Hasil untuk "Industrial psychology"

Sidney C. Sufrin

Albert H. Segars

H. Eysenck

R. Baron, D. Byrne, W. Griffitt

W. Hollway

Anjali Sharma, Manish Roshan Thakur, Shiv Prasad

This text discusses a case report of a pregnant anemic female in her second trimester with a diagnosis of bipolar depression and comorbid obsessive compulsive disorder (OCD). Managing both mood stabilization and obsessive compulsive symptoms simultaneously in a pregnant female presents a real challenge, especially considering that serotonin reuptake inhibitors (SSRI), the first-line treatment for OCD, can induce manic or mixed mood states in bipolar disorder patients. This case is unique in view of limited options left with pregnancy in place. Lamotrigine was chosen as an agent and was introduced in this patient which is rated as FDA pregnancy risk category C which amounts to risk cannot be ruled out (human data lacking, animal studies positive or not done). Patient responded well and maintained well on monotherapy of lamotrigine and showed significant improvement in both symptoms of depression and OCD. USG-level II examination shows no abnormality on repeated scans.

Samira Abdelrahman, Hossam Farag, Gilberto Berardinelli

Emerging 6G industrial networks envision autonomous in-X subnetworks to support efficient and cost-effective short range, localized connectivity for autonomous control operations. Supporting timely transmission of event-driven, critical control traffic is challenging in such networks is challenging due to limited radio resources, dynamic device activity, and high mobility. In this paper, we propose a distributed, learning-based random access protocol that establishes implicit inter-subnetwork coordination to minimize the collision probability and improves timely delivery. Each subnetwork independently learns and selects access configurations based on a contention signature signal broadcast by a central access point, enabling adaptive, collision-aware access under dynamic traffic and mobility conditions. The proposed approach features lightweight neural models and online training, making it suitable for deployment in constrained industrial subnetworks. Simulation results show that our method significantly improves the probability of timely packet delivery compared to baseline methods, particularly in dense and high-load scenarios. For instance, our proposed method achieves 21% gain in the probability of timely packet delivery compared to a classical Multi-Armed Bandit (MAB) for an industrial setting of 60 subnetworks and 5 radio channels.

Bastien Muraccioli, Mathieu Celerier, Mehdi Benallegue et al.

Physical Human-Robot Interaction (pHRI) is critical for implementing Industry 5.0, which focuses on human-centric approaches. However, few studies explore the practical alignment of pHRI to industrial-grade performance. This paper introduces a versatile control framework designed to bridge this gap by incorporating the torque-based control modes: compliance control, null-space compliance, and dual compliance, all in static and dynamic scenarios. Thanks to our second-order Quadratic Programming (QP) formulation, strict kinematic and collision constraints are integrated into the system as safety features, and a weighted hierarchy guarantees singularity-robust task tracking performance. The framework is implemented on a Kinova Gen3 collaborative robot (cobot) equipped with a Bota force/torque sensor. A DualShock 4 game controller is attached to the robot's end-effector to demonstrate the framework's capabilities. This setup enables seamless dynamic switching between the modes, and real-time adjustments of parameters, such as transitioning between position and torque control or selecting a more robust custom-developed low-level torque controller over the default one. Built on the open-source robotic control software mc_rtc, our framework ensures reproducibility for both research and industrial deployment, this framework demonstrates a step toward industrial-grade performance and repeatability, showcasing its potential as a robust pHRI control system for industrial environments.

Chentao Shen, Ding Pan, Mingyu Mei et al.

Visual pose tracking is playing an increasingly vital role in industrial contexts in recent years. However, the pose tracking for industrial metal objects remains a challenging task especially in the real world-environments, due to the reflection characteristic of metal objects. To address this issue, we propose a novel 6DoF pose tracking method based on active control points. The method uses image control points to generate edge feature for optimization actively instead of 6DoF pose-based rendering, and serve them as optimization variables. We also introduce an optimal control point regression method to improve robustness. The proposed tracking method performs effectively in both dataset evaluation and real world tasks, providing a viable solution for real-time tracking of industrial metal objects. Our source code is made publicly available at: https://github.com/tomatoma00/ACPTracking.

