Search results for: rabbits and principal component analysis

Authors: Ying Ge

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Wetlands are one of the essential types of ecosystems in the world. They are of great value to human society thanks to their special ecosystem functions and services, such as protecting biodiversity, adjusting hydrology and climate, providing essential habitats and, products and tourism resources. However, wetlands worldwide are degrading severely due to climate change, accelerated urbanization, and rapid economic development. Both nature and human factors drive wetland change, and the influences are variable from wetland types. Thus, the objectives of this study were to (1) to compare the changes in China’s wetland area during the three decades rapid growth period (1978-2008); (2) to analyze the effects of social economy and environmental factors on wetlands change (area loss and change of wetland types) in China during the high-speed economic development. The socio-economic influencing factors include population, income, education, development of agriculture, industry, infrastructure, wastewater amount, etc. Several statistical methods (canonical correlation analysis, principal component analysis, and regression analysis) were employed to analyze the relationship between socio-economic indicators and wetland area change. This study will determine the relevant driving socio-economic factors on wetland changes, which is of great significance for wetland protection and management.

Keywords: socioeconomic effects, China, wetland change, wetland type

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Authors: Robert Rusinek, Marek Gancarz, Jolanta Wawrzyniak, Marzena Gawrysiak-Witulska, Dariusz Wiącek, Agnieszka Nawrocka

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Investigations were performed to examine the possibility of using an electronic nose to monitor the development of fungal microflora during the first eighteen days of rapeseed storage. The Cyranose 320 device with polymer-composite sensors was used. Each sample of infected material was divided into three parts, and the degree of spoilage was measured in three ways: analysis of colony forming units (CFU), determination of ergosterol content (ERG), and measurement with the eNose. Principal component analysis (PCA) was performed on the generated patterns of signals, and six groups of different spoilage levels were isolated. The electronic nose with polymer-composite sensors under laboratory conditions distinguished between species of spoiled and unspoiled seeds with 100% accuracy. Despite some minor differences in the CFU and ergosterol content, the electronic nose provided responses correctly corresponding to the level of spoilage with 85% accuracy. Therefore, the main conclusion from the study is that the electronic nose is a promising tool for quick and non-destructive detection of the level of oil seed spoilage. The research was supported by the National Centre for Research and Development (NCBR), Grant No. PBS2/A8/22/2013.

Keywords: colony forming units, electronic nose, ergosterol, rapeseed

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Authors: M. E. Khiav, D. J. Borah, H. T. S. Santos, V. T. Faria

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For automobile companies, it has become very important to ensure sound quality in manufacturing and assembling in order to prevent occurrence of defects and to reduce the amount of parts replacements to be done in the service centers during the warranty period. Statistical Process Control (SPC) is widely used as the tool to analyze the quality of such processes and plays a significant role in the improvement of the processes by identifying the patterns and the location of the defects. In this paper, a case study has been conducted on an Iranian automobile company. This paper performs a quality analysis of a particular component called “Internal Bearing for the Back Wheel” of a particular car model, manufactured by the company, based on the 10 million data received from its service centers located all over the country. By creating control charts including X bar–S charts and EWMA charts, it has been observed after the year 2009, the specific component underwent frequent failures and there has been a sharp dip in the average distance covered by the cars till the specific component requires replacement/maintenance. Correlation analysis was performed to find out the reasons that might have affected the quality of the specific component in all the cars produced by the company after the year 2009. Apart from manufacturing issues, some political and environmental factors have been identified to have a potential impact on the quality of the component. A maiden attempt has been made to analyze the quality issues within an Iranian automobile manufacturer; such issues often get neglected in developing countries. The paper also discusses the possibility of political scenario of Iran and the country’s environmental conditions affecting the quality of the end products, which not only strengthens the extant literature but also provides a new direction for future research.

