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Decoding joint complaints in children: a diagnostic approach to rheumatic and non-rheumatic disorders
No full textIntroduction. Musculoskeletal (MSK) complaints are common in children, with many cases ranging from benign to serious conditions. While these patients are often referred to pediatric rheumatologists, only about half are diagnosed with rheumatic diseases. The objective of this study is to identify key clues that assist in the differential diagnosis of patients presenting with joint complaints at pediatric rheumatology outpatient clinic. Material Methods. In this one-year study, patients with joint pain were assessed using a standardized form. Demographic, clinical, and examination data were collected and anonymized. Initial assessments were done by pediatric rheumatology fellows or residents, with final diagnoses confirmed by a pediatric rheumatologist. Results. This study included 414 patients aged 0-18 years with joint pain, of whom 273 were diagnosed with rheumatologic conditions and 141 with non-rheumatologic conditions. Patients with rheumatologic conditions had significantly higher laboratory values, including leukocyte counts, neutrophils, platelets, C-reactive protein, and erythrocyte sedimentation rate, all with p < 0.001. Multivariable analysis revealed that arthritis (adjusted odds ratio [aOR] 2.63, 95% confidence interval [CI] 1.09-6.33) and rash (aOR 4.37, 95% CI 1.38-13.86) predicted rheumatic disease in acute presentations, while in chronic complaints arthritis (aOR 2.61, 95% CI 1.30-5.21), morning stiffness (aOR 3.47, 95% CI 1.69-7.11), migratory pain (aOR 3.45, 95% CI 1.01-11.80), and fever (aOR 12.89, 95% CI 4.41-37.68) were independent predictors, whereas myalgia was associated with non-rheumatic conditions (aOR 0.35, 95% CI 0.15-0.83). Conclusion. In conclusion, this study emphasized the importance of clinical clues in diagnosing rheumatic diseases in children with joint pain. An accurate diagnosis depends on a thorough history and physical examination. Improving the differential diagnosis of joint pain is essential to reduce unnecessary referrals and enhance the efficiency of healthcare services
Beyond Accuracy: Cross-Linguistic Equity and Socio-Technical Dimensions of Large Language Models
No full textEvaluation of large language models in clinical neuroanatomy: a comparative scoring analysis based on accuracy, concordance, insight, and anatomical terminology accuracy.
No full textA Fuzzy Logic-Based Model for Measuring Perception of Urban Spaces During Walking Experience
No full textTÜKETİCİ DENEYİMİNDE YENİLİKÇİ YAKLAŞIM: ARTIRILMIŞ GERÇEKLİK VE MARKA İLİŞKİSİ
No full textTüketici deneyimlerinin gelişen teknolojiyle birlikte dönüşümü, markalar için önemli fırsatlar sunmaktadır. Bu dönüşüm sürecinde artırılmış gerçeklik teknolojisi, tüketici etkileşimlerini zenginleştirerek markaların müşteri deneyimini yenilikçi bir şekilde şekillendirmelerine olanak tanımaktadır. Bu çalışmanın amacı, FLO mobil uygulamalarında kullanılan artırılmış gerçeklik uygulamasının kalitesinin, markaya yönelik tutum üzerindeki etkilerini incelemektir. Ayrıca, teknoloji kaygısının, AR uygulamasının kalitesi ile markaya yönelik tutum arasındaki ilişkide bir moderatör rolü oynayıp oynamadığı araştırılmıştır. Bununla birlikte, bu iki değişken arasındaki ilişkinin cinsiyetler arasında farklılık gösterip göstermediği de incelenmiştir. Çalışma, çevrim içi ortamda 328 katılımcıdan elde edilen verilerle gerçekleştirilmiştir. Verilerin analizi için, PLS yapısal eşitlik modellemesi ve multigroup analiz yöntemleri kullanılmıştır. Analizler sonucunda, AR uygulamasının kalitesinin markaya yönelik tutumu pozitif yönde etkilediği bulgusu elde edilmiştir. Ancak, teknoloji kaygısının AR kalitesi ile markaya yönelik tutum arasındaki ilişki üzerinde moderatör etkisi olmadığı ve cinsiyetler arasında bu ilişkinin farklılaşmadığı tespit edilmiştir. Elde edilen bulgular, AR teknolojisinin markaya yönelik tutumları iyileştirme potansiyeline sahip olduğunu, ancak teknoloji kaygısının bu etkileşimde belirgin bir rol oynamadığını ve cinsiyetin bu etkileşimi farklılaştırmadığını göstermektedir. Bu çalışma, tüketici deneyiminde AR teknolojisinin etkinliğini anlamak ve uygulama kalitesinin marka algısı üzerindeki etkilerini değerlendirmek adına önemli katkılar sunmaktadır.The transformation of consumer experiences with advancing technology presents significant opportunities for brands. In this transformation process, augmented reality technology enriches consumer interactions, enabling