Real-time Monitoring Data Research Articles

Sepsis increases the risk of in-hospital cardiac arrest: a population-based analysis.

Sepsis patients have a high risk of developing in-hospital cardiac arrest (IHCA), which portends poor survival. However, little is known about whether the increased incidence of IHCA is due to sepsis itself or to comorbidities harbored by sepsis patients. We conducted a retrospective population-based cohort study comprising 20,022 patients admitted with sepsis to hospitals in Taiwan using the National Health Insurance Research Database (NHIRD). We constructed three non-sepsis comparison cohorts using risk set sampling and propensity score (PS) matching. We used univariate conditional logistic regression to evaluate the risk of IHCA and associated mortality. We identified 12,790 inpatients without infection (matched cohort 1), 12,789 inpatients with infection but without sepsis (matched cohort 2), and 10,536 inpatients with end-organ dysfunction but without sepsis (matched cohort 3). In the three PS-matched cohorts, the odds ratios (OR) for developing ICHA were 21.17 (95% CI 17.19, 26.06), 18.96 (95% CI: 15.56, 23.10), and 1.23 (95% CI: 1.13, 1.33), respectively (p < 0.001 for all ORs). In conclusion, in our study of inpatients across Taiwan, sepsis was independently associated with an increased risk of IHCA. Further studies should focus on identifying the proxy causes of IHCA using real-time monitoring data to further reduce the incidence of cardiopulmonary insufficiency in patients with sepsis.

A New Perspective on Digital Twin-Based Mechanical Design in Industrial Engineering

The advent of digital twin methodologies in industrial engineering is inspiring a transformative wave in mechanical design processes. This innovative framework signifies the convergence of real and virtual worlds, enabling an unprecedented level of synergy between design, simulation, production, and evaluation cycles. The resultant 'digital counterparts' serve as dynamic blueprints that echo the life cycle of their physical kin, unraveling sophisticated opportunities for predictive maintenance, accelerated prototyping, and mitigated risks. Residing at the cusp of this evolution, mechanical design draws immense strategic advantage. The adoption of digital twins unearths potential for holistic design enhancements through real-time condition monitoring, performance prediction, and comprehensive data analytics. These processes structure efficient decision-making protocols, fostering increased product reliability, enhanced operational efficiency, and reduced time-to-market. Moreover, digital twins open new avenues for developing complex systems and large-scale machinery through seamless integration of interdisciplinary skills. Intricate simulations aid in isolating potential hurdles and foreseeing outcomes with substantial accuracy, ensuring a meticulous yet fluid design process. Ultimately, the emergence of digital twins in mechanical design embodies a remarkable shift pivoting towards Industry 4.0, redefining the paradigms of industrial engineering with the blend of cornerstone technologies. Leveraging these state-of-the-art methodologies hints towards an optimistic future regarding sustainable industry practices and technological advancements.

Wireless network handheld terminal-based green ecological sustainable design evaluation system: Improved data communication and reduced packet loss rate

Abstract In response to the challenge of high packet loss rates due to poor mobility of PCs, we introduce a sustainable design evaluation system centered around wireless network handheld terminals. This system combines hardware components, including microprocessor and wireless communication modules, to achieve wireless data transmission and program debugging. In the software domain, a configurable communication protocol is employed to establish an ecological data communication module supporting wireless network handheld terminals. Leveraging real-time monitoring data from multiple points, we introduce an information fusion algorithm and construct a green ecological sustainability design evaluation model. Notably, experimental results reveal that our system outperforms alternatives based on sensor and RFID technologies. It exhibits a lower packet loss rate and extends the distance of zero packet loss, highlighting its advantages in data communication.

Smart Greenhouse and Warehouse Monitoring with Disease Detection using Machine Learning

Global climatic changes in the recent era have resulted in a significant global food shortage. Where there is a vast population and a diverse agricultural sector, food security is a major problem. Growing competition for land, water, and energy will undermine our capital food production, and the effect of climate change will continue to be a challenge. The integrated greenhouse and warehouse provide quality and sustainable food production. In this paper an integrated green house with warehouse monitoring hardware implementation, with novel feature of customized disease detection with convolution neural network (CNN) algorithm is presented. The real time monitoring of data of different sensors are displayed on the screen/ dashboard developed by the android application. This aids farmers in making wise choices on crop management and disease detection of greenhouse crops and effective monitoring.

