Hsiuping University of Science and Technology

This paper presents a real-time digital twin framework for robotic manipulation that integrates ROS2 with Unity for synchronized execution and monitoring. The system combines a HIWIN RA610-1869 robotic arm, a CCV camera, and a YOLO-based perception pipeline to classify and manipulate ten classes of industrial components placed randomly in a workspace. Upon receiving a command, the robot identifies, localizes, and performs pick-and-place tasks, while Unity mirrors all motions in real time, enhancing transparency and operator validation. Experimental trials demonstrate high accuracy under controlled conditions and high reliability in manipulation. A comparative study of two YOLO models trained on different datasets further highlights the challenge of domain adaptation: the real-image model achieved stable validation (precision <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\approx 0.90$</tex>, recall <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\approx 0.92$</tex>, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{mAP50} \approx 0.90$</tex>), whereas the synthetic-only model exhibited strong in-domain results but <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\approx 90 \%$</tex> mislabeling in real-world scenes. These findings confirm system robustness while underscoring the need for domain adaptation in digital twinenabled robotics.

This paper proposes a real-time, cross-platform learning system that combines YOLOv8-based visual recognition with digital reality to accelerate skill acquisition for the Class C Pneumatic Technician Certification. A custom dataset of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{1, 2 0 0}$</tex> high-resolution images was collected, annotated at symbol level, and augmented (hue <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\pm 5.4^{\circ}$</tex>, rotation <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\pm 5^{\circ}$</tex>, scale <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$0.5-1.5 \times$</tex>) to ensure cross-brand recognition consistency. The trained YOLOv8 model achieved mAP@0.5 of 0.94 and recall above 0.90, supporting low-latency (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$&lt;40 ~\text{ms}$</tex>) recognition on edge devices. The system integrates six interactive modules—including component recognition, virtual assembly, disassembly, and circuit wiring—allowing unlimited practice without physical hardware. User testing with 30 students showed a 32 % reduction in assembly errors and a 27 % improvement in task completion time compared with traditional training. By removing equipment and time constraints, reducing material waste, and enabling anytime-anywhere practice on PC, Android, and iOS, the proposed system provides a scalable and cost-effective approach to pneumatic skills training, significantly enhancing learner preparedness for certification and real-world applications.

This study examines the effectiveness of augmented reality (AR) in digital learning by comparing three learning modalities: paper-based, computer-based, and AR-based presentations of artwork. A within-subject experimental design was employed with 10 participants, using a Latin Square Design to counterbalance the order of learning modalities and control for sequence effects. Each participant experienced all three modalities, followed by immediate and delayed recall tests to assess memory retention. The results will provide insights into how AR influences learning outcomes compared to traditional and digital methods. Findings from this study will contribute to the understanding of AR’s impact on memory performance and its potential applications in education.

A time-dependent third-order shear deformation theory (TSDT) approach on the displacements of thick functionally graded material (FGM) conical shells under dynamic thermal vibration is studied. Dynamic equations of motion with TSDT for thick FGM conical shells are applied directly with the partial derivative of variable R*θ in the curve coordinates (x, θ, z) instead of y in the Cartesian coordinates (x, y, z) for thick FGM plates, where R* is the middle-surface radius at any point on conical shells. The generalized differential quadrature (GDQ) numerical method is used to solve the dynamic differential equations in equilibrium matrix forms under thermal loads. It is the novelty of the current study to identify the parametric effects of shear correction coefficient, environment temperature, TSDT model, and FGM power law index on the displacements and stresses in the thick conical shells only subjected to sinusoidal heating loads. The physical parts with values on the length-to-thickness ratio equals 5, and 10 FGMs can be used in an area of an airplane engine that usually operates near more than 1000 K of temperatures when the thermal stress is considered and affected. The important findings of the presented study are listed as follows. The values of normal stress are in decreasing tendencies with time in cases when the coefficient c1 equals 0.925925/mm2 in TSDT and length-to-thickness ratio equals 5. The shear stress values in x plane z direction on the minor middle-surface radius (r) equals the major middle-surface radius (R) over 8 and length-to-thickness ratio equals to 5 can withstand T = 1000 K of pressure.

