Hasil untuk "Industrial psychology"

Jian Yang, Wei Zhang, Jiajun Wu et al.

Industrial software development across chip design, GPU optimization, and embedded systems lacks expert reasoning traces showing how engineers reason about hardware constraints and timing semantics. In this work, we propose InCoder-32B-Thinking, trained on the data from the Error-driven Chain-of-Thought (ECoT) synthesis framework with an industrial code world model (ICWM) to generate reasoning traces. Specifically, ECoT generates reasoning chains by synthesizing the thinking content from multi-turn dialogue with environmental error feedback, explicitly modeling the error-correction process. ICWM is trained on domain-specific execution traces from Verilog simulation, GPU profiling, etc., learns the causal dynamics of how code affects hardware behavior, and enables self-verification by predicting execution outcomes before actual compilation. All synthesized reasoning traces are validated through domain toolchains, creating training data matching the natural reasoning depth distribution of industrial tasks. Evaluation on 14 general (81.3% on LiveCodeBench v5) and 9 industrial benchmarks (84.0% in CAD-Coder and 38.0% on KernelBench) shows InCoder-32B-Thinking achieves top-tier open-source results across all domains.GPU Optimization

Leila Ismail, Abdelmoneim Abdelmoti, Arkaprabha Basu et al.

With the increasing complexity of industrial systems, there is a pressing need for predictive maintenance to avoid costly downtime and disastrous outcomes that could be life-threatening in certain domains. With the growing popularity of the Internet of Things, Artificial Intelligence, machine learning, and real-time big data analytics, there is a unique opportunity for efficient predictive maintenance to forecast equipment failures for real-time intervention and optimize maintenance actions, as traditional reactive and preventive maintenance practices are often inadequate to meet the requirements for the industry to provide quality-of-services of operations. Central to this evolution is digital twin technology, an adaptive virtual replica that continuously monitors and integrates sensor data to simulate and improve asset performance. Despite remarkable progress in digital twin implementations, such as considering DT in predictive maintenance for industrial engineering. This paper aims to address this void. We perform a retrospective analysis of the temporal evolution of the digital twin in predictive maintenance for industrial engineering to capture the applications, middleware, and technological requirements that led to the development of the digital twin from its inception to the AI-enabled digital twin and its self-learning models. We provide a layered architecture of the digital twin technology, as well as a taxonomy of the technology-enabled industrial engineering applications systems, middleware, and the used Artificial Intelligence algorithms. We provide insights into these systems for the realization of a trustworthy and efficient smart digital-twin industrial engineering ecosystem. We discuss future research directions in digital twin for predictive maintenance in industrial engineering.

Nikhita Joshi, Daniel Vogel

Writing longer prompts for an AI assistant to generate a short story increases psychological ownership, a user's feeling that the writing belongs to them. To encourage users to write longer prompts, we evaluated two interaction techniques that modify the prompt entry interface of chat-based generative AI assistants: pressing and holding the prompt submission button, and continuously moving a slider up and down when submitting a short prompt. A within-subjects experiment investigated the effects of such techniques on prompt length and psychological ownership, and results showed that these techniques increased prompt length and led to higher psychological ownership than baseline techniques. A second experiment further augmented these techniques by showing AI-generated suggestions for how the prompts could be expanded. This further increased prompt length, but did not lead to improvements in psychological ownership. Our results show that simple interface modifications like these can elicit more writing from users and improve psychological ownership.

Kai R. Larsen, Sen Yan, Roland M. Mueller et al.

Psychological measurement is critical to many disciplines. Despite advances in measurement, building nomological networks, theoretical maps of how concepts and measures relate to establish validity, remains a challenge 70 years after Cronbach and Meehl proposed them as fundamental to validation. This limitation has practical consequences: clinical trials may fail to detect treatment effects, and public policy may target the wrong outcomes. We introduce Analysis of Latent Indicators to Generate Nomological Structures (ALIGNS), a large language model-based system trained with validated questionnaire measures. ALIGNS provides three comprehensive nomological networks containing over 550,000 indicators across psychology, medicine, social policy, and other fields. This represents the first application of large language models to solve a foundational problem in measurement validation. We report classification accuracy tests used to develop the model, as well as three evaluations. In the first evaluation, the widely used NIH PROMIS anxiety and depression instruments are shown to converge into a single dimension of emotional distress. The second evaluation examines child temperament measures and identifies four potential dimensions not captured by current frameworks, and questions one existing dimension. The third evaluation, an applicability check, engages expert psychometricians who assess the system's importance, accessibility, and suitability. ALIGNS is freely available at nomologicalnetwork.org, complementing traditional validation methods with large-scale nomological analysis.

