Search results for: data reduction

Authors: Adarsh Shroff

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Big data is a collection of dataset so large and complex that it becomes difficult to process using data base management tools. To perform operations like search, analysis, visualization on big data by using data mining; which is the process of extraction of patterns or knowledge from large data set. In recent years, the data mining applications become stale and obsolete over time. Incremental processing is a promising approach to refreshing mining results. It utilizes previously saved states to avoid the expense of re-computation from scratch. This project uses i2MapReduce, an incremental processing extension to Map Reduce, the most widely used framework for mining big data. I2MapReduce performs key-value pair level incremental processing rather than task level re-computation, supports not only one-step computation but also more sophisticated iterative computation, which is widely used in data mining applications, and incorporates a set of novel techniques to reduce I/O overhead for accessing preserved fine-grain computation states. To optimize the mining results, evaluate i2MapReduce using a one-step algorithm and three iterative algorithms with diverse computation characteristics for efficient mining.

Keywords: big data, map reduce, incremental processing, iterative computation

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Authors: Jerry Q. Cheng

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Currently, in analyzing large-scale recurrent event data, there are many challenges such as memory limitations, unscalable computing time, etc. In this research, a divide-and-conquer method is proposed using parametric frailty models. Specifically, the data is randomly divided into many subsets, and the maximum likelihood estimator from each individual data set is obtained. Then a weighted method is proposed to combine these individual estimators as the final estimator. It is shown that this divide-and-conquer estimator is asymptotically equivalent to the estimator based on the full data. Simulation studies are conducted to demonstrate the performance of this proposed method. This approach is applied to a large real dataset of repeated heart failure hospitalizations.

Keywords: big data analytics, divide-and-conquer, recurrent event data, statistical computing

Procedia PDF Downloads 168

Authors: Saleh Alkarri, Melinda Frame, Dimple Sharma, John Cairney, Lee Maddan, Jin H. Kim, Jonathan O. Rayner, Teresa M. Bergholz, Muhammad Rabnawaz

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Reported herein is an investigation of anti-bacterial and anti-virus properties, mode of action of Mg(OH)₂ and copper-infused Mg(OH)₂ nanoplatelets (NPs) on melt-compounded and thermally embossed polypropylene (PP) surfaces. The anti-viral activity for the NPs was studied in aqueous liquid suspensions against SARS-CoV-2, and the mode of action was investigated on neat NPs and PP samples that were thermally embossed with NPs. Anti-bacterial studies for melt-compounded NPs in PP confirmed approximately 1 log reduction of E. coli populations in 24 h, while for thermally embossed NPs, an 8 log reduction of E. coli populations was observed. In addition, the NPs exhibit anti-viral activity against SARS-CoV-2. Fluorescence microscopy revealed that reactive oxygen species (ROS) is the main mode of action through which Mg(OH)₂ and Cu-Infused Mg(OH)₂act against microbes. Plastics with anti-microbial surfaces from where biocides are non-leachable are highly desirable. This work provides a general fabrication strategy for developing anti-microbial plastic surfaces.

Keywords: anti-microbial activity, E. coli K-12 MG1655, anti-viral activity, SARS-CoV-2, copper-infused magnesium hydroxide, non-leachable, ROS, compounding, surface embossing, dyes

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Authors: Karim Hajjar, Luis Lillo, Diego Martinez, Manuel Hermosilla, Nicholas Risko

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Objectives: This study examines the relationship between universal health coverage (UCH) and the burden of emergency diseases at a global level. Methods: Data on Disability-Adjusted Life Years (DALYs) from emergency conditions were extracted from the Institute for Health Metrics and Evaluation (IHME) database for the years 2015 and 2019. Data on UHC, measured using two variables, 1) coverage of essential health services and 2) proportion of population spending more than 10% of household income on out-of-pocket health care expenditure, was extracted from the World Bank Database for years preceding our outcome of interest. Linear regression was performed, analyzing the effect of the UHC variables on the DALYs of emergency diseases, controlling for other variables. Results: A total of 133 countries were included. 44.4% of the analyzed countries had coverage of essential health services index of at least 70/100, and 35.3% had at least 10% of their population spend greater than 10% of their household income on healthcare. For every point increase in the coverage of essential health services index, there was a 13-point reduction in DALYs of emergency medical diseases (95% CI -16, -11). Conversely, for every percent decrease in the population with large household expenditure on healthcare, there was a 0.48 increase in DALYs of emergency medical diseases (95% CI -5.6, 4.7). Conclusions: After adjusting for multiple variables, an increase in coverage of essential health services was significantly associated with improvement in DALYs for emergency conditions. There was, however, no association between catastrophic health expenditure and DALYs.

Keywords: emergency medicine, universal healthcare, global health, health economics

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Authors: Phusit Phukamchanoad

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This research was to analyze personality’s activities based on sufficiency economy philosophy of people and community leaders in urban communities. The data were collected through questionnaires administered to 392 people and interviewed with community leaders. It was found that most people revealed that their lives depend on activities in accordance with the sufficiency economy philosophy in high level especially, being honest and aware on sufficiency, occupations, peacefulness in the community leaders’ side, they reported on extravagant reduction, planting home vegetable garden, having household accounting, expense planning by dividing into 3 categories; 1) saving for illness cover 2) saving for business cover, and 3) household daily expense. The samples were also adjusted their livings quite well with the rapid change of urbanization. Although those people have encountered with any hardships, their honesty in occupations and awareness on sufficiency remain to survive happily.

