Hasil untuk "Industrial psychology"

Jian Yang, Wei Zhang, Jiajun Wu et al.

Recent code large language models have achieved remarkable progress on general programming tasks. Nevertheless, their performance degrades significantly in industrial scenarios that require reasoning about hardware semantics, specialized language constructs, and strict resource constraints. To address these challenges, we introduce InCoder-32B (Industrial-Coder-32B), the first 32B-parameter code foundation model unifying code intelligence across chip design, GPU kernel optimization, embedded systems, compiler optimization, and 3D modeling. By adopting an efficient architecture, we train InCoder-32B from scratch with general code pre-training, curated industrial code annealing, mid-training that progressively extends context from 8K to 128K tokens with synthetic industrial reasoning data, and post-training with execution-grounded verification. We conduct extensive evaluation on 14 mainstream general code benchmarks and 9 industrial benchmarks spanning 4 specialized domains. Results show InCoder-32B achieves highly competitive performance on general tasks while establishing strong open-source baselines across industrial domains.

Junfeng Jiao, Saleh Afroogh, Kevin Chen et al.

The rise of Generative AI (GAI) and Large Language Models (LLMs) has transformed industrial landscapes, offering unprecedented opportunities for efficiency and innovation while raising critical ethical, regulatory, and operational challenges. This study conducts a text-based analysis of 160 guidelines and policy statements across fourteen industrial sectors, utilizing systematic methods and text-mining techniques to evaluate the governance of these technologies. By examining global directives, industry practices, and sector-specific policies, the paper highlights the complexities of balancing innovation with ethical accountability and equitable access. The findings provide actionable insights and recommendations for fostering responsible, transparent, and safe integration of GAI and LLMs in diverse industry contexts.

Despina Tomkou, George Fatouros, Andreas Andreou et al.

This paper introduces a novel integration of Retrieval-Augmented Generation (RAG) enhanced Large Language Models (LLMs) with Extended Reality (XR) technologies to address knowledge transfer challenges in industrial environments. The proposed system embeds domain-specific industrial knowledge into XR environments through a natural language interface, enabling hands-free, context-aware expert guidance for workers. We present the architecture of the proposed system consisting of an LLM Chat Engine with dynamic tool orchestration and an XR application featuring voice-driven interaction. Performance evaluation of various chunking strategies, embedding models, and vector databases reveals that semantic chunking, balanced embedding models, and efficient vector stores deliver optimal performance for industrial knowledge retrieval. The system's potential is demonstrated through early implementation in multiple industrial use cases, including robotic assembly, smart infrastructure maintenance, and aerospace component servicing. Results indicate potential for enhancing training efficiency, remote assistance capabilities, and operational guidance in alignment with Industry 5.0's human-centric and resilient approach to industrial development.

Ruiyang Ma, Tianhao Wei, Jiaxi Zhang et al.

As hardware design complexity increases, hardware fuzzing emerges as a promising tool for automating the verification process. However, a significant gap still exists before it can be applied in industry. This paper aims to summarize the current progress of hardware fuzzing from an industry-use perspective and propose solutions to bridge the gap between hardware fuzzing and industrial verification. First, we review recent hardware fuzzing methods and analyze their compatibilities with industrial verification. We establish criteria to assess whether a hardware fuzzing approach is compatible. Second, we examine whether current verification tools can efficiently support hardware fuzzing. We identify the bottlenecks in hardware fuzzing performance caused by insufficient support from the industrial environment. To overcome the bottlenecks, we propose a prototype, HwFuzzEnv, providing the necessary support for hardware fuzzing. With this prototype, the previous hardware fuzzing method can achieve a several hundred times speedup in industrial settings. Our work could serve as a reference for EDA companies, encouraging them to enhance their tools to support hardware fuzzing efficiently in industrial verification.

