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Modeling the effects of soil improvement on train induced random ground-borne vibrations
(Isik University, 2025-05-01) Bayındır, Cihan; Kesten, Ali Sercan; Etminan, Ehsan
Ground-borne vibrations by railway trains are generated at the rail-wheel interface due to the passage of wheels and due to irregularities of wheels and tracks. These vibrations need to be predicted and controlled during the design and service of the railway for the safety and serviceability of the railway to avoid possible vibrationinduced problems such as settlement and differential settlement due to their compaction effect, liquefaction, and discomfort of people. While such railway vibrations are modeled by different techniques, only a few studies do exist to analyze them in the case of soilimproved conditions. In this study, we propose a mathematical framework to study the effects of soil improvement on the ground-borne vibrations induced by railway trains. We use an experimentally calibrated model that utilizes the evolutionary random process approach to model the time-varying transfer functions between the axles of the train and the fixed observation point. The railway is modeled as a Winkler foundation with rail pads and corresponding transfer functions are used. The target area of this study is the Emin¨on¨u-Alibeyk¨oy Tramway Line in ˙Istanbul, which is under construction. Due to poor soil conditions at the specific stations along the proposed tramway route, soil improvement by the application of geo-synthetics is performed at the site and taken into account in our model. The improvement in soil conditions is modeled as increased vertical soil stiffness in the Winkler foundation of the evolutionary random process model. To model the various tramway loading conditions, both the 5-axle and 6-axle tramway configurations with non-uniform axle spacing are considered. We show that by increasing the vertical soil stiffness ksb, the vibration velocity and acceleration levels can be reduced significantly. By implementing the model proposed, we present the reduction of the vibration velocity and acceleration levels as the functions of soil improvement parameters and discuss our findings and the applicability of the model.
Enhancing real estate listings through image classification and enhancement: a comparative study
(Multidisciplinary Digital Publishing Institute (MDPI), 2025-05-22) Küp, Eyüp Tolunay; Sözdinler, Melih; Işık, Ali Hakan; Doksanbir, Yalçın; Akpınar, Gökhan
We extended real estate property listings on the online prop-tech platform. On the platform, the images were classified into the specified classes according to quality criteria. The necessary interventions were made by measuring the platform’s appropriateness level and increasing the advertisements’ visual appeal. A dataset of 3000 labeled images was utilized to compare different image classification models, including convolutional neural networks (CNNs), VGG16, residual networks (ResNets), and the LLaVA large language model (LLM). Each model’s performance and benchmark results were measured to identify the most effective method. In addition, the classification pipeline was expanded using image enhancement with contrastive unsupervised representation learning (CURL). This method assessed the impact of improved image quality on classification accuracy and the overall attractiveness of property listings. For each classification model, the performance was evaluated in binary conditions, with and without the application of CURL. The results showed that applying image enhancement with CURL enhances image quality and improves classification performance, particularly in models such as CNN and ResNet. The study results enable a better visual representation of real estate properties, resulting in higher-quality and engaging user listings. They also underscore the importance of combining advanced image processing techniques with classification models to optimize image presentation and categorization in the real estate industry. The extended platform offers information on the role of machine learning models and image enhancement methods in technology for the real estate industry. Also, an alternative solution that can be integrated into intelligent listing systems is proposed in this study to improve user experience and information accuracy. The platform proves that artificial intelligence and machine learning can be integrated for cloud-distributed services, paving the way for future innovations in the real estate sector and intelligent marketplace platforms.
