevates the risk of fatal events like myocardial infarction. High cholesterol levels contribute to atherosclerosis in the aorta and coronary arteries. The severity of coronary artery disease is influenced by plaque formation characterized by calcium deposits. Obesity is also linked to hypertension and increased ventricular mass, exacerbating cardiovascular risks. Hypercholesterolemia and hypertension correlate with a high Body Mass Index (BMI). Obesity is intricately connected to insulin resistance, particularly evident in childhood obesity. It entails a gradual decline in insulin sensitivity, leading to elevated insulin levels in the bloodstream. Insulin resistance is a central factor in the development of Type 2 Diabetes Mellitus. Obesity also results in elevated triglyceride levels and reduced high-density lipoproteins, contributing to atherogenic dyslipidemia and a heightened risk of atherosclerosis. Additionally, obesity is associated with various other disorders, including epilepsy, depression, and neuropsychological problems. Beyond its physiological impact, obesity is associated with significant psychosocial challenges. Studies indicate that individuals with obesity face a higher risk of depressive symptoms, often related to overeating and unhealthy dietary patterns. The social withdrawal tendencies of obese individuals exacerbate these symptoms, with higher rates of depression observed in this group. OSA, a sleep disorder characterized by intermittent breathing cessation during sleep, is strongly correlated with obesity. Approximately 58% of obese individuals experience OSA, with higher Body Mass Index (BMI) associated with an increased risk. Weight reduction has been found to mitigate the severity of OSA and related arrhythmias. Continuous positive airway pressure (CPAP) therapy demonstrates effectiveness in reducing visceral fat accumulation and leptin levels. This comprehensive review underscores the intricate web of health implications associated with obesity, emphasizing the critical need for preventive measures and intervention strategies to address the multifaceted challenges posed by this global health concern.

Brain plasticity is induced by learning during wakefulness and is consolidated during sleep. But the molecular mechanisms involved are poorly understood and their relation to experience-dependent changes in brain activity remains to be clarified. Localized mRNA translation is im-portant for the structural changes at synapses supporting brain plasticity consolidation. Sleep has been shown activate the translation mTOR pathway, via phosphorylation of 4E-BPs, during brain plasticity, but whether this activation is specific to synapses is not known. We investigated this question using acute exposure of rats to an enriched environment (EE). We measured brain activity with EEGs and 4E-BPs phosphorylation at cortical and cerebellar synapses with Western Blot. Sleep significantly increased the conversion of 4E-BPs to its hyperphosphorylated form at synapses, especially after EE exposure. EE exposure increased oscillations in the alpha band dur-ing active exploration and in the theta to beta (4-30Hz) range, as well as spindle density, during NREM sleep. Theta activity during exploration and NREM spindle frequency predicted changes in 4E-BPs hyperphosphorylation at synapses. Our results provide a link between EEG and mo-lecular markers of plasticity across wake and sleep.

Background: To examine the effects of continuous positive airway pressure (CPAP) therapy on central corneal thickness (CCT) in patients with obstructive sleep apnea (OSA) followed for 12 months. Methods: Participants were patients with OSA and an indication for CPAP who were enrolled after diagnosis and graded according to the apnea hypopnea index (AHI) determined in an overnight polysomnography. Each subject underwent a full ophthalmologic examination including ultrasound pachymetry (USP) with an SP3000 USP (Tomey Inc) at the time points just before CPAP onset, and after 3 and 12 months of CPAP treatment. Results: The study sample comprised 40 eyes of 20 patients. Adherence to CPAP was adequate (5.23 ± 1.77 h/night). After receiving 12 months of CPAP, positive correlation was observed between hours of CPAP use and CCT (r = 0.382, p = 0.015). Intermittent hypoxemia pre-CPAP or hypoxia severity pre-CPAP were also positively correlated with the CCT recorded after 12 months of CPAP (r = 0.394, p = 0.012; r = 0.324, p = 0.041). Significant correlations were, however, not observed after only 3 months of CPAP treatment. In addition, compared to measurements at 3 months, intraocular pressure was higher after 12 months of treatment. Conclusions: Our findings indicate that 12 months of correct adherence to CPAP are needed to detect significant CCT thickening, and that the greater the pre-CPAP hypoxemia, the greater the capacity for an increase in CCT to be produced in response to 12 months of this therapy.

