Search results for: high data rate

Authors: Mohd Izzat Md. Nor, Norfazlina Jaffar, Noor Zaitulakma Md. Zain, Nur Izyanti Mohd Suppian, Subhashini Balakrishnan, Geetha Kandavello

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During the Coronavirus (COVID-19) pandemic, Malaysia has been focusing on building herd immunity by introducing vaccination programs into the community. Hospital Standard Operating Procedures (SOP) were developed to prevent inpatient transmission. Objective: In this study, we focus on the positivity rate of inpatient Person Under Surveillance (PUS) becoming COVID-19 positive and compare this to the National rate in order to see the outcomes of the patient who becomes COVID-19 positive in relation to their vaccination status. Methodology: This is a retrospective observational study carried out from 1 January until 30 March 2022 in Institut Jantung Negara (IJN). There were 5,255 patients admitted during the time of this study. Pre-admission Polymerase Chain Reaction (PCR) swab was done for all patients. Patients with positive PCR on pre-admission screening were excluded. The patient who had exposure to COVID-19-positive staff or patients during hospitalization was defined as PUS and were quarantined and monitored for potential COVID-19 infection. Their frequency and risk of exposure (WHO definition) were recorded. A repeat PCR swab was done for PUS patients that have clinical deterioration with or without COVID symptoms and on their last day of quarantine. The severity of COVID-19 infection was defined as category 1-5A. All patients' vaccination status was recorded, and they were divided into three groups: fully immunised, partially immunised, and unvaccinated. We analyzed the positivity rate of PUS patients becoming COVID-positive, outcomes, and correlation with the vaccination status. Result: Total inpatient PUS to patients and staff was 492; only 13 became positive, giving a positivity rate of 2.6%. Eight (62%) had multiple exposures. The majority, 8/13(72.7%), had a high-risk exposure, and the remaining 5 had medium-risk exposure. Four (30.8%) were boostered, 7(53.8%) were fully vaccinated, and 2(15.4%) were partial/unvaccinated. Eight patients were in categories 1-2, whilst 38% were in categories 3-5. Vaccination status did not correlate with COVID-19 Category (P=0.641). One (7.7%) patient died due to COVID-19 complications and sepsis. Conclusion: Within the first quarter of 2022, our institution's positivity rate (2.6%) is significantly lower than the country's (14.4%). High-risk exposure and multiple exposures to positive COVID-19 cases increased the risk of PUS becoming COVID-19 positive despite their underlying vaccination status.

Keywords: COVID-19, boostered, high risk, Malaysia, quarantine, vaccination status

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Authors: V. Khosravi, F. D. Ardejani, A. Aryafar, M. Sedighi

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Heavy metals have a damaging impact for the environment, animals and humans due to their extreme toxicity and removing them from wastewaters is a very important and interesting task in the field of water pollution control. Biosorption is a relatively new method for treatment of wastewaters and recovery of heavy metals. In this study, a continuous fixed bed study was carried out by using Bacillus thuringiensis as a biosorbent for the removal of Cu and Mn ions from Sarcheshmeh Acid Mine Drainage (AMD). The effect of operating parameters such as flow rate and bed height on the sorption characteristics of B. thuringiensis was investigated at pH 6.0 for each metal ion. The experimental results showed that the breakthrough time decreased with increasing flow rate and decreasing bed height. The data also indicated that the equilibrium uptake of both metals increased with decreasing flow rate and increasing bed height. BDST, Thomas, and Yoon–Nelson models were applied to experimental data to predict the breakthrough curves. All models were found suitable for describing the whole dynamic behavior of the column with respect to flow rate and bed height. In order to regenerate the adsorbent, an elution step was carried out with 1 M HCl and five adsorption-desorption cycles were carried out in continuous manner.

Keywords: acid mine drainage, bacillus thuringiensis, biosorption, cu and mn ions, fixed bed

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Authors: Christopher Deekia Nwimae, Nigel Simms, Liyun Lao

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Erosion in pipe bends caused by particles is a major obstacle in the oil and gas fields and might cause the breakdown of production equipment. This work studied the effects imposed by flow velocity and impact of solid particles diameter in an elbow; erosion rate was verified with experimental data using the computational fluid dynamics (CFD) approach. Two-way coupled Euler-Lagrange and discrete phase model was employed to calculate the air/solid particle flow in an elbow. One erosion model and three-particle rebound models were used to predict the erosion rate on the 90° elbows. The generic erosion model was used in the CFD-based erosion model, and after comparing it with experimental data, results showed agreement with the CFD-based predictions as observed.

Keywords: erosion, prediction, elbow, computational fluid dynamics

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Authors: Iman Nezamzadeh, Ali Faghih, Behnam Oveisi

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In order to reduce earthquake hazards in urban areas, it is necessary to perform comprehensive studies to understand the dynamics of the active faults and identify potentially high risk areas. The fault slip-rates in Late Quaternary sediment are critical indicators of seismic hazard and also provide valuable data to recognize young crustal deformations. To measure slip-rates accurately, is needed to displacement of geomorphic markers and ages of quaternary sediment samples of alluvial deposit that deformed by movements on fault. In this study we produced information about Intermediate term slip rate of Sabzpushan Fault Zone (SPF) within the central part of the Zagros Mountains of Iran using OSL dating technique to make better analysis of seismic hazard and seismic risk reduction for Shiraz city. For this purpose identifiable geomorphic fluvial surfaces help us to provide a reference frame to determine differential or absolute horizontal and vertical deformation. Optically stimulated luminescence (OSL) is an alternative and independent method of determining the burial age of mineral grains in Quaternary sediments. Field observation and satellite imagery show geomorphic markers that deformed horizontally along the Sabzpoushan Fault. Here, drag folds is forming because of evaporites material of Miocen Formation. We estimate 2.8±0.5 mm/yr (mm/y) horizontal slip rate along the Sabzpushan fault zone, where ongoing deformation is involve with drug folding. The Soltan synclinal structure, close to the Sabzpushan fault, shows slight uplift rate due to active core-extrousion.

