Xia Yan¹, Wei Wang¹, Kai Shen^2,*, Yanqing Feng¹, Junyi Sun¹, Xiaogang Li¹, Wentao Zhu¹, Binbin Shi¹, Guanglong Sheng^2,3

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.070360 - 27 December 2025

Abstract In the development of coalbed methane (CBM) reservoirs using multistage fractured horizontal wells, there often exist areas that are either repeatedly stimulated or completely unstimulated between fracturing stages, leading to suboptimal reservoir performance. Currently, there is no well-established method for accurately evaluating the effectiveness of such stimulation. This study introduces, for the first time, the concept of the Fracture Network Bridging Coefficient (FNBC) as a novel metric to assess stimulation performance. By quantitatively coupling the proportions of unstimulated and overstimulated volumes, the FNBC effectively characterizes the connectivity and efficiency of the fracture network. A background… More >

Jinglu Chen¹, Mengpan Chen², Wenhao Zhang^2,*, Huihui Ren², Daniel Dajun Zeng^1,2

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-25, 2026, DOI:10.32604/cmc.2025.069331 - 09 December 2025

Abstract Temporal knowledge graph completion (TKGC), which merges temporal information into traditional static knowledge graph completion (SKGC), has garnered increasing attention recently. Among numerous emerging approaches, translation-based embedding models constitute a prominent approach in TKGC research. However, existing translation-based methods typically incorporate timestamps into entities or relations, rather than utilizing them independently. This practice fails to fully exploit the rich semantics inherent in temporal information, thereby weakening the expressive capability of models. To address this limitation, we propose embedding timestamps, like entities and relations, in one or more dedicated semantic spaces. After projecting all embeddings into… More >

Jiajun Sun, Shunshun Ji, Chao Zhang^*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-23, 2026, DOI:10.32604/cmc.2025.068668 - 09 December 2025

Abstract Asparagus stem blight is a devastating crop disease, and the early detection of its pathogenic spores is essential for effective disease control and prevention. However, spore detection is still hindered by complex backgrounds, small target sizes, and high annotation costs, which limit its practical application and widespread adoption. To address these issues, a semi-supervised spore detection framework is proposed for use under complex background conditions. Firstly, a difficulty perception scoring function is designed to quantify the detection difficulty of each image region. For regions with higher difficulty scores, a masking strategy is applied, while the… More >

Yu-Xiang Zhao^*, Yi Lee

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 791-810, 2025, DOI:10.32604/cmc.2025.065048 - 09 June 2025

Abstract Bird species classification is not only a challenging topic in artificial intelligence but also a domain closely related to environmental protection and ecological research. Additionally, performing edge computing on low-level devices using small neural networks can be an important research direction. In this paper, we use the EfficientNetV2B0 model for bird species classification, applying transfer learning on a dataset of 525 bird species. We also employ the BiRefNet model to remove backgrounds from images in the training set. The generated background-removed images are mixed with the original training set as a form of data augmentation.… More >

WENZHUO YANG^1,#, HAODONG CHEN^2,#, ZHILAN ZHANG³, ZHIYONG XIA³, YUANYUAN JIN^1,*, ZHAOYONG YANG^1,*

Oncology Research, Vol.33, No.6, pp. 1485-1494, 2025, DOI:10.32604/or.2025.059426 - 29 May 2025

Abstract Background: Human natural killer (NK) cells have attracted widespread attention as a potential adoptive cell therapy (ACT). However, the therapeutic effects of NK cell infusion in patients with solid tumors are limited. There is an urgent need to explore a suitable new treatment plan to overcome weaknesses and support the superior therapeutic activity of NK cells. Methods: In this study, the mechanisms underlying the susceptibility of gastric cancer (GC) cell lines AGS, HGC-27, and NCI-N87 to NK cell-mediated cytotoxicity were explored. Results: Lactic dehydrogenase (LDH) release assays showed that all three GC cell lines were susceptible… More >

MING YANG^1,#, LIANGZHAO CHU^1,#, SHUKAI LIN², HAN PENG¹, NIYA LONG¹, KAYA XU¹, HUA YANG¹, FENG HAN^1,*, JIAN LIU^1,*

Oncology Research, Vol.33, No.5, pp. 1189-1198, 2025, DOI:10.32604/or.2024.055102 - 18 April 2025