Keywords: sufficiency economy philosophy, individual and household activities, urban community

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Authors: Ana Magana

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Youth are entering the juvenile justice system at alarming rates. For the youth of color entering the system, the outcomes are far worse than for their white counterparts. In fact, the youth of color are more likely to be arrested and sentenced for longer periods of time than white youth. Race disproportionality in the juvenile justice system is evident, but what happens to the youth that exit the juvenile justice system? Who supports them after they are incarcerated and who can prevent them from re-offending? There are several diversion programs that have been implemented in the US to aid the reduction of juvenile incarceration and help reduce recidivism. The program interviewed for this study is a community-based diversion program (CBDP). The CBDP is a pre-filing diversion non-profit organization based in South Seattle. The objective of this exploratory research study is to provide a space and platform for the CBDP team to speak about their background experiences and the influence their background has on their current approach and practice with juveniles. A qualitative, exploratory study was conducted. Interviews were conducted with staff and provided oral consent. The interview included six open-ended, semi-structured questions. Interviews were digitally recoded and transcribed. The aim of this study was to understand how the influence of the participant’s backgrounds and previous experiences impact their current practice approaches with the CBDP youth and young adults. Ecological systems theory was the guiding framework for analysis. After careful analysis, three major themes emerged: 1) strong influence of participant’s background, 2) participants belonging to community and 3) strong self-identity with the CBDP. Within these three themes, subthemes were developed based on participant’s responses. It was concluded that the participant’s approach is influenced by their background experiences. This corresponds to the ecological systems theory and the community-based lens which underscores theoretical analysis. The participant’s approach is grounded in interpersonal relationships within the client’s systems, meaning that the participants understand and view their clients within an ecological systems perspective. When choosing participants that reflect the population being served, the clients receive a balanced, inclusive and caring approach. Youth and young adults are searching for supportive adults to be there for them, it is essential for diversion programs to provide a space for shared background experiences and have people that hold similar identities. Grassroots organizations such as CBDP have the tools and experience to work with marginalized populations that are constantly being passed on. While articles and studies focus on the reduction of recidivism and re-offending it is important to question the reasons behind this data. For instance, there can be a reduction in statistics, but at whose expense. Are the youth and young adults truly being supported? Or is it just a requirement that they are completing in order to remove their charge? This research study can serve as the beginning of a series of studies conducted at CBDP to further understand and validate the need to employ individuals with similar backgrounds as the participants CBDP serves.

Keywords: background experience, diversion, ecological systems theory, relationships

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Authors: Gabriela Wasileski, Debra L. Stanley

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Maryland efforts to decrease the juvenile criminality and recidivism shifts towards evidence based sentencing. While in theory the evidence based sentencing has an impact on the reduction of juvenile delinquency and drug abuse; the assessment of juveniles’ risk and needs usually lacks crucial information about juvenile’s prior victimization. The Maryland Comprehensive Assessment and Service Planning (MCASP) Initiative is the primary tool for developing and delivering a treatment service plan for juveniles at risk. Even though it consists of evidence-based screening and assessment instruments very little is currently known regarding the effectiveness and the impact of the assessment in general. In keeping with Maryland’s priority to develop successful evidence-based recidivism reduction programs, this study examined results of assessments based on MCASP using a representative sample of the juveniles at risk and their assessment results. Specifically, it examined: (1) the results of the assessments in an electronic database (2) areas of need that are more frequent among delinquent youth in a system/agency, (3) the overall progress of youth in an agency’s care (4) the impact of child victimization and trauma experiences reported in the assessment. The project will identify challenges regarding the use of MCASP in Maryland, and will provide a knowledge base to support future research and practices.

Keywords: Juvenile Justice, assessment of risk and need, victimization and crime, recidivism

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Authors: Ashiff Khan, A. Seetharaman, Abhijit Dasgupta

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The new era of big data (BD) is influencing chemical industries tremendously, providing several opportunities to reshape the way they operate and help them shift towards intelligent manufacturing. Given the availability of free software and the large amount of real-time data generated and stored in process plants, chemical industries are still in the early stages of big data adoption. The industry is just starting to realize the importance of the large amount of data it owns to make the right decisions and support its strategies. This article explores the importance of professional competencies and data science that influence BD in chemical industries to help it move towards intelligent manufacturing fast and reliable. This article utilizes a literature review and identifies potential applications in the chemical industry to move from conventional methods to a data-driven approach. The scope of this document is limited to the adoption of BD in chemical industries and the variables identified in this article. To achieve this objective, government, academia, and industry must work together to overcome all present and future challenges.