Keywords: slip rate, active tectonics, OSL, geomorphic markers, Sabzpushan Fault Zone, Zagros, Iran

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Authors: Ahmed Z. Mazen, Nejat Rahmanian, Iqbal Mujtaba, Ali Hassanpour

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In oil and gas well drilling, the drill bit is an important part of the Bottom Hole Assembly (BHA), which is installed and designed to drill and produce a hole by several mechanisms. The efficiency of the bit depends on many drilling parameters such as weight on bit, rotary speed, and mud properties. When the bit is pulled out of the hole, the evaluation of the bit damage must be recorded very carefully to guide engineers in order to select the bits for further planned wells. Having a worn bit for hole drilling may cause severe damage to bit leading to cutter or cone losses in the bottom of hole, where a fishing job will have to take place, and all of these will increase the operating cost. The main factor to reduce the cost of drilling operation is to maximize the rate of penetration by analyzing real-time data to predict the drill bit wear while drilling. There are numerous models in the literature for prediction of the rate of penetration based on drilling parameters, mostly based on empirical approaches. One of the most commonly used approaches is Bourgoyne and Young model, where the rate of penetration can be estimated by the drilling parameters as well as a wear index using an empirical correlation, provided all the constants and coefficients are accurately determined. This paper introduces a new methodology to estimate the eight coefficients for Bourgoyne and Young model using the gPROMS parameters estimation GPE (Version 4.2.0). Real data collected form similar formations (12 ¼’ sections) in two different fields in Libya are used to estimate the coefficients. The estimated coefficients are then used in the equations and applied to nearby wells in the same field to predict the bit wear.

Keywords: Bourgoyne and Young model, bit wear, gPROMS, rate of penetration

Procedia PDF Downloads 128

Authors: Samaneh Nazeri, Bagher Mojazi Amiri, Hamid Farahmand

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Production of high quality fish eggs with reasonable hatching rate makes a success in aquaculture industries. It is influenced by the environmental stimulators and inhibitors. Diazinon is a widely-used pesticide in Golestan province (Southern Caspian Sea, North of Iran) which is washed to the aquatic environment (3 mg/L in the river). It is little known about the effect of this pesticide on the embryogenesis of sturgeon fish, the valuable species of the Caspian Sea. Hormonal content of the egg is an important factor to guaranty the successful passes of embryonic stages. In this study, the fate of Persian sturgeon embryo to 24, 48, 72, and 96-hours exposure of diazinon (LC₅₀ dose) was tested. Also, the effect of thyroid hormones (T3 and T4) on these embryos was tested concurrently or separately with diazinon LC ₅₀ dose. Fertilized eggs are exposed to T3 (low dose: 1 ng/ml, high dose: 10 ng/ml), T4 (low dose: 1 ng/ml, high dose: 10 ng/ml). Six eggs were randomly selected from each treatment (with three replicates) in five developmental stages (two cell- division, neural, heart present, heart beaten, and hatched larvae). The possibility of changing T3, T4, and cortisol contents of the embryos were determined in all treated groups and in every mentioned embryonic stage. The hatching rate in treated groups was assayed at the end of the embryogenesis to clarify the effect of thyroid hormones and diazinon. The results indicated significant differences in thyroid hormone contents, but no significant differences were recognized in cortisol levels at various early life stages of embryos. There was also significant difference in thyroid hormones in (T3, T4) + diazinon treated embryos (P˂0.05), while no significant difference between control and treatments in cortisol levels was observed. The highest hatching rate was recorded in HT3 treatment, while the lowest hatching rate was recorded for diazinon LC₅₀ treatment. The result confirmed that Persian sturgeon embryo is less sensitive to diazinon compared to teleost embryos, and thyroid hormones may increase hatching rate even in the presence of diazinon.

Keywords: Persian sturgeon, diazinon, thyroid hormones, cortisol, embryo

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Authors: Obiageli Helen Ezeh, Chuks Clement Ezeh

