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Querying search engines with the keyword “jaguars” returns results as diverse as web sites about cars, computer games, attack planes, American football, and animals. More and more search engines offer options to organize query results by categories or, given a document, to return a list of links to topically related documents. While information retrieval traditionally defines similarity of documents in terms of contents, it seems natural to expect that the very structure of the Web carries important information about the topical similarity of documents. Here we study the role of a matrix constructed from weighted co‐citations (documents referenced by the same document), weighted couplings (documents referencing the same document), incoming, and outgoing links for the clustering of documents on the Web. We present and discuss three methods of clustering based on this matrix construction using three clustering algorithms, K‐means, Markov and Maximum Spanning Tree, respectively. Our main contribution is a clustering technique based on the Maximum Spanning Tree technique and an evaluation of its effectiveness comparatively to the two most robust alternatives: K‐means and Markov clustering.

Nowadays, ample industries are fascinated to look for high strength and light weight materials for the development of robust parts. Because of light weight and high stiffness to weight ratio; usage of aluminum parts is growing rapidly, especially in automotive engineering. Process improvement of Al alloys and their grain structure refinement is the current area of interest in casting companies. In this research work, an investigation has been carried out to enhance the process improvement of die casting by optimization of various significant parameters and their refinement of grains by the effect of Nb-C novel grain refiner.

L27 orthogonal array (OA) has been considered to optimize the preferred casting input parameters such as molten metal temperature (°C), die temperature (°C), injection pressure (bar), Al-3.5Nb-1.5 C novel grain refiner and Ni alloying additions as key process parameters in order to increase the quality and efficiency of Al-9Si-3Cu aluminum alloy die casting by reducing the porosity formation.

It was observed that the porosity values have significantly decreased from 0.88% to 0.25% particularly at 0.1 wt.% of new grain refiner and 0.5 wt. % of Al-6Ni master alloy. As per the ANOVA results, it was observed that Al-3.5FeNb-1.5 C grain refiner (F value 2609.22), Al-6Ni alloying addition (F value 1329.13), molten metal temperature (F value 1002.43) and, injection pressure (F value 448.06) are the factors that significantly affects the porosity, whereas die temperature was found to be insignificant. The results show that new grain refiner is one the most significant factor among the other selected parameters. The contribution of the new grain refiner to the variation of mean casting porosity is around 57.74%. confidence interval (CI) has also been estimated as 0.013 for 95% consistency level to validate the predicted range of optimum casting porosity of aforesaid alloy.

To the best of the authors' knowledge, no study has been conducted in the past to investigate the combined effect of these die casting parameters and composition factors for the development of Al-Si robust cast parts. The paper represents original research and provides new information for the fabrication of die casting parts.

The purpose of this paper is to propose a debt rating index system for small industrial enterprises that significantly distinguishes the default state. This debt rating system is constructed using the F-test and correlation analysis method, with the small industrial enterprise loans of a Chinese commercial bank as the data sample. This study establishes the weighting principle for the debt scoring model: “the more significant the default state, the larger is the weight.” The debt rating system for small industrial enterprises is constructed based on the standard “the higher the debt rating, the lower is the loss given default.”

In this study, the authors selected indexes that pass the homogeneity of variance test based on the principle that a greater deviation of the default sample’s mean from the whole sample’s mean leads to greater significance in distinguishing the default samples from the non-default samples. The authors removed correlated indexes based on the results of the correlation analysis and constructed a debt rating index system for small industrial enterprises that included 23 indexes.

Among the 23 indexes, the weights of 12 quantitative indexes add up to 0.547, while the weights of the remaining 11 qualitative indexes add up to 0.453. That is, in the debt rating of the small industry enterprises, the financial indexes are not capable of reflecting all the debt situations, and the qualitative indexes play a more important role in debt rating. The weights of indexes “X17 Outstanding loans to all assets ratio” and “X59 Date of the enterprise establishment” are 0.146 and 0.133, respectively; both these are greater than 0.1, and the indexes are ranked first and second, respectively. The weights of indexes “X6 EBIT-to- current liabilities ratio,” “X13 Ratio of capital to fixed” and “X78 Legal dispute number” are between 0.07 and 0.09, these indexes are ranked third to fifth. The weights of indexes “X3 Quick ratio” and “X50 Per capital year-end savings balance of Urban and rural residents” are both 0.013, and these are the lowest ranked indexes.

