Hasil untuk "Industrial psychology"

Saima Ahmed, Rajesh Kumar, Niska Sinha et al.

Background: Obsessive-compulsive disorder (OCD) is a chronic psychiatric disorder characterized by persistent, distressing obsessive thoughts and compulsive behaviors. In OCD, the level of insight is classified as good, poor, or absent. Poorer insight is associated with a more complex clinical presentation and a poorer prognosis. Aim: The aim of our research was to investigate the relationship between the level of insight in individuals with OCD and various neuropsychological and psychopathological factors. Materials and Methods: This cross-sectional study recruited a total of 100 participants diagnosed with OCD. The Brown assessment of beliefs scale (BABS) was used to evaluate the insight of the patients. Psychopathology was assessed using the Yale–Brown obsessive compulsive scale (YBOCS) and Hamilton depression rating scale (HDRS). Neuropsychological assessments included the Stroop test, digit span test, controlled oral word association (COWA) test, trail making test, and Wisconsin card sorting test. Results: The majority of the patients had good insight (54%), mild depression (48%), and moderate symptom severity (47%). Patients with poor insight had significantly higher scores on the YBOCS and HAM-D. They also performed significantly worse on the WCST and TMT-A. Patients with comorbid depression (mild/moderate) showed significantly poor performance on the WCST compared to those without depression. Conclusion: The findings of our study indicate that patients with poor insight exhibit more severe forms of OCD, display greater psychopathology, and show more pronounced neuropsychological dysfunction.

Tiago Brandão

The Innovation Delusion: How Our Obsession with the New Has Disrupted the Work That Matters Most (2020), by Lee Vinsel and Andrew L. Russell, presents a blistering critique of the contemporary ideology of innovation, exposing what the authors call «innovation-speak» – a hegemonic discourse that glorifies disruptive change and marginalises the essential work of maintenance. The Innovation Delusion, by Lee Vinsel and Andrew L. Russell, published not many years ago (2020), is among the scholar books one must read, especially for younger generations and policymakers around the world. Many years ago, Steven Shapin (1989) unearthed the role of the technician in modern science. Innovation Delusion does the same for hidden activities in innovation — i.e., activities related to technology and engineering. Maintenance, upkeep and care is the motto behind Vinsel and Russell’s book.

Chen Y, Feng Z, Wang H

Yezi Chen,1 Zhouqi Feng,1 Haibin Wang2 1School of Urban Governance and Public Affairs, Suzhou City University, Suzhou, Jiangsu, People’s Republic of China; 2School of Educational Science, Huangshan University, Huangshan, Anhui, People’s Republic of ChinaCorrespondence: Haibin Wang, School of Educational Science, Huangshan University, Huangshan, Anhui, People’s Republic of China, Tel +86 13855910608, Email asdwhb@163.comPurpose: Emotional awareness, fundamental to emotional intelligence, involves recognizing and describing emotions in oneself and others, critically influencing mental health and relationships. Therefore, this study aimed to analyze the emotional awareness of pre-service teachers in Chinese through the revised emotional awareness scale (LEAS).Participants and Methods: The two-stage study included 455 pre-service teachers for LEAS revision (Study 1) and 773 pre-service teachers (randomly sampled) alongside in-service teachers as a contrast group (Study 2).Results: The revised LEAS showed a strong reliability (total α = 0.888; self/others-awareness α = 0.860/0.822) and validity, with self/others-awareness subscales highly correlated (r = 0.797) and strongly linked to total scores (r = 0.937/0.925). In addition, the criterion-related validity test found that the LEAS was significantly and positively correlated with the TMMS and QYEI. Pre-service teachers’ mean emotional awareness score (2.730) was below the theoretical midpoint (3). Females scored higher than males, awareness increased with academic grade, and liberal arts students outperformed science peers. In-service teachers had higher self/others-awareness than pre-service groups, but total scores of senior pre-service teachers matched in-service levels.Conclusion: The revised LEAS exhibited good reliability and validity and could be used as an effective tool to measure emotional awareness. Pre-service teachers’ emotional awareness ability, in general, is low to medium and needs improvement. Pre-service teachers’ emotional awareness differed significantly by gender, grade, and profession. The cultivation of emotional awareness is very important and necessary for teachers and is more important for pre-service teachers.Keywords: pre-service teacher, emotional awareness, LEAS, investigation, China

Yarnaphat Shaengchart, Nalinpat Bhumpenpein, Tanpat Kraiwanit et al.

