Hasil untuk "Industrial psychology"

Keno Moenck, Adrian Philip Florea, Julian Koch et al.

Autonomous vision applications in production, intralogistics, or manufacturing environments require perception capabilities beyond a small, fixed set of classes. Recent open-vocabulary methods, leveraging 2D Vision-Language Foundation Models (VLFMs), target this task but often rely on class-agnostic segmentation models pre-trained on non-industrial datasets (e.g., household scenes). In this work, we first demonstrate that such models fail to generalize, performing poorly on common industrial objects. Therefore, we propose a training-free, open-vocabulary 3D perception pipeline that overcomes this limitation. Instead of using a pre-trained model to generate instance proposals, our method simply generates masks by merging pre-computed superpoints based on their semantic features. Following, we evaluate the domain-adapted VLFM "IndustrialCLIP" on a representative 3D industrial workshop scene for open-vocabulary querying. Our qualitative results demonstrate successful segmentation of industrial objects.

Liora Manelis-Baram, Tal Barami, Michal Ilan et al.

Abstract Background Difficulties with non-verbal communication, including atypical use of facial expressions, are a core feature of autism. Quantifying atypical use of facial expressions during naturalistic social interactions in a reliable, objective, and direct manner is difficult, but potentially possible with facial analysis computer vision algorithms that identify facial expressions in video recordings. Methods We analyzed > 5 million video frames from 100 verbal children, 2-7 years-old (72 with autism and 28 controls), who were recorded during a ~ 45-minute ADOS-2 assessment using modules 2 or 3, where they interacted with a clinician. Three different facial analysis algorithms (iMotions, FaceReader, and Py-Feat) were used to identify the presence of six facial expressions (anger, fear, sadness, surprise, disgust, and happiness) in each video frame. We then compared results across algorithms and across autism and control groups using robust non-parametric statistical tests. Results There were significant differences in the performance of the three facial analysis algorithms including differences in the proportion of frames identified as containing a face and frames classified as containing each of the six examined facial expressions. Nevertheless, analyses across all three algorithms demonstrated that there were no significant differences in the quantity of any facial expression produced by children with autism and controls. Furthermore, the quantity of facial expressions did not correlate with autism symptom severity as measured by ADOS-2 CSS scores. Limitations The current findings are limited to verbal children with autism who completed ADOS-2 assessments using modules 2 and 3 and were able to sit in a stable manner while facing a wall-mounted camera. Furthermore, the analyses focused on comparing the quantity of facial expressions across groups rather than their quality, timing, or social context. Conclusions Commonly used automated facial analysis algorithms exhibit large variability in their output when identifying facial expressions of young children during naturalistic social interactions. Nonetheless, all three algorithms did not identify differences in the quantity of facial expressions across groups, suggesting that atypical production of facial expressions in verbal children with autism is likely related to their quality, timing, and social context rather than their quantity during natural social interaction.

Brodie

Utilising qualitative methodology, this research explored the impact of team coaching on enhancing team mindfulness traits in the context of teams in the workplace. With teams facing ongoing changes and stressors, the quality of interpersonal interactions between team members can suffer, affecting a team’s capacity to be mindful. The research sought to identify whether team coaching fosters an environment whereby team mindfulness behaviours can be heightened. Four areas of perceived change within teams were characterised as relational, behavioural, cognitive and emotional. However, the effectiveness of the intervention can be hindered by the leader’s behaviours. This article highlights the benefits of developing team mindfulness traits through team coaching and offers practical suggestions for coaches to support teams in dynamic environments.

Rubens Kim, Stephan Carney, Yvonne Fonken et al.

We examine whether measured cognitive processes predict cyber-attack behavior. We analyzed data that included psychometric scale responses and labeled attack behaviors from cybersecurity professionals who conducted red-team operations against a simulated enterprise network. We employed multilevel mixed-effects Poisson regression with technique counts nested within participants to test whether cognitive processes predicted technique-specific usage. The scales significantly predicted technique use, but effects varied by technique rather than operating uniformly. Neither expertise level nor experimental treatment condition significantly predicted technique patterns, indicating that cognitive processes may be stronger drivers of technique selection than training or experience. These findings demonstrate that individual cognitive differences shape cyber-attack behavior and support the development of psychology-informed defense strategies.

