The Internet of Things (IoT) has been implemented by multiple manufacturing companies into their production chain as this technology is the main source of digitalization in a production plant. It improves the data assembling, productivity of the operation, communication efficiency, and overall manufacturing performance. IoT is also serves to be a good means for improved and efficient warehouse automation. It makes the delivery of products more efficient by calculating the least routes and also reduces the time that is consumed during the management of inventory. The basic objective of Industry 4.0 is to lessen the participation of human operators and to emphasize the automation systems. However, this objective has changed in Industry 5.0, which aims to achieve the maximum benefits through the human–machine interaction by maintaining a balance. Industry 5.0 aims to strengthen the interaction between ever-increasing powerful machinery and the productive abilities of human beings. This paper introduces a detailed overview of IoT in enabling digital transformations and Industry 4.0. The authors have discussed the application of IoT in different industrial sectors, and how the concept of IIoT has evolved. In addition to this, the present paper highlights several research studies that enable the authors to elicit the major challenges, implementation analysis, and future scope of IIoT.

This review article comprehensively delves into the rapidly evolving field of domain adaptation in computer and robotic vision. It offers a detailed technical analysis of the opportunities and challenges associated with this topic. Domain adaptation methods play a pivotal role in facilitating seamless knowledge transfer and enhancing the generalization capabilities of computer and robotic vision systems. Our methodology involves systematic data collection and preparation, followed by the application of diverse assessment metrics to evaluate the efficacy of domain adaptation strategies. This study assesses the effectiveness and versatility of conventional, deep learning-based, and hybrid domain adaptation techniques within the domains of computer and robotic vision. Through a cross-domain analysis, we scrutinize the performance of these approaches in different contexts, shedding light on their strengths and limitations. The findings gleaned from our evaluation of specific domains and models offer valuable insights for practical applications while reinforcing the validity of the proposed methodologies.

Robotics and automation have been a growing area within K–12 educational institutions for the past decade. Across secondary educational institutions, students are introduced to robotics in classes, after-school clubs, and competition leagues through various educational platforms, vendors, and kits. Robotics was initially implemented in schools to help drive more interest in STEM through hands-on application of mechanical, electrical, structural, and computer engineering concepts. Recently, the trend of K–12 robotics has become very niche, focusing more on mobile robotics or robotics competitions. Because of this trend, students have limited exposure to emerging technological advances, such as those found in Industry 4.0. Exciting technological areas, such as digital twins, are not covered in curricula, and this lack of exposure negatively influences the direction of student interest in the “T” and “E” of STEM, with many students never pursuing computer science, technology, or robotics in higher education. The primary goal of this research is to provide a methodology to expose secondary students to Industry 4.0 technologies by leveraging accessible technologies, such as Unity and the Robot Operating System (ROS), to develop a low-cost, high-fidelity digital twin of a pick-and-place robot in a smart warehouse operation. This digital twin prototype will help students to learn about Industry 4.0 trends, such as next-generation automation systems, digital twins, digital manufacturing, intelligent automation, and additive manufacturing, using ROS–Unity integration and hardware accessible to secondary schools to simulate a pick-and-place robotic application. By harnessing the accessibility of Unity and ROS to create a low-cost digital twin prototype for a secondary school, this research has a secondary goal of improving the pipeline of students interested in pursuing STEM-related learning in higher education, thereby ensuring a future STEM workforce that can research, design, develop, operate, and maintain the systems and technologies of Industry 4.0.

In recent years, CubeSats have gained popularity as secondary payloads in space missions due to their uniquely small size and minimal weight. This allows for the quick and inexpensive development of high-risk, high-reward investigations. The success of cube-shaped CubeSats has led to the development of a new class of small-scale and low-cost scientific platforms known as CanSats, which maintain a unique cylindrical shape. CanSats offer an even more economical alternative for conducting high-risk investigations, although they are typically constrained by having to operate within Earth’s atmosphere, which contributes to their reduced costs. However, the ability to test and improve space-bound hardware makes the CanSat a potential intermediary technology for continued space exploration. This survey paper seeks to provide a technical definition of CanSats and summarize the current state of the art in CanSat-based research. This paper covers the history of CanSats, their current mainstream applications, and their potential impact on the technology pipeline for space exploration. CanSats have proven to be versatile in various applications, including Earth science, aeronautics, and educational purposes. The lower cost of CanSats provides a wider range of researchers and educational institutions access to near-space science. Therefore, this paper also aims to explore the potential future applications of CanSats, particularly as an intermediary technology for testing and improving space-bound hardware, with potential benefits for future space missions. The findings from this survey could help to guide the further research and development of CanSats, as well as help to shape future space exploration efforts.

Domain adaptation (DA) is essential for developing robust machine learning models capable of operating across different domains with minimal retraining. This study explores the application of domain adaptation techniques to 3D datasets for industrial object detection, with a focus on short-range and long-range scenarios. While 3D data provide superior spatial information for detecting industrial parts, challenges arise due to domain shifts between training data (often clean or synthetic) and real-world conditions (noisy and occluded environments). Using the MVTec ITODD dataset, we propose a multi-level adaptation approach that leverages local and global feature alignment through PointNet-based architectures. We address sensor variability by aligning data from high-precision, long-range sensors with noisier short-range alternatives. Our results demonstrate an 85% accuracy with a minimal 0.02% performance drop, highlighting the resilience of the proposed methods. This work contributes to the emerging needs of Industry 5.0 by ensuring adaptable and scalable automation in manufacturing processes, empowering robotic systems to perform precise, reliable object detection and manipulation under challenging, real-world conditions, and supporting seamless human–robot collaboration.

Artificial intelligence (AI) shows great promise for transforming the agriculture sector and can enable the development of many modern farming practices over conventional methods. Nowadays, AI agents and agentic AI have attained popularity due to their autonomous structure and working mechanism. This research work proposes an agentic AI framework that integrates multiple agents developed for farming land to promote climate-smart agriculture and support United Nations (UN) sustainable development goals (SDGs). The developed structure has four agents: Agent A for monitoring soil properties, Agent B for weather sensing, Agent C for disease detection vision sensing in rice crops, and Agent D, a multi-agent supervisor agent chatbot connected with the other agents. The overall objective was to connect all agents on a single platform to obtain sensor data and perform a predictive analysis. This will help farmers and landowners obtain information about weather conditions, soil properties, and vision-based disease detection so that appropriate measures can be taken on agricultural land for rice crops. For soil properties (nitrogen, phosphorus, and potassium) from Agent A and climate data (temperature and humidity) from Agent B, we deployed the long short-term memory (LSTM), gated recurrent unit (GRU), and one-dimensional convolutional neural network (1D-CNN) predictive models, which achieved an accuracy of 93.4%, 94%, and 96% for Agent A; a 0.27 mean absolute error (MAE) for temperature; and a 2.9 MAE for humidity on the Agent B data. For Agent C, we used vision transformer (ViT), MobileViT, and RiceNet (with a diffusion model layer as a feature extractor) models to detect disease. The models achieved accuracies of 95%, 98.5%, and 85.4% during training respectively. Overall, the proposed framework demonstrates how agentic AI can be used to transform conventional farming practices into a digital process, thereby supporting smart agriculture.