This research investigates how big data influences online purchasing behavior and identifies the key factors shaping consumer decisions in Thailand. Utilizing a quantitative approach, data were initially gathered from 760 Thai participants through convenience sampling. After a data cleaning process, the final analysis focused on 661 individuals who had experience with online shopping. The study employed statistical tools such as percentages, mean values, and binary logistic regression to analyze the data in depth. The results underscore the important role that media-based product exposure plays in driving online purchases. Additionally, the findings reveal complex interactions among variables such as age, educational background, income level, and frequency of media use. Gaining insight into these dynamics is crucial for businesses, marketers, and related stakeholders seeking to develop effective, targeted strategies in the digital marketplace.

Xiangrui Liu, Man Luo, Agneet Chatterjee et al.

Hallucination is a long-standing problem that has been actively investigated in Vision-Language Models (VLMs). Existing research commonly attributes hallucinations to technical limitations or sycophancy bias, where the latter means the models tend to generate incorrect answers to align with user expectations. However, these explanations primarily focus on technical or externally driven factors, and may have neglected the possibility that hallucination behaviours might mirror cognitive biases observed in human psychology. In this work, we introduce a psychological taxonomy, categorizing VLMs' cognitive biases that lead to hallucinations, including sycophancy, logical inconsistency, and a newly identified VLMs behaviour: appeal to authority. To systematically analyze these behaviours, we design AIpsych, a scalable benchmark that reveals psychological tendencies in model response patterns. Leveraging this benchmark, we investigate how variations in model architecture and parameter size influence model behaviour when responding to strategically manipulated questions. Our experiments reveal that as model size increases, VLMs exhibit stronger sycophantic tendencies but reduced authority bias, suggesting increasing competence but a potential erosion of response integrity. A human subject study further validates our hypotheses and highlights key behavioural differences between VLMs and human respondents. This work suggests a new perspective for understanding hallucination in VLMs and highlights the importance of integrating psychological principles into model evaluation.

Shahed Masoudian, Gustavo Escobedo, Hannah Strauss et al.

As Large Language Models (LLMs) are increasingly used across different applications, concerns about their potential to amplify gender biases in various tasks are rising. Prior research has often probed gender bias using explicit gender cues as counterfactual, or studied them in sentence completion and short question answering tasks. These formats might overlook more implicit forms of bias embedded in generative behavior of longer content. In this work, we investigate gender bias in LLMs using gender stereotypes studied in psychology (e.g., aggressiveness or gossiping) in an open-ended task of narrative generation. We introduce a novel dataset called StereoBias-Stories containing short stories either unconditioned or conditioned on (one, two, or six) random attributes from 25 psychological stereotypes and three task-related story endings. We analyze how the gender contribution in the overall story changes in response to these attributes and present three key findings: (1) While models, on average, are highly biased towards male in unconditioned prompts, conditioning on attributes independent from gender stereotypes mitigates this bias. (2) Combining multiple attributes associated with the same gender stereotype intensifies model behavior, with male ones amplifying bias and female ones alleviating it. (3) Model biases align with psychological ground-truth used for categorization, and alignment strength increases with model size. Together, these insights highlight the importance of psychology-grounded evaluation of LLMs.

Md Bokhtiar Al Zami, Shaba Shaon, Vu Khanh Quy et al.

Industrial networks are undergoing rapid transformation driven by the convergence of emerging technologies that are revolutionizing conventional workflows, enhancing operational efficiency, and fundamentally redefining the industrial landscape across diverse sectors. Amidst this revolution, Digital Twin (DT) emerges as a transformative innovation that seamlessly integrates real-world systems with their virtual counterparts, bridging the physical and digital realms. In this article, we present a comprehensive survey of the emerging DT-enabled services and applications across industries, beginning with an overview of DT fundamentals and its components to a discussion of key enabling technologies for DT. Different from literature works, we investigate and analyze the capabilities of DT across a wide range of industrial services, including data sharing, data offloading, integrated sensing and communication, content caching, resource allocation, wireless networking, and metaverse. In particular, we present an in-depth technical discussion of the roles of DT in industrial applications across various domains, including manufacturing, healthcare, transportation, energy, agriculture, space, oil and gas, as well as robotics. Throughout the technical analysis, we delve into real-time data communications between physical and virtual platforms to enable industrial DT networking. Subsequently, we extensively explore and analyze a wide range of major privacy and security issues in DT-based industry. Taxonomy tables and the key research findings from the survey are also given, emphasizing important insights into the significance of DT in industries. Finally, we point out future research directions to spur further research in this promising area.