Luciana Ciringione, Emma Franchino, Simone Reigl et al.

Math anxiety poses significant challenges for university psychology students, affecting their career choices and overall well-being. This study employs a framework based on behavioural forma mentis networks (i.e. cognitive models that map how individuals structure their associative knowledge and emotional perceptions of concepts) to explore individual and group differences in the perception and association of concepts related to math and anxiety. We conducted 4 experiments involving psychology undergraduates from 2 samples (n1 = 70, n2 = 57) compared against GPT-simulated students (GPT-3.5: n2 = 300; GPT-4o: n4 = 300). Experiments 1, 2, and 3 employ individual-level network features to predict psychometric scores for math anxiety and its facets (observational, social and evaluational) from the Math Anxiety Scale. Experiment 4 focuses on group-level perceptions extracted from human students, GPT-3.5 and GPT-4o's networks. Results indicate that, in students, positive valence ratings and higher network degree for "anxiety", together with negative ratings for "math", can predict higher total and evaluative math anxiety. In contrast, these models do not work on GPT-based data because of differences in simulated networks and psychometric scores compared to humans. These results were also reconciled with differences found in the ways that high/low subgroups of simulated and real students framed semantically and emotionally STEM concepts. High math-anxiety students collectively framed "anxiety" in an emotionally polarising way, absent in the negative perception of low math-anxiety students. "Science" was rated positively, but contrasted against the negative perception of "math". These findings underscore the importance of understanding concept perception and associations in managing students' math anxiety.

Qishan Wang, Shuyong Gao, Junjie Hu et al.

Multi-class Unsupervised Anomaly Detection algorithms (MUAD) are receiving increasing attention due to their relatively low deployment costs and improved training efficiency. However, the real-world effectiveness of MUAD methods is questioned due to limitations in current Industrial Anomaly Detection (IAD) datasets. These datasets contain numerous classes that are unlikely to be produced by the same factory and fail to cover multiple structures or appearances. Additionally, the defects do not reflect real-world characteristics. Therefore, we introduce the Heterogeneous Same-Sort Industrial Anomaly Detection (HSS-IAD) dataset, which contains 8,580 images of metallic-like industrial parts and precise anomaly annotations. These parts exhibit variations in structure and appearance, with subtle defects that closely resemble the base materials. We also provide foreground images for synthetic anomaly generation. Finally, we evaluate popular IAD methods on this dataset under multi-class and class-separated settings, demonstrating its potential to bridge the gap between existing datasets and real factory conditions. The dataset is available at https://github.com/Qiqigeww/HSS-IAD-Dataset.

Haijun Li, Tianqi Shi, Zifu Shang et al.

Machine translation (MT) has become indispensable for cross-border communication in globalized industries like e-commerce, finance, and legal services, with recent advancements in large language models (LLMs) significantly enhancing translation quality. However, applying general-purpose MT models to industrial scenarios reveals critical limitations due to domain-specific terminology, cultural nuances, and stylistic conventions absent in generic benchmarks. Existing evaluation frameworks inadequately assess performance in specialized contexts, creating a gap between academic benchmarks and real-world efficacy. To address this, we propose a three-level translation capability framework: (1) Basic Linguistic Competence, (2) Domain-Specific Proficiency, and (3) Cultural Adaptation, emphasizing the need for holistic evaluation across these dimensions. We introduce TransBench, a benchmark tailored for industrial MT, initially targeting international e-commerce with 17,000 professionally translated sentences spanning 4 main scenarios and 33 language pairs. TransBench integrates traditional metrics (BLEU, TER) with Marco-MOS, a domain-specific evaluation model, and provides guidelines for reproducible benchmark construction. Our contributions include: (1) a structured framework for industrial MT evaluation, (2) the first publicly available benchmark for e-commerce translation, (3) novel metrics probing multi-level translation quality, and (4) open-sourced evaluation tools. This work bridges the evaluation gap, enabling researchers and practitioners to systematically assess and enhance MT systems for industry-specific needs.

