Hasil untuk "Industrial psychology"

Pengfei Yue, Xiaokang Jiang, Yilin Lu et al.

Industrial Anomaly Detection (IAD) is vital for manufacturing, yet traditional methods face significant challenges: unsupervised approaches yield rough localizations requiring manual thresholds, while supervised methods overfit due to scarce, imbalanced data. Both suffer from the "One Anomaly Class, One Model" limitation. To address this, we propose Referring Industrial Anomaly Segmentation (RIAS), a paradigm leveraging language to guide detection. RIAS generates precise masks from text descriptions without manual thresholds and uses universal prompts to detect diverse anomalies with a single model. We introduce the MVTec-Ref dataset to support this, designed with diverse referring expressions and focusing on anomaly patterns, notably with 95% small anomalies. We also propose the Dual Query Token with Mask Group Transformer (DQFormer) benchmark, enhanced by Language-Gated Multi-Level Aggregation (LMA) to improve multi-scale segmentation. Unlike traditional methods using redundant queries, DQFormer employs only "Anomaly" and "Background" tokens for efficient visual-textual integration. Experiments demonstrate RIAS's effectiveness in advancing IAD toward open-set capabilities. Code: https://github.com/swagger-coder/RIAS-MVTec-Ref.

Sid Terason, Pirayut Pattanayanon, Chaithanaskorn Phawitpiriyakliti

This study investigates the impact of digital brand interaction on brand loyalty in Thailand’s automotive consumers, focusing on the mediating role of customer relationship quality. It hypothesizes that different types of digital brand interaction—information-based, interaction-based, and service-based—have varying effects on brand loyalty and customer relationship quality. The participants were 605 car owners from Bangkok, who responded to a structured questionnaire. Employing structural equation modeling, this research analyzed how these types of digital brand interaction influence brand loyalty through customer relationship quality. The digital brand interaction was categorized into three types: information-based interaction involving passive content delivery, interaction-based interaction that facilitate two-way communication, and service-based interaction providing customer support and services. The outcome measures focused on the perceived customer relationship quality and brand loyalty as influenced by these types of interaction. The findings revealed that information-based interaction had a negligible effect on both customer relationship quality and brand loyalty, indicating that mere provision of information is insufficient to foster loyalty. Conversely, interaction-based interaction significantly enhanced customer relationship quality and, subsequently, brand loyalty. Service-based interaction also positively impacted these variables but to a lesser extent than interactive methods, underscoring the importance of emotional connections facilitated by digital brand interaction. Conclusively, the study suggests that the effectiveness of digital brand interaction in enhancing brand loyalty significantly relies on their capacity to improve customer relationship quality. These insights are crucial for marketers in the automotive industry aiming at leveraging digital platforms for more effective customer engagement and retention strategies in a competitive market.

Tianle Yang, Luyao Chang, Jiadong Yan et al.

As industrial products become abundant and sophisticated, visual industrial defect detection receives much attention, including two-dimensional and three-dimensional visual feature modeling. Traditional methods use statistical analysis, abnormal data synthesis modeling, and generation-based models to separate product defect features and complete defect detection. Recently, the emergence of foundation models has brought visual and textual semantic prior knowledge. Many methods are based on foundation models (FM) to improve the accuracy of detection, but at the same time, increase model complexity and slow down inference speed. Some FM-based methods have begun to explore lightweight modeling ways, which have gradually attracted attention and deserve to be systematically analyzed. In this paper, we conduct a systematic survey with comparisons and discussions of foundation model methods from different aspects and briefly review non-foundation model (NFM) methods recently published. Furthermore, we discuss the differences between FM and NFM methods from training objectives, model structure and scale, model performance, and potential directions for future exploration. Through comparison, we find FM methods are more suitable for few-shot and zero-shot learning, which are more in line with actual industrial application scenarios and worthy of in-depth research.

