Climate change, primarily driven by human activities such as burning fossil fuels, is causing significant long-term changes in temperature and weather patterns. To mitigate these impacts, there is an increased focus on renewable energy sources. However, optimizing power consumption through effective usage control and waste recycling also offers substantial potential for reducing energy demands. This study explores non-intrusive load monitoring (NILM) to estimate disaggregated energy consumption from a single household meter, leveraging advancements in deep learning such as convolutional neural networks. The study uses the UK-DALE dataset to extract and plot power consumption data from the main meter and identify five household appliances. Convolutional neural networks (CNNs) are trained with transfer learning using VGG16 and MobileNet. The models are validated, tested on split datasets, and combined using ensemble methods for improved performance. A new voting scheme for ensembles is proposed, named weighted average confidence voting (WeCV), and it is used to create combinations of the best 3 and 5 models and applied to NILM. The base models achieve up to 97% accuracy. The ensemble methods applying WeCV show an increased accuracy of 98%, surpassing previous state-of-the-art results. This study shows that CNNs with transfer learning effectively disaggregate household energy use, achieving high accuracy. Ensemble methods further improve performance, offering a promising approach for optimizing energy use and mitigating climate change.

Electrical power systems are crucial, yet vulnerable, due to their complex and interconnected nature, necessitating effective fault detection and diagnostics to ensure stability and prevent disruptions. Advances in artificial intelligence (AI) and the Internet of Things (IoT) have transformed the ability to identify and resolve electrical system problems efficiently. Electrical systems operate at various scales, ranging from individual households to large-scale regional grids. In this study, we focus on medium-scale urban infrastructures. These environments present unique electrical challenges, such as phase imbalances and transient voltage fluctuations, which require robust fault detection mechanisms. This work investigates the use of AI with dynamic programming and a support vector machine (SVM) to improve fault detection. The data collected from voltage measurements in urban office buildings with smart meters over a period of six weeks was used to develop an AI model, demonstrating its applicability to similar urban infrastructures. This model achieved high accuracy in detecting system failures, identifying them with a performance greater than 99%, highlighting the potential of smart sensing technologies combined with AI to improve urban infrastructure management. The integration of smart sensors and advanced data analytics significantly increases the reliability and efficiency of energy systems, promoting sustainable and resilient urban environments.

This article present the design and construction of a PID controller for direct current motors motion control in low-cost robotic applications. The reasons which led us to the make the proposal and design of the system were, on the one hand, the need for robotic developments by students in Colombians academic environments with the resources available locally, and on the other hand, the need for a teaching of the robotics based on laboratory practices of the most commonly used control algorisms in this area, such as: the inverse kinematics based PID control and the dynamic control by calculated torque.

Introduction: eccentric hamstring strength measured during the Nordic hamstring exercise is used to profile and monitor soccer players, but its individual interpretation requires reference values obtained in the same population and with the same device and protocol. Objective: to describe the distribution of absolute and body mass-normalized peak eccentric hamstring strength during the Nordic hamstring exercise in Colombian under-20 soccer players and to pro-pose a descriptive percentile-based classification. Methodology: in this cross-sectional study, 142 male players aged 17 to 20 years from five clubs in the department of Atlántico, Colombia, were assessed at the end of the 2025 preseason. Peak force of each limb was recorded over three repetitions with a load cell device of known reliability (ICC (3,1) = 0.85) and ex-pressed in newtons (N) and N/kg. Percentiles 5 to 95 were calculated, and five categories were defined using the 10th, 25th, 75th and 90th percentiles as cut-off points. Results: the dominant limb, defined as the limb with the higher peak force, produced 387.34 ± 109.60 N (5.45 ± 1.36 N/kg) and the non-dominant limb 339.52 ± 92.69 N (4.78 ± 1.16 N/kg). The median bilateral average was 360.14 N (IQR: 287.73–424.40) and 5.04 N/kg (IQR: 4.31–5.92). Body mass correlated with absolute strength (r = 0.43; p < 0.001) but not with relative strength. Conclusion: these values are a descriptive reference for under-20 players assessed with the same device and protocol; the categories are not clinically validated and should not be used to predict inju-ries.

It is here presented the mechatronic design of an system with automatic storing and picking of small products. The design concept presents the system as a four degrees-of-freedom robot capable of storing objects at different heights. The novelty of the design lies in the space optimization using stacked trays for storing the desired objetcs. Other key components of the system are a device for entry and exit of products, and a robotic arm for manipulating objects. A typical application of such a system is the storage of products inside safe-deposit boxes is presented.

Introduction: Hamstring injuries are common in soccer and are associated with eccentric strength deficits and inter-limb functional asymmetries. The Nordic Hamstring Curl (NHC) is effective for prevention; however, the influence of execution velocity on functional adaptations in young athletes remains unclear. Objective: To determine the effects of two execution velocities of the Nordic Hamstring Curl on eccentric knee flexor strength and functional asymmetry in young soccer players. Methods: A quasi-experimental study with random assignment was conducted in male U-17 soccer players (n=20), divided into a fast-velocity group (FG) and a slow-velocity group (SG). The intervention lasted 4 weeks (3 sessions per week) with progressive volume. Bilateral eccentric strength was recorded in real time during the Nordic Hamstring Curl using a validated load-cell device; inter-limb asymmetry was calculated. Results: Improvements in eccentric strength were observed in both groups. Statistical significance was reached only in the non-dominant limb of the FG (p=0.0157). No between-group differences were found bilaterally. Functional asymmetry was significantly reduced in the FG (p=0.0119), while it remained stable in the SG. Discussion: A short Nordic Curl training cycle improved eccentric strength and reduced inter-limb asymmetry, especially with a fast tempo as a key modulator, showing both agreements and discrepancies with previous studies. Conclusions: The execution velocity of the Nordic Curl emerges as a key factor in short-term adaptations: both tempos improve eccentric strength, and the fast, braking-emphasized variant additionally promotes the correction of asymmetries.