Anna Messina, Anna Maria Annoni, Rebecca Amati et al.

Aims: The growing use of technology in healthcare has contributed to the development of digital interventions for informal caregivers of people living with dementia. However, the marked heterogeneity of interventions poses challenges in evaluating their effectiveness. We conducted a review to delineate the distinctive features and development of the interventions, with focus on participatory methods. Methods: We searched the following databases: Cochrane; Cinahl; Pubmed; Psychinfo; Scopus; Web of Knowledge, and IEEE, and screened and selected studies based on titles, abstracts and full texts. We used standardized procedure to abstract and synthetize relevant data of primary studies, and the Mixed Methods Appraisal Tool to assess their quality. Results: Of 3136 records, 20 studies met the inclusion criteria. Most of the studies were web-based interventions, with multiple components and interactive features. The design and development of eight interventions employed participatory methods with large variations in the underlying framework and application. Conclusions: This review sheds light on the design and development of digital interventions for dementia caregivers. The limited and heterogeneous use of participatory methods, along with inadequate reporting, hinders a clear understanding of intervention efficacy and implementation. Formal standardization of participatory action research methods is necessary to improve the design, development, and evaluation of digital interventions for caregivers of people with dementia.

Zhu L, Huang M, Fang Z et al.

Lijun Zhu,1,&ast; Mengyun Huang,2,&ast; Zhengmei Fang,1,&ast; Jiani Tong,1 Zhiyin Pan,1 Long Hua,1 Pu Dong,1 Liying Wen,1 Weiwei Chang,1 Yingshui Yao,1 Yan Chen,1 Yuelong Jin1 1Department of Epidemiology and Biostatistics, School of Public Health, Wannan Medical College/ Institute of Chronic Disease Prevention and Control, Wuhu, 241002, People’s Republic of China; 2Department of Clinical Medicine, Anhui College of Traditional Chinese Medicine, Wuhu, 241003, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Yuelong Jin; Yan Chen, Department of Epidemiology and Biostatistics, School of Public Health, and Institute of Chronic Disease Prevention and Control, Wannan Medical College, No. 22, Wenchang Road, Wuhu, Anhui, 241002, People’s Republic of China, Email jinyl0803@wnmc.edu.cn; chenyan2010@wnmc.edu.cnBackground: The prevalence of aggressive behavior among adolescents in higher education settings, particularly within the unique context of China’s one-child policy, has become an increasingly significant issue. This study aims to elucidate the interplay between aggressive behaviors, parenting styles, and self-esteem among college students who are only children.Methods: Conducted as a cross-sectional study from September 2022 to September 2023, the research involved a sample of students from four higher education institutions in Anhui Province, employing a convenience sampling method stratified by year of study and academic major. A total of 5,700 college students participated, with 5,431 valid responses obtained, resulting in a 95.28% validity rate. The average age of the participants was 19.16 ± 1.1 years, with 50.19% male and 49.81% female. The Buss-Perry Aggression Questionnaire (BPAQ), a revised Chinese version of the short Egan’s Memories of Parents’ Education (s-EMBU) questionnaire, and the French version of the Rosenberg Self-Esteem Scale (R-SES) were utilized to assess levels of aggression, parental styles, and self-esteem, respectively. Data were analyzed using SPSS26.0 and SPSS PROCESS Macro.Results: The findings revealed that only children exhibited significantly higher levels of physical aggression (19.26± 5.33 vs 18.41± 4.87, p < 0.001) and hostility (18.64± 5.68 vs 18.30± 5.33, p = 0.046) compared to their non-only child peers. Regression analysis showed that both paternal (β = 0.670, p = 0.004) and maternal rejection (β = 1.095, p < 0.001) positively predicted aggressive behavior, while self-esteem negatively correlated with aggression (β = − 0.375, p < 0.001). Mediation analysis indicated that self-esteem partially mediated the relationship between parental rejection and aggression, accounting for 6.90% and 6.54% of the variance in paternal and maternal rejection, respectively. This suggests that self-esteem nurturing may be a potential strategy to reduce aggression among only-child college students.Conclusion: The study concludes that nurturing self-esteem among only-child college students in China may be a pivotal strategy in curbing aggressive behaviors, underscoring the necessity to comprehend the intricate relationship between parenting styles, self-esteem, and aggression. The insights gained from this research are instrumental in enhancing social cohesion by addressing the distinctive needs of this demographic.Keywords: aggressive behavior, only-child, parenting styles, self-esteem, Chinese college students

