Search results for: secure data aggregation

Authors: Diego Rodrigues, Marcelo Lisboa, Elismar Batista, Marcos Dias

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Based on data on child labor, this work arises questions about how to understand and locate the factors that make up the child labor rates, and which properties are important to analyze these cases. Using data mining techniques to discover valid patterns on Brazilian social databases were evaluated data of child labor in the State of Tocantins (located north of Brazil with a territory of 277000 km2 and comprises 139 counties). This work aims to detect factors that are deterministic for the practice of child labor and their relationships with financial indicators, educational, regional and social, generating information that is not explicit in the government database, thus enabling better monitoring and updating policies for this purpose.

Keywords: social data, government decision making, association of social data, data mining

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Authors: Zhi Zhu, Boquan Zhang, Tian Jing, Jingjing Li, Tao Wang

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Data assimilation is a model and data hybrid-driven method that dynamically fuses new observation data with a numerical model to iteratively approach the real system state. It is widely used in state prediction and parameter inference of continuous systems. Because of the discrete event system’s non-linearity and non-Gaussianity, traditional Kalman Filter based on linear and Gaussian assumptions cannot perform data assimilation for such systems, so particle filter has gradually become a technical approach for discrete event simulation data assimilation. Hence, we proposed a particle filter-based discrete event simulation data assimilation method and took the unmanned aerial vehicle (UAV) maintenance service system as a proof of concept to conduct simulation experiments. The experimental results showed that the filtered state data is closer to the real state of the system, which verifies the effectiveness of the proposed method. This research can provide a reference framework for the data assimilation process of other complex nonlinear systems, such as discrete-time and agent simulation.

Keywords: discrete event simulation, data assimilation, particle filter, model and data-driven

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Authors: Ravi Raghav Sonani, Niraj Kumar Singh, Jitendra Kumar, Datta Madamwar

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Phycobiliproteins (PBPs) are light harvesting proteins showing very strong absorbance and fluorescence in the visible range of the solar spectrum. Phycoerythrin (PE) and phycocyanin (PC) are majorly found PBPs in the cyanobacteria and red algae. In the present study, we have investigated the reactive oxygen species (ROS)-averting capacity of purified PE and PC of cyanobacterial origin. Furthermore, the possibility - whether the ROS-averting potential of PBPs can be explored in the therapeutics of oxidative stress associated physiological anomalies including aging and neurodegenerative diseases. The nematode Caenorhabditis elegans has been used as model organism in this study. PE and PC treatment moderated normal aging and associated physiological functionalities like pharyngeal pumping and locomotion of C. elegans. Moreover, PE-treatment enhanced the stress (oxidative and heat) tolerance upon PE and PC treatment. Specifically, PE treatment was also noted to moderate the progression of Alzheimer’s disease in transgenic C. elegans CL4176. However, PC-treatment curtailed the polyQ aggregation mediated proteotoxicity in C. elegans AM141 (Huntington disease model) under stressed (paraquat stress) as well as normal conditions. The effectiveness of PE and PC in expanding the lifespan of mutant C. elegans knockout for some up- (daf 16) and down- (daf-2 and age-1) stream regulators of insulin/IGF-1 signalling (IIS) shows the independency of their effects from DAF-2–AGE-1–DAF-16 signalling pathway. In conclusion, the present report demonstrates the anti-aging and neuro-protective potential of cyanobacterial PE and PC.

Keywords: phycobiliproteins, aging, alzheimer, huntington, C. elegans

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Authors: Sadam Alwadi

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Outlier values become a problem that frequently occurs in the data observation or recording process. Thus, the need for data imputation has become an essential matter. In this work, it will make use of the methods described in the prior work to detect the outlier values based on a collection of stock market data. In order to implement the detection and find some solutions that maybe helpful for investors, real closed price data were obtained from the Amman Stock Exchange (ASE). Tukey and Maximum Overlapping Discrete Wavelet Transform (MODWT) methods will be used to impute the detect the outlier values.

