The Hopfield network is one of the easiest types, and its architecture is such that each neuron in the network connects to the other, thus called a fully connected neural network. In addition, this type is considered auto-associative memory, because the network returns the pattern immediately upon recognition, this network has many limitations, including memory capacity, discrepancy, orthogonally between patterns, weight symmetry, and local minimum. This paper proposes a new strategy for designing Hopfield based on XOR operation; A new strategy is proposed to solve these limitations by suggesting a new algorithm in the Hopfield network design, this strategy will increase the performance of Hopfield by modifying the architecture of t

Abstract: The issue of rebuilding the strategiccapacity of Iraq after the occupation and the US invasion in 2003 has been oneof the most contentious issues of debate and debate. It raises a number ofpolitical, social, economic, military and security problems. We say politicalprimarily because the climate and political conditions play a large and activerole along with other factors complementary to them. The important questionthat arises here is: How can the ruling political blocs move to the stage ofreconstruction without reservations? In order to answer this question, wedivided the study into three main axes. The first deals with the concept ofpower and its types. The second axis deals with the relationship betweenstrategic capa

In this study, an analytical model depending on experimental results for InPInGaAs
avalanche photodiode at low bias was presented and the characteristics of
gain for this photodiode were determined directly by the impulse response. The
model have considered the most important mechanisms contributing the
photocurrent, they are trapping, photogeneration in the undepleted region and
charge-carriers velocity due to the built-in electrical field. Also, the bandwidth
was determined as a function to the total gain of photodiode and it was mainly
determined by diffusion and trapping processes at low gain regarding to the multilayer
structure considered in this study

Background: Rheumatoid arthritis is a chronic inflammatory autoimmune disease characterized by joint inflammation, involvement of exocrine salivary and lacrimal glands may occur as extra-articular mani¬festations in this disease. This study aimed to provide evidence of altered in function and composition of salivary gland in patients with rheumatoid arthritis by determine salivary flow rate and some biochemical parameters(total protein, amylase, peroxidase) and to investigate the relationship between disease activity and changes in function and composition of salivary gland. Materials and Methods: Fifty five patients with RA (7 males and 48 females) were enrolled in this study with age range (20-69) years. The patients were separated int

In this work, we synthesized thirteen compounds of 1-(2-furoyl)thiourea derivatives 1-13 by conversion of 2-furoyl chloride to 2-furoyl isothiocyanate by reacting it with potassium thiocyanate in dry acetone in a quite short reflux time then, in the same pot, different of (primary and secondary amines) were added individually to achieve thiourea derivatives. The products were characterized spectroscopically using (FT-IR, 1H NMR and 13C NMR) techniques. Some of them were evaluated as antioxidant agents using DPPH radical scavenging method, and all were examined theoretically as enzyme inhibitors against Bacillus pasteurii urease (pdb id: 4ubp) and  by studying  molecular docking using Autodock (4.2.6) software.

Problem: Cancer is regarded as one of the world's deadliest diseases. Machine learning and its new branch (deep learning) algorithms can facilitate the way of dealing with cancer, especially in the field of cancer prevention and detection. Traditional ways of analyzing cancer data have their limits, and cancer data is growing quickly. This makes it possible for deep learning to move forward with its powerful abilities to analyze and process cancer data. Aims: In the current study, a deep-learning medical support system for the prediction of lung cancer is presented. Methods: The study uses three different deep learning models (EfficientNetB3, ResNet50 and ResNet101) with the transfer learning concept. The three models are trained using a