WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.14627/6
Browse
14 results
Search Results
Article Short-Term Effects of Targeted Movement Training on Gait Kinematics in Children with Juvenile Idiopathic Arthritis: A Motion Analysis Study(MDPI, 2026) Argunsah, Hande; Dönmez, İrem; Ayaz, Nuray Aktay; Yekdaneh, Asena; Albayrak, Asya; Arman, Nilay; Aktay Ayaz, Nuray; Özbal, SibelBackground: Children with juvenile idiopathic arthritis (JIA) exhibit gait abnormalities, postural instability, and compensatory movement strategies due to joint pain, inflammation, and reduced neuromuscular control. These alterations negatively affect functional mobility and movement efficiency. Although gait retraining is commonly recommended in rehabilitation, objective evidence on its short-term biomechanical effects remains limited. This study aimed to evaluate the immediate impact of a single-session standardized movement training intervention on gait biomechanics in children with JIA. Methods: Seventeen children with JIA underwent pre-post gait assessments using the Xsens MVN Awinda wearable motion capture system. The intervention focused on step symmetry, stride length, heel-toe progression, and upright trunk posture, delivered by an experienced physiotherapist following a standardized protocol. Scalar kinematic outcomes were analyzed using paired statistical tests, and time-normalized kinematic waveforms were compared with healthy reference data from 25 age-matched participants derived from the COMPWALK-ACL dataset. Results: Significant improvements were observed in multiple gait parameters following the intervention. Trunk lateral lean decreased significantly (p = 0.0002; d = -1.35), indicating enhanced postural stability. Significant changes were also found in ankle dorsiflexion-plantarflexion (p = 0.0081; d = 0.83) and knee flexion-extension (p = 0.0252; d = 0.68). Waveform analyses showed increased similarity to healthy patterns, particularly in trunk and knee kinematics. Spatiotemporal parameters reflected a slower, more controlled gait pattern, with increased stride time and stance duration. Conclusions: A single session of standardized movement training can produce immediate improvements in gait biomechanics in children with JIA, especially in trunk control and lower-limb kinematics. Wearable motion analysis provides a sensitive tool for detecting these short-term adaptations and supports the inclusion of structured movement training in pediatric JIA rehabilitation.Article Fault-Tolerant QCA-Based Parity Pre-Filtering Circuits for Lightweight Edge-IoT Transaction Screening(MDPI, 2026) Ahmadpour, Seyed-Sajad; Ajlouni, Naim; Zohaib, Muhammad; Selvi, OsmanEdge Internet of Things (IoT) blockchain deployments increasingly rely on continuous transaction ingestion from resource-constrained IoT devices to nearby edge gateways over heterogeneous wireless links. In this setting, transient channel noise and packet corruption can inject invalid payloads into the edge processing pipeline and trigger unnecessary buffering, parsing, and, most critically, computationally expensive cryptographic operations such as digital signature verification. This leads to wasted computation, increased latency, and reduced energy efficiency at the edge, particularly under dense IoT traffic. This paper presents an energy-aware and fault-tolerant Quantum-Dot Cellular Automata (QCA)-based integrity pre-filter for IoT-to-edge blockchain transaction ingestion. At the circuit level, we adapt and modify a previously reported fault-tolerant five-input majority gate (MV5) structure and use it as a robust primitive for nanoscale integrity-screening circuits. Building on this modified MV5, we design a set of QCA integrity blocks, including a parity checker, a compact XNOR gate circuit, a parity-bit generation circuit, and a sender-to-channel/receiver nano-communication integrity workflow suitable for early screening of corrupted payloads. Compared with the best previously reported baseline considered in this study, the modified MV5 achieves 76.47% tolerance to single-cell omission defects, corresponding to a 17.47 percentage-point increase and an approximately 29.61% relative improvement over the prior 59% omission-tolerance result, while preserving 100% tolerance against extra-cell deposition defects. At the system level, the proposed circuit is discussed as a potential early screening stage for edge-IoT blockchain transaction ingestion. A bounded analytical model is used to estimate the possible reduction in unnecessary signature-verification workload under assumed corruption and detection conditions. This analysis is not intended as a deployment-level validation; full edge-node implementation, throughput measurement, queueing-delay evaluation, real traffic traces, retransmission behavior, and empirical signature-verification profiling remain future work. The proposed parity/chunk-parity pre-filter is designed for low-cost detection of random transmission-induced corruption and does not replace cryptographic authentication, hashing, digital signatures, CRC-based detection, or blockchain validation. All proposed designs are validated using QCADesigner tools.Article A Delta-Targeted Hybrid Deep Learning Architecture for Short-Term Scrap Steel Price Forecasting: A Comparative Study(MDPI, 2026) Ugurlu, Onur; Cifci, Nihan Sena; Karatay, Melike; Aygul, Yesim; Demirel, YaseminForecasting