Simulation of Electronic Waste Reverse Chains for the Sao Paulo Circular Economy: An Artificial Intelligence-Based Approach for Economic and Environmental Optimizations.

The objective of this study was to apply simulation and genetic algorithms for the economic and environmental optimization of the reverse network (manufacturers, waste managers, and recyclers in Sao Paulo, Brazil) of waste from electrical and electronic equipment (WEEE) to promote the circular economy. For the economic evaluation, the reduction in fuel, drivers, insurance, depreciation, maintenance, and charges was considered. For the environmental evaluation, the impact of abiotic, biotic, water, land, air, and greenhouse gases was measured. It was concluded that the optimized structure of the WEEE reverse chains for Sao Paulo, Brazil provided a reduction in the number of collections, thus making the most of cubage. It also generated economic and environmental gains, contributing to the strategic actions of the circular economy. Therefore, the proposed approach is replicable in organizational practice, which is mainly required to meet the 2030 agenda of reducing the carbon footprint generated by transport in large cities. Thus, this study can guide companies in structuring the reverse WEEE chains in Sao Paulo, Brazil, and other states and countries for economic and environmental optimization, which is an aspect of great relevance considering the exponential generation of WEEE.

Low-Cost Sensors Technologies for Monitoring Sustainability and Safety Issues in Mining Activities: Advances, Gaps, and Future Directions in the Digitalization for Smart Mining.

Nowadays, monitoring aspects related to sustainability and safety in mining activities worldwide are a priority, to mitigate socio-environmental impacts, promote efficient use of water, reduce carbon footprint, use renewable energies, reduce mine waste, and minimize the risks of accidents and fatalities. In this context, the implementation of sensor technologies is an attractive alternative for the mining industry in the current digitalization context. To have a digital mine, sensors are essential and form the basis of Industry 4.0, and to allow a more accelerated, reliable, and massive digital transformation, low-cost sensor technology solutions may help to achieve these goals. This article focuses on studying the state of the art of implementing low-cost sensor technologies to monitor sustainability and safety aspects in mining activities, through the review of scientific literature. The methodology applied in this article was carried out by means of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and generating science mapping. For this, a methodological procedure of three steps was implemented: (i) Bibliometric analysis as a quantitative method, (ii) Systematic review of literature as a qualitative method, and (iii) Mixed review as a method to integrate the findings found in (i) and (ii). Finally, according to the results obtained, the main advances, gaps, and future directions in the implementation of low-cost sensor technologies for use in smart mining are exposed. Digital transformation aspects for data measurement with low-cost sensors by real-time monitoring, use of wireless network systems, artificial intelligence, machine learning, digital twins, and the Internet of Things, among other technologies of the Industry 4.0 era are discussed.

Performance Analysis of Embedded Multilayer Perceptron Artificial Neural Networks on Smart Cyber-Physical Systems for IoT Environments.

At present, modern society is experiencing a significant transformation. Thanks to the digitization of society and manufacturing, mainly because of a combination of technologies, such as the Internet of Things, cloud computing, machine learning, smart cyber-physical systems, etc., which are making the smart factory and Industry 4.0 a reality. Currently, most of the intelligence of smart cyber-physical systems is implemented in software. For this reason, in this work, we focused on the artificial intelligence software design of this technology, one of the most complex and critical. This research aimed to study and compare the performance of a multilayer perceptron artificial neural network designed for solving the problem of character recognition in three implementation technologies: personal computers, cloud computing environments, and smart cyber-physical systems. After training and testing the multilayer perceptron, training time and accuracy tests showed each technology has particular characteristics and performance. Nevertheless, the three technologies have a similar performance of 97% accuracy, despite a difference in the training time. The results show that the artificial intelligence embedded in fog technology is a promising alternative for developing smart cyber-physical systems.

Data Science Methods and Tools for Industry 4.0: A Systematic Literature Review and Taxonomy.

