Search results for: missing at random (MAR)

Authors: Ersun N. Kurtulus

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One of the strategic goals of left-wing terrorism, both in its Anarchist and Marxist-Leninist forms, was mobilization of masses as a first step in launching a revolution. However, in the canonical texts of left-wing terrorist literature (such as the works of Brousse, Nachaev, Bakunin, Kropotkin, Most, Heinzen, Guevara and Marighella) it is not clear how resort to terrorist tactics such as assassinations or bomb attacks will lead to mobilization of masses. This link is usually presumed and taken for granted. However, in other, less known terrorist texts, where there is some elaboration upon this link, two conflicting views emerge: (i) terrorist attacks are supposed to cause state repression which in turn radicalizes masses and opens up the way for recruitment and mobilization versus (ii) terrorist attacks are supposed to demonstrate the hollowness of the already existent state repression and thereby encourage mobilization of masses that are already radicalized but inactive due fear caused by state repression. The paper argues that terrorism studies have largely overemphasized the former while the latter has remained more or less unnoticed.

Keywords: terrorism, repression, radical left, mobilization of masses

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Authors: Ilker Ali Ozkan

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In this study, an artificial neural network model has been developed to estimate chronic kidney failure which is a common disease. The patients’ age, their blood and biochemical values, and 24 input data which consists of various chronic diseases are used for the estimation process. The input data have been subjected to preprocessing because they contain both missing values and nominal values. 147 patient data which was obtained from the preprocessing have been divided into as 70% training and 30% testing data. As a result of the study, artificial neural network model with 25 neurons in the hidden layer has been found as the model with the lowest error value. Chronic kidney failure disease has been able to be estimated accurately at the rate of 99.3% using this artificial neural network model. The developed artificial neural network has been found successful for the estimation of chronic kidney failure disease using clinical data.

Keywords: estimation, artificial neural network, chronic kidney failure disease, disease diagnosis

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Authors: Veronica Diaz Quezada

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The society of the 21st century requires training of engineers capable of solving routine and non-routine problems in applications of the limit of real functions, as part of the course Calculus I. For this purpose, research was conducted with a methodological design that combines quantitative and qualitative procedures and that aims, to identify and to characterize the types of problems according to their nature and context, through the application of a mathematics test; to know— through a questionnaire— the opinion of difficulties in their solution, previous and missing knowledge of some students of three engineering careers of a state university in Chile. This research is completed with three case studies. The results favor the performance of students in solving problems of a fantasist and realistic context, but these do not guarantee mathematical skills which are necessary to solve non-routine problems of limit applications. In conclusion, through this research, it became clear that the students of the three engineerings do not have all the necessary skills to solve problems of application of the limit of a function of the real variable.

Keywords: case studies, engineering program, limits, problem solving

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Authors: Kelechi E. Ezeji

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The inclusion of relevant content in curricula of architecture schools is vital for attainment of Computer Aided Design (CAD) proficiency by graduates. Implementing this content involves, among other variables, the presence of competent tutors. Consequently, this study sought to investigate the importance of personnel recruitment for inclusion of content vital to the implementation of CAD in the curriculum for architecture education. This was with a view to developing a framework for appropriate implementation of CAD curriculum. It was focused on departments of architecture in universities in south-east Nigeria which have been accredited by National Universities Commission. Survey research design was employed. Data were obtained from sources within the study area using questionnaires, personal interviews, physical observation/enumeration and examination of institutional documents. A multi-stage stratified random sampling method was adopted. The first stage of stratification involved random sampling by balloting of the departments. The second stage involved obtaining respondents’ population from the number of staff and students of sample population. Chi Square analysis tool for nominal variables and Pearson’s product moment correlation test for interval variables were used for data analysis. With ρ < 0.5, the study found significant correlation between the number of CAD literate academic staff and use of CAD in design studio/assignments; that increase in the overall number of teaching staff significantly affected total CAD credit units in the curriculum of the department. The implications of these findings were that for successful implementation leading to attainment of CAD proficiency to occur, CAD-literacy should be a factor in the recruitment of staff and a policy of in-house training should be pursued.

