Real-time Data Research Articles

OTs Should Be Using Video Modeling to Teach Self-Care to Children with NDDs

ABSTRACT An evidence-based project aimed at finding whether utilizing video modeling as an occupational therapy (OT) intervention increased basic self-care skills in school-aged children with neurodevelopmental disorders (NDDs). A total of 11 pediatric outpatient OT clients, 6–12 years old, with one or more NDDs, participated in this project. Pre- and post-intervention surveys completed by their parents garnered information regarding parental views on their children’s self-care skills, parental stress, and quality of life. Real-time data gathered during the intervention revealed the efficacy of video modeling, showing an overall increase in the completion of basic self-care skills from 16.1% at baseline to 96.4%. Meaningful gains were achieved in all eight self-care skills addressed and with all participants, elucidating a combined mean percentage of 79.3% improvement using video modeling. Post-intervention, qualitative data showed resultant perspectives of less parental stress, improved quality of life for the children and their parents, and improved independence with basic self-care skills. Considering the increasing diagnosis of NDDs, the call for evidence-based interventions from both payers and NDD-related organizations, and the future socioeconomic ramifications of not addressing basic self-care skills, this project reveals the efficacy of video modeling as a viable, accessible, cost-effective intervention which can be utilized across environments to address these concerns.

Water Pollution Detection Using Video Surveillance and Deep Learning

Aiming at the problems that the traditional water quality prediction model is generally not high in prediction accuracy and robustness, a water pollution prediction using deep learning in water environment monitoring big data is proposed. Objective. To optimize and improve the prediction accuracy of the water quality prediction model. Firstly, in the water environment monitoring system, the Internet of Things big data technology is used to accurately sense and monitor the real-time data of sewage treatment equipment and sewage quality. Then, the deep belief network (DBN) is used to build the water pollution prediction model, and the collected sewage treatment data is analyzed to predict the water quality status. Finally, particle swarm optimization algorithm is used to dynamically optimize the number of hidden layer neural units and learning rate in the DBN prediction model, which makes the prediction results more scientific and accurate. Based on the sampling data of Shanghai Jinze Reservoir, the proposed model is experimentally analyzed. The results show that the probability of accurate location of the pollution source is not less than70%. And under the two indicators of chemical oxygen demand and biological oxygen demand, the root mean square error and correlation coefficient are 3.073, 0.9892 and 1.958, 0.9565, respectively, which are better than other comparison models.

Enhanced Secured and Real-Time Data Transmissions in Wireless Sensor Networks using SFRT Routing Protocol

Enhanced Secured and Real-Time Data Transmissions in Wireless Sensor Networks using SFRT Routing Protocol

Enhancing Safety in Cargo Handling Units Using Thermal and Motion IoT Sensors

To improve supply chain efficiency by establishing real-time communication via Internet of Things (IoT) devices between cargo units and Logistics Control Systems (LCS). The method makes it possible to continuously monitor characteristics like location, temperature, humidity, and handling affects by carefully placing IoT sensors within cargo containers. These Internet of Things (IoT) devices work together to create a network that sends data in real time to a centralized LCS platform. This data gives logistics operators fast insights for proactive disruption response, risk mitigation, and optimized route planning. The research uses encryption techniques, redundancy plans, and flexible software interfaces to address issues with data security, device dependability, and integration into current infrastructure. Furthermore, by integrating machine learning and predictive analytics, the LCS can anticipate potential problems, which helps promotes increased customer happiness, decreased losses, and greater operational performance in contemporary logistics and freight transportation.

Wireless Diagnosis and Control of DC–DC Converter for Off-Grid Photovoltaic Systems

Integrating a photovoltaic (PV) microgrid system with wireless network control heralds a new era for renewable energy systems. This fusion capitalizes on the strengths of photovoltaic technology, leveraging solar energy for electricity generation while incorporating advanced networked control capabilities. Although employing network communication to facilitate information exchange among system elements offers benefits, it also introduces novel challenges which can hinder fault diagnosis, such as packet loss and communication delay. This paper focuses on a cloud-based fault detection approach for an effective boost converter within a photovoltaic system. Faults are diagnosed using a detection algorithm based on the Lyapunov function, ensuring power optimization. The effectiveness of our approach is demonstrated through simulations of a PV generator model utilizing real-time weather data collected in Brazil, illustrating its robustness through the acquired results.