The Fourth Industrial Revolution, also named Industry 4.0, is leveraging several modern computing fields. Industry 4.0 comprises automated tasks in manufacturing facilities, which generate massive quantities of data through sensors. These data contribute to the interpretation of industrial operations in favor of managerial and technical decision-making. Data science supports this interpretation due to extensive technological artifacts, particularly data processing methods and software tools. In this regard, the present article proposes a systematic literature review of these methods and tools employed in distinct industrial segments, considering an investigation of different time series levels and data quality. The systematic methodology initially approached the filtering of 10,456 articles from five academic databases, 103 being selected for the corpus. Thereby, the study answered three general, two focused, and two statistical research questions to shape the findings. As a result, this research found 16 industrial segments, 168 data science methods, and 95 software tools explored by studies from the literature. Furthermore, the research highlighted the employment of diverse neural network subvariations and missing details in the data composition. Finally, this article organized these results in a taxonomic approach to synthesize a state-of-the-art representation and visualization, favoring future research studies in the field.

Improving startups through excellence initiatives: addressing circular economy and innovation.

This study analyses how startups implement circular business models supported by innovation and Industry 4.0, in which strategic stakeholders for value creation are to be found in this specific business ecosystem. The theoretical framework of circular business models supported by innovation was used for analysing the startups based on some assumptions of stakeholder theory. Fifty-one startups were selected, which correspond to the unit of analysis for this study on the improvement of supply chains through circular business models supported by innovation aimed at sustainability in organizations. We conducted a multiple case study whose results suggest that: (i) circularity is strategic for the business to assume its commitment to a sustainable development agenda, especially regarding pollution prevention and proactive action; (ii) visionary entrepreneurs are actively engaged with circular economy practices and technological innovation to promote a circular flow for their business ecosystem; (iii) Industry 4.0 is still incipient, but it is synergistic and beneficial for a successful circular economy in startups; and (iv) primary stakeholders are the activators of circular cycles in the startups surveyed. The present study contributes to the literature in four ways by: (i) presenting a framework which brings together exploratory theoretical propositions on strategic stakeholders for startups, innovative capabilities and assumptions of circular business models; (ii) validating exploratory theoretical propositions with 51 startups; (iii) providing lessons learned so far by the startups which are in line with the assumptions of circular business models for triggering their innovation capabilities and promoting Industry 4.0; (iv) providing an original typology of circular economy assumptions and technological innovations adopted by startups. The originality of this study lies in presenting useful insights for motivating managers to: (i) invest in circular business to become one of the first entrants and earn extra profits; (ii) make investments in circular business and technological innovations to obtain efficiency, practicality and process optimization; (iii) internalize Industry 4.0 technologies concomitantly with technological innovations and circular economy to generate systemic effects; (iv) integrate relevant stakeholders of the business ecosystem to generate a synergistic and effective effect for sustainable development.

Opinions Related to the Potential Application of Artificial Intelligence (AI) by the Responsible in Charge of the Administrative Management Related to the Logistics and Supply Chain of Medical Stock in Health Centers in North of Chile.

The research evaluated the opinion of those in charge of the administrative management of the logistics and supply chain of medical and pharmaceutical stocks of health care centers in the north of Chile and a potential improvement of their operations through the use of artificial intelligence (AI). The identification of the problem arose from the empirical analysis, where serious deficiencies in the manual handling and management of the stock of medicines and hospital supplies were evidenced. This deficiency does not allow a timely response to the demand of the logistics and supply chain, causing stock ruptures in health centers. Based on this finding, we asked ourselves how AI was observed as the most efficient tool to solve this difficulty. The results were obtained through surveys of personnel in charge of hospital and pharmacy supplies. The questions focused on the level of training, seniority in positions related to the problem, knowledge of regulations, degree of innovation in the procedures used in logistics and supply chain and procurement. However, a very striking fact was related to the importance of the use of AI, where, very surprisingly, 64.7% considered that it would not help to reduce human errors generated in the areas analyzed.

Sustainable digital transformation in small and medium enterprises (SMEs): A review on performance.

