Search results for: regional vector

Authors: Emmanuel Osaze Oshoiribhor, Adetokunbo MacGregor John-Otumu

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There has been a need in recent years to predict student academic achievement prior to graduation. This is to assist them in improving their grades, especially for those who have struggled in the past. The purpose of this research is to use supervised learning techniques to create a model that predicts student academic progress. Many scholars have developed models that predict student academic achievement based on characteristics including smoking, demography, culture, social media, parent educational background, parent finances, and family background, to mention a few. This element, as well as the model used, could have misclassified the kids in terms of their academic achievement. As a prerequisite to predicting if the student will perform well in the future on related courses, this model is built using a logistic regression classifier with basic features such as the previous semester's course score, attendance to class, class participation, and the total number of course materials or resources the student is able to cover per semester. With a 96.7 percent accuracy, the model outperformed other classifiers such as Naive bayes, Support vector machine (SVM), Decision Tree, Random forest, and Adaboost. This model is offered as a desktop application with user-friendly interfaces for forecasting student academic progress for both teachers and students. As a result, both students and professors are encouraged to use this technique to predict outcomes better.

Keywords: artificial intelligence, ML, logistic regression, performance, prediction

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Authors: Javeria Ahmad, Ayesha Maryam, Zahid Rizwan, Nadeem Nasir, Yasir Nawab, Hafiz Shehbaz Ahmad

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Mitigation of electromagnetic interference (EMI) through thin, lightweight, and cost-effective materials is critical for electronic appliances as well as human health. The present research work discusses the design of composites that are suitable to minimize EMI through various stacking sequences. The carbon fibers reinforced composite structures impregnated with dielectric (MWCNTs) and magnetic nanofillers (Fe₂O₃) were developed to investigate their microwave absorption properties. The composite structure comprising a single type of nanofillers, each of MWCNTs & Fe₂O₃, was developed, and then their layers were stacked over each other with various stacking sequences to investigate the best stacking sequence, which presents good microwave absorption characteristics. A vector network analyzer (VNA) was used to analyze the microwave absorption properties of these developed composite structures. The composite structures impregnated with the layers of a dielectric nanofiller and sandwiched between the layers of a magnetic nanofiller show the highest EMI shielding value of 59 dB and a dielectric conductivity of 35 S/cm in the frequency range of 0.1 to 13.6 GHz. The results also demonstrate that the microwave absorption properties of the developed composite structures were dominant over reflection properties. The absence of an external peak in X-ray diffraction (XRD), marked the purity of the added nanofillers.

Keywords: nanocomposites, microwave absorption, EMI shielding, skin depth, reflection loss

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Authors: P. L. Cheng, I. N. Umar

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Online discussion forum is a popular e-learning technique that allows participants to interact and construct knowledge. This study aims to examine the levels of participation, categories of participants and the structure of their interactions in a forum. A convenience sampling of one course coordinator and 23 graduate students was selected in this study. The forums’ log file and the Social Network Analysis software were used in this study. The analysis reveals 610 activities (including viewing forum’s topic, viewing discussion thread, posting a new thread, replying to other participants’ post, updating an existing thread and deleting a post) performed by them in this forum, with an average of 3.83 threads posted. Also, this forum consists of five at-risk participants, six bridging participants, four isolated participants and five leaders of information. In addition, the network density value is 0.15 and there exist five reciprocal interactions in this forum. The closeness value varied between 28 and 68 while the eigen vector centrality value varied between 0.008 and 0.39. The finding indicates that the participants tend to listen more rather than express their opinions in the forum. It was also revealed that those who actively provide supports in the discussion forum were not the same people who received the most responses from their peers. This study found that cliques do not exist in the forum and the participants are not selective to whom they response to, rather, it was based on the content of the posts made by their peers. Based upon the findings, further analysis with different method and population, larger sample size and a longer time frame are recommended.

Keywords: e-learning, learning management system, online forum, social network analysis

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Authors: Wajid Mumtaz, Aamir Saeed Malik, Syed Saad Azhar Ali, Mohd Azhar Mohd Yasin

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In this paper, a technique based on electroencephalogram (EEG) analysis is presented, aiming for diagnosing major depressive disorder (MDD) among a potential population of MDD patients and healthy controls. EEG is recognized as a clinical modality during applications such as seizure diagnosis, index for anesthesia, detection of brain death or stroke. However, its usability for psychiatric illnesses such as MDD is less studied. Therefore, in this study, for the sake of diagnosis, 2 groups of study participants were recruited, 1) MDD patients, 2) healthy people as controls. EEG data acquired from both groups were analyzed involving inter-hemispheric asymmetry and composite permutation entropy index (CPEI). To automate the process, derived quantities from EEG were utilized as inputs to classifier such as logistic regression (LR) and support vector machine (SVM). The learning of these classification models was tested with a test dataset. Their learning efficiency is provided as accuracy of classifying MDD patients from controls, their sensitivities and specificities were reported, accordingly (LR =81.7 % and SVM =81.5 %). Based on the results, it is concluded that the derived measures are indicators for diagnosing MDD from a potential population of normal controls. In addition, the results motivate further exploring other measures for the same purpose.

