Search results for: regional vector

Authors: Julien Caudeville, Muriel Ismert

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Analyzing the relationship between environment and health has become a major preoccupation for public health as evidenced by the emergence of the French national plans for health and environment. These plans have identified the following two priorities: (1) to identify and manage geographic areas, where hotspot exposures are suspected to generate a potential hazard to human health; (2) to reduce exposure inequalities. At a regional scale and fine resolution of exposure outcome prerequisite, environmental monitoring networks are not sufficient to characterize the multidimensionality of the exposure concept. In an attempt to increase representativeness of spatial exposure assessment approaches, risk composite indicators could be built using additional available databases and theoretical framework approaches to combine factor risks. To achieve those objectives, combining data process and transfer modeling with a spatial approach is a fundamental prerequisite that implies the need to first overcome different scientific limitations: to define interest variables and indicators that could be built to associate and describe the global source-effect chain; to link and process data from different sources and different spatial supports; to develop adapted methods in order to improve spatial data representativeness and resolution. A GIS-based modeling platform for quantifying human exposure to chemical substances (PLAINE: environmental inequalities analysis platform) was used to build health risk indicators within the Lorraine region (France). Those indicators combined chemical substances (in soil, air and water) and noise risk factors. Tools have been developed using modeling, spatial analysis and geostatistic methods to build and discretize interest variables from different supports and resolutions on a 1 km2 regular grid within the Lorraine region. By example, surface soil concentrations have been estimated by developing a Kriging method able to integrate surface and point spatial supports. Then, an exposure model developed by INERIS was used to assess the transfer from soil to individual exposure through ingestion pathways. We used distance from polluted soil site to build a proxy for contaminated site. Air indicator combined modeled concentrations and estimated emissions to take in account 30 polluants in the analysis. For water, drinking water concentrations were compared to drinking water standards to build a score spatialized using a distribution unit serve map. The Lden (day-evening-night) indicator was used to map noise around road infrastructures. Aggregation of the different factor risks was made using different methodologies to discuss weighting and aggregation procedures impact on the effectiveness of risk maps to take decisions for safeguarding citizen health. Results permit to identify pollutant sources, determinants of exposure, and potential hotspots areas. A diagnostic tool was developed for stakeholders to visualize and analyze the composite indicators in an operational and accurate manner. The designed support system will be used in many applications and contexts: (1) mapping environmental disparities throughout the Lorraine region; (2) identifying vulnerable population and determinants of exposure to set priorities and target for pollution prevention, regulation and remediation; (3) providing exposure database to quantify relationships between environmental indicators and cancer mortality data provided by French Regional Health Observatories.

Keywords: health risk, environment, composite indicator, hotspot areas

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Authors: Anupam Das

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Although financial liberalization has been one of the most important policy prescriptions of international organizations like the World Bank and the IMF, the effect of financial liberalization on economic growth in developing countries is far from unanimous. Since the '80s, South Asian countries made a significant development in liberalization the financial sector. However, due to unavailability of a sufficient number of time series observations, the relationship between economic growth and financial development has not been investigated adequately. We aim to fill this gap by examining time series data of five developing countries from the South Asian region: Bangladesh, India, Pakistan, Sri Lanka, and Nepal. Applying the cointegration tests and Granger causality within the vector error correction model (VECM), we do not find unanimous evidence of financial development on positive economic growth. These results are helpful for developing countries which have been trying to liberalize the financial sector in recent decades.

Keywords: economic growth, financial development, Granger causality, South Asia

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Authors: Hichem Kmimech, Achref Jabeur Telmoudi, Lotfi Nabli

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The estimation of the initial marking minimum (MIM) is a crucial problem in labeled Petri nets. In the case of multiple choices, the search for the initial marking leads to a problem of optimization of the minimum allocation of resources with two constraints. The first concerns the firing sequence that could be legal on the initial marking with respect to the firing vector. The second deals with the total number of tokens that can be minimal. In this article, the MIM problem is solved by the meta-heuristic particle swarm optimization (PSO). The proposed approach presents the advantages of PSO to satisfy the two previous constraints and find all possible combinations of minimum initial marking with the best computing time. This method, more efficient than conventional ones, has an excellent impact on the resolution of the MIM problem. We prove through a set of definitions, lemmas, and examples, the effectiveness of our approach.

