Real-time Collection Research Articles

What Drives I4.0 Adoption? Establishing the Importance of Strategy

This study investigates adoption patterns of I4.0 technologies in the automotive component sector and links I4.0 adoption to competitive strategies. Analysis of survey data concerning 288 automotive suppliers reveals that most firms currently focus on implementing real-time collection and analysis of production data (i.e., the Internet of Things, Big Data and Artificial Intelligence), while other technologies such as additive manufacturing and blockchain are infrequently adopted. I4.0 adoption is associated with a simultaneous strategic emphasis on cost, quality and delivery, thus suggesting that automotive suppliers look at I4.0 as a mean to overcome performance trade-offs. Findings also confirm that small and medium-sized firms are less likely to adopt I4.0. Based on these results, implications for academics, managers and policy makers are suggested.

Automatic patient functionality assessment from multimodal data using deep learning techniques – Development and feasibility evaluation

Wearable devices and mobile sensors enable the real-time collection of an abundant source of physiological and behavioural data unobtrusively. Unlike traditional in-person evaluation or ecological momentary assessment (EMA) questionnaire-based approaches, these data sources open many possibilities in remote patient monitoring. However, defining robust models is challenging due to the data's noisy and frequently missing observations.This work proposes an attention-based Long Short-Term Memory (LSTM) neural network-based pipeline for predicting mobility impairment based on WHODAS 2.0 evaluation from such digital biomarkers. Furthermore, we addressed the missing observation problem by utilising hidden Markov models and the possibility of including information from unlabelled samples via transfer learning. We validated our approach using two wearable/mobile sensor data sets collected in the wild and socio-demographic information about the patients.Our results showed that in the WHODAS 2.0 mobility impairment prediction task, the proposed pipeline outperformed a prior baseline while additionally providing interpretability with attention heatmaps. Moreover, using a much smaller cohort via task transfer learning, the same model could learn to predict generalised anxiety severity accurately based on GAD-7 scores.

DB-YOLOv5: A UAV Object Detection Model Based on Dual Backbone Network for Security Surveillance

Unmanned aerial vehicle (UAV) object detection technology is widely used in security surveillance applications, allowing for real-time collection and analysis of image data from camera equipment carried by a UAV to determine the category and location of all targets in the collected images. However, small-scale targets can be difficult to detect and can compromise the effectiveness of security surveillance. In this work, we propose a novel dual-backbone network detection method (DB-YOLOv5) that uses multiple composite backbone networks to enhance the extraction capability of small-scale targets’ features and improve the accuracy of the object detection model. We introduce a bi-directional feature pyramid network for multi-scale feature learning and a spatial pyramidal attention mechanism to enhance the network’s ability to detect small-scale targets during the object detection process. Experimental results on the challenging UAV aerial photography dataset VisDrone-DET demonstrate the effectiveness of our proposed method, with a 3% improvement over the benchmark model. Our approach can enhance security surveillance in UAV object detection, providing a valuable tool for monitoring and protecting critical infrastructure.

A Wearable Multimodal Wireless Sensing System for Respiratory Monitoring and Analysis.

Wireless sensing systems are required for continuous health monitoring and data collection. It allows for patient data collection in real time rather than through time-consuming and expensive hospital or lab visits. This technology employs wearable sensors, signal processing, and wireless data transfer to remotely monitor patients' health. The research offers a novel approach to providing primary diagnostics remotely with a digital health system for monitoring pulmonary health status using a multimodal wireless sensor device. The technology uses a compact wearable with new integration of acoustics and biopotentials sensors to monitor cardiovascular and respiratory activity to provide comprehensive and fast health status monitoring. Furthermore, the small wearable sensor size may stick to human skin and record heart and lung activities to monitor respiratory health. This paper proposes a sensor data fusion method of lung sounds and cardiograms for potential real-time respiration pattern diagnostics, including respiratory episodes like low tidal volume and coughing. With a p-value of 0.003 for sound signals and 0.004 for electrocardiogram (ECG), preliminary tests demonstrated that it was possible to detect shallow breathing and coughing at a meaningful level.

