Search results for: gender specific data

Authors: S. Neelima, P. S. Subramanyam

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The major tool for distribution planning is load forecasting, which is the anticipation of the load in advance. Artificial neural networks have found wide applications in load forecasting to obtain an efficient strategy for planning and management. In this paper, the application of neural networks to study the design of short term load forecasting (STLF) Systems was explored. Our work presents a pragmatic methodology for short term load forecasting (STLF) using proposed two-stage model of wavelet transform (WT) and artificial neural network (ANN). It is a two-stage prediction system which involves wavelet decomposition of input data at the first stage and the decomposed data with another input is trained using a separate neural network to forecast the load. The forecasted load is obtained by reconstruction of the decomposed data. The hybrid model has been trained and validated using load data from Telangana State Electricity Board.

Keywords: electrical distribution systems, wavelet transform (WT), short term load forecasting (STLF), artificial neural network (ANN)

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Authors: Jamal Alqinai

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A pragmatic approach to translation studies the rules and principles governing the use of language over and above the rules of syntax or morphology, and what makes some uses of language more appropriate than others in [communicative] situations. It attempts to explain translation as a procedure and product from the point of view of how, why and what is done by the source text author (ST) and what is to be done in the target text (TT) rendition. The latter will be subject to evaluation not as generated by the linguistics system but as conveyed and manipulated by participants in a communicative situation according to the referential and pragmatic standards employed. The failure of a purely lexical or structural translation stems from ignoring the relation between words as signs and the effect they have on their users. A more refined approach would also consider those processes that are sometimes labeled extra-linguistic or intuitive and which translators strive to reproduce unscathed in the translation process. We need to grasp the kind of actions an ST author performs on his readers by combining linguistic and non-linguistic elements against a backdrop of beliefs and cultural values. In other words, aside from considering the cohesive ties at the textual level, one needs to understand how the whole ST discourse hangs together logically in order to reproduce a coherent TT. The latter can only be achieved by an analysis of the pragmatic elements of presuppositions, implicatures and acts performed in the ST. Establishing cohesive ties within a text may require seeking reference outside the immediate text. The illocutionary functions manifested in one language/culture are relatively autonomous cultural/linguistic categories, but are imaginable by members of other cultures and, to some extent , are translatable though not, of course, without translation loss. Globalization and the spread of literacy worldwide may have created a universal empathy to comprehend the performative aspect of utterances when explained by approximate glosses or by paraphrase. Yet, it is often the multilayered and the culture-specific nature of illocutionary functions that de-universalize their possible interpretations. This paper addresses the pragmatic interpretation of culturally specific texts with examples adduced from a number of distinct settings to illustrate the influence of the pragmatic factors at stake.

Keywords: pragmatic, presupposition, implicature, cohesion

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Authors: Jeh Patel, Chandrahas Paidi, Ahmed Hambaba

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Traditional methods for analyzing athlete movement often lack the detail and immediacy required for optimal training. This project aims to address this limitation by developing a Real-time human pose estimation system specifically designed to enhance athlete training across various sports. This system leverages the power of convolutional neural networks (CNNs) to provide a comprehensive and immediate analysis of an athlete’s movement patterns during training sessions. The core architecture utilizes dilated convolutions to capture crucial long-range dependencies within video frames. Combining this with the robust encoder-decoder architecture to further refine pose estimation accuracy. This capability is essential for precise joint localization across the diverse range of athletic poses encountered in different sports. Furthermore, by quantifying movement efficiency, power output, and range of motion, the system provides data-driven insights that can be used to optimize training programs. Pose estimation data analysis can also be used to develop personalized training plans that target specific weaknesses identified in an athlete’s movement patterns. To overcome the limitations posed by outdoor environments, the project employs strategies such as multi-camera configurations or depth sensing techniques. These approaches can enhance pose estimation accuracy in challenging lighting and occlusion scenarios, where pose estimation accuracy in challenging lighting and occlusion scenarios. A dataset is collected From the labs of Martin Luther King at San Jose State University. The system is evaluated through a series of tests that measure its efficiency and accuracy in real-world scenarios. Results indicate a high level of precision in recognizing different poses, substantiating the potential of this technology in practical applications. Challenges such as enhancing the system’s ability to operate in varied environmental conditions and further expanding the dataset for training were identified and discussed. Future work will refine the model’s adaptability and incorporate haptic feedback to enhance the interactivity and richness of the user experience. This project demonstrates the feasibility of an advanced pose detection model and lays the groundwork for future innovations in assistive enhancement technologies.

Keywords: computer vision, deep learning, human pose estimation, U-NET, CNN

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Authors: Mina Jafari, Kobra Hamraee, Saied Hossein Hosseini

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The prediction of breast cancer disease is one of the challenges in medicine. In this paper we collected 528 records of women’s information who live in Kabul including demographic, life style, diet and pregnancy data. There are many classification algorithm in breast cancer prediction and tried to find the best model with most accurate result and lowest error rate. We evaluated some other common supervised algorithms in data mining to find the best model in prediction of breast cancer disease among afghan women living in Kabul regarding to momography result as target variable. For evaluating these algorithms we used Cross Validation which is an assured method for measuring the performance of models. After comparing error rate and accuracy of three models: Decision Tree, Naive Bays and Rule Induction, Decision Tree with accuracy of 94.06% and error rate of %15 is found the best model to predicting breast cancer disease based on the health care records.