Keywords: major depressive disorder, diagnosis based on EEG, EEG derived features, CPEI, inter-hemispheric asymmetry

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Authors: Miladi Yasmine, Bouhlel Salah, Garnit Hechmi

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Hydrothermal vein-type deposits of the Hammam Jedidi F-Ba(Pb-Zn-Cu) are hosted in Lower Jurassic, Cretaceous and Tertiary series, and located near a very important structural lineament (NE-SW) corresponding to the Hammam Jedidi Fault in the Tunisian Dorsale. The circulation of the ore forming fluid is triggered by a regional tectonic compressive phase which occurred during the miocène time. Mineralization occurs as stratabound and vein-type orebodies adjacent to the Triassic salt diapirs and within fault in Jurassic limestone. Fluid inclusions data show that two distinct fluids were involved in the mineralisation deposition: a warmer saline fluid (180°C, 20 wt % NaCl equivalent) and cooler less saline fluid (126°C, 5wt%NaCl equivalent). The contrasting salinities and halogen ratios suggest that this two fluid derived from one of the brine originated after the dissolution of halite as suggested by its high salinity. The other end member, as indicated by the low Cl/Br ratios, acquired its low salinity by dilution of Br enriched evaporated seawater. These results are compatible with Mississippi-Valley- type mineralization.

Keywords: Jebel Oust, fluid inclusions, North Eastern Tunisia, mineralization

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Authors: Rabab M. Ramadan, Elaraby A. Elgallad

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With extensive application, the performance of unimodal biometrics systems has to face a diversity of problems such as signal and background noise, distortion, and environment differences. Therefore, multimodal biometric systems are proposed to solve the above stated problems. This paper introduces a bimodal biometric recognition system based on the extracted features of the human palm print and iris. Palm print biometric is fairly a new evolving technology that is used to identify people by their palm features. The iris is a strong competitor together with face and fingerprints for presence in multimodal recognition systems. In this research, we introduced an algorithm to the combination of the palm and iris-extracted features using a texture-based descriptor, the Scale Invariant Feature Transform (SIFT). Since the feature sets are non-homogeneous as features of different biometric modalities are used, these features will be concatenated to form a single feature vector. Particle swarm optimization (PSO) is used as a feature selection technique to reduce the dimensionality of the feature. The proposed algorithm will be applied to the Institute of Technology of Delhi (IITD) database and its performance will be compared with various iris recognition algorithms found in the literature.

Keywords: iris recognition, particle swarm optimization, feature extraction, feature selection, palm print, the Scale Invariant Feature Transform (SIFT)

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Authors: Remy Jonkam Oben

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Using a time series approach, this study investigates how natural resources impact financial development from a global perspective over the 1980-2019 period. Some important determinants of financial development (economic growth, trade openness, population growth, and investment) have been added to the model as control variables. Unit root tests have revealed that all the variables are integrated into order one. Johansen's cointegration test has shown that the variables are in a long-run equilibrium relationship. The vector error correction model (VECM) has estimated the coefficient of the error correction term (ECT), which suggests that the short-run values of natural resources, economic growth, trade openness, population growth, and investment contribute to financial development converging to its long-run equilibrium level by a 23.63% annual speed of adjustment. The estimated coefficients suggest that global natural resource rent has a statistically-significant negative impact on global financial development in the long-run (thereby validating the financial resource curse) but not in the short-run. Causality test results imply that neither global natural resource rent nor global financial development Granger-causes each other.

Keywords: financial development, natural resources, resource curse hypothesis, time series analysis, Granger causality, global perspective

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Authors: V. Baweja, K. K. Gupta, V. Dubey, C. Keshavam

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Antimicrobial activity of six indigenous plants such as Tulsi Ocimum sanctum, Neem Azadirachta indica, Aloe vera, Turmeric Curcuma longa, Lantana Lantana camara, and Clove Syzygium aromaticum was assessed against the gut microbiota of the dengue fever mosquito Aedes aegypti, keeping in view that the presence of midgut bacteria may affect the ability of the vector to transmit pathogens. Eleven different types of bacterial clones were isolated from the midgut of lab-reared fourth instar larvae of Aedes aegypti and were grown on LB agar medium at an optimum temperature of 25 ºC. Identification of these bacteria was done on the basis of their colony characteristic such as colony size, shape, opacity, elevation, consistency, and growth. Light microscopic studies of the gut microbiota revealed dominance of Gram-negative cocci over gram positive cocci and bacilli and Gram-negative bacilli. Identification of species was done by chemical characterization of the colonies. Crude extracts of all test plants were screened for their antimicrobial activities against gut microbiota by disc diffusion assay. The zone of exclusion seen after 24 hr of incubation in different assays revealed the most potent antibacterial activities in neem followed by clove and turmeric. Lantana and Aloe vera were least effective.

