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A real-time production scheduling method for semiconductor back-end manufacturing process becomes increasingly important in industry 4.0. Semiconductor back-end manufacturing process is always accompanied by order splitting and merging; besides, in each stage of the process, there are always multiple machine groups that have different production capabilities and capacities. This paper studies a multi-agent based scheduling architecture for the radio frequency identification (RFID)-enabled semiconductor back-end shopfloor, which integrates not only manufacturing resources but also human factors.

The architecture includes a task management (TM) agent, a staff instruction (SI) agent, a task scheduling (TS) agent, an information management center (IMC), machine group (MG) agent and a production monitoring (PM) agent. Then, based on the architecture, the authors developed a scheduling method consisting of capability & capacity planning and machine configuration modules in the TS agent.

The authors used greedy policy to assign each order to the appropriate machine groups based on the real-time utilization ration of each MG in the capability & capacity (C&C) planning module, and used a partial swarm optimization (PSO) algorithm to schedule each splitting job to the identified machine based on the C&C planning results. At last, we conducted a case study to demonstrate the proposed multi-agent based real-time production scheduling models and methods.

This paper proposes a multi-agent based real-time scheduling framework for semiconductor back-end industry. A C&C planning and a machine configuration algorithm are developed, respectively. The paper provides a feasible solution for semiconductor back-end manufacturing process to realize real-time scheduling.

This paper aims to investigates a novel design of a modular moving magnet linear oscillating actuator (MMM-LOA) with the capability of coupling modules, based on their application and space requirements.

Proposed design comprised of modules, and modules are separated by using nonmagnetic materials. Movable part of the proposed design of LOA is composed of permanent magnets (PMs) having axial magnetization direction and tubular structure. Stator of the proposed design is composed of one coil individually in a module. Dimensions of the design parameters are optimized through parametric analysis using COMSOL Multi Physics software. This design is analyzed up to three modules and their response in term of electromagnetic (EM) force and stroke are presented. Influence of adding modules is analyzed for both directions of direct current (DC) and alternating input loadings.

Proposed LOA shows linear increase in magnitude of EM force by adding modules. Motor constant of the investigated LOA is 264 N/A and EM force per PM mass is 452.389 N/kg, that shows significant improvement. Moreover, proposed LOA operates in feasible region of stroke for compressor application. Furthermore, this design uses axially magnetized PMs which are low cost and available in compact tubular structure.

Proposed LOA shows the influence of adding modules and its effect in term of EM force is analyzed for DC and alternating current (AC). Moreover, overall performance and structural topology is compared with state-of-the-art designs of LOA. Improvement with regard of motor constant and EM force per PM mass shows originality and scope of this paper.

This case study explores how universal design for learning (UDL)-informed online instruction modules developed during COVID-19 can better support student information literacy outcomes. This study will also examine how hybrid learning lends itself to UDL and may resolve some of the issues within library instruction.

This case study explores how a team of librarians at Utah State University developed three UDL-informed modules to support library instruction and hybrid learning during the height of the COVID-19 pandemic. A survey was sent to composition instructors to understand how they utilized the three new UDL-informed modules and if the modules helped their students reach information literacy outcomes.

Findings from this case study describe how academic libraries should adopt the UDL framework to support best practices for online learning as well as inclusive pedagogies. The findings indicate that the UDL-informed modules developed for hybrid instruction help students meet information literacy outcomes and goals.

The authors present a case study examining the current climate of information literacy instruction and UDL while providing actionable instructional practices that can be of use to librarians implementing hybrid instruction.

The aim of this paper is to provide an overview of a novel offender personality disorder (OPD) higher education programme and the research evaluation results collected over a three-year period. Data from Phase 1 was collected from a face-to-face mode of delivery, and Phase 2 data collected from the same programme was from an online mode of delivery because of the COVID-19 pandemic.

In Phase 1, three modules were developed and delivered in a fully face-to-face format before the pandemic in 2019–2020 (n = 52 student participants). In 2020–2021 (n = 66 student participants), training was adapted into a fully online mode of delivery in Phase 2. This mixed-methods study evaluated participant confidence and compassion. Pre-, post- and six-month follow-up questionnaires were completed. Qualitative interviews were conducted across both phases to gain in-depth feedback on this programme (Phase 1: N = 7 students, Phase 2: N = 2 students, N = 5 leaders). Data from Phase 1 (face-to-face) and Phase 2 (online) are synthesised for comparison.

In Phase 1 (N = 52), confidence in working with people with personality disorder or associated difficulties improved significantly, while compassion did not change. In Phase 2 (N = 66), these results were replicated, with statistically significant improvements in confidence reported. Compassion, however, was reduced in Phase 2 at the six-month follow-up. Results have been integrated and have assisted in shaping the future of modules to meet the learning needs of students.

