Xujing Zhang¹, Hatem Abushammala², Debora Puglia³, Binbao Lu¹, Pengwu Xu¹, Weijun Yang^1,*, Piming Ma¹

Journal of Renewable Materials, Vol.12, No.5, pp. 895-908, 2024, DOI:10.32604/jrm.2024.049810

Abstract Fluorescent nanomaterials have attracted much attention, due to their unique luminescent properties and promising applications in biomedical areas. In this study, lignin based fluorescent nanoparticles (LFNP) with high yield (up to 32.4%) were prepared from lignin nanoparticles (LNP) by one-pot hydrothermal method with ethylenediamine (EDA) and citric acid. Morphology and chemical structure of LFNP were investigated by SEM, FT-IR, and zeta potential, and it was found that the structure of LFNP changed with the increase of citric acid addition. LFNP showed the highest fluorescence intensity under UV excitation at wavelengths of 375–385 nm, with emission More > Graphic Abstract

Loghman Khodakarami^*

Revue Internationale de Géomatique, Vol.33, pp. 221-246, 2024, DOI:10.32604/rig.2024.050908

Abstract Urban areas globally are escalating contributors to carbon dioxide (CO) emissions, challenging sustainable development. This study proposes a novel micro-scale approach utilizing GIS to quantify CO emission spatial distribution, enhancing urban sustainability assessment. Employing a “bottom-up” methodology, emissions were calculated for various sources, revealing Isfahan’s urban area emits 13,855,525 tons of CO annually. Major contributors include stationary and mobile sources such as power plants (50.61%), road and rail transport (17.18%), and residential sectors (21.78%). Spatial distribution mapping showed that 81.68% of CO emissions originate from stationary sources, notably power plants. Furthermore, mobile sources, including road More >

Youshen Jiang¹, Tongqing Zhou¹, Zhilin Wang², Zhiping Cai^1,*, Qiang Ni³

Intelligent Automation & Soft Computing, Vol.39, No.3, pp. 585-597, 2024, DOI:10.32604/iasc.2023.030221

Abstract Due to the increasingly severe challenges brought by various epidemic diseases, people urgently need intelligent outbreak trend prediction. Predicting disease onset is very important to assist decision-making. Most of the existing work fails to make full use of the temporal and spatial characteristics of epidemics, and also relies on multivariate data for prediction. In this paper, we propose a Multi-Scale Location Attention Graph Neural Networks (MSLAGNN) based on a large number of Centers for Disease Control and Prevention (CDC) patient electronic medical records research sequence source data sets. In order to understand the geography and… More >

Yue Wan, Nan Jiang^*, Ziyu Liu

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 3017-3034, 2024, DOI:10.32604/cmes.2024.051101

Abstract The Social Internet of Things (SIoT) integrates the Internet of Things (IoT) and social networks, taking into account the social attributes of objects and diversifying the relationship between humans and objects, which overcomes the limitations of the IoT’s focus on associations between objects. Artificial Intelligence (AI) technology is rapidly evolving. It is critical to build trustworthy and transparent systems, especially with system security issues coming to the surface. This paper emphasizes the social attributes of objects and uses hypergraphs to model the diverse entities and relationships in SIoT, aiming to build an SIoT hypergraph generation… More >

Hongchi Liu¹, Xing Deng^1,*, Haijian Shao^1,2

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2397-2424, 2024, DOI:10.32604/cmes.2024.049737

Abstract The degradation of optical remote sensing images due to atmospheric haze poses a significant obstacle, profoundly impeding their effective utilization across various domains. Dehazing methodologies have emerged as pivotal components of image preprocessing, fostering an improvement in the quality of remote sensing imagery. This enhancement renders remote sensing data more indispensable, thereby enhancing the accuracy of target identification. Conventional defogging techniques based on simplistic atmospheric degradation models have proven inadequate for mitigating non-uniform haze within remotely sensed images. In response to this challenge, a novel UNet Residual Attention Network (URA-Net) is proposed. This paradigmatic approach… More > Graphic Abstract

