Search results for: kernel principal component analysis

Authors: Serge Provost, Yishan Zang

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Copulas are currently utilized in finance, reliability theory, machine learning, signal processing, geodesy, hydrology and biostatistics, among several other fields of scientific investigation. It follows from Sklar's theorem that the joint distribution function of a multidimensional random vector can be expressed in terms of its associated copula and marginals. Since marginal distributions can easily be determined by making use of a variety of techniques, we address the problem of securing the distribution of the copula. This will be done by using several approaches. For example, we will obtain bivariate least-squares approximations of the empirical copulas, modify the kernel density estimation technique and propose a criterion for selecting appropriate bandwidths, differentiate linearized empirical copulas, secure Bernstein polynomial approximations of suitable degrees, and apply a corollary to Sklar's result. Illustrative examples involving actual observations will be presented. The proposed methodologies will as well be applied to a sample generated from a known copula distribution in order to validate their effectiveness.

Keywords: copulas, Bernstein polynomial approximation, least-squares polynomial approximation, kernel density estimation, density approximation

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Authors: Fuad A. A. Trayek, Tunku Badariah Tunku Ahmad, Mohamad Sahari Nordin, Mohammed AM Dwikat

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This paper reports on the results of an exploratory factor analysis procedure applied on the e-learning readiness data obtained from a survey of four hundred and seventy-nine (N = 479) teachers from secondary schools in Nablus, Palestine. The data were drawn from a 23-item Likert questionnaire measuring e-learning readiness based on Chapnick's conception of the construct. Principal axis factoring (PAF) with Promax rotation applied on the data extracted four distinct factors supporting four of Chapnick's e-learning readiness dimensions, namely technological readiness, psychological readiness, infrastructure readiness and equipment readiness. Together these four dimensions explained 56% of the variance. These findings provide further support for the construct validity of the items and for the existence of these four factors that measure e-learning readiness.

Keywords: e-learning, e-learning readiness, technological readiness, psychological readiness, principal axis factoring

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Authors: Abderrahmen Aloulou, Younes Boujelbene

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The current paper displays a new method of extracting information content from options prices by eliminating biases caused by daily variation of contract maturity. Based on Kernel regression tool, this non-parametric technique serves to obtain a spectrum of interpolated options with constant maturity horizons from negotiated optional contracts on the S&P TSX 60 index. This method makes it plausible to compare daily risk neutral densities from which extracting time continuous indicators allows the detection traders attitudes’ evolution, such as, belief homogeneity, asymmetry and extreme Risk Perception. Our findings indicate that the applied method contribute to develop effective trading strategies and to adjust monetary policies through controlling trader’s reactions to economic and monetary news.

Keywords: risk neutral densities, kernel, constant maturity horizons, homogeneity, asymmetry and extreme risk perception

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Authors: Rym Ghariani, Younes Boujelbene

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This study aimed to examine the impact of digitalization and supply chain integration on the financial performance of companies in the agro-industrial sector in Tunisia, highlighting the growing importance of digital technologies in modern economies. The results were analyzed using a questionnaire and using principal component analysis, as well as linear regression modeling with SPSS26. The results demonstrate that the digitalization and integration of the supply chain have a significant impact on the financial results of Tunisian agro-industrial companies. In theory, this study provides a better understanding of the effects of digital advancements and supply chain strategies on financial results in this specific area. This study, therefore, studies the relationship between these variables and financial efficiency, highlighting the significant impacts of these technological and strategic elements on the financial results of agro-industrial companies in Tunisia.

Keywords: digitalization, supply chain integration, financial performance, Tunisian agro-industrial sector

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Authors: Rorlinda Yusof, Siti Aishah Hassan, Afifah Mohamad Radzi, Mohd Hakimie Zainal Abidin, Amran Rasli, Inderbir Sandhu

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The purpose of this study is to examine the self-identities of gifted and talented students and the relationship between self-identity and academic accomplishment. A random sample of 300 students enrolled in a secondary education programme at the Pusat GENIUS@pintar Negara was chosen as respondents of a 151-item holistic-identity component development tool. The validity of the instrument was assessed using Principal Components Analysis and Factor Analysis via an inter-Item Correlation Matrix (Loading values 0.44 to 0.86), which resulted in the formation of eight dimensions. The Cronbach's Alpha was calculated to determine the instrument's reliability (the overall result was 0.98). The results showed that students' holistic-identity profiles were relatively high (mean=4.09, standard deviation=0.449). In addition, spiritual identity received the greatest mean score (4.34) out of the eight components of identity investigated, while leadership identity received the lowest mean score (3.88). A conceptual framework for Islamic school leadership is recommended to implement spiritual values without differentiation to harmonize spiritual and intellectual intelligence among all the students. Some benchmarking studies with other centres for gifted and talented students are recommended for further research.

