The objective of this work is the study of the procedure for flow rate measurement derived from temperature profiles. This method is deemed appropriate because of the inability to mount conventional flow meters in multiple-zone oil wells. In this work, a reduced-scale prototype well with horizontal geometry was developed to study and validate a method of flow profiling by temperature measurements in the well column based on a heat transfer mathematical model studied by Ramey in 1962. Flow sensors were installed at key points to provide validation data for the flow measurements derived from the temperature. The plant was automated and all the tests were managed from a workstation. It was possible to test different situations to provide a variability of evaluation scenarios. The initial experiments used injected fluid flow rates of 15 and 18 L/min in the well inlet. The results of the calculated flow values in different work conditions were compared with a relatively low error reference meter.

This paper presents the development and implementation of a centralized industrial network for an automatic purified water production system used in the pharmaceutical industry. This implementation is part of a project to adapt an industrial plant to cope with advances in industrial technology to achieve the level of Industry 4.0. The adequacy of the instruments and the interconnection of the controllers made it possible to monitor the process steps by transforming a manual plant, with discontinuous production into an automated plant, improving the efficiency and quality of the produced water. The development of a supervisory system provides the operator with a panoramic view of the process, informing in real-time the behavior of the variables in the process steps, as well as storing data, event history and alarms. This system also prevented the collection of erroneous or manipulated data, making the process more transparent and reliable. Accordingly, we have been able to tailor this water treatment plant to operate within the minimum requirements required by the regulator.

This research explores the design of a system for monitoring driver drowsiness and supervising seat belt usage in interprovincial buses. In Peru, road accidents involving long-distance bus transportation amounted to 5449 in 2022, and the human factor plays a significant role. It is essential to understand how the use of non-invasive sensors for monitoring and supervising passengers and drivers can enhance safety in interprovincial transportation. The objective of this research is to develop a system using a Raspberry Pi 4 and Arduino Nano that allows for the storage of monitoring data. To achieve this, a conventional camera and MediaPipe were used for driver drowsiness detection, while passenger supervision was carried out using a combination of commercially available sensors as well as custom-built sensors. RS485 communication was utilized to store data related to both the driver and passengers. The simulations conducted demonstrate a high level of reliability in detecting driver drowsiness under specific conditions and the correct operation of the sensors for passenger supervision. Therefore, the proposed system is feasible and can be implemented for real-world testing. The implications of this research suggest that the system’s cost is not a barrier to its implementation, thus contributing to improved safety in interprovincial transportation.

The use of recurrent neural networks has proven effective in time series prediction tasks such as weather. However, their use in resource-limited systems such as MCUs presents difficulties in terms of both size and stability with longer prediction windows. In this context, we propose a variant of the LSTM model, which we call SE-LSTM (Single Embedding LSTM), which uses embedding techniques to vectorially represent seasonality and latent patterns through variables such as temperature and humidity. The proposal is systematically compared in two parts: The first compares it against other reference architectures such as CNN-LSTM, TCN, LMU, and TPA-LSTM. The second stage, which includes implementation, compares it against the CNN-LSTM, LSTM, and TCN networks. Metrics such as the MAE and MSE are used along with the network weight, a crucial aspect for MCUs such as an ESP32 or Raspberry Pi Pico. An analysis of the memory usage, energy consumption, and generalization across different regions is also included. The results show that the use of embedding optimizes the network space without sacrificing the performance, which is crucial for edge computing. This research is part of a larger project, which focuses on improving agricultural monitoring systems.

This paper presents a methodology for detecting the pest Aleurothrixus floccosus in citrus crops in Pedregal de Arequipa, Peru. The study employs simple random sampling during image collection to minimize bias, alternating and extracting leaves from different citrus trees. Image processing techniques, including noise reduction, edge smoothing, and segmentation, are applied for pest detection. Machine learning algorithms are used to classify the images, culminating in a robust detection methodology. A dataset of 1200 images was analyzed during the study.

This article details the design, assembly, and tuning of a multipurpose FPV (First Person View) drone designed as a budget-friendly and customizable alternative to professional-grade drone model. Built with a focus on performance, adaptability, durability, and ease of repair, the APdrone (All Purpose Drone) incorporates high-quality components such as a carbon fiber frame, brushless motors, and advanced flight controllers. To ensure optimal flight characteristics, a comprehensive tuning process was performed using empirical adjustments, supported by controlled testing environments, to optimize PID parameters ($K_p$, $K_i$, $K_d$) to achieve stable and precise flight performance. Testing demonstrated a flight time of approximately 8 min under standard conditions with a maximum payload capacity of 0.98 kg. Importantly, the APdrone offers operational flexibility and significant cost savings (USD 647.09), compared to high-end drones like the DJI Mavic 3 Pro, while maintaining comparable functionality and allowing for customization. To encourage accessibility, reproducibility and further development, open-source design files, including CAD schematics, firmware configurations, and assembly instructions, are provided. The APdrone serves as a scalable platform for research, recreational use, and advancements within the UAV (Unmanned Aerial Vehicle) field.