Na Liu¹, Yan Zhang², Junxiu Wang^3,4,*

International Journal of Mental Health Promotion, Vol.26, No.6, pp. 449-462, 2024, DOI:10.32604/ijmhp.2024.049334

Abstract Background: In the current social environment, scarcity, as a universally present objective state, profoundly impacts individuals’ decision-making and health through the subjective feeling it induces, known as a “scarcity mindset.” Particularly, the feeling of scarcity related to money and sleep time is not only widespread but also directly linked to an individual’s mental health. Purpose: This study aims to delve into the relationship between the feeling of scarcity and mental health, with a specific focus on the relationship between the feeling of money scarcity or sleep time scarcity and mental health, as well as the… More >

Yuting Zhou¹, Xuemei Yang¹, Junping Yin^2,3,4,*, Shiqi Liu¹

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5313-5333, 2024, DOI:10.32604/cmc.2024.052060

Abstract Gliomas have the highest mortality rate of all brain tumors. Correctly classifying the glioma risk period can help doctors make reasonable treatment plans and improve patients’ survival rates. This paper proposes a hierarchical multi-scale attention feature fusion medical image classification network (HMAC-Net), which effectively combines global features and local features. The network framework consists of three parallel layers: The global feature extraction layer, the local feature extraction layer, and the multi-scale feature fusion layer. A linear sparse attention mechanism is designed in the global feature extraction layer to reduce information redundancy. In the local feature… More >

Yehao Long, Jing Zhong^*, Tongdi Zhang, Li Chen, Lijun Zhang^*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3435-3453, 2024, DOI:10.32604/cmc.2024.051629

Abstract Physical Vapor Deposited (PVD) TiAlN coatings are extensively utilized as protective layers for cutting tools, renowned for their excellent comprehensive performance. To optimize quality control of TiAlN coatings for cutting tools, a multi-scale simulation approach is proposed that encompasses the microstructure evolution of coatings considering the entire preparation and service lifecycle of PVD TiAlN coatings. This scheme employs phase-field simulation to capture the essential microstructure of the PVD-prepared TiAlN coatings. Moreover, cutting simulation is used to determine the service temperature experienced during cutting processes at varying rates. Cahn-Hilliard modeling is finally utilized to consume the More >

Tingting Su¹, Jia Wang^1,*, Wei Hu^2,*, Gaoqiang Dong¹, Jeon Gwanggil³

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4433-4448, 2024, DOI:10.32604/cmc.2024.051535

Abstract Along with the progression of Internet of Things (IoT) technology, network terminals are becoming continuously more intelligent. IoT has been widely applied in various scenarios, including urban infrastructure, transportation, industry, personal life, and other socio-economic fields. The introduction of deep learning has brought new security challenges, like an increment in abnormal traffic, which threatens network security. Insufficient feature extraction leads to less accurate classification results. In abnormal traffic detection, the data of network traffic is high-dimensional and complex. This data not only increases the computational burden of model training but also makes information extraction more… More >

Zhenyu Qian¹, Yizhang Jiang¹, Zhou Hong¹, Lijun Huang², Fengda Li³, KhinWee Lai⁶, Kaijian Xia^4,5,6,*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4741-4762, 2024, DOI:10.32604/cmc.2024.050920

Abstract In this paper, we introduce a novel Multi-scale and Auto-tuned Semi-supervised Deep Subspace Clustering (MAS-DSC) algorithm, aimed at addressing the challenges of deep subspace clustering in high-dimensional real-world data, particularly in the field of medical imaging. Traditional deep subspace clustering algorithms, which are mostly unsupervised, are limited in their ability to effectively utilize the inherent prior knowledge in medical images. Our MAS-DSC algorithm incorporates a semi-supervised learning framework that uses a small amount of labeled data to guide the clustering process, thereby enhancing the discriminative power of the feature representations. Additionally, the multi-scale feature extraction… More > Graphic Abstract