Chong Gao, Xiaolong Zhao, Shuang Li School of Design and Architecture, Zhejiang University of Technology, Hangzhou, Zhejiang, People’s Republic of ChinaCorrespondence: Shuang Li, School of Design and Architecture, Zhejiang University of Technology, Hangzhou, 310023, People’s Republic of China, Tel +86 571 85290827, Email leesh@zjut.edu.cnPurpose: Cognitive reappraisal can improve mental state by reinterpreting events and thoughts. This study investigated the effects of a five-day cognitive reappraisal training program on mental resilience, depression level, and emotional state in 63 Chinese individuals with depressive disorder.Patients and Methods: This pre–post intervention study recruited participants through verified online depression support groups. Training was conducted daily over five consecutive days (each session lasting 60 minutes). A total of 63 individuals participated (age range 16– 42 years, M=24, SD=5), including 26 males and 37 females. Based on baseline SDS scores, 27.0% met criteria for minor depression, 31.7% for moderate depression, and 41.3% for major depression. A 5-day intervention comprising daily 30– 40-minute sessions of cognitive reappraisal training using IAPS images was implemented. Patient-generated reappraisal strategies were collected as written responses and systematically coded by researchers for analysis. Participants completed the Reappraisal Inventiveness Test (RIT), Brief Resilience Scale (BRS), Self-Rating Depression Scale (SDS), and Positive and Negative Affect Scale (PANAS) before training, on day 2 and day 5 during training, and 10 days post-training. Data were analyzed using SPSS 27.0, including paired-sample t-tests, Wilcoxon signed-rank tests, Spearman correlation, and independent-sample t-tests with Bonferroni correction.Results: Cognitive reappraisal ability (CR1: mean increase from 2.349 to 3.365, P< 0.05), mental resilience (mean increase from 12.333 to 13.540, P< 0.05), and emotional state improved during training (ES: mean increase from 29.825 to 40.095, P< 0.05), while depression level declined, though changes in depression were not statistically significant. Improvements persisted to 10 days after training, albeit slightly diminished. Cognitive reappraisal ability correlated positively with mental resilience (P< 0.05) and negatively with depression level (P< 0.05). The strategy of “humor interpretation” significantly enhanced mental resilience and reduced depression (P< 0.05), while “generating positive aspects” only significantly enhanced mental resilience (P< 0.05). The differences in emotional state across strategies were not significant. Demographic analyses indicated a larger reduction in depression levels among younger participants (16– 25 years)(P< 0.05) and those with minor depression (p< 0.01), while gender and regional differences were nonsignificant.Conclusion: Cognitive reappraisal training can improve mental well-being in the short-term in individuals with depressive disorder. Extended training may enhance these effects.Keywords: reappraisal, depressive disorder, mental health, emotion, pre-post intervention
Despite rapid advances in autonomous driving technology, current autonomous vehicles (AVs) lack effective bidirectional human-machine communication, limiting their ability to personalize the riding experience and recover from uncertain or immobilized states. This limitation undermines occupant comfort and trust, potentially hindering the adoption of AV technologies. We propose PACE-ADS (Psychology and Cognition Enabled Automated Driving Systems), a human-centered autonomy framework enabling AVs to sense, interpret, and respond to both external traffic conditions and internal occupant states. PACE-ADS uses an agentic workflow where three foundation model agents collaborate: the Driver Agent interprets the external environment; the Psychologist Agent decodes passive psychological signals (e.g., EEG, heart rate, facial expressions) and active cognitive inputs (e.g., verbal commands); and the Coordinator Agent synthesizes these inputs to generate high-level decisions that enhance responsiveness and personalize the ride. PACE-ADS complements, rather than replaces, conventional AV modules. It operates at the semantic planning layer, while delegating low-level control to native systems. The framework activates only when changes in the rider's psychological state are detected or when occupant instructions are issued. It integrates into existing AV platforms with minimal adjustments, positioning PACE-ADS as a scalable enhancement. We evaluate it in closed-loop simulations across diverse traffic scenarios, including intersections, pedestrian interactions, work zones, and car-following. Results show improved ride comfort, dynamic behavioral adjustment, and safe recovery from edge-case scenarios via autonomous reasoning or rider input. PACE-ADS bridges the gap between technical autonomy and human-centered mobility.
