Predictive maintenance in machines aims to anticipate failures. In rotating machines, the component that suffers the most wear and tear is the bearings. Currently, based on the Industry 4.0 paradigm, advances have been made in obtaining data, specifically, vibration signals that can be used to predict deterioration using different techniques. In this study, we have applied vibration analysis to obtain features that can be used in an optimal Machine Learning model using a public dataset from CWRU, widely used in research, which contains data on bearing failures. The main objective of this research is to detect bearing failures using a minimum set of observations and selecting the minimum number of features. To achieve this, frequency domain vibration analysis, combined with envelope analysis, is utilized as an effective method for detecting bearing failures. The results were further improved by incorporating an optimal bandwidth determined using the kurtogram. When the results of the envelope analysis are applied to various machine learning models, using the calculated amplitudes as predictors, the Kernel Naive Bayes model has achieved an accuracy of 94.4%. Meanwhile, the Decision Tree (Fine Tree) and KNN (Fine KNN) models demonstrate exceptional accuracy, achieving a perfect accuracy rate of 100%.

Clustering in wireless sensor networks has been widely discussed in the literature as a strategy to reduce power consumption. However, aspects such as cluster formation and cluster head (CH) node assignment strategies have a significant impact on quality of service, as energy savings imply restrictions in application usage and data traffic within the network. Regarding the first aspect, this article proposes a hierarchical routing protocol based on the k-d tree algorithm, taking a partition data structure of the space to organize nodes into clusters. For the second aspect, we propose a reactive mechanism for the formation of CH nodes, with the purpose of improving delay, jitter, and throughput, in contrast with the low-energy adaptive clustering hierarchy/hierarchy-centralized protocol and validating the results through simulation.

Opportunistic networks (OppNet) a new paradigm emerges after the great advancement of wireless technology and MANETs (Mobile Ad-hoc Networks). Store-Carry-forward phenomena is used to route the messages in OppNets; therefore, it is called delay tolerant network (DTNs). OppNet is such a heterogeneous flexible network that works in hurricane affected regions and volcanoes environment. The objective of this article is to determine, evaluate, elect and synthesize all terrific quality research work on the basis of Research Questions (RQ) which is significant to use for opportunistic routing. Systematic literature review on state-of-the-art researches on routing protocols are presented with comparative analysis. To eliciting relevant work for review, advanced exploration is conducted on contrasting digital libraries. This work gives the opportunity to the readers to understand what has been done in field of Opportunistic Routing (OR), what parameters effect the performance of protocol, name of the organization; who works in this field. At last, issues, challenges, and upcoming future terms of this network are discussed.

Currently, we have around us many Smart Objects. With the use of these objects, we can obtain benefits in our daily lives, as well as recommendations and help when we travel. Alternatively, we may increase and improve our industrial processes through the automation of certain tasks. Notwithstanding, we need to use specific software or to develop our own applications. Nevertheless, the main problem arises when we need to develop our own application because we need to save money, or in other cases, the existing applications are not adapted to us. In this case, it is possible that we need to learn new things, the money will then be spent, and such a process is likely to involve problems related to the Software Crisis. So, the main motivation is to create an environment which can reuse the previous knowledge and help people without knowledge about programming to create Smart Objects. Then, the research question of this paper is the following: Could we enable the creation of Smart Objects in an easy and efficient way for people who do not have programming skills? As a possible solution, we have developed a graphic Domain-Specific Language using the Midgar platform. In order to validate our proposal, we make an evaluation split into different phases; the first one consisted in measuring data obtained from participants when they were performing a specific task, and the second one consisted of a survey to collect their opinions about our proposal. Moreover, we also did a comparison of the measured data between two graphical editors and two different participant profiles according to their knowledge about Smart Objects.

Throughout this article, an alternative depreciation method for electric vehicles (EVs) is presented, addressing the challenge of information asymmetry—a common issue in secondary markets. The proposed method is contrasted with traditional models, such as the Straight-Line Method (SLM), the Declining Balance Method, and the Sum-of-Years Digits (SYD) method, as these classic approaches fail to adequately consider key factors such as mileage and secondary aspects like battery degradation and rapid technological obsolescence, which critically impact the residual value of used EVs. The presented approach employs an adverse selection model that incorporates buyers’ and sellers’ perceptions of vehicle quality from the information recorded on e-commerce platforms, improving the depreciation estimation. The results show that the proposed method offers greater accuracy by leveraging asymmetric information extracted from web portals. Specifically, the method identifies a characteristic intersection point, marking the moment when the model aligns most closely with the data obtained through traditional methods in terms of precision. The analysis through the density of price estimations by vehicle model year indicates that, beyond 1.8 months, the proposed model provides more reliable results than traditional methods. The proposed model allows buyers to identify undervalued assets and sellers to obtain a fair market value, mitigating the risks associated with adverse selection, reducing uncertainty, and increasing market transparency and trust. It fosters equitable pricing between buyers and sellers by addressing the implications of adverse selection, where sellers—possessing more information about the vehicle’s condition than buyers—can dominate market transactions. This model restores balance by ensuring fairer valuation based on vehicle usage, primarily addressing the lack of critical data available on e-commerce platforms, such as battery certifications, among others.

Calculating forward and inverse kinematics for robotic agents is one of the most time-intensive tasks when controlling the robot movement in any environment. This calculation is then encoded to control the motors and validated in a simulator. The feedback produced by the simulation can be used to correct the code or to implement the code can be implemented directly in the robotic agent. However, the simulation process executes instructions that are not native to the robotic agents, extending development time or making it preferable to validate the code directly on the robot, which in some cases might result in severe damage to it. The use of Domain-Specific Languages help reduce development time in simulation tasks. These languages simplify code generation by describing tasks through an easy-to-understand language and free the user to use a framework or programming API directly for testing purposes. This article presents the language PyDSLRep, which is characterized by the connection and manipulation of movement in mobile robotic agents in the V-Rep simulation environment. This language is tested in three different environments by twenty people, against the framework given by V-Rep, demonstrating that PyDSLRep reduces the average development time by 45.22%, and the lines of code by 76.40% against the Python framework of V-Rep.