Keywords: holistic self-identity, academic achievement, self-development programme, counselling services, gifted and talented students

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Authors: Masoud Sheidai

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Biologists with a special field of population genetics and phylogeny have different research tasks such as populations’ genetic variability and divergence, species relatedness, the evolution of genetic and morphological characters, and identification of DNA SNPs with adaptive potential. To tackle these problems and reach a concise conclusion, they must use the proper and efficient statistical and bioinformatic methods as well as suitable genetic and morphological characteristics. In recent years application of different bioinformatic and statistical methods, which are based on various well-documented assumptions, are the proper analytical tools in the hands of researchers. The species delineation is usually carried out with the use of different clustering methods like K-means clustering based on proper distance measures according to the studied features of organisms. A well-defined species are assumed to be separated from the other taxa by molecular barcodes. The species relationships are studied by using molecular markers, which are analyzed by different analytical methods like multidimensional scaling (MDS) and principal coordinate analysis (PCoA). The species population structuring and genetic divergence are usually investigated by PCoA and PCA methods and a network diagram. These are based on bootstrapping of data. The Association of different genes and DNA sequences to ecological and geographical variables is determined by LFMM (Latent factor mixed model) and redundancy analysis (RDA), which are based on Bayesian and distance methods. Molecular and morphological differentiating characters in the studied species may be identified by linear discriminant analysis (DA) and discriminant analysis of principal components (DAPC). We shall illustrate these methods and related conclusions by giving examples from different edible and medicinal plant species.

Keywords: GWAS analysis, K-Means clustering, LFMM, multidimensional scaling, redundancy analysis

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Authors: Hamid Mustafa, Huson J. Heather, Kim Eiusoo, McClure Matt, Khalid Javed, Talat Nasser Pasha, Afzal Ali1, Adeela Ajmal, Tad Sonstegard

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Genetic differences associated with speciation, breed formation or local adaptation can help to preserve and effective utilization of animals in selection programs. Analyses of population structure and breed diversity have provided insight into the origin and evolution of cattle. In this study, we used a high-density panel of SNP markers to examine population structure and diversity among ten Pakistani indigenous cattle breeds. In total, 25 individuals from three cattle populations, including Achi (n=08), Bhagnari (n=04) and Cholistani (n=13) were genotyped for 777, 962 single nucleotide polymorphism (SNP) markers. Population structure was examined using the linkage model in the program STRUCTURE. After characterizing SNP polymorphism in the different populations, we performed a detailed analysis of genetic structure at both the individual and population levels. The whole-genome SNP panel identified several levels of population substructure in the set of examined cattle breeds. We further searched for spatial patterns of genetic diversity among these breeds under the recently developed spatial principal component analysis framework. Overall, such high throughput genotyping data confirmed a clear partitioning of the cattle genetic diversity into distinct breeds. The resulting complex historical origins associated with both natural and artificial selection have led to the differentiation of numerous different cattle breeds displaying a broad phenotypic variety over a short period of time.

Keywords: Pakistan, cattle, genetic diversity, population structure

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Authors: Zouhair Haddi, Stephane Delliaux, Jean-Francois Pons, Ismail Kechaf, Jean-Claude De Haro, Mustapha Ouladsine

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Atrial fibrillation (AF) has been considered as the most common cardiac arrhythmia, and a major public health burden associated with significant morbidity and mortality. Nowadays, telemedical approaches targeting cardiac outpatients situate AF among the most challenged medical issues. The automatic, early, and fast AF detection is still a major concern for the healthcare professional. Several algorithms based on univariate analysis have been developed to detect atrial fibrillation. However, the published results do not show satisfactory classification accuracy. This work was aimed at resolving this shortcoming by proposing multivariate data analysis methods for automatic AF detection. Four publicly-accessible sets of clinical data (AF Termination Challenge Database, MIT-BIH AF, Normal Sinus Rhythm RR Interval Database, and MIT-BIH Normal Sinus Rhythm Databases) were used for assessment. All time series were segmented in 1 min RR intervals window and then four specific features were calculated. Two pattern recognition methods, i.e., Principal Component Analysis (PCA) and Learning Vector Quantization (LVQ) neural network were used to develop classification models. PCA, as a feature reduction method, was employed to find important features to discriminate between AF and Normal Sinus Rhythm. Despite its very simple structure, the results show that the LVQ model performs better on the analyzed databases than do existing algorithms, with high sensitivity and specificity (99.19% and 99.39%, respectively). The proposed AF detection holds several interesting properties, and can be implemented with just a few arithmetical operations which make it a suitable choice for telecare applications.