Guillaume Rochette, Mathieu Rochat, Matthew J. Vowels

psifx is a plug-and-play multi-modal feature extraction toolkit, aiming to facilitate and democratize the use of state-of-the-art machine learning techniques for human sciences research. It is motivated by a need (a) to automate and standardize data annotation processes that typically require expensive, lengthy, and inconsistent human labour; (b) to develop and distribute open-source community-driven psychology research software; and (c) to enable large-scale access and ease of use for non-expert users. The framework contains an array of tools for tasks such as speaker diarization, closed-caption transcription and translation from audio; body, hand, and facial pose estimation and gaze tracking with multi-person tracking from video; and interactive textual feature extraction supported by large language models. The package has been designed with a modular and task-oriented approach, enabling the community to add or update new tools easily. This combination creates new opportunities for in-depth study of real-time behavioral phenomena in psychological and social science research.

Jinfan Liu, Yichao Yan, Junjie Li et al.

Video anomaly detection (VAD) is a challenging task aiming to recognize anomalies in video frames, and existing large-scale VAD researches primarily focus on road traffic and human activity scenes. In industrial scenes, there are often a variety of unpredictable anomalies, and the VAD method can play a significant role in these scenarios. However, there is a lack of applicable datasets and methods specifically tailored for industrial production scenarios due to concerns regarding privacy and security. To bridge this gap, we propose a new dataset, IPAD, specifically designed for VAD in industrial scenarios. The industrial processes in our dataset are chosen through on-site factory research and discussions with engineers. This dataset covers 16 different industrial devices and contains over 6 hours of both synthetic and real-world video footage. Moreover, we annotate the key feature of the industrial process, ie, periodicity. Based on the proposed dataset, we introduce a period memory module and a sliding window inspection mechanism to effectively investigate the periodic information in a basic reconstruction model. Our framework leverages LoRA adapter to explore the effective migration of pretrained models, which are initially trained using synthetic data, into real-world scenarios. Our proposed dataset and method will fill the gap in the field of industrial video anomaly detection and drive the process of video understanding tasks as well as smart factory deployment.

Qian Liang, Yi Zeng, Menghaoran Tang

Musical mode is one of the most critical element that establishes the framework of pitch organization and determines the harmonic relationships. Previous works often use the simplistic and rigid alignment method, and overlook the diversity of modes. However, in contrast to AI models, humans possess cognitive mechanisms for perceiving the various modes and keys. In this paper, we propose a spiking neural network inspired by brain mechanisms and psychological theories to represent musical modes and keys, ultimately generating musical pieces that incorporate tonality features. Specifically, the contributions are detailed as follows: 1) The model is designed with multiple collaborated subsystems inspired by the structures and functions of corresponding brain regions; 2)We incorporate mechanisms for neural circuit evolutionary learning that enable the network to learn and generate mode-related features in music, reflecting the cognitive processes involved in human music perception. 3)The results demonstrate that the proposed model shows a connection framework closely similar to the Krumhansl-Schmuckler model, which is one of the most significant key perception models in the music psychology domain. 4) Experiments show that the model can generate music pieces with characteristics of the given modes and keys. Additionally, the quantitative assessments of generated pieces reveals that the generating music pieces have both tonality characteristics and the melodic adaptability needed to generate diverse and musical content. By combining insights from neuroscience, psychology, and music theory with advanced neural network architectures, our research aims to create a system that not only learns and generates music but also bridges the gap between human cognition and artificial intelligence.

Xiao Wang, Yutong Wang, Jing Yang et al.

As the concept of Industries 5.0 develops, industrial metaverses are expected to operate in parallel with the actual industrial processes to offer ``Human-Centric" Safe, Secure, Sustainable, Sensitive, Service, and Smartness ``6S" manufacturing solutions. Industrial metaverses not only visualize the process of productivity in a dynamic and evolutional way, but also provide an immersive laboratory experimental environment for optimizing and remodeling the process. Besides, the customized user needs that are hidden in social media data can be discovered by social computing technologies, which introduces an input channel for building the whole social manufacturing process including industrial metaverses. This makes the fusion of multi-source data cross Cyber-Physical-Social Systems (CPSS) the foundational and key challenge. This work firstly proposes a multi-source-data-fusion-driven operational architecture for industrial metaverses on the basis of conducting a comprehensive literature review on the state-of-the-art multi-source data fusion methods. The advantages and disadvantages of each type of method are analyzed by considering the fusion mechanisms and application scenarios. Especially, we combine the strengths of deep learning and knowledge graphs in scalability and parallel computation to enable our proposed framework the ability of prescriptive optimization and evolution. This integration can address the shortcomings of deep learning in terms of explainability and fact fabrication, as well as overcoming the incompleteness and the challenges of construction and maintenance inherent in knowledge graphs. The effectiveness of the proposed architecture is validated through a parallel weaving case study. In the end, we discuss the challenges and future directions of multi-source data fusion cross CPSS for industrial metaverses and social manufacturing in Industries 5.0.

