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A growing number of researchers have investigated the role of key factors that affect transport mode choice. Scant studies, however, have tried to incorporate the built environment factors at origin and destination, weather condition, departure time, and different trip purposes into mode choice models. To address these shortcomings, we developed four multinomial logit (MNL) models to analyze travel mode choice decision for different purposes in the context of a developing country, Iran. Travel data drawn from household travel survey conducted by Isfahan Municipality in 2015 and weather parameters were retrieved from five stations located inside the city. The results of models reveal some important insights. While entropy index and average block size strongly influence transport mode decisions, other built environment factors have weak associations with transport modes. Further, low temperature and low relative humidity decrease the probability of transit, motorcycle and bicycle usage over automobile. The impact of weather condition on discretionary trips is stronger than that of work trips. Apart from mentioned variables, socio-demographic characteristics and departure time of travel are other important variables. Findings of this paper indicate that nonphysical strategies in tandem with land use policies should be considered based on local condition.

This study aims to promote the high-quality development of regional agriculture. This study proposes a data-driven method for regional agricultural analysis and evaluation. Based on the data collection and processing related to regional agricultural development, the location entropy index was used to analyze the industrial agglomeration level, and the shift-share model was constructed to evaluate the industrial structure and competitiveness. Based on the above analysis and evaluation, high-quality development policy suggestions for regional agriculture were provided. Taking the agricultural development of the Yangtze River Delta from 2010 to 2019 as an example, this study shows the implementation process of the method. From the perspective of high-quality agricultural development in the Anhui Province, this paper proposes policy suggestions on industrial structure adjustment and promoting competitiveness. This study provides theoretical and methodological support for the development of high-quality regional agriculture.

Entropy indexes are widely used to measure the complexity of precipitation and its spatiotemporal variability, due to their advantages in quantifying the complex and nonlinear interactions of climate variables. However, different entropy indexes may provide different outcomes on the complexity of precipitation, thus causing difficulties in the assessment of actual complexity. In this study, we identified and chose the optimal entropy index from six entropy indexes based on the reliability and stability of entropy values, to assess the complexity of precipitation. The six entropy indexes considered are: information entropy (IE), fuzzy entropy (FE), approximate entropy (AE), sample entropy (SE), wavelet entropy (WE), and permutation entropy (PE). We implemented the approach on the monthly precipitation data observed over the period 1968–2017 at 58 meteorological stations in Beijing, China. The results indicated that the SE index was the optimal index with the best reliability and stability and, thus, was used to examine the complexity of the 58 monthly precipitation series in Beijing. The results also suggested that the complexity of precipitation can be viewed in terms of four sub-regions in Beijing. The precipitation in the ‘Northern Mountain Area’, with high complexity, was more affected by the interactions between the terrain and climate variability, compared with that in the ‘Southern Mountain Area’ with low complexity. The precipitation in the ‘Central Urban Area’ was more significantly impacted by the extensive urbanization than that in the ‘Northeast Suburban Area’, causing higher complexity in the former. At temporal scale, the complexity of precipitation in the ‘Southern Mountain Area’ exhibited the largest increasing rate due to the highest impacts of climate variability, followed by the ‘Central Urban Area’ with a moderate increasing rate of complexity of precipitation due to the impacts of human activities. However, the complexity of precipitation in the ‘Northern Mountain Area’ and ‘Northeast Suburban Area’ exhibited small changes due to relatively weak sensitivity to climate variability and human activities.

The primary objective of this research was to assess the efficiency of RS and GIS for predicting the flood risk in Raiganj Sub-division, Eastern India using the Frequency ratio, Entropy index, and Weight of evidence-information Value models. Consequently, for spatial analyses fourteen flood conditioning variables are constructed. The research region is experiencing floods consequently in the past with moderate to high intensities. The assessment demonstrates that factors such as elevation, LULC, rainfall, distance from rivers, and drainage density contributed significantly to the occurrence of floods. From the estimation, it is found that about 11.02 per cent (Frequency Ratio result), 13.90 per cent (Entropy’s result), and 11.50 per cent (WofE-IV results) of the total area has very high vulnerability status respectively. Around 33 per cent to 47 per cent of the total area of each block in the subdivision is projected to be in danger of floods. The validation of the results indicates that the success rate of the presently constructed maps was 0.933 for the FR model, 0.917 for the SEI model, and 0.907 for the WofE model indicating that the frequency ratio model for mapping flood risk in the study region is more authentic, reliable, and useful for delineating flood vulnerable areas and potential flood risk sites. The findings of the analysis will help planners to develop flood prevention measures as part of regional flood risk management programs, as well as provide a foundation for future research in the study area.