Keywords: chemical engineering, big data analytics, industrial revolution, professional competence, data science

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Authors: Sourabh Jain, Vikas Jain, Dharmendra Kumnar

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The present study investigated the antidiabetic potential of Cedrus deodara heart wood aqueous extract and its relationship in alloxan-induced diabetic rats. Aqueous extract of Cedrus deodara was found to reduce blood sugar level in alloxan induced diabetic rats. Reduction in blood sugar could be seen from 5th day after continuous administration of the extract and on 21st day sugar levels were found to be reduced by 40.20%. Oxidative stress produced by alloxan was found to be significantly lowered by the administration of Cedrus deodara aqueous extract (500 mg/kg). This was evident from a significant decrease in lipid per oxidation level in liver induced by alloxan. The level of Glutathione, Catalase, Superoxide dismutase and Glutathione-S-Transferase in liver, kidney and pancreas tissue were found to be increased significantly after drug administration. The results obtained in the present study suggest that the Cedrus deodara aqueous extract effectively and significantly reduced the oxidative stress induced by alloxan and produced a reduction in blood sugar level.

Keywords: Cedrus deodara, heartwood, antioxidant, anti-diabetic, anti-inflammatory

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Authors: Salhi Nesrine, Salama M. El-Darier, Halilat M. El-Taher

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The present study aimed to investigate the effect of two type of soil (clay and sandy soils) in the potential allelopathic effects of Artemisia herba-alba, Oudneya africana crude powder on some growth parameters and phytomass of two weeds (Bromus tectorum and Melilotus indica) under laboratory conditions (pot experiment). The experimental findings have reported that the donor species crude powder concentrations were suppressing to shoot length (SL), root length (RL), fresh and dry weight of shoot and root (SFw, RFw, SDw and RDw, respectively and the leaf number (LN)) in both soil types and caused a gradual reduction particularly when they are high. However, the reduction degree was varied and species, concentration dependent. The suppressive effect of all the eight donors on the two weedy species was in the following order Bromus tectorum> Melilotus indica. Generally, the growth parameters of two recipient species were significantly decreased with the increase of each of the donor species crude powder concentration levels. Concerning the type of sol the t-test indicated that the difference was insignificant between clay and sandy soils.

Keywords: allelopathy, soil, Artemisia herba-alba, Oudneya africana, growth, weeds

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Authors: M. Sumana, K. S. Hareesha

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Secure computations are essential while performing privacy preserving data mining. Distributed privacy preserving data mining involve two to more sites that cannot pool in their data to a third party due to the violation of law regarding the individual. Hence in order to model the private data without compromising privacy and information loss, secure multiparty computations are used. Secure computations of product, mean, variance, dot product, sigmoid function using the additive and multiplicative homomorphic property is discussed. The computations are performed on vertically partitioned data with a single site holding the class value.

Keywords: homomorphic property, secure product, secure mean and variance, secure dot product, vertically partitioned data

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Authors: Nino G. Vepkhvadze, Tea Enukidze

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Every year the number of countries around the world face the risk of the spread of infectious diseases that bring significant ecological and social-economic damage. Hence, the importance of food product safety is emphasized that is the issue of interest for many countries. To solve them, it’s necessary to conduct preventive measures against the diseases, have accurate diagnostic results, leadership, and management. The Peste des petits ruminants (PPR) disease is caused by a morbillivirus closely related to the rinderpest virus. PPR is a transboundary disease as it emerges and evolves, considered as one of the top most damaging animal diseases. The disease imposed a serious threat to sheep-breeding when the farms of sheep, goats are significantly growing within the country. In January 2016, PPR was detected in Georgia. Up to present the origin of the virus, the age relationship of affected ruminants and the distribution of PPRV in Georgia remains unclear. Due to the nature of PPR, and breeding practices in the country, reemerging of the disease in Georgia is highly likely. The purpose of the studies is to provide laboratories with efficient tools allowing the early detection of PPR emergence and re-emergences. This study is being accomplished under the Biological Threat Reduction Program project with the support of the Defense Threat Reduction Agency (DTRA). The purpose of the studies is to investigate the samples and identify areas at high risk of the disease. Georgia has a high density of small ruminant herds bred as free-ranging, close to international borders. Kakheti region, Eastern Georgia, will be considered as area of high priority for PPR surveillance. For this reason, in 2019, in Kakheti region investigated n=484 sheep and goat serum and blood samples from the same animals, utilized serology and molecular biology methods. All samples were negative by RT-PCR, and n=6 sheep samples were seropositive by ELISA-Ab. Future efforts will be concentrated in areas where the risk of PPR might be high such as international bordering regions of Georgia. For diagnostics, it is important to integrate the PPRV knowledge with epidemiological data. Based on these diagnostics, the relevant agencies will be able to control the disease surveillance.