Yu yang

In recent years, breakthroughs in artificial intelligence (AI) technology have triggered global industrial transformations, with applications permeating various fields such as finance, healthcare, education, and manufacturing. However, this rapid iteration is accompanied by irrational development, where enterprises blindly invest due to technology hype, often overlooking systematic value assessments. This paper develops a multi-dimensional evaluation model that integrates information theory's entropy reduction principle, economics' bounded rationality framework, and psychology's irrational decision theories to quantify AI product value. Key factors include positive dimensions (e.g., uncertainty elimination, efficiency gains, cost savings, decision quality improvement) and negative risks (e.g., error probability, impact, and correction costs). A non-linear formula captures factor couplings, and validation through 10 commercial cases demonstrates the model's effectiveness in distinguishing successful and failed products, supporting hypotheses on synergistic positive effects, non-linear negative impacts, and interactive regulations. Results reveal value generation logic, offering enterprises tools to avoid blind investments and promote rational AI industry development. Future directions include adaptive weights, dynamic mechanisms, and extensions to emerging AI technologies like generative models.

Naval Kishore Mehta, Arvind, Himanshu Kumar et al.

Detecting and interpreting operator actions, engagement, and object interactions in dynamic industrial workflows remains a significant challenge in human-robot collaboration research, especially within complex, real-world environments. Traditional unimodal methods often fall short of capturing the intricacies of these unstructured industrial settings. To address this gap, we present a novel Multimodal Industrial Activity Monitoring (MIAM) dataset that captures realistic assembly and disassembly tasks, facilitating the evaluation of key meta-tasks such as action localization, object interaction, and engagement prediction. The dataset comprises multi-view RGB, depth, and Inertial Measurement Unit (IMU) data collected from 22 sessions, amounting to 290 minutes of untrimmed video, annotated in detail for task performance and operator behavior. Its distinctiveness lies in the integration of multiple data modalities and its emphasis on real-world, untrimmed industrial workflows-key for advancing research in human-robot collaboration and operator monitoring. Additionally, we propose a multimodal network that fuses RGB frames, IMU data, and skeleton sequences to predict engagement levels during industrial tasks. Our approach improves the accuracy of recognizing engagement states, providing a robust solution for monitoring operator performance in dynamic industrial environments. The dataset and code can be accessed from https://github.com/navalkishoremehta95/MIAM/.

Linda Kölbel, Markus Hornsteiner, Stefan Schönig

In industry, the networking and automation of machines through the Internet of Things (IoT) continues to increase, leading to greater digitalization of production processes. Traditionally, business and production processes are controlled, optimized and monitored using business process management methods that require process discovery. However, these methods cannot be fully applied to industrial production processes. Nevertheless, processes in the industry must also be monitored and discovered for this purpose. The aim of this paper is to develop an approach for process discovery methods and to adapt existing process discovery methods for application to industrial processes. The adaptations of classic discovery methods are presented as universally applicable guidelines specifically for the Industrial Internet of Things (IIoT). In order to create an optimal process model based on process evaluation, different methods are combined into a standardized discovery approach that is both efficient and cost-effective.

Xi Jiang, Jian Li, Hanqiu Deng et al.

In the field of industrial inspection, Multimodal Large Language Models (MLLMs) have a high potential to renew the paradigms in practical applications due to their robust language capabilities and generalization abilities. However, despite their impressive problem-solving skills in many domains, MLLMs' ability in industrial anomaly detection has not been systematically studied. To bridge this gap, we present MMAD, the first-ever full-spectrum MLLMs benchmark in industrial Anomaly Detection. We defined seven key subtasks of MLLMs in industrial inspection and designed a novel pipeline to generate the MMAD dataset with 39,672 questions for 8,366 industrial images. With MMAD, we have conducted a comprehensive, quantitative evaluation of various state-of-the-art MLLMs. The commercial models performed the best, with the average accuracy of GPT-4o models reaching 74.9%. However, this result falls far short of industrial requirements. Our analysis reveals that current MLLMs still have significant room for improvement in answering questions related to industrial anomalies and defects. We further explore two training-free performance enhancement strategies to help models improve in industrial scenarios, highlighting their promising potential for future research.

Jinpeng Hu, Tengteng Dong, Luo Gang et al.