Abstract Background: Glioblastoma (GBM) is one of the most malignant types of central nervous system tumors. Oxygen deprivation in the tumor microenvironment is thought to be an important factor in promoting GBM progression. However, the mechanisms of hypoxia-promoted tumor progression remain elusive. Methods: Alternative splicing of diacylglycerol kinase gamma (DGKG)-Δ exon13 was amplified and verified by PCR-Sanger sequencing. The functions of DGKG and DGKG-Δ exon13 were analyzed by Cell counting kit-8 (CCK-8), Transwell, Matrigel-transwell experiments, and in vivo orthotropic GBM animal models. Transcriptome analyses were done to find out the regulated genes. Results: In this study, we found… More > Graphic Abstract

Marwa Rezgui^1,#,*, Wided Ben Ammar¹, Muhammad Nazim^2,3,#, Walid Soufan⁴, Chiraz Chaffei Haouari¹

Phyton-International Journal of Experimental Botany, Vol.94, No.1, pp. 137-155, 2025, DOI:10.32604/phyton.2025.058851 - 24 January 2025

Abstract Nickel (Ni) toxicity significantly impairs plant growth, photosynthesis, and metabolism by inducing oxidative stress. This study evaluates the potential of exogenous Alpha-Ketoglutarate (AKG) in mitigating Ni-induced stress in Peganum harmala L. Seedlings were exposed to 0, 200, 500, and 750 μM NiCl₂, with or without AKG supplementation. Under 750 μM Ni stress, dry weight (DW) decreased by 33.7%, tissue water content (TWC) by 39.9%, and chlorophyll a and total chlorophyll levels were reduced by 17% and 15%, respectively. Ni exposure also significantly increased secondary metabolite production, with leaf anthocyanin content rising by 131%, and superoxide dismutase (SOD)… More >

Xin Leng^#, Jiakai Chen^#, Jianping Huang^*, Lei Zhang, Zongxuan Li

Phyton-International Journal of Experimental Botany, Vol.93, No.11, pp. 2963-2981, 2024, DOI:10.32604/phyton.2024.057166 - 30 November 2024

Abstract Tea, a globally cultivated crop renowned for its unique flavor profile and health-promoting properties, ranks among the most favored functional beverages worldwide. However, diseases severely jeopardize the production and quality of tea leaves, leading to significant economic losses. While early and accurate identification coupled with the removal of infected leaves can mitigate widespread infection, manual leaves removal remains time-consuming and expensive. Utilizing robots for pruning can significantly enhance efficiency and reduce costs. However, the accuracy of object detection directly impacts the overall efficiency of pruning robots. In complex tea plantation environments, complex image backgrounds, the… More >

Zhi Li¹, Yufei Zhao², Yueming Ji², Hanwen Gu², Zaibin Jiao^2,*

Energy Engineering, Vol.121, No.12, pp. 3899-3927, 2024, DOI:10.32604/ee.2024.055899 - 22 November 2024

Abstract By integrating advanced digital technologies such as cloud computing and the Internet of Things in sensor measurement, information communication, and other fields, the digital DC distribution network can efficiently and reliably access Distributed Generator (DG) and Energy Storage Systems (ESS), exhibiting significant advantages in terms of controllability and meeting requirements of Plug-and-Play (PnP) operations. However, during device plug-in and -out processes, improper system parameters may lead to small-signal stability issues. Therefore, before executing PnP operations, conducting stability analysis and adjusting parameters swiftly is crucial. This study introduces a four-stage strategy for parameter optimization to enhance… More >

Bo Cao^1,*, Qinghua Xing², Longyue Li², Huaixi Xing¹, Zhanfu Song¹

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1251-1275, 2024, DOI:10.32604/cmc.2024.052842 - 18 July 2024

Abstract As a core part of battlefield situational awareness, air target intention recognition plays an important role in modern air operations. Aiming at the problems of insufficient feature extraction and misclassification in intention recognition, this paper designs an air target intention recognition method (KGTLIR) based on Knowledge Graph and Deep Learning. Firstly, the intention recognition model based on Deep Learning is constructed to mine the temporal relationship of intention features using dilated causal convolution and the spatial relationship of intention features using a graph attention mechanism. Meanwhile, the accuracy, recall, and F1-score after iteration are introduced More >