Search results for: principal component analysis PCA

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

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The relationship between antibacterial activity of eighteen different substituted benzimidazole derivatives and their molecular characteristics was studied using chemometric QSAR (Quantitative Structure–Activity Relationships) approach. QSAR analysis has been carried out on inhibitory activity towards Staphylococcus aureus, by using molecular descriptors, as well as minimal inhibitory activity (MIC). Molecular descriptors were calculated from the optimized structures. Principal component analysis (PCA) followed by hierarchical cluster analysis (HCA) and multiple linear regression (MLR) was performed in order to select molecular descriptors that best describe the antibacterial behavior of the compounds investigated, and to determine the similarities between molecules. The HCA grouped the molecules in separated clusters which have the similar inhibitory activity. PCA showed very similar classification of molecules as the HCA, and displayed which descriptors contribute to that classification. MLR equations, that represent MIC as a function of the in silico molecular descriptors were established. The statistical significance of the estimated models was confirmed by standard statistical measures and cross-validation parameters (SD = 0.0816, F = 46.27, R = 0.9791, R2CV = 0.8266, R2adj = 0.9379, PRESS = 0.1116). These parameters indicate the possibility of application of the established chemometric models in prediction of the antibacterial behaviour of studied derivatives and structurally very similar compounds.

Keywords: antibacterial, benzimidazole, molecular descriptors, QSAR

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Authors: Djamila Meghraoui, Bachir Boudraa, Thouraya Meksen, M.Boudraa

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Parkinson's disease is a pathology that involves characteristic perturbations in patients’ voices. This paper describes a proposed method that aims to diagnose persons with Parkinson (PWP) by analyzing on line their voices signals. First, Thresholds signals alterations are determined by the Multi-Dimensional Voice Program (MDVP). Principal Analysis (PCA) is exploited to select the main voice principal componentsthat are significantly affected in a patient. The decision phase is realized by a Mul-tinomial Bayes (MNB) Classifier that categorizes an analyzed voice in one of the two resulting classes: healthy or PWP. The prediction accuracy achieved reaching 98.8% is very promising.

Keywords: Parkinson’s disease recognition, PCA, MDVP, multinomial Naive Bayes

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Authors: Nargess Fasihmardanloo

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The aim of this study was to comparatively study the motivations of women to physical activity in Iran and selected Arab countries Based on individual, social and Islamic components. The present study was a descriptive comparative study that was performed by field method. The statistical population of the study included female athletes in Iran and Arab countries. A total of 184 people from Iran and 179 people from Arab countries (Iraq, UAE, and Jordan) were selected through available sampling as a research sample. The research tool included a questionnaire. The validity of the questionnaire was confirmed and its reliability in a pilot study was 0.95 through Cronbach's alpha. The questionnaire was translated into Persian in Iran and translated into Arabic for the selected countries and was provided to the participants electronically and through cyberspace. Finally, 363 questionnaires were collected. Manova multivariate analysis of variance using spss22 software was used to analyze the data. Findings showed that between Iranian women athletes and women athletes in selected Arab countries in the components of intrapersonal motivation (p = 0.009 and f = 6.978), interpersonal motivation (p = 0.050 and f = 3.875), There is a significant difference between social motives (p = 0.001 and f = 27.619) and Islamic motives (p = 0.001 and f = 11.339). And this difference is significant at the level of p <0.01 and p <0.05. In other words, in the component of intrapersonal motivations, the average of this component in Iranian female athletes (M = 59.77) was higher than female athletes in selected Arab countries (M = 55.53). In the interpersonal motivations component, the average of this component in Iranian female athletes (M = 26.87) was lower than in female athletes in selected Arab countries (M = 28.62). In the component of social motivations, the average of this component in Iranian female athletes (M = 33.08) was lower than female athletes in selected Arab countries (M = 39.64). In the component of Islamic motives, the average of this component in Iranian female athletes (M = 21.55) was higher than female athletes in selected Arab countries (M = 19.04).

