Bo Su¹, Mwansa Chambalile¹, Shihao He¹, Wan Sun², Enyuan Zhang¹, Tong Guo³, Jianming Hao⁴, Md. Mahbub Alam^5,*

FDMP-Fluid Dynamics & Materials Processing, Vol.22, No.1, 2026, DOI:10.32604/fdmp.2026.076681 - 06 February 2026

Abstract Predicting the precise impacts of climate change on extreme winds remains challenging, yet strong storms are widely expected to occur more frequently in a warming climate. Wind barriers are commonly used on bridges to reduce aerodynamic loads on trains through blocking effects. This study develops a novel wind barrier based on Tesla valves, which not only blocks incoming flow but also dissipates mechanical energy through fluid collision. To demonstrate this energy-dissipation capability, a Tesla plate is placed in a circular duct to examine its influence on pressure drop. Experimental tests and numerical simulations comparing a… More >

Jing Li¹, Bo Ning^1,*, Bo Li², Xuemei Jin¹, Dezhi Qiu¹, Fenlan Ou¹

FDMP-Fluid Dynamics & Materials Processing, Vol.22, No.1, 2026, DOI:10.32604/fdmp.2026.074335 - 06 February 2026

Abstract As a controllable power generation method requiring no energy storage, Ocean Thermal Energy Conversion (OTEC) technology demonstrates characteristics of abundant reserves, low pollution, and round-the-clock stable operation. The free-standing cold-water pipe (CWP) in the system withstands various complex loads during operation, posing potential failure risks. To reveal the deformation and stress mechanisms of OTEC CWPs, this study first analyzes wave particle velocity and acceleration to determine wave loads at different water depths. Based on the Euler-Bernoulli beam model, a quasi-static load calculation model for OTEC CWPs was established. The governing equations were discretized using the… More >

Dmytro A. Kiriziy¹, Oksana V. Dubrovna¹, Oksana G. Sokolovska-Sergiienko¹, Alina S. Holoboroda¹, Victor V. Rohach^1,2, Oleg O. Stasik^1,*

Phyton-International Journal of Experimental Botany, Vol.95, No.1, 2026, DOI:10.32604/phyton.2026.075371 - 30 January 2026

Abstract Water scarcity severely constrains the genetic potential of wheat yield worldwide. Proline is among the most versatile stress-related metabolites in plants, and targeting genes involved in proline synthesis and degradation represents a promising strategy for developing drought-tolerant wheat genotypes. This study evaluates the performance of the photosynthetic apparatus in transgenic wheat line with RNAi-mediated suppression of proline dehydrogenase (ProDH) and in the original (wild-type) genotype, under both drought and recovery conditions. Drought was induced at the flowering stage by lowering soil moisture to 30% field capacity for 7 days, compared with 70% field capacity in… More >

Lu Chen^1,2,#, Zhijie Ren^2,#, Guangmin Zhao², Xuan He², Legong Li², Sheng Teng^1,*, Yikun He^2,*, Fang Bao^2,*

Phyton-International Journal of Experimental Botany, Vol.95, No.1, 2026, DOI:10.32604/phyton.2025.073817 - 30 January 2026

Abstract ERD4 proteins, members of the early responsive-to-dehydration family, act as plasma membrane ion channels that contribute to ion homeostasis and modulate plant response to abiotic stresses. However, the functions of ERD4 homologs in non-vascular species remain largely unexplored. Here, we characterized an ERD4 family homolog in Physcomitrium patens (Hedw.) Mitt., PpCSC1 (Calcium-permeable Stress-responsive Cation Channel 1), and investigated its role in salt stress response. PpCSC1 localized to the plasma membrane and functioned as a non-selective cation channel permeable to Na⁺, K⁺, Ca²⁺, and Mg²⁺. Under salt treatment, PpCSC1 transcripts were markedly downregulated, whereas overexpression lines exhibited enhanced salt sensitivity.… 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 >

Yun-Im Kang¹, Youn Jung Choi¹, Su Young Lee¹, Young-Ran Lee¹, Ki-Byung Lim^2,3, Yun-Jae Ahn^2,3,*

Phyton-International Journal of Experimental Botany, Vol.95, No.1, 2026, DOI:10.32604/phyton.2026.073138 - 30 January 2026

Abstract Cucumber mosaic virus (CMV) threatens lily production by reducing floral quality and enabling carry-over via infected planting stock. To explore tissue-specific host responses, we analyzed a legacy, single-replicate RNA-seq dataset from two cultivars, ‘Cancun’ and ‘Connecticut King’ (CK), profiling leaf (source) and bulb (sink) tissues at 0 and 28 days post-inoculation (dpi), alongside leaf DAS-ELISA. Principal component analysis indicated that tissue identity dominated the transcriptome (PC1 = 47.7%), with CMV treatment driving within-tissue shifts over time. Exploratory Gene Ontology/KEGG summaries and a focused marker panel revealed a consistent split: in leaves, genes linked to jasmonate/WRKY-associated… 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 >

Moein Tosan^1,*, Vahid Nourani^2,3, Ozgur Kisi^4,5,6, Yongqiang Zhang⁷, Sameh A. Kantoush⁸, Mekonnen Gebremichael⁹, Ruhollah Taghizadeh-Mehrjardi¹⁰, Jinhui Jeanne Huang¹¹

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.074768 - 29 January 2026

Abstract The integration of machine learning (ML) into geohazard assessment has successfully instigated a paradigm shift, leading to the production of models that possess a level of predictive accuracy previously considered unattainable. However, the black-box nature of these systems presents a significant barrier, hindering their operational adoption, regulatory approval, and full scientific validation. This paper provides a systematic review and synthesis of the emerging field of explainable artificial intelligence (XAI) as applied to geohazard science (GeoXAI), a domain that aims to resolve the long-standing trade-off between model performance and interpretability. A rigorous synthesis of 87 foundational… More >

Menwa Alshammeri^1,2,*, Noshina Tariq³, NZ Jhanji^4,5, 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 >

Xianrui Lyu, Xiaodan Ren^*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.074388 - 29 January 2026

Abstract Inverse design of advanced materials represents a pivotal challenge in materials science. Leveraging the latent space of Variational Autoencoders (VAEs) for material optimization has emerged as a significant advancement in the field of material inverse design. However, VAEs are inherently prone to generating blurred images, posing challenges for precise inverse design and microstructure manufacturing. While increasing the dimensionality of the VAE latent space can mitigate reconstruction blurriness to some extent, it simultaneously imposes a substantial burden on target optimization due to an excessively high search space. To address these limitations, this study adopts a Variational… More >