Keywords: atrial fibrillation, multivariate data analysis, automatic detection, telemedicine

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Authors: Ashish Kumar Dwivedi

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Uttar Pradesh is largest part of Indian Federal system encapsulating twenty two million populations and has huge cultural, economic and religious diversity. The newly elected 18 months old state leadership of Uttar Pradesh has envisaged and initiated various proactive strides for the public grievance redressal and inclusive development schemes for all the sections of population from its very day of assumption of the office by Hon’ble Chief Minster Shri Yogi Adtiyanath. These initiatives also include Departmental responses via social media handles as Twitter, Facebook Page, and Web interaction. In the same course, every department of state government has been guided for the correct usage of verified social media handle separately and in co-ordination with other departments. These guidelines included making new WhatsApp groups to connect technocrats and politicians to communicate on common platform. Minister for Department of Infrastructure and Industrial Development, Shri Satish Mahana is a very popular leader and very intuitive statesman, has thousands of followers on social media and his accounts receive almost three hundred individually mentioned notifications from the various parts of Uttar Pradesh. These notifications primarily include problems related to livelihood and grievances concerned to department. To address these communications, a body of five experts has been set who are actively responding on various levels and increase bureaucratic engagements with marginalized sections of society. With reference to above background, this piece of research was conducted to analyze, categorize and derive effective implementation of public policies via social media platforms. This act of responsiveness has brought positive change in the mindset of population for the government, which was missed earlier. Department of Industrial Development in the Government is also inclined to attract investors aiming to become first trillion-dollar economy of India henceforth department also organized two major successful events in last one year. These events were also frame worked on social media platform to update 2.5 million population of state who is actively using social media in many ways. To analyze change scientifically, this study has been conducted and big data has been collected from October 2017 to September 2018 from the departmental social media handles as Twitter, Facebook, and emails. For this data, a statistical study has been conducted to analyze sentiments and expectations, specific and common requirement of communities, nature of grievances and their effective elucidation fitted into government policies. The control sample has also been taken from previous government activities to analyze the change. The statistical study used tools such as correlation study and principal component analysis. Also in this research communication, the modus operandi of grievance redressal, proliferation of government policies, connections to their beneficiaries and quick response procedure will be discussed.

Keywords: correlation study, principal component analysis, bureaucratic engagements, social media

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Authors: Rym Ghariani, Younes Boujelbene

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In contemporary times, global technological advancements, particularly those in the realm of digital technology, have emerged as pivotal instruments for enterprises in fostering viable partnerships and forging meaningful alliances with other firms. The advent of these digital innovations is poised to revolutionize nearly every facet and operation within corporate entities. The primary objective of this study is to explore the correlation between digitization, integration of supply chains, and the financial efficacy of the agro-industrial sector in Tunisia. To accomplish this, data collection employed a questionnaire as the primary research instrument. Subsequently, the research queries were addressed, and hypotheses were examined by subjecting the gathered data to principal component analysis and linear regression modeling, facilitated by the utilization of SPSS26 software. The findings revealed that digitalization within the supply chain, along with external supply chain integration, exerted discernible impacts on the financial performance of the organization.

Keywords: digitalization, supply chain integration, financial performance, Tunisian agro-industrial sector

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Authors: Khalid Kaddoura, Tarek Zayed

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Sewer networks are constructed to protect the communities and the environment from any contact with the sewer mediums. Pipelines, being laterals or sewer mains, and manholes form the huge underground infrastructure in every urban city. Due to the sewer networks importance, the infrastructure asset management field has extensive advancement in condition assessment and rehabilitation decision models. However, most of the focus was devoted to pipelines giving little attention toward manholes condition assessment. In fact, recent studies started to emerge in this area to preserve manholes from any malfunction. Therefore, the main objective of this study is to propose a condition assessment model for sewer manholes. The model divides the manhole into several components and determines the relative importance weight of each component using the Analytic Network Process (ANP) decision-making method. Later, the condition of the manhole is computed by aggregating the condition of each component with its corresponding weight. Accordingly, the proposed assessment model will enable decision-makers to have a final index suggesting the overall condition of the manhole and a backward analysis to check the condition of each component. Consequently, better decisions are made pertinent to maintenance, rehabilitation, and replacement actions.

