The intersection of artificial intelligence and psychological science has experienced remarkable growth, with annual publications expanding from 859 papers in 2000 to 29,979 by 2025. However, this rapid evolution has created methodological fragmentation where similar computational techniques are independently developed across isolated psychological domains. This survey introduces the first systematic taxonomy that organizes AI-driven psychology tasks by computational processing patterns rather than application domains, categorizing them into four fundamental types: classification, regression, structured relational, and generative interactive tasks. Through analysis of over 300 representative works spanning the pre-trained model era and large language model era, we examine how computational approaches evolved from task-specific feature engineering to transfer learning and few-shot adaptation. We provide systematic coverage of datasets, evaluation metrics, and benchmarks while addressing fundamental challenges including interpretability, label uncertainty, privacy constraints, and cross-cultural validity. This computational perspective reveals transferable methodological patterns previously obscured by domain-centric organization, enabling systematic knowledge transfer and accelerated progress in computational psychology.
The convergence of artificial intelligence, cyber-physical systems, and cross-enterprise data ecosystems has propelled industrial intelligence to unprecedented scales. Yet, the absence of a unified trust foundation across data, services, and knowledge layers undermines reliability, accountability, and regulatory compliance in real-world deployments. While existing surveys address isolated aspects, such as data governance, service orchestration, and knowledge representation, none provides a holistic, cross-layer perspective on trustworthiness tailored to industrial settings. To bridge this gap, we present \textsc{Trisk} (TRusted Industrial Data-Service-Knowledge governance), a novel conceptual and taxonomic framework for trustworthy industrial intelligence. Grounded in a five-dimensional trust model (quality, security, privacy, fairness, and explainability), \textsc{Trisk} unifies 120+ representative studies along three orthogonal axes: governance scope (data, service, and knowledge), architectural paradigm (centralized, federated, or edge-embedded), and enabling technology (knowledge graphs, zero-trust policies, causal inference, etc.). We systematically analyze how trust propagates across digital layers, identify critical gaps in semantic interoperability, runtime policy enforcement, and operational/information technologies alignment, and evaluate the maturity of current industrial implementations. Finally, we articulate a forward-looking research agenda for Industry 5.0, advocating for an integrated governance fabric that embeds verifiable trust semantics into every layer of the industrial intelligence stack. This survey serves as both a foundational reference for researchers and a practical roadmap for engineers to deploy trustworthy AI in complex and multi-stakeholder environments.
Georgina Warner, Sandra Gupta Löfving, Emma Geijer-Simpson
Introduction: Although there is a high need for mental health support among individuals with experience of forced migration, there are barriers to accessing in-person interventions. Online delivery offers an alternative. This study aimed to examine the feasibility and acceptability of online-delivered Teaching Recovery Techniques (TRT) for young people who had experienced forced migration and reported symptoms of PTSD. Methods: The study used an open, single-arm trial design with a mixed-methods approach. Participants (n = 16; 62.5% male; 17–23 years) were recruited via an upper secondary school in Sweden. Fidelity checklists were used to capture adherence to the manual, psychological symptom and life satisfaction questionnaires were administered (n = 16), and a focus group discussion (n = 3) and interview (n = 1) explored participants' perspectives. Results: High fidelity was observed, with all components delivered except for elements of the final session. Technical challenges were noted, including limited platform functionality for private communication and unstable internet connectivity, and privacy concerns were raised where participants lacked private spaces. The format was adapted, including merging groups, delivering sessions twice weekly rather than weekly, and reducing session duration from 90 to 60 min. Of 16 participants, 9 completed post-intervention measures; descriptive data suggested completers were less likely to be female and had higher symptom scores. Qualitative data indicated symptom improvements and emphasised facilitators' relational qualities, but benefits were described as transient and insufficient to address ongoing stressors, with participants preferring in-person delivery. Conclusion: Online TRT needs enhanced technical support, privacy safeguards, and closure procedures; future trials should test efficacy and long-term outcomes.
