A novel simulation-based analysis of a stochastic HIV model with the time delay using high order spectral collocation technique.

The economic impact of Human Immunodeficiency Virus (HIV) goes beyond individual levels and it has a significant influence on communities and nations worldwide. Studying the transmission patterns in HIV dynamics is crucial for understanding the tracking behavior and informing policymakers about the possible control of this viral infection. Various approaches have been adopted to explore how the virus interacts with the immune system. Models involving differential equations with delays have become prevalent across various scientific and technical domains over the past few decades. In this study, we present a novel mathematical model comprising a system of delay differential equations to describe the dynamics of intramural HIV infection. The model characterizes three distinct cell sub-populations and the HIV virus. By incorporating time delay between the viral entry into target cells and the subsequent production of new virions, our model provides a comprehensive understanding of the infection process. Our study focuses on investigating the stability of two crucial equilibrium states the infection-free and endemic equilibriums. To analyze the infection-free equilibrium, we utilize the LaSalle invariance principle. Further, we prove that if reproduction is less than unity, the disease free equilibrium is locally and globally asymptotically stable. To ensure numerical accuracy and preservation of essential properties from the continuous mathematical model, we use a spectral scheme having a higher-order accuracy. This scheme effectively captures the underlying dynamics and enables efficient numerical simulations.

Brain Tumor Segmentation Using Deep Learning on MRI Images.

Brain tumor (BT) diagnosis is a lengthy process, and great skill and expertise are required from radiologists. As the number of patients has expanded, so has the amount of data to be processed, making previous techniques both costly and ineffective. Many academics have examined a range of reliable and quick techniques for identifying and categorizing BTs. Recently, deep learning (DL) methods have gained popularity for creating computer algorithms that can quickly and reliably diagnose or segment BTs. To identify BTs in medical images, DL permits a pre-trained convolutional neural network (CNN) model. The suggested magnetic resonance imaging (MRI) images of BTs are included in the BT segmentation dataset, which was created as a benchmark for developing and evaluating algorithms for BT segmentation and diagnosis. There are 335 annotated MRI images in the collection. For the purpose of developing and testing BT segmentation and diagnosis algorithms, the brain tumor segmentation (BraTS) dataset was produced. A deep CNN was also utilized in the model-building process for segmenting BTs using the BraTS dataset. To train the model, a categorical cross-entropy loss function and an optimizer, such as Adam, were employed. Finally, the model's output successfully identified and segmented BTs in the dataset, attaining a validation accuracy of 98%.

Score-Level Fusion of 3D Face and 3D Ear for Multimodal Biometric Human Recognition.

A novel multimodal biometric system is proposed using three-dimensional (3D) face and ear for human recognition. The proposed model overcomes the drawbacks of unimodal biometric systems and solves the 2D biometric problems such as occlusion and illumination. In the proposed model, initially, the principal component analysis (PCA) is utilized for 3D face recognition. Thereafter, the iterative closest point (ICP) is utilized for 3D ear recognition. Finally, the 3D face is fused with a 3D ear using score-level fusion. The simulations are performed on the Face Recognition Grand Challenge database and the University of Notre Dame Collection F database for 3D face and 3D ear datasets, respectively. Experimental results reveal that the proposed model achieves an accuracy of 99.25% using the proposed score-level fusion. Comparative analyses show that the proposed method performs better than other state-of-the-art biometric algorithms in terms of accuracy.

Insight into the photocatalytic properties of diatomite@Ni/NiO composite for effective photo-degradation of malachite green dye and photo-reduction of Cr (VI) under visible light.

Diatomite frustules decorated by nano Ni/NiO nanoparticles (Diatomite@Ni/NiO) were synthesized as a novel photocatalyst for effective degradation of malachite green cationic dye (M.G) and photocatalytic-reduction of Cr (VI) ions. The composite was characterized by different analytical techniques and revealed enhancing in the surface area (400 m2/g), 5.8 nm as average pore diameter and showed lower band gap energy (1.71 eV) than NiO as single phase. The photocatalytic activity of the composite in the removal of M.G and reduction of Cr (VI) was evaluated under visible light considering the pH, illumination time, catalyst mass, and the pollutants concentrations. The results revealed complete removal of 25 mg/L M.G can be achieved using 20 mg, 30 mg, 40 mg and 50 mg of the after 150 min, 90 min, 60 min, and 30 min, respectively. The complete degradation of 50 mg/L can be obtained after 240 min, 90 min, and 60 min using 20 mg, 40 mg, and 50 mg of the catalyst, respectively. This also was reported for the photocatlytic-reduction of 25 mg/L of Cr(VI) ions as the complete reduction was estimated after 180 min, 60 min and 30 min using 20 mg, 40 mg, and 50 mg, respectively. Also, 50 mg/L of Cr (VI) can be completely reduced after 240 min, 90 min, and 60 min using 20 mg, 40 mg, and 50 mg as catalyst dosage, respectively. The photocatalytic degradation of M.G controlled mainly by the generated electron-hole pairs and the superoxide species while the photocatalytic-reduction of Cr (VI) controlled mainly by the directly excited electrons of Ni/NiO and partially by the formed superoxide radicals. Hence, the synthetic diatomite@Ni/NiO composite can be considered as potential photocatalyst in the degradation of M.G dye and photoreduction of Cr (VI) ions.

Facile synthesis of bentonite/biopolymer composites as low-cost carriers for 5-fluorouracil drug; equilibrium studies and pharmacokinetic behavior.

Two types of bentonite/biopolymer composites (bentonite/chitosan (BE/CH) and bentonite/Co-Poly 2-hydroxyethyl methacrylate-methyl methacrylate (BE/HEMA-MMA)) were synthesized after modification of bentonite by an organic surfactant (BE/CTAB). The products were characterized as low-cost carriers for the 5-fluorouracil drug of high loading properties and controlled releasing behavior. The experimental loading results revealed the suitability of BE, BE/CTAB, BE/CH and BE/HEMA-MMA to load 114 mg/g, 230 mg/g, 273 mg/g, and 310 mg/g, respectively. The loading behaviors of BE/CTAB, BE/CH, and BE/HEMA-MMA are of excellent fitting with the Langmuir model. The adsorption energies and the thermodynamic studies revealed a physisorption mechanism (coulombic attractive forces) for the drug molecules. The thermodynamic parameters reflected spontaneous loading reactions of endothermic nature. The releasing profile showed significant enhancement with the formation of bentonite/biopolymer composites to extend for 160 h without attending the complete release either in the intestinal fluid (pH 7.4) or the gastric fluid (pH 1.2) with a preference for BE/HEMA-MMA composite. The inspected pharmacokinetics reflected erosion mechanism for the releasing of 5FU from BE/HEMA-MMA and the releasing properties of it from BE/CTAB and BE/CH controlled by a combination of erosion and diffusion mechanisms.