Small and medium enterprises (SMEs) are responsible for 90% of all business and 50% of employment globally, mostly female jobs. Therefore, measuring SMEs' performance under the digital transformation (DT) through methods that encompass sustainability represents an essential tool for reducing poverty and gender inequality (United Nations Sustainable Development Goals). We aimed to describe and analyze the state-of-art performance evaluations of digital transformation in SMEs, mainly focusing on performance measurement. Also, we aimed to determine whether the tools encompass the three pillars of sustainability (environmental, social, and economic). Through a systematic literature review (SLR), a search on Web of Science (WoS) and Scopus resulted in the acceptance of 74 peer-reviewed papers published until December 2021. Additionally, a bibliometrics investigation was executed. Although there was no time restriction, the oldest paper was published in 2016, indicating that DT is a new research topic with increasing interest. Italy, China, and Finland are the countries that have the most published on the theme. Based on the results, a conceptual framework is proposed. Also, two future research directions are presented and discussed, one for theoretical and another for practical research. Among the theoretical development, it is essential to work on a widely accepted SME definition. Among the practical research, nine directions are identified-e.g., applying big data, sectorial and regional prioritization, cross-temporal investigations etc. Researchers can follow the presented avenues and roads to guide their researchers toward the most relevant topics with the most urgent necessity of investigation.

A Machine Learning Approach to Robot Localization Using Fiducial Markers in RobotAtFactory 4.0 Competition.

Localization is a crucial skill in mobile robotics because the robot needs to make reasonable navigation decisions to complete its mission. Many approaches exist to implement localization, but artificial intelligence can be an interesting alternative to traditional localization techniques based on model calculations. This work proposes a machine learning approach to solve the localization problem in the RobotAtFactory 4.0 competition. The idea is to obtain the relative pose of an onboard camera with respect to fiducial markers (ArUcos) and then estimate the robot pose with machine learning. The approaches were validated in a simulation. Several algorithms were tested, and the best results were obtained by using Random Forest Regressor, with an error on the millimeter scale. The proposed solution presents results as high as the analytical approach for solving the localization problem in the RobotAtFactory 4.0 scenario, with the advantage of not requiring explicit knowledge of the exact positions of the fiducial markers, as in the analytical approach.

An Interpretable Digital Twin for Self-Aware Industrial Machines.

This paper presents a proposed three-step methodology designed to enhance the performance and efficiency of industrial systems by integrating Digital Twins with particle swarm optimization (PSO) algorithms while prioritizing interpretability. Digital Twins are becoming increasingly prevalent due to their capability to offer a comprehensive virtual representation of physical systems, thus facilitating detailed simulations and optimizations. Concurrently, PSO has demonstrated its effectiveness for real-time parameter estimation, especially in identifying both standard and unknown components that influence the dynamics of a system. Our methodology, as exemplified through DC Motor and Hydraulic Actuator simulations, underscores the potential of Digital Twins to augment the self-awareness of industrial machines. The results indicate that our approach can proficiently optimize system parameters in real-time and unveil previously unknown components, thereby enhancing the adaptive capacities of the Digital Twin. While the reliance on accurate data to develop Digital Twin models is a notable consideration, the proposed methodology serves as a promising framework for advancing the efficiency of industrial applications. It further extends its relevance to fault detection and system control. Central to our approach is the emphasis on interpretability, ensuring a more transparent understanding and effective usability of such systems.

Enhanced Modbus/TCP Security Protocol: Authentication and Authorization Functions Supported.

The Zero Trust concept is being adopted in information technology (IT) deployments, while human users remain to be the main risk for operational technology (OT) deployments. This article proposes to enhance the new Modbus/TCP Security protocol with authentication and authorization functions that guarantee security against intentional unauthorized access. It aims to comply with the principle of never trusting the person who is accessing the network before carrying out a security check. Two functions are tested and used in order to build an access control method that is based on a username and a password for human users with knowledge of industrial automation control systems (IACS), using simple means, low motivation, and few resources. A man-in-the-middle (MITM) component was added in order to intermediate the client and the server communication and to validate these functions. The proposed scenario was implemented using the Node-RED programming platform. The tests implementing the functions and the access control method through the Node-RED software have proven their potential and their applicability.