Keywords: capability analysis, car manufacturing, statistical process control, quality control, quality tools

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

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The New Zealand White breed of rabbit is one of the most commonly used, well adapted exotic breeds in India. Earlier studies were limited only to analyze the environmental factors affecting the growth and reproductive performance. In the present study, the population of the New Zealand White rabbits in a closed herd was evaluated for its genetic structure. Data on pedigree information (n=2508) for 18 years (1995-2012) were utilized for the study. Pedigree analysis and the estimates of population genetic parameters based on gene origin probabilities were performed using the software program ENDOG (version 4.8). The analysis revealed that the mean values of generation interval, coefficients of inbreeding and equivalent inbreeding were 1.489 years, 13.233 percent and 17.585 percent, respectively. The proportion of population inbred was 100 percent. The estimated mean values of average relatedness and the individual increase in inbreeding were 22.727 and 3.004 percent, respectively. The percent increase in inbreeding over generations was 1.94, 3.06 and 3.98 estimated through maximum generations, equivalent generations, and complete generations, respectively. The number of ancestors contributing the most of 50% genes (fₐ₅₀) to the gene pool of reference population was 4 which might have led to the reduction in genetic variability and increased amount of inbreeding. The extent of genetic bottleneck assessed by calculating the effective number of founders (fₑ) and the effective number of ancestors (fₐ), as expressed by the fₑ/fₐ ratio was 1.1 which is indicative of the absence of stringent bottlenecks. Up to 5th generation, 71.29 percent pedigree was complete reflecting the well-maintained pedigree records. The maximum known generations were 15 with an average of 7.9 and the average equivalent generations traced were 5.6 indicating of a fairly good depth in pedigree. The realized effective population size was 14.93 which is very critical, and with the increasing trend of inbreeding, the situation has been assessed to be worse in future. The proportion of animals with the genetic conservation index (GCI) greater than 9 was 39.10 percent which can be used as a scale to use such animals with higher GCI to maintain balanced contribution from the founders. From the study, it was evident that the herd was completely inbred with very high inbreeding coefficient and the effective population size was critical. Recommendations were made to reduce the probability of deleterious effects of inbreeding and to improve the genetic variability in the herd. The present study can help in carrying out similar studies to meet the demand for animal protein in developing countries.

Keywords: effective population size, genetic structure, pedigree analysis, rabbit genetics

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Authors: Raef Cherif, Houssine Bakkali, Wafaa El Khatiri, Yacine Yaddaden

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The analysis of how vibrations are transmitted between components is required in many engineering applications. Transfer path analysis (TPA) has been a valuable engineering tool for solving Noise, Vibration, and Harshness (NVH problems using sub-structuring applications. The most challenging part of a TPA analysis is estimating the equivalent forces at the contact points between the active and the passive side. Component TPA in situ Method calculates these forces by inverting the frequency response functions (FRFs) measured at the passive subsystem, relating the motion at indicator points to forces at the interface. However, matrix inversion could pose problems due to the ill-conditioning of the matrices leading to inaccurate results. This paper establishes a TPA model for an academic system consisting of two plates linked by four springs. A numerical study has been performed to improve the interface forces identification. Several parameters are studied and discussed, such as the singular value rejection and the number and position of indicator points chosen and used in the inversion matrix.

Keywords: transfer path analysis, matrix inverse method, indicator points, SVD decomposition

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Authors: Lavanya Srinivasan

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Biometric identification is to identify unique features in a person like fingerprints, iris, ear, and voice recognition that need the subject's permission and physical contact. Gait biometric is used to identify the unique gait of the person by extracting moving features. The main advantage of gait biometric to identify the gait of a person at a distance, without any physical contact. In this work, the gait biometric is used for person re-identification. The person walking naturally compared with the same person walking with bag, coat, and case recorded using longwave infrared, short wave infrared, medium wave infrared, and visible cameras. The videos are recorded in rural and in urban environments. The pre-processing technique includes human identified using YOLO, background subtraction, silhouettes extraction, and synthesis Gait Entropy Image by averaging the silhouettes. The moving features are extracted from the Gait Entropy Energy Image. The extracted features are dimensionality reduced by the principal component analysis and recognised using different classifiers. The comparative results with the different classifier show that linear discriminant analysis outperforms other classifiers with 95.8% for visible in the rural dataset and 94.8% for longwave infrared in the urban dataset.

Keywords: biometric, gait, silhouettes, YOLO

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Authors: Carolina Cuéllar, Paz González

