Search results for: data space connector

Authors: Mahsa Mousavi, Hamid Reza Pourshaghaghi, Mohammad Tahghighi, Henk Corporaal

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Recently, Statistic Random-Access Memory-based (SRAM-based) Field-Programmable Gate Arrays (FPGAs) are widely used in aeronautics and space systems where high dependability is demanded and considered as a mandatory requirement. Since design’s circuit is stored in configuration memory in SRAM-based FPGAs; they are very sensitive to Single Event Upsets (SEUs). In addition, the adverse effects of SEUs on the electronics used in space are much higher than in the Earth. Thus, developing fault tolerant techniques play crucial roles for the use of SRAM-based FPGAs in space. However, fault tolerance techniques introduce additional penalties in system parameters, e.g., area, power, performance and design time. In this paper, an accurate estimation of configuration memory vulnerability to SEUs is proposed for approximate-tolerant applications. This vulnerability estimation is highly required for compromising between the overhead introduced by fault tolerance techniques and system robustness. In this paper, we study applications in which the exact final output value is not necessarily always a concern meaning that some of the SEU-induced changes in output values are negligible. We therefore define and propose Approximate-based Configuration Memory Vulnerability Factor (ACMVF) estimation to avoid overestimating configuration memory vulnerability to SEUs. In this paper, we assess the vulnerability of configuration memory by injecting SEUs in configuration memory bits and comparing the output values of a given circuit in presence of SEUs with expected correct output. In spite of conventional vulnerability factor calculation methods, which accounts any deviations from the expected value as failures, in our proposed method a threshold margin is considered depending on user-case applications. Given the proposed threshold margin in our model, a failure occurs only when the difference between the erroneous output value and the expected output value is more than this margin. The ACMVF is subsequently calculated by acquiring the ratio of failures with respect to the total number of SEU injections. In our paper, a test-bench for emulating SEUs and calculating ACMVF is implemented on Zynq-7000 FPGA platform. This system makes use of the Single Event Mitigation (SEM) IP core to inject SEUs into configuration memory bits of the target design implemented in Zynq-7000 FPGA. Experimental results for 32-bit adder show that, when 1% to 10% deviation from correct output is considered, the counted failures number is reduced 41% to 59% compared with the failures number counted by conventional vulnerability factor calculation. It means that estimation accuracy of the configuration memory vulnerability to SEUs is improved up to 58% in the case that 10% deviation is acceptable in output results. Note that less than 10% deviation in addition result is reasonably tolerable for many applications in approximate computing domain such as Convolutional Neural Network (CNN).

Keywords: fault tolerance, FPGA, single event upset, approximate computing

Procedia PDF Downloads 200

Authors: P. Daniels, T. Adonis, P. September-Brown, R. Comalie

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The community engagement unit at the University of the Western Cape was tasked with establishing a community engagement database. The database would store information of all community engagement projects related to the university. The wealth of knowledge obtained from the various disciplines would be used to facilitate interdisciplinary collaboration within the university, as well as facilitating community university partnership opportunities. The purpose of this qualitative study was to explore electronic data collection through the development of a database. Two types of electronic data collection platforms were used, namely online questionnaire and email. The semi structured questionnaire was used to collect data related to community engagement projects from different faculties and departments at the university. There are many benefits for using an electronic data collection platform, such as reduction of costs and time, ease in reaching large numbers of potential respondents, and the possibility of providing anonymity to participants. Despite all the advantages of using the electronic platform, there were as many challenges, as depicted in our findings. The findings suggest that certain barriers existed by using an electronic platform for data collection, even though it was in an academic environment, where knowledge and resources were in abundance. One of the challenges experienced in this process was the lack of dissemination of information via email to staff within faculties. The actual online software used for the questionnaire had its own limitations, such as only being able to access the questionnaire from the same electronic device. In a few cases, academics only completed the questionnaire after a telephonic prompt or face to face meeting about "Is higher education in South Africa ready to embrace electronic platform in data collection?"

Keywords: community engagement, database, data collection, electronic platform, electronic tools, knowledge sharing, university

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Authors: Divya Juneja, Sukhjeet Kaur Matharu

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Purpose: The paper is aimed at identifying the challenging factors experienced by the women entrepreneurs in India in operating their enterprises amidst the challenges posed by the COVID-19 pandemic. Methodology: The sample for the study comprised 396 women entrepreneurs from different regions of India. A purposive sampling technique was adopted for data collection. Data was collected through a self-administered questionnaire. Analysis was performed using the SPSS package for quantitative data analysis. Findings: The results of the study state that entrepreneurial characteristics, resourcefulness, networking, adaptability, and continuity have a positive influence on the resiliency of women entrepreneurs when faced with a crisis situation. Practical Implications: The findings of the study have some important implications for women entrepreneurs, organizations, government, and other institutions extending support to entrepreneurs.

