Relin John Renny, Srikrishna Prasad Panda, Vishwavijet Mopagar
et al.
Background:
Urbanization and modernization have led to increasing marital disputes, a major cause of stress affecting the mental health and quality of life among couples. Poor marital adjustment is commonly associated with psychological complaints. Internet addiction may function both as a maladaptive coping mechanism and as a factor contributing to marital discord. These difficulties not only affect the couple but can also have lasting implications for family dynamics and child functioning. These factors must be considered when designing effective interventions targeting intergenerational associations and improving psychosocial outcomes in an urban environment.
Aim:
To determine the marital adjustment, psychological distress, and Internet addiction among married couples in an urban community through cross-sectional design.
Materials and Methods:
106 married couples were studied for marital discord. A simple random sampling approach was employed for recruitment. Standardized questionnaires measuring marital adjustment, psychological wellbeing, and Internet addiction were used to collect self-reported data. Statistical analysis was undertaken to evaluate how psychosocial variables relate to the extent of marital discord.
Results:
22.6% females and 20.7% males experienced poor marital adjustment. Education and occupation were significantly associated with marital discord, particularly among men. Marital adjustment showed a significant inverse correlation with psychological distress, whereas no such association was found with Internet addiction.
Conclusion:
Marital discord was significantly associated with psychological distress, with women experiencing higher distress levels. Internet addiction showed no direct link with discord but correlated with psychological distress. These findings highlight the need for integrated interventions addressing marital, mental health and digital wellbeing among urban couples.
Automated semantic understanding of dense point clouds is a prerequisite for Scan-to-BIM pipelines, digital twin construction, and as-built verification--core tasks in the digital transformation of the construction industry. Yet for industrial mechanical, electrical, and plumbing (MEP) facilities, this challenge remains largely unsolved: TLS acquisitions of water treatment plants, chiller halls, and pumping stations exhibit extreme geometric ambiguity, severe occlusion, and extreme class imbalance that architectural benchmarks (e.g., S3DIS or ScanNet) cannot adequately represent. We present Industrial3D, a terrestrial LiDAR dataset comprising 612 million expertly labelled points at 6 mm resolution from 13 water treatment facilities. At 6.6x the scale of the closest comparable MEP dataset, Industrial3D provides the largest and most demanding testbed for industrial 3D scene understanding to date. We further establish the first industrial cross-paradigm benchmark, evaluating nine representative methods across fully supervised, weakly supervised, unsupervised, and foundation model settings under a unified benchmark protocol. The best supervised method achieves 55.74% mIoU, whereas zero-shot Point-SAM reaches only 15.79%--a 39.95 percentage-point gap that quantifies the unresolved domain-transfer challenge for industrial TLS data. Systematic analysis reveals that this gap originates from a dual crisis: statistical rarity (215:1 imbalance, 3.5x more severe than S3DIS) and geometric ambiguity (tail-class points share cylindrical primitives with head-class pipes) that frequency-based re-weighting alone cannot resolve. Industrial3D, along with benchmark code and pre-trained models, will be publicly available at https://github.com/pointcloudyc/Industrial3D.
Tolgahan Bardakci, Andreas Faes, Mutlu Beyazit
et al.
Large Language Models (LLMs) are increasingly used to support software testing tasks, yet there is little evidence of their effectiveness for REST API testing in industrial settings. To address this gap, we replicate our earlier work on LLM-based REST API test amplification within an industrial context at one of the largest logistics companies in Belgium. We apply LLM-based test amplification to six representative endpoints of a production microservice embedded in a large-scale, security-sensitive system, where there is in-depth complexity in authentication, stateful behavior, and organizational constraints. Our experience shows that LLM-based test amplification remains practically useful in industry by increasing coverage and revealing various observations and anomalies.
Psychological client simulators have emerged as a scalable solution for training and evaluating counselor trainees and psychological LLMs. Yet existing simulators exhibit unrealistic over-compliance, leaving counselors underprepared for the challenging behaviors common in real-world practice. To bridge this gap, we present ResistClient, which systematically models challenging client behaviors grounded in Client Resistance Theory by integrating external behaviors with underlying motivational mechanisms. To this end, we propose Resistance-Informed Motivation Reasoning (RIMR), a two-stage training framework. First, RIMR mitigates compliance bias via supervised fine-tuning on RPC, a large-scale resistance-oriented psychological conversation dataset covering diverse client profiles. Second, beyond surface-level response imitation, RIMR models psychologically coherent motivation reasoning before response generation, jointly optimizing motivation authenticity and response consistency via process-supervised reinforcement learning. Extensive automatic and expert evaluations show that ResistClient substantially outperforms existing simulators in challenge fidelity, behavioral plausibility, and reasoning coherence. Moreover, ResistClient facilities evaluation of psychological LLMs under challenging conditions, offering new optimization directions for mental health dialogue systems.