Keywords: computer-aided design, education, personnel recruitment, curriculum

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Authors: Abeer A. Aljohani

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COVID-19 virus spread has been one of the extreme pandemics across the globe. It is also referred to as coronavirus, which is a contagious disease that continuously mutates into numerous variants. Currently, the B.1.1.529 variant labeled as omicron is detected in South Africa. The huge spread of COVID-19 disease has affected several lives and has surged exceptional pressure on the healthcare systems worldwide. Also, everyday life and the global economy have been at stake. This research aims to predict COVID-19 disease in its initial stage to reduce the death count. Machine learning (ML) is nowadays used in almost every area. Numerous COVID-19 cases have produced a huge burden on the hospitals as well as health workers. To reduce this burden, this paper predicts COVID-19 disease is based on the symptoms and medical history of the patient. This research presents a unique architecture for COVID-19 detection using ML techniques integrated with feature dimensionality reduction. This paper uses a standard UCI dataset for predicting COVID-19 disease. This dataset comprises symptoms of 5434 patients. This paper also compares several supervised ML techniques to the presented architecture. The architecture has also utilized 10-fold cross validation process for generalization and the principal component analysis (PCA) technique for feature reduction. Standard parameters are used to evaluate the proposed architecture including F1-Score, precision, accuracy, recall, receiver operating characteristic (ROC), and area under curve (AUC). The results depict that decision tree, random forest, and neural networks outperform all other state-of-the-art ML techniques. This achieved result can help effectively in identifying COVID-19 infection cases.

Keywords: supervised machine learning, COVID-19 prediction, healthcare analytics, random forest, neural network

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Authors: Vishal Jaunky, Jonas Grafström

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The development of the European Union’s (EU) single economic market and rapid technological change has resulted in major structural changes in EU’s member states economies. The general liberalization process that the countries has undergone together has convinced the governments of the member states of need to upgrade their economic and training systems in order to be able to face the economic globalization. Several signs of economic convergence have been found but less is known about the knowledge production. This paper addresses the convergence pattern of technological innovation in 13 European Union (EU) states over the time period 1990-2011 by means of parametric and non-parametric techniques. Parametric approaches revolve around the neoclassical convergence theories. This paper reveals divergence of both the β and σ types. Further, we found evidence of stochastic divergence and non-parametric convergence approach such as distribution dynamics shows a tendency towards divergence. This result is supported with the occurrence of γ-divergence. The policies of the EU to reduce technological gap among its member states seem to be missing its target, something that can have negative long run consequences for the market.

Keywords: convergence, patents, panel data, European union

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Authors: Vladimir A. Grachev

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Today, in the context of rapidly developing technosphere and hourly emerging new technologies, the industrial and environmental safety issue is ever more pressing. The article is devoted to the relationship of social, environmental, and industrial policies with industrial safety, occupational health and safety, environmental safety, and environmental protection. The author assesses the up-to-day situation through system analysis and on the basis of the existing practices. A complex system of the policies implementation without "gaps" and missing links ensures preservation of human lives, health and a favorable living environment. The author demonstrates that absence of an "environmental safety" high-priority link can lead to a significant loss of human lives and health and the global changes in the environment. The role of implementing the environmental policy of enterprises and organizations, and of economic sectors in the implementation of national environmental policy is shown. It was established that the system for implementing environmental policy should be based on a system analysis.

Keywords: environmental protection, environmental safety, industrial safety, occupational health and safety

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Authors: John Akpesiri Olotewo

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One of the controversies in existing marketing literatures is on how to retain existing and new customers to have repurchase intention in the long-run; however, empirical answer to this question is scanty in existing studies. Thus, this study investigates the moderating effect of consumer loyalty on long-run repurchase intentions in telecommunication industry using Lagos State environs. The study adopted field survey research design using questionnaire to elicit responses from 250 respondents who were selected using random and stratified random sampling techniques from the telecommunication industry in Lagos State, Nigeria. The internal consistency of the research instrument was verified using the Cronbach’s alpha, the result of 0.89 implies the acceptability of the internal consistency of the survey instrument. The test of the research hypotheses were analyzed using Pearson Product Method of Correlation (PPMC), simple regression analysis and inferential statistics with the aid of Statistical Package for Social Science version 20.0 (SPSS). The study confirmed that customer satisfaction has a significant relationship with customer loyalty in the telecommunication industry; also Service quality has a significant relationship with customer loyalty to a brand; loyalty programs have a significant relationship with customer loyalty to a network operator in Nigeria and Customer loyalty has a significant effect on the long run repurchase intentions of the customer. The study concluded that one of the determinants of long term profitability of a business entity is the long run repurchase intentions of its customers which hinges on the level of brand loyalty of the customer. Thus, it was recommended that service providers in Nigeria should improve on factors like customer satisfaction, service quality, and loyalty programs in order to increase the loyalty of their customer to their brands thereby increasing their repurchase intentions.

