A monoclonal antibody-based enzyme immunoassay for detecting parasite antigenaemia and antigenuria in Schistosomiasis mansoni

Both specific polycolonal antibody and monoclonal antibody against the microsomal antigen of adult S. mansoni were used to detect antigenaemia and antigenuria by antigen-capture sandwich ELISA in the sera and urine of patients infected with S. mansoni and other parasites. Antigenaemia was detected in 22 sera out of 100 of patients infected with S. mansoni but no antigenuria was detected. None of the sera of S. mansoni free patients were positive for microsomal antigen. More standardization of the technique and more refining of the reagents used is required to improve the sensitivity of the test.     

High-pressure steam curing of high-slag cement. II. Strength of cement-sand mixes

The strength of dense high-slag cement-sand mixes autoclaved at 180 lbf/in², and of certain aerated mixes autoclaved at pressures ranging from 120 to 210 lbf/in², has been determined. It was found that maximum strengths were developed in an autoclaving period of 5 h at 180 lbf/in², and that longer periods, as well as higher autoclaving pressures, yielded lower strengths. The maximum strength values obtained for compacted 1: 3 (w/w) mixes were 425 kg/cm² in the absence of fine quartz, and 510 kg/cm² in the presence of suitable addition of fine quartz. The corresponding values for Portland cement mixes were, respectively, 330 and 460 kg/cm² for a heating period of 5 h, and 360 and 505 kg/cm² for a period of 6 h.     

Studies on the anodic polarization of composite amalgams. The behaviour of manganese—Zinc amalgams in alkaline solutions

The anodic oxidation of manganese-zinc amalgams of different concentrations was studied in 0·1, 1·0, 2·0 and 4·0N NaOH solutions at 25°C. The polarization curves obtained under constant currents exhibited several steps corresponding to the formation of Zn(OH)₂, Mn(OH)₂, MnO₃ and oxidation of mercury before O₂ evolution took place. The effect of presence of zinc on the oxidation step of manganese is explained.     

Bifurcation analysis of a nonlinear coupled pitch–roll ship

The response of a two-degree-of-freedom system with quadratic coupling under a modulated amplitude sinusoidal excitation is studied and solved. The method of multiple scale perturbation technique is applied to obtain the bifurcation response equation near the combination resonance case in the presence of internal resonance of this system. The main attention is focused on the stability of the periodic solution and dynamical properties of local bifurcations of this system. The numerical solution and the effects of some parameters on the vibrating system are investigated and reported in this paper.     

Rheology and thermal conductivity of calcium grease containing multi-walled carbon nanotube

Recently, nanofluids attract considerable interest for enhanced rheological behavior and thermal performance. The aim of this research is to study the influence of additives Multi-Walled Carbon Nanotubes (MWCNTs) on the rheological behavior and its structure, thermal conductivity, and the influence of shear thinning rate on oil separation at different temperatures for calcium grease. Various concentrations of MWCNTs (0.5, 1, 2, 3, and 4%) have been added to the grease to obtain the best percentages that improve the properties of nanofluid. The microstructure of MWCNTs and nanofluid were examined by X-ray diffraction (XRD), Transmission Electron Microscope (TEM), and Scanning Electron Microscope (SEM). These experimental investigations were evaluated with a Brookfield programmable Rheometer DV-III ULTRA. The results indicated that the optimum concentration of MWCNTs was 3%, and the dropping point increasing about 11%. The rheological behaviors of the nanofluids show that the grease with various concentrations of MWCNTs demonstrates non Newtonian behaviors and the results indicated that the shear stress, apparent viscosity and thermal conductivity increase with the increase of volume concentration of MWCNTs to 65%, 52%, and% 56, respectively.     