Keywords: outlier values, imputation, stock market data, detecting, estimation

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Authors: P. Jayashree, S. Rajkumar

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With social networks and smart devices generating a multitude of data, effective data management is the need of the hour for networks and cloud applications. Some applications need effective storage while some other applications need effective communication over networks and data reduction comes as a handy solution to meet out both requirements. Most of the data compression techniques are based on data statistics and may result in either lossy or lossless data reductions. Though lossy reductions produce better compression ratios compared to lossless methods, many applications require data accuracy and miniature details to be preserved. A variety of data compression algorithms does exist in the literature for different forms of data like text, image, and multimedia data. In the proposed work, a generic progressive compression algorithm, based on probabilistic encoding, called PEINS is projected as an enhancement over irrational number stored coding technique to cater to storage issues of increasing data volumes as a cost effective solution, which also offers data security as a secondary outcome to some extent. The proposed work reveals cost effectiveness in terms of better compression ratio with no deterioration in compression time.

Keywords: compression ratio, generic compression, irrational number storage, probabilistic encoding

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Authors: Femi Elegbeleye, Omobayo Esan, Muienge Mbodila, Patrick Bowe

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This paper focused on cost effective storage architecture using fog and cloud data storage gateway and presented the design of the framework for the data privacy model and data analytics framework on a real-time analysis when using machine learning method. The paper began with the system analysis, system architecture and its component design, as well as the overall system operations. The several results obtained from this study on data privacy model shows that when two or more data privacy model is combined we tend to have a more stronger privacy to our data, and when fog storage gateway have several advantages over using the traditional cloud storage, from our result shows fog has reduced latency/delay, low bandwidth consumption, and energy usage when been compare with cloud storage, therefore, fog storage will help to lessen excessive cost. This paper dwelt more on the system descriptions, the researchers focused on the research design and framework design for the data privacy model, data storage, and real-time analytics. This paper also shows the major system components and their framework specification. And lastly, the overall research system architecture was shown, its structure, and its interrelationships.

Keywords: IoT, fog, cloud, data analysis, data privacy

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Authors: Nordila Ahmad, Thamer Mohammad, Bruce W. Melville, Zuliziana Suif

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Current methods for predicting local scour at wide bridge piers, were developed on the basis of laboratory studies and very limited scour prediction were tested with field data. Laboratory wide pier scour equation from previous findings with field data were presented. A wide range of field data were used and it consists of both live-bed and clear-water scour. A method for assessing the quality of the data was developed and applied to the data set. Three other wide pier-scour equations from the literature were used to compare the performance of each predictive method. The best-performing scour equation were analyzed using statistical analysis. Comparisons of computed and observed scour depths indicate that the equation from the previous publication produced the smallest discrepancy ratio and RMSE value when compared with the large amount of laboratory and field data.

Keywords: field data, local scour, scour equation, wide piers

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Authors: D. Pugazhenthi, B. Sree Vidya

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Cloud computing is one of the emerging technologies that enables end users to use the services of cloud on ‘pay per usage’ strategy. This technology grows in a fast pace and so is its security threat. One among the various services provided by cloud is storage. In this service, security plays a vital factor for both authenticating legitimate users and protection of information. This paper brings in efficient ways of authenticating users as well as securing information on the cloud. Initial phase proposed in this paper deals with an authentication technique using multi-factor and multi-dimensional authentication system with multi-level security. Unique identification and slow intrusive formulates an advanced reliability on user-behaviour based biometrics than conventional means of password authentication. By biometric systems, the accounts are accessed only by a legitimate user and not by a nonentity. The biometric templates employed here do not include single trait but multiple, viz., iris and finger prints. The coordinating stage of the authentication system functions on Ensemble Support Vector Machine (SVM) and optimization by assembling weights of base SVMs for SVM ensemble after individual SVM of ensemble is trained by the Artificial Fish Swarm Algorithm (AFSA). Thus it helps in generating a user-specific secure cryptographic key of the multimodal biometric template by fusion process. Data security problem is averted and enhanced security architecture is proposed using encryption and decryption system with double key cryptography based on Fuzzy Neural Network (FNN) for data storing and retrieval in cloud computing . The proposing scheme aims to protect the records from hackers by arresting the breaking of cipher text to original text. This improves the authentication performance that the proposed double cryptographic key scheme is capable of providing better user authentication and better security which distinguish between the genuine and fake users. Thus, there are three important modules in this proposed work such as 1) Feature extraction, 2) Multimodal biometric template generation and 3) Cryptographic key generation. The extraction of the feature and texture properties from the respective fingerprint and iris images has been done initially. Finally, with the help of fuzzy neural network and symmetric cryptography algorithm, the technique of double key encryption technique has been developed. As the proposed approach is based on neural networks, it has the advantage of not being decrypted by the hacker even though the data were hacked already. The results prove that authentication process is optimal and stored information is secured.