Keywords: athletes, motivation, women, Islamic

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Authors: Ramkumar Gunasekaran, John Cater, Vinod Suresh, Haribalan Kumar

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The main objective of this project is to develop a statistical shape model using principal component analysis that could be used for analyzing the shape of the human airway. The ultimate goal of this project is to identify geometric risk factors for diagnosis and management of Obstructive Sleep Apnoea (OSA). Anonymous CBCT scans of 25 individuals were obtained from the Otago Radiology Group. The airways were segmented between the hard-palate and the aryepiglottic fold using snake active contour segmentation. The point data cloud of the segmented images was then fitted with a bi-cubic mesh, and pseudo landmarks were placed to perform PCA on the segmented airway to analyze the shape of the airway and to find the relationship between the shape and OSA risk factors. From the PCA results, the first four modes of variation were found to be significant. Mode 1 was interpreted to be the overall length of the airway, Mode 2 was related to the anterior-posterior width of the retroglossal region, Mode 3 was related to the lateral dimension of the oropharyngeal region and Mode 4 was related to the anterior-posterior width of the oropharyngeal region. All these regions are subjected to the risk factors of OSA.

Keywords: medical imaging, image processing, FEM/BEM, statistical modelling

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Authors: Rute Faria, Digo Moraes, Amanda Santos, Dione Morais, Maria Sartori

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Seed vigor is a fundamental component for the good performance of the entire soybean production process. Seeds with mechanical damage at harvest time will be more susceptible to fungal and insect attack during storage, which will invariably reduce their vigor to the field, compromising uniformity and final stand performance. Harvesters, even the most modern ones, when not properly regulated or operated, can cause irreversible damages to the seeds, compromising even their commercialization. Therefore, the control of an efficient harvest is necessary in order to guarantee a good quality final product. In this work, the damage caused by two different harvesters (one rented, and another one) was evaluated, traveling in two speeds (4 and 8 km / h). The design was completely randomized in 2 x 2 factorial, with four replications. To evaluate the physiological quality seed germination and vigor tests were carried out over a period of six months. A multivariate analysis of Principal Components (PCA) and clustering allowed us to verify that the leased machine had better performance in the incidence of immediate damages in the seeds, but after a storage period of 6 months the vigor of these seeds reduced more than own machine evidencing that such a machine would bring more damages to the seeds.

Keywords: Glycine max (L.), cluster analysis, PCA, vigor

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Authors: Ana B. Carrera-Aguilar , Rodrigo T. Sepulveda-Hirose, Diego A. Bernal-Gurrusquieta, Francisco A. Ramirez Casas

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In recent years, Mexico City has presented high levels of atmospheric pollution; the city is also an example of inequality and poverty that impact metropolitan areas around the world. This combination of social and economic exclusion, coupled with high levels of pollution evidence the loss of well-being among the population. The effect of air pollution on quality of life is an area of study that has been overlooked. The purpose of this study is to find relations between air quality and quality of life in Mexico City through statistical analysis of a regression model and principal component analysis of several atmospheric contaminants (CO, NO₂, ozone, particulate matter, SO₂) and well-being indexes (HDI, poverty, inequality, life expectancy and health care index). The data correspond to official information (INEGI, SEDEMA, and CEPAL) for 2000-2018. Preliminary results show that the Human Development Index (HDI) is affected by the impacts of pollution, and its indicators are reduced in the presence of contaminants. It is necessary to promote a strong interest in this issue in Mexico City. Otherwise, the problem will not only remain but will worsen affecting those who have less and the population well-being in a generalized way.