Keywords: plant extract, aedes, dengue, antimicrobial activity

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Authors: Mohammadhossein Moghimi, Robert Stone, Pia Rotshtein

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Recording psychological and physiological correlates of human performance within virtual environments and interpreting their impacts on human engagement, ‘immersion’ and related emotional or ‘effective’ states is both academically and technologically challenging. By exposing participants to an effective, real-time (game-like) virtual environment, designed and evaluated in an earlier study, a psychophysiological database containing the EEG, GSR and Heart Rate of 30 male and female gamers, exposed to 10 games, was constructed. Some 174 features were subsequently identified and extracted from a number of windows, with 28 different timing lengths (e.g. 2, 3, 5, etc. seconds). After reducing the number of features to 30, using a feature selection technique, K-Nearest Neighbour (KNN) and Support Vector Machine (SVM) methods were subsequently employed for the classification process. The classifiers categorised the psychophysiological database into four effective clusters (defined based on a 3-dimensional space – valence, arousal and dominance) and eight emotion labels (relaxed, content, happy, excited, angry, afraid, sad, and bored). The KNN and SVM classifiers achieved average cross-validation accuracies of 97.01% (±1.3%) and 92.84% (±3.67%), respectively. However, no significant differences were found in the classification process based on effective clusters or emotion labels.

Keywords: virtual reality, effective computing, effective VR, emotion-based effective physiological database

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Authors: Aref Aasi, Sahar Ebrahimi Bajgani, Erfan Aasi

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Breast cancer (BC) continues to be the most frequent cancer in females and causes the highest number of cancer-related deaths in women worldwide. Inspired by recent advances in studying the relationship between different patient attributes and features and the disease, in this paper, we have tried to investigate the different classification methods for better diagnosis of BC in the early stages. In this regard, datasets from the University Hospital Centre of Coimbra were chosen, and different machine learning (ML)-based and neural network (NN) classifiers have been studied. For this purpose, we have selected favorable features among the nine provided attributes from the clinical dataset by using a random forest algorithm. This dataset consists of both healthy controls and BC patients, and it was noted that glucose, BMI, resistin, and age have the most importance, respectively. Moreover, we have analyzed these features with various ML-based classifier methods, including Decision Tree (DT), K-Nearest Neighbors (KNN), eXtreme Gradient Boosting (XGBoost), Logistic Regression (LR), Naive Bayes (NB), and Support Vector Machine (SVM) along with NN-based Multi-Layer Perceptron (MLP) classifier. The results revealed that among different techniques, the SVM and MLP classifiers have the most accuracy, with amounts of 96% and 92%, respectively. These results divulged that the adopted procedure could be used effectively for the classification of cancer cells, and also it encourages further experimental investigations with more collected data for other types of cancers.

Keywords: breast cancer, diagnosis, machine learning, biomarker classification, neural network

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Authors: S. K. Jain, M. K. Mishra

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Oblique propagation of ion-acoustic solitons in magnetized electron-positron-ion (EPI) plasma with warm adiabatic ions and isothermal electrons has been studied. Korteweg-de Vries (KdV) equation using reductive perturbation method has been derived for the system, which admits an obliquely propagating soliton solution. It is found that for the selected set of parameter values, the system supports only compressive solitons. Investigations reveal that an increase in positron concentration diminishes the amplitude as well as the width of the soliton. It is also found that the temperature ratio of electron to positron (γ) affects the amplitude of the solitary wave. An external magnetic field do not affect the amplitude of ion-acoustic solitons, but obliqueness angle (θ), the angle between wave vector and magnetic field affects the amplitude. The amplitude of the ion-acoustic solitons increases with increase in angle of obliqueness. Magnetization and obliqueness drastically affect the width of the soliton. An increase in ionic temperature decreases the amplitude and width. For the fixed set of parameters, profiles have been drawn to study the combined effect with variation of two parameters on the characteristics of the ion-acoustic solitons (i.e., amplitude and width). The result may be applicable to plasma in the laboratory as well as in the magnetospheric region of the earth.

