Crystallinity,particle size,and mechanical properties of fibers in some egyptian and american cotton cultivars

The main objective of this study was to measure the crystallinity, particle size, and mechanical properties of fibers and to assess their relationship in 18 Egyptian and American cotton cultivars. Remarkable variations in crystallinity were found, ranging between 49 and 90%, whereas the range of differences in particle size was somewhat narrow, and so its effect on mechanical properties was not detectable. The cotton cultivars that have higher values of crystallinity are characterized by higher values of tenacity at either $ \frac{1}{8} $ in. or zero gauge and stiffness and by reduced elongation. © 1993 John Wiley & Sons, Inc.     

Reliability analysis of reverse osmosis plant

The increasing complexity of desalting plants demands consideration of operational and maintenance factors in the design phase. Applications of the fault tree techniques to RO sea water desalination systems are illustrated; critical failure problem areas are identified and methods of improving the performance of the RO desalination plants are recommended. Failure data has been extracted from operation and maintenance reports of the operating RO desalination plants in Jeddah, Saudi Arabia. An assessment is also made of the impact of failures and outages on the availability of the plant.     

Interoperability evaluation models: A systematic review

Interoperability is defined as the ability for two (or more) systems or components to exchange information and to use the information that has been exchanged. There is increasing demand for interoperability between individual software systems. Developing an interoperability evaluation model between software and information systems is difficult, and becoming an important challenge. An interoperability evaluation model allows knowing the degree of interoperability, and lead to the improvement of interoperability. This paper describes the existing interoperability evaluation models, and performs a comparative analysis among their findings to determine the similarities and differences in their philosophy and implementation. This analysis yields a set of recommendations for any party that is open to the idea of creating or improving an interoperability evaluation model.     

Environmental control of the dominant phytoplankton in Aras Reservoir (Iran): A multivariate approach

This study was undertaken to determine the phytoplankton structure and the environmental variables comprising the driving factors leading to cyanobacterial blooms in Aras Reservoir. Sampling was carried out seasonally at five sampling sites along the main body of the reservoir. Samples were collected for phytoplankton identification and enumeration, chemical analyses and chlorophyll‐a (Chl‐a) concentrations at each sampling site. Principal component analysis (PCA) and two‐way unweighted pair group method with arithmetic mean (UPGMA) were performed to determine the environmental variables affecting phytoplankton community dynamics. Seventy‐two species belonging to five divisions were determined during this study. Cyanobacteria contained the highest density (74%) during the study period, with Pseudanabaena limnetica being the most abundant species. The Shannon diversity index was low (0.44–1.87), indicating a high level of cyanobacteria dominance and eutrophic water status. Principal component analysis indicated that total phosphorus and temperature were significantly associated with cyanobacteria growth. Two‐way clustering by UPGMA indicated a close relationship among sampling sites during the same season from the perspective of the phytoplankton density. The findings of this study suggested Aras Reservoir could be highly affected by agricultural runoff and untreated sewage loadings. Thus, the proper use of fertilizers and sewage treatment should be taken into account in considering effective conservation and management plans.     

HDM-Analyser: a hybrid analysis approach based on data mining techniques for malware detection

Today’s security threats like malware are more sophisticated and targeted than ever, and they are growing at an unprecedented rate. To deal with them, various approaches are introduced. One of them is Signature-based detection, which is an effective method and widely used to detect malware; however, there is a substantial problem in detecting new instances. In other words, it is solely useful for the second malware attack. Due to the rapid proliferation of malware and the desperate need for human effort to extract some kinds of signature, this approach is a tedious solution; thus, an intelligent malware detection system is required to deal with new malware threats. Most of intelligent detection systems utilise some data mining techniques in order to distinguish malware from sane programs. One of the pivotal phases of these systems is extracting features from malware samples and benign ones in order to make at least a learning model. This phase is called “Malware Analysis” which plays a significant role in these systems. Since API call sequence is an effective feature for realising unknown malware, this paper is focused on extracting this feature from executable files. There are two major kinds of approach to analyse an executable file. The first type of analysis is “Static Analysis” which analyses a program in source code level. The second one is “Dynamic Analysis” that extracts features by observing program’s activities such as system requests during its execution time. Static analysis has to traverse the program’s execution path in order to find called APIs. Because it does not have sufficient information about decision making points in the given executable file, it is not able to extract the real sequence of called APIs. Although dynamic analysis does not have this drawback, it suffers from execution overhead. Thus, the feature extraction phase takes noticeable time. In this paper, a novel hybrid approach, HDM-Analyser, is presented which takes advantages of dynamic and static analysis methods for rising speed while preserving the accuracy in a reasonable level. HDM-Analyser is able to predict the majority of decision making points by utilising the statistical information which is gathered by dynamic analysis; therefore, there is no execution overhead. The main contribution of this paper is taking accuracy advantage of the dynamic analysis and incorporating it into static analysis in order to augment the accuracy of static analysis. In fact, the execution overhead has been tolerated in learning phase; thus, it does not impose on feature extraction phase which is performed in scanning operation. The experimental results demonstrate that HDM-Analyser attains better overall accuracy and time complexity than static and dynamic analysis methods.     

