Mujahid Ali^*

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2161-2194, 2024, DOI:10.32604/cmc.2024.058888 - 18 November 2024

Abstract Forecasting travel demand requires a grasp of individual decision-making behavior. However, transport mode choice (TMC) is determined by personal and contextual factors that vary from person to person. Numerous characteristics have a substantial impact on travel behavior (TB), which makes it important to take into account while studying transport options. Traditional statistical techniques frequently presume linear correlations, but real-world data rarely follows these presumptions, which may make it harder to grasp the complex interactions. Thorough systematic review was conducted to examine how machine learning (ML) approaches might successfully capture nonlinear correlations that conventional methods may… More >

Yuanzhen Hou¹, YinBo Zhu¹, Heng-an Wu^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.4, pp. 1-2, 2024, DOI:10.32604/icces.2024.011353

Abstract Traditional materials are emerging increasingly severe problems such as environmental pollution, non-renewability, and resource waste. As the most abundant natural biomass in nature, nanocellulose materials are expected to become a new generation of green, biodegradable, high-performance structural materials and contribute to sustainable development. Starting from the intrinsic relationship between hydrogen bonding network and microstructure deformation in nanocellulose, we performs the bottom-up multiscale mechanics methods, combing theoretical modeling, experimental characterization and material preparation, to reveal the physical mechanism and key characteristic parameters of the microstructure-regulated mechanical behaviors of nanocellulose materials, further establishing the cross-scale relationship between… More >

Qianshuo Liu, Jun Zhao^*

Phyton-International Journal of Experimental Botany, Vol.93, No.10, pp. 2663-2681, 2024, DOI:10.32604/phyton.2024.056054 - 30 October 2024

Abstract Traditional machine vision algorithms have difficulty handling the interference of light and shadow changes, broken rows, and weeds in the complex growth circumstances of soybean fields, which leads to erroneous navigation route segmentation. There are additional shortcomings in the feature extractFion capabilities of the conventional U-Net network. Our suggestion is to utilize an improved U-Net-based method to tackle these difficulties. First, we use ResNet’s powerful feature extraction capabilities to replace the original U-Net encoder. To enhance the concentration on characteristics unique to soybeans, we integrate a multi-scale high-performance attention mechanism. Furthermore, to do multi-scale feature… More >

Alan F.N. Boss¹, Manuella G.C. Munhoz¹, Gisele Amaral-Labat², Rodrigo G.A. Lima², Leonardo I. Medeiros^2,3, Nila C.F.L. Medeiros^2,3, Beatriz C.S. Fonseca², Flavia L. Braghiroli^4,*, Guilherme F.B. Lenz e Silva¹

Journal of Renewable Materials, Vol.12, No.10, pp. 1639-1659, 2024, DOI:10.32604/jrm.2024.056004 - 23 October 2024

Abstract Radar Absorbing Materials (RAM) are a class of composites that can attenuate incident electromagnetic waves to avoid radar detection. Most carbon allotropes that have the potential to be used as RAM are either carbon nanotubes (CNTs), graphene, carbon black (CB) and ultimately, sustainable porous carbon (SPC). Here, black wattle bark waste (following tannin extraction) was used as a sustainable source to produce SPC made from biomass waste. It was characterized and used as a filler for a silicone rubber matrix to produce a flexible RAM. The electromagnetic performance of this composite was compared with composites… More >

JPM Editorial Office

Journal of Polymer Materials, Vol.41, No.3, pp. 205-205, 2024, DOI:10.32604/jpm.2024.058921 - 30 September 2024

Abstract This article has no abstract. More >

Fatima-Ezzahra Soussani^1,2,3, Youssef Ait Rahou^1,2,4, Chayma Ikan^1,2, Allal Douira³, Rachid Benkirane³, Essaid Ait Barka⁵, Rachid Lahlali^6,*, Abdelilah Meddich^1,2,7,*

Phyton-International Journal of Experimental Botany, Vol.93, No.9, pp. 2415-2438, 2024, DOI:10.32604/phyton.2024.055050 - 30 September 2024

