Piotr Musznicki¹, Marek Turzyński¹, Lyu Guanghua², Ghulam E Mustafa Abro^3,*, Viola Gierszewska¹, Arsalan Muhammad Soomar¹, Syed Hadi Hussain Shah²

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.082254 - 13 August 2026

Abstract This paper presents an accurate and efficient methodology for parameter extraction in complex impedance models using Differential Evolution (DE), an evolutionary optimization technique. The proposed approach targets equivalent RLC circuit topologies and aims to match measured impedance characteristics across a wide frequency spectrum. By formulating the extraction process as a global optimization problem, DE enables precise identification of component values, even for high-order models with multiple resonances. The method is implemented in Python using open-source libraries, facilitating reproducibility and integration into broader modeling workflows. Validation is performed on both analytically derived resonant circuits and physically More >

Oncology Research Editorial Office

Oncology Research, Vol.34, No.9, 2026, DOI:10.32604/or.2026.090479 - 13 August 2026

Abstract This article has no abstract. More >

Sugeng Rifqi Mubaroq^1,*, Rolly Maulana Awangga², Tegar Ditya Pragama¹, Sidiq Fathummubin³, Ali Yusuf Abdulhaq¹

Intelligent Automation & Soft Computing, Vol.41, pp. 27-46, 2026, DOI:10.32604/iasc.2026.088039 - 11 August 2026

Abstract Decoding cognitive workload from electroencephalography (EEG) underpins passive brain–computer interfaces and adaptive learning technology, yet practical decoders share two weaknesses: they rely on a single family of features, and their accuracy collapses on recordings from an unfamiliar device, montage, or task. We address both. We first build a multi-specialist stacking decoder that fuses complementary spectral, Riemannian, and spatial views through a meta-learner. Evaluated across two public corpora under the leave-one-subject-out MOABB benchmarking protocol, it outperforms the best single specialist on the binary workload contrasts, and the strongest of these effects survives family-wide false-discovery-rate correction. The… More >

Kui Hu, Peng Zhang^*, Xiang Xiao, Yuan Xu, Yunhuang Zhang, Yuan Yuan, Zhiyuan Feng

Energy Engineering, Vol.123, No.9, 2026, DOI:10.32604/ee.2026.078340 - 06 August 2026

Abstract The rapid development of gas-cooled microreactors (GMRs) for remote and modular power supply necessitates highly efficient and autonomous core power monitoring systems. Traditional monitoring systems, such as those deployed in commercial power plants, rely on dense in-core instrumentation, whereas due to the limitation of space and simplicity in hardware designs, only sparse ex-core detectors are employed in microreactor designs. To address this challenge, this study proposes an advanced online power reconstruction approach based on the higher-order harmonic expansion. A dedicated higher-order harmonic calculation module was developed within a multi-group diffusion framework, capable of executing rapid… More >

Xiaoguang Wang^1,2, Dehua Liu^1,2,*, Zhenping Liu³, Zhen Luo³, Guihua Yang^1,2, Zongfa Li^1,2, Lijuan Huang^1,2,*

FDMP-Fluid Dynamics & Materials Processing, Vol.22, No.7, 2026, DOI:10.32604/fdmp.2026.079864 - 31 July 2026

Abstract Low-permeability conglomerate reservoirs are characterized by complex pore structures and poor sweep efficiency, making the optimization of carbon dioxide-water alternating gas (CO₂-WAG) injection critical for enhanced oil recovery. To evaluate the displacement performance of CO₂-WAG and determine the lower limit of effectively swept pore throats, this study presents long-core flooding experiments conducted on cores from the KS Formation of the B Oilfield in the Junggar Basin under reservoir conditions (20.1 MPa and 58°C). Continuous CO₂ flooding and CO₂-WAG schemes with varying cycle numbers and gas-water ratios (GWRs) were systematically compared. In situ online nuclear magnetic resonance (NMR) monitoring… More >

Nan Zhao^1,2,*, Qianru Zhao¹, Xiangyu Wu¹, Jiawei Hong¹, Luanmei Lu¹

Phyton-International Journal of Experimental Botany, Vol.95, No.7, 2026, DOI:10.32604/phyton.2026.083994 - 30 July 2026

Abstract The growth and landscape application of Bougainvillea, a widely cultivated ornamental plant, are often influenced by light conditions. The effects of shade on two Bougainvillea cultivars, ‘Imperial Delight (ID)’ and ‘San Diego Red (SDR)’, cultivated under full sunlight (CK) and 50% shade (S) were investigated. Under shading, both cultivars presented decreased leaf thickness; increased chlorophyll a, b and total contents; and reduced light compensation points (LCPs) and dark respiration rates (R_ds), reflecting morphological and photosynthetic acclimatization. Decreased superoxide dismutase (SOD) activity and malondialdehyde (MDA) contents indicated attenuated membrane lipid peroxidation. In ‘ID’, shading significantly reduced the leaf,… More >

Sahil Tripathi¹, Manaswi Kulahara², Abdul Khader Jilani Saudagar³, Hatoon S. AlSagri^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083755 - 27 July 2026

Abstract Spatial reasoning, defined as the ability to infer and compose relations among entities—is fundamental to geoinformatics applications such as spatial querying and map understanding. However, existing works such as Bidirectional Encoder Representations from Transformers (BERT)-based spatial Question Answering (QA) models and neuro-symbolic models rely on dataset-specific patterns, leading to shortcut learning, where reliance on superficial lexical cues rather than true relational understanding. Recent Large Language Models (LLMs)-based works, including fine-tuning and Chain-of-Thought (CoT) prompting, partially alleviate shortcut learning but remain limited by non-causal reasoning, where predictions depend on spurious correlations rather than stable relational structure. To address these… More >

Daniel Martín-Pérez¹, Francesc Rodríguez-Díaz¹, David Gutiérrez-Avilés², Alicia Troncoso¹, Francisco Martínez-Álvarez^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.082712 - 27 July 2026

Abstract Hybrid classical-quantum architectures have emerged as a practical response to the data and compute demands of modern deep learning, since a pretrained classical backbone can carry the feature extraction while a compact quantum head provides the trainable component. Quantum transfer learning is the most active instance of this idea. However, existing quantum transfer learning pipelines have been evaluated in isolation, typically on a single software framework and without a structured treatment of noise or statistical significance, which makes it difficult to assess how this paradigm contributes over fair classical baselines. A methodological benchmark for quantum… More >

Oncology Research Editorial Office

Oncology Research, Vol.34, No.8, 2026, DOI:10.32604/or.2026.088697 - 16 July 2026

Abstract This article has no abstract. More >

Bin Lan¹, Jie Tan¹, Qing Wang¹, Siyuan Zeng^2,*

Oncology Research, Vol.34, No.8, 2026, DOI:10.32604/or.2026.081711 - 16 July 2026

Abstract Backgrounds: Cyclin-dependent kinase 7 (CDK7) plays key roles in transcription and cell cycle regulation, and its inhibition has been proposed as a potential therapeutic strategy for hepatocellular carcinoma (HCC). The primary research objective of this study is to identify and validate CDK7-associated prognostic genes in HCC using bioinformatics approaches, construct a reliable prognostic risk model, and explore the functional role of CDK7 in HCC progression through in vitro and in vivo experiments, with a specific focus on its association with RelA/p65 phosphorylation, so as to provide evidence supporting CDK7 as a potential prognostic biomarker and therapeutic target for… More >