The confluence of Artificial Intelligence and Computational Psychology presents an opportunity to model, understand, and interact with complex human psychological states through computational means. This paper presents a comprehensive, multi-faceted framework designed to bridge the gap between isolated predictive modeling and an interactive system for psychological analysis. The methodology encompasses a rigorous, end-to-end development lifecycle. First, foundational performance benchmarks were established on four diverse psychological datasets using classical machine learning techniques. Second, state-of-the-art transformer models were fine-tuned, a process that necessitated the development of effective solutions to overcome critical engineering challenges, including the resolution of numerical instability in regression tasks and the creation of a systematic workflow for conducting large-scale training under severe resource constraints. Third, a generative large language model (LLM) was fine-tuned using parameter-efficient techniques to function as an interactive "Personality Brain." Finally, the entire suite of predictive and generative models was architected and deployed as a robust, scalable microservices ecosystem. Key findings include the successful stabilization of transformer-based regression models for affective computing, showing meaningful predictive performance where standard approaches failed, and the development of a replicable methodology for democratizing large-scale AI research. The significance of this work lies in its holistic approach, demonstrating a complete research-to-deployment pipeline that integrates predictive analysis with generative dialogue, thereby providing a practical model for future research in computational psychology and human-AI interaction.
The combination of embodied intelligence and robots has great prospects and is becoming increasingly common. In order to work more efficiently, accurately, reliably, and safely in industrial scenarios, robots should have at least general knowledge, working-environment knowledge, and operating-object knowledge. These pose significant challenges to existing embodied intelligent robotics (EIR) techniques. Thus, this paper first briefly reviews the history of industrial robotics and analyzes the limitations of mainstream EIR frameworks. Then, a new knowledge-driven technical framework of embodied intelligent industrial robotics (EIIR) is proposed for various industrial environments. It has five modules: a world model, a high-level task planner, a low-level skill controller, a simulator, and a physical system. The development of techniques related to each module are also thoroughly reviewed, and recent progress regarding their adaption to industrial applications are discussed. A case study of real-world assembly system is given to demonstrate the newly proposed EIIR framework's applicability and potentiality. Finally, the key challenges that EIIR encounters in industrial scenarios are summarized and future research directions are suggested. The authors believe that EIIR technology is shaping the next generation of industrial robotics and EIIR-based industrial systems supply a new technological paradigm for intelligent manufacturing. It is expected that this review could serve as a valuable reference for scholars and engineers that are interested in industrial embodied intelligence. Together, scholars can use this research to drive their rapid advancement and application of EIIR techniques. The authors would continue to track and contribute new studies in the project page https://github.com/jackyzengl/EIIR
José María Villanueva Núñez-Lagos, Ana García-Mina Freire, Gonzalo Aza Blanc
et al.
This article addresses a gap in the literature by offering the first structured reconstruction of the origins, motivations, and development of Ignatian Leadership, connecting its spiritual roots, conceptual foundations, and institutional applications within a coherent and transferable framework. The study explores the genesis, evolution, and contemporary relevance of Ignatian Leadership as a transformative model that combines organisational management principles with the spirituality of Saint Ignatius of Loyola and the Jesuit tradition. Through a qualitative methodology, we conducted an exhaustive review of 54 documents and interviews with key experts, incorporating diverse phenomenological perspectives. The findings show that this leadership model emerged to renew the apostolic mission of the Society of Jesus, modernise its educational management, and empower both laypeople and Jesuits in leadership roles. Grounded in Ignatian spirituality—particularly in the practice of discernment aimed at promoting actions inspired by the Magis, in ever deeper and greater service to the most universal good—it seeks to serve others and promote the common good. Over time, the model has expanded beyond religious contexts, offering a counter-cultural and ethically grounded leadership style applicable in educational, managerial and civic settings. This shift of focus not only paves the way for institutional change but also guides individuals towards a more authentic and meaningful life.
