Search results for: count data

Authors: Aisha Arshad, Samina Naz Mukry, Tahir Shamsi

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Background: Immune thrombocytopenia (ITP) is an autoimmune disorder. Besides platelets counts, immature platelets fraction (IPF) can be used as tool to predict megakaryocytic activity in ITP patients. The clinical biomarkers like Neutrophils to lymphocytes ratio (NLR) and platelet to lymphocytes ratio(PLR) predicts inflammation and can be used as prognostic markers.The present study was planned to assess the ratios in ITP and their utility in predicting prognosis after treatment. Methods: A total of 111 patients of ITP with same number of healthy individuals were included in this case control study during the period of January 2015 to December 2017.All the ITP patients were grouped according to guidelines of International working group of ITP. A 3cc blood was collected in EDTA tube and blood parameters were evaluated using Sysmex 1000 analyzer.The ratios were calculated by using absolute counts of Neutrophils,Lymphocytes and platelets.The significant (p=<0.05) difference between ITP patients and healthy control groups was determined by Kruskal wallis test, Dunn’s test and spearman’s correlation test was done using SPSS version 23. Results: The significantly raised total leucocytes counts (TLC) and IPF along with low platelets counts were observed in ITP patients as compared to healthy controls.In ITP groups,very low platelet count with median and IQR of 2(3.8)3x109/l with highest mean and IQR IPF 25.4(19.8)% was observed in newly diagnosed ITP group. The NLR was high with prognosis of disease as higher levels were observed in P-ITP. The PLR was significantly low in ND-ITP ,P-ITP, C-ITP, R-ITP and compared to controls with p=<0.001 as platelet were less in number in all ITP patients. Conclusion: The IPF can be used in evaluation of bone marrow response in ITP. The simple, reliable and calculated NLR and PLR ratios can be used in predicting prognosis and response to treatment in ITP and to some extend the severity of disease.

Keywords: neutrophils, platelets, lymphocytes, infection

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Authors: Ahmed Rashad Harb Riad Ismail

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The purpose of this research is to study the potential of Dynamic Pricing if deployed by mobile operators and analyse its effects from both operators and consumers side. Furthermore, to conclude, throughout the research study, the recommended conditions for successful Dynamic Pricing deployment, recommended factors identifying the type of markets where Dynamic Pricing can be effective, and proposal for a Dynamic Pricing stakeholders’ framework were presented. Currently, the mobile telecommunications industry is witnessing a dramatic growth rate in the data consumption, being fostered mainly by higher data speed technology as the 4G LTE and by the smart devices penetration rates. However, operators’ revenue from data services lags behind and is decupled from this data consumption growth. Pricing strategy is a key factor affecting this ecosystem. Since the introduction of the 4G LTE technology will increase the pace of data growth in multiples, consequently, if pricing strategies remain constant, then the revenue and usage gap will grow wider, risking the sustainability of the ecosystem. Therefore, this research study is focused on Dynamic Pricing for 4G LTE data services, researching the drivers, benefits and challenges of 4G LTE Dynamic Pricing and the feasibility of its deployment in practice from different perspectives including operators, regulators, consumers, and telecommunications equipment manufacturers point of views.

Keywords: LTE, dynamic pricing, EPC, research

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Authors: S. Adjiri-Bailiche, S. M. Boudia, H. Daaou, S. Hadouche, A. Benzaoui

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In this work the study of changes in the wind speed depending on the altitude is calculated and described by the model of the neural networks, the use of measured data, the speed and direction of wind, temperature and the humidity at 10 m are used as input data and as data targets at 50m above sea level. Comparing predict wind speeds and extrapolated at 50 m above sea level is performed. The results show that the prediction by the method of artificial neural networks is very accurate.

Keywords: MATLAB, neural network, power low, vertical extrapolation, wind energy, wind speed

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Authors: Ahmad Badawi Saluy, Novawiguna Kemalasari

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This study aims to examine, analyze and explain the impacts of compensation, career development and employee engagement to employee’s performance partially and simultaneously (Case Study at PT Bank XYZ). The research design used is quantitative descriptive research causality involving 30 respondents. Sources of data are from primary and secondary data, primary data obtained from questionnaires distribution and secondary data obtained from journals and books. Data analysis used model test using smart application PLS 3 that consists of test outer model and inner model. The results showed that compensation, career development and employee engagement partially have a positive impact on employee performance, while they have a positive and significant impact on employee performance simultaneously. The independent variable has the greatest impact is the employee engagement.