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Research has shown that school leadership has a significant indirect effect on students’ achievements. In Chile, evidence has also revealed that this impact is stronger in vulnerable schools. With the aim of strengthening school leadership, public policy has taken up the challenge of enhancing capabilities of novice principals through the implementation of induction programs, which include a mentoring component, entrusting the task of delivering these programs to universities. The importance of using mentoring or coaching models in the preparation of novice school leaders has been emphasized in the international literature. Thus, it can be affirmed that building leadership capacity through partnership is crucial to facilitate cognitive and affective support required in the initial phase of the principal career, gain role clarification and socialization in context, stimulate reflective leadership practice, among others. In Chile, mentoring is a recent phenomenon in the field of school leadership and it is even more new in the preparation of new principals who work in public schools. This study, funded by the Chilean Ministry of Education, sought to explore the challenges and lessons arising from the design and implementation of mentoring processes which are part of the induction programs, according to the perception of the different actors involved: ministerial agents, university coordinators, mentors and novice principals. The investigation used a qualitative design, based on a study of three cases (three induction programs). The sources of information were 46 semi-structured interviews, applied in two moments (at the beginning and end of mentoring). Content analysis technique was employed. Data focused on the uniqueness of each case and the commonalities within the cases. Five main challenges and lessons emerged in the design and implementation of mentoring within the induction programs for new principals from Chilean public schools. They comprised the need of (i) developing a shared conceptual framework on mentoring among the institutions and actors involved, which helps align the expectations for the mentoring component within the induction programs, along with assisting in establishing a theory of action of mentoring that is relevant to the public school context; (ii) recognizing trough actions and decisions at different levels that the role of a mentor differs from the role of a principal, which challenge the idea that an effective principal will always be an effective mentor; iii) improving mentors’ selection and preparation processes trough the definition of common guiding criteria to ensure that a mentor takes responsibility for developing critical judgment of novice principals, which implies not limiting the mentor’s actions to assist in the compliance of prescriptive practices and standards; (iv) generating common evaluative models with goals, instruments and indicators consistent with the characteristics of mentoring processes, which helps to assess expected results and impact; and (v) including the design of a mentoring structure as an outcome of the induction programs, which helps sustain mentoring within schools as a collective professional development practice. Results showcased interwoven elements that entail continuous negotiations at different levels. Taking action will contribute to policy efforts aimed at professionalizing the leadership role in public schools.

Keywords: induction programs, mentoring, novice principals, school leadership preparation

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Authors: Naveed Ul Hassan, Bilal Aziz, Maryam Mushtaq, Imran Ameen Khan

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Equity risk premium (ERP) is the stock return in excess of risk free return. Even though it is an essential topic of finance but still there is no common consensus upon its forecasting. For forecasting ERP, apart from the macroeconomic variables attention is devoted to technical indicators as well. For this purpose, set of 14 technical and 14 macro-economic variables is selected and all forecasts are generated based on a standard predictive regression framework, where ERP is regressed on a constant and a lag of a macroeconomic variable or technical indicator. The comparative results showed that technical indicators provide better indications about ERP estimates as compared to macro-economic variables. The relative strength of ERP predictability is also investigated by using National Bureau of Economic Research (NBER) data of business cycle expansion and recessions and found that ERP predictability is more than twice for recessions as compared to expansions.

Keywords: equity risk premium, forecasting, macroeconomic indicators, technical indicators

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Authors: Raed Fawzi Mohammed Ameen, Haider I. Alyasari, Maryam Altaweel

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Due to rapid urbanization, developing countries faced significant urban challenges that accompanied the population growth such as the inability to provide adequate housing; sustain human and community's health and wellbeing; ensure the safety in urban areas; the prevalence corruption; lack of jobs; and a shortage of investment. The destruction, degradation, and lack of planning are acute in countries such as Iraq that have suffered for more than four decades because of war and international sanctions, resulting in severe damages to the ecology sector, social utilities, housing, infrastructure, as well as the disruption of the economic sector. Many of significant urban development, housing, and regeneration projects are currently underway in different regions in Iraq, labelled as a means to reform the environmental, social, and economic sectors. However, most often with absence of public participation. Hence, there is an urgent need for understanding public perception, especially of urban socio-economic challenges, which represents a crucial concern for many planners, designers, and policy-makers in order to develop effective policies in addition to increasing their participation. The aim of this study is to investigate stakeholder perceptions of the socio-economic challenges of urban development and their priorities in the all Iraqi provinces. A nationwide questionnaire has been conducted (N = 643) across Iraq, using 19- item structured questionnaire where the stakeholder’s perspectives were collected on a 5-point Likert-type scale. The indicators were identified through deep investigation in previous studies. Principal component analysis (PCA) and statistical tests were utilized to the collected responses in order to investigate the linkage between the perceptions of socio- economic challenges and demographic factors. A high value of internal consistency and reliability of the instrument has been achieved (Cronbach’s alpha= 0.867). Five principal components have been identified, namely: economic, cultural aspects, design context, employment, security and housing demands. The item ‘safety of public places' was ranked as the most important, followed by the items 'minimize unplanned housing', and ‘provision of affordable housing’, respectively. Promote high-rise housing from the housing demands group, was ranked the lowest component between all indicators. 'Using sustainable local materials in construction' item had the second lowest mean score. The results also illustrate a link between deficiencies in the social and economic infrastructure because of the destruction and degradation caused by political instability in Iraq in the last few decades.