Background and Objectives: Light therapy (LT) is used as an adjunctive treatment for sleep problems. This study evaluates the impact of LT on sleep quality and sleep-related parameters in patients with sleep disorders. Materials and Methods: We performed a pilot randomized, open-label clinical trial. Fourteen patients aged 20–60 years with sleep disorders for more than three months were randomized into the control and LT groups (1:1 ratio). The LT group was instructed to use a device that provides bright LT (6000 K, 380 lux, wavelength 480 nm) for at least 25 minutes before 09:00 am for two weeks. A self-reported questionnaire was used to evaluate circadian preference, mood, and sleep-related parameters. We analyzed serum cortisol levels and clock genes expression. Results: The Epworth Sleepiness Scale (ESS), insomnia severity index, and Pittsburgh Sleep Quality index were significantly improved within the LT group only after the two-week period. When comparing the two groups, only the change in ESS significant was significant (mean difference; control: -0.14 vs LT: -1.43, p=0.021) after adjusting for baseline characteristics. There were no significant differences in serum cortisol or clock genes expression. Conclusions: LT can improve daytime sleepiness in patients with sleep disorders; however, further well-designed studies are warranted to confirm its efficacy.

Background: Elevated fasting plasma glucose and visceral fat area (VFA) is highly prevalent in obese adults. This study investigated the associations between systemic arterial hypertension (SAH) and laboratory, anthropometric, heart rate variability (HRV), and obstructive sleep apnea markers. Methods: Cross-sectional study with 95 obese patients treated at Obesity Treatment and Surgery Center, located in Salvador, BA, Brazil. SAH data were obtained from electronic medical records of patients. To evaluate the association of SAH with the predictor variables, the sample was stratified in Normotense Group (NG) and Hypertensive Group (HG), and laboratory markers, body composition, polysomnography data, and HRV were measured. Results: The average age of the NG was 36.3 ± 10.1 and HG 40.4 ± 10.6 years, 73.7% were women in the NG and 57.9% in HG; 82.4% in HG had insulin resistance. In the multivarious logistics regression model with adjustments age, sex, height, and oxyhemoglobin saturation, SAH was inversely associated with fasting plasma glucose mg/dL (odds ratio [OR] = 0.96; 95% interval confidence [CI] = 0.92 - 0.99) and VFA cm² (OR = 0.98; 95% CI = 0.97 - 0.99). The area under curve the VFA was 0.728; CI 95% (0.620 - 0.836) and fasting plasma glucose 0.693; CI 95% (0.582 - 0.804). Conclusions: Lower VFA and fasting plasma glucose concentrations were inversely associated with SAH. These results indicate opportunities to improve the outcome in obese patients through counseling and clinical interventions.

Pulse oximetry is the current standard for detecting drops in arterial blood oxygen saturation (SpO₂) associated with obstructive sleep apnea and hypopnea events in polysomnographic (PSG) testing. In cases of hypoxic challenge, such as occurs during apneic events, regulatory mechanisms restrict blood flow to the skin to preferentially maintain SpO₂ for more vital organs. As a result, a measure related to skin tissue oxygenation is likely to be more sensitive to inadequate breathing during sleep than pulse oximetry. Energy Conversion Monitoring (ECM) provides a method for measuring skin tissue oxygen-dependent energy conversion and, as such, is promising for more sensitively detecting sleep disordered breathing (SDB) events compared to pulse oximetry. We hypothesized that ECM would detect hypoxia occurring with SDB events associated with drops in SpO₂ but also would detect hypoxic challenge occurring with SDB events not associated with drops in SpO₂ (hypopneas defined by a drop in nasal pressure occurring in conjunction with an arousal, respiratory-related arousals, and primary snoring). Primary snoring is of particular interest with respect to the potential of ECM because it is statistically associated with co-morbidities of SDB, such as hypertension, but is not considered pathological because of the lack of a proximal measure of pathology occurring with PSG. In this article we review ECM technology and methodology, present preliminary data indicating that it detects hypoxia occurring in the skin during SDB events that is not detected as blood desaturation by pulse oximetry, and make the case that it is a promising tool for identifying pathology occurring at the mild end of the SDB spectrum.