An IOT framework for detecting cardiac arrhythmias in real-time using deep learning resnet model

A cardiac arrhythmia poses a serious health risk to patients and can have serious consequences for their health. A clinical assessment of arrhythmia disorders could save a person's life. The Internet of Things (IoT) will revolutionize the healthcare sector by continuously monitoring cardiac arrhythmia diseases remotely and minimally invasively. We propose a frame-work that will facilitate the development of a practical diagnostic tool for the identification of cardiac arrhythmias in real-time in this work. An Electrocardiogram (ECG) signal is processed using the Pan Tompkins QRS (Quantum Resonance System) detection method in order to extract the dynamic properties of the signal. The inter beat (RR) intervals are derived from an ECG signal in order to determine the characteristics of heart rate variability. The electrocardiogram is primarily used to identify irregular heartbeats (cardiac arrhythmias). Therefore, in our study, we evaluated other factors such as the heartbeat of the individual. As part of our IoT deployment, we are storing and analyzing data collected by the Pulse Sensor on the ThingSpeak IoT platform. The designed circuit's real-time collection of heartbeat and beats per minute values was uploaded to Thingspeak. Over the course of more than a week, we collected a variety of heart data. We propose Multi Channel Residual Network (MCHResNet) a deep-learning based solution that relies on multi-channel convolutions to detect both spatial and frequency features from electrocardiograms to facilitate the classification process. Based on the well-known Massachusetts Institute of Technology-Beth Israel Hospital Arrhythmia (MIT-BIH-AR) database, we evaluate the proposed framework against MCH ResNet. Our IoT-based framework has been shown to be effective based on the results reported in this paper.

Multi-Terminal Realization on Construction Engineering Safety and Quality Supervision Integrated Platform Based on Real-Time Image Information Collection Network

Multi-Terminal Realization on Construction Engineering Safety and Quality Supervision Integrated Platform Based on Real-Time Image Information Collection Network

2022 Annual Report of the Seoul Poison Control Center

Purpose: The Korea University Anam Hospital Seoul Poison Control Center (SeoulPCC) was established in accordance with Seoul Metropolitan Government Ordinance No. 7524 “Seoul Metropolitan Government Ordinance on the Prevention of Toxic Substances Poisoning and Accident Safety.” Herein, the center’s annual performance in terms of project results and consultation information for 2022 are reported.Methods: SeoulPCC operates a helpline (Help Call, 1855-2221) that the general public can use from 9:00 AM to 5:00 PM on weekdays, as well as chatting and chatbot counseling through KakaoTalk’s “Seoul Poison Control Center,” and one-on-one online counseling through the website. Additionally, it has constructed a system for communicating with the general public through social media. Poisoning disease information collected from SeoulPCC from January to December 2022 was analyzed according to the number of requests, age of exposure, gender, location, and reason. Requests from the general public were summarized, and a brief image presenting information on poisoning disease-related consultations was produced.Results: SeoulPCC has a database containing information on 188,065 toxic substances collected by public institutions and provides this information to the general public and medical staff through its website. In 2022, consultations were performed through phone calls and SNS (social networking service) for 577 cases of poison information and first aid treatment due to exposure to toxic substances. There were 1,431 instances of providing poison information services. The annual requests included 512 exposure cases and 65 non-exposure cases. Furthermore, 366 cases were in Seoul, 145 cases were outside of Seoul, and 66 had an unknown location. The exposure cases included 161 requests from the general public and 351 requests from medical staff.Conclusion: This is the first annual report in Korea to analyze the occurrence of poisoning based on consultations. It is of major significance that this report serves as a starting point for identifying and tracking the aspects and characteristics of poisoning cases in the pre-hospital stage. In the future, poisoning-related disease information provided through consultations and at the emergency room should be linked, and through real-time collection and analysis, this information should be used as basic data for poisoning disease management policies.

Non-contact type dynamic responses test of wind turbines

The performance analysis of wind turbine systems should be considered when calculating the wind speed relative to the wind turbine structure, and is essential for wind turbine design. Since the conditions are precarious in transient state and the operating environments are challenging, the wind turbine is a complex, multivariate, nonlinear system. This paper presents a novel dynamic response test method for wind turbines based on three-dimensional digital speckle measurement. This method use a real-time speckle image collection of objects in various stages using binocular stereo vision to perform stereo matching of deformation points on object surface. A digital image correlation algorithm is used to rebuild three-dimensional space coordinates of matching points so as to achieve wind turbine dynamic response. A laboratory-scale experimental platform is constructed to test the dynamic response of the wind turbine system. In order to verify the accuracy of the proposed method, a three-dimensional model of a wind turbine is built. With dynamic structure response process adopted to carry out dynamic analysis and compare theoretical results with test results, the results vary by less than 10 %, indicating that the test method presented in the paper is feasible and effective.