Vyron Kampourakis, Christos Smiliotopoulos, Vasileios Gkioulos et al.

Traditional air gaps in industrial systems are disappearing as IT technologies permeate the OT domain, accelerating the integration of wireless solutions like Wi-Fi. Next-generation Wi-Fi standards (IEEE 802.11ax/be) meet performance demands for industrial use cases, yet their introduction raises significant security concerns. A critical knowledge gap exists regarding the empirical prevalence and security configuration of Wi-Fi in real-world industrial settings. This work addresses this by mining the global crowdsourced WiGLE database to provide a data-driven understanding. We create the first publicly available dataset of 1,087 high-confidence industrial Wi-Fi networks, examining key attributes such as SSID patterns, encryption methods, vendor types, and global distribution. Our findings reveal a growing adoption of Wi-Fi across industrial sectors but underscore alarming security deficiencies, including the continued use of weak or outdated security configurations that directly expose critical infrastructure. This research serves as a pivotal reference point, offering both a unique dataset and practical insights to guide future investigations into wireless security within industrial environments.

Moussa Koulako Bala Doumbouya, Dan Jurafsky, Christopher D. Manning

Work in psychology has highlighted that the geometric model of similarity standard in deep learning is not psychologically plausible because its metric properties such as symmetry do not align with human perception of similarity. In contrast, Tversky (1977) proposed an axiomatic theory of similarity with psychological plausibility based on a representation of objects as sets of features, and their similarity as a function of their common and distinctive features. This model of similarity has not been used in deep learning before, in part because of the challenge of incorporating discrete set operations. In this paper, we develop a differentiable parameterization of Tversky's similarity that is learnable through gradient descent, and derive basic neural network building blocks such as the Tversky projection layer, which unlike the linear projection layer can model non-linear functions such as XOR. Through experiments with image recognition and language modeling neural networks, we show that the Tversky projection layer is a beneficial replacement for the linear projection layer. For instance, on the NABirds image classification task, a frozen ResNet-50 adapted with a Tversky projection layer achieves a 24.7% relative accuracy improvement over the linear layer adapter baseline. With Tversky projection layers, GPT-2's perplexity on PTB decreases by 7.8%, and its parameter count by 34.8%. Finally, we propose a unified interpretation of both types of projection layers as computing similarities of input stimuli to learned prototypes for which we also propose a novel visualization technique highlighting the interpretability of Tversky projection layers. Our work offers a new paradigm for thinking about the similarity model implicit in modern deep learning, and designing neural networks that are interpretable under an established theory of psychological similarity.

Chang S, Zhang Y, Wang C et al.

Song Chang,1,2 Yaohua Zhang,1,2 Chunxu Wang,1 Fan Xu,1 Yunyun Huang,1,2 Sufei Xin1,2 1College of Education, Ludong University, Yantai, Shandong, People’s Republic of China; 2Collaborative Innovation Center for the Mental Health of Youth from the Era of Conversion of New and Old Kinetic Energy along the Yellow River Basin, Yantai, Shandong, People’s Republic of ChinaCorrespondence: Sufei Xin, College of Education, Ludong University; Collaborative Innovation Center for the Mental Health of Youth from the Era of Conversion of New and Old Kinetic Energy along the Yellow River Basin, No. 186, Hongqi Middle Road, Yantai, Shandong, People’s Republic of China, Email xinsufei2016@ldu.edu.cnPurpose: The alleviating effects of a growth mindset on depression are promising. However, whether a growth mindset can attenuate the effect of low family socioeconomic status (SES) on depressive symptoms among adolescents remains unknown. Based on the Family Stress Model, the current study explores whether a growth mindset could moderate the associations between family SES, interparental conflict, and adolescent depressive symptoms.Methods: The participants were 1572 Chinese adolescents (Mage = 13.35 years, SD = 1.16, 51.84% female). They completed the family SES questionnaire, Children’s Perceptions of Interparental Conflict scale, Growth Mindset scale, and Center for Epidemiologic Studies Depression scale. We tested the moderation, mediation, and moderated mediation models using the SPSS macro program PROCESS.Results: A growth mindset moderated the association between family SES and depressive symptoms. Family SES was significantly related to depressive symptoms in adolescents with a lower growth mindset, but not in those with a higher growth mindset. After incorporating the mediating effect of interparental conflict, the growth mindset did not exert a significant moderating influence on the direct path; however, it significantly moderated the mediating effect of interparental conflict on depressive symptoms. Specifically, while a lower growth mindset in adolescents was associated with an increased risk of depressive symptoms due to interparental conflict, those with a higher growth mindset showed a less pronounced effect.Conclusion: A growth mindset attenuates the link between family SES and depressive symptoms among adolescents. These findings highlight the benefits of a growth mindset on mental health, especially for low-SES adolescents.Keywords: growth mindset, socioeconomic status, depression, adolescent, interparental conflict

