Real-time Data Research Articles

Adaptive AI in precision agriculture: A review: Investigating the use of self-learning algorithms in optimizing farm operations based on real-time data

Adaptive AI in precision agriculture: A review: Investigating the use of self-learning algorithms in optimizing farm operations based on real-time data

Harnessing big data for tailored health communication: A systematic review of impact and techniques

In recent years, the convergence of healthcare and big data analytics has opened new avenues for tailored health communication, enabling personalized interventions and improving healthcare outcomes. This systematic review investigates the impact and techniques of harnessing big data for tailored health communication. The review synthesizes findings from diverse studies spanning healthcare sectors, including public health campaigns, clinical interventions, and patient engagement initiatives. It examines the effectiveness of tailored communication strategies in addressing various health challenges, such as chronic diseases, infectious outbreaks, and mental health disorders. Key findings highlight the significant positive impact of personalized health communication on health behavior change, treatment adherence, and patient empowerment. Big data analytics enable the segmentation of diverse populations based on socio-demographic, behavioral, and clinical characteristics, facilitating the delivery of targeted messages tailored to individual preferences and needs. Personalization enhances engagement, fosters trust, and motivates individuals to adopt healthier lifestyles and adhere to medical recommendations. Furthermore, the review explores the diverse techniques and technologies employed in harnessing big data for tailored health communication. Machine learning algorithms, natural language processing, and predictive modeling are leveraged to analyze vast datasets, predict health outcomes, and tailor communication messages in real-time. Mobile health applications, social media platforms, and wearable devices serve as channels for delivering personalized interventions and collecting real-time health data. However, the review also identifies challenges and limitations, including privacy concerns, data security risks, and the digital divide. Ethical considerations regarding data collection, consent, and transparency are paramount in ensuring the responsible use of big data in health communication. This systematic review underscores the transformative potential of harnessing big data for tailored health communication. By leveraging advanced analytics and technology, healthcare stakeholders can deliver personalized interventions that resonate with individuals, ultimately driving positive health behavior change and improving healthcare outcomes on a population scale.

Employee Monitoring System Using Power BI

Abstract: This paper introduces an HR attendance and task monitoring system, which has been developed utilizing Power BI. The system provides a comprehensive dashboard for HR personnel to visualize and track employee activity effectively. The dashboard includes key aspects such as Attendance Monitoring system works on real-time or historical attendance data can be showcased, letting HR to identifies trends, patterns, and potential attendance problems. Employee Skills & Roles this section presents a clear view of employee skillsets and assigned roles, helping in informed project allocation and resource management accurately. Project Assignments the dashboard keeps track of current project assignments for each employee, empowering HR to understand workload distribution and make adjustments when needed. By utilizing Power BI's data visualization and analysis abilities to the fullest extent, this system augments HR to gain crucial insights into employee activity. This assists in better decision-making in areas like attendance management, project allocation, and resource optimization, ultimately contributes to a more efficient and productive workforce

Smart Agriculture Automation System using ML

Abstract: The "Smart Agriculture Automation System using Machine Learning" project envisages a future where farming transcends manual labor and embraces data-driven automation. In response to the escalating demand for food amidst a burgeoning global population, traditional farming methods are rendered insufficient. However, Smart Agriculture Automation Systems offer a transformative solution by enhancing crop production efficiency and maintaining yield quality. Climate changeinduced challenges such as erratic weather patterns and heightened pest attacks further underscore the necessity for innovation in agriculture. At the heart of this system lies real-time sensor data, enabling precision farming practices that optimize resource utilization while fostering environmental sustainability. Leveraging Machine Learning algorithms, the system not only predicts and mitigates agricultural challenges but also detects early signs of crop diseases and nutrient deficiencies. This proactive approach ensures higher yields minimizes manual tasks and minimizes wastage. The integration of IoT technology enables remote monitoring and seamless data exchange, unlocking capabilities such as precise crop prediction, weather forecasting, and fertilizer recommendations. By analyzing historical data alongside current environmental factors, these systems accurately forecast crop yields and offer insights for informed decision-making in planting and harvesting cycles. Smart Agriculture Automation Systems embody adaptability, sustainability, and accessibility, heralding a new era of intelligent and efficient modern farming. This project integrates the Internet of Things (IoT) sensors and an innovative marketplace to revolutionize the agricultural landscape. Through this system, we aim to empower farmers with data-driven insights,

A comparative review of subsea navigation technologies in offshore engineering projects