Keywords: customer loyalty, long run repurchase intentions, brands, service quality and customer satisfaction

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Authors: Odeh Regina Comfort, Angelina Okewu Ogwuche

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The study investigated transformational leadership style of principal and conflict management in secondary schools in North Central Nigeria. A descriptive survey design was adopted. The population of the study comprised 34,473 teachers in 1949 public secondary schools in the study area. Proportionate stratified random sampling and simple random sampling techniques were used to select 39 public secondary schools and 689 respondents, respectively, for the study. The researcher utilized a self-structured questionnaire titled 'Influence of Transformational Leadership Style Questionnaire (ITLSQ)'. Face and content validity were ensured. The reliability index of 0.86 was obtained through Cronbach alpha statistics. The instrument was a modified Likert rating scale of Very High Extent (4), High Extent (3), Low Extent (2) and Very Low Extent (1). Mean, and standard deviation were used to answer 2 research questions, while chi-square goodness of fit was used to test the 2 hypotheses at 0.05 level of significance. The results among others indicate: that intellectual stimulation and individualized components of transformational leadership style of principal in public secondary schools in the study area have significant influence on conflict management in secondary schools. Based on the results, it was recommended that principals of secondary schools should be encouraged to practice the intellectual stimulation component of transformational leadership style that would help to consider teachers' levels of knowledge to decide what suits them to reach high levels of attainment thereby minimizing conflict in school settings; also transformational leadership should be taught to all people at all levels of secondary school especially that which pertains to individualized consideration to have a positive impact on the overall performance of teachers and this would help to minimize conflict in schools.

Keywords: conflict management, individualized consideration, intellectual stimulation, transformational leadership style

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Authors: D. Calogine, O. Chau, S. Dotti, O. Ramiarinjanahary, P. Rasoavonjy, F. Tovondahiniriko

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Mafate is a natural circus in the north-western part of Reunion Island, without an electrical grid and road network. A micro-grid concept is being experimented in this area, composed of a photovoltaic production combined with electrochemical batteries, in order to meet the local population for self-consumption of electricity demands. This work develops a discrete model as well as a stochastic model in order to reach an optimal equilibrium between production and consumptions for a cluster of houses. The management of the energy power leads to a large linearized programming system, where the time interval of interest is 24 hours The experimental data are solar production, storage energy, and the parameters of the different electrical devices and batteries. The unknown variables to evaluate are the consumptions of the various electrical services, the energy drawn from and stored in the batteries, and the inhabitants’ planning wishes. The objective is to fit the solar production to the electrical consumption of the inhabitants, with an optimal use of the energies in the batteries by satisfying as widely as possible the users' planning requirements. In the discrete model, the different parameters and solutions of the linear programming system are deterministic scalars. Whereas in the stochastic approach, the data parameters and the linear programming solutions become random variables, then the distributions of which could be imposed or established by estimation from samples of real observations or from samples of optimal discrete equilibrium solutions.

Keywords: photovoltaic production, power consumption, battery storage resources, random variables, stochastic modeling, estimations of probability distributions, mixed integer linear programming, smart micro-grid, self-consumption of electricity.

Procedia PDF Downloads 87

Authors: Benjamin Leiby, Darryl Ahner

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This study demonstrates an alternative stochastic imputation approach for large datasets when preferred commercial packages struggle to iterate due to numerical problems. A large country conflict dataset motivates the search to impute missing values well over a common threshold of 20% missingness. The methodology capitalizes on correlation while using model residuals to provide the uncertainty in estimating unknown values. Examination of the methodology provides insight toward choosing linear or nonlinear modeling terms. Static tolerances common in most packages are replaced with tailorable tolerances that exploit residuals to fit each data element. The methodology evaluation includes observing computation time, model fit, and the comparison of known values to replaced values created through imputation. Overall, the country conflict dataset illustrates promise with modeling first-order interactions while presenting a need for further refinement that mimics predictive mean matching.

Keywords: correlation, country conflict, imputation, stochastic regression

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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: Ehsan Sadie

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Today, the cost of repair and maintenance of structures is very important and by studying the behavior of reinforced concrete structures Will become specified several factors such as : Design and calculation errors, lack of proper implementation of structural changes, the damage caused by the introduction of random loads, concrete corrosion and environmental conditions reduce durability of the structures . Meanwhile building codes alteration also cause changes in the assessment and review of the design and structure rather if necessary will be improved and strengthened in the future.