Keywords: Analytic Network Process (ANP), condition assessment, decision-making, manholes

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Authors: Tao Peng, Jing Zhao, Yanqing Xu, Jing Cai

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Due to missing/ambiguous boundaries between the prostate and neighboring structures, the presence of shadow artifacts, as well as the large variability in prostate shapes, ultrasound prostate segmentation is challenging. To handle these issues, this paper develops a hybrid method for ultrasound prostate segmentation by combining an optimized closed principal curve-based method and the evolutionary neural network; the former can fit curves with great curvature and generate a contour composed of line segments connected by sorted vertices, and the latter is used to express an appropriate map function (represented by parameters of evolutionary neural network) for generating the smooth prostate contour to match the ground truth contour. Both qualitative and quantitative experimental results showed that our proposed method obtains accurate and robust performances.

Keywords: ultrasound prostate segmentation, optimized closed polygonal segment method, evolutionary neural network, smooth mathematical model, principal curve

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Authors: Qazi Mahdia Ghyas

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Electronic Government Procurement (e-GP) has implemented in Bangladesh to ensure the good Governance. e-GP has transformed Bangladesh's procurement process electronically. But, to our best knowledge, there is no study to understand the key features of e-GP in Bangladesh. So, this study tries to identify the features of performance improvement after implementing an e-GP system that will help for further improvements. Data was collected from the PROMIS Overall Report (Central Procurement Technical Unit website) for the financial year from Q1 _July- Sep 2015-16 to Q4 _Apr- Jun 2021-22. This study did component factor analysis on KPIs and found nineteen KPIs that are statistically significant and represent time savings, efficiency, accountability, anti-corruption and compliance key features in procurement activities of e-GP. Based on the analysis, some practical measures have been recommended for better improvement of e-GP. This study has some limitations. Because of having multicollinearity issues, all the 42 KPIs (except 19) did not show a good fit for component factor analysis.

Keywords: public procurement, electronic government procurement, KPI, performance evaluation

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Authors: Arthur C. Tsai, Chii-Shyang Kuo, Vincent S. C. Chien, Michelle Liou, Philip E. Cheng

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Meditation enhances mental abilities and it is an antidote to anxiety. However, very little is known about brain mechanisms and cortico-subcortical interactions underlying meditation-induced anxiety relief. In this study, the changes of phase-amplitude coupling (PAC) in which the amplitude of the beta frequency band were modulated in phase with delta rhythm were investigated after eight-week of meditation training. The study hypothesized that through a concentrate but relaxed mental training the delta-beta coupling in the frontal regions is attenuated. The delta-beta coupling analysis was applied to within and between maximally-independent component sources returned from the extended infomax independent components analysis (ICA) algorithm on the continuous EEG data during mediation. A unique meditative concentration task through relaxing body and mind was used with a constant level of moderate mental effort, so as to approach an ‘emptiness’ meditative state. A pre-test/post-test control group design was used in this study. To evaluate cross-frequency phase-amplitude coupling of component sources, the modulation index (MI) with statistics to calculate circular phase statistics were estimated. Our findings reveal that a significant delta-beta decoupling was observed in a set of frontal regions bilaterally. In addition, beta frequency band of prefrontal component were amplitude modulated in phase with the delta rhythm of medial frontal component.

Keywords: phase-amplitude coupling, ICA, meditation, EEG

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Authors: Boulis M. Ibrahim, Iordanis A. Kalaitzoglou

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A micro structural pricing model is proposed in which price components account for learning by incorporating changing expectations of the trading intensity and the risk level of incoming trades. An analysis of European carbon futures transactions finds expected trading intensity to increase the information component and decrease the liquidity component of price changes, but at different rates. Among the results, the expected persistence in trading intensity explains the majority of the auto correlations in the level and the conditional volatility of price changes, helps predict hourly patterns in the bid–ask spread and differentiates between the impact of buy versus sell and continuing versus reversing trades.

Keywords: CO2 emission allowances, market microstructure, duration, price discovery

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Authors: Alaa H. J. Al-Rkaby, A. Chegenizadeh, H. R. Nikraz

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Anisotropy is one of the major aspects that affect soil behavior, and extensive efforts have investigated its effect on the mechanical properties of soil. However, very little attention has been given to the combined effect of anisotropy and fine contents. Therefore, in this paper, the anisotropic strength of sand containing different fine content (F) of 5%, 10%, 15%, and 20%, was investigated using hollow cylinder tests under different principal stress directions of α = 0° and α = 90°. For a given principal stress direction (α), it was found that increasing fine content resulted in decreasing deviator stress (q). Moreover, results revealed that all fine contents showed anisotropic strength where there is a clear difference between the strength under 0° and the strength under 90°. This anisotropy was greatest under F = 5% while it decreased with increasing fine contents, particularly at F = 10%. Mixtures with low fine content show low contractive behavior and tended to show more dilation. Moreover, all sand-clay mixtures exhibited less dilation and more compression at α = 90° compared with that at α = 0°.