The data of index i are divided into two categories: default and non-default. A greater deviation in the mean of the default sample from that of the whole sample leads to greater deviation from the non-default sample’s mean as well; thus, the index can easily distinguish the default and the non-default samples. Following this line of thought, the authors select indexes that pass the F-test for the debt rating system that identifies whether or not the sample is default. This avoids the disadvantages of the existing research in which the standard for selecting the index has nothing to do with the default state; further, this presents a new way of debt rating. When the correlation coefficient of two indexes is greater than 0.8, the index with the smaller F-value is removed because of its weaker prediction capacity. This avoids the mistake of eliminating an index that has strong ability to distinguish default and non-default samples. The greater the deviation of the default sample’s mean from the whole sample’s mean, the greater is the capability of the index to distinguish the default state. According to this rule, the authors assign a larger weight to the index that exhibits the ability to identify the default state. This is different from the existing index system, which does not take into account the ability to identify the default state.

The purpose of this paper is to provide an in‐depth examination of country‐of‐origin (COO) perceptions of consumers in a multinational setting. It shows how explanatory factors like demographics, familiarity with a country's products, purchase behaviour and psychological variables jointly work to explain consumers' COO perceptions.

This is a quantitative study using a drop‐off and pick‐up survey among three samples of consumers in Canada, Morocco and Taiwan. The final sample size was comprised of 506 male consumers. The data were analyzed using factor analysis to group countries of origin and analyses of variance to relate COO perceptions to the explanatory variables.

The familiarity with products made in a country was the strongest predictor of country perceptions, followed by nationality and the manufacturing process and product complexity dimensions of country evaluation. Canadians had the highest propensity to distinguish between countries of origin on the basis of product technological complexity and manufacturing dimensions and Moroccans the least. Taiwanese appeared to show animosity towards China.

The study used an only‐male sample from a limited number of countries. Future research should seek to develop a multi‐dimensional scale for the familiarity construct. They should also explore the concept of consumer capacity to distinguish between COOs. Cross‐national studies using cognitive style scales should be carried out. A qualitative examination of Taiwanese's COO perceptions is also recommended.

It seems important to increase consumers' familiarity with a COO and its products to improve its overall perception. Products made in Latin American countries have the lowest level of familiarity in general. Thus, increasing familiarity with their products is particularly important to achieve export success.

This study contributes to the marketing and international business literatures and provides insights to international marketers by bringing valuable information that can help make decisions as to where to manufacture and how to promote global products. It provides guidance as to what types of nations are likely to require multi‐dimensional information about countries of origin.

The aim of this study was to measure the readiness of science and mathematics supervisors to utilize technology and online learning platforms for teachers' plans and professional development, during and after the period of the COVID-19 pandemic.

To achieve this aim, the researchers developed a questionnaire comprising of 55 items based on the instruments used in pertinent studies. A mixed-methods research design was employed, whereby a quantitative online survey was supplemented by focus group discussions with selected supervisors. Survey data were subjected to one-way analysis of variance and t-test, while information obtained via focus groups was coded to identify common themes related to the obstacles and challenges supervisors face.

When completing the survey, the supervisors approached proficiency using technology; however, focus group discussions revealed misconceptions related to e-leaning and limitations in their abilities to use technology in schools, as well as obstacles imposed by the structure and management of the educational system. T

These findings indicate that supervisors need support in acquiring the competencies required for integrating technology in education, and that their support to teacher community needs to be grounded in clear and systematic approaches and best educational practices.

These findings indicate that supervisors need support in acquiring the competencies required for integrating technology in education, and that their support to teacher community needs to be grounded in clear and systematic approaches and best educational practices.

This study examines the behaviors of eight Asian emerging market Single Country‐Closed End Funds’ (SCCEFs) market prices, net asset values (NAV) and price to net asset value ratios from January 5, 1996 to February 25, 2000, bracketing the period of the Asian currency crisis. The purpose of the study is to discern the degree of change of SCCEFs’ market prices and net asset values (NAV) in conjunction with changes in certain objective economic factors as explanatory variables, particularly changes in exchange rates, that may shed light on the probable reasons for the stickiness of market prices and yet speedy adjustment of NAVs. Results of statistical analysis suggest asymmetric information holding explanation to be the major reason for the observed phenomenon that can be exploited for profitable SCCEF investment decisions.

Sentiment analysis and opinion mining are emerging areas of research for analyzing Web data and capturing users’ sentiments. This research aims to present sentiment analysis of an Indian movie review corpus using natural language processing and various machine learning classifiers.

In this paper, a comparative study between three machine learning classifiers (Bayesian, naïve Bayesian and support vector machine [SVM]) was performed. All the classifiers were trained on the words/features of the corpus extracted, using five different feature selection algorithms (Chi-square, info-gain, gain ratio, one-R and relief-F [RF] attributes), and a comparative study was performed between them. The classifiers and feature selection approaches were evaluated using different metrics (F-value, false-positive [FP] rate and training time).