The thick functionally graded material (FGM) circular cylindrical shells with advanced varied shear correction coefficient and third-order shear deformation theory (TSDT) under advanced thermal vibration are studied by the method of generalized differential quadrature (GDQ). The coefficient of displacement model of TSDT is applied to derive the equations of motion for the thick FGM circular cylindrical shells. The stiffness in simpler forms of thick FGM circular cylindrical shells and temperature rise in linear expression of the heat conduction equation are used. The differential equations in dynamic equilibrium state of thick FGM circular cylindrical shells can be obtained and rewritten into displacements and shear rotations in partial derivative expressions under dynamic thermal loads in partial derivative expressions. Parametric effect studies including advanced nonlinear varied shear correction coefficient, nonlinear coefficient c1 value, power law index and thermal temperature difference of external heating loads on the displacements and stresses of thick FGM cylindrical shells under thermal dynamic vibration are investigated.

The purpose of this study is to examine whether organizational project management maturity in the project management consultancy industry offers any competitive advantages to a business when it tenders for contracts. We collected 150 responses from both members and former members of professional Australian project management associations in order to examine and understand any potential effects of project management maturity on management and organizations. The statistical software SPSS was used to analyze the data collected with the confidence interval (alpha) set at 5%. The results of this study reveal that organizational project management maturity has an effect on competitive advantages as noted by the project managers (p < .0001; 99.99%; H0 -rejected). The study also shows that the perception of competitive advantages for organizational project management maturity is based on the level of maturity reached. It also reveals that an organization is winning contracts/jobs due to various other competencies, particularly soft skills such as client relationships, stakeholder management, communications skills, and modes of client engagement.

The effects of advanced nonlinear varied shear coefficient and third-order shear deformation theory (TSDT) on the dynamic responses of thick functionally graded material (FGM) plates under thermal vibration are investigated. The nonlinear coefficient of the displacement field of TSDT is used to obtain the expression of advanced varied shear coefficient for the thick FGM plates. The dynamic displacements, shear rotations and stresses in numerical results under sinusoidal applied heat loads are obtained and investigated. Two parametric effects of environment temperature and FGM power law index on the dynamic responses of thermal stress and center deflection of thick FGM plates are also investigated. The transient responses of center deflection are found for the cases of simply homogeneous equation and fully homogeneous equation. Also, the transient responses of center deflection are found for cases of nonlinear and linear varied-modified coefficient of shear correction.

Due to the social awareness of risk control, we are witnessing the popularization of the insurance concept and the rapid development of financial insurance. The performance of the insurance industry is highly competitive; thus, in order to develop new and old business from existing clients, information on the renewal of client premiums, purchase of new policies, and new client referrals has become an important research topic in this field. However, based on a review of published literature, few scholars have engaged in relevant research on the above topics by data mining, which motivated the formation of this study, hoping to bridge this gap. We constructed 10 mixed classification prediction models (called Models A–J) using advanced data mining techniques. Moreover, 19 conditional attributes (coded as X1–X19) were selected from the collected insurance client database, plus three different decision attributes (coded as X20–X22): whether to pay the renewal insurance premium, whether to buy a new insurance policy, and whether to introduce new clients. In terms of technical methods, we used two data pretreatment techniques, attribute selection and data discretization, combined with different methods of disassembly in proportion and data cross-validation to conduct data analysis of the collected experimental data set. We also combined and calculated 23 important classification algorithms (or classifiers) in seven different classifications of data mining techniques (i.e., decision tree, Bayes, Function, Lazy, Meta, Mise, and Rule). In terms of the experimental results of insurance data, this study has the following important contributions and findings: (1) finding the best classifier; (2) finding the optimal mixed classification model; (3) determining the best disassembly in proportion; (4) comparing the performance of different disassembly in proportion and data cross-validation methods; (5) determining the important factors influencing the decision attribute “whether to purchase a new insurance policy”, including the time interval to the first purchase, the number of valid policies, the total number of purchased policies, the family salary structure, and gender; and (6) building a knowledge base of decision rules and criteria with the decision tree C4.5 technology, which shall be provided to relevant stakeholders such as insurance dealers and insurance salespeople as a reference for looking for valid clients in the future, and is conducive to the rapid expansion of insurance business. Finally, the important research findings and management implications of this study can serve as a basis for further study of sustainable insurance by academic researchers.