Keywords: women entrepreneurs, analysis, data analysis, positive influence, resiliency

Procedia PDF Downloads 118

Authors: Mohammed Abdullah Alshahrani

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The research focuses on investigating the use of partial least squares (PLS) methodology for addressing challenges associated with high-dimensional correlated data. Recent technological advancements have led to experiments producing data characterized by a large number of variables compared to observations, with substantial inter-variable correlations. Such data patterns are common in chemometrics, where near-infrared (NIR) spectrometer calibrations record chemical absorbance levels across hundreds of wavelengths, and in genomics, where thousands of genomic regions' copy number alterations (CNA) are recorded from cancer patients. PLS serves as a widely used method for analyzing high-dimensional data, functioning as a regression tool in chemometrics and a classification method in genomics. It handles data complexity by creating latent variables (components) from original variables. However, applying PLS can present challenges. The study investigates key areas to address these challenges, including unifying interpretations across three main PLS algorithms and exploring unusual negative shrinkage factors encountered during model fitting. The research presents an alternative approach to addressing the interpretation challenge of predictor weights associated with PLS. Sparse estimation of predictor weights is employed using a penalty function combining a lasso penalty for sparsity and a Cauchy distribution-based penalty to account for variable dependencies. The results demonstrate sparse and grouped weight estimates, aiding interpretation and prediction tasks in genomic data analysis. High-dimensional data scenarios, where predictors outnumber observations, are common in regression analysis applications. Ordinary least squares regression (OLS), the standard method, performs inadequately with high-dimensional and highly correlated data. Copy number alterations (CNA) in key genes have been linked to disease phenotypes, highlighting the importance of accurate classification of gene expression data in bioinformatics and biology using regularized methods like PLS for regression and classification.

Keywords: partial least square regression, genetics data, negative filter factors, high dimensional data, high correlated data

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Authors: Maisyatus Suadaa Irfana, Nove Eka Variant Anna, Dyah Puspitasari Sri Rahayu

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Technological developments provide convenience to all the people. Nowadays, the communication of human with the computer is done via text. With the development of technology, human and computer communications have been conducted with a voice like communication between human beings. It provides an easy facility for many people, especially those who have special needs. Voice search technology is applied in the search of book collections in the OPAC (Online Public Access Catalog), so library visitors will find it faster and easier to find books that they need. Integration with Google is needed to convert the voice into text. To optimize the time and the results of searching, Server will download all the book data that is available in the server database. Then, the data will be converted into JSON format. In addition, the incorporation of some algorithms is conducted including Decomposition (parse) in the form of array of JSON format, the index making, analyzer to the result. It aims to make the process of searching much faster than the usual searching in OPAC because the data are directly taken to the database for every search warrant. Data Update Menu is provided with the purpose to enable users perform their own data updates and get the latest data information.

Keywords: OPAC, voice, searching, faster

Procedia PDF Downloads 350

Authors: M. Uysal, M. Yilmaz, I. Tiryakioğlu

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Digital Terrain Model (DTM) is a digital numerical representation of the Earth's surface. DTMs have been applied to a diverse field of tasks, such as urban planning, military, glacier mapping, disaster management. In the expression of the Earth' surface as a mathematical model, an infinite number of point measurements are needed. Because of the impossibility of this case, the points at regular intervals are measured to characterize the Earth's surface and DTM of the Earth is generated. Hitherto, the classical measurement techniques and photogrammetry method have widespread use in the construction of DTM. At present, RADAR, LiDAR, and stereo satellite images are also used for the construction of DTM. In recent years, especially because of its superiorities, Airborne Light Detection and Ranging (LiDAR) has an increased use in DTM applications. A 3D point cloud is created with LiDAR technology by obtaining numerous point data. However recently, by the development in image mapping methods, the use of unmanned aerial vehicles (UAV) for photogrammetric data acquisition has increased DTM generation from image-based point cloud. The accuracy of the DTM depends on various factors such as data collection method, the distribution of elevation points, the point density, properties of the surface and interpolation methods. In this study, the random data reduction method is compared for DTMs generated from image based point cloud data. The original image based point cloud data set (100%) is reduced to a series of subsets by using random algorithm, representing the 75, 50, 25 and 5% of the original image based point cloud data set. Over the ANS campus of Afyon Kocatepe University as the test area, DTM constructed from the original image based point cloud data set is compared with DTMs interpolated from reduced data sets by Kriging interpolation method. The results show that the random data reduction method can be used to reduce the image based point cloud datasets to 50% density level while still maintaining the quality of DTM.