Keywords: marking, production system, labeled Petri nets, particle swarm optimization

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Authors: Christine Lassen, Elizabeth Leonard, Matthew Oliver

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The management of trauma is often complex and outcomes dependent on clinical expertise, effective teamwork, and a supported trauma system. The implementation of a statewide trauma education program should be accessible to all clinicians who manage trauma, but this can be challenging due to diverse individual needs, trauma service needs and geography. The NSW Institute of Trauma and Injury Management (ITIM) is a government funded body, responsible for coordinating and supporting the NSW Trauma System. The aim of this presentation is to describe how education initiatives have been implemented across the state. Simulation: In 2006, ITIM developed a Trauma Team Training Course - aimed to educate clinicians on the technical and non-technical skills required to manage trauma. The course is now independently coordinated by trauma services across the state at major trauma centres as well as in regional and rural hospitals. ITIM is currently in the process of re-evaluating and updating the Trauma Team Training Course to allow for the development of new resources and simulation scenarios. Trauma Education Evenings: In 2013, ITIM supported major trauma services to develop trauma education evenings which allowed the provision of free education to staff within the area health service and local area. The success of these local events expanded to regional hospitals. A total of 75 trauma education evenings have been conducted within NSW, with over 10,000 attendees. Wed-Based Resources: Recently, ITIM commenced free live streaming of the trauma education evenings which have now had over 3000 live views. The Trauma App developed in 2015 provides trauma clinicians with a centralised portal for trauma information and works on smartphones and tablets that integrate with the ITIM website. This supports pre-hospital and bedside clinical decisions and allows for trauma care to be more standardised, evidence-based, timely, and appropriate. Online e-Learning modules have been developed to assist clinicians, reduce unwarranted clinical variation and provide up to date evidence based education. The modules incorporate clinically focused education content with summative and formative assessments. Conclusion: Since 2005, ITIM has helped to facilitate the development of trauma education programs for doctors, nurses, pre-hospital and allied health clinicians. ITIM has been actively involved in more than 100 specialized trauma education programs, seminars and clinical workshops - attended by over 12,000 staff. The provision of state-wide trauma education is a challenging task requiring collaboration amongst numerous agencies working towards a common goal – to provide easily accessible trauma education.

Keywords: education, simulation, team-training, trauma

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Authors: Luigi Delle Rose, Oliver Fischer, Ahmed Hammad

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In this article, we study the prospects of the proposed Large Hadron electron Collider (LHeC) in the search for heavy neutral scalar particles. We consider a minimal model with one additional complex scalar singlet that interacts with the Standard Model (SM) via mixing with the Higgs doublet, giving rise to an SM-like Higgs boson and a heavy scalar particle. Both scalar particles are produced via vector boson fusion and can be tested via their decays into pairs of SM particles, analogously to the SM Higgs boson. Using multivariate techniques, we show that the LHeC is sensitive to heavy scalars with masses between 200 and 800 GeV down to scalar mixing of order 0.01.

Keywords: beyond the standard model, large hadron electron collider, multivariate analysis, scalar singlet

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Authors: Nadia el Atlas, Mohammed el Aroussi, Mohammed Wahbi

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In this paper a novel technique of mass characterization based on robust features-fusion is presented. The proposed method consists of mainly four stages: (a) the first phase involves segmenting the masses using edge information’s. (b) The second phase is to calculate and fuse the most relevant morphological features. (c) The last phase is the classification step which allows us to classify the images into benign and malignant masses. In this step we have implemented Support Vectors Machines (SVM) and Artificial Neural Networks (ANN), which were evaluated with the following performance criteria: confusion matrix, accuracy, sensitivity, specificity, receiver operating characteristic ROC, and error histogram. The effectiveness of this new approach was evaluated by a recently developed database: INBREAST database. The fusion of the most appropriate morphological features provided very good results. The SVM gives accuracy to within 64.3%. Whereas the ANN classifier gives better results with an accuracy of 97.5%.

Keywords: breast cancer, mammography, CAD system, features, fusion

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Authors: D. Pugazhenthi, B. Sree Vidya

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

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

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Authors: K. Thamizhmaran, Akshaya Devi Arivazhagan, M. Anitha

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On the case of most important systematic issue that must need to be solved in means of implementing a data transmission algorithm on the source of Mobile adhoc networks (MANETs). That is, how to save mobile nodes energy on meeting the requirements of applications or users as the mobile nodes are with battery limited. On while satisfying the energy saving requirement, hence it is also necessary of need to achieve the quality of service. In case of emergency work, it is necessary to deliver the data on mean time. Achieving quality of service in MANETs is also important on while. In order to achieve this requirement, Hence, we further implement the Energy-Aware routing protocol for system of Mobile adhoc networks were it being proposed, that on which saves the energy as on every node by means of efficiently selecting the mode of energy efficient path in the routing process by means of Enhanced AODV routing protocol.

