Vardhelli Soukya¹, Soumen Maitra^1,*, Nandita Sahana², Saikat Das³, Rupsanatan Mandal³, Arpita Mandal Khan¹, Arindam Das⁴, Ashok Choudhury⁵, Prodyut Kumar Paul⁶, Ahmed Gaber⁷, Mohammed M. Althaqafi⁸, Akbar Hossain^9,*

Phyton-International Journal of Experimental Botany, Vol.94, No.11, pp. 3493-3508, 2025, DOI:10.32604/phyton.2025.071450 - 01 December 2025

Abstract The present study investigated the genetic diversity of 24 germplasms of Polianthes tuberosa L. via 16 inter simple sequence repeat (ISSR) marker techniques. The research findings revealed that the ISSR markers presented higher levels of band reproducibility and were more efficient at clustering germplasms. Among the 16 markers examined in this study, 12 had a complete polymorphism rate of 100%. The molecular analysis revealed a PIC ranging from 0.079 to 0.373, with a mean value of 0.30, whereas the range of the marker index was from 0.0001 to 0.409, with an average value of 0.03, and More >

Neethu Rose Thomas^1,2, J. Anitha², Cristina Popirlan³, Claudiu-Ionut Popirlan³, D. Jude Hemanth^2,*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4407-4440, 2025, DOI:10.32604/cmc.2025.070689 - 23 October 2025

Abstract Integration of artificial intelligence in image processing methods has significantly improved the accuracy of the medical diagnostics pathway for early detection and analysis of kidney tumors. Computer-assisted image analysis can be an effective tool for early diagnosis of soft tissue tumors located remotely or in inaccessible anatomical locations. In this review, we discuss computer-based image processing methods using deep learning, convolutional neural networks (CNNs), radiomics, and transformer-based methods for kidney tumors. These techniques hold significant potential for automated segmentation, classification, and prognostic estimation with high accuracy, enabling more precise and personalized treatment planning. Special focus More >

Xi Chen^1,3, Wanru Lin¹, Yuefu Liu^2,*

Journal of Psychology in Africa, Vol.35, No.5, pp. 661-669, 2025, DOI:10.32604/jpa.2025.068787 - 24 October 2025

Abstract Accurate assessment of climate anxiety is crucial, yet the cross-cultural transportability of existing instruments remains an open question. This study translated and validated the Hogg Climate Anxiety Scale for the Chinese context. A total of 959 students (females = 69.7%; M age = 19.60 years, SD = 1.40 years) completed the Hogg Climate Anxiety Scale, with the Climate Change Anxiety Scale and the Anxiety Presence Subscale served as criterion measures for concurrent validity. Test–retest reliability was evaluated with a subset after one month. Confirmatory factor analysis supported the original four-factor structure and measurement invariance across genders.… More >

Muhammad Salman Qamar^1,2,*, Muhammad Fahad Munir², Athar Waseem²

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3557-3584, 2025, DOI:10.32604/cmes.2025.067447 - 30 September 2025

Abstract Air pollution, specifically fine particulate matter (PM2.5), represents a critical environmental and public health concern due to its adverse effects on respiratory and cardiovascular systems. Accurate forecasting of PM2.5 concentrations is essential for mitigating health risks; however, the inherent nonlinearity and dynamic variability of air quality data present significant challenges. This study conducts a systematic evaluation of deep learning algorithms including Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and the hybrid CNN-LSTM as well as statistical models, AutoRegressive Integrated Moving Average (ARIMA) and Maximum Likelihood Estimation (MLE) for hourly PM2.5 forecasting. Model performance is… More >

Puranam Revanth Kumar¹, Gouse Baig Mohammad², Pallati Narsimhulu³, Dharnisha Narasappa⁴, Lakshmana Phaneendra Maguluri⁵, Subhav Singh^6,7,8, Shitharth Selvarajan^9,10,11,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 2779-2818, 2025, DOI:10.32604/cmes.2025.066365 - 30 September 2025

Abstract Blockchain Technology (BT) has emerged as a transformative solution for improving the efficacy, security, and transparency of supply chain intelligence. Traditional Supply Chain Management (SCM) systems frequently have problems such as data silos, a lack of visibility in real time, fraudulent activities, and inefficiencies in tracking and traceability. Blockchain’s decentralized and irreversible ledger offers a solid foundation for dealing with these issues; it facilitates trust, security, and the sharing of data in real-time among all parties involved. Through an examination of critical technologies, methodology, and applications, this paper delves deeply into computer modeling based-blockchain framework… More >