Elette van den Berg, Sebastiaan Rothmann

Purpose: This study investigates the associations between financial accounting students’ self-perceived graduate employability, well-being and institutional embeddedness in a higher education institution. Design/methodology/approach: Financial accounting students (N = 102) participated in a cross-sectional survey. Three measuring instruments were administered: the Self-Perceived Graduate Employability Scale, the Mental Health Continuum – Short Form and the adapted Global Job Embeddedness Scale. Findings/results: The results highlight the strategic role of employability perceptions – particularly internal perceived employability and university commitment – in promoting key aspects of financial accounting student well-being and embeddedness. Internal perceived employability predicted both psychological and social well-being, while university commitment emerged as a robust predictor of social and emotional well-being and university embeddedness. Practical implications: Enhancing financial accounting students’ perceived internal employability and strengthening their commitment to the university can significantly improve their psychological, social and emotional well-being. These factors also support greater university embeddedness, highlighting their value for individual development and institutional retention strategies. Originality/value: Initiatives aimed at enhancing students’ confidence in their employability and strengthening their commitment to the institution may yield broad individual well-being and institutional benefits. Investing in employability could be vital for universities seeking to foster the well-being and embeddedness of financial accounting students within the institution.

Julius März, Lianne P. de Vries, Hanneke Scholten et al.

The Experience Sampling Method (ESM) can help young people gain insight into their fluctuating emotions through multiple daily surveys. This can act as an intervention to improve well-being and mental health. However, the effectiveness of ESM interventions is expected to depend on compliance, i.e., how often participants respond to these surveys. We aimed to understand compliance patterns among young people during an ESM-based intervention, explored predictors of these patterns, and examined if the intervention's impact on well-being and mental health varied with compliance levels.Dutch adolescents and young adults (N = 1139, 12–25 years, mean age = 17.67; 79 % female) completed baseline questionnaires, responded to five daily ESM surveys over three weeks using the Grow It! app, and completed follow-up questionnaires.Despite overall low compliance (mean compliance = 33.8 %), latent class growth analyses identified four compliance patterns: stable high (N = 176; M = 78.8 %), stable medium (N = 193; M = 50.1 %), high initial and decreasing (N = 272; M = 30.9 %), and low initial and decreasing (N = 498; M = 13.2 %). These patterns were not consistently associated with age, gender, education, baseline well-being, or depressive and anxiety symptoms, and did not influence the intervention's effect on well-being and mental health outcomes.We identified specific ESM compliance patterns among young people but found no clear predictors or outcomes of these patterns. To improve compliance and intervention effectiveness, future ESM interventions can potentially be enhanced by personalized designs, incorporating intrinsic and extrinsic motivators, and investigating situational factors and additional participant characteristics.

Petronella Jonck, Calvin Mabaso

Purpose: The research examined how decent work conditions influence economic growth, more specifically the impact on private household direct retirement investments. Pension or retirement fund contributions, which ought to be invested, accruing interest, served as a proxy for long-term direct investments, which would theoretically lead to economic growth, highlighting the role of improved labour standards in driving economic growth. Design/methodology/approach: The rationale for the study was to address the lacuna of empirical evidence underscoring decent work conditions in the agricultural sector and the effect thereof on economic growth, such as long-term investment in a retirement fund. Data from 1006 agricultural workers obtained by means of the Quarterly Labour Force Survey administered by Statistics South Africa were analysed quantitatively. The sample was generated by means of a stratified two-pronged sampling technique. Descriptive and inferential statistical analyses were computed. Findings/results: All the facets of decent work statistically significantly influenced the economic growth proxy except for the employee contract. A significant portion of the sample reported having access to fundamental rights such as paid leave and paid sick leave. Conversely, 91.7% of the sample did not have trade union membership, limiting collective bargaining power within the sector. Practical implications: The agricultural sector could increase its contribution to economic growth by stimulating direct investment, for example, by providing agricultural employees retirement fund benefits. Originality/value: The study highlighted gaps in the sectoral decent work conditions and emphasises the role of employee benefits in fostering economic growth, providing actionable insights for policy makers to improve labour conditions.