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Background: It is estimated that more than 330 million persons are living with HIV-infection globally and in Nigeria about 3.4 persons are living with the infection, with an annual death rate of 180,000. Psychological morbidity often accompany chronic illnesses and may be associated with substance abuse, poor health seeking behavior and adherence to treatment program; it may worsen existing health problems and the overall quality of life. Until the burden is effectively identified, intervention cannot be planned. Until there is a cure, the goal is to manage and cope effectively with HIV-infection. Little if any studies have been done in this area in the North West geo-political zone of Nigeria. The study would help to identify high risk groups and prevent the progression and spread of the infection. Aim: To identify HIV-infected persons with psychological morbidity, accessing HIV- clinic at Shika Hospital, Zaria, Kaduna State; and analyze their socio-demographic profile. Methods: A cross sectional descriptive study was carried out to assess and analyze the socio-demographic characteristics of HIV-infected persons attending Shika hospital Zaria Nigeria, who screened positive for psychological morbidity. A total of 109 HIV-infected persons receiving HAART at Shika clinic, Zaria, Kaduna State, Nigeria, were administered questionnaires, the General Health Questionnaire (GHQ-12)measuring psychological morbidity and socio-demographic data. The participants ranged in age between 18 and 75 years. Results: Data were analyzed using SPSS software 15. Both descriptive and inferential Statistics were performed on the data. Results indicate a total prevalent rate of psychological morbidity of 78 percent among participants. Of this, about 16.2 percent were severely distressed, 25.1 percent moderately distressed and 36.7percent were mildly distressed. More females (65 percent of those with psychological morbidity) were found to be distressed than their male (55 percent) counterparts. It was (44 percent) for patients whose HIV-infection was of relatively shorter duration(2-4 years) than those of longer duration(5-9 years; and 10 years/above). The age group (21-30 years) was the most affected (35 percent). The rate was also 55 percent for Christians and 45 percent for Muslims. For married patients with partners it was 20 percent and for singles 30 percent; for the widowed (12 percent) and divorced (38 percent). At the level of tribal/ethnic groups, it was 13 percent for Ibos, 22 percent for Yorubas, 27 percent for Hausas and 33 percent for all the other minority tribes put together. Conclusion/Recommendation: The study has been able to identify the presence of psychological morbidity among HIV-infected persons as high and analyze the socio-demographic factors associated with it as significant. Periodic screening of HIV-infected persons for psychological morbidity and psychosocial intervention was recommended.

Keywords: socio-demographics, psychological morbidities, HIV-Infection, HAART

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Authors: Puji Sucia Sukmaningrum, Achsania Hendratmi, Noven Suprayogi, Muhammad Madyan

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Theoretically, Islamic banks in Indonesia and Malaysia not necessarily aware to the interest rate fluctuation, since they don’t use interest-based instruments. Both countries use dual banking system in which Islamic and conventional banking system are exist. This situation makes the profit-sharing level of the Islamic banks will be indirectly affected by the interest rate fluctuation from the conventional banks system. One of the risk management tools for anticipating the risk of interest rate fluctuation is gap management, which has purpose to narrow the difference between Rate Sensitive Asset (RSA) and Rate Sensitive Liability (RSL). This formed gap will give the information about the risk potential in Islamic banks which respect to the fluctuation on the interest rate. This study aims to determine the position of the gap formed at Islamic Maybank Indonesia and Islamic Maybank Malaysia, and analyze the difference in the formation of gap based on the period of sensitivity. This study is a quantitative research with comparative study using sensitivity gap analysis, independent sample t-test, and Mann-Whitney method. The data being used was secondary data from Maturity Profile contained in the Annual Financial Report of Islamic Maybank Indonesia and Islamic Maybank Malaysia from 2011 to 2015 period. The result shows that, cumulatively the formation of the gap was negative gap. From the results of independent sample t-test and Mann-Whitney, the formation of the gap in Islamic Maybank Indonesia and Islamic Maybank Malaysia for a period of sensitivity of ≤ 1 month and >1-3 months show a significant difference, while the period of sensitivity >3-12 months does not. The result shows, even though Indonesia and Malaysia using same dual banking systems, the gap values are different. The difference in debt policy between Indonesia and Malaysia also affecting the gap sensitivity in debt. In can be concluded that each country needs an appropriate gap management to support its Islamic banking performance specifically.

Keywords: assets and liability management, gap management, interest rate risk, Islamic bank

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Authors: Syed Iftikhar Hussain Shah, Vasilis Peristeras, Ioannis Magnisalis

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Organizations, including governments, generate (big) data that are high in volume, velocity, veracity, and come from a variety of sources. Public Administrations are using (big) data, implementing base registries, and enforcing data sharing within the entire government to deliver (big) data related integrated services, provision of insights to users, and for good governance. Government (Big) data ecosystem actors represent distinct entities that provide data, consume data, manipulate data to offer paid services, and extend data services like data storage, hosting services to other actors. In this research work, we perform a systematic literature review. The key objectives of this paper are to propose a robust definition of government (big) data ecosystem and a classification of government (big) data ecosystem actors and their roles. We showcase a graphical view of actors, roles, and their relationship in the government (big) data ecosystem. We also discuss our research findings. We did not find too much published research articles about the government (big) data ecosystem, including its definition and classification of actors and their roles. Therefore, we lent ideas for the government (big) data ecosystem from numerous areas that include scientific research data, humanitarian data, open government data, industry data, in the literature.

Keywords: big data, big data ecosystem, classification of big data actors, big data actors roles, definition of government (big) data ecosystem, data-driven government, eGovernment, gaps in data ecosystems, government (big) data, public administration, systematic literature review

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Authors: Byung Ho Jung, Dong Hoon Lim

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

Keywords: big data, logistic regression, MapReduce, RHadoop

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Authors: Behnoush Dousti, Ye Choi, Gil S. Lee

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Supercapacitors which are also known as ultra supercapacitors play a significant role in development of energy storage devices owing to their high power density and rate capability. Nobel research has been conducted on micro scale energy storage systems currently to address the demand for smaller wearable technology and portable devices. Improving the performance of these microsupercapacitors have been always a challenge. Here, we demonstrate a facile fabrication of a microsupercapacitor (MSC) with interdigitated electrodes using novel structure of carbon nanotube sheets which are spun directly from as-grown carbon nanotube forests. Stability and performance of the device was tested using an aqueous PVA-H3PO4 gel electrolyte that also offers desirable electrochemical capacitive properties. High Coulombic efficiency around 100%, great rate capability and excellent capacitance retention over 15,000 cycles were obtained. Capacitive performance greatly improved with surface modification with acid and nitrogen doping of the CNT sheets. The high power density and stable cycling performance make this microsupercapacitor a suitable candidate for verity of energy storage application.