Keywords: air quality, Mexico City, quality of life, statistics

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Authors: Dishant Shahi, Aradhya Shandilya, Nand Kumar

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We study here the impact of the black component referred to as X component in the text on Real estate transactions. The X component involved not only acts as friction in transaction but also leads to dysfunctionality in the capital market of real estate. The effect of the component is presented by using a model of economy which seeks resemblance with that of India involving property deals. The rigidities which hinder smooth transactions in property or land deals are depicted and their impact on the economy as a whole has been modelled. The effect of subprime crisis (2007) on Indian housing capital market and the role which the X component played during it, is also included in one of the sections. In the entire text, we have utilised 4 Quadrant graphs to study supply and demand causalities involved in commercial real estate. At the end we have included the impact of demonetisation as a move to counter the problem of overvaluation in the property assets arising due to the X component. The case of Demonetisation which has been the latest move by the Indian Government to control huge amount of black money in circulation has been included along with its impact on the housing and rent as well as the capital market.

Keywords: X-component, 4Q graph, real estate, capital markets, demonetisation, consumer sentiments

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Authors: Somayeh Khanjani, Hamideh Abolghasemi, Hadi Shirzad, Samaneh Nabavi

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At world market can be currently encountered a wide range of gemological objects that are incorrectly declared, treated, or it concerns completely different materials that try to copy precious objects more or less successfully. Counterfeiting of precious commodities is a problem faced by governments in most countries. Police have seized many counterfeit coins that looked like the real coins and because the feeling to the touch and the weight were very similar to those of real coins. Most people were fooled and believed that the counterfeit coins were real ones. These counterfeit coins may have been made by big criminal organizations. To elucidate the manufacturing process, not only the quantitative analysis of the coins but also the comparison of their morphological characteristics was necessary. Several modern techniques have been applied to prevent counterfeiting of coins. The objective of this study was to demonstrate the potential of X-ray Fluorescence (XRF) technique and the other analytical techniques for example SEM/EDX/WDX, FT-IR/ATR and Raman Spectroscopy. Using four elements (Cu, Ag, Au and Zn) and obtaining XRF for several samples, they could be discriminated. XRF technique and SEM/EDX/WDX are used for study of chemical composition. XRF analyzers provide a fast, accurate, nondestructive method to test the purity and chemistry of all precious metals. XRF is a very promising technique for rapid and non destructive counterfeit coins identification in forensic science.

Keywords: counterfeit coins, X-ray fluorescence, forensic, FT-IR

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Authors: E. Izsó, M. Bartalné-Berceli, Sz. Gergely, A. Salgó

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The aims of this investigation is to elaborate near-infrared methods for testing and recognition of chemical components and quality in “Pannon wheat” allied (i.e. true to variety or variety identified) milling fractions as well as to develop spectroscopic methods following the milling processes and evaluate the stability of the milling technology by different types of milling products and according to sampling times, respectively. This wheat categories produced under industrial conditions where samples were collected versus sampling time and maximum or minimum yields. The changes of the main chemical components (such as starch, protein, lipid) and physical properties of fractions (particle size) were analysed by dispersive spectrophotometers using visible (VIS) and near-infrared (NIR) regions of the electromagnetic radiation. Close correlation were obtained between the data of spectroscopic measurement techniques processed by various chemometric methods (e.g. principal component analysis (PCA), cluster analysis (CA) and operation condition of milling technology. Its obvious that NIR methods are able to detect the deviation of the yield parameters and differences of the sampling times by a wide variety of fractions, respectively. NIR technology can be used in the sensitive monitoring of milling technology.

Keywords: near infrared spectroscopy, wheat categories, milling process, monitoring

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Authors: Chirawat Wattanapanich, Hong Wei

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This study presents new gait representations for improving gait recognition accuracy on cross gait appearances, such as normal walking, wearing a coat and carrying a bag. Based on the Gait Energy Image (GEI), two ideas are implemented to generate new gait representations. One is to append lower knee regions to the original GEI, and the other is to apply convolutional operations to the GEI and its variants. A set of new gait representations are created and used for training multi-class Support Vector Machines (SVMs). Tests are conducted on the CASIA dataset B. Various combinations of the gait representations with different convolutional kernel size and different numbers of kernels used in the convolutional processes are examined. Both the entire images as features and reduced dimensional features by Principal Component Analysis (PCA) are tested in gait recognition. Interestingly, both new techniques, appending the lower knee regions to the original GEI and convolutional GEI, can significantly contribute to the performance improvement in the gait recognition. The experimental results have shown that the average recognition rate can be improved from 75.65% to 87.50%.