Robotic foundation models (RFMs) are emerging as a promising route towards flexible, instruction- and demonstration-driven robot control, however, a critical investigation of their industrial applicability is still lacking. This survey gives an extensive overview over the RFM-landscape and analyses, driven by concrete implications, how industrial domains and use cases shape the requirements of RFMs, with particular focus on collaborative robot platforms, heterogeneous sensing and actuation, edge-computing constraints, and safety-critical operation. We synthesise industrial deployment perspectives into eleven interdependent implications and operationalise them into an assessment framework comprising a catalogue of 149 concrete criteria, spanning both model capabilities and ecosystem requirements. Using this framework, we evaluate 324 manipulation-capable RFMs via 48,276 criterion-level decisions obtained via a conservative LLM-assisted evaluation pipeline, validated against expert judgements. The results indicate that industrial maturity is limited and uneven: even the highest-rated models satisfy only a fraction of criteria and typically exhibit narrow implication-specific peaks rather than integrated coverage. We conclude that progress towards industry-grade RFMs depends less on isolated benchmark successes than on systematic incorporation of safety, real-time feasibility, robust perception, interaction, and cost-effective system integration into auditable deployment stacks.
This paper explores how inclusive and environmentally focused research and innovation policies challenge dominant models by reshaping directionality and governance for social transformation. It contributes to Critical and Transformative Innovation Studies by addressing key intertwined gaps: the role of agency, the political economy of policy instruments, the politics of continuity, and its territorial grounding. Analytically, it expands a Knowledge Systems approach, promoting a broader, symmetrical view of innovation that values diverse actors, policies, infrastructures, and knowledges. It challenges competitiveness-driven assumptions by exploring how alternative normative directions are negotiated over time. Through two case studies in Argentina—Yogurito (a probiotic yogurt to address malnutrition) and the Paraná River Aquarium (focused on biodiversity conservation)—the paper traces innovation journeys as a process where multiple actors vie to steer its course. Directionality is framed as both a political process of prioritization and decision making amid competing interests and its negotiated outcome, shaped by actors’ visions, knowledge, and policy preferences. The paper also proposes a framework to empirically trace and analyze these evolving pathways. It shows how innovation is steered, which orientations take precedence, and the limits and possibilities of STI as a development driver under enduring structural constraints.
Sarah Depenbusch, Niclas Schaper, Mirko Schürmann
et al.
In the context of digitalization, work processes are subject to constant change. To achieve overall process efficiency, it should be ensured that employees have a deep understanding of the work processes in which they are involved. Preliminary research has shown that the utilization of virtual reality (VR) environments, which visualize employees' workspaces and present VR avatars that demonstrate work process steps, can enhance employees' understanding of (future) work processes. However, implementing such virtual environments entails certain challenges, such as the necessity of training employees in the utilization of VR technology. Thus, the delivery of VR avatar simulations in a video format (VR-based avatar video) may present a flexible alternative solution. Focusing on related work, it can be assumed that VR-based avatar videos (VRA videos) help learners build a coherent mental model of their work processes by providing contextualized visual information that is close to real life. Furthermore, the visual design elements included in a VRA video (e.g., the VR avatar and virtual workspace) may increase employees' motivation to learn. Despite the potential benefits of VRA videos, critics may argue that these videos contain an excessive amount of visual detail, thus increasing learners' cognitive load. Due to these contradicting opinions, the present study investigates the potential advantages of a VRA video in enhancing employees' understanding of work processes compared to a schematically designed voice-over slides video (VOS video). Furthermore, the study compares the motivational impact of both videos. In an online experimental study, participants (N = 121) were randomly assigned to either the VRA or the VOS video group. One-way ANOVAs revealed that the VRA video group achieved significantly better transfer scores than the VOS video group. Results of the motivation questionnaires (based on the ARCS model) demonstrated that attention (ARCS-A), relevance (ARCS-R), and satisfaction (ARCS-S) were significantly higher in the VRA video group than in the VOS video group.