Keywords: DTM, Unmanned Aerial Vehicle (UAV), uniform, random, kriging

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Authors: Chen Chou, Feng-Tyan Lin

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Big Data has been attracted a lot of attentions in many fields for analyzing research issues based on a large number of maternal data. Electronic Toll Collection (ETC) is one of Intelligent Transportation System (ITS) applications in Taiwan, used to record starting point, end point, distance and travel time of vehicle on the national freeway. This study, taking advantage of ETC big data, combined with urban planning theory, attempts to explore various phenomena of inter-city transportation activities. ETC, one of government's open data, is numerous, complete and quick-update. One may recall that living area has been delimited with location, population, area and subjective consciousness. However, these factors cannot appropriately reflect what people’s movement path is in daily life. In this study, the concept of "Living Area" is replaced by "Influence Range" to show dynamic and variation with time and purposes of activities. This study uses data mining with Python and Excel, and visualizes the number of trips with GIS to explore influence range of Tainan city and the purpose of trips, and discuss living area delimited in current. It dialogues between the concepts of "Central Place Theory" and "Living Area", presents the new point of view, integrates the application of big data, urban planning and transportation. The finding will be valuable for resource allocation and land apportionment of spatial planning.

Keywords: Big Data, ITS, influence range, living area, central place theory, visualization

Procedia PDF Downloads 280

Authors: Tania M. Guerrero, Ileana Cortes Santiago

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Estamos Unidos Asylum Project has served more than 2,000 asylum seekers and migrants who are under the Migrant Protection Protocols (MPP) policy in Ciudad Juarez, Mexico. The U.S. policy, implemented in January 2019, has stripped asylum seekers of their rights—forcing people fleeing violence and discrimination to wait in similar or worse conditions from which they fled and navigate their entire asylum process in a different country. Several civil rights groups, including the American Civil Liberties Union (ACLU), challenged MPP in U.S. federal courts in February 2019, arguing a violation of international U.S. obligations towards refugees and asylum-seekers under the 1951 Refugee Convention and the Refugee Act of 1980 in regards to the non-refoulement principle. MPP has influenced Mexico's policies, enforcement, and prioritization of the presence of asylum seekers and migrants; it has also altered the way international non-governmental organizations work at the Mexican Northern border. Estamos Unidos is a project situated in a logistical conundrum, as it provides needed legal services to a population in a legal and humanitarian void, i.e., a liminal space. The liminal space occupied by asylum seekers living under MPP is one that, in today's world, should not be overlooked; it dilutes asylum law and U.S. commitments to international protections. This paper provides analysis of and broader implications from a project whose main goal is to uphold the protections of asylum seekers and international refugee law. The authors identified and analyzed four critical points based on field work conducted since August 2019: (1) strategic coalition building with international, local, and national organizations; (2) brokering between domestic and international contexts and critical legal constraints; (3) flexibility to sudden policy changes and the diverse needs of the multiethnic groups of migrants and asylum seekers served by the project; and (4) the complexity of providing legal assistance to asylum seekers who are survivors of trauma. The authors concur with scholarship when highlighting the erosion of protections of asylum seekers and migrants as a dangerous and unjust global phenomenon.

Keywords: asylum, human rights, migrant protection protocols, refugees law

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Authors: Tapan Jain, Davender Singh Saini

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Clustering is a useful mechanism in wireless sensor networks which helps to cope with scalability and data transmission problems. The basic aim of our research work is to provide efficient clustering using Hierarchical agglomerative clustering (HAC). If the distance between the sensing nodes is calculated using their location then it’s quantitative HAC. This paper compares the various agglomerative clustering techniques applied in a wireless sensor network using the quantitative data. The simulations are done in MATLAB and the comparisons are made between the different protocols using dendrograms.

Keywords: routing, hierarchical clustering, agglomerative, quantitative, wireless sensor network

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Authors: Sophia Shi

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Acute kidney injury (AKI) is the sudden onset of kidney damage in which the kidneys cannot filter waste from the blood, requiring emergency hospitalization. AKI patient mortality rate is high in the ICU and is virtually impossible for doctors to predict because it is so unexpected. Currently, there is no hybrid model predicting AKI that takes advantage of two types of data. De-identified patient data from the MIMIC-III database and de-identified kidney images and corresponding patient records from the Beijing Hospital of the Ministry of Health were collected. Using data features including serum creatinine among others, two numeric models using MIMIC and Beijing Hospital data were built, and with the hospital ultrasounds, an image-only model was built. Convolutional neural networks (CNN) were used, VGG and Resnet for numeric data and Resnet for image data, and they were combined into a hybrid model by concatenating feature maps of both types of models to create a new input. This input enters another CNN block and then two fully connected layers, ending in a binary output after running through Softmax and additional code. The hybrid model successfully predicted AKI and the highest AUROC of the model was 0.953, achieving an accuracy of 90% and F1-score of 0.91. This model can be implemented into urgent clinical settings such as the ICU and aid doctors by assessing the risk of AKI shortly after the patient’s admission to the ICU, so that doctors can take preventative measures and diminish mortality risks and severe kidney damage.

