Search results for: foundation models

Authors: Nani Manvelishvili

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Soviet propaganda tried to shape common public opinion through Soviet Press. The activation of propaganda gained special importance to increase the fighting ability of the military and people behind the front During the Great Patriotic war (1941-1945). The state propaganda used unnecessary intervention in Press and created characters who were supposed to be role models for society. The new female role models were identified, which were supported by the authorities. The representation of the mother, warrior woman, working woman, victim, feminine woman, etc., in the works aimed to raise the fighting ability of the Soviet citizen and incite patriotism. This paper analyzes the soviet Press (The newspaper “Komunisti”) that was written and published during the Great Patriotic war in Soviet Georgia. The study aims to find propagandistic content in Press that used Soviet ideology during the Great Patriotic war. We analyzed the Soviet Newspaper "Komunisti," published during wartime. Soviet Press had the most significant impact on the formation of public opinion. The Soviet government actively used this resource to increase combat capability. While at the beginning of the war, women were supposed to replace men, propaganda by the end of the war moved to reassert conservative gender politics. Women returned to their traditional roles.

Keywords: Great Patriotic War, Soviet Georgia, women in war, women's history, Soviet press

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Authors: Niloofar Yousefi, Marie Alaghband, Ivan Garibay

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With the increase of credit card usage, the volume of credit card misuse also has significantly increased, which may cause appreciable financial losses for both credit card holders and financial organizations issuing credit cards. As a result, financial organizations are working hard on developing and deploying credit card fraud detection methods, in order to adapt to ever-evolving, increasingly sophisticated defrauding strategies and identifying illicit transactions as quickly as possible to protect themselves and their customers. Compounding on the complex nature of such adverse strategies, credit card fraudulent activities are rare events compared to the number of legitimate transactions. Hence, the challenge to develop fraud detection that are accurate and efficient is substantially intensified and, as a consequence, credit card fraud detection has lately become a very active area of research. In this work, we provide a survey of current techniques most relevant to the problem of credit card fraud detection. We carry out our survey in two main parts. In the first part, we focus on studies utilizing classical machine learning models, which mostly employ traditional transnational features to make fraud predictions. These models typically rely on some static physical characteristics, such as what the user knows (knowledge-based method), or what he/she has access to (object-based method). In the second part of our survey, we review more advanced techniques of user authentication, which use behavioral biometrics to identify an individual based on his/her unique behavior while he/she is interacting with his/her electronic devices. These approaches rely on how people behave (instead of what they do), which cannot be easily forged. By providing an overview of current approaches and the results reported in the literature, this survey aims to drive the future research agenda for the community in order to develop more accurate, reliable and scalable models of credit card fraud detection.

Keywords: Credit Card Fraud Detection, User Authentication, Behavioral Biometrics, Machine Learning, Literature Survey

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Authors: Yuna Lee

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In the current policy process, there has been a growing interest in more open approaches that incorporate creativity and innovation based on the forecasting groups composed by the public and experts together into scientific data-driven foresight methods to implement more effective policymaking. Especially, citizen participation as collective intelligence in policymaking with design and deep scale of innovation at the global level has been developed and human-centred design thinking is considered as one of the most promising methods for strategic foresight. Yet, there is a lack of a common theoretical foundation for a comprehensive approach for the current situation of and post-COVID-19 era, and substantial changes in policymaking practice are insignificant and ongoing with trial and error. This project hypothesized that rigorously developed policy systems and tools that support strategic foresight by considering the public understanding could maximize ways to create new possibilities for a preferable future, however, it must involve a better understating of Behavioural Insights, including individual and cultural values, profit motives and needs, and psychological motivations, for implementing holistic and multilateral foresight and creating more positive possibilities. To what extent is the policymaking system theoretically possible that incorporates the holistic and comprehensive foresight and policy process implementation, assuming that theory and practice, in reality, are different and not connected? What components and environmental conditions should be included in the strategic foresight system to enhance the capacity of decision from policymakers to predict alternative futures, or detect uncertainties of the future more accurately? And, compared to the required environmental condition, what are the environmental vulnerabilities of the current policymaking system? In this light, this research contemplates the question of how effectively policymaking practices have been implemented through the synthesis of scientific, technology-oriented innovation with the strategic design for tackling complex societal challenges and devising more significant insights to make society greener and more liveable. Here, this study conceptualizes the notions of a new collaborative way of strategic foresight that aims to maximize mutual benefits between policy actors and citizens through the cooperation stemming from evolutionary game theory. This study applies mixed methodology, including interviews of policy experts, with the case in which digital transformation and strategic design provided future-oriented solutions or directions to cities’ sustainable development goals and society-wide urgent challenges such as COVID-19. As a result, artistic and sensual interpreting capabilities through strategic design promote a concrete form of ideas toward a stable connection from the present to the future and enhance the understanding and active cooperation among decision-makers, stakeholders, and citizens. Ultimately, an improved theoretical foundation proposed in this study is expected to help strategically respond to the highly interconnected future changes of the post-COVID-19 world.