Frida Johnson, Sara Frygner Holm, Andrea Hess Engström

Background: Pelvic girdle pain, low back pain, and pelvic floor dysfunction can affect women's mobility, quality of life, and well-being during pregnancy and the postpartum period. Digital interventions for treating perinatal depression and lifestyle changes have been studied. Research on digital physiotherapy for musculoskeletal issues related to pregnancy and the postpartum period is sparse. Methods: This qualitative study involved in-depth, semi-structured interviews with 19 participants, of whom six were pregnant and 13 had given birth. Participants were recruited from a private clinic in Sweden through convenience sampling and had received digital physiotherapy prior to the interviews. An interview guide with questions exploring participants' experiences of digital physiotherapy, including its impact on musculoskeletal issues and daily life, and their motivation for seeking digital healthcare was used. Data were analyzed using a qualitative content analysis with an inductive approach. Results: The analysis resulted in two main categories: Finding a new way into physiotherapy treatment and Personalized progress through tailored physiotherapy. These main categories encompassed four generic categories: Convenience and dissatisfaction motivators for digital physiotherapy, A dual experience – appreciated but not always comprehensive, Being involved in the rehabilitation process, and Perceived physical and mental improvements after digital physiotherapy. Conclusion: Digital physiotherapy was well-accepted and perceived as beneficial for managing musculoskeletal symptoms during pregnancy and after childbirth. High accessibility and flexibility were considered advantages. However, inability to undergo a physical assessment was a challenge. Digital physiotherapy may be recommended as a complement to usual care, particularly for women with limited access to a physiotherapist specialized in women's health. Future studies exploring digital physiotherapy's efficacy for musculoskeletal issues during pregnancy and after childbirth are highly recommended.

Jie Sun, Mahlagha Dehghan, Yaser Soltanmoradi et al.

Abstract Background The Covid-19 pandemic has affected all areas of society, including students. However, medical students have faced many challenges due to direct contact with patients. The present study was conducted with the aim of investigating and comparing the quality of life (QoL), anxiety and mindfulness between Iranian medical and non-medical students during the COVID-19 pandemic. Method Five hundred and six students (both medical and non-medical students) participated in the study from August to October 2022 with a convenience sampling method. The data were collected using an online questionnaire including a demographic form, the QoL Questionnaire (WHOQOL- BREF), the Generalized Anxiety Disorder 7-item survey (GAD‐7) and the Relaxation/Meditation/Mindfulness Tracker t-Persian version survey (RMMt-P). Pearson correlation and independent t-test and multivariate linear regression were used to determine the relationship between the study variables. Results The samples included 272 medical students and 234 non-medical students with a mean age of 21.99 ± 3.46 and 24.17 ± 6.54 years respectively. Most of the medical and non-medical students were female, single and had a bachelor’s degree. The mean scores of medical and non-medical students’ QoL during the COVID-19 pandemic were 57.86 ± 13.26 and 56.75 ± 14.42, respectively which indicates the higher quality of life of medical students. Anxiety and mindfulness predicted 29% of the variance of the QoL in the medical students, while anxiety and mindfulness and having a chronic disease predicted 30% of the variance of the QoL in the non-medical students. No significant difference existed in the QoL and its subscales during the COVID-19 pandemic between medical and non-medical students (p > 0.05). There was a significant difference in terms of anxiety (p = 0.02) and mindfulness (p = 0.03) between medical and non-medical students during the prevalence of COVID-19. Discussion and conclusion The findings of the present study indicated that medical students exhibited lower levels of anxiety and higher levels of mindfulness. Therefore, interventions aimed at reducing anxiety and increasing mindfulness among non-medical students are necessary. It is recommended that preventive approaches and psychological interventions to improve students’ quality of life become an integral part of crisis response during the COVID-19 pandemic. Additionally, reducing anxiety and enhancing mindfulness can improve the quality of education and professional performance of medical students, while also contributing to their mental well-being and effective clinical communication.