Mental health has attracted substantial attention in recent years and LLM can be an effective technology for alleviating this problem owing to its capability in text understanding and dialogue. However, existing research in this domain often suffers from limitations, such as training on datasets lacking crucial prior knowledge and evidence, and the absence of comprehensive evaluation methods. In this paper, we propose a specialized psychological large language model (LLM), named PsycoLLM, trained on a proposed high-quality psychological dataset, including single-turn QA, multi-turn dialogues and knowledge-based QA. Specifically, we construct multi-turn dialogues through a three-step pipeline comprising multi-turn QA generation, evidence judgment, and dialogue refinement. We augment this process with real-world psychological case backgrounds extracted from online platforms, enhancing the relevance and applicability of the generated data. Additionally, to compare the performance of PsycoLLM with other LLMs, we develop a comprehensive psychological benchmark based on authoritative psychological counseling examinations in China, which includes assessments of professional ethics, theoretical proficiency, and case analysis. The experimental results on the benchmark illustrate the effectiveness of PsycoLLM, which demonstrates superior performance compared to other LLMs.

Mónica Edwards-Schachter

Von Schomberg offers a compelling examination of key open science principles and their potential role in fostering responsible research and innovation (RRI). Utilizing Merton's Ethos of Science framework, the paper constructs a series of arguments supporting a central thesis: “the transition towards open science is vital to facilitate RRI.” This transition necessitates significant institutional reforms within the scientific community and adjustments to incentive structures that promote the adoption of open and mutually responsive practices. The manuscript reframes the discourse surrounding responsibility and responsiveness in light of the evolving landscape of open science, shifting the focus from normative commitments to actionable frameworks in research and open science practices. Overall, the position paper strives to bridge the gap between idealised models of scientific communities based on RRI principles and the reality of actual scientific endeavour (Anderson et al., 2007; Politi, 2021, 2024). However, it is important to acknowledge certain omissions that could enrich the analysis. Firstly, a more comprehensive examination of the profound crisis facing science amidst the increasing marketisation and commodification of academia and research would provide valuable context beyond discussions of system failures related to productivity and reproducibility. Secondly, a more nuanced and critical approach to conceptualising open science would enrich the discussion, considering its multifaceted nature and potential pitfalls. Thirdly, the validity of the Mertonian framework and its selective analysis of values, particularly its exclusive focus on the norm of communism. Lastly, a deeper exploration of the challenges and promises inherent in the pursuit of responsible Open Science within ongoing institutional processes.

Anica Cvetkovic, Nina Savela, Rita Latikka et al.

The fourth industrial revolution is bringing artificial intelligence (AI) into various workplaces, and many businesses worldwide are already capitalizing on AI assistants. Trust is essential for the successful integration of AI into organizations. We hypothesized that people have higher trust in human assistants than AI assistants and that people trust AI assistants more if they have more control over their activities. To test our hypotheses, we utilized a survey experiment with 828 participants from Finland. Results showed that participants would rather entrust their schedule to a person than to an AI assistant. Having control increased trust in both human and AI assistants. The results of this study imply that people in Finland still have higher trust in traditional workplaces where people, rather than smart machines, perform assisting work. The findings are of relevance for designing trustworthy AI assistants, and they should be considered when integrating AI technology into organizations.

Guanghui Fu, Qing Zhao, Jianqiang Li et al.

In the contemporary landscape of social media, an alarming number of users express negative emotions, some of which manifest as strong suicidal intentions. This situation underscores a profound need for trained psychological counselors who can enact effective mental interventions. However, the development of these professionals is often an imperative but time-consuming task. Consequently, the mobilization of non-professionals or volunteers in this capacity emerges as a pressing concern. Leveraging the capabilities of artificial intelligence, and in particular, the recent advances in large language models, offers a viable solution to this challenge. This paper introduces a novel model constructed on the foundation of large language models to fully assist non-professionals in providing psychological interventions on online user discourses. This framework makes it plausible to harness the power of non-professional counselors in a meaningful way. A comprehensive study was conducted involving ten professional psychological counselors of varying expertise, evaluating the system across five critical dimensions. The findings affirm that our system is capable of analyzing patients' issues with relative accuracy and proffering professional-level strategies recommendations, thereby enhancing support for non-professionals. This research serves as a compelling validation of the application of large language models in the field of psychology and lays the groundwork for a new paradigm of community-based mental health support.

Tianyu Zhang, Gang Wang, Chuanyu Xue et al.