Keywords: policymaking, strategic design, sustainable innovation, evolution of cooperation

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Authors: F. Saptouw

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This paper will examine the value of introducing social justice issues into the tertiary fine art curriculum at a first-year level. The paper will present detail of the conceptual impetus and the logistics related to the execution of a collaborative teaching project. The cohort of students was registered for the Fine Art Foundation course at the Michaelis School of Fine Art at the University of Cape Town. The course is dedicated to the development of critical thinking, communication skills, and varied approaches to knowledge construction within the first-year cohort. A core component of the course is the examination of the representation of gender, identity, politics, and power. These issues are examined within a range of public and private representations like art galleries, museum spaces, and contemporary popular culture. This particular project was a collaborative project with the Office of Inclusivity and Change, and the project leaders were Fabian Saptouw and Gabriel Khan. The paper will conclude by presenting an argument for the importance of such projects within the tertiary environment.

Keywords: art, education, gender-based violence, social responsiveness

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Authors: František Šebek, Petr Kubík, Jindřich Petruška, Jiří Hůlka

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Present study is aimed on the cutting process of circular cross-section rods where the fracture is used to separate one rod into two pieces. Incorporating the phenomenological ductile fracture model into the explicit formulation of finite element method, the process can be analyzed without the necessity of realizing too many real experiments which could be expensive in case of repetitive testing in different conditions. In the present paper, the steel AISI 1045 was examined and the tensile tests of smooth and notched cylindrical bars were conducted together with biaxial testing of the notched tube specimens to calibrate material constants of selected phenomenological ductile fracture models. These were implemented into the Abaqus/Explicit through user subroutine VUMAT and used for cutting process simulation. As the calibration process is based on variables which cannot be obtained directly from experiments, numerical simulations of fracture tests are inevitable part of the calibration. Finally, experiments regarding the cutting process were carried out and predictive capability of selected fracture models is discussed. Concluding remarks then make the summary of gained experience both with the calibration and application of particular ductile fracture criteria.

Keywords: ductile fracture, phenomenological criteria, cutting process, explicit formulation, AISI 1045 steel

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Authors: Mohsen Azarmjoo, Yasaman Azarmjoo, Zahra Alikhani Koopaei

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This article introduces devices that can transfer sunlight to buildings that do not have access to direct sunlight during the day. The transmission and reflection of sunlight are done through the movement of movable mirrors. The focus of this article is on two models of sun tracker systems designed and built by the Macad team. In fact, this article will reveal the distinction between the two Macad devices and the previously built competitor device. What distinguishes the devices built by the Macad team from the competitor's device is the different mode of operation and the difference in the location of the sensors. Given that the devices have the same results, the Macad team has tried to reduce the defects of the competitor's device as much as possible. The special feature of the second type of device built by the Macad team has enabled buildings with different construction positions to use sun tracking systems. This article will also discuss diagrams of the path of sunlight transmission and more details of the device. It is worth mentioning that fixed mirrors are also placed next to the main devices. So that the light shining on the first device is reflected to these mirrors, this light is guided within the light receiver space and is transferred to the different parts around by steel sheets built in the light receiver space, and finally, these spaces benefit from sunlight.

Keywords: design, construction, mechatronic device, sun tracker system, sun tracker, sunlight

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Authors: Rafat Fazeli, Reza Fazeli

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This paper focuses on the study of the welfare state and social wage in the leading liberal economy of the United States. The welfare state acquired a broad acceptance as a major socioeconomic achievement of the liberal democracy in the Western industrialized countries during the postwar boom period. The modern and modified vision of capitalist democracy offered, on the one hand, the possibility of high growth rate and, on the other hand, the possibility of continued progression of a comprehensive system of social support for a wider population. The economic crises of the 1970s, provided the ground for a great shift in economic policy and ideology in several Western countries, most notably the United States and the United Kingdom (and to a lesser extent Canada under Prime Minister Brian Mulroney). In the 1980s, the free market oriented reforms undertaken under Reagan and Thatcher greatly affected the economic outlook not only of the United States and the United Kingdom, but of the whole Western world. The movement which was behind this shift in policy is often called neo-conservatism. The neoconservatives blamed the transfer programs for the decline in economic performance during the 1970s and argued that cuts in spending were required to go back to the golden age of full employment. The agenda for both Reagan and Thatcher administrations was rolling back the welfare state, and their budgets included a wide range of cuts for social programs. The question is how successful were Reagan and Thatcher’s efforts to achieve retrenchment? The paper involves an empirical study concerning the distributive role of the welfare state in the two countries. Other studies have often concentrated on the redistributive effect of fiscal policy on different income brackets. This study examines the net benefit/ burden position of the working population with respect to state expenditures and taxes in the postwar period. This measurement will enable us to find out whether the working population has received a net gain (or net social wage). This study will discuss how the expansion of social expenditures and the trend of the ‘net social wage’ can be linked to distinct forms of economic and social organizations. This study provides an empirical foundation for analyzing the growing significance of ‘social wage’ or the collectivization of consumption and the share of social or collective consumption in total consumption of the working population in the recent decades. The paper addresses three other major questions. The first question is whether the expansion of social expenditures has posed any drag on capital accumulation and economic growth. The findings of this study provide an analytical foundation to evaluate the neoconservative claim that the welfare state is itself the source of economic stagnation that leads to the crisis of the welfare state. The second question is whether the increasing ideological challenges from the right and the competitive pressures of globalization have led to retrenchment of the American welfare states in the recent decades. The third question is how social policies have performed in the presence of the rising inequalities in the recent decades.