Siqi Wang, Chao Liang, Yunfan Gao et al.

Industrial parks are critical to urban economic growth. Yet, their development often encounters challenges stemming from imbalances between industrial requirements and urban services, underscoring the need for strategic planning and operations. This paper introduces IndustryScopeKG, a pioneering large-scale multi-modal, multi-level industrial park knowledge graph, which integrates diverse urban data including street views, corporate, socio-economic, and geospatial information, capturing the complex relationships and semantics within industrial parks. Alongside this, we present the IndustryScopeGPT framework, which leverages Large Language Models (LLMs) with Monte Carlo Tree Search to enhance tool-augmented reasoning and decision-making in Industrial Park Planning and Operation (IPPO). Our work significantly improves site recommendation and functional planning, demonstrating the potential of combining LLMs with structured datasets to advance industrial park management. This approach sets a new benchmark for intelligent IPPO research and lays a robust foundation for advancing urban industrial development. The dataset and related code are available at https://github.com/Tongji-KGLLM/IndustryScope.

Francesco Sanfedino, Paolo Iannelli, Daniel Alazard et al.

To overcome the innovation gap of the Guidance, Navigation and Control (GNC) design process between research and industrial practice a benchmark of industrial relevance has been developed and is presented. This initiative is driven as well by the necessity to train future GNC engineers and the GNC space community on a set of identified complex problems. It allows to demonstrate the relevance of state-of-the-art modeling, control and analysis algorithms for future industrial adoption. The modeling philosophy for robust control synthesis, analysis including the control architecture that enables the simulation of the mission, i.e. the acquisition of a high pointing space mission, are provided.

Haojie Xie, Yirong Chen, Xiaofen Xing et al.

Currently, large language models (LLMs) have made significant progress in the field of psychological counseling. However, existing mental health LLMs overlook a critical issue where they do not consider the fact that different psychological counselors exhibit different personal styles, including linguistic style and therapy techniques, etc. As a result, these LLMs fail to satisfy the individual needs of clients who seek different counseling styles. To help bridge this gap, we propose PsyDT, a novel framework using LLMs to construct the Digital Twin of Psychological counselor with personalized counseling style. Compared to the time-consuming and costly approach of collecting a large number of real-world counseling cases to create a specific counselor's digital twin, our framework offers a faster and more cost-effective solution. To construct PsyDT, we utilize dynamic one-shot learning by using GPT-4 to capture counselor's unique counseling style, mainly focusing on linguistic style and therapy techniques. Subsequently, using existing single-turn long-text dialogues with client's questions, GPT-4 is guided to synthesize multi-turn dialogues of specific counselor. Finally, we fine-tune the LLMs on the synthetic dataset, PsyDTCorpus, to achieve the digital twin of psychological counselor with personalized counseling style. Experimental results indicate that our proposed PsyDT framework can synthesize multi-turn dialogues that closely resemble real-world counseling cases and demonstrate better performance compared to other baselines, thereby show that our framework can effectively construct the digital twin of psychological counselor with a specific counseling style.