Keywords: compensation, career development, employee engagement, employee performance

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Authors: Thomas L. McCullough, John D. Hague, Marylesa M. Howard, Matthew K. Kiser, Michael A. Mazur, Lance K. McLean, Johanna L. Turk

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Radiological search and mapping depends on the successful recognition of anomalies in large data sets which contain varied and dynamic backgrounds. We present a new algorithmic approach for real-time anomaly detection which is resistant to common detector imperfections, avoids the limitations of a source template library and provides immediate, and easily interpretable, user feedback. This algorithm is based on a continuous wavelet transform for variance reduction and evaluates the deviation between a foreground measurement and a local background expectation using methods from linear algebra. We also present a technique for recognizing and visualizing spectrally similar clusters of data. This technique uses Laplacian Eigenmap Manifold Learning to perform dimensional reduction which preserves the geometric "closeness" of the data while maintaining sensitivity to outlying data. We illustrate the utility of both techniques on real-world data sets.

Keywords: radiological search, radiological mapping, radioactivity, radiation protection

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Authors: Rhaed Khiati, Muhammad Hanif

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In the past decade, smart healthcare systems have been on an ascendant drift, especially with the evolution of hospitals and their increasing reliance on bioinformatics and software specializing in healthcare. Doctors have become reliant on technology more than ever, something that in the past would have been looked down upon, as technology has become imperative in reducing overall costs and improving the quality of patient care. With patient-doctor interactions becoming more necessary and more complicated than ever, systems must be developed while taking into account costs, patient comfort, and patient data, among other things. In this work, we proposed a smart hospital bed, which mixes the complexity and big data usage of traditional healthcare systems with the comfort found in soft beds while taking certain concerns like data confidentiality, security, and maintaining SLA agreements, etc. into account. This research work potentially provides users, namely patients and doctors, with a seamless interaction with to their respective nurses, as well as faster access to up-to-date personal data, including prescriptions and severity of the condition in contrast to the previous research in the area where there is lack of consideration of such provisions.

Keywords: big data, smart healthcare, distributed systems, bioinformatics

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Authors: Tero Huhtala, Minna Pikkarainen, Saila Saraniemi

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Information technology has long been used as an enabler of exchange for goods and services. Services are evolving from generic to personalized, and the reverse use of customer data has been discussed in both academia and industry for the past few years. This article presents the results of an empirical case study in the area of preventive health care services. The primary data were gathered in workshops, in which future personal data-based services were conceptualized by analyzing future scenarios from a business perspective. The aim of this study is to understand business model transformation in emerging personal data ecosystems. The work was done as a case study in the context of occupational healthcare. The results have implications to theory and practice, indicating that adopting personal data management principles requires transformation of the business model, which, if successfully managed, may provide access to more resources, potential to offer better value, and additional customer channels. These advantages correlate with the broadening of the business ecosystem. Expanding the scope of this study to include more actors would improve the validity of the research. The results draw from existing literature and are based on findings from a case study and the economic properties of the healthcare industry in Finland.

Keywords: ecosystem, business model, personal data, preventive healthcare

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Authors: Syed Nauman Bin Asghar Bukhari, Mohtashim Mansoor, Mohammad Nouman

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Engine test-Bed system is a fundamental tool to measure dynamic parameters, economic performance, and reliability of an aircraft Engine, and its automation and accuracy directly influences the precision of acquired and analysed data. In this paper, we present the design of digital Data Acquisition (DAQ) system for a vintage aircraft engine test bed that lacks the capability of displaying all the analyzed parameters at one convenient location (one panel-one screen). Recording such measurements in the vintage test bed is not only time consuming but also prone to human errors. Digitizing such measurement system requires a Data Acquisition (DAQ) system capable of recording these parameters and displaying them on one screen-one panel monitor. The challenge in designing upgrade to the vintage systems arises with a need to build and integrate digital measurement system from scratch with a minimal budget and modifications to the existing vintage system. The proposed design not only displays all the key performance / maintenance parameters of the gas turbine engines for operator as well as quality inspector on separate screens but also records the data for further processing / archiving.

Keywords: Gas turbine engine, engine test cell, data acquisition, instrumentation

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Authors: Mona Vijayaran, Gurleen Oberoi, Sanjay Mishra

