Artificial Intelligence of Things (AIoT) is the most emerging era of a combination of Artificial Intelligence (AI) and the Internet of Things (IoT), which is exponentially gaining researchers' attention with every passing day because of its broad applicability in various sectors. Technology 4.0 in the industry focuses on effective object sorting of conveyors and energy saving. There is an increase in the need for industrial employee training. However, the new technology of conveyor systems to enhance performance has yet to be widespread. However, the new technology of conveyor systems to enhance performance has yet to be widespread. Owing to the learning material for training is expensive. It is also not possible to simulate the operation of a comprehensive system. Industrial employees need to understand the big picture, causing problems in learning and lacking practical skills. Especially new industrial employees may need more knowledge and experience when operating errors; they can have problem-solving skills to seek solutions. Therefore, we proposed the implementation of an object-sorting training kit based on AIoT technology. In the experiment, nineteen industrial employees from many industrial enterprises participated in three days of training activities. The results show increased employee performance with an AIoT training kit that can simulate object sorting with color and size and then offer a dashboard. They can understand how to save energy in manufacturing. Mainly, they have practical skills and positive perceptions toward this study.

In the industrial sector, Artificial Intelligence (AI) technology has been extensively utilized and integrated into educational curricula and teaching methodologies to enhance educational effectiveness and respond to the needs of entrepreneurs. However, due to its costliness, more learning tools and technology are needed to ensure AI education. The Arduino board offers a user-friendly and cost- effective solution that enables the study of complex applications involving the integration of artificial intelligence with Arduino programming. This study presents a review process to investigate trends of using Arduino to enhance AI study. This study highlights the increasing complexity of using Arduino boards with artificial intelligence algorithms. Specifically, it explores their applications in domains such as multiple linear regression (MLR), particle swarm optimization (PSO), adaptive neuro-fuzzy inference systems (ANFIS), and Node-RED. The findings of this study will serve as a valuable reference for scholars interested in this domain and as a guide for AI education in the future.

Technology 4.0 in the industry focuses on effective energy saving. There is an increase in the need for industrial worker training, especially in compressed air systems, air kept under more significant pressure than atmospheric pressure. It is an important medium for the transfer of energy in industrial processes. However, the new technology of energy management systems to enhance performance has yet to be widespread. Owing to the learning material for training is expensive. It is also not possible to simulate the operation of a comprehensive system. The employees lack an understanding of the big picture, causing problems in learning and lacking practical skills. Especially new employees may need more knowledge and experience and have operation errors or problem-solving skills. In this study, we proposed a training model consisting of a compressed air systems training kit based on the Artificial Intelligence of Things (AIoT), and the energy-saving scenarios consist of 1) controlling the compressed air pressure fed to the air cylinder while being subjected to loads of different sizes. 2) controlling the compressed air pressure supplied to the air cylinder while operating the air cylinder without load. 3) controlling the compressed air supplied to the machine while it is stopped. In addition, we use an outcome-based approach to drive training activities. The results show that the training model can simulate the process of energy management systems of compressed air systems and show it on the dashboard. It can be implemented in employee or engineer practical training in the future.

Machine vision is even more critical for sorting workpieces in quality control of automated production systems for Industry 4.0. Use the image processing system principle to inspect the workpiece with a high-speed vision sensor camera. It results in high accuracy of sorting performance and can replace labor in automated production processes. The learning process of vision sensors used for inspecting workpieces with industrial cameras for mechatronics and robotics engineering students is concerned. Most barriers need more teaching materials due to the relatively high cost of the training kit. That is causing students' lack of knowledge and practical skills during the professional training experience in the workplace. This study proposed designing a sorting system using a machine vision (SSMV) training kit for mechatronics and robotics engineering students. It can be a learning tool to promote the students' learning in related fields, automated manufacturing systems, and engineering education.

Nowadays, education in industrial engineering places a significant emphasis on pneumatic systems due to their widespread application in conveyor belt systems and various automated industrial processes. Components within pneumatic systems include compressors, air preparation units, directional control valves, actuators, and gripping devices. Therefore, understanding pneumatic systems necessitates having a solid understanding of how to employ these components efficiently. In educational management, the development of learning materials holds paramount importance. These materials play a pivotal role in facilitating enhanced comprehension among students. Integrating augmented reality technology further enriches the learning experience, specifically in understanding pneumatic systems. This technology provides students with a comprehensive system overview before engaging with actual equipment in practical tasks. This study proposed the design of a pneumatics system learning material with AR technology for vocational education students. It encourages the student’s motivation in the concept of pneumatic systems. We created a model using 3D and AR application programs to generate learning material to display on mobile devices. The circuits of the function of the cylinder work in various conditions. It has been used to simulate the use of training skills for students or interested people to apply such knowledge to work in the workplace in the future.

The industry sector has a complex manufacturing process and technology, mainly utilizing robots and automation, which are necessary for factory operation. Many vocational colleges try to use methods to promote the students' experience of learning automation technology to control robots with a programmable logic controller (PLC). Preparing students who will use technology in Industry 4.0 with robot arm operation is essential. However, the learning tools used to practice based are high-cost, and the technology is constantly changing and evolving. Hence, this research aims to design a training tool for an industrial robot operating using a programmable logic controller (IRO- PLC). The proposal shows that the design training model can simulate steps of the learning activity to control the robot with PLC and then display it on the dashboard with an IoT device. It can increase the student's performance in practical vocational education training in future studies.