Smart Poultry Farm Automation

Our case-study paper entitled "Smart Poultry Farm Automation" aims to enhance the efficiency and productivity of poultry farming through the application of advanced automation technologies. Poultry farming plays a crucial role in meeting the growing global demand for poultry products. However, traditional farming methods often face challenges related to labor intensity, resource management, and monitoring. This project proposes the design and implementation of a comprehensive automation system for poultry farms. The system will integrate sensors, actuators, and IoT (Internet of Things) technologies to automate various tasks such as environmental control, feeding, watering, and health monitoring of poultry. Real-time data collection and analysis will enable farmers to optimize farm conditions, improve bird health, and minimize resource wastage. Key components of the proposed system include environmental sensors for monitoring temperature, humidity, and ventilation; automated feeders and waterers controlled by IoT devices; and health monitoring systems utilizing image processing or sensor data for early disease detection. The system will be designed to provide farmers with remote access and control through a user-friendly interface, allowing them to monitor farm operations and receive alerts or notifications. The expected outcomes of this project include increased productivity, reduced operational costs, improved animal welfare, and better decision-making for farmers. By leveraging automation and IoT technologies, the proposed system will contribute to the modernization and sustainability of poultry farming practices

Influence of emotions on the aggressive driving behavior of online ­car-hailing drivers based on association rule mining

Emotion is an important factor that can lead to the occurrence of aggressive driving. This paper proposes an association rule mining-based method for analysing contributing factors associated with aggressive driving behaviour among online car-hailing drivers. We collected drivers’ emotion data in real time in a natural driving setting. The findings show that 29 of the top 50 association rules for aggressive driving are related to emotions, revealing a strong relationship between driver emotions and aggressive driving behaviour. The emotions of anger, surprised, happy and disgusted are frequently associated with aggressive driving behaviour. Negative emotions combined with other factors (for example, driving at high speeds and high acceleration rates and with no passengers in the vehicle) are more likely to lead to aggressive driving behaviour than negative emotions alone. The results of this study provide practical implications for the supervision and training of car-hailing drivers.

Personalized Management of Fatigue in Individuals With Myalgic Encephalomyelitis/Chronic Fatigue Syndrome and Long COVID Using a Smart Digital mHealth Solution: Protocol for a Participatory Design Approach.

Fatigue is the most common symptom in myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) and long COVID, impacting patients' quality of life; however, there is currently a lack of evidence-based context-aware tools for fatigue self-management in these populations. This study aimed to (1) address fatigue in ME/CFS and long COVID through the development of digital mobile health solutions for self-management, (2) predict perceived fatigue severity using real-time data, and (3) assess the feasibility and potential benefits of personalized digital mobile health solutions. The MyFatigue project adopts a patient-centered approach within the participatory health informatics domain. Patient representatives will be actively involved in decision-making processes. This study combines inductive and deductive research approaches, using qualitative studies to generate new knowledge and quantitative methods to test hypotheses regarding the relationship between factors like physical activity, sleep behaviors, and perceived fatigue in ME/CFS and long COVID. Co-design methods will be used to develop a personalized digital solution for fatigue self-management based on the generated knowledge. Finally, a pilot study will evaluate the feasibility, acceptance, and potential benefits of the digital health solution. The MyFatigue project opened to enrollment in November 2023. Initial results are expected to be published by the end of 2024. This study protocol holds the potential to expand understanding, create personalized self-management approaches, engage stakeholders, and ultimately improve the well-being of individuals with ME/CFS and long COVID. PRR1-10.2196/50157.

Data on Ocrelizumab Treatment Collected by MS Patients in Germany Using Brisa App

Background: With a rising number of multiple sclerosis (MS) cases and increasing pressure on health systems, digital companion apps like Brisa, designed specifically for people with MS, can play an important role in the patient journey. These apps enable the collection of real-time longitudinal data that are critical to our understanding of the pathophysiology and progression of MS. Methods: This retrospective, descriptive analysis consists of data from Brisa users who registered between 6 August 2021 and 8 September 2022. Of the unique users, 37.7% (n = 1593) fulfilled the inclusion criteria including information about medication and demographics and tracked one or more symptoms and/or patient-reported outcomes. Users were classified as moderate-efficacy treatment users, high-efficacy treatment users and ocrelizumab users, and the reporting frequency and scores of symptoms and patient-reported outcomes were analyzed. Results: The largest cohort of Brisa users (405) reported treatment with ocrelizumab and were mostly diagnosed 2–5 years before the survey. The most reported MS symptoms were similar between OUs (ocrelizumab users), HETUs (high-efficacy treatment users) and METUs (moderate-efficacy treatment users). OUs on average reported symptoms and answered questionnaires more frequently. Baseline scores between HETUs and OUs were similar, whereas baseline scores of METUs were slightly lower in comparison. In a further analysis of OUs, disability scores increased with age; users aged 26–45 years had higher pain scores than 18–25-year-olds. No significant differences were found in quality of life, bowel control and vision between age groups. Conclusion: These findings show that the characteristics of the Brisa cohort are similar to the results of other studies and registries and can provide a representative overview of everyday disease management. Thereby, these results can bridge the gap between clinical research and real patient experience, but they also raise new questions, such as how often the hard-and-early therapy approach is already used and whether baseline characteristics and reasons for choosing a particular treatment contribute to the different outcomes over time. Answering these questions requires further research and analysis.

