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Cyclops and Phoenix retire, ushering in a new era for the X-Men.

Nowadays, due to the increasing use of electric vehicles, manufacturers are making more and more innovations in the batteries used in electromobility, in order to make these vehicles more efficient and provide them with greater autonomy. This has led to the need to evaluate and compare the efficiency of different batteries used in electric vehicles to determine which one is the best to be implemented. This paper characterises, models and compares three batteries used in electromobility: lithium-ion, lead-acid, and nickel metal hydride, and determines which of these three is the most efficient based on their state of charge. The main drawback to determine the state of charge is that there are a great variety of methods and models used for this purpose; in this article, the Thévenin model and the Coulomb Count method are used to determine the state of charge of the battery. When obtaining the electrical parameters, the simulation of the same is carried out, which indicates that the most efficient battery is the Lithium-ion battery presenting the best performance of state of charge, reaching 99.05% in the charging scenario, while, in the discharge scenario, it reaches a minimum value of 40.68%; in contrast, the least efficient battery is the lead acid battery, presenting in the charging scenario a maximum value of 98.42%, and in the discharge scenario a minimum value of 10.35%, presenting a deep discharge. This indicates that the lithium-ion battery is the most efficient in both the charge and discharge scenarios, and is the best option for use in electric vehicles. In this paper, it was decided to use the Coulomb ampere counting method together with the Thévenin equivalent circuit model because it was determined that the combination of these two methods to estimate the SOC can be applied to any battery, not only applicable to electric vehicle batteries, but to battery banks, BESS systems, or any system or equipment that has batteries for its operation, while the models based on Kalman, or models based on fuzzy mathematics and neural networks, as they are often used and are applicable only to a specific battery system.

In the field of artificial intelligence, control systems for mobile robots have undergone significant advancements, particularly within the realm of autonomous learning. However, previous studies have primarily focused on predefined paths, neglecting real-time obstacle avoidance and trajectory reconfiguration. This research introduces a novel algorithm that integrates reinforcement learning with the Deep Q-Network (DQN) to empower an agent with the ability to execute actions, gather information from a simulated environment in Gazebo, and maximize rewards. Through a series of carefully designed experiments, the algorithm’s parameters were meticulously configured, and its performance was rigorously validated. Unlike conventional navigation systems, our approach embraces the exploration of the environment, facilitating effective trajectory planning based on acquired knowledge. By leveraging randomized training conditions within a simulated environment, the DQN network exhibits superior capabilities in computing complex functions compared to traditional methods. This breakthrough underscores the potential of our algorithm to significantly enhance the autonomous learning capacities of mobile robots.

This study presents an advanced energy demand management approach within residential microgrids using bipartite models for optimal demand response. The methodology relies on linear programming, specifically the Simplex algorithm, to optimize power distribution while minimizing costs. The model aims to reduce residential energy consumption by flattening the demand curve through demand response programs. Additionally, the Internet of Things (IoT) is integrated as a communication channel to ensure efficient energy management without compromising user comfort. The research evaluates energy resource allocation using bipartite graphs, modeling the generation of energy from renewable and conventional high-efficiency sources. Various case studies analyze scenarios with and without market constraints, assessing the impact of demand response at different levels (5%, 10%, 15%, and 20%). Results demonstrate a significant reduction in reliance on external grids, with optimized energy distribution leading to potential cost savings for consumers. The findings suggest that intelligent demand response strategies can enhance microgrid efficiency, supporting sustainability and reducing carbon footprints.

During the Industry 4.0 era, the open source-based robotic arms control applications have been developed, in which the control algorithms apply for movement precision in the trajectory tracking paths based on direct or reverse kinematics. Therefore, small errors in the joint positions can summarize in large position errors of the end-effector in the industrial activities. Besides the change of the end-effector position for a given variation of the set-point in manipulator joint positions depends on the manipulator configuration. This research proposes a control based on Proportional Derivative (PD) Control with gravity compensation to show the robustness of this control scheme in the robotic arm’s industrial applications. The control algorithm is developed using a low-cost board like Raspberry Pi (RPI) where the Robot Operating System (ROS) is installed. The novelty of this approach is the development of new functions in ROS to make the PD control with gravity compensation in low-cost systems. This platform brings a fast exchange of information between the Kuka™ youBot robotic arm and a graphical user’s interface that allows a transparent interaction between them.

The fourth industrial revolution has transformed industrial ergonomics through the adoption of wearable technologies to enhance workplace safety and well-being. This study conducts a comprehensive scoping review, structured according to PRISMA guidelines, examining how wearable devices are revolutionizing ergonomic practices within Industry 4.0. After analyzing 1319 articles from major databases including SpringerLink, MDPI, Scopus, and IEEEXplore, 36 relevant studies were selected for detailed analysis. The review specifically focuses on how wearable technologies improve worker comfort and safety, promoting more productive work environments. The findings reveal that wearable devices have significantly impacted ergonomic conditions in industrial settings, with artificial intelligence integration showing the highest presence in analyzed applications. Over the past years, wearable technology implementations have demonstrated a 38% improvement in optimizing ergonomic conditions compared to traditional approaches.