Keywords: the welfare state, social wage, The United States, limits to growth

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Authors: Tsega Asresa, Getahun Tigistu, Melaku Bayih

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Computer vision is a subfield of artificial intelligence that allows computers and systems to extract meaning from digital images and video. It is used in a wide range of fields of study, including self-driving cars, video surveillance, medical diagnosis, manufacturing, law, agriculture, quality control, health care, facial recognition, and military applications. Aromatic medicinal plants are botanical raw materials used in cosmetics, medicines, health foods, essential oils, decoration, cleaning, and other natural health products for therapeutic and Aromatic culinary purposes. These plants and their products not only serve as a valuable source of income for farmers and entrepreneurs but also going to export for valuable foreign currency exchange. In Ethiopia, there is a lack of technologies for the classification and identification of Aromatic medicinal plant parts and disease type cured by aromatic medicinal plants. Farmers, industry personnel, academicians, and pharmacists find it difficult to identify plant parts and disease types cured by plants before ingredient extraction in the laboratory. Manual plant identification is a time-consuming, labor-intensive, and lengthy process. To alleviate these challenges, few studies have been conducted in the area to address these issues. One way to overcome these problems is to develop a deep learning model for efficient identification of Aromatic medicinal plant parts with their corresponding disease type. The objective of the proposed study is to identify the aromatic medicinal plant parts and their disease type classification using computer vision technology. Therefore, this research initiated a model for the classification of aromatic medicinal plant parts and their disease type by exploring computer vision technology. Morphological characteristics are still the most important tools for the identification of plants. Leaves are the most widely used parts of plants besides roots, flowers, fruits, and latex. For this study, the researcher used RGB leaf images with a size of 128x128 x3. In this study, the researchers trained five cutting-edge models: convolutional neural network, Inception V3, Residual Neural Network, Mobile Network, and Visual Geometry Group. Those models were chosen after a comprehensive review of the best-performing models. The 80/20 percentage split is used to evaluate the model, and classification metrics are used to compare models. The pre-trained Inception V3 model outperforms well, with training and validation accuracy of 99.8% and 98.7%, respectively.

Keywords: aromatic medicinal plant, computer vision, convolutional neural network, deep learning, plant classification, residual neural network

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Authors: Jefferson Hernandez, Juan Padilla

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Estimation of price elasticity of demand is a valuable tool for the task of price settling. Given its relevance, it is an active field for microeconomic and statistical research. Price elasticity in the industry of oil and gas, in particular for fuels sold in gas stations, has shown to be a challenging topic given the market and state restrictions, and underlying correlations structures between the types of fuels sold by the same gas station. This paper explores the Lotka-Volterra model for the problem for price elasticity estimation in the context of fuels; in addition, it is introduced multivariate random effects with the purpose of dealing with errors, e.g., measurement or missing data errors. In order to model the underlying correlation structures, the Inverse-Wishart, Hierarchical Half-t and LKJ distributions are studied. Here, the Bayesian paradigm through Markov Chain Monte Carlo (MCMC) algorithms for model estimation is considered. Simulation studies covering a wide range of situations were performed in order to evaluate parameter recovery for the proposed models and algorithms. Results revealed that the proposed algorithms recovered quite well all model parameters. Also, a real data set analysis was performed in order to illustrate the proposed approach.