Keywords: artificial fish swarm algorithm (AFSA), biometric authentication, decryption, encryption, fingerprint, fusion, fuzzy neural network (FNN), iris, multi-modal, support vector machine classification

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Authors: A. Mohammadpour, Ebrahim Najafi Kajabad, Ghazale Ipakchi

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Presently, computer networks’ security rise in importance and many studies have also been conducted in this field. By the penetration of the internet networks in different fields, many things need to be done to provide a secure industrial and non-industrial network. Fire walls, appropriate Intrusion Detection Systems (IDS), encryption protocols for information sending and receiving, and use of authentication certificated are among things, which should be considered for system security. The aim of the present study is to use the outcome of several algorithms, which cause decline in IDS errors, in the way that improves system security and prevents additional overload to the system. Finally, regarding the obtained result we can also detect the amount and percentage of more sub attacks. By running the proposed system, which is based on the use of multi-algorithmic outcome and comparing that by the proposed single algorithmic methods, we observed a 78.64% result in attack detection that is improved by 3.14% than the proposed algorithms.

Keywords: intrusion detection systems, clustering, k-means, k-medoids, SV clustering, ensemble

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Authors: Inkyu Kim, SangMan Moon

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This IEEE 802.11b protocol provides up to 11Mbps data rate, whereas aerospace industry wants to seek higher data rate COTS data link system in the UAV. The Total Maximum Throughput (TMT) and delay time are studied on many researchers in the past years This paper provides theoretical data throughput performance of UAV formation flight data link using the existing 802.11b performance theory. We operate the UAV formation flight with more than 30 quad copters with 802.11b protocol. We may be predicting that UAV formation flight numbers have to bound data link protocol performance limitations.

Keywords: UAV datalink, UAV formation flight datalink, UAV WLAN datalink application, UAV IEEE 802.11b datalink application

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Authors: Radoslav Židek, Veronika Šidlová, Radovan Kasarda, Birgit Fuerst-Waltl

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Machine learning represents a set of topics dealing with the creation and evaluation of algorithms that facilitate pattern recognition, classification, and prediction, based on models derived from existing data. The data can present identification patterns which are used to classify into groups. The result of the analysis is the pattern which can be used for identification of data set without the need to obtain input data used for creation of this pattern. An important requirement in this process is careful data preparation validation of model used and its suitable interpretation. For breeders, it is important to know the origin of animals from the point of the genetic diversity. In case of missing pedigree information, other methods can be used for traceability of animal´s origin. Genetic diversity written in genetic data is holding relatively useful information to identify animals originated from individual countries. We can conclude that the application of data mining for molecular genetic data using supervised learning is an appropriate tool for hypothesis testing and identifying an individual.

Keywords: genetic data, Pinzgau cattle, supervised learning, machine learning

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Authors: Nidhi Gopal

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Routing is the process of moving a packet of data from source to destination and enables messages to pass from one computer to another and eventually reach the target machine. A router is a networking device that forwards data packets between computer networks. It is connected to two or more data lines from different networks (as opposed to a network switch, which connects data lines from one single network). This paper mainly emphasizes upon the study of router device, its top level architecture, and how various sub-modules of router i.e. Register, FIFO, FSM and Synchronizer are synthesized, and simulated and finally connected to its top module.

Keywords: data packets, networking, router, routing

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Authors: Sara Muslat Alsahali, Nout Mohammed Alqahtani

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Digital technology has changed the world, and with the increasing number of children accessing the Internet, it has now become an integral part of children's lives from their early years. With the rapid development of digital technology, the risks children face on the internet also evolve from cyberbullying to misuse, sexual exploitation, and abuse of their private information over the Internet. Digital technology, with its advantages and disadvantages, is now a fact of our life. Therefore, knowledge of how to reduce its risks and maximize its benefits will help shape the growth and future of a new generation of digital citizens. This paper will discuss access control techniques that help to create secure cyberspace where children can be safe without depriving them of their rights and freedom to use the internet and preventing them from its benefits. Also, it sheds light on its challenges and problems by classifying the methods of parental controlling into two possibilities asynchronous and synchronous techniques and choosing YouTube as a case study of access control techniques.