Keywords: Acute kidney injury, Convolutional neural network, Hybrid deep learning, Patient record data, ResNet, Ultrasound kidney images, VGG

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Authors: Freda Adomaa, Samuel Oppong Boampong, Charles Gyamfi Rahman

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The living and working environment of migrants and their access to healthcare services induce good or poor health. This study was conducted to assess the factors associated with rural-urban migration and its associated health hazards among female adolescents. A sample size of two hundred (200) was chosen in which all responded to questionnaires comprising closed-ended questions, which were distributed to gather data from the respondents, after which it was analyzed using the Statistical Package for Social Sciences (SPSS) version 20. The utilized three causes of rural-urban migration thus political, economic and socio-cultural. The study revealed that political situations such as regional inequality (65.4%) and ethnic conflicts (78.2%) whereas economic factors such as lack of amenities (82.3%), lack of employment in rural communities (77.4%), lack of education (74%), and poverty (85.3%) as well as socio-cultural factors such as divorced parents (65.6%), media influence (79.1%), family conflicts (59.4%) and appealing urban informal sector (65.2%) are major causes of migration. Respondents’ encountered challenges such as poor remuneration for services (87.2%), being maltreated by a colleague or worker (69%), sleeping in open space (73.3%), and harassment by the task force (71.4%) and teenage pregnancies (58.5%). The study concluded that the three variables play a key role in adolescent migration and when they travel they end up getting involved in serious health hazardous behaviors such as rapes as well as physical and psychological harassments’. The study, therefore, recommends that vocational training of the rural people on small scale industries (non-farm) activities that could generate an income for the rural household should be introduced.

Keywords: rural, urban, migration, female health hazards

Procedia PDF Downloads 138

Authors: Taner Ersoz, Filiz Ersoz

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This study was designed enable application of multivariate technique in the interpretation of categorical data for measuring health care services satisfaction in Turkey. The data was collected from a total of 17726 respondents. The establishment of the sample group and collection of the data were carried out by a joint team from The Ministry of Health and Turkish Statistical Institute (Turk Stat) of Turkey. The multiple correspondence analysis (MCA) was used on the data of 2882 respondents who answered the questionnaire in full. The multiple correspondence analysis indicated that, in the evaluation of health services females, public employees, younger and more highly educated individuals were more concerned and complainant than males, private sector employees, older and less educated individuals. Overall 53 % of the respondents were pleased with the improvements in health care services in the past three years. This study demonstrates the public consciousness in health services and health care satisfaction in Turkey. It was found that most the respondents were pleased with the improvements in health care services over the past three years. Awareness of health service quality increases with education levels. Older individuals and males would appear to have lower expectancies in health services.

Keywords: multiple correspondence analysis, multivariate categorical data, health care services, health satisfaction survey

Procedia PDF Downloads 249

Authors: Kais Amir Kadhim

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Context: The study focuses on the rhetorical structure of chapters in engineering final year projects, specifically the Introduction chapter, written by UMT (University of Marine Technology) engineering students. Existing research has explored the use of genre-based approaches to analyze the writing of final year projects in various disciplines. Research Aim: The aim of this study is to investigate the rhetorical structure of Introduction chapters in engineering final year projects by UMT students. The study aims to identify the frequency of communicative moves and their constituent steps within the Introduction chapters, as well as understand how students justify their research projects. Methodology: The research design will utilize a mixed method approach, combining both quantitative and qualitative methods. Forty Introduction chapters from two different fields in UMT engineering undergraduate programs will be selected for analysis. Findings: The study intends to identify the types of moves present in the Introduction chapters of engineering final year projects by UMT students. Additionally, it aims to determine if these moves and steps are obligatory, conventional, or optional. Theoretical Importance: The study draws upon Bunton's modified CARS (Creating a Research Space) model, which is a conceptual framework used for analyzing the introduction sections of theses. By applying this model, the study contributes to the understanding of the rhetorical structure of Introduction chapters in engineering final year projects. Data Collection: The study will collect data from forty Introduction chapters of engineering final year projects written by UMT engineering students. These chapters will be selected from two different fields within UMT's engineering undergraduate programs. Analysis Procedures: The analysis will involve identifying and categorizing the communicative moves and their constituent steps within the Introduction chapters. The study will utilize both quantitative and qualitative analysis methods to examine the frequency and nature of these moves. Question Addressed: The study aims to address the question of how UMT engineering students structure and justify their research projects within the Introduction chapters of their final year projects. Conclusion: The study aims to contribute to the knowledge of rhetorical structure in engineering final year projects by investigating the Introduction chapters written by UMT engineering students. By using a mixed method research design and applying the modified CARS model, the study intends to identify the types of moves and steps employed by students and explore their justifications for their research projects. The findings have the potential to enhance the understanding of effective academic writing in engineering disciplines.