Ahmed Gailani, Peter Taylor

Decarbonising the industrial sector is vital to reach net zero targets. The deployment of industrial decarbonisation technologies is expected to increase industrial electricity demand in many countries and this may require upgrades to the existing electricity network or new network investment. While the infrastructure requirements to support the introduction of new fuels and technologies in industry, such as hydrogen and carbon capture, utilisation and storage are often discussed, the need for investment to increase the capacity of the electricity network to meet increasing industrial electricity demands is often overlooked in the literature. This paper addresses this gap by quantifying the requirements for additional electricity network capacity to support the decarbonisation of industrial sectors across Great Britain (GB). The Net Zero Industrial Pathways model is used to predict the future electricity demand from industrial sites to 2050 which is then compared spatially to the available headroom across the distribution network in GB. The results show that network headroom is sufficient to meet extra capacity demands from industrial sites over the period to 2030 in nearly all GB regions and network scenarios. However, as electricity demand rises due to increased electrification across all sectors and industrial decarbonisation accelerates towards 2050, the network will need significant new capacity (71 GW + by 2050) particularly in the central, south, and north-west regions of England, and Wales. Without solving these network constraints, around 65% of industrial sites that are large point sources of emissions would be constrained in terms of electric capacity by 2040. These sites are responsible for 69% of industrial point source emissions.

Ashwini Padmashali

Wang C, Zhou R, Zhang X

Chenyu Wang,1 Rui Zhou,2 Xing Zhang3 1School of Music, Jiangxi Normal University, Nanchang, People’s Republic of China; 2College of Marxism, Sichuan University, Chengdu, People’s Republic of China; 3School of Psychology, Jiangxi Normal University, Nanchang, People’s Republic of ChinaCorrespondence: Xing Zhang, School of Psychology, Jiangxi Normal University, 99, Ziyang Avenue, Nanchang, Jiangxi Province, 330022, People’s Republic of China, Email zhangxing@jxnu.edu.cn Rui Zhou, College of Marxism, Sichuan University, Chuanda Road, Shuangliu County, Chengdu, 610207, People’s Republic of China, Email ruizhou_283@163.comPurpose: In light of the ongoing COVID-19 pandemic, mental health concerns have become more prevalent worldwide. However, there is a lack of research specifically addressing the mental well-being of college art students. Therefore, the purpose of this study is to examine the prevalence of depressive symptoms among college music students and explore the factors that predict and alleviate these symptoms amidst the challenges posed by the COVID-19 pandemic.Materials and Methods: An online survey was conducted among college music students (n = 407) from two universities at May 2022 in China. Self-report scales were used to measure levels of depression (Zung Self-Rating Depression Scale), positive childhood experiences (Benevolent Childhood Experiences Scale), social support (Multi-Dimensional Scale of Perceived Social Support), and regulatory emotional self-efficacy (Regulatory Emotional Self-Efficacy Scale). Hayes PROCESS macro for SPSS was used to test the hypothesized effects of regulatory emotional self-efficacy and social support in the relationship between positive childhood experiences and depression.Results: Results showed that, the prevalence of depression symptoms of the current study sample was 64.13%, positive childhood experiences had a significant and negative predictive effect on the depression of college music students, and the relation was partially mediated by regulatory emotional self-efficacy. Furthermore, social support moderated the relationship between positive childhood experiences and regulatory emotional self-efficacy, the relation was significant only for students with higher levels of social support, social support may enhance and amplify the positive impacts of positive childhood experiences on regulatory emotional self-efficacy.Conclusion: The findings reveal a significant prevalence of depression among college music students during the COVID-19 epidemic, underscoring the seriousness of the issue. Moreover, this study contributes to a deeper comprehension of how positive childhood experiences alleviate depression among college music students. These insights hold potential for informing mental health education initiatives tailored to college art students in the post-pandemic era, offering valuable guidance for promoting their well-being and resilience.Keywords: COVID-19, college music students, positive childhood experiences, regulatory emotional self-efficacy, social support, depression

Li D, Zhang L, Bai T et al.