Further research into the impact of different modes of delivery is important for the future of education in a post-pandemic digitalised society. Comparisons of blended learning approaches were not covered but would be beneficial to explore and evaluate in the future.

This comparison provided informed learning for consideration in the development of non-related educational programmes and, hence, was of use to other educational providers.

This paper provides a comparison of a student-evaluated training programme, thus providing insights into the impact of delivering a relational-focused training programme in both face-to-face and online distance learning delivery modes. From this pedagogic research evaluation, the authors were able to derive unique insights into the outcomes of this programme.

Attention is one of the most important factors to affect the academic performance of students. Effectively analyzing students' attention in class can promote teachers' precise teaching and students' personalized learning. To intelligently analyze the students' attention in classroom from the first-person perspective, this paper proposes a fusion model based on gaze tracking and object detection. In particular, the proposed attention analysis model does not depend on any smart equipment.

Given a first-person view video of students' learning, the authors first estimate the gazing point by using the deep space–time neural network. Second, single shot multi-box detector and fast segmentation convolutional neural network are comparatively adopted to accurately detect the objects in the video. Third, they predict the gazing objects by combining the results of gazing point estimation and object detection. Finally, the personalized attention of students is analyzed based on the predicted gazing objects and the measurable eye movement criteria.

A large number of experiments are carried out on a public database and a new dataset that is built in a real classroom. The experimental results show that the proposed model not only can accurately track the students' gazing trajectory and effectively analyze the fluctuation of attention of the individual student and all students but also provide a valuable reference to evaluate the process of learning of students.

The contributions of this paper can be summarized as follows. The analysis of students' attention plays an important role in improving teaching quality and student achievement. However, there is little research on how to automatically and intelligently analyze students' attention. To alleviate this problem, this paper focuses on analyzing students' attention by gaze tracking and object detection in classroom teaching, which is significant for practical application in the field of education. The authors proposed an effectively intelligent fusion model based on the deep neural network, which mainly includes the gazing point module and the object detection module, to analyze students' attention in classroom teaching instead of relying on any smart wearable device. They introduce the attention mechanism into the gazing point module to improve the performance of gazing point detection and perform some comparison experiments on the public dataset to prove that the gazing point module can achieve better performance. They associate the eye movement criteria with visual gaze to get quantifiable objective data for students' attention analysis, which can provide a valuable basis to evaluate the learning process of students, provide useful learning information of students for both parents and teachers and support the development of individualized teaching. They built a new database that contains the first-person view videos of 11 subjects in a real classroom and employ it to evaluate the effectiveness and feasibility of the proposed model.

This paper aims to investigate the bonding of the photonic integrated circuit (PIC) chip with the heat sink using the AlNi self-propagating soldering method.

Compared to industrial optical modules, optical modules for aerospace applications require better reliability and stability, which is hard to achieve via the dispensing adhesive process that is used for traditional industrial optical modules. In this paper, 25 µm SAC305 solder foils and the AlNi nanofoil heat source were used to bond the back of the PIC chip with the heat sink. The temperature field and temperature history were analyzed by the finite element analysis (FEA) method. The junction-to-case thermal resistance is 0.0353°C/W and reduced by 85% compared with the UV hybrid epoxy joint.

The self-propagating reaction ends within 2.82 ms. The maximum temperature in the PIC operating area during the process is 368.5°C. The maximum heating and cooling rates of the solder were 1.39 × 107°C/s and −5.15 × 106°C/s, respectively. The microstructure of SAC305 under self-propagating reaction heating is more refined than the microstructure of SAC305 under reflow. The porosity of the heat sink-SAC305-PIC chip self-propagating joint is only 4.7%. Several metastable phases appear as AuSn3.4 and AgSn3.

A new bonding technology was used to form the bonding between the PIC chip with the heat sink for the aerospace optical module. The reliability and thermal resistance of the joint are better than that of the UV hybrid epoxy joint.

Autonomous mobile robots (AMRs) play a crucial role in industrial and service fields. The paper aims to build a LiDAR-based simultaneous localization and mapping (SLAM) system used by AMRs to overcome challenges in dynamic and changing environments.