Political institutions and public administration (General)
Xue He,1,* Cong Li,1,2,* Yan Wang,1,3,* Zhenchao Du,1,4 Jianrong Jiang,1,5 Wenli Zhang,1,4 Jingyan Peng,1,5 Zhishen Peng,1,5 Tengda Huang,1,3 Heng Li,1,4 Yu Kuang,1 Honghua Yu,1 Lei Liu,1 Xiaohong Yang1,3 1Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People’s Republic of China; 2Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Sciences, Guangzhou, People’s Republic of China; 3School of Medicine, South China University of Technology, Guangzhou, People’s Republic of China; 4School of Medicine, Shantou University Medical College, Shantou, People’s Republic of China; 5The Second School of Clinical Medicine, Southern Medical University, Guangzhou, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xiaohong Yang, Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People’s Republic of China, Email syyangxh@scut.edu.cn Lei Liu, Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People’s Republic of China, Email liulei@gdph.org.cnBackground: Coronary heart disease (CHD) and depression are highly comorbid and increase mortality risk. Although age-related eye diseases (AREDs) are independently associated with CHD and depression, their link to comorbidity remains unknown. Therefore, we aim to investigate the association between AREDs and the comorbidity of CHD and depression.Methods: Using UK Biobank data, we conducted a prospective cohort analysis with baseline assessments from March 2006 to December 2010 and follow-up until July 2021. AREDs include age-related macular degeneration, glaucoma, cataract, and diabetes-related eye diseases (DRED). Incident cases were identified via self-reports and hospital records. Multivariable Cox proportional hazard regression models were applied to investigate the association between AREDs and comorbidity risk.Results: Among 116,501 participants free of CHD and depression at baseline, 7,750 (6.65%), 3,682 (3.16%), and 741 (0.64%) developed CHD, depression, and their comorbidity over a mean of 11.82 years (inter-quartile range: 11.51– 13.11) of follow-up. After adjusting for confounders, individuals with AREDs had a higher risk of developing CHD (hazard ratio [HR] 1.10, 95% confidence interval [CI]: 1.03– 1.17), depression (HR 1.28, 95% CI: 1.16– 1.42), and comorbidity (HR 1.37, 95% CI: 1.12– 1.67). Compared to those without AREDs, individuals with cataract were associated with increased risks of comorbidity (HR 1.57, 95% CI: 1.23– 2.03) and depression (HR 1.26, 95% CI: 1.10– 1.43), while those with DRED had an increased risk of incident CHD (HR 1.33, 95% CI: 1.13– 1.56).Conclusion: The study found that individuals with AREDs had a higher risk of comorbid CHD and depression than of either condition independently. Our findings highlighted the importance of screening for the comorbidity of CHD and depression in the longitudinal management of AREDs.Keywords: age-related eye diseases, coronary heart disease, depression, comorbidity
Mostafa Mohammadian, Reza Kazemi, Sina Mollahoseini
et al.
Background: In open-plan office environments, irrelevant speech noise (ISN) is a common complaint among employees, leading to reduced performance. This study aimed to assess the impact of music on the working memory performance of individuals exposed to ISN in simulated open-plan offices. Additionally, we sought to examine any differential effects of music between male and female participants.Methods: In this experimental study, participants were selected through convenient sampling. Their working memory performance was evaluated using n-back (n=1, 2) tests conducted with software while they were exposed to irrelevant speech noise (ISN) alone and a combination of ISN and music. Sampling took place over one month during the spring season in the acoustic laboratory of the Faculty of Health in Shiraz, Iran.Results: Thirty students, including 15 females, with an ags range of 18 to 38 (Mean=25.27, Standard Deviation=6.03), participated in the study. The results showed a significant increase in the accuracy of participants’ responses to both simple and difficult tasks of the n-back (n=1, 2) test when music was played compared to the ISNonly condition. However, there was no significant difference between the conditions regarding reaction times in the working memory test.Conclusion: In the present study, the inclusion of music, specifically “For Elise,” emerged as a crucial factor in enhancing working memory amidst the presence of open-plan office noise. This finding underscores the potential of utilizing music as an effective strategy for improving cognitive performance in such environments. Given its cost-effectiveness and simplicity of implementation, incorporating background music like “For Elise” can be recommended as a favorable method for mitigating the negative impacts of noise in open-plan offices.
In the field of psychology, traditional assessment methods, such as standardized scales, are frequently critiqued for their static nature, lack of personalization, and reduced participant engagement, while comprehensive counseling evaluations are often inaccessible. The complexity of quantifying psychological traits further limits these methods. Despite advances with large language models (LLMs), many still depend on single-round Question-and-Answer interactions. To bridge this gap, we introduce PsyDI, a personalized and progressively in-depth chatbot designed for psychological measurements, exemplified by its application in the Myers-Briggs Type Indicator (MBTI) framework. PsyDI leverages user-related multi-modal information and engages in customized, multi-turn interactions to provide personalized, easily accessible measurements, while ensuring precise MBTI type determination. To address the challenge of unquantifiable psychological traits, we introduce a novel training paradigm that involves learning the ranking of proxy variables associated with these traits, culminating in a robust score model for MBTI measurements. The score model enables PsyDI to conduct comprehensive and precise measurements through multi-turn interactions within a unified estimation context. Through various experiments, we validate the efficacy of both the score model and the PsyDI pipeline, demonstrating its potential to serve as a general framework for psychological measurements. Furthermore, the online deployment of PsyDI has garnered substantial user engagement, with over 3,000 visits, resulting in the collection of numerous multi-turn dialogues annotated with MBTI types, which facilitates further research. The source code for the training and web service components is publicly available as a part of OpenDILab at: https://github.com/opendilab/PsyDI
Edoardo Sebastiano De Duro, Enrique Taietta, Riccardo Improta
et al.