The variations in socioeconomic status (SES) between different social classes of a population correspond to differences in accessibility to all resources available and able to improve global health. SES can influence global health trajectory for an individual or a community, depending if SES is low or high. Sleep is sensitive to environmental stimuli, as well as living conditions. Plenty of studies linked sleep complaints with mood disorders, allostatic load or circadian disruption; but very few or none investigated deeply what happened earlier to sleep depending of SES. While SES is now known as one of the main determinants for a good health and a good aging, its influence on sleep disorders (SD) is not well understood. SES is a concept, not directly observable but estimated using indicators like income, education, occupational status and area of living. Even if recent evidence suggested that few of SES indicators like occupational status are linked with sleep disturbances, the relation between SES and health in general with sleep as an outcome or a mediator is not well documented. This scoping review synthetized studies which investigated physiological and psychological mechanisms resulting from a low SES and linked them with sleep disturbances as consequences or as mediators. This review also explore a possible role played by sleep in the relation between socioeconomic status and health inequalities.

Adolescence is a critical developmental period associated with an increase in stress, the appearance of anxiety and depressive symptoms, and changes in sleep patterns. Even though the disruption of sleep patterns in stress and anxiety and depressive disorders is well known, the independent effects of childhood trauma and stressful life events on sleep patterns are less understood. We tested the independent effects of stress (childhood trauma and stressful life events) while controlling for anxiety and depression on adolescent sleep patterns. Seven hundred fifty-two adolescents completed self-report questionnaires about childhood trauma, stressful life events, anxiety, and depression. Four sleep factors identifying movement during sleep, sleep regularity, sleep disturbances and sleep pressure were extracted in the principal component analysis of sleep questions. Both childhood trauma and recent stressful life events were significantly associated with sleep disturbances before and after controlling for anxiety and depression.

Research into the causes of neurotoxicity in Alzheimer’s Dementia (AD) has focused on neurofibrillary tangles and beta amyloid (Aβ) plaques. This paper proposes the heterodox theory that these hallmarks of AD are the visible effects, not direct causes of neuronal necrosis. Rather AD results from a combination of age-induced, disproportional decline in physiological support for aerobic metabolism, and dysregulation of the sleep cycle processes. The hypothesis is that the decimation of neurons in AD results from a combination of neurotoxicity and increased apoptosis caused by: 1. direct damage from toxic waste products of anaerobic glycolysis due to a progressive decline in the capacity of neurons to perform oxidative phosphorylation (OXPHOS) and an increased reliance on anaerobic glycolysis to meet metabolic needs; 2. impaired cellular repair and effluent release due to dysregulation of non-rapid eye movement (NREM) sleep allowing damage to cell membranes and synaptic junctions to accumulate inducing a chronic inflammatory response; 3. indirect damage from products produced by inflammatory reaction to toxic metabolites; 4. neuronal apoptosis from the AβPP-mediated pathway due to the age-induced decline of growth hormone (GH), GH-releasing hormone (GHRH) and insulin-like growth factor (IGF).

Today, due to recent developments in technology, children devote plenty of time for screen viewing. However, its harmful effects are not yet clear. The purpose of present study was to examine the associations among screen viewing and sleep duration, and body mass index (BMI) in under-five years old children. This cross-sectional study was conducted with 322 under-five healthy children that were selected using multistage stratified cluster sampling method in 2017. The data that were gathered by time-use diary method were analyzed using Kolmogorov-Smirnov test, Spearman correlation tests, multiple linear regression analysis, one-way ANCOVA, two-way ANCOVA. There was a negative correlation between screen time and sleep duration (rs = -0.42, p = 0.00), positive correlation between screen time and BMI (rs = 0.38, p = 0.00) and sleep duration negatively correlated with BMI (rs = -0.22, p = 0.00). screen viewing was a predictive factor for both sleep duration (β = -0.26, p = 0.00) and BMI (β = -0.26, p = 0.00). screen viewing had a significant impact on sleep duration (4, 314) = 5.02, P = 0.001) and BMI (F (4, 314) = 1.16, P=0.298). Results of this study indicated that screen viewing is related to sleep duration and BMI in under-five children. furthermore, screen time has an impact on sleep duration and BMI scores of children. findings of our study suggest that sleep duration negatively is associated with BMI in under-five-year-old children.