Keywords: cohesive markers, learning, meaning, students

Procedia PDF Downloads 78

Authors: Larissa M. Vargas, Julia M. Sacchi, Renata O. Pereira, Lucas S. Asano, Iara C. Araújo, Patricia Fiorino, Vera Farah

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The aim of this study was to evaluate the occurrence of subclinical organ injuries induced by high-fat diet. Male wistar rats (n=5/group) were divided in control diet group (CD), commercial rat chow, and hyperlipidic diet (30% lipids) group (HD) administrated during 8 weeks, starting after weaning. All the procedures followed the rules of the Committee of Research and Ethics of the Mackenzie University (CEUA Nº 077/03/2011). At the end of protocol the animals were euthanized by anesthesia overload and the left kidney was removed. Intrarenal lipid deposition was evaluated by histological analyses with oilred. Kidney slices were stained with picrosirius red to evaluate the area of the Bowman's capsule (AB) and space (SB), and glomerular tuft area (GT). The renal expression of sterol regulatory element–binding protein (SREBP-2) was performed by Western Blotting. Creatinine concentration (serum and urine) and lipid profile were determined by colorimetric kit (Labtest). At the end of the protocol there was no differences in body weight between the groups, however the HD showed a marked increase in lipid deposits, glomeruli and tubules, and biochemical analysis for cholesterol and triglycerides. Moreover, in the kidney, the high-fat diet induced a reduction in the AB (13%), GT (18%) and SB (17%) associated with a reduction in glomerular filtration rate (creatinine clearance). The renal SRBP2 expression was increased in HD group. These data suggests that consumption of high-fat diet starting in childhood is associated with subclinical renal damage and function.

Keywords: high-fat diet, kidney, intrarenal lipid deposition, SRBP2

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Authors: Paul Dallyn, Ashraf El-Hamalawi, Alessandro Palmeri, Bob Knight

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In order to better understand the long term implications of the grout wear failure mode in large-diameter plain-sided grouted connections, a numerical model has been developed and calibrated that can take advantage of existing operational plant data to predict the wear accumulation for the actual load conditions experienced over a given period, thus limiting the need for expensive monitoring systems. This model has been derived and calibrated based on site structural condition monitoring (SCM) data and supervisory control and data acquisition systems (SCADA) data for two operational wind turbine generator substructures afflicted with this challenge, along with experimentally derived wear rates.

Keywords: grouted connection, numerical model, offshore structure, wear, wind energy

Procedia PDF Downloads 458

Authors: Ons Slimene, Aroua Taamallah, Maha Khemaja

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In recent years, human activity recognition (HAR) has been a key area of research due to its diverse applications. It has garnered increasing attention in the field of computer vision. HAR plays an important role in people’s daily lives as it has the ability to learn advanced knowledge about human activities from data. In HAR, activities are usually represented by exploiting different types of sensors, such as embedded sensors or visual sensors. However, these sensors have limitations, such as local obstacles, image-related obstacles, sensor unreliability, and consumer concerns. Recently, several deep learning-based approaches have been proposed for HAR and these approaches are classified into two categories based on the type of data used: vision-based approaches and sensor-based approaches. This research paper highlights the importance of multimodal data fusion from skeleton data obtained from videos and data generated by embedded sensors using deep neural networks for achieving HAR. We propose a deep multimodal fusion network based on a twostream architecture. These two streams use the Convolutional Neural Network combined with the Bidirectional LSTM (CNN BILSTM) to process skeleton data and data generated by embedded sensors and the fusion at the feature level is considered. The proposed model was evaluated on a public OPPORTUNITY++ dataset and produced a accuracy of 96.77%.

Keywords: human activity recognition, action recognition, sensors, vision, human-centric sensing, deep learning, context-awareness

Procedia PDF Downloads 107

Authors: Sikander Nawaz Khan

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Natural disasters like flood, earthquake, cyclone, volcanic eruption and others are causing immense losses to the property and lives every year. Current status and actual loss information of natural hazards can be determined and also prediction for next probable disasters can be made using different remote sensing and mapping technologies. Global Positioning System (GPS) calculates the exact position of damage. It can also communicate with wireless sensor nodes embedded in potentially dangerous places. GPS provide precise and accurate locations and other related information like speed, track, direction and distance of target object to emergency responders. Remote Sensing facilitates to map damages without having physical contact with target area. Now with the addition of more remote sensing satellites and other advancements, early warning system is used very efficiently. Remote sensing is being used both at local and global scale. High Resolution Satellite Imagery (HRSI), airborne remote sensing and space-borne remote sensing is playing vital role in disaster management. Early on Geographic Information System (GIS) was used to collect, arrange, and map the spatial information but now it has capability to analyze spatial data. This analytical ability of GIS is the main cause of its adaption by different emergency services providers like police and ambulance service. Full potential of these so called 3S technologies cannot be used in alone. Integration of GPS and other remote sensing techniques with GIS has pointed new horizons in modeling of earth science activities. Many remote sensing cases including Asian Ocean Tsunami in 2004, Mount Mangart landslides and Pakistan-India earthquake in 2005 are described in this paper.