Adaptive locally connected recurrent unit (ALCRU)
(Springer Science and Business Media Deutschland GmbH, 2025-07-03) Özçelik, Şuayb Talha; Tek, Faik Boray
Research has shown that adaptive locally connected neurons outperform their fully connected (dense) counterparts, motivating this study on the development of the Adaptive Locally Connected Recurrent Unit (ALCRU). ALCRU modifies the Simple Recurrent Neuron Model (SimpleRNN) by incorporating spatial coordinate spaces for input and hidden state vectors, facilitating the learning of parametric local receptive fields. These modifications add four trainable parameters per neuron, resulting in a minor increase in computational complexity. ALCRU is implemented using standard frameworks and trained with back-propagation-based optimizers. We evaluate the performance of ALCRU using diverse benchmark datasets, including IMDb for sentiment analysis, AdditionRNN for sequence modelling, and the Weather dataset for time-series forecasting. Results show that ALCRU achieves accuracy and loss metrics comparable to GRU and LSTM while consistently outperforming SimpleRNN. In particular, experiments with longer sequence lengths on AdditionRNN and increased input dimensions on IMDb highlight ALCRU’s superior scalability and efficiency in processing complex data sequences. In terms of computational efficiency, ALCRU demonstrates a considerable speed advantage over gated models like LSTM and GRU, though it is slower than SimpleRNN. These findings suggest that adaptive local connectivity enhances both the accuracy and efficiency of recurrent neural networks, offering a promising alternative to standard architectures.
ANN activation function estimators for homomorphic encrypted inference
(Institute of Electrical and Electronics Engineers Inc., 2025-06-13) Harb, Mhd Raja Abou; Çeliktaş, Barış
Homomorphic Encryption (HE) enables secure computations on encrypted data, facilitating machine learning inference in sensitive environments such as healthcare and finance. However, efficiently handling non-linear activation functions, specifically Sigmoid and Tanh, remains a significant computational challenge for encrypted inference using Artificial Neural Networks (ANNs). This study introduces a lightweight, ANN-based estimator designed to accurately approximate activation functions under homomorphic encryption. Unlike traditional polynomial and piecewise linear approximations, the proposed ANN estimators achieve superior accuracy with lower computational overhead associated with bootstrapping or high-degree polynomial techniques. These estimators are trained on plaintext data and seamlessly integrated into encrypted inference pipelines, significantly outperforming conventional methods. Experimental evaluations demonstrate notable improvements, with ANN estimators enhancing accuracy by approximately 2% for Sigmoid and up to 73% for Tanh functions, improving F1-scores by approximately 2% for Sigmoid and up to 88% for Tanh, and markedly reducing Mean Square Error (MSE) by up to 96% compared to polynomial approximations. The ANN estimator achieves an accuracy of 97.70% and an AUC of 0.9997 when integrated into a CNN architecture on the MNIST dataset, and an accuracy of 85.25% with an AUC of 0.9459 on the UCI Heart Disease dataset during ciphertext inference. These results underscore the estimator’s practical effectiveness and computational feasibility, making it suitable for secure and efficient ANN inference in encrypted environments.
The comparison of functional connectivity in Parkinson’s Disease patients with and without Parkin gene mutations
(Turkish Neuropsychiatric Society, 2025-06-19) Çebi, Merve; Ay, Ulaş; Kıçik, Ani; Erdoğdu, Emel; Tepgeç, Fatih; Uyguner, Zehra Oya; Tüfekçioğlu, Zeynep; Samancı, Bedia; Bilgiç, Başar; Emre, Murat; Demiralp, Tamer; Hanağası, Haşmet Ayhan
Introduction: Mapping the functional connectivity of brain regions became appealing in recent research in neurology. Accordingly, a growing body of evidence shows resting-state functional connectivity (rsFC) changes in neurodegenerative disorders including Parkinson’s Disease (PD). As characterised by extensive and progressive dopaminergic loss in the substantia nigra, PD emerges with serious motor and non-motor dysfunctions. In the literature, the minority of PD cases have been associated with certain genetic mutations. The aim of this study was to investigate the rsFC in a group of PD patients having Parkin gene mutation. Method: Twelve PD patients with Parkin mutation (PP-PD), 12 PD patients without Parkin mutation (PN-PD) and 12 healthy controls (HC) were included in the study. All participants underwent a resting-state functional magnetic resonance imaging as well as a neuropsychological assessment and clinical examination. Results: Results indicated that PP-PD had longer disease duration, a higher rate of dyskinesia and lower scores on complex visual perception tests. The resting state networks showed that all PD (consisting of PP-PD and PN-PD) and PP-PD groups had increased functional connectivity in the frontoparietal network as compared to the HC. In addition, the PP-PD group displayed decreased functional connectivity in the dorsal attention network compared to the PN-PD. Conclusion: In conclusion, our data suggests that PD with Parkin gene mutation might be emerging with distinct resting state functional connectivity changes in the brain.
