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In a dual-robot system, the relative position error is a superposition of errors from each mono-robot, resulting in deteriorated coordination accuracy. This study aims to enhance relative accuracy of the dual-robot system through direct compensation of relative errors. To achieve this, a novel calibration-driven transfer learning method is proposed for relative error prediction in dual-robot systems.

A novel local product of exponential (POE) model with minimal parameters is proposed for error modeling. And a two-step method is presented to identify both geometric and nongeometric parameters for the mono-robots. Using the identified parameters, two calibrated models are established and combined as one dual-robot model, generating error data between the nominal and calibrated models’ outputs. Subsequently, the calibration-driven transfer, involving pretraining a neural network with sufficient generated error data and fine-tuning with a small measured data set, is introduced, enabling knowledge transfer and thereby obtaining a high-precision relative error predictor.

Experimental validation is conducted, and the results demonstrate that the proposed method has reduced the maximum and average relative errors by 45.1% and 30.6% compared with the calibrated model, yielding the values of 0.594 mm and 0.255 mm, respectively.

First, the proposed calibration-driven transfer method innovatively adopts the calibrated model as a data generator to address the issue of real data scarcity. It achieves high-accuracy relative error prediction with only a small measured data set, significantly enhancing error compensation efficiency. Second, the proposed local POE model achieves model minimality without the need for complex redundant parameter partitioning operations, ensuring stability and robustness in parameter identification.

The brake controller is a key component of the locomotive brake system. It is essential to study its safety.

This paper summarizes and analyzes typical faults of the brake controller, and proposes four categories of faults: position sensor faults, microswitch faults, mechanical faults and communication faults. Suggestions and methods for improving the safety of the brake controller are also presented.

In this paper, a self-judgment and self-learning dynamic calibration method is proposed, which integrates the linear error of the sensor and the manufacturing and assembly errors of the brake controller to solve the output drift. This paper also proposes a logic for diagnosing and handling microswitch faults. Suggestions are proposed for other faults of brake controller.

The methods proposed in this paper can greatly improve the usability of the brake controller and reduce the failure rate.

In recent years, additive manufacturing (AM) has started to be used for manufacturing real functional parts and assemblies for critical applications in aerospace, automotive, and machinery industries. Most complex or assembled parts require internal features (IF) such as holes, channels, slots, or guides for locational and mating requirements. Therefore, it is critical to understand and compare the structural and mechanical properties of additively manufactured and conventionally machined IFs.

In this study, mechanical and microstructural properties of Inconel 718 (Inc718) alloy internal features, manufactured either as-built with AM or machining of additively manufactured (AMed) part thereafter were investigated.

The results showed that the average ultimate tensile strength (UTS) of additively manufactured center internal feature (AM-IF) is almost analogous to the machined internal feature (M-IF). However, the yield strength of M-IF is greater than that of AM-IF due the greater surface roughness of the internal feature in AM-IF, which is deemed to surpass the effect of microstructure on the mechanical performance. The results of digital image correlation (DIC) analysis suggest that AM-IF and M-IF conditions have similar strain values under the same stress levels but the specimens with as built IF have a more locally ductile region around their IF, which is confirmed by hardness test results. But this does not change global elongation behavior. The microstructural evolution starting from as-built (AB) and heat-treated (HT) samples to specimens with IF are examined. The microstructure of HT specimens has bimodal grain structure with d phase while the AB specimens display a very fine dendritic microstructure with the presence of carbides. Although they both have close values, machined specimens have a higher frequency of finer grains based on SEM images.

It was shown that the concurrent creation of the IF during AM can provide a final part with a preserved ultimate tensile strength and elongation but a decreased yield strength. The variation in UTS of AM-IF increases due to the surface roughness near the internal feature as compared to smooth internal surfaces in M-IF. Hence, the outcomes of this study are believed to be valuable for the industry in terms of determining the appropriate production strategy of parts with IF using AM and postprocessing processes.

This study experimentally aims to determine the degree of influence that mindfulness training exerts on learning capacity at the university level and contrasts it with previous observational or relational studies that have shown contradictory results.

A quasi-experiment was carried out to measure the variation of six academic learning abilities – a) self-efficacy, b) organization of and attention to studies, c) stress control due to time pressure and the environment, d) involvement with college activity, e) emotional satisfaction, and f) class communication – which together comprise the research questionnaire called the college learning effectiveness inventory (CLEI). The CLEI questionnaire was administered before and after the participants were trained in the mindfulness technique. The study was conducted in Ecuador, and the participants were selected from among the graduate students of a local university.

The learning ability measured by the CLEI was improved by a statistically significant margin in the two groups.

The treatment groups consisted of graduate students who did not have opportunities for full-time activities on campus, as they were limited to attending regular classes at specific times, usually at night. The dropout rate was 14% due to inconveniences caused by the pandemic. These conditions could have affected the study results both positively and negatively. In addition, the pandemic limited academic interactions, which are required to evaluate the learning results after applying the research instrument. This limitation was especially critical for people who had experienced online classes only.