Over the past few years, the industry has experienced significant growth, leading to what is now known as Industry 4.0. This advancement has been characterized by the automation of robots. Industries have embraced mobile robots to enhance efficiency in specific manufacturing tasks, aiming for optimal results and reducing human errors. Moreover, robots can perform tasks in areas inaccessible to humans, such as hard-to-reach zones or hazardous environments. However, the challenge lies in the lack of knowledge about the operation and proper use of the robot. This work presents the development of a teleoperation system using HTC Vive Pro 2 virtual reality goggles. This allows individuals to immerse themselves in a fully virtual environment to become familiar with the operation and control of the KUKA youBot robot. The virtual reality experience is created in Unity, and through this, robot movements are executed, followed by a connection to ROS (Robot Operating System). To prevent potential damage to the real robot, a simulation is conducted in Gazebo, facilitating the understanding of the robot’s operation.

Immunisation against COVID-19 is crucial for controlling the pandemic, yet global challenges persist in vaccine coverage and equitable distribution. A well-integrated primary health care approach can enhance vaccination programmes. To explore the relationship between perceived PC (primary care)-PH (public health) integration, as well as other vaccination program implementation factors, and national COVID-19 vaccination coverage. A convenience sample of self-identified primary care professionals completed an online survey on COVID-19 vaccination programme implementation and their perceptions of PC-PH integration. Countries with ≥5 responses were included in the data analysis. COVID-19 vaccination implementation approach and perceived PC-PH integration against COVID-19 vaccination coverage was investigated using bivariate and subgroup analyses, Spearman correlation, and linear regression. A total of 394 responses from 32 countries were analysed. Participants included primary care providers, academics, and researchers. The median national COVID-19 vaccination coverage was 28.41% at time of study. Perceived barriers included patient hesitancy and vaccine supply shortages, while facilitators included vaccine product choices, equity, and community engagement. The study revealed a positive relationship between perceptions of PC-PH integration and national vaccination coverage in upper-middle and lower-middle income countries. Perceived PC-PH integration increased with decreasing economic quartiles and this perception was linked to actual national vaccination coverage. Integration may be especially important for countries with lesser vaccine supply. High-income countries may benefit from increased collaboration between PC and PH to enhance vaccination efficiency. The findings contribute to understanding the role of PC-PH integration in vaccination programmes in different settings.

This paper focuses on the implementation of a constrained Dynamic Matrix Control (DMC) approach within the level processes of the FESTO™ MPS-PA Compact Workstation plant in the context of the Industrial Internet of Things (IIoT) paradigm. The goal is to develop an industrial control application with decentralized logic that optimizes the operation of the plant while adhering to specific constraints. The implementation is carried out using the IEC-61499 standard and the OPC-UA protocol, enabling seamless communication between devices and systems. The authors utilize the 4diac-IDE and 4diac-FORTE as the development and runtime environments, respectively, to enable the execution of the control application on low-cost devices. The Beagle Bone Black (BBB) card is used for data acquisition and actuator control. Three types of constraints are considered: control increment (Δu(k)), output (ym(k)), and control (u(k)) constraints, to prevent unnecessary stress on the actuator and avoid damage to the plant. The QP algorithm is employed to optimize the objective function and address these constraints effectively. By integrating advanced control strategies into industrial processes in the IIoT paradigm and implementing them on low-cost devices, this paper demonstrates the feasibility and effectiveness of improving system performance, resource utilization, and overall productivity while considering system limitations and constraints.

Existing models for estimating hyporheic solute mass flux often require numerous parameters related to flow, bed, and channel characteristics, which are frequently unavailable. We performed a meta‐analysis on existing data set, enhanced with high Reynolds number cases from a validated Computational Fluid Dynamics model, to identify key parameters influencing effective diffusivity at the sediment water interface. We applied multiple linear regression to generate empirical models for predicting eddy diffusivity. To simplify this, we developed two single‐parameter models using either a roughness or permeability‐based Reynolds number. These models were validated against existing models and literature data. The model using roughness Reynolds number is easy to use and can provide an estimate of the mass transfer coefficient for solutes like dissolved oxygen, particularly in scenarios where detailed bed characteristics such as permeability might not be readily available. Plain Language Summary Current methods for estimating solute mass transfer across the sediment‐water interface of rivers often require a lot of information about flow and riverbed characteristics. Unfortunately, this information is often not readily available. We evaluated existing data from flume experiments and the field and added new data from a verified computational model, in order to identify which factors are most important in determining how much solute moves toward the bed at the sediment‐water interface. Using statistical tools, we developed two simple models that require minimal information about the stream and streambed. One model considers sediment size, the other looks at riverbed permeability. We validated these models by comparing them to existing methods and data from other studies, and they performed well. The model based on sediment size, which also reflects the roughness of the riverbed, performs best and is the most user‐friendly because it does not require information about permeability, which is harder to estimate. This model can be further applied for dissolved oxygen transfer and provide a reliable estimate of how oxygen moves at the sediment‐water interface, particularly when specific details about the riverbed are not available. Key Points We used a validated numerical model to expand the available data set of hyporheic mass exchange under various bed and flow conditions We performed reanalysis of flume/field data combined with numerical results to develop models for the hyporheic mass exchange rate We proposed unifying single‐parameter models for the estimation of hyporheic mass transfer coefficient in open‐channel flows