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Introduction: p190 BCR‐ABL1 in CML is often associated with monocytosis. In the case described here, monocytosis is associated with coexisting p210 BCR‐ABL and CMML clones. Mutation analysis using next‐generation sequence (NGS) in our case showed TET2 and SRSF2 mutations. Aims & Objectives: A 75-year male was evaluated for monocytosis and thrombocytopenia. CBC showed Hb-11.8g/dl, TLC-12,060/cmm, Monocytes-35%, Platelets-39,000/cmm. Materials & Methods: Bone marrow examination showed a hypercellular marrow with myeloid series showing sequential maturation up to neutrophils with 30% monocytes. Immunophenotyping by flow cytometry from bone marrow had 3% blasts. Making chronic myelomonocytic leukemia as the likely diagnosis. NGS for myeloid mutation panel had TET2 (48.9%) and SRSF2 (32.5%) mutations. This report further supported the diagnosis of CMML. To fulfil the WHO diagnostic criteria for CMML, a BCR ABL1 by RQ-PCR was sent. The report came positive for p210 (B3A2, B2A2) Major Transcript (M-BCR) % IS of 38.418. Result: The patient was counselled regarding the unique presentation of the presence of 2 clones- P210 CML and CMML. After discussion with an international faculty with vast experience in CMML. It was decided to start this elderly gentleman on Imatinib 200mg and not on azacytidine, as ASXL1 was not present; hence, his chances of progressing to AML would be less and on the other end, if CML is left untreated then chances of progression to blast phase would always be a possibility. After 3 months on Imatinib his platelet count improved to 80,000 to 90,000/cmm, but his monocytosis persists. His 3rd month BCR-ABL1 IS% is 0.004%. Conclusion: After searching the literature, there were no case reports of a coexisting CML p210 with CMML. This case might be the first case report. p190 BCR ABL1 is often associated with monocytosis. There are few case reports of p210 BCR ABL1 positivity in patients with monocytosis but none with coexisting CMML. This case highlights the need for extensively evaluating patients with monocytosis with next-generation sequencing for myeloid mutation panel and BCR-ABL1 by RT-PCR to correctly diagnose and treat them.

Keywords: CMML, NGS, p190 CML, Imatinib

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Authors: Khoi A. Nguyen, Rodney A. Stewart, Hong Zhang

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‘Water-related energy’ is energy use which is directly or indirectly influenced by changes to water use. Informatics applying a range of mathematical, statistical and rule-based approaches can be used to reveal important information on demand from the available data provided at second, minute or hourly intervals. This study aims to combine these two concepts to improve the current water end use disaggregation problem through applying a wide range of most advanced pattern recognition techniques to analyse the concurrent high-resolution water-energy consumption data. The obtained results have shown that recognition accuracies of all end-uses have significantly increased, especially for mechanised categories, including clothes washer, dishwasher and evaporative air cooler where over 95% of events were correctly classified.

Keywords: deep learning network, smart metering, water end use, water-energy data

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Authors: Abeer A. Aljohani

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COVID-19 virus spread has been one of the extreme pandemics across the globe. It is also referred to as coronavirus, which is a contagious disease that continuously mutates into numerous variants. Currently, the B.1.1.529 variant labeled as omicron is detected in South Africa. The huge spread of COVID-19 disease has affected several lives and has surged exceptional pressure on the healthcare systems worldwide. Also, everyday life and the global economy have been at stake. This research aims to predict COVID-19 disease in its initial stage to reduce the death count. Machine learning (ML) is nowadays used in almost every area. Numerous COVID-19 cases have produced a huge burden on the hospitals as well as health workers. To reduce this burden, this paper predicts COVID-19 disease is based on the symptoms and medical history of the patient. This research presents a unique architecture for COVID-19 detection using ML techniques integrated with feature dimensionality reduction. This paper uses a standard UCI dataset for predicting COVID-19 disease. This dataset comprises symptoms of 5434 patients. This paper also compares several supervised ML techniques to the presented architecture. The architecture has also utilized 10-fold cross validation process for generalization and the principal component analysis (PCA) technique for feature reduction. Standard parameters are used to evaluate the proposed architecture including F1-Score, precision, accuracy, recall, receiver operating characteristic (ROC), and area under curve (AUC). The results depict that decision tree, random forest, and neural networks outperform all other state-of-the-art ML techniques. This achieved result can help effectively in identifying COVID-19 infection cases.

Keywords: supervised machine learning, COVID-19 prediction, healthcare analytics, random forest, neural network

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Authors: Saroj Panthi, Sher Singh Thagunna