The results of this study show that, for the maximum number of features, the RF feature selection approach was found to be the best, with better F-values, a low FP rate and less time needed to train the classifiers, whereas for the least number of features, one-R was better than RF. When the evaluation was performed for machine learning classifiers, SVM was found to be superior, although the Bayesian classifier was comparable with SVM.

This is a novel research where Indian review data were collected and then a classification model for sentiment polarity (positive/negative) was constructed.

The novel coronavirus disease 2019 (COVID-19) pandemic has presented unique challenges in terms of understanding its unique characteristics of transmission and predicting its spread. The purpose of this study is to present a simple, parsimonious and accurate model for forecasting mortality caused by COVID-19.

The presented Bass Model is compared it with several alternative existing models for forecasting the spread of COVID-19. This study calibrates the model for deaths for the period, March 21 to April 30 for the USA as a whole and as the US States of New York, California and West Virginia. The daily data from the COVID-19 Tracking Project has been used, which is a volunteer organization launched from The Atlantic. Every day, data is collected on testing and patient outcomes from all the 50 states, 5 territories and the District of Columbia. This data set is widely used by policymakers and scholars. The fit of the model (F-value and its significance, R-squared value) and the statistical significance of the variables (t-values) for each one of the four estimates are examined. This study also examines the forecast of deaths for a three-day period, May 1 to 3 for each one of the four estimates – US, and States of New York, California and West Virginia. Based on these metrics, the viability of the Bass Model is assessed. The dependent variable is the number of deaths, and the two independent variables are cumulative number of deaths and its squared value.

The findings of this paper show that compared to other forecasting methods, the Bass Model performs remarkably well. In fact, it may even be argued that the Bass Model does better with its forecast. The calibration of models for deaths in the USA, and States of New York, California and West Virginia are all found to be significant. The F values are large and the significance of the F values is low, that is, the probability that the model is wrong is very miniscule. The fit as measured by R-squared is also robust. Further, each of the two independent variables is highly significant in each of the four model calibrations. These forecasts also approximate the actual numbers reasonably well.

This study illustrates the applicability of the Bass Model to estimate the diffusion of COVID-19 with some preliminary but important empirical analyses. This study argues that while the more sophisticated models may produce slightly better estimates, the Bass model produces robust and reasonably accurate estimates given the extreme parsimony of the model. Future research may investigate applications of the Bass Model for pandemic management using additional variables and other theoretical lenses.

The Bass Model offers effective forecasting of mortality resulting from COVID-19 to help understand how the curve can be flattened, how hospital capacity could be overwhelmed and how fatality rates might climb based on time and geography in the upcoming weeks and months.

This paper demonstrates the efficacy of the Bass Model as a parsimonious, accessible and theory-based approach that can predict the mortality rates of COVID-19 with minimal data requirements, simple calibration and accessible decision calculus. For all these reasons, this paper recommends further and continued examination of the Bass Model as an instrument for forecasting COVID-19 (and other epidemic/pandemic) mortality and health resource requirements. As this paper has demonstrated, there is much promise in this model.

Existing research on the early careers of graduates has provided some useful information but has also suffered from several limitations. It has not examined the full range of graduate experiences, nor has it investigated differences between organizations or stages in early career. Reports a study designed to overcome these and other limitations. Presents data concerning the reported experiences of 797 graduates in the first three years of their careers with eight substantial UK‐based recruiters of graduates. The most pervasive finding is that graduates’ experiences varied a great deal between organizations. Thus it is important for managers and researchers to evaluate individual organizations rather than using aggregated data. Nevertheless, some general statements can be made. Graduates felt their social relationships at work were harmonious, but this co‐existed with less than adequate performance feedback from bosses, and some negative opinions about colleagues. They tended to feel that career prospects in the organization were fairly attractive, but were unsure about exactly what paths were available, or how to get onto them. Graduates generally did not feel their work taxed their skills, but it nevertheless developed them, and offered considerable autonomy. Training courses were rated quite positively, but some doubt was expressed about the overall planning of training and development. Organizational systems were seen as neither helpful nor obstructive. Perceptions of the adequacy of pay and benefits varied greatly between companies. With increasing tenure, graduates’ work involved more decision making and supervision of others, but not more autonomy or visibility within the company. Training was perceived more positively in the first year than subsequently. Graduates did not become clearer about career paths with increasing tenure. Overall these results paint a complex and differentiated view of graduate experiences at work. Some trends identified in other work are confirmed, but others are not. Specific areas of concern are identified. The data provide a benchmark against which other organizations can be compared.