Associations between cerebral perfusion pressure, hemodynamic parameters, and cognitive test values in normal-tension glaucoma patients, Alzheimer’s disease patients, and healthy controls
(Multidisciplinary Digital Publishing Institute (MDPI), 2025-05-24) Stoskuviene, Akvile; Chaleckas, Edvinas; Grusauskiene, Evelina; Bartusis, Laimonas; Çelikkaya, Güven; Januleviciene, Ingrida; Vaitkus, Antanas; Ragauskas, Arminas; Hamarat, Yasin
Background/Objectives: Glaucoma and Alzheimer’s disease (AD) are neurodegenerative conditions with vascular underpinnings. This study aimed to explore the relationship between blood pressure parameters such as mean arterial pressure (MAP), pulse pressure (PP), and cerebral perfusion pressure (CPP) and cognitive performance in patients with AD, normal-tension glaucoma (NTG), and healthy controls. We hypothesized that NTG patients, like those with mild cognitive impairment (MCI), may experience subtle cognitive changes related to vascular dysregulation. Methods: Ninety-eight participants (35 NTG, 17 AD, 46 controls) were assessed for CPP, MAP, OPP, and cognitive performance. Statistical analyses compared groups and examined correlations. Results: AD patients showed lower CPP and MAP (p < 0.001), indicating systemic vascular dysfunction, while NTG patients had higher ocular perfusion pressure (OPP) (p = 0.008), suggesting compensatory mechanisms. CPP correlated with visuospatial abilities in AD (r = 0.492, p = 0.045). MAP correlated with the Clock drawing test (CDT) scores in the NTG group (r = 0.378, p = 0.025). PP negatively correlated with cognition in AD (r = −0.527, p = 0.016 for CDT scores) and controls (r = −0.440, p = 0.002 for verbal fluency and r = −0.348, p = 0.019 for total ACE scores). Conclusions: The study highlights distinct hemodynamic profiles: systemic dysfunction in AD and localized dysregulation in NTG. These findings emphasize the role of vascular dysregulation in neurodegeneration, with implications for personalized treatment approaches targeting vascular health in neurodegenerative conditions.
Investigation of symptom-specific functional connectivity patterns in Parkinson’s disease
(Springer-Verlag Italia S.R.L., 2025-06-14) Kıçik, Ani; Bayram, Ali; Erdoğdu, Emel; Kurt, Elif; Sarıdede, Dilek Betül; Cengiz, Sevim; Bilgiç, Başar; Hanağası, Haşmet; Öztürk Işık, Esin; Gürvit, Hakan; Tüzün, Erdem; Demiralp, Tamer
Parkinson’s disease (PD) is a complex neurodegenerative disease, characterized by pronounced heterogeneity in symptoms. This study investigates the functional connectivity (FC) patterns associated with distinct symptom clusters, aiming to elucidate the heterogeneity in PD and uncover the neural mechanisms underlying its motor and cognitive symptoms. Resting-state functional MRI (rs-fMRI) data from 55 non-demented PD patients and 24 healthy controls (HC) were used to perform seed-to-seed FC analyses. A clustering algorithm was applied to the cognitive and motor scores of all PD patients to generate relatively homogeneous symptomatic subgroups. PD patients exhibited a general decrease in FC within a network comprising the sensorimotor network (SMN) and the visual network (VN) regions. Symptom-based clustering revealed three relatively homogeneous subgroups, exhibiting a gradient pattern: patients with greater motor deficits showed significant disconnection within the SMN, whereas patients with greater visuospatial deficits exhibited reduced FC in an extended subnetwork, with pronounced disconnections between the VN and SMN areas. Our study demonstrated a notable disconnection between the SMN and VN, indicating impaired visual-motor integration in PD. Stronger disconnection within the SMN was associated with greater motor dysfunction, and stronger visual-sensorimotor disconnections were associated with greater visuospatial deficits. These findings suggest that at least two separate routes of functional disconnection may be responsible for the inhomogeneous symptom distribution in PD.