Christiaan Verwijs, Evelien Acun-Roos, Daniel Russo
As hybrid, distributed, and asynchronous work models become more prevalent, continuous learning in Agile Software Development (ASD) gains renewed importance. Communities of Practice (CoPs) are increasingly adopted to support social learning beyond formal education, often relying on virtual communication. Psychological safety, a prerequisite for effective learning, remains insufficiently understood in these settings. This mixed-methods study investigates psychological safety within Agile CoPs through survey data from 143 participants. Results indicate that psychological safety is significantly lower in online interactions compared to face-to-face settings. Moreover, low psychological safety reduces participants' intent to continue contributing and avoidance of interpersonal risk. No significant differences emerged based on gender, community seniority, or content creation activity. However, differences by role and age group suggest potential generational or role-related effects. Thematic analysis revealed exclusionary behavior, negative interaction patterns, and hostility as primary threats to psychological safety, often reinforced by tribalism and specific community dynamics. Suggested interventions include establishing explicit norms, structured facilitation, and active moderation. The findings were validated through member checking with 30 participants. This study provides a comparative perspective on interaction modalities and offers practical guidance for organizers seeking to cultivate inclusive, high-impact CoPs and similarly structured virtual or hybrid work environments.
Cavitation intensity recognition (CIR) is a critical technology for detecting and evaluating cavitation phenomena in hydraulic machinery, with significant implications for operational safety, performance optimization, and maintenance cost reduction in complex industrial systems. Despite substantial research progress, a comprehensive review that systematically traces the development trajectory and provides explicit guidance for future research is still lacking. To bridge this gap, this paper presents a thorough review and analysis of hundreds of publications on intelligent CIR across various types of mechanical equipment from 2002 to 2025, summarizing its technological evolution and offering insights for future development. The early stages are dominated by traditional machine learning approaches that relied on manually engineered features under the guidance of domain expert knowledge. The advent of deep learning has driven the development of end-to-end models capable of automatically extracting features from multi-source signals, thereby significantly improving recognition performance and robustness. Recently, physical informed diagnostic models have been proposed to embed domain knowledge into deep learning models, which can enhance interpretability and cross-condition generalization. In the future, transfer learning, multi-modal fusion, lightweight network architectures, and the deployment of industrial agents are expected to propel CIR technology into a new stage, addressing challenges in multi-source data acquisition, standardized evaluation, and industrial implementation. The paper aims to systematically outline the evolution of CIR technology and highlight the emerging trend of integrating deep learning with physical knowledge. This provides a significant reference for researchers and practitioners in the field of intelligent cavitation diagnosis in complex industrial systems.
Artemis Stefanidou, Panagiotis Radoglou-Grammatikis, Vasileios Argyriou
et al.
The recent tremendous advancements in the areas of Artificial Intelligence (AI) and Deep Learning (DL) have also resulted into corresponding remarkable progress in the field of Computer Vision (CV), showcasing robust technological solutions in a wide range of application sectors of high industrial interest (e.g., healthcare, autonomous driving, automation, etc.). Despite the outstanding performance of CV systems in specific domains, their development and exploitation at industrial-scale necessitates, among other, the addressing of requirements related to the reliability, transparency, trustworthiness, security, safety, and robustness of the developed AI models. The latter raises the imperative need for the development of efficient, comprehensive and widely-adopted industrial standards. In this context, this study investigates the current state of play regarding the development of industrial computer vision AI standards, emphasizing on critical aspects, like model interpretability, data quality, and regulatory compliance. In particular, a systematic analysis of launched and currently developing CV standards, proposed by the main international standardization bodies (e.g. ISO/IEC, IEEE, DIN, etc.) is performed. The latter is complemented by a comprehensive discussion on the current challenges and future directions observed in this regularization endeavor.
