In this research a recent developed practical modeling technique is applied for the glucose regulation system identification. By using this technique a set of mathematical models is obtained instead of single one to compensate for the  loss of information caused by the optimization technique in curve fitting algorithms, the diversity of members inside the single set is interpreted in term of restricted range of its parameters, also a diagnosis criteria is developed for detecting any disorder in the glucose regulation system by investigating the influence of variation of the parameters on the response of the system, this technique is applied in this research practically  for 20 cases with association of  National Center for

Neural cryptography deals with the problem of “key exchange” between two neural networks by using the mutual learning concept. The two networks exchange their outputs (in bits) and the key between two communicating parties ar eventually represented in the final learned weights, when the two networks are said to be synchronized. Security of neural synchronization is put at risk if an attacker is capable of synchronizing with any of the two parties during the training process.

The Internet of Things (IoT) technology is every object around us and it is used to connect these objects to the Internet to verify Machine to Machine (M2M) communication. The smart house system is the most important application of IoT technology; it is increase the quality of life and decrease the efforts. There were many problems that faced the existing smart house networking systems, including the high cost of implementation and upgrading, high power consumption, and supported limited features. Therefore, this paper presents the design and implementation of smart house network system (SHNS) using Raspberry Pi and Arduino platforms as network infrastructure with ZigBee technology as wireless communication. SHNS consists of two mai

This work is concerned with the synthesis of two types of composites thin films (Sb2O3:WO3 and (Sb2O3:In2O3) with different concentrations (0.0,0.05,0.1, and 0.15). The prepared thin films are characterized with the use of the X-ray diffraction atomic force microscope AFM and UV-Vis–infrared spectrophotometer. The structural examination shows that both composite's thin films were polycrystalline with nano size with cubic phase as majority and orthorhombic phase as minority phase. The crystal size decreases from 34.6 to 24.7 and 21.7 nm for (Sb2O3:WO3) and (Sb2O3:In2O3) thin films, respectively. The average roughness shows non-regular growth by increasing the concentration of both oxides. The optical energy band gap shows a red shi

The evolution in the field of Artificial Intelligent (AI) with its training algorithms make AI very important in different aspect of the life. The prediction problem of behavior of dynamical control system is one of the most important issue that the AI can be employed to solve it. In this paper, a Convolutional Multi-Spike Neural Network (CMSNN) is proposed as smart system to predict the response of nonlinear dynamical systems. The proposed structure mixed the advantages of Convolutional Neural Network (CNN) with Multi -Spike Neural Network (MSNN) to generate the smart structure. The CMSNN has the capability of training weights based on a proposed training algorithm. The simulation results demonstrated that the proposed

The developing countries can be distinguished by spatial disparities and by this a wide gap between urban and rural settlements were produced as well as the appearance of primate cities. The effect of spatial development as a dynamic and continuous process can be perceived in the state of population distribution inside settlements inter and intra regions as well as the hierarchy of urban settlements according to time series. The research proved that the improvement judgment of the structure of the urban system using Gene factor is not accurate because it cannot be accounted for the internal components of the system which make a contrariety between the whole judgment (country) and partial components (Provinces including Sulaimaniy

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In this study, optical fibers were designed and implemented as a chemical sensor based on surface plasmon resonance (SPR) to estimate the age of the oil used in electrical transformers. The study depends on the refractive indices of the oil. The sensor was created by embedding the center portion of the optical fiber in a resin block, followed by polishing, and tapering to create the optical fiber sensor. The tapering time was 50 min. The multi-mode optical fiber was coated with 60 nm thickness gold metal. The deposition length was 4 cm. The sensor's resonance wavelength was 415 nm. The primary sensor parameters were calculated, including sensitivity (6.25), signal-to-noise ratio (2.38), figure of merit (4.88), and accuracy (3.2)

PAN/PMMA) with different blend ratios (80/20, 75/25,70/30,65/35, and 60/40) wt% undoped and doped with various lithium (Li) salts (LiCO3, Li2SO4H2O, and LiCl) at 20 wt %. Different techniques were employed to investigate the composition and optical properties of the prepared blend samples undoped and doped with Lithium salts like FT-IR and UV-Visible spectroscopies. The optical properties analysis declared that the energy gap increased from (4.0 eV to 4.20 eV) by increasing of PMMA ratio from 20 to 40 wt %. In contrast, the energy gap reduced and shifted toward the low energy side by adding lithium salts; the minimum energy gap value was 2 eV obtained from 65 % PAN and 35 % PMMA doped with Li2CO3. The optical constants were determ

Ground-based active optical sensors (GBAOS) have been successfully used in agriculture to predict crop yield potential (YP) early in the season and to improvise N rates for optimal crop yield. However, the models were found weak or inconsistent due to environmental variation especially rainfall. The objectives of the study were to evaluate if GBAOS could predict YP across multiple locations, soil types, cultivation systems, and rainfall differences. This study was carried from 2011 to 2013 on corn (Zea mays L.) in North Dakota, and in 2017 in potatoes in Maine. Six N rates were used on 50 sites in North Dakota and 12 N rates on two sites, one dryland and one irrigated, in Maine. Two active GBAOS used for this study were GreenSeeker and Holl