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 >

Guebin Choi^*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.073985 - 29 January 2026

Abstract This study proposes a novel forecasting framework that simultaneously captures the strong periodicity and irregular meteorological fluctuations inherent in solar radiation time series. Existing approaches typically define inter-regional correlations using either simple correlation coefficients or distance-based measures when applying spatio-temporal graph neural networks (STGNNs). However, such definitions are prone to generating spurious correlations due to the dominance of periodic structures. To address this limitation, we adopt the Elastic-Band Transform (EBT) to decompose solar radiation into periodic and amplitude-modulated components, which are then modeled independently with separate graph neural networks. The periodic component, characterized by strong More >

Farhad Soleimanian Gharehchopogh^*, Keyvan Fattahi Rishakan

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.073808 - 29 January 2026

Abstract Optimization algorithms are crucial for solving NP-hard problems in engineering and computational sciences. Metaheuristic algorithms, in particular, have proven highly effective in complex optimization scenarios characterized by high dimensionality and intricate variable relationships. The Mountain Gazelle Optimizer (MGO) is notably effective but struggles to balance local search refinement and global space exploration, often leading to premature convergence and entrapment in local optima. This paper presents the Improved MGO (IMGO), which integrates three synergistic enhancements: dynamic chaos mapping using piecewise chaotic sequences to boost exploration diversity; Opposition-Based Learning (OBL) with adaptive, diversity-driven activation to speed up… More >

Wei Chen, Yang Wu^*, Tingting Pei, Jie Lin, Guojing Yuan

Energy Engineering, Vol.123, No.2, 2026, DOI:10.32604/ee.2025.070905 - 27 January 2026

Abstract In view of the insufficient utilization of condition-monitoring information and the improper scheduling often observed in conventional maintenance strategies for photovoltaic (PV) modules, this study proposes a predictive maintenance (PdM) strategy based on Remaining Useful Life (RUL) estimation. First, a RUL prediction model is established using the Transformer architecture, which enables the effective processing of sequential degradation data. By employing the historical degradation data of PV modules, the proposed model provides accurate forecasts of the remaining useful life, thereby supplying essential inputs for maintenance decision-making. Subsequently, the RUL information obtained from the prediction process is… More >

Jun Guo^1,*, Maoyuan Chen¹, Yuyang Li¹, Sibo Feng^2,3, Guangyu Fu³

Energy Engineering, Vol.123, No.2, 2026, DOI:10.32604/ee.2025.068894 - 27 January 2026

Abstract The author proposes a dual layer source grid load storage collaborative planning model based on Benders decomposition to optimize the low-carbon and economic performance of the distribution network. The model plans the configuration of photovoltaic (3.8 MW), wind power (2.5 MW), energy storage (2.2 MWh), and SVC (1.2 Mvar) through interaction between upper and lower layers, and modifies lines 2–3, 8–9, etc. to improve transmission capacity and voltage stability. The author uses normal distribution and Monte Carlo method to model load uncertainty, and combines Weibull distribution to describe wind speed characteristics. Compared to the traditional… More >

Yunpeng Wen^1,*, Bingyang Ke², Junrong Ding³

Chalcogenide Letters, Vol.23, No.1, 2026, DOI:10.32604/cl.2026.076587 - 26 January 2026

Abstract This study reports an active learning (AL)-guided strategy to optimize the sulfurization–selenization processing conditions of Sb₂(S,Se)₃ thin-film photovoltaic absorbers for enhanced power conversion efficiency (PCE). By coupling Gaussian process modeling with iterative experimental feedback, we explored 20 targeted annealing conditions across the full compositional spectrum (x = 0–1) and identified an optimal S/(S + Se) ratio of 0.40 (x = 0.60), which yielded a band gap (Eg) of ~1.34 eV, close to the theoretical Shockley–Queisser optimum. The optimized process employed a controlled two-step 420°C anneal with sequential H₂Se→H₂S exposure, which produced large plate-like grains (300–500 nm)… More >

Haotian Zhang, Jing Li^*, Ming Zhu, Zhiyong Zhao, Hongli Su, Liming Sun

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072381 - 12 January 2026

Abstract In the wireless energy transmission service composition optimization problem, a key challenge is accurately capturing users’ preferences for service criteria under complex influencing factors, and optimally selecting a composition solution under their budget constraints. Existing studies typically evaluate satisfaction solely based on energy transmission capacity, while overlooking critical factors such as price and trustworthiness of the provider, leading to a mismatch between optimization outcomes and user needs. To address this gap, we construct a user satisfaction evaluation model for multi-user and multi-provider scenarios, systematically incorporating service price, transmission capacity, and trustworthiness into the satisfaction assessment… More >

Zixuan Guo¹, Houbin Wang², Sameer Kumar^1,*, Yuanfang Chen³

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072064 - 12 January 2026

Abstract With the rapid development of digital culture, a large number of cultural texts are presented in the form of digital and network. These texts have significant characteristics such as sparsity, real-time and non-standard expression, which bring serious challenges to traditional classification methods. In order to cope with the above problems, this paper proposes a new ASSC (ALBERT, SVD, Self-Attention and Cross-Entropy)-TextRCNN digital cultural text classification model. Based on the framework of TextRCNN, the Albert pre-training language model is introduced to improve the depth and accuracy of semantic embedding. Combined with the dual attention mechanism, the… More >

Longqi Liao¹, Jing Li², Yuhua Li³, Yuemin Wang³, Jinhua Li^1,*, Liyuan Cao^4,*, Chunxiang Li^4,*

Structural Durability & Health Monitoring, Vol.20, No.1, 2026, DOI:10.32604/sdhm.2025.072237 - 08 January 2026

Abstract Quantitative detection of sleeve grouting compactness is a technical challenge in civil engineering testing. This study explores a novel quantitative detection method based on ultrasonic time-frequency dual-domain analysis. It establishes a mapping relationship between sleeve grouting compactness and characteristic parameters. First, this study made samples with gradient defects for two types of grouting sleeves, G18 and G20. These included four cases: 2D, 4D, 6D defects (where D is the diameter of the grouting sleeve), and no-defect. Then, an ultrasonic input/output data acquisition system was established. Three-dimensional sound field distribution data were obtained through an orthogonal… More >

Weichen Wang¹, Shaofeng Wang¹, Wenjing Liu^1,*, Luncai Zhou², Erqing Zhang¹, Ting Gao³, Grigory Petrishin⁴

Structural Durability & Health Monitoring, Vol.20, No.1, 2026, DOI:10.32604/sdhm.2025.071278 - 08 January 2026

Abstract In dry-coupled ultrasonic thickness measurement, thick rubber layers introduce high-amplitude parasitic echoes that obscure defect signals and degrade thickness accuracy. Existing methods struggle to resolve overlap-ping echoes under variable coupling conditions and non-stationary noise. This study proposes a novel dual-criterion framework integrating energy contribution and statistical impulsivity metrics to isolate specimen re-flections from coupling-layer interference. By decomposing A-scan signals into Intrinsic Mode Functions (IMFs), the framework employs energy contribution thresholds (>85%) and kurtosis indices (>3) to autonomously select IMFs containing valid specimen echoes. Hybrid time-frequency thresholding further suppresses interference through amplitude filtering and spectral focusing. More >