While supply voltage and frequency directly affect circuit soft errors, thermal response from tuning these two parameters also provides a moderate side effect. This study firstly improves the accuracy of logic soft error estimation by taking into consideration the thermal impact from supply voltage and frequency variations. In the presence of the inversion of the temperature effect where the drive current of some modern designs increases at high temperature, we also take a benefit of this effect to develop a novel soft error mitigation technique by adaptively regulating the chip temperature. This technique can moderately reduce high soft error rate of combinational blocks during low voltage operation with neglectable power overhead.

Teaching and learning in engineering courses need to emphasize practicality to acquire integrated knowledge and skills. It is necessary to learn processes controlled by mathematical equations in mechatronic engineering to apply scientific principles to use automation control systems technology to support Industry 4.0, such as automatic control with a Programmable Logic Controller (PLC) control device. In addition, they are app lying the data of engineering process control to real-time applications with Supervisory Control and Data Acquisition (SCADA) software and PLC Network. Due to the outbreak of Coronavirus (COVID -19), lack of practice face-to-face interaction, so the effectiveness of online learning practice is significantly reduced. This study presents the combination of remote-control technology and multi- material learning to promote engineering students' conceptual and practical experiments during online learning. The finding shows that this multi-material learning can operate and motivate engineering students in the distance learning situation.

The traditional teaching methods of teachers are lectures and assignments to students' tasks in the classroom. Sometimes the students lack enthusiasm, resulting in poor academic performance. Providing teaching and learning that allows students to experience real -life situations is a prerequisite for future work. Furt hermore, in the industry, Programmable Logic Controllers (PLC) are used in automatic control systems. Therefore, studying in a PLC course, students should be exposed to real -world situations. Role -playing teaching is one way to help students experience real- life situations for future work and makes the learning process more interesting, fun, and motivating. Thus, this study proposes using role -playing for learning on PLC courses for vocational certificate students. Role -playing activities to help in learnin g can promote students to functional skills for using PLC for controlling the operation of the input/output devices. Moreover, the student can develop their knowledge, skills, and abilities according to the course competencies and use them for real work in the future.

Engineering education is one of the most demanded topics in Thailand. With the traditional instruction, pre-service engineering teachers are not well promoted to learn actively. Based on this point of view, this study developed a series of active learning activities in corresponding to the Robotic based on STEM framework. This aims to help enhance students’ understanding on the robotics, functionality, and applications. This study documented perceptions of STEM (science, technology, engineering and mathematics) content and careers for vocational students participating in STEM activities focused on industrial robotics. The analysis of the answers from 578 students in vocational education attended the activities and completed the questionnaires that showed the results showed that student’s attitude after the activities were compared across gender groups (men=421, female=157). The finding of this study is not only fruitful for students, especially pre-service engineering teachers, but also shed light of the revolutionized teaching activities for other teachers in different fields. Since this is a very first implementation of the proposed framework, it requires more investigations and improvements in the future.

In FinFET-based VLSI designs, heat issues from increase of driving current with temperature and self-heating effect profoundly influence the circuit performance and reliability. This work evaluates the performance of FinFET-based combinational circuits considering BTI stress and thermal effect of supply voltage and frequency variations. The proposed simulation framework is applied to selected benchmark circuits implemented with the 14-nm tri-gate bulk FinFET technology. The experimental results reveal that as temperature increases, BTI aging delay increasingly worsens, yet it is overridden by performance gain from the increase in driving current. We also prove that BTI degradation is dependent on power supply and frequency through their thermal impacts. Further, we introduce a DVFS based power reduction approach that scales down the supply voltage of hot circuits to maintain the performance. The results show that power reduction yielded from the proposed technique is larger for all circuits working at higher ambient temperature (as large as 66% in some experimental circuits working at 20 oC above the baseline ambient temperature) with a slight decrease in BTI degradation.

In the era of deep-nanoscale transistors, 3D structure of a FinFET strengthens the self-heating effect (SHE) that intensifies aging degradation. However, unlike planar devices, heat in FinFETs can improve the circuit performance due to the temperature effect inversion (TEI). This work investigates the impact of bias temperature instability (BTI) on long-term circuit performance in the presence of TEI. To accurately evaluate the device delay, the proposed unified TEI and BTI model takes into consideration temperature variation extracted from power dissipation in the device. The experimental results are carried out through selected combinational benchmark circuits under temperature and frequency variations. Compared to other recent approaches, this proposed method provides highly optimistic results in which either performance degradation (up to 2.3%) or improvement (up to 10%) can be found. Further, the results reveal that performance degradation due to the integration of BTI and TEI tends to worsen at high temperature, yet weaken at high frequency.

With the advancement of technological innovations for ICT in education, this study focuses on building effective computer assisted learning based on the framework of ADDIE instructional development model to improve visual impaired students’ conceptual learning progression on Marketing course. To examine the effectiveness of the developed computer assisted learning, an experiment was conducted by assigning twenty-four diploma students into three groups consisting of five blind students, seven partial visual impaired students, and twelve non-visual impaired students. They are permitted to select the learning instructions regarding to their vision level. The results of this study show that the developed computer assisted learning could help the students improve their conceptual learning progression. Additionally, the students reveal positive satisfaction towards the developed computer assisted learning.