Keywords: animal disease, especially dangerous pathogen, laboratory diagnostics, virus

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Authors: Zaid Jamal Saeed Almahmoud

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Smart Grid is the vision of the future power system that combines advanced monitoring and communication technologies to provide energy in a smart, efficient, and user-friendly manner. This proposal considers a demand response model in the Smart Grid based on utility maximization. Given a set of consumers with conflicting preferences in terms of consumption and a utility company that aims to minimize the peak demand and match demand to supply, we study the problem of aggregating these preferences while modelling the problem as a game. We also investigate whether an equilibrium can be reached to maximize the social benefit. Based on such equilibrium, we propose a dynamic pricing heuristic that computes the equilibrium and sets the prices accordingly. The developed approach was analysed theoretically and evaluated experimentally using real appliances data. The results show that our proposed approach achieves a substantial reduction in the overall energy consumption.

Keywords: heuristics, smart grid, aggregation, mechanism design, equilibrium

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Authors: Ratheesh Radhakrishnan, P. C. Sreekumar, K. Krishnamoorthy

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Heat transfer augmentation techniques ultimately results in the reduction of thermal resistance in a conventional heat exchanger by generating higher convective heat transfer coefficient. It also results in reduction of size, increase in heat duty, decrease in approach temperature difference and reduction in pumping power requirements for heat exchangers. Present study deals with compound augmentation technique, which is not widely used. The study deals with the use of Alumina (Al2O3)/water nanofluid and baffled twisted tape inserts in double pipe heat exchanger as compound augmentation technique. Experiments were conducted to evaluate the heat transfer coefficient and friction factor for the flow through the inner tube of heat exchanger in turbulent flow range (8000Keywords: enhancement, heat transfer coefficient, friction factor, twisted tape, nanofluid

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Authors: Jee-Young Lee, Joaquim J. Ferreira, Hyeo-il Ma, José-Francisco Rocha, Beomseok Jeon

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The effective management of wearing-off is a key driver of medication changes for patients with Parkinson’s disease (PD) treated with levodopa (L-DOPA). While L-DOPA is well tolerated and efficacious, its clinical utility over time is often limited by the development of complications such as dyskinesia. Still, common first-line option includes adjusting the daily L-DOPA dose followed by adjunctive therapies usually counting for the L-DOPA equivalent daily dose (LEDD). The LEDD conversion formulae are a tool used to compare the equivalence of anti-PD medications. The aim of this work is to compare the effects of opicapone (OPC) 50 mg, a catechol-O-methyltransferase (COMT) inhibitor, and an additional 100 mg dose of L-DOPA in reducing the off time in PD patients with early motor fluctuations receiving different daily L-DOPA doses. OPC was found to be well tolerated and efficacious in advanced PD population. This work utilized patients' home diary data from a 4-week Phase 2 pharmacokinetics clinical study. The Korean ADOPTION study randomized (1:1) patients with PD and early motor fluctuations treated with up to 600 mg of L-DOPA given 3–4 times daily. The main endpoint was change from baseline in off time in the subgroup of patients receiving 300–400 mg/day L-DOPA at baseline plus OPC 50 mg and in the subgroup receiving >300 mg/day L-DOPA at baseline plus an additional dose of L-DOPA 100 mg. Of the 86 patients included in this subgroup analysis, 39 received OPC 50 mg and 47 L-DOPA 100 mg. At baseline, both L-DOPA total daily dose and LEDD were lower in the L-DOPA 300–400 mg/day plus OPC 50 mg group than in the L-DOPA >300 mg/day plus L-DOPA 100 mg. However, at Week 4, LEDD was similar between the two groups. The mean (±standard error) reduction in off time was approximately three-fold greater for the OPC 50 mg than for the L-DOPA 100 mg group, being -63.0 (14.6) minutes for patients treated with L-DOPA 300–400 mg/day plus OPC 50 mg, and -22.1 (9.3) minutes for those receiving L-DOPA >300 mg/day plus L-DOPA 100 mg. In conclusion, despite similar LEDD, OPC demonstrated a significantly greater reduction in off time when compared to an additional 100 mg L-DOPA dose. The effect of OPC appears to be LEDD independent, suggesting that caution should be exercised when employing LEDD to guide treatment decisions as this does not take into account the timing of each dose, onset, duration of therapeutic effect and individual responsiveness. Additionally, OPC could be used for keeping the L-DOPA dose as low as possible for as long as possible to avoid the development of motor complications which are a significant source of disability.

Keywords: opicapone, levodopa, pharmacokinetics, off-time

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Authors: Chao Jen-Chih, Hsu Kuo-Wei

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Climate change and natural disasters take effect on urban development. It has been discussed urban renewals can prevent natural disasters. Integrating green infrastructure and urban renewals may have great effect on adapting the impact of climate change. To highlight the economic value of green infrastructure development on urban renewals, some strategies need to be carry on to reduce environmental impact. A number of urban renewals studies has been conducted on right transfer, financial risk, urban renewal policy, and public participation. Little research has been devoted on the subject of the economic value of green infrastructure development on urban renewals. The purpose of this study is to investigate the affecting factors on the economic value of green infrastructure development on urban renewals. This study will present the benefits of green infrastructure development and summarize the critical factors of green infrastructure develop on urban renewals for real essence and non-real essence on economic value from literature. Our results indicate that factors of housing price, land value, floor area incentive, and facilitation of the construction industry affect the outcome of real essence economic value. Factors of enhancement of urban disaster prevention, improvement of urban environment and landscape, crime reduction, climate control, pollution reduction, biological diversity, health impacts, and leisure space affects the outcome of non-real essence economic value.

