Abstract: Recently, there is increasing interest in using mode-division multipelexing (MDM) technique  to enhace  data rate transmission over multimode fibers.  In this technique, each fiber mode is treated as a separate optical carrier to transfer its own data.  This paper presents a broadband, compact, and low loss three-mode (de)multiplexer designed for C+L band using subwavelength grating (SWG) technology and built-in silicon-on-insulator SOI platform. SWG offers refractive index engineering for wider operating bandwidth and compact devices compared to conventional ones. The designed (de)multiplex deals with three modes (TE0, TE1, and TE2) and has a loss > -1 dB and crosstalk < −15 dB, and its operation c

Nano gamma alumina was prepared by double hydrolysis process using aluminum nitrate nano hydrate and sodium aluminate as an aluminum source, hydroxyle poly acid and CTAB (cetyltrimethylammonium bromide) as templates. Different crystallization temperatures (120, 140, 160, and 180) ⁰C and calcinations temperatures (500, 550, 600, and 650) ⁰C were applied. All the batches were prepared at PH equals to 9. XRD diffraction technique and infrared Fourier transform spectroscopy were used to investigate the phase formation and the optical properties of the nano gamma alumina. N₂ adsorption-desorption (BET) was used to measure the surface area and pore volume of the prepared nano alumina, the particle size and the

The objective of this study was to develop neural network algorithm, (Multilayer Perceptron), based correlations for the prediction overall volumetric mass-transfer coefficient (k_La), in slurry bubble column for gas-liquid-solid systems. The Multilayer Perceptron is a novel technique based on the feature generation approach using back propagation neural network. Measurements of overall volumetric mass transfer coefficient were made with the air - Water, air - Glycerin and air - Alcohol systems as the liquid phase in bubble column of 0.15 m diameter. For operation with gas velocity in the range 0-20 cm/sec, the overall volumetric mass transfer coefficient was found to decrease w

The method of predicting the electricity load of a home using deep learning techniques is called intelligent home load prediction based on deep convolutional neural networks. This method uses convolutional neural networks to analyze data from various sources such as weather, time of day, and other factors to accurately predict the electricity load of a home. The purpose of this method is to help optimize energy usage and reduce energy costs. The article proposes a deep learning-based approach for nonpermanent residential electrical ener-gy load forecasting that employs temporal convolutional networks (TCN) to model historic load collection with timeseries traits and to study notably dynamic patterns of variants amongst attribute par

Background: This study aimed to determine the cephalometric values of tetragon analysis on a sample of Iraqi adults with normal occlusion. Material and methods: Forty digital true lateral cephalometric radiographs belong to 20 males and 20 females having normal dental relation were analyzed using AutoCAD program 2009. Descriptive statistics and sample comparison with Fastlicht norms were obtained. Results: The results showed that maxillary and mandibular incisors were more proclined and the maxillary/mandibular planes angle was lower in Iraqi sample than Caucasian sample. Conclusion: It's recommended to use result from this study when using tetragon analysis for Iraqis to get more accurate result.

This research had been achieved to identify the image of the subsurface structure representing the Tertiary period in the Galabat Field northeast of Iraq using 2D seismic survey measurements. Synthetic seismograms of the Galabat-3 well were generated in order to identify and pick the reflectors in seismic sections. Structural Images were drawn in the time domain and then converted to the depth domain by using average velocities. Structurally, seismic sections illustrate these reflectors are affected by two reverse faults affected on the Jeribe Formation and the layers below with the increase in the density of the reverse faults in the northern division. The structural maps show Galabat field, which consists of longitudinal Asymmetrical narr

In today's digital era, the importance of securing information has reached critical levels. Steganography is one of the methods used for this purpose by hiding sensitive data within other files. This study introduces an approach utilizing a chaotic dynamic system as a random key generator, governing both the selection of hiding locations within an image and the amount of data concealed in each location. The security of the steganography approach is considerably improved by using this random procedure. A 3D dynamic system with nine parameters influencing its behavior was carefully chosen. For each parameter, suitable interval values were determined to guarantee the system's chaotic behavior. Analysis of chaotic performance is given using the