Correctly defining and grouping electrical feeders is of great importance for electrical system operators. In this paper, we compare two different clustering techniques, K-means and hierarchical agglomerative clustering, applied to real data from the east region of Paraguay. The raw data were pre-processed, resulting in four data sets, namely, (i) a weekly feeder demand, (ii) a monthly feeder demand, (iii) a statistical feature set extracted from the original data and (iv) a seasonal and daily consumption feature set obtained considering the characteristics of the Paraguayan load curve. Considering the four data sets, two clustering algorithms, two distance metrics and five linkage criteria a total of 36 models with the Silhouette, Davies–Bouldin and Calinski–Harabasz index scores was assessed. The K-means algorithms with the seasonal feature data sets showed the best performance considering the Silhouette, Calinski–Harabasz and Davies–Bouldin validation index scores with a configuration of six clusters.

In the present work the problem of Optimal Placement of remote controlled switches, through the replacement of installed manual switches, in Electric Power Distribution Systems with radial configuration is approached, considering economic, technical, operational and social aspects. The approach used for the identification of strategic points is based on the implementation of meta-heuristic algorithms for optimization with multiple objectives. For the evaluation of each solution, an evaluating function based on the Monte Carlo method is implemented, which estimates the defined indices through the simulation of failures in the network, whose probabilities are obtained using the Reliability Block Diagram method. Three different multi-objective algorithms were implemented and their results compared using the hypervolume metric, having the Generalized Differential Evolution 3 algorithm the best performance.

This survey presents a comprehensive review of data-driven approaches for industrial asset maintenance, emphasizing the use of data mining and machine learning techniques, including deep learning, for condition-based and predictive maintenance. It examines 534 references from 1995 to 2023, along with three additional articles from 2024 on natural language processing and large language models in industrial maintenance. The study categorizes two main techniques, four specialized approaches, and 27 methodologies, resulting in over 100 variations of algorithms tailored to specific maintenance needs for industrial assets. It details the data types utilized in the industrial sector, with the most frequently mentioned being time series data, event timestamp data, and image data. The survey also highlights the most frequently referenced data mining algorithms, such as the proportional hazard model, expert systems, support vector machines, random forest, autoencoder, and convolutional neural networks. Additionally, the survey proposes four level classes of asset complexity and studies five asset types, including mechanical, electromechanical, electrical, electronic, and computing assets. The growing adoption of deep learning is highlighted alongside the continued relevance of traditional approaches such as shallow machine learning and rule-based and model-based techniques. Furthermore, the survey explores emerging trends in machine learning and related technologies, identifies future research directions, and underscores their critical role in advancing condition-based and predictive maintenance frameworks.

This paper presents a data set with information on meteorological data and electricity consumption in the department of Alto Paraná, Paraguay. The meteorological data were registered every three hours at the Aeropuerto Guarani, Department of Alto Paraná, which belongs to the Dirección Nacional de Aeronáutica Civil of Paraguay. The final data consists of a total of 22.445 records of temperature, relative humidity, wind speed and atmospheric pressure. On the other hand, the electrical energy consumption data set contains a total of 1.848.947 records, all of them coming from the one hundred and fifteen feeders located throughout the Alto Paraná region of Paraguay. Electrical energy consumption data was provided by Administración Nacional de Electricidad (ANDE). The analysis of this data can yield insights regarding the energy consumption in the area.

Decision making in orthodontic treatment, especially regarding irreversible tooth extractions, is a complex and controversial challenge due to its impact on facial aesthetics and long-term functional stability. Artificial Intelligence and Machine Learning are advancing dentistry, aiding in tooth extraction decisions. Here we propose a Deep Learning solution to the Dental Extraction decision problem. We compare Intraoral and Extraoral image from a Dataset, which is an important contribution of this paper, with 1720 images from 215 patients, a small number considering the typical Deep Learning data sets. We compare five well known pretrained Convolutional Neural Network applying several balancing and optimization techniques. We significantly improved the baseline, achieving an F1-Score of 89.52% and an AUC of 95.21% using VGG19, one of the best-performing architectures. Oversampling of the minority class proved to be the most effective balancing strategy. These results are promising considering the small dataset. Models trained with intraoral and extraoral views showed superior performance. The main outcome of this paper is that Deep Learning with Occlusal images have a great potential to be an excellent orthodontic clinical decision support system.

Diabetic retinopathy is a vision impairment associated with diabetes mellitus, a common medical condition. Retinographic imaging analysis of retinal fundus images is the predominant method of diagnosing this eye complication. Recent advances in deep learning show effectiveness in detecting diabetic retinopathy, rivaling the diagnostic accuracy of human inspection. However, the success of these deep learning methods largely depends on the architecture model. In our research, we introduce a method for training a deep learning model using a Low-Rank Adaptation (LoRA) technique to differentiate between three stages of diabetic retinopathy: no diabetic retinopathy, non-proliferative diabetic retinopathy, and proliferative diabetic retinopathy. LoRA significantly reduces the training parameters needed by employing a low-ank representation. Our experimental results on four data sets of different sizes show that the LoRA model delivers top-tier results in classifying stages of diabetic retinopathy.