MESI protocol for multicore processors based on FPGA

In modern techniques of building processors, manufactures using more than one processor in the integrated circuit (chip) and each processor called a core. The new chips of processors called a multi-core processor. This new design makes the processors to work simultaneously for more than one task or all the cores working in parallel for the same task. All cores are similar in their design, and each core has its own cache memory, while all cores shares the same main memory. So, if one core requests a block of data from main memory to its cache, there should be a protocol to declare the situation of this block in the main memory and other cores. This is called the cache coherency or cache consistency of multi-core. In this paper a special circuit is designed using VHDL coding and implemented using ISE Xilinx software, one protocol was used in this design, the MESI (Modify, Exclusive, Shared and Invalid) protocol. Test results were taken by using test bench, and showed all the states of the protocols are working correctly

New Algorithm For Detection of Spinal Cord Tumor using OpenCV

The spinal cord one of the most sensitive and significant parts of the human body lies protected inside the spine the backbone and contains bundles of nerves Any minor problem in the spinal cord can cause debilitation of internal and external functions of the human body One of the complications in the spinal cord is tumor - abnormal growth of tissue In this project we present a new algorithm based on OpenCV to detect spinal cord tumors from MRI sagittal image without human intervention The new algorithm can detect tumor-like substances adjacent to the spinal cord Tests carried out on spinal cord MRI images 33 cervical spinal images showed approximately 90 91 of accuracy rate in detecting tumor

Metal Complexes of Macrocyclic Schiff-Base Ligand: Preparation, Characterisation, and Biological Activity

A new macrocyclic multidentate Schiff-base ligand Na4L consisting of two submacrocyclic units (10,21-bis-iminomethyl-3,6,14,17-tricyclo[17.3.1.18,12]tetracosa-1(23),2,6,8,10,12(24),13,17,19,21,-decaene-23,24-disodium) and its tetranuclear metal complexes with Mn(II), Co(II), Ni(II), Cu(II), and Zn(II) are reported. Na4L was prepared via a template approach, which is based on the condensation reaction of sodium 2,4,6-triformyl phenolate with ethylenediamine in mole ratios of 2 : 3. The tetranuclear macrocyclic-based complexes were prepared from the reaction of the corresponding metal chloride with the ligand. The mode of bonding and overall geometry of the compounds were determined through physicochemical and spectroscopic methods. These studies revealed tetrahedral geometries about Mn, Co, and Zn atoms. However, square planar geometries have been suggested for NiII and CuII complexes. Biological activity of the ligand and its metal complexes against Gram positive bacterial strain Staphylococcus aureus and Gram negative bacteria Escherichia coli revealed that the metal complexes become more potentially resistive to the microbial activities as compared to the free ligand. However, these metal complexes do not exhibit any effects on the activity of Pseudomonas aeruginosa bacteria. There is therefore no inhibition zone

Generating and Validating DSA Private Keys from Online Face Images for Digital Signatures

Signing digital documents is attracting more attention in recent years, according to the rapidly growing number of digital documents being exchanged online. The digital signature proves the authenticity of the document and the sender’s approval on the contents of the document. However, storing the private keys of users for digital signing imposes threats toward gaining unauthorized access, which can result in producing false signatures. Thus, in this paper, a novel approach is proposed to extract the private component of the key used to produce the digital signature from online face image. Hence, this private component is never stored in any database, so that, false signatures cannot be produced and the sender’s approval cannot be denied. The proposed method uses a convolutional neural network that is trained using a semi-supervised approach, so that, the values used for the training are extracted based on the predictions of the neural network. To avoid the need for training a complex neural network, the proposed neural network makes use of existing pretrained neural networks, that already have the knowledge about the distinctive features in the faces. The use of the MTCNN for face detection and Facenet for face recognition, in addition to the proposed neural network, to achieved the best performance. The performance of the proposed method is evaluated using the Colored FERET Faces Database Version 2 and has achieved robustness rate of 13.48% and uniqueness of 100%

Stress-strain modelling of reinforced concrete membrane sructures

In this study, a nonlinear finite element (FE) model is proposed to investigate the behaviour and failure mechanism of reinforced concrete membrane structures. Proven accurate stress-strain relation is incorporated in the model to describe the stress-strain behaviour of the concrete under compression for uniaxial and biaxial stress system. The nonlinearity behaviour of the materials in the compressive stress field is considered for the concrete in the orthogonal directions. The effect of micro cracking confinement and softening on the stress-strain relationship under biaxial stresses are included by employing the equivalent uniaxial strain concept. Tension stiffening effect by concrete in tension is modelled in the ascending and descending parts. The model allows for the progressive local failure of the reinforced concrete materials. The applicability of the proposed FE model is investigated by demonstrating the nonlinear structural response and failure mechanism of a simple deep beam and validated with published experimental work. Good agreement is achieved between the developed FE model and the experimental test results which gives confidence that the approach is fundamentally correct