scrap steel prices is crucial for the economic sustainability of recycling operations, yet it remains challenging due to inherent volatility and non-stationary behavior. In this study, we develop and evaluate a delta-targeted Hybrid forecasting pipeline for short horizons of 1, 3, and 7 days. We benchmark classical baselines (Naive, Seasonal Autoregressive Integrated Moving Average (SARIMA), and Exponential Smoothing (ETS)) against recurrent deep learning models (Simple Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM)) and recent neural forecasting baselines, including Decomposition-Linear (DLinear), Convolutional Kolmogorov-Arnold Network (C-KAN), and Neural Basis Expansion Analysis for Time Series (N-BEATS), using real-world daily scrap steel price data. The results indicate that delta-targeting generally yields more stable predictive performance than direct raw-price forecasting as the prediction horizon increases. For example, at the 7-day horizon, the predictive fit improves from approximately R-2 approximate to 0.87 for raw-price LSTM to around R-2 approximate to 0.90 for delta-trained recurrent models. At the same horizon, a delta-based RNN achieves the lowest Mean Absolute Percentage Error (MAPE) among the evaluated models (approximately 1.39%), while the proposed Hybrid model remains competitive across all tested horizons and maintains a goodness-of-fit of approximately R-2 approximate to 0.90 without uniformly minimizing point error relative to the best-performing recurrent baseline. Attention profiling and permutation-based feature importance analyses indicate that the model places relatively higher weight on calendar-related inputs, consistent with the presence of weekly patterns in the data; these results should be interpreted as sensitivity diagnostics rather than causal evidence. Overall, the findings suggest that delta-transformed targets provide a more suitable prediction space than raw-price targets for short-horizon scrap steel forecasting, while the Hybrid design offers a balanced combination of predictive performance and diagnostic interpretability for operational decision support.Article Prevalence and Risk of Carpal Tunnel Syndrome in Parkinson’s Disease: A Systematic Review and Meta-Analysis(MDPI, 2026) Raafat, Kareem Wael; Amin, Ahmed M.; Ezz, Mohamed R.; Sabry, Ehab Naser; Ibrahim, Ismail A.; Attia, Amir N.; Mohammed, Mariam M.Background: Parkinson's disease (PD) is a progressive neurodegenerative disorder characterised by motor and non-motor symptoms. Several studies have reported varying prevalence of Carpal Tunnel Syndrome (CTS) among individuals with PD. Objective: This study aimed to estimate the pooled prevalence of CTS in people with PD and explore any potential association between the two conditions. Methods: This systematic review and meta-analysis was conducted and reported in accordance with the PRISMA 2020 guidelines. A systematic search was performed across PubMed, the Cochrane Central Register of Controlled Trials (CENTRAL), Web of Science (WoS), Scopus, and EMBASE from inception to April 2024. Studies reporting CTS prevalence data in individuals with PD were included. Methodological quality was assessed using the National Institutes of Health (NIH) quality assessment tool. Pooled prevalence estimates were calculated using a random-effects model. Risk difference (RD) and risk ratio (RR) were calculated to assess the association between PD and CTS compared with control groups. Results: A total of 7 studies involving 411 participants (343 with PD and 68 controls) met the inclusion criteria, with 679 wrists assessed. The pooled prevalence of CTS in PD was estimated at 15% (95% CI: 0.07-0.28) with significant heterogeneity (p < 0.001, I-2 = 91%). The RD was 10% (95% CI: 0.04-0.16, p = 0.002), with low heterogeneity (p = 0.29, I-2 = 19%). The RR of CTS in PD compared with controls was 3.31 (95% CI: 0.60-18.42, p = 0.17), with moderate heterogeneity (p = 0.13, I-2 = 52%). Conclusions: This meta-analysis provides preliminary pooled estimates indicating a potentially increased prevalence of carpal tunnel syndrome in individuals with PD. Although the findings suggest a possible association, clinicians should maintain increased vigilance for CTS symptoms in patients with PD presenting with upper-limb sensory or motor complaints. From a biomechanical and functional perspective, these findings highlight the importance of routine upper-limb screening and the implementation of rehabilitation strategies targeting hand use, dexterity, and sensorimotor control within physiotherapy practice. Further high-quality studies with larger, well-characterised samples are required to confirm this relationship and clarify its clinical and functional implications.Article Assessment of Treatment Effectiveness in Acute and Chronic Anal Fissures(MDPI, 2026) Dincer, Onur Ilkay; Turksoy, Vugar Ali; Cakmak, Erol; Felek, DuyguBackground and Objectives: Anal fissures are a common condition in the general population, for which there are multiple treatment options. It is essential to select the most appropriate treatment for the right patient. This study aimed to observe and evaluate the effect of topical antibiotherapy, which is widely used in the management of wounds and chronic infections, on the healing of acute and chronic anal fissures. Materials and Methods: Hospital records of 625 individuals diagnosed with an anal fissure