Keywords: decision tree, breast cancer, probability, data mining

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Authors: Preeti Kumari, Ridhi Kapoor

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 In any communication, security is the most important issue in today’s world. In this paper, steganography is the process of hiding the important data into other data, such as text, audio, video, and image. The interest in this topic is to provide availability, confidentiality, integrity, and authenticity of data. The steganographic technique that embeds hides content with unremarkable cover media so as not to provoke eavesdropper’s suspicion or third party and hackers. In which many applications of compression, encryption, decryption, and embedding methods are used for digital image steganography. Due to compression, the nose produces in the image. To sustain noise in the image, the LSB insertion technique is used. The performance of the proposed embedding system with respect to providing security to secret message and robustness is discussed. We also demonstrate the maximum steganography capacity and visual distortion.

Keywords: steganography, LSB, encoding, information hiding, color image

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Authors: Awatef Hicheur Cairns, Billel Gueni, Hind Hafdi, Christian Joubert, Nasser Khelifa

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Given the ever changing needs of the job markets, education and training centers are increasingly held accountable for student success. Therefore, education and training centers have to focus on ways to streamline their offers and educational processes in order to achieve the highest level of quality in curriculum contents and managerial decisions. Educational process mining is an emerging field in the educational data mining (EDM) discipline, concerned with developing methods to discover, analyze and provide a visual representation of complete educational processes. In this paper, we present our distributed computation platform which allows different education centers and institutions to load their data and access to advanced data mining and process mining services. To achieve this, we present also a comparative study of the different clustering techniques developed in the context of process mining to partition efficiently educational traces. Our goal is to find the best strategy for distributing heavy analysis computations on many processing nodes of our platform.

Keywords: educational process mining, distributed process mining, clustering, distributed platform, educational data mining, ProM

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Authors: Sonia Kudlacik-Kramarczyk, Anna Drabczyk, Dagmara Malina, Bozena Tyliszczak, Agnieszka Sobczak-Kupiec

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Introduction: In the preparation of polymer materials, compounds of natural origin are currently gaining more and more interest. This is particularly noticeable in the case of synthesis of materials considered for biomedical use. Then, selected material has to meet many requirements. It should be characterized by non-toxicity, biodegradability and biocompatibility. Therefore special attention is directed to substances such as polysaccharides, proteins or substances that are the basic building components of proteins, i.e. amino acids. These compounds may be crosslinked with other reagents that leads to the preparation of polymer matrices. Such amino acids as e.g. cysteine or histidine. On the other hand, previously mentioned requirements may be met by polymers obtained as a result of biosynthesis, e.g. polyhydroxybutyrate. This polymer belongs to the group of aliphatic polyesters that is synthesized by microorganisms (selected strain of bacteria) under specific conditions. It is possible to modify matrices based on given polymer with substances of various origin. Such a modification may result in the change of their properties or/and in providing the material with new features desirable in viewpoint of specific application. Described materials are synthesized using UV radiation. Process of photopolymerization is fast, waste-free and enables to obtain final products with favorable properties. Methodology: Polymer matrices have been prepared by means of photopolymerization. First step involved the preparation of solutions of particular reagents and mixing them in the appropriate ratio. Next, crosslinking agent and photoinitiator have been added to the reaction mixture and the whole was poured into the Petri dish and treated with UV radiation. After the synthesis, polymer samples were dried at room temperature and subjected to the numerous analyses aimed at the determining their physicochemical properties. Firstly, sorption properties of obtained polymer matrices have been determined. Next, mechanical properties have been characterized, i.e. tensile strength. The ability to deformation under applied stress of all prepared polymer matrices has been checked. Such a property is important in viewpoint of the application of analyzed materials e.g. as wound dressings. Wound dressings have to be elastic because depending on the location of the wound and its mobility, such a dressing has to adhere properly to the wound. Furthermore, considering the use of the materials for biomedical purposes it is essential to determine its behavior in environments simulating these ones occurring in human body. Therefore incubation studies using selected liquids have also been conducted. Conclusions: As a result of photopolymerization process, polymer matrices based on natural compounds have been prepared. These exhibited favorable mechanical properties and swelling ability. Moreover, biocompatibility in relation to simulated body fluids has been stated. Therefore it can be concluded that analyzed polymer matrices constitute an interesting materials that may be considered for biomedical use and may be subjected to the further more advanced analyses using specific cell lines.