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Reliable and sufficient information regarding status, distribution and habitat preference of red panda (Ailurus fulgens fulgens) is lacking in Nepal. The research activities on red panda in the mid-western Nepal are very limited, so the status of red panda in the region is quite unknown. The study conducted during May, 2013 in three Village Development Committees (VDCs) namely Godhemahadev, Malikathata and Tamti of Jumla district was an important step for providing vital information including distribution and habitat preference of this species. The study included the reconnaissance, key informants survey, interviews, and consultation for the most potential area identification, opportunistic survey comprising the direct observation and indirect sign count method for the presence and distribution, habitat assessment consisting vegetation sampling and ocular estimation. The study revealed the presence of red panda in three forests namely Bahirepatan, Imilchadamar and Tyakot of Godhemahadev, Tamti and Malikathata VDCs respectively. The species was found distributed between 2880 and 3244 m with an average dropping encounter rate of 1.04 per hour of searching effort and 12 pellets per dropping. Red panda mostly preferred the habitat in the elevation range of 2900 - 3000 m with southwest facing steep slopes (36˚ - 45˚), associated with water sources at the distance of ≤100 m. Trees such as Acer spp., Betula utilis and Quercus semecarpifolia, shrub species of Elaeagnus parvifolia, Drepanostachyum spp. and Jasminum humile, and the herbs like Polygonatum cirrhifolium, Fragaria nubicola and Galium asperifolium were found to be the most preferred species by red panda. The red panda preferred the habitat with dense crown coverage ( >20% - 100%) and 31% - 50% ground cover. Fallen logs (39%) were the most preferred substrate used for defecation.

Keywords: distribution, habitat preference, jumla, red panda

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Authors: Aminah Muchdar, Nuraeni, Eddy

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The aims of the research are: (1) to verify the cropsyst plant model of experimental data in the field of soybean plants and (2) to predict planting time and potential yield soybean plant with the use of cropsyst model. This research is divided into several stages: (1) first calibration stage which conducted in the field from June until September 2015.(2) application models stage, where the data obtained from calibration in the field will be included in cropsyst models. The required data models are climate data, ground data/soil data,also crop genetic data. The relationship between the obtained result in field with simulation cropsyst model indicated by Efficiency Index (EF) which the value is 0,939.That is showing that cropsyst model is well used. From the calculation result RRMSE which the value is 1,922%.That is showing that comparative fault prediction results from simulation with result obtained in the field is 1,92%. The conclusion has obtained that the prediction of soybean planting time cropsyst based models that have been made valid for use. and the appropriate planting time for planting soybeans mainly on rain-fed land is at the end of the rainy season, in which the above study first planting time (June 2, 2015) which gives the highest production, because at that time there was still some rain. Tanggamus varieties more resistant to slow planting time cause the percentage decrease in the yield of each decade is lower than the average of all varieties.

Keywords: soybean, Cropsyst, calibration, efficiency Index, RRMSE

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Authors: Noorulaine Maqsood, Saeed Khan

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Background: Concern regarding incident reporting and bio-safety training in clinical laboratories in Pakistan has increased remarkably in the last few years due to rapid increase in diagnosis and research on infectious organisms. In order to ensure the safety of employees, this issue needs to be addressed immediately. Bio-safety training sessions and lectures are necessary for the protection of laboratory workers in order to ensure safe practices and minimize the count of incident reporting in the lab. Objective: To carry out an inter-departmental survey in lab regarding the awareness of bio-safety practices among lab employees before and after conducting bio-safety training sessions. Methodology: We conducted a 30 questions survey of laboratory workers in June 2013 (before training session) to gather information related to bio-safety awareness. Afterwards, we conducted another survey after training sessions and workshops related to bio-safety. Result: The survey regarding bio-safety level showed that before the training session 32% of the participants were aware of bio-safety level being used in their lab whereas after the session this percentage increased to 72%. 48% of the participants had information about the proper usage of PPE which increased to 76%. Awareness regarding proper management of hazardous waste increased from 32% to 64%. The incident reporting practice, sample handling and hand hygiene awareness was previously reported to be 40%, 65%, and 52% that increased to 80%, 85% and 88% respectively after the training session was completed. Conclusion: The first survey results showed lack of awareness that suggest nearly all senior scientists, faculty, medical technologist, lab attendant and housekeeping staff working in laboratories are required to have bio-safety training, and required inspection at least twice a year by a bio-safety officer and also required to renew their bio-safety training. After the training session, significant changes in awareness level and attitude of the participants regarding biosafety practices were observed. Therefore, such bio-safety sessions should be carried out regularly in clinical laboratories.

Keywords: biosafety practices, clinical laboratory, Pakistan, survey

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Authors: Reza Safdari, Goli Arji, Robab Abdolkhani Maryam zahmatkeshan

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Background and purpose: Cardiovascular diseases are among the most common diseases in all societies. The most important step in minimizing myocardial infarction and its complications is to minimize its risk factors. The amount of medical data is increasingly growing. Medical data mining has a great potential for transforming these data into information. Using data mining techniques to generate predictive models for identifying those at risk for reducing the effects of the disease is very helpful. The present study aimed to collect data related to risk factors of heart infarction from patients’ medical record and developed predicting models using data mining algorithm. Methods: The present work was an analytical study conducted on a database containing 350 records. Data were related to patients admitted to Shahid Rajaei specialized cardiovascular hospital, Iran, in 2011. Data were collected using a four-sectioned data collection form. Data analysis was performed using SPSS and Clementine version 12. Seven predictive algorithms and one algorithm-based model for predicting association rules were applied to the data. Accuracy, precision, sensitivity, specificity, as well as positive and negative predictive values were determined and the final model was obtained. Results: five parameters, including hypertension, DLP, tobacco smoking, diabetes, and A+ blood group, were the most critical risk factors of myocardial infarction. Among the models, the neural network model was found to have the highest sensitivity, indicating its ability to successfully diagnose the disease. Conclusion: Risk prediction models have great potentials in facilitating the management of a patient with a specific disease. Therefore, health interventions or change in their life style can be conducted based on these models for improving the health conditions of the individuals at risk.