Intelligent health monitoring in 6G networks: machine learning-enhanced VLC-based medical body sensor networks
(Multidisciplinary Digital Publishing Institute (MDPI), 2025-05-23) Antaki, Bilal; Dalloul, Ahmed Hany; Miramirkhani, Farshad
Recent advances in Artificial Intelligence (AI)-driven wireless communication are driving the adoption of Sixth Generation (6G) technologies in crucial environments such as hospitals. Visible Light Communication (VLC) leverages existing lighting infrastructure to deliver high data rates while mitigating electromagnetic interference (EMI); however, patient movement induces fluctuating signal strength and dynamic channel conditions. In this paper, we present a novel integration of site-specific ray tracing and machine learning (ML) for VLC-enabled Medical Body Sensor Networks (MBSNs) channel modeling in distinct hospital settings. First, we introduce a Q-learning-based adaptive modulation scheme that meets target symbol error rates (SERs) in real time without prior environmental information. Second, we develop a Long Short-Term Memory (LSTM)-based estimator for path loss and Root Mean Square (RMS) delay spread under dynamic hospital conditions. To our knowledge, this is the first study combining ray-traced channel impulse response modeling (CIR) with ML techniques in hospital scenarios. The simulation results demonstrate that the Q-learning method consistently achieves SERs with a spectral efficiency (SE) lower than optimal near the threshold. Furthermore, LSTM estimation shows that D1 has the highest Root Mean Square Error (RMSE) for path loss (1.6797 dB) and RMS delay spread (1.0567 ns) in the Intensive Care Unit (ICU) ward, whereas D3 exhibits the highest RMSE for path loss (1.0652 dB) and RMS delay spread (0.7657 ns) in the Family-Type Patient Rooms (FTPRs) scenario, demonstrating high estimation accuracy under realistic conditions.
Sustainable soil stabilization using colemanite: experimental and numerical analysis of sandy soils for improved geotechnical properties
(Springer Nature, 2025-06-12) Koçak Dinç, Beste; Dehghanian, Kaveh; Etminan, Ehsan
This paper discusses the use of colemanite, a boron compound, which is a natural additive to geotechnically improved sandy soils, thus providing an eco-friendly alternative to conventional soil stabilization. Clean angular sand was the base material with the addition of colemanite in amounts of 0%, 5%, 10%, and 15% by dry mass. Various laboratory tests, such as Atterberg limits, void ratio, specific gravity, compaction, permeability, and unconsolidated undrained triaxial tests, were carried out to determine the physical and mechanical characteristics of the produced mixtures. Numerical modeling, adopted by the PLAXIS finite element program, was used to carry out simulations under various conditions for soil profiles to determine and compare soil behavior. The findings revealed that the addition of colemanite significantly reduced permeability and void ratios while enhancing stiffness and strength, with 15% colemanite yielding the best performance. This study is one of those that focuses on the introduction of colemanite, which can also act as an effective stabilizer and is a much greener and more environmentally friendly option. Apart from this, it has other advantages both economically and ecologically by reducing the amount of cement, which is a high carbon source required for building based on this. The discoveries bring in the further development of green geotechnical engineering, which also includes the construction of sustainable infrastructures.
Retinal disease diagnosis in OCT scans using a foundational model
(Springer Science and Business Media Deutschland GmbH, 2025) Nazlı, Muhammet Serdar; Turkan, Yasemin; Tek, Faik Boray; Toslak, Devrim; Bulut, Mehmet; Arpacı, Fatih; Öcal, Mevlüt Celal
This study examines the feasibility and performance of using single OCT slices from the OCTA-500 dataset to classify DR (Diabetic Retinopathy) and AMD (Age-Related Macular Degeneration) with a pre-trained transformer-based model (RETFound). The experiments revealed the effective adaptation capability of the pretrained model to the retinal disease classification problem. We further explored the impact of using different slices from the OCT volume, assessing the sensitivity of the results to the choice of a single slice (e.g., “middle slice”) and whether analyzing both horizontal and vertical cross-sectional slices could improve outcomes. However, deep neural networks are complex systems that do not indicate directly whether they have learned and generalized the disease appearance as human experts do. The original dataset lacked disease localization annotations. Therefore, we collected new disease classification and localization annotations from independent experts for a subset of OCTA-500 images. We compared RETFound’s explainability-based localization outputs with these newly collected annotations and found that the region attributions aligned well with the expert annotations. Additionally, we assessed the agreement and variability between experts and RETFound in classifying disease conditions. The Kappa values, ranging from 0.35 to 0.69, indicated moderate agreement among experts and between the experts and the model. The transformer-based RETFound model using single or multiple OCT slices, is an efficient approach to diagnosing AMD and DR.