Keywords: photopolymerization, polymer matrices, simulated body fluids, swelling properties

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Authors: Akinwumi Omotayo, Badu-B Apraku, Joseph Olobasola, Petra Abdul Saghir, Yinka Sobowale

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In southwestern Nigeria, climate change has led to loss of at least two months of rainfall. Consequently, only one cycle of maize can now be grown because of the shorter duration of rainy season as against two cycles in the past. The Early and Extra-early maturing varieties of maize were originally developed for the semi-arid and arid zones of West and Central Africa where there are seasonal challenges of water threatening optimum performance of the traditional maize grown, which are commonly late in maturity (115 to 120 days). The early varieties of maize mature in 90 to 95 days; while the Extra-Early maize varieties reach physiological maturity in less than 90 days. It was broadly hypothesized that the extra early varieties of maize could mitigate the effects of climate change in southwestern Nigeria with higher levels of rainfall by reinstating the original two cycles of rain-fed maize crop. Trials were therefore carried out in southwestern Nigeria on the possibility of adapting the extra early maize to mitigate the effects of climate change. The trial was the Mother/Baby design. The mother trial involves the evaluation of extra-early varieties following ideal recommendations and closely supervised centrally at the University research farm and the Agricultural Development Programmes (ADPs). This requires farmers to observe and evaluate the technology and the management regime meant to precede the second stage of evaluation at several satellite farmers field managed by selected farmers. The Baby Trial is expected to provide a realistic assessment of the technology by farmers in their own environment. A stratified selection of thirty farmers for the Baby Trial ensured appropriate representation across the different categories of the farming population by age and gender. Data from the trials indicate that extra early maize can be grown in two cycles rain fed in south west Nigeria and a third and fourth cycle could be obtained with irrigation. However the long duration varieties outyielded the extra early maize in both the mother and baby trials. When harvested green, the extra early maize served as source of food between March and May when there was scarcity of food. This represents a major advantage. The study recommends that further work needs to be done to improve the yield of extra early maize to encourage farmers to adopt.

Keywords: adaptation, climate change, extra early, maize varieties, mitigation

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Authors: Luleka Dlamini, Olivier Crespo, Jos van Dam

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Accurately estimating crop growth and yield is pivotal for timely sustainable agricultural management and ensuring food security. Crop models and remote sensing can complement each other and form a robust analysis tool to improve crop growth and yield estimations when combined. This study thus aims to systematically evaluate how research that exclusively focuses on assimilating RS data into crop models varies among countries, crops, data assimilation methods, and farming conditions. A strict search string was applied in the Scopus and Web of Science databases, and 497 potential publications were obtained. After screening for relevance with predefined inclusion/exclusion criteria, 123 publications were considered in the final review. Results indicate that over 81% of the studies were conducted in countries associated with high socio-economic and technological advancement, mainly China, the United States of America, France, Germany, and Italy. Many of these studies integrated MODIS or Landsat data into WOFOST to improve crop growth and yield estimation of staple crops at the field and regional scales. Most studies use recalibration or updating methods alongside various algorithms to assimilate remotely sensed leaf area index into crop models. However, these methods cannot account for the uncertainties in remote sensing observations and the crop model itself. l. Over 85% of the studies were based on commercial and irrigated farming systems. Despite a great global interest in data assimilation into crop models, limited research has been conducted in resource- and data-limited regions like Africa. We foresee a great potential for such application in those conditions. Hence facilitating and expanding the use of such an approach, from which developing farming communities could benefit.

Keywords: crop models, remote sensing, data assimilation, crop yield estimation

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Authors: Luleka Dlamini, Olivier Crespo, Jos van Dam

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Accurately estimating crop growth and yield is pivotal for timely sustainable agricultural management and ensuring food security. Crop models and remote sensing can complement each other and form a robust analysis tool to improve crop growth and yield estimations when combined. This study thus aims to systematically evaluate how research that exclusively focuses on assimilating RS data into crop models varies among countries, crops, data assimilation methods, and farming conditions. A strict search string was applied in the Scopus and Web of Science databases, and 497 potential publications were obtained. After screening for relevance with predefined inclusion/exclusion criteria, 123 publications were considered in the final review. Results indicate that over 81% of the studies were conducted in countries associated with high socio-economic and technological advancement, mainly China, the United States of America, France, Germany, and Italy. Many of these studies integrated MODIS or Landsat data into WOFOST to improve crop growth and yield estimation of staple crops at the field and regional scales. Most studies use recalibration or updating methods alongside various algorithms to assimilate remotely sensed leaf area index into crop models. However, these methods cannot account for the uncertainties in remote sensing observations and the crop model itself. l. Over 85% of the studies were based on commercial and irrigated farming systems. Despite a great global interest in data assimilation into crop models, limited research has been conducted in resource- and data-limited regions like Africa. We foresee a great potential for such application in those conditions. Hence facilitating and expanding the use of such an approach, from which developing farming communities could benefit.