Keywords: price elasticity, volume, correlation structures, Bayesian models

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Authors: Luis Fonseca, Filipe Cabral Pinto, Susana Sargento

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The increase of the world population, especially in large urban centers, has resulted in new challenges particularly with the control and optimization of public safety. Thus, in the present work, a solution is proposed for the prediction of criminal occurrences in a city based on historical data of incidents and demographic information. The entire research and implementation will be presented start with the data collection from its original source, the treatment and transformations applied to them, choice and the evaluation and implementation of the Machine Learning model up to the application layer. Classification models will be implemented to predict criminal risk for a given time interval and location. Machine Learning algorithms such as Random Forest, Neural Networks, K-Nearest Neighbors and Logistic Regression will be used to predict occurrences, and their performance will be compared according to the data processing and transformation used. The results show that the use of Machine Learning techniques helps to anticipate criminal occurrences, which contributed to the reinforcement of public security. Finally, the models were implemented on a platform that will provide an API to enable other entities to make requests for predictions in real-time. An application will also be presented where it is possible to show criminal predictions visually.

Keywords: crime prediction, machine learning, public safety, smart city

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Authors: Erick Pruchnicki, Nikhil Padhye

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Plates are important engineering structures which have attracted extensive research since the 19th century. The subject of this work is statical analysis of a linearly elastic homogenous plate under small deformations. A 'thin plate' is a three-dimensional structure comprising of a small transverse dimension with respect to a flat mid-surface. The general aim of any plate theory is to deduce a two-dimensional model, in terms of mid-surface quantities, to approximately and accurately describe the plate's deformation in terms of mid-surface quantities. In recent decades, a common starting point for this purpose is to utilize series expansion of a displacement field across the thickness dimension in terms of the thickness parameter (h). These attempts are mathematically consistent in deriving leading-order plate theories based on certain a priori scaling between the thickness and the applied loads; for example, asymptotic methods which are aimed at generating leading-order two-dimensional variational problems by postulating formal asymptotic expansion of the displacement fields. Such methods rigorously generate a hierarchy of two-dimensional models depending on the order of magnitude of the applied load with respect to the plate-thickness. However, in practice, applied loads are external and thus not directly linked or dependent on the geometry/thickness of the plate; thus, rendering any such model (based on a priori scaling) of limited practical utility. In other words, the main limitation of these approaches is that they do not furnish a single plate model for all orders of applied loads. Following analogy of recent efforts of deploying Fourier-series expansion to study convergence of reduced models, we propose two-dimensional model(s) resulting from truncation of the potential energy and rigorously prove the convergence of these two-dimensional plate models to the parent three-dimensional linear elasticity with increasing truncation order of the potential energy.

Keywords: plate theory, Fourier-series expansion, convergence result, Legendre polynomials

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Authors: Jonathan Josephs-Spaulding, Hannah Rettig, Simon Graspeunter, Jan Rupp, Christoph Kaleta

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Background: Recurrent urinary tract infections (rUTIs) are a global cause of emergency room visits and represent a significant burden for public health systems. Therefore, metatranscriptomic approaches to investigate metabolic exchange and crosstalk between uropathogenic Escherichia coli (UPEC), which is responsible for 90% of UTIs, and collaborating pathogens of the urogenital microbiome is necessary to better understand the pathogenetic processes underlying rUTIs. Objectives: This study aims to determine the level in which uropathogens optimize the host urinary metabolic environment to succeed during invasion. By developing patient-specific metabolic models of infection, these observations can be taken advantage of for the precision treatment of human disease. Methods: To date, we have set up an rUTI patient cohort and observed various urine-associated pathogens. From this cohort, we developed patient-specific metabolic models to predict bladder microbiome metabolism during rUTIs. This was done by creating an in silico metabolomic urine environment, which is representative of human urine. Metabolic models of uptake and cross-feeding of rUTI pathogens were created from genomes in relation to the artificial urine environment. Finally, microbial interactions were constrained by metatranscriptomics to indicate patient-specific metabolic requirements of pathogenic communities. Results: Metabolite uptake and cross-feeding are essential for strain growth; therefore, we plan to design patient-specific treatments by adjusting urinary metabolites through nutritional regimens to counteract uropathogens by depleting essential growth metabolites. These methods will provide mechanistic insights into the metabolic components of rUTI pathogenesis to provide an evidence-based tool for infection treatment.

Keywords: recurrent urinary tract infections, human microbiome, uropathogenic Escherichia coli, UPEC, microbial ecology

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Authors: Nikola Tomasevic, Marko Batic, Sanja Vranes

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Emerging Semantic Web technologies can be seen as the next step in evolution of the intelligent facility management systems. Particularly, this considers increased usage of open source and/or standardized concepts for data classification and semantic interpretation. To deliver such facility management systems, providing the comprehensive integration and interoperability platform in from of the facility data model is a prerequisite. In this paper, one of the possible modelling approaches to provide such integrative facility data model which was based on the ontology modelling concept was presented. Complete ontology development process, starting from the input data acquisition, ontology concepts definition and finally ontology concepts population, was described. At the beginning, the core facility ontology was developed representing the generic facility infrastructure comprised of the common facility concepts relevant from the facility management perspective. To develop the data model of a specific facility infrastructure, first extension and then population of the core facility ontology was performed. For the development of the full-blown facility data models, Malpensa and Fiumicino airports in Italy, two major European air-traffic hubs, were chosen as a test-bed platform. Furthermore, the way how these ontology models supported the integration and interoperability of the overall airport energy management system was analyzed as well.