Keywords: convolutional image, lower knee, gait

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Authors: Yuhanis Ibrahim, Sung-Pil Lee

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This study attempts to address factors that contribute to outline public art factors assessment, memorial monument specifically. Memorial monuments hold significant and rich message whether the intention of the art is to mark and commemorate important event or to inform younger generation about the past. Public monument should relate to the public and raise awareness about the significant issue. Therefore, by investigating the impact of the existing public memorial art will hopefully shed some lights to the upcoming public art projects’ stakeholders to ensure the lucid memorial message is delivered to the public directly. Public is the main actor as public is the fundamental purpose that the art was created. Perception is framed as one of the reliable evaluation tools to assess the public art impact factors. The Malaysia National Monument was selected to be the case study for the investigation. The public’s perceptions were gathered using a questionnaire that involved (n-115) participants to attain keywords, and next Semantical Differential Methodology (SDM) was adopted to evaluate the perceptions about the memorial monument. These perceptions were then measured with Reliability Factor and then were factorised using Factor Analysis of Principal Component Analysis (PCA) method to acquire concise factors for the monument assessment. The result revealed that there are four factors that influence public’s perception on the monument which are aesthetic, audience, topology, and public reception. The study concludes by proposing the factors for public memorial art assessment for the next future public memorial projects especially in Malaysia.

Keywords: factor analysis, public art, public perception, semantical differential methodology

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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: Kaining Zhang, Lang Chen, Danyang Liu, Jianying Lu, Kun Yang, Junying Wu

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In order to solve the problem of a large amount of simulation and limited simulation scale in the first-principle molecular dynamics simulation of energetic material detonation reaction, we established an artificial intelligence model for analyzing and predicting the detonation reaction mechanism of CL-20 based on the first-principle molecular dynamics simulation of the multiscale shock technique (MSST). We employed principal component analysis to identify the dominant charge features governing molecular reactions. We adopted the K-means clustering algorithm to cluster the reaction paths and screen out the key reactions. We introduced the neural network algorithm to construct the mapping relationship between the charge characteristics of the molecular structure and the key reaction characteristics so as to establish a calculation method for predicting detonation reactions based on the charge characteristics of CL-20 and realize the rapid analysis of the reaction mechanism of energetic materials.

Keywords: energetic material detonation reaction, first-principle molecular dynamics simulation of multiscale shock technique, neural network, CL-20

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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: Yayun Hsu, Henry Horng-Shing Lu

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In this paper, a new method is proposed to extending the method of connected component labeling from processing binary images to multi-scale modeling of images. By using the adaptive threshold of multi-scale attributes, this approach minimizes the possibility of missing those important components with weak intensities. In addition, the computational cost of this approach remains similar to that of the typical approach of component labeling. Then, this methodology is applied to grain boundary detection and Drosophila Brain-bow neuron segmentation. These demonstrate the feasibility of the proposed approach in the analysis of challenging microscopy images for scientific discovery.

Keywords: microscopic image processing, scientific data mining, multi-scale modeling, data mining

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Authors: Athdath Waduge Susantha Janaka Kumara, Xiefei Zhi, Zin Mie Mie Sein

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Understanding the air temperature variability is crucially important for disaster risk reduction and management. In this study, we used 15 synoptic meteorological stations to assess the spatiotemporal variability of air temperature over Sri Lanka during 1972–2021. The empirical orthogonal function (EOF), Principal component analysis (PCA), Mann-Kendall test, power spectrum analysis and correlation coefficient analysis were used to investigate the long-term trends of air temperature and their possible relation to sea surface temperature (SST) over the region. The results indicate that an increasing trend in air temperature was observed with the abrupt climate change noted in the year 1994. The spatial distribution of EOF1 (63.5%) shows the positive and negative loading dipole patterns from south to northeast, while EOF2 (23.4%) explains warmer (colder) in some parts of central (south and east) areas. The power spectrum of PC1 (PC2) indicates that there is a significant period of 3-4 years (quasi-2 years). Moreover, Indian Ocean Dipole (IOD) provides a strong positive correlation with the air temperature of Sri Lanka, while the EL Nino Southern Oscillation (ENSO) presents a weak negative correlation. Therefore, IOD events led to higher temperatures in the region. This study’s findings can help disaster risk reduction and management in the country.