Adoption of Industry 4.0 technologies by organizations: a maturity levels perspective.

This study employs a structured literature analysis considering Industry 4.0 technologies and their adoption stages (intention, adoption, implementation, routinization, continuance, and diffusion). We identify the technology adoption stage for each technology type, which in turn supports a maturity level categorization, as well as future research suggestions and challenging open research questions. By considering an integrated view of all the adoption stages of Industry 4.0 key technologies, we reveal the key technologies and their development stages, as well as a novel maturity level categorization perspective. The proposed categorization brings valuable research insights in the form of guidelines for practitioners and decision-makers interested in gaining a deeper understanding of the maturity level of key Industry 4.0 technologies.

Evaluating Brazilian Agriculturalists' IoT Smart Agriculture Adoption Barriers: Understanding Stakeholder Salience Prior to Launching an Innovation.

The study sought to: (1) evaluate agriculturalists' characteristics as adopters of IoT smart agriculture technologies, (2) evaluate traits fostering innovation adoption, (3) evaluate the cycle of IoT smart agriculture adoption, and, lastly, (4) discern attributes and barriers of information communication. Researchers utilized a survey design to develop an instrument composed of eight adoption constructs and one personal characteristic construct and distributed it to agriculturalists at an agricultural exposition in Rio Grande do Sul. Three-hundred-forty-four (n = 344) agriculturalists responded to the data collection instrument. Adopter characteristics of agriculturalists were educated, higher consciousness of social status, larger understanding of technology use, and more likely identified as opinion leaders in communities. Innovation traits advantageous to IoT adoption regarding smart agriculture innovations were: (a) simplistic, (b) easily communicated to a targeted audience, (c) socially accepted, and (d) larger degrees of functionality. Smart agriculture innovation's elevated levels of observability and compatibility coupled with the innovation's low complexity were the diffusion elements predicting agriculturalists' adoption. Agriculturalists' beliefs in barriers to adopting IoT innovations were excessive complexity and minimal compatibility. Practitioners or change agents should promote IoT smart agriculture technologies to opinion leaders, reduce the innovation's complexity, and amplify educational opportunities for technologies. The existing sum of IoT smart agriculture adoption literature with stakeholders and actors is descriptive and limited, which constitutes this inquiry as unique.

Somos todes youtubers? Indústria 4.0 e precarização do trabalho docente em tempos de pandemia / Are we all youtubers? Industry 4.0 and precariousness of teaching work in times of pandemic

Resumo: Sob a justificativa de que são necessárias ao isolamento social nem à preservação da saúde pública, as plataformas digitais nestes tempos de pandemia de covid-19 se tornaram parte do cotidiano profissional de diversas áreas do trabalho, incluindo a da educação. A era informacional da Indústria 4.0 se intensificou abruptamente à vida universitária. Examinamos alguns dos nefastos impactos do Ensino Emergencial Remoto para a docência em Serviço Social: que formação para qual projeto societário? Somos todes youtubers?

Abstract: Under the justification that they are necessary for social isolation and the preservation of public health, digital platforms in these times of the covid-19 pandemic have become part of the professional routine in various areas of work, including education. The information age of Industry 4.0 abruptly intensified into university life. We examine some of the disastrous impacts of Remote Emergency Education on Social Work teaching: which training for which society project? Are we all youtubers?

UAV-Based Smart Educational Mechatronics System Using a MoCap Laboratory and Hardware-in-the-Loop.

Within Industry 4.0, drones appear as intelligent devices that have brought a new range of innovative applications to the industrial sector. The required knowledge and skills to manage and appropriate these technological devices are not being developed in most universities. This paper presents an unmanned aerial vehicle (UAV)-based smart educational mechatronics system that makes use of a motion capture (MoCap) laboratory and hardware-in-the-loop (HIL) to teach UAV knowledge and skills, within the Educational Mechatronics Conceptual Framework (EMCF). The macro-process learning construction of the EMCF includes concrete, graphic, and abstract levels. The system comprises a DJI Phantom 4, a MoCap laboratory giving the drone location, a Simulink drone model, and an embedded system for performing the HIL simulation. The smart educational mechatronics system strengthens the assimilation of the UAV waypoint navigation concept and the capacity for drone flight since it permits the validation of the physical drone model and testing of the trajectory tracking control. Moreover, it opens up a new range of possibilities in terms of knowledge construction through best practices, activities, and tasks, enriching the university courses.