Offering graduate students the opportunity to learn about and adopt a mindfulness practice helps to improve their academic outcomes, as reflected through the statistical measurement of the CLEI indicator.

This study is especially relevant within the context of sanitary conditions due to the pandemic and the intensive use of technology for managing academic interactions, both of which have replaced physical contact between participants.

The main contributions of this study are related to the determination of the practical effects of mindfulness training in postgraduate university settings and the identification of the mechanisms involving participants' reflecting upon, learning and understanding the importance of perfecting their soft skills to facilitate their learning processes and face today's uncertain environments.

本研究採用實驗方法、來釐定正念認知對在大學水平上的學習能力的影響程度，並對比於以往顯示了相互矛盾的研究結果的觀察性研究或關係研究。

研究人員進行準實驗、來量度六個學術學習能力的變化。這六個學習能力為：(一) 自我效能, (二) 學習的組織能力和注意力，(三) 控制因時間壓力和環境因素而產生的壓力, (四) 大學活動的參與，(五) 情緒方面的滿足、及 (六) 課堂之溝通。這六個學習能力合起來就構成本研究的調查問卷，我們稱之為大學學習效能清單。問卷調查工作分別於研究參與者接受正念認知技巧訓練之前及之後進行。本研究是在厄瓜多爾進行的，而參與者則選自當地一間大學的研究生。

研究結果顯示、根據大學學習效能清單的測量，有關的兩個組別均顯示明顯數據差額範圍上的學習能力提昇。

若為研究生提供學習正念認知技巧的機會，並讓他們應用這技巧，他們的學習成果將會得到提昇，這正是透過數據上量度有關的大學學習效能清單指標所顯示的現象。

本研究的結果，就現時的衛生狀況而言特具意義。這是由於大流行病的發生，以及在管理學術互動上大量使用應用技術，參與者之間的身體接觸也不需要了。

本研究的主要貢獻為、研究結果確認了在大學研究院的學習環境裡，正念認知的培訓是有其實用效果的;研究亦找出了參與者應如何有效地反省思考和學習提昇其軟技能的方法，並了解其重要性，以能增強他們的學習能力，以及更能應對今天不明確的環境。

The purpose of this study is to establish a method for accurately extracting torch and seam features. This will improve the quality of narrow gap welding. An adaptive deflection correction system based on passive light vision sensors was designed using the Halcon software from MVtec Germany as a platform.

This paper proposes an adaptive correction system for welding guns and seams divided into image calibration and feature extraction. In the image calibration method, the field of view distortion because of the position of the camera is resolved using image calibration techniques. In the feature extraction method, clear features of the weld gun and weld seam are accurately extracted after processing using algorithms such as impact filtering, subpixel (XLD), Gaussian Laplacian and sense region for the weld gun and weld seam. The gun and weld seam centers are accurately fitted using least squares. After calculating the deviation values, the error values are monitored, and error correction is achieved by programmable logic controller (PLC) control. Finally, experimental verification and analysis of the tracking errors are carried out.

The results show that the system achieves great results in dealing with camera aberrations. Weld gun features can be effectively and accurately identified. The difference between a scratch and a weld is effectively distinguished. The system accurately detects the center features of the torch and weld and controls the correction error to within 0.3mm.

An adaptive correction system based on a passive light vision sensor is designed which corrects the field-of-view distortion caused by the camera’s position deviation. Differences in features between scratches and welds are distinguished, and image features are effectively extracted. The final system weld error is controlled to 0.3 mm.

This research paper aims to optimize the calibration process for an ABB IRB 120 robot, specifically for robotic orbital milling applications, by introducing and validating the use of the observability index and telescopic ballbar for accuracy enhancement.

The study uses the telescopic ballbar and an observability index for the calibration of an ABB IRB 120 robot, focusing on robotic orbital milling. Comparative simulation analysis selects the O₃ index. Experimental tests, both static and dynamic, evaluate the proposed calibration approach within the robot’s workspace.

The proposed calibration approach significantly reduces circularity errors, particularly in robotic orbital milling, showcasing effectiveness in both static and dynamic modes at various tool center point speeds.

The study focuses on a specific robot model and application (robotic orbital milling), limiting generalizability. Further research could explore diverse robot models and applications.

The findings offer practical benefits by enhancing the accuracy of robotic systems, particularly in precision tasks like orbital milling, providing a valuable calibration method.

While primarily technological, improved robotic precision can have social implications, potentially influencing fields where robotic applications are crucial, such as manufacturing and automation.

This study’s distinctiveness lies in advancing the accuracy and precision of industrial robots during circular motions, specifically tailored for orbital milling applications. The innovative approach synergistically uses the observability index and telescopic ballbar to achieve these objectives.

The purpose of this paper is to propose a system dynamic simulated process model for maintenance work management incorporating the Fourth Industrial Revolution (4IR) technologies.