Keywords: airport ontology, energy management, facility data model, ontology modeling

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Authors: Chokri Slim

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The objective of this research is attempting to respond to this question: Is there a significant difference between the regression model and statistical learning models in predicting credit risk management? A Multiple Linear Regression (MLR) model was compared with neural networks including Multi-Layer Perceptron (MLP), and a Support vector regression (SVR). The population of this study includes 50 listed Banks in Tunis Stock Exchange (TSE) market from 2000 to 2016. Firstly, we show the factors that have significant effect on the quality of loan portfolios of banks in Tunisia. Secondly, it attempts to establish that the systematic use of objective techniques and methods designed to apprehend and assess risk when considering applications for granting credit, has a positive effect on the quality of loan portfolios of banks and their future collectability. Finally, we will try to show that the bank governance has an impact on the choice of methods and techniques for analyzing and measuring the risks inherent in the banking business, including the risk of non-repayment. The results of empirical tests confirm our claims.

Keywords: credit risk management, multiple linear regression, principal components analysis, artificial neural networks, support vector machines

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Authors: Stuti Modi, Aditi Kambli

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This paper addresses the need for intelligent water management and distribution system in smart cities to ensure optimal consumption and distribution of water for drinking and sanitation purposes. Water being a limited resource in cities require an effective system for collection, storage and distribution. In this paper, applications of two mostly widely used particular types of data-driven models, namely artificial neural networks (ANN) and fuzzy logic-based models, to modelling in the water resources management field are considered. The objective of this paper is to review the principles of various types and architectures of neural network and fuzzy adaptive systems and their applications to integrated water resources management. Final goal of the review is to expose and formulate progressive direction of their applicability and further research of the AI-related and data-driven techniques application and to demonstrate applicability of the neural networks, fuzzy systems and other machine learning techniques in the practical issues of the regional water management. Apart from this the paper will deal with water storage, using ANN to find optimum reservoir level and predicting peak daily demands.

Keywords: artificial neural networks, fuzzy systems, peak daily demand prediction, water management and distribution

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Authors: K. Veluraja

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Glycan of glycolipids and glycoproteins is playing a significant role in living systems particularly in molecular recognition processes. Molecular recognition processes are attributed to their occurrence on the surface of the cell, sequential arrangement and type of sugar molecules present in the oligosaccharide structure and glyosidic linkage diversity (glycoinformatics) and conformational diversity (glycoconformatics). Molecular Dynamics Simulation study is a theoretical-cum-computational tool successfully utilized to establish glycoconformatics of glycan. The study on various oligosaccharides of glycan clearly indicates that oligosaccharides do exist in multiple conformational states and these conformational states arise due to the flexibility associated with a glycosidic torsional angle (φ,ψ) . As an example: a single disaccharide structure NeuNacα(2-3) Gal exists in three different conformational states due to the differences in the preferential value of glycosidic torsional angles (φ,ψ). Hence establishing three dimensional structural and conformational models for glycan (cartesian coordinates of every individual atoms of an oligosaccharide structure in a preferred conformation) is quite crucial to understand various molecular recognition processes such as glycan-toxin interaction and glycan-virus interaction. The gycoconformatics models obtained for various glycan through Molecular Dynamics Simulation stored in our 3DSDSCAR (3DSDSCAR.ORG) a public domain database and its utility value in understanding the molecular recognition processes and in drug design venture will be discussed.

Keywords: glycan, glycoconformatics, molecular dynamics simulation, oligosaccharide

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Authors: Irene Yi

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Machine learning, natural language processing, and neural network models of language are becoming more and more prevalent in the fields of technology and linguistics today. Training data for machines are at best, large corpora of human literature and at worst, a reflection of the ugliness in society. Computational linguistics is a growing field dealing with such issues of data collection for technological development. Machines have been trained on millions of human books, only to find that in the course of human history, derogatory and sexist adjectives are used significantly more frequently when describing females in history and literature than when describing males. This is extremely problematic, both as training data, and as the outcome of natural language processing. As machines start to handle more responsibilities, it is crucial to ensure that they do not take with them historical sexist and misogynistic notions. This paper gathers data and algorithms from neural network models of language having to deal with syntax, semantics, sociolinguistics, and text classification. Computational analysis on such linguistic data is used to find patterns of misogyny. Results are significant in showing the existing intentional and unintentional misogynistic notions used to train machines, as well as in developing better technologies that take into account the semantics and syntax of text to be more mindful and reflect gender equality. Further, this paper deals with the idea of non-binary gender pronouns and how machines can process these pronouns correctly, given its semantic and syntactic context. This paper also delves into the implications of gendered grammar and its effect, cross-linguistically, on natural language processing. Languages such as French or Spanish not only have rigid gendered grammar rules, but also historically patriarchal societies. The progression of society comes hand in hand with not only its language, but how machines process those natural languages. These ideas are all extremely vital to the development of natural language models in technology, and they must be taken into account immediately.

