Real-time Data Collection Research Articles

Real-Time Monitoring System of Landslide Based on LoRa Architecture

The equal time interval sampling in the existing landslide monitoring system cannot detect the abnormal change of landslides in real time. This study proposes a novel landslide monitoring method based on the long range (LoRa) network and intelligent sensing Internet of Things (IoT) to address these drawbacks. The overall structure, hardware circuit, and software design of intelligent sensing monitoring technology are described comprehensively. The designed adaptive data collection strategy can effectively capture abnormal changes in the landslide monitoring parameters, which provides a better alternative monitoring scheme for the real-time data collection of disaster body status. Furthermore, it can ensure data accuracy for the research of intelligent large-scale landslide disaster monitoring and prediction. The field test demonstrates that the proposed system presents the technical characteristics of low power consumption, self-organizing network, stable and reliable communication, and long transmission distance, which is effective for mountain landslide monitoring in harsh environments.

Electrochemical-Based Extraction, Separation, and Purification of Coumarin Compounds from Trifolium chinensis

In order to deal with the problems of the unstable transformation process and volatile palatability of coumarin compounds in C. chinensis, a method for the extraction, separation, and purification of coumarins in Chinese medicinal materials based on electrochemistry was prepared. First, an electrochemical distance-independent localization mechanism is used to determine node locations and routing mechanisms, and an extraction method is designed to achieve automatic and accurate real-time collection, aggregation, and transmission of ever-growing data. Environmental information for C. chinensis is collected. On the basis of this method, according to the chemical properties of coumarin and the extraction rate of coumarin as an index, the two-phase solvent extraction method and macroporous adsorption resin method were used to separate and purify it, and the best separation was determined. The purification process achieves efficient purification of coumarin. The experimental results show that the retention rate of coumarin by ethyl acetate is 34.5–56.8% higher than that of the other three extractants. When the optimal process determined in this study is adopted, the coumarin adsorption rate is greater than 98% and the recovery rate is greater than 85%. The purity of samples with 50% coumarin content can be increased to more than 97%, which verifies the correctness and advancement of the separation and purification process in this study.

Internet of Things Approaches for Monitoring and Control of Smart Greenhouses in Industry 4.0

In recent decades, climate change and a shortage of resources have brought about the need for technology in agriculture. Farmers have been forced to use information and innovation in communication in order to enhance production efficiency and crop resilience. Systems engineering and information infrastructure based on the Internet of Things (IoT) are the main novel approaches that have generated growing interest. In agriculture, IoT solutions according to the challenges for Industry 4.0 can be applied to greenhouses. Greenhouses are protected environments in which best plant growth can be achieved. IoT for smart greenhouses relates to sensors, devices, and information and communication infrastructure for real-time monitoring and data collection and processing, in order to efficiently control indoor parameters such as exposure to light, ventilation, humidity, temperature, and carbon dioxide level. This paper presents the current state of the art in the IoT-based applications to smart greenhouses, underlining benefits and opportunities of this technology in the agriculture environment.

Challenges and Obstacles Facing Data in the Big Data Environment

Big data is dubbed “today’s digital oil” and the “new raw resource of the twenty-first century”. BD is synonymous with the future of innovation, competition, and productivity. It can produce and find corporate value by analyzing data in ways that older methodologies could not. Regardless of its benefits, the development of big data continues to encounter various barriers, the most important of which are security and privacy concerns. As a result, this study is motivated by the need to address and evaluate big data challenges. Thus, by comparing and contrasting big data difficulties with available and potential solutions, users, developers, and businesses can find pertinent and timely responses to specific dangers, resulting in the best possible big data-based services. The objective of this essay is to highlight the inherent challenges of big data and some essential strategies for overcoming them.The purpose of this article was to extract and analyze significant works in order to contribute to the corpus of literature by emphasizing many critical difficulties in the big data domain and throwing light on how these challenges affect a range of domains, including users, sites, and business. Many issues such as data privacy, information sharing, failures in big data technologies and infrastructure, poor data quality management, managers and policymakers’ inability to learn and adapt, the absence of government policies and plans, the lack of successfully implemented big data analytics projects, and the lack of human experience are among the most frequently mentioned issues. Obstacles also exist in the areas of real-time data collection, as well as real-time data processing and visualization, among others.By combining previously identified solutions, this research addressed these concerns. The consequences for both researchers and practitioners have been discussed. Aiming to help scholars gain a comprehensive understanding of these issues and confirm the approaches used to address them, this study’s theoretical focus is broad. This study uses tried-and-true solutions to overcome these obstacles. Business and individuals using big data analytics systems will benefit from these solutions.