Keywords: ion-acoustic solitons, Korteweg-de Vries (KdV) equation, magnetized electron-positron-ion (EPI) plasma, reductive perturbation method

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Authors: Gaurav Bisht, Rahul Rathnakumar, Ravikumar Duggirala

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Predictive simulations are one of the key focus areas in safety-critical industries such as aerospace and high-performance automotive engineering. The stone-chipping study is one such effort taken up by the industry to predict and evaluate the damage caused due to gravel impact on vehicles. This paper describes a finite elements based method that can simulate the ejection of gravel chips from a vehicle tire. The FE simulations were used to obtain the initial ejection velocity of the stones for various driving conditions using a computational contact mechanics approach. To verify the accuracy of the tire model, several parametric studies were conducted. The FE simulations resulted in stone loft velocities ranging from 0–8 m/s, regardless of tire speed. The stress on the tire at the instant of initial contact with the stone increased linearly with vehicle speed. Mesh convergence studies indicated that a highly resolved tire mesh tends to result in better momentum transfer between the tire and the stone. A fine tire mesh also showed a linearly increasing relationship between the tire forward speed and stone lofting speed, which was not observed in coarser meshes. However, it also highlighted a potential challenge, in that the ejection velocity vector of the stone seemed to be sensitive to the mesh, owing to the FE-based contact mechanical formulation of the problem.

Keywords: abaqus, contact mechanics, foreign object debris, stone chipping

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Authors: Abdelkarim M. Ertiame, D. W. Yu, D. L. Yu, J. B. Gomm

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In this paper, a new robust fault detection and isolation (FDI) scheme is developed to monitor a multivariable nonlinear chemical process called the Chylla-Haase polymerization reactor when it is under the cascade PI control. The scheme employs a radial basis function neural network (RBFNN) in an independent mode to model the process dynamics and using the weighted sum-squared prediction error as the residual. The recursive orthogonal Least Squares algorithm (ROLS) is employed to train the model to overcome the training difficulty of the independent mode of the network. Then, another RBFNN is used as a fault classifier to isolate faults from different features involved in the residual vector. The several actuator and sensor faults are simulated in a nonlinear simulation of the reactor in Simulink. The scheme is used to detect and isolate the faults on-line. The simulation results show the effectiveness of the scheme even the process is subjected to disturbances and uncertainties including significant changes in the monomer feed rate, fouling factor, impurity factor, ambient temperature and measurement noise. The simulation results are presented to illustrate the effectiveness and robustness of the proposed method.

Keywords: Robust fault detection, cascade control, independent RBF model, RBF neural networks, Chylla-Haase reactor, FDI under closed-loop control

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Authors: Sachoiba Inkah

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The Northeast region has not been a major factor in India’s foreign policy calculation since independence. Instead, the region was ignored and marginalized even to the extent of using force and repressive Acts such as AFSPA(Armed Forces Special Powers Act) to suppress the voices of both states and non-state actors. The liberalization of the economy in the 90s in the wake of globalization gave India a new outlook and the Look East Policy (LEP) was a paradigm shift in India’s engagement with the Southeast Asian nations as it seeks to explore the benefits of the ASEAN. The reorienting of India’s foreign policy to ‘Neighborhood First” is attributed to the present political dispensation, which is further widened to include ‘Extended Neighborhood.’ As a result, the Northeastern states have become key players in India’s participation in regional groupings such as SAARC, BIMSTEC, and BCIM. The need for external balancing, diplomacy and development has reset India’s foreign policy priorities as the Northeast states lie in the confluence of South Asia, Southeast and East Asia, and a stakeholder in Act East Policy. The paper will explore the role of Northeastern states in the framework of Indian foreign policy as it shares international boundaries with China, Bhutan, Bangladesh, and Myanmar and most importantly, study the case of Nagas who are spread across Manipur, Nagaland, and Arunachal Pradesh bordering Myanmar. The Indo-Myanmar border is an area of conflict and various illegal activities such as arms trafficking, illegal migrants, drug, and human trafficking are still being carried out and in order to address this issue, both India and Myanmar need to take into consideration the various communities living across the border. And conflict and insurgency should not be a yardstick to curtailed development of infrastructures such as roads, health facilities, transport, and communication in the contested region. The realities, perceptions, and contentions of the Northeastern states and the different communities living in the border areas need a wider discourse as the region the potential to drive India’s diplomatic relations with its neighbors and extended neighborhood. The methods employed are analytical and more of a descriptive analysis on India’s foreign policy framework with a focus on Nagas in Myanmar, drawing from both primary and secondary sources. Primary sources include official documents, data, and statistics released by various governmental agencies, parliamentary debates, political speeches, press releases, treaties and agreements, historical biographies and organizational policy papers, protocols and procedures of government conferences, regional organization study reports etc. The paper concludes that the recent proactive engagement between India and Myanmar on trade, defense, economic, and infrastructure development are positive signs cementing bilateral ties, but there is not much room for the people-to-people connect, especially for people living in the borderland. The Freedom of Movement Regime that is in place is limited and there is more scope for improvement as people in the borderland looks towards trade and commerce to not only uplift the border economy but also act as a catalyst for robust engagement between the two countries, albeit with more infrastructure such as road, healthcare, education, a tourist hotspot, trade centers, mobile connectivity, etc.

