Shijie Lin¹, Zimo Wang¹, Hongbo Zhu², Conghui Wang³, Hongfeng Wang², Dawei Zhang¹, Tianbing Gou¹, Guangdao Bao¹, Ye Luo¹, Huaijiang He¹, Zhonghui Zhang^1,*

Phyton-International Journal of Experimental Botany, Vol., , DOI:10.32604/phyton.2024.046989

Abstract Tilia amurensis is an economically valuable broadleaf tree species in Northeast China. The production of high-quality T. amurensis varieties at commercial scales has been greatly limited by the low germination rates. There is thus a pressing need to develop an organogenesis protocol for in vitro propagation of T. amurensis to alleviate a shortage of high-quality T. amurensis seedlings. Here, we established a rapid in vitro propagation system for T. amurensis from mature zygotic embryos and analyzed the effects of plant growth regulators and culture media in different stages. We found that Woody plant medium (WPM) was the optimal primary culture… More >

Zhifen Shi^1,3, Fumei Pan^1,3, Xiaotian Kong², Jiaqi Lang³, Mingyan Ye³, Qian Wu⁴, Guangzhi Wang^1,*, Liang Han^5,*, Nong Zhou^3,*

Phyton-International Journal of Experimental Botany, Vol., , DOI:10.32604/phyton.2024.046452

Abstract Fritillaria taipaiensis P. Y. Li is a widely used medicinal herb in treating pulmonary diseases. In recent years, its wild resources have become scarce, and the demand for efficient artificial cultivation has significantly increased. This article is the first to apply phosphate solubilizing bacteria isolated from the rhizosphere soil of F. taipaiensis P. Y. Li to the cultivation process of F. taipaiensis P. Y. Li. The aim is to identify suitable reference strains for the artificial cultivation and industrial development of F. taipaiensis P. Y. Li by examining the effects of various phosphate solubilizing bacteria and their combinations on photosynthesis,… More >

Dongmei Chen¹, Peipei Cao¹, Lijie Yan¹, Huidong Chen¹, Jia Lin¹, Xin Li², Lin Yuan³, Kaihua Wu^1,*

Phyton-International Journal of Experimental Botany, Vol., , DOI:10.32604/phyton.2024.046331

Abstract Tea leaf picking is a crucial stage in tea production that directly influences the quality and value of the tea. Traditional tea-picking machines may compromise the quality of the tea leaves. High-quality teas are often handpicked and need more delicate operations in intelligent picking machines. Compared with traditional image processing techniques, deep learning models have stronger feature extraction capabilities, and better generalization and are more suitable for practical tea shoot harvesting. However, current research mostly focuses on shoot detection and cannot directly accomplish end-to-end shoot segmentation tasks. We propose a tea shoot instance segmentation model based on multi-scale mixed attention… More >

YAN ZHANG, HEYANG ZHANG, JING WANG, XIN WEI, YI QU, FENG XU, LIJUN ZHANG^*

Oncology Research, Vol., , DOI:10.32604/or.2023.043922

Abstract Background: Bortezomib results in peripheral neuropathy (PN) in approximately 50% of patients, during multiple myeloma (MM) treatment, a complication known as Bortezomib-induced peripheral neuropathy (BIPN). The drug response varies among individuals. Genetic factor may play an important role in BIPN. Methods: A next-generation sequencing (NGS) panel containing 1659 targets from 233 genes was used to identify risk variants for developing BIPN in 204 MM patients who received bortezomib therapy. mRNA expression of MTHFR and ALDH1A1 in 62 peripheral blood samples was detected by real-time quantitative PCR (RT-qPCR). Serum homocysteine (Hcy) levels were detected in 40 samples by chemiluminescent microparticle immunoassay… More > Graphic Abstract

Lifeng Li¹, Zaimin Yang¹, Xiongping Yang¹, Jiaming Li², Qianyufan Zhou^3,*, Ping Yang³