Keywords: computational analysis, gendered grammar, misogynistic language, neural networks

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Authors: Ammara Mehmood, Aneela Zameer, Muhammad Asif Zahoor Raja, Muhammad Faisal Fateh

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In this work, the strength of bio-inspired computational intelligence based technique is exploited for parameter estimation for the periodic signals using Continuous Differential Evolution (CDE) by defining an error function in the mean square sense. Multidimensional and nonlinear nature of the problem emerging in sinusoidal signal models along with noise makes it a challenging optimization task, which is dealt with robustness and effectiveness of CDE to ensure convergence and avoid trapping in local minima. In the proposed scheme of Continuous Differential Evolution based Signal Parameter Estimation (CDESPE), unknown adjustable weights of the signal system identification model are optimized utilizing CDE algorithm. The performance of CDESPE model is validated through statistics based various performance indices on a sufficiently large number of runs in terms of estimation error, mean squared error and Thiel’s inequality coefficient. Efficacy of CDESPE is examined by comparison with the actual parameters of the system, Genetic Algorithm based outcomes and from various deterministic approaches at different signal-to-noise ratio (SNR) levels.

Keywords: parameter estimation, bio-inspired computing, continuous differential evolution (CDE), periodic signals

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Authors: Khadidja Rahmani, Nahla Bouaziz

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Algeria is known for its rich heritage. It owns an immense historical heritage with a universal reputation. Unfortunately, this wealth is withered because of abundance. This research focuses on the Ksourian model, which constitutes a large portion of this wealth. In fact, the Ksourian model is not just a witness to a great part of history or a vernacular culture, but also it includes a panoply of assets in terms of energetic efficiency. In this context, the purpose of our work is to evaluate the performance of the old techniques which are derived from the Ksourian model , and that using the simulation tools. The proposed method is decomposed in two steps; the first consists of isolate and reintroduce each device into a basic model, then run a simulation series on acquired models. And this in order to test the contribution of each of these dialectal processes. In another scale of development, the second step consists of aggregating all these processes in an aboriginal model, then we restart the simulation, to see what it will give this mosaic on the environmental and energetic plan .The model chosen for this study is one of the ksar units of Knadsa city of Bechar (Algeria). This study does not only show the ingenuity of our ancestors in their know-how, and their adapting power to the aridity of the climate, but also proves that their conceptions subscribe in the current concerns of energy efficiency, and respond to the requirements of sustainable development.

Keywords: dialectal processes, energy efficiency, evaluation, Ksourian model, simulation tools

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Authors: Enrique Bazan-Zurita, Sittipong Jarernprasert, Jacobo Bielak

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We study the effects of soil-structure interaction (SSI) on the inelastic seismic response of a single-degree-of-freedom system whose hysteretic behaviour exhibits stiffness and/or strength degrading characteristics. Two sets of accelerograms are used as seismic input: the first comprising 87 record from stiff to medium stiff sites in California, and the second comprising 66 records from the soft lakebed of Mexico City. This study focuses in three seismic response parameters: ductility demand, inter-story drift, and total lateral displacement. The results allow quantitative estimates of changes in such parameters in an SSI system in comparison with those corresponding to the associated fixed-base system. We found that degrading features affect significantly both the response of fixed-base structures and the impact of soil-structure interaction. We propose a procedure to incorporate the results of this and similar studies in seismic design regulations for SSI system with anticipated nonlinear degrading behaviour.

Keywords: inelastic, seismic, building, foundation, interaction

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Authors: Xin Gao, Jun Wu

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'In Dialogue with Humanity’ and ‘In Dialogue with Nature’ are two compulsory General Education Foundation (GEF) courses for all undergraduates at the Chinese University of Hong Kong (CUHK). These courses aim to enrich students’ intellectual pursuits and enhance their thinking capabilities through classic readings. To better understand and evaluate students’ thinking habits and abilities, GEF introduced Narrative Qualitative Analysis (NQA) in 2014 and has continued the study since then. Through the NQA study, a two-way evaluation scheme has been developed, including both student self-evaluation and teacher evaluation. This study will first introduce the theoretical background and research framework of the NQA study and then focus on student self-evaluation. An interactive online application, MASCOT, has been developed to facilitate students’ self-evaluation of their own thinking complexity. In this presentation, the design and development of MASCOT will be explained, and the main results will be reported when applying it in classroom teaching. An obvious discrepancy has been observed between students’ self-evaluations and teachers’ evaluations.