Development of a Surveillance Robot with a Rocker Bogie Design for Versatile Terrain Navigation

This project focuses on the design and development of a multi-terrain robot based on the rocker bogie mechanism. The robot is equipped with a camera, GPS module, Wi-Fi module, ultrasonic sensor for obstacle detection, and gas sensors for detecting carbon monoxide, methane, smoke levels, and AQI. It utilizes internet connectivity to transmit data and presents the collected information through a website dashboard. The project explores the potential applications of the robot, including surveillance, environmental monitoring, search and rescue, industrial inspections, and agriculture. The integration of various sensors and connectivity capabilities allows for real-time monitoring and analysis of critical data in challenging terrains and environments.

Design, Develop, Fabricate, and Statistical Quantification of Air Quality System Along with Web Application

Our Air Quality Index (AQI) measurement and analytics system is specifically designed for household, office, or single-room use, addressing concerns related to pollution's impact on respiratory health and visibility. The system incorporates dedicated sensors such as the PM Sensor (PMS5003), the ozone sensor (MQ- 131), the VOC Sensor (MP503), the CO2 Sensor (MH- Z19B), and the temperature and humidity sensor (DHT22). These sensors communicate with a web application server through the ESP8266-01 Wi-Fi module. The sensor values are displayed on a TFT (Thin Film Transistor) display, ensuring continuous monitoring with a time delay. The Arduino Pro Mini Board, powered by an FTDI (Future Technology Devices International Ltd.) chip, facilitates the sensor readings by operating on specific protocols. The ESP-01 module provides internet capability, enabling the system to host a custom web server. The sensor data is utilized to generate graphs on a dashboard, presenting the air quality information in a visually accessible manner. The displayed values are aligned with the standard guidelines set by the Central Pollution Control Board (CPCB). Moreover, the system supports historical data retrieval through integration with a PostgreSQL database, allowing users to analyze air quality trends over time. By providing real-time monitoring, comprehensive analytics, and historical data insights, our AQI measurement and analytics system empowers users to make informed decisions to improve air quality and mitigate the potential risks associated with pollution.

IoT-Enabled Fault Prediction and Maintenance for Smart Charging Piles

With the application of the Internet of Things (IoT), smart charging piles, which are important facilities for new energy electric vehicles (NEVs), have become an important part of the smart grid. Since the smart charging piles are generally deployed in complex environments and prone to failure, it’s significant to perform efficient fault diagnosis and timely maintenance for them. One of the key problems to be solved is how to conduct fault prediction based on limited data collected through IoT in the early stage and develop reasonable preventive maintenance strategies. In this paper, a real-time fault prediction method combining costsensitive logistic regression (CS-LR) and cost-sensitive support vector machine classification (CS-SVM) is proposed. CS-LR is firstly used to classify the fault data of smart charging piles, then the CS-SVM is adopted to predict the faults based on the classified data. The feasibility of the proposed model is illustrated through the case study on fault prediction of real-world smart charging piles. To demonstrate the advantage in prediction accuracy, the proposed fault prediction model is compared with the classic baseline models such as LR, SVM, decision tree (DT), K-nearest neighbor (KNN), and back propagation neural network (BPNN). Finally, based on the proposed fault prediction method, preventive maintenance based on a probability threshold with the minimum total expected cost is proposed. Simulation results show that the proposed maintenance strategy has a better performance in reducing the total maintenance cost compared with traditional periodic maintenance. This is valuable for the development of preventive maintenance strategies for repairable systems under early real-time monitoring data.

