Visually impaired and blind people (VIBP) have to face significant difficulties locating public transport vehicles and bus stops due to their vision restrictions. Over the past decade, diverse assistance systems have been developed to solve this problem. However, most of them are based upon the global positioning system (GPS) and present satellite coverage problems in indoor environments. Some others are wearable prototypes that turn out to be onerous for the user. This paper presents an assistance system for VIBP in the use of public transportation. The proposed system uses Bluetooth Low Energy (BLE) technology for location and communication purposes, and a mobile application for user-smartphone interaction. The BLE beacons are installed on buses and their stops; accordingly, the mobile application tracks them in real-time and provides the relevant information to the user employing verbal instructions; transportation line, destination, next stop name, and current location. This information allows the user to properly select the desired bus in advance and get off at the correct destination stop. The proposed system has been tested in two scenarios: 1) under controlled conditions and 2) in a real environment. The results show that the proposed system is 97.6% effective when VIBP travel independently from one point to another. In addition, according to an assessment sheet completed by the participants, the proposed system grants them greater confidence and independence than GPS-based systems because of the following reasons; firstly, it can work with an internet connection or without an internet connection. Secondly, it is not an onerous system; information about the location of vehicles and stops is provided in real-time. Last but not least, it does not present satellite coverage problems in indoor environments.

This manuscript presents a fully detailed methodology in order to identify the hydrodynamic parameters of a mini autonomous underwater vehicle (mini-AUV) and evaluate its performance using different controllers. The methodology consists of close-to-reality simulation using a Computed Fluid Dynamics (CFD) module of the ANSYS™ Workbench software, the processing of the data, obtained by simulation, with a set of Savistky–Golay filters; and, the application of the Least Square Method in order to estimate the hydrodynamic parameters of the mini-AUV. Finally, these parameters are considered to design the three different controllers that are based on the robot manipulators theory. Numerical simulations are carried out to evaluate the performance of the controllers.

According to the World Health Organization (WHO), melanoma is the most severe type of skin cancer and is the leading cause of death from skin cancer worldwide. Certain features of melanoma include size, shape, color, or texture changes of a mole. In this work, a novel, robust and efficient method for the detection and classification of melanoma in simple and dermatological images is proposed. It is achieved by using HSV (Hue, Saturation, Value) color space along with mathematical morphology and a Gaussian filter to detect the region of interest and estimate four descriptors: symmetry, edge, color, and size. Although these descriptors have been used for several years, the way they are computed for this proposal is one of the things that enhances the results. Subsequently, a multilayer perceptron is employed to classify between malignant and benign melanoma. Three datasets of simple and dermatological images commonly used in the literature were employed to train and evaluate the performance of the proposed method. According to k-fold cross-validation, the method outperforms three state-of-art works, achieving an accuracy of 98.5% and 98.6%, a sensitivity of 96.68% and 98.05%, and a specificity of 98.15%, and 98.01%, in simple and dermatological images, respectively. The results have proven that its use as an assistive device for the detection of melanoma would improve reliability levels compared to conventional methods.

This article presents a methodology to improve the performance of learning of students in the major of Automation Engineering, through the development of a multidisciplinary project that involves the use of contemporary technological tools and knowledge of previous subjects. It is sought to add in their educational training, theory and practice for the student, learning, new knowledge, and skills within their training as an engineer. Despite advances in teaching methods and the inclusion of contemporary tools in the learning process, the success rate is low. A qualitative and quantitative analysis of student learning is made by integrating a multidisciplinary project based on contemporary tools compared with other reviewed works. This laboratory based on a field‐programmable gate array helps the student to acquire skills related to the implementation of reconfigurable logic and instrumentation applied to control theory. The development of the final project involves the design of a graphic user interface, hardware description, communication protocols, and implementation of motion control, it is looking for is that the student can relate theory to practice in order to motivate and increase skills and knowledge, as well as solving both didactic and industrial problems. The results obtained show that the students are satisfied with the course and that the experience with the integration of this type of multidisciplinary projects directly impacts the motivation and learning of the student, considerably increased developed based on the specific indicators that the Querétaro State University has accredited by Accreditation Board for Engineering and Technology since 2016.

Early leak detection in water distribution networks is essential to minimize losses and improve operational efficiency. This systematic review analyzes 53 studies published between 2018 and 2025 that employed machine learning, deep learning, and hybrid approaches. The results show that pressure is the most widely used and most sensitive input variable for identifying hydraulic anomalies. Most datasets originate from EPANET-generated simulations, while experimental and field data are less common due to their high costs and operational complexity. Machine learning models, particularly SVMs, achieve accuracies between 94 and 100%, demonstrating stability with noisy data and low computational cost, while in deep learning, CNNs are most effective for multiclass classification and localization, typically reaching 95–99% accuracy. Hybrid approaches that combine automatic feature extraction (e.g., CNNs or autoencoders) with conventional classifiers (such as SVMs or LSSVMs) yield the best results, surpassing 97% accuracy and achieving localization errors below 0.2 m. Based on these findings, a theoretical model is proposed using a hybrid CNN + SVM approach to enhance accuracy, robustness, and adaptability in real-time monitoring systems.

Active Mobility (AM) currently presents an opportunity to change the paradigm of the competitive and dispersed city created by motorized mobility, revaluing the role of walking and cycling in generating more sustainable urban ecosystems. This article addresses the challenges and opportunities for AM to contribute to the regeneration of urban systems and the capacity for anticipation. This article analyzes AM using the Ecosystemic Urbanism (EU) as an analysis framework within its four axes: social cohesion, complexity, efficiency, and compactness and functionality. Through this analysis, the points of incidence of AM were identified within each of these axes. The study highlights the potential of AM to act as a transformative driver in urban development, integrating an ecological framework where urban systems are interconnected and mutually reinforced. This perspective reveals walking and cycling as a catalyst for reshaping urban interactions. In light of this, future cities must adopt a human urban scale through compactness that fosters complexity and diverse and engaging urban interactions. In addition, the enjoyability achieved through AM brings significant ecosystem benefits by promoting awareness of others, nature, and the interconnectedness between the individual and the city. This represents a new paradigm shift in which the automobile does not play the central role, allowing more sustainable ways of living together.