We are living in the era of the fourth industrial revolution, namely Industry 4.0. This paper presents the main aspects related to Industry 4.0, the technologies that will enable this revolution, and the main application domains that will be affected by it. The effects that the introduction of Internet of Things (IoT), Cyber-Physical Systems (CPS), crowdsensing, crowdsourcing, cloud computing and big data will have on industrial processes will be discussed. The main objectives will be represented by improvements in: production efficiency, quality and cost-effectiveness; workplace health and safety, as well as quality of working conditions; products’ quality and availability, according to mass customisation requirements. The paper will further discuss the common denominator of these enhancements, i.e., data collection and analysis. As data and information will be crucial for Industry 4.0, crowdsensing and crowdsourcing will introduce new advantages and challenges, which will make most of the industrial processes easier with respect to traditional technologies.

/pdf/how-data-will-transform-industrial-processes-crowdsensing-2ifpzs39ib.pdf

How data will transform industrial processes: crowdsensing, crowdsourcing and big data as pillars of industry 4.0

The concentration in the new era of healthcare is the medical Internet of Things (IoT) according to preventive and prediction ( $p^{2}~Health$ ). In large scale and general perspective, the effective parameters from behavioral, ambient, and physiological domains as the most influencing fields of interest in healthcare must be monitored. In personalized healthcare monitoring, wearables are playing an important role in terms of data measurement and collection. We aim at creating a configurable and adaptable platform for comprehensive parameters monitoring, according to the convenient mode of wearability. Hence, we develop an innovative wrist-worn prototype for ambient monitoring and a flexible IoT gateway. The prototype measures the most critical parameters from an ambient domain. In this platform, via IoT gateway as an intermediate hub between the wearables and the IoT server, bidirectional communication between the end user and medics is established in real time. In addition, the physician as the real-time observer of patients are given the possibility to set up the required parameters for measurement through the IoT gateway and activate/deactivate the sensors on the wearables. Therefore, depending on the target investigation, status of patients, requirements, and demands, medics can determine the setup parameters for measurement. Thus, the application of this platform is not limited to specific groups, but widely may be applied whether in daily routine or medical research investigation. Under the flexible IoT gateway, the new wearables can readily be integrated and synchronized into the platform. The users are not restricted to select only one specific product but there are alternatives as well. To support the solution, experimental results are provided.

A Flexible and Pervasive IoT-Based Healthcare Platform for Physiological and Environmental Parameters Monitoring

Impact of Edge Computing Paradigm on Energy Consumption in IoT

The Internet of Things (IoT) is a paradigm aimed at connecting everyday objects to the internet. IoT applications include smart cities, healthcare, agriculture, as well as the industry and manufacturing. The ability to monitor and control the physical world using information technology creates many opportunities. However, it also comes with some costs. The exponential growth of connected devices, the heterogeneity of IoT use cases, and the diversity of the network technologies yield a concern regarding IoT sustainability. With this work, we aim to contribute to this concern. In doing so, we introduce a novel representation model that is destined for (i) monitoring the IoT environment at runtime, (ii) expressing the overall quality of the system, and (iii) helping to utilize the available resources efficiently. We also define a feature set that describes the best the expectations of decentralized IoT platforms. Furthermore, we describe a quality-enabled decentralized IoT architecture too that incorporates the specified feature set as well as our representation model. Such solutions are necessary to improve and maintain IoT of the future and all its application domains, including the Industrial Internet of Things (IIoT). With the presented research, we aim to encourage the efficient utilization of resources and simplify the production of next-generation IoT solutions.

Quality-enabled decentralized IoT architecture with efficient resources utilization

Unstructured clinical medical text, as an important part of the electronic health records, is characterized by large quantities and can store substantial disease-related information of patients. Currently, the disease risk assessment model based on the analysis of clinical medical text designs relevant characteristics aiming at certain diseases, and different characteristics are identified from the text using different methods. In this way, changes of disease performance characteristics are difficult to adapt. Furthermore, it is hard to use the risk assessment model in other disease applications. As a result, this paper establishes the universal disease risk assessment model using the data of clinical medical text, conducts the independent study, and extracts disease characteristics from substantial historical data to avoid the limitations designing disease characteristics. First, this paper analyzes the medial clinical text to determine the contents related to the disease characteristics of patients. Second, learn the representation of clinical text with unsupervised learning methods, and study and extract the disease characteristics from the substantial historical data of patients in the convolutional neural network to assess disease risks. Finally, make a contrast experiment of disease risk assessment using the clinical text data of patients with cerebral infarction, patients with pulmonary infection, and patients with coronary atherosclerotic heart disease from the data of a second grade-A hospital in China and related methods. The experiments show that the approach proposed in this paper achieves the disease risk assessment for different diseases with the same structure.