Keywords: foreign policy, infrastructure development, insurgency, people to people connect

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Authors: Haifeng Wang, Haili Zhang

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Most movie recommendation systems have been developed for customers to find items of interest. This work introduces a predictive model usable by small and medium-sized enterprises (SMEs) who are in need of a data-based and analytical approach to stock proper movies for local audiences and retain more customers. We used classification models to extract features from thousands of customers’ demographic, behavioral and social information to predict their movie genre preference. In the implementation, a Gaussian kernel support vector machine (SVM) classification model and a logistic regression model were established to extract features from sample data and their test error-in-sample were compared. Comparison of error-out-sample was also made under different Vapnik–Chervonenkis (VC) dimensions in the machine learning algorithm to find and prevent overfitting. Gaussian kernel SVM prediction model can correctly predict movie genre preferences in 85% of positive cases. The accuracy of the algorithm increased to 93% with a smaller VC dimension and less overfitting. These findings advance our understanding of how to use machine learning approach to predict customers’ preferences with a small data set and design prediction tools for these enterprises.

Keywords: computational social science, movie preference, machine learning, SVM

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Authors: Ali Bayati, Jamileh Azarnoush

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Nowadays according to increasing the population all around the world, consuming of fossil fuels increased dramatically. Many believe that most of the atmospheric pollution comes by using fossil fuels. The process of natural sources entering cities shows one of the large challenges in consumption sources management. Nowadays, everyone considered about the consumption of fossil fuels and also Reduction of consumption civil energy in megacities that play a key role in solving serious problems such as air pollution, producing greenhouse gasses, global warming and damage ozone layer. In the construction industry, we should use the materials with the lowest need to energy for making and carrying them, and also the materials which need the lowest energy and expenses to recycling. In this way, the kind of usage material, the way of processing, regional materials and the adaptation with the environment is critical. Otherwise, the isolation should be use and mention in the long term. Accordingly, in this article we investigates the new ways in order to reduce environmental pollution and save more energy by using materials that are not harmful to the environment, fully insulated materials in buildings, sustainable and diversified buildings, suitable urban design and using solar energy more efficiently in order to reduce energy consumption.

Keywords: building design, construction masonry, insulation, sustainable construction

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Authors: Ruth Yunji Nange

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Tourism has continued to increase as a significant industry, enhancing economic growth and development in Cameroon; due to the tremendous success of this industry, community participation (CP) has enhanced tourism development (TD). While gaining augmented attractiveness, considering how local CP is encouraged in such initiatives has become imperative. It has become essential to examine the importance of CP in the development of policies and planning processes for the equitable distribution of benefits and the effects of TD in the country. This study will exactly explore the cases of CP in the most populated cities in Cameroon (Douala and Yaoundé) and also understand how local CP is incorporated into tourism to enhance development in the tourism industry in particular and Cameroon in general. This paper is based on a qualitative research method, semi-structured interviews, and in-depth, face-to-face interviews carried out with the top administrators of tourism, both in the public and private sectors, such as the minister, provincial and regional delegates of tourism, non-governmental organizations (NGO), leaders of local community associations and tour operators. The forms and surveys were open-ended with a high level of flexibility. The findings of this study will pose implications for the development of CP in tourism initiatives programs in Cameroon and other developing economies.

Keywords: Cameroon, local community, participation and planning, tourism

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Authors: Jennifer Chen, Pei Y. Hsu, Yu W. Chen

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Groundwater resources play a vital role in regional water supply because over 1/3 of total demand is satisfied by groundwater resources. Because over-pumpage might cause environmental impact such as land subsidence, a sustainable management of groundwater resource is required. In this study, a blueprint of smart management for groundwater resource is proposed and planned. The framework of the smart management can be divided into two major parts, hardware and software parts. First, an internet of groundwater (IoG) which is inspired by the internet of thing (IoT) is proposed to observe the migration of groundwater usage and the associated response, groundwater levels. Second, algorithms based on data mining and signal analysis are proposed to achieve the goal of providing highly efficient management of groundwater. The entire blueprint is a 4-year plan and this year is the first year. We have finished the installation of 50 flow meters and 17 observation wells. An underground hydrological model is proposed to determine the associated drawdown caused by the measured pumpages. Besides, an alternative to the flow meter is also proposed to decrease the installation cost of IoG. An accelerometer and 3G remote transmission are proposed to detect the on and off of groundwater pumpage.