Keywords: crop models, remote sensing, data assimilation, crop yield estimation

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Authors: Hassan Gholibeigian, Kazem Gholibeigian

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In this paper, our goal is determination of loading versus time in crust. For this goal, we present a computational procedure to propose a cumulative strain energy time profile which can be used to predict the approximate location and time of the next major earthquake (M > 4.5) along a specific fault, which we believe, is more accurate than many of the methods presently in use. In the coming pages, after a short review of the research works presently going on in the area of earthquake analysis and prediction, earthquake mechanisms in both the jerk and sequence earthquake direction is discussed, then our computational procedure is presented using differential equations of equilibrium which govern the nonlinear dynamic response of a system of finite elements, modified with an extra term to account for the jerk produced during the quake. We then employ Von Mises developed model for the stress strain relationship in our calculations, modified with the addition of an extra term to account for thermal effects. For calculation of the strain energy the idea of Pulsating Mantle Hypothesis (PMH) is used. This hypothesis, in brief, states that the mantle is under diurnal cyclic pulsating loads due to unbalanced gravitational attraction of the sun and the moon. A brief discussion is done on the Denali fault as a case study. The cumulative strain energy is then graphically represented versus time. At the end, based on some hypothetic earthquake data, the final results are verified.

Keywords: pulsating mantle hypothesis, inner core’s dislocation, outer core’s bulge, constitutive model, transient hydro-magneto-thermo-mechanical load, diurnal stress, jerk, fault behaviour

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Authors: Elaheh Vaezpour

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Data sensing in wireless sensor networks is done by query deceleration the network by the users. In many applications of the wireless sensor networks, many users send queries to the network simultaneously. If the queries are processed separately, the network’s energy consumption will increase significantly. Therefore, it is very important to aggregate the queries before sending them to the network. In this paper, we propose a multiple query optimization framework based on sensors physical and temporal correlation. In the proposed method, queries are merged and sent to network by considering correlation among the sensors in order to reduce the communication cost between the sensors and the base station.

Keywords: wireless sensor networks, multiple query optimization, data correlation, reducing energy consumption

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Authors: Yoonsuh Jung

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As DNA microarray data contain relatively small sample size compared to the number of genes, high dimensional models are often employed. In high dimensional models, the selection of tuning parameter (or, penalty parameter) is often one of the crucial parts of the modeling. Cross-validation is one of the most common methods for the tuning parameter selection, which selects a parameter value with the smallest cross-validated score. However, selecting a single value as an "optimal" value for the parameter can be very unstable due to the sampling variation since the sample sizes of microarray data are often small. Our approach is to choose multiple candidates of tuning parameter first, then average the candidates with different weights depending on their performance. The additional step of estimating the weights and averaging the candidates rarely increase the computational cost, while it can considerably improve the traditional cross-validation. We show that the selected value from the suggested methods often lead to stable parameter selection as well as improved detection of significant genetic variables compared to the tradition cross-validation via real data and simulated data sets.

Keywords: cross validation, parameter averaging, parameter selection, regularization parameter search

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Authors: Vasti Oelofse, Niekie van der Merwe, Dorita du Toit

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This study addresses the need for a unified best practice model for Physical Education across BRICS nations, as current research primarily offers individual country recommendations. Drawing on relevant literature within the framework of Bronfenbrenner’s Ecological Systems Theory, as well as data from open-ended questionnaires completed by Physical Education experts from the BRICS countries, , the study develops a best practice model based on identified challenges and effective practices in Physical Education. A model is proposed that incorporates flexible and resource-efficient strategies tailored to address PE challenges specific to these countries, enhancing outcomes for learners, empowering teachers, and fostering systemic collaboration among BRICS members. The proposed model comprises six key areas: “Curriculum and policy requirements”, “General approach”, “Theoretical basis”, “Strategies for presenting content”, “Teacher training”, and “Evaluation”. The “Strategies for presenting program content” area addresses both well-resourced and poorly resourced schools, adapting curriculum, teaching strategies, materials, and learner activities for varied socio-economic contexts. The model emphasizes a holistic approach to learner development, engaging environments, and continuous teacher training. A collaborative approach among BRICS countries, focusing on shared best practices and continuous improvement, is vital for the model's successful implementation, enhancing Physical Education programs and outcomes across these nations.

Keywords: BRICS countries, physical education, best practice model, ecological systems theory

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Authors: Ssekamanya, Mastura Badzis, Khamsiah Ismail, Dayang Shuzaidah Bt Abduludin

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With rising incidents of school violence worldwide, educators and researchers are trying to understand and find ways to enhance the safety of children at school. The purpose of this study was to investigate the extent to which the demographic variables of gender, age, length of service, position, academic qualification, and school location predicted teachers’ awareness about school safety practices in Malaysian primary schools. A stratified random sample of 380 teachers was selected in the central Malaysian states of Kuala Lumpur and Selangor. Multiple regression analysis revealed that none of the factors was a good predictor of awareness about school safety training, delivery methods of school safety information, and available school safety programs. Awareness about school safety activities was significantly predicted by school location (whether the school was located in a rural or urban area). While these results may reflect a general lack of awareness about school safety among primary school teachers in the selected locations, a national study needs to be conducted for the whole country.