A STUDY ON WORK LIFE BALANCE OF WOMEN IT EMPLOYEES POST COVID-19 PANDEMIC

Work-life balance states that the level of prioritization between personal and professional activities of an individual. Work life balance encourages increased productivity, less instances of sickness and absenteeism, a happier, less stressed workforce, improvements in employee mental health and well-being and more engaged staff. The work life imbalance leads to mental disorders, such as anxiety and depression. Many studies have explored the impacts of work life balance but one or two covered COVID pandemic affected work scenario. After this mostly in IT industry work from home culture was introduced. This brings in a new change in the existing paradigm. At present this causes Job switch overs and resignations while people called on to work from office. In this context this study aims to measure the level of work life balance among IT women employees. For this purpose existing models were reviewed and analyzed to bring out an improved model. The review data of forty previous studies were analyzed and a hypothetical model is built as a result. In future the model is planned to be validated through real time data collection and analysis. keywords: work life balance(WLB), work from home, COVID-19, personal life variable, professional life variables and psychological variables

Training room management based on speech recognition and artificial intelligence

This paper starts from the basic principles of speech recognition, starting from acoustic models and features, and applies deep convolutional neural networks to speech recognition processing. It also introduces the overall process of speech recognition and mainstream algorithms to classify and summarize the speech recognition system. Due to the technological innovation of speech recognition and offline speech recognition systems, we found that traditional systems have low interactivity, poor flexibility, poor recognition command library, and other problems. We provide solutions for this paper according to the specific application environment. This paper introduces the design and implementation of an artificial intelligence teaching management system based on the Internet of Things (IoT) technology. Based on the design of the entire system, the system databases such as the video surveillance module, artificial intelligence security module, and remote control module are designed and realized the functions of these modules. Finally, RFID technology and database technology will be used to realize the informatization of training room personnel and equipment management, and the positioning and storage inspection of training room equipment. Secondly, from the perspective of environmental monitoring of the training room, temperature/ humidity sensors and optical sensors are used to complete the collection of real-time environmental data in the training room, thereby transmitting the data to the host system. It realizes the pairing through the Zigbee wireless communication module. Real-time monitoring of training room and management system. This paper introduces speech recognition and artificial intelligence into the management system of the training room so that the training can be better recorded and managed.

Exploring the Use of Software Metrics in Saudi Enterprises

This study was aimed to evaluate the application of software metrics used by the software enterprises present in Saudi Arabia. Extensive literature reviews were conducted to comprehend the current body of knowledge on the use of software metrics in Saudi enterprises. These literature review and studies elapsed approximately two decades. Based on the drawbacks, shortcomings, and fallacies of the existing studies a series of interview questionnaires were developed. Interviews were conducted for collection of real-time and actual data. Around seven Saudi enterprises were selected, and each enterprise was considered and regarded as a unit, and the manager of the enterprise was acting as a unit for our case study. 40 managers were interviewed, and their responses were analyzed. Respondents' responses indicate that the software is useful enough to support business processes. In an attempt to assess the complexity of implementing this software, effective feedback was received, suggesting that there is a lack of communication between the developers and managements’ intent. Moreover, the findings of this study showed that the organization need to give more attention to quality and productivity management. In addition, the results indicate that when agile development is undertaken through software effectiveness, the enterprise's services are implemented appropriately.