Anomaly detection and localization in automated industrial manufacturing can significantly enhance production efficiency and product quality. Existing methods are capable of detecting surface defects in pre-defined or controlled imaging environments. However, accurately detecting workpiece defects in complex and unstructured industrial environments with varying views, poses and illumination remains challenging. We propose a novel anomaly detection and localization method specifically designed to handle inputs with perturbative patterns. Our approach introduces a new framework based on a collaborative distillation heterogeneous teacher network (HetNet), an adaptive local-global feature fusion module, and a local multivariate Gaussian noise generation module. HetNet can learn to model the complex feature distribution of normal patterns using limited information about local disruptive changes. We conducted extensive experiments on mainstream benchmarks. HetNet demonstrates superior performance with approximately 10% improvement across all evaluation metrics on MSC-AD under industrial conditions, while achieving state-of-the-art results on other datasets, validating its resilience to environmental fluctuations and its capability to enhance the reliability of industrial anomaly detection systems across diverse scenarios. Tests in real-world environments further confirm that HetNet can be effectively integrated into production lines to achieve robust and real-time anomaly detection. Codes, images and videos are published on the project website at: https://zihuatanejoyu.github.io/HetNet/
Accurate 6D pose estimation is essential for robotic manipulation in industrial environments. Existing pipelines typically rely on off-the-shelf object detectors followed by cropping and pose refinement, but their performance degrades under challenging conditions such as clutter, poor lighting, and complex backgrounds, making detection the critical bottleneck. In this work, we introduce a standardized and plug-in pipeline for 2D detection of unseen objects in industrial settings. Based on current SOTA baselines, our approach reduces domain shift and background artifacts through low-light image enhancement and background removal guided by open-vocabulary detection with foundation models. This design suppresses the false positives prevalent in raw SAM outputs, yielding more reliable detections for downstream pose estimation. Extensive experiments on real-world industrial bin-picking benchmarks from BOP demonstrate that our method significantly boosts detection accuracy while incurring negligible inference overhead, showing the effectiveness and practicality of the proposed method.
Eva Selenko, Anahi Van Hootegem, Mindy Shoss
et al.
This paper investigates how changes to work (caused by the COVID-19 pandemic) and job insecurity relate to identification and subsequent compliance to public health mitigation guidelines. Specifically, we argue that job insecurity and certain changes to social contacts, workload and autonomy can be related to a lowered identification with the working population. Three-country survey data from Belgium, the UK and the US collected at the height of the pandemic supports that people who reported changes that were in line with what most people experienced (e.g., a decrease in social contacts at work) and changes that symbolised more importance (e.g., an increase in workload due to the pandemic) identified more strongly with the working population. Higher job insecurity was associated with less identification with the working population. Changes to autonomy did not play a role. Identification with the working population was related to higher compliance with national COVID-19 health guidelines. A 2SLS instrumental variable estimation controlled for endogeneity issues and confirmed the relationship between identification and health compliance. The study brings important general lessons for the societal impact of work-related changes and discusses applications beyond the pandemic context.
Pragnya Pillarisetti, Vishnu Priya Dikkala, P. S. Murthy
et al.
Background:
As a prerequisite to start a medical practice in India, Foreign Medical graduates on returning have to sit for FMGE (Foreign Medical Graduate’s Exam), organized by NBE. The time and effort involved by the students with adjustments to various changes in educational and examination pattern may manifest as psychiatric morbidities while awaiting a positive outcome.
Aim:
To evaluate depression, anxiety, stress, and insomnia in Foreign Medical Graduate students and to understand the various factors associated with them.
Materials and Methods:
A total of 80 MBBS students appearing for FMGE were enrolled during the period of April 2023 to May 2023, after obtaining their written consent on the Google form to participate in this study. The participants answered a semi-structured proforma consisting of information about sociodemographic data, DASS-21 (depression, anxiety, and stress severity scale), ISI scale (insomnia severity index), and RSES (Rosenberg self-esteem scale).
Results:
In this study, 76.2%, 80%, 72.5%, and 78.8% of students were found to be suffering from depression, anxiety, stress, and insomnia, respectively. A significant statistical correlation was found between anxiety and the students appearing for July FMGE 2023. Stress and depression were associated with insecurities/comparisons faced by the participants due to peers clearing the examination before them. Low self-esteem was associated with 3 or more failed attempts for FMGE and the presence of other stressors preceding/concurrent while preparing for FMGE. Clinical insomnia was found to be statistically significant with depression, anxiety as well as stress in this study. Multiple regression analysis showed that stress and anxiety predicted depression while the young age of the student, low socioeconomic status, low self-esteem, stress, and depression predicted anxiety. Stress was found to be associated with anxiety, depression, and insomnia. Insomnia was corelated with having extension in the UG course and stress while low self-esteem was corelated with students who were attempting for the July 2023 examination and anxiety.
Conclusion:
The significantly high proportion of psychiatric morbidities among Foreign Medical Graduates is suggestive of the need for the necessary psychological aid and counseling as these foreign medical graduates can be viewed as an opportunity to correct India’s physician shortage.