Keywords: school safety awareness, predictors of school safety, multiple regression analysis, malaysian primary schools

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Authors: Amar Partap Singh Pharwaha, Balkrishan Jindal

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In this paper, a new method is developed for hiding image in a digital image with multilayer security. In the proposed method, the secret image is encrypted in the first instance using a flexible matrix based symmetric key to add first layer of security. Then another layer of security is added to the secret data by encrypting the ciphered data using Pythagorean Theorem method. The ciphered data bits (4 bits) produced after double encryption are then embedded within digital image in the spatial domain using Least Significant Bits (LSBs) substitution. To improve the image quality of the stego-image, an improved form of pixel adjustment process is proposed. To evaluate the effectiveness of the proposed method, image quality metrics including Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), entropy, correlation, mean value and Universal Image Quality Index (UIQI) are measured. It has been found experimentally that the proposed method provides higher security as well as robustness. In fact, the results of this study are quite promising.

Keywords: Pythagorean theorem, pixel adjustment, ciphered data, image hiding, least significant bit, flexible matrix

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Authors: Byung Ho Jung, Dong Hoon Lim

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Logistic regression is a statistical method for analyzing a dataset in which there are one or more independent variables that determine an outcome. Logistic regression is used extensively in numerous disciplines, including the medical and social science fields. In this paper, we address the problem of estimating parameters in the logistic regression based on MapReduce framework with RHadoop that integrates R and Hadoop environment applicable to large scale data. There exist three learning algorithms for logistic regression, namely Gradient descent method, Cost minimization method and Newton-Rhapson's method. The Newton-Rhapson's method does not require a learning rate, while gradient descent and cost minimization methods need to manually pick a learning rate. The experimental results demonstrated that our learning algorithms using RHadoop can scale well and efficiently process large data sets on commodity hardware. We also compared the performance of our Newton-Rhapson's method with gradient descent and cost minimization methods. The results showed that our newton's method appeared to be the most robust to all data tested.

Keywords: big data, logistic regression, MapReduce, RHadoop

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Authors: Chae Hoon Park, Jong Kang Park, Jong Tae Kim

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Electrical characteristics of capacitive touchscreen need to be accurately analyzed to result in better performance for multi-channel capacitance sensing. In this paper, we extracted the panel resistances and capacitances of the touchscreen by comparing measurement data and model data. By employing a lumped RC model for driver-to-receiver paths in touchscreen, we estimated resistance and capacitance values according to the physical lengths of channel paths which are proportional to the RC model. As a result, we obtained the model having 95.54% accuracy of the measurement data.

Keywords: electrical characteristics of capacitive touchscreen, iterative extraction, lumped RC model, physical lengths of channel paths

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Authors: Jun Ming Moey, Zhiyaun Chen, David Nicholson

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Bad actors are often hard to detect in data that imprints their behaviour patterns because they are comparatively rare events embedded in non-bad actor data. An unsupervised machine learning framework is applied here to detect bad actors in financial crime datasets that record millions of transactions undertaken by hundreds of actors (<0.01% bad). Specifically, the framework combines ‘shallow’ (PCA, Isolation Forest) and ‘deep’ (Autoencoder) methods to detect outlier patterns. Detection performance analysis for both the individual methods and their combination is reported.

Keywords: detection, machine learning, deep learning, unsupervised, outlier analysis, data science, fraud, financial crime

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Authors: Anna M. Pretorius

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The emerging markets category for portfolio investment was introduced in 1986 in an attempt to promote capital market development in less developed countries. Investors traditionally diversified their portfolios by investing in different developed markets. However, high growth opportunities forced investors to consider emerging markets as well. Examples include the rapid growth of the “Asian Tigers” during the 1980s, growth in Latin America during the 1990s and the increased interest in emerging markets during the global financial crisis. As such, portfolio flows to emerging markets have increased substantially. In 2002 7% of all equity allocations from advanced economies went to emerging markets; this increased to 20% in 2012. The stronger links between advanced and emerging markets led to increased synchronization of asset price movements. This increased level of stock market integration for emerging markets is confirmed by various empirical studies. Against the background of increased interest in emerging market assets and the increasing level of integration of emerging markets, this paper focuses on the determinants of stock market integration of emerging market countries. Various studies have linked the level of financial market integration with specific economic variables. These variables include: economic growth, local inflation, trade openness, local investment, budget surplus/ deficit, market capitalization, domestic bank credit, domestic institutional and legal environment and world interest rates. The aim of this study is to empirically investigate to what extent trade-related determinants have an impact on stock market integration. The panel data sample include data of 16 emerging market countries: Brazil, Chile, China, Colombia, Czech Republic, Hungary, India, Malaysia, Pakistan, Peru, Philippines, Poland, Russian Federation, South Africa, Thailand and Turkey for the period 1998-2011. The integration variable for each emerging stock market is calculated as the explanatory power of a multi-factor model. These factors are extracted from a large panel of global stock market returns. Trade related explanatory variables include: exports as percentage of GDP, imports as percentage of GDP and total trade as percentage of GDP. Other macroeconomic indicators – such as market capitalisation, the size of the budget deficit and the effectiveness of the regulation of the securities exchange – are included in the regressions as control variables. An initial analysis on a sample of developed stock markets could not identify any significant determinants of stock market integration. Thus the macroeconomic variables identified in the literature are much more significant in explaining stock market integration of emerging markets than stock market integration of developed markets. The three trade variables are all statistically significant at a 5% level. The market capitalisation variable is also significant while the regulation variable is only marginally significant. The global financial crisis has highlighted the urgency to better understand the link between the financial and real sectors of the economy. This paper comes to the important finding that, apart from the level of market capitalisation (as financial indicator), trade (representative of the real economy) is a significant determinant of stock market integration of countries not yet classified as developed economies.

