Olga S. Tsiftsoglou^1,*, Vladimir Mihailovic², Nikola Sreckovic², Jelena S. Katanic Stankovic³, Kyriakos Michail Dimitriadis¹, Michalis K. Stefanakis⁴, Diamanto Lazari¹

Phyton-International Journal of Experimental Botany, Vol.95, No.1, 2026, DOI:10.32604/phyton.2026.075566 - 30 January 2026

Abstract Achillea species are known for their healing properties since ancient times. There is extensive literature on their pharmacological action due to their bioactive compounds. The present study aimed to investigate the antioxidant and antimicrobial effects of hydroalcoholic extracts from the inflorescences and leaves of the species Achillea crithmifolia Waldst. and Kit., A. grandifolia Friv. and A. millefolium L. The phytochemical profiles of all extracts were evaluated both by NMR spectroscopy and LC-MS analysis, and the results were consistent with the spectrophotometrically determined total phenolic (TP: 125.42–191.98 mg/g) and total flavonoid (TF: 47.34–180.02 mg/g) contents. All the extracts were tested More >

Zakia Akter¹, Sumona Akter Jannat², Sheikh Md. Shibly¹, Afroza Sultana¹, Amdadul Hoque Amran¹, Joairia Hossain Faria¹, Sabina Yeasmin¹, Md. Parvez Anwar^1,*

Phyton-International Journal of Experimental Botany, Vol.95, No.1, 2026, DOI:10.32604/phyton.2026.075524 - 30 January 2026

Abstract Seaweed extract contains plant growth regulators and bio-stimulants that enhance plant growth and development. In Bangladesh, winter rice (Boro rice) in the nursery bed often shows poor seed emergence and weak seedling growth due to low temperature. This problem can be addressed by using seaweed extract as a seed priming agent and bio-stimulant. The objective of this study was to evaluate the effectiveness of seaweed extract (Crop Plus) on seed emergence, seedling growth, and vigor of winter rice in the nursery. Two experiments were conducted at Bangladesh Agricultural University using BRRI dhan89. The laboratory experiment… 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 >

Xiang Luo¹, Yuxuan Peng², Renghong Xie¹, Peng Li³, Yuwen Qian^3,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073700 - 12 January 2026

Abstract Deep learning has made significant progress in the field of oriented object detection for remote sensing images. However, existing methods still face challenges when dealing with difficult tasks such as multi-scale targets, complex backgrounds, and small objects in remote sensing. Maintaining model lightweight to address resource constraints in remote sensing scenarios while improving task completion for remote sensing tasks remains a research hotspot. Therefore, we propose an enhanced multi-scale feature extraction lightweight network EM-YOLO based on the YOLOv8s architecture, specifically optimized for the characteristics of large target scale variations, diverse orientations, and numerous small objects… More >

Marija Macenić, Tomislav Kurevija^*, Tin Herbst

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.067067 - 27 December 2025

Abstract The increased interest in geothermal energy is evident, along with the exploitation of traditional hydrothermal systems, in the growing research and projects developing around the reuse of already-drilled oil, gas, and exploration wells. The Republic of Croatia has around 4000 wells, however, due to a long period since most of these wells were drilled and completed, there is uncertainty about how many are available for retrofitting as deep-borehole heat exchangers. Nevertheless, as hydrocarbon production decreases, it is expected that the number of wells available for the revitalization and exploitation of geothermal energy will increase. The… More >

Meixi Chu¹, Xinyu Jiang^1,*, Yushu Tao²

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-23, 2026, DOI:10.32604/cmc.2025.071533 - 09 December 2025

Abstract Industrial operators need reliable communication in high-noise, safety-critical environments where speech or touch input is often impractical. Existing gesture systems either miss real-time deadlines on resource-constrained hardware or lose accuracy under occlusion, vibration, and lighting changes. We introduce Industrial EdgeSign, a dual-path framework that combines hardware-aware neural architecture search (NAS) with large multimodal model (LMM) guided semantics to deliver robust, low-latency gesture recognition on edge devices. The searched model uses a truncated ResNet50 front end, a dimensional-reduction network that preserves spatiotemporal structure for tubelet-based attention, and localized Transformer layers tuned for on-device inference. To reduce… More >

Baixuan Han¹, Yueping Peng^1,*, Zecong Ye², Hexiang Hao¹, Xuekai Zhang¹, Wei Tang¹, Wenchao Kang¹, Qilong Li¹

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-31, 2026, DOI:10.32604/cmc.2025.071071 - 09 December 2025

Abstract Aiming at the problem of imbalance between detection accuracy and algorithm model lightweight in UAV aerial image target detection algorithm, a lightweight multi-category abnormal behavior detection algorithm based on improved YOLOv11n is designed. By integrating multi-head grouped self-attention mechanism and Partial-Conv, a two-way feature grouping fusion module (DFPF) was designed, which carried out effective channel segmentation and fusion strategies to reduce redundant calculations and memory access. C3K2 module was improved, and then unstructured pruning and feature distillation technology were used. The algorithm model is lightweight, and the feature extraction ability for airborne visual abnormal behavior… More >

Xinwei Zhu, Jianxun Zhang^*, Huan Zeng

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-25, 2026, DOI:10.32604/cmc.2025.068578 - 09 December 2025

Abstract Underwater images often affect the effectiveness of underwater visual tasks due to problems such as light scattering, color distortion, and detail blurring, limiting their application performance. Existing underwater image enhancement methods, although they can improve the image quality to some extent, often lead to problems such as detail loss and edge blurring. To address these problems, we propose FENet, an efficient underwater image enhancement method. FENet first obtains three different scales of images by image downsampling and then transforms them into the frequency domain to extract the low-frequency and high-frequency spectra, respectively. Then, a distance… More >

Jiang Mi¹, Zhijian Gan¹, Pengliu Tan^2,*, Xin Chang², Zhi Wang², Haisheng Xie²

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-22, 2026, DOI:10.32604/cmc.2025.069348 - 10 November 2025

Abstract In response to the challenges in highway pavement distress detection, such as multiple defect categories, difficulties in feature extraction for different damage types, and slow identification speeds, this paper proposes an enhanced pavement crack detection model named Star-YOLO11. This improved algorithm modifies the YOLO11 architecture by substituting the original C3k2 backbone network with a Star-s50 feature extraction network. The enhanced structure adjusts the number of stacked layers in the StarBlock module to optimize detection accuracy and improve model efficiency. To enhance the accuracy of pavement crack detection and improve model efficiency, three key modifications to… More >

Kwok Tai Chui^1,*, Varsha Arya¹, Brij B. Gupta^2,3,4,*, Miguel Torres-Ruiz⁵, Razaz Waheeb Attar⁶

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-23, 2026, DOI:10.32604/cmc.2025.068842 - 10 November 2025

Abstract Parkinson’s disease (PD) is a debilitating neurological disorder affecting over 10 million people worldwide. PD classification models using voice signals as input are common in the literature. It is believed that using deep learning algorithms further enhances performance; nevertheless, it is challenging due to the nature of small-scale and imbalanced PD datasets. This paper proposed a convolutional neural network-based deep support vector machine (CNN-DSVM) to automate the feature extraction process using CNN and extend the conventional SVM to a DSVM for better classification performance in small-scale PD datasets. A customized kernel function reduces the impact… More >