Fragile X syndrome (FXS) is the most frequent monogenic form of autism spectrum disorder (ASD). Autistic FXS is caused by loss of the fmr1 gene product, the fragile X mental retardation protein (FMRP), triggering physiological and behavioral abnormalities. It is correlated with clock components for behavioral circadian rhythm. Mutation of this gene causes the disturbances in sleep patterns and circadian behavior commonly observed in patients with autistic FXS, accompanied by frequent dysregulation of melatonin synthesis and melatonin-dependent signaling. These changes impair vigilance, learning and memory, and are also linked to autistic behavior including the abnormal anxiety response. However, although several possible causes, symptoms, and clinical features of ASD have been investigated, the correlation between an altered circadian rhythm and autistic FXS has not been extensively studied. Recent works have highlighted the impact of melatonin on the nervous, immune, and metabolic systems. Even though utilization of melatonin for sleep disorder in ASD has been considered in clinical research, further studies should be aimed at its neuroprotective role in ASD during developmental period. In this review, we focus on the regulatory circuits involved in melatonin dysregulation and circadian system disruption in those with autistic FXS. Additionally, we discuss the neuroprotective effect of melatonin intervention. This may improve neuroplasticity and physical capability. We also review the underlying molecular mechanisms, and suggest that melatonin may be a useful novel treatment for autistic FXS, countering the adverse effects of circadian variation.

Gaze tracking of high-speed moving targets is a novel application mode for low Earth orbit microsatellites. In this mode, small satellites are equipped with high-resolution narrow-field-of-view video cameras for stable gaze-tracking imaging of high-speed moving targets. This paper proposes a high-precision attitude adaptive integral sliding mode control method with a feedforward compensation disturbance observer to enhance the capability of a microsatellite attitude control system for gaze tracking of high-speed moving targets. Specifically, first, we present the attitude control system model for microsatellites and the calculation method for the desired attitude of target tracking based on image feedback. Then, an adaptive integral sliding mode attitude control algorithm with a feedforward compensation disturbance observer, which meets the requirements of high-precision tracking control, is designed. The developed algorithm utilizes the disturbance observer to observe the friction torque of the flywheel and compensates for it through feedforward control. It also employs the adaptive integral sliding mode control algorithm to reduce the impact of uncertain disturbances, decrease the steady-state error of the system, and enhance the attitude control precision. Simulation experiments demonstrated that the designed disturbance observer can successfully observe the frictional disturbance torque of the flywheel. The attitude Euler angle control precision for high-speed moving target tracking reached 0.009°, and the angular velocity control precision reached 0.005°/s, validating the effectiveness of the proposed approach.

This paper proposed an optimal tracking control scheme through adaptive dynamic programming(ADP) for a class of partially unknown discrete-time nonlinear systems based on radial basis function neural network(RBF-NN). In order to acquire the unknown system dynamics, we use two RBF-NNs, the one is used to construct the identifier, and the another is used to directly approximate the steady-state control input, where a novel adaptive law is proposed to update neural network weights. While the optimal feedback control and the cost function are derived via feedforward neural networks approximating, it is proposed to regulate the tracking error, the critic network and the actor network are then trained online to obtain the solution of the associated Hamilton–Jacobi–Bellman (HJB) equation being built under the ADP framework. Simulations verify the effectiveness of the optimal tracking control technique using the neural networks.