As large language models (LLMs) are increasingly used in human-centered tasks, assessing their psychological traits is crucial for understanding their social impact and ensuring trustworthy AI alignment. While existing reviews have covered some aspects of related research, several important areas have not been systematically discussed, including detailed discussions of diverse psychological tests, LLM-specific psychological datasets, and the applications of LLMs with psychological traits. To address this gap, we systematically review six key dimensions of applying psychological theories to LLMs: (1) assessment tools; (2) LLM-specific datasets; (3) evaluation metrics (consistency and stability); (4) empirical findings; (5) personality simulation methods; and (6) LLM-based behavior simulation. Our analysis highlights both the strengths and limitations of current methods. While some LLMs exhibit reproducible personality patterns under specific prompting schemes, significant variability remains across tasks and settings. Recognizing methodological challenges such as mismatches between psychological tools and LLMs' capabilities, as well as inconsistencies in evaluation practices, this study aims to propose future directions for developing more interpretable, robust, and generalizable psychological assessment frameworks for LLMs.
Memory, a fundamental component of human cognition, exhibits adaptive yet fallible characteristics as illustrated by Schacter's memory "sins".These cognitive phenomena have been studied extensively in psychology and neuroscience, but the extent to which artificial systems, specifically Large Language Models (LLMs), emulate these cognitive phenomena remains underexplored. This study uses human memory research as a lens for understanding LLMs and systematically investigates human memory effects in state-of-the-art LLMs using paradigms drawn from psychological research. We evaluate seven key memory phenomena, comparing human behavior to LLM performance. Both people and models remember less when overloaded with information (list length effect) and remember better with repeated exposure (list strength effect). They also show similar difficulties when retrieving overlapping information, where storing too many similar facts leads to confusion (fan effect). Like humans, LLMs are susceptible to falsely "remembering" words that were never shown but are related to others (false memories), and they can apply prior learning to new, related situations (cross-domain generalization). However, LLMs differ in two key ways: they are less influenced by the order in which information is presented (positional bias) and more robust when processing random or meaningless material (nonsense effect). These results reveal both alignments and divergences in how LLMs and humans reconstruct memory. The findings help clarify how memory-like behavior in LLMs echoes core features of human cognition, while also highlighting the architectural differences that lead to distinct patterns of error and success.
Theories on stereotype content suggest that job applicants should be perceived as more hireable when they belong to a social group whose stereotype content matches that of the occupation. However, few experimental studies have examined matching effects from the perspective of the stereotype content model (SCM; Fiske et al. 2002), simultaneously focusing on the combination of multiple social categories. This survey experiment examines whether matching occurs at the intersection of gender and sexual orientation, which gives rise to different warmth and competence stereotype content profiles according to the SCM. Similarly, matching is examined in relation to occupations characterized by different warmth and competence profiles. Participants consisted of 1,563 employees recruited through Prolific. They were asked to assess job suitability for (otherwise identical) gay and straight male and female jobseekers who had ostensibly submitted letters of interest for positions belonging to occupations whose stereotype content matched (e.g., personal assistant) or were opposite (e.g., accountant) of that, of the job-seeker’s social group (e.g., gay men). The results yielded no support for a matching effect. Instead, gay applicants received more positive evaluations regardless of gender and occupation. Field experimental research on actual hiring decisions is needed to examine whether the lack of matching effect is replicated in the real labor market.