Optimization Water Conservation Through IoT Sensor Implementation At Smartneasy Nusantara

The use of IoT sensors in technology is a fascinating research subject due to the possibility of real-time monitoring of water usage and temperature effects. Developing tools and systems that make observations of water usage through Max6675 sensors can optimize data collection and processing through remote monitoring. Utilizing the IoT technique, the ESP8266 WeMOS D1 R2 microcontroller and the Max6675 temperature sensor are utilized to regulate the water pump via a relay. This approach enhances water sustainability for plant assumptions based on the predetermined temperature. The device activates the water pump when the temperature surpasses the standard level at the observation site. The outcome indicated that this device operates accordingly with a 100% success rate. The device was determined to function effectively by activating the water pump based on temperature. Additionally, it can provide real-time monitoring data and process sensor data for analysis.

Positive Effect Observed on Reducing Criteria Pollutant Emissions Provided by Provisional Local Regulations during the 2022 Winter Olympics

This study examined the impact of temporary air quality control measures on reducing pollutants during the 2022 Winter Olympics in China, utilizing real-time monitoring data from 2017 and 2022 to assess spatial and temporal variations in critical air pollutant concentrations. The results showed that concentrations of PM2.5, PM10, CO, SO2, and NO2 in the Beijing–Tianjin–Hebei region during the Olympic Games showed a marked decrease compared to the historical period, with reductions of 36.59%, 20.35%, 33.95%, 28.90%, and 22.70%, respectively. Significant north–south spatial differences were observed in Beijing, Tianjin, and Hebei Province during the Olympic period. The cities of Zhangjiakou, Chengde, Qinhuangdao, Beijing, and Tangshan showed the most significant pollution reduction. Based on assessments conducted during the Olympic period, it was noted that more than 95% of the daily average concentrations of pollutants are below the maximum values set by the World Health Organization for the interim target. Our research shows that provisional regulations effectively control the emission of air pollutants, providing a solid reference and basis for ensuring air quality during major international events.

Fine-Auth: A Fine-Grained User Authentication and Key Agreement Protocol Based on Physical Unclonable Functions for Wireless Body Area Networks

Wireless body area networks (WBANs) can be used to realize the real-time monitoring and transmission of health data concerning the human body based on wireless communication technology. With the transmission of these sensitive health data, security and privacy protection issues have become increasingly prominent. Fine-grained authentication allows physicians to run authentication checks of another specific entity according to their identifying attributes. Hence, it plays a key role in preserving the security and privacy of WBANs. In recent years, substantial research has been carried out on fine-grained authentication. However, these studies have put considerable effort into WBAN performances, resulting in weakened security. This paper proposes a fine-grained user authentication and key agreement protocol based on physical unclonable functions (PUFs) while maintaining robust security and performance. This will allow physicians to perform mutual authentication and obtain key agreements with authorized body area sensor nodes according to their identity parameters, such as occupation type and title. We then provide comprehensive security and heuristic analyses to demonstrate the security of the proposed protocol. Finally, the performance comparison shows that the proposed protocol is more robust in security, cost-effective communication, and computational overheads compared to three leading alternatives.

Real-Time Generation of Operational Characteristic Curves for Municipal Water Pumping Systems: An Approach to Energy Efficiency and Carbon Footprint

Water supply represents a significant electrical load worldwide. The operation of inefficient pumps contributes to increased energy demand and carbon footprint. Current methods for evaluating industrial water pumps present technical and economic challenges that need to be addressed. For these reasons, we developed a practical and cost-effective virtual instrument called the Pumping Evaluation System (PES). The PES tool performs real-time monitoring of electrical, hydraulic, and efficiency data, as well as the Operating Characteristic (OC) curves. The PES tool was implemented on a vertical pump currently used by a municipal water distribution plant in Mexicali, Mexico. A comparative analysis between the experimental data and the manufacturer’s data revealed potential energy savings of 361,455 kWh/year (24.35%) and an equivalent carbon footprint reduction of 157,233 kg of CO2eq/year if the old pump is replaced by a new one, which is the lowest cost alternative according to a Life Cycle Cost (LCC).

