Business analytics in industry 4.0: a systematic review

Recently, the term “Industry 4.0” has emerged to characterize several Information Technology and Communication (ICT) adoptions in production processes (e.g., Internet-of-Things, implementation of digital production support information technologies). Business Analytics is often used within the Industry 4.0, thus incorporating its data intelligence (e.g., statistical analysis, predictive modelling, optimization) expert system component. In this paper, we perform a Systematic Literature Review (SLR) on the usage of Business Analytics within the Industry 4.0 concept, covering a selection of 169 papers obtained from six major scientific publication sources from 2010 to March 2020. The selected papers were first classified in three major types, namely, Practical Application, Reviews and Framework Proposal. Then, we analysed with more detail the practical application studies which were further divided into three main categories of the Gartner analytical maturity model, Descriptive Analytics, Predictive Analytics and Prescriptive Analytics. In particular, we characterized the distinct analytics studies in terms of the industry application and data context used, impact (in terms of their Technology Readiness Level) and selected data modelling method. Our SLR analysis provides a mapping of how data-based Industry 4.0 expert systems are currently used, disclosing also research gaps and future research opportunities.The work of P. Cortez was supported by FCT - Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. We would like to thank to the three anonymous reviewers for their helpful suggestions

REPAC: Reliable estimation of phase-amplitude coupling in brain networks

Recent evidence has revealed cross-frequency coupling and, particularly, phase-amplitude coupling (PAC) as an important strategy for the brain to accomplish a variety of high-level cognitive and sensory functions. However, decoding PAC is still challenging. This contribution presents REPAC, a reliable and robust algorithm for modeling and detecting PAC events in EEG signals. First, we explain the synthesis of PAC-like EEG signals, with special attention to the most critical parameters that characterize PAC, i.e., SNR, modulation index, duration of coupling. Second, REPAC is introduced in detail. We use computer simulations to generate a set of random PAC-like EEG signals and test the performance of REPAC with regard to a baseline method. REPAC is shown to outperform the baseline method even with realistic values of SNR, e.g., -10 dB. They both reach accuracy levels around 99%, but REPAC leads to a significant improvement of sensitivity, from 20.11% to 65.21%, with comparable specificity (around 99%). REPAC is also applied to a real EEG signal showing preliminary encouraging results

Evolution of ICT for the improvement of quality of life

In today's society, chronic diseases are a well-known issue related to the average increasing age of a population, especially in the most developed countries. The elderly, who are often the most impaired individuals, experience a significant reduction of independence in their daily life. This, consequently, affects their psychological conditions as well as their social attitudes and relationships. Therefore, industry, academia, and government health organizations are actively investigating and testing large-scale affordable solutions to improve the overall Quality of Life (QoL) in this population

A simple and accessible inkjet platform for ultra-short concept-to-prototype sEMG electrodes production

Inkjet-printing is a well-known technology that has been recently revalued for the production of flexible sensors and biosensors, thank to the use of engineered nanostructured inks. In a previous work, we developed a general-purpose biosensors printing platform that made use of a simple and low-cost consumer printer and allowed to produce customized flexible electrodes with an ultra-short concept-to-prototype time, without requiring any sintering step. In this study we show the preliminary results about the use of such a newly easily-accessible, low-cost inkjet-based platform to produce flexible and fully customizable electrodes for reliable surface electromyographic (sEMG) recordings

Modeling Value of Information in Remote Sensing from Correlated Sources

This paper investigates remote sensing networks and discusses different models to characterize the Value of Information (VoI), a metric that describes how informative the data transmitted by the sensors are. For each sensor, the VoI evaluations comprise the average node-specific Age of Information (AoI), the average cost spent for sending updates, and the AoI of neighbor nodes, assumed to be correlated sources of information and therefore benefiting the VoI of other sensors nearby. We discuss how this metric can be tracked through a two-dimensional Markov chain, but we also show how this representation can be simplified by including the impact of neighbor nodes within the transition probabilities, so as to obtain a simpler model that gives the same insight in terms of VoI evaluations

Challenges of the Age of Information Paradigm for Metrology in Cyberphysical Ecosystems

We are facing a transition towards interconnection of computing systems, people, and things, where boundaries are disappearing and new challenges are emerging. This trend also applies to smart living environments, which are becoming a cyberphysical ecosystem of devices and individuals. Generally, meta-descriptors such as age of information are exploited to obtain efficient content representation and semantic characterization, with the advantage of better data handling. However, the strong relevance of living support in the involved applications imposes to rethink of this approach whenever it is important to factor the human-in-the-loop. In this paper, we discuss how the investigations related to age of information, in particular aimed at statistical descriptions and/or network operation modeling, can be influenced in such scenarios, for what concerns overarching machine learning for data classification and its impact on the sensing frequency, as well as the presence of data correlation that allows for a parsimonious handling of the updates

Tackling Age of Information in Access Policies for Sensing Ecosystems

Recent technological advancements such as the Internet of Things (IoT) and machine learning (ML) can lead to a massive data generation in smart environments, where multiple sensors can be used to monitor a large number of processes through a wireless sensor network (WSN). This poses new challenges for the extraction and interpretation of meaningful data. In this spirit, age of information (AoI) represents an important metric to quantify the freshness of the data monitored to check for anomalies and operate adaptive control. However, AoI typically assumes a binary representation of the information, which is actually multi-structured. Thus, deep semantic aspects may be lost. In addition, the ambient correlation of multiple sensors may not be taken into account and exploited. To analyze these issues, we study how correlation affects AoI for multiple sensors under two scenarios of (i) concurrent and (ii) time-division multiple access. We show that correlation among sensors improves AoI if concurrent transmissions are allowed, whereas the benefits are much more limited in a time-division scenario. Furthermore, we discuss how ML can be applied to extract relevant information from data and show how it can further optimize the transmission policy with savings of resources. Specifically, we demonstrate, through simulations, that ML techniques can be used to reduce the number of transmissions and that classification errors have no influence on the AoI of the system

Shapley Value as an Aid to Biomedical Machine Learning: a Heart Disease Dataset Analysis

This paper investigates the decision making process aided by machine learning for biomedical problems and how to improve it through meta assessments of the most relevant features. Classification algorithms are usually trained and exploited with high dimensional datasets (i.e., with an extremely large number of features), which is inefficient and costly. It would be beneficial to identify the most meaningful features that contribute the most to assigning a category to a subject, and in particular, diagnosing a pathological condition. A helpful support can come from cooperative game theory, through the computation of the Shapley value, an indicator of desirable properties according to which the players, in our case the input features, can be ranked. We apply such a framework to a supervised machine learning scenario of a random forest tree classifier applied to heart disease detection. From a publicly available dataset, we identify the most relevant features that can affect the decision, thus obtaining practical guidelines for a compact yet efficient description based on an analytical rationale