Bowen Zheng

To address the challenges of 3D modeling and structural simulation in industrial environment, such as the difficulty of equipment deployment, and the difficulty of balancing accuracy and real-time performance, this paper proposes an integrated workflow, which integrates high-fidelity 3D reconstruction based on monocular video, finite element simulation analysis, and mixed reality visual display, aiming to build an interactive digital twin system for industrial inspection, equipment maintenance and other scenes. Firstly, the Neuralangelo algorithm based on deep learning is used to reconstruct the 3D mesh model with rich details from the surround-shot video. Then, the QuadRemesh tool of Rhino is used to optimize the initial triangular mesh and generate a structured mesh suitable for finite element analysis. The optimized mesh is further discretized by HyperMesh, and the material parameter setting and stress simulation are carried out in Abaqus to obtain high-precision stress and deformation results. Finally, combined with Unity and Vuforia engine, the real-time superposition and interactive operation of simulation results in the augmented reality environment are realized, which improves users 'intuitive understanding of structural response. Experiments show that the method has good simulation efficiency and visualization effect while maintaining high geometric accuracy. It provides a practical solution for digital modeling, mechanical analysis and interactive display in complex industrial scenes, and lays a foundation for the deep integration of digital twin and mixed reality technology in industrial applications.

Francesco Aurelio Pironti, Angelo Furfaro, Francesco Blefari et al.

The security of Industrial Control Systems (ICSs) is critical to ensuring the safety of industrial processes and personnel. The rapid adoption of Industrial Internet of Things (IIoT) technologies has expanded system functionality but also increased the attack surface, exposing ICSs to a growing range of cyber threats. Honeypots provide a means to detect and analyze such threats by emulating target systems and capturing attacker behavior. However, traditional ICS honeypots, often limited to software-based simulations of a single Programmable Logic Controller (PLC), lack the realism required to engage sophisticated adversaries. In this work, we introduce a modular honeynet framework named ICSLure. The framework has been designed to emulate realistic ICS environments. Our approach integrates physical PLCs interacting with live data sources via industrial protocols such as Modbus and Profinet RTU, along with virtualized network components including routers, switches, and Remote Terminal Units (RTUs). The system incorporates comprehensive monitoring capabilities to collect detailed logs of attacker interactions. We demonstrate that our framework enables coherent and high-fidelity emulation of real-world industrial plants. This high-interaction environment significantly enhances the quality of threat data collected and supports advanced analysis of ICS-specific attack strategies, contributing to more effective detection and mitigation techniques.

Jannick Stranghöner, Philipp Hartmann, Marco Braun et al.

High-mix low-volume (HMLV) industrial assembly, common in small and medium-sized enterprises (SMEs), requires the same precision, safety, and reliability as high-volume automation while remaining flexible to product variation and environmental uncertainty. Current robotic systems struggle to meet these demands. Manual programming is brittle and costly to adapt, while learning-based methods suffer from poor sample efficiency and unsafe exploration in contact-rich tasks. To address this, we present SHaRe-RL, a reinforcement learning framework that leverages multiple sources of prior knowledge. By (i) structuring skills into manipulation primitives, (ii) incorporating human demonstrations and online corrections, and (iii) bounding interaction forces with per-axis compliance, SHaRe-RL enables efficient and safe online learning for long-horizon, contact-rich industrial assembly tasks. Experiments on the insertion of industrial Harting connector modules with 0.2-0.4 mm clearance demonstrate that SHaRe-RL achieves reliable performance within practical time budgets. Our results show that process expertise, without requiring robotics or RL knowledge, can meaningfully contribute to learning, enabling safer, more robust, and more economically viable deployment of RL for industrial assembly.

Parul Khanna, Sameer Prabhu, Ramin Karim et al.

The construction industry is presently going through a transformation led by adopting digital technologies that leverage Artificial Intelligence (AI). These industrial AI solutions assist in various phases of the construction process, including planning, design, production and management. In particular, the production phase offers unique potential for the integration of such AI-based solutions. These AI-based solutions assist site managers, project engineers, coordinators and other key roles in making final decisions. To facilitate the decision-making process in the production phase of construction through a human-centric AI-based solution, it is important to understand the needs and challenges faced by the end users who interact with these AI-based solutions to enhance the effectiveness and usability of these systems. Without this understanding, the potential usage of these AI-based solutions may be limited. Hence, the purpose of this research study is to explore, identify and describe the key factors crucial for developing AI solutions in the construction industry. This study further identifies the correlation between these key factors. This was done by developing a demonstrator and collecting quantifiable feedback through a questionnaire targeting the end users, such as site managers and construction professionals. This research study will offer insights into developing and improving these industrial AI solutions, focusing on Human-System Interaction aspects to enhance decision support, usability, and overall AI solution adoption.