Keywords: decision trees, neural network, myocardial infarction, Data Mining

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Authors: Fuad Noman, Sh-Hussain Salleh, Chee-Ming Ting, Hadri Hussain, Syed Rasul

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In this paper, a data-driven dictionary approach is proposed for the automatic detection and classification of cardiovascular abnormalities. Electrocardiography (ECG) signal is represented by the trained complete dictionaries that contain prototypes or atoms to avoid the limitations of pre-defined dictionaries. The data-driven trained dictionaries simply take the ECG signal as input rather than extracting features to study the set of parameters that yield the most descriptive dictionary. The approach inherently learns the complicated morphological changes in ECG waveform, which is then used to improve the classification. The classification performance was evaluated with ECG data under two different preprocessing environments. In the first category, QT-database is baseline drift corrected with notch filter and it filters the 60 Hz power line noise. In the second category, the data are further filtered using fast moving average smoother. The experimental results on QT database confirm that our proposed algorithm shows a classification accuracy of 92%.

Keywords: electrocardiogram, dictionary learning, sparse coding, classification

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Authors: Qiuxiao Chen, Yan Hou, Ning Wu

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As there are deficiencies of the classic Douglas-Peucker Algorithm (DPA), such as high risks of deleting key nodes by mistake, high complexity, time consumption and relatively slow execution speed, a new Deletion-Cost Based Compression Algorithm (DCA) for linear vector data was proposed. For each curve — the basic element of linear vector data, all the deletion costs of its middle nodes were calculated, and the minimum deletion cost was compared with the pre-defined threshold. If the former was greater than or equal to the latter, all remaining nodes were reserved and the curve’s compression process was finished. Otherwise, the node with the minimal deletion cost was deleted, its two neighbors' deletion costs were updated, and the same loop on the compressed curve was repeated till the termination. By several comparative experiments using different types of linear vector data, the comparison between DPA and DCA was performed from the aspects of compression quality and computing efficiency. Experiment results showed that DCA outperformed DPA in compression accuracy and execution efficiency as well.

Keywords: Douglas-Peucker algorithm, linear vector data, compression, deletion cost

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Authors: Janusz Bobulski, Mariusz Kubanek

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The main goal of the research is to develop a multimedia container structure containing three types of images: RGB, lidar and infrared, properly calibrated to each other. An additional goal is to develop program libraries for creating and saving this type of file and for restoring it. It will also be necessary to develop a method of data synchronization from lidar and RGB cameras as well as infrared. This type of file could be used in autonomous vehicles, which would certainly facilitate data processing by the intelligent autonomous vehicle management system. Autonomous cars are increasingly breaking into our consciousness. No one seems to have any doubts that self-driving cars are the future of motoring. Manufacturers promise that moving the first of them to showrooms is the prospect of the next few years. Many experts believe that creating a network of communicating autonomous cars will be able to completely eliminate accidents. However, to make this possible, it is necessary to develop effective methods of detection of objects around the moving vehicle. In bad weather conditions, this task is difficult on the basis of the RGB(red, green, blue) image. Therefore, in such situations, you should be supported by information from other sources, such as lidar or infrared cameras. The problem is the different data formats that individual types of devices return. In addition to these differences, there is a problem with the synchronization of these data and the formatting of this data. The goal of the project is to develop a file structure that could be containing a different type of data. This type of file is calling a multimedia container. A multimedia container is a container that contains many data streams, which allows you to store complete multimedia material in one file. Among the data streams located in such a container should be indicated streams of images, films, sounds, subtitles, as well as additional information, i.e., metadata. This type of file could be used in autonomous vehicles, which would certainly facilitate data processing by the intelligent autonomous vehicle management system. As shown by preliminary studies, the use of combining RGB and InfraRed images with Lidar data allows for easier data analysis. Thanks to this application, it will be possible to display the distance to the object in a color photo. Such information can be very useful for drivers and for systems in autonomous cars.