Energy Engineering, Vol., , DOI:10.32604/ee.2023.046447

Abstract As the global demand for renewable energy grows, solar energy is gaining attention as a clean, sustainable energy source. Accurate assessment of solar energy resources is crucial for the siting and design of photovoltaic power plants. This study proposes an integrated deep learning-based photovoltaic resource assessment method. Ensemble learning and deep learning methods are fused for photovoltaic resource assessment for the first time. The proposed method combines the random forest, gated recurrent unit, and long short-term memory to effectively improve the accuracy and reliability of photovoltaic resource assessment. The proposed method has strong adaptability and high accuracy even in the… More >

Aimin Gao¹, Xiaobo Cui^2,*, Guoqiang Yu¹, Jianjun Shu¹, Tianhai Zhang¹

Energy Engineering, Vol., , DOI:10.32604/ee.2023.045805

Abstract Gas turbines play core roles in clean energy supply and the construction of comprehensive energy systems. The control performance of primary frequency modulation of gas turbines has a great impact on the frequency control of the power grid. However, there are some control difficulties in the primary frequency modulation control of gas turbines, such as the coupling effect of the fuel control loop and speed control loop, slow tracking speed, and so on. To relieve the abovementioned difficulties, a control strategy based on the desired dynamic equation proportional integral (DDE-PI) is proposed in this paper. Based on the parameter stability… More >

Xinyu Zhang¹, Wenjie Xia², Yang Wang^3,4, Liang Wang^1,*, Xiaofeng Liu¹

CMES-Computer Modeling in Engineering & Sciences, Vol., , DOI:10.32604/cmes.2023.046922

Abstract Graphene aerogel (GA), as a novel solid material, has shown great potential in engineering applications due to its unique mechanical properties. In this study, the mechanical performance of GA under high-velocity projectile impacts is thoroughly investigated using full-atomic molecular dynamics (MD) simulations. The study results show that the porous structure and density are key factors determining the mechanical response of GA under impact loading. Specifically, the impact-induced penetration of the projectile leads to the collapse of the pore structure, causing stretching and subsequent rupture of covalent bonds in graphene sheets. Moreover, the effects of temperature on the mechanical performance of… More >

Yifan Huang¹, Zikang Zhou^1,2, Mingyu Li¹, Xuedong Luo^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol., , DOI:10.32604/cmes.2024.045947

Abstract Accurately estimating blasting vibration during rock blasting is the foundation of blasting vibration management. In this study, Tuna Swarm Optimization (TSO), Whale Optimization Algorithm (WOA), and Cuckoo Search (CS) were used to optimize two hyperparameters in support vector regression (SVR). Based on these methods, three hybrid models to predict peak particle velocity (PPV) for bench blasting were developed. Eighty-eight samples were collected to establish the PPV database, eight initial blasting parameters were chosen as input parameters for the prediction model, and the PPV was the output parameter. As predictive performance evaluation indicators, the coefficient of determination (R²), root mean square… More >

Pengchao Li^1,2,3,*, Fang Xu^1,2,3,4, Jintao Wang^1,2, Haibing Guo⁴, Mingmin Liu⁴, Zhenjun Du⁴

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2023.047057

Abstract We propose a novel image segmentation algorithm to tackle the challenge of limited recognition and segmentation performance in identifying welding seam images during robotic intelligent operations. Initially, to enhance the capability of deep neural networks in extracting geometric attributes from depth images, we developed a novel deep geometric convolution operator (DGConv). DGConv is utilized to construct a deep local geometric feature extraction module, facilitating a more comprehensive exploration of the intrinsic geometric information within depth images. Secondly, we integrate the newly proposed deep geometric feature module with the Fully Convolutional Network (FCN8) to establish a high-performance deep neural network algorithm… More >

Yuchun Li^1,4, Mengxing Huang^1,*, Yu Zhang², Zhiming Bai³

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2023.046883

Abstract The precise and automatic segmentation of prostate magnetic resonance imaging (MRI) images is vital for assisting doctors in diagnosing prostate diseases. In recent years, many advanced methods have been applied to prostate segmentation, but due to the variability caused by prostate diseases, automatic segmentation of the prostate presents significant challenges. In this paper, we propose an attention-guided multi-scale feature fusion network (AGMSF-Net) to segment prostate MRI images. We propose an attention mechanism for extracting multi-scale features, and introduce a 3D transformer module to enhance global feature representation by adding it during the transition phase from encoder to decoder. In the… More >