Manish Kuchakulla^1,2,*, Hriday P. Bhambhvani^1,2, Robert Fisch^1,2, Runzhuo Ma^1,2, Jonathan Gal², Marc Goldstein²

Canadian Journal of Urology, Vol.33, No.2, pp. 441-449, 2026, DOI:10.32604/cju.2025.071275 - 20 April 2026

Abstract Objectives: Men with obstructive azoospermia (OA) or infertility often require surgical sperm retrieval for assisted reproductive techniques. While sperm can be successfully obtained from either the testis or epididymis in these patients, sperm DNA integrity may differ between retrieval sites, which could influence reproductive outcomes. This study aimed to determine whether bilateral epididymal and/or testicular sperm extraction is necessary in men with OA or infertility and elevated DNA fragmentation index (DFI). Methods: We retrospectively analyzed men who underwent bilateral testicular biopsy and/or microscopic epididymal sperm aspiration (MESA) by a single surgeon from 2020–2022. TUNEL assays… More >

Daniil Kamakaev, Khaled Mahbub^*

Journal on Artificial Intelligence, Vol.8, pp. 183-202, 2026, DOI:10.32604/jai.2026.077823 - 15 April 2026

Abstract This paper presents an innovative system designed to automate the analysis of candidate interviews by integrating multiple analytical techniques into a single multimodal framework. This system combines text sentiment analysis, audio sentiment analysis, keyword extraction, and Mel-Frequency Cepstral Coefficients (MFCC) feature extraction to evaluate candidate performance holistically. This system employs text sentiment analysis using VADER and transformer-based sentiment features (probability-based outputs), audio sentiment analysis with an SVM model trained on both IEMOCAP and MELD datasets, keyword extraction via KeyBERT, and audio feature extraction including MFCCs, delta MFCCs, pitch, and energy to evaluate candidate performance holistically. More >

Dongwei Shao, Hao Sun, Qi Wang, Ping Han, Yiwei Liu, Jiyi Luan, Lin Jia, Qiang He, Bo Cui^*

Journal of Polymer Materials, Vol.43, No.1, 2026, DOI:10.32604/jpm.2026.074541 - 03 April 2026

Abstract The valorization of agricultural waste into high-value nanomaterials is crucial for advancing sustainable biorefineries. This study presents an efficient approach for extracting carboxylated cellulose nanocrystals (CNCs) from poplar leaf waste (PL), an abundant and underutilized biomass. The process involved alkaline treatment and hydrogen peroxide bleaching to purify cellulose (PL-CEL), followed by sequential periodate-chlorite oxidation to produce dicarboxylic cellulose nanocrystals (PL-CNCs). The resulting nanocrystals were comprehensively characterized using compositional analysis, XRD, FTIR, TEM, TGA, and zeta potential measurements. XRD analysis confirmed a high crystallinity index of 82% for PL-CEL, which decreased to 72.2% after oxidation due… More >

Yajuan Lu^*, Yongtao Hu, Jie Li, Jinping Zhang, Jingjing Si

Structural Durability & Health Monitoring, Vol.20, No.2, 2026, DOI:10.32604/sdhm.2025.071110 - 31 March 2026

Abstract To address challenges in wind turbine blade defect detection models, primarily due to insufficient feature extraction capabilities and the difficulty of deploying models on drone-type edge devices, this study proposes a wind turbine blade defect detection model, WtCS-YOLO11, that incorporates multiscale feature extraction and an attention mechanism. Firstly, the cross-stage partial with two kernels and a wavelet convolution module (C3k2_WTConv) is proposed by introducing wavelet convolution into the module. The cross-stage partial with two kernels (C3k2) module in the necking network is replaced with the C3k2_WTConv module to increase the model’s receptive field, enable multiscale… More >

Sheng Luo¹, Rashid Abbasi^1,*, Hao Wang², Jinghua Xu³, Dongyang Lyu⁴, Aaron Zhang¹, Farhan Amin^5,*, Isabel de la Torre⁶, Gerardo Mendez Mezquita⁷, Henry Fabian Gongora⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.075080 - 30 March 2026