Incidence and clinical phenotype of multisystem inflammatory syndrome in children after infection with the SARS-CoV-2 delta variant by vaccination status: a Danish nationwide prospective cohort study

BackgroundMultisystem inflammatory syndrome in children (MIS-C) occurs after infection with SARS-CoV-2 and its incidence is likely to depend on multiple factors, including the variant of the preceding SARS-CoV-2 infection and vaccine effectiveness. We aimed to estimate the incidence of MIS-C, and describe the clinical phenotype, following the delta variant of SARS-CoV-2 (B.1.617.2 and sublineages) according to vaccination status. We aimed to compare the incidence and clinical phenotype of MIS-C from our cohort during the pre-delta era.MethodsThis prospective, population-based cohort study included patients aged 0–17 years hospitalised with MIS-C in Denmark, according to the US Centers for Disease Control and Prevention case definition, from Aug 1, 2021, to Feb 1, 2022, a period dominated by the delta variant. We identified MIS-C cases via a nationwide research collaboration involving real-time data collection from all 18 paediatric departments. Aggregated number of SARS-CoV-2 infections by vaccination status was obtained from the Danish COVID-19 surveillance registries. The incidence of MIS-C was calculated using the estimated number of infected individuals by vaccination status. We calculated the incidence of MIS-C per 1 000 000 vaccinated and unvaccinated person-years, and estimated vaccine effectiveness as 1–incidence rate ratio using Poisson regression. Incidence and phenotype of MIS-C were compared with MIS-C cases from the first year of the pandemic. This study is registered at ClinicalTrials.gov, NCT05186597.FindingsWe identified 51 MIS-C cases among unvaccinated individuals and one in a fully vaccinated adolescent. The incidence of MIS-C was one in 3400 unvaccinated individuals (95% CI 2600–4600) with the delta variant and one in 9900 vaccinated individuals (95% CI 1800–390 000) with breakthrough infection. The estimated vaccine effectiveness against MIS-C after the delta variant was 94% (95% CI 55–99; p=0·0061) in individuals aged 5–17 years. The clinical phenotype during the delta wave was comparable to the pre-delta era.InterpretationWe found the incidence and phenotype of MIS-C in unvaccinated children during the delta wave to be similar to the incidence during the first year of the pandemic. We found vaccine effectiveness to be high against MIS-C, which we suggest was due to protection from infection and, possibly, a decreased incidence of MIS-C after breakthrough infection. Knowledge of the incidence of MIS-C after different SARS-CoV-2 variants and the effect of vaccination might contribute to the elucidation of the extent to which MIS-C is a vaccine-preventable disease.FundingNational Ministry of Higher Education and Science and Innovation Fund Denmark.

AUV-Aided Hybrid Data Collection Scheme Based on Value of Information for Internet of Underwater Things

The current Internet of Underwater Things (IoUT) for marine observations and emergency responses suffers from two critical issues: 1) energy efficient and 2) timely data collection. Autonomous underwater vehicles (AUVs), serving as tools for collecting and forwarding distributed data, can deal with the unbalanced power consumption in a traditional multihop underwater communication network. However, the low speed of the AUV has not been able to guarantee the timeliness of delay-sensitive data. In this article, we introduce a hybrid data collection scheme (HDCS), taking both real-time data collection and energy efficiency (EE) issues into consideration. All sensor nodes (SNs) are first clustered based on their locations in the network. We develop an analytic expression to describe the attenuation of Value of Information (VoI), involving the relationship between the importance degree and timeliness; initial VoI could be measured by historical data. The emergency can be recognized by the presented criterion, and the transmission mode of cluster heads (CHs) in the same layer is judged by CHs themselves according to VoI. The selected CHs shall transmit the urgent data via multihop routing to avoid over attenuation of VoI. The normal data are collected by AUVs visiting all remaining CHs, and the shortest trajectory is achieved by addressing a variation of the classic traveling salesman problem (TSP). Our simulation experiments show that this mechanism can effectively increase long-term VoI while significantly improving EE.

Analysing the drivers for adoption of Industry 4.0 technologies in a functional paper – cement – sugar circular sharing network

