Evolution of structural and magnetic properties in Ta/Ni_81Fe_(19) multilayer thin films

The interdiffusion kinetics in short period (12.8 nm) Ta/Ni81Fe19 polycrystalline multilayer films has been investigated and related to the evolution of soft magnetic properties upon thermal annealing in the temperature range 300-600-degrees-C. Small angle x-ray diffraction and transmission electron microscopy were used to estimate the multilayer period. Interdiffusion in the multilayers was directly computed from the decay of the satellites near (000) in a small angle x-ray diffraction spectrum. A kinetic analysis of interdiffusion suggests that grain growth is concurrent with grain boundary diffusion of Ta in Ni81Fe19. The evolution of soft magnetic properties of Ni81Fe19, i.e., lowering of 4piM(s) and increase in coercivity H(c), also lend support to the above analysis

Extended of TEA: A 256 bits block cipher algorithm for image encryption

This paper introduces an effective image encryption approach that merges a chaotic map and polynomial with a block cipher. According to this scheme, there are three levels of encryption. In the first level, pixel positions of the image are scuffled into blocks randomly based on a chaotic map. In the second level, the polynomials are constructed by taking N unused pixels from the permuted blocks as polynomial coefficients. Finally, the third level a proposed secret-key block cipher called extended of tiny encryption algorithm (ETEA) is used. The proposed ETEA algorithm increased the block size from 64-bit to 256-bit by using F-function in type three Feistel network design. The key schedule generation is very straightforward through admixture the entire major subjects in the identical manner for every round. The proposed ETEA algorithm is word-oriented, where wholly internal operations are executed on words of 32 bits. So, it is possible to efficiently implement the proposed algorithm on smart cards. The results of the experimental demonstration that the proposed encryption algorithm for all methods are efficient and have high security features through statistical analysis using histograms, correlation, entropy, randomness tests, and the avalanche effect

The Factors of Influence towards Knowledge Sharing Among TVET Educators: A Study on TVET Educators within Hulu Langat District

Similar to other higher education institutions, knowledge sharing among highly skilled instructors and other instructors is important in improving the quality of training and skills development in the Technical Vocational Education and Training (TVET) institutions. In this borderless world, information technology enables knowledge sharing activities to be carried out more efficiently, quickly and widely. Understanding the influencing factors of knowledge sharing activities in TVET institutions is important since a number of failures have been reported due to the lack of awareness in contributing new knowledge. Therefore, this study is conducted to identify the factors that influence knowledge sharing activities among instructors in TVET institutions and to propose a framework to the knowledge sharing activities. A quantitative study involving 96 respondents was conducted and the data that was analysed using multiple regression analysis found that there are four (4) factors that contribute to knowledge sharing activities in TVET institutions: organizational, content, cooperation and technological. The proposed framework is expected to assist in the development of a repository of knowledge sharing among instructors in TVET institutions

Inappropriate use of antibiotics in the treatment of pharyngo-tonsilitis in children in Khartoum, Sudan

Background: Pharyngo-tonsillitis represents a major public health problem all over the world. Recent studies in Sudan have revealed alarming results reporting antibiotics over prescription in the management of acute tonsillitis.Objectives: The aim of this study was to estimate the prevalence of Group A Beta hemolytic streptococci and document the appropriateness of using antibiotics in the treatment of acute tonsillo-pharyngitis in Jaafar Ibn Auf Pediatrics Hospital.Materials and Methods: A cross sectional hospital based study was conducted in the period January to August 2012 in Jaafar Ibn Auf Hospital, the largest tertiary pediatrics hospital in Sudan. The sample size was 100 including children aged 2-17 years and got antibiotics treatment for their current sore throat. A pharyngeal swab was collected at presentation from tonsils and posterior pharynx. Gram staining was done first, and then Group A Beta hemolytic streptococci were isolated and identified in the laboratory by their growth characteristics. All children included in the study were assessed clinically and subjected to a structural questionnaire. Data were analyzed by SPSS version 19.Results: The estimated prevalence of Group A Beta hemolytic streptococci tonsillitis and/or pharyngitis was 22%, nevertheless the proportion of antibiotic prescription was 100%.Conclusion: Most children were treated inappropriately regarding the need for using antibiotics, the type of antibiotics used and the duration of management. As many studies suggested that increased using of antibiotics may be due to uncertainty of diagnosis, requesting scoring system or rapid diagnostic test can contribute to the reduction of the rate of antibiotics prescription.Keyword: Pharyngo-tonsillitis, inappropriate, children, antibiotics, Suda

