The acquisition of ECG signals offers physicians and specialists a very important tool in the diagnosis of cardiovascular diseases. However, very often these signals are affected by noise from various sources, including noise generated by movement during physical activity. This type of noise is known as Motion Artifact (MA) which changes the waveform of the signal, leading to erroneous readings. The elimination of this noise is performed by different filtering techniques, where the adaptive filtering using the LMS (least mean squares) algorithm stands out. The objective of this article is to determine which algorithms best deal with motion artifacts, taking into account the use of instruments or wearable equipment, in different conditions of physical activity. A comparison between different algorithms derived from LMS (NLMS, PNLMS and IPNLM) used in adaptive filtering is carried out using indicators such as: Pearson's Correlation Coefficient, Signal to Noise Ratio (SNR) and Mean Squared Error (MSE) as metrics to evaluate them. For this purpose, the mHealth database was used, which contains ECG signals taken during moderate to medium intensity physical activities. The results show that filtering by IPNLMS as well as PNLMS offers an improvement both visually and in terms of SNR, Pearson, and MSE indicators.

Water quality is a key factor in environmental and agronomic sustainability. Due to the influence of human activity and industrial development, the composition of rivers or lakes can experience significant variations both immediately and over time. In order to obtain a more accurate and documented assessment of these data, distributed monitoring with multiple sampling points is necessary. This paper presents the design and implementation of a scalable monitoring network based on long range (LoRa) and Message Queuing Telemetry Transport (MQTT), integrating a submersible sensor module (SSM) that works as a static measuring station or as a complement to sediment collectors, capable of measuring key water quality parameters such as TDS, turbidity, pH, temperature, and river kinematics with a gyroscope. The system includes a LoRa repeater (LRR) and a gateway, in addition to the SSM, which manages information transmission to a monitoring server (MS) using a tree topology. This configuration allows for dynamic antenna power adjustment based on the Received Signal Strength Indicator (RSSI) between the LRR and the gateway. Evaluations were performed on the Chil River in Arequipa, Peru, a rapid river that demonstrated ideal characteristics for validating the system’s efficacy. The results confirm the design’s efficacy and its capacity for real-time remote water quality monitoring.

We address the real case of a large-scale market-driven and captive company in Brazil, with a particular focus on the production planning of its foundry sector. The situation involves a wide range of item types produced from different alloy types, and capacitated furnaces and production lines. Given the internal and external demand of each item type over a specific time horizon, the company aims to determine which and how many items to produce and store each day, while minimizing total inventory and the number of setups required. To represent and solve this problem, we introduce a novel optimization model and incorporate it into a decision-support tool. We are not aware of any other solution approach that encompasses all the specificities of the studied company. Computational experiments using real-world data provided by the company indicate that our approach is an effective tool for supporting efficient planning in the foundry sector.

This research presents the design and development of a robot for the collection of surface aquatic sediments in shallow water environments. The robot’s primary function is to enhance traditional sampling techniques and ensure precise data for environmental assessment. The robot’s working principle involves being manually placed at the sampling point, where it descends under its own weight through the water column until reaching the sediment bed. At this point, it employs an innovative drilling system that combines rotational and linear movements, enabling a conical‐shaped collector to gather samples from the uppermost 52 mm of sediment while minimizing the loss of fine sediment. Primarily constructed with 3D‐printed parts, the robot features a modular layered design in its mechanical structure, facilitating both assembly and maintenance. The control system is composed of a Raspberry Pi (RPI), a stepper motor driver, an H‐bridge, and optical sensors for position and velocity feedback. Additionally, a monitoring system was implemented through a local Wi‐Fi network to visualize drilled distance, linear velocity, angular velocity, and battery voltage during the sampling process. Multiple tests have been carried out in the laboratory, where the amount of sediment collected ranged from 84.2 to 151 g depending on the sampling duration, demonstrating the robot’s capability and consistency in meeting standard requirements.

Currently, resource optimization in industrial automation requires continuously exploring new solutions that enhance production efficiency. This work presents the design and implementation of a dedicated SCADA system, developed in Python, for the supervision and control of a real liquid level module using a Siemens S7-1200 PLC. Open-source SCADA platforms such as PySCADA provide generalized architectural solutions, where as the proposed implementation, designed for liquid level control, adopts an application-focused architecture that facilitates real-time monitoring, reduces deployment complexity, and minimizes development overhead. The system integrates Modbus TCP/IP communication, a streamlined interface for level monitoring, and experimental validation on a real testbed. These results highlight a practical, lightweight, and scalable approach suitable for industrial and academic applications.