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The study explored how PSTs perceived a learning design, using a decision simulator, a self-reflection guide and a peer mentoring guide as tools to mediate reflections on approaches to the teacher’s role. The individual characteristics of PSTs were also considered, including role clarity, self-efficacy and affective commitment, as predictors of the learning design’s perceived relevance.

Informed by earlier research exploring the teacher’s role, a scenario-based simulation was supported by a tool for self-reflection on the results and a peer mentoring tool for the further development of an individual’s teaching role. Using structural equation modelling, the study assessed the statistical strength of the relationships between relevant factors to explore how a decision simulator and associated research-based tools were perceived by preservice teachers (PSTs).

The results indicate that regardless of PSTs’ individual characteristics, the decision simulator and associated peer mentoring tools have the potential to enhance learning and reflection. Therefore, the proposed approach can facilitate peer mentoring and enhance PSTs’ learning potential.

The paper explores the use of innovative approaches to mentoring by introducing peer mentoring grounded in experiences using a decision simulator.

This study aims to propose a methodology aimed at understanding the cognitive and physiological processes inherent in cadet pilot operations. Through analyzing responses from two cadet pilots with varied experience levels across diverse simulation scenarios, the research uses descriptive statistics, t-test, one-way ANOVA and percentage change analysis to explore crucial variables, including heart rate (HR), heart rate variability (HRV) and respiratory rate (RR).

The investigation meticulously examines HR, HRV and RR under circumstances encompassing resting state, visual flight rules and instrument flight rules with engine failure. Pilots undergo comprehensive analyses employing statistical techniques and visual representations to comprehend cognitive loads and physiological adaptations.

Significant disparities emerge between the two pilots, elucidating the profound impact of experience on cognitive and physiological outcomes. Novice cadet pilots exhibit heightened variability during scenario transitions, while experienced cadet pilot demonstrate controlled responses, indicative of adaptability. Visual flight simulations evoke distinct responses, whereas instrument-based scenarios, particularly those simulating emergencies, lead to pronounced physiological changes.

The findings of this research hold practical significance in introducing the proposed novel methodology for monitoring Cadet pilots to refine pilot training simulation protocols and enhance aviation safety by illuminating the interplay between experience levels and scenario complexities.

This study proposes a novel methodology for investigating cognitive and physiological responses in pilot operations, mainly investigating cadet pilots’ vital parameters through diverse analytical methods and an exploration of scenario-specific demands.

This article introduces SigBERT, a novel approach that fine-tunes bidirectional encoder representations from transformers (BERT) for the purpose of distinguishing between intact and impaired structures by analyzing vibration signals. Structural health monitoring (SHM) systems are crucial for identifying and locating damage in civil engineering structures. The proposed method aims to improve upon existing methods in terms of cost-effectiveness, accuracy and operational reliability.

SigBERT employs a fine-tuning process on the BERT model, leveraging its capabilities to effectively analyze time-series data from vibration signals to detect structural damage. This study compares SigBERT's performance with baseline models to demonstrate its superior accuracy and efficiency.

The experimental results, obtained through the Qatar University grandstand simulator, show that SigBERT outperforms existing models in terms of damage detection accuracy. The method is capable of handling environmental fluctuations and offers high reliability for non-destructive monitoring of structural health. The study mentions the quantifiable results of the study, such as achieving a 99% accuracy rate and an F-1 score of 0.99, to underline the effectiveness of the proposed model.

SigBERT presents a significant advancement in SHM by integrating deep learning with a robust transformer model. The method offers improved performance in both computational efficiency and diagnostic accuracy, making it suitable for real-world operational environments.

This study aims to tackle control challenges in soft robots by proposing a visually-guided reinforcement learning approach. Precise tip trajectory tracking is achieved for a soft arm manipulator.

A closed-loop control strategy uses deep learning-powered perception and model-free reinforcement learning. Visual feedback detects the arm’s tip while efficient policy search is conducted via interactive sample collection.

Physical experiments demonstrate a soft arm successfully transporting objects by learning coordinated actuation policies guided by visual observations, without analytical models.

