Yang Zhang¹, Xiaoping Yang¹, Yalan Zhang¹, Mingzhe Han¹, Jiayi Sun², Zhengsheng Xia^3,*

FDMP-Fluid Dynamics & Materials Processing, Vol.22, No.1, 2026, DOI:10.32604/fdmp.2026.075388 - 06 February 2026

Abstract Self-suspended proppants, which enable clear-water fracturing, represent a promising new class of materials for reservoir stimulation. Given the economic limitations associated with their exclusive use, this study investigates proppant transport behavior in hybrid systems combining self-suspended proppants with conventional 40/70 mesh quartz sand at various mixing ratios. A dedicated experimental apparatus was developed to replicate field-relevant complex fracture networks, consisting of a main fracture and two branching fractures with different deflection angles. Using this system, sand bank formation and proppant distribution were examined for both conventional quartz sand fracturing and fracturing augmented with self-suspended proppants.… More >

Erdinç Uslan^1,*, Emin Ulugergerli²

Revue Internationale de Géomatique, Vol.35, pp. 31-51, 2026, DOI:10.32604/rig.2026.075081 - 05 February 2026

Abstract Climate classification systems are essential tools for analyzing regional climatic behavior, assessing long-term aridity patterns, and evaluating the impacts of climate change on water resources and ecosystem resilience. This study introduces a new Climate Classification Method based on uniform and unitless variables, referred to as the U2 Climate Classification (U2CC). The proposed U2 Index was designed to overcome structural limitations of the classical De Martonne (1942) and Erinç (1949) indices, which rely on raw precipitation–temperature ratios and are sensitive to extreme values, particularly subzero temperatures. The U2 methodology consisted of two key steps: (i) normalization… More >

Olha Mykhailenko^1,2,3,#,*, Zigmantas Gudžinskas⁴, Liudas Ivanauskas⁵, Victoriya Georgiyants¹, Chia-Hung Yen^6,7,8, Chung-Fan Hsieh⁹, Riong Seulina Panjaitan⁶, Tsong-Long Hwang^10,11,12,13, Bing-Hung Chen^14,15, Michal Korinek^6,7,8,#,*

Phyton-International Journal of Experimental Botany, Vol.95, No.1, 2026, DOI:10.32604/phyton.2026.074209 - 30 January 2026

Abstract The sustainable sourcing of novel bioactive compounds from natural sources is crucial to the success of the pharmaceutical, food, and cosmetics industries. Iris bucharica Foster (syn. Juno bucharica (Foster) Vved.) is a promising source of novel bioactive molecules, particularly phenolic compounds, which are renowned for their antioxidant properties. In this study, we developed a reliable HPLC-UV-DAD method to identify and quantify phenolic compounds in the leaves and bulbs of I. bucharica, establishing the first set of quality control markers for this species. A total of 21 phenolic compounds were identified in the leaves, with flavonoids isoorientin, guaijaverin, hyperoside, More >

Mario D’Ambrosio¹, Elisabetta Bigagli^1,*, Lorenzo Cinci¹, Cecilia Brunetti^2,*, Edgardo Giordani³, Francesco Ferrini³, Cristina Luceri¹

Phyton-International Journal of Experimental Botany, Vol.95, No.1, 2026, DOI:10.32604/phyton.2026.073250 - 30 January 2026

Abstract Moringa oleifera (MO) is traditionally used to mitigate inflammatory-mediated disorders; however, the influence of ecotypic variation on its anti-inflammatory activity remains poorly understood. In this study, we compared the phytochemical composition and anti-inflammatory activity of ethanolic extracts obtained from fresh and dried leaves of four MO ecotypes (India, Paraguay, Mozambique, and Pakistan), all grown under the same outdoor conditions, as well as two commercial powders (Just Moringa and WISSA), using LPS-stimulated RAW 264.7 macrophages. Extracts from fresh leaves were 19–43% more cytotoxic than those from dried leaves, depending on the ecotype, likely due to higher cyanogenic… More >

Amine Batbat^1,2, Khaoula Habbadi², Mohamed Jeddi³, Samiah Hamad Al-Mijalli⁴, Hanae Naceiri Mrabti⁵, Fahad M. Alshabrmi⁶, Naif Hesham Moursi⁷, Hassane Greche¹, Naoufal El Hachlafi^8,*

Phyton-International Journal of Experimental Botany, Vol.95, No.1, 2026, DOI:10.32604/phyton.2026.072398 - 30 January 2026