Aims: To assess the influence of agricultural production diversity of marginal and small farmers Study Design: Purposive Random Sampling Place and Duration of Study: Kanyakumari and Perambalur districts in Tamil Nadu were purposefully chosen for the survey, which was conducted through primary data collection from July to August 2020. Methodology: According to the Tamil Nadu state planning commission report 2017, Kanyakumari and Perambalur districts were chosen at random from the top five and bottom five districts of the food security index to analyze dietary diversity. The crop diversification index was calculated using the entropy index. The Tobit model is used to investigate the impact of diverse agriculture production among marginal and small farming households. Results: The findings clearly show that, of the two districts, Perambalur has diversified more than Kanyakumari. Farmers in Perambalur district cultivate agricultural crops, whereas farmers in Kanyakumari district selected block cultivate horticultural crops, particularly plantation crops. According to the Tobit results, the number of cattle, farm size, credit available, and distance from the market all had a positive and significant influence on crop diversification. Improving female-headed household decision-making, promoting household investment and infrastructure facilities, and suggesting crop development as crop diversification measures. Conclusion: The results conclude that farmers in Perambalur district have highly diversified cropping than Kanyakumari district.

This research paper investigates urban sprawl in Thiruvananthapuram Urban Agglomeration (UA) and attempts to delineate Urban Growth Boundary (UGB) for promoting urban sustenance. A 112% rise in the spatial expanse of Thiruvananthapuram UA from 256.22 km2 in 2001 to 542.57 km2 in 2011 might induce urban sprawl in the peripheral areas. The Landsat satellite imagery for the years 1987, 1997, 2007, and 2017 were extracted to examine the spatiotemporal urban growth pattern. Shannon’s entropy index was employed to detect urban sprawl in Thiruvananthapuram UA. The UGB delineation process involved future urban growth prediction using the MOLUSCE (Modules for Land Use Change Simulations) plug-in of QGIS software. ANN-MLP (Artificial Neural Network-Multi Layer Perceptron) and CA (Cellular Automata) model was preferred in MOLUSCE to predict future urban growth for the year 2027. Thereafter, hexagons of one square kilometer were used to demarcate the Contiguous built-up Growth Boundary (CGB), and later, sub-administrative units were selected to delineate UGB. The results revealed a rise in the built-up areas from 36.04 km2 in 1987 to 140.69 km2 in 2017. Shannon’s entropy index indicated the prevalence of urban sprawl in Thiruvananthapuram UA. The future growth prediction by 2027 exhibited a further rise in built-up areas to 173.31 km2. The total area within CGB is 213.58 km2, while UGB accounted for 355.59 km2, which included 16 sub-administrative units. This study exhibited a unique methodology to delineate the urban growth boundary, which optimizes the future land requirements in developing nations.

PurposeThis paper aims to shed light on gender inequality in Nigeria exploring new available data. It makes a case for attention to women empowerment and likely economic outcomes. The general objective of the research work is to ascertain the direction of gender inequality and show the pattern of inequality. Also, sectoral trends are obtained by analyzing and examining income inequality in Nigeria.Design/methodology/approachThe paper obtained data from the Living Standard Measurement Survey Wave 3, published 2017 with emphasis on the earnings that accrued to both male and female. The study employed the ordinary least square (OLS) method to show the relationship between the mean income and other parameters such as the sector of employment, marital status and education level. Theil’s entropy index was used to measure the within and between inequality that exist in the economy and across regions and sectors while adopting the overcrowding theory.FindingsThe result shows that gender inequality is more pronounced across the region, location and in some sectors of employment than the others. Geographical area has a higher effect on earnings disparity but is more pronounced among females. Also, the result showed that gender within inequality was high in the regions, education, location, and marital status while a higher level of education contributes to high wages for women. However, married women are more deprived.Originality/valueThis study has further revealed the need to bridge the gap gender inequality has caused in Nigeria, especially related to income, education and geographical location, with a focus on both opportunities and outcomes.