Keywords: carbon nanotube sheet, energy storage, solid state electrolyte, supercapacitor

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Authors: Mohamad Mohsen Yavarizadeh

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Polystyrene (PS) and related homopolymers are brittle materials that typically fail in tensile tests at very low strains. These polymers can be toughened by the addition of rubbery particles which initiate a large number of crazes that produce substantial plastic strain at relatively low stresses. Considerable energy is dissipated in the formation of these crazes, producing a relatively tough material that shows an impact toughness of more than 5 times of pure PS. While cross linking of rubbery phase is necessary in aforementioned mechanism of toughening, another mechanism of toughening was also introduced in which low molecular weight liquid rubbers can also toughen PS when dispersed in the form of small pools in the glassy matrix without any cross linking. However, this new mechanism which is based on local plasticization, fails to act properly at high strain rate deformations, i.e. impact tests. In this work, the idea of combination of these two mechanisms was tried. To do so, Polybutadiene rubbers (PB) with bimodal distribution of molecular weight were prepared in which, comparable fractions of very high and very low molecular weight rubbers were mixed. Incorporation of these materials in PS matrix in a reactive process resulted in more significant increases in toughness of PS. In other words, although low molecular weight PB is ineffective in high strain rate impact test by itself, it showed a significant synergistic effect when combined with high molecular weight PB. Surprisingly, incorporation of just 10% of low molecular weight PB doubled the impact toughness of regular high impact PS (HIPS). It was observed that most of rubbery particles could initiate crazes. The effectiveness of low molecular weight PB in impact test was attributed to low strain rate deformation of each individual craze as a result of producing a large number of crazes in this material. In other words, high molecular weight PB chains make it possible to have an appropriate dispersion of rubbery phase in order to create a large number of crazes in the PS matrix and consequently decrease the velocity of each craze. Low molecular weight PB, in turn, would have enough time to locally plasticize craze fibrils and enhance the energy dissipation.

Keywords: molecular weight distribution, polystyrene, toughness, homopolymer

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Authors: Goodness Onwuka, Khaled Abou-El-Hossein

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Borosilicate-crown (BK7) glass has found broad application in the optic and automotive industries and the growing demands for nanometric surface finishes is becoming a necessity in such applications. Thus, it has become paramount to optimize the parameters influencing the surface roughness of this precision lens. The research was carried out on a 4-axes Nanoform 250 precision lathe machine with an ultra-high precision grinding spindle. The experiment varied the machining parameters of feed rate, wheel speed and depth of cut at three levels for different combinations using Box Behnken design of experiment and the resulting surface roughness values were measured using a Taylor Hobson Dimension XL optical profiler. Acoustic emission monitoring technique was applied at a high sampling rate to monitor the machining process while further signal processing and feature extraction methods were implemented to generate the input to a neural network algorithm. This paper highlights the training and development of a back propagation neural network prediction algorithm through careful selection of parameters and the result show a better classification accuracy when compared to a previously developed response surface model with very similar machining parameters. Hence artificial neural network algorithms provide better surface roughness prediction accuracy in the ultra-high precision grinding of BK7 glass.

Keywords: acoustic emission technique, artificial neural network, surface roughness, ultra-high precision grinding

Procedia PDF Downloads 287

Authors: Ren Moses

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The process of classifying vehicles on a highway is hereby viewed as a pattern recognition problem in which connectionist techniques such as artificial neural networks (ANN) can be used to assign vehicles to their correct classes and hence to establish optimum axle spacing thresholds. In the United States, vehicles are typically classified into 13 classes using a methodology commonly referred to as “Scheme F”. In this research, the ANN model was developed, trained, and applied to field data of vehicles. The data comprised of three vehicular features—axle spacing, number of axles per vehicle, and overall vehicle weight. The ANN reduced the classification error rate from 9.5 percent to 6.2 percent when compared to an existing classification algorithm that is not ANN-based and which uses two vehicular features for classification, that is, axle spacing and number of axles. The inclusion of overall vehicle weight as a third classification variable further reduced the error rate from 6.2 percent to only 3.0 percent. The promising results from the neural networks were used to set up new thresholds that reduce classification error rate.

Keywords: artificial neural networks, vehicle classification, traffic flow, traffic analysis, and highway opera-tions

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Authors: B. Suraj Aravind, M. H. M. Krishna Prasad

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There exist emerging applications of data streams that require association rule mining, such as network traffic monitoring, web click streams analysis, sensor data, data from satellites etc. Data streams typically arrive continuously in high speed with huge amount and changing data distribution. This raises new issues that need to be considered when developing association rule mining techniques for stream data. This paper proposes to introduce an improved data stream association rule mining algorithm by eliminating the limitation of resources. For this, the concept of cloud computing is used. Inclusion of this may lead to additional unknown problems which needs further research.