Keywords: economic value, green infrastructure, urban renewals, urban development

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Authors: Navneet Kaur, S. D. Tiwari

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Superparamagnetism is an interesting phenomenon and observed in small particles of magnetic materials. It arises due to a reduction in particle size. In the superparamagnetic state, as the thermal energy overcomes magnetic anisotropy energy, the magnetic moment vector of particles flip their magnetization direction between states of minimum energy. Superparamagnetic nanoparticles have been attracting the researchers due to many applications such as information storage, magnetic resonance imaging, biomedical applications, and sensors. For information storage, thermal fluctuations lead to loss of data. So that nanoparticles should have high blocking temperature. And to achieve this, nanoparticles should have a higher magnetic moment and magnetic anisotropy constant. In this work, the magnetic anisotropy constant of the antiferromagnetic nanoparticles system is determined. Magnetic studies on nanoparticles of NiO (nickel oxide) are reported well. This antiferromagnetic nanoparticle system has high blocking temperature and magnetic anisotropy constant of order 105 J/m3. The magnetic study of NiO nanoparticles in the superparamagnetic region is presented. NiO particles of two different sizes, i.e., 6 and 8 nm, are synthesized using the chemical route. These particles are characterized by an x-ray diffractometer, transmission electron microscope, and superconducting quantum interference device magnetometry. The magnetization vs. applied magnetic field and temperature data for both samples confirm their superparamagnetic nature. The blocking temperature for 6 and 8 nm particles is found to be 200 and 172 K, respectively. Magnetization vs. applied magnetic field data of NiO is fitted to an appropriate magnetic expression using a non-linear least square fit method. The role of particle size distribution and magnetic anisotropy is taken in to account in magnetization expression. The source code is written in Python programming language. This fitting provides us the magnetic anisotropy constant for NiO and other magnetic fit parameters. The particle size distribution estimated matches well with the transmission electron micrograph. The value of magnetic anisotropy constants for 6 and 8 nm particles is found to be 1.42 X 105 and 1.20 X 105 J/m3, respectively. The obtained magnetic fit parameters are verified using the Neel model. It is concluded that the effect of magnetic anisotropy should not be ignored while studying the magnetization process of nanoparticles.

Keywords: anisotropy, superparamagnetic, nanoparticle, magnetization

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Authors: Pradeep Singh, Shrish Verma

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Software fault prediction models are created by using the source code, processed metrics from the same or previous version of code and related fault data. Some company do not store and keep track of all artifacts which are required for software fault prediction. To construct fault prediction model for such company, the training data from the other projects can be one potential solution. The earlier we predict the fault the less cost it requires to correct. The training data consists of metrics data and related fault data at function/module level. This paper investigates fault predictions at early stage using the cross-project data focusing on the design metrics. In this study, empirical analysis is carried out to validate design metrics for cross project fault prediction. The machine learning techniques used for evaluation is Naïve Bayes. The design phase metrics of other projects can be used as initial guideline for the projects where no previous fault data is available. We analyze seven data sets from NASA Metrics Data Program which offer design as well as code metrics. Overall, the results of cross project is comparable to the within company data learning.

Keywords: software metrics, fault prediction, cross project, within project.

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Authors: Vesna Kirandziska, Nevena Ackovska, Ana Madevska Bogdanova

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The problem of emotion recognition is a challenging problem. It is still an open problem from the aspect of both intelligent systems and psychology. In this paper, both voice features and facial features are used for building an emotion recognition system. A Support Vector Machine classifiers are built by using raw data from video recordings. In this paper, the results obtained for the emotion recognition are given, and a discussion about the validity and the expressiveness of different emotions is presented. A comparison between the classifiers build from facial data only, voice data only and from the combination of both data is made here. The need for a better combination of the information from facial expression and voice data is argued.

Keywords: emotion recognition, facial recognition, signal processing, machine learning

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Authors: Sartaj Singh, Amar Singh, Ashok Sharma, Sandeep Kaur

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With upcoming threats in a digital world, we need to work continuously in the area of security in all aspects, from hardware to software as well as data modelling. The rise in social media activities and hunger for data by various entities leads to cybercrime and more attack on the privacy and security of persons. Cryptography has always been employed to avoid access to important data by using many processes. Symmetric key and asymmetric key cryptography have been used for keeping data secrets at rest as well in transmission mode. Various cryptosystems have evolved from time to time to make the data more secure. In this research article, we are studying various cryptosystems in asymmetric cryptography and their application with usefulness, and much emphasis is given to Elliptic curve cryptography involving algebraic mathematics.