This paper explores the potential benefits and challenges of integrating Electric Vehicles (EVs) and Autonomous Ground Vehicles (AGVs) in industrial settings to improve sustainability and operational efficiency. While EVs offer environmental advantages, barriers like high costs and limited range hinder their widespread use. Similarly, AGVs, despite their autonomous capabilities, face challenges in technology integration and reliability. To address these issues, the paper develops a fleet management tool tailored for coordinating electric AGVs in industrial environments. The study focuses on simulating electric AGV performance in a primary aluminum plant to provide insights into their effectiveness and offer recommendations for optimizing fleet performance.
Being able to recognise defects in industrial objects is a key element of quality assurance in production lines. Our research focuses on visual anomaly detection in RGB images. Although Convolutional Neural Networks (CNNs) achieve high accuracies in this task, end users in industrial environments receive the model's decisions without additional explanations. Therefore, it is of interest to enrich the model's outputs with further explanations to increase confidence in the model and speed up anomaly detection. In our work, we focus on (1) CNN-based classification models and (2) the further development of a model-agnostic explanation algorithm for black-box classifiers. Additionally, (3) we demonstrate how we can establish an interactive interface that allows users to further correct the model's output. We present our NearCAIPI Interaction Framework, which improves AI through user interaction, and show how this approach increases the system's trustworthiness. We also illustrate how NearCAIPI can integrate human feedback into an interactive process chain.
Giampaolo Cuozzo, Enrico Testi, Salvatore Riolo
et al.
The Industrial Internet of Things (IIoT) paradigm has emerged as a transformative force, revolutionizing industrial processes by integrating advanced wireless technologies into traditional procedures to enhance their efficiency. The importance of this paradigm shift has produced a massive, yet heterogeneous, proliferation of scientific contributions. However, these works lack a standardized and cohesive characterization of the IIoT framework coming from different entities, like the 3rd Generation Partnership Project (3GPP) or the 5G Alliance for Connected Industries and Automation (5G-ACIA), resulting in divergent perspectives and potentially hindering interoperability. To bridge this gap, this article offers a unified characterization of (i) the main IIoT application domains, (ii) their respective requirements, (iii) the principal technological gaps existing in the current literature, and, most importantly, (iv) we propose a systematic approach for assessing and addressing the identified research challenges. Therefore, this article serves as a roadmap for future research endeavors, promoting a unified vision of the IIoT paradigm and fostering collaborative efforts to advance the field.
This paper delves into the contrasting roles of data within academic and industrial spheres, highlighting the divergence between Data-Centric AI and Model-Agnostic AI approaches. We argue that while Data-Centric AI focuses on the primacy of high-quality data for model performance, Model-Agnostic AI prioritizes algorithmic flexibility, often at the expense of data quality considerations. This distinction reveals that academic standards for data quality frequently do not meet the rigorous demands of industrial applications, leading to potential pitfalls in deploying academic models in real-world settings. Through a comprehensive analysis, we address these disparities, presenting both the challenges they pose and strategies for bridging the gap. Furthermore, we propose a novel paradigm: Model-Based Data-Centric AI, which aims to reconcile these differences by integrating model considerations into data optimization processes. This approach underscores the necessity for evolving data requirements that are sensitive to the nuances of both academic research and industrial deployment. By exploring these discrepancies, we aim to foster a more nuanced understanding of data's role in AI development and encourage a convergence of academic and industrial standards to enhance AI's real-world applicability.
Francisco Tomás González-Fernández, Gabriel Delgado-García, Jesús Siquier Coll
et al.
Abstract The aim of this study was to analyze the relationships among physical cognitive ability, academic performance, and physical fitness regarding age and sex in a group of 187 students (53.48% male, 46.52% female) from one town of Norwest of Jaén, Andalusia (Spain), aged between 9 and 15 years old (M = 11.97, SD = 1.99). The D2 attention test was used in order to analyze selective attention and concentration. Physical fitness, reflected on maximal oxygen uptake (VO2max), was evaluated using the 6 min Walking Test (6MWT). The analysis taken indicated a significant relationship between physical fitness level, attention, and concentration, as in the general sample looking at sex (finding differences between boys and girls in some DA score in almost all age categories [p < 0.05]) and at age category (finding some differences between the younger age category groups and the older age category groups in some DA scores (p < 0.05), not finding any significant interaction between sex and age category (p > 0.05). In sum, the present study revealed that students with better aerobic fitness can present better-processed elements and smaller omission errors. Moreover, girls and older students seem to present better cognitive functioning scores than boys and younger. Our findings suggest that more research is necessary to elucidate the cognitive function between ages, sexes, and physical fitness and anthropometry levels of students.