Keywords: data stream, association rule mining, cloud computing, frequent itemsets

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Authors: A. Seddiki, M. Djebbouri, D. Guerchi

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Medical imaging produces human body pictures in digital form. Since these imaging techniques produce prohibitive amounts of data, compression is necessary for storage and communication purposes. Many current compression schemes provide a very high compression rate but with considerable loss of quality. On the other hand, in some areas in medicine, it may be sufficient to maintain high image quality only in region of interest (ROI). This paper discusses a contribution to the lossless compression in the region of interest of Scintigraphic images based on SPIHT algorithm and global transform thresholding using Huffman coding.

Keywords: global thresholding transform, huffman coding, region of interest, SPIHT coding, scintigraphic images

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Authors: Prashant Verma, K. K. Singh, Anjali Singh, Ujjaval Srivastava

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Contraceptive policies have been derived to achieve desired reductions in the growth rate and also, applied to the data of Uttar-Pradesh, India for illustration. Using the Lotka’s integral equation for the stable population, expressions for the proportion of contraceptive users at different ages have been obtained. At the age of 20 years, 42% of contraceptive users is imperative to reduce the present annual growth rate of 0.036 to 0.02, assuming that 40% of the contraceptive users discontinue at the age of 25 years and 30% again continue contraceptive use at age 30 years. Further, presuming that 75% of women start using contraceptives at the age of 23 years, and 50% of the remaining women start using contraceptives at the age of 28 years, while the rest of them start using it at the age of 32 years. If we set a minimum age of marriage as 20 years, a reduction of 0.019 in growth rate will be obtained. This study describes how the level of contraceptive use at different age groups of women reduces the growth rate in the state of Uttar Pradesh. The article also promotes delayed marriage in the region.

Keywords: child bearing, contraceptive devices, contraceptive policies, population growth, stable population

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Authors: Xiaobo Liu, Rakesh Mishra, Yun Zhang

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Continuous monitoring of forest canopy height with large coverage is essential for obtaining forest carbon stocks and emissions, quantifying biomass estimation, analyzing vegetation coverage, and determining biodiversity. LiDAR can be used to collect accurate woody vegetation structure such as canopy height. However, LiDAR’s coverage is usually limited because of its high cost and limited maneuverability, which constrains its use for dynamic and large area forest canopy monitoring. On the other hand, optical satellite images, like Sentinel-2, have the ability to cover large forest areas with a high repeat rate, but they do not have height information. Hence, exploring the solution of integrating LiDAR data and Sentinel-2 images to enlarge the coverage of forest canopy height prediction and increase the prediction repeat rate has been an active research topic in the environmental remote sensing community. In this study, we explore the potential of training a Random Forest Regression (RFR) model and a Convolutional Neural Network (CNN) model, respectively, to develop two predictive models for predicting and validating the forest canopy height of the Acadia Forest in New Brunswick, Canada, with a 10m ground sampling distance (GSD), for the year 2018 and 2021. Two 10m airborne LiDAR-derived canopy height models, one for 2018 and one for 2021, are used as ground truth to train and validate the RFR and CNN predictive models. To evaluate the prediction performance of the trained RFR and CNN models, two new predicted canopy height maps (CHMs), one for 2018 and one for 2021, are generated using the trained RFR and CNN models and 10m Sentinel-2 images of 2018 and 2021, respectively. The two 10m predicted CHMs from Sentinel-2 images are then compared with the two 10m airborne LiDAR-derived canopy height models for accuracy assessment. The validation results show that the mean absolute error (MAE) for year 2018 of the RFR model is 2.93m, CNN model is 1.71m; while the MAE for year 2021 of the RFR model is 3.35m, and the CNN model is 3.78m. These demonstrate the feasibility of using the RFR and CNN models developed in this research for predicting large-coverage forest canopy height at 10m spatial resolution and a high revisit rate.

Keywords: remote sensing, forest canopy height, LiDAR, Sentinel-2, artificial intelligence, random forest regression, convolutional neural network

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Authors: Cynthia A. Gallardo, Kelly S. Hall, Kristopher Garza, Linda Challoo, Mais Nijim

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Dual-enrollment programs have become more prevalent in college and high school settings. Also known as early college programs, dual-enrollment programs help students acquire a head start in earning college credit for post-secondary studies. The number and percentage of high school students who take college courses while in high school is growing. However, little is known about how dual-enrolled students fare. The classroom environment is important to learning. This study compares dually enrolled high school students who take courses that yield college credit either within their high school or at some other location. Mann-Whitney U was the statistical test used. Mean proportions were compared for each of the five standard letter grades earned across the state of Texas. Results indicated that students earn similar passing A, B, and C grades when they take dual-enrollment courses at their high school location but are more likely to fail if they take dual-enrollment courses at non-high school locations. Implications of results are that student success rate of dual-enrollment college courses may have a significant difference between the locations and student performance.

Keywords: educational leadership, dual-enrollment, student performance, college

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Authors: Joseph K. A. Johnson

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Hypertension affects 20 percent of the people within the ages 55 upward in Ghana. Of these, almost one-third are unaware of their condition. Also at the age of 55, more men turned to have hypertension than women. After that age, the condition becomes more prevalent with women. Hypertension is significantly more common in African Americans of both sexes than the racial or ethnic groups. This study was conducted to determine the causes of high blood pressure in Ashanti Region, Ghana. The study employed One Hundred and Seventy (170) respondents. The sample population for the study was all the available respondents at the time of the data collection. The research was conducted using primary data where convenience sampling was used to locate the respondents. A set of questionnaire were used to gather the data for the study. The gathered data was analysed using principal component analysis. The study revealed that, personal description, lifestyle behavior and risk awareness as some of the causes of high blood pressure in Ashanti Region. The study therefore recommend that people must be advice to see to their personal characteristics that may contribute to high blood pressure such as controlling of their temper and how to react perfectly to stressful situations. They must be educated on the factors that may increase the level of their blood pressure such as the essence of seeing a medical doctor before taking in any drug. People must also be made known by the public health officers to those lifestyles behaviour such as smoking and drinking of alcohol which are major contributors of high blood pressure.

