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  • Open Access

    ARTICLE

    Hydroalcoholic Extracts of Achillea spp. from Greece: A Study on Phenolic Content and Their Biological Activities

    Olga S. Tsiftsoglou1,*, Vladimir Mihailovic2, Nikola Sreckovic2, Jelena S. Katanic Stankovic3, Kyriakos Michail Dimitriadis1, Michalis K. Stefanakis4, Diamanto Lazari1

    Phyton-International Journal of Experimental Botany, Vol.95, No.1, 2026, DOI:10.32604/phyton.2026.075566 - 30 January 2026

    Abstract Achillea species are known for their healing properties since ancient times. There is extensive literature on their pharmacological action due to their bioactive compounds. The present study aimed to investigate the antioxidant and antimicrobial effects of hydroalcoholic extracts from the inflorescences and leaves of the species Achillea crithmifolia Waldst. and Kit., A. grandifolia Friv. and A. millefolium L. The phytochemical profiles of all extracts were evaluated both by NMR spectroscopy and LC-MS analysis, and the results were consistent with the spectrophotometrically determined total phenolic (TP: 125.42–191.98 mg/g) and total flavonoid (TF: 47.34–180.02 mg/g) contents. All the extracts were tested More >

  • Open Access

    ARTICLE

    Spikelet Filling Characteristics in Early-Season Rice Experiencing High Temperatures during Ripening

    Jiazhou Li1,2, Mingyu Zhang1, Xing Li1,3, Fangbo Cao1,2, Jiana Chen1,2, Weiqin Wang1,2, Huabin Zheng1,2, Min Huang1,2,4,*

    Phyton-International Journal of Experimental Botany, Vol.95, No.1, 2026, DOI:10.32604/phyton.2025.075255 - 30 January 2026

    Abstract Spikelet filling characteristics in early-season rice in southern China may be distinctive due to its exposure to high temperatures during the ripening period. However, limited information is currently available on these characteristics. This study aimed to characterize spikelet filling in early-season rice and identify the key factors contributing to its improvement. Field experiments were conducted over two years (2021 and 2022) to mainly investigate the proportions of fully-filled, partially-filled, and empty spikelets, along with the biomass-fertilized spikelet ratio and harvest index, in 11 early-season rice varieties. The results revealed significant varietal variation in spikelet filling,… More >

  • Open Access

    ARTICLE

    Morpho-Anatomical and Biochemical Defense Responses of Pigeon Pea Varieties to Phytophthora Blight

    Kirti A. Yadav1, Yachana Jha1, Haiam O. Elkatry2, Heba I. Mohamed3,*, Ahmed Mahmoud Ismail4, Abdelrahman R. Ahmed2,*

    Phyton-International Journal of Experimental Botany, Vol.95, No.1, 2026, DOI:10.32604/phyton.2026.074570 - 30 January 2026

    Abstract Phytophthora blight is a devastating disease of pigeon pea (Cajanus cajan) that severely impacts plant growth and productivity. This study investigates the morphological, anatomical, and biochemical responses of a susceptible variety (ICPL 11260) and a resistant variety (IPAC-02) following infection by Phytophthora. Morphological analyses showed that infection caused a drastic reduction in root length, shoot length, leaf number, fresh weight, and dry weight in the susceptible ICPL 11260 variety, with reductions ranging from 0.5- to 2-fold compared to non-infected controls. Anatomical observations revealed pronounced cellular damage and mycelial invasion in infected ICPL 11260 plants by 30… More >

  • Open Access

    ARTICLE

    Prediction of Root Zone Temperature Dynamics at Effective Depth on Lettuce Production in Greenhouse Using Sensitivity and Feature Importance Analysis with XGBoost

    Hasan Kaan Kucukerdem*

    Phyton-International Journal of Experimental Botany, Vol.95, No.1, 2026, DOI:10.32604/phyton.2026.074188 - 30 January 2026

    Abstract Root-zone temperature (RZT) strongly affects plant growth, nutrient uptake and tolerance to environmental stress, making its regulation a key challenge in greenhouse cultivation in cold climates. This study aimed to assess the potential of passive techniques, namely black polyethylene mulch and row covers, for modifying RZT dynamics in lettuce (Lactuca sativa L.) production and to evaluate the predictive performance of the eXtreme Gradient Boosting (XGBoost) algorithm. Experiments were conducted in Iğdır, Türkiye, over a 61-day period, with soil temperature continuously monitored at depths of 1–30 cm under mulched and non-mulched conditions, alongside measurements of greenhouse air… More >

  • Open Access

    ARTICLE

    A Subdomain-Based GPU Parallel Scheme for Accelerating Perdynamics Modeling with Reduced Graphics Memory