were reviewed. Previous treatments, including 0.4% glyceryl trinitrate and 5% lidocaine, were recorded. A total of 118 patients were included: 49 patients who received additional topical metronidazole due to inflammation, induration and minimal purulent discharge, in the absence of an abscess; and 69 uncomplicated patients who received only standard treatment, as per the exclusion criteria. Results: The mean age of the participants was 41.06 +/- 10.70 years. No significant differences were found between the groups regarding age or sex (p = 0.616 and p = 0.665, respectively). However, prior treatment history and mucosal healing differed significantly between the two groups (p = 0.001 and p = 0.024, respectively). There were no significant differences in follow-up intervals, additional treatment requirements or improvement in VAS scores (p = 0.546, 0.904 and 0.154, respectively). Conclusions: Topical metronidazole may be associated with improved mucosal healing in selected patients with acute anal fissures presenting with clinical features such as local inflammation, minimal discharge or incision-related changes. However, the observed benefit does not appear to be uniform across all patients, and, in the absence of microbiological data, the extent of microbial involvement remains uncertain. Accordingly, topical metronidazole may be considered for carefully selected cases of acute anal fissure based on clinical judgement, while avoiding routine or indiscriminate antibiotic use.Article A Data-Efficient Machine Learning Approach for Breast Ultrasound Lesion Classification Integrating Image-Derived Features and Sonographic Descriptors(MDPI, 2026) Karacor, Adil Gursel; Sahin, SevimBackground/Objectives: Breast ultrasound is widely used for the diagnostic evaluation of breast lesions; however, reliable lesion characterization remains challenging due to substantial image heterogeneity and the limited size of most clinically available datasets. These constraints reduce the generalizability of end-to-end deep learning approaches in routine practice. The objective of this study was to evaluate a data-efficient diagnostic framework that integrates image-derived features with clinical sonographic descriptors to improve breast ultrasound lesion classification in small cohorts. Methods: Ultrasound images from the publicly available BrEaST-Lesions dataset were processed using a pretrained convolutional neural network to extract compact image feature representations from full images, lesion masks, and cropped tumor regions. These features were combined with manually recorded sonographic descriptors after label encoding to form a unified tabular dataset. Gradient-boosted tree models were trained using descriptor-only and fused feature sets with fivefold stratified cross-validation and evaluated on an independent external hold-out test set. Results: Using sonographic descriptors alone, the best-performing model (LightGBM) achieved an external validation accuracy of 0.88, with an area under the receiver operating characteristic curve (AUC) of 0.95. Incorporation of image-derived features improved diagnostic performance on the external test set, yielding an accuracy of 0.88, an AUC of 0.96, and a sensitivity of 1.00 for malignant lesion detection. The fused framework demonstrated more stable generalization than descriptor-only models, particularly for malignant cases. Conclusions: Combining image-derived features with clinical sonographic descriptors within a tabular learning framework provides a robust and data-efficient approach for breast ultrasound-based lesion classification. This strategy supports diagnostic decision-making in small ultrasound datasets and represents a clinically realistic alternative when large-scale deep learning models are impractical.Article Machine Learning Model for Predicting Multidrug Resistance in Clinical Klebsiella pneumoniae Isolates(MDPI, 2026) Akkaya, Yuksel; Aydin, Irfan; Tanyildizi-Kokkulunk, Handan; Erturk, Ayse; Kilic, Ibrahim HalilBackground/Objectives: Klebsiella pneumoniae is an opportunistic pathogen increasingly resistant to carbapenems and broad-spectrum antibiotics, complicating timely infection management. In critical cases like septic shock, where initiating effective antibiotics within 3 h improves survival, culture-based resistance testing is often too slow. This study evaluates machine learning (ML) algorithms for faster antimicrobial resistance prediction than conventional methods. Methods: In this retrospective study, antibiogram results of 607 Klebsiella pneumoniae isolates collected between 2017 and 2024 were combined with demographic and clinical information of the patients from whom the isolates were obtained. Four different ML algorithms, namely Decision Tree (DT), Support Vector Classifier (SVC), K-Nearest Neighbors (KNN) and Random Forest (RF), were applied to classify the resistance status for 22 antibiotics. Model performances were evaluated using accuracy, precision, recall, F-score, AUC and feature importance metrics. Results: The RF model showed the highest overall performance in accurately predicting resistance to 22 antibiotics, achieving an average AUC value of 0.96. In particular, it predicted resistance to treatment-critical antibiotics such as Ertapenem (100%), Imipenem (93%) and Meropenem (95%) with high accuracy. Conclusions: ML models, especially RF, offer a powerful tool for rapid