Keywords: narrative qualitative analysis, thinking complexity, student self-evaluation, interactive online application

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Authors: Anoop Kumar Suraj

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Dr Rammanohar Lohia (1910-67), a radical Indian socialist thinker, left an unfinished and critical oeuvre of works on ‘Social Revolution’, argued for the necessity of fundamentally reordering our social structures and offered the ideological framework for such a radical change. An alternative kind of democratic political action called Saat Krantiya, or ‘seven revolutions’, sought to establish socialism with a strong cultural and historical foundation in Indian society. Lohia cautiously adopted civil disobedience [a Gandhian tool] as a means of seven revolutions as a mode of revolution. He saw Indian youth as the vanguard of the social revolution and claimed that the ideas of ‘constructive militancy’ and ‘militant construction’ were at the core of such a revolution. This paper demonstrates that Lohia presented a unique short theoretical paradigm to interpret history and revolution, and Sapta Kranti was a normative framework to arrive at an egalitarian society.

Keywords: Rammanohar Lohia, Sapt Kranti, matter and spirit, caste-class, human approximation

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Authors: Mohammad Bakhshi, Firas Al Janabi

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High resolution rain data are very important to fulfill the input of hydrological models. Among models of high-resolution rainfall data generation, the temporal disaggregation was chosen for this study. The paper attempts to generate three different rainfall resolutions (4-hourly, hourly and 10-minutes) from daily for around 20-year record period. The process was done by DiMoN tool which is based on random cascade model and method of fragment. Differences between observed and simulated rain dataset are evaluated with variety of statistical and empirical methods: Kolmogorov-Smirnov test (K-S), usual statistics, and Exceedance probability. The tool worked well at preserving the daily rainfall values in wet days, however, the generated data are cumulated in a shorter time period and made stronger storms. It is demonstrated that the difference between generated and observed cumulative distribution function curve of 4-hourly datasets is passed the K-S test criteria while in hourly and 10-minutes datasets the P-value should be employed to prove that their differences were reasonable. The results are encouraging considering the overestimation of generated high-resolution rainfall data.

Keywords: DiMoN Tool, disaggregation, exceedance probability, Kolmogorov-Smirnov test, rainfall

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Authors: Areej Al-Hamad, Cheryl Forchuk, Abe Oudshoorn, Gerald Patrick Mckinley

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Syrian refugee women face many obstacles when accessing health services in host countries that are influenced by various cultural, structural, and practical factors. This paper is based on critical ethnographic research undertaken in Canada to explore Syrian refugee women's migration experiences. Also, we aim to critically examine how the intersection of gender, trauma, violence and the political and economic conditions of Syrian refugee women shapes their everyday lives and health. The study also investigates the strategies and practices by which Syrian refugee women are currently addressing their healthcare needs and the models of care that are suggested for meeting their physical and mental health needs. Findings show that these women experienced constant worries, hardship, vulnerability, and intrusion of dignity. These experiences and challenges were aggravated by the structure of the Canadian social and health care system. This study offers a better understanding of the impact of migration and trauma on Syrian refugee women's roles, responsibilities, gender dynamics, and interaction with Ontario's healthcare system to improve interaction and outcomes. Health care models should address these challenges among Syrian refugee families in Canada.

Keywords: Syrian refugee women, intersectionality, critical ethnography, migration

Procedia PDF Downloads 93

Authors: Farida, M. Shehu, Shehu U. R. Aliyu

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Access to finance has been recognized in the literature as one of the major impediments confronting small scale businesses (SSBs). This largely arises due to high lending rate, religious inclinations, collateral, etc. Islamic mode finance operates under Profit and Loss Sharing (PLS) arrangement between a borrower (business owner) and a lender (Islamic bank). This paper empirically assesses the determinants of predisposition of small scale business operators in Fagge local government area, Kano State, Nigeria, towards the PLS. Cross-sectional data from a sample of 291 small scale business operators was analyzed using logit and probit regression models. Empirical results reveal that while awareness and religion inclination positively drive interest towards the PLS, lending rate and collateral work against it. The paper, therefore, strongly recommends more advocacy campaigns and setting up of more Islamic banks in the country to cater for the financing and religious needs of SSBs in the study area.