Keywords: Ad-Hoc networks, MANET, routing, AODV, EAODV

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Authors: Kongmeng Ly

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The management of transboundary river basins across developing countries, such as the Lower Mekong River Basin (LMB), is frequently challenging given the development and conservation divergences of the basin countries. Driven by needs to sustain economic performance and reduce poverty, the LMB countries (Cambodia, Lao PDR, Thailand, Viet Nam) are embarking on significant land use changes in the form hydropower dam, to fulfill their energy requirements. This pathway could lead to irreversible changes to the ecosystem of the Mekong River, if not properly managed. Given the uncertain trade-offs of hydropower development and operation, the Lower Mekong River Basin Countries through the technical support of the Mekong River Commission (MRC) Secretariat embarked on decade long the development of Technical Guidelines for Transboundary Environmental Impact Assessment. Through a series of workshops, seminars, national and regional consultations, and pilot studies and further development following the recommendations generated through legal and institutional reviews undertaken over two decades period, the LMB Countries jointly adopted the MRC Technical Guidelines for Transboundary Environmental Impact Assessment (TbEIA Guidelines). These guidelines were developed with particular regard to the experience gained from MRC supported consultations and technical reviews of the Xayaburi Dam Project, Don Sahong Hydropower Project, Pak Beng Hydropower Project, and lessons learned from the Srepok River and Se San River case studies commissioned by the MRC under the generous supports of development partners around the globe. As adopted, the TbEIA Guidelines have been designed as a supporting mechanism to the national EIA legislation, processes and systems in each Member Country. In recognition of the already agreed mechanisms, the TbEIA Guidelines build on and supplement the agreements stipulated in the 1995 Agreement on the Cooperation for the Sustainable Development of the Mekong River Basin and its Procedural Rules, in addressing potential transboundary environmental impacts of development projects and ensuring mutual benefits from the Mekong River and its resources. Since its adoption in 2022, the TbEIA Guidelines have already been voluntary implemented by Lao PDR on its underdevelopment Sekong A Downstream Hydropower Project, located on the Sekong River – a major tributary of the Mekong River. While this implementation is ongoing with results expected in early 2024, the implementation thus far has strengthened cooperation among concerned Member Countries with multiple successful open dialogues organized at national and regional levels. It is hope that lessons learnt from this application would lead to a wider application of the TbEIA Guidelines for future water resources development projects in the LMB.

Keywords: transboundary, EIA, lower mekong river basin, mekong river

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Authors: Varis Ransibrahmanakul, Doug Pirhalla, Scott Sheridan, Cameron Lee

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U.S. coastal areas and ecosystems are facing multiple sea level rise threats and effects: heavy rain events, cyclones, and changing wind and weather patterns all influence coastal flooding, sedimentation, and erosion along critical barrier islands and can strongly impact habitat resiliency and water quality in protected habitats. These impacts are increasing over time and have accelerated the need for new tracking techniques, models and tools of flood risk to support enhanced preparedness for coastal management and mitigation. To address this issue, NOAA National Ocean Service (NOS) evaluated new metrics from satellite altimetry AVISO/Copernicus and MODIS IR flood extents to isolate nodes atmospheric variability indicative of elevated sea level and nuisance flood events. Using de-trended time series of cross-shelf sea surface heights (SSH), we identified specific Self Organizing Maps (SOM) nodes and transitions having a strongest regional association with oceanic spatial patterns (e.g., heightened downwelling favorable wind-stress and enhanced southward coastal transport) indicative of elevated coastal sea levels. Results show the impacts of the inverted barometer effect as well as the effects of surface wind forcing; Ekman-induced transport along broad expanses of the U.S. eastern coastline. Higher sea levels and corresponding localized flooding are associated with either pattern indicative of enhanced on-shore flow, deepening cyclones, or local- scale winds, generally coupled with an increased local to regional precipitation. These findings will support an integration of satellite products and will inform seasonal outlook model development supported through NOAAs Climate Program Office and NOS office of Center for Operational Oceanographic Products and Services (CO-OPS). Overall results will prioritize ecological areas and coastal lab facilities at risk based on numbers of nuisance flood projected and inform coastal management of flood risk around low lying areas subjected to bank erosion.