Keywords: high blood pressure, principal component analysis, hypertension, public health

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Authors: Muhammad Noor Ul Ane, Dong-Soon Kim, Myron P. Zalucki

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Models can be useful to help understand population dynamics of insects under diverse environmental conditions and in developing strategies to manage pest species better. Adult longevity and fecundity of Helicoverpa armigera (Hübner) were evaluated against a wide range of constant temperatures (15, 20, 25, 30, 35 and 37.5ᵒC). The modified Sharpe and DeMichele model described adult aging rate and was used to estimate adult physiological age. Maximum fecundity of H. armigera was 973 egg/female at 25ᵒC decreasing to 72 eggs/female at 37.5ᵒC. The relationship between adult fecundity and temperature was well described by an extreme value function. Age-specific cumulative oviposition rate and age-specific survival rate were well described by a two-parameter Weibull function and sigmoid function, respectively. An oviposition model was developed using three temperature-dependent components: total fecundity, age-specific oviposition rate, and age-specific survival rate. The oviposition model was validated against independent field data and described the field occurrence pattern of egg population of H. armigera very well. Our model should be a useful component for population modeling of H. armigera and can be independently used for the timing of sprays in management programs of this key pest species.

Keywords: cotton bollworm, life table, temperature-dependent adult development, temperature-dependent fecundity

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Authors: Fuad M. Alkoot

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Different experimental technologies such as microarray sequencing have been proposed to generate high-resolution genetic data, in order to understand the complex dynamic interactions between complex diseases and the biological system components of genes and gene products. However, the generated samples have a very large dimension reaching thousands. Therefore, hindering all attempts to design a classifier system that can identify diseases based on such data. Additionally, the high overlap in the class distributions makes the task more difficult. The data we experiment with is generated for the identification of autism. It includes 142 samples, which is small compared to the large dimension of the data. The classifier systems trained on this data yield very low classification rates that are almost equivalent to a guess. We aim at reducing the data dimension and improve it for classification. Here, we experiment with applying a multistage PCA on the genetic data to reduce its dimensionality. Results show a significant improvement in the classification rates which increases the possibility of building an automated system for autism detection.

Keywords: PCA, gene expression, dimensionality reduction, classification, autism

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Authors: Dipak Subedi, Anil Giri, Christine Whitt, Tia McDonald

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This study aims to understand the impact of the pandemic on the overall economic well-being of the agricultural sector of the United States. The two key metrics used to examine the economic well-being are the bankruptcy rate of the U.S. farm operations and the operating profit margin. One of the primary reasons for farm operations (in the U.S.) to file for bankruptcy is continuous negative profit or a significant decrease in profit. The pandemic caused significant supply and demand shocks in the domestic market. Furthermore, the ongoing trade disruptions, especially with China, also impacted the prices of agricultural commodities. The significantly reduced demand for ethanol and closure of meat processing plants affected both livestock and crop producers. This study uses data from courts to examine the bankruptcy rate over time of U.S. farm operations. Preliminary results suggest there wasn’t an increase in farm operations filing for bankruptcy in 2020. This was most likely because of record high Government payments to producers in 2020. The Federal Government made direct payments of more than $45 billion in 2020. One commonly used economic metric to measure farm profitability is the operating profit margin (OPM). Operating profit margin measures profitability as a share of the total value of production and government payments. The Economic Research Service of the United States Department of Agriculture defines a farm operation to be in a) a high-risk zone if the OPM is less than 10 percent and b) a low-risk zone if the OPM is higher than 25 percent. For this study, OPM was calculated for small, medium, and large-scale farm operations using the data from the Agriculture Resource Management Survey (OPM). Results show that except for small family farms, the share of farms in high-risk zone decreased in 2020 compared to the most recent non-pandemic year, 2019. This was most likely due to higher commodity prices at the end of 2020 and record-high government payments. Further investigation suggests a lower share of smaller farm operations receiving lower average government payments resulting in a large share (over 70 percent) being in the critical zone. This study should be of interest to multiple stakeholders, including policymakers across the globe, as it shows the resilience of the U.S. agricultural system as well as (some) impact of government payments.

Keywords: U.S. farm sector, COVID-19, operating profit margin, farm bankruptcy, ag finance, government payments to the farm sector