Keywords: anisotropy, principal stress direction, fine content, hollow cylinder sample

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Authors: Anton Hristozov, Eric Matson, Eric Dietz, Marcus Rogers

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Components are heavily used in many software systems, including robotics systems. The growth of sophistication and diversity of new capabilities for robotic systems presents new challenges to their architectures. Their complexity is growing exponentially with the advent of AI, smart sensors, and the complex tasks they have to accomplish. Such complexity requires a more rigorous approach to the creation, use, and interoperability of software components. The issue is exacerbated because robotic systems are becoming more and more reliant on third-party components for certain functions. In order to achieve this kind of interoperability, including dynamic component replacement, we need a way to standardize their interfaces. A formal approach is desperately needed to specify what an interface of a robotic software component should contain. This study performs an analysis of the issue and presents a universal and generic approach to standardizing component interfaces for robotic systems. Our approach is inspired by well-established robotic architectures such as ROS, PX4, and Ardupilot. The study is also applicable to other software systems that share similar characteristics with robotic systems. We consider the use of JSON or Domain Specific Languages (DSL) development with tools such as Antlr and automatic code and configuration file generation for frameworks such as ROS and PX4. A case study with ROS2 is presented as a proof of concept for the proposed methodology.

Keywords: CPS, robots, software architecture, interface, ROS, autopilot

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Authors: Daningsih Sulaeman, Achmad Sjarmidi, Djoko T. Iskandar

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Komodo National Park can be associated with the implementation of ecotourism program. The result of Principal Components Analysis is synthesized, tested, and compared to the basic concept of ecotourism with some field adjustments. Principal aspects of professional management should involve ecotourism and wildlife welfare. The awareness should be focused on the future of the Natural Park as 7^th Wonder Natural Heritage and its wildlife components, free from human wastes and beneficial to wildlife and local people. According to perceptions and expectations of visitors from various results of tourism programs, the visitor’s perceptions showed that the tourism management in Komodo National Park should pay more attention to visitor's satisfaction and expectation and gives positive impact directly to the ecosystem sustainability, local community and transparency to the conservation program.

Keywords: 7th wonders of nature, ecotourism, Komodo dragon, visitor’s perceptions, wildlife management

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Authors: Ergun Guntekin

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Elastic constants of Fir wood (Abies cilicica) have been investigated by means of ultrasound and compression tests. Three modulus of elasticity in principal directions (EL, ER, ET), six Poisson’s ratios (ʋLR, ʋLT, ʋRT, ʋTR, ʋRL, ʋTL) and three shear modules (GLR, GRT, GLT) were determined. 20 x 20 x 60 mm samples were conditioned at 65 % relative humidity and 20ºC before testing. Three longitudinal and six shear wave velocities propagating along the principal axes of anisotropy, and additionally, three quasi-shear wave velocities at 45° angle with respect to the principal axes of anisotropy were measured. 2.27 MHz longitudinal and 1 MHz shear sensors were used for obtaining sound velocities. Stress-strain curves of the samples in compression tests were obtained using bi-axial extensometer in order to calculate elastic constants. Test results indicated that most of the elastic constants determined in the study are within the acceptable range. Although elastic constants determined from ultrasound are usually higher than those determined from compression tests, the values of EL and GLR determined from compression tests were higher in the study. The results of this study can be used in the numerical modeling of elements or systems under load using Fir wood.

Keywords: compression tests, elastic constants, fir wood, ultrasound

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Authors: Josselyn Mata Calidonio, Arianna I. Maddox, Kimberly Hamad-Schifferli

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Rapid diagnostics are critical infectious disease tools that are designed to detect a known biomarker using antibodies specific to that biomarker. However, a way to detect unknown viruses has not yet been achieved in a paper test format. We describe here a route to make an adaptable paper immunoassay that can detect an unknown biomarker, demonstrating it on SARS-CoV-2 variants. The immunoassay repurposes cross-reactive antibodies raised against the alpha variant. Gold nanoparticles of two different colors conjugated to two different antibodies create a colorimetric signal, and machine learning of the resulting colorimetric pattern is used to train the assay to discriminate between variants of alpha and Omicron BA.5. By using principal component analysis, the colorimetric test patterns can pick up and discriminate an unknown that it has not encountered before, Omicron BA.1. The test has an accuracy of 100% and a potential calculated discriminatory power of 900. We show that it can be used adaptively and that it can be used to pick up emerging variants without the need to raise new antibodies.