Recent studies have established the relevance and importance of Industry 4.0 (I4.0) technologies in circular economy (CE) models. However, due to the vast contrast between the two concepts and their recent popularity, the factors that drive the integration of I4.0 technologies in CE models have not been explored so far. This paper, therefore, contributes to the literature by investigating the various driving factors that underpin the adoption of I4.0 technologies in a functional circular sharing network (CSN). The case CSN that is chosen for this study is an industrial ecosystem consisting of sugar factory, paper industry, spirit manufacturing industry, and cement industry. A total of 13 drivers are determined through an extensive literature review and by incorporating inputs of Expert Committees (ECs). An integrated approach of Total Interpretive Structural Modelling (TISM) and Cross-Impact Matrix Multiplication applied to classification (MICMAC) is employed to find the interdependencies between the drivers identified and to analyse them systematically. The TISM method establishes a 6-level hierarchical model of the drivers, while MICMAC analysis classifies the drivers into 4 categories. The findings reveal that Real-time data collection is the most influential driver, while gaining a competitive edge is the most dependent among the drivers that influence the adoption of I4.0 in a functional CSN. This paper aims to provide insights to industrial decision-makers and practitioners to effectively strategize the implementation of I4.0 technologies to enhance performance and complement CE principles. It also lays the foundations of future research on factors involved in the integration of I4.0 and CE.

Genesis of improved quality in imaging through a national Australian echocardiography registry.

BackgroundDespite rapid technological advances and growth, quality in imaging has not received the focus seen elsewhere in cardiovascular medicine, resulting in significant gaps between guidelines and practice. Contemporary echocardiography practice requires comprehensive real-time data collection to allow dynamic auditing and benchmarking of key performance indices. The American College of Cardiology (ACC) proposed additional data standardisation, structured reporting identifying key data elements and imaging registries. In the absence of an Australian echocardiography registry, we developed a national clinical quality registry (GenesisCare Cardiovascular Outcomes Echo Registry). We hypothesised that measurement and local reporting of data would improve compliance of echo studies with quality guidelines and hence their clinical value.Methods and resultsWe prospectively collected data on 4 099 281 echocardiographic studies entered directly into a central electronic database from 63 laboratories across four Australian states between 2010 and 2021. Real-time auditing of key data elements and introduction of quality improvement pathways were performed to maximise completeness and uniformity of data acquisition and reporting. We compared completeness of key data element acquisition (AV peak velocity, left ventricular ejection fraction, E/e’, LA area, rhythm, RVSP) by time and state using de-identified data. Key performance outcomes benchmarked against the aggregated study cohort and international standards were reported to individual sites to drive quality improvement. Between 2010 and 2014 there were significant improvements in data completeness (72.0%+/-26.8% vs 86.8%+/-13.5%, p=0.02), which were maintained to 2020. In addition, interstate variability fell for both EF and E/e’ (p<0.002).ConclusionsThis large-scale collaboration provides a platform for the development of major quality improvement initiatives in echocardiography. Introduction of local quality assurance programmes via a unified national data set significantly improved the completeness of reporting of key echo quality measures. This in turn significantly improved the quality of, and reduced the interstate variability of, echo data. Developing a centralised database allowed rapid adoption nationally of local quality improvements.

Perancangan Sistem Informasi Bank Darah Menggunakan Basis Data NoSQL

The existence of a blood exchange transaction process through a system carried out by the community, causing storing and managing data on a large scale is now causing problems in terms of scalability for blood bank managers in Yogyakarta. The process of blood transactions carried out by PMI units and hospitals at any time, makes the structure of relations in the blood banking system more complicated. In fact, every PMI unit and hospital in Yogyakarta requires a real-time data collection and storage process, considering that access to mobility of blood transactions is carried out almost 24 hours a day. Implementing an information system using a non-relational database model, is an alternative in the process of simplifying the relational structure in data relationships, this is because the non-relational database uses a NoSQL database model that provides support for database speed so that it remains optimal. In the research conducted, an information system was created that applied a non-relational database model to a case study of a blood bank information system. Where the NoSQL database is suitable for blood bank systems because it has high performance and is non-relational with real-time data storage and retrieval processes. The research conducted resulted in a blood bank information system that was developed using the Firebase database, where this system can display some data from the database such as viewing blood stocks, hospitals, blood donor events, and other information that is carried out in real time.