Anju Rani, Daniel Ortiz-Arroyo, Petar Durdevic

In recent years, 3D point clouds (PCs) have gained significant attention due to their diverse applications across various fields, such as computer vision (CV), condition monitoring (CM), virtual reality, robotics, autonomous driving, etc. Deep learning (DL) has proven effective in leveraging 3D PCs to address various challenges encountered in 2D vision. However, applying deep neural networks (DNNs) to process 3D PCs presents unique challenges. This paper provides an in-depth review of recent advancements in DL-based industrial CM using 3D PCs, with a specific focus on defect shape classification and segmentation within industrial applications. Recognizing the crucial role of these aspects in industrial maintenance, the paper offers insightful observations on the strengths and limitations of the reviewed DL-based PC processing methods. This knowledge synthesis aims to contribute to understanding and enhancing CM processes, particularly within the framework of remaining useful life (RUL), in industrial systems.

Sharon Ferguson, Georgia Van de Zande, Alison Olechowski

The data created from virtual communication platforms presents the opportunity to explore automated measures for monitoring team performance. In this work, we explore one important characteristic of successful teams - Psychological Safety - or the belief that a team is safe for interpersonal risk-taking. To move towards an automated measure of this phenomenon, we derive virtual communication characteristics and message keywords related to elements of Psychological Safety from the literature. Using a mixed methods approach, we investigate whether these characteristics are present in the Slack messages from two design teams - one high in Psychological Safety, and one low. We find that some usage characteristics, such as replies, reactions, and user mentions, might be promising metrics to indicate higher levels of Psychological Safety, while simple keyword searches may not be nuanced enough. We present the first step towards the automated detection of this important, yet complex, team characteristic.

Rafael Souza, Jia-Hao Lim, Alexander Davis

Psychological consultation is essential for improving mental health and well-being, yet challenges such as the shortage of qualified professionals and scalability issues limit its accessibility. To address these challenges, we explore the use of large language models (LLMs) like GPT-4 to augment psychological consultation services. Our approach introduces a novel layered prompting system that dynamically adapts to user input, enabling comprehensive and relevant information gathering. We also develop empathy-driven and scenario-based prompts to enhance the LLM's emotional intelligence and contextual understanding in therapeutic settings. We validated our approach through experiments using a newly collected dataset of psychological consultation dialogues, demonstrating significant improvements in response quality. The results highlight the potential of our prompt engineering techniques to enhance AI-driven psychological consultation, offering a scalable and accessible solution to meet the growing demand for mental health support.

Anna Topchylo

Kang L, Li C, Chen D et al.