Keywords: an autonomous car, image processing, lidar, obstacle detection

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Authors: Mulue Girmay Gebreslasie

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Entomopathogenic fungi are naturally safe and eco-friendly biological agents. Their potential of host specificity and ease handling made them appealing options to substitute synthetic pesticides in pest control programs. However, they are highly delicate and unstable under field conditions. Therefore, the current experiment was held to search out persistent fungal strains by defining the relationship between invitro heat tolerance and field persistence. Current results on leaf and soil persistence assay revealed that strains of Metarhizium species, M. pingshaense (F2685), M. pingshaense (MS2) and M. brunneum (F709) exhibit maximum cumulative CFUs count, relative survival rate and least percent of CFUs reductions showed significant difference at 7 days and 28 days post inoculations (dpi) in hot seasons from sampled soils and leaves and in cold season from soil samples. Whereas relative survival of B. brongniartii (TNO6) found significantly higher in cold weather leaf treatment application as compared to hot season and found as persistent as other fungal strains, while higher deterioration of fungal conidia seen with M. pingshaense (MS2). In the current study, strains of Beauveria brongniartii (TNO6) and Cordyceps javanica (Czy-LP) were relatively vulnerable in field condition with utmost colony forming units (CFUs) reduction and least survival rates. Further, the relationship of the two parameters (heat tolerance and field persistence) was seen with strong linear positive correlations elucidated that heat test could be used in selection of field persistent fungal strains for hot season applications.

Keywords: integrated pest management, biopesticides, Insect pathology and microbial control, entomology

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Authors: Helen K. Buteme, Rebecca Axelsson-Robertson, Moses L. Joloba, Henry W. Boom, Gunilla Kallenius, Markus Maeurer

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Background: The Ugandan population is heavily affected by infectious diseases and Human leukocyte antigen (HLA) diversity plays a crucial role in the host-pathogen interaction and affects the rates of disease acquisition and outcome. The identification of HLA class 1 alleles and determining which alleles are associated with tuberculosis (TB) outcomes would help in screening individuals in TB endemic areas for susceptibility to TB and to predict resistance or progression to TB which would inevitably lead to better clinical management of TB. Aims: To be able to determine the HLA class 1 phenotype distribution in a Ugandan TB cohort and to establish the relationship between these phenotypes and active and latent TB. Methods: Blood samples were drawn from 32 HIV negative individuals with active TB and 45 HIV negative individuals with latent MTB infection. DNA was extracted from the blood samples and the DNA samples HLA typed by the polymerase chain reaction-sequence specific primer method. The allelic frequencies were determined by direct count. Results: HLA-A*02, A*01, A*74, A*30, B*15, B*58, C*07, C*03 and C*04 were the dominant phenotypes in this Ugandan cohort. There were differences in the distribution of HLA types between the individuals with active TB and the individuals with LTBI with only HLA-A*03 allele showing a statistically significant difference (p=0.0136). However, after FDR computation the corresponding q-value is above the expected proportion of false discoveries (q-value 0.2176). Key findings: We identified a number of HLA class I alleles in a population from Central Uganda which will enable us to carry out a functional characterization of CD8+ T-cell mediated immune responses to MTB. Our results also suggest that there may be a positive association between the HLA-A*03 allele and TB implying that individuals with the HLA-A*03 allele are at a higher risk of developing active TB.

Keywords: HLA, phenotype, tuberculosis, Uganda

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Authors: Monojit Manna, Arpan Adhikary

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In Mobile cloud sensing across the globe, an emerging paradigm is selected by the user to compute sensing tasks. In urban cities current days, Mobile vehicles are adapted to perform the task of data sensing and data collection for universality and mobility. In this work, we focused on the optimality and mobile nodes that can be selected in order to collect the maximum amount of data from urban areas and fulfill the required data in the future period within a couple of minutes. We map out the requirement of the vehicle to configure the maximum data optimization problem and budget. The Application implementation is basically set up to generalize a realistic online platform in which real-time vehicles are moving apparently in a continuous manner. The data center has the authority to select a set of vehicles immediately. A deep learning-based scheme with the help of mobile vehicles (DLMV) will be proposed to collect sensing data from the urban environment. From the future time perspective, this work proposed a deep learning-based offline algorithm to predict mobility. Therefore, we proposed a greedy approach applying an online algorithm step into a subset of vehicles for an NP-complete problem with a limited budget. Real dataset experimental extensive evaluations are conducted for the real mobility dataset in Rome. The result of the experiment not only fulfills the efficiency of our proposed solution but also proves the validity of DLMV and improves the quantity of collecting the sensing data compared with other algorithms.

