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  • Open Access

    ARTICLE

    The Hydraulic Fracturing Optimization for Stacked Tight Gas Reservoirs Using Multilayers and Multiwells Fracturing Strategies

    Yuanyuan Yang1, Xian Shi1,2,*, Cheng Ji3, Yujie Yan3, Na An3, Teng Zhang4

    Energy Engineering, Vol.121, No.12, pp. 3667-3688, 2024, DOI:10.32604/ee.2024.056266 - 22 November 2024

    Abstract Based on a geology-engineering sweet spot evaluation, the high-quality reservoir zones and horizontal well landing points were determined. Subsequently, fracture propagation and production were simulated with a multilayer fracturing scenario. The optimal hydraulic fracturing strategy for the multilayer fracturing network was determined by introducing a vertical asymmetry factor. This strategy aimed to minimize stress shadowing effects in the vertical direction while maximizing the stimulated reservoir volume (SRV). The study found that the small vertical layer spacing of high-quality reservoirs and the presence of stress-masking layers (with a stress difference of approximately 3~8 MPa) indicate that… More > Graphic Abstract

    The Hydraulic Fracturing Optimization for Stacked Tight Gas Reservoirs Using Multilayers and Multiwells Fracturing Strategies

  • Open Access

    ARTICLE

    A Recurrent Neural Network for Multimodal Anomaly Detection by Using Spatio-Temporal Audio-Visual Data

    Sameema Tariq1, Ata-Ur- Rehman2,3, Maria Abubakar2, Waseem Iqbal4, Hatoon S. Alsagri5, Yousef A. Alduraywish5, Haya Abdullah A. Alhakbani5,*

    CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2493-2515, 2024, DOI:10.32604/cmc.2024.055787 - 18 November 2024

    Abstract In video surveillance, anomaly detection requires training machine learning models on spatio-temporal video sequences. However, sometimes the video-only data is not sufficient to accurately detect all the abnormal activities. Therefore, we propose a novel audio-visual spatiotemporal autoencoder specifically designed to detect anomalies for video surveillance by utilizing audio data along with video data. This paper presents a competitive approach to a multi-modal recurrent neural network for anomaly detection that combines separate spatial and temporal autoencoders to leverage both spatial and temporal features in audio-visual data. The proposed model is trained to produce low reconstruction error… More >

  • Open Access

    ARTICLE

    A Dynamic YOLO-Based Sequence-Matching Model for Efficient Coverless Image Steganography

    Jiajun Liu1, Lina Tan1,*, Zhili Zhou2, Weijin Jiang1, Yi Li1, Peng Chen1

    CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3221-3240, 2024, DOI:10.32604/cmc.2024.054542 - 18 November 2024

    Abstract Many existing coverless steganography methods establish a mapping relationship between cover images and hidden data. One issue with these methods is that as the steganographic capacity increases, the number of images stored in the database grows exponentially. This makes it challenging to build and manage a large image database. To improve the image library utilization and anti-attack capability of the steganography system, we propose an efficient coverless scheme based on dynamically matched substrings. We utilize You Only Look Once (YOLO) for selecting optimal objects and create a mapping dictionary between these objects and scrambling factors.… More >

  • Open Access

    PROCEEDINGS

    Study on the Effect of Welding Sequence on Residual Stress in Post Internal-Welding Joint of Bimetal Composite Pipe

    Zhenhua Gao1, Bin Han1,*, Shengyuan Niu1, Liying Li1

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.3, pp. 1-4, 2024, DOI:10.32604/icces.2024.013339

    Abstract With the rapid development of industry and globalization, the demand and strategic importance of oil and natural gas have become increasingly significant, leading to energy extraction in more complex corrosive environments [1, 2]. Bimetallic composite pipes, which offer strength and corrosion resistance, exhibit promising potential. For the welding of bimetallic composite plates, it is optimal to follow the welding sequence of the base layer, transition layer, and inner layer [3, 4]. For the welding of bimetal composite pipes, due to the diameter limit, the inner layer is usually welded first, followed by the transition layer,… More >

  • Open Access

    PROCEEDINGS

    Topology Optimization for Multi-Axis Additive Manufacturing

    Yifan Guo1,3, Jikai Liu2, Yongsheng Ma3,*, Rafiq Ahmad1,*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.2, pp. 1-2, 2024, DOI:10.32604/icces.2024.011177

    Abstract Multi-axis additive manufacturing (AM) is an advanced manufacturing method with advantages over traditional 3-axis additive manufacturing. A formidable challenge in AM is widely acknowledged in utilizing support materials, a process characterized by temporal and material resource consumption. Extensive research endeavors have been dedicated to mitigating or eliminating reliance on support materials, particularly emphasizing pioneering self-supporting design strategies. Empirical investigations reveal that when the overhang angle of a structure surpasses a predefined threshold (typically 45°), support structures may become dispensable for assisted printing. In traditional 3-axis AM systems, achieving support-free printing for structures exhibiting overhang angles… More >

