Both the idea and technology for connecting sensors and actuators to a network to remotely monitor and control physical systems have been known for many years and developed accordingly. However, a little more than a decade ago the concept of the Internet of Things (IoT) was coined and used to integrate such approaches into a common framework. Technology has been constantly evolving and so has the concept of the Internet of Things, incorporating new terminology appropriate to technological advances and different application domains. This paper presents the changes that the IoT has undertaken since its conception and research on how technological advances have shaped it and fostered the arising of derived names suitable to specific domains. A two-step literature review through major publishers and indexing databases was conducted; first by searching for proposals on the Internet of Things concept and analyzing them to find similarities, differences, and technological features that allow us to create a timeline showing its development; in the second step the most mentioned names given to the IoT for specific domains, as well as closely related concepts were identified and briefly analyzed. The study confirms the claim that a consensus on the IoT definition has not yet been reached, as enabling technology keeps evolving and new application domains are being proposed. However, recent changes have been relatively moderated, and its variations on application domains are clearly differentiated, with data and data technologies playing an important role in the IoT landscape.

This study evaluates the effect of conversion between tacit and explicit knowledge, and its influence on Software engineering and Software Process Improvement in the context of a small school software company in which undergraduate students participate as personnel. A survey measurement instrument was applied to the last three generations of students. The effect was measured from an interaction of the four modes of the SECI model knowledge conversion using regression analysis associated with four hypotheses study. The findings show that students are able to generate tacit and explicit knowledge in a similar way to software organizations. This study is considered a contribution of both academia and software industry that encourages this type of experiences in undergraduate students and prepares them as intellectual capital with an organizational culture that shares knowledge.

Biosensors are small analytical devices incorporating a biological recognition element and a physico-chemical transducer to convert a biological signal into an electrical reading. Nowadays, their technological appeal resides in their fast performance, high sensitivity and continuous measuring capabilities; however, a full understanding is still under research. This paper aims to contribute to this growing field of biotechnology, with a focus on Glucose-Oxidase Biosensor (GOB) modeling through statistical learning methods from a regression perspective. We model the amperometric response of a GOB with dependent variables under different conditions, such as temperature, benzoquinone, pH and glucose concentrations, by means of several machine learning algorithms. Since the sensitivity of a GOB response is strongly related to these dependent variables, their interactions should be optimized to maximize the output signal, for which a genetic algorithm and simulated annealing are used. We report a model that shows a good generalization error and is consistent with the optimization.

This document presents a Knowledge Management (KM) model aimed at promoting a strong energy culture regarding Saving and Efficient Use of Energy (SEUE) in higher education students. A survey was carried out and the analysis revealed a significant association between the study subjects’ prior tacit knowledge about SEUE and their KM situation and attitudes about it. We have also used factor analysis as a statistic technique to extract latent variables from the ones already existing. The findings suggest the design and use of didactic materials and educational strategies that promote training and development practices in the context of an effective and transparent knowledge transfer process, as well as individual practices in the change of energy use behaviour. The educational strategies must prevent previously inherited beliefs from distorting the new necessary knowledge, i.e., the tacit knowledge higher education students must acquire, assimilate and disseminate to strengthen a culture of SEUE.

The filling of channels in porous media with particles of a material can be interpreted in a first approximation as a packing of spheres in cylindrical recipients. Numerous studies on micro- and nanoscopic scales show that they are, as a rule, not ideal cylinders. In this paper, the channels, which have an irregular shape and a circular cross-section, as well as the packing algorithms are investigated. Five patterns of channel shapes are detected to represent any irregular porous structures. A novel heuristic packing algorithm for monosized spheres and different irregularities is proposed. It begins with an initial configuration based on an fcc unit cell and the subsequent densification of the obtained structure by shaking and gravity procedures. A verification of the algorithm was carried out for nine sinusoidal axisymmetric channels with different D_min/D_max ratio by MATLAB^® simulations, reaching a packing fraction of at least 0.67 (for sphere diameters of 5%D_min or less), superior to a random close packing density. The maximum packing fraction was 73.01% for a channel with a ratio of D_min/D_max = 0.1 and a sphere size of 5%D_min. For sphere diameters of 50%D_min or larger, it was possible to increase the packing factor after applying shaking and gravity movements.

One of the challenges the organizations confront is to extract data from the information systems to know the reality of their processes to improve their efficiency. In this study, the application of Process Mining is addressed as an opportunity in the specific context of an SME dedicated to software development, implementing the L* life cycle model methodology from a layered Software Engineering approach. This research is carried out based on process improvement in an initial SME project. Subsequently, it is compared with a second project, using different Process Mining perspectives such as control flow, case, organization, and time, with the aim of extending the process model. This holistic view allows not only to better understand the processes involved, but also to identify and analyze the similarities and differences between the two projects. As a result, the Process Mining analysis shows crucial aspects such as the representation of integrated models, traces on sequences of actions, and the interaction of activities with specific roles and deviations in the flow of activities that compromise the quality of the process and the product. At the same time, the challenges that emerged during the improvement cycle are highlighted. These challenges cover issues such as data extraction, fluid communication between those involved, and the documentation associated with the processes. This study contributes to the body of knowledge of Process Mining. Likewise, the case study results offer a vision for other SMEs seeking to incorporate Process Mining as part of their improvement strategies.