Keywords: access control, cyber security, kids, parental monitoring

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Authors: Julius Onyancha, Valentina Plekhanova

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One of the significant issues facing web users is the amount of noise in web data which hinders the process of finding useful information in relation to their dynamic interests. Current research works consider noise as any data that does not form part of the main web page and propose noise web data reduction tools which mainly focus on eliminating noise in relation to the content and layout of web data. This paper argues that not all data that form part of the main web page is of a user interest and not all noise data is actually noise to a given user. Therefore, learning of noise web data allocated to the user requests ensures not only reduction of noisiness level in a web user profile, but also a decrease in the loss of useful information hence improves the quality of a web user profile. Noise Web Data Learning (NWDL) tool/algorithm capable of learning noise web data in web user profile is proposed. The proposed work considers elimination of noise data in relation to dynamic user interest. In order to validate the performance of the proposed work, an experimental design setup is presented. The results obtained are compared with the current algorithms applied in noise web data reduction process. The experimental results show that the proposed work considers the dynamic change of user interest prior to elimination of noise data. The proposed work contributes towards improving the quality of a web user profile by reducing the amount of useful information eliminated as noise.

Keywords: web log data, web user profile, user interest, noise web data learning, machine learning

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Authors: Zeba Mahmood

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The modern business organizations are adopting technological advancement to achieve competitive edge and satisfy their consumer. The development in the field of Information technology systems has changed the way of conducting business today. Business operations today rely more on the data they obtained and this data is continuously increasing in volume. The data stored in different locations is difficult to find and use without the effective implementation of Data mining and Knowledge management techniques. Organizations who smartly identify, obtain and then convert data in useful formats for their decision making and operational improvements create additional value for their customers and enhance their operational capabilities. Marketers and Customer relationship departments of firm use Data mining techniques to make relevant decisions, this paper emphasizes on the identification of different data mining and Knowledge management techniques that are applied to different business industries. The challenges and issues of execution of these techniques are also discussed and critically analyzed in this paper.

Keywords: knowledge, knowledge management, knowledge discovery in databases, business, operational, information, data mining

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Authors: Adarsh Shroff

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Big data is a collection of dataset so large and complex that it becomes difficult to process using data base management tools. To perform operations like search, analysis, visualization on big data by using data mining; which is the process of extraction of patterns or knowledge from large data set. In recent years, the data mining applications become stale and obsolete over time. Incremental processing is a promising approach to refreshing mining results. It utilizes previously saved states to avoid the expense of re-computation from scratch. This project uses i2MapReduce, an incremental processing extension to Map Reduce, the most widely used framework for mining big data. I2MapReduce performs key-value pair level incremental processing rather than task level re-computation, supports not only one-step computation but also more sophisticated iterative computation, which is widely used in data mining applications, and incorporates a set of novel techniques to reduce I/O overhead for accessing preserved fine-grain computation states. To optimize the mining results, evaluate i2MapReduce using a one-step algorithm and three iterative algorithms with diverse computation characteristics for efficient mining.

Keywords: big data, map reduce, incremental processing, iterative computation

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Authors: Jerry Q. Cheng

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Currently, in analyzing large-scale recurrent event data, there are many challenges such as memory limitations, unscalable computing time, etc. In this research, a divide-and-conquer method is proposed using parametric frailty models. Specifically, the data is randomly divided into many subsets, and the maximum likelihood estimator from each individual data set is obtained. Then a weighted method is proposed to combine these individual estimators as the final estimator. It is shown that this divide-and-conquer estimator is asymptotically equivalent to the estimator based on the full data. Simulation studies are conducted to demonstrate the performance of this proposed method. This approach is applied to a large real dataset of repeated heart failure hospitalizations.

