Antonios Chrysargyris^1,*, Demetris Antoniou², Timos Boyias², Nikolaos Tzortzakis^1,*

Phyton-International Journal of Experimental Botany, Vol.95, No.1, 2026, DOI:10.32604/phyton.2026.076565 - 30 January 2026

Abstract Cyprus has an extensive record in grape production and winemaking. Grapevine is essential for the economic and environmental sustainability of the agricultural sector, as it is in other Mediterranean regions. Intensive agriculture can overuse and exhaust natural resources, including soil and water. The current study evaluated how conservation strategies, including no tillage and semi-tillage (as a variation of strip tillage), affected grapevine growth and grape quality when compared to conventional tillage application. Two cultivars were used: Chardonnay and Maratheftiko (indigenous). Soil pH decreased, and EC increased after tillage applications, in both vineyards. Tillage lowered soil… 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 >

Wenfeng Zheng¹, Guangyu Xu², Siyu Lu³, Junmin Lyu⁴, Feng Bao^5,*, Lirong Yin^6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.075741 - 29 January 2026

Abstract Graph Neural Networks (GNNs), as a deep learning framework specifically designed for graph-structured data, have achieved deep representation learning of graph data through message passing mechanisms and have become a core technology in the field of graph analysis. However, current reviews on GNN models are mainly focused on smaller domains, and there is a lack of systematic reviews on the classification and applications of GNN models. This review systematically synthesizes the three canonical branches of GNN, Graph Convolutional Network (GCN), Graph Attention Network (GAT), and Graph Sampling Aggregation Network (GraphSAGE), and analyzes their integration pathways More >

Van-Viet Nguyen¹, Huu-Khanh Nguyen², Kim-Son Nguyen¹, Thi Minh-Hue Luong¹, Duc-Quang Vu¹, Trung-Nghia Phung³, The-Vinh Nguyen^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.075442 - 29 January 2026

Abstract It remains difficult to automate the creation and validation of Unified Modeling Language (UML) diagrams due to unstructured requirements, limited automated pipelines, and the lack of reliable evaluation methods. This study introduces a cohesive architecture that amalgamates requirement development, UML synthesis, and multimodal validation. First, LLaMA-3.2-1B-Instruct was utilized to generate user-focused requirements. Then, DeepSeek-R1-Distill-Qwen-32B applies its reasoning skills to transform these requirements into PlantUML code. Using this dual-LLM pipeline, we constructed a synthetic dataset of 11,997 UML diagrams spanning six major diagram families. Rendering analysis showed that 89.5% of the generated diagrams compile correctly, while… More >

Amel Ksibi^*, Hatoon Albadah, Ghadah Aldehim, Manel Ayadi

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.073533 - 29 January 2026

Abstract Sustainable energy systems will entail a change in the carbon intensity projections, which should be carried out in a proper manner to facilitate the smooth running of the grid and reduce greenhouse emissions. The present article outlines the TransCarbonNet, a novel hybrid deep learning framework with self-attention characteristics added to the bidirectional Long Short-Term Memory (Bi-LSTM) network to forecast the carbon intensity of the grid several days. The proposed temporal fusion model not only learns the local temporal interactions but also the long-term patterns of the carbon emission data; hence, it is able to give… More >

Jihoon Moon^*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.071052 - 29 January 2026

Abstract Predicting the behavior of renewable energy systems requires models capable of generating accurate forecasts from limited historical data, a challenge that becomes especially pronounced when commissioning new facilities where operational records are scarce. This review aims to synthesize recent progress in data-efficient deep learning approaches for addressing such “cold-start” forecasting problems. It primarily covers three interrelated domains—solar photovoltaic (PV), wind power, and electrical load forecasting—where data scarcity and operational variability are most critical, while also including representative studies on hydropower and carbon emission prediction to provide a broader systems perspective. To this end, we examined… More >

Seenaa Khudhayer Salman¹, Shaymaa Husham Abdulmalek^2,*, Ali Ahmed Gitan¹, Thamer Khalif Salem³, Raaid Rashad Jassem Al-Doury³

Energy Engineering, Vol.123, No.2, 2026, DOI:10.32604/ee.2025.073940 - 27 January 2026

Abstract Operating Lithium-ion batteries at their temperature limits is a challenging design task due to explosion risk at high temperatures and rapid degradation at low temperatures. Depending on the battery package design, those risks can be solved with passive solutions, which require no active cooling or heating. The current work aims to optimize the pack design and materials of the type-NCR18650B battery based on a wide range of operation temperature. The lower limit was denoted by cold case while the maximum limit was expressed by hot case. A combined analytical-numerical approach was developed to model the… More >

Firas Mahmood Younis^1,*, Omar Mohammad Hamdoon², Ayad Younis Abdulla¹

Energy Engineering, Vol.123, No.2, 2026, DOI:10.32604/ee.2025.073483 - 27 January 2026

Abstract This paper presents an experimental analysis of a solar-assisted powered underfloor heating system, designed primarily to boost energy efficiency and achieve reliable desired steady-state temperature in buildings. We thoroughly tested the system’s thermal and operational features by subjecting it to three distinct scenarios that mimicked diverse solar irradiance and environmental conditions. Our findings reveal a strong correlation between variations in solar input and overall system performance. The Solar Fraction (SF), our key energy efficiency metric, varied significantly across the cases, ranging from 63.1% up to 88.7%. This high reliance on renewables resulted in a substantial… More >

Fadhil M. Oleiwi¹, Jaber O. Dahloos², Amer Resen Kalash³, Hasanain A. Abdul Wahhab³, Miqdam T. Chaichan^1,4,*

Energy Engineering, Vol.123, No.2, 2026, DOI:10.32604/ee.2025.073313 - 27 January 2026

Abstract In the present study, researchers examined a solar off-grid-connected photovoltaic system for a family house in the city of Baghdad. The design was created with the help of the “How to Design PV Program” and the “Renewable Energy Investment Calculator (REICAL)” software (Version 1.1). In Iraq, the national grid provides around 71% of the overall electricity demand, though this drops to nearly 50% during extremely hot and cold months, where the supply alternates between four hours on and four hours off. During the off periods, power is generated by local generators at high costs. To… More >

Cheng Yang^*, Hanjie Qi, Qing Yin

Energy Engineering, Vol.123, No.2, 2026, DOI:10.32604/ee.2025.070957 - 27 January 2026

Abstract In order to enhance the off-peak performance of gas turbine combined cycle (GTCC) units, a novel collaborative power generation system (CPG) was proposed. During off-peak operation periods, the remaining power of the GTCC was used to drive the adiabatic compressed air energy storage (ACAES), while the intake air of the GTCC was heated by the compression heat of the ACAES. Based on a 67.3 MW GTCC, under specific demand load distribution, a CPG system and a benchmark system (BS) were designed, both of which used 9.388% of the GTCC output power to drive the ACAES.… More >