Bruno Santos, Rogério Luís C. Costa, Leonel Santos

Unlocking the potential of Industry 5.0 hinges on robust cybersecurity measures. This new Industrial Revolution prioritises human-centric values while addressing pressing societal issues such as resource conservation, climate change, and social stability. Recognising the heightened risk of cyberattacks due to the new enabling technologies in Industry 5.0, this paper analyses potential threats and corresponding countermeasures. Furthermore, it evaluates the existing industrial implementation frameworks, which reveals their inadequacy in ensuring a secure transition from Industry 4.0 to Industry 5.0. Consequently, the paper underscores the necessity of developing a new framework centred on cybersecurity to facilitate organisations' secure adoption of Industry 5.0 principles. The creation of such a framework is emphasised as a necessity for organisations.

El-Ashry AM, Hussein Ramadan Atta M, Alsenany SA et al.

Ayman Mohamed El-Ashry,1 Mohamed Hussein Ramadan Atta,1 Samira Ahmed Alsenany,2 Sally Mohammed Farghaly Abdelaliem,3 Mahmoud Abdelwahab Khedr1 1Department of Psychiatric and Mental Health Nursing, Faculty of Nursing, Alexandria University, Alexandria, Egypt; 2Department of Community Health Nursing, College of Nursing, Princess Nourah bint Abdulrahman University, Riyadh, 11671, Saudi Arabia; 3Department of Nursing Management and Education, College of Nursing, Princess Nourah bint Abdulrahman University, Riyadh, 11671, Saudi ArabiaCorrespondence: Sally Mohammed Farghaly Abdelaliem, Alyasmeen 153, Riyadh, 13326, Saudi Arabia, Tel +966550773686, Email smfarghaly@pnu.edu.saBackground: Distress tolerance skills have the potential to decrease problematic internet use and improve psychological wellbeing by cognitive reframing and goal-oriented problem-solving.Aim: To assess the impact of distress tolerance training on problematic internet use and psychological wellbeing among university nursing students.Methods: A randomized control trial used at the faculty of nursing using simple random sampling method. Tools: Distress Tolerance Scale, problematic internet use questionnaire, and Ryff psychological wellbeing scale. Data were collected from 60 nursing students over a period of 4 months.Results: Distress tolerance level was increased among study group from 20.75± 14.29 to 72.75± 24.09 and sustained for 3 months to 62.44 ± 20.77 with statistically significant (f=7.090, p=0.006) and large effect size 0.75. When compared to no change among the control group as mean scare start by 22.35± 14.29 to 23.44± 16.09 and after 3 months to 21.75± 17.44 with insignificant difference (f=0.454, p=0.574). The mean score of problematic internet use shows highly statistically significant decrement in the study group between three period of time (pretest= 62.93, immediately post= 52.13, and post 3 months=52.70) with large effect size 0.78 (f=95.029, p< 0.001), in compared to control group that showed insignificant no changes (pretest= 64.0± 14.54, immediately post= 63.13± 12.87, and post 3 months=63.53± 11.36) with (f=1.012, p=0.332). In addition, the total mean score of psychological well-being was increased immediately after therapy and later for three months of therapy (pretest= 128.47, immediately post=148.77, and post 3 months= 153.60) with highly statistically significant (f=41.197, p< 0.001) with effect size 0.85, compared to no change among control group (pretest=125.97± 32.58, immediately post= 126.23± 30.86, and post 3 months=126.43± 29.78) with (f=0.208, p=0.698).Conclusion: Efficacy of distress tolerance skills intervention had been proven in altering poor psychological wellbeing among students with problematic internet use. It helps students to learn new skills and use more adaptive strategies to overcome distress tolerance difficulties.Keywords: distress tolerance training, problematic internet use, psychological wellbeing, nursing students