Keywords: Islamic finance, logit and probit models, profit and loss sharing small scale businesses, finance, commerce

Procedia PDF Downloads 368

Authors: Abdelmalek Ouannou, Adil Brouri, Laila Kadi, Tarik

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Switched reluctance motor (SRM) has a major interest in a large domain as in electric vehicle driving because of its wide range of speed operation, high performances, low cost, and robustness to run under degraded conditions. The purpose of the paper is to develop a new analytical approach for modeling SRM parameters. Then, an identification scheme is proposed to obtain the SRM parameters. Since the SRM is featured by a highly nonlinear behavior, modeling these devices is difficult. Then, it is convenient to develop an accurate model describing the SRM. Furthermore, it is always operated in the magnetically saturated mode to maximize the energy transfer. Accordingly, it is shown that the SRM can be accurately described by a generalized polynomial Hammerstein model, i.e., the parallel connection of several Hammerstein models having polynomial nonlinearity. Presently an analytical identification method is developed using a chirp excitation signal. Afterward, the parameters of the obtained model have been determined using Finite Element Method analysis. Finally, in order to show the effectiveness of the proposed method, a comparison between the true and estimate models has been performed. The obtained results show that the output responses are very close.

Keywords: switched reluctance motor, swept-sine signal, generalized Hammerstein model, nonlinear system

Procedia PDF Downloads 234

Authors: L. S. Chathurika

Abstract:

Early prediction of student performance is an important factor to be gained academic excellence. Whatever the study stream in secondary education, students lay the foundation for higher studies during the first year of their degree or diploma program in Sri Lanka. The information technology (IT) field has certain improvements in the education domain by selecting specialization areas to show the talents and skills of students. These specializations can be software engineering, network administration, database administration, multimedia design, etc. After completing the first-year, students attempt to select the best path by considering numerous factors. The purpose of this experiment is to predict the best study path using machine learning algorithms. Five classification algorithms: decision tree, support vector machine, artificial neural network, Naïve Bayes, and logistic regression are selected and tested. The support vector machine obtained the highest accuracy, 82.4%. Then affecting features are recognized to select the best study path.

Keywords: algorithm, classification, evaluation, features, testing, training

Procedia PDF Downloads 118

Authors: Rizwan Rizwan

Abstract:

This paper provides an overview of Federated Learning (FL) and its application in enhancing data security, privacy, and efficiency. FL utilizes three distinct architectures to ensure privacy is never compromised. It involves training individual edge devices and aggregating their models on a server without sharing raw data. This approach not only provides secure models without data sharing but also offers a highly efficient privacy--preserving solution with improved security and data access. Also we discusses various frameworks used in FL and its integration with machine learning, deep learning, and data mining. In order to address the challenges of multi--party collaborative modeling scenarios, a brief review FL scheme combined with an adaptive gradient descent strategy and differential privacy mechanism. The adaptive learning rate algorithm adjusts the gradient descent process to avoid issues such as model overfitting and fluctuations, thereby enhancing modeling efficiency and performance in multi-party computation scenarios. Additionally, to cater to ultra-large-scale distributed secure computing, the research introduces a differential privacy mechanism that defends against various background knowledge attacks.

Keywords: federated learning, differential privacy, gradient descent strategy, convergence, stability, threats

Procedia PDF Downloads 29

Authors: Tetsuji Tanaka, Jin Guo

Abstract:

The extant literature has not succeeded in uncovering the common determinants of price volatility transmissions of agricultural commodities from international to local markets, and further, has rarely investigated the role of self-sufficiency measures in the context of national food security. We analyzed various factors to determine the degree of price volatility transmissions of wheat, rice, and maize between world and domestic markets using GARCH models with dynamic conditional correlation (DCC) specifications and panel-feasible generalized least square models. We found that the grain autarky system has the potential to diminish volatility pass-throughs for three grain commodities. Furthermore, it was discovered that the substitutive commodity consumption behavior between maize and wheat buffers the volatility transmissions of both, but rice does not function as a transmission-relieving element, either for the volatilities of wheat or maize. The effectiveness of grain consumption substitution to insulate the pass-throughs from global markets is greater than that of cereal self-sufficiency. These implications are extremely beneficial for developing governments to protect their domestic food markets from uncertainty in foreign countries and as such, improves food security.

Keywords: food security, GARCH, grain self-sufficiency, volatility transmission

Procedia PDF Downloads 153

Authors: Joseph G. Grzywacz, Azza O. Abdelmoneium, Abdallah M. Badahdah

Abstract:

Qatar provides an interesting context for studying family-friendly policies for working adults because it is governed by monarchy and Sharia law provides the foundation for legislation. This study considers gender variation in the perceived importance of and satisfaction with 12 family-friendly benefits (e.g., paid parental leave, flexible work hours, onsite childcare). Data were from a 2017 national sample of native Qatari adults. The perceived importance of family-friendly benefits was comparable for women and men for 7 of 12 benefits. Men rated paid paternity leave higher than women, but women rated telecommuting, dedicated breastfeeding room, onsite childcare, and opportunities to job share higher than men. Women and men reported similar experiences for 9 of 12 family-friendly benefits, but men’s expectations were better met than women’s for flexible work arrangements, telecommuting, and financial support for athletic clubs.

Keywords: culture, family-friendly benefits, gender, work-family

Procedia PDF Downloads 224