Developed Method of Information Hiding in Video AVI File Based on Hybrid Encryption and Steganography

This paper produces a development of an AVI Hiding Information System (HIS) based on steganography techniques to prevent intruders to obtain the transmitted information. This work is based on a combination of steganography and cryptography techniques to increase the level of security and to make the system more complex to be defeated by attackers. In this work AVI file is separated into two parts, video and audio. The video is a stream of frames; each frame is stored as a bmp file image and a number of frames required or needed to be used as a cover are chosen. The algorithm that is used for encryption is the Type-3 Feistel Network of The 128-bits block size improved Blowfish encryption it is a symmetric uses a variable-length up to 129 bytes, making it useful for both domestic and exportable use and a variable-length key would make cryptoanalysis more difficult for potential attackers. Two methods of hiding are used in this work, the first method is the Least Significant Bit (LSB) and the second is the Haar Wavelet Transform (HWT). The proposed HIS system was tested using standard subjective measures such as Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR). All of the measures obtained as the test results indicate good results for PSNR (above 50db) and they increase when the number of frames used as a cover increases

Comparative study of herbal plants on the phenolic and flavonoid content, antioxidant activities and toxicity on cells and zebrafish embryo

Natural antioxidants derived from plants have shown a tremendous inhibitory effect on free radicals in actively metabolizing cells. Overproduction of free radicals increases the risk factor of chronic diseases associated with diabetes, cancer, arthritis and cardiovascular disease. Andrographis paniculata, Cinnamon zeylanicum, Curcuma xanthorrhiza, Eugenia polyantha and Orthosiphon stamineus are ethnomedicinal plants used in the Asian region to treat various illnesses from a common fever to metabolic disease. In this study, we have quantified the total phenolic (TPC) and flavonoid content (TFC) in these plants and its inhibitory effect on 1,1-diphenyl-2-picrylhydrazyl radical (DPPH) and 2,2′-azinobis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) free radicals as well as the cytotoxicity effect on cell lines proliferation and zebrafish embryogenesis. Results showed that Cinnamon zeylanicum and E. polyantha have the highest phenolic and flavonoid content. Furthermore, both herbs significantly inhibited the formation of DPPH and ABTS free radicals. Meanwhile, O. stamineus exhibited minimum cytotoxicity and embryotoxicity on tested models. Good correlation between IC50 of 3T3-L1 cells and LC50 embyrotoxicity was also found. This study revealed the potent activity of antioxidant against free radical and the toxicology levels of the tested herbal plants

Ensemble learning method for the prediction of new bioactive molecules

Pharmacologically active molecules can provide remedies for a range of different illnesses and infections. Therefore, the search for such bioactive molecules has been an enduring mission. As such, there is a need to employ a more suitable, reliable, and robust classification method for enhancing the prediction of the existence of new bioactive molecules. In this paper, we adopt a recently developed combination of different boosting methods (Adaboost) for the prediction of new bioactive molecules. We conducted the research experiments utilizing the widely used MDL Drug Data Report (MDDR) database. The proposed boosting method generated better results than other machine learning methods. This finding suggests that the method is suitable for inclusion among the in silico tools for use in cheminformatics, computational chemistry and molecular biology. This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication

Spatial downscaling of satellite precipitation data in humid tropics using a site-specific seasonal coefficient