An innovative framework for real-time monitoring of pollutant point sources in river networks

Simultaneous identification of the location and release history of pollutant sources in river networks is an ill-posed and complicated problem, particularly in the case of multiple sources with time-varying release patterns. This study presents an innovative method for solving this problem using minimum observational data. To do so, a procedure is proposed in which, the number and the suspected reaches to the existence of pollutant sources are determined. This is done by defining two different types of monitoring stations with an adaptive arrangement in addition to real-time data collection and reliable flow and transport mathematical models. In the next step, the sources’ location and their release history are identified by solving the inverse source problem employing a geostatistical approach. Different scenarios are discussed for different conditions of number, release history and location of pollutant sources in the river network. Results indicated the capability of the proposed method in identifying the characteristics of the sources in complicated cases. Hence, it can be effectively used for the comprehensive monitoring of river networks for different purposes.

Can we do real inquiry online? Influence of real-time data collection on students’ views of inquiry in an online, multi-site masters’ degree on environmental education

In a previous study we detected that a number of inquiry stages (data collection, analysis and conclusions) went unnoticed by the students of an in situ joint online/onsite master’s degree via online teaching. In this paper we analyse the effect of improved instruction, in which students fully experienced and became aware of all the stages that comprise the inquiry-based teaching approach. In the article we show the differences between the initial and improved instruction. The comparison of student comments as exhibited in the online class diary forum between the initial and improved instruction has allowed us to analyse the influence of this improvement in the level of depth of the students’ discourse. Two codings have been employed to analyse the forums: the first (deductive) detected which stages of inquiry appeared in the comments. The second (inductive) involved the recoding of each of the previously classified comments based on five levels of communicative quality that emerged. Our main finding was that as well as being more aware of the different stages of inquiry, the students of the improved investigation were able to explain and identify them with specific examples. In other words, the investment of time in developing each of the stages in question helped them to define, afford reality to, and increase the explicative quality of their comments.

New Challenges towards Smart Systems’ Efficiency by Digital Twin in Water Distribution Networks

Nowadays, in the management of water distribution networks (WDNs), particular attention is paid to digital transition and the improvement of the energy efficiency of these systems. New technologies have been developed in the recent years and their implementation can be crucial to achieve a sustainable level of water networks, namely, in water and energy losses. In particular, Digital Twins (DT) represents a very innovative technology, which relies on the integration of virtual network models, optimization algorithms, real time data collection, and smart actuators information with Geographic Information System (GIS) data. This research defines a new methodology for an efficient application of DT expertise within water distribution networks. Assuming a DMA of a real water distribution network as a case study, it was demonstrated that a fast detection of leakage along with an optimal setting of pressure control valves by means of DT together with an optimization procedure can ensure up to 28% of water savings, contributing to significantly increase the efficiency of the whole system.

Human-Computer Interaction of Networked Vehicles Based on Big Data and Hybrid Intelligent Algorithm

Intelligent connected vehicle (ICV) refers to realizing the exchange and sharing of intelligent information between vehicles, roads, people, clouds, and so on by carrying advanced on-board sensors, controllers, actuators, and other devices and combining modern communication and network technology. In the age of big data, the information of everything can be transformed into digital resources, and transportation big data has become a basic resource. This paper constructs a big data platform for traffic data processing to realize the function of real-time collection, processing, and analysis of traffic data. Based on the proposed big data platform, the parallel programming framework of MapReduce and HDFS distributed storage system are used to process the real-time vehicle dynamic information in parallel, and the output result is used as the input of running genetic algorithm simulated annealing (GA-SA) for parallel calculation. At the same time, it studies the impact of various elements on users’ interactive behavior, constructs the demand framework and design model of automobile human-computer interaction, and then realizes fast and comprehensive search. The experimental results show that the human-computer interaction method of intelligent networked vehicle can find the optimal driving path, transmit it to each networked vehicle through the human-computer interaction system, realize human-computer interaction, reduce the impact of user unintentional operation on redundant motion, reduce the motion error accumulation of the system, and improve the performance of human-computer interaction system.

Underwater to above water LoRaWAN networking: Theoretical analysis and field tests

The aim of this paper is to demonstrate the exploitability of Long Range Wide Area Network (LoRaWAN) networking to set up a wireless transmission channel allowing the real time data collection from underwater depths up to more than 1 m. To this aim, a theoretical analysis is carried out to determine the actual feasibility of this link taking into account the technical features of the LoRa channel. Following this analysis, field tests in a swimming pool have been performed analyzing the transmission performance in terms of Received Signal Strength Indicator (RSSI) and Signal to Noise Ratio (SNR) for different transmission parameters and at increasing depths. Moreover, two test campaigns varying the antenna’s sensor node and the packet payload length have been performed. Experimental results have seen a good corroboration of the theoretical analysis with efficient underwater LoRaWAN data transmission at depths up to 110 ±5 cm, considering 14 dBm transmitted power.