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Authors: Abdulmalek Ahmed

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Formation pressure is the main function that affects drilling operation economically and efficiently. Knowing the pore pressure and the parameters that affect it will help to reduce the cost of drilling process. Many empirical models reported in the literature were used to calculate the formation pressure based on different parameters. Some of these models used only drilling parameters to estimate pore pressure. Other models predicted the formation pressure based on log data. All of these models required different trends such as normal or abnormal to predict the pore pressure. Few researchers applied artificial intelligence (AI) techniques to predict the formation pressure by only one method or a maximum of two methods of AI. The objective of this research is to predict the pore pressure based on both drilling parameters and log data namely; weight on bit, rotary speed, rate of penetration, mud weight, bulk density, porosity and delta sonic time. A real field data is used to predict the formation pressure using five different artificial intelligence (AI) methods such as; artificial neural networks (ANN), radial basis function (RBF), fuzzy logic (FL), support vector machine (SVM) and functional networks (FN). All AI tools were compared with different empirical models. AI methods estimated the formation pressure by a high accuracy (high correlation coefficient and low average absolute percentage error) and outperformed all previous. The advantage of the new technique is its simplicity, which represented from its estimation of pore pressure without the need of different trends as compared to other models which require a two different trend (normal or abnormal pressure). Moreover, by comparing the AI tools with each other, the results indicate that SVM has the advantage of pore pressure prediction by its fast processing speed and high performance (a high correlation coefficient of 0.997 and a low average absolute percentage error of 0.14%). In the end, a new empirical correlation for formation pressure was developed using ANN method that can estimate pore pressure with a high precision (correlation coefficient of 0.998 and average absolute percentage error of 0.17%).

Keywords: Artificial Intelligence (AI), Formation pressure, Artificial Neural Networks (ANN), Fuzzy Logic (FL), Support Vector Machine (SVM), Functional Networks (FN), Radial Basis Function (RBF)

Procedia PDF Downloads 133

Authors: Gwi Hyun Lee, Mun Soo Na

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Solar energy has many advantages of infinite and clean source, and also it can be used for reduction of greenhouse gases and environment pollution. Concentrated solar system is a very useful to achieve reasonably high thermal efficiency. Different types of solar concentrating systems have been developed such as parabolic trough and parabolic dish. Conical solar concentrator is one of the most reliable and promising renewable energy systems for higher temperature applications. The objectives of this study were to investigate the influence of flow rate affecting the thermal efficiency of a conical solar collector, which has a double tube absorber placed at focal axis for collecting solar radiation. A conical solar concentrator consists of a conical reflector, which reflects direct solar radiation into an absorber. A double tube absorber was placed at the center of focal axis for collecting the solar radiation reflected from a conical reflector. A dual tracking system consists of a linear actuator and slew drive with driving cycle of 6 seconds. Water was used as circulating fluid, which flows from inlet to outlet of an absorber for collecting solar radiation. Three identical conical solar concentrator systems were installed side by side at the same place for the accurate performance analysis under the same environmental conditions. Performance evaluations were carried out with different volumetric flow rate of 2, 4 and 6 L/min to find the influence of flow rate affecting on thermal efficiency. The results indicated that average thermal efficiency was 73.24%, 81.96%, and 79.78% for each flow rate of 2 L/min, 4 L/min, and 6 L/min. It shows that the flow rate of circulating water has a significant effect on the thermal efficiency of the conical solar concentrator. It is concluded that an optimum flow rate of conical solar concentrator is 6 L/min.

Keywords: conical solar concentrator, performance evaluation, solar energy, solar energy system

Procedia PDF Downloads 257

Authors: Syed Iftikhar Hussain Shah, Vasilis Peristeras, Ioannis Magnisalis

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Data that is high in volume, velocity, veracity and comes from a variety of sources is usually generated in all sectors including the government sector. Globally public administrations are pursuing (big) data as new technology and trying to adopt a data-centric architecture for hosting and sharing data. Properly executed, big data and data analytics in the government (big) data ecosystem can be led to data-driven government and have a direct impact on the way policymakers work and citizens interact with governments. In this research paper, we conduct a systematic literature review. The main aims of this paper are to highlight essential aspects of the government (big) data ecosystem and to explore the most critical socio-technical factors that contribute to the successful implementation of government (big) data ecosystem. The essential aspects of government (big) data ecosystem include definition, data types, data lifecycle models, and actors and their roles. We also discuss the potential impact of (big) data in public administration and gaps in the government data ecosystems literature. As this is a new topic, we did not find specific articles on government (big) data ecosystem and therefore focused our research on various relevant areas like humanitarian data, open government data, scientific research data, industry data, etc.

Keywords: applications of big data, big data, big data types. big data ecosystem, critical success factors, data-driven government, egovernment, gaps in data ecosystems, government (big) data, literature review, public administration, systematic review

Procedia PDF Downloads 200

Authors: Mohammad Saleh Al-Aboody

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In the present study 442 fecal samples from cattle, buffaloes, and sheep for contamination with helminthes. Samples were examined from 171 cattle, 128 buffaloes, and 143 sheep. The testing, during the period from May 2014 to April 2015, showed that 81 out of 171cattle were positive for helminthes infection (47.3%), with the rate of infection higher in females (55%) than in males (40%). In buffaloes, 41 of 128 tested were positive, a 32% rate of infection. Again, the infection rate was higher in females (47%) than in males (22%). In sheep, the rate of infection was highest of all three species. The results showed that, the infection rate among cattle were 50.3 % and Trichostrongyle species were the predominant parasites among both cattle and buffaloes. The prevalence rate was much higher in females than males. Regarding seasonal dynamics the highest infection rates with helminthes reported was in spring season.