AI-Powered Intelligent Seaport Mobility: Enhancing Container Drayage Efficiency through Computer Vision and Deep Learning

The rapid urbanization phenomenon has introduced multifaceted challenges across various domains, including housing, transportation, education, health, and the economy. This necessitates a significant transformation of seaport operations in order to optimize smart mobility and facilitate the evolution of intelligent cities. This conceptual paper presents a novel mathematical framework rooted in deep learning techniques. Our innovative model accurately identifies parking spaces and lanes in seaport environments based on crane positions, utilizing live Closed-Circuit Television (CCTV) camera data for real-time monitoring and efficient parking space allocation. Through a comprehensive literature review, we explore the advantages of merging artificial intelligence (AI) and computer vision (CV) technologies in parking facility management. Our framework focuses on enhancing container drayage efficiency within seaports, emphasizing improved traffic management, optimizing parking space allocation, and streamlining container movement. The insights from our study provide a foundation that could have potential implications for real-world applications. By integrating cutting-edge technologies, our proposed framework not only enhances the efficiency of seaport operations, but also lays the foundation for sustainable and intelligent seaport systems. It signifies a significant leap toward the realization of intelligent seaport operations, contributing profoundly to the advancement of urban logistics and transportation networks. Future research endeavors will concentrate on the practical implementation and validation of this pioneering mathematical framework in real-world seaport environments. Additionally, our work emphasizes the crucial need to explore further applications of AI and CV technologies in seaport logistics, adapting the framework to address the evolving urbanization and transportation challenges. These efforts will foster continuous advancements in the field, shaping the future of intelligent seaport operations.

Sustainable Farm Care System Using IoT

Throughout history, the quest for sustenance has been a constant struggle. With the advent of agriculture, humankind has witnessed remarkable progress over the centuries. In the present era, the Internet of Things (IoT) has permeated every sphere of life. Inspired by this, We have embarkedon a project to develop an IoT-based application tailored specifically for the agricultural domain, with a focus on revolutionizing plant management. Plant management encompasses a range of agricultural activities, including seedbed preparation, seed sowing, and plant maintenance. Ourwork aims to automate crucial processes such as water irrigation, water storage management, artificial lighting, and plant security systems. To achieve this, We have devised a dedicated mobile application that interfaces withIoT devices. These interconnected devices form a network, enabling real-time data monitoring and seamless activation of various operations within the system. This ambitious work consists of four major modules. To handle each module effectively, an individual nodemcu device has been allocated. By combining the power of IoT technology and agriculture, this work strives to optimize plant management, enhance productivity, and ultimately contribute to the advancement of this vital field

A data-driven operating improvement method for the thermal power unit with frequent load changes

In the near and medium term, thermal power generation will still play an important peak-shaving role in providing grid connection to volatile renewable energy. Thermal power units need to adjust from the current load to the given load within a given time period, which provides space for operational improvement. In this paper, we propose an operating improvement method for the thermal power unit based on real-time monitoring data by observation dividing, feature construction and selection, clustering, and machine learning. The unit operation data is categorized into three feature subsets based on domain knowledge, which is used to distinguish different functions of historical data. Subsequently, the optimal feature subset and observations are selected for building regression models, including linear regression (LR), ensemble tree regression (ETR), neural network regression (NNR), and support vector regression (SVR). R-squared (R2), root mean square error (RMSE), and mean absolute error (MAE) are adopted to test the performance of the proposed regression model on the real-time monitoring data of a thermal unit, which has 80,000 observations of 36 different variables. Compared with benchmark methods, the proposed method has lower regression error in the numerical experiment. Thus, we can thereby improve the efficiency of operational management based on the built learning model. Furthermore, in response to peak shaving requirements, the proposed method in this article considers operational optimization over time periods containing multiple time points compared to the traditional operational optimization perspective. With the continuous arrival of monitoring data, the above method can be updated in a timely manner based on the new database to address model adjustments caused by unit aging and other factors.