Andrea Bussolan, Stefano Baraldo, Oliver Avram et al.

Human-robot collaboration (HRC) is a key focus of Industry 5.0, aiming to enhance worker productivity while ensuring well-being. The ability to perceive human psycho-physical states, such as stress and cognitive load, is crucial for adaptive and human-aware robotics. This paper introduces MultiPhysio-HRC, a multimodal dataset containing physiological, audio, and facial data collected during real-world HRC scenarios. The dataset includes electroencephalography (EEG), electrocardiography (ECG), electrodermal activity (EDA), respiration (RESP), electromyography (EMG), voice recordings, and facial action units. The dataset integrates controlled cognitive tasks, immersive virtual reality experiences, and industrial disassembly activities performed manually and with robotic assistance, to capture a holistic view of the participants' mental states. Rich ground truth annotations were obtained using validated psychological self-assessment questionnaires. Baseline models were evaluated for stress and cognitive load classification, demonstrating the dataset's potential for affective computing and human-aware robotics research. MultiPhysio-HRC is publicly available to support research in human-centered automation, workplace well-being, and intelligent robotic systems.

Oscar Adamuz-Hinojosa, Felix Delgado-Ferro, Núria Domènech et al.

The digital transformation driven by Industry 4.0 relies on networks that support diverse traffic types with strict deterministic end-to-end latency and mobility requirements. To meet these requirements, future industrial automation networks will use time-sensitive networking, integrating 5G as wireless access points to connect production lines with time-sensitive networking bridges and the enterprise edge cloud. However, achieving deterministic end-to-end latency remains a challenge, particularly due to the variable packet transmission delay introduced by the 5G system. While time-sensitive networking bridges typically operate with latencies in the range of hundreds of microseconds, 5G systems may experience delays ranging from a few to several hundred milliseconds. This paper investigates the potential of configuring the 5G time division duplex pattern to minimize packet transmission delay in industrial environments. Through empirical measurements using a commercial 5G system, we evaluate different TDD configurations under varying traffic loads, packet sizes and full buffer status report activation. Based on our findings, we provide practical configuration recommendations for satisfying requirements in industrial automation, helping private network providers increase the adoption of 5G.

Anwar Ahmed Khan, Shama Siddiqui, Indrakshi Dey

Industrial Internet of Things (IIoT) promises to revolutionize industrial operations and productions through utilizing Machine-to-Machine (M2M) communications. Since each node in such environments generates various types of data with diverse service requirements, MAC protocol holds crucial importance to ensure efficient delivery. In this context, simple to complex MAC schemes are found in literature. This paper focuses on evaluating the performance of two major techniques "slot stealing" and "packet fragmentation" for the IIoT; representative protocols SS-MAC and FROG-MAC have been chosen from each category respectively. We conducted realistic simulations for the two protocols using Contiki. Delay and packet loss comparison for SS-MAC and FROG-MAC indicates the superiority of FROG-MAC due to reduction in the waiting time for urgent traffic. Thus, a simple fragmentation scheme could be deployed for efficient scheduling of heterogenous traffic in the industrial environments.

Wan J, Liu YJ, Zhou WJ et al.