Relationships among organizational-level maturities in artificial intelligence, cybersecurity, and digital transformation: a survey-based analysis
(Institute of Electrical and Electronics Engineers Inc., 2025-05-19) Kubilay, Burak; Çeliktaş, Barış
The rapid development of digital technology across industries has highlighted the growing need for enhanced competencies in Artificial Intelligence (AI), Cyber security (CS), and Digital Transformation (DT). While there is extensive research on each of these domains in isolation, few studies have investigated their relationship and joint impact on organizational maturity. This study aims to address this gap by analyzing the relationships among the maturity levels of AI, CS, and DT at the organizational level using Structural Equation Modeling (SEM) and descriptive statistical methods. A mixed-methods design combines quantitative survey data with synthetic modeling techniques to assess organizational preparedness. The findings demonstrate significant bidirectional correlations among AI, CS, and DT, with technology and finance being more advanced than government and education. The research highlights the necessity of an integrated AI-CS strategy and provides actionable recommendations to increase investments in these domains. In contrast to the preceding fragmented evaluations, the current research establishes a comprehensive, empirically grounded framework that acts as a strategic reference point for digital resilience. Follow-up studies will involve collecting real-world industry data in support of empirical validation and predictive ability in measuring AI and CS maturity. This research adds to the existing literature by filling the gaps among fragmented digital maturity models and providing a consistent empirical base for organizations to thrive in an evolving technological environment.
Goal-Oriented Random Access (GORA)
(Institute of Electrical and Electronics Engineers Inc., 2025-08) Topbaş, Ahsen; Ari, Çağrı; Kaya, Onur; Uysal, Elif
We propose Goal-Oriented Random Access (GORA), where transmitters jointly optimize what to send and when to access the shared channel to a common access point, considering the ultimate goal of the information transfer at its final destination. This goal is captured by an objective function, which is expressed as a general (not necessarily monotonic) function of the Age of Information. Our findings reveal that, under certain conditions, it may be desirable for transmitters to delay channel access intentionally and, when accessing the channel, transmit aged samples to reach a specific goal at the receiver.
Drought analysis based on nonparametric multivariate standardized drought index in the Seyhan River Basin
(Springer Science and Business Media B.V., 2025-05) Terzi, Tolga Barış; Önöz, Bihrat
Drought is a detrimental natural hazard that is a threat to the social and ecological aspects of life. Unlike other natural hazards, drought occurs slowly and gradually, making it difficult to detect its formation, leading to severe consequences in the affected area. Therefore, precise and reliable monitoring of drought is crucial to implement effective drought mitigation strategies. Drought indices are significant tools for drought monitoring; single variable indices are quite frequently used in the literature to assess drought conditions. Although these indices are generally accurate at characterizing the specific type of drought they were developed for, they fail to provide a comprehensive representation of drought conditions. Hence, this study applies a nonparametric multivariate standardized drought index (MSDI) that integrates meteorological and hydrological drought to investigate the dynamics of drought events within the Seyhan River Basin (SRB). Trend analyses were conducted to detect any directional changes in the drought patterns within the SRB. Additionally, this study examined the potential effects of El Nino-Southern Oscillation events on the MSDI series to determine their impact on drought conditions in the SRB. The results indicate that the MSDI outperforms the single variable indices in characterizing drought conditions within the basin. The calculations conducted for 5 different time scales 1, 3, 6, 9 and 12-months showed satisfactory results in multivariate analysis of drought. Upon examining the trend analyses, MSDI series showed an insignificant negative trend in all stations within the SRB. The MSDI series was strongly influenced by Nino 3.4 and Arctic Oscillation (AO) indices while sunspot activities had a relatively weak impact on the MSDI series.