Blockchain Enhanced Student Physical Performance Analysis using Machine Learning-IoT and Apriori Algorithm in Physical Education Network Teaching

In the digital era, particularly with the rise of online teaching, traditional approaches to college physical education face challenges in adequately monitoring and enhancing students’ physical fitness. This study introduces a novel approach that integrates blockchain technology with a Machine Learning-IoT framework to evaluate and improve students' physical performance. Utilizing the Apriori algorithm, enhanced with particle swarm optimization and an improved K-means methodology, this system offers a robust tool for correlating student behavior with sports performance in a secure and decentralized manner. The proposed system uses blockchain for safe data management and IoT for real-time data collection, ensuring privacy as well as efficiency. The algorithm's accuracy, recall, and F1 values on the Iris dataset are 0.947, 0.931, and 0.928, respectively, with a considerable Calinski Harabasz score of more than 240. When applied to university student behavior data, the blockchain-enhanced system successfully mined association rules with a maximum confidence level of 0.923.

Concert Halls as Nearly Adaptive Spaces

Concert halls have led to increasingly complex spaces that cannot be thought of as static ‘containers’ anymore. This complexity makes them viable to be launched towards industry 4.0 and to be considered a function of the activities that they can provide during their life cycle. They are characterized by dynamic objects that contain sophisticated sub-systems and add to the capability to influence both environmental variables and user behavior. This article explains an adaptive concert hall at an early stage, in which a network of sensors that gather real-time data on environmental factors such as temperature, air humidity and air velocity are considered, focusing on their direct and indirect intercorrelations with the acoustic quantities to optimize the room acoustic response. The proposed methodology is controlled by a digital twin (DT) based on building information modeling (BIM), integrated with sensors, actuators, and acoustic measurements and algorithms. By analyzing the data, algorithms identify patterns, and an autonomous fine-tune setting is achieved, including the novelty for which a natural variable acoustic field becomes possible during a musical execution without the use of any electroacoustic system support. The hall becomes a natural active instrument to be included in the composer’s score. A case study is presented.

High resolution chlorophyll-a in-situ fluorescence sensors versus in-vitro chlorophyll-a measurements in mesocosms with contrasting nutrient and temperature treatments

Harmful algal blooms (HABs) are a significant threat to freshwater ecosystems, and monitoring for changes in biomass is therefore important. Fluorescence in-situ sensors enable rapid and high frequency real-time data collection and have been widely used to determine chlorophyll-a (Chla) concentrations that are used as an indicator of the total algal biomass. However, conversion of fluorescence to equivalent Chla concentrations is often complicated due to biofouling, phytoplankton composition and the type of equipment used. Here, we validated measurements from 24 Chla and 12 phycocyanin (cyanobacteria indicator) fluorescence in-situ sensors (Cyclops-7F, Turner Designs) against spectrophotometrically (in-vitro) determined Chla and tested a data-cleaning procedure for eliminating data errors and impacts of non-photochemical quenching. The test was done across a range of freshwater plankton communities in 24 mesocosms (i.e. experimental tanks) with a 2x3 (high and low nutrient x ambient, IPCC-A2 and IPCC-A2+50% temperature scenarios) factorial design. For most mesocosms (tanks), we found accurate (r2 ≥ 0.7) calibration of in-situ Chla fluorescence data using simple linear regression. An exception was tanks with high in-situ phycocyanin fluorescence, for which multiple regressions were employed, which increased the explained variance by >16%. Another exception was the low Chla concentration tanks (r2 < 0.3). Our results also show that the high frequency in-situ fluorescence data recorded the timing of sudden Chla variations, while less frequent in-vitro sampling sometimes missed these or, when recorded, the duration of changes was inaccurately determined. Fluorescence in-situ sensors are particularly useful to detect and quantify sudden phytoplankton biomass variations through high frequency measurements, especially when using appropriate data-cleaning methods and accounting for factors that can impact the fluorescence readings.