Keywords: concrete building , expandable cement, honeycombed surface , reinforcement corrosion

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Authors: Said Ali Al-Hadhrami, Amer Ibrahim Al-Omari

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In this paper, Bayesian estimation for the mean of exponential distribution is considered using Moving Extremes Ranked Set Sampling (MERSS). Three priors are used; Jeffery, conjugate and constant using MERSS and Simple Random Sampling (SRS). Some properties of the proposed estimators are investigated. It is found that the suggested estimators using MERSS are more efficient than its counterparts based on SRS.

Keywords: Bayesian, efficiency, moving extreme ranked set sampling, ranked set sampling

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Authors: Harman Jaggi, David Steinsaltz, Shripad Tuljapurkar

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Understanding the effect of fluctuations on populations is crucial in the context of increasing habitat fragmentation, climate change, and biological invasions, among others. Migration in response to environmental disturbances enables populations to escape unfavorable conditions, benefit from new environments and thereby ride out fluctuations in variable environments. Would populations disperse if there is no uncertainty? Karlin showed in 1982 that when sub-populations experience distinct but fixed growth rates at different sites, greater mixing of populations will lower the overall growth rate relative to the most favorable site. Here we ask if and when environmental variability favors migration over no-migration. Specifically, in random environments, would a small amount of migration increase the overall long-run growth rate relative to the zero migration case? We use analysis and simulations to show how long-run growth rate changes with migration rate. Our results show that when fitness (dis)advantages fluctuate over time across sites, migration may allow populations to benefit from variability. When there is one best site with highest growth rate, the effect of migration on long-run growth rate depends on the difference in expected growth between sites, scaled by the variance of the difference. When variance is large, there is a substantial probability of an inferior site experiencing higher growth rate than its average. Thus, a high variance can compensate for a difference in average growth rates between sites. Positive correlations in growth rates across sites favor less migration. With multiple sites and large fluctuations, the length of shortest cycle (excursion) from the best site (on average) matters, and we explore the interplay between excursion length, average differences between sites and the size of fluctuations. Our findings have implications for conservation biology: even when there are superior sites in a sea of poor habitats, variability and habitat quality across space may be key to determining the importance of migration.

Keywords: migration, variable-environments, random, dispersal, fluctuations, habitat-quality

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Authors: Mahzabin Afroz, Indubhushan Patnaikuni, Srikanth Venkatesan

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It is well-known that fly ash can be used in high volume as a partial replacement of cement to get beneficial effects on concrete. High volume fly ash (HVFA) concrete is currently emerging as a popular option to strengthen by fiber. Although studies have supported the use of fibers with fly ash, a unified model along with the incorporation into finite element software package to estimate the maximum flexural loads need to be developed. In this study, nonlinear finite element analysis of steel fiber reinforced high strength HVFA concrete beam under static loadings was conducted to investigate their failure modes in terms of ultimate load. First of all, the experimental investigation of mechanical properties of high strength HVFA concrete was done and validates with developed numerical model with the appropriate modeling of element size and mesh by ANSYS 16.2. To model the fiber within the concrete, three-dimensional random fiber distribution was simulated by spherical coordinate system. Three types of high strength HVFA concrete beams were analyzed reinforced with 0.5, 1 and 1.5% volume fractions of steel fibers with specific mechanical and physical properties. The result reveals that the use of nonlinear finite element analysis technique and three-dimensional random fiber orientation exhibited fairly good agreement with the experimental results of flexural strength, load deflection and crack propagation mechanism. By utilizing this improved model, it is possible to determine the flexural behavior of different types and proportions of steel fiber reinforced HVFA concrete beam under static load. So, this paper has the originality to predict the flexural properties of steel fiber reinforced high strength HVFA concrete by numerical simulations.

Keywords: finite element analysis, high volume fly ash, steel fibers, spherical coordinate system

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Authors: Amjad Al Shammari, Ashraf Ibrahim, Laila Seada

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Background and Objectives: Incidence of thyroid carcinoma has been increasing world-wide. In the present study, we evaluated diagnostic accuracy of Fine needle aspiration (FNA) and its efficiency in early detecting neoplastic lesions of thyroid gland over a 3-year period. Methods: Data have been retrieved from pathology files in King Khalid Hospital. For each patient, age, gender, FNA, site & size of nodule and final histopathologic diagnosis were recorded. Results: Study included 490 cases where 419 of them were female and 71 male. Male to female ratio was 1:6. Mean age was 43 years for males and 38 for females. Cases with confirmed histopathology were 131. In 101/131 (77.1%), concordance was found between FNA and histology. In 30/131 (22.9%), there was discrepancy in diagnosis. Total malignant cases were 43, out of which 14 (32.5%) were true positive and 29 (67.44%) were false negative. No false positive cases could be found in our series. Conclusion: FNA could diagnose benign nodules in all cases, however, in malignant cases, ultrasound findings have to be taken into consideration to avoid missing of a microcarcinoma in the contralateral lobe.