Keywords: groundwater management, internet of groundwater, underground hydrological model, alternative of flow meter

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Authors: Hanmei Peng, Yiqun Wang, Mao Tan, Zhuocen Dai, Yongxin Su

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Efficient energy management in multi-microgrid distribution systems holds significant importance for enhancing the economic benefits of regional power grids. To better balance conflicts among various stakeholders, a two-stage game-based collaborative optimization approach is proposed in this paper, effectively addressing the realistic scenario involving both competition and collaboration among stakeholders. The first stage, aimed at maximizing individual benefits, involves constructing a non-cooperative tariff game model for the distribution network and surplus microgrid. In the second stage, considering power flow and physical line capacity constraints we establish a cooperative P2P game model for the multi-microgrid distribution system, and the optimization involves employing the Lagrange method of multipliers to handle complex constraints. Simulation results demonstrate that the proposed approach can effectively improve the system economics while harmonizing individual and collective rationality.

Keywords: cooperative game, collaborative optimization, multi-microgrid distribution system, non-cooperative game

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Authors: Hyojin Lim, Cuong Nguyen Khac, Yeongyu Choi, Ho-Youl Jung

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In this paper, we propose a simple moving object detection, which is based on motion vectors obtained from pyramidal Lucas-Kanade optical flow. The proposed method detects moving objects such as pedestrians, the other vehicles and some obstacles at the front-side of the host vehicle, and it can provide the warning to the driver. Motion vectors are obtained by using pyramidal Lucas-Kanade optical flow, and some outliers are eliminated by comparing the amplitude of each vector with the pre-defined threshold value. The background model is obtained by calculating the mean and the variance of the amplitude of recent motion vectors in the rectangular shaped local region called the cell. The model is applied as the reference to classify motion vectors of moving objects and those of background. Motion vectors are clustered to rectangular regions by using the unsupervised clustering K-means algorithm. Labeling method is applied to label groups which is close to each other, using by distance between each center points of rectangular. Through the simulations tested on four kinds of scenarios such as approaching motorbike, vehicle, and pedestrians to host vehicle, we prove that the proposed is simple but efficient for moving object detection in parking lots.

Keywords: moving object detection, dynamic scene, optical flow, pyramidal optical flow

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Authors: A. S. CohenMiller, Jenifer Lewis, Gwen McEvoy, Kristy Kelly

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This international, interdisciplinary study presents the first stage of a gender mainstreaming project within one university as a microcosm of society in Kazakhstan to make concrete policy recommendations and set up the potential for new research to monitor change over time. Local, regional, and UN representatives have noted the critical need and interest in gender related issues in Kazakhstan. Gender mainstreaming has been noted as a strategy to understand and address gender equality and equity such as within the academy in exploring and examining organizational/management issues, university decision-making and leadership, assessing the overall academic climate, discrimination issues, hiring and promotion, and student recruitment and retention. This presentation provides preliminary findings from the university gender audit, highlighting key elements for moving forward in gender mainstreaming. The full study analyzes findings from the full gender audit including interview with key stakeholders, time-use surveys, participant-observations and interviews with female students, staff and faculty, and reviews of formal organizational policies and practices.

Keywords: academia, equity, Eurasia, gender audit, gender mainstreaming, Kazakhstan, policy, time-use survey

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Authors: Yousif Mohamed Y. Abdallah, Eltayeb Wagi Allah Eltayeb, Mohamed E. Gar-elnabi, Mohamed Ahmed Ali

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To characterize coronary artery obstruction and related findings in ischemic heart patients using cardiac scintigraphy for the identification of myocardial ischemia, 146 patients were studied at basal conditions and also asked for fasting after night till the intravenous injection of the radiopharmaceutical. After the injection time about 15 to 20 minutes, the patient should eat a fatty meal and chocolate for the good excretion of the gall bladder, to evaluate the performance and regional wall motion of the left ventricle (LV). The results showed that the body mass index percentage in this sample was in range of 43.05 to 61.05. The number of patients who were catheter candidates were 56 with 43% and the patients that were not candidate to cathode were 74 patients with 57% of all patients. For the group of patients where type of ischemia was assessed, 29.5% of patients had reversible posterior and inferior wall, 15.1% of patients had fixed large from apex to base, 9.6% of patients had mild basal inferior wall, 4.8 % of patients had mild anterior wall, 6.2% of patients had antro-septal and 34.9% of patients had moderate ischemia.