Development of a text message-based headache diary in adolescents and children.

International guidelines recommend diaries in migraine trials for prospective collection of headache symptoms. Studies in other patient populations suggest higher adherence with electronic diaries instead of pen-and-paper. This study examines the feasibility of a text message-based (texting) diary for children and adolescents with headache. This is a secondary analysis of data from a study validating a pediatric scale of treatment expectancy. We developed a Health Insurance Portability and Accountability Act-compliant texting diary collecting headache characteristics, medication use, and disability with 3-5 core daily questions for 4 or 12 weeks depending on headache treatment. Adherence was incentivized. 93 participants consented to the expectancy study. Five participants opted for a paper diary for follow-up. 88 participants chose the texting diary with 28 4-week and 60 12-week participants. Five participants did not complete the enrollment visit. Of those remaining 83, 89% of 4-week and 93% of 12-week participants responded on at least 80% of days. On average, participants fully completed 88% (4-week cohort) and 90% (12-week) of diary entries. Text messages are a promising method for collecting patient-reported data. Adherence was similar to that reported for paper diaries in other pediatric migraine trials, but time-stamped entries ensure real-time data collection.

An Intelligent Collection System of Big Data in Medical and Health Education Based on the Internet of Things.

The Internet of Medical Things has developed rapidly as an important direction in the field of Internet of Things, especially through the use of the new generation of information technology, theoretical and applied research on medical IoT intelligent health management that provides “full-service.” It has become a research hotspot of many universities and research institutions. Therefore, conducting research on intelligent health management in the network of medical things is of great engineering importance and theoretical guidance to improve the level of medical information. For health management in the network of medical things to conduct research around the goal of health management “Everything full spectrum for everyone,” analyzed the lack of sharing of health information in current health management, lack of continuous monitoring and management of health indicators, etc., a new “individual-family-community-hospital” four-level intelligent health management service model is proposed, the hardware architecture of intelligent healthcare management and the software maintenance system have been built. Through methods such as real-time multi-source data collection, mobile sensing, cloud computing, multi-network fusion technology, continuous monitoring and intelligent management of health data is realized convenient, fast and efficient. It solves the problems that the existing medical system cannot meet the multi-level health needs, personal data security and privacy protection, etc., it has achieved the goal of real-time interactive health management of regionalization, multi-level and multi-center, and whole-person, whole-process and all-round.

Grassland Ecological Protection Monitoring and Management Application Based on ZigBee Wireless Sensor Network

Grassland plays a key role in human production and life, especially in the protection and improvement of the natural environment, which cannot be replaced by other ecosystems, such as maintaining water and soil, preventing wind and sand fixation, maintaining carbon balance, affecting climate change, and producing biological products,.. More importantly, contemporary society attaches great importance to the development of agriculture and animal husbandry. Protecting and nurturing grassland plants and animals, maintaining biodiversity, rational grazing, and maintaining the sustainable development of the grassland ecological environment have become the top priorities. In view of the current serious grassland degradation and the decline of livestock carrying capacity, this paper proposes a grassland environmental monitoring system based on the ZigBee wireless sensor network. The system consists of a grassland wireless monitoring network and a remote PC, which realizes real-time and remote monitoring of environmental information, such as air temperature and humidity, light intensity, and rainfall that affect the growth of grassland pastures. Based on the requirements of the agricultural field data monitoring system, the overall framework of the system was designed and built. The system mainly includes two parts: the ZigBee wireless sensor network subsystem and the remote management software subsystem, and data communication is realized between the two through the Ethernet system data exchange protocol. Among them, the ZigBee wireless sensor network subsystem is deployed in the grassland area and mainly realizes the functions of real-time collection, processing, and wireless transmission of grassland data. The remote management software subsystem is mainly used for data reception, storage, and display and can maintain communication with the gateway node to support real-time monitoring of grassland data by remote browsers.