brands to innovate and enhance customer experiences. This study aims to examine the effects of the quality of the AR application used in FLO mobile applications on brand attitude. Additionally, it investigates whether technology anxiety moderates the relationship between AR application quality and brand attitude. Furthermore, the study explores whether this relationship differs between genders. The research was conducted using data collected from 328 participants in an online setting. For data analysis, PLS structural equation modeling, and multi-group analysis methods were employed. The findings indicate that the quality of the AR application positively influences brand attitude. However, no moderating effect of technology anxiety on the relationship between AR quality and brand attitude was found, nor were any gender-based differences observed in this relationship. These findings suggest that while AR technology has the potential to enhance brand attitudes, technology anxiety does not play a significant role in this interaction, and gender does not differentiate this effect. This study provides valuable insights into the effectiveness of AR technology in consumer experiences and evaluates the impact of application quality on brand perception. </p
Integrated thermal management strategies for electric vehicles: a system-level review of battery cooling, heat pump technologies, PCM integration, and cold-climate performance
No full textAssessing the Early Impact of the EU ETS on Turkish-Flagged Vessels: Operational and Legal Insights
No full textThis study examines the initial implications of the entrance of shipping industry into European Union Emissions Trading System (EU ETS) for Turkish-flagged vessels by combining empirical observation with a legal interpretation. While the existing studies predominantly focused on EU-based fleets, the present research addresses a clear gap by evaluating the indirect legal and operational effects of the EU ETS on Turkish maritime actors that operate within European port jurisdictions. Within this scope, monitoring, reporting and verification (MRV) data for the years 2023 (pre-ETS) and 2024 (initial year of EU ETS) were comparatively assessed, covering approximately one hundred Turkish-flagged vessels listed in both reporting periods. Descriptive statistical analysis was carried out with respect to three core parameters: total CO2 emissions, emission intensity per nautical mile, and annual time spent at sea. The findings suggest that while total emissions remained largely stable, emission intensity were declined, while the average duration of vessel activity at sea was increased. These results indicate that Turkish operators have begun to make operational adjustments that reflect an evolving awareness of emissions efficiency, rather than a fully consolidated compliance practice. Beyond the empirical dimension, the study provides a legal reflection on the developing compliance framework and its potential to reshape maritime governance dynamics in Türkiye. In this regard, the research contributes both conceptually and practically to the understanding of Türkiye’s preparedness for a broader integration with the EU ETS and seeks to fill a notable gap in regional discussions on maritime decarbonization.</p
A Study of Sensitive Fault Detection for Lithium-Ion Batteries Being Recharged Using Support Vector Machine Classifier and Receiver Operating Characteristics
No full textSeveral installationsequipped with lithium-ion batteries may require additional precautions. Whilelithium-ion batteries offer good performance relative to other rechargeablebatteries, their state of health should be monitored. Faulty lithium-ionbatteries may be vulnerable to thermal runaway or explosion. Early detection ofthose vulnerabilities can be done accurately by using an effectivecharging-anomaly detection method. In this paper, a binary support vectormachine classification method was used to detect faulty lithium-ion batteriesthat are being recharged with constant voltage. The support vector machinealgorithm was trained on battery data acquired after the recharging wasfinished. The battery data consisted of temperature, voltage, and varyingrecharging current measured inside the lithium-ion battery. Estimation losses,sensitivity, and receiver operating characteristic curves were computed andpresented after training and testing the algorithm. Class labels andclassifier’s generalization performance information were also displayed. Anestimation loss of 7% was found at the end of this research.</p