The agricultural industry places a high priority on crop protection, especially when it comes to protecting cotton crops against pink bollworm infestations. Rapid disease identification is essential for effective crop management at the same time. In order to overcome these difficulties, this study offers a thorough process for utilizing the Robot Operating System (ROS) to deploy an autonomous robot. The robot is intended to identify infestations of pink bollworm in addition to counting and tracking red Disease that impact cotton harvests. Drones are used to check the condition of cotton fields, and the autonomous robot's path planning component is closely related to this process. Large fields are photographed by these drones in multispectral mode, and the robot's course planning technique is done by calculating the Normalized Difference Vegetation Index (NDVI) based on these images. This approach ensures targeted surveillance and intervention, optimizing the use of resources. A customized dataset was created especially for this application to improve the robot's detecting abilities. The dataset was utilized to train a YOLOV8 model, a state-of-the-art object detection architecture. Performance characteristics of the trained model are impressive; it has a mean Average Precision (mAP) of 67.1%, Precision of 67.9%, and Recall of 61.8%. These metrics highlight how well the model works to precisely locate and measure interesting occurrences in the cotton fields. In order to address the particular challenges presented by pink bollworm infestations and crop diseases in the context of cotton cultivation, this research contributes a comprehensive solution for autonomous crop monitoring and protection by seamlessly integrating ROS, drones, NDVI calculations, and a robust detection model.

The motion of Unmanned Surface Vehicles (USVs) is frequently disturbed by the ocean wind, wave and current. A poorly designed controller will lead to failures and even safety problems during actual navigation. In order to obtain satisfied control performance of the motion for the USVs, a Model Predictive Control (MPC) based on improved Nonlinear Disturbance Observer (NDO) is proposed. Firstly, the USV model is approximately linearized and MPC is designed for the multivariable system with constraints. In order to compensate the influence of disturbances, an improved NDO is designed, with which the calculation time for the MPC saved a lot. Finally, comparison experiments are conducted between the MPC with NDO and the MPC with improved NDO, and the results show that they have similar performance to the USVs. However, the proposed method has less parameter to be tuned and it is much more time-saving compared to the MPC with traditional NDO.

In this paper, the distributed formation tracking control problem of quadrotor unmanned aerial vehicles (UAVs) is considered. Adaptive backstepping technology is used to design flight control schemes for quadrotor UAVs. For the position subsystem, a distributed robust formation tracking control scheme is developed to achieve formation flight of quadrotor UAVs and track the desired flight trajectory. For the attitude subsystem, an adaptive disturbance rejection control scheme is proposed to achieve attitude stabilization during UAV flight under uncertain disturbances. Compared with the existing results, the novelty of this paper is that the proposed formation control scheme does not require the use of the quadrotor model parameters. Finally, a quadrotor UAV swarm system is used to verify the effectiveness of the proposed control scheme.

Abstract: Embodied pedagogy maintains that teaching and learning abstract concepts can benefit significantly from integrating bodily movements in the process. Here we explored the involvement of eye movements in the process of collective embodied learning of a concept in physics - angular velocity. Embodied learning was accomplished by the subjects forming a line that rotated around a central object, in this case, a bottle. We tracked the gaze resulting from eye and head movements in 12 subjects, who both actively participated in the collective embodied exercise and passively watched it. The tracking data of 7 of these 12 subjects passed our tracking reliability criteria in all trials and are reported here. During active learning, the learners tended to look ahead of the rotating line (by 35.18±14.82 degrees). In contrast, while passively watching others performing the task, the learners tended to look directly at the line. Interestingly, while the learners were performing the collective exercise they were unaware of looking ahead of the rotating line. We discuss possible differences between active and passive embodied learning that might be indicated by the observed differences in gaze control.