Nezir Aydin^1,2,*, Melike Cari³, Betul Kara³, Ertugrul Ayyildiz^1,3

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2625-2650, 2025, DOI:10.32604/cmc.2025.067290 - 23 September 2025

Abstract Transportation systems are rapidly transforming in response to urbanization, sustainability challenges, and advances in digital technologies. This review synthesizes the intersection of artificial intelligence (AI), fuzzy logic, and multi-criteria decision-making (MCDM) in transportation research. A comprehensive literature search was conducted in the Scopus database, utilizing carefully selected AI, fuzzy, and MCDM keywords. Studies were rigorously screened according to explicit inclusion and exclusion criteria, resulting in 73 eligible publications spanning 2006–2025. The review protocol included transparent data extraction on methodological approaches, application domains, and geographic distribution. Key findings highlight the prevalence of hybrid fuzzy AHP and… More >

Sunawar Khan¹, Tehseen Mazhar^1,2,*, Naila Sammar Naz¹, Fahed Ahmed¹, Tariq Shahzad³, Atif Ali⁴, Muhammad Adnan Khan^5,*, Habib Hamam^6,7,8,9

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2347-2401, 2025, DOI:10.32604/cmc.2025.066932 - 23 September 2025

Abstract Feature selection (FS) plays a crucial role in medical imaging by reducing dimensionality, improving computational efficiency, and enhancing diagnostic accuracy. Traditional FS techniques, including filter, wrapper, and embedded methods, have been widely used but often struggle with high-dimensional and heterogeneous medical imaging data. Deep learning-based FS methods, particularly Convolutional Neural Networks (CNNs) and autoencoders, have demonstrated superior performance but lack interpretability. Hybrid approaches that combine classical and deep learning techniques have emerged as a promising solution, offering improved accuracy and explainability. Furthermore, integrating multi-modal imaging data (e.g., Magnetic Resonance Imaging (MRI), Computed Tomography (CT), Positron… More >

Pasqualino Corigliano^*, Giulia Palomba

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 1935-1955, 2025, DOI:10.32604/cmes.2025.067094 - 31 August 2025

Abstract Experimental tests are essential for evaluating S-N curves and assessing the fatigue life of welded joints. However, in the case of complex geometries, experimental tests often cannot provide the necessary stress-strain data for specific materials and welded joints. Therefore, finite element (FE) analyses are frequently utilized to assess fatigue behavior in complex geometries and address the discontinuities induced by welding processes. In this study, the fatigue properties of titanium welded joints, produced using an innovative laser source and welded without the use of filler materials, were analyzed through numerical methods. Two different FE methods were… More >

Yuriy Mayasin¹, Maria Osinnikova¹, Daria Osadchaya¹, Victoria Dmitrienko¹, Anna Gorodilova¹, Chulpan Kharisova¹, Kristina Kitaeva¹, Ivan Filin¹, Valeria Solovyeva¹, Albert Rizvanov^1,2,*

Oncology Research, Vol.33, No.9, pp. 2221-2242, 2025, DOI:10.32604/or.2025.064677 - 28 August 2025

Abstract Melanoma is a malignant neoplasm with a high propensity to metastasize, arising from melanocytes and contributing significantly to global morbidity and mortality. Despite the demonstrated efficacy of many immunotherapy approaches, these methods rely on direct destruction of tumor cells with minimal impact on the aggregate of nearby non-tumor cells, the extracellular matrix, and blood vessels that form the tumor microenvironment (TME). The TME is known to be heterogeneous and dynamic, exerting both antitumor and pro-tumor effects depending on the specific features and stage of carcinogenesis. TME has been shown in several studies to promote… More >

Saranya Velmurugan¹, Dapkupar Wankhar², Vijayalakshmi Paramasivan³, Gowtham Kumar Subbaraj^1,*

BIOCELL, Vol.49, No.8, pp. 1363-1390, 2025, DOI:10.32604/biocell.2025.065891 - 29 August 2025

Abstract Cancer rates are increasing globally, making it more urgent than ever to enhance research and treatment strategies. This study aims to investigate how innovative technology and integrated multi-omics techniques could help improve cancer diagnosis, knowledge, and therapy. A complete literature search was undertaken using PubMed, Elsevier, Google Scholar, ScienceDirect, Embase, and NCBI. This review examined the articles published from 2010 to 2025. Relevant articles were found using keywords and selected using inclusion criteria New sequencing methods, like next-generation sequencing and single-cell analysis, have transformed our ability to study tumor complexity and genetic mutations, paving the… More >