This paper addresses the challenges of vision-based manipulation for autonomous cutting and unpacking of transparent plastic bags in industrial setups, aligning with the Industry 4.0 paradigm. Industry 4.0, driven by data, connectivity, analytics, and robotics, promises enhanced accessibility and sustainability throughout the value chain. The integration of autonomous systems, including collaborative robots (cobots), into industrial processes is pivotal for efficiency and safety. The proposed solution employs advanced Machine Learning algorithms, particularly Convolutional Neural Networks (CNNs), to identify transparent plastic bags under varying lighting and background conditions. Tracking algorithms and depth sensing technologies are utilized for 3D spatial awareness during pick and placement. The system addresses challenges in grasping and manipulation, considering optimal points, compliance control with vacuum gripping technology, and real-time automation for safe interaction in dynamic environments. The system's successful testing and validation in the lab with the FRANKA robot arm, showcases its potential for widespread industrial applications, while demonstrating effectiveness in automating the unpacking and cutting of transparent plastic bags for an 8-stack bulk-loader based on specific requirements and rigorous testing.
Fabrice Harel-Canada, Hanyu Zhou, Sreya Muppalla
et al.
Evaluations of creative stories generated by large language models (LLMs) often focus on objective properties of the text, such as its style, coherence, and diversity. While these metrics are indispensable, they do not speak to a story's subjective, psychological impact from a reader's perspective. We introduce the Psychological Depth Scale (PDS), a novel framework rooted in literary theory that measures an LLM's ability to produce authentic and narratively complex stories that provoke emotion, empathy, and engagement. We empirically validate our framework by showing that humans can consistently evaluate stories based on PDS (0.72 Krippendorff's alpha). We also explore techniques for automating the PDS to easily scale future analyses. GPT-4o, combined with a novel Mixture-of-Personas (MoP) prompting strategy, achieves an average Spearman correlation of 0.51 with human judgment while Llama-3-70B with constrained decoding scores as high as 0.68 for empathy. Finally, we compared the depth of stories authored by both humans and LLMs. Surprisingly, GPT-4 stories either surpassed or were statistically indistinguishable from highly-rated human-written stories sourced from Reddit. By shifting the focus from text to reader, the Psychological Depth Scale is a validated, automated, and systematic means of measuring the capacity of LLMs to connect with humans through the stories they tell.
M. Henderson, J. P. Edelen, J. Einstein-Curtis
et al.
Industrial particle accelerators typically operate in dirtier environments than research accelerators, leading to increased noise in RF and electronic systems. Furthermore, given that industrial accelerators are mass produced, less attention is given to optimizing the performance of individual systems. As a result, industrial accelerators tend to underperform their own hardware capabilities. Improving signal processing for these machines will improve cost and time margins for deployment, helping to meet the growing demand for accelerators for medical sterilization, food irradiation, cancer treatment, and imaging. Our work focuses on using machine learning techniques to reduce noise in RF signals used for pulse-to-pulse feedback in industrial accelerators. Here we review our algorithms and observed results for simulated RF systems, and discuss next steps with the ultimate goal of deployment on industrial systems.
Secure and efficient communication to establish a seamless nexus between the five levels of a typical automation pyramid is paramount to Industry 4.0. Specifically, vertical and horizontal integration of these levels is an overarching requirement to accelerate productivity and improve operational activities. Vertical integration can improve visibility, flexibility, and productivity by connecting systems and applications. Horizontal integration can provide better collaboration and adaptability by connecting internal production facilities, multi-site operations, and third-party partners in a supply chain. In this paper, we propose an Edge-computing-based Industrial Gateway for interfacing information technology and operational technology that can enable Industry 4.0 vertical and horizontal integration. Subsequently, we design and develop a working prototype to demonstrate a remote production-line maintenance use case with a strong focus on security aspects and the edge paradigm to bring computational resources and data storage closer to data sources.
Nathan P. Lawrence, Seshu Kumar Damarla, Jong Woo Kim
et al.