Keywords: big data analytics, divide-and-conquer, recurrent event data, statistical computing

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Authors: Nikeshala Weerasekara, Roshan Ajward

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Contemporary business environment, given the circumstance of stringent scrutiny toward corporate behavior, organizations are under pressure to develop and implement solid overarching Corporate Social Responsibility (CSR) strategies. In that milieu, in order to differentiate themselves from competitors and maintain stakeholder confidence banks spend millions of dollars on CSR programmes. However, knowledge on how non-western bank employees perceive such activities is inconclusive. At the same time recently only researchers have shifted their focus on positive effects of compassion at work or the organizational conditions under which it arises. Nevertheless, mediation mechanisms between CSR and compassion at work have not been adequately examined leaving a vacuum to be explored. Despite finding a purpose in work that is greater than extrinsic outcomes of the work is important to employees, meaningful work has not been examined adequately. Thus, in addition to examining the direct relationship between CSR and compassion at work, this study examined the mediating capability of meaningful work between these variables. Specifically, the researcher explored how CSR enables employees to sense work as meaningful which in turn would enhance their level of compassion at work. Hypotheses were developed to examine the direct relationship between CSR and compassion at work and the mediating effect of meaningful work on the relationship between CSR and compassion at work. Both Social Identity Theory (SIT) and Social Exchange Theory (SET) were used to theoretically support the relationships. The sample comprised of 450 respondents covering different levels of the bank. A convenience sampling strategy was used to secure responses from 13 local licensed commercial banks in Sri Lanka. Data was collected using a structured questionnaire which was developed based on a comprehensive review of literature and refined using both expert opinions and a pilot survey. Structural equation modeling using Smart Partial Least Square (PLS) was utilized for data analysis. Findings indicate a positive and significant (p < .05) relationship between CSR and compassion at work. Also, it was found that meaningful work partially mediates the relationship between CSR and compassion at work. As per the findings it is concluded that bank employees’ perception of CSR engagement not only directly influence compassion at work but also impact such through meaningful work as well. This implies that employees consider working for a socially responsible bank since it creates greater meaningfulness of work to retain with the organization, which in turn trigger higher level of compassion at work. By utilizing both SIT and SET in explaining relationships between CSR and compassion at work it amounts to theoretical significance of the study. Enhance existing literature on CSR and compassion at work. Also, adds insights on mediating capability of psychologically related variables such as meaningful work. This study is expected to have significant policy implications in terms of increasing compassion at work where managers must understand the importance of including CSR activities into their strategy in order to thrive. Finally, it provides evidence of suitability of using Smart PLS to test models with mediating relationships involving non normal data.

Keywords: compassion at work, corporate social responsibility, employee commitment, meaningful work, positive affect

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Authors: Degale Desta, Cheng Jian

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The most important factor in identifying a person is their face. Even identical twins have their own distinct faces. As a result, identification and face recognition are needed to tell one person from another. A face recognition system is a verification tool used to establish a person's identity using biometrics. Nowadays, face recognition is a common technique used in a variety of applications, including home security systems, criminal identification, and phone unlock systems. This system is more secure because it only requires a facial image instead of other dependencies like a key or card. Face detection and face identification are the two phases that typically make up a human recognition system.The idea behind designing and creating a face recognition system using deep learning with Azure ML Python's OpenCV is explained in this paper. Face recognition is a task that can be accomplished using deep learning, and given the accuracy of this method, it appears to be a suitable approach. To show how accurate the suggested face recognition system is, experimental results are given in 98.46% accuracy using Fast-RCNN Performance of algorithms under different training conditions.

Keywords: deep learning, face recognition, identification, fast-RCNN

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Authors: Ashiff Khan, A. Seetharaman, Abhijit Dasgupta

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The new era of big data (BD) is influencing chemical industries tremendously, providing several opportunities to reshape the way they operate and help them shift towards intelligent manufacturing. Given the availability of free software and the large amount of real-time data generated and stored in process plants, chemical industries are still in the early stages of big data adoption. The industry is just starting to realize the importance of the large amount of data it owns to make the right decisions and support its strategies. This article explores the importance of professional competencies and data science that influence BD in chemical industries to help it move towards intelligent manufacturing fast and reliable. This article utilizes a literature review and identifies potential applications in the chemical industry to move from conventional methods to a data-driven approach. The scope of this document is limited to the adoption of BD in chemical industries and the variables identified in this article. To achieve this objective, government, academia, and industry must work together to overcome all present and future challenges.

