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  • Open Access

    ARTICLE

    SARS-CoV2 et cancer : quel impact psychologique ?
    Expérience du service d’oncologie médicale du centre hospitalier universitaire Hassan-II de Fès, Maroc

    L. Amaadour, I. Lahrch, O. Siyouri, K. Oualla, Z. Benbrahim, S. Arifi, C. Aarab, S. El Fakir, N. Mellas

    Psycho-Oncologie, Vol.17, No.1, pp. 38-43, 2023, DOI:10.3166/pson-2022-0221

    Abstract Introduction: La Covid-19 constitue une cause de plusieurs affections mentales, notamment chez les patients atteints de cancer qui sont déjà considérés comme une population vulnérable. Ainsi, l’objectif de la présente étude était d’évaluer les troubles psychologiques des patients suivis pour une maladie tumorale maligne, sous traitement médical systémique, ayant eu une infection au SARS-CoV2 ; ainsi que les conséquences que ces troubles psychologiques peuvent avoir sur l’adhésion aux soins oncologiques.
    Méthode: Il s’agit d’une étude transversale sur une période de quatre mois (pic de la pandémie) menée au département d’oncologie médicale du centre hospitalier universitaire Hassan-II de… More >

  • Open Access

    ARTICLE

    Experimental Study for the Cementation Effect of Dust Soil by Using Soybean Urease

    Jianwei Zhang1, Yue Yin1, Lei Shi1, Yi Han2, Mi Zhou3,*, Xihong Zhang4

    Journal of Renewable Materials, Vol.11, No.6, pp. 2893-2911, 2023, DOI:10.32604/jrm.2023.025436

    Abstract Dust is an environmental and health hazard. In this study, a new technology for dust suppressant is introduced using soybean urease with an optimal cementing solution. Calcium carbonate is produced by soybean urease and cementing solution, which bonds the soil particles towards a dust suppressant. A laboratory wind tunnel test is carried out to examine its effectiveness and discover possible optimization solutions. Several factors, including soybean meal concentration, cementing solution concentration, and volume of solution per unit area, are examined to quantify their influences on soil transport mass, evaporation ratio, evaporation rate, surface strength, water… More >

  • Open Access

    ARTICLE

    Research on Adaptive TSSA-HKRVM Model for Regression Prediction of Crane Load Spectrum

    Dong Qing1,*, Qi Song1, Shuangyun Huang2, Gening Xu1

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2345-2370, 2023, DOI:10.32604/cmes.2023.026552

    Abstract For the randomness of crane working load leading to the decrease of load spectrum prediction accuracy with time, an adaptive TSSA-HKRVM model for crane load spectrum regression prediction is proposed. The heterogeneous kernel relevance vector machine model (HKRVM) with comprehensive expression ability is established using the complementary advantages of various kernel functions. The combination strategy consisting of refraction reverse learning, golden sine, and Cauchy mutation + logistic chaotic perturbation is introduced to form a multi-strategy improved sparrow algorithm (TSSA), thus optimizing the relevant parameters of HKRVM. The adaptive updating mechanism of the heterogeneous kernel RVM… More >

  • Open Access

    ARTICLE

    Enhanced Energy Efficient with a Trust Aware in MANET for Real-Time Applications

    M. V. Narayana1, Vadla Pradeep Kumar2, Ashok Kumar Nanda2,*, Hanumantha Rao Jalla3, Subba Reddy Chavva4

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 587-607, 2023, DOI:10.32604/cmc.2023.034773

    Abstract Mobile ad hoc networks (MANETs) are subjected to attack detection for transmitting and creating new messages or existing message modifications. The attacker on another node evaluates the forging activity in the message directly or indirectly. Every node sends short packets in a MANET environment with its identifier, location on the map, and time through beacons. The attackers on the network broadcast the warning message using faked coordinates, providing the appearance of a network collision. Similarly, MANET degrades the channel utilization performance. Performance highly affects network performance through security algorithms. This paper developed a trust management… More >

  • Open Access

    ARTICLE

    Toxic and Antifeedant Effects of Different Pesticidal Plant Extracts against Beet Armyworm (Spodoptera exigua)

    Muhammad Asad1, Rashad Rasool Khan2,*, Ahmed B. Aljuboory3, Muhammad Haroon U. Rashid4, Uttam Kumar5, Inzamam Ul Haq6, Aqsa Hafeez7, Ahmed Noureldeen8, Khadiga Alharbi9,*

    Phyton-International Journal of Experimental Botany, Vol.92, No.4, pp. 1161-1172, 2023, DOI:10.32604/phyton.2023.026513