With the rise of deep learning, there has been renewed interest within the process industries to utilize data on large-scale nonlinear sensing and control problems. We identify key statistical and machine learning techniques that have seen practical success in the process industries. To do so, we start with hybrid modeling to provide a methodological framework underlying core application areas: soft sensing, process optimization, and control. Soft sensing contains a wealth of industrial applications of statistical and machine learning methods. We quantitatively identify research trends, allowing insight into the most successful techniques in practice. We consider two distinct flavors for data-driven optimization and control: hybrid modeling in conjunction with mathematical programming techniques and reinforcement learning. Throughout these application areas, we discuss their respective industrial requirements and challenges. A common challenge is the interpretability and efficiency of purely data-driven methods. This suggests a need to carefully balance deep learning techniques with domain knowledge. As a result, we highlight ways prior knowledge may be integrated into industrial machine learning applications. The treatment of methods, problems, and applications presented here is poised to inform and inspire practitioners and researchers to develop impactful data-driven sensing, optimization, and control solutions in the process industries.
Neave O'Clery, Juan Chaparro, Andres Gomez-Lievano
et al.
What drives formal employment creation in developing cities? We find that larger cities, home to an abundant set of complex industries, employ a larger share of their working age population in formal jobs. We propose a hypothesis to explain this pattern, arguing that it is the organised nature of formal firms, whereby workers with complementary skills are coordinated in teams, that enables larger cities to create more formal employment. From this perspective, the growth of formal employment is dependent on the ability of a city to build on existing skills to enter new complex industries. To test our hypothesis, we construct a variable which captures the skill-proximity of cities' current industrial base to new complex industries, termed 'complexity potential'. Our main result is that complexity potential is robustly associated with subsequent growth of the formal employment rate in Colombian cities.
Yu Gao,1 Haiyan Liu,2 Yuechi Sun2 1School of Psychology, Shandong Second Medical University, Weifang, Shandong, People’s Republic of China; 2School of Economics and Management, China University of Geosciences (Beijing), Beijing, People’s Republic of ChinaCorrespondence: Haiyan Liu, School of Economics and Management, China University of Geosciences (Beijing), 29 Xueyuan Road, Haidian District, Beijing, 100083, People’s Republic of China, Email liuhy@cugb.edu.cnBackground: In the digital age, people’s attitudes and psychological security towards public health emergencies will be shared. Similar or identical psychological security states are prone to clustering and differentiation, while differentiated group psychological security is more prone to polarization, leading to group psychological security risks and then posing a threat to social stability and national security. However, existing studies mostly use qualitative analysis methods to study group emotional risks. There are still limitations in the study of dynamics of group psychological security risks through mining real data of social media.Purpose: The study aims to use intelligent analysis methods to understand how group psychological security risks dynamically change.Methods: The study draws on text sentiment analysis, Markov chains and time series analysis to construct a framework for the evolution of group psychological security risks. Based on this framework, text data was crawled on Sina Weibo platform, mainly consisting of posts during public health emergencies (March 1st to June 30th, 2022) in Shanghai, and a psychological security lexicon in the field of public health emergencies was constructed. This laid the foundation for identifying the tendencies, intensity, and transitions of individual text psychological security, and then exploring the evolution trend of group psychological security risks.Results: Compared with the generation and reduction periods, group psychological security risks are more likely to occur during the outbreak and recovery periods, and the intensity level is also higher. The overall intensity of group psychological security risks shows an evolution trend of first increasing, then decreasing, and then increasing again.Conclusion: The paper provides an opportunity to explore the dynamics of psychological security in the digital space. Meanwhile, we call on the government and relevant management departments to pay more attention to the group psychological security risks formed during the outbreak and recovery periods of public health emergencies, and take corresponding measures in a timely manner to guide the public to transform the extreme psychological security state into the normal psychological security state, in order to prevent and resolve group psychological security risks, promote social stability and national security.Keywords: group psychological security risks, psychological security lexicon, machine learning, evolution trend
Katie Crabtree, Gavin Clark, Tracy Donachie
et al.