Keywords: chemical engineering, big data analytics, industrial revolution, professional competence, data science

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Authors: Syamsul Syafiq, Maslina Daud, Hafizah Hasan, Ahmad Zairi, Shazil Imri, Ezaini Akmar, Norbazilah Rahim

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Over recent years, web development has changed significantly. Driven largely by the rise of trends like mobiles, the world of development is rapidly evolving. The rise of the Internet makes web applications crucial nowadays. The web application has been an interface for a company and one of the ways they present their portfolio to the client. On the other hand, the web has become part of the file management system which takes over the role of paper. Due to high demand in web applications, developers are required to develop a web application that are cost-effective, secure and well coded. A framework has been proposed to develop an application rather than using library style development. The framework is helping the developer in creating the structure of a web automatically. This paper will compare the advantages and disadvantages of web development using framework against library-style development. This comparison is based on a previous research paper focusing on two main indicators, which are the impact to management and impact to the developer.

Keywords: framework, library style development, web application development, traditional web, static web, dynamic web

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Authors: Serge Provost, Yishan Zang

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Copulas are currently utilized in finance, reliability theory, machine learning, signal processing, geodesy, hydrology and biostatistics, among several other fields of scientific investigation. It follows from Sklar's theorem that the joint distribution function of a multidimensional random vector can be expressed in terms of its associated copula and marginals. Since marginal distributions can easily be determined by making use of a variety of techniques, we address the problem of securing the distribution of the copula. This will be done by using several approaches. For example, we will obtain bivariate least-squares approximations of the empirical copulas, modify the kernel density estimation technique and propose a criterion for selecting appropriate bandwidths, differentiate linearized empirical copulas, secure Bernstein polynomial approximations of suitable degrees, and apply a corollary to Sklar's result. Illustrative examples involving actual observations will be presented. The proposed methodologies will as well be applied to a sample generated from a known copula distribution in order to validate their effectiveness.

Keywords: copulas, Bernstein polynomial approximation, least-squares polynomial approximation, kernel density estimation, density approximation

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Authors: Vijaykumar Patel, P. Bahadur

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Microstructural evolution of a cationic gemini surfactant 12-4-12 micelles in the presence of bile salts has been investigated using different techniques. A negative value of interaction parameter evaluated from surface tension measurements is a signature of strong synergistic interaction between oppositely charged surfactants. Both the bile salts compete with each other in inducing the micellar transition of 12-4-12 micelles depending on their hydrophobicity. Viscosity measurements disclose that loading of bile salts induces morphological changes in 12-4-12 micelles; sodium deoxycholate is more efficient in altering the aggregation behaviour of 12-4-12 micelles compared to sodium cholate and presents pronounced increase in viscosity and micellar growth which is suppressed at elevated temperatures. A remarkable growth of 12-4-12 micelles in the presence of sodium deoxycholate at low pH has been ascribed to the solubilization of bile acids formed in acidic medium. Small angle neutron scattering experiments provided size and shape of 12-4-12/bile salt mixed micelles are explicated on the basis of hydrophobicity of bile salts. The location of bile salts in micelle was determined from nuclear overhauser effect spectroscopy. The present study characterizes 12-4-12 gemini-bile salt mixed systems which significantly enriches our knowledge, and such a structural transition provides an opportunity to use these bioamphiphiles as delivery vehicles and in some pharmaceutical formulations.

Keywords: gemini surfactants, bile salts, SANS (small angle neutron scattering), NOESY (nuclear overhauser effect spectroscopy)

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Authors: Zamira Sattinova, Tassybek Bekenov

