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Yayın An effective maintenance policy for a multi-component dynamic system using factored POMDPs(Springer Verlag, 2019-09-20) Kıvanç, İpek; Özgür Ünlüakın, DemetWith the latest advances in technology, almost all systems are getting substantially more uncertain and complex. Since increased complexity costs more, it is challenging to cope with this situation. Maintenance optimization plays a critical role in ensuring effective decision-making on the correct maintenance actions in multi-component systems. A Partially Observable Markov Decision Process (POMDP) is an appropriate framework for such problems. Nevertheless, POMDPs are rarely used for tackling maintenance problems. This study aims to formulate and solve a factored POMDP model to tackle the problems that arise with maintenance planning of multi-component systems. An empirical model consisting of four partially observable components deteriorating in time is constructed. We resort to Symbolic Perseus solver, which includes an adapted variant of the point-based value iteration algorithm, to solve the empirical model. The obtained maintenance policy is simulated on the empirical model in a finite horizon for many replications and the results are compared to the other predefined maintenance policies. Drawing upon the policy results of the factored representation, we present how factored POMDPs offer an effective maintenance policy for the multi-component systems.Yayın Decision making, emotion recognition and childhood traumatic experiences in murder convicts ımprisoned with aggravated life sentence: a prison study(Turkish Neuropsychiatric Society, 2025-03) Çıkrıkçılı, Uğur; Yıldırım, Elif; Buker, Seda; Ger, Can; Erözden, Ozan; Gürvit, Hakan; Saydam, BilginIntroduction: Decision-making and emotion recognition are two fundamental themes in social cognition. Disorders in these areas can lead to interpersonal, psychosocial, and legal problems for the individual and society. The likelihood of consequent aggression and crime makes them foci of forensic psychiatry over time. In this study, two developmental disorders that have a clear relationship with crime, that are antisocial personality disorder (ASPD), and psychopathy are investigated for their relationship with these social cognitive deficits.Methods: The present study involved 23 male prison inmates who were diagnosed with both antisocial personality disorder and psychopathy, as well as 23 control participants who were matched for age, gender, and level of education. Following the psychiatric interview, Reading the Mind in the Eyes Test (RMET), the Iowa Gambling Test (IGT), Toronto Alexithymia Scale (TAS), Defense Styles Questionnaire (DSQ), Childhood Psychic Trauma Scale (CTQ), Hare Psychopathy Checklist (PCL-R) were administered to all participants. Results: The results of the study showed that ASPD group performed statistically worse than healthy controls in TAS, CTQ, all items of DSQ, PCL-R Factor 1 and 2, and all the IGT scores (p<0.05). There were no statistically significant difference between in the RMET test performancesConclusion: These results suggest that ASPD and psychopathy lead to impaired decision-making behaviors due to the inability to recognize one’s own emotions and impulsivity, and that these characteristics play a critical role in the criminal behavior of individuals. In addition, contrary to expectations, the results of affective theory of mind assessed with the RMET showed similar characteristics in homicide convicts and healthy controls. These data indicate the need for further research in the field of forensic psychiatry.Yayın Designing a scalable agricultural information system for pest detection and decision support in hazelnut cultivation(World Scientific Publishing Company, 2025-11-12) Aydın, ŞahinThis study presents a microservices-based, multi-tiered information system to detect, monitör and manage pest species that cause yield losses in hazelnut production. The system integrates a deep learning model for classifying pest images submitted by field users, the generation of pest density maps and location-based early warning mechanisms for growers. Delivered through mobile, web and desktop platforms, the system enables data sharing among farmers, researchers and decision-makers, supporting agricultural decisions. Experimental findings show that the DNN+ResNet50 architecture achieved the highest accuracy (91.88%) among all tested CNN models. Performance evaluations indicated that the Authentication and Heatmap services sustained high stability under loads of up to 1000 requests, while the Bug Classification Service was reliable up to 750 requests before reaching a critical resource threshold. The usability test resulted in an overall score of 38 out of 50, with sub-scores of Appropriateness Recognizability (0.73, Acceptable), Learnability (0.71, Acceptable), Operability (0.65, Questionable), User Error Protection (0.86, Good), User Interface Aesthetics (0.83, Good) and Accessibility (0.74, Acceptable). With its robust technical architecture and practical implementation, the proposed system can generate economic, social and commercial outcomes. This study provides a software engineering-oriented approach to the digitalization of agricultural production and the sustainable management of pests.