Keywords: disaster mitigation, GIS, GPS, remote sensing

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Authors: Burcu Guvenek, Duygu Baysal Kurt

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The impact of foreign trade on economic growth has been discussed since the Classical Economists. Today, foreign trade has become more important for the country's economy with the increasing globalization. When it comes to foreign trade, policies which may vary from country to country and from time to time as protectionism or free trade are implemented. In general, the positive effect of foreign trade on economic growth is alleged. However, as studies supporting this general acceptance take place in the economics literature, there are also studies in the opposite direction. In this paper, the impact of foreign trade on economic growth will be investigated with the help of panel data analysis. For this research, 24 OECD countries’ GDP and foreign trade data, including the period of 1990 and 2010, will be used.

Keywords: foreign trade, economic growth, OECD countries, panel data analysis

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Authors: A. O. Glazkov, A. S. Krylova, G. G. Nadareishvili, A. S. Terenchenko, S. I. Yudin

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The topic of the presented materials concerns the design of the exhaust system for a certain internal combustion engine. The exhaust system can be divided into two parts. The first is the engine exhaust manifold, turbocharger, and catalytic converters, which are called “hot part.” The second part is the gas exhaust system, which contains elements exclusively for reducing exhaust noise (mufflers, resonators), the accepted designation of which is the "cold part." The design of the exhaust system from the point of view of acoustics, that is, reducing the exhaust noise to a predetermined level, consists of working on the second part. Modern computer technology and software make it possible to design "cold part" with high accuracy in a given frequency range but with the condition of accurately specifying the input parameters, namely, the amplitude spectrum of the input noise and the acoustic impedance of the noise source in the form of an engine with a "hot part". Getting this data is a difficult problem: high temperatures, high exhaust gas velocities (turbulent flows), and high sound pressure levels (non-linearity mode) do not allow the calculated results to be applied with sufficient accuracy. The aim of this work is to obtain the most reliable acoustic output parameters of an engine with a "hot part" based on a complex of computational and experimental studies. The presented methodology includes several parts. The first part is a finite element simulation of the "cold part" of the exhaust system (taking into account the acoustic impedance of radiation of outlet pipe into open space) with the result in the form of the input impedance of "cold part". The second part is a finite element simulation of the "hot part" of the exhaust system (taking into account acoustic characteristics of catalytic units and geometry of turbocharger) with the result in the form of the input impedance of the "hot part". The next third part of the technique consists of the mathematical processing of the results according to the proposed formula for the convergence of the mathematical series of summation of multiple reflections of the acoustic signal "cold part" - "hot part". This is followed by conducting a set of tests on an engine stand with two high-temperature pressure sensors measuring pulsations in the nozzle between "hot part" and "cold part" of the exhaust system and subsequent processing of test results according to a well-known technique in order to separate the "incident" and "reflected" waves. The final stage consists of the mathematical processing of all calculated and experimental data to obtain a result in the form of a spectrum of the amplitude of the engine noise and its acoustic impedance.

Keywords: acoustic impedance, engine exhaust system, FEM model, test stand

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Authors: Lillian Musila, Valerie Oundo, Daniel Erwin, Willie Sang

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Staphylococcus aureus infections are a major cause of nosocomial infections in Kenya. Methicillin resistant S. aureus (MRSA) infections are a significant burden to public health and are associated with considerable morbidity and mortality. At a hospital in Western Kenya two clusters of MRSA cases emerged within short periods of time. In this study we explored whether these clusters represented a nosocomial outbreak by characterizing the isolates using phenotypic and molecular assays and examining epidemiological data to identify possible transmission patterns. Specimens from the site of infection of the subjects were collected, cultured and S. aureus isolates identified phenotypically and confirmed by APIStaph™. MRSA were identified by cefoxitin disk screening per CLSI guidelines. MRSA were further characterized based on their antibiotic susceptibility patterns and spa gene typing. Characteristics of cases with MRSA isolates were compared with those with MSSA isolated around the same time period. Two cases of MRSA infection were identified in the two week period between 21 April and 4 May 2015. A further 2 MRSA isolates were identified on the same day on 7 September 2015. The antibiotic resistance patterns of the two MRSA isolates in the 1st cluster of cases were different suggesting that these were distinct isolates. One isolate had spa type t2029 and the other had a novel spa type. The 2 isolates were obtained from urine and an open skin wound. In the 2nd cluster of MRSA isolates, the antibiotic susceptibility patterns were similar but isolates had different spa types: one was t037 and the other a novel spa type different from the novel MRSA spa type in the first cluster. Both cases in the second cluster were admitted into the hospital but one infection was community- and the other hospital-acquired. Only one of the four MRSA cases was classified as an HAI from an infection acquired post-operatively. When compared to other S. aureus strains isolated within the same time period from the same hospital only one spa type t2029 was found in both MRSA and non-MRSA strains. None of the cases infected with MRSA in the two clusters shared any common epidemiological characteristic such as age, sex or known risk factors for MRSA such as prolonged hospitalization or institutionalization. These data suggest that the observed MRSA clusters were multi strain clusters and not an outbreak of a single strain. There was no clear relationship between the isolates by spa type suggesting that no transmission was occurring within the hospital between these cluster cases but rather that the majority of the MRSA strains were circulating in the community. There was high diversity of spa types among the MRSA strains with none of the isolates sharing spa types. Identification of disease clusters in space and time is critical for immediate infection control action and patient management. Spa gene typing is a rapid way of confirming or ruling out MRSA outbreaks so that costly interventions are applied only when necessary.