Keywords: FNA, hail, histopathology, thyroid

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Authors: Jiheon Park, Taekwang Kim, Kwang Ryel Ryu

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Electric automated guided vehicles (AGVs) are becoming popular in many automated container terminals nowadays because they are pollution-free and environmentally friendly vehicles for transporting the containers within the terminal. Since efficient operation of AGVs is critical for the productivity of the container terminal, the replacement of batteries of the AGVs must be conducted in a strategic way to minimize undesirable transportation interruptions. While a too frequent replacement may lead to a loss of terminal productivity by delaying container deliveries, missing the right timing of battery replacement can result in a dead AGV that causes a severer productivity loss due to the extra efforts required to finish post treatment. In this paper, we propose a strategy for battery replacement based on a scoring function of multiple criteria taking into account the current battery level, the distances to different battery stations, and the progress of the terminal job operations. The strategy is optimized using a genetic algorithm with the objectives of minimizing the total time spent for battery replacement as well as maximizing the terminal productivity.

Keywords: AGV operation, automated container terminal, battery replacement, electric AGV, strategy optimization

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Authors: Mohammad Ba’azm

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The self-referentiality method as the missing ring of the Qur’an by Qur’an’s interpretation has a precise application at the level of the Quranic vocabulary, but after entering the domain of the verses, chapters and the whole Qur’an, it reveals its defect. Self-referentiality cannot show the clear concept of the Quranic scriptures, unlike the Qur’an by Qur’an’s interpretation method that guides us to the comprehension and exact hermeneutics. The Qur’an by Qur’an’s interpretation is a solid way of comprehension of the verses of the Qur'an and does not use external resources to provide implications and meanings with different theoretical and practical supports. In this method, theoretical supports are based on the basics and modalities that support and validate the legitimacy and validity of the interpretive method discussed, and the practical supports also relate to the practitioners of the religious elite. The combination of these two methods illustrates the exact understanding of the Qur'an at the level of Quranic verses, chapters, and the whole Qur’an. This study by examining the word 'book' in the Qur'an shows the difference between the two methods, and the necessity of attachment of these, in order to attain a desirable level for comprehensions meaning of the Qur'an. In this article, we have proven that by aspects of the meaning of the Quranic words, we cannot say any word has an exact meaning.

Keywords: Qur’an’s hermeneutic, self-referentiality, The Qur’an by Qur’an’s Interpretation, polysemy

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Authors: Tao Peng, Jing Zhao, Yanqing Xu, Jing Cai

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Due to missing/ambiguous boundaries between the prostate and neighboring structures, the presence of shadow artifacts, as well as the large variability in prostate shapes, ultrasound prostate segmentation is challenging. To handle these issues, this paper develops a hybrid method for ultrasound prostate segmentation by combining an optimized closed principal curve-based method and the evolutionary neural network; the former can fit curves with great curvature and generate a contour composed of line segments connected by sorted vertices, and the latter is used to express an appropriate map function (represented by parameters of evolutionary neural network) for generating the smooth prostate contour to match the ground truth contour. Both qualitative and quantitative experimental results showed that our proposed method obtains accurate and robust performances.

Keywords: ultrasound prostate segmentation, optimized closed polygonal segment method, evolutionary neural network, smooth mathematical model, principal curve

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Authors: Eman W. Boghdady, A. R. Shehata, M. A. Azem

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Database (DB) security demands permitting authorized users and prohibiting non-authorized users and intruders actions on the DB and the objects inside it. Organizations that are running successfully demand the confidentiality of their DBs. They do not allow the unauthorized access to their data/information. They also demand the assurance that their data is protected against any malicious or accidental modification. DB protection and confidentiality are the security concerns. There are four types of controls to obtain the DB protection, those include: access control, information flow control, inference control, and cryptographic. The cryptographic control is considered as the backbone for DB security, it secures the DB by encryption during storage and communications. Current cryptographic techniques are classified into two types: traditional classical cryptography using standard algorithms (DES, AES, IDEA, etc.) and chaos cryptography using continuous (Chau, Rossler, Lorenz, etc.) or discreet (Logistics, Henon, etc.) algorithms. The important characteristics of chaos are its extreme sensitivity to initial conditions of the system. In this paper, DB-security systems based on chaotic algorithms are described. The Pseudo Random Numbers Generators (PRNGs) from the different chaotic algorithms are implemented using Matlab and their statistical properties are evaluated using NIST and other statistical test-suits. Then, these algorithms are used to secure conventional DB (plaintext), where the statistical properties of the ciphertext are also tested. To increase the complexity of the PRNGs and to let pass all the NIST statistical tests, we propose two hybrid PRNGs: one based on two chaotic Logistic maps and another based on two chaotic Henon maps, where each chaotic algorithm is running side-by-side and starting from random independent initial conditions and parameters (encryption keys). The resulted hybrid PRNGs passed the NIST statistical test suit.