Analysis of Anthropogenic Waste Heat Emission from an Academic Data Center

The rapid growth in computing and data transmission has significant energy and environmental implications. While there is considerable interest in waste heat emission and reuse in commercial data centers, opportunities in academic data centers remain largely unexplored. In this study, real-time onsite waste heat data were collected from a typical academic data center and an analysis framework was developed to determine the quality and quantity of waste heat that can be contained for reuse. In the absence of a comprehensive computer room monitoring system, real-time thermal data were collected from the data center using two arrays of thermometers and thermo-anemometers in the server room. Additionally, a computational fluid dynamics model was used to simulate temperature distribution and identify “hot spots” in the server room. By simulating modification of the server room with a hot air containment system, the return air temperature increased from 23 to 46 °C and the annual waste heat energy increased from 377 to 2004 MWh. Our study emphasizes the importance of containing waste heat so that it can be available for reuse, and also, that reusing the waste heat has value in not releasing it to the environment.

Comparative Analysis of Meteorological versus In Situ Variables in Ship Thermal Simulations

Thermal simulations have become increasingly popular in assessing energy efficiency and predicting thermal behaviors in various structures. Calibration of these simulations is essential for accurate predictions. A crucial aspect of this calibration involves investigating the influence of meteorological variables. This study aims to explore the impact of meteorological variables on thermal simulations, particularly focusing on ships. Using TRNSYS (TRaNsient System Simulation) software (v17), renowned for its capability to model complex energy systems within buildings, the significance of incorporating meteorological data into thermal simulations was analyzed. The investigation centered on a patrol vessel stationed in a port in Galicia, northwest Spain. To ensure accuracy, we not only utilized the vessel’s dimensions but also conducted in situ temperature measurements onboard. Furthermore, a dedicated weather station was installed to capture real-time meteorological data. Data from multiple sources, including Meteonorm and MeteoGalicia, were collected for comparative analysis. By juxtaposing simulations based on meteorological variables against those relying solely on in situ measurements, we sought to discern the relative merits of each approach in enhancing the fidelity of thermal simulations.

In Situ Monitoring of the Curing of Highly Filled Epoxy Molding Compounds: The Influence of Reaction Type and Silica Content on Cure Kinetic Models

Monitoring of molding processes is one of the most challenging future tasks in polymer processing. In this work, the in situ monitoring of the curing behavior of highly filled EMCs (silica filler content ranging from 73 to 83 wt%) and the effect of filler load on curing kinetics are investigated. Kinetic modelling using the Friedman approach was applied using real-time process data obtained from in situ DEA measurements, and these online kinetic models were compared with curing analysis data obtained from offline DSC measurements. For an autocatalytic fast-reacting material to be processed above the glass transition temperature Tg and for an autocatalytic slow-reacting material to be processed below Tg, time–temperature–transformation (TTT) diagrams were generated to investigate the reaction behavior regarding Tg progression. Incorporating a material containing a lower silica filler content of 10 wt% enabled analysis of the effects of filler content on sensor sensitivity and curing kinetics. Lower silica particle content (and a larger fraction of organic resin, respectively) favored reaction kinetics, resulting in a faster reaction towards Tg1. Kinetic analysis using DEA and DSC facilitated the development of highly accurate prediction models using the Friedman model-free approach. Lower silica particle content resulted in enhanced sensitivity of the analytical method, leading, in turn, to more precise prediction models for the degree of cure.

Selection of reference genes for normalizing gene expression data across seasons in spermatozoa of water buffalo (Bubalus bubalis).

Selection of the most stably expressed reference genes is key to monitoring accurate target gene expression across any tissue or cell type. The mRNA in spermatozoa stores valuable information related to changes in spermatogenesis due to variations in environmental conditions, especially during heat stress, which affects various sperm functions. Semen quality in buffalo bulls is significantly influenced by the seasons. In the study, a panel of nine genes was evaluated to identify the most stably expressed internal control gene (ICG) for the normalization of real-time gene expression data generated across various seasons for Murrah buffalo bulls' spermatozoa. Sperm cells were purified from the semen samples collected during different seasons, with temperature-humidity index (THI) ranging from 80.80 ± 1.47 (hot summer) to 55.88 ± 1.98 (winter), using the BoviPure™ gradient purification method. The RNA isolated from the purified spermatozoa fraction was quality checked prior to reverse transcription and subjected to qPCR (quantitative real-time PCR) based expression analysis. An automated 'endoGene' pipeline was employed to apply the geNorm, NormFinder, and BestKeeper algorithms for data analysis. The result indicated that GAPDH and PP1A were the most stably expressed among the gene panel, whereas ATPSF1 and ACTB were the two least stable expressed reference genes. Further, the most suitable ICGs identified were validated by normalization of real time expression data of heat stress and sperm quality genes, HSFY2 and AKAP4, respectively. The genes identified would help in generating the most reliable results for the expression profiling of the genes dictating sperm quality and heat stress cope-up mechanism in buffalo spermatozoa, collected during different seasons.