Keywords: public participation in development, socio-economic challenges, urban development, urban sustainability

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Authors: Ankur Dixit, Hiroaki Wagatsuma

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The crack detection on a concrete bridge is an important and constant task in civil engineering. Chronically, humans are checking the bridge for inspection of cracks to maintain the quality and reliability of bridge. But this process is very long and costly. To overcome such limitations, we have used a drone with a digital camera, which took some images of bridge deck and these images are processed by morphological component analysis (MCA). MCA technique is a very strong application of sparse coding and it explores the possibility of separation of images. In this paper, MCA has been used to decompose the image into coarse and fine components with the effectiveness of two dictionaries namely anisotropic diffusion and wavelet transform. An anisotropic diffusion is an adaptive smoothing process used to adjust diffusion coefficient by finding gray level and gradient as features. These cracks in image are enhanced by subtracting the diffused coarse image into the original image and the results are treated by Sobel edge detector and binary filtering to exhibit the cracks in a fine way. Our results demonstrated that proposed MCA framework using anisotropic diffusion followed by Sobel operator and binary filtering may contribute to an automation of crack detection even in open field sever conditions such as bridge decks.

Keywords: anisotropic diffusion, coarse component, fine component, MCA, Sobel edge detector and wavelet transform

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Authors: Arbolino R., Boffardi R., L. De Simone

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The success of a development policy requires the definition of a proper investment planning on behalf of policymakers. Such planning should consider both tangible and intangible features characterizing a territory and, moreover, evaluate both state of place and an ideal situation to be achieved, that represents the final goal of the policy. The aim of this research is to propose a methodological approach to implement this ideal solution or the best solution appliable to the Italian regions. It consists of two steps: the first one is a measure of regional inequality through building a composite indicator for analyzing the level of development and compare the differences among the regions. It is constructed by means of a principal component analysis. Ranking regions according to the scores achieved is useful as benchmark, to identify a best solution towards which other regions should strive. Thus, this distance is measured through a revised Sen index that allows to assign a weight on which calculate the financing resource programming. The results show that this approach is a good instrument to fairly and efficiently allocate public funds, in order to reduce regional inequalities.

Keywords: public economics, inequalities, growth, development

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Authors: Roshanak Afrakhteh, Abdolrasoul Salman Mahini, Mahdi Motagh, Hamidreza Kamyab

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Urban Heat Islands (UHIs) are distinctive urban areas characterized by densely populated central cores surrounded by less densely populated peripheral lands. These areas experience elevated temperatures, primarily due to impermeable surfaces and specific land use patterns. The consequences of these temperature variations are far-reaching, impacting the environment and society negatively, leading to increased energy consumption, air pollution, and public health concerns. This paper emphasizes the need for simplified approaches to comprehend UHI temperature dynamics and explains how urban development patterns contribute to land surface temperature variation. To illustrate this relationship, the study focuses on the Guilan Plain, utilizing techniques like principal component analysis and generalized additive models. The research centered on mapping land use and land surface temperature in the low-lying area of Guilan province. Satellite data from Landsat sensors for three different time periods (2002, 2012, and 2021) were employed. Using eCognition software, a spatial unit known as a "city block" was utilized through object-based analysis. The study also applied the normalized difference vegetation index (NDVI) method to estimate land surface radiance. Predictive variables for urban land surface temperature within residential city blocks were identified categorized as intrinsic (related to the block's structure) and neighboring (related to adjacent blocks) variables. Principal Component Analysis (PCA) was used to select significant variables, and a Generalized Additive Model (GAM) approach, implemented using R's mgcv package, modeled the relationship between urban land surface temperature and predictor variables.Notable findings included variations in urban temperature across different years attributed to environmental and climatic factors. Block size, shared boundary, mother polygon area, and perimeter-to-area ratio were identified as main variables for the generalized additive regression model. This model showed non-linear relationships, with block size, shared boundary, and mother polygon area positively correlated with temperature, while the perimeter-to-area ratio displayed a negative trend. The discussion highlights the challenges of predicting urban surface temperature and the significance of block size in determining urban temperature patterns. It also underscores the importance of spatial configuration and unit structure in shaping urban temperature patterns. In conclusion, this study contributes to the growing body of research on the connection between land use patterns and urban surface temperature. Block size, along with block dispersion and aggregation, emerged as key factors influencing urban surface temperature in residential areas. The proposed methodology enhances our understanding of parameter significance in shaping urban temperature patterns across various regions, particularly in Iran.