Keywords: AVISO satellite altimetry SSHA, MODIS IR flood map, nuisance flood, remote sensing of flood

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Authors: Jamal Takhchi

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The understanding of vibration propagation in complex structures such as automotive body in white remains a challenging issue in car design regarding NVH performances. The current analysis is limited to the low frequency range where modal concepts are dominant. Higher frequencies, between 200 and 1000 Hz, will become critical With the rise of electrification. EVs annoying sounds are mostly whines created by either Gears or e-motors between 300 Hz and 2 kHz. Structural intensity analysis was Experienced a few years ago on finite element models. The application was promising but limited by the fact that the propagating 3D intensity vector field is masked by a rotational Intensity field. This rotational field should be filtered using a differential operator. The expression of this operator in the framework of finite element modeling is not yet known. The aim of the proposed work is to implement this operator in the current dynamic solver (NASTRAN) of Stellantis and develop the Expected methodology for the mid-frequency structural analysis of electrified vehicles.

Keywords: structural intensity, NVH, body in white, irrotatational intensity

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Authors: Abdulla D. Alblooshi

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The stock market is a representation of human behavior and psychology, such as fear, greed, and discipline. Those are manifested in the form of price movements during the trading sessions. Therefore, predicting the stock movement and prices is a challenging effort. However, those trading sessions produce a large amount of data that can be utilized to train an AI agent for the purpose of predicting the stock movement. Predicting the stock market price action will be advantageous. In this paper, the stock movement data of three DFM listed stocks are studied using historical price movements and technical indicators value and used to train an agent using KNN and SVM methods to predict the future price movement. MATLAB Toolbox and a simple script is written to process and classify the information and output the prediction. It will also compare the different learning methods and parameters s using metrics like RMSE, MAE, and R².

Keywords: KNN, ANN, style, SVM, stocks, technical indicators, RSI, MACD, moving averages, RMSE, MAE

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Authors: Nadia Chagtmi, Nejla Rejab, Noureddine Boulejfen

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This paper presents a calibration approach based on artificial neural networks (ANN) to determine the envelop signal (I+jQ) of a six-port based receiver (SPR). The memory effects called also dynamic behavior and the nonlinearity brought by diode based power detector have been taken into consideration by the ANN. Experimental set-up has been performed to validate the efficiency of this method. The efficiency of this approach has been confirmed by the obtained results in terms of waveforms. Moreover, the obtained error vector magnitude (EVM) and the mean absolute error (MAE) have been calculated in order to confirm and to test the ANN’s performance to achieve I/Q recovery using the output voltage detected by the power based detector. The baseband signal has been recovered using ANN with EVMs no higher than 1 % and an MAE no higher than 17, 26 for the SPR excited different type of signals such QAM (quadrature amplitude modulation) and LTE (Long Term Evolution).

Keywords: six-port based receiver; calibration, nonlinearity, memory effect, artificial neural network

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Authors: Farshideh Einsele, Hassan Foroosh

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In this paper, we present a robust algorithm to recognize extracted text from grocery product images captured by mobile phone cameras. Recognition of such text is challenging since text in grocery product images varies in its size, orientation, style, illumination, and can suffer from perspective distortion. Pre-processing is performed to make the characters scale and rotation invariant. Since text degradations can not be appropriately defined using wellknown geometric transformations such as translation, rotation, affine transformation and shearing, we use the whole character black pixels as our feature vector. Classification is performed with minimum distance classifier using the maximum likelihood criterion, which delivers very promising Character Recognition Rate (CRR) of 89%. We achieve considerably higher Word Recognition Rate (WRR) of 99% when using lower level linguistic knowledge about product words during the recognition process.

Keywords: camera-based OCR, feature extraction, document, image processing, grocery products

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Authors: Jerald Ruta, Ricardo Villar, Jojemar Bantugan, Nycel Barbadillo, Jigg Pelayo

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Streams are important in providing water supply for industrial, agricultural and human consumption, In short when there are streams there are lives. Identifying streams are essential since many developed cities are situated in the vicinity of these bodies of water and in flood management, it serves as basin for surface runoff within the area. This study aims to process and generate features from high-resolution digital terrain model (DTM) with 1-meter resolution using Hydrology Tools of ArcGIS. The raster was then filled, processed flow direction and accumulation, then raster calculate and provide stream order, converted to vector, and clearing undesirable features using the ancillary or google earth. In field validation streams were classified whether perennial, intermittent or ephemeral. Results show more than 90% of the extracted feature were accurate in assessment through field validation.