Keywords: emerging markets, financial market integration, panel data, trade

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Authors: Rasha Elhawari

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Heritage language learners (HLL) are part of the linguistic reality in Foreign Language Learning (FLL). These learners present several characteristics that are different from non-heritage language learners. They have a personal connection with the language and their motivation to learn the language is partly because of this personal connection. In Canada there is a large diversity in the foreign language learning classroom; the Arabic language classroom is no exception. The Arabic HLL is unique for more than one reason. First, is the fact that the Arabic language is spoken across twenty-two Arab countries across the Arab World. Across the Arab World there is a standard variation and a local dialect that co-exist side by side, i.e. diaglossia exists in a strong and unique way as a feature of Arabic. Second, Arabic is the language that all Muslims across the Muslim World use for their prayers. This raises a number of points when we consider Arabic as a Heritage Language; namely the role of diaglossia, culture and religion. The fact that there is a group of leaners that can be regarded as HLL who are not of Arabic speaking background but are Muslims and use the language for religious purposes is unique, thus course developers and language instructors need take this into consideration. The paper takes a closer look at this distinction and establishes sub-groups the Arabic HLLs in a language and/or culture specific way related mainly to the Arabic HLL. It looks at the learners at the beginners’ Arabic class at the undergraduate university level over a period of three years in order to define this learner. Learners belong to different groups and backgrounds but they all share common characteristics. The paper presents a detailed look at the learner types present at this class in order to help prepare and develop material for this specific learner group. The paper shows that separate HLL and non-HLL courses, especially at the introductory and intermediate level, is successful in resolving some of the pedagogical problems that occur in the Arabic as a Foreign Language classroom. In conclusion, the paper recommends the development of HLL courses at the early levels of language learning. It calls for a change in the pedagogical practices to overcome some of the challenges learner in the introductory Arabic class can face.

Keywords: Arabic, Heritage Language, langauge learner, teaching

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Authors: Priyanka Dixit

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Reproductive health problem is an important public health issue in most of the developing countries like India. It is a common problem in India for women in the reproductive age group to suffer from reproductive illnesses and not seek care. Existing literatures tell us very little about the several dimensions of reproductive morbidity. In addition the general perception says, metros have better medical infrastructure, so its residents should lead a healthier life. However some of the studies reveal a very different picture. Therefore, the present study is conducted with the specific objectives to find out the prevalence of reproductive health problem and treatment seeking behavior of currently married women in four metro cities in India namely; Mumbai, Delhi, Chennai and Kolkata. In addition, this paper also examines the effect of socio-economic and demographic factors on self-reported reproductive health problems. Bi-variate and multivariate regression have been applied to achieve the proposed objectives. Study is based on National Family Health Survey 2015-16 data. The analysis shows that the prevalence of any reproductive health problem among women is the highest in Mumbai followed by Delhi, Chennai, and Kolkata. A bulk of women in all four metro cities has reported abdominal pain, itching and burning sensation as the major problems while urinating. However, in spite of the high prevalence of reproductive health problems, a huge proportion of such women in all these cities do not seek any advice or treatment for these problems. This study also investigates determinants that affect the prevalence of reproductive health problem to policy makers plan for proper interventions for improving women’s reproductive health.

Keywords: reproductive health, India, national family health survey-4, city

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Authors: Jan Stodt, Christoph Reich

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The usage of machine learning models for prediction is growing rapidly and proof that the intended requirements are met is essential. Audits are a proven method to determine whether requirements or guidelines are met. However, machine learning models have intrinsic characteristics, such as the quality of training data, that make it difficult to demonstrate the required behavior and make audits more challenging. This paper describes an ML audit framework that evaluates and reviews the risks of machine learning applications, the quality of the training data, and the machine learning model. We evaluate and demonstrate the functionality of the proposed framework by auditing an steel plate fault prediction model.

Keywords: audit, machine learning, assessment, metrics

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Authors: Keerthi S. Shetty, Annappa Basava

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In recent years, modelling of biological systems represented by biochemical reactions has become increasingly important in Systems Biology. Biological systems represented by biochemical reactions are highly stochastic in nature. Probabilistic model is often used to describe such systems. One of the main challenges in Systems biology is to combine absolute experimental data into probabilistic model. This challenge arises because (1) some molecules may be present in relatively small quantities, (2) there is a switching between individual elements present in the system, and (3) the process is inherently stochastic on the level at which observations are made. In this paper, we describe a novel idea of combining absolute experimental data into probabilistic model using tool R2. Through a case study of the Transcription Process in Prokaryotes we explain how biological systems can be written as probabilistic program to combine experimental data into the model. The model developed is then analysed in terms of intrinsic noise and exact sampling of switching times between individual elements in the system. We have mainly concentrated on inferring number of genes in ON and OFF states from experimental data.