Keywords: myocardial ischemia, myocardial scintigraphy, contrast ventriculography, coronary artery obstruction

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Authors: Amit Bhattacharyya

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The economic scenario of any region does not show the real picture for the measurement of overall development. Therefore, economic development must be accompanied by social development to be able to make an assessment to measure the level of development. The spatial variation with respect to social development has been discussed taking into account the quality of functioning of a social system in a specific area. In this paper, an attempt has been made to study the spatial distribution of social infrastructural facilities and analyze the magnitude of regional disparities at inter- block level in Barddhman district. It starts with the detailed account of the selection process of social infrastructure indicators and describes the methodology employed in the empirical analysis. Analyzing the block level data, this paper tries to identify the disparity among the blocks in the levels of social development. The results have been subsequently explained using both statistical analysis and geo spatial technique. The paper reveals that the social development is not going on at the same rate in every part of the district. Health facilities and educational facilities are concentrated at some selected point. So overall development activities come to be concentrated in a few centres and the disparity is seen over the blocks.

Keywords: disparity, inter-block, social development, spatial variation

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Authors: Sandeep Santosh, O. P. Sahu

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In this paper, performance comparison of wideband covariance matrix sparse representation (W-CMSR) method with other existing wideband Direction of Arrival (DOA) estimation methods has been made.W-CMSR relies less on a priori information of the incident signal number than the ordinary subspace based methods.Consider the perturbation free covariance matrix of the wideband array output. The diagonal covariance elements are contaminated by unknown noise variance. The covariance matrix of array output is conjugate symmetric i.e its upper right triangular elements can be represented by lower left triangular ones.As the main diagonal elements are contaminated by unknown noise variance,slide over them and align the lower left triangular elements column by column to obtain a measurement vector.Simulation results for W-CMSR are compared with simulation results of other wideband DOA estimation methods like Coherent signal subspace method (CSSM), Capon, l1-SVD, and JLZA-DOA. W-CMSR separate two signals very clearly and CSSM, Capon, L1-SVD and JLZA-DOA fail to separate two signals clearly and an amount of pseudo peaks exist in the spectrum of L1-SVD.

Keywords: W-CMSR, wideband direction of arrival (DOA), covariance matrix, electrical and computer engineering

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Authors: Arth Bohra, Marwa Mahmoud

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In this paper, we utilize computer vision to develop a tool for youth coaches to formulate set-piece tactics for their players. We used the Soccernet database to extract the ResNet features and camera calibration data for over 3000 corner kick across 500 professional matches in the top 6 European leagues (English Premier League, UEFA Champions League, Ligue 1, La Liga, Serie A, Bundesliga). Leveraging the provided homography matrix, we construct a feature vector representing the formation of players on these corner kicks. Additionally, labeling the videos manually, we obtained the pass-trajectory of each of the 3000+ corner kicks by segmenting the field into four zones. Next, after determining the localization of the players and ball, we used event data to give the corner kicks a rating on a 1-4 scale. By employing a Convolutional Neural Network, our model managed to predict the success of a corner kick given the formations of players. This suggests that with the right formations, teams can optimize the way they approach corner kicks. By understanding this, we can help coaches formulate set-piece tactics for their own teams in order to maximize the success of their play. The proposed model can be easily extended; our method could be applied to even more game situations, from free kicks to counterattacks. This research project also gives insight into the myriad of possibilities that artificial intelligence possesses in transforming the domain of sports.

Keywords: soccer, corner kicks, AI, computer vision

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Authors: Halima Benazzouz

Abstract:

It is often believed that British English and American English are the foremost varieties of the English Language serving as reference norms for other varieties;that is the reason why they have obviously been compared and contrasted.Meanwhile,the terms “British English” and “American English” are used differently by different people to refer to: 1) Two national varieties each subsuming regional and other sub-varieties standard and non-standard. 2) Two national standard varieties in which each one is only part of the range of English within its own state, but the most prestigious part. 3) Two international varieties, that is each is more than a national variety of the English Language. 4) Two international standard varieties that may or may not each subsume other standard varieties.Furthermore,each variety serves as a reference norm for users of the language elsewhere. Moreover, without a clear identification, as primarily belonging to one variety or the other, British English(Br.Eng) and American English (Am.Eng) are understood as national or international varieties. British English and American English are both “variants” and “varieties” of the English Language, more similar than different.In brief, the following may justify general categories of difference between Standard American English (S.Am.E) and Standard British English (S.Br.e) each having their own sociolectic value: A difference in pronunciation exists between the two foremost varieties, although it is the same spelling, by contrast, a divergence in spelling may be recognized, eventhough the same pronunciation. In such case, the same term is different but there is a similarity in spelling and pronunciation. Otherwise, grammar, syntax, and punctuation are distinctively used to distinguish the two varieties of the English Language. Beyond these differences, spelling is noted as one of the chief sources of variation.