Dandan Li,1– 3,* Long Zhang,4,* Tongjian Bai,4,* Bensheng Qiu,5 Chunyan Zhu,1– 3 Kai Wang1,2,4,6,7 1School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, People’s Republic of China; 2Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, People’s Republic of China; 3Research Center for Translational Medicine, Second Hospital of Anhui Medical University, Hefei, People’s Republic of China; 4Department of Neurology, First Affiliated Hospital of Anhui Medical University, Hefei, People’s Republic of China; 5Hefei National Laboratory for Physical Sciences at the Microscale and the Centers for Biomedical Engineering, University of Science and Technology of China., Hefei, People’s Republic of China; 6Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, People’s Republic of China; 7Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei, People’s Republic of China*These authors contributed equally to this workCorrespondence: Chunyan Zhu; Kai Wang, Email ayswallow@126.com; wangkai1964@126.comIntroduction: Empathy traits are highly heritable and linked with reward processing. It is implicated that common variations of the oxytocin-receptor gene (OXTR) play a modulatory effect on empathic performance. However, it is unclear about the neural substrates underlying the modulatory effect of the OXTR genotype on empathic performance. This study aimed to characterize the modulatory effect of common OXTR variations on reward-circuitry function and its relationship with empathy.Methods: Based on the seed of the nucleus accumbens (NAcc; a key hub of reward circuitry), we examined differences in spontaneous local activity and functional connectivity between OXTR rs2268493 genotype groups and their relationship with empathic performance among 402 high-homogeneity participants.Results: Comparing with C carriers (CC/CT) group, the individuals with the rs2268493 TT genotype exhibited lower functional connectivity of the right NAcc with the medial prefrontal cortex (mPFC) and inferior frontal gyrus. Similarly lower functional connectivity was found between the left NAcc and mPFC. Consequently, no significant difference was found in the spontaneous local activity of NAcc.Discussion: Our findings suggested that common OXTR variations have a modulatory effect on the connection of the NAcc with the hub of empathic networks (mPFC and IFG), which may provide insight on the neural substrate underlying the modulatory effect of OXTR on empathic behavior.Keywords: empathy, oxytocin-receptor gene, reward network, RS-fMRI, rs2268493

Ankush Meshram, Markus Karch, Christian Haas et al.

Since 2010, multiple cyber incidents on industrial infrastructure, such as Stuxnet and CrashOverride, have exposed the vulnerability of Industrial Control Systems (ICS) to cyber threats. The industrial systems are commissioned for longer duration amounting to decades, often resulting in non-compliance to technological advancements in industrial cybersecurity mechanisms. The unavailability of network infrastructure information makes designing the security policies or configuring the cybersecurity countermeasures such as Network Intrusion Detection Systems (NIDS) challenging. An empirical solution is to self-learn the network infrastructure information of an industrial system from its monitored network traffic to make the network transparent for downstream analyses tasks such as anomaly detection. In this work, a Python-based industrial communication paradigm-aware framework, named PROFINET Operations Enumeration and Tracking (POET), that enumerates different industrial operations executed in a deterministic order of a PROFINET-based industrial system is reported. The operation-driving industrial network protocol frames are dissected for enumeration of the operations. For the requirements of capturing the transitions between industrial operations triggered by the communication events, the Finite State Machines (FSM) are modelled to enumerate the PROFINET operations of the device, connection and system. POET extracts the network information from network traffic to instantiate appropriate FSM models (Device, Connection or System) and track the industrial operations. It successfully detects and reports the anomalies triggered by a network attack in a miniaturized PROFINET-based industrial system, executed through valid network protocol exchanges and resulting in invalid PROFINET operation transition for the device.

Yuan Luo, Song Han, Jingjing Liu

Machine learning is expected to enable the next Industrial Revolution. However, lacking standardized and automated assembly networks, ML faces significant challenges to meet ever-growing enterprise demands and empower broad industries. In the Perspective, we argue that ML needs to first complete its own Industrial Revolution, elaborate on how to best achieve its goals, and discuss new opportunities to enable rapid translation from ML's innovation frontier to mass production and utilization.

Marjaana Sianoja, Christine Syrek, Jessica de Bloom et al.

