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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.

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.

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.

The purpose of this paper is to develop a prototype of a wearable medical device in the form of a bandage with a real-time data monitoring platform, which can be used domestically for diabetic patients to identify the possibility of foot ulceration at the early stage.

The prototype can measure blood volumetric change and temperature variation in the forefoot area simultaneously. The waveform extracted using a pulsatile-blood-flow signal was used to assess blood perfusion-related information, and hence, predict ischemic ulcers. The temperature difference between ulcerated and the reference was used to predict neuropathic ulcers. The medical device can be used as a bandage during the application wherein the sensory module is placed inside the hollow pocket of the bandage. A platform was developed through a mobile application where doctors can extract real-time information, and hence, determine the possibility of ulceration.

The height of the peaks in the pulsatile-blood-flow signal measured from the subject with foot ischemic ulcers is significantly less than that of the subject without ischemic ulcers. In the presence of ischemic ulcers, the captured waveform flattens. Therefore, the blood perfusion from arteries to the tissue of the forefoot is considerably low for the subject with ischemic ulcers. According to the temperature difference data measured over 25 consecutive days, the temperature difference of the subject with neuropathic ulcers occasionally exceeded the 4 °F range but mostly had higher values closer to the 4 °F range. However, the temperature difference of the subject who had no complications of neuropathic ulcers did not exceed the 4 °F range, and the majority of the measurements occupy a narrow range from −2°F to 2 °F.

The proposed prototype of wearable medical apparatus can monitor both temperature variation and pulsatile-blood-flow signal on the forefoot simultaneously and thereby predict both ischemic and neuropathic diabetes using a single device. Most importantly, the wearable medical device can be used domestically without clinical assistance with a real-time data monitoring platform to predict the possibility of ulceration and the course of action thereof.

The sustainability of the global food system hinges on its environmental resiliency and safety, including the health and well-being of its labor force. Single disciplinary courses in liberal arts or science often fail to highlight the overlap between environmental and social vulnerabilities that lead to food insecurity and diminish the sustainability of food systems. This paper aims to present the design and delivery of a successfully co-taught, interdisciplinary module on agricultural labor and sustainable food systems as a case study.

The authors designed a co-taught module in which they joined each other’s respective history and science class sessions at the undergraduate business college where they teach. Innovating the cross-disciplinary content of food security, immigration status, labor exploitation and pesticide exposure, they approached sustainability from the disciplinary perspectives of labor history and environmental science to show how these elements had both unique and overlapping impacts across food systems levels. Comparisons between pre- and post-module survey responses, alongside assessments of a co-authored exam question, measured the effectiveness of this module is changing students’ perspectives as food consumers and as citizens.

This module altered students’ understanding and perspectives around issues of food systems sustainability. Assessments indicated that students increased their awareness of agricultural workers at the front end of the food system, during production; students also gained awareness beyond consumption as they came to see the connections between workforce invisibility and ecosystem degradation.

These insights are valuable to educators at all institutional levels who seek to collaborate on sustainability initiatives and teaching, both in the singular, robust modules and in building modules that will lead to the development of entire courses focused on sustainability. The module described here builds on previous demonstrations of the value, significance and effectiveness of cross-disciplinary collaborations; it pioneers the use of the food system as the link between social and environmental sustainability education.

Classification of remote sensing images (RSI) is a challenging task in computer vision. Recently, researchers have proposed a variety of creative methods for automatic recognition of RSI, and feature fusion is a research hotspot for its great potential to boost performance. However, RSI has a unique imaging condition and cluttered scenes with complicated backgrounds. This larger difference from nature images has made the previous feature fusion methods present insignificant performance improvements.

This work proposed a two-convolutional neural network (CNN) fusion method named main and branch CNN fusion network (MBC-Net) as an improved solution for classifying RSI. In detail, the MBC-Net employs an EfficientNet-B3 as its main CNN stream and an EfficientNet-B0 as a branch, named MC-B3 and BC-B0, respectively. In particular, MBC-Net includes a long-range derivation (LRD) module, which is specially designed to learn the dependence of different features. Meanwhile, MBC-Net also uses some unique ideas to tackle the problems coming from the two-CNN fusion and the inherent nature of RSI.

Extensive experiments on three RSI sets prove that MBC-Net outperforms the other 38 state-of-the-art (STOA) methods published from 2020 to 2023, with a noticeable increase in overall accuracy (OA) values. MBC-Net not only presents a 0.7% increased OA value on the most confusing NWPU set but also has 62% fewer parameters compared to the leading approach that ranks first in the literature.

MBC-Net is a more effective and efficient feature fusion approach compared to other STOA methods in the literature. Given the visualizations of grad class activation mapping (Grad-CAM), it reveals that MBC-Net can learn the long-range dependence of features that a single CNN cannot. Based on the tendency stochastic neighbor embedding (t-SNE) results, it demonstrates that the feature representation of MBC-Net is more effective than other methods. In addition, the ablation tests indicate that MBC-Net is effective and efficient for fusing features from two CNNs.