antibiotic resistance prediction, supporting accurate empirical treatment decisions and antimicrobial stewardship.Article Evaluation of Barriers to the Integration of Renewable Energy Technologies into Industries in Türkiye(MDPI, 2026) Caloglu Buyukselcuk, Elif; Turan, HakanThe transition to renewable energy technologies is one of the most important ways to achieve the sustainable development goals (SDGs) of affordable and clean energy (SDG7); industry, innovation and infrastructure (SDG9); responsible production and consumption (SDG12); and climate action (SDG13). The widespread use of renewable energy technologies in developing countries will reduce dependence on imported fossil resources, increase industrial competitiveness, and support low-carbon development. Despite all their advantages, the integration of renewable energy technologies into industrial and domestic systems in developing countries remains slow due to a number of barriers. Financial constraints, technical and technological deficiencies, political restrictions and uncertainties, and organizational and managerial inadequacies are some of the barriers to the widespread adoption of renewable energy technologies. This study aims to identify, classify, and prioritize the barriers to the implementation of renewable energy technologies by applying multi-criteria decision-making methods in a fuzzy environment, with T & uuml;rkiye considered as a case study. The relative importance of the barriers identified using the Single-Valued Spherical Fuzzy SWARA method was assessed, and their interconnections and significance were systematically demonstrated. The findings will contribute to the development of policy and management strategies aligned with global sustainability goals, thereby facilitating a more effective and equitable transition to clean and resilient energy systems.Article From Data to Autonomy: Integrating Demographic Factors and AI Models for Expert-Free Exercise Coaching(MDPI, 2026) Ozbalkan, Ugur; Turna, Ozgur CanThis study investigates the performance of three deep learning architectures-LSTM with Attention, GRU with Attention, and Transformer-in the context of real-time, self-guided exercise classification, using coordinate data collected from 103 participants via a dual-camera system. Each model was evaluated over ten randomized runs to ensure robustness and statistical validity. The GRU + Attention and LSTM + Attention models demonstrated consistently high test accuracy (mean approximate to 98.9%), while the Transformer model yielded significantly lower accuracy (mean approximate to 96.6%) with greater variance. Paired t-tests confirmed that the difference between LSTM and GRU models was not statistically significant (p = 0.9249), while both models significantly outperformed the Transformer architecture (p < 0.01). In addition, participant-specific features, such as athletic experience and BMI, were found to affect classification accuracy. These findings support the feasibility of AI-based feedback systems in enhancing unsupervised training, offering a scalable solution to bridge the gap between expert supervision and autonomous physical practice.Article Evaluation of Octenidine Dihydrochloride-Induced Cytotoxicity, Apoptosis, and Inflammatory Responses in Human Ocular Epithelial and Retinal Cells(MDPI, 2025) Ciftci, Ihsan Hakki; Deveci Ozkan, Asuman; Erman, Gulay; Kilbas, Imdat; Aydemir, OzlemBackground/Objectives: Octenidine dihydrochloride (OCT-D) is a broad-spectrum antiseptic with high chemical stability, low toxicity, and no reported microbial resistance, making it a strong candidate for use on mucosal surfaces. Despite increasing interest in its potential ophthalmic applications, limited data exist regarding its cellular effects on ocular tissues. This study aimed to investigate the cytotoxic, apoptotic, inflammatory, and transcriptional responses induced by OCT-D in human conjunctival (IOBA-NHC) and retinal pigment epithelial (ARPE-19) cells. Methods: Cells were exposed to varying concentrations of OCT-D, and viability was assessed using the WST-1 assay to determine IC50 and IC50/2 values. These concentrations were subsequently used in molecular assays. Pro-inflammatory cytokines (IL-6, IL-1 beta, TNF-alpha, IFN-gamma) were quantified by ELISA. Apoptotic activation was evaluated through caspase-3/7 activity assays. Gene expression analysis of apoptotic (Bax, Bcl-2), DNA damage-related (ATM, Rad51), and inflammatory markers was performed using RT-qPCR. Results: OCT-D induced a marked, dose-dependent reduction in cell viability in both cell lines, with ARPE-19 showing greater sensitivity. Caspase-3/7 activity increased significantly at IC50 and IC50/2, confirming intrinsic apoptotic activation. OCT-D markedly suppressed the release of key inflammatory cytokines and downregulated transcription of inflammatory genes. RT-qPCR revealed upregulation of pro-apoptotic and DNA damage-associated genes, demonstrating coordinated activation of apoptotic and genomic stress pathways. Conclusion: OCT-D triggers integrated cytotoxic, apoptotic, and immunomodulatory responses in conjunctival and retinal epithelial cells. While these findings provide important mechanistic insights into OCT-D's cellular effects, further studies using primary cells, advanced 3D ocular models, and disease-relevant systems are required to support its potential translational use in ophthalmology.