Yu Zhang^1,2,#, Shihan Tan^1,#, Guangyao Lian², Congying Dun³, Qiwei Hu^1,*, Chiming Guo^1,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.081931 - 15 June 2026

Abstract Gearboxes are critical components in the transmission systems of various mechanical equipment. Subjected to complex and harsh operating conditions for a long time, they suffer from a high failure rate and potentially severe consequences. Traditional fault diagnosis methods are limited by problems such as noise interference, and can hardly meet the requirements in terms of diagnostic accuracy, generalization ability, and reliability. To tackle the deficiencies of traditional gearbox fault diagnosis methods, including insufficient utilization of features, poor generalization under small-sample conditions, and weak model interpretability, this paper proposes a fault diagnosis method based on multi-dimensional… More >

Huaixuan Yan, Yan Gong^*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.080178 - 15 June 2026

Abstract To correctly diagnose faults in farm machinery, we need to know a lot about the field and have experience with maintenance. However, most of this important information is stored in old, unstructured documents like technical manuals and expert logs. These documents don’t have a standard way to be represented digitally, which makes it very hard to build automated diagnosis systems. There are three main technical problems with getting structured knowledge out of this kind of text: noise from optical character recognition (OCR) during digitization, the extreme lack of labeled samples in specialized fields (low-resource constraints),… More >

Poluru Eswaraiah¹, Uddagiri Sirisha^2,*, Shaik Abdul Nabi³, Revathi Durgam⁴, Pallavi Malavath⁵, Gilakara Muni Nagamani⁶

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.078511 - 15 June 2026

Abstract The growing volume of digital text complicates the extraction of relevant information from unstructured data. Transformer models such as BERT, ALBERT, and RoBERTa are powerful, but they may face challenges in hyperparameter optimization and adaptation to new domains. To address this issue, a hybrid ensemble BERT model is suggested, optimized using the Walrus Optimization Algorithm (WaOA). The framework applies PCA to reduce dimensionality, ontology normalization, and K-means clustering to improve semantic comprehension. Experimental results on the SQuAD 2.0 and MS MARCO datasets show that the proposed model outperforms the baseline models. WaOA (Weighted Average of More >

Miao Ye¹, Ziheng Wang¹, Qiuxiang Jiang¹, Xingsi Xue², Wenxi Liu³, Yu Ning¹, Cheng Zhu^1,4,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.078282 - 15 June 2026

Abstract In recent years, anomaly detection in Wireless Sensor Networks (WSNs) has been widely studied using Graph Neural Networks and Transformer-based methods. However, in multi-node and multi-modal data scenarios, these approaches still face challenges such as insufficient extraction of spatiotemporal correlation features, limited modeling capabilities when relying solely on either time-domain or frequency-domain information, and high computational overhead. To address these issues, this work aims to develop an anomaly detection model that balances detection performance with computational efficiency, enabling effective identification of complex anomaly patterns. Specifically, we propose a time–frequency feature extraction method with topological information… More >

Manda Vais Jatul Fitri¹, Melbi Mahardika^2,3,4,*, Yuni Kusumastuti^1,*, Mochamad Asrofi⁵

Journal of Renewable Materials, Vol.14, No.5, 2026, DOI:10.32604/jrm.2026.02025-0197 - 28 May 2026

Abstract The development of seaweed-derived products, particularly carrageenan, is increasingly prioritized in Indonesia to support sustainability and strengthen the local economy. Despite extensive studies on carrageenan extraction, systematic comparisons between locally extracted carrageenan and specific local commercial products remain limited. This study addresses this gap by directly comparing carrageenan extracted from Eucheuma cottonii harvested in Lombok, Indonesia, with a locally produced commercial carrageenan as a quality benchmark. Carrageenan extraction was performed using alkaline KOH treatment followed by ethanol precipitation. The extracted carrageenan exhibited a relatively high viscosity (61.16 cP) and a low sulfate content (11.58%). FTIR analysis More >

Adib Hafiizhullah Mohamad Prim Nasir^1,2, Mohd Nurazzi Norizan^1,2,3,*, Nur Izzaati Saharudin^1,2, Sumarni Mansur^1,2

Journal of Renewable Materials, Vol.14, No.5, 2026, DOI:10.32604/jrm.2025.02025-0156 - 28 May 2026

Abstract Plant-based starch has emerged as a promising natural binder in biocomposites owing to its biodegradability, renewability, and functional adaptability. This study critically reviews the extraction, modification, and performance of starches derived from sources such as corn, potato, and cassava, with particular attention to their calorific behaviour as measured through bomb calorimetry. Calorimetric analysis provides insight into the energy density and combustion efficiency of starch binders, parameters that influence both processing and End-of-life valorisation of biocomposites. Through physical, chemical, enzymatic, and genetic modifications, the inherent limitations of native starch such as moisture sensitivity and low mechanical More > Graphic Abstract

Natalia Prieto-Fernández¹, Martín Bayón-Gutiérrez^1,*, Sergio Fernández-Blanco¹, Álvaro Fernández-Blanco¹, Francisco Carro-De-Lorenzo², José Alberto Benítez-Andrades¹

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.080540 - 27 May 2026

Abstract Feature-based Simultaneous Localization and Mapping (SLAM) using 2D Light Detection and Ranging (LiDAR) in structured indoor environments commonly relies on the extraction of straight segments and corners from raw scan data. The quality of these landmarks depends not only on the fitting algorithm, but also on how uncertainty is modeled and propagated from line estimates to derived corner features. Although the magnitude of LiDAR uncertainty has been widely studied, the influence of line parameterization and geometric conditioning on uncertainty propagation has received less attention. In particular, the scale ambiguity inherent to implicit line representations can… More >

Thanh Tuan Nguyen¹, Cuong Nguyen Dinh Hoa^2,3,*

Intelligent Automation & Soft Computing, Vol.41, pp. 1-24, 2026, DOI:10.32604/iasc.2026.078344 - 12 May 2026

Abstract Accurate crude oil price forecasting is critical for global economic stability but remains an exceptionally challenging task due to the data’s complex, non-linear, and non-stationary nature. Deep learning models like LSTMs are widely favored. However, the dominant research trend currently focuses on increasingly complex hybrid and ensemble architectures. These models often suffer from high computational overhead, intricate tuning processes, and potential overfitting, raising critical questions about their necessity. In this paper, we challenged the assumption that complexity is required for high performance by proposing and evaluating a streamlined 1D-CNN model. We conducted a comprehensive evaluation… More >

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 >