Ting-Yen Wang

This study introduces a novel framework for estimating industrial heterogeneity by integrating maximum entropy (ME) estimation of production functions with Zonotope-based measures. Traditional production function estimations often rely on restrictive parametric models, failing to capture firm behavior under uncertainty. This research addresses these limitations by applying Hang K. Ryu's ME method to estimate production functions using World Bank Enterprise Survey (WBES) data from Bangladesh, Colombia, Egypt, and India. The study normalizes entropy values to quantify heterogeneity and compares these measures with a Zonotope-based Gini index. Results demonstrate the ME method's superiority in capturing nuanced, functional heterogeneity often missed by traditional techniques. Furthermore, the study incorporates a "Tangent Against Input Axes" method to dynamically assess technical change within industries. By integrating information theory with production economics, this unified framework quantifies structural and functional differences across industries using firm-level data, advancing both methodological and empirical understanding of heterogeneity. A numerical simulation confirms the ME regression functions can approximate actual industrial heterogeneity. The research also highlights the superior ability of the ME method to provide a precise and economically meaningful measure of industry heterogeneity, particularly for longitudinal analyses.

Muhammad Aqeel, Shakiba Sharifi, Marco Cristani et al.

Anomaly detection is fundamental for ensuring quality control and operational efficiency in industrial environments, yet conventional approaches face significant challenges when training data contains mislabeled samples-a common occurrence in real-world scenarios. This paper presents RAD, a robust anomaly detection framework that integrates Normalizing Flows with Model-Agnostic Meta-Learning to address the critical challenge of label noise in industrial settings. Our approach employs a bi-level optimization strategy where meta-learning enables rapid adaptation to varying noise conditions, while uncertainty quantification guides adaptive L2 regularization to maintain model stability. The framework incorporates multiscale feature processing through pretrained feature extractors and leverages the precise likelihood estimation capabilities of Normalizing Flows for robust anomaly scoring. Comprehensive evaluation on MVTec-AD and KSDD2 datasets demonstrates superior performance, achieving I-AUROC scores of 95.4% and 94.6% respectively under clean conditions, while maintaining robust detection capabilities above 86.8% and 92.1% even when 50% of training samples are mislabeled. The results highlight RAD's exceptional resilience to noisy training conditions and its ability to detect subtle anomalies across diverse industrial scenarios, making it a practical solution for real-world anomaly detection applications where perfect data curation is challenging.

Dikshit Chauhan, Nitin Gupta, Anupam Yadav

Industrial and reliability optimization problems often involve complex constraints and require efficient, interpretable solutions. This paper presents AI-AEFA, an advanced parameter reconfiguration-based metaheuristic algorithm designed to address large-scale industrial and reliability-redundancy allocation problems. AI-AEFA enhances search space exploration and convergence efficiency through a novel log-sigmoid-based parameter adaptation and chaotic mapping mechanism. The algorithm is validated across twenty-eight IEEE CEC 2017 constrained benchmark problems, fifteen large-scale industrial optimization problems, and seven reliability-redundancy allocation problems, consistently outperforming state-of-the-art optimization techniques in terms of feasibility, computational efficiency, and convergence speed. The additional key contribution of this work is the integration of SHAP (Shapley Additive Explanations) to enhance the interpretability of AI-AEFA, providing insights into the impact of key parameters such as Coulomb's constant, charge, acceleration, and electrostatic force. This explainability feature enables a deeper understanding of decision-making within the AI-AEFA framework during the optimization processes. The findings confirm AI-AEFA as a robust, scalable, and interpretable optimization tool with significant real-world applications.

Subhashini Gopal, Lakshmi Venkatraman, B Suhavana et al.