Keyfitz entropy index is a new indicator that measures the sensitivity of life expectancy to a change in mortality rate. Understanding the characteristics of this indicator can significantly help life table studies in survival analysis. In this paper, we take a closer look at some mathematical properties of Keyfitz entropy index. First, using theoretical studies we show that in some cases this index belongs to the interval [0, 1] and in other cases, it is greater than 1. We also provide two inequalities for Keyfitz entropy using Shannon entropy and pth central moments of random variables. Then, we present an empirical value for it. This value can be useful and provides initial information about Keyfitz entropy value to the researcher, especially before estimating the population survival function with common parametric and nonparametric methods. Second, we propose a new nonparametric method for estimating the survival function in life table using information theory which applies existing information from the population, such as average and moments. The survival function estimated by this method provides the maximum value for Keyfitz entropy indicating the maximum sensitivity of life expectancy to changes in age-specific mortality rates. We also demonstrate that the survival function estimated by this method can be a powerful competitor to its counterparts which are estimated by common parametric and nonparametric methods.

We examine the impact of sector-based reform on income inequality, concentrating on state banking deregulation in the USA, for which we employ annual balanced panel data from all 50 states and the District of Columbia, covering the period from 1970 to 2000, for our baseline analysis. The estimation strategy exploits the variation across states and years in the enactment of laws that remove restrictions on in-state bank branch geographical expansion and cross-state bank business operational expansion to compute the effects of developments in the financial sector on income inequality. We find evidence that inequality on average decreases with within-state branching reform, whereas it on average increases with between-state banking deregulation. Utilizing five different measures of inequality (top decile income share, Atkinson index, the Gini coefficient, relative mean deviation, and Theil entropy index), we determine that our finding materially depends on which measure of income inequality is being considered. We argue that this has not been stressed in the previous literature.

Walking is greatly significant to achieve good health, and ultimately, social sustainability. There have been numerous walkability indices developed to measure the effectiveness of walking. Limited number of these indices have been developed objectively in the context of developing countries. The current research is focused on developing a walkability index objectively, as well as finding a comparison between non-gated and gated neighborhoods, and to see how the developed walkability index is associated with walking in developing countries. The study area for this research is Karachi, Pakistan. The walkability index has been developed by utilizing entropy index for land-use-mix and Space Syntax for street connectivity, while gross housing density was considered. In order to measure the walking at neighborhood level, a questionnaire (n=1042) was used. Binary logistic regression analysis and Independent sample t-test were used for attaining results. The results show that the walkability of non-gated neighborhoods is higher than gated and there is a positive correlation between walkability and walking. The study concludes that an objectively measured walkability index can be developed in even those areas where a central database doesn’t exist. Furthermore, the promulgation of walking at neighborhood level doesn’t necessitate the policy of promoting the gating of neighborhoods.

An attempt has been made to assess the status and trends of ecosystems of different types of reservoirs in Europe and Asia (the Saratov and Volgograd reservoirs, closing the Volga cascade, and the Iriklinskii Reservoir, which is the largest on the Ural River) on the basis of the relationship among basic hydrochemical parameters. The ecological status of water bodies was considered in terms of the stability of their ecosystems, which was evaluated using some statistical approaches based on the data of long-term monitoring. The proportion of variability of the main components and the index of plasticity (reliability) corresponding to the average module of the correlation coefficients of hydrochemical parameters were calculated. The desirability function method for qualitative assessment of aquatic ecosystems, characterized by a large number of hydrochemical parameters, was applied. This method allowed minimizing a large amount of information and overcoming the dimension problem. The results of the application of the calculated statistical indices allow us to consider the volume of water flow and summer water temperature as the main factors affecting the content of the basic hydrochemical components in the Volgograd and Iriklinskii reservoirs. The hydrochemical regime of the Saratov Reservoir, due to its high flow rate, is determined mainly by the waters coming from the Kuibyshev storage reservoir, and therefore, such dependences were not established. During the study period, the entropy index in the Volga reservoirs fluctuated at the same level but increased significantly in the Iriklinskii Reservoir. The highest values of plasticity are characteristic for the Iriklinskii Reservoir, which has the greatest spatial and seasonal heterogeneity of its aquatic environment. During the study period, there have been trends of increasing plasticity in the Saratov Reservoir and decreasing in the Iriklinskii Reservoir. A decrease in the desirability function indicates some destabilization of their ecosystems.