With the introduction of Cyber-Physical Systems (CPS) and Internet of Things (IoT) technologies, the automation industry is undergoing significant changes, particularly in improving production efficiency and reducing maintenance costs. Industrial automation applications often need to transmit time- and safety-critical data to closely monitor and control industrial processes. Several Ethernet-based fieldbus solutions, such as PROFINET IRT, EtherNet/IP, and EtherCAT, are widely used to ensure real-time communications in industrial automation systems. These solutions, however, commonly incorporate additional mechanisms to provide latency guarantees, making their interoperability a grand challenge. The IEEE 802.1 Time Sensitive Networking (TSN) task group was formed to enhance and optimize IEEE 802.1 network standards, particularly for Ethernet-based networks. These solutions can be evolved and adapted for cross-industry scenarios, such as large-scale distributed industrial plants requiring multiple industrial entities to work collaboratively. This paper provides a comprehensive review of current advances in TSN standards for industrial automation. It presents the state-of-the-art IEEE TSN standards and discusses the opportunities and challenges of integrating TSN into the automation industry. Some promising research directions are also highlighted for applying TSN technologies to industrial automation applications.

Myeonghwan Bang, Chan Woong Jang, Hyoung Seop Kim et al.

Background: As the number of individuals suffering from cognitive diseases continues to rise, dealing with the diminished cognitive function that comes with age has become a serious public health concern. While the use of mobile applications (apps) as digital treatments for cognitive training shows promise, the analysis of their content and quality remains unclear. Objective: The aim of this study was to systematically search and assess cognitive training apps using the multidimensional mobile app rating scale (MARS) to rate objective quality and identify critical points. Methods: A search was conducted on the Google Play Store and Apple App Store in February 2022 using the terms “cognitive training” and “cognitive rehabilitation.” The cognitive domains provided by each app were analyzed, and the frequency and percentage according to the apps were obtained. The MARS, a mHealth app quality rating tool including multidimensional measures, was used to analyze the quality of the apps. The relationship between the MARS score, the number of reviews, and 5-star ratings were examined. Results: Of the 53 apps, 52 (98 %) included memory function, 48 (91 %) included attention function, 24 (45 %) included executive function, and 19 (36 %) included visuospatial function. The mean (SD) scores of MARS, 5-star ratings, and reviews of 53 apps were 3.09 (0.61), 4.33 (0.30), and 62,415.43 (121,578.77). From the between-section comparison, engagement (mean 2.97, SD 0.68) obtained lower scores than functionality (mean 3.18, SD 0.62), aesthetics (mean 3.13, SD 0.72), and information (mean 3.11, SD 0.54). The mean quality score and reviews showed a statistically significant association (r = 0.447 and P = .001*). As the number of domains increased, the mean quality score showed a statistically significant increasing trend (P = .002*). Conclusions: Most apps provided training for the memory and attention domains, but few apps included executive function or visuospatial domains. The quality of the apps improved significantly when more domains were provided, and was positively associated with the number of reviews received. These results could be useful for the future development of mobile apps for cognitive training.

Callum Stephenson, Elnaz Moghimi, Gilmar Gutierrez et al.

Introduction: In the last few years, online psychotherapy programs have burgeoned since they are a more accessible and scalable treatment option compared to in-person therapies. While these online programs are promising, understanding the user experience and perceptions of care is essential for program optimization. Methods: This study investigated the experiences of end-users who had previously received online psychotherapy through a web-based platform. A 35-item multiple-choice survey was developed by the research team and distributed to past users to capture their perceptions of the program. Results: The survey yielded 163 responses, with a 90 % completion rate. Participants were predominantly white and female, with an average age of 42 years. While most participants preferred in-person therapy, they also reported the benefits of the online psychotherapy program. Participants had positive perceptions of the platform, the quality and interaction of their therapist, and the homework assignments and skills covered. Lack of motivation to complete weekly homework assignments was cited as a common struggle. Discussion: The findings support online psychotherapy as a beneficial digital mental health tool and highlight some areas for improvement. Scalability and accessibility are key benefits of the platform. At the same time, improvements in participant engagement, including those from equity-seeking and equity-deserving groups, may enhance the efficacy of the programs offered.

Jake A. Weiss, Neal Outland, Gabriel Plummer et al.

Emerging literature indicates the critical value of employee coachability for individual, coaching practice, and organisational effectiveness across contexts. To expand our understanding of coachability and maximize its application within organisations, we require a greater understanding of its antecedents. Thus, this paper explicates and examines trait, motivational, and behaviourally based individual differences underlying employees’ coachability. Findings from this investigation demonstrate feedback orientation, expressed humility, and the instrumental feedback motive significantly influence employees’ coachability. This research contributes to the growing body of coachability literature and provides a strong foundation for enhancing its identification and development in organisational settings.

