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YENİLİKÇİ REÇETE GELİŞTİRME: ZEYTİN YAPRAĞI VE ARI EKMEĞİ İLE BESİN DEĞERİ ZENGİNLEŞTİRİLMİŞ FONKSİYONEL KRAKER ÜRETİMİ
No full textFİLİZLENDİRİLMİŞ PSEUDO TAHIL UNLARI İLE ZENGİNLEŞTİRİLMİŞ KRAKERLERİN DUYUSAL VE FONKSİYONEL ÖZELLİKLERİNİN BELİRLENMESİ
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The Relationship Between the Gross Motor Function Classification System, Functional Mobility Scale, Observational Gait Scale, and the Amsterdam Gait Classification in Children with Cerebral Palsy During Long-Term Treatment with Botulinum Toxin Injections and Combined Integrated, Intensive Rehabilitation
No full textPatients with cerebral palsy (CP) experience complex gait disorders that change with age, leading to reduced activity and social participation. This study aimed to analyse how gait patterns developed over five years and to examine the relationships between the Observational Gait Scale (OGS), Amsterdam Gait Classification (AGC), Gross Motor Function Classification System (GMFCS), and the Functional Mobility Scale (FMS) at 5 and 50 m (FMS 5/50) during treatment. This retrospective, single-centre observational study involved annual assessments over a five-year period, which were analysed. Patients underwent a rehabilitation programme including physiotherapy, orthotics, multilevel botulinum toxin type A injections (BoNT-A), and serial casting. Data regarding BoNT-A treatment, casting, physiotherapy, orthoses, GMFCS levels, and FMS 5/50 scores were obtained from medical records. OGS and AGC were evaluated through two-plane clinical video recordings conducted in the same gait laboratory for all children. A cohort of 200 pediatric subjects (120 boys and 80 girls) diagnosed with bilateral cerebral palsy, predominantly classified as GMFCS II (48%) and III (36%), was analyzed. The average initial age was 32.23 months (±6.96), and GMFCS levels improved in 33. 5% of children and worsened in 2% (p < 0.001). Improvements were observed in 50% of children with GMFCS III and 40% with GMFCS IV levels. FMS 5 and 50 improved by 54% and 52%, respectively. OGS scores showed improvement in 74% and 76% of patients, respectively, while deterioration was observed in 5% and 7% for the right and left lower limbs, respectively. Most changes in OGS scores ranged from 1 to 4 points. A negative correlation was found between OGS and GMFCS (p < 0.001), and a positive correlation was found between OGS scores and FMS 5 and FMS 50 (p < 0.001). Additionally, significant relationships were identified between AGC and GMFCS, as well as FMS at 5 and 50 m. Complex gait disorders identified by the AGC are associated with higher GMFCS E&R scores and lower FMS scores. During the five-year follow-up, relationships were observed among GMFCS, FMS, OGS, and AGC. Our findings indicate that integrated treatment has a positive effect on functional mobility and gait patterns in patients with CP
Do Large Language Models Perform Equally Across Languages? A Comparison of Responses to Frequently Asked Questions in Anesthesiology
No full textBackground: Material/Methods: Results: Conclusions: With the increasing use of large language model (LLM) chatbots in healthcare, evaluating their ability to provide reliable and understandable information in multiple languages is critical, particularly in fields such as anesthesia, where patient education is essential. The study primarily aimed to compare the quality of ChatGPT 4.0’s and DeepSeek V3’s English responses, with secondary aims to evaluate content and communication differences between English and Turkish responses. Anesthesiologists proficient in both languages were recruited as experts. Ten frequently asked questions in anesthesia were selected and translated for evaluation. Responses from ChatGPT 4.0 and DeepSeek V3 in both English and Turkish were assessed for overall quality and content quality (accuracy, comprehensiveness, and safety) and communication quality (understanding, empathy/tone, and ethics), and Turkish and English responses were compared by the evaluators. Eleven experts evaluated the responses. English responses of ChatGPT 4.0 were superior to the English responses of DeepSeek V3 in overall (P<0.001). English responses of ChatGPT 4.0 were superior to the Turkish responses in the terms of overall, content, and communication quality (P<0.001 each) and English responses of DeepSeek V3 were superior to the Turkish responses in the terms of overall (P<0.001), content (P<0.001) and communication (P=0.001) quality. ChatGPT 4.0 performed better than DeepSeek V3 in the English language in terms of overall quality of responses to 10 frequently asked questions in the field of anesthesia and the English responses provided by ChatGPT 4.0 and DeepSeek V3 outperformed the Turkish responses
Model-Agreement-Aware Multi-Objective Optimization for High-Frequency Transformers in EV Onboard Chargers
No full textDevelopments in electric vehicle (EV) technology are pushing on-board chargers (OBCs) toward higher power density and efficiency, making high-frequency transformer loss prediction a critical design bottleneck. However, the accuracy of commonly used analytical winding-loss models varies strongly with frequency, conductor type (Litz/solid), window fill factor, and winding layout (e.g., interleaved), which can render single-model-based optimization unreliable. In this study, six analytical copper-loss models from the literature were independently reimplemented in a unified Python 3.11.5 workflow with a standardized interface to enable fair comparison under identical geometry and operating conditions. The models were benchmarked against 2D finite-element simulations on test scenarios with increasing physical complexity, including high fill-factor Litz windings and interleaved arrangements. The results confirm a regime-dependent behavior: no single model consistently outperforms others across the full design space, and model dispersion increases in geometrically stressed and higher-frequency regions. To manage this uncertainty, variance maps were generated and model disagreement was quantified using the coefficient of variation (CV). Finally, a reliability-oriented multi-objective optimization framework based on NSGA-II was developed, where a SmartTransformerRouter selects a reference loss estimate per candidate and CV is incorporated via constraints/penalties, with optional FEM triggering in high-uncertainty regions.</jats:p