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The article is devoted to modeling the technology of hot casting of beryllium oxide ceramics. The stages of ultrasonic activation of beryllium oxide slurry in the plant vessel to improve the rheological property, hot casting in the moulding cavity with cooling and solidification of the casting are described. Thermoplastic slurry (hereinafter referred to as slurry) shows the rheology of a non-Newtonian fluid with yield and plastic viscosity. Cooling-solidification of the slurry in the forming cavity occurs in the liquid, taking into account crystallization and solid state. In this work is the method of calculation of hot casting of the slurry using the method of effective molecular viscosity of viscoplastic fluid. It is shown that the slurry near the cooled wall is in a state of crystallization and plasticity, and the rest may still be in the liquid phase. Nonuniform distribution of temperature, density and concentration of kinetically free binder takes place along the cavity section. This leads to compensation of shrinkage by the influx of slurry from the liquid into the crystallization zones and plasticity of the castings. In the plasticity zone, the shrinkage determined by the concentration of kinetically free binder is compensated under the action of the pressure gradient. The solidification mechanism, as well as the mechanical behavior of the casting mass during casting, the rheological and thermophysical properties of the thermoplastic BeO slurry due to ultrasound exposure have not been well studied. Nevertheless, experimental data allow us to conclude that the effect of ultrasonic vibrations on the slurry mass leads to it: a change in structure, an increase in technological properties, a decrease in heterogeneity and a change in rheological properties. In the course of experiments, the effect of ultrasonic treatment and its duration on the change in viscosity and ultimate shear stress of the slurry depending on temperature (55-75℃) and the mass fraction of the binder (10 - 11.7%) have been studied. At the same time, changes in these properties before and after ultrasound exposure have been analyzed, as well as the nature of the flow in the system under study. The experience of operating the unit with ultrasonic impact has shown that at the same time, the casting capacity of the slurry increases by an average of 15%, and the viscosity decreases by more than half. Experimental study of physicochemical properties and phase change with simultaneous consideration of all factors affecting the quality of products in the process of continuous casting is labor-intensive. Therefore, an effective way to control the physical processes occurring in the formation of articles with predetermined properties and shapes is to simulate the process and determine its basic characteristics. The results of the calculations show the whole stage of hot casting of beryllium oxide slurry, taking into account the change in its state of aggregation. Ultrasonic treatment improves rheological properties and increases the fluidity of the slurry in the forming cavity. Calculations show the influence of velocity, temperature factors and structural data of the cavity on the cooling-solidification process of the casting. In the calculations, conditions for molding with shrinkage of the slurry by hot casting have been found, which makes it possible to obtain a solidifying product with a uniform beryllium oxide structure at the outlet of the cavity.

Keywords: hot casting, thermoplastic slurry molding, shrinkage, beryllium oxide

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Authors: Mohammed A. M. Ibrahim

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This paper investigate the utilization of wire and wireless networks to be platform for distributed educational monitoring system. Universities in developing countries suffer from a lot of shortages(staff, equipment, and finical budget) and optimal utilization of the wire and wireless network, so universities can mitigate some of the mentioned problems and avoid the problems that maybe humble the education processes in many universities by using our implementation of the examinations system as a test-bed to utilize the network as a solution to the shortages for academic staff in Taiz University. This paper selects a two areas first one quizzes activities is only a test bed application for wireless network learning environment system to be distributed among students. Second area is the features and the security of wireless, our tested application implemented in a promising area which is the use of WLAN in higher education for leering environment.

Keywords: networking wire and wireless technology, wireless network security, distributed computing, algorithm, encryption and decryption

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Authors: Pradeep Singh, Shrish Verma

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Software fault prediction models are created by using the source code, processed metrics from the same or previous version of code and related fault data. Some company do not store and keep track of all artifacts which are required for software fault prediction. To construct fault prediction model for such company, the training data from the other projects can be one potential solution. The earlier we predict the fault the less cost it requires to correct. The training data consists of metrics data and related fault data at function/module level. This paper investigates fault predictions at early stage using the cross-project data focusing on the design metrics. In this study, empirical analysis is carried out to validate design metrics for cross project fault prediction. The machine learning techniques used for evaluation is Naïve Bayes. The design phase metrics of other projects can be used as initial guideline for the projects where no previous fault data is available. We analyze seven data sets from NASA Metrics Data Program which offer design as well as code metrics. Overall, the results of cross project is comparable to the within company data learning.

Keywords: software metrics, fault prediction, cross project, within project.

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Authors: Shama Urooj

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The present work investigates the relationship among financial inclusion, modernization, and energy performance in SCO member countries during the years 2011–2021. PCA is used to create composite indexes of financial inclusion, modernization, and energy performance. We used panel regression models that are both reliable and heteroscedasticity-consistent to look at the relationship among variables. The findings indicate that financial inclusion (FI) and modernization, along with the increased FDI, all appear to contribute to the energy performance in the SCO member countries. However, per capita GDP has a negative impact on energy performance. These results are unbiased and consistent with the robust results obtained by applying different econometric models. Feasible Generalized Least Square (FGLS) estimation is also used for checking the uniformity of the main model results. This research work concludes that there has been no policy coherence in SCO member countries regarding the coordination of growing financial inclusion and modernization for energy sustainability in recent years. In order to improve energy performance with modern development, policies regarding financial inclusion and modernization need be integrated both at national as well as international levels.

Keywords: financial inclusion, energy performance, modernization, technological development, SCO.