Keywords: urban heat island, land surface temperature, LST modeling, GAM, Gilan province

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Authors: Jude A. Mbanasor, Chigozirim N. Onwusiribe

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The enigma climate change is no longer an illusion but a reality. In the recent years, the Nigeria climate has changed and the changes are shown by the changing patterns of rainfall, the sunshine, increasing level carbon and nitrous emission as well as deforestation. This study analyzed the behavioural response of bee keepers to variations in the climate and the adaptation techniques developed in response to the climate variation. Beekeeping is a viable economic activity for the alleviation of poverty as the products include honey, wax, pollen, propolis, royal jelly, venom, queens, bees and their larvae and are all marketable. The study adopted the multistage sampling technique to select 120 beekeepers from the five states of Southeast Nigeria. Well-structured questionnaires and focus group discussions were adopted to collect the required data. Statistical tools like the Principal component analysis, data envelopment models, graphs, and charts were used for the data analysis. Changing patterns of rainfall and sunshine with the increasing rate of deforestation had a negative effect on the habitat of the bees. The bee keepers have adopted the Kenya Top bar and Langstroth hives and they establish the bee hives on fallow farmland close to the cultivated communal farms with more flowering crops.

Keywords: climate, farmer, response, smart

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Authors: G. V. Novikov, V. S. Sivozhelezov, S. S. Kolesnikov, K. V. Shaitan

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The study reports about the influence of binding of orthosteric ligands as well as point mutations on the conformational dynamics of β-2-adrenoreceptor. Using molecular dynamics simulation we found that there was a little fraction of active states of the receptor in its apo (ligand free) ensemble corresponded to its constitutive activity. Analysis of MD trajectories indicated that such spontaneous activation of the receptor is accompanied by the motion in intracellular part of its alpha-helices. Thus receptor’s constitutive activity directly results from its conformational dynamics. On the other hand the binding of a full agonist resulted in a significant shift of the initial equilibrium towards its active state. Finally, the binding of the inverse agonist stabilized the receptor in its inactive state. It is likely that the binding of inverse agonists might be a universal way of constitutive activity inhibition in vivo. Our results indicate that ligand binding redistribute pre-existing conformational degrees of freedom (in accordance to the Monod-Wyman-Changeux-Model) of the receptor rather than cause induced fit in it. Therefore, the ensemble of biologically relevant receptor conformations is encoded in its spatial structure, and individual conformations from that ensemble might be used by the cell in conformity with the physiological behaviour.

Keywords: seven-transmembrane receptors, constitutive activity, activation, x-ray crystallography, principal component analysis, molecular dynamics simulation

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Authors: M. Bodner, M. Scampicchio

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Fourier Transform Infrared (FT-IR) spectroscopy coupled with chemometrics was used to distinguish between butter samples and non-butter samples. Further, quantification of the content of margarine in adulterated butter samples was investigated. Fingerprinting region (1400-800 cm^–1) was used to develop unsupervised pattern recognition (Principal Component Analysis, PCA), supervised modeling (Soft Independent Modelling by Class Analogy, SIMCA), classification (Partial Least Squares Discriminant Analysis, PLS-DA) and regression (Partial Least Squares Regression, PLS-R) models. PCA of the fingerprinting region shows a clustering of the two sample types. All samples were classified in their rightful class by SIMCA approach; however, nine adulterated samples (between 1% and 30% w/w of margarine) were classified as belonging both at the butter class and at the non-butter one. In the two-class PLS-DA model’s (R² = 0.73, RMSEP, Root Mean Square Error of Prediction = 0.26% w/w) sensitivity was 71.4% and Positive Predictive Value (PPV) 100%. Its threshold was calculated at 7% w/w of margarine in adulterated butter samples. Finally, PLS-R model (R² = 0.84, RMSEP = 16.54%) was developed. PLS-DA was a suitable classification tool and PLS-R a proper quantification approach. Results demonstrate that FT-IR spectroscopy combined with PLS-R can be used as a rapid, simple and safe method to identify pure butter samples from adulterated ones and to determine the grade of adulteration of margarine in butter samples.