Business transformation through sustainability based on Industry 4.0.

The growing global concern with sustainability has driven companies to rethink their business model and seek new ways to operate and face this challenge. Industry 4.0 (I4.0) has shown itself capable of contributing to the development or reformulation of organizational processes to make them more competitive and sustainable. Thus, this article aims to propose drivers for the development of corporate sustainability via I4.0. To this end, a literature review and content analysis were used as research methods to identify and analyze, in recent scientific literature, boosting elements that enable organizational processes to become more sustainable via I4.0. Based on these elements, six drivers were systematized and proposed: strategy; product and process design; energy and material resources; people; smart production; and supply chain. Each driver was discussed in light of the scientific literature to generate recommendations for companies to develop the economic, social, and environmental dimensions of sustainability. The main theoretical-scientific contribution of this work is the deepening and expansion of the knowledge block that articulates corporate sustainability with I4.0, which strengthens the basis for the development of new research on these topics and creates a reference for the analysis and discussion of empirical studies. As an applied-managerial contribution, the drivers proposed in this work will provide organizations and their managers with a point of reference to effectively move towards sustainability, making their businesses greener, fairer, and more profitable. A limitation of this study is that the proposed drivers were based on the 30 most cited articles and did not consider other sources, such as documentation from companies. Therefore, for future studies, we suggest increasing the article base and include organizational repositories and identify how SMEs can become sustainable through I4.0 in a way that strengthens the social dimension of sustainability.

Industry 4.0 Technologies Applied to Inland Waterway Transport: Systematic Literature Review.

The focus of this article is inland waterway transport. Different problems in this domain have been studied due to the increase in waterway traffic globally. Industry 4.0 technologies have become an alternative for the possible solution of these problems. For this reason, this paper aims to answer the following research questions: (1) What are the main problems in transporting cargo by inland waterway? (2) What technological strategies are being studied to solve these problems? (3) What technologies from Industry 4.0 are used within the technological strategies to solve the exposed problems? This study adopts a Systematic Literature Review (SLR) approach. For this work, were recovered 645 articles, 88 of which were eligible, from which we could identify five domains corresponding to (1) traffic monitoring, (2) smart navigation, (3) emission reduction, (4) analytics with big data, and (5) cybersecurity. The strategies currently being considered combine navigation technologies, such as AIS (Automatic Identification System), which offers a large amount of data, with Industry 4.0 tools and mainly machine learning techniques, to take advantage of data collected over a long time. This study is, to our knowledge, one of the first to show how Industry 4.0 technologies are currently being used to tackle inland waterway transport problems and current application trends in the scientific community, which is a first step for the development of future studies and more advanced solutions.

A bibliometric and thematic approach to agriculture 4.0.

Researchers are developing digital solutions for agriculture. Humanity has perfected agriculture throughout history because this activity is fundamental to our existence. The agricultural sector is currently incorporating new technologies from other areas. This phenomenon is agriculture 4.0. However, a challenge to research is the integration of technologies from different knowledge fields, and this has caused theoretical and practical difficulties. Thus, our purpose with this study has been to understand the core agriculture 4.0 research themes. We have used a bibliometric analysis, and guided the data collection by the PRISMA protocol. VosViewer and Bibliometrix software generated the results. We found two main research fronts, one focussed on agriculture 4.0 development, and another on the impacts of agriculture 4.0, which may be positive or negative. We found 21 main keywords or topics researched in agriculture 4.0 related to these research fronts. These themes are within five different axes. We managed to establish a good understanding of the topics around agriculture 4.0. Future studies could focus on the responsible development of digital solutions for agriculture. This is because the social, environmental, and economic impacts of these new solutions may be positive or negative. We conclude that digital agriculture is the node technologies integration for the automation of agricultural activities.

