This research includes depositionof thin film of semiconductor, CdSe by vaccum evaporation on conductor polymers substrate to the poly aniline where, the polymer deposition on the glass substrats by polymerization oxidation tests polymeric films and studied the structural and optical properties through it,s IR and UV-Vis , XRD addition to thin film CdSe, on of the glass substrate and on the substrate of polymer poly-aniline and when XRD tests was observed to improve the properties of synthetic tests as well as the semiconductor Hall effect proved to improve the electrical properties significantly

Molecular interactions between 2-isopropenylnaphthalene-methacrylic acid (IPNMA) block copolymer( as a model for water- soluble polymer) and methanol at several temperatures were studied using fluorescence techniques , Fluorescence spectrum for (IPNMA) exhibits two emission bands at around 342 nm and 387 nm corresponding to the monomer and the excimer bands , respectively .The fluorescence spectra of dilute solution of (IPNMA) in methanol were recorded in temperature range of 8- 45?C . Plot of the excimer to monomer intensity ratio Ie/Im versus temperature was obtained, which shows double lines with positive slopes crossing at 25?C , the increasing of slope value above this temperature is s

This work predicts the effect of thermal load distribution in polymer melt inside a mold and a die during injection and extrusion processes respectively on the structure properties of final product. Transient thermal and structure models of solidification process for polycarbonate polymer melt in a steel mold and die are studied in this research. Thermal solution obtained according to solidify the melt from 300 to 30Cand Biot number of 16 and 112 respectively for the mold and from 300 to 30 Cand Biot number of 16 for die. Thermal conductivity, and shear and Young Modulus of polycarbonate are temperature depending. Bonded contact between the polycarbonate and the steel surfaces is suggested to transfer the thermal load. The temperat

Abstract: The international community now places significant emphasis on achieving zero carbon emissions, requiring both new researchers and experienced policymakers to prioritise this goal. This article examines the effects of carbon taxes, carbon cap and trade, renewable energy (RE) production and consumption, and economic growth (EG) on carbon emission reduction in the United States, Japan, Canada, and Australia. The study collected secondary data from the World Development Indicators (WDI) secondary source spanning the years 1991 to 2022. The study examines the relationship between variables using the cross-sectionally augmented autoregressive distributed lag (CS-ARDL) approach. The findings indicate that carbon taxes, carbon cap and tr

General Background: Deep image matting is a fundamental task in computer vision, enabling precise foreground extraction from complex backgrounds, with applications in augmented reality, computer graphics, and video processing. Specific Background: Despite advancements in deep learning-based methods, preserving fine details such as hair and transparency remains a challenge. Knowledge Gap: Existing approaches struggle with accuracy and efficiency, necessitating novel techniques to enhance matting precision. Aims: This study integrates deep learning with fusion techniques to improve alpha matte estimation, proposing a lightweight U-Net model incorporating color-space fusion and preprocessing. Results: Experiments using the AdobeComposition-1k

The convolutional neural networks (CNN) are among the most utilized neural networks in various applications, including deep learning. In recent years, the continuing extension of CNN into increasingly complicated domains has made its training process more difficult. Thus, researchers adopted optimized hybrid algorithms to address this problem. In this work, a novel chaotic black hole algorithm-based approach was created for the training of CNN to optimize its performance via avoidance of entrapment in the local minima. The logistic chaotic map was used to initialize the population instead of using the uniform distribution. The proposed training algorithm was developed based on a specific benchmark problem for optical character recog

   Regarding to the computer system security, the intrusion detection systems are fundamental components for discriminating attacks at the early stage. They monitor and analyze network traffics, looking for abnormal behaviors or attack signatures to detect intrusions in early time. However, many challenges arise while developing flexible and efficient network intrusion detection system (NIDS) for unforeseen attacks with high detection rate. In this paper, deep neural network (DNN) approach was proposed for anomaly detection NIDS. Dropout is the regularized technique used with DNN model to reduce the overfitting. The experimental results applied on NSL_KDD dataset. SoftMax output layer has been used with cross entropy loss funct

Background/Objectives: The purpose of this study was to classify Alzheimer’s disease (AD) patients from Normal Control (NC) patients using Magnetic Resonance Imaging (MRI). Methods/Statistical analysis: The performance evolution is carried out for 346 MR images from Alzheimer's Neuroimaging Initiative (ADNI) dataset. The classifier Deep Belief Network (DBN) is used for the function of classification. The network is trained using a sample training set, and the weights produced are then used to check the system's recognition capability. Findings: As a result, this paper presented a novel method of automated classification system for AD determination. The suggested method offers good performance of the experiments carried out show that the