Keywords: adaptive immunoassay, detecting unknown viruses, gold nanoparticles, paper immunoassay, repurposing antibodies

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Authors: Baso Hamdani, Arniati Massinai, Jamaluddin Jompa

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Coral diseases are one of the factors affect reef degradation. This research had analysed the progression rate, incidence, and prevalence of Black Band Disease (BBD) on stony coral (Pachyseris sp.) in relation to the environmental parameters (pH, nitrate, phospate, Dissolved Organic Matter (DOM), and turbidity). The incidence of coral disease was measured weekly for 6 weeks using Belt Transect Method. The progression rate of BBD was measured manually. Furthermore, the prevalence and incidence of BBD were calculated each colonies infected. The relationship between environmental parameters and the progression rate, prevalence and incidence of BBD was analysed by Principal Component Analysis (PCA). The results showed the average of progression rate is 0,07 ± 0,02 cm/ hari. The prevalence of BBD increased from 0,92% - 19,73% in 7 weeks observation with the average incidence of new infected colonies coral 0,2 - 0,65 colony/day The environment factors which important were pH, Nitrate, Phospate, DOM, and Turbidity.

Keywords: progression rate, incidence, prevalence, Black Band Disease, Barranglompo

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Authors: Saul Ngarava, Leocadia Zhou, Nomakhaya Monde

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Water crises have the fourth most significant societal impact after weapons of mass destruction, climate change, and extreme weather conditions, ahead of natural disasters. Intricacies between women and water are central to achieving the 2030 Sustainable Development Goals (SDGs). The majority of the 1.2 billion poor people worldwide, with two-thirds being women, and mostly located in Sub Sahara Africa (SSA) and South Asia, do not have access to safe and reliable sources of water. There exist gendered differences in water security based on the division of labour associating women with water. Globally, women and girls are responsible for water collection in 80% of the households which have no water on their premises. Women spend 16 million hours a day collecting water, while men and children spend 6 million and 4 million per day, respectively, which is time foregone in the pursuit of other livelihood activities. Due to their proximity and activities concerning water, women are vulnerable to water insecurity through exposures to water-borne diseases, fatigue from physically carrying water, and exposure to sexual and physical harassment, amongst others. Proximity to treated water and their wellbeing also has an effect on their sensitivity and adaptive capacity to water insecurity. The great distances, difficult terrain and heavy lifting expose women to vulnerabilities of water insecurity. However, few studies have quantified the vulnerabilities and burdens on women, with a few taking a phenomenological qualitative approach. Vulnerability studies have also been scanty in the water security realm, with most studies taking linear forms of either quantifying exposures, sensitivities or adaptive capacities in climate change studies. The current study argues for the need for a water insecurity vulnerability assessment, especially for women into research agendas as well as policy interventions, monitoring, and evaluation. The study sought to identify and provide pathways through which female-headed households were water insecure in South Africa, the 30th driest country in the world. This was through linking the drinking water decision as well as the vulnerability frameworks. Secondary data collected during the 2016 General Household Survey (GHS) was utilised, with a sample of 5928 female-headed households. Principal Component Analysis and Structural Equation Modelling were used to analyse the data. The results show dynamic relationships between water characteristics and water treatment. There were also associations between water access and wealth status of the female-headed households. Association was also found between water access and water treatment as well as between wealth status and water treatment. The study concludes that there are dynamic relationships in water insecurity (exposure, sensitivity, and adaptive capacity) for female-headed households in South Africa. The study recommends that a multi-prong approach is required in tackling exposures, sensitivities, and adaptive capacities to water insecurity. This should include capacitating and empowering women for wealth generation, improve access to water treatment equipment as well as prioritising the improvement of infrastructure that brings piped and safe water to female-headed households.

Keywords: gender, principal component analysis, structural equation modelling, vulnerability, water insecurity

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Authors: R. J. Kuo, W. C. Cheng, W. C. Lien, T. J. Yang

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Taiwan is a hyper endemic area for the Hepatitis B virus (HBV). The estimated total number of HBsAg carriers in the general population who are more than 20 years old is more than 3 million. Therefore, a case record review is conducted from January 2003 to June 2007 for all patients with a diagnosis of acute hepatitis who were admitted to the Emergency Department (ED) of a well-known teaching hospital. The cost for the use of medical resources is defined as the total medical fee. In this study, principal component analysis (PCA) is firstly employed to reduce the number of dimensions. Support vector regression (SVR) and artificial neural network (ANN) are then used to develop the forecasting model. A total of 117 patients meet the inclusion criteria. 61% patients involved in this study are hepatitis B related. The computational result shows that the proposed PCA-SVR model has superior performance than other compared algorithms. In conclusion, the Child-Pugh score and echogram can both be used to predict the cost of medical resources for patients with acute hepatitis in the ED.