Keywords: adulterated butter, margarine, PCA, PLS-DA, PLS-R, SIMCA

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Authors: Michael Adedotun Oke

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This paper, therefore, under study the various multiplier usages of the different Animal Dungs, from the animals such as Rabbits, Cows, Fishes, Sheep, and Poultry manure in the areas council of the Federal Capital Territory Abuja, Nigeria. Thus the various observations, with the pictorial representation, that was taken with the field survey from the different farms in Gwagawalada. Shows that the rabbits dungs are being used in some of the vegetables and crop farms, which serves as the nutrients, reduces the cost of production, ensure profitability, which also increases the different vegetative growth, early maturity, and the development of the crop and this is also applicable to some crops like maize, sweet potatoes. While the manure of the poultry products are being incorporated to fish ponds and the cows dungs are being used to serve as some manure to some certain crops, e.g. Okro, Maize, Pepper. Which provides the necessary nutritious values, but the various number of quantity of different bags of the various application are lacking, and the time of usage, it is also a life germane questions, which there are needs for further adaptive research, that will be involved and the reintroduction of new technology, that will be used in terms of the different methodology such as broadcasting and ring applications, of the dungs at large, while the seasons of the various applications. Thus the paper, therefore, suggested a training programs and production of manuals that will guide the various applications and usage and the effective dissemination of the various used of the simple technology, that will advances and teaching of a new mode of and the time of applications and the various quantity to used, during the applications.

Keywords: animals, usage, livestock, dungs, feaces, gwagawalada

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Authors: Ali Ulvi Uzer

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Data reduction is an important topic in the field of pattern recognition applications. The basic concept is the reduction of multitudinous amounts of data down to the meaningful parts. The Principal Component Analysis (PCA) method is frequently used for data reduction. The Support Vector Machine (SVM) method is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data, the algorithm outputs an optimal hyperplane which categorizes new examples. This study offers a hybrid approach that uses the PCA for data reduction and Support Vector Machines (SVM) for classification. In order to detect the accuracy of the suggested system, two boreholes taken from the soil sample was used. The classification accuracies for this dataset were obtained through using ten-fold cross-validation method. As the results suggest, this system, which is performed through size reduction, is a feasible system for faster recognition of dataset so our study result appears to be very promising.

Keywords: feature selection, sequential forward selection, support vector machines, soil sample

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Authors: Monisha Pattanaik

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In scenarios where users have to work with large amounts of hierarchical data structures combined with visualizations (For example, Construction 3d Models, Manufacturing equipment's models, Gantt charts, Building Plans), the data structures have a high density in terms of consisting multiple parent nodes up to 50 levels and their siblings to descendants, therefore convey an immediate feeling of complexity. With customers moving to consumer-grade enterprise software, it is crucial to make sophisticated features made available to touch devices or smaller screen sizes. This paper evaluates the UI component that allows users to scroll through all deep density levels using a slider overlay on top of the hierarchy table, performing several actions to focus on one set of objects at any point in time. This overlay component also solves the problem of excessive horizontal scrolling of the entire table on a fixed pane for a hierarchical table. This component can be customized to navigate through parents, only siblings, or a specific component of the hierarchy only. The evaluation of the UI component was done by End Users of application and Human-Computer Interaction (HCI) experts to test the UI component's usability with statistical results and recommendations to handle complex hierarchical data visualizations.

Keywords: building information modeling, digital twin, navigation, UI component, user interface, usability, visualization

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Authors: Mohammad Shoraka, Francesco Preti, Karen Angeles, Raulina Wojtkiewicz, Karthik Ramanathan

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Catastrophe modeling has evolved significantly over the last four decades. Verisk introduced its pioneering comprehensive inland flood model tailored for the U.K. in 2008. Over the course of the last 15 years, Verisk has built a suite of physically driven flood models for several countries and regions across the globe. This paper aims to spotlight a selection of these advancements tailored to the development of vulnerability estimation, which forms an integral part of a forthcoming update to Verisk’s U.K. inland flood model. Vulnerability functions are critical to evaluating and robust modeling flood-induced damage to buildings and contents. The subsequent damage assessments then allow for direct quantification of losses for entire building portfolios. Notably, today’s flood loss models more often prioritize enhanced development of hazard characterization, while vulnerability functions often lack sufficient granularity for a robust assessment. This study proposes a novel, engineering-driven, physically based component-level flood vulnerability framework for the U.K. Various aspects of the framework, including component classification and comprehensive cost analysis, meticulously tailored to capture the distinct building characteristics unique to the U.K., will be discussed. This analysis will elucidate how the cost distribution across individual components contributes to translating component-level damage functions into building-level damage functions. Furthermore, a succinct overview of essential datasets employed to gauge building regional vulnerability will be highlighted.