Keywords: algorithms and data structure, DB security, encryption, chaotic algorithms, Matlab, NIST

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Authors: Trad Khodja Rafik, Mahtout Amine, Ghoul Rachid, Benbouali Amine, Boulahlib Amine, Hariza Abdelmalik

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Cranioplasty with custom implant realized using 3D printing technology. Cranioplasty is a surgical act that aims restoring cranial bone losses in order to protect the brain from external aggressions and to improve the patient aesthetic appearance. This objective can be achieved with taking advantage of the current technological development in computer science and biomechanics. The objective of this paper it to present an approach for the realization of high precision biocompatible cranial implants using new 3D printing technologies at the lowest cost. The proposed method is to reproduce the missing part of the skull by referring to its healthy contralateral part. Once the model is validated by the neurosurgeons, a mold is 3D printed for the production of a biocompatible implant in Poly-Methyl-Methacrylate (PMMA) acrylic cement. Using this procedure four patients underwent this procedure with excellent aesthetic results.

Keywords: cranioplasty, cranial bone loss, 3D printing technology, custom-made implants, PMMA

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Authors: Durga Toshniwal, Lokesh Agrawal

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As the web continues to play an increasing role in information exchange, and conducting daily activities, computer users have become the target of miscreants which infects hosts with malware or adware for financial gains. Unfortunately, even a single visit to compromised web site enables the attacker to detect vulnerabilities in the user’s applications and force the downloading of multitude of malware binaries. We provide an approach to effectively scan the so-called drive-by downloads on the Internet. Drive-by downloads are result of URLs that attempt to exploit their visitors and cause malware to be installed and run automatically. To scan the web for malicious pages, the first step is to use a crawler to collect URLs that live on the Internet, and then to apply fast prefiltering techniques to reduce the amount of pages that are needed to be examined by precise, but slower, analysis tools (such as honey clients or antivirus programs). Although the technique is effective, it requires a substantial amount of resources. A main reason is that the crawler encounters many pages on the web that are legitimate and needs to be filtered. In this paper, to characterize the nature of this rising threat, we present implementation of a web crawler on Python, an approach to search the web more efficiently for pages that are likely to be malicious, filtering benign pages and passing remaining pages to antivirus program for detection of malwares. Our approaches starts from an initial seed of known, malicious web pages. Using these seeds, our system generates search engines queries to identify other malicious pages that are similar to the ones in the initial seed. By doing so, it leverages the crawling infrastructure of search engines to retrieve URLs that are much more likely to be malicious than a random page on the web. The results shows that this guided approach is able to identify malicious web pages more efficiently when compared to random crawling-based approaches.

Keywords: web crawler, malwares, seeds, drive-by-downloads, security

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Authors: Peter Olasupo Ogunjuyigbe, Sarafa Shittu

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This paper emphasizes the relevance of conjugal relationship and spousal communication towards enhancing men’s involvement in contraceptive use among the Yorubas of South Western Nigeria. An understanding of males influence and the role they play in reproductive decision making can throw better light on mechanisms through which egalitarianness of husband/wife decision making influences contraceptive use. The objective of this study was to investigate how close conjugal relationships can be a good indicator of joint decision making among couples using data derived from a survey conducted in three states of South Western Nigeria. The study sample consisted of five hundred and twenty one (521) male respondents aged 15-59 years and five hundred and forty seven (547) female respondents aged 15-49 years. The study used both quantitative and qualitative approached to elicit information from the respondents. In order that the study would be truly representative of the towns, each of the study locations in the capital cities was divided into four strata: The traditional area, the migrant area, the mixed area (i.e. traditional and migrant), and the elite area. In the rural areas, selection of the respondents was by simple random sampling technique. However, the random selection was made in such a way that all the different parts of the locations were represented. Generally, the data collected were analysed at univariate, bivariate, and multivariate levels. Logistic regression models were employed to examine the interrelationships between male reproductive behaviour, conjugal relationship and contraceptive use. The study indicates that current use of contraceptive is high among this major ethnic group in Nigeria because of the improved level of communication among couples. The problem, however, is that men still have lower exposure rate when it comes to question of family planning information, education and counseling. This has serious implications on fertility regulation in Nigeria.