Keywords: air temperature, interannaul variability, ENSO, IOD

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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: C. M. Mat Isa, H. Mohd Saman, S. R. Mohd Nasir, A. Jaapar

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International market expansion involves a strategic process of market entry decision through which a firm expands its operation from domestic to the international domain. Hence, entry timing choices require the needs to balance the early entry risks and the problems in losing opportunities as a result of late entry into a new market. Questionnaire surveys administered to 115 Malaysian construction firms operating in 51 countries worldwide have resulted in 39.1 percent response rate. Factor analysis was used to determine the most significant factors affecting entry timing choices of the firms to penetrate the international market. A logistic regression analysis used to examine the firms’ entry timing choices, indicates that the model has correctly classified 89.5 per cent of cases as late movers. The findings reveal that the most significant factor influencing the construction firms’ choices as late movers was the firm factor related to the firm’s international experience, resources, competencies and financing capacity. The study also offers valuable information to construction firms with intention to internationalize their businesses.

Keywords: factors, early movers, entry timing choices, late movers, logistic regression model, principal axis factorial analysis, Malaysian construction firms

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Authors: Takahiro Nishigaki, Katsumi Nitta, Takashi Onoda

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In this paper, we present a method of applying Independent Topic Analysis (ITA) to increasing the number of document data. The number of document data has been increasing since the spread of the Internet. ITA was presented as one method to analyze the document data. ITA is a method for extracting the independent topics from the document data by using the Independent Component Analysis (ICA). ICA is a technique in the signal processing; however, it is difficult to apply the ITA to increasing number of document data. Because ITA must use the all document data so temporal and spatial cost is very high. Therefore, we present Incremental ITA which extracts the independent topics from increasing number of document data. Incremental ITA is a method of updating the independent topics when the document data is added after extracted the independent topics from a just previous the data. In addition, Incremental ITA updates the independent topics when the document data is added. And we show the result applied Incremental ITA to benchmark datasets.

Keywords: text mining, topic extraction, independent, incremental, independent component analysis

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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: Shivani Jha, Klaus Baier, Rafiq Azzam, Ramakar Jha

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In India, generally people migrate from rural area to the urban area for better infra-structural facilities, high standard of living, good job opportunities and advanced transport/communication availability. In fact, unplanned urban development due to migration of people causes seriou damage to the land use, water pollution and available water resources. In the present work, an attempt has been made to use satellite data of different years for urban change detection of Chennai metropolitan city along with pattern analysis to generate future scenario of urban development using buffer zoning in GIS environment. In the analysis, SRTM (30m) elevation data and IRS-1C satellite data for the years 1990, 2000, and 2014, are used. The flow accumulation, aspect, flow direction and slope maps developed using SRTM 30 m data are very useful for finding suitable urban locations for industrial setup and urban settlements. Normalized difference vegetation index (NDVI) and Principal Component Analysis (PCA) have been used in ERDAS imagine software for change detection in land use of Chennai metropolitan city. It has been observed that the urban area has increased exponentially in Chennai metropolitan city with significant decrease in agriculture and barren lands. However, the water bodies located in the study regions are protected and being used as freshwater for drinking purposes. Using buffer zone analysis in GIS environment, it has been observed that the development has taken place in south west direction significantly and will do so in future.