Keywords: mobile crowdsensing, deep learning, vehicle recruitment, sensing coverage, data collection

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Authors: Ramon Santana

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The use of biometric identifiers in the field of information security, access control to resources, authentication in ATMs and banking among others, are of great concern because of the safety of biometric data. In the general architecture of a biometric system have been detected eight vulnerabilities, six of them allow obtaining minutiae template in plain text. The main consequence of obtaining minutia templates is the loss of biometric identifier for life. To mitigate these vulnerabilities several models to protect minutiae templates have been proposed. Several vulnerabilities in the cryptographic security of these models allow to obtain biometric data in plain text. In order to increase the cryptographic security and ease of reversibility, a minutiae templates protection model is proposed. The model aims to make the cryptographic protection and facilitate the reversibility of data using two levels of security. The first level of security is the data transformation level. In this level generates invariant data to rotation and translation, further transformation is irreversible. The second level of security is the evaluation level, where the encryption key is generated and data is evaluated using a defined evaluation function. The model is aimed at mitigating known vulnerabilities of the proposed models, basing its security on the impossibility of the polynomial reconstruction.

Keywords: fingerprint, template protection, bio-cryptography, minutiae protection

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Authors: Veysel Çelik, Aynur Aker, Ebru Güç

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Developments in information and communication technologies have increased both the speed of producing information and the speed of accessing new information. Accordingly, the daily lives of individuals have started to change. New concepts such as e-mail, e-government, e-school, e-signature have emerged. For this reason, prospective teachers who will be future teachers or school administrators are expected to have a high awareness of digital data security. The aim of this study is to reveal the effect of the digital storytelling technique on the data security awareness of pre-service teachers of computer and instructional technology education departments. For this purpose, participants were selected based on the principle of volunteering among third-grade students studying at the Computer and Instructional Technologies Department of the Faculty of Education at Siirt University. In the research, the pretest/posttest half experimental research model, one of the experimental research models, was used. In this framework, a 6-week lesson plan on digital data security awareness was prepared in accordance with the digital narration technique. Students in the experimental group formed groups of 3-6 people among themselves. The groups were asked to prepare short videos or animations for digital data security awareness. The completed videos were watched and evaluated together with prospective teachers during the evaluation process, which lasted approximately 2 hours. In the research, both quantitative and qualitative data collection tools were used by using the digital data security awareness scale and the semi-structured interview form consisting of open-ended questions developed by the researchers. According to the data obtained, it was seen that the digital storytelling technique was effective in creating data security awareness and creating permanent behavior changes for computer and instructional technology students.

Keywords: digital storytelling, self-regulation, digital data security, teacher candidates, self-efficacy

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Authors: Kamel Allaw, Jocelyne Adjizian Gerard, Makram Chehayeb, Nada Badaro Saliba

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In developing countries, such as Lebanon, the demographic data are hardly available due to the absence of the mechanization of population system. The aim of this study is to evaluate, using only remote sensing data, the correlations between the number of population and the characteristics of roads network (length of primary roads, length of secondary roads, total length of roads, density and percentage of roads and the number of intersections). In order to find the influence of the different factors on the demographic data, we studied the degree of correlation between each factor and the number of population. The results of this study have shown a strong correlation between the number of population and the density of roads and the number of intersections.

Keywords: population, road network, statistical correlations, remote sensing

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Authors: Dewan Hasin Mahtab, Farzana Sadia

Abstract:

At present, wastewater treatment has become essential to maintain a constant supply of safe water as well as to protect the environment. Due to overpopulation and overconsumption, the water quality from various surface water sources is degrading every day. Being one of the megacities in the world, Dhaka City, is going through rapid industrialization and urbanization. The effluents from these industries and factories are mostly discharged directly into the rivers without any treatment. As such, the quality of water of Buriganga is being afflicted with a noisome problem of pollution. The water of the Buriganga River has become detrimental to humans, animals, and the environment. It has become crucial to conserve the environment so that we can save both ourselves and the environment. The first step towards it should be analyzing the wastewater to decide the further steps of the treatment process. Increased population and increased consumption both contribute to water pollution. Mohammadpur is a developing area of Dhaka City, and Kamrangirchar is one of the largest slum areas in Dhaka City. The total study area is 6.13 sq. Km of Dhaka city with a population of 4,73,310 people. Of them, 86.47% had their own latrine, 47% were directly connected to the drain, 55% had septic tanks, and 70.09% of them cleaned their septic tank once a year. The pH, Dissolved Oxygen, Chemical Oxygen Demand, Biochemical Oxygen Demand, Total Dissolved Solid, Total Suspended and total coliforms of wastewater from two samples of both Mohammadpur and Kamrangirchar was analyzed. The DO level from the water bodies of Kamrangirchar was found very low, making the water bodies inhabitable for aquatic plants and animals. The BOD and COD level was extremely high from samples collected from Mohammadpur. The total coliforms count was found too high during the wet season, making it a potential health concern in the wet season in these two areas.