Keywords: catastrophe modeling, inland flood, vulnerability, cost analysis

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Authors: Diana Reckien

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Vulnerability assessments are increasingly used to support policy-making in complex environments, like urban areas. Usually, vulnerability studies include the construction of aggregate (sub-) indices and the subsequent mapping of indices across an area of interest. Vulnerability studies show a couple of advantages: they are great communication tools, can inform a wider general debate about environmental issues, and can help allocating and efficiently targeting scarce resources for adaptation policy and planning. However, they also have a number of challenges: Vulnerability assessments are constructed on the basis of a wide range of methodologies and there is no single framework or methodology that has proven to serve best in certain environments, indicators vary highly according to the spatial scale used, different variables and metrics produce different results, and aggregate or composite vulnerability indicators that are mapped easily distort or bias the picture of vulnerability as they hide the underlying causes of vulnerability and level out conflicting reasons of vulnerability in space. So, there is urgent need to further develop the methodology of vulnerability studies towards a common framework, which is one reason of the paper. We introduce a social vulnerability approach, which is compared with other approaches of bio-physical or sectoral vulnerability studies relatively developed in terms of a common methodology for index construction, guidelines for mapping, assessment of sensitivity, and verification of variables. Two approaches are commonly pursued in the literature. The first one is an additive approach, in which all potentially influential variables are weighted according to their importance for the vulnerability aspect, and then added to form a composite vulnerability index per unit area. The second approach includes variable reduction, mostly Principal Component Analysis (PCA) that reduces the number of variables that are interrelated into a smaller number of less correlating components, which are also added to form a composite index. We test these two approaches of constructing indices on the area of New York City as well as two different metrics of variables used as input and compare the outcome for the 5 boroughs of NY. Our analysis yields that the mapping exercise yields particularly different results in the outer regions and parts of the boroughs, such as Outer Queens and Staten Island. However, some of these parts, particularly the coastal areas receive the highest attention in the current adaptation policy. We imply from this that the current adaptation policy and practice in NY might need to be discussed, as these outer urban areas show relatively low social vulnerability as compared with the more central parts, i.e. the high dense areas of Manhattan, Central Brooklyn, Central Queens and the Southern Bronx. The inner urban parts receive lesser adaptation attention, but bear a higher risk of damage in case of hazards in those areas. This is conceivable, e.g., during large heatwaves, which would more affect more the inner and poorer parts of the city as compared with the outer urban areas. In light of the recent planning practice of NY one needs to question and discuss who in NY makes adaptation policy for whom, but the presented analyses points towards an under representation of the needs of the socially vulnerable population, such as the poor, the elderly, and ethnic minorities, in the current adaptation practice in New York City.

Keywords: vulnerability mapping, social vulnerability, additive approach, Principal Component Analysis (PCA), New York City, United States, adaptation, social sensitivity

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Authors: Bounmediene Farida, Nouani Abdelouahab, Bellal Mouloud

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The present study examined the influence of two production parameters namely genetic factor (race) and physiological factors (stage of lactation) on the composition of the traditional goat cheese made using the enzyme extract of caprine origin and commercial rennet. The results obtained show that the goat cheese of the Alpine race is richer in fat and protein than Saanen and Local breeds. Similar variations were observed depending on the stage of lactation for the third stage. Thus, analysis of the products obtained show that there is no difference in quality between the cheeses obtained with rennet and those obtained with goat coagulase. In addition, principal component analysis (PCA) made from individuals (races and stages of lactation) and variables (physicochemical parameters goat cheese) divides people into two groups: The first group includes cheeses races Alpine, Saanen and local third stages of lactation. This group corresponds to samples of the richest cheese in a useful matter. The second group includes cheeses from the three races in the second stage of lactation. This group corresponds to cheeses that have low contents in a useful matter.