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Authors: Theo Neethling

Abstract:

The United States Africa Command (AFRICOM) was established in 2007 as a military and diplomatic entity and is intended to assist African states and military actors to address their security needs. At the same time, AFRICOM is clearly an extension of US strategic interests on the African continent. The challenge for the US Department of Defence is to project AFRICOM as a cooperative and willing partner. This implies a partner that offers needed services and resources, and supports African security and military priorities with no presumption of having a privileged role in defining the African future. However, one of AFRICOM’s main challenges relates to the point that it has not been able to secure a firm partnership with South Africa as a key player on the continent: South Africa has continuously taken a lukewarm, if not cold, approach towards AFRICOM since its formation. The main aim of this article is to examine and discuss South Africa’s political-military relations with AFRICOM and to assess the underlying reasons currently inhibiting AFRICOM from achieving a fully productive relationship with the South African government.

Keywords: AFRICOM, South African foreign policy, US interests, defence capabilities in Africa, US-Africa relations

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Authors: Vesna Kirandziska, Nevena Ackovska, Ana Madevska Bogdanova

Abstract:

The problem of emotion recognition is a challenging problem. It is still an open problem from the aspect of both intelligent systems and psychology. In this paper, both voice features and facial features are used for building an emotion recognition system. A Support Vector Machine classifiers are built by using raw data from video recordings. In this paper, the results obtained for the emotion recognition are given, and a discussion about the validity and the expressiveness of different emotions is presented. A comparison between the classifiers build from facial data only, voice data only and from the combination of both data is made here. The need for a better combination of the information from facial expression and voice data is argued.

Keywords: emotion recognition, facial recognition, signal processing, machine learning

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Authors: Tesfaldet Mekonnen Estifanos, Chen Hui, Afewerki Weldezgi

Abstract:

Background: Intimate partner violence (hereafter IPV) is a major global public health challenge that tortures and disables women in the place where they are ought to be most secure within their own families. The fact that the family unit is commonly viewed as a private circle, violent acts towards women remains undermined. There are limited research and knowledge about the influencing factors linked to IPV in Sierra Leone. This study, therefore, estimates the prevalence rate and the predicting factors associated with IPV. Methods: Data were taken from Sierra-Leone Demographic and Health Survey (SDHS, 2013): the first in its form to incorporate information on domestic violence. Multistage cluster sampling research design was used, and information was gathered by a standard questionnaire. A total of 5185 respondents selected were interviewed, out of whom 870 were never been in union, thus excluded. To analyze the two dependent variables: experience of IPV, ‘ever’ and 'last 12 months prior to the survey', a total of 4315 (currently or formerly married) and 4029 women (currently in union) were included respectively. These dependent variables were constructed from the three forms of violence namely physical, emotional and sexual. Data analysis was applied using SPSS version 23, comprising three-step process. First, descriptive statistics were used to show the frequency distribution of both the outcome and explanatory variables. Second, bivariate analysis adopting chi-square test was applied to assess the individual relationship between the outcome and explanatory variables. Third, multivariate logistic regression analysis was undertaken using hierarchical modeling strategy to identify the influence of the explanatory variables on the outcome variables. Odds ratio (OR) and 95% confidence interval (CI) were utilized to examine the association of the variables considering p-values less than 0.05 statistically significant. Results: The prevalence of lifetime IPV among ever married women was 48.4%, while 39.8% of those currently married experienced IPV in the previous year preceding the survey. Women having 1 to 4 and more than 5 number of ever born babies were almost certain to encounter lifetime IPV. However, women who own a property, and those who referenced 3-5 reasons for which wife-beating is acceptable were less probably to experience lifetime IPV. Attesting parental violence, partner’s dominant marital behavior, and women afraid of their partner were the variables related to both experience of IPV ‘ever’ and ‘the previous year prior to the survey’. Respondents who concur that wife-beating is sensible in certain situations and occupations under the professional category had diminished chances of revealing IPV in the year prior to the data collection. Conclusion: This study indicated that factors significantly correlated with IPV in Sierra-Leone are mostly linked with husband related factors specifically, marital controlling behaviors. Addressing IPV in Sierra-Leone requires joint efforts that target men raise awareness to address controlling behavior and empower security in affiliations.

Keywords: husband behavior, married women, partner violence, Sierra Leone

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