Keywords: cluster, Kenya, MRSA, spa typing

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Authors: Emanuel Koseos

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Data-Driven Decision Making (DDDM) refers to making decisions that are based on historical data in order to inform practice, develop strategies and implement policies that benefit organizational settings. In educational technology, DDDM facilitates the implementation of differential educational learning approaches such as Educational Data Mining (EDM) and Competency-Based Education (CBE), which commonly target university classrooms. There is a current need for DDDM models applied to middle and secondary schools from a concern for assessing the needs, progress and performance of students and educators with respect to regional standards, policies and evolution of curriculums. To address these concerns, we propose a DDDM reference model developed using educational key process initiatives as inputs to a machine learning framework implemented with statistical software (SAS, R) to provide a best-practices, complex-free and automated approach for educators at their regional level. We assessed the efficiency of the model over a six-year period using data from 45 schools and grades K-12 in the Langley, BC, Canada regional school district. We concluded that the model has wider appeal, such as business learning systems.

Keywords: competency-based learning, data-driven decision making, machine learning, secondary schools

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Authors: Enerit Sacdanaku, Idriz Haxhiu

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This study was conducted in the area of Vlora Bay, Albania. Data about Sea Turtles Caretta caretta and Chelonia mydas, belonging to two periods of time (1984–1991; 2008–2014) are given. All data gathered were analyzed using recent methodologies. For all turtles captured (as by catch), the Curve Carapace Length (CCL) and Curved Carapace Width (CCW) were measured. These data were statistically analyzed, where the mean was 67.11 cm for CCL and 57.57 cm for CCW of all individuals studied (n=13). All untagged individuals of marine turtles were tagged using metallic tags (Stockbrand’s titanium tag) with an Albanian address. Sex was determined and resulted that 45.4% of individuals were females, 27.3% males and 27.3% juveniles. All turtles were studied for the presence of the epibionts. The area of Vlora Bay is used from marine turtles (Caretta caretta) as a migratory corridor to pass from the Mediterranean to the northern part of the Adriatic Sea.

Keywords: Caretta caretta, Chelonia mydas, CCL, CCW, tagging, Vlora Bay

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Authors: Parth Prajapati, A. R. Srinivas

Abstract:

The purpose of this paper is to validate the concept of the Lotus Mechanism using Computer Aided Engineering (CAE) tools considering the statics and dynamics through actual time dependence involving inertial forces acting on the mechanism joints. For a 1.2 m mirror made of hexagonal segments, with simple harnesses and three-point supports, the maximum diameter is 400 mm, minimum segment base thickness is 1.5 mm, and maximum rib height is considered as 12 mm. Manufacturing challenges are explored for the segments using manufacturing research and development approaches to enable use of large lightweight mirrors required for the future space system.

Keywords: dynamics, manufacturing, reflectors, segmentation, statics

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Authors: Farhat Imtiaz, Umar Farooq

Abstract:

In this work, we developed a computational model to predict ply level failure in impacted composite laminates. Data obtained from physical testing from flat and round nose impacts of 8-, 16-, 24-ply laminates were considered. Routine inspections of the tested laminates were carried out to approximate ply by ply inflicted damage incurred. Plots consisting of load–time, load–deflection, and energy–time history were drawn to approximate the inflicted damages. Impact test generated unwanted data logged due to restrictions on testing and logging systems were also filtered. Conventional filters (built-in, statistical, and numerical) reliably predicted load thresholds for relatively thin laminates such as eight and sixteen ply panels. However, for relatively thick laminates such as twenty-four ply laminates impacted by flat nose impact generated clipped data which can just be de-noised using oscillatory algorithms. The literature search reveals that modern oscillatory data filtering and extrapolation algorithms have scarcely been utilized. This investigation reports applications of filtering and extrapolation of the clipped data utilising fast Fourier Convolution algorithm to predict load thresholds. Some of the results were related to the impact-induced damage areas identified with Ultrasonic C-scans and found to be in acceptable agreement. Based on consistent findings, utilizing of modern data filtering and extrapolation algorithms to data logged by the existing machines has efficiently enhanced data interpretations without resorting to extra resources. The algorithms could be useful for impact-induced damage approximations of similar cases.