Keywords: digital terrain models, hydrology tools, strahler method, stream classification

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Authors: Edlira Donefski, Tina Donefski, Lorenc Ekonomi

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Edgeworth Approximation, Bootstrap, and Monte Carlo Simulations have considerable impacts on achieving certain results related to different problems taken into study. In our paper, we have treated a financial case related to the effect that has the components of a cash-flow of one of the most successful businesses in the world, as the financial activity, operational activity, and investment activity to the cash and cash equivalents at the end of the three-months period. To have a better view of this case, we have created a vector autoregression model, and after that, we have generated the impulse responses in the terms of asymptotic analysis (Edgeworth Approximation), Monte Carlo Simulations, and residual bootstrap based on the standard errors of every series created. The generated results consisted of the common tendencies for the three methods applied that consequently verified the advantage of the three methods in the optimization of the model that contains many variants.

Keywords: autoregression, bootstrap, edgeworth expansion, Monte Carlo method

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Authors: Mehdi Janbaz

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The global financial crisis and the credit crunch that followed magnified the importance of credit risk management and its crucial role in the stability of all financial sectors and the whole of the system. Many believe that risks faced by the sovereign sector are highly interconnected with banking risks and most likely to trigger and reinforce each other. This study aims to examine (1) the impact of banking and interbank risk factors on the sovereign credit risk of Eurozone, and (2) how the EU Credit Default Swaps spreads dynamics are affected by the Crude Oil price fluctuations. The hypothesizes are tested by employing fitting risk measures and through a four-staged linear modeling approach. The sovereign senior 5-year Credit Default Swap spreads are used as a core measure of the credit risk. The monthly time-series data of the variables used in the study are gathered from the DataStream database for a period of 2008-2019. First, a linear model test the impact of regional macroeconomic and market-based factors (STOXX, VSTOXX, Oil, Sovereign Debt, and Slope) on the CDS spreads dynamics. Second, the bank-specific factors, including LIBOR-OIS spread (the difference between the Euro 3-month LIBOR rate and Euro 3-month overnight index swap rates) and Euribor, are added to the most significant factors of the previous model. Third, the global financial factors including EURO to USD Foreign Exchange Volatility, TED spread (the difference between 3-month T-bill and the 3-month LIBOR rate based in US dollars), and Chicago Board Options Exchange (CBOE) Crude Oil Volatility Index are added to the major significant factors of the first two models. Finally, a model is generated by a combination of the major factor of each variable set in addition to the crisis dummy. The findings show that (1) the explanatory power of LIBOR-OIS on the sovereign CDS spread of Eurozone is very significant, and (2) there is a meaningful adverse co-movement between the Crude Oil price and CDS price of Eurozone. Surprisingly, adding TED spread (the difference between the three-month Treasury bill and the three-month LIBOR based in US dollars.) to the analysis and beside the LIBOR-OIS spread (the difference between the Euro 3M LIBOR and Euro 3M OIS) in third and fourth models has been increased the predicting power of LIBOR-OIS. Based on the results, LIBOR-OIS, Stoxx, TED spread, Slope, Oil price, OVX, FX volatility, and Euribor are the determinants of CDS spreads dynamics in Eurozone. Moreover, the positive impact of the crisis period on the creditworthiness of the Eurozone is meaningful.

Keywords: CDS, crude oil, interbank risk, LIBOR-OIS, OVX, sovereign credit risk, TED

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Authors: Kamel Laidi, Khelifa Benmansour, Ouahid Bouchhida

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Neural networks-based approach for 2-cells serial converter has been developed and implemented. The approach is based on a behavioural description of the different operating modes of the converter. Each operating mode represents a well-defined configuration, and for which is matched an operating zone satisfying given invariance conditions, depending on the capacitors' voltages and the load current of the converter. For each mode, a control vector whose components are the control signals to be applied to the converter switches has been associated. Therefore, the problem is reduced to a classification task of the different operating modes of the converter. The artificial neural nets-based approach, which constitutes a powerful tool for this kind of task, has been adopted and implemented. The application to a 2-cells chopper has allowed ensuring efficient and robust control of the load current and a high capacitors voltages balancing.

Keywords: neural nets, control, multicellular converters, 2-cells chopper

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Authors: Assaad Ghazouani, Ati Abdessatar

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In the world, agriculture maintains a social and economic importance in the national economy. Its importance is distinguished by its ripple effects not only downstream but also upstream vis-à-vis the non-agricultural sector. However, the situation is relatively fragile because of weather conditions. In this work, we propose a model to highlight the impacts of climate change (CC) on economic growth in the world where agriculture is considered as a strategic sector. The CC is supposed to directly and indirectly affect economic growth by reducing the performance of the agricultural sector. The model is tested for Tunisia. The results validate the hypothesis that the potential economic damage of the CC is important. Indeed, an increase in CO2 concentration (temperatures and disruption of rainfall patterns) will have an impact on global economic growth particularly by reducing the performance of the agricultural sector. Analysis from a vector error correction model also highlights the magnitude of climate impact on the performance of the agricultural sector and its repercussions on economic growth

Keywords: Climate Change, Agriculture, Economic Growth, World, VECM, Cointegration.