Readiness levels of Industry 4.0 technologies applied to aircraft manufacturing-a review, challenges and trends.

The present paper provides an overview of the state-of-the-art research, outlining the applications of the Industry 4.0 (I4.0) technologies on the aircraft manufacturing sector and their maturity state based on the technology readiness level (TRL) scale. A literature review has been conducted for the identification, selection, and evaluation of the published research. A total of 57 papers extracted from the two most relevant scientific databases for the area (Web of Science and Scopus), from 2010 to March 2021, were analysed and summarized. The research, analysis, and evaluation of these papers has provided an outlook of how the aircraft manufacturing industry is inserted into the I4.0 context, based on a classification of the I4.0 technologies maturity for this industrial branch. Then, a survey was performed with 12 specialists from 5 different aircraft manufacturing companies aiming to report the practical point-of-view in this area. Thus, this paper highlights and discusses the gaps found in the literature related to the I4.0 technologies applied to aircraft manufacturing and their main useful implications not only from the academic point-of-view but also from competitive business aspects, providing recommendations for industrial managers, engineers, and stakeholders. Finally, this paper proposes new opportunities and challenges for future research.

Industry 4.0 Technologies Applied to the Rail Transportation Industry: A Systematic Review.

BACKGROUND: Industry 4.0 technologies have been widely used in the railway industry, focusing mainly on maintenance and control tasks necessary in the railway infrastructure. Given the great potential that these technologies offer, the scientific community has come to use them in varied ways to solve a wide range of problems such as train failures, train station security, rail system control and communication in hard-to-reach areas, among others. For this reason, this paper aims to answer the following research questions: what are the main issues in the railway transport industry, what are the technologic strategies that are currently being used to solve these issues and what are the technologies from industry 4.0 that are used in the railway transport industry to solve the aforementioned issues? METHODS: This study adopts a systematic literature review approach. We searched the Science Direct and Web of Science database inception from January 2017 to November 2021. Studies published in conferences or journals written in English or Spanish were included for initial process evaluation. The initial included papers were analyzed by authors and selected based on whether they helped answer the proposed research questions or not. RESULTS: Of the recovered 515 articles, 109 were eligible, from which we could identify three main application domains in the railway industry: monitoring, decision and planification techniques, and communication and security. Regarding industry 4.0 technologies, we identified 9 different technologies applied in reviewed studies: Artificial Intelligence (AI), Internet of Things (IoT), Cloud Computing, Big Data, Cybersecurity, Modelling and Simulation, Smart Decision Support Systems (SDSS), Computer Vision and Virtual Reality (VR). This study is, to our knowledge, one of the first to show how industry 4.0 technologies are currently being used to tackle railway industry problems and current application trends in the scientific community, which is highly useful for the development of future studies and more advanced solutions. FUNDING: Colombian national organizations Minciencias and the Mining-Energy Planning Unit.

A Bibliometric Network Analysis of Recent Publications on Digital Agriculture to Depict Strategic Themes and Evolution Structure.

The agriculture sector is one of the backbones of many countries' economies. Its processes have been changing to enable technology adoption to increase productivity, quality, and sustainable development. In this research, we present a scientific mapping of the adoption of precision techniques and breakthrough technologies in agriculture, so-called Digital Agriculture. To do this, we used 4694 documents from the Web of Science database to perform a Bibliometric Performance and Network Analysis of the literature using SciMAT software with the support of the PICOC protocol. Our findings presented 22 strategic themes related to Digital Agriculture, such as Internet of Things (IoT), Unmanned Aerial Vehicles (UAV) and Climate-smart Agriculture (CSA), among others. The thematic network structure of the nine most important clusters (motor themes) was presented and an in-depth discussion was performed. The thematic evolution map provides a broad perspective of how the field has evolved over time from 1994 to 2020. In addition, our results discuss the main challenges and opportunities for research and practice in the field of study. Our findings provide a comprehensive overview of the main themes related to Digital Agriculture. These results show the main subjects analyzed on this topic and provide a basis for insights for future research.