In recent decades, there has been considerable interest in developing underwater remotely operated vehicles (ROVs) due to their vital role in exploring ocean depths to perform missions in various applications, including offshore oil and gas, military and defense, scientific research, and aquaculture. To this end, researchers must consider multiple aspects to develop ROVs, such as general design, power and thrust system, navigation and control, and obstacle avoidance. Accordingly, this paper proposes an integrated framework for designing and implementing an ROV prototype, considering the mechanical, electrical, and software systems. Eventually, image processing was implemented using Python to examine the ROV’s capabilities in performing underwater missions. The proposed design employs six thrusters to provide controllability of the ROV in six-degrees-of-freedom (DOF). We coated the track width of the printed circuit board (PCB) with a composite mixture of tin, silver, and gold to resist corrosion and harsh environments, enhance the circuit performance and solderability, and increase its life span. The PCB was designed to sustain 30 A with 10 cm × 10 cm dimensions. The image processing results revealed that the proposed ROV could successfully identify the benthic species, follow the desired routes, detect cracks, and analyze obstacles.

Industry Revolution Five (Industry 5.0) will shift the focus away from technology and rely more on to the collaboration between humans and AI-powered robots. This approach emphasizes a more human-centric perspective, enhanced resilience, optimized workplace processes, and a stronger commitment to sustainability. The humanoid robot market has experienced substantial growth, fueled by technological advancements and the increasing need for automation in industries such as service, customer support, and education. However, challenges like high costs, complex maintenance, and societal concerns about job displacement remain. Despite these issues, the market is expected to continue expanding, supported by innovations that enhance both accessibility and performance. Therefore, this article proposes the design and implementation of low-cost, remotely controlled humanoid robots via a mobile application for home-assistant applications. The humanoid robot boasts an advanced mechanical structure, high-performance actuators, and an array of sensors that empower it to execute a wide range of tasks with human-like dexterity and mobility. Incorporating sophisticated control algorithms and a user-friendly Graphical User Interface (GUI) provides precise and stable robot operation and control. Through an in-house developed code, our research contributes to the growing field of humanoid robotics and underscores the significance of advanced control systems in fully harnessing the capabilities of these human-like machines. The implications of our findings extend to the future development and deployment of humanoid robots across various industries and societal contexts, making this an ideal area for students and researchers to explore innovative solutions.

The nexus of sustainable manufacturing and Industry 4.0 technologies is redefining modern industrial practices. Conventional manufacturing, characterized by intensive energy use, resource depletion, and waste generation, is increasingly unsustainable in the face of environmental pressures and evolving regulations. Industry 4.0 technologies—including IoT, artificial intelligence, data analytics, cloud computing platforms, and, recently, digital twins—provide opportunities to embed sustainability by enabling real-time monitoring, predictive analytics, and adaptive decision-making. This paper addresses key methods and strategies for sustainability and Industry 4.0 nexus. It involves IoT systems for data-driven monitoring, AI for process optimization, cloud platforms for supply chain sustainability, and emphasizes the use of digital twins for predictive maintenance. Organizational strategies such as cross-functional collaboration, customized software, dual-focus performance metrics, and workforce reskilling are explored, alongside barriers including high capital costs, cybersecurity risks, and system integration challenges. The findings present a structured perspective on harmonizing sustainability and Industry 4.0, demonstrating how this nexus can reduce environmental impact, enhance efficiency, and support long-term industrial resilience.

With the recent growth in space exploration, the problem of space debris is becoming increasingly important. This debris could endanger active spacecraft. So, many methods are suggested to clear space debris. The best and most efficient removal method must be selected from these available options. This is our main objective. In this study, we suggest using the Weighted Sum Model (WSM) to order the chosen removal methods according to a set of deciding criteria. WSM is one of the most common Multi-Criteria Decision Analysis (MCDA) techniques. Based on how widely applicable they are, we selected five active removal methods. Thirteen decision criteria were chosen carefully. The Relative Frequency approach was used to determine the weight of these criteria. The WSM equation was used in the final stage. According to our major findings, Tethered-Deployed Nets, with a performance score of 78.9%, is comparatively the best method among the selected methods. More in-depth research on this approach is recommended for future work. To obtain more accurate results for our future research, we will use more decision criteria.