Keywords: cryptography, symmetric key cryptography, asymmetric key cryptography

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Authors: Pei-Jyun Hsieh, Cheng-Hsuan Li, Bor-Chen Kuo

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For hyperspectral image classification, feature reduction is an important pre-processing for avoiding the Hughes phenomena due to the difficulty for collecting training samples. Hence, lots of researches developed feature selection methods such as F-score, HSIC (Hilbert-Schmidt Independence Criterion), and etc., to improve hyperspectral image classification. However, most of them only consider the class separability in the original space, i.e., a linear class separability. In this study, we proposed a nonlinear class separability measure based on kernel trick for selecting an appropriate feature subset. The proposed nonlinear class separability was formed by a generalized RBF kernel with different bandwidths with respect to different features. Moreover, it considered the within-class separability and the between-class separability. A genetic algorithm was applied to tune these bandwidths such that the smallest with-class separability and the largest between-class separability simultaneously. This indicates the corresponding feature space is more suitable for classification. In addition, the corresponding nonlinear classification boundary can separate classes very well. These optimal bandwidths also show the importance of bands for hyperspectral image classification. The reciprocals of these bandwidths can be viewed as weights of bands. The smaller bandwidth, the larger weight of the band, and the more importance for classification. Hence, the descending order of the reciprocals of the bands gives an order for selecting the appropriate feature subsets. In the experiments, three hyperspectral image data sets, the Indian Pine Site data set, the PAVIA data set, and the Salinas A data set, were used to demonstrate the selected feature subsets by the proposed nonlinear feature selection method are more appropriate for hyperspectral image classification. Only ten percent of samples were randomly selected to form the training dataset. All non-background samples were used to form the testing dataset. The support vector machine was applied to classify these testing samples based on selected feature subsets. According to the experiments on the Indian Pine Site data set with 220 bands, the highest accuracies by applying the proposed method, F-score, and HSIC are 0.8795, 0.8795, and 0.87404, respectively. However, the proposed method selects 158 features. F-score and HSIC select 168 features and 217 features, respectively. Moreover, the classification accuracies increase dramatically only using first few features. The classification accuracies with respect to feature subsets of 10 features, 20 features, 50 features, and 110 features are 0.69587, 0.7348, 0.79217, and 0.84164, respectively. Furthermore, only using half selected features (110 features) of the proposed method, the corresponding classification accuracy (0.84168) is approximate to the highest classification accuracy, 0.8795. For other two hyperspectral image data sets, the PAVIA data set and Salinas A data set, we can obtain the similar results. These results illustrate our proposed method can efficiently find feature subsets to improve hyperspectral image classification. One can apply the proposed method to determine the suitable feature subset first according to specific purposes. Then researchers can only use the corresponding sensors to obtain the hyperspectral image and classify the samples. This can not only improve the classification performance but also reduce the cost for obtaining hyperspectral images.

Keywords: hyperspectral image classification, nonlinear feature selection, kernel trick, support vector machine

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Authors: Abdelkader T. Ahmed

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Greywater is any wastewater draining from a household including kitchen sinks and bathroom tubs, except toilet wastes. Although this used water may contain grease, food particles, hair, and any number of other impurities, it may still be suitable for reuse after treatment. Greywater reusing serves two purposes including reduction the amount of freshwater needed to supply a household, and reduction the amount of wastewater entering sewer systems. This study aims to investigate and design a simple and cheap unit to treat the greywater in household via using ceramic membranes and reuse it in supplying water for toilet flushing. The study include an experimental program for manufacturing several tablet ceramic membranes from clay and sawdust with three different mixtures. The productivity and efficiency of these ceramic membranes were investigated by chemical and physical tests for greywater before and after filtration through these membranes. Then a treatment unit from this ceramic membrane was designed based on the experimental results of lab tests. Results showed that increase sawdust percent with the mixture increase the flow rate and productivity of treated water but decrease in the same time the water quality. The efficiency of the new ceramic membrane reached 95%. The treatment unit save 0.3 m3/day water for toilet flushing without need to consume them from the fresh water supply network.

Keywords: ceramic membranes, filtration, greywater, wastewater treatment

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Authors: Priyanka Kalra

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Background: Complementary and alternative medicine (CAM) includes various practices like Ayurveda, Yoga & Meditation Acupressure Acupuncture and Reiki. These practices are frequently used by patients with spinal cord injury (SCI). They have shown effectiveness in the management of pain and stress consequently improving overall quality of life post injury. Objective: The goals of the present case series were to evaluate the feasibility of 1) Using of Ayurvedic herbal oil massages in shoulder pain management, 2) Using yoga & meditation on managing the stress in spinal cord injury. Methodology: 15 SCI cases with muscular pain around shoulder were treated with Ayurvedic herbal oil massage for 10 days in CAM Department. Each session consisted of 30 min oil massage followed by 10 min hot towel fomentation. The patients continued regular therapy medications along with CAM. Another 15 SCI cases were treated with yoga and meditation for 15 days 30 min yoga (20 min Asana+ 10 min Pranayam + 15 min Meditation) in isolated yoga room of CAM department. Results: On the VAS scale the patients reported a reduction in their pain score by 70 %. On the PSS scale, the patients reported a reduction in their stress score by 80 %. Conclusion: These case series may encourage more people to explore CAM therapies.