Illegal removal of road barricades without the notice of road emergency officials and road users has resulted in fatalities, injuries, and property damages. It is only after an incident has occurred or someone noticed the removal and alerted the authorities for the barricade to be placed back at its intended location. There is a need for traditional barricades to be equipped with mechanisms that would alert officials on illegal road barricade removal and warn road users of a road/lane(s) closure to prevent fatalities, injuries, and property damages to the traveling public. This research has used the Global Positioning System (GPS) module to implement the detection of barricade displacement and an alerting system for emergency officials and road users. The barricade displacement was estimated from the haversine distance formula, corrected for errors, and interfaced with the displacement threshold value for the road users within a geofenced area to be alerted. The geofenced area radius was estimated to be 1.04 miles from the barricade location using AASHTO, NSC, and TransGuide ITS manuals. The non-parametric bootstrapping method estimated the GPS position error to 10.5 feet and corrected the measured distances. Data from a clear sunny day shows the best response to barricade movements compared to a cloudy day where movements can’t be explained easily. The proposed monitoring and warning systems would warn road users and alert emergency officials of the danger when the physical barricades have been removed illegally hence saving lives and reducing property damages.

Feral cats are both difficult to manage and harder to monitor. We analysed the cost-efficacy of monitoring the pre- and post-bait abundance of feral cats via camera-traps or track counts using four years of data from the Matuwa Indigenous protected Area. Additionally, we report on the recovery of the feral cat population and the efficacy of subsequent Eradicat® aerial baiting programs following 12 months of intensive feral cat control in 2019 that consisted of aerial baiting and leg-hold trapping. Significantly fewer cats were captured in 2020 (n = 8) compared to 2019 (n = 126). Pre-baiting surveys for 2020 and 2021 suggested that the population of feral cats on Matuwa was very low, at 5.5 and 4.4 cats/100 km respectively, which is well below our target threshold of 10 cats/100 km. Post-baiting surveys then recorded 3.6 and 3.0 cats/100 km respectively, which still equates to a 35% and 32% reduction in cat activity. Track counts recorded significantly more feral cats than camera traps and were cheaper to implement. We recommend that at least two methods of monitoring cats be implemented to prevent erroneous conclusions.

The ever-burgeoning growth of autonomous unmanned aerial vehicles (UAVs) has demonstrated a promising platform for utilization in real-world applications. In particular, UAV equipped with a vision system could be leveraged for surveillance applications. This paper proposes a learning-based UAV system for achieving autonomous surveillance, in which the UAV can be of assistance in autonomously detecting, tracking, and following a target object without human intervention. Specifically, we adopted the YOLOv4-Tiny algorithm for semantic object detection and then consolidated it with a 3D object pose estimation method and Kalman Filter to enhance the perception performance. In addition, a back-end UAV path planning for surveillance maneuver is integrated to complete the fully autonomous system. The perception module is assessed on a quadrotor UAV, while the whole system is validated through flight experiments. The experiment results verified the robustness, effectiveness, and reliability of the autonomous object tracking UAV system in performing surveillance tasks. The source code is released to the research community for future reference.

Gastric emptying rate (GER) may impact diabetes and obesity in humans and could provide a method to reduce canine weight gain. Starch, the most common source of carbohydrates (CHOs) in pet food, is classified as rapidly or slowly digestible, or resistant to digestion. This study investigated starch source effects in commercial extruded dog foods on the GER of 11 healthy adult Siberian Huskies (5.63±0.72 years; mean±SEM). Test diets were classified as traditional, grain-free, whole-grain, and vegan. Dogs received each diet once, a glucose control twice, and acetaminophen (Ac) as a marker for GER in a randomized, partially-replicated, 6x6 Latin square design. Pre- and post-prandial blood samples were collected at 16 timepoints from -15 to 480 minutes. Serum Ac concentrations were assessed via standard spectrophotometric assays and fitted with a mathematical model to estimate parameters of GER. Data was analyzed using a repeated measures ANOVA, followed by a Tukey-Kramer post-hoc test when significant (p<0.05). More total emptying (p= 0.0430) occurred at faster rate (p=0.0668) in dogs fed the grain-free diet, which contained the lowest total starch (34.03 ± 0.23%) and highest resistant starch (0.52 ± 0.007%). This research may benefit future diet formulations to reduce the prevalence of canine weight gain.