Abstract Human pose estimation is crucial across diverse applications, from healthcare to human–computer interaction. Integrating inertial measurement units (IMUs) with monocular vision methods holds great potential for leveraging complementary modalities; however, existing approaches are often limited by IMU drift, noise, and underutilization of visual information. To address these limitations, we propose a novel dual-stream feature extraction framework that effectively combines temporal IMU data and single-view image features for improved pose estimation. Short-term dependencies in IMU sequences are captured with convolutional layers, while a Transformer-based architecture models long-range temporal dynamics. To mitigate IMU drift and inter-sensor inconsistencies, More >

Huda F. Al-Shahad^1,2, Razali Yaakob^1,*, Nurfadhlina Mohd Sharef¹, Hazlina Hamdan¹, Hasyma Abu Hassan³, Xiaoyi Jiang⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2025.074109 - 30 March 2026

Abstract Currently, thyroid diseases are prevalent worldwide; therefore, it is necessary to develop techniques that help doctors improve their diagnostic skills for such diseases. In previous studies, 2-dimensional convolutional neural network (2D CNN) techniques were employed to classify thyroid nodules as benign and malignant without detecting the presence of thyroid nodules in the obtained ultrasound images. To address this issue, we propose a 3-dimensional convolutional neural network (3D CNN) for thyroid nodule detection. The proposed CNN exploits the 3D information and spatial features contained in ultrasound images and generates distinctive features during its training using multiple… More >

Weimin Shi^1,*, Kuntao Lv¹, Chang Xuan¹, Ji Wu²

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.075203 - 12 March 2026

Abstract The application of deep learning in fabric defect detection has become increasingly widespread. To address false positives and false negatives in fabric roll seam detection, and to improve automation efficiency and product quality, we propose the Multi-scale Context DeepLabV3+ (MSC-DeepLabV3+), a semantic segmentation network designed for fabric roll seam detection, based on DeepLabV3+. The model improvements include enhancing the backbone performance through optimization of the UIB-MobileNetV2 network; designing the Dynamic Atrous and Sliding-window Fusion (DASF) module to improve adaptability to multi-scale seam structures with dynamic dilation rates and a sliding-window mechanism; and utilizing the Progressive… More >

Yuanmeng Chang, Hongmei Liu^*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.074204 - 12 March 2026

Abstract Driven by rapid advances in deep learning, object detection has been widely adopted across diverse application scenarios. However, in low-light conditions, critical visual cues of target objects are severely degraded, posing a significant challenge for accurate low-light object detection. Existing methods struggle to preserve discriminative features while maintaining semantic consistency between low-light and normal-light images. For this purpose, this study proposes a DL-YOLO model specially tailored for low-light detection. To mitigate target feature attenuation introduced by repeated downsampling, we design a Multi-Scale Feature Convolution (MSF-Conv) module that captures rich, multi-level details via multi-scale feature learning, More >

Seunggyu Byeon¹, Jung-hun Lee², Jong-Deok Kim^3,*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.074033 - 12 March 2026

Abstract This paper introduces a fuzzy C-means-based pooling layer for convolutional neural networks that explicitly models local uncertainty and ambiguity. Conventional pooling operations, such as max and average, apply rigid aggregation and often discard fine-grained boundary information. In contrast, our method computes soft memberships within each receptive field and aggregates cluster-wise responses through membership-weighted pooling, thereby preserving informative structure while reducing dimensionality. Being differentiable, the proposed layer operates as standard two-dimensional pooling. We evaluate our approach across various CNN backbones and open datasets, including CIFAR-10/100, STL-10, LFW, and ImageNette, and further probe small training set restrictions More >

Rafika Harrabi^1,2,*, Slim Ben Chaabane^1,2, Hassene Seddik²

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.072054 - 12 March 2026

Abstract Securing restricted zones such as airports, research facilities, and military bases requires robust and reliable access control mechanisms to prevent unauthorized entry and safeguard critical assets. Face recognition has emerged as a key biometric approach for this purpose; however, existing systems are often sensitive to variations in illumination, occlusion, and pose, which degrade their performance in real-world conditions. To address these challenges, this paper proposes a novel hybrid face recognition method that integrates complementary feature descriptors such as Fuzzy-Gabor 2D Fisher Linear Discriminant (FG-2DFLD), Generalized 2D Linear Discriminant Analysis (G2DLDA), and Modular-Local Binary Patterns (Modular-LBP)… More >