Structure and Rheological Properties of Bovine Aortic Heart Valve and Pericardium Tissue: Implications in Bioprosthetic and Tissue-Engineered Heart Valves

Heart valve (HV) diseases are among the leading causes of cardiac failure and deaths. Of the various HV diseases, damaged HV leaflets are among the primary culprits. In many cases, impaired HV restoration is not always possible, and the replacement of valves becomes necessary. Bioprosthetic HVs have been used for the replacement of the diseased valves, which is obtained from the sources of bovine and porcine origin, while tissue-engineered heart valves (TEHV) have emerged as a promising future solution. The bioprosthetic valves are prone to become calcified, and thus they last for only ten to fifteen years. The adequate understanding of the correlations between the biomechanics and rheological properties of native HV tissues can enable us to improve the durability of the bioprosthetic HV as well as help in the development of tissue-engineered heart valves (TEHV). In this study, the structural and rheological properties of native bovine aortic HV and pericardium tissues were investigated. The microstructures of the tissues were investigated using scanning electron microscopy, while the rheological properties were studied using oscillatory shear measurement and creep test. The reported results provide significant insights into the correlations between the microstructure and viscoelastic properties of the bovine aortic HV and pericardium tissues.Scopu

A combined weighting for the feature-based method on topological parameters in semantic taxonomy using social media

The textual analysis has become most important task due to the rapid increase of the number of texts that have been continuously generated in several forms such as posts and chats in social media, emails, articles, and news. The management of these texts requires efficient and effective methods, which can handle the linguistic issues that come from the complexity of natural languages. In recent years, the exploitation of semantic features from the lexical sources has been widely investigated by researchers to deal with the issues of “synonymy and ambiguity” in the tasks involved in the Social Media like document clustering. The main challenges of exploiting the lexical knowledge sources such as 1WordNet 3.1 in these tasks are how to integrate the various types of semantic relations for capturing additional semantic evidence, and how to settle the high dimensionality of current semantic representing approaches. In this paper, the proposed weighting of features for a new semantic feature-based method as which combined four things as which is “Synonymy, Hypernym, non-taxonomy, and Glosses”. Therefore, this research proposes a new knowledge-based semantic representation approach for text mining, which can handle the linguistic issues as well as the high dimensionality issue. Thus, the proposed approach consists of two main components: a feature-based method for incorporating the relations in the lexical sources, and a topic-based reduction method to overcome the high dimensionality issue. The proposed method approach will evaluated using WordNet 3.1 in the text clustering and text classification

A young boy with abdominal pain

At times, acute diffuse abdominal pain can be a diagnostic dilemma, especially when the symptoms appear to be out of proportion to the findings on physical examination. The case of a young boy with abdominal pain is presented

p53 and PCNA expression in benign, atypical and malignant meningiomas

Objective: Alterations: p53 genes are turning out to be the most common genetic alterations in human cancers. Due to long half-life of mutated p53, its detection is possible by immunohistochemistry. Proliferating cell nuclear antigen (PCNA) is expressed by dividing cells, hence has been shown to correlate with prognosis. We have used monoclonal antibodies protein DO-7 (p53) and PC10 (PCNA) to see whether their expression correlates with histological grading in meningethelial tumour.Material and Methods: a Twenty nine meningiomas (20 benign, 7 atypical and 2 malignant) were selected from the records of our laboratory. p53 and PCNA expression was sought by immunohistochemistry using Peroxidase Anti Peroxidase (PAP) technique.Results: Four benign and 2 atypical meningiomas showed weak staining for p53. Both malignant meningiomas showed strong positivity for p53. Six benign meningiomas had less than 5% PCNA positivity, one 10% positivity and three showed 20% positivity. PCNA positivity ranged for 10-80% in atypical meningiomas. In two malignant meningiomas PCNA positivity was 70% and 90%.Conclusion: It is worthwhile to include p53 and PCNA expression along with histologic assessment in predicting outcome of meningiomas. A larger series with complete follow-up is essential in assessing value of these markers which unfortunately remains a dream in our country