Grammar or crammer? the role of morphology in distinguishing orthographically similar but semantically unrelated words
(Institute of Electrical and Electronics Engineers Inc., 2025) Ercan, Gökhan; Yıldız, Olcay Taner
We show that n-gram-based distributional models fail to distinguish unrelated words due to the noise in semantic spaces. This issue remains hidden in conventional benchmarks but becomes more pronounced when orthographic similarity is high. To highlight this problem, we introduce OSimUnr, a dataset of nearly one million English and Turkish word-pairs that are orthographically similar but semantically unrelated (e.g., grammar - crammer). These pairs are generated through a graph-based WordNet approach and morphological resources. We define two evaluation tasks - unrelatedness identification and relatedness classification - to test semantic models. Our experiments reveal that FastText, with default n-gram segmentation, performs poorly (below 5% accuracy) in identifying unrelated words. However, morphological segmentation overcomes this issue, boosting accuracy to 68% (English) and 71% (Turkish) without compromising performance on standard benchmarks (RareWords, MTurk771, MEN, AnlamVer). Furthermore, our results suggest that even state-of-the-art LLMs, including Llama 3.3 and GPT-4o-mini, may exhibit noise in their semantic spaces, particularly in highly synthetic languages such as Turkish. To ensure dataset quality, we leverage WordNet, MorphoLex, and NLTK, covering fully derivational morphology supporting atomic roots (e.g., '-co_here+ance+y' for 'coherency'), with 405 affixes in Turkish and 467 in English.
Cognitive reserve and aging: impacts on theory of mind and executive functions
(Routledge, 2025-03) Şandor, Serra; Hıdıroğlu Ongun, Ceren; Yıldırım, Elif
Aim: This study examines the effects of cognitive reserve (CR) on Executive Functions (EF) and Theory of Mind (ToM). While CR is suggested to mitigate age-related cognitive decline, its relationship with social cognition remains limited and inconsistent in the literature. It was hypothesized that the effect of CR on ToM might be indirect, mediated by EF and working memory. Methods: 225 cognitively healthy participants were included. CR was measured with the Cognitive Reserve Index Questionnaire, EF with verbal fluency and the Stroop Test, and WM using digit span tasks. Structural Equation Modeling was used to analyze the relationships among CR, EF, WM, and SC, controlling for age and gender. Results: CR was significantly associated with both RMET and FPRT performances. Mediation analysis revealed the direct effects of CR on RMET performance, while the effects on FPRT performance were mediated by executive functions. WM had a partial mediating effect on EF and ToM, but did not directly influence FPRT. Education was most strongly associated with RMET performance, while leisure activities were linked to FPRT performance. Conclusion: These findings suggest that CR indirectly supports ToM by enhancing EF and highlight the importance of interventions aimed at strengthening executive control to support social cognition in aging.
Exploring the impact of Flash technique on test anxiety among adolescents
(SAGE Publications Ltd, 2025-07) Çitil Akyol, Canan; İnci İzmir, Sevim Berrin
This study aims to investigate the specific effects of Flash Technique (FT) on adolescents with test anxiety. This follow-up study consists of 38 adolescents, 14–17 years of age (M = 15.39, SD = 1.13). Pre-post assessments were conducted using the Test Anxiety Inventory (TAI), Scale of Attitudes Negatively Affecting the Performance I/Test (POET), and Beck Anxiety Inventory (BAI) at baseline, at the end of the 4thand 12thweeks of therapy. The FT was applied for 12 weeks, with one weekly session as an intervention. As a result of the therapy process, the baseline means of total BAI scores decreased from 25.26 to 2.18; the baseline means of TAI decreased from 149.79 to 39.13, and the baseline mean of POET decreased from 298.47 to 73.84 at the end of the 12th week of therapy. Also, the baseline means of SUD scores decreased from 9.42 to zero at the end of the 12th week of treatment. All the adolescents showed complete improvement after the 12th week of the FT. The study findings showed that the test anxiety symptoms significantly decreased with the treatment of the FT. FT can be an effective intervention for test anxiety in adolescents.