Shreyak Chandel, Swati Choudhary, Vipindeep K Sandhu

Eric Rosen, Devesh K. Jha

We address the problem of teleoperating an industrial robot manipulator via a commercially available Virtual Reality (VR) interface. Previous works on VR teleoperation for robot manipulators focus primarily on collaborative or research robot platforms (whose dynamics and constraints differ from industrial robot arms), or only address tasks where the robot's dynamics are not as important (e.g: pick and place tasks). We investigate the usage of commercially available VR interfaces for effectively teleoeprating industrial robot manipulators in a variety of contact-rich manipulation tasks. We find that applying standard practices for VR control of robot arms is challenging for industrial platforms because torque and velocity control is not exposed, and position control is mediated through a black-box controller. To mitigate these problems, we propose a simplified filtering approach to process command signals to enable operators to effectively teleoperate industrial robot arms with VR interfaces in dexterous manipulation tasks. We hope our findings will help robot practitioners implement and setup effective VR teleoperation interfaces for robot manipulators. The proposed method is demonstrated on a variety of contact-rich manipulation tasks which can also involve very precise movement of the robot during execution (videos can be found at https://www.youtube.com/watch?v=OhkCB9mOaBc)

J. P. Edelen, M. J. Henderson, J. Einstein-Curtis et al.

Industrial particle accelerators inherently operate in much dirtier environments than typical research accelerators. This leads to an increase in noise both in the RF system and in other electronic systems. Combined with the fact that industrial accelerators are mass produced, there is less attention given to optimizing the performance of an individual system. As a result, industrial systems tend to under perform considering their hardware hardware capabilities. With the growing demand for accelerators for medical sterilization, food irradiation, cancer treatment, and imaging, improving the signal processing of these machines will increase the margin for the deployment of these systems. Our work is focusing on using machine learning techniques to reduce the noise of RF signals used for pulse-to-pulse feedback in industrial accelerators. We will review our algorithms, simulation results, and results working with measured data. We will then discuss next steps for deployment and testing on an industrial system.

Philippe Carvalho, Alexandre Durupt, Yves Grandvalet

The field of industrial defect detection using machine learning and deep learning is a subject of active research. Datasets, also called benchmarks, are used to compare and assess research results. There is a number of datasets in industrial visual inspection, of varying quality. Thus, it is a difficult task to determine which dataset to use. Generally speaking, datasets which include a testing set, with precise labeling and made in real-world conditions should be preferred. We propose a study of existing benchmarks to compare and expose their characteristics and their use-cases. A study of industrial metrics requirements, as well as testing procedures, will be presented and applied to the studied benchmarks. We discuss our findings by examining the current state of benchmarks for industrial visual inspection, and by exposing guidelines on the usage of benchmarks.

Bram van den Akker, Olivier Jeunen, Ying Li et al.

The bandit paradigm provides a unified modeling framework for problems that require decision-making under uncertainty. Because many business metrics can be viewed as rewards (a.k.a. utilities) that result from actions, bandit algorithms have seen a large and growing interest from industrial applications, such as search, recommendation and advertising. Indeed, with the bandit lens comes the promise of direct optimisation for the metrics we care about. Nevertheless, the road to successfully applying bandits in production is not an easy one. Even when the action space and rewards are well-defined, practitioners still need to make decisions regarding multi-arm or contextual approaches, on- or off-policy setups, delayed or immediate feedback, myopic or long-term optimisation, etc. To make matters worse, industrial platforms typically give rise to large action spaces in which existing approaches tend to break down. The research literature on these topics is broad and vast, but this can overwhelm practitioners, whose primary aim is to solve practical problems, and therefore need to decide on a specific instantiation or approach for each project. This tutorial will take a step towards filling that gap between the theory and practice of bandits. Our goal is to present a unified overview of the field and its existing terminology, concepts and algorithms -- with a focus on problems relevant to industry. We hope our industrial perspective will help future practitioners who wish to leverage the bandit paradigm for their application.

Bo Ying Su, Zhongqi Wei, James McCann et al.

Tactile skins made from textiles enhance robot-human interaction by localizing contact points and measuring contact forces. This paper presents a solution for rapidly fabricating, calibrating, and deploying these skins on industrial robot arms. The novel automated skin calibration procedure maps skin locations to robot geometry and calibrates contact force. Through experiments on a FANUC LR Mate 200id/7L industrial robot, we demonstrate that tactile skins made from textiles can be effectively used for human-robot interaction in industrial environments, and can provide unique opportunities in robot control and learning, making them a promising technology for enhancing robot perception and interaction.