A novel algorithm for maximum power point tracking using computer vision (CVMPPT).

The behavior of an illuminated solar module can be characterized by its power-voltage curve. Tracking the peak of this curve is essential to harvest the maximum power by the module. The position of the peak varies with temperature and irradiance and needs to be traced. Under partial shading conditions, the number of peaks increases and makes it more difficult to find the global maximum power point (MPP). Various methods are used for maximum power point tracking (MPPT) that are based on iterations. These methods are time-consuming and fail to work satisfactorily under rapidly changing environmental conditions. In this paper, a novel algorithm is proposed that for the first time, utilizes computer vision to find the global maximum power point. This algorithm, which is implemented in Matlab/Simulink, is free of voltage iterations and gives the real-time data for the maximum power point. The proposed algorithm increases the speed and the reliability of the MPP tracking via replacing analogue electronics calculations by digital means. The validity of the algorithm is experimentally verified.

Risk Reduction in Transportation Systems: The Role of Digital Twins According to a Bibliometric-Based Literature Review

Urban areas, with their dense populations and complex infrastructures, are increasingly susceptible to various risks, including environmental challenges and infrastructural strain. This paper delves into the transformative potential of digital twins—virtual replicas of physical entities—for mitigating these risks. It specifically explores the role of digital twins in reducing disaster risks, such as those posed by earthquakes and floods, through a comprehensive bibliometric-based literature review. Digital twins could contribute to risk reduction by combining data analytics, simulation, and predictive modeling by creating virtual replicas of physical entities and integrating real-time data streams to better address and manage risks in urban environments. In detail, they can help city planners and decision-makers analyze complex urban systems, simulate potential scenarios, and predict potential outcomes. This proactive approach allows both the identification of vulnerabilities and better implementation of targeted mitigation strategies to enhance urban resilience and sustainability. More informed decisions can be made relying on simulations, and it can also be possible to optimize resource allocation and better respond to emerging challenges. This work reviews the key publications in this domain, with the aim of finding relevant papers that can be useful to urban planners and policy-makers. The paper concludes by discussing the broader implications of these findings and identifying challenges in the widespread adoption of digital twin technology, including data privacy concerns and the need for interdisciplinary collaboration. It also outlines prospective avenues for future research in this emerging field.

A Survey of Security Challenges in Cloud-Based SCADA Systems

Supervisory control and data acquisition (SCADA) systems enable industrial organizations to control and monitor real-time data and industrial processes. Migrating SCADA systems to cloud environments can enhance the performance of traditional systems by improving storage capacity, reliability, and availability while reducing technical and industrial costs. However, the increasing frequency of cloud cyberattacks poses a significant challenge to such systems. In addition, current research on cloud-based SCADA systems often focuses on a limited range of attack types, with findings scattered across various studies. This research comprehensively surveys the most common cybersecurity vulnerabilities and attacks facing cloud-based SCADA systems. It identifies four primary vulnerability factors: connectivity with cloud services, shared infrastructure, malicious insiders, and the security of SCADA protocols. This study categorizes cyberattacks targeting these systems into five main groups: hardware, software, communication and protocol-specific, control process, and insider attacks. In addition, this study proposes security solutions to mitigate the impact of cyberattacks on these control systems.

An examination of state and trait urgency in individuals with binge-eating disorder.

Negative urgency (i.e., acting rashly when experiencing negative affect; NU), is a theorised maintenance factor in binge-eating type eating disorders. This study examined the association between trait NU and eating disorder severity, momentary changes in state NU surrounding episodes of binge eating, and the momentary mechanistic link between affect, rash action, and binge-eating risk. Participants were 112 individuals with binge-eating disorder (BED). Baseline measures included the UPPS-P Impulsive Behaviour Scale to assess trait NU and the Eating Disorders Examination to assess binge-eating frequency and global eating disorder severity. Ecological momentary assessment captured real-time data on binge eating, negative affect, and state NU. Multiple regression analysis revealed a strong association between trait NU and eating disorder severity. Generalised estimating equations showed that state NU increased before and decreased after binge-eating episodes, and that this pattern was not moderated by trait-level NU. Finally, a multilevel structural equation model indicated that increases in rash action mediated the momentary relationship between states of high negative affect and episodes of binge eating. These findings underscore the importance of both trait and state NU in binge-eating type eating disorders, and suggest NU as a potential key target for intervention.