This study focuses on the effect of cross‐linking mechanisms in the development of hydrogel‐based flexible ionic diodes (HBIDs) with desirable features for ionic devices. The research explores one p‐type hydrogel and a series of n‐type hydrogels with varying cross‐linking ratios, investigating their ion‐conducting properties, morphological and electronic structures, as well as the performance of flexible HBIDs constructed using these hydrogels. The results indicate that the cross‐linking ratio significantly influences ionic conductivity, bulk resistance, and capacitance. Additionally, the HBIDs' ability to maintain their electrical performance under repeated mechanical bending is studied concerning the cross‐linking ratio. Notably, the HBID with the highest cross‐linking ratio (HG/12) exhibits relatively higher current densities (up to 8.9 mA cm⁻²) and considerably higher rectification ratios (up to 1269). Even after 500 cycles of deformation, the HG/12 diode maintained a rectification ratio of 987, demonstrating its excellent performance and durability.

Background/Objectives: This paper aims to design an explainable and accurate ML framework to support the automatic classification of Carpal Tunnel Syndrome (CTS) severity from structured patient data. Methods: For the experiment, an open-source dataset of 1037 samples was used. Following stratified partitioning, 305 samples were held out as the test set; the remaining training set (n = 732) was augmented to 1216 balanced samples via ADASYN, yielding an 80/20 train/test ratio relative to the final dataset (n = 1521). In order to solve the problem of imbalance associated with CTS cases of moderate and severe severity, the Adaptive Synthetic Sampling (ADASYN) technique was employed. The model’s predictive capacity was increased by means of feature engineering methods, such as polynomial transformations and clinically relevant interactions. Specifically, four ensemble learning models (XGBoost, Random Forest, LightGBM, and CatBoost) were optimized and ensembled with the use of a stacking approach with a base algorithm of LightGBM. The explainability of the model was ensured through SHAP and LIME analysis. Results: As a result, the stacking ensemble was able to reach a test accuracy of 91.15%, an F1-score of 91.13%, and an ROC-AUC of 0.9708. The proposed ensemble performed superiorly compared to any other individual algorithm while having stable performance across all severity categories. Conclusions: Through the explainability analysis, it was observed that such a classification model relies on important clinically relevant predictors, including cross-sectional area (CSA), duration of symptoms, pain level measured by the numeric rating scale of pain (NRS), and palmar bowing (PB).

Beyaz kan hücreleri, insanların bağışıklık sisteminin en önemli yapısı olup, kan ve lenf dokularında kemik iliği tarafından üretilmektedir. Bu hücreler insan vücudunu hastalık ve yabancı organizmalara karşı koruyan savunma mekanizmalarıdır ve kandaki oranı düştüğünde Lökopeni ile karşılaşılabilir. Bu hücrelerin insan vücudundaki oranının belirlenmesi ve hastalığın tespit ve tedavisi için yoğun emek harcaması gerekmektedir. Bu çalışmada, derin öğrenme modellerini kullanarak beyaz kan hücreleri sınıflandırma performansının iyileştirilmesi amaçlanmıştır. Sınıflandırma işlemini daha verimli gerçekleştirmek için evrişimli sinir ağı modelleri kullanılmıştır. Beyaz kan hücresi çeşitleri olan eozinofil, lenfosit, monosit ve nötrofil arasında ayrım yapmak için Densenet201, ResNet50 ve Alexnet birleştirilmiştir. Elde edilen özellik haritalarının sınıflandırılması için K-En yakın komşuluk, Destek Vektör Makinesi ve Naïve Bayes olmak üzere üç farklı makine öğrenmesi sınıflandırıcısı kullanılmıştır. Derin Öğrenme (DÖ) ile eğitilen Kaggle veri kümesi görüntülerine CLAHE ve Gauss filtreleri uygulanarak bu görüntüler üç ESA ağı ile yeniden sınıflandırılmıştır. Bu filtreler uygulandıktan sonra elde edilen sonuçların, orijinal verilerle elde edilen sınıflandırma sonuçlardan daha yüksek olduğu ortaya konmuştur.