Keywords: spinal cord injury, Ayurveda, complementary and alternative medicine, yoga, meditation

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Authors: Babu Shersad, Partha Banerjee

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Reduction in haemoglobin levels has been implicated to be a cause for reduced exercise tolerance and cardiovascular complications of chronic renal diseases. Trends of hemoglobin levels in patients on haemodialysis could be an indicator of efficacy of hemodialysis and an indicator of quality of life in haemodialysis patients. In the UAE, the rate of growth (of patients on dialysis) is 10 to 15 per cent per year. The primary mode of haemodialysis in the region is based on in-patient hospital-based hemodialysis units. The increase in risk of cardiovascular and cerebrovascular morbidity as well as mortality in pre-dialysis Chronic Renal Disease has been reported. However, data on the health burden on haemodialysis in standalone dialysis facilities is very scarce. This is mainly due to the paucity of ambulatory centres for haemodialysis in the region. AMSA is the first center to offer standalone dialysis in the UAE and a study over a one year period was performed. Patient data was analyzed using a questionnaire for 45 patients with an average of 2.5 dialysis sessions per week. All patients were on chronic haemodialysis as outpatients. The trends of haemoglobin levels as an independent variable were evaluated. These trends were interpreted in comparison with other parameters of renal function (creatinine, uric acid, blood pressure and ferritin). Trends indicate an increase in hemoglobin levels with increased supplementation of iron and erythropoietin over time. The adequacy of hemodialysis shows improvement concomitantly. This, in turn, correlates with better patient outcomes and has a direct impact on morbidity and mortality. This study is a pilot study and further studies are indicated so that objective parameters can be studied and validated for hemodialysis in the region.

Keywords: haemodialysis, haemoglobin in haemodialysis, haemodialysis parameters, erythropoietic agents in haemodialysis

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Authors: Tim Steinhaus, Christian Beidl

Abstract:

Urban air quality and climate protection are two major challenges for future mobility systems. Despite the steady reduction of pollutant emissions from vehicles over past decades, local immission load within cities partially still reaches heights, which are considered hazardous to human health. Although traffic-related emissions account for a major part of the overall urban pollution, modeling the exact interaction remains challenging. In this paper, a novel approach for the determination of the emission-immission interaction on the basis of neural network modeling for traffic induced NO2-immission load within a near-traffic hot-spot scenario is presented. In a detailed sensitivity analysis, the significance of relevant influencing variables on the prevailing NO2 concentration is initially analyzed. Based on this, the generation process of the model is described, in which not only environmental influences but also the vehicle fleet composition including its associated segment- and certification-specific real driving emission factors are derived and used as input quantities. The validity of this approach, which has been presented in the past, is re-examined in this paper using updated data on vehicle emissions and recent immission measurement data. Within the framework of a final scenario analysis, the future development of the immission load is forecast for different developments in the vehicle fleet composition. It is shown that immission levels of less than half of today’s yearly average limit values are technically feasible in hot-spot situations.

Keywords: air quality, emission, emission-immission-interaction, immission, NO2, zero impact

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Authors: Rong-Terng Juang

Abstract:

Autonomous vehicles offer the possibility of significant benefits to social welfare. However, fully automated cars might not be going to happen in the near further. To speed the adoption of the self-driving technologies, many governments worldwide are passing laws requiring data recorders for the testing of autonomous vehicles. Currently, the self-driving vehicle, (e.g., shuttle bus) has to be monitored from a remote control center. When an autonomous vehicle encounters an unexpected driving environment, such as road construction or an obstruction, it should request assistance from a remote operator. Nevertheless, large amounts of data, including images, radar and lidar data, etc., have to be transmitted from the vehicle to the remote center. Therefore, this paper proposes a data compression method of in-vehicle networks for remote monitoring of autonomous vehicles. Firstly, the time-series data are rearranged into a multi-dimensional signal space. Upon the arrival, for controller area networks (CAN), the new data are mapped onto a time-data two-dimensional space associated with the specific CAN identity. Secondly, the data are sampled based on differential sampling. Finally, the whole set of data are encoded using existing algorithms such as Huffman, arithmetic and codebook encoding methods. To evaluate system performance, the proposed method was deployed on an in-house built autonomous vehicle. The testing results show that the amount of data can be reduced as much as 1/7 compared to the raw data.

Keywords: autonomous vehicle, data compression, remote monitoring, controller area networks (CAN), Lidar

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Authors: Apolinar Picado, Steve Alfaro, Rafael Gamero

Abstract:

Energy and exergy analyses of thin-layer drying of pineapple slices (Ananas comosus L.) were conducted in a laboratory tunnel dryer. Drying experiments were carried out at three temperatures (100, 115 and 130 °C) and an air velocity of 1.45 m/s. The effects of drying variables on energy utilisation, energy utilisation ratio, exergy loss and exergy efficiency were studied. The enthalpy difference of the gas increased as the inlet gas temperature increase. It is observed that at the 75 minutes of the drying process the outlet gas enthalpy achieves a maximum value that is very close to the inlet value and remains constant until the end of the drying process. This behaviour is due to the reduction of the total enthalpy within the system, or in other words, the reduction of the effective heat transfer from the hot gas flow to the vegetable being dried. Further, the outlet entropy exhibits a significant increase that is not only due to the temperature variation, but also to the increase of water vapour phase contained in the hot gas flow. The maximum value of the exergy efficiency curve corresponds to the maximum value observed within the drying rate curves. This maximum value represents the stage when the available energy is efficiently used in the removal of the moisture within the solid. As the drying rate decreases, the available energy is started to be less employed. The exergetic efficiency was directly dependent on the evaporation flux and since the convective drying is less efficient that other types of dryer, it is likely that the exergetic efficiency has relatively low values.