Turkey and Ukraine’s strategic positioning: economic, energy, and military cooperation in the geopolitical landscape between the European Union and Russia
(Taylor and Francis, 2025-01-01) Karakaya Polat, Rabia; Lebduška, Michal
The geopolitical positioning of Ukraine and Turkey between the European Union (EU) and Russia holds significant implications for their economies. The analysis first highlights how Turkey’s balancing act between the West and Russia has been further complicated by the war in Ukraine, offering both challenges and opportunities in economic, energy, and military fields. The chapter then turns to Ukraine showing how the annexation of the Crimea and military intervention in Donbas in 2014 prompted extensive internal reforms and a definitive shift towards European and Euro-Atlantic integration. The chapter argues that the domestic political trajectories in Turkey and Ukraine involve sequences of opening and closing, democratic reform and authoritarian resurgence, influenced by internal factors but simultaneously linked to the EU’s ambiguity towards their European aspirations and Russia’s geopolitical aspirations.
Treatment and long-term outcome of mental disorders: The grim picture from a quasi-epidemiological investigation in 54,826 subjects from 40 countries
(Elsevier Ireland Ltd, 2025-06) Fountoulakis, Konstantinos N.; Karakatsoulis, Gregory; Abraham, Seri; Adorjan, Kristina; Uddin Ahmed, Helal; Alarcòn, Renato Daniel; Arai, Kiyomi; Auwal, Sani Salihu; Berk, Michael; Levaj, Sarah; Yılmaz Kafalı, Helin
Introduction: This study registered rates of specific treatment options for mental disorders as well as their long-term outcome. Material and methods: The history of mental disorders was used as a proxy for diagnosis. The data came from the COMET-G study (40 countries; 54,826 subjects, 64.73 % females, 35.45±13.51 years old). The analysis included descriptive statistics, Risk Ratios, t-tests, and ANCOVA's. Results: 24.14 % reported a history of any mental disorder (depression >12 %, non-affective psychosis and Bipolar disorder 1 % each, >20 % self-injury, >10 % had attempted suicide, 7.17 % illegal substance abuse). Most patients were not under any kind of treatment (59.44 %) and most were not receiving treatment as recommended (e.g. 90 % of Bipolar and 2/3 of psychotic patients). No treatment at all and psychotherapy as monotherapy were consistently related to poorer outcomes. In anxiety or depression, only antidepressant monotherapy and benzodiazepines, in Bipolar disorder only antipsychotic monotherapy in males and antidepressant monotherapy in females and in non-affective psychosis antipsychotics and psychotherapy in females only, were related to good outcomes. No treatment modality was related to a good outcome in those with a history of self-harm, suicidal attempts, or illegal substance use. Only depression and treatment with antidepressants were related to metabolic syndrome. Discussion: In the community, the overwhelming majority of mental patients do not receive appropriate treatment or, even worse, no treatment at all. The outcome is unfavourable for the majority and only a few selective treatment options seem to make a difference.
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, Bilgin
Introduction: 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.
Design and control of high-frequency buck converter fed six-step drive for air-core PMSM
(Institute of Electrical and Electronics Engineers Inc., 2025-02) Jena, Sritam; Kumar, Saurabh; Deshmukh, Akshay Vijayrao; Hava, Ahmet Masum; Akın, Bilal; Gabrys, Christopher; Rodgers, Timothy
Air-core permanent magnet synchronous motors (PMSMs) machines are becoming known for their higher efficiency, lighter weight designs, and superior performance compared to widely utilized induction motors (IMs). They hold great potential for diverse industrial applications. However, effectively harnessing this potential requires overcoming drive hardware and control challenges. This research introduces a silicon carbide (SiC)-based two-phase interleaved buck-converter-fed quasi-current source inverter (quasi-CSI) drive tailored for driving low-inductance air-core PMSMs which is ideal for heavy-duty fan and pump applications. Operating in the discontinuous current mode (DCM) with an effective switching frequency of 1 MHz, this drive is designed to address efficiency and the very low-cost market constraints while simultaneously reducing control complexity an issue associated with its high switching frequency. The article also analyzes two critical control challenges of mitigating high current spikes due to air-core machines' low inductance and finding solutions to overcome microcontroller resource limitations when executing time-critical functions within interrupt subroutines (ISRs). The culmination of this work is a 300 V dc-bus and five-horsepower electric drive prototype with closed-loop speed control. Experimental results illustrate a 2% enhancement in overall efficiency compared to conventional induction machine (IM) drives in similar applications (e.g., fan and pump) and ratings, alongside a significant 50% reduction in drive volume.

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