Smartphones have become an integral part of our everyday lives and keep us busy while doing other primary activities such as driving, cycling or walking in traffic. The problem of digital distraction among drivers has been largely addressed, and interest is growing also on vulnerable road users as well: In fact, high percentages of pedestrians and cyclists are accustomed to checking their devices while moving in traffic. This research links to the presented theme and aims to investigate the extent to which digital distraction in the form of social media app checking influences pedestrian behavior. The focus of the study is specifically on signalized intersections. An outdoor, eye-tracking experiment was conducted on a specific route consisting of various elements typical of urban areas. Participants were asked to walk the predefined route twice, en-countering three signalized intersections: the first time they were asked to walk with their smartphone in hand, the second time without. The recordings of each participant's route were then analyzed, examining reaction time, crossing time and speed, fixations, and gaze paths. The results show a clear impact of digital devices on pedestrians' attention by increasing their reaction and crossing times and decreasing crossing speeds. In addition, the analysis of fixations found that 82.54% of the time was devoted to the smartphone, while interest in other street ele-ments decreased from 16.64% to 4.03%.

We present the hardware of a cheap multi-sensor magnetometric setup, where a relatively large set of magnetic field components is measured in several positions by calibrated magnetoresistive detectors. The setup is developed to map the (inhomogeneous) field generated by a known magnetic source, which is measured and then discerned from the background (homogeneous) geomagnetic field. The data output from this hardware can be successfully and reliably used to retrieve the position and orientation of the magnetic source with respect to the sensor frame, together with the orientation of the frame with respect to the environmental field. Possible applications of the setup are briefly discussed, and a synthetic description of the methods of data elaboration and analysis is provided.

Hand tracking enables controller-free interaction with virtual environments, which can, compared to traditional handheld controllers, make virtual reality (VR) experiences more natural and immersive. As naturalness hinges on both technological and user-based features, fine-tuning the former while assessing the latter can be used to increase usability. For a grab-and-place use case in immersive VR, we compared a prototype of a camera-based hand tracking interface (Leap Motion) with customized design elements to the standard Leap Motion application programming interface (API) and a traditional controller solution (Oculus Touch). Usability was tested in 32 young healthy participants, whose performance was analyzed in terms of accuracy, speed and errors as well as subjective experience. We found higher performance and overall usability as well as overall preference for the handheld controller compared to both controller-free solutions. While most measures did not differ between the two controller-free solutions, the modifications made to the Leap API to form our prototype led to a significant decrease in accidental drops. Our results do not support the assumption of higher naturalness for hand tracking but suggest design elements to improve the robustness of controller-free object interaction in a grab-and-place scenario.

Particle size can pose a challenge to random embankment compaction control methods, where practical techniques have hardly been developed and procedural control is used instead. In order to develop new quality control procedures for slate random fill, the necessary fieldwork and laboratory tests were carried out. This involved the revision of certain methods such as the wheel-tracking or topographic settlement tests. More than six hundred in-situ density and moisture content measurements were carried out for this research. In addition, more three hundred topographic settlements and three hundred wheel-tracking carriage tests were performed. The quality control processes were completed with more than thirty plate bearing tests. Possible evidence of statistical correlations between compaction control tests were identified. An analysis of variance (ANOVA) was performed. When testing proved relationships between them, the replacement of one of them by the other was assessed by deduction. Finally, the study suggests new procedures for compaction quality control of random slate fill used in crown area.

Prediction of the likely evolution in the traffic scenes is a challenging task because of high uncertainty of sensing technology and dynamic environment. It leads to failure of planning for intelligent agents like autonomous vehicles. In this paper, we propose a fluid-based physical model to present the influence of surrounding object's motion on driving safety. In our pipeline, the input sensor could be LiDAR, camera, or multi-modal data. We use a Kalman filter to estimate the state space of each detected object, and adopt the properties of stable fluid to build a riskmap based on the density field. The noisy state space are then modeled as the boundary conditions in the simulation of advection and diffusion process. We test our approach on the public KITTI dataset and find this model could handle the short-term prediction in case of misdetection and tracking failure caused by object occlusion, which shows promising in collision risk assessment on road scenes.