Keywords: efficiency, energy, exergy, thin-layer drying

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Authors: P. Chonsin, N. Neelapaichit, N. Piaseu

Abstract:

This household survey aimed to describe food behaviors and nutritional status of households in suburb nearby Bangkok, Thailand. Through convenience sampling, sample included 187 food providers from 125 households in three communities. Data were collected by structured interview and nutritional assessment. Results revealed that majority of the sample were female (68.4 %), aged between 18 to 91 years. The households selected raw foods concerning quality as the first priority (46.5%), cooking for their family members as 91.2%, using seasonings as 71.2%. The most favorite tastes were sweet (19.8%), salty (20.3%), and fatty (1.6%). Food related health problems were hypertension (40.1%), diabetes (26.7%), and dyslipidemia (19.3%). Approximately half of the overall samples (55.1%) and the sample with hypertension (84.5%) had excessive body mass index (BMI). Moreover, one-fourth of the sample with hypertension (25.3%) had salty food preference. Results suggest approaches to promote behavioral modification for sodium reduction particularly in food providers of households with hypertension and excessive BMI.

Keywords: food behavior, nutrition status, household, suburb

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Authors: Chen Zhang, Nan Chen

Abstract:

High-dimensional process monitoring becomes increasingly important in many application domains, where usually the process distribution is unknown and much more complicated than the normal distribution, and the between-stream correlation can not be neglected. However, since the process dimension is generally much bigger than the reference sample size, most traditional nonparametric multivariate control charts fail in high-dimensional cases due to the curse of dimensionality. Furthermore, when the process goes out of control, the influenced variables are quite sparse compared with the whole dimension, which increases the detection difficulty. Targeting at these issues, this paper proposes a new nonparametric monitoring scheme for high-dimensional processes. This scheme first projects the high-dimensional process into several subprocesses using random projections for dimension reduction. Then, for every subprocess with the dimension much smaller than the reference sample size, a local nonparametric control chart is constructed based on the spatial rank test to detect changes in this subprocess. Finally, the results of all the local charts are fused together for decision. Furthermore, after an out-of-control (OC) alarm is triggered, a diagnostic framework is proposed. using the square-root LASSO. Numerical studies demonstrate that the chart has satisfactory detection power for sparse OC changes and robust performance for non-normally distributed data, The diagnostic framework is also effective to identify truly changed variables. Finally, a real-data example is presented to demonstrate the application of the proposed method.

Keywords: random projection, high-dimensional process control, spatial rank, sequential change detection

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Authors: Aditi Taunk, George Iskander, Kitty Ka Kit Ho, Mark Willcox, Naresh Kumar

Abstract:

Bacterial infections on biomaterial implants and medical devices accounts for 60-70% of all hospital acquired infections (HAIs). Treatment or removal of these infected devices results in high patient mortality and morbidity along with increased hospital expenses. In addition, with no effective strategies currently available and rapid development of antibacterial resistance has made device-related infections extremely difficult to treat. Therefore, in this project we have developed biomaterial surfaces using antibacterial compounds that inhibit biofilm formation by interfering with the bacterial communication mechanism known as quorum sensing (QS). This study focuses on covalent attachment of potent quorum sensing (QS) inhibiting compounds, halogenated furanones (FUs) and dihydropyrrol-2-ones (DHPs), onto glass surfaces. The FUs were attached by photoactivating the azide groups on the surface, and the acid functionalized DHPs were immobilized on amine surface via EDC/NHS coupling. The modified surfaces were tested in vitro against pathogenic organisms such as Staphylococcus aureus and Pseudomonas aeruginosa using confocal laser scanning microscopy (CLSM). Successful attachment of compounds on the substrates was confirmed by X-ray photoelectron spectroscopy (XPS) and contact angle measurements. The antibacterial efficacy was assessed, and significant reduction in bacterial adhesion and biofilm formation was observed on the FU and DHP coated surfaces. The activity of the coating was dependent upon the type of substituent present on the phenyl group of the DHP compound. For example, the ortho-fluorophenyl DHP (DHP-2) exhibited 79% reduction in bacterial adhesion against S. aureus and para-fluorophenyl DHP (DHP-3) exhibited 70% reduction against P. aeruginosa. The results were found to be comparable to DHP coated surfaces prepared in earlier study via Michael addition reaction. FUs and DHPs were able to retain their in vitro antibacterial efficacy after covalent attachment via azide chemistry. This approach is a promising strategy to develop efficient antibacterial biomaterials to reduce device related infections.

Keywords: antibacterial biomaterials, biomedical device-related infections, quorum sensing, surface functionalization

Procedia PDF Downloads 268