Keywords: acute hepatitis, medical resource cost, artificial neural network, support vector regression

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Authors: Jessyca A. Bessa, Darlan A. Barroso, Jonas P. Reges, Auzuir R. Alexandria

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This work aims to study the rocker. This is a device of the suspension of Formula SAE vehicle that receives efforts from the motion scrolling of the vehicle and transmits them to the chassis frame minimized by a momentum ratio and smoothed by the set spring - damper. A review of parameters used in vehicle dynamics and a geometric analysis of the forces and stresses caused by such was carried out. The main function of the rocker is to reduce the force transmitted to the frame due to movement of rolling and subsequent application of the suspension. This functions is taken as satisfactory, since the force applied to the wheel and which would be transmitted to the chassis is reduced from 3833.9N to 3496.48N. From these values can be further more detailed simulations using the finite element method aimed at mass reduction or even rocker manufacturing feasibility aluminum. Then, the analysis by the finite element method was applied. This analysis uses the theory of discretization of systems and examines the strength of the component based on the distortion energy, determining the maximum straining experienced by the component and the region of higher demand.

Keywords: rocker, suspension, the finite element method, mechatronics engineering

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Authors: Bikshalu Kalagadda, Srikanth Rangu

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The important object of images segmentation is to extract objects with respect to some input features. One of the important methods for image segmentation is Level set method. Generally medical images and synthetic images with low contrast of pixel profile, for such images difficult to locate interested features in images. In conventional level set function, develops irregularity during its process of evaluation of contour of objects, this destroy the stability of evolution process. For this problem a remedy is proposed, a new hybrid algorithm is Clustering Level Set Evolution. Kernel fuzzy particles swarm optimization clustering with the Distance Regularized Level Set (DRLS) and Selective Binary, and Gaussian Filtering Regularized Level Set (SBGFRLS) methods are used. The ability of identifying different regions becomes easy with improved speed. Efficiency of the modified method can be evaluated by comparing with the previous method for similar specifications. Comparison can be carried out by considering medical and synthetic images.

Keywords: segmentation, clustering, level set function, re-initialization, Kernel fuzzy, swarm optimization

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Authors: Payal Patel

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Textile machinery is one of the fastest evolving areas which has an application of mechanical engineering. The modular approach towards the processing right from the stage of cotton to the fabric, allows us to observe the result of each process on its input. Cost and space being the major constraints. The flyer is a component of roving machine, which is used as a part of spinning process. In the present work using the application of Hyper Works, the flyer arm has been modified which saves the material used for manufacturing the flyer. The size optimization of the flyer is carried out with the objective of reduction in weight under the constraints of standard operating conditions. The new design of the flyer is proposed and validated using the module of HyperWorks which is equally strong, but light weighted compared to the existing design. Dynamic balancing of the optimized model is carried out to align a principal inertia axis with the geometric axis of rotation. For the balanced geometry of flyer, air resistance is obtained theoretically and with Gambit and Fluent. Static analysis of the balanced geometry has been done to verify the constraint of operating condition. Comparison of weight, deflection, and factor of safety has been made for different aluminum alloys.

Keywords: flyer, size optimization, textile, weight

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Authors: Timothy E. Frank, Josh R. Aldred, Sophie B. Boulware, Michelle K. Cabonce, Justin H. White

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Materials degrade at different rates in different environments depending on factors such as temperature, aridity, salinity, and solar radiation. Therefore, predicting asset longevity depends, in part, on the environmental conditions to which the asset is exposed. Heating, ventilation, and air conditioning (HVAC) systems are critical to building operations yet are responsible for a significant proportion of their energy consumption. HVAC energy use increases substantially with slight operational inefficiencies. Understanding the environmental influences on HVAC degradation in detail will inform maintenance schedules and capital investment, reduce energy use, and increase lifecycle management efficiency. HVAC inspection records spanning 14 years from 21 locations across the United States were compiled and associated with the climate conditions to which they were exposed. Three environmental features were explored in this study: average high temperature, average low temperature, and annual precipitation, as well as four non-environmental features. Initial insights showed no correlations between individual features and the rate of HVAC component degradation. Using neighborhood component analysis, however, the most critical features related to degradation were identified. Two models were considered, and results varied between them. However, longitude and latitude emerged as potentially the best predictors of average HVAC component degradation. Further research is needed to evaluate additional environmental features, increase the resolution of the environmental data, and develop more robust models to achieve more conclusive results.