Keywords: systems biology, probabilistic model, inference, biology, model

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Authors: E. Vazquez-Santacruz, M. Gamboa-Zuniga

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In this paper, we show a methodology for bodies classification in lying state using HOG descriptors and pressures sensors positioned in a matrix form (14 x 32 sensors) on the surface where bodies lie down. it will be done in real time. Our system is embedded in a care robot that can assist the elderly patient and medical staff around to get a better quality of life in and out of hospitals. Due to current technology a limited number of sensors is used, wich results in low-resolution data array, that will be used as image of 14 x 32 pixels. Our work considers the problem of human posture classification with few information (sensors), applying digital process to expand the original data of the sensors and so get more significant data for the classification, however, this is done with low-cost algorithms to ensure the real-time execution.

Keywords: real-time classification, sensors, robots, health care, elderly patients, artificial intelligence

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Authors: Fatma Kürüm Varolgüneş, Fatih Canan

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Mardin is one of the unique cities in Turkey with its rich cultural and historical heritage. Mardin’s traditional dwellings have been affected both by natural data such as climate and topography and by cultural data like lifestyle and belief. However, in the new settlements, housing is formed with modern approaches and unsuitable forms clashing with Mardin’s culture and environment. While the city is expanding, traditional textures are ignored. Thus, traditional settlements are losing their identity and are vanishing because of the rapid change and transformation. The main aim of this paper is to determine the physical and social data needed to define the characteristic features of Mardin’s old and new settlements. In this context, based on social and cultural data, old and new settlement formations of Mardin have been investigated from various aspects. During this research, the following methods have been utilized: observations, interviews, public surveys, literature review, as well as site examination via maps, photographs and questionnaire methodology. In conclusion, this paper focuses on how changes in the physical forms of cities affect the typology and the identity of cities, as in the case of Mardin.

Keywords: urban and local identity, historical city center, traditional settlements, Mardin

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Authors: Zihan Geng, L. Quentin Dixon

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It has been proposed that early bilingual experience confers a number of advantages in the development of executive control mechanisms. Although the literature provides empirical evidence for bilingual benefits, some studies also reported null or mixed results. To make sense of these contradictory findings, the current review synthesize recent empirical studies investigating bilingual effects on children’s executive function and attention control. The publication time of the studies included in the review ranges from 2010 to 2017. The key searching terms are bilingual, bilingualism, children, executive control, executive function, and attention. The key terms were combined within each of the following databases: ERIC (EBSCO), Education Source, PsycINFO, and Social Science Citation Index. Studies involving both children and adults were also included but the analysis was based on the data generated only by the children group. The initial search yielded 137 distinct articles. Twenty-eight studies from 27 articles with a total of 3367 participants were finally included based on the selection criteria. The selective studies were then coded in terms of (a) the setting (i.e., the country where the data was collected), (b) the participants (i.e., age and languages), (c) sample size (i.e., the number of children in each group), (d) cognitive outcomes measured, (e) data collection instruments (i.e., cognitive tasks and tests), and (f) statistic analysis models (e.g., t-test, ANOVA). The results show that the majority of the studies were undertaken in western countries, mainly in the U.S., Canada, and the UK. A variety of languages such as Arabic, French, Dutch, Welsh, German, Spanish, Korean, and Cantonese were involved. In relation to cognitive outcomes, the studies examined children’s overall planning and problem-solving abilities, inhibition, cognitive complexity, working memory (WM), and sustained and selective attention. The results indicate that though bilingualism is associated with several cognitive benefits, the advantages seem to be weak, at least, for children. Additionally, the nature of the cognitive measures was found to greatly moderate the results. No significant differences are observed between bilinguals and monolinguals in overall planning and problem-solving ability, indicating that there is no bilingual benefit in the cooperation of executive function components at an early age. In terms of inhibition, the mixed results suggest that bilingual children, especially young children, may have better conceptual inhibition measured in conflict tasks, but not better response inhibition measured by delay tasks. Further, bilingual children showed better inhibitory control to bivalent displays, which resembles the process of maintaining two language systems. The null results were obtained for both cognitive complexity and WM, suggesting no bilingual advantage in these two cognitive components. Finally, findings on children’s attention system associate bilingualism with heightened attention control. Together, these findings support the hypothesis of cognitive benefits for bilingual children. Nevertheless, whether these advantages are observable appears to highly depend on the cognitive assessments. Therefore, future research should be more specific about the cognitive outcomes (e.g., the type of inhibition) and should report the validity of the cognitive measures consistently.