    Zuokun Yang1, Jun Li1,2,*, Xin Lai1,2, Lisheng Liu1,2,*

    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 >

  • Open Access

    ARTICLE

    Learning-Based Prediction of Soft-Tissue Motion for Latency Compensation in Teleoperation

    Guangyu Xu1,2, Yuxin Liu1, Bo Yang1, Siyu Lu3,*, Chao Liu4, Junmin Lyu5, Wenfeng Zheng1,*

    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 >

  • Open Access

    ARTICLE

    Explainable Ensemble Learning Framework for Early Detection of Autism Spectrum Disorder: Enhancing Trust, Interpretability and Reliability in AI-Driven Healthcare

    Menwa Alshammeri1,2,*, Noshina Tariq3, NZ Jhanji4,5, Mamoona Humayun6, Muhammad Attique Khan7

    CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.074627 - 29 January 2026

    Abstract Artificial Intelligence (AI) is changing healthcare by helping with diagnosis. However, for doctors to trust AI tools, they need to be both accurate and easy to understand. In this study, we created a new machine learning system for the early detection of Autism Spectrum Disorder (ASD) in children. Our main goal was to build a model that is not only good at predicting ASD but also clear in its reasoning. For this, we combined several different models, including Random Forest, XGBoost, and Neural Networks, into a single, more powerful framework. We used two different types More >

  • Open Access

    ARTICLE

    Adaptability Analysis of Dual Clearing Systems in Spot Electricity Markets Based on Fuzzy Evaluation Metrics: An Inner Mongolia Case Study

    Kai Xie1, Shaoqing Yuan2, Dayun Zou1, Jinran Wang1,*, Genjun Chen1, Ciwei Gao3, Yinghao Cao1

    Energy Engineering, Vol.123, No.2, 2026, DOI:10.32604/ee.2025.070733 - 27 January 2026

    Abstract The construction of spot electricity markets plays a pivotal role in power system reforms, where market clearing systems profoundly influence market efficiency and security. Current clearing systems predominantly adopt a single-system architecture, with research focusing primarily on accelerating solution algorithms through techniques such as high-efficiency parallel solvers and staggered decomposition of mixed-integer programming models. Notably absent are systematic studies evaluating the adaptability of primary-backup clearing systems in contingency scenarios—a critical gap given redundant systems’ expanding applications in operational environments. This paper proposes a comprehensive evaluation framework for analyzing dual-system adaptability, demonstrated through an in-depth case… More >

  • Open Access

    ARTICLE

    Ab initio Investigation of Structural Units and Raman Vibrational Characteristics in Ge-Se-Te Glasses

    Xuecai Han, Yilin Tong, Jiaqi Bao, Kan Yu*

    Chalcogenide Letters, Vol.23, No.1, 2026, DOI:10.32604/cl.2026.075604 - 26 January 2026

    Abstract Chalcogenide glasses in the Ge-Se-Te system possess wide infrared transparency and strong optical nonlinearity, yet the microscopic origin of their vibrational behavior remains unclear. Using ab initio calculations, we analyzed Raman-active modes in GeSexTe4−x (x = 0–4) tetrahedra, edge-sharing tetrahedra, and ethane-like Ge2Se2xTe6−2x (x = 0–3) clusters. For GeSexTe4−x (x = 0–4) tetrahedra, the symmetric stretching vibrations exhibit two families: Ge-Se-dominated and Ge-Te-dominated modes, both showing monotonic redshifts as the number of same-type bonds increases. In edge-sharing tetrahedra, the Ge-Ch-Ge-Ch (Ch = Se or Te) four-membered-ring breathing frequency decreases with higher Te content, and a comparable softening is More >

  • Open Access

    ARTICLE

    Machine Learning Models for Predicting Smoking-Related Health Decline and Disease Risk

    Vaskar Chakma1,*, Md Jaheid Hasan Nerab1, Abdur Rouf1, Abu Sayed2, Hossem Md Saim3, Md. Nournabi Khan3

    Journal of Intelligent Medicine and Healthcare, Vol.4, pp. 1-35, 2026, DOI:10.32604/jimh.2026.074347 - 23 January 2026

    Abstract Smoking continues to be a major preventable cause of death worldwide, affecting millions through damage to the heart, metabolism, liver, and kidneys. However, current medical screening methods often miss the early warning signs of smoking-related health problems, leading to late-stage diagnoses when treatment options become limited. This study presents a systematic comparative evaluation of machine learning approaches for smoking-related health risk assessment, emphasizing clinical interpretability and practical deployment over algorithmic innovation. We analyzed health screening data from 55,691 individuals, examining various health indicators including body measurements, blood tests, and demographic information. We tested three advanced… More >

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