Recovery from work stress during workday breaks, free evenings, weekends, and vacations is known to benefit employee health and well-being. However, how recovery at different temporal settings is interconnected is not well understood. We hypothesized that on days when employees engage in recovery-enhancing lunchtime activities, they will experience higher resources when leaving home from work (i.e., low fatigue and high positive affect) and consequently spend more time on recovery-enhancing activities in the evening, thus creating a positive recovery cycle. In this study, 97 employees were randomized into lunchtime park walk and relaxation groups. As evening activities, we measured time spent on physical exercise, physical activity in natural surroundings, and social activities. Afternoon resources and time spent on evening activities were assessed twice a week before, during, and after the intervention, for five weeks. Our results based on multilevel analyses showed that on days when employees completed the lunchtime park walk, they spent more time on evening physical exercise and physical activity in natural surroundings compared to days when the lunch break was spent as usual. However, neither lunchtime relaxation exercises nor afternoon resources were associated with any of the evening activities. Our findings suggest that other factors than afternoon resources are more important in determining how much time employees spend on various evening activities. Fifteen-minute lunchtime park walks inspired employees to engage in similar health-benefitting activities during their free time.

Alfaifi A, Darraj A, El-Setouhy M

Abdulrahman Alfaifi,1 Abdulaziz Darraj,1 Maged El-Setouhy2,3 1Jazan Health Affairs, Ministry of Health, Jazan, Saudi Arabia; 2Department of Family and Community Medicine, Faculty of Medicine, Jazan University, Jazan, Saudi Arabia; 3Department of Community, Environmental and Occupational Medicine, Faculty of Medicine, Ain Shams University, Cairo, EgyptCorrespondence: Abdulrahman Alfaifi, Jazan Health Affairs, Ministry of Health, PO 96, Jazan, 45142, Saudi Arabia, Tel +966 56 887 3773, Email Abalfaify1@gmail.comBackground: COVID-19 is a viral infectious disease that spreads quickly through droplets. It is highly contagious and could overwhelm the health system. Because of that, many governments established health quarantines for suspected infected people to minimize the spread of this disease.Objective: This study aimed to assess the prevalence of depression, anxiety, and stress symptoms and to address the associated risk factors among quarantined non-healthcare workers, quarantined healthcare workers, and medical staff in the Ministry of Health quarantine facility.Patients and Methods: We conducted an analytical cross-sectional study at the health quarantine in Jazan, Saudi Arabia. The total number of participants was 301 individuals. Furthermore, the study questionnaire was composed of three sections, the first two were the background and clinical characteristics, and the last one was DASS 21 scale. Also, we used SPSS software to analyze the data. Lastly, we implemented logistic regression to assess the predictors of depression, anxiety, and stress symptoms.Results: The prevalence of depression, anxiety, and stress symptoms among quarantined non-healthcare workers were 51.9%, 60.2%, and 40.6%, respectively. These prevalences were 25.0%, 29.8%, and 16.9% among quarantined healthcare workers and 20.5%, 20.5%, and 27.3% among the medical staff. The predictors of depression, anxiety, and stress symptoms among the study participants were female gender, perceived COVID-19 stigma, presence of other relatives in quarantine, comorbidities, and abnormal sleep duration.Conclusion and Recommendations: Health quarantine is an environment that could negatively affect people’s mental health. The quarantined non-healthcare workers were the most affected study participants inside this environment. Therefore, the availability of mental health services there could minimize their depression, anxiety, and stress symptoms. Moreover, a home quarantine would be better to reduce these negative symptoms whenever possible.Keywords: depression, anxiety, stress, prevalence, risk factors

Maurice Koch, Kuno Kurzhals, Michael Burch et al.

Technical progress in hardware and software enables us to record gaze data in everyday situations and over long time spans. Among a multitude of research opportunities, this technology enables visualization researchers to catch a glimpse behind performance measures and into the perceptual and cognitive processes of people using visualization techniques. The majority of eye tracking studies performed for visualization research is limited to the analysis of gaze distributions and aggregated statistics, thus only covering a small portion of insights that can be derived from gaze data. We argue that incorporating theories and methodology from psychology and cognitive science will benefit the design and evaluation of eye tracking experiments for visualization. This book chapter provides an overview of how eye tracking can be used in a variety of study designs. Further, we discuss the potential merits of cognitive models for the evaluation of visualizations. We exemplify these concepts on two scenarios, each focusing on a different eye tracking study. Lastly, we identify several call for actions.