Abstract This study assesses the effectiveness of using arbuscular mycorrhizal fungi (M) and compost applied alone or in combination (vs. conventional chemical fertilizer application (NPK)) on tomato resistance to vascular wilt caused by Verticillium dahliae. In this study, ten treatments were applied: (1) Control (2) M: Rhizophagus irregularis (3) C: Compost (4) MC: The combination of M and compost (5) NPK: conventional chemical fertilizer (6) V: plants infected with V. dahliae (7) MV: plants infected with a conidial suspension of V. dahliae and M, (8) CV: The combination of compost and V. dahliae, (9) MCV: The combination of M, compost and V. dahliae,… More >

Mingzhao Han¹, Susilawati Kasim^1,*, Zhongming Yang², Xi Deng², Noor Baity Saidi³, Md Kamal Uddin¹, Effyanti Mohd Shuib¹

Phyton-International Journal of Experimental Botany, Vol.93, No.9, pp. 2149-2166, 2024, DOI:10.32604/phyton.2024.054009 - 30 September 2024

Abstract It is imperative to enhance crop yield to meet the demands of a burgeoning global population while simultaneously safeguarding the environment from adverse impacts, which is one of the dominant challenges confronting humanity in this phase of global climate change. To overcome this problem and reduce dependency on chemical fertilizer, scientists now view the implementation of biostimulant strategies as a cost-effective and environmentally friendly approach to achieving sustainable agriculture. Plant extracts are rich in bioactive phytocompounds, which can enhance plant resistance to disease, pest, and abiotic stresses (e.g., drought, salinity, and extreme temperature), and promote… More >

Mohamed Abdel-Mongy¹, Mudassir Iqbal², M. Farag³, Ahmed. M. Yosri^1,*, Fahad Alsharari¹, Saif Eldeen A. S. Yousef⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 525-543, 2024, DOI:10.32604/cmes.2024.052505 - 20 August 2024

Abstract Alkali-activated materials/geopolymer (AAMs), due to their low carbon emission content, have been the focus of recent studies on ecological concrete. In terms of performance, fly ash and slag are preferred materials for precursors for developing a one-part geopolymer. However, determining the optimum content of the input parameters to obtain adequate performance is quite challenging and scarcely reported. Therefore, in this study, machine learning methods such as artificial neural networks (ANN) and gene expression programming (GEP) models were developed using MATLAB and GeneXprotools, respectively, for the prediction of compressive strength under variable input materials and content… More >

Tareq Hamadneh¹, Khalid Kaabneh², Omar Alssayed³, Gulnara Bektemyssova^4,*, Galymzhan Shaikemelev⁴, Dauren Umutkulov⁴, Zoubida Benmamoun⁵, Zeinab Monrazeri⁶, Mohammad Dehghani^6,*

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2005-2030, 2024, DOI:10.32604/cmc.2024.052401 - 15 August 2024

Abstract The efficiency of businesses is often hindered by the challenges encountered in traditional Supply Chain Management (SCM), which is characterized by elevated risks due to inadequate accountability and transparency. To address these challenges and improve operations in green manufacturing, optimization algorithms play a crucial role in supporting decision-making processes. In this study, we propose a solution to the green lot size optimization issue by leveraging bio-inspired algorithms, notably the Stork Optimization Algorithm (SOA). The SOA draws inspiration from the hunting and winter migration strategies employed by storks in nature. The theoretical framework of SOA is… More >

Apip Amrullah¹, Widya Fatriasari², Novia Amalia Sholeha³, Edy Hartulistiyoso⁴, Obie Farobie^4,*

Journal of Renewable Materials, Vol.12, No.6, pp. 1087-1102, 2024, DOI:10.32604/jrm.2024.050201 - 02 August 2024

Abstract The escalating demand for energy coupled with environmental concerns necessitates exploring sustainable alternatives to fossil fuels. The study explores the viability of using large ocean-based seaweeds as a source of third-generation biomass, specifically focusing on their conversion to biofuel via the process of pyrolysis. Sargassum plagiophyllum and Ulva lactuca represent prevalent forms of macroalgae, posing significant discharge challenges for coastal regions globally. However, the exploration of their potential for bio-oil generation via pyrolysis remains limited. This study investigates the pyrolysis process of S. plagiophyllum and U. lactuca for biofuel production, aiming to provide valuable insights into their utilization and… More > Graphic Abstract