Machine psychology aims to reconstruct the mindset of Large Language Models (LLMs), i.e. how these artificial intelligences perceive and associate ideas. This work introduces PhDGPT, a prompting framework and synthetic dataset that encapsulates the machine psychology of PhD researchers and professors as perceived by OpenAI's GPT-3.5. The dataset consists of 756,000 datapoints, counting 300 iterations repeated across 15 academic events, 2 biological genders, 2 career levels and 42 unique item responses of the Depression, Anxiety, and Stress Scale (DASS-42). PhDGPT integrates these psychometric scores with their explanations in plain language. This synergy of scores and texts offers a dual, comprehensive perspective on the emotional well-being of simulated academics, e.g. male/female PhD students or professors. By combining network psychometrics and psycholinguistic dimensions, this study identifies several similarities and distinctions between human and LLM data. The psychometric networks of simulated male professors do not differ between physical and emotional anxiety subscales, unlike humans. Other LLMs' personification can reconstruct human DASS factors with a purity up to 80%. Furthemore, LLM-generated personifications across different scenarios are found to elicit explanations lower in concreteness and imageability in items coding for anxiety, in agreement with past studies about human psychology. Our findings indicate an advanced yet incomplete ability for LLMs to reproduce the complexity of human psychometric data, unveiling convenient advantages and limitations in using LLMs to replace human participants. PhDGPT also intriguingly capture the ability for LLMs to adapt and change language patterns according to prompted mental distress contextual features, opening new quantitative opportunities for assessing the machine psychology of these artificial intelligences.
Internet of Everything (IoE) is a newly emerging trend, especially in homes. Marketing forces toward smart homes are also accelerating the spread of IoE devices in households. An obvious risk of the rapid adoption of these smart devices is that many lack controls for protecting the privacy and security of end users from attacks designed to disrupt lives and incur financial losses. Today the smart home is a system for managing the basic life support processes of both small systems, e.g., commercial, office premises, apartments, cottages, and largely automated complexes, e.g., commercial and industrial complexes. One of the critical tasks to be solved by the concept of a modern smart home is the problem of preventing the usage of IoE resources. Recently, there has been a rapid increase in attacks on consumer IoE devices. Memory corruption vulnerabilities constitute a significant class of vulnerabilities in software security through which attackers can gain control of an entire system. Numerous memory corruption vulnerabilities have been found in IoE firmware already deployed in the consumer market. This paper aims to analyze and explain the resource usage attack and create a low-cost simulation environment to aid in the dynamic analysis of the attack. Further, we perform controlled resource usage attacks while measuring resource consumption on resource-constrained victims' IoE devices, such as CPU and memory utilization. We also build a lightweight algorithm to detect memory usage attacks in the IoE environment. The result shows high efficiency in detecting and mitigating memory usage attacks by detecting when the intruder starts and stops the attack.
As an emerging technology that enables seamless integration between the physical and virtual worlds, the Metaverse has great potential to be deployed in the industrial production field with the development of extended reality (XR) and next-generation communication networks. This deployment, called the Industrial Metaverse, is used for product design, production operations, industrial quality inspection, and product testing. However, there lacks of in-depth understanding of the enabling technologies associated with the Industrial Metaverse. This encompasses both the precise industrial scenarios targeted by each technology and the potential migration of technologies developed in other domains to the industrial sector. Driven by this issue, in this article, we conduct a comprehensive survey of the state-of-the-art literature on the Industrial Metaverse. Specifically, we first analyze the advantages of the Metaverse for industrial production. Then, we review a collection of key enabling technologies of the Industrial Metaverse, including blockchain (BC), digital twin (DT), 6G, XR, and artificial intelligence (AI), and analyze how these technologies can support different aspects of industrial production. Subsequently, we present numerous formidable challenges encountered within the Industrial Metaverse, including confidentiality and security concerns, resource limitations, and interoperability constraints. Furthermore, we investigate the extant solutions devised to address them. Finally, we briefly outline several open issues and future research directions of the Industrial Metaverse.