  • Open Access

    ARTICLE

    Adaptive Successive POI Recommendation via Trajectory Sequences Processing and Long Short-Term Preference Learning

    Yali Si1,2, Feng Li1,*, Shan Zhong1,2, Chenghang Huo3, Jing Chen4, Jinglian Liu1,2

    CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 685-706, 2024, DOI:10.32604/cmc.2024.055141 - 15 October 2024

    Abstract Point-of-interest (POI) recommendations in location-based social networks (LBSNs) have developed rapidly by incorporating feature information and deep learning methods. However, most studies have failed to accurately reflect different users’ preferences, in particular, the short-term preferences of inactive users. To better learn user preferences, in this study, we propose a long-short-term-preference-based adaptive successive POI recommendation (LSTP-ASR) method by combining trajectory sequence processing, long short-term preference learning, and spatiotemporal context. First, the check-in trajectory sequences are adaptively divided into recent and historical sequences according to a dynamic time window. Subsequently, an adaptive filling strategy is used to… More >

  • Open Access

    ARTICLE

    Linkage Mapping and QTL Analysis of Isoflavones Composition in Soybean Seeds

    Songnan Yang1, Miao Zhang1, Rongrong Yao1, Liangyu Chen1, Weixuan Cong1, Dan Yao2, Jian Zhang1,3, Jun Zhang1,*, Xueying Li1,*

    Phyton-International Journal of Experimental Botany, Vol.93, No.9, pp. 2209-2225, 2024, DOI:10.32604/phyton.2024.055046 - 30 September 2024

    Abstract The high isoflavones content of soybeans is an important breeding goal due to the demonstrated benefits of isoflavones to human health and their association with plant resistance. In this study, quantitative trait loci (QTL) mapping for soybean isoflavone aglycones, including daidzin, glycerin, and genistin, and total isoflavones content was performed in a population of 178 F2:6 recombinant inbred lines (RILs) which was generated from a cross between varieties Jinong 17 and Jinong 18. A genetic linkage map covering 1248 cM was constructed using the simple sequence repeat (SSR) molecular markers. The results revealed 22 isoflavone-related QTLs,… More >

  • Open Access

    REVIEW

    Wood By-Products as UV Protection: A Consequence Review

    Naglaa Salem El‑Sayed, Mohamed Hasanin, Samir Kamel*

    Journal of Renewable Materials, Vol.12, No.4, pp. 699-720, 2024, DOI:10.32604/jrm.2024.049118 - 12 June 2024

    Abstract In recent decades, the ozone layer has suffered considerable damage, increasing the entry of ultraviolet (UV) light into the atmosphere and reaching the earth’s surface, negatively affecting life. Accordingly, researchers aimed to solve this problem by synthesizing advanced UV-shielding materials. On the other hand, developing an easy and green strategy to prepare functional materials with outstanding properties based on naturally abundant and environmentally friendly raw materials is highly desirable for sustainable development. Because biomass-derived materials are sustainable and biodegradable, they present a promising substitute for petroleum-based polymers. The three main structural constituents of the plant More > Graphic Abstract

    Wood By-Products as UV Protection: A Consequence Review

  • Open Access

    ARTICLE

    Landscape of Sequence Variations in Homologous Copies of FAD2 and FAD3 in Rapeseed (Brassica napus L.) Germplasm with High/Low Linolenic Acid Trait

    Haoxue Wu#, Xiaohan Zhang§,#, Xiaoyu Chen, Kang Li, Aixia Xu, Zhen Huang, Jungang Dong, Chengyu Yu*

    Phyton-International Journal of Experimental Botany, Vol.93, No.3, pp. 627-640, 2024, DOI:10.32604/phyton.2024.050321 - 28 March 2024

    Abstract Genetic manipulation (either restraint or enhancement) of the biosynthesis pathway of α-linolenic acid (ALA) in seed oil is an important goal in Brassica napus breeding. B. napus is a tetraploid plant whose genome often harbors four and six homologous copies, respectively, of the two fatty acid desaturases FAD2 and FAD3, which control the last two steps of ALA biosynthesis during seed oil accumulation. In this study, we compared their promoters, coding sequences, and expression levels in three high-ALA inbred lines 2006L, R8Q10, and YH25005, a low-ALA line A28, a low-ALA/high-oleic-acid accession SW, and the wildtype ZS11. The expression… More >

  • Open Access

    ARTICLE

    TSCND: Temporal Subsequence-Based Convolutional Network with Difference for Time Series Forecasting

    Haoran Huang, Weiting Chen*, Zheming Fan

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3665-3681, 2024, DOI:10.32604/cmc.2024.048008 - 26 March 2024

    Abstract Time series forecasting plays an important role in various fields, such as energy, finance, transport, and weather. Temporal convolutional networks (TCNs) based on dilated causal convolution have been widely used in time series forecasting. However, two problems weaken the performance of TCNs. One is that in dilated casual convolution, causal convolution leads to the receptive fields of outputs being concentrated in the earlier part of the input sequence, whereas the recent input information will be severely lost. The other is that the distribution shift problem in time series has not been adequately solved. To address… More >

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