In this work, novel copolymers of poly(adipic anhydride-co-mannitol) were synthesized by melting condensation polymerization of poly(adipic anhydride) with five percentages of mannitol sugar, 1 to 5 Wt.%. These copolymers were purified and then, characterized by FT-IR, which was proved that the cross-linking reaction was caused by nucleophilic attack of mannitol hydroxyl group to acidic anhydride groups of poly(adipic anhydride) backbone and new ester groups were formed and appeared. Also, modified organic-soluble chitosan, N-maleoyl-chitosan, were synthesized by grafting reaction of chitosan with maleic anhydride in DMF as solvent, and it was also purified and characterized by FT-IR. Biodegradation in vitro of the IPNs of poly(adipic anhyd

<p>The demand for internet applications has increased rapidly.  Providing quality of service (QoS) requirements for varied internet application is a challenging task. One important factor that is significantly affected on the QoS service is the transport layer. The transport layer provides end-to-end data transmission across a network. Currently, the most common transport protocols used by internet application are TCP (Transmission Control Protocol) and UDP (User Datagram Protocol). Also, there are recent transport protocols such as DCCP (data congestion control protocol), SCTP (stream congestion transmission protocol), and TFRC (TCP-friendly rate control), which are in the standardization process of Internet Engineering Task

The chlorine concentration variation in Baghdad water networks was studied. The
chlorine data were collected from Mayoralty of Baghdad and Ministry of Environment
(MOE) for the networks for both sides of the city Karkh and Rasafa for (2008-2009). The
study of these data indicates that there are no systematic testing program .Classified GIS
maps showed that the areas far from the treatment plants have almost always low
chlorine concentration .This indicates that the problem of the low chlorine concentration
in the far areas is due to cracks of pipe along the conveyance path ,as expected. The area's
most frequently have low concentration are Al-sadir,Al-Kadhimya, and Al-Amiria . It
was found also that the chlorine c

Face recognition is a crucial biometric technology used in various security and identification applications. Ensuring accuracy and reliability in facial recognition systems requires robust feature extraction and secure processing methods. This study presents an accurate facial recognition model using a feature extraction approach within a cloud environment. First, the facial images undergo preprocessing, including grayscale conversion, histogram equalization, Viola-Jones face detection, and resizing. Then, features are extracted using a hybrid approach that combines Linear Discriminant Analysis (LDA) and Gray-Level Co-occurrence Matrix (GLCM). The extracted features are encrypted using the Data Encryption Standard (DES) for security

This research presents a method for calculating stress ratio to predict fracture pressure gradient. It also, describes a correlation and list ideas about this correlation. Using the data collected from four wells, which are the deepest in southern Iraqi oil fields (3000 to 6000) m and belonged to four oil fields. These wells are passing through the following formations: Y, Su, G, N, Sa, Al, M, Ad, and B. A correlation method was applied to calculate fracture pressure gradient immediately in terms of both overburden and pore pressure gradient with an accurate results. Based on the results of our previous research , the data were used to calculate and plot the effective stresses. Many equations relating horizontal effective stress and vertica

Image compression plays an important role in reducing the size and storage of data while increasing the speed of its transmission through the Internet significantly. Image compression is an important research topic for several decades and recently, with the great successes achieved by deep learning in many areas of image processing, especially image compression, and its use is increasing Gradually in the field of image compression. The deep learning neural network has also achieved great success in the field of processing and compressing various images of different sizes. In this paper, we present a structure for image compression based on the use of a Convolutional AutoEncoder (CAE) for deep learning, inspired by the diversity of human eye

The complexity and variety of language included in policy and academic documents make the automatic classification of research papers based on the United Nations Sustainable Development Goals (SDGs) somewhat difficult. Using both pre-trained and contextual word embeddings to increase semantic understanding, this study presents a complete deep learning pipeline combining Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Network (CNN) architectures which aims primarily to improve the comprehensibility and accuracy of SDG text classification, thereby enabling more effective policy monitoring and research evaluation. Successful document representation via Global Vector (GloVe), Bidirectional Encoder Representations from Tra