Keywords: climate, degradation, HVAC, neighborhood component analysis

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Authors: Rachid Belhachemi, Pierre Rostan, Alexandra Rostan

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This paper develops a discrete-time option pricing model for index options. The model consists of two key ingredients. First, daily stock return innovations are driven by a continuous hidden threshold mixed skew-normal (HTSN) distribution which generates conditional non-normality that is needed to fit daily index return. The most important feature of the HTSN is the inclusion of a latent state variable with a continuum of states, unlike the traditional mixture distributions where the state variable is discrete with little number of states. The HTSN distribution belongs to the class of univariate probability distributions where parameters of the distribution capture the dependence between the variable of interest and the continuous latent state variable (the regime). The distribution has an interpretation in terms of a mixture distribution with time-varying mixing probabilities. It has been shown empirically that this distribution outperforms its main competitor, the mixed normal (MN) distribution, in terms of capturing the stylized facts known for stock returns, namely, volatility clustering, leverage effect, skewness, kurtosis and regime dependence. Second, heteroscedasticity in the model is captured by a threeexogenous-factor GARCH model (GARCHX), where the factors are taken from the principal components analysis of various world indices and presents an application to option pricing. The factors of the GARCHX model are extracted from a matrix of world indices applying principal component analysis (PCA). The empirically determined factors are uncorrelated and represent truly different common components driving the returns. Both factors and the eight parameters inherent to the HTSN distribution aim at capturing the impact of the state of the economy on price levels since distribution parameters have economic interpretations in terms of conditional volatilities and correlations of the returns with the hidden continuous state. The PCA identifies statistically independent factors affecting the random evolution of a given pool of assets -in our paper a pool of international stock indices- and sorting them by order of relative importance. The PCA computes a historical cross asset covariance matrix and identifies principal components representing independent factors. In our paper, factors are used to calibrate the HTSN-GARCHX model and are ultimately responsible for the nature of the distribution of random variables being generated. We benchmark our model to the MN-GARCHX model following the same PCA methodology and the standard Black-Scholes model. We show that our model outperforms the benchmark in terms of RMSE in dollar losses for put and call options, which in turn outperforms the analytical Black-Scholes by capturing the stylized facts known for index returns, namely, volatility clustering, leverage effect, skewness, kurtosis and regime dependence.

Keywords: continuous hidden threshold, factor models, GARCHX models, option pricing, risk-premium

Procedia PDF Downloads 295

Authors: Sanja O. Podunavac-Kuzmanović, Lidija R. Jevrić, Strahinja Z. Kovačević

Abstract:

Benzimidazoles are a group of compounds with significant antibacterial, antifungal and anticancer activity. The studied compounds consist of the main benzimidazole structure with different combinations of substituens. This study is based on the two-dimensional and three-dimensional molecular modeling and calculation of molecular descriptors (physicochemical and lipophilicity descriptors) of structurally diverse benzimidazoles. Molecular modeling was carried out by using ChemBio3D Ultra version 14.0 software. The obtained 3D models were subjected to energy minimization using molecular mechanics force field method (MM2). The cutoff for structure optimization was set at a gradient of 0.1 kcal/Åmol. The obtained set of molecular descriptors was used in principal component analysis (PCA) of possible similarities and dissimilarities among the studied derivatives. After the molecular modeling, the quantitative structure-property relationship (QSPR) analysis was applied in order to get the mathematical models which can be used in prediction of pKb values of structurally similar benzimidazoles. The obtained models are based on statistically valid multiple linear regression (MLR) equations. The calculated cross-validation parameters indicate the high prediction ability of the established QSPR models. This study is financially supported by COST action CM1306 and the project No. 114-451-347/2015-02, financially supported by the Provincial Secretariat for Science and Technological Development of Vojvodina.

Keywords: benzimidazoles, chemometrics, molecular modeling, molecular descriptors, QSPR

Procedia PDF Downloads 281

Authors: Chro Kawyan

Abstract:

Endothelin-1 (ET-1), the principal individual from the newfound mammalian endothelin group of organically dynamic peptides, was initially distinguished as a 21 buildup powerful vasoconstrictor peptide in vascular endothelial cells. However, it has since been demonstrated to have a wide range of pharmacological activities in tissues both inside and outside the cardiovascular system. Additionally, peptides that have a striking resemblance to ET-1 have been identified as the primary toxic component of snake venom. In addition, late examinations have proposed that warm blooded creatures, including people, produce three unmistakable individuals from this peptide family, ET-1, ET-2 and ET-J, which might have various profiles of organic action and may follow up on particular subtypes of endothelin receptor. Masashi Yanagisawa and Tomoh Masaki survey the ongoing status of the organic chemistry and sub-atomic science of endothelin.

Keywords: thelin, microbiology, molecular biology, cell

Procedia PDF Downloads 66