In this work, a pollution-sensitive Photonic Crystal Fiber (PCF) based on Surface Plasmon Resonance (SPR) technology is designed and implemented for sensing refractive indices and concentrations of polluted  water . The overall construction of the sensor is achieved by splicing short lengths of PCF (ESM-12) solid core on one side with traditional multimode fiber (MMF) and depositing a gold nanofilm of 50nm thickness on the end of the PCF sensor. The PCF- SPR experiment was carried out with various samples of polluted water including(distilled water, draining water, dirty pond water, chemical water, salty  water and oiled water). The location of the resonant wavelength peaks is seen to move to longer wavelengths (red shift)

ABSTRACT : Bacillus cereus and Pseudomonas aeruginosa is the ability to produce a wide antimicrobial active compounds (Bacillin and S-Pyocin) against pathogenic microorganism. In vitro assay with the antagonists of both crude bacteriocin and partial by precipitation 75% ammonium sulfate showed that the effectively inhibited growth of the following (Candida kefyer and Fusarium spp) and Propionibacterium acnes. The results showed the inhibition zone of reached Bacillin (9-13 mm), while Pyocin (13 - 16mm) in solid medium.

Several stress-strain models were used to predict the strengths of steel fiber reinforced concrete, which are distinctive of the material. However, insufficient research has been done on the influence of hybrid fiber combinations (comprising two or more distinct fibers) on the characteristics of concrete. For this reason, the researchers conducted an experimental program to determine the stress-strain relationship of 30 concrete samples reinforced with two distinct fibers (a hybrid of polyvinyl alcohol and steel fibers), with compressive strengths ranging from 40 to 120 MPa. A total of 80% of the experimental results were used to develop a new empirical stress-strain model, which was accomplished through the application of the parti

The efficiency evaluation of the railway lines performance is done through a set of indicators and criteria, the most important are transport density, the productivity of enrollee, passenger vehicle production, the productivity of freight wagon, and the productivity of locomotives. This study includes an attempt to calculate the most important of these indicators which transport density index from productivity during the four indicators, using artificial neural network technology. Two neural networks software are used in this study, (Simulnet) and (Neuframe), the results of second program has been adopted. Training results and test to the neural network data used in the study, which are obtained from the international in

Community detection is useful for better understanding the structure of complex networks. It aids in the extraction of the required information from such networks and has a vital role in different fields that range from healthcare to regional geography, economics, human interactions, and mobility. The method for detecting the structure of communities involves the partitioning of complex networks into groups of nodes, with extensive connections within community and sparse connections with other communities. In the literature, two main measures, namely the Modularity (Q) and Normalized Mutual Information (NMI) have been used for evaluating the validation and quality of the detected community structures. Although many optimization algo

Community detection is useful for better understanding the structure of complex networks. It aids in the extraction of the required information from such networks and has a vital role in different fields that range from healthcare to regional geography, economics, human interactions, and mobility. The method for detecting the structure of communities involves the partitioning of complex networks into groups of nodes, with extensive connections within community and sparse connections with other communities. In the literature, two main measures, namely the Modularity (Q) and Normalized Mutual Information (NMI) have been used for evaluating the validation and quality of the detected community structures. Althoug

In this study, we investigate the behavior of the estimated spectral density function of stationary time series in the case of missing values, which are generated by the second order Autoregressive (AR (2)) model, when the error term for the AR(2) model has many of continuous distributions. The Classical and Lomb periodograms used to study the behavior of the estimated spectral density function by using  the simulation.

In this work, an estimation of the key rate of measurement-device-independent quantum key distribution (MDI-QKD) protocol in free space was performed. The examined free space links included satellite-earth downlink, uplink and intersatellite link. Various attenuation effects were considered such as diffraction, atmosphere, turbulence and the efficiency of the detection system. Two cases were tested: asymptotic case with infinite number of decoy states and one-decoy state case. The estimated key rate showed the possibility of applying MDI-QKD in earth-satellite and intersatellite links, offering longer single link distance to be covered.

     A key agreement protocol (KAP) is a fundamental block in any cryptosystem since it ensures secure communication between two parties. Furthermore, KAP should include advanced features in limited-resource environments such as IoT, in which this protocol must be lightweight and efficient in consuming resources. Despite the Elliptic Curve Diffie-Hellman (ECDH) algorithm having often been considered efficient in providing an acceptable security degree with less resource consumption, it has suffered from weakness against Man-In-The-Middle Attacks (MITMA). This paper presents two versions of the Variant Elliptic Curve Diffie-Hellman (VECDH) algorithms as a key agreement protocol. The security analysis indicates that the proposed algori

Survival analysis is widely applied in data describing for the life time of item until the occurrence of an event of interest such as death or another event of understudy . The purpose of this paper is to use the dynamic approach in the deep learning neural network method, where in this method a dynamic neural network that suits the nature of discrete survival data and time varying effect. This neural network is based on the Levenberg-Marquardt (L-M) algorithm in training, and the method is called Proposed Dynamic Artificial Neural Network (PDANN). Then a comparison was made with another method that depends entirely on the Bayes methodology is called Maximum A Posterior (MAP) method. This method was carried out using numerical algorithms re