Keywords: urban change, satellite data, the Chennai metropolis, change detection

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Authors: Fan Chih-Wen

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The Ministry of Education (2014) released the 12-year National Basic Education Curriculum Syllabus. Curriculum implementation has evolved from a loose connection of cooperation to a closely structured relationship of coordination and collaboration. Collaboration opens the door to teachers' culture of isolation and classrooms and allows them to discuss educational issues from multiple perspectives and achieve shared goals. The purpose of study is to investigate facilitating factors of collaborative school culture and implications for principal curriculum leadership. The development and implementation of the new curriculum involves collaborative governance across systems and levels, including cooperation between central governments and schools. First, it analyzes the connotation of the 12-year National Basic Education Curriculum; Second, it analyzes the meaning of collaborative culture； Third, it analyzes the motivating factors of collaborative culture. Finally, based on this, it puts forward relevant suggestions for principal curriculum leadership.

Keywords: curriculum leadership, collaboration culture, tracher culture, school improvement

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Authors: Uzma Ayaz, Sanam Bashir, Shahid Iqbal Awan, Muhammad Ilyas, Muhammad Fareed Khan

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Drought stress directly affects growth along with the productivity of plants by altering plant water status. Sunflower (Helianthus annuus L.), an oilseed crop, is adversely affected by abiotic stresses. The present study was carried out to characterize the genetic variability for seedling and morpho-physiological parameters in different sunflower genotypes under water-stressed conditions. A total of twenty-seven genotypes, including two hybrids, eight advanced lines and seventeen accessions of sunflower (Helianthus annuus L.) were tested against drought stress at Seedling stages by Polyethylene glycol (PEG). Significant means were calculated among traits using analysis of variance (ANOVA) whereas, correlation and principal component analysis also confirmed that germination percentage, root length, shoot length, chlorophyll content, stomatal frequency are positively linked with each other hence, these traits were responsible for most of the variation among genotypes. The cluster analysis results showed that genotypes Ausun, line-3, line-2, and 17578, line-1, line-7, line-6 and 17562 as more diverse among all the genotypes. These most divergent genotypes could be utilized in the development of drought-tolerant inbreed lines which could be subsequently used in future heterosis breeding programs.

Keywords: sunflower, drought, stress, polyethylene- glycol, screening

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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: 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: 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: Lavanya Srinivasan

Abstract:

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: Ying Ge

Abstract:

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: Marianna Ciaccia, Gennaro Agrimi, Isabella Pisano, Maurizio Bettiga, Silvia Rapacioli, Giulia Mensa, Monica Marzagalli

Abstract:

Objective: Untargeted metabolomic analysis of extracellular metabolites is a powerful approach that focuses on comprehensively profiling in the extracellular space. In this study, we applied extracellular metabolomic analysis to investigate the metabolism of two probiotic microorganisms with health benefits that extend far beyond the digestive tract and the immune system. Methods: Analytical techniques employed in extracellular metabolomic analysis encompass various technologies, including mass spectrometry (MS), which enables the identification of metabolites present in the fermentation media, as well as the comparison of metabolic profiles under different experimental conditions. Multivariate statistical analysis techniques like principal component analysis (PCA) or partial least squares-discriminant analysis (PLS-DA) play a crucial role in uncovering metabolic signatures and understanding the dynamics of metabolic networks. Results: Different types of supernatants from fermentation processes, such as dairy-free, not dairy-free media and media with no cells or pasteurized, were subjected to metabolite profiling, which contained a complex mixture of metabolites, including substrates, intermediates, and end-products. This profiling provided insights into the metabolic activity of the microorganisms. The integration of advanced software tools has facilitated the identification and characterization of metabolites in different fermentation conditions and microorganism strains. Conclusions: In conclusion, untargeted extracellular metabolomic analysis, combined with software tools, allowed the study of the metabolites consumed and produced during the fermentation processes of probiotic microorganisms. Ongoing advancements in data analysis methods will further enhance the application of extracellular metabolomic analysis in fermentation research, leading to improved bioproduction and the advancement of sustainable manufacturing processes.

Keywords: biotechnology, metabolomics, lactic bacteria, probiotics, postbiotics

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

Abstract:

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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