Constraints potentially include simulator gaps and dynamical variations. Future work will focus on enhancing adaptation capabilities.

By eliminating assumptions on precise analytical models or instrumentation requirements, the proposed data-driven framework offers a practical solution for real-world control challenges in soft systems.

This research provides an effective methodology integrating robust machine perception and learning for intelligent autonomous control of soft robots with complex morphologies.

This study aims to address the issue of random movement and non coordination between docking mechanisms and locking mechanisms, and proposes a comprehensive dynamic docking control architecture that integrates perception, planning, and motion control.

Firstly, the proposed dynamic docking control architecture uses laser sensors and a charge-coupled device camera to perceive the pose of the target. The sensor data are mapped to a high-dimensional potential field space and fused to reduce interference caused by detection noise. Next, a new potential function based on multi-dimensional space is developed for docking path planning, which enables the docking mechanism based on Stewart platform to rapidly converge to the target axis of the locking mechanism, which improves the adaptability and terminal docking accuracy of the docking state. Finally, to achieve precise tracking and flexible docking in the final stage, the system combines a self-impedance controller and an impedance control algorithm based on the planned trajectory.

Extensive simulations and experiments have been conducted to validate the effectiveness of the dynamic docking system and its control architecture. The results indicate that even if the target moves randomly, the system can successfully achieve accurate, stable and flexible dynamic docking.

This research can provide technical guidance and reference for docking task of unmanned vehicles under the ground conditions. It can also provide ideas for space docking missions, such as space simulator docking.

Parallelogram linkages are used to increase the stiffness of manipulators and allow precise control of end-effectors. They help maintain the orientation of connected links when the manipulator changes its position. They are implemented in many palletizing robots connected with binary, ternary and quaternary links through both active and passive joints. This limits the motion of some joints and hence results in relative and negative joint angles when assigning coordinate axes. This study aims to provide a simplified accurate model for manipulators built with parllelogram linkages to ease the kinematics calculations.

This study introduces a simplified model, replacing each parallelogram linkage with a single (binary) link with an active and a passive joint at the ends. This replacement facilitates countering motion while preserving subsequent link orientations. Validation of kinematics is performed on palletizing manipulators from five different OEMs. The validation of Dobot Magician and ABB IRB1410 was carried out in real time and in their control software. Other robots from ABB, Yaskawa, Kuka and Fanuc were validated using control environments and simulators.

The proposed model enables the straightforward derivation of forward kinematics and transforms hybrid robots into equivalent serial-link robots. The model demonstrates high accuracy streamlining the derivation of kinematics.

The proposed model facilitates the use of classical methods like the Denavit–Hartenberg procedure with ease. It not only simplifies kinematics derivation but it also helps in robot control and motion planning within the workspace. The approach can also be implemented to simplify the parallelogram linkages of robots with higher degrees of freedom such as the IRB1410.

Authorities have set up numerous security checkpoints during times of armed conflict to control the flow of commercial and humanitarian trucks into and out of areas of conflict. These security checkpoints have become highly utilized because of the complex security procedures and increased truck traffic, which significantly slow the delivery of relief aid. This paper aims to improve the process at security checkpoints by redesigning the current process to reduce processing time and relieve congestion at checkpoint entrance gates.

A decision-support tool (clearing function distribution model [CFDM]) is used to minimize the effects of security checkpoint congestion on the entire humanitarian supply network using a hybrid simulation-optimization approach. By using a business process simulation, the current and reengineered processes are both simulated, and the simulation output was used to estimate the clearing function (capacity as a function of the workload). For both the AS-IS and TO-BE models, key performance indicators such as distribution costs, backordering and process cycle time were used to compare the results of the CFDM tool. For this, the Kerem Abu Salem security checkpoint south of Gaza was used as a case study.

The comparison results demonstrate that the CFDM tool performs better when the output of the TO-BE clearing function is used.

The efforts will contribute to improving the planning of any humanitarian network experiencing congestion at security checkpoints by minimizing the impact of congestion on the delivery lead time of relief aid to the final destination.