Data analysis of physical recovery and injury prevention in sports teaching based on wearable devices

Athletes can also cause damage to some parts of their body during training, so specialized preparation activities should be carried out before athlete training to reduce the damage caused to the athlete's body, allowing the stressed parts to move and distribute the load. Excessive recovery has a significant effect on improving the performance level of the athletes studied and preventing sports injuries. This article studies the data analysis of body recovery and injury prevention in physical education teaching based on wearable devices. Real time collection of students' exercise data, including indicators such as exercise volume, heart rate, steps, distance, etc., by wearing wearable devices. By using Internet of Things technology to transmit data to cloud servers, data analysis and mining techniques are used to process the data and study issues related to body recovery and injury prevention. Specifically, this article adopts methods such as time series analysis, machine learning algorithms, and artificial neural networks to analyze the relationship between exercise data and body recovery and injury prevention, providing scientific guidance and support for physical education teaching. This method can monitor students' exercise data in real-time, predict the risk of physical recovery and injury, and provide corresponding prevention and guidance suggestions.

The Use of Patient-Reported Outcome Measures in Clinical Practice and Clinical Decision Making.

Patient-reported outcome measures (PROMs) are highly effective measures of quality of care and outcomes that matter to patients regarding their physical, mental, and social health. While PROMs have played a notable role in research and registry reporting, they are also useful as clinical tools. Real-time PROM collection can be integrated into routine clinical care with immediate access to scores within the electronic health record. This can be integral when discussing treatment options and using decision aids. PROM scores can also be useful for postoperative monitoring. Various approaches to quantifying clinical efficacy have been developed, including the minimal clinically important difference, the substantial clinical benefit, and the patient acceptable symptom state (PASS). As the patient experience and patient-reported outcome measurement of health-related outcomes become increasingly emphasized in patient-centered, high value care, so too will the importance of methods to gauge clinical benefit using these instruments for improved clinical decision-making.

Cloud-fog-based approach for smart wildfire monitoring

Wildfires have disturbed the delicate balance of the Earth's ecosystem, exacerbating global warming, floods, droughts, and causing a reduction in oxygen level. Early detection of wildfires is crucial in order to mitigate the risk of losses. The Internet of Things (IoT) has emerged as a critical tool in bridging disparate technologies, allowing for the real-time collection of data from sensors and further analysis of factors influencing the occurrence of wildfires (FIWs) on fog devices or cloud servers. However, most deployment of these sensor nodes in hostile and unmanned environments presents significant challenges to the reliability information collection of FIWs and the effectiveness of energy-conserving technologies. To address these challenges, this paper proposes a novel wildfire monitoring model based on an IoT-Fog-Cloud three-tier architecture. This model incorporates energy-conserving and fault-tolerant scheduling algorithms in the fog layer, ensuring that sensor nodes operate reliably. In the cloud layer, the multi-Grained Cascade Forest (GC-Forest) algorithm is utilized to analyze both spectral and FIWs data. Furthermore, in order to accurately assess the spread trend of forest fires, the Wang Zhengfei model is improved. Experimental results demonstrate that the energy-conserving algorithm reduces energy consumption by approximately 25.48%, while the fault-tolerant scheduling algorithm remains a fault detection rate of over 98.7%. The GC-Forest algorithm exhibits a low error rate of only 16.25%, with a hit rate of 83.75%, and a Hanssen-Kuiper skill score of 63.73%.

Circular economy and household e-waste management in India. Part II: A case study on informal e-waste collectors (Kabadiwalas) in India

Rapid advances in electronics and telecommunications technologies have contributed significantly to making e-waste one of the largest and fastest-growing solid waste streams. India produces over 3.23 million tonnes of e-waste each year. Over 90% of the total e-waste generated in the country is processed by the informal waste management sector. Informal waste collectors in India are known as ‘kabadiwalas’. Many people in India employ their services to discard household solid waste items, including e-waste. However, the percentage of people disposing of their e-waste to kabadiwalas is unknown. Moreover, detailed research studies on kabadiwalas’ waste collection activities are very limited to date. A case study was therefore formulated to determine the likelihood of e-waste disposal to kabadiwalas and their solid waste collection strategies, including e-waste fraction. A global positioning system (GPS) based real-time waste collection route tracking method was employed. The results indicated that many people in India (∼45% of survey respondents) prefer to dispose of their e-waste to kabadiwalas. Based on the GPS tracking results, the informal waste collectors were found to travel considerable distances using manual and modified three-wheeler vehicles (20–30 km per day) to collect household waste, out of which e-waste accounted for about 10%. It was deduced that their large workforce and low operating costs enable them to collect e-waste from households all over the country effectively. The results from this study indicate that kabadiwalas play an integral role in the e-waste management system of India and that their integration into the existing formal e-waste industry could help establish improved e-waste management and value recovery operations in India.