This research introduces SLAM-RAMU, a lifelong SLAM system that addresses these challenges by providing precise and consistent relocalization and autonomous map updating (RAMU). During the mapping process, local odometry is obtained using iterative error state Kalman filtering, while back-end loop detection and global pose graph optimization are used for accurate trajectory correction. In addition, a fast point cloud segmentation module is incorporated to robustly distinguish between floor, walls and roof in the environment. The segmented point clouds are then used to generate a 2.5D grid map, with particular emphasis on floor detection to filter the prior map and eliminate dynamic artifacts. In the positioning process, an initial pose alignment method is designed, which combines 2D branch-and-bound search with 3D iterative closest point registration. This method ensures high accuracy even in scenes with similar characteristics. Subsequently, scan-to-map registration is performed using the segmented point cloud on the prior map. The system also includes a map updating module that takes into account historical point cloud segmentation results. It selectively incorporates or excludes new point cloud data to ensure consistent reflection of the real environment in the map.

The performance of the SLAM-RAMU system was evaluated in real-world environments and compared against state-of-the-art (SOTA) methods. The results demonstrate that SLAM-RAMU achieves higher mapping quality and relocalization accuracy and exhibits robustness against dynamic obstacles and environmental changes.

Compared to other SOTA methods in simulation and real environments, SLAM-RAMU showed higher mapping quality, faster initial aligning speed and higher repeated localization accuracy.

Few resources exist to incorporate principles of modular approach to course design. This research aimed to help instructors by presenting principles for practical and empirically informed modular course design in engineering education.

In the first phase, a systematic literature review was completed to identify categories addressing a modular course design. Search and screening procedures resulted in 33 qualifying articles describing the development of a modular course. In the second phase, 6 expert interviews were conducted to elaborate on the identified categories.

Guided by the interview results and the ADDIE (Analyze, Design, Develop, Implement, and Evaluate) course design model, the categories were compiled into six design principles. To present the design principles in relation to the guiding principles of modular approach, an overarching conceptual model was developed.

Here, we present our innovation; a foundation for an evidence-based systematic approach to modular course design. Implications have value for supporting flexibility and autonomy in learning.

The Open University (OU) in the UK has been providing distance learning since 1969. The purpose of this case study is to outline the impact that The OU Library in the UK has had on student learning outcomes by embedding academic literature and digital and information literacy (DIL) skills materials in the curriculum.

The case study presents an overview of the university context, including how the curriculum is developed. It discusses the role of the library in this process, outlining how librarians work with academic staff to embed skills and literature in the curriculum. Unique in-house technical solutions are presented to aid future approaches to providing distance library services.

The impact of the library on university education is discussed. Findings from qualitative research are presented, outlining the value the university places on the role of the library as an educational partner. Quantitative research studies are also presented, outlining the positive relationships between library content access and training attendance with student success.

As universities are considering their distance-learning offerings post-COVID-19, it is hoped that this case study will help both library and university administrators examine the role of their libraries in this strategy.

A case study on the approach The OU Library takes to support education in its broadest sense has not been published before.

There are insufficient health and nutrition education interventions targeting adolescent females in rural contexts in Indonesia. There is also a paucity of research evaluating the impact of implemented programs. This paper aims to develop and test the validity of a tailored education module to improve the knowledge, attitude and skills of adolescent females on health and nutrition.

The study was conducted between 2019 and 2023 in Ban Village, Karangasem Regency. This study used an explanatory sequential mixed methods research approach consisting of three stages: formative research using mixed methods, validation and review of the module by experts using the Delphi technique and pilot testing of the module. In the formative research stage, there were 40 female adolescent respondents implicated, in the validation and module review stage, there were 14 nutrition and public health experts implicated, and in the pilot test, a new cohort of 60 female adolescents were recruited. Validity was assessed by exploring the feasibility, reliability and linguistics of the module. The Delphi score was measured by the mean score and standard deviation.

The Health and Balanced Nutrition Education Module was impactful in improving the health and nutrition of female adolescents in Ban Village. The validation score of the module shows that from the total score of 4, construct reliability obtained a score of 3.18 with a 0.35 standard deviation. The construct feasibility and language revealed better scores, which were 3.31 with 0.4 standard deviations and 3.29 with 0.46 standard deviations, respectively. After dissemination of the module, participants’ mean score of knowledge on the importance of balanced nutrition significantly improved by 68.8% (p-value = 0.0001).

The Health and Balance Nutrition Education Module has been proven to improve the awareness of adolescents on balanced nutrition. Nevertheless, this study also has limitations due to the small number of respondents attending the information sessions and the pilot testing. Further studies should consider using implementation research for scale-up in other parts of Bali.

This study provides insight for health and nutrition educators for creating modules that better align with the context and information needs of the target group particularly for adolescents in rural areas, which are seldom neglected.

This study indicates that the trialed education materials can play a role in improving female adolescents’ knowledge of nutrition throughout their life cycle and their role in preventing stunting and noncommunicable diseases in later adult life.

The health and nutrition module trialed in the study is tailored specifically to the context of rural areas of Bali and validated by public health experts, then tested with 60 adolescents.