Lili Kang,1 Changle Li,1 Duohui Chen,2 Xinxin Bao3 1School of Health Management, Fujian Medical University, Fuzhou, People’s Republic of China; 2College of Public Health Sciences, Chulalongkorn University, Bangkok, Thailand; 3School of Foreign Language Studies, Inner Mongolia Medical University, Hohhot, People’s Republic of ChinaCorrespondence: Xinxin Bao, School of Foreign Language Studies, Inner Mongolia Medical University, Hohhot, 010110, People’s Republic of China, Email 20120027@immu.edu.cnObjective: This study was conducted to identify the factors (especially parental involvement, academic self-efficacy, and depression) associated with academic performance among Chinese K-12 students during the COVID-19 pandemic.Methods: This cross-sectional study used data from the 2020 China Family Panel Studies (CFPS). The CFPS was conducted from July to December 2020 during the COVID-19 pandemic. A multistage probability sample proportional to size was used for the survey. The final sample consisted of 1747 K-12 students. This study used the 14-item Chinese Parental Involvement and Support Scale, the Responsibility Scale, and the 8-item Center for Epidemiologic Studies Depression Scale to measure parental involvement, academic self-efficacy, and depression, respectively. An ordered probit regression and structural equation models were employed to analyze the factors associated with academic performance. A multiple imputation technique was applied to compute missing values in selected variables.Results: We found that parental involvement and academic self-efficacy were positively associated with good academic performance. In contrast, depression was negatively associated with good academic performance. Moreover, academic stress, male, rural residency, middle school, family size, high income, online gaming daily, reading, and intelligence quotient were statistically significant predictors on academic performance.Conclusion: The empirical findings suggested that parental involvement and academic self-efficacy were positively and significantly associated with good academic performance. However, depression was negatively and significantly associated with good academic performance. These results showed that policymakers and practitioners can help improve academic success and address educational inequalities among K-12 students by implementing a series of reforms.Keywords: parental involvement, academic self-efficacy, depression, academic performance, K-12, COVID-19 pandemic, China

Dmitry Zhevnenko, Mikhail Kazantsev, Ilya Makarov

The paper deals with the problem of controlling the state of industrial devices according to the readings of their sensors. The current methods rely on one approach to feature extraction in which the prediction occurs. We proposed a technique to build a scalable model that combines multiple different feature extractor blocks. A new model based on sequential sensor space analysis achieves state-of-the-art results on the C-MAPSS benchmark for equipment remaining useful life estimation. The resulting model performance was validated including the prediction changes with scaling.

Sérgio F. Chevtchenko, Elisson da Silva Rocha, Monalisa Cristina Moura Dos Santos et al.

Anomaly detection is critical in the smart industry for preventing equipment failure, reducing downtime, and improving safety. Internet of Things (IoT) has enabled the collection of large volumes of data from industrial machinery, providing a rich source of information for Anomaly Detection. However, the volume and complexity of data generated by the Internet of Things ecosystems make it difficult for humans to detect anomalies manually. Machine learning (ML) algorithms can automate anomaly detection in industrial machinery by analyzing generated data. Besides, each technique has specific strengths and weaknesses based on the data nature and its corresponding systems. However, the current systematic mapping studies on Anomaly Detection primarily focus on addressing network and cybersecurity-related problems, with limited attention given to the industrial sector. Additionally, these studies do not cover the challenges involved in using ML for Anomaly Detection in industrial machinery within the context of the IoT ecosystems. This paper presents a systematic mapping study on Anomaly Detection for industrial machinery using IoT devices and ML algorithms to address this gap. The study comprehensively evaluates 84 relevant studies spanning from 2016 to 2023, providing an extensive review of Anomaly Detection research. Our findings identify the most commonly used algorithms, preprocessing techniques, and sensor types. Additionally, this review identifies application areas and points to future challenges and research opportunities.

Jane Savitri, Heliany Kiswantomo, Grace Naulee Tambun

Student engagement is essential for academic success and holistic development, yet many studies have examined only isolated predictors of engagement. While self-esteem and life satisfaction have each been linked to engagement, few studies have investigated their combined effect, particularly within non-Western contexts such as Indonesia. Addressing this gap, the present study examines the joint and individual contributions of self-esteem and life satisfaction to student engagement among 397 active university students in Bandung City. Using a quantitative correlational design, data were collected through the University Student Engagement Inventory (USEI), the Rosenberg Self-Esteem Scale (RSES), and the Satisfaction with Life Scale (SWLS). Multiple regression analysis revealed that self-esteem and life satisfaction significantly predicted student engagement, both independently and simultaneously (R² = 0.31, p < .001). Self-esteem emerged as the stronger predictor, accounting for 21.3% of the variance, compared to 9.7% from life satisfaction. These findings underscore the importance of fostering both self-esteem and life satisfaction in educational settings to enhance student engagement. The study contributes a novel perspective by demonstrating the synergistic influence of these two psychological factors in a culturally specific context. Practical implications include the need for integrated student development programs that promote self-worth and subjective well-being. The study calls for future research to examine how each predictor influences different dimensions of engagement—behavioral, emotional, and cognitive—over time.