Intelligent machine plus production line digital twin model construction technology

Digital twin is one of the key technology of intelligent digital manufacturing field, the related research is still in the initial stage This article in complex parts machining production line as the research object, from the geometric physical behavior rules four aspects to explore the digital twin modeling technology of production line Through the geometric model and the size of the physical model visualization expression line shape Based on the three-layer model of geometric physical behavior, the digital twin behavior model is constructed to describe the operation failure rule of the machine and unit. Driven by real-time information collection and fusion technology, closed-loop control and iterative optimization are realized Finally, the application value of digital twin technology in intelligent monitoring information feedback decision control is demonstrated through the intelligent control system of machining production line.

ID: 215794 ECAP-Based SCS for the Treatment of Chronic Pain: Crossover and 36-Month Evoke Study Outcomes

Utilization of objective neurophysiological measures to guide clinicians in providing optimal spinal cord stimulation (SCS) for chronic pain patients is a novel concept to neuromodulation. This clear and transparent mechanistic approach provides a better understanding to clinicians, patients, and payors allowing for proper evaluation of clinical outcomes. A novel SCS system delivers evoked-compound action potentials (ECAPs)-based therapy to 1) Guide programming and confirm activation of the intended target (i.e., ECAP-guided programming); and 2) Deliver closed-loop therapy to maintain accurate and consistent neural activation on every stimulus (i.e., ECAP-controlled closed-loop SCS). This therapy has now been studied in a 24-month double-blind randomized controlled trial (RCT) with self-selected crossover through 36 months. ECAP-based therapy enables real-time collection of continuous, objective, in-vivo neurophysiological data. This provides proper evaluation of the therapy and interpretation of the clinical outcomes, resulting in a level of evidence unmatched in neuromodulation.

Application of AI-Based Models for Flood Water Level Forecasting and Flood Risk Classification

Owing to global climate change, the frequency of disasters has increased twelve-fold, with a corresponding approximately seventeen-fold increase in economic damages over the past six decades. Notably, severe flood damage has been occurring in Asia along the Pacific coast due to extreme weather events, including torrential rains and typhoons, which have been becoming increasingly frequent and prolonging the rainy season. In the Eastern Visayas region, the management and monitoring facilities for flood observation data, as well as the forecasting and warning systems suitable for the local area, are insufficient. The warning system introduced through overseas grants is limited in operation in some areas of the city. Furthermore, although an organization has jurisdiction over flood forecasting and warning, the system's operation is not systematic and is limited. Additionally, there is a shortage of technical manpower. In this study, we utilized deep learning models to forecast flood water levels in the CarayCaray Basin on Biliran Island, located in Eastern Visayas, the Philippines. Additionally, a flood risk classification was applied to evaluate the degree of risk associated with the predicted water levels. The predicted water levels for each model were compared with the observed water level data. The evaluation of the predictive performance of each model resulted in an NRMSE value of 9.48. Moreover, the accuracy of the DNN model was found to be the best among the flood water level prediction models. To implement the flood risk classification, we utilized extreme gradient boosting, random forest, and decision tree models. The application of these models resulted in an F1-score of 0.92 for the extreme gradient boost model, which exhibited the highest accuracy. With an increasing need for disaster (flood) management, AI-based predictive models are anticipated to reduce the damage caused by natural disasters and enhance disaster mitigation systems. Real-time collection of rainfall and water level data enables continuous learning. Furthermore, if a clear flood warning based on learned flood level patterns is issued, preemptive measures can be taken before intense flood damage occurs.

Inflatable Humanoid Cybernetic Avatar for Physical Human–Robot Interaction

In a digital twin, a humanoid robot can be the counterpart of a simulated agent in the real world. In addition, a human, virtual avatar, and avatar robot might constitute digital triplets. We propose an inflatable cybernetic avatar (CA) with a humanoid upper body using an inflatable structure that can represent gestures. This inflatable CA is much lighter, safer, and cheaper than conventional humanoid robots and can be folded when deflated. These properties are ideal for physical human–robot interaction (pHRI) and allow real-time collection of human behavior through interaction. In the experiment, basic movements such as nodding and raising arms were measured using motion capture systems. This paper demonstrates the proposed inflatable CA in a hybrid event. We also conducted an experiment to measure the touch interactions using tactile sensors attached to the fabric of the inflatable part. A psychologically secure inflatable humanoid CA is a promising platform for physical interaction experiments.