This paper described the development of a spatial downscaling algorithm to produce finer grid resolution for satellite precipitation data (0.05°) in humid tropics. The grid resolution provided by satellite precipitation data (>0.25°) was unsuitable for practical hydrology and meteorology applications in the high hydrometeorological dynamics of Southeast Asia. Many downscaling algorithms have been developed based on significant seasonal relationships, without vegetation and climate conditions, which were inapplicable in humid, equatorial, and tropical regions. Therefore, we exploited the potential of the low variability of rainfall and monsoon characteristics (period, location, and intensity) on a local scale, as a proxy to downscale the satellite precipitation grid and its corresponding rainfall estimates. This study hypothesized that the ratio between the satellite precipitation and ground rainfall in the low-variance spatial rainfall pattern and seasonality region of humid tropics can be used as a coefficient (constant value) to spatially downscale future satellite precipitation datasets. The spatial downscaling process has two major phases: the first is the derivation of the high-resolution coefficient (0.05°), and the second is applying the coefficient to produce the high-resolution precipitation map. The first phase utilized the long-term bias records (1998-2008) between the high-resolution areal precipitation (0.05°) that was derived from dense network of ground precipitation data and re-gridded satellite precipitation data (0.05°) from the Tropical Rainfall Measuring Mission (TRMM) to produce the site-specific coefficient (SSC) for each individual pixel. The outcome of the spatial downscaling process managed to produce a higher resolution of the TRMM data from 0.25° to 0.05° with a lower bias (average: 18%). The trade-off for the process was a small decline in the correlation between TRMM and ground rainfall. Our results indicate that the SSC downscaled method can be used to spatially downscale satellite precipitation data in humid, tropical regions, where the seasonal rainfall is consistent

Intercomparison and Assessment of Stand-Alone and Wavelet-Coupled Machine Learning Models for Simulating Rainfall-Runoff Process in Four Basins of Pothohar Region, Pakistan

The science of hydrological modeling has continuously evolved under the influence of rapid advancements in software and hardware technologies. Starting from simple rational formulae for estimating peak discharge and developing into sophisticated univariate predictive models, accurate conversion of rainfall into runoff and the assessment of inherent uncertainty has been a prime focus for researchers. Therefore, alternative data-driven methods have gained widespread attention in hydrology. Moreover, scientists often couple conventional machine learning models with data pre-processing techniques, i.e., wavelet transformation (WT), to enhance modelling accuracy. In this context, this research work attempts to explore the latent linkage between rainfall and runoff in Pothohar region of Pakistan by developing a novel linkage of five streamline techniques of machine learning, including single decision tree (SDT), decision tree forest (DTF), tree boost (TB), multilayer perceptron (MLP), and gene expression modeling (GEP), with a more sophisticated variant of WT, i.e., maximal overlap discrete wavelet transformation (MODWT), for boundary correction of the transformed components of timeseries data. This study also implements these machine learning models in a stand-alone mode for a more comprehensive comparative analysis of performances. Furthermore, the study uses a combined-basin approach that divides Pothohar region into two basins to compensate for the complex topographic division of the study area. The results indicate that MODWT-based DTF outperformed other stand-alone and hybrid models in terms of modeling accuracy. In the first scenario, considering the Bunha-Kahan River basin, MODWT-DTF yielded the highest NSE (0.86) and the lowest RMSE (220.45 mm) and R2 (0.92 at lag order 3 (Lo3)) when transformed with daubechies4 (db4) at level three. While in the Soan-Haro River basin, MODWT-DTF produced the highest accuracy modeling at lag order 4 (Lo4) (NSE = 0.88, RMSE = 21.72 m(3)/s, and R2 = 0.91). The highly accurate performance of 3- and 4-days lagged models reflects the temporal consistency in hydrological response of the study area. The comparison of simple and hybrid model performance indicates up to a 55% increase in modeling accuracy due to data pre-processing with wavelet transformation