Keywords: behavior, conjugal, communication, counseling, spouse

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Authors: Ambali Alade Odebowale, Andargachew Mekonnen Berhe, Haroldo T. Hattori, Andrey E. Miroshnichenko

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Solar thermophotovoltaic systems (STPV) have emerged as a promising solution to overcome the Shockley-Queisser limit, a significant impediment in the direct conversion of solar radiation into electricity using conventional solar cells. The STPV system comprises essential components such as an optical concentrator, selective emitter, and a thermophotovoltaic (TPV) cell. The pivotal element in achieving high efficiency in an STPV system lies in the design of a spectrally selective emitter or absorber. Traditional methods for designing and optimizing selective emitters are often time-consuming and may not yield highly selective emitters, posing a challenge to the overall system performance. In recent years, the application of machine learning techniques in various scientific disciplines has demonstrated significant advantages. This paper proposes a novel nanostructure composed of four-layered materials (SiC/W/SiO2/W) to function as a selective emitter in the energy conversion process of an STPV system. Unlike conventional approaches widely adopted by researchers, this study employs a machine learning-based approach for the design and optimization of the selective emitter. Specifically, a random forest algorithm (RFA) is employed for the design of the selective emitter, while the optimization process is executed using genetic algorithms. This innovative methodology holds promise in addressing the challenges posed by traditional methods, offering a more efficient and streamlined approach to selective emitter design. The utilization of a machine learning approach brings several advantages to the design and optimization of a selective emitter within the STPV system. Machine learning algorithms, such as the random forest algorithm, have the capability to analyze complex datasets and identify intricate patterns that may not be apparent through traditional methods. This allows for a more comprehensive exploration of the design space, potentially leading to highly efficient emitter configurations. Moreover, the application of genetic algorithms in the optimization process enhances the adaptability and efficiency of the overall system. Genetic algorithms mimic the principles of natural selection, enabling the exploration of a diverse range of emitter configurations and facilitating the identification of optimal solutions. This not only accelerates the design and optimization process but also increases the likelihood of discovering configurations that exhibit superior performance compared to traditional methods. In conclusion, the integration of machine learning techniques in the design and optimization of a selective emitter for solar thermophotovoltaic systems represents a groundbreaking approach. This innovative methodology not only addresses the limitations of traditional methods but also holds the potential to significantly improve the overall performance of STPV systems, paving the way for enhanced solar energy conversion efficiency.

Keywords: emitter, genetic algorithm, radiation, random forest, thermophotovoltaic

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Authors: Reza Miresmaeili, Asghar Heydari Astaraee, Fereshteh Dolati

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In the present study, an analytical model based on dislocation density model was developed to simulate grain refinement in surface mechanical attrition treatment (SMAT). The correlation between SMAT time and development in plastic strain on one hand, and dislocation density evolution, on the other hand, was established to simulate the grain refinement in SMAT. A dislocation density-based constitutive material law was implemented using VUHARD subroutine. A random sequence of shots is taken into consideration for multiple impacts model using Python programming language by utilizing a random function. The simulation technique was to model each impact in a separate run and then transferring the results of each run as initial conditions for the next run (impact). The developed Finite Element (FE) model of multiple impacts describes the coverage evolution in SMAT. Simulations were run to coverage levels as high as 4500%. It is shown that the coverage implemented in the FE model is equal to the experimental coverage. It is depicted that numerical SMAT coverage parameter is adequately conforming to the well-known Avrami model. Comparison between numerical results and experimental measurements for residual stresses and depth of deformation layers confirms the performance of the established FE model for surface engineering evaluations in SMA treatment. X-ray diffraction (XRD) studies of grain refinement, including resultant grain size and dislocation density, were conducted to validate the established model. The full width at half-maximum in XRD profiles can be used to measure the grain size. Numerical results and experimental measurements of grain refinement illustrate good agreement and show the capability of established FE model to predict the gradient microstructure in SMA treatment.