Green synthesis of silver nanoparticles and their applications as colorimetric probe for determination of Fe3+ and Hg2+ ions

Silver nanoparticles (AgNPs) were prepared by a green method using Cordia myxa leaf extract. They were characterised by UV–vis spectroscopy, Fourier transform infrared spectroscopy and their X‐ray diffraction pattern. Their sizes were determined by scanning electron micrographs, transmission electron micrographs imaging and dynamic light scattering analysis. The shapes of nanoparticles were spherical or truncated triangular and their average size was determined to be 51.6 nm. Their solution was stable at least for one month. The prepared AgNPs were used as a selective chemical sensor for determination of iron(III) (only when Cl⁻ ions were present in the medium) and mercury(II) ions with detection limits of 0.084 and 0.037 nM, respectively. It was shown that the mechanism of these detections is through oxidation of Ag atoms by Fe³⁺ and Hg²⁺ ions.Inspec keywords: visible spectra, nanoparticles, transmission electron microscopy, nanofabrication, ultraviolet spectra, chemical sensors, scanning electron microscopy, silver, X‐ray diffraction, Fourier transform infrared spectra, oxidationOther keywords: Ag, Cordia myxa leaf extract, iron(III) ions, mercury(II) ions, oxidation, scanning electron micrographs, Fourier transform infrared spectroscopy, silver nanoparticles, chemical sensor, dynamic light scattering analysis, transmission electron micrographs, X‐ray diffraction pattern, UV–vis spectroscopy, colorimetric probe, green synthesis     

Superabsorbent polymers achieved by surface cross linking of poly(sodium acrylate) using microwave method

Surface cross linking is a post-treatment process for superabsorbent polymers (SAPs) leading to an increase in the absorbency under load (AUL). This process is typically carried out through conventional heating method. In the current study, for the first time, microwave method was used for surface treatment process of SAPs based on poly(sodium acrylate). Diglycidyl materials such as 1,4-butanediol diglycidyl ether (BDDGE), polyethylene diglycidyl ether (PEGDGE), and ethylene glycol diglycidyl ether (EGDGE) were utilized as surface cross-linking agents. Also, N,N-Dimethylaniline was used as a catalyst for surface treatment of poly(sodium acrylate) SAP with diglycidyl materials as the external cross linkers. The results showed that surface treatment time can be reduced from 1 to 3 h in the conventional heating to a few minutes by microwave method. The use of catalyst in surface treatment solution resulted in higher AULs. The AULs of SAPs were increased from 14 g/g for unmodified SAP to 17.5, 19 and 20.7 g/g after surface treatment for surface-treated SAPs with BDDGE, PEGDGE and EGDGE, respectively. These results present the microwave method as an effective alternative candidate for thermal surface treatment of SAP which can have economic benefits from the viewpoint of time and energy consumption industrially.

     

A Novel SOI MESFET to Improve the Equipotential Contour Distributions by Using an Oxide Barrier

Silicon - A new Metal Semiconductor Field Effect Transistor (MESFET) structure in Silicon on Insulator (SOI) technology will be introduced in this paper. In our idea, an oxide barrier is used on...     

Eugenol-Induced Autophagy and Apoptosis in Breast Cancer Cells via PI3K/AKT/FOXO3a Pathway Inhibition

The use of natural compounds is promising in approaches to prevent and treat cancer. The long-term application of most currently employed chemotherapy techniques has toxic side effects. Eugenol, a phenolic phytochemical extracted from certain essential oils, has an anti-cancer effect. The modulation of autophagy can promote either the survival or apoptosis of cancer cells. Triple-negative (MDA-MB-231) and HER2 positive (SK-BR-3) breast cancer cell lines were treated with different doses of eugenol. Apoptosis was detected by a flow-cytometry technique, while autophagy was detected by acridine orange. Real-time PCR and Western blot assays were applied to investigate the effect of eugenol on the gene and protein expression levels of autophagy and apoptotic genes. Treating cells with different concentrations of eugenol significantly inhibited cell proliferation. The protein levels of AKT serine/threonine kinase 1 (AKT), forkhead box O3 (FOXO3a), cyclin dependent kinase inhibitor 1A (p21), cyclin-dependent kinase inhibitor (p27), and Caspase-3 and -9 increased significantly in Eugenol-treated cells. Eugenol also induced autophagy by upregulating the expression levels of microtubule-associated protein 1 light chain 3 (LC3) and downregulating the expression of nucleoporin 62 (NU p62). Eugenol is a promising natural anti-cancer agent against triple-negative and HER2-positive breast cancer. It appears to work by targeting the caspase pathway and by inducing autophagic cell death.     

Re-examination of the phenolic hydroxyl contents of coals

In connection with studies of the dependence of liquefaction behaviour on coal characteristics, parameters were needed that might effectively characterize the organic chemical structures. Accordingly, the phenolic hydroxyl contents of 37 coals from three geological provinces of the USA have been determined, by acetylation with ¹⁴C-labelled acetic anhydride. The results, when expressed as fractions of the total organic matter in the coals, showed a good inverse correlation with the carbon contents (dmmf). However, application of a stepwise multiple regression analysis to the data developed a linear equation relating hydroxyl content to the vitrinite reflectance, calorific value and vitrinite content, the fraction of variance explained being 92%. When the hydroxyl contents were expressed in the alternative manner, as fractions of the total oxygen content, no correlation could be seen with carbon content. However, in the wide scatter of points on the graph, the data are seen to fall into three reasonably distinct populations such that at the same level of rank, hydroxyl contents typically decrease for coals from the three provinces in the order Interior > Eastern > Rocky Mountain, implying that coals from these areas differ in structure as a result of differing antecedents. Statistical analyses showed that $\text{OH}\text{O}$ has some significance in determining liquefaction behaviour, but it is not among the coal properties found most significant.