Design and Optimization of Smart Campus Framework Based on Artificial Intelligence22

In this study, an artificial intelligence (AI)--based smart campus framework is built and optimized with the aim of improving user happiness, raising AI model performance, maximizing resource utilization, and promoting smart campus adoption. The study technique employs a mixed-methods approach that combines quantitative data analysis and qualitative user feedback in order to completely evaluate the effectiveness of the framework. Literature reviews, Questionnaires of 544, interviews of 56 persons, and observations are used to collect data on user satisfaction, AI model performance, optimization strategies, and adoption of smart campuses AI models are built using statistical methodology and AI techniques for performance evaluation. In the Smart Campus Framework based on Artificial Intelligence, we gathered the data by constructing IoT sensor networks for real-time monitoring and merging student data to provide insights into academic performance and student engagement. The findings indicate that, on average, users are satisfied, and the performance ratings for the AI models vary from 7.25 to 8.25. The smart campus framework is effective, as evidenced by the optimization metric's 7.53 average score. A score of 7.4 for smart campus adoption combines user knowledge, perceived utility, and perceived ease of use. The practical implications include better user experience, cost optimization, and smart campus architecture. Theoretical implications include the verification of the mixed-methods strategy and the creation of a framework for AI model optimization. The study's findings act as a model for upcoming smart campus research, spurring creativity and change in institutions of higher learning. The study’s limitations suggest that results can be generalized with minor contextual change and this is the biggest challenge for researchers and policy makers.

Developing a Circular Business Model for Machinery Life Cycle Extension by Exploiting Tools for Digitalization

Digitalization technologies have been identified as enablers for the adoption of circular economy practices. The machinery-value chain addressed in this study is affected by the introduction of digital technologies that enable real-time monitoring of data on product condition and control optimization, the deployment of predictive analytics techniques, as well as offering circular-based services. Machinery-lifetime extension can be digitally enabled on both old and new machines. The research objectives were to investigate how digital technologies enable the adoption of circular economy-based business models by manufacturing companies and provide answers regarding (i) which Life Cycle Extension Strategy is suitable for digital circular-business model adoption and (ii) how digitalization of machines enables manufacturing companies to innovate their business models. The correlation matrix is the tool developed from the proposed approach and it aims to support manufacturers in their first contact with circular business models. In the European RECLAIM project context, two manufacturers have applied the approach. The next steps are expected to introduce quantitative indicators to define thresholds for the steps toward circularity without replacing the qualitative approach, as this guarantees its applicability in a context that has never considered circularity yet.

Design of a Multi-Standard UWB-LoRa Antenna Structure and Transceiver Board for High-Accuracy and Long-Range Localization Applications

Long-Range Wide-Area Networks (LoRaWAN) allow the transmission of data via radio link from sensors, which are potentially isolated or difficult to access, to gateways and servers that are connected to cellular networks for data processing, exchange, or relay, with low transmission power. This concept employs Long-Range (LoRa) modulation and has led to the emergence of many applications for the monitoring and tracking of objects. However, due to its characteristic of a low data rate for low-power communication, the transmission of information with LoRa technology is not suitable for the fast real-time monitoring of data. Additionally, due to its narrow bandwidth, an attempt to perform localization through the LoRa modulation technique will result in very limited accuracy because of its inability to resolve multipath problems. Thus, in this paper, we propose a multi-standard Ultra-Wide Bandwidth (UWB) and LoRa end-device that is capable of measuring location with high accuracy using UWB technology and then transmitting the location information through LoRa method to gateways and the Internet of Things Network. The results of measurements in indoor and outdoor scenarios show a UWB localization accuracy that is of sub-meter level, being between 10 and 33 cm, and a UWB range of 124 m in Line-of-Sight (LOS) and 55 m in Non-Line-of-Sight (NLOS) applications, respectively.

