Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (2,554)
  • Open Access

    REVIEW

    Renewable Polymers in Biomedical Applications: From the Bench to the Market

    Rauany Cristina Lopes1, Tamires Nossa2, Wilton Rogério Lustri1, Gabriel Lombardo3,4,5, Maria Inés Errea3,4, Eliane Trovatti1,*

    Journal of Renewable Materials, Vol., , DOI:10.32604/jrm.2024.048957

    Abstract Polymers from renewable resources have been used for a long time in biomedical applications and found an irreplaceable role in some of them. Their uses have been increasing because of their attractive properties, contributing to the improvement of life quality, mainly in drug release systems and in regenerative medicine. Formulations using natural polymer, nano and microscale particles preparation, composites, blends and chemical modification strategies have been used to improve their properties for clinical application. Although many studies have been carried out with these natural polymers, the way to reach the market is long and only very few of them become… More >

  • Open Access

    ARTICLE

    Fabrication of Core-Shell Hydrogel Bead Based on Sodium Alginate and Chitosan for Methylene Blue Adsorption

    Xiaoyu Chen*

    Journal of Renewable Materials, Vol., , DOI:10.32604/jrm.2024.048470

    Abstract A novel core-shell hydrogel bead was fabricated for effective removal of methylene blue dye from aqueous solutions. The core, made of sodium alginate-g-polyacrylamide and attapulgite nanofibers, was cross-linked by Calcium ions (Ca2+). The shell, composed of a chitosan/activated carbon mixture, was then coated onto the core. Fourier transform infrared spectroscopy confirmed the grafting polymerization of acrylamide onto sodium alginate. Scanning electron microscopy images showed the core-shell structure. The core exhibited a high water uptake ratio, facilitating the diffusion of methylene blue into the core. During the diffusion process, the methylene blue was first adsorbed by the shell and then further… More > Graphic Abstract

    Fabrication of Core-Shell Hydrogel Bead Based on Sodium Alginate and Chitosan for Methylene Blue Adsorption

  • Open Access

    ARTICLE

    Bio-Based Rigid Polyurethane Foams for Cryogenic Insulation

    Laima Vevere*, Beatrise Sture, Vladimir Yakushin, Mikelis Kirpluks, Ugis Cabulis

    Journal of Renewable Materials, Vol., , DOI:10.32604/jrm.2024.047350

    Abstract Cryogenic insulation material rigid polyurethane (PU) foams were developed using bio-based and recycled feedstock. Polyols obtained from tall oil fatty acids produced as a side stream of wood biomass pulping and recycled polyethylene terephthalate were used to develop rigid PU foam formulations. The 4th generation physical blowing agents with low global warming potential and low ozone depletion potential were used to develop rigid PU foam cryogenic insulation with excellent mechanical and thermal properties. Obtained rigid PU foams had a thermal conductivity coefficient as low as 0.0171 W/m·K and an apparent density of 37–40 kg/m3. The developed rigid PU foams had… More > Graphic Abstract

    Bio-Based Rigid Polyurethane Foams for Cryogenic Insulation

  • Open Access

    ARTICLE

    Machine Learning Empowered Security and Privacy Architecture for IoT Networks with the Integration of Blockchain

    Sohaib Latif1,*, M. Saad Bin Ilyas1, Azhar Imran2, Hamad Ali Abosaq3, Abdulaziz Alzubaidi4, Vincent Karovič Jr.5

    Intelligent Automation & Soft Computing, Vol., , DOI:10.32604/iasc.2024.047080

    Abstract The Internet of Things (IoT) is growing rapidly and impacting almost every aspect of our lives, from wearables and healthcare to security, traffic management, and fleet management systems. This has generated massive volumes of data and security, and data privacy risks are increasing with the advancement of technology and network connections. Traditional access control solutions are inadequate for establishing access control in IoT systems to provide data protection owing to their vulnerability to single-point OF failure. Additionally, conventional privacy preservation methods have high latency costs and overhead for resource-constrained devices. Previous machine learning approaches were also unable to detect denial-of-service… More >

  • Open Access

    ARTICLE

    Trading in Fast-Changing Markets with Meta-Reinforcement Learning

    Yutong Tian1, Minghan Gao2, Qiang Gao1,*, Xiao-Hong Peng3

    Intelligent Automation & Soft Computing, Vol., , DOI:10.32604/iasc.2024.042762

    Abstract How to find an effective trading policy is still an open question mainly due to the nonlinear and non-stationary dynamics in a financial market. Deep reinforcement learning, which has recently been used to develop trading strategies by automatically extracting complex features from a large amount of data, is struggling to deal with fast-changing markets due to sample inefficiency. This paper applies the meta-reinforcement learning method to tackle the trading challenges faced by conventional reinforcement learning (RL) approaches in non-stationary markets for the first time. In our work, the history trading data is divided into multiple task data and for each… More >