    Abstract The beet armyworm (BAW), Spodoptera exigua (Lepidoptera: Noctuidae) is a highly destructive pest of vegetables and field crops. Management of beet armyworm primarily relies on synthetic pesticides, which is threatening the beneficial community and environment. Most importantly, the BAW developed resistance to synthetic pesticides with making it difficult to manage. Therefore, alternative and environment-friendly pest management tactics are urgently required. The use of pesticidal plant extracts provides an effective way for a sustainable pest management program. To evaluate the use of pesticidal plant extracts against BAW, we selected six plant species (Lantana camara, Aloe vera, Azadirachta indica, Cymbopogon citratus, Nicotiana tabacum , and… More >

  • Open Access

    ARTICLE

    Short-Term Power Load Forecasting with Hybrid TPA-BiLSTM Prediction Model Based on CSSA

    Jiahao Wen, Zhijian Wang*

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 749-765, 2023, DOI:10.32604/cmes.2023.023865

    Abstract Since the existing prediction methods have encountered difficulties in processing the multiple influencing factors in short-term power load forecasting, we propose a bidirectional long short-term memory (BiLSTM) neural network model based on the temporal pattern attention (TPA) mechanism. Firstly, based on the grey relational analysis, datasets similar to forecast day are obtained. Secondly, the bidirectional LSTM layer models the data of the historical load, temperature, humidity, and date-type and extracts complex relationships between data from the hidden row vectors obtained by the BiLSTM network, so that the influencing factors (with different characteristics) can select relevant… More >

  • Open Access

    ARTICLE

    Shared Cache Based on Content Addressable Memory in a Multi-Core Architecture

    Allam Abumwais*, Mahmoud Obaid

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 4951-4963, 2023, DOI:10.32604/cmc.2023.032822

    Abstract Modern shared-memory multi-core processors typically have shared Level 2 (L2) or Level 3 (L3) caches. Cache bottlenecks and replacement strategies are the main problems of such architectures, where multiple cores try to access the shared cache simultaneously. The main problem in improving memory performance is the shared cache architecture and cache replacement. This paper documents the implementation of a Dual-Port Content Addressable Memory (DPCAM) and a modified Near-Far Access Replacement Algorithm (NFRA), which was previously proposed as a shared L2 cache layer in a multi-core processor. Standard Performance Evaluation Corporation (SPEC) Central Processing Unit (CPU)… More >

  • Open Access

    ARTICLE

    A Novel Approximate Message Passing Detection for Massive MIMO 5G System

    Nidhi Gour1, Rajneesh Pareek1, Karthikeyan Rajagopal2,3, Himanshu Sharma1, Mrim M. Alnfiai4, Mohammed A. AlZain4, Mehedi Masud5, Arun Kumar6,*

    Computer Systems Science and Engineering, Vol.45, No.3, pp. 2827-2835, 2023, DOI:10.32604/csse.2023.033341

    Abstract Massive-Multiple Inputs and Multiple Outputs (M-MIMO) is considered as one of the standard techniques in improving the performance of Fifth Generation (5G) radio. 5G signal detection with low propagation delay and high throughput with minimum computational intricacy are some of the serious concerns in the deployment of 5G. The evaluation of 5G promises a high quality of service (QoS), a high data rate, low latency, and spectral efficiency, ensuring several applications that will improve the services in every sector. The existing detection techniques cannot be utilised in 5G and beyond 5G due to the high More >

  • Open Access

    ARTICLE

    Metabolites Composition of Bacillus subtilis HussainT-AMU Determined by LC-MS and Their Effect on Fusarium Dry Rot of Potato Seed Tuber

    Touseef Hussain1,*, Abrar A. Khan1, Heba I. Mohamed2,*

    Phyton-International Journal of Experimental Botany, Vol.92, No.3, pp. 783-799, 2023, DOI:10.32604/phyton.2022.026045

    Abstract Fusarium dry rot is considered to be the most critical soilborne and postharvest disease that damages potato tubers worldwide when they are stored for a long time. This study was performed to demonstrate the effect of crude extract, culture filtrate, and cell suspension obtained from the bacterium Bacillus subtilis HussainT-AMU, on the net house and field. From oil-contaminated soil, through the serial dilution method, biosurfactant bacteria were isolated on nutrient agar medium. To isolate and screen the prospective biosurfactant strain, various biosurfactant screening methods were used. Standard protocols were carried out for morphological, molecular, and chemical… More >

  • Open Access

    ARTICLE

    Night Vision Object Tracking System Using Correlation Aware LSTM-Based Modified Yolo Algorithm

    R. Anandha Murugan1,*, B. Sathyabama2

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 353-368, 2023, DOI:10.32604/iasc.2023.032355

    Abstract Improved picture quality is critical to the effectiveness of object recognition and tracking. The consistency of those photos is impacted by night-video systems because the contrast between high-profile items and different atmospheric conditions, such as mist, fog, dust etc. The pictures then shift in intensity, colour, polarity and consistency. A general challenge for computer vision analyses lies in the horrid appearance of night images in arbitrary illumination and ambient environments. In recent years, target recognition techniques focused on deep learning and machine learning have become standard algorithms for object detection with the exponential growth of… More >

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