Maria Krantz, Alexander Windmann, Rene Heesch et al.

The increasing complexity of Cyber-Physical Systems (CPS) makes industrial automation challenging. Large amounts of data recorded by sensors need to be processed to adequately perform tasks such as diagnosis in case of fault. A promising approach to deal with this complexity is the concept of causality. However, most research on causality has focused on inferring causal relations between parts of an unknown system. Engineering uses causality in a fundamentally different way: complex systems are constructed by combining components with known, controllable behavior. As CPS are constructed by the second approach, most data-based causality models are not suited for industrial automation. To bridge this gap, a Uniform Causality Model for various application areas of industrial automation is proposed, which will allow better communication and better data usage across disciplines. The resulting model describes the behavior of CPS mathematically and, as the model is evaluated on the unique requirements of the application areas, it is shown that the Uniform Causality Model can work as a basis for the application of new approaches in industrial automation that focus on machine learning.

Ziwei Wan, Zhen Gao, Marco Di Renzo et al.

The fourth industrial revolution, i.e., Industry 4.0, is evolving all around the globe. In this article, we introduce the landscape of Industry 4.0 and beyond empowered by the seamless collaboration of communication technology (CT), information technology (IT), and operation technology (OT), i.e., CIOT collaboration. Specifically, CIOT collaboration is regarded as a main improvement of Industry 4.0 compared to the previous industrial revolutions. We commence by reviewing the previous three industrial revolutions and we argue that the key feature of Industry 4.0 is the CIOT collaboration. More particularly, CT domain supports ubiquitous connectivity of the industrial elements and further bridges the physical world and the cyber world, which is a pivotal prerequisite. Then, we present the potential impacts of CIOT collaboration on typical industrial use cases with the objective of creating a more intelligent and human-friendly industry. Furthermore, the technical challenges of paving the way for the CIOT collaboration with an emphasis on the CT domain are discussed. Finally, we shed light on a roadmap for Industry 4.0 and beyond. The salient steps to be taken in the future CIOT collaboration are highlighted, which may be expected to expedite the paradigm shift towards the next industrial revolution.

A. M. M. Todd, R. Agustsson, D. L. Bruhwiler et al.

The purpose of this white paper is to discuss the importance of having a world class domestic industrial vendor base, capable of supporting the needs of the particle accelerator facilities, and the necessary steps to support and develop such a base in the United States. The paper focuses on economic, regulatory, and policy-driven barriers and hurdles, which presently limit the depth and scope of broader industrial participation in US accelerator facilities. It discusses the international competition landscape and proposes steps to improve the strength and vitality of US industry.

Marida Steurer, Leoni van der Vaart, Sebastiaan Rothmann

Orientation: Graduate employability remains high on researchers’ and practitioners’ agendas. Consequently, many studies have been conducted on the topic (also from a managerial perspective). A synthesis of these studies is however lacking, complicating decision-making for stakeholders with a vested interest in the topic. Research purpose: This study aimed to give a scientific overview of managerial expectations of new graduate employability attributes through a scoping review of the available literature. Motivation for the study: A synthesis of these studies is required to facilitate stakeholders’ (researchers and practitioners) decision-making. Research approach/design and method: This study included 63 peer-reviewed articles as part of the review. The researcher analysed the data using conventional content analysis. Main findings: Four main categories of graduate employability attributes were identified: personal, interpersonal, workplace and applied knowledge attributes. The term personal attributes refers to an individual’s unique make-up that enables them to be successful in all aspects of life and lays the foundation for the way all other attributes are applied. Interpersonal attributes dictate new graduates’ ability to communicate or interact well with other individuals. The way in which new graduates adapt and function at work will be determined by their workplace attributes whilst their applied knowledge attributes build on the first three categories and enable new graduates to apply their theoretical and empirical learning in practice. Practical/managerial implications: Not only could the results inform further studies but the additional insight into the complexity of graduate employability could also guide future developmental interventions. Contribution/value-add: The present study aimed to make a scientifically founded contribution towards literature by identifying the most important expectations managers have regarding new graduate employability.