This exploratory feasibility study examined a wellbeing coaching programme developed from the theory of socio-cognitive mindfulness (Langer, 1989). Six participants were recruited to attend the six-week programme and to complete surveys measuring mindfulness and wellbeing at baseline, post-intervention and follow-up. High attendance and completion rates suggest that the study and intervention procedures were feasible, with a preliminary assessment of outcomes indicating that the intervention may be effective in some cases for improving mindfulness and wellbeing. Participant responses infer that the coaching programme was acceptable and well-received but with suggestions for improvement which can inform intervention refinement and potential coach training.
Special aspects of education, Industrial psychology
Background: Comorbid sleep disturbances are common among individuals with chronic pain, and Cognitive Behavioural Therapy for Insomnia (CBT-i) has proven effective for such individuals. Nonetheless, research on web-based CBT-i tailored for patients with both chronic pain and insomnia is limited. This study aimed to evaluate the feasibility and efficacy of internet-based CBT-i and to explore potential mechanisms underlying treatment outcomes. Methods: In this study, 85 participants suffering from comorbid insomnia and chronic pain were randomized into two groups: Internet-based CBT for Insomnia (ICBT-i) and Internet-based Applied Relaxation (IAR). Both interventions spanned eight weeks, supported by therapeutic guidance throughout. Results: Participation was modest, with an average module completion of 2.0 out of 8 for ICBT-i and 2.4 for IAR. Both interventions significantly alleviated insomnia symptoms on one of the insomnia measures post-treatment, without notable differences between them. Directly after treatment, IAR outperformed ICBT-i in reducing pain interference, anxiety, and in enhancing self-rated health, though these differences lessened at the 6-month follow-up. Potential therapeutic mechanisms may involve attenuating maladaptive sleep beliefs and augmenting sleep-related willingness. Conclusions: The study encountered low engagement rates, with approximately one-third of participants not completing any module. The limited efficacy of ICBT-i may be due to low treatment involvement, with few patients completing key techniques like sleep compression and stimulus control. Despite the low adherence, both interventions yielded post-treatment improvements in insomnia symptoms, but to establish internet-based treatments for insomnia as a viable option in chronic pain management, patient engagement must be improved.
Nova Nita Anggun Prasiska, Hudaniah Hudaniah, Devina Andriany
A mother who decides to work often experiences role conflict because she cannot divide her time to carry out her roles, both as a mother and worker, which can cause stress and low life satisfaction. This has an impact on her subjective well-being. Subjective well-being is a person’s evaluation of their life, including life satisfaction, experienced emotions, and fulfillment. The social support of a spouse or husband is one of the variables that influence it. Therefore, this study aims to determine how much influence the husband’s support has on the subjective well-being of working mothers. This study uses a quantitative design with a simple regression analysis calculation method. The number of samples in this study was 140 participants who were actively involved, obtained through the purposive sampling technique. The scale used to measure the husband’s support was Receipt of Spousal Support Items, while the scale to measure subjective well-being used the Satisfaction with Life Scale (SWLS) and Positive Affect Negative Affect Schedule (PANAS). This study shows that there is an influence of the husband’s support on the 3 aspects of subjective well-being in working mothers with the value of p < 0.01. p (1, 138) = 64.11, F = 0,317, 2R < 0.01 The husband’s support also explained a significant proportion of the variation in positive aspect scores, (138) = 0.027, t= 0.217 p < 0.01. p (1, 138) = 52.71, F = 0,276, R2 < 0.01 The husband’s also explained a significant proportion of the variation in positive affect scores, (138) = 0.038, t= .278. p < 0.01. p (1, 138) = 71.10, F = 3,40, R2 < .01. The husband’s also explained a significant proportion of the variation in negative affect scores, (138) = 0.042, t = 0.-353. The implication of the findings of this study is that the husband’s support is able to balance emotions in a working mother so as to create satisfaction in life, which is an aspect of forming subjective well-being.