The extant literature in physical assets maintenance depicts that poor maintenance management is predominantly because of a lack of a clearly defined maintenance work management process model, resulting in poor management of maintenance work. This paper solves this complex phenomenon using a combination of conceptual process modeling and system dynamics simulation incorporating 4IR technologies. A process for maintenance work management and its control actions on scheduled maintenance tasks versus unscheduled maintenance tasks is modeled, replicating real-world scenarios with a digital lens (4IR technologies) for predictive maintenance strategy.

A process for maintenance work management is thus modeled and simulated as a dynamic system. Post-model validation, this study reveals that the real-world maintenance work management process can be replicated using system dynamics modeling. The impact analysis of 4IR technologies on maintenance work management systems reveals that the implementation of 4IR technologies intensifies asset performance with an overall gain of 27.46%, yielding the best maintenance index. This study further reveals that the benefits of 4IR technologies positively impact equipment defect predictability before failure, thereby yielding a predictive maintenance strategy.

The study focused on maintenance work management system without the consideration of other subsystems such as cost of maintenance, production dynamics, and supply chain management.

The maintenance real-world quantitative data is retrieved from two maintenance departments from company A, for a period of 24 months, representing years 2017 and 2018. The maintenance quantitative data retrieved represent six various types of equipment used at underground Mines. The maintenance management qualitative data (Organizational documents) in maintenance management are retrieved from company A and company B. Company A is a global mining industry, and company B is a global manufacturing industry. The reliability of the data used in the model validation have practical implications on how maintenance work management system behaves with the benefit of 4IR technologies' implementation.

This research study yields an overall benefit in asset management, thereby intensifying asset performance. The expected learnings are intended to benefit future research in the physical asset management field of study and most important to the industry practitioners in physical asset management.

This paper provides for a model in which maintenance work and its dynamics is systematically managed. Uncontrollable corrective maintenance work increases the complexity of the overall maintenance work management. The use of a system dynamic model and simulation incorporating 4IR technologies adds value on the maintenance work management effectiveness.

This article aims to explore the so-called illusion of explanatory depth (IOED) of managers regarding their understanding of digital technologies and examines the effect of knowledge visualization one’s current understanding and decision making. Its purpose is to show that managers think they know more than they do and that this affects decision making but can be reduced through knowledge visualization.

In two experiments with experienced managers, the authors investigate the size and impact of the IOED bias in decision making and examine if sketched self-explanations are as effective as written self-explanations to reduce the bias.

The findings show that experienced managers suffer from a significant illusion concerning their explanatory understanding of digital technologies and that sketching one’s current level of explanatory understanding of these technologies supports the accurate calibration of one’s knowledge. The findings indicate that sketching knowledge is a helpful modality for the detection and subsequent recalibration of biased knowledge in domain-dependent decision making.

This article is the first to explore the effect of sketched knowledge externalization on the calibration of explanatory knowledge of managers. It extends the literature on both, the IOED and on knowledge visualization as an instrument of knowledge calibration.

In recent days, there has been an increasing interest towards achieving sustainable tourism objectives globally and specifically in Saudi Arabia. The benefits can be maximized if the government is successful in attracting current pilgrims and influence their future intention to revisit the country as tourists. Hence, the purpose of this paper is to measure pilgrims’ revisit intentions to understand more about the possibility of their potential contribution towards the Saudi tourism and hospitality industry in the evolving circumstances.

This paper uses configuration theory to identify the “ideal” type of the pilgrims and compares this to the rest to establish if they differ and if that difference matters. Data were collected from 278 visitors to the Holy Mosque in Makkah, Saudi Arabia, to perform Umrah.

The findings show that a large deviation from the “ideal pilgrim” is negatively related to revisiting intentions and dissemination of positive word of mouth (PWOM).

The development of profiles gives a better understanding of organizations or people across several dimensions looked at holistically. Fundamental to the theory is that there are only a limited number of configurations that achieve optimal performance (however defined).

The analytical approach adopted in this paper leads to achieving verbal and statistical correspondence in tests of “gestalts”. The interest is in establishing whether this difference matters to intentions to revisit and providing PWOM.

The aim of the paper is to propose a global, automated and continuous curvature calibration strategy for bending sensors, which is used for the angle feedback control of soft fingers.

In this work, the proposed curvature calibration strategy for bending sensors is based on the constant curvature bending properties of soft fingers. The strategy is to install the bending sensor on the soft finger and use the laser distance sensor to assist calibration, then calculate the relationship between the curvature and the voltage of the bending sensor through geometric conversion. In addition, this work also develops a full set of standard calibration systems and collection procedures for the bending sensor curvature calibration and uses machine learning algorithms to fit the collected data.

First, compared with the traditional calibration methods, the proposed curvature calibration strategy can achieve constant curvature measurement with the advantages of better continuity. Second, using the sensor data obtained by the proposed calibration method as the feedback signal for the soft finger bending angle control, the control effect is better than that of the traditional method.

This work proposes and verifies a global, automated and continuous curvature calibration strategy for bending sensors and is used for the angle feedback control of soft fingers. In addition, this work also develops a full set of standard calibration systems and collection procedures, which can be applied to a variety of flexible bending sensors with a good adaptability.