Jin Wan, Yu Jie Liu, Wen Jun Zhou, Si Yuan Wu School of Economics and Management, East China Jiaotong University, Nanchang, Jiangxi, 330013, People’s Republic of ChinaCorrespondence: Jin Wan, Email 244022935@qq.comIntroduction: To investigate the impact of interpersonal sensitivity on the subjective well-being of accompanying children of migrant workers and the role of perception of exclusion and peer support in the process.Methods: A questionnaire survey was conducted among 304 migrant workers’ accompanying children and 501 urban children in grades 4– 9 in seven schools in Jiangxi Province, China. Hierarchical regression and bootstrap analysis were used.Results: Interpersonal sensitivity not only had a significant direct negative effect on the subjective well-being of migrant workers’ accompanying children (β= − 0.27, 95% CI = [− 0.37, − 0.17]), but also had an indirect effect through perception of exclusion (β= − 0.06, 95% CI = [− 0.11, − 0.03]). Peer support negatively moderated the relationship between interpersonal sensitivity and perception of exclusion (β= − 0.18, 95% CI = [− 0.28, − 0.08]) and the mediating effect of perceptions of exclusion between interpersonal sensitivity and subjective well-being (β = 0.06, CI = [0.02, 0.11]).Conclusion: The subjective well-being of migrant children is indeed lower than that of urban children, and one of the most important reasons is their higher interpersonal sensitivity. Interpersonal sensitivity not only directly reduces their subjective well-being, but also reduces it by triggering their perception of exclusion, while peer support can effectively mitigate this negative effect. Therefore, one way to improve the subjective well-being of these children is to reduce their excessive interpersonal sensitivity. Their parents should help them to adapt to urban life, to develop correct professional values and to deal correctly with “occupational stigma”, to overcome feelings of inferiority, while communities can create specialized activity centers to provide more social opportunities and psychological counseling services for these children.Keywords: migrant workers’ accompanying children, interpersonal sensitivity, perception of exclusion, peer support, subjective well-being

Muwaga Musa, Fuad Nashori

This study investigates the relationship between altruism and teacher performance in private secondary schools in Iganga District, Uganda—a context that remains underrepresented in existing literature. While previous research has explored altruism within corporate and public service sectors, limited attention has been paid to its role in non-profit educational environments, particularly in Sub-Saharan Africa. To address this contextual and content-related gap, a mixed-methods approach was employed, combining a cross-sectional survey design with both quantitative and qualitative techniques. Data were collected from 88 respondents, including head teachers and teachers with administrative responsibilities, using validated questionnaires and semi-structured interviews. Quantitative data were analyzed using Pearson product-moment correlation, while qualitative insights were interpreted through content analysis. The results reveal a statistically significant and strong positive relationship (r = .644, p < .01) between altruism and teacher performance. Qualitative findings further illuminate how altruistic behaviors—such as empathy, collaboration, and voluntary support—contribute to sustained performance and institutional cohesion. The study offers a novel contribution by demonstrating how altruism functions as a critical behavioral factor in enhancing teacher effectiveness in resource-constrained school settings. These findings underscore the importance of cultivating altruistic values in school management practices and teacher development programs. Future research should explore comparative analyses between school types and examine institutional support mechanisms that sustain altruism.

Wahab S, Azmi AD, Thind A et al.

Suzaily Wahab,1 Amirul Danial Azmi,1 Ashwin Thind,2 Nor Fazreana Athira Ismail Zulkarnain,1 Mohammad Affieq Aiman Mohammad Azhar1 1Department of Psychiatry, Universiti Kebangsaan Malaysia Medical Centre, Cheras, Kuala Lumpur, Malaysia; 2PENGASIH Kuala Lumpur (Main HQ), PENGASIH Malaysia Association, Kuala Lumpur, Wilayah Persekutuan Kuala Lumpur, MalaysiaCorrespondence: Suzaily Wahab, Email suzaily@ppukm.ukm.edu.myIntroduction: Cases of amphetamine-type stimulants (ATS) use have been increasing over the past decade. Cravings are considered a causal factor for recurrent relapses in ATS use cases. The absence of questionnaires measuring cravings for ATS in the local population necessitates the creation of one, especially considering the rising number of cases.Objective: This study aimed to adapt and validate the Cocaine Cravings Questionnaire into a questionnaire suitable for measuring cravings for ATS in the local population.Methodology: The original questionnaire was adapted by substituting “cocaine” with “ATS”. The process involved a back-to-back translation, followed by a round of face and content validation. The participants included people who use drugs (PWUD) with a history of ATS use recruited from rehabilitation centers in Malaysia. A set of questionnaires consisting of demographic items and the adapted ATS Cravings Questionnaire (ATS-CQ) were given.Results: This cross-sectional study recruited a total of 205 PWUD, mostly single men, with a mean age of 33.32 (s.d.=13.14). The mean age of ATS initiation was 22.89 (s.d.=9.39), with a median duration of ATS use of 60 months (IQR=24.00, 120.00). The adapted questionnaire received a good score for content validation. Unlike the original, this adapted version was found to have only three factors showing good internal consistency, ranging from 0.707 to 0.918 for all three factors. Test-retest reliability also showed good results, with an interclass correlation coefficient of 0.875 (95% CI=0.835, 0.905).Conclusion: The translated ATS-CQ has been finalized and deemed valid and reliable for use among Malaysian substance users to measure ATS cravings.Keywords: substance use, craving, addiction, psychometric