94 Characterizing Yearling Beef Steers Grazing on Smooth Bromegrass Pasture Using Global Positioning Technology

Abstract Extensive grazing systems often vary in pasture size and terrain, making it challenging for ranchers to accurately locate livestock through visual appraisal. Using satellite-based global positioning systems (GPS), accurate position data can be provided to track animal movements that could inform grazing management decisions. This study aimed to: 1) assess two modern GPS devices for functionality by calculating fix success rate, and 2) evaluate the relationship between average daily distance traveled and average daily weight gain (ADG) performance on yearling steers grazing smooth bromegrass pasture using the GPS devices. Yearling steers (n = 25) grazed a 10.8-ha smooth bromegrass pasture for 156 days from May to September 2021. The temperature ranged from 4.1 to 38.8ºC with an average 22.7ºC and received 490 mm of rainfall. Cattle ADG was 0.65 ± 0.13 kg/d. Each steer was assigned two GPS collars for 28 days to compare two types of sensors. The sensors evaluated were i-gotU GT-600 travel loggers (GU) and Yabby LoRaWAN GPS sensors (YB) utilizing the IoT (Internet of Things) technology. The YB sensor possessed real-time monitoring and remote data access, while data from the GU sensor needed to be removed from the animals and manually downloaded. The YB sensors had a manufacturer declared finest positioning accuracy of 4.6 m, while that of the GU sensor being 9.1 m. With a 10-min sampling rate, battery life of the YB sensors lasted 4 weeks, while the GU sensors demonstrated unreliable battery performance that ranged between 1 to 4 weeks. Fixed success rate (FSR) was calculated for each sensor. Using PROC MIXED of SAS (v 9.4), on average the GU sensors had a 67.1% FSR and the YB sensors observed an 89.9% FSR (P &amp;lt; 0.0001). The YB sensor outweighed the GU sensor on battery life and FSR, thus was utilized throughout the duration of the grazing season. Individual animal traveled distance was calculated after converting the GPS coordinates in decimal degrees to Universal Transverse Mercator coordinates (WGS 1984 UTM zone 14N) in meters. The Euclidean distance between consecutive coordinates was calculated and summed daily to represent the daily traveled distance of an animal. Using all coordinates recorded from YB, steers traveled 2,718 ± 809 m/d. Statistical analysis was conducted using PROC CORR of SAS to determine the correlation between the daily averages of distance traveled and ADG. All values were considered significant α ≤ 0.05. There was no correlation observed between distance traveled and ADG (P ≥ 0.76). Results demonstrate that modern GPS sensors utilizing IoT technology have benefits over traditional GPS sensors and can be used as a tool for extensive grazing livestock tracking.

Research on Safety State Evaluation of Cable-Stayed Bridge Structures across the Sea

In response to the lack of relevant research on the health monitoring system and assessment of the structural safety status of a cable-stayed bridge across the sea, the real-time monitoring data was determined by the health monitoring system installed on the bridge structure. Strain and displacement data for the bridge structure were analyzed and processed. Based on operational research principles, a safety condition assessment of the Channel 1 Bridge was conducted using the Analytic Hierarchy Process (AHP). First, the reserve strength method is applied to determine the weight proportion of monitoring indicators such as strain and displacement. Secondly, the weight proportion of each monitoring indicator at different cross-sectional positions is determined. Then, the hierarchical division of the cable-stayed bridge structure is performed from the lowest layer, and the rating level of the Channel 1 Bridge structure is obtained using the variable weight synthesis method. This provides a basis for management decisions in the bridge maintenance department and serves as an example for future safety assessments of medium-span cable-stayed bridges over the sea.

AutoEMage: automatic data transfer, preprocessing, real-time display and monitoring in cryo-EM

Cryo-electron microscopy (cryo-EM), especially single-particle analysis, has become a powerful technique for visualizing the structure of biological macromolecules at high resolution. However, data acquisition in cryo-EM is time consuming because it requires the collection of thousands of images to achieve a high-quality reconstruction. Real-time preprocessing and display of the images can greatly enhance the efficiency and quality of data collection. This study presents AutoEMage, a new open-source software package that automates data transfer, preprocessing and real-time display in cryo-EM experiments. AutoEMage also includes a real-time data monitoring system that alerts users to issues with their data, allowing them to take corrective actions accordingly. The software is equipped with an easy-to-use graphical user interface that provides seamless data screening and real-time feedback on data quality and microscope status.