Research Purposes: This research aims to determine the application of good governance principles in zakat institutions accompanied by concrete evidence that has been implemented by zakat management institutions in Indonesia.
Design, Methodology, Approach: This research is library research, data sources are obtained from journals, books and other documents.
Research Findings: good governance principles that can be implemented by zakat institutions include: the principles of transparency, accountability, responsibility, independence and justice, as an effort to increase the professionalism of zakat institutions while increasing muzakki's trust in zakat management institutions. . Concrete evidence of the application of good governance principles has been actualized by many zakat management institutions in Indonesia, both Baznas and Laznas.
Research Contribution: research provides new insights related to the implementation of good governance by zakat management institutions
Tujuan Penelitian: Penelitian ini bertujuan untuk mengetahui penerapan prinsip good governance pada lembaga zakat disertai dengan bukti nyata yang telah diterapkan oleh lembaga pengelola zakat di Indonesia.
Desain, Metodologi, Pendekatan: Penelitian ini merupakan penelitian kepustakaan, sumber data diperoleh dari jurnal, buku dan dokumen lainnya.
Temuan penelitian: prinsip-prinsip good governance yang dapat diterapkan oleh lembaga zakat antara lain: prinsip transparansi, akuntabilitas, responsibilitas, independensi dan keadilan, sebagai upaya meningkatkan profesionalisme lembaga zakat sekaligus meningkatkan kepercayaan muzakki terhadap lembaga pengelola zakat. Bukti nyata penerapan prinsip good governance telah diaktualisasikan oleh banyak lembaga pengelola zakat di Indonesia, baik Baznas maupun Laznas.
Kontribusi Penelitian: penelitian memberikan wawasan baru terkait dengan penerapan good governance lembaga pengelola zakat
Economics as a science, Management. Industrial management
Anurag Solanki, Manushree Gupta, Swarndeep Singh
et al.
Background:
Only a few studies have systematically assessed sexual dysfunction and marital adjustment in married men with alcohol dependence in India.
Aim:
To assess the prevalence and pattern of sexual dysfunction and marital adjustment in a clinical sample of married men with alcohol dependence. The association of sexual dysfunction and marital adjustment with the severity of alcohol dependence was also explored.
Materials and Methods:
This cross-sectional study included 100 consecutive married men attending psychiatry outpatient department (OPD) with a diagnosis of alcohol dependence syndrome (ADS). A pre-designed study proformawas used for collecting relevant socio-demographic and clinical details of the study participants. Sexual dysfunction, marital adjustment, and alcohol dependence were assessed using the following validated tools: the Arizona Sexual Experiences Scale (ASEX), the Marital Adjustment Questionnaire (MAQ), and the Severity of Alcohol Dependence Questionnaire (SADQ), respectively.
Results:
Of 100 patients, about 15% had clinical sexual dysfunction based on ASEX. The most commonly reported sexual dysfunction was difficulty with erection (24.0%), followed by problems in desire (18.0%), sexual arousal (12.0%), ability to reach orgasm (12.0%), and satisfaction with orgasm (9.0%). Sexual dysfunction (ASEX score) showed significant positive correlation (rs = 0.345; P < 0.001) with alcohol dependence severity (SADQ score). Also, the severity of alcohol dependence was negatively correlated (rs = -0.240; P = 0.016) with the overall level of marital adjustment (MAQ score) with spouse.
Conclusion:
Sexual dysfunction is common in married men with ADS, with both sexual dysfunction intensity and marital adjustment difficulties being positively associated with the severity of alcohol dependence.
The 2023 Odisha train accident in India is one of the deadliest train accidents in recent history, which is expected to have a massive psychological impact on the survivors and their families. Despite train accidents being common, there is an apparent lack of a process to support the psychosocial needs of the survivors. This narrative review highlights the catastrophic nature of the accident and possible psychological consequences of train accidents based on the literature and discusses approaches that can be taken to provide mental health support for the survivors. While there was appreciable support for the treatment of the injuries, financial compensation, and other practical help, it appeared that there were unmet needs for psychological support. With a greater understanding of trauma manifestation and effective strategies, it is feasible to set up an implementation plan that can take care of survivors from crisis support to long-term psychological intervention and rehabilitation. It would need multidisciplinary and multilevel cooperation and support. While preventing accidents should be the primary focus, providing timely and appropriate care for the survivors is of paramount importance. This review highlights the gap in psychological support for train accident survivors and provides a feasible approach that can be easily integrated into the existing health and social care system.