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Authors: Susanne Shunnaq

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During the past decade, an increasing number of higher education institutions in Oman have sought accreditation in an attempt to assure the quality of their programs. Sultan Qaboos University (SQU), the only public university in the country, has also been seeking accreditation. Hence, the university administration has been encouraging departments to evaluate their programs for development purposes. The Department of English, where 100% of the students are learners of English as a foreign language, already produced a self-study report that outlined the strength and weaknesses of the current program. The department came to the realization that due to a changing local and regional job market, transferrable communication skills are high in demand among stakeholders in the public and private sectors. Failure to equip English literature students, for example, with excellent verbal communicative skills in English may have detrimental effects for undergraduate job-seekers who have to compete for jobs in employment sectors with a predominantly English-speaking workforce. Ongoing extensive discussions about restructuring the current literature program by means of partially replacing literature courses with skills courses, hoping to produce higher quality graduates who are equipped with effective communication skills for local and regional markets, have sparked the idea for this research. The researcher, who is an American Literature specialist at SQU, has set out to investigate to what extent senior American literature students have been able to apply transferable communication skills in an advanced literature course. The study also attempts to unearth performance inhibitors and causes for communication breakdown. The primary data source for the study were audio-recordings of 6 in-class peer-group discussions in an advanced contemporary American literature course during the academic year 2016/2017. The significance of this research lies in the rarity of studies focusing on verbal communication skills in Omani higher education literature classrooms at a time when English programs are in the process of being re-visited and revamped both for accreditation purposes and for meeting job-market demands. The results showed a considerable variation in Omani students' verbal communicative abilities and English proficiency levels. The study also raises crucial questions and provides important recommendations for administrators and teachers alike who are in the process of restructuring English programs in the region and in non-English speaking countries worldwide.

Keywords: job-market, literature, Oman, tertiary education, oral communication skills

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Authors: Sahbi Farhani

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This study investigates the relationship between external debt and military spending in case of India over the period of 1970–2012. In doing so, we have applied the structural break unit root tests to examine stationarity properties of the variables. The Auto-Regressive Distributed Lag (ARDL) bounds testing approach is used to test whether cointegration exists in presence of structural breaks stemming in the series. Our results indicate the cointegration among external debt, military spending, debt servicing, and economic growth. Moreover, military spending and debt servicing add in external debt. Economic growth helps in lowering external debt. The Vector Error Correction Model (VECM) analysis and Granger causality test reveal that military spending and economic growth cause external debt. The feedback effect also exists between external debt and debt servicing in case of India.

Keywords: external debt, military spending, ARDL approach, India

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Authors: Pegah Eshraghi, Zahra Sadat Zomorodian, Mohammad Tahsildoost

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Over the past decade, energy consumption in educational buildings has steadily increased. The purpose of this research is to provide a method to quickly predict the energy consumption of buildings using separate evaluation of zones and decomposing the building to eliminate the complexity of geometry at the early design stage. To produce this framework, machine learning algorithms such as Support vector regression (SVR) and Artificial neural network (ANN) are used to predict energy consumption and thermal comfort metrics in a school as a case. The database consists of more than 55000 samples in three climates of Iran. Cross-validation evaluation and unseen data have been used for validation. In a specific label, cooling energy, it can be said the accuracy of prediction is at least 84% and 89% in SVR and ANN, respectively. The results show that the SVR performed much better than the ANN.

Keywords: early stage of design, energy, thermal comfort, validation, machine learning

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Authors: Deepa Das, Susmita Das

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Cognitive Radio (CR) is an emerging technology to combat the spectrum scarcity issues. This is achieved by consistently sensing the spectrum, and detecting the under-utilized frequency bands without causing undue interference to the primary user (PU). In soft decision fusion (SDF) based cooperative spectrum sensing, various evolutionary algorithms have been discussed, which optimize the weight coefficient vector for maximizing the detection performance. In this paper, we propose the hybrid invasive weed optimization and particle swarm optimization (IWO/PSO) algorithm as a fast and global optimization method, which improves the detection probability with a lesser sensing time. Then, the efficiency of this algorithm is compared with the standard invasive weed optimization (IWO), particle swarm optimization (PSO), genetic algorithm (GA) and other conventional SDF based methods on the basis of convergence and detection probability.