Keywords: Dhaka, environmental conservation rule, sanitation, wastewater

Procedia PDF Downloads 130

Authors: Tuğçe Yücel

Abstract:

A Wireless Sensor Network(WSN) is a collection of sensor nodes which are deployed randomly in an area for surveillance. Efficient utilization of limited battery energy of sensors for increased network lifetime as well as data security are major design objectives for WSN. Moreover secure transmission of data sensed to a base station for further processing. Producing multiple copies of data packets and sending them on different paths is one of the strategies for this purpose, which leads to redundant energy consumption and hence reduced network lifetime. In this work we develop a restricted multi-copy multipath strategy where data move through ‘frequently’ or ‘heavily’ used sensors is copied by the sensor incident to such central nodes and sent on node-disjoint paths. We develop a mixed integer programing(MIP) model and heuristic approach present some preleminary test results.

Keywords: MIP, sensor, telecommunications, WSN

Procedia PDF Downloads 510

Authors: L. Rajaonarivo, M. N. Bessagnet, C. Sallaberry, A. Le Parc Lacayrelle, L. Leveque

Abstract:

TCVPYR is a European FEDER (European Regional Development Fund) project which aims to promote tourism in the French Pyrenees region by leveraging its cultural heritage. It involves scientists from various domains (geographers, historians, anthropologists, computer scientists...). This paper presents a fully automated process to publish any dataset as Wikipedia articles as well as the corresponding linked information on Wikidata and Wikimedia Commons. We validate this process on a sample of geo-referenced cultural heritage data collected by TCVPYR researchers in different regions of the Pyrenees. The main result concerns the technological prerequisites, which are now in place. Moreover, we demonstrated that we can automatically publish cultural heritage data on Wikimedia.

Keywords: cultural heritage dissemination, digital humanities, open data, Wikimedia automated publishing

Procedia PDF Downloads 127

Authors: Alaa Abdullah Altaee

Abstract:

This paper presents an adaptive decision feedback equalizer (ADFE) for multi-Gbps serial links utilizing a fix-step error signal extracted from cross-points of received data symbols. The extracted signal is generated based on violation of received data symbols with minimum detection requirements at the clock and data recovery (CDR) stage. The iterations of the adaptation process search for the optimum feedback tap coefficients to maximize the data eye-opening and minimize the adaptation convergence time. The effectiveness of the proposed architecture is validated using the simulation results of a serial link designed in an IBM 130 nm 1.2V CMOS technology. The data link with variable channel lengths is analyzed using Spectre from Cadence Design Systems with BSIM4 device models.

Keywords: adaptive DFE, CMOS equalizer, error detection, serial links, timing jitter, wire-line communication

Procedia PDF Downloads 120

Authors: Rik van Leeuwen, Ger Koole

Abstract:

Within hospitality, marketing departments use segmentation to create tailored strategies to ensure personalized marketing. This study provides a data-driven approach by segmenting guest profiles via hierarchical clustering based on an extensive set of features. The industry requires understandable outcomes that contribute to adaptability for marketing departments to make data-driven decisions and ultimately driving profit. A marketing department specified a business question that guides the unsupervised machine learning algorithm. Features of guests change over time; therefore, there is a probability that guests transition from one segment to another. The purpose of the study is to provide steps in the process from raw data to actionable insights, which serve as a guideline for how hospitality companies can adopt an algorithmic approach.

Keywords: hierarchical cluster analysis, hospitality, market segmentation

Procedia PDF Downloads 108

Authors: Amara Rafik, Mostefa Belhadj Aissa

Abstract:

Radar data is one of the many data sources used by ATM Air Traffic Management systems. These data come from air navigation radar antennas. These radars intercept signals emitted by the various aircraft crossing the controlled airspace and calculate the position of these aircraft and retransmit their positions to the Air Traffic Management System. For greater reliability, these radars are positioned in such a way as to allow their coverage areas to overlap. An aircraft will therefore be detected by at least one of these radars. However, the position coordinates of the same aircraft and sent by these different radars are not necessarily identical. Therefore, the ATM system must calculate a single position (radar track) which will ultimately be sent to the control position and displayed on the air traffic controller's monitor. There are several techniques for calculating the radar track. Furthermore, the geographical nature of the problem requires the use of a Geographic Information System (GIS), i.e. a geographical database on the one hand and geographical processing. The objective of this work is to propose a GIS for traffic simulation which reconstructs the evolution over time of aircraft positions from a multi-source radar data set and by applying these different techniques.

Keywords: ATM, GIS, radar data, simulation

Procedia PDF Downloads 117