Background: Cognitive impairments in individuals with psychotic disorders impact day-to-day activities and social and occupational functioning (Bowie CR, 2006). Most of the cognitive interventions were developed in the west focusing mainly on clinical research and were not available in routine care. Adaptability and accessibility of these techniques in low-resource settings like India had major challenges. Keeping this in mind, Compensatory Cognitive Training (CCT), being an economical and noncomputerized intervention, was adapted to be used for an urban English-speaking population in India. Aim: The study aimed to determine the acceptability and feasibility of delivering CCT to persons with schizophrenia through virtual one-on-one sessions. Materials and Methods: Patients with a diagnosis of schizophrenia were assessed for their subjective and objective cognitive deficits. CCT was delivered for 13 participants as a virtual one – one session. Three participants dropped out midway. Semistructured interview was conducted with all ten participants who completed the intervention to understand their acceptability of Virtual CCT. Feasibility was assessed using a visual analog scale on their attendance, involvement, and comprehending ability. The mean percentile scores on cognitive domains at baseline and end of intervention were analyzed. Results: Significant change was observed in specific domains of cognition. Participant involvement, lesser dropout rates, and their feedback indicated that Virtual CCT is a feasible and acceptable intervention. Conclusion: Virtually delivered CCT appears to be an acceptable and feasible intervention to increase access to cognitive interventions for persons with schizophrenia in LAMI countries. This needs to be tested in larger populations.

Ipsita Basu, Srikrishna Prasad Panda, Prateek Yadav

Obsessive-compulsive disorder (OCD) is characterized by intrusive, distressing thoughts and/or repetitive behaviors. Transcranial direct current stimulation (tDCS) is a promising neuromodulation technique for augmenting pharmacotherapy in OCD. This case series identified patients diagnosed with OCD, who showed residual symptoms even after two adequate trials of pharmacotherapy corroborated using the Yale-Brown Obsessive Compulsive Scale (Y-BOCS) (score of eight and above taken as cutoff). tDCS was given in the form of two milliamperes current with anode at SMA and cathode at the right orbitofrontal area, for twenty minutes, for six sessions (one session per day) in addition to pharmacotherapy. Clinical assessment was performed using the Y BOCS at baseline and post treatment. The results revealed a reduction in OCD symptom severity in all the patients (as per both subjective report and Y-BOCS scores). The patients did not have any severe adverse effects. tDCS appears to be a potential augmentation therapy for individuals with OCD. However, further research is warranted to optimize stimulation parameters and elucidate the long-term effects of tDCS as part of a comprehensive treatment approach for OCD.

Priyank Singh Mehta, Priyadarshee Patra, Santosh Tapasi

Background: Alcohol dependence syndrome is a major public health problem, and it impacts the social, psychological, medical, economic, and religious spheres of our existence. Persistent alcohol abuse impacts sexual functioning negatively and leads to the onset of sexual dysfunction. Aim: This study was conducted to determine erectile dysfunction in males diagnosed with alcohol dependence syndrome and its association with the severity of alcohol dependence. Materials and Methods: The descriptive, non-interventional, cross-sectional study was conducted at the Department of Psychiatry in a tertiary care hospital where 78 subjects diagnosed with alcohol dependence syndrome were assessed for severity of dependence with the Severity of Alcohol Dependence Questionnaire (SADQ-C). Erectile dysfunction in these subjects was assessed with the International Index of Erectile Function scale (IIEF) and the severity of the same was correlated with the severity of alcohol dependence. Results: The results of our study indicated that erectile dysfunction was common in individuals having alcohol dependence syndrome and its severity was positively correlated with the severity of alcohol dependence. Unidentified sexual dysfunction may perpetuate alcohol dependence with poor response to deaddiction therapy. This information about sexual dysfunction due to alcohol dependence can also be used in motivational counseling of heavy drinkers to provide an impetus for change. Conclusions: The prevalence of erectile dysfunction was significantly higher than that of the general population. The same was significantly elevated in patients with severe alcohol dependence as compared to those with mild/moderate alcohol dependence.

Saptaningsih Sumarmi, Heru Kurnianto Tjahjono, Ika Nurul Qamari et al.

