Nowadays, governments and companies are looking for solutions to increase the collection level of various waste types by using new technologies and devices such as smart sensors, Internet of Things (IoT), cloud platforms etc. In order to fulfil this need, this paper presents solutions provided by a research project involving the design, development and implementation of fully automated waste collection systems with an increased usage degree, productivity and storage capacity. The paper will focus on the main results of this research project in turning the automated waste collection system into a smart system so that it can be easily integrated in any smart city infrastructure. For this purpose, the Internet of Things platform for the automated waste collection system provided by the project will allow real time monitoring and communication with central systems. Details about each module are sent to the central systems: various modules' statuses (working, blocked, needs repairs or maintenance etc.); equipment status; storage systems status (allowing full reports for all waste types); the amount of waste for each module, allowing optimal discharging; route optimization for waste discharging etc. To do that, we describe here an IoT cloud solution integrating device connection, data processing, analytics and management.

Although the new economic paradigm is based on the rapid evolution of technology, it is not clear if this evolution is only dependent on a spectacular transformation of human resources or if the evolution of human resources has imposed major changes at a technical level as well. The main focus of this paper is to identify how to cope with these new technologies as educational actors, using a diagnosis of contemporary generation characteristics. The fourth industrial revolution (Industry 4.0) imposes a rapid evolution (or revolution) of the human resources paradigm in engineering: iMillennials should adapt to that paradigm, and the paradigm should be adapted to them. The research objectives were to identify some relevant characteristics of iMillennials’ technological background and to create a map of the abilities of this generation as required by the evolution of new technologies. For a batch of students with a technical background, two psychological inventories that describe emotional intelligence and motivation acquisition were applied. Each inventory used focuses on certain features that describe motivational achievement (AMI) or emotional intelligence (EQ-I). Besides the motivational features, the AMI questionnaire also refers to socio-emotional abilities. A correlation between the parameters of the two inventories occurred. Three correlated parameters (assertiveness, reality testing, and commitment) were identified. Based on these results, a constellation map of soft skills was designed to match characteristic features of iMillennials with necessary competencies for an Industry 4.0 environment. Furthermore, this paper proposes a tool for educational actors to cope with these transformations based on the new technologies of Industry 4.0 and the characteristics of the iMillennials generation.

Lately, many scientists have focused their research on subjects like smart buildings, sensor devices, virtual sensing, buildings management, Internet of Things (IoT), artificial intelligence in the smart buildings sector, improving life quality within smart homes, assessing the occupancy status information, detecting human behavior with a view to assisted living, maintaining environmental health, and preserving natural resources. The main purpose of our review consists of surveying the current state of the art regarding the recent developments in integrating supervised and unsupervised machine learning models with sensor devices in the smart building sector with a view to attaining enhanced sensing, energy efficiency and optimal building management. We have devised the research methodology with a view to identifying, filtering, categorizing, and analyzing the most important and relevant scientific articles regarding the targeted topic. To this end, we have used reliable sources of scientific information, namely the Elsevier Scopus and the Clarivate Analytics Web of Science international databases, in order to assess the interest regarding the above-mentioned topic within the scientific literature. After processing the obtained papers, we finally obtained, on the basis of our devised methodology, a reliable, eloquent and representative pool of 146 papers scientific works that would be useful for developing our survey. Our approach provides a useful up-to-date overview for researchers from different fields, which can be helpful when submitting project proposals or when studying complex topics such those reviewed in this paper. Meanwhile, the current study offers scientists the possibility of identifying future research directions that have not yet been addressed in the scientific literature or improving the existing approaches based on the body of knowledge. Moreover, the conducted review creates the premises for identifying in the scientific literature the main purposes for integrating Machine Learning techniques with sensing devices in smart environments, as well as purposes that have not been investigated yet.

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This paper investigates the impact of several factors related to manufacturing, design, and post-processing on the dimensional accuracy of holes built in the additively manufactured parts obtained by material extrusion process (MEX). Directly fabricated holes in the 3D prints are commonly used for joining with other parts by means of mechanical fasteners, thus producing assemblies or larger parts, or have other functional purposes such as guiding the drill in the case of patient-personalized surgical guides. However, despite their spread use and importance, the relationship between the 3D-printed holes’ accuracy and printing settings is not well documented in the literature. Therefore, in this research, test parts were manufactured by varying the number of shells, printing speed, layer thickness, and axis orientation angles for evaluating their effect on the dimensional accuracy of holes of different diameters. In the same context of limited existing information, the influence of material, 3D printer, and slicing software is also investigated for determining the dimensional accuracy of hole-type features across different manufacturing sites, a highly relevant aspect when using MEX to produce spare or end-use parts in a delocalized production paradigm. The results of this study indicated that the layer thickness is the most relevant influence factor for the diameter accuracy, followed by the number of shells around the holes. Considering the tested values, the optimal set of values found as optimizing the accuracy and printing time was 0.2 mm layer thickness, two shells, and 50 mm/s printing speed for the straight holes. Data on the prints manufactured on different MEX equipment and slicers indicated no statistically significant difference between the diameters of the holes. The evaluation of 3D-printed polylactic acid test parts mimicking a surgical template device with inclined holes showed that the medical decontamination process had more impact on the holes’ dimensional variability than on their dimensional accuracy.