Multiple Disease Risk Assessment With Uniform Model Based on Medical Clinical Notes

Customer churn is one of the most important concerns for large companies. Currently, massive data are often encountered in customer churn analysis, which bring new challenges for model computation. To cope with these concerns, sub‐sampling methods are often used to accomplish data analysis tasks of large scale. To cover more informative samples in one sampling round, classic sub‐sampling methods need to compute non‐uniform sampling probabilities for all data points. However, this method creates a huge computational burden for data sets of large scale and therefore, is not applicable in practice. In this study, we propose a sequential one‐step (SOS) estimation method based on repeated sub‐sampling data sets. In the SOS method, data points need to be sampled only with uniform probabilities, and the sampling step is conducted repeatedly. In each sampling step, a new estimate is computed via one‐step updating based on the newly sampled data points. This leads to a sequence of estimates, of which the final SOS estimate is their average. We theoretically show that both the bias and the standard error of the SOS estimator can decrease with increasing sub‐sampling sizes or sub‐sampling times. The finite sample SOS performances are assessed through simulations. Finally, we apply this SOS method to analyse a real large‐scale customer churn data set in a securities company. The results show that the SOS method has good interpretability and prediction power in this real application.

Sequential one‐step estimator by sub‐sampling for customer churn analysis with massive data sets

Research on health management and risk warning based on sensor data

The internationalization of the futures market has a certain impact on the internationalization of the RMB, and the opening up of futures varieties to the outside world is the most important link in the internationalization of the futures market, and it is necessary to quantitatively study the quantitative impact of the opening up of futures varieties on the internationalization of the RMB. Based on the monthly data from 2018 to 2022, this paper adopts the quantitative research method of cointegration-related theoretical model for the domestic international commodity futures (crude oil, iron ore, PTA) variables (futures price, turnover, position volume) on the variables of RMB internationalization (RMB international payment global market share, cross-border trade RMB business settlement amount, foreign exchange reserves, RMB real effective exchange rate index, RMB financial transactions), and studies the overall impact of international commodity futures on RMB internationalization. The study shows that the three indicators of the international commodity futures variable have different degrees of influence on the RMB internationalization index, of which the influence of price and open interest is significantly greater than the turnover. In the long run, the international futures price and open interest have a positive impact on the internationalization of the RMB, the turnover has a negative impact, and the impact of crude oil futures prices and open interest is stronger than that of iron ore and PTA. In the short term, the international commodity futures indicators have a certain impact on the internationalization of the RMB, crude oil and iron ore have a greater impact than the PTA, there are differences in the international development of different commodity markets, and the fitting effect of the model crude oil is better than other varieties. It is recommended to promote balanced development of the futures market internationalization through a variety of measures, continuously improve the international pricing power and influence of futures varieties, increase the open interest of futures, moderately reduce the turnover, and effectively promote the further development of RMB internationalization. 

/pdf/effects-of-futures-varieties-opening-up-on-currency-2jx9aid3.pdf

Effects of Futures Varieties Opening Up on Currency Internationalization: Evidence from China

In adverse working conditions, environmental parameters such as metallic dust, noise, and environmental temperature, directly affect the health condition of manufacturing workers. It is therefore important to implement health monitoring and management based on important physiological parameters (e.g., heart rate, blood pressure, and body temperature). In recent years, new technologies, such as body area networks, cloud computing, and smart clothing, have allowed the improvement of the quality of services. In this article, we first give five-layer architecture for health monitoring and management of manufacturing workers. Then, we analyze the system implementation process, including environmental data processing, physical condition monitoring and system services and management, and present the corresponding algorithms. Finally, we carry out an evaluation and analysis from the perspective of insurance and compensation for manufacturing workers in adverse working conditions. The proposed scheme will contribute to the improvement of workplace conditions, realize health monitoring and management, and protect the interests of manufacturing workers.

Health Monitoring and Management for Manufacturing Workers in Adverse Working Conditions

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Papers

Sequential one‐step estimator by sub‐sampling for customer churn analysis with massive data sets

Research on health management and risk warning based on sensor data

Effects of Futures Varieties Opening Up on Currency Internationalization: Evidence from China

Health Monitoring and Management for Manufacturing Workers in Adverse Working Conditions