  • Open Access

    ARTICLE

    U-Net Inspired Deep Neural Network-Based Smoke Plume Detection in Satellite Images

    Ananthakrishnan Balasundaram1,2, Ayesha Shaik1,2,*, Japmann Kaur Banga2, Aman Kumar Singh2

    CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2024.048362

    Abstract Industrial activities, through the human-induced release of Green House Gas (GHG) emissions, have been identified as the primary cause of global warming. Accurate and quantitative monitoring of these emissions is essential for a comprehensive understanding of their impact on the Earth’s climate and for effectively enforcing emission regulations at a large scale. This work examines the feasibility of detecting and quantifying industrial smoke plumes using freely accessible geo-satellite imagery. The existing system has so many lagging factors such as limitations in accuracy, robustness, and efficiency and these factors hinder the effectiveness in supporting timely response to industrial fires. In this… More >

  • Open Access

    ARTICLE

    Enhancing Cancer Classification through a Hybrid Bio-Inspired Evolutionary Algorithm for Biomarker Gene Selection

    Hala AlShamlan*, Halah AlMazrua*

    CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2024.048146

    Abstract In this study, our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization (GWO) with Harris Hawks Optimization (HHO) for feature selection. The motivation for utilizing GWO and HHO stems from their bio-inspired nature and their demonstrated success in optimization problems. We aim to leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification. We selected leave-one-out cross-validation (LOOCV) to evaluate the performance of both two widely used classifiers, k-nearest neighbors (KNN) and support vector machine (SVM), on high-dimensional cancer microarray… More >

  • Open Access

    ARTICLE

    Combined CNN-LSTM Deep Learning Algorithms for Recognizing Human Physical Activities in Large and Distributed Manners: A Recommendation System

    Ameni Ellouze1, Nesrine Kadri2, Alaa Alaerjan3,*, Mohamed Ksantini1

    CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2024.048061

    Abstract Recognizing human activity (HAR) from data in a smartphone sensor plays an important role in the field of health to prevent chronic diseases. Daily and weekly physical activities are recorded on the smartphone and tell the user whether he is moving well or not. Typically, smartphones and their associated sensing devices operate in distributed and unstable environments. Therefore, collecting their data and extracting useful information is a significant challenge. In this context, the aim of this paper is twofold: The first is to analyze human behavior based on the recognition of physical activities. Using the results of physical activity detection… More >

  • Open Access

    ARTICLE

    Cervical Cancer Prediction Empowered with Federated Machine Learning

    Muhammad Umar Nasir1, Omar Kassem Khalil2, Karamath Ateeq3, Bassam SaleemAllah Almogadwy4, M. A. Khan5, Khan Muhammad Adnan6,*

    CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2024.047874

    Abstract Cervical cancer is an intrusive cancer that imitates various women around the world. Cervical cancer ranks in the fourth position because of the leading death cause in its premature stages. The cervix which is the lower end of the vagina that connects the uterus and vagina forms a cancerous tumor very slowly. This pre-mature cancerous tumor in the cervix is deadly if it cannot be detected in the early stages. So, in this delineated study, the proposed approach uses federated machine learning with numerous machine learning solvers for the prediction of cervical cancer to train the weights with varying neurons… More >

  • Open Access

    ARTICLE

    An Enhanced Multiview Transformer for Population Density Estimation Using Cellular Mobility Data in Smart City

    Yu Zhou1, Bosong Lin1, Siqi Hu2, Dandan Yu3,*

    CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2024.047836

    Abstract This paper addresses the problem of predicting population density leveraging cellular station data. As wireless communication devices are commonly used, cellular station data has become integral for estimating population figures and studying their movement, thereby implying significant contributions to urban planning. However, existing research grapples with issues pertinent to preprocessing base station data and the modeling of population prediction. To address this, we propose methodologies for preprocessing cellular station data to eliminate any irregular or redundant data. The preprocessing reveals a distinct cyclical characteristic and high-frequency variation in population shift. Further, we devise a multi-view enhancement model grounded on the… More >

Displaying 41-50 on page 5 of 2554. Per Page