Keywords: cognitive radio, spectrum sensing, soft decision fusion, GA, PSO, IWO, hybrid IWO/PSO

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Authors: Alena Sicakova

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Air-cooled Blast furnace slag aggregate (BFSA) is usually referred to as a material providing for unique properties of concrete. On the other hand, negative influences are also presented in many aspects. The freeze-thaw resistance of concrete is dependent on many factors, including regional specifics and when a concrete mix is specified it is still difficult to tell its exact freeze-thaw resistance due to the different components affecting it. An important consideration in working with BFSA is the granularity and whether slag is sorted or not. The experimental part of the article represents a comparative testing of concrete using both the sorted and unsorted BFSA through the freeze-thaw resistance as an indicator of durability. Unsorted BFSA is able to be successfully used for concretes as they are specified for exposure class XF4 with providing that the type of cement is precisely selected.

Keywords: blast furnace slag aggregate, concrete, freeze-thaw resistance

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Authors: Sondos M. Hammad, Sherin M. ElGokhy, Mahmoud M. Fahmy, Elsayed A. Sallam

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In this paper, an ensemble-based Support Vector Machine (SVM) classification approach is proposed. It is used for miRNA prediction. Three problems, commonly associated with previous approaches, are alleviated. These problems arise due to impose assumptions on the secondary structural of premiRNA, imbalance between the numbers of the laboratory checked miRNAs and the pseudo-hairpins, and finally using a training data set that does not consider all the varieties of samples in different species. We aggregate the predicted outputs of three well-known SVM classifiers; namely, Triplet-SVM, Virgo and Mirident, weighted by their variant features without any structural assumptions. An additional SVM layer is used in aggregating the final output. The proposed approach is trained and then tested with balanced data sets. The results of the proposed approach outperform the three base classifiers. Improved values for the metrics of 88.88% f-score, 92.73% accuracy, 90.64% precision, 96.64% specificity, 87.2% sensitivity, and the area under the ROC curve is 0.91 are achieved.

Keywords: MiRNAs, SVM classification, ensemble algorithm, assumption problem, imbalance data

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Authors: F. Agbele

Abstract:

Elections are core democratic institutions. Consequently, voter participation during elections is paramount to democratic governance as it serves as a medium to legitimize authority and make the privileges of electoral democracy meaningful to citizens. To this effect, the topic of voter mobilization and subsequent turnout level have been largely studied in advanced democracies. In young and consolidating democracies, the debate has, however, revolves around the huge reliance on ethnic and regional appeals. According to the Author’s knowledge, studies on electoral mobilization especially within the African context have argued the use of ethnic linkages by political parties to mobilize voters during elections. Literature has however not differentiated between the level of democratic dispensation among African countries and the use of ethnic linkages. The question, however, is whether the state of the country’s democracy determines the strategies employed by political parties to induce voter participation. In other words, do parties simply play ethno-regional cards as strongly suggested by literature or will consider an arrayed of strategies to mobilize voters? Additionally, studies have not differentiated the impact of mobilization strategy within a country, i.e. between high to low turnout areas. They have also not distinguished between strategies employed by an incumbent or an opposition party. This paper, therefore, is a comparative analysis of voter mobilization in Ghana. It uses original survey and interview data from three constituencies in Ghana: Nanton, Assin North, and Ellembelle, which are typical cases of high, average and low turnout areas, respectively. The data were concurrently collected during fieldworks conducted in November 2016 to February 2017, and again from July to August 2017. The study found that political parties within a consolidating democracy employ a blend of strategies to ensure turnout by both parties’ faithful and swing voters. The dominant strategies used depends on whether the party is an incumbent or in opposition. While an incumbent may depend more on personalistic and clientelistic strategies, parties in opposition will largely use programmatic strategies, which entails making many campaign promises. Additionally, opposition parties do use clientelistic tactics, but not on the same level as the incumbent. Similarly, within the context of this study, the use of ethnic linkage by political parties to mobilize voters has not been found to be as strong as suggested in the literature. Further, location was key in determining the strategy to use. In all, the consolidation process of a democratic country like Ghana means the change of mobilization strategies used by political parties, which entail a gradual shift from ethnic linkages to programmatic and other forms of non-programmatic strategies.