Introduction/Main Objectives: This research explores Dynamic Adaptive Capability to achieve Team Performance in higher education institutions, focusing on the challenges posed by a rapidly changing global environment. The study emphasizes the importance of dynamic adaptability as a mediating variable, connecting Authentic Leadership and Justice Climate as key elements influencing Team Performance. Background Problems: The main question of this research is whether Authentic Leadership improves Team Performance through Dynamic Adaptive Capability. The urgency of this research in Indonesia's higher education context is critical, considering that the ever-changing global environment demands rapid and effective adaptation to remain competitive and relevant. Novelty: Empirical evidence explaining how Authentic Leadership can improve Team Performance in Indonesian higher education still needs to be explored. Therefore, research that finds this mechanism is still required and adds Dynamic Adaptive Capability as a mediator. Research Methods: This research collected a survey of the Head of the Study Program as a team representative, used purposive sampling, and utilized AMOS SEM analysis to test the hypothesis. Finding/Results: The research shows that Authentic Leadership positively impacts Dynamic Adaptive Capability and Team Performance. Meanwhile, Justice Climate has no relationship with Team Performance but positively impacts Dynamic Adaptive Capability. Dynamic Adaptive Capability positively mediates Authentic Leadership and Team Performance while negatively mediating Justice Climate and Team Performance. Conclusion: In addition to supporting several hypotheses, this research also highlights the complex and diverse nature of these relationships, prompting recommendations for strategic planning and further investigation to deepen our understanding of the dynamics in organizational environments.

Xiaoshan Huang, Haolun Wu, Xue Liu et al.

This study explores the impact of peer acknowledgement on learner engagement and implicit psychological attributes in written annotations on an online social reading platform. Participants included 91 undergraduates from a large North American University. Using log file data, we analyzed the relationship between learners' received peer acknowledgement and their subsequent annotation behaviours using cross-lag regression. Higher peer acknowledgements correlate with increased initiation of annotations and responses to peer annotations. By applying text mining techniques and calculating Shapley values to analyze 1,969 social annotation entries, we identified prominent psychological themes within three dimensions (i.e., affect, cognition, and motivation) that foster peer acknowledgment in digital social annotation. These themes include positive affect, openness to learning and discussion, and expression of motivation. The findings assist educators in improving online learning communities and provide guidance to technology developers in designing effective prompts, drawing from both implicit psychological cues and explicit learning behaviours.

Silvia García-Méndez, Francisco de Arriba-Pérez, María del Carmen Somoza-López

Artificial Intelligence (AI) can potentially transform the industry, enhancing the production process and minimizing manual, repetitive tasks. Accordingly, the synergy between high-performance computing and powerful mathematical models enables the application of sophisticated data analysis procedures like Machine Learning. However, challenges exist regarding effective, efficient, and flexible processing to generate valuable knowledge. Consequently, this work comprehensively describes industrial challenges where AI can be exploited, focusing on the dairy industry. The conclusions presented can help researchers apply novel approaches for cattle monitoring and farmers by proposing advanced technological solutions to their needs.

Karthik Muthineni, Alexander Artemenko, Josep Vidal et al.

The precise and accurate indoor positioning using cellular communication technology remains to be a prerequisite for several industrial applications, including the emergence of a new topic of Integrated Sensing and Communication (ISAC). However, the frequently occurring Non-Line-of-Sight (NLoS) conditions in a heavy multipath dominant industrial scenario challenge the wireless signal propagation, leading to abnormal estimation errors (outliers) in the signal measurements taken at the receiver. In this paper, we investigate the iterative positioning scheme that is robust to the outliers in the Time of Arrival (ToA) measurements. The Iteratively Reweighted Least Squares (IRLS) positioning scheme formulated on the Least Squares (LS) is implemented to reject the outlier measurements and reweight the available ToA samples based on their confidence. Our positioning scheme is validated under 5G frequency bands, including the C-band (3.7 GHz) and the mmWave-band (26.8 GHz) in a Ray-Tracing enabled industrial scenario with different emulation setups.

L. Bowker, Dorothy Kenny, Jennifer Pearson

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