Keywords: attention, bilingual advantage, children, executive function

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Authors: Mohammed Hussein Ahmed, Bahar Mohammed Kareem

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This study is concerned with the pragmatic discoursal study of hedging constructions in English language. Hedging is a mitigated word used to lessen the impact of the utterance uttered by the speakers. Hedging could be either adverbs, adjectives, verbs and sometimes it may consist of clauses. It aims at finding out the extent to which speakers and participants of the discourse use hedging constructions during their conversations. The study also aims at finding out whether or not there are any significant differences in the types and functions of the frequency of hedging constructions employed by male and female. It is hypothesized that hedging constructions are frequent in English discourse more than any other languages due to its formality and that the frequency of the types and functions are influenced by the gender of the participants. To achieve the aims of the study, two types of procedures have been followed: theoretical and practical. The theoretical procedure consists of presenting a theoretical background of hedging topic which includes its definitions, etymology and theories. The practical procedure consists of selecting a sample of texts and analyzing them according to an adopted model. A number of conclusions will be drawn based on the findings of the study.

Keywords: hedging, pragmatics, politeness, theoretical

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Authors: Fall Abdourahmane, Lazrak Meryem, El Hsaini Houda, El Ammari Laila, Gamih Hasnae, Yahyane Abdelhakim, Benjouad Abdelaziz, Aguenaou Hassan, El Kari Khalid

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Introduction: The double burden of malnutrition (DBM), characterized by the coexistence of undernutrition and overnutrition, is a significant health challenge, particularly in low- and middle-income countries. In Morocco, 61.3% of women of reproductive age (WRA) are overweight or obese, including 30.4% who were obese, while 34.4% were anaemic, and 49.7% have iron deficiency anaemia. Objective: This study aims to determine the prevalence of DBM at the individual level among Moroccan WRA, defined by the coexistence of iron deficiency anaemia and overweight/obesity. Methods: a cross-sectional national survey was conducted among a representative sample of 2090 Moroccan WRA. Data collected included socio-economic parameters, anthropometric measurements, and blood samples. Haemoglobin levels were measured photometrically using Hemocue, while ferritin and CRP were assessed through immunoturbudimetry. Results: The prevalence of overweight/obesity, iron deficiency, anaemia and iron deficiency anaemia among WRA in Morocco were 60.2%, 30.6%, 34.4% and 50.0% respectively. The coexistence of overweight/obesity with anaemia and iron deficiency was observed in 19.2% and 16.3% of women, respectively. Among overweight/obese women, 32.5% were anaemic, 28.4% were iron deficient, and 47.6% had iron deficiency anaemia. the prevalence of DBM was higher in urban areas compared to rural settings. Conclusion: The coexistence of undernutrition and overnutrition among WRA highlights the urgent need for integrated public health interventions addressing both anaemia and obesity simultaneously. Tailored strategies should consider the specific socio-economic and geographical contexts to effectively combat this dual burden.

Keywords: the double burden of malnutrition, iron deficiency anaemia, overweight, obesity

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Authors: Mina Salamatmanesh, Elizabeth Fitzpatrick, Tim Ramsay, Josee Lagacé, Lindsey Sikora, JoAnne Whittingham

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Introduction: Hearing loss (HL) is one of the most common disorders that presents at birth and in early childhood. Universal newborn hearing screening (UNHS) has been adopted based on the assumption that with early identification of HL, children will have access to optimal amplification and intervention at younger ages, therefore, taking advantage of the brain’s maximal plasticity. One particular challenge for parents in the early years is achieving consistent hearing aid (HA) use which is critical to the child’s development and constitutes the first step in the rehabilitation process. This study examined the consistency of hearing aid use in young children based on data logging information documented during audiology sessions in the first three years after hearing aid fitting. Methodology: The first 100 children who were diagnosed with bilateral HL before 72 months of age since 2003 to 2015 in a pediatric audiology clinic and who had at least two hearing aid follow-up sessions with available data logging information were included in the study. Data from each audiology session (age of child at the session, average hours of use per day (for each ear) in the first three years after HA fitting) were collected. Clinical characteristics (degree of hearing loss, age of HA fitting) were also documented to further understanding of factors that impact HA use. Results: Preliminary analysis of the results of the first 20 children shows that all of them (100%) have at least one data logging session recorded in the clinical audiology system (Noah). Of the 20 children, 17(85%) have three data logging events recorded in the first three years after HA fitting. Based on the statistical analysis of the first 20 cases, the median hours of use in the first follow-up session after the hearing aid fitting in the right ear is 3.9 hours with an interquartile range (IQR) of 10.2h. For the left ear the median is 4.4 and the IQR is 9.7h. In the first session 47% of the children use their hearing aids ≤5 hours, 12% use them between 5 to 10 hours and 22% use them ≥10 hours a day. However, these children showed increased use by the third follow-up session with a median (IQR) of 9.1 hours for the right ear and 2.5, and of 8.2 hours for left ear (IQR) IQR is 5.6 By the third follow-up session, 14% of children used hearing aids ≤5 hours, while 38% of children used them ≥10 hours. Based on the primary results, factors like age and level of HL significantly impact the hours of use. Conclusion: The use of data logging information to assess the actual hours of HA provides an opportunity to examine the: a) challenges of families of young children with HAs, b) factors that impact use in very young children. Data logging when used collaboratively with parents, can be a powerful tool to identify problems and to encourage and assist families in maximizing their child’s hearing potential.

Keywords: hearing loss, hearing aid, data logging, hours of use

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