In this work, an optical fiber biomedical sensor for detecting the ratio of the hemoglobin in the blood is presented. A surface plasmon resonance (SPR)-based coreless optical fiber was developed and implemented using single- and multi-mode optical fibers. The sensor is also utilized to evaluate refractive indices and concentrations of hemoglobin in blood samples, with 40 nm thickness of (20 nm Au and 20 nm Ag) to increase the sensitivity. It is found in practice that when the sensitive refractive index increases, the resonant wavelength increases due to the decrease in energy.

Disease diagnosis with computer-aided methods has been extensively studied and applied in diagnosing and monitoring of several chronic diseases. Early detection and risk assessment of breast diseases based on clinical data is helpful for doctors to make early diagnosis and monitor the disease progression. The purpose of this study is to exploit the Convolutional Neural Network (CNN) in discriminating breast MRI scans into pathological and healthy. In this study, a fully automated and efficient deep features extraction algorithm that exploits the spatial information obtained from both T2W-TSE and STIR MRI sequences to discriminate between pathological and healthy breast MRI scans. The breast MRI scans are preprocessed prior to the feature

   The petroleum industry, which is one of the pillars of the national economy, has the potential to generate vast wealth and employment possibilities. The transportation of petroleum products is complicated and changeable because of the hazards caused by the corrosion consequences. Hazardous chemical leaks caused by natural disasters may harm the environment, resulting in significant economic losses. It significantly threatens the aim for sustainable development. When a result, determining the likelihood of leakage and the potential for environmental harm, it becomes a top priority for decision-makers as they develop maintenance plans. This study aims to provide an in-depth understanding of the risks associated with oil and gas pipeli

Photodetector based on Rutile and Anatase TiO₂ nanostructures/n-Si Heterojunction

This paper proposes a new encryption method. It combines two cipher algorithms, i.e., DES and AES, to generate hybrid keys. This combination strengthens the proposed W-method by generating high randomized keys. Two points can represent the reliability of any encryption technique. Firstly, is the key generation; therefore, our approach merges 64 bits of DES with 64 bits of AES to produce 128 bits as a root key for all remaining keys that are 15. This complexity increases the level of the ciphering process. Moreover, it shifts the operation one bit only to the right. Secondly is the nature of the encryption process. It includes two keys and mixes one round of DES with one round of AES to reduce the performance time. The W-method deals with

Finding communities of connected individuals in complex networks is challenging, yet crucial for understanding different real-world societies and their interactions. Recently attention has turned to discover the dynamics of such communities. However, detecting accurate community structures that evolve over time adds additional challenges. Almost all the state-of-the-art algorithms are designed based on seemingly the same principle while treating the problem as a coupled optimization model to simultaneously identify community structures and their evolution over time. Unlike all these studies, the current work aims to individually consider this three measures, i.e. intra-community score, inter-community score, and evolution of community over