Trusted Network Difference Data Mining Algorithm Based on Artificial Bee Colony Optimization

Abstract Trusted network is characterized by a large amount of data, abnormal dispersion, and high complexity. Traditional methods are easily affected by trusted network environment, resulting in unreliable mining results. Therefore, a new real-time mining method of trusted network difference data is proposed. Real-time collection of trusted network difference data through history system is performed on the basis of determining the principle of trusted network difference data mining and collecting and extracting the characteristics of difference data. The process of trusted network differential data mining is designed through the artificial bee colony algorithm. According to the process, differential data mining is carried out from three aspects: constructing a trusted network differential data transmission path, updating pheromone, and establishing a differential data transmission path set. The experimental results show that the proposed method can effectively realize the real-time mining of difference data, and the mining accuracy is more accurate.

Impact of COVID-19 pandemic waves on health-care worker hand hygiene activity in department of medicine and ICU as measured by an automated monitoring system.

Hand hygiene (HH) compliance among health-care workers is important for preventing transmission of infectious diseases. To describe health-care worker hand hygiene activity in ICU and non-ICU patients' rooms, using an automated monitoring system (AMS), before and after the onset of the COVID-19 pandemic. At the Intercommunal Hospital of Créteil, near Paris, France, alcohol-based hand sanitizer (ABHS) consumption in the Department of Medicine (DM) and ICU was recorded using an AMS during four periods: before, during, and after the first wave of the COVID-19 pandemic, and during its second wave. From 1st February to 30th November 2020, in the DM, the mean number of doses per patient-day for each of the four periods was, respectively, 5.7 (±0.3), 19.4 (±1.3), 17.6 (±0.7), and 7.9 (±0.2, P<0.0001). In contrast, ICU ABHS consumption remained relatively constant. In the DM, during the pandemic waves, ABHS consumption was higher in rooms of COVID-19 patients than in other patients' rooms. Multivariate analysis showed ABHS consumption was associated with the period in the DM, and with the number of HCWs in the ICU. An AMS allows real-time collection of ABHS consumption data that can be used to adapt training and prevention measures to specific hospital departments.

Crossing the AI Chasm in Neurocritical Care

Despite the growing interest in possible applications of computer science and artificial intelligence (AI) in the field of neurocritical care (neuro-ICU), widespread clinical applications are still missing. In neuro-ICU, the collection and analysis in real time of large datasets can play a crucial role in advancing this medical field and improving personalized patient care. For example, AI algorithms can detect subtle changes in brain activity or vital signs, alerting clinicians to potentially life-threatening conditions and facilitating rapid intervention. Consequently, data-driven AI and predictive analytics can greatly enhance medical decision making, diagnosis, and treatment, ultimately leading to better outcomes for patients. Nevertheless, there is a significant disparity between the current capabilities of AI systems and the potential benefits and applications that could be achieved with more advanced AI technologies. This gap is usually indicated as the AI chasm. In this paper, the underlying causes of the AI chasm in neuro-ICU are analyzed, along with proposed recommendations for utilizing AI to attain a competitive edge, foster innovation, and enhance patient outcomes. To bridge the AI divide in neurocritical care, it is crucial to foster collaboration among researchers, clinicians, and policymakers, with a focus on specific use cases. Additionally, strategic investments in AI technology, education and training, and infrastructure are needed to unlock the potential of AI technology. Before implementing a technology in patient care, it is essential to conduct thorough studies and establish clinical validation in real-world environments to ensure its effectiveness and safety. Finally, the development of ethical and regulatory frameworks is mandatory to ensure the secure and efficient deployment of AI technology throughout the process.

Online Prediction of Deformation Resistance for Strip Tandem Cold Rolling Based on Data-Driven

An online model is proposed for predicting deformation resistance in the strip tandem cold rolling by combining the back propagation neural network optimized by the mind evolutionary algorithm (MEA-BP) and the deformation resistance analytical model. The real-time collection of hot and cold rolling process data is achieved by constructing a “hot and cold rolling” cross-process data platform. Based on this, a dataset including historical production data of hot and cold rolling is established to train and test the model. The application result of the proposed model shows that the deformation resistance prediction error can be reduced from ±12% to ±5% compared with the traditional analytical model, which demonstrates the model established in this work can effectively improve the prediction accuracy of the deformation resistance in the strip tandem cold rolling.