Keywords: helminthes, prevalence, ruminants, trichostrongyle

Procedia PDF Downloads 351

Authors: M. Safiqul Islam, Md Arafat Hossain Sarkar

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Brachytherapy is one of the most important cancer treatment management systems in radiotherapy department. Brachytherapy treatment is moved into High Dose Rate (HDR) after loader from Low Dose Rate (LDR) after loader due to radiation protection advantage. HDR Brachytherapy is a highly multipurpose system for enhancing cure and achieving palliation in many common cancers disease of developing countries. High-dose rate (HDR) Brachytherapy is a type of internal radiation therapy that delivers radiation from implants placed close to or inside, the tumor(s) in the body. This procedure is very effective at providing localized radiation to the tumor site while minimizing the patient’s whole body dose. Brachytherapy has proven to be a highly successful treatment for cancers of the prostate, cervix, endometrium, breast, skin, bronchus, esophagus, and head and neck, as well as soft tissue sarcomas and several other types of cancer. For the time being in our country we have 10 new HDR Remote after loading Brachytherapy. Right now 4 HDR Brachytherapy is already installed and running for patient’s treatment out of 10 HDR Brachytherapy. Ir-192 source is more comfortable than Co-60. In that case people or expert personnel prefer Ir-192 source for different kind of cancer patients. Ir-192 are economically, more flexible and familiar in our country.

Keywords: Ir-192, brachytherapy, cancer treatment, prostate, cervix, endometrium, breast, skin, bronchus, esophagus, soft tissue sarcomas

Procedia PDF Downloads 406

Authors: I. Tzoka, V. A. Chirayath, A. Brandt, J. Asaadi, Melvin J. Aviles, Stephen Clarke, Stefan Cwik, Michael R. Foley, Cole J. Hamel, Alexey Lyashenko, Michael J. Minot, Mark A. Popecki, Michael E. Stochaj, S. Shin

Abstract:

Micro-channel plate photo-multiplier tubes (MCP-PMTs) have become ubiquitous and are widely considered potential candidates for next generation High Energy Physics experiments due to their picosecond timing resolution, ability to operate in strong magnetic fields, and low noise rates. A key factor that determines the applicability of MCP-PMTs in their lifetime, especially when they are used in high event rate experiments. We have developed a novel method for the investigation of the aging behavior of an MCP-PMT on an accelerated basis. The method involves exposing a localized region of the MCP-PMT to photons at a high repetition rate. This pixel-based method was inspired by earlier results showing that damage to the photocathode of the MCP-PMT occurs primarily at the site of light exposure and that the surrounding region undergoes minimal damage. One advantage of the pixel-based method is that it allows the dynamics of photo-cathode damage to be studied at multiple locations within the same MCP-PMT under different operating conditions. In this work, we use the pixel-based accelerated lifetime test to investigate the aging behavior of a 20 cm x 20 cm Large Area Picosecond Photo Detector (LAPPD) manufactured by INCOM Inc. at multiple locations within the same device under different operating conditions. We compare the aging behavior of the MCP-PMT obtained from the first lifetime test conducted under high gain conditions to the lifetime obtained at a different gain. Through this work, we aim to correlate the lifetime of the MCP-PMT and the rate of ion feedback, which is a function of the gain of each MCP, and which can also vary from point to point across a large area (400 $cm^2$) MCP. The tests were made possible by the uniqueness of the LAPPD design, which allows independent control of the gain of the chevron stacked MCPs. We will further discuss the implications of our results for optimizing the operating conditions of the detector when used in high event rate experiments.

Keywords: electron multipliers (vacuum), LAPPD, lifetime, micro-channel plate photo-multipliers tubes, photoemission, time-of-flight

Procedia PDF Downloads 144

Authors: M. Sakthivel, A. Devaki, D. Balasubramanyam, P. Kumarasamy, A. Raja, R. Anilkumar, H. Gopi

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The genetic parameters of growth traits in the New Zealand White rabbits maintained at Sheep Breeding and Research Station, Sandynallah, The Nilgiris, India were estimated by partitioning the variance and covariance components. The (co)variance components of body weights at weaning (W42), post-weaning (W70) and marketing (W135) age and growth efficiency traits viz., average daily gain (ADG), relative growth rate (RGR) and Kleiber ratio (KR) estimated on a daily basis at different age intervals (1=42 to 70 days; 2=70 to 135 days and 3=42 to 135 days) from weaning to marketing were estimated by restricted maximum likelihood, fitting six animal models with various combinations of direct and maternal effects. Data were collected over a period of 15 years (1998 to 2012). A log-likelihood ratio test was used to select the most appropriate univariate model for each trait, which was subsequently used in bivariate analysis. Heritability estimates for W42, W70 and W135 were 0.42 ± 0.07, 0.40 ± 0.08 and 0.27 ± 0.07, respectively. Heritability estimates of growth efficiency traits were moderate to high (0.18 to 0.42). Of the total phenotypic variation, maternal genetic effect contributed 14 to 32% for early body weight traits (W42 and W70) and ADG1. The contribution of maternal permanent environmental effect varied from 6 to 18% for W42 and for all the growth efficiency traits except for KR2. Maternal permanent environmental effect on most of the growth efficiency traits was a carryover effect of maternal care during weaning. Direct maternal genetic correlations, for the traits in which maternal genetic effect was significant, were moderate to high in magnitude and negative in direction. Maternal effect declined as the age of the animal increased. The estimates of total heritability and maternal across year repeatability for growth traits were moderate and an optimum rate of genetic progress seems possible in the herd by mass selection. The estimates of genetic and phenotypic correlations among body weight traits were moderate to high and positive; among growth efficiency traits were low to high with varying directions; between body weights and growth efficiency traits were very low to high in magnitude and mostly negative in direction. Moderate to high heritability and higher genetic correlation in body weight traits promise good scope for genetic improvement provided measures are taken to keep the inbreeding at the lowest level.

Keywords: genetic parameters, growth traits, maternal effects, rabbit genetics

Procedia PDF Downloads 431