Mishra PK, Rašticová M

Pawan Kumar Mishra, Martina Rašticov&#x00E1Faculty of Business and Economics, Mendel University in Brno, Brno, CzechiaCorrespondence: Pawan Kumar Mishra, Email pawan.mishra@mendelu.czAbstract: The revolution in technology has impacted the work and personal lives of human beings greatly. While it has introduced the mankind to a more comfortable life, it has brought in the stress too in the form of technostress, the situation where a person fails to cope up with the ever-advancing technology and experiences stress symptoms. The increasing intensity of technostress calls for more research on technostress diving deeper into the causes and coping mechanisms. However, technostress research requires successful and reliable assessment of stress. It has been observed in recent years that biomarkers such as cortisol and salivary alpha amylase are reliable indicators of stress. There are several reports where the researchers have used questionnaires and surveys to assess the technostress, but the number of studies using biomarkers for technostress assessment is limited. It has been established that biomarker assessment is an important complement to the surveys to study the technostress. Here, we summarize the important studies done on technostress using the biomarkers along with the rationale of using these biomarkers.Keywords: technostress, biomarkers, cortisol, skin conductance, salivary alpha amylase, heart rate variability

Benjamin Alt, Julia Dvorak, Darko Katic et al.

Over the past decade, deep learning helped solve manipulation problems across all domains of robotics. At the same time, industrial robots continue to be programmed overwhelmingly using traditional program representations and interfaces. This paper undertakes an analysis of this "AI adoption gap" from an industry practitioner's perspective. In response, we propose the BANSAI approach (Bridging the AI Adoption Gap via Neurosymbolic AI). It systematically leverages principles of neurosymbolic AI to establish data-driven, subsymbolic program synthesis and optimization in modern industrial robot programming workflow. BANSAI conceptually unites several lines of prior research and proposes a path toward practical, real-world validation.

Eldar Knar

The chronological hierarchy and classification of psychological types of individuals are examined. The anomalous nature of psychological activity in individuals involved in scientific work is highlighted. Certain aspects of the introverted thinking type in scientific activities are analyzed. For the first time, psychological archetypes of scientists with pronounced introversion are postulated in the context of twelve hypotheses about the specifics of professional attributes of introverted scientific activities. A linear regression and Bayesian equation are proposed for quantitatively assessing the econometric degree of introversion in scientific employees, considering a wide range of characteristics inherent to introverts in scientific processing. Specifically, expressions for a comprehensive assessment of introversion in a linear model and the posterior probability of the econometric (scientometric) degree of introversion in a Bayesian model are formulated. The models are based on several econometric (scientometric) hypotheses regarding various aspects of professional activities of introverted scientists, such as a preference for solo publications, low social activity, narrow specialization, high research depth, and so forth. Empirical data and multiple linear regression methods can be used to calibrate the equations. The model can be applied to gain a deeper understanding of the psychological characteristics of scientific employees, which is particularly useful in ergonomics and the management of scientific teams and projects. The proposed method also provides scientists with pronounced introversion the opportunity to develop their careers, focusing on individual preferences and features.

Mike Allenspach, Michael Pantic, Rik Girod et al.