Keywords: goat cheese, goat coagulase, rennet, coagulation

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Authors: Rachida Ouysse

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This paper proposes a constrained principal components (CnPC) estimator for efficient estimation of large-dimensional factor models when errors are cross sectionally correlated and the number of cross-sections (N) may be larger than the number of observations (T). Although principal components (PC) method is consistent for any path of the panel dimensions, it is inefficient as the errors are treated to be homoskedastic and uncorrelated. The new CnPC exploits the assumption of bounded cross-sectional dependence, which defines Chamberlain and Rothschild’s (1983) approximate factor structure, as an explicit constraint and solves a constrained PC problem. The CnPC method is computationally equivalent to the PC method applied to a regularized form of the data covariance matrix. Unlike maximum likelihood type methods, the CnPC method does not require inverting a large covariance matrix and thus is valid for panels with N ≥ T. The paper derives a convergence rate and an asymptotic normality result for the CnPC estimators of the common factors. We provide feasible estimators and show in a simulation study that they are more accurate than the PC estimator, especially for panels with N larger than T, and the generalized PC type estimators, especially for panels with N almost as large as T.

Keywords: high dimensionality, unknown factors, principal components, cross-sectional correlation, shrinkage regression, regularization, pseudo-out-of-sample forecasting

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Authors: Adeleh Moshtaghi Zonouz, Issa Eskandari

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A simple, efficient, and green one-pot, four-component synthesis of highly substituted polyhydronaphthalenes in aqueous media is described. The method has such advantages as short reaction times, high yields, mild reaction conditions, operational simplicity and environmentally benign.

Keywords: polyhydronaphthalene, 2, 6-dicyanoanilines, multi-component reaction, aqueous media

Procedia PDF Downloads 347

Authors: Karthik Mittal

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This study performs an unsupervised machine learning analysis to find clusters of related SNPs which highlight biological pathways that are important for the biological mechanisms of breast cancer. Studying genetic variations in isolation is illogical because these genetic variations are known to modulate protein production and function; the downstream effects of these modifications on biological outcomes are highly interconnected. After extracting the SNPs and their effect on different types of breast cancer using the MRBase library, two unsupervised machine learning clustering algorithms were implemented on the genetic variants: a k-means clustering algorithm and a hierarchical clustering algorithm; furthermore, principal component analysis was executed to visually represent the data. These algorithms specifically used the SNP’s beta value on the three different types of breast cancer tested in this project (estrogen-receptor positive breast cancer, estrogen-receptor negative breast cancer, and breast cancer in general) to perform this clustering. Two significant genetic pathways validated the clustering produced by this project: the MAPK signaling pathway and the connection between the BRCA2 gene and the ESR1 gene. This study provides the first proof of concept showing the importance of unsupervised machine learning in interpreting GWAS summary statistics.

Keywords: breast cancer, computational biology, unsupervised machine learning, k-means, PCA

Procedia PDF Downloads 117

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: 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

Procedia PDF Downloads 238

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

Procedia PDF Downloads 586

Authors: I. P. Ogbuewu, I. F. Etuk, V. U. Odoemelam, I. C. Okoli, M. U. Iloeje

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The effect of dietary ginger rhizome meal on libido and semen characteristics of post-pubertal rabbit bucks was investigated in an experiment that lasted for 12 weeks. Thirty-six post-pubertal bucks were randomly assigned to 4 dietary groups of 9 rabbits each in a completely randomized design. Four experimental diets were formulated to contain ginger rhizome meal at 0 g/kg feed (BT0), 5g/kg feed (BT5), 10 g/kg feed (BT10), and 15g/kg feed (BT15) were fed ad libitum to the experimental animals. Results revealed that semen colour changed from cream milky to milky. Data on semen pH and sperm concentration were similar (p>0.05) among the dietary groups. Semen volume for the bucks in BT0 (0.64 mL) and BT5 (0.60 mL) groups were significantly (p<0.05) higher than those in BT10 (0.44 mL) and BT15 (0.46 mL) groups. Total spermatozoa concentration value was significantly (p<0.05) higher in BT0 and BT5 groups than those in BT10 and BT15 groups. Sperm motility and percent live sperm declined (p<0.05) progressively among the treatment groups. Percent dead sperm were significantly (p<0.05) lower for bucks in BT0 group than in BT10 and BT15 groups. Reaction time had a dose-dependent increase; however, the observed difference was not significant (p>0.05). These results indicate that the inclusion of ginger rhizome meal at 5-15g per kg feed in ration for post-pubertal rabbit bucks could cause mild depressive effect on semen production and quality.

Keywords: rabbits, semen, libido, ginger

Procedia PDF Downloads 534

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

Procedia PDF Downloads 81

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

Procedia PDF Downloads 51