This study investigates the influence of fear, refuge, and migration in a predator–prey model, where the interactions between the species follow an asymmetric function response. In contrast to some other findings, we propose that prey develop an anti-predator response in response to a concentration of predators, which in turn increases the fear factor of the predators. The conditions under which all ecologically meaningful equilibrium points exist are discussed in detail. The local and global dynamics of the model are determined at all equilibrium points. The model admits several interesting results by changing the rate of fear of predators and predator aggregate sensitivity. Numerical simulations have been performed to verify our theoret

The Moisture damage is considered as one of the main challenge for the experts in the field of asphalt pavement design. The aims of the present study is to modify moisture resistance of the asphalt concrete by utilizing ceramic fibers as a type of reinforcement incorporated with hydrated lime. For this purpose, a penetration grade of the asphalt cement (40-50) was utilized as a binder with an aggregate of the maximum nominal size of 12.5mm and mineral filler limestone dust. A series of specimens has been fabricated by utilizing 0.50, 1.0, 1.5, and 2.0 percentages of ceramic fibers. For each of these contents, another subsequent group of specimens with hydrated lime with 0.0, 1.0, 1.5, and 2.0 percentages were moulded. For the additi

Breast cancer is the second most common cancer in women world. Multiple Cytokines appear to have a dominant role in human breast cancer formation. Estimation of the in situ expression of  IL-6 and IL-1β in breast cancer patients. A sixty patients with breast cancer BC were divided into two clinical subgroups, (30) with malignant breast cancer MBC and (30) with benign breast tumor as a control group according to histological examination. In situ hybridization technique used for detection of IL-6 and IL-1β mRNA sequence in two groups.  The results showed that percentages of mRNA expression of IL-6 and IL-1β were in (≥ 11-50%) for malignant breast cancer. This research also investigated that (73.3%) of beni

Lung cancer is one of the most serious and prevalent diseases, causing many deaths each year. Though CT scan images are mostly used in the diagnosis of cancer, the assessment of scans is an error-prone and time-consuming task. Machine learning and AI-based models can identify and classify types of lung cancer quite accurately, which helps in the early-stage detection of lung cancer that can increase the survival rate. In this paper, Convolutional Neural Network is used to classify Adenocarcinoma, squamous cell carcinoma and normal case CT scan images from the Chest CT Scan Images Dataset using different combinations of hidden layers and parameters in CNN models. The proposed model was trained on 1000 CT Scan Images of cancerous and non-c