Keywords: Greek, Latin, French pronunciation expert, varieties of English language

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Authors: Sharifah Mousli, Sona Taheri, Jiayuan He

Abstract:

Autism spectrum disorder (ASD) is a complex developmental condition involving persistent difficulties with social communication, restricted interests, and repetitive behavior. The challenges associated with ASD can interfere with an affected individual’s ability to function in social, academic, and employment settings. Although there is no effective medication known to treat ASD, to our best knowledge, early intervention can significantly improve an affected individual’s overall development. Hence, an accurate diagnosis of ASD at an early phase is essential. The use of machine learning approaches improves and speeds up the diagnosis of ASD. In this paper, we focus on the application of unsupervised clustering methods in ASD as a large volume of ASD data generated through hospitals, therapy centers, and mobile applications has no pre-existing labels. We conduct a comparative analysis using seven clustering approaches such as K-means, agglomerative hierarchical, model-based, fuzzy-C-means, affinity propagation, self organizing maps, linear vector quantisation – as well as the recently developed optimization-based clustering (COMSEP-Clust) approach. We evaluate the performances of the clustering methods extensively on real-world ASD datasets encompassing different age groups: toddlers, children, adolescents, and adults. Our experimental results suggest that the COMSEP-Clust approach outperforms the other seven methods in recognizing ASD with well-separated clusters.

Keywords: autism spectrum disorder, clustering, optimization, unsupervised machine learning

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Authors: Adetoun Adedotun Amubode

Abstract:

Nigeria's Textile Industry was the second largest in Africa after Egypt, with above 250 vibrant factories and over 50 percent capacity utilization contributing to foreign exchange earnings and employment generation. Currently, multifaceted challenges such as epileptic power supply, inconsistent government policies, growing digitalization, smuggling of foreign textiles, insecurity and the inability of the local industries to compete with foreign products, especially Chinese textile, has created a hostile environment for the sector. This led to the closure of most of the textile industries. China's textile industry has experienced institutional change and industrial restructuring, having 30% of the world's market share. This paper examined the strategies adopted by China in transforming her textile and clothing industries and designed a model for the integration of these strategies to improve the competitive strength and growth of the Nigerian textile and clothing industries in a dynamic and changing market. The paper concludes that institutional support, regional production, export-oriented policy, value-added and branding cultivation, technological upgrading and enterprise resource planning be integrated into the Nigerian clothing and textile industries.

Keywords: clothing, industry, integration, Nigerian, textile, transformation.

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Authors: Margaret S. Wright

Abstract:

Background/Significance: During many recent public health emergencies/disasters, public health nursing data has been missing or delayed, potentially impacting the decision-making and response. Data used as evidence for decision-making in response, planning, and mitigation has been erratic and slow, decreasing the ability to respond. Methodology: Applying best practices in data management and data use in public health settings, and guided by the concepts outlined in ‘Disaster Standards of Care’ models leads to the development of recommendations for a model of best practices in data management and use in public health disasters/emergencies by public health nurses. As the ‘patient’ in public health disasters/emergencies is the community (local, regional or national), guidelines for patient documentation are incorporated in the recommendations. Findings: Using model public health nurses could better plan how to prepare for, respond to, and mitigate disasters in their communities, and better participate in decision-making in all three phases bringing public health nursing data to the discussion as part of the evidence base for decision-making.

Keywords: data management, decision making, disaster planning documentation, public health nursing

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Authors: Paul Ojeaga

Abstract:

Can the level of innovation use drive un-validated business models across regions? To what extent does industrial sector attractiveness drive firm’s success across regions at the time of start-up? This study examines the role of innovation on start-up success in six regions of the world (namely Sub Saharan Africa, the Middle East and North Africa, Latin America, South East Asia Pacific, the European Union and the United States representing North America) using macroeconomic variables. While there have been studies using firm level data, results from such studies are not suitable for national policy decisions. The need to drive a regional innovation policy also begs for an answer, therefore providing room for this study. Results using dynamic panel estimation show that innovation counts in the early infancy stage of new business life cycle. The results are robust even after controlling for time fixed effects and the study present variance-covariance estimation robust standard errors.

Keywords: industrial economics, un-validated business models, scalable models, entrepreneurship

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