Abstract Origanum elongatum (OE) is an aromatic, medicinal plant endemic to Morocco that is widely used in traditional medicine due to its biological properties. This study aimed to elucidate the chemical composition of the essential oil (EO) obtained from O. elongatum (OEEO) at three stages of its life cycle, including vegetative stage (OEEO-VS), flowering stage (OEEO-FS), and post-flowering (OEEO-PFS), as well as to evaluate its biological and antiradical characteristics. The chemical analysis of the essential oil was conducted using gas chromatography-mass spectrometry (GC-MS). The antibacterial activity was evaluated in vitro through distinct methodologies, namely, disc diffusion and microatmosphere assay;… More >

Zuokun Yang¹, Jun Li^1,2,*, Xin Lai^1,2, Lisheng Liu^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2026.075980 - 29 January 2026

Abstract Peridynamics (PD) demonstrates unique advantages in addressing fracture problems, however, its nonlocality and meshfree discretization result in high computational and storage costs. Moreover, in its engineering applications, the computational scale of classical GPU parallel schemes is often limited by the finite graphics memory of GPU devices. In the present study, we develop an efficient particle information management strategy based on the cell-linked list method and on this basis propose a subdomain-based GPU parallel scheme, which exhibits outstanding acceleration performance in specific compute kernels while significantly reducing graphics memory usage. Compared to the classical parallel scheme,… More >

Nuh Azginoglu^*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2026.075834 - 29 January 2026

Abstract Breast cancer screening programs rely heavily on mammography for early detection; however, diagnostic performance is strongly affected by inter-reader variability, breast density, and the limitations of conventional computer-aided detection systems. Recent advances in deep learning have enabled more robust and scalable solutions for large-scale screening, yet a systematic comparison of modern object detection architectures on nationally representative datasets remains limited. This study presents a comprehensive quantitative comparison of prominent deep learning–based object detection architectures for Artificial Intelligence-assisted mammography analysis using the MammosighTR dataset, developed within the Turkish National Breast Cancer Screening Program. The dataset comprises… More >

Mong-Fong Horng^1,#, Thanh-Lam Nguyen^1,#, Thanh-Tuan Nguyen^2,*, Chin-Shiuh Shieh^1,*, Lan-Yuen Guo³, Chen-Fu Hung⁴, Chun-Chih Lo¹

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.075064 - 29 January 2026

Abstract The global population is rapidly expanding, driving an increasing demand for intelligent healthcare systems. Artificial intelligence (AI) applications in remote patient monitoring and diagnosis have achieved remarkable progress and are emerging as a major development trend. Among these applications, mouth motion tracking and mouth-state detection represent an important direction, providing valuable support for diagnosing neuromuscular disorders such as dysphagia, Bell’s palsy, and Parkinson’s disease. In this study, we focus on developing a real-time system capable of monitoring and detecting mouth state that can be efficiently deployed on edge devices. The proposed system integrates the Facial… More >

Guangyu Xu^1,2, Yuxin Liu¹, Bo Yang¹, Siyu Lu^3,*, Chao Liu⁴, Junmin Lyu⁵, Wenfeng Zheng^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.074938 - 29 January 2026

Abstract Soft-tissue motion introduces significant challenges in robotic teleoperation, especially in medical scenarios where precise target tracking is critical. Latency across sensing, computation, and actuation chains leads to degraded tracking performance, particularly around high-acceleration segments and trajectory inflection points. This study investigates machine learning-based predictive compensation for latency mitigation in soft-tissue tracking. Three models—autoregressive (AR), long short-term memory (LSTM), and temporal convolutional network (TCN)—were implemented and evaluated on both synthetic and real datasets. By aligning the prediction horizon with the end-to-end system delay, we demonstrate that prediction-based compensation significantly reduces tracking errors. Among the models, TCN More >

Naveed Ahmad¹, Muhammad Kaleem², Mourad Elloumi³, Muhammad Azhar Mushtaq², Ahlem Fatnassi⁴, Mohd Fazil⁵, Anas Bilal^6,*, Abdulbasit A. Darem^7,4

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.074902 - 29 January 2026

Abstract Network-on-Chip (NoC) systems are progressively deployed in connecting massively parallel megacore systems in the new computing architecture. As a result, application mapping has become an important aspect of performance and scalability, as current trends require the distribution of computation across network nodes/points. In this paper, we survey a large number of mapping and scheduling techniques designed for NoC architectures. This time, we concentrated on 3D systems. We take a systematic literature review approach to analyze existing methods across static, dynamic, hybrid, and machine-learning-based approaches, alongside preliminary AI-based dynamic models in recent works. We classify them… More >