In real-world industrial environments, modern robots often rely on human operators for crucial decision-making and mission synthesis from individual tasks. Effective and safe collaboration between humans and robots requires systems that can adjust their motion based on human intentions, enabling dynamic task planning and adaptation. Addressing the needs of industrial applications, we propose a motion control framework that (i) removes the need for manual control of the robot's movement; (ii) facilitates the formulation and combination of complex tasks; and (iii) allows the seamless integration of human intent recognition and robot motion planning. For this purpose, we leverage a modular and purely reactive approach for task parametrization and motion generation, embodied by Riemannian Motion Policies. The effectiveness of our method is demonstrated, evaluated, and compared to \remove{state-of-the-art approaches}\add{a representative state-of-the-art approach} in experimental scenarios inspired by realistic industrial Human-Robot Interaction settings.

Pamela Adelino Ramos, Helen Silva Gonçalves, Felipe Araujo Pereira et al.

Work ability is a facilitating factor for management in terms of life, work, and productivity. This research aimed to identify the work ability factors (WAF) in the face of the COVID-19 pandemic, correlating these estimations according to the gender and age of workers, personal development, and finances. An exploratory survey was conducted with an intentional non-probabilistic sample and factor analysis to refine the data and identify the most-applied WAF. We executed the Levene test, ANOVA analysis, Kruskal-Wallis, and Bonferroni to make estimations considering the gender and age factors. The SEM related the elements of personal development and finances. The results show no significant change in the measures of work ability according to gender and age. It is observed that personal development and work alignment represent the most notable work ability factors. The relevance of studies on work ability remains essential to assessing working conditions.

Tang X

Xin Tang School of Law, Chongqing University, Chongqing, 400044, People’s Republic of ChinaCorrespondence: Xin Tang, Email xintang@cqu.edu.cnIntroduction: Since the reform and opening up, the social economy has developed rapidly. The competition in the employer market is fierce, which leads leaders to have strict requirements for workers, and workplace stress increases. The blind pursuit of corporate economic benefits has led to the neglect of workers’ mental health. Employee retaliation against the corporate occurs frequently. The perfection of the legal system for occupational mental health protection is imminent.Methods: Based on the above questions, this study first introduces the research background, significance, and purpose in the introduction. Second, in the literature review, the current status of research is sorted out, the problems in the existing research are summarized, and the innovation points of this study are highlighted. Then, in the method section, the algorithms and models used here are introduced, including convolutional neural networks, long short-term memory networks, and the design of interview processes. Finally, the results of the questionnaire survey and the experimental test are analyzed.Results: (1) There is further room for optimization of intelligent lie recognition technology. (2) The employee assistance program system can effectively solve the mental health problems of employees. (3) There is a need to expand the legislative mechanism for workers’ mental health protection at the legal level.Discussion: This study mainly explores the loopholes of occupational mental health protection under the formulation of laws and regulations. Intelligent lie recognition technology reduces workers’ adverse physical and mental health risks due to work. It is dedicated to protecting workers’ legitimate rights and interests from the formulation of laws and regulations.Keywords: workplace stress, intelligent lie recognition, employee assistance program system, legal protection, occupational mental health

Vongai Ruzungunde, Willie T. Chinyamurindi, Chioneso S. Marange

Orientation: In South African organisations, a dual work is argued as important: first, the promotion of decent working conditions and secondly, encouraging workplaces that safe-guard the mental well-being of employees. Research purpose: This study was aimed at investigating the determinants of mental health accounting for the role of organisational climate and decent work among public service employees in South Africa. Motivation of the study: There is a need for organisations to pay attention to those aspects that improve the mental well-being of employees. This also includes the promotion of workplace that in turn emphasises the promotion of decent work. Research approach/design and method: A cross-sectional quantitative research design was adopted, using a self-administered questionnaire. A convenience sampling technique was used. Data were collected from a sample of 289 public service employees working in the South African public service in the Eastern Cape province of South Africa. Main findings: The study found organisational climate to have a direct and positive association with decent work. Further, there was support for the mediation of decent work on the relationship between organisational climate and employee mental health. Practical/managerial implications: The main practical implication of the study is the need to argue for the promotion of decent working conditions through organisational interventions in supporting employee mental health. Contribution/value-add: This becomes crucial in business environments where employees often suffer challenges that affect their well-being.