Subsea navigation technologies play a critical role in enhancing precision and efficiency in offshore engineering projects. This comparative review examines the evolution and application of subsea navigation systems, with a focus on improving precision in offshore construction and surveying. The study evaluates various subsea navigation technologies, including acoustic positioning systems, inertial navigation systems, and optical imaging systems, highlighting their capabilities and limitations. The review begins by discussing the historical development of subsea navigation technologies, tracing their evolution from early acoustic systems to modern integrated solutions. It then explores the key components and operation principles of each technology, providing insights into their functionalities and suitability for different offshore applications. The study also examines the challenges and advancements in subsea navigation, including issues related to signal interference, accuracy, and real-time data processing. It discusses how these challenges have been addressed through technological innovations, such as improved sensor technologies and advanced data fusion algorithms. Furthermore, the review assesses the practical application of subsea navigation technologies in offshore engineering projects, including pipeline installation, subsea infrastructure maintenance, and underwater surveying. It analyzes case studies and industry practices to illustrate the effectiveness of these technologies in improving precision, reducing costs, and mitigating risks in offshore operations. Overall, this comparative review provides a comprehensive overview of subsea navigation technologies in offshore engineering projects. It highlights the evolution, capabilities, and applications of these technologies, emphasizing their role in enhancing precision and efficiency in offshore construction and surveying. The study concludes with recommendations for future research and development to further improve the performance and reliability of subsea navigation systems in offshore operations.

Utilizing comprehensive data dashboards to improve service delivery: Insights from U.S. case studies

In an era driven by data, the efficient utilization of comprehensive data dashboards has emerged as a crucial tool for enhancing service delivery across various sectors. This review provides insights into the impact and effectiveness of employing such dashboards, drawing from case studies conducted in the United States. Effective service delivery requires a deep understanding of operations, client needs, and performance metrics. Comprehensive data dashboards offer a solution by aggregating, visualizing, and analyzing vast amounts of data in real-time, providing decision-makers with actionable insights. Through a synthesis of case studies, this review elucidates how organizations in the U.S. leverage data dashboards to optimize service delivery. Firstly, the review examines the healthcare sector, where data dashboards play a pivotal role in improving patient care. Case studies highlight how hospitals utilize dashboards to monitor patient outcomes, manage resources efficiently, and identify areas for quality improvement. By tracking key performance indicators such as patient wait times, readmission rates, and medication errors, healthcare providers can implement targeted interventions to enhance service quality and patient satisfaction. Furthermore, the review delves into the realm of education, showcasing how schools and educational institutions leverage data dashboards to personalize learning experiences and improve student outcomes. Through the analysis of student performance data, attendance records, and demographic information, educators can identify at-risk students, tailor instructional strategies, and allocate resources effectively, thereby fostering academic success and equitable education opportunities. Additionally, this review explores the utilization of data dashboards in government agencies to enhance public service delivery. Case studies demonstrate how municipalities employ dashboards to track infrastructure projects, manage budgets, and address citizen concerns promptly. By fostering transparency and accountability, these dashboards facilitate citizen engagement and enable governments to deliver services that align with community needs and priorities. In conclusion, the review underscores the transformative potential of comprehensive data dashboards in optimizing service delivery across diverse sectors. By leveraging real-time data analytics, organizations can enhance efficiency, accountability, and client satisfaction, ultimately driving positive outcomes and fostering continuous improvement in service delivery practices.

IOT Based Pill Reminder and Monitoring System

Abstract: Introducing an innovative IoT-based pill reminder and monitoring system, this solution aims to tackle medication nonadherence challenges effectively. Leveraging interconnected devices and advanced sensors, personalized medication reminders are delivered to users, enhancing adherence rates efficiently. Accessible via smartphones or other devices, a user-friendly interface allows patients to configure medication schedules and receive timely reminders tailored to their needs. The system tracks pill intakefor dosage accuracy and provides real-time data to healthcare providers for remote monitoring and intervention. Integration with electronic health records streamlines communication between patients and healthcare providers, fostering collaborative care. This IoT solution holds significant potential for addressing medication non-adherence issues and improving overall healthcare outcomes, with further research warranted for broader implementation and evaluation across diverse patient populations.

Environmental Monitoring in Internet of Things (IOT)

Abstract: The Internet of Things (IoT) connects physical objects to the internet, enabling autonomous data collection and analysis. In environmental monitoring, IoT plays a crucial role in gathering real-time data on parameters like air and water quality, weather, wildlife behavior, and urban heat islands. This abstract explores how IoT is used in environmental monitoring, covering standards, device types, and applications. IoT comprises interconnected devices that collect environmental data and transmit it for analysis, facilitating continuous observation and better resource management. Standards like MQTT and communication technologies like Wi-Fi govern IoT implementation. Environmental monitoring involves assessing air and water quality, weather conditions, wildlife behavior, and urban heat islands. Air quality monitoring measures pollutant levels in the atmosphere using IoT-based sensor networks. Water quality monitoring assesses chemical and biological characteristics of water bodies. Weather and climate monitoring observe atmospheric conditions using IoT-based sensor networks. Wildlife monitoring tracks animal movements and behavior using IoT-enabled devices. Urban heat island monitoring assesses temperature distribution in urban areas using IoT-based sensors. IoT revolutionizes environmental monitoring by enabling real-time, remote observation across various domains, contributing to better resource management and sustainability efforts.

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.