The purpose of this paper is to enhance the performance of robots in peg-in-hole assembly tasks, enabling them to swiftly and robustly accomplish the task. It also focuses on the robot’s ability to generalize across assemblies with different hole sizes.

Human behavior in peg-in-hole assembly serves as inspiration, where individuals visually locate the hole firstly and then continuously adjust the peg pose based on force/torque feedback during the insertion process. This paper proposes a novel framework that integrate visual servo and adjustment based on force/torque feedback, the authors use deep neural network (DNN) and image processing techniques to determine the pose of hole, then an incremental learning approach based on a broad learning system (BLS) is used to simulate human learning ability, the number of adjustments required for insertion process is continuously reduced.

The author conducted experiments on visual servo, adjustment based on force/torque feedback, and the proposed framework. Visual servo inferred the pixel position and orientation of the target hole in only about 0.12 s, and the robot achieved peg insertion with 1–3 adjustments based on force/torque feedback. The success rate for peg-in-hole assembly using the proposed framework was 100%. These results proved the effectiveness of the proposed framework.

This paper proposes a framework for peg-in-hole assembly that combines visual servo and adjustment based on force/torque feedback. The assembly tasks are accomplished using DNN, image processing and BLS. To the best of the authors’ knowledge, no similar methods were found in other people’s work. Therefore, the authors believe that this work is original.

Rural indigenous schools in Malaysia can be far from equipped with facilities and conducive learning environments, especially in schools serving a large and diverse community. SK RPS (Rancangan Penempatan Semula/Resettlement Programme) Banun is an all-indigenous school located in the interiors of Gerik, Perak in Malaysia. Being the only school in the vicinity of an Orang Asli settlement comprising 18 villages of the Je hai and Temiar tribes, this public school is said to be a one stop hub for the community. Despite the school's existence, there are several challenges which hinders children of the Orang Asli community to attain a quality education or in some cases to even go to school. This paper is written based on the lived experiences of three teachers from mid-2022 to mid-2023 and amplifies the day-to-day challenges of the school and its community. The paper also discusses the initiatives implemented to tackle some of the challenges, highlighting key successes and suggestions to improve some areas particularly in enhancing and reimagining pedagogical approaches as well as learning environments in Orang Asli schools in Malaysia.

The purpose of this study is to investigate the factors facilitating and influencing the adoption of DevOps practices specifically tailored to mobile software development, with a focus on understanding the influence of mobile-specific requirements on DevOps integration.

The study employs a qualitative methodology, including a literature review, exploratory case research and partial quantitative assessments through DORA metrics and survey applications. This approach, guided by the Technology-Organization-Environment (TOE) framework, prioritizes in-depth insights into the adoption of DevOps practices and explores strategies for integrating DevOps in mobile software development.

The research identifies several key themes specific to Mobile DevOps adoption, including tool integration issues, testing complexities, deployment challenges and security concerns. These findings underscore the necessity for tailored DevOps solutions that can effectively address the unique demands of mobile software development. The study also proposes actionable strategies to overcome these challenges, thereby enhancing the efficiency, quality and security of mobile applications.

The insights gained from this study provide valuable guidance for practitioners in the mobile software development sector. By understanding and addressing the specific challenges of Mobile DevOps, organizations can improve their DevOps practices and achieve better outcomes in terms of project delivery speed, quality and security. For example, implementing robust testing strategies, investing in compatible tools and developing well-defined rollback procedures can significantly enhance Mobile DevOps effectiveness. Furthermore, incorporating continuous security measures and improving cross-functional collaboration can lead to more secure and efficient mobile application deployments.

This study offers valuable starting points for researching Mobile DevOps in real-world settings, based on insights from practical DevOps implementations in a single-case organization. Organizations can use this information to compare their own DevOps approaches with those of the studied organization, and can facilitate self-assessment and improvement.

This study contributes to the limited literature on Mobile DevOps adoption and proposing actionable strategies. By incorporating the TOE framework, it provides a comprehensive guide that enhances understanding and management of DevOps practices throughout the mobile application development lifecycle and offers significant value to practitioners and researchers alike.