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Commercial educational robots provide an accessible entry point into the world of robotics. However, their programming is often limited to specific platforms, which can make it challenging to acquire the skills necessary for industry and research. In this study, we introduce an open-access tool developed using C++ and Arduino IDE that enables us to manage a commercial mobile robot through the Robot Operating System (ROS) middleware. This provides programmers with the ability to work in a powerful programming environment, such as Python. The robot used is the CrowBot BOLT, a kit based on ESP32 that enables wireless communication and includes various peripherals for application development. The mobile robot topics include robot velocities, RGB LEDs, a buzzer, a programmable button, and proximity, light, and line sensors. The proposal is assessed using two controllers: one for proximity and the other for tracking angular light. Both controllers are developed using Visual Studio Code. The experimental results demonstrated the proper functioning of the tool. Additionally, the response time was evaluated, and it was found that optimal performance is achieved at a frequency of 10 Hz. In summary, this proposal provides an accessible option for students and developers seeking to gain skills in robotics using ROS. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) technologies and sensors have recently received significant interest in intellectual agriculture. Accelerating the application of AI technologies and agriculture sensors in intellectual agriculture is urgently required for the growth of modern agriculture and will help promote smart agriculture. Automatic irrigation scheduling systems were highly required in the agricultural field due to their capability to manage and save water deficit irrigation techniques. Automatic learning systems devise an alternative to conventional irrigation management through the automatic elaboration of predictions related to the learning of an agronomist. With this motivation, this study develops a modified black widow optimization with a deep belief network-based smart irrigation system (MBWODBN-SIS) for intelligent agriculture. The MBWODBN-SIS algorithm primarily enables the Internet of Things (IoT) based sensors to collect data forwarded to the cloud server for examination purposes. Besides, the MBWODBN-SIS technique applies the deep belief network (DBN) model for different types of irrigation classification: average, high needed, highly not needed, and not needed. The MBWO algorithm is used for the hyperparameter tuning process. A wideranging experiment was conducted, and the comparison study stated the enhanced outcomes of the MBWODBN-SIS approach to other DL models with maximum accuracy of 95.73%. [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "Effects of a dual intervention (motor and virtual reality-based cognitive) on cognition in patients with mild cognitive impairment: a single-blind, randomized controlled trial" published in the Journal of NeuroEngineering & Rehabilitation. The correction addresses an error in the author's name, which was incorrectly written as 'Carolina de Valle Soto' instead of 'Carolina Del-Valle-Soto'. The original article has been corrected. The publisher, Springer Nature, maintains a neutral stance on jurisdictional claims and institutional affiliations. The authors of the article are Jorge Buele, Fátima Avilés-Castillo, Carolina Del-Valle-Soto, José Varela-Aldás, and Guillermo Palacios-Navarro. [Extracted from the article]

With the fast-growing interconnection of smart technologies, the Industrial Internet of Things (IIoT) has revolutionized how industries work by connecting devices and sensors and automating regular operations via the Internet of Things (IoTs). IoT devices provide seamless diversity and connectivity in different application domains. This system and its transmission channels are subjected to targeted cyberattacks due to their round-the-clock connectivity. Accordingly, a multilevel security solution is needed to safeguard the industrial system. By analyzing the data packet, the Intrusion Detection System (IDS) counteracts the cyberattack for the targeted attack in the IIoT platform. Various research has been undertaken to address the concerns of cyberattacks on IIoT networks using machine learning (ML) and deep learning (DL) approaches. This study introduces a new Bayesian Machine Learning with the Sparrow Search Algorithm for Cyberattack Detection (BMLSSA-CAD) technique in the IIoT networks. The proposed BMLSSA-CAD technique aims to enhance security in IIoT networks by detecting cyberattacks. In the BMLSSA-CAD technique, the min-max scaler normalizes the input dataset. Additionally, the method utilizes the Chameleon Optimization Algorithm (COA)-based feature selection (FS) approach to identify the optimal feature set. The BMLSSA-CAD technique uses the Bayesian Belief Network (BBN) model for cyberattack detection. The hyperparameter tuning process employs the sparrow search algorithm (SSA) model to enhance the BBN model performance. The performance of the BMLSSA-CAD method is examined using UNSWNB51 and UCI SECOM datasets. The experimental validation of the BMLSSA-CAD method highlighted superior accuracy outcomes of 97.84% and 98.93% compared to recent techniques on the IIoT platform., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

Purpose: Assistive technologies based on IoT can contribute to improve quality of living of patients with severe motor difficulties by providing partial or total independence. The aim of this work was to analyse the usability and performance of an assistive system based on the IoT when is evaluated by a child patient with spinal muscular atrophy type 1 (SMA-I)., Materials and Methods: The study involved a child with SMA-I and his caregiver. The materials used include an M5Stack Core2 kit, a mobile app, and a smart switch based on the ESP-01S card. The patient sends requests to the caregiver from the app installed on the M5Stack Core2 to a mobile app, and controls smart switches located in the rooms. The system was tested by the participants for a period of 30 days to later evaluate its usability and performance., Results: The results show that the control function of smart switches is the most used and there is no decrease in interactions over the days for the system in general. In addition, the scores obtained from both usability tests (patient and caregiver) were 87.5% and 90%, respectively. The average performance of the entire system was 93.33%., Conclusion: The application of assistive technologies based on the IoT allows obtaining a practical solution that improves the development of daily activities in a patient with SMA-I.