Probability and non-probability sampling methods are used by researchers to learn about a population (Maree & Pietersen, 2016). The adequacy of these samples is determined by the composition, size (Vasileiou et al., 2018) and the chosen sample’s representativeness (Hanges & Wang, 2012). Despite being at the heart of research, psychology still pays little attention to sampling methodology (Fisher & Sandell, 2015; Robinson, 2014). Scholtz, De Klerk and De Beer (2020) found a lack of transparency in top-tier miscellaneous international psychology journals regarding the sampling methods. This lack of transparency was evident in articles regardless of the applied research method (e.g. qualitative and quantitative research methods). Furthermore, the justification for using specific sampling techniques is rare in industrial and organisational Orientation: Articles from three African psychology journals were reviewed to indicate their use and reporting practices of convenience samples.
E. Kossek, Matthew B. Perrigino, Alyson Gounden Rock
Abstract Looking over fifty years, we review the careers literature,grounded in vocational psychology; and the work-family literature,rooted in industrial-organizational psychology and organizational behavior (IO/OB); in order to identify commonalities and gaps. Historically, these streams were not well-integrated, developed at separate speeds, and differed in gender focus. Early career studies targeted men's careers. Work-family studies centered on women's careers. The 71 studies identified clustered into three main approaches: careers studies emphasizing vocational psychology lenses; work-family theories from IO/OB research, and dual-realm focused theories from other disciplines. Two-thirds of the articles were conceptual. Most empirical articles took a trade-off lens, assuming an incompatibility between high dual role investments in career and family. To guide the next decades' future research, we propose expansion to four integrative lenses: Whole Life Demands-Resources; Linked-Lives of Family Life Course and Career Stages; Diversity, Inclusion and Multiple Identities; and Ideal Work in Changing Social, Technological, and Economic Contexts.
Haoping Bai, Shancong Mou, Tatiana Likhomanenko
et al.
Despite progress in vision-based inspection algorithms, real-world industrial challenges -- specifically in data availability, quality, and complex production requirements -- often remain under-addressed. We introduce the VISION Datasets, a diverse collection of 14 industrial inspection datasets, uniquely poised to meet these challenges. Unlike previous datasets, VISION brings versatility to defect detection, offering annotation masks across all splits and catering to various detection methodologies. Our datasets also feature instance-segmentation annotation, enabling precise defect identification. With a total of 18k images encompassing 44 defect types, VISION strives to mirror a wide range of real-world production scenarios. By supporting two ongoing challenge competitions on the VISION Datasets, we hope to foster further advancements in vision-based industrial inspection.
Developmental psychologists have spent decades devising experiments to test the intelligence and knowledge of infants and children, tracing the origin of crucial concepts and capacities. Moreover, experimental techniques in developmental psychology have been carefully designed to discriminate the cognitive capacities that underlie particular behaviors. We propose that using classical experiments from child development is a particularly effective way to probe the computational abilities of AI models, in general, and LLMs in particular. First, the methodological techniques of developmental psychology, such as the use of novel stimuli to control for past experience or control conditions to determine whether children are using simple associations, can be equally helpful for assessing the capacities of LLMs. In parallel, testing LLMs in this way can tell us whether the information that is encoded in text is sufficient to enable particular responses, or whether those responses depend on other kinds of information, such as information from exploration of the physical world. In this work we adapt classical developmental experiments to evaluate the capabilities of LaMDA, a large language model from Google. We propose a novel LLM Response Score (LRS) metric which can be used to evaluate other language models, such as GPT. We find that LaMDA generates appropriate responses that are similar to those of children in experiments involving social understanding, perhaps providing evidence that knowledge of these domains is discovered through language. On the other hand, LaMDA's responses in early object and action understanding, theory of mind, and especially causal reasoning tasks are very different from those of young children, perhaps showing that these domains require more real-world, self-initiated exploration and cannot simply be learned from patterns in language input.