Keywords: comparative analysis, elections, mobilization strategies, voter turnout

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Authors: Lula Chou

Abstract:

At the turn of the 20th century, Chicago faced a large real estate boom and technological advances through industrialization that led to the rise of the commercial skyscrapers. Focusing on creating a Midwest regional character and new functional meanings of structural art, architects like Sullivan, Adler, Burnham, and Root dominated the first Chicago School of Architecture. After they spearheaded the arena of modern skyscrapers, other cities in the United States like New York soon followed the trend. While battling with eclecticism and Beaux-Arts beliefs in decorative style, Chicago architects adapted Classical monumentality into their modern expressions that emphasized organicism and functionalism. With various experiments of material possibilities in the steel-framed constructions, Chicago architecture succeeded in forming humanitarian aesthetics alongside fulfilling functional requirements of the new generation.

Keywords: Chicago school, modernity, monumentality, skyscrapers, Sullivan

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Authors: Gurjeet Singh, Harinder Singh

Abstract:

In this paper, we present "The Language of Science in Higher Education: Related Questions and Discussions". Linguists have written and researched in depth the role of language in science. On this basis, it is clear that language is not just a medium or vehicle for communicating knowledge and ideas. Nor are there mere signs of language knowledge and conversion of ideas into code. In the process of reading and writing, everyone thinks deeply and struggles to understand concepts and make sense. Linguistics play an important role in achieving concepts. In the context of such linguistic diversity, there is no straightforward and simple answer to the question of which language should be the language of advanced science and technology. Many important topics related to this issue are as follows: Involvement in practical or Deep theoretical issues. Languages for the study of science and other subjects. Language issues of science to be considered separate from the development of science, capitalism, colonial history, the worldview of the common man. The democratization of science and technology education in India is possible only by providing maximum reading/resource material in regional languages. The scientific research should be increase to chances of understanding the subject. Multilingual instead or monolingual. As far as deepening the understanding of the subject is concerned, we can shed light on it based on two or three experiences. An attempt was made to make the famous sociological journal Economic and Political Weekly Hindi almost three decades ago. There were many obstacles in this work. The original articles written in Hindi were not found, and the papers and articles of the English Journal were translated into Hindi, and a journal called Sancha was taken out. Equally important is the democratization of knowledge and the deepening of understanding of the subject. However, the question is that if higher education in science is in Hindi or other languages, then it would be a problem to get job. In fact, since independence, English has been dominant in almost every field except literature. There are historical reasons for this, which cannot be reversed. As mentioned above, due to colonial rule, even before independence, English was established as a language of communication, the language of power/status, the language of higher education, the language of administration, and the language of scholarly discourse. After independence, attempts to make Hindi or Hindustani the national language in India were unsuccessful. Given this history and current reality, higher education should be multilingual or at least bilingual. Translation limits should also be increased for those who choose the material for translation. Writing in regional languages on science, making knowledge of various international languages available in Indian languages, etc., is equally important for all to have opportunities to learn English.

Keywords: language, linguistics, literature, culture, ethnography, punjabi, gurmukhi, higher education

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Authors: Marcelino Caetano Noronha, Vosco Pereira, Jose Soares Pinto, Ferdinando Da C. Saores

Abstract:

One particularly popular social media platform is Youtube. It’s a video-sharing platform where users can submit videos, and other users can like, dislike or comment on the videos. In this study, we conduct a binary classification task on YouTube’s video comments and review from the users regarding the accession process of Timor Leste to become the eleventh member of the Association of South East Asian Nations (ASEAN). We scrape the data directly from the public YouTube video and apply several pre-processing and weighting techniques. Before conducting the classification, we categorized the data into two classes, namely positive and negative. In the classification part, we apply Support Vector Machine (SVM) algorithm. By comparing with Naïve Bayes Algorithm, the experiment showed SVM achieved 84.1% of Accuracy, 94.5% of Precision, and Recall 73.8% simultaneously.

Keywords: classification, YouTube, sentiment analysis, support sector machine

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Authors: Subodh Chandra Shakya, Rajendra Sapkota, Aakash Tamang, Shushant Pudasaini, Sujan Adhikari, Sajjan Adhikari

Abstract:

Lots of efforts have been made in order to measure the semantic similarity between the text corpora in the documents. Techniques have been evolved to measure the similarity of two documents. One such state-of-art technique in the field of Natural Language Processing (NLP) is word to vector models, which converts the words into their word-embedding and measures the similarity between the vectors. We found this to be quite useful for the task of resume ranking. So, this research paper is the implementation of the word2vec model along with other Natural Language Processing techniques in order to rank the resumes for the particular job description so as to automate the process of hiring. The research paper proposes the system and the findings that were made during the process of building the system.

Keywords: chunking, document similarity, information extraction, natural language processing, word2vec, word embedding

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