An efficient concept-based retrieval model for enhancing text retrieval quality

Most of the common techniques in text retrieval are based on the statistical analysis terms (words or phrases). Statistical analysis of term frequency captures the importance of the term within a document only. Thus, to achieve a more accurate analysis, the underlying model should indicate terms that capture the semantics of text. In this case, the model can capture terms that represent the concepts of the sentence, which leads to discovering the topic of the document. In this paper, a new concept-based retrieval model is introduced. The proposed concept-based retrieval model consists of conceptual ontological graph (COG) representation and concept-based weighting scheme. The COG representation captures the semantic structure of each term within a sentence. Then, all the terms are placed in the COG representation according to their contribution to the meaning of the sentence. The concept-based weighting analyzes terms at the sentence and document levels. This is different from the classical approach of analyzing terms at the document level only. The weighted terms are then ranked, and the top concepts are used to build a concept-based document index for text retrieval. The concept-based retrieval model can effectively discriminate between unimportant terms with respect to sentence semantics and terms which represent the concepts that capture the sentence meaning. Experiments using the proposed concept-based retrieval model on different data sets in text retrieval are conducted. The experiments provide comparison between traditional approaches and the concept-based retrieval model obtained by the combined approach of the conceptual ontological graph and the concept-based weighting scheme. The evaluation of results is performed using three quality measures, the preference measure (bpref), precision at 10 documents retrieved (P(10)) and the mean uninterpolated average precision (MAP). All of these quality measures are improved when the newly developed concept-based retrieval model is used, confirming that such model enhances the quality of text retrieval.     

An ASN.1 Model and Integration Approach for Structured Text

A language for manipulating ASN.1 print files is defined as an addition to the ASN.1 data definition language as specified in the ISO standards 8824 and 8825. The language has syntax similar in spirit to SQL, and provides analogous operations. Together, ASN.1 and the manipulation language define a model that can represent attribute hierarchies, multivalued attributes, optional attributes, both ordered and unordered attributes and tuples, and unnamed attributes—constructs that are absent in the traditional relational model. In addition, the proposed model allows the semantics of order, homogeneity, optionality, choosability, and uniqueness to be attached to constructs and construct elements. The approach is well suited for modeling data where form is important, such as text and flat file databases having ad hoc formats. It also supports modeling relations. The primary motivation for developing this language is to provide a common model for a database integration approach where some of the databases are in the form of formatted flat file text, while others are relational. Although the approach is general and can be applied in many different areas, scientific database and tool integration was the primary motive for its development.     

Evolving comprehensible neural network trees using genetic algorithms

This paper presents a comprehensible neural network tree (CNNTREE). CNNTREE is a proposed general modular neural network structure, where each node in this tree is a comprehensible expert neural network (CENN). One advantage of using CNNTREE is that it is a “gray box”; because it can be interpreted easily for symbolic systems; where each node in the CNNTREE is equivalent for symbolic operator in the symbolic system. Another advantage of CNNTREE is that it can be trained as any normal multi layer feed forward neural network. An evolutionary algorithm is given for designing the CNNTREE. Back propagation is also checked as local learning algorithm that fits for real time learning constraints. The tree generalization and training performance are examined using experiments with a digit recognition problem. The article is published in the original. Elsayed Mostafa. Received the B.Sc. degree in electrical (Communication) Eng., Cairo University at 1967. Dipl.-Ing, and Dr-Ing. from Stuttgart University at 1977, 1981 respectively. He is a member of ECS and EEES. He is currently a professor of electronic circuits, Faculty of Engineering, University of Helwan. Amr Kamel. Graduated from Computer Department, Faculty of Engineering of Ain Shams University, Egypt in 1999, and studying M.Sc. degree in computer engineering from the Faculty of Engineering of Helwan University. His special fields of interest include neural networks and genetic algorithms. Alaa Hamdy. Was born in Giza in Egypt, on August 17, 1966. He graduated from the Telecommunications and Electronics Department, Faculty of Engineering and Technology of Helwan University, Cairo, Egypt in 1989. He received the M.Sc. degree in computer engineering from the same university in 1996 and the Ph.D. degree from the Faculty of Electrical Engineering, Poznan University of Technology, Poland in 2004. Currently he is working as a lecturer in the Faculty of Engineering of Helwan University. His special fields of interest, include image processing, pattern analysis, and machine vision.