Keywords: fibre reinforced laminates, fast Fourier algorithms, mechanical testing, data filtering and extrapolation

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Authors: M. Naidoo

Abstract:

The concept of moral personhood extending from the moral status of an artificial intelligence system has been explored – but predominantly from a Western conception of personhood. African personhood, however, is distinctly different from Western personhood in that communitarianism is central to the underpinnings of personhood - rather than Western individualism. Personhood in the African context is not an inherent property that a human is born with; rather, it is an ontological journey that one goes on in his or her life with the hopes of attaining personhood. Given the decolonization, projects happening in Africa, and the law-making that is happening in this space within South Africa, it is of paramount importance to consider these views.

Keywords: artificial intelligence, bioethics, law, legal personality

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Authors: Mohammad Reza Mohammadian, Dariush Sattarzadeh, Mir Mohammad Javad Poor Hadi Hosseini

Abstract:

Population growth, urban development and urban buildup have disturbed the balance between the nature and the city, and so leading to the loss of quality of sustainability of proximity to rivers. While in the past, the sides of urban rivers were considered as urban green space. Urban rivers and their sides that have environmental, social and economic values are important to achieve sustainable development. So far, efforts have been made at various scales in various cities around the world to revitalize these areas. On the other hand, biophilic design is an innovative design approach in which attention to natural details and relation to nature is a fundamental concept. The purpose of this study is to provide an integrated framework of urban design using the potential of urban rivers (in order to increase sustainability) with a biophilic design approach to be used in cities in developing countries. The methodology of the research is based on the collection of data and information from research and projects including a study on biophilic design, investigations and projects related to the urban rivers, and a review of the literature on sustainable urban development. Then studying the boundary of urban rivers is completed by examining case samples. Eventually, integrated framework of urban design, to design the boundaries of urban rivers in the cities of developing countries is presented regarding the factors affecting the design of these areas. The result shows that according to this framework, the potential of the river banks is utilized to increase not only the environmental sustainability but also social, economic and physical stability with regard to water, light, and the usage of indigenous materials, etc.

Keywords: urban rivers, biophilic design, urban sustainability, nature

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Authors: Amon Olimov, Umarov Jamshid, Dae-Ho Kim, Chol-U Lee, Ryum-Duck Oh

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This paper proposes IoT virtualization platform based incident information system. IoT information based environment is the platform that was developed for the purpose of collecting a variety of data by managing regionally scattered IoT devices easily and conveniently in addition to analyzing data collected from roads. Moreover, this paper configured the platform for the purpose of providing incident information based on sensed data. It also provides the same input/output interface as UNIX and Linux by means of matching IoT devices with the directory of file system and also the files. In addition, it has a variety of approaches as to the devices. Thus, it can be applied to not only incident information but also other platforms. This paper proposes the incident information system that identifies and provides various data in real time as to urgent matters on roads based on the existing USN/M2M and IoT visualization platform.

Keywords: incident information system, IoT, virtualization platform, USN, M2M

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Authors: Tadas Telksnys, Zenonas Navickas, Minvydas Ragulskis

Abstract:

Necessary and sufficient conditions for the existence of second order solitary solutions to the Hodgkin-Huxley equation are derived in this paper. The generalized multiplicative operator of differentiation helps not only to construct closed-form solitary solutions but also automatically generates conditions of their existence in the space of the equation's parameters and initial conditions. It is demonstrated that bright, kink-type solitons and solitary solutions with singularities can exist in the Hodgkin-Huxley equation.

Keywords: Hodgkin-Huxley equation, solitary solution, existence condition, operator method

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Authors: Nabilah Zainal Abidin, Fawazul Khair Ibrahim, Raja Nafida Raja Shahminan

Abstract:

This paper discusses on how componential analysis is used in architecture, mainly in determining the absence and presence of furniture in Traditional Malay Houses. The house samples were retrieved from the reports archived by the Centre of Built Environment in the Malay World (KALAM) of Universiti Teknologi Malaysia (UTM). Findings from the analysis indicate that furniture available in the spaces of the houses is determined by the culture of the people and the availability of certain furniture is influenced by the activities that are carried out within the space.

Keywords: componential analysis, sustainable furniture, traditional malay house

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Authors: Sean McCarthy, Patrice M. Ludwig, Will Watson

Abstract:

This exploratory case study explores the deployment of Social Network Analysis (SNA) in mapping community assets in an interdisciplinary, undergraduate, team-taught course focused on income insecure populations in a rural area in the US. Specifically, it analyzes how students were taught to collect data on community assets and to visualize the connections between those assets using Kumu, an SNA data visualization tool. Further, the case study shows how social network data was also collected about student teams via their written communications in Slack, an enterprise messaging tool, which enabled instructors to manage and guide student research activity throughout the semester. The discussion presents how SNA methods can simultaneously inform both community-based research and social innovation pedagogy through the use of data visualization and collaboration-focused communication technologies.

Keywords: social innovation, social network analysis, pedagogy, problem-based learning, data visualization, information communication technologies

Procedia PDF Downloads 151