Keywords: dislocation density, grain refinement, severe plastic deformation, simulation, surface mechanical attrition treatment

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Authors: Chris Ewe, Naoum Jamous, Holger Schrödl

Abstract:

Like most entrepreneurs, data center operators pursue goals such as profit-maximization, improvement of the company’s reputation or basically to exist on the market. Part of those aims is to guarantee a given quality of service. Quality characteristics are specified in a contract called the service level agreement. Central part of this agreement is non-functional properties of an IT service. The system availability is one of the most important properties as it will be shown in this paper. To comply with availability requirements, data center operators can use virtualization technologies. A clear model to assess the effect of virtualization functions on the parts of a data center in relation to the system availability is still missing. This paper aims to introduce a basic model that shows these connections, and consider if the identified effects are positive or negative. Thus, this work also points out possible disadvantages of the technology. In consequence, the paper shows opportunities as well as risks of data center virtualization in relation to system availability.

Keywords: availability, cloud computing IT service, quality of service, service level agreement, virtualization

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Authors: Nicoleta O. Tanase, Ciprian S. Mateescu

Abstract:

This paper is concerned with the numerical study of the flow through porous media. The purpose of this project is to determine the permeability of a medium and its connection to porosity to be able to identify how the permeability of said medium can be altered without changing the porosity. The numerical simulations are performed in 2D flow configurations with the laminar solvers implemented in Workbench - ANSYS Fluent. The direction of flow of the working fluid (water) is axial, from left to right, and in steady-state conditions. The working fluid is water. The 2D geometry is a channel with 300 mm length and 30 mm width, with a different number of circles that are positioned differently, modelling a porous medium. The permeability of a porous medium can be altered without changing the porosity by positioning the circles differently (by missing the same number of circles) in the flow domain, which induces a change in the flow spectrum. The main goal of the paper is to investigate the flow pattern and permeability under controlled perturbations induced by the variation of velocity and porous medium. Numerical solutions provide insight into all flow magnitudes, one of the most important being the WSS distribution on the circles.

Keywords: CFD, porous media, permeability, flow spectrum

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Authors: Hossam Abdelrahman, Sama Rostom, Reem Yassein, Yara Mohamed, Salma Salah, Nour Awny

Abstract:

In today’s environment, people are becoming increasingly interested in their appearance. However, they are afraid of their unknown appearance after a plastic surgery or treatment. Accidents, burns and genetic problems such as bowing of body parts of people have a negative impact on their mental health with their appearance and this makes them feel uncomfortable and underestimated. The approach presents a revolutionary deep learning-based image inpainting method that analyses the various picture structures and corrects damaged images. In this study, A model is proposed based on the in-painting of medical images with Stable Diffusion Inpainting method. Reconstructing missing and damaged sections of an image is known as image inpainting is a key progress facilitated by deep neural networks. The system uses the input of the user of an image to indicate a problem, the system will then modify the image and output the fixed image, facilitating for the patient to see the final result.

Keywords: generative adversarial network, large mask inpainting, stable diffusion inpainting, plastic surgery

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Authors: Brahmanage S. Rangika, Dinithi C. Peiris

Abstract:

Boiled Aqueous Flower Extract (AFE) of Nyctanthes arbor-tristis L. (Family: Oleaceae) is used in traditional Sri Lankan medicinal system to treat diabetes. However, this is not scientifically proven and the mechanisms by which the flowers reduce diabetes have not been investigated. The present study was carried out to examine the hypoglycemic potential and toxicity effects of aqueous flower extract of N. arbor-tristis. AFE was prepared and mice were treated orally either with 250, 500, and 750 mg/kg of AFE or distilled water (Control). Fasting and random blood glucose levels were determined. In addition, the toxicity of AFE was determined using chronic oral administration. In normoglycemic mice, mid dose (500mg/kg) of AFE significantly (p < 0.01) reduced fasting blood glucose levels by 49% at 4h post treatment. Further, 500mg/kg of AFE significantly (p < 0.01) lowered random blood glucose level of non-fasted normoglycemic mice. AFE significantly lowered total cholesterol and triglyceride levels while increasing the HDL levels in the serum. Further, AFE significantly inhibited the glucose absorption from the lumen of the intestine and it increases the diaphragm uptake of glucose. Alpha-amylase inhibitory activity was also evident. However, AFE did not induce any overt signs of toxicity or hepatotoxicity. There were no adverse effects on food and water intake and body weight of mice during the experimental period. It can be concluded that AFE of N. arbor-tristis posses safe oral anti diabetic potentials mediated via multiple mechanisms. Results of the present study scientifically proved the claims made about the uses of N. arbor-tristis in the treatment of diabetes mellitus in traditional Sri Lankan medicinal system. Further, flowers can also be used for as a remedy to improve blood lipid profile.

Keywords: aqueous extract, hypoglycemic hypolipidemic, Nyctanthes arbor-tristis flowers, hepatotoxicity

Procedia PDF Downloads 345