The paper proposes a new method for the integrated assessment of aquatic ecosystems based on dissolved oxygen concentration and water body temperature. The study proves the validity of assessing the state of aquatic ecosystems in terms of entropy change and the possibility of assessing the ecological state of water bodies based on the point values of the entropy index. The algorithm for the calculation of the entropy index based on water temperature and oxygen concentration according to point measurements is described, and the theoretically reasoned scale for the estimation of the state of water ecosystems is proposed. Based on long-term data on dissolved oxygen and water temperature obtained at the stations of the state observation network and Roshydromet automatic water quality control stations, as well as on experimental data, the entropy index is calculated for some water bodies in the Russian Federation. The advantages and prospects of practical application of the method for the surface water monitoring are discussed.

This study discusses the evaluation of the effect of using different weighting approaches in the process of landslide susceptibility assessment. Weighting process is needed, especially for landslide susceptibility assessment using bivariate statistical analysis, and can radically affect the resulting susceptibility map. The bivariate analysis belongs to a set of quantitative methods. The initial point for the bivariate analysis is selection and processing of input factors in the form of parametric maps, factors that play a dominant role in slope stability. The parametric maps were subsequently evaluated related to landslide inventory map. Another essential part of the bivariate analysis is the determination of the weight of the given input factors. Herein, four methods were applied to determine the weights of each class within reclassified input factors, as well as the total weight of the individual input factors. As a study area, the district of Kysucke Nove Mesto in Slovakia was chosen. Four prognostic maps were the result using entropy index, AHP method (AHP—analytic hierarchy process) (the input factors weighted as a whole), frequency ratio and landslide index (the weights were calculated for each class of input factor). Final landslide susceptibility maps were verified trough ROC curves (ROC—receiver operating characteristics). The accuracy of maps was ascertained by the size area (AUC—area under curve) under ROC curve. The highest accuracy was obtained for maps using weights calculated from the landslide index (88.5%) and the frequency ratio (88.4%).

Spatial Interaction Pattern and Characteristics of Individual and Household Commuters between Regencies/Cities in Greater Bandung Differences in regional development in Greater Bandung caused spatial interaction in order to meet the shortcomings in origin areas. One of the form spatial interaction is commuting. This study aimed to analyze the pattern of spatial interaction between regencies/cities in Greater Bandung and determine individual and household commuter characteristics. Variables used to analyze the pattern of spatial interactions were number of commuters, distances, regional development index, built up area ratio, and population density of regency/city units in Greater Bandung. Method used were Entropy Index and Augmented Doubly Constrained Entropy Model. Variables used to determine individual and household commuter characteristics were age, marital status, highest education completed, number of household members, monthly household expenditure, and the main reason for choosing settlement location. Data were originated from 2017 Greater Bandung Commuter Survey held by Statistics Indonesia (BPS), which consists of 15,082 individuals and 4,531 households. The method used was correspondence analysis. Analysis results shows that Bandung Regency, Sumedang Regency, and Bandung City had positive effects on spatial interactions in Greater Bandung. Commuters in Greater Bandung mostly come from regency areas with productive age, low education level, big number of household members, have low and high economic level, live in regency area because of affordable house price and closeness to family. Meanwhile, commuters in Greater Bandung mostly move toward city areas have young and productive age, high education level, have low and high economic level, and variety number of household members.

Land use land cover (LULC) classification of Churu district of Rajasthan state in India was done through hybrid classification technique. Landsat imageries of 1998, 2008 and 2018 were used to ascertain the rate and nature of spatio-temporal LULC changes. Major focus was given on vegetative cover change detection of the study area. On the basis of field survey and standard classification classes, the land use classes of the study area were divided into eleven classes. A detailed vegetative classification has been done while categorizing the classes. This classification method employed maximum likelihood and ISODATA clustering. The decision tree approach was used to create the multi-temporal hybrid LULC classification. The accuracy assessment results have shown excellent results at 91% overall accuracy with a kappa of 0.92. The results indicated that agriculture, crop land dominated the land use of Churu district while natural vegetation (forest areas) had the least share in land cover during the entire study period from 1998 to 2018. Shannon’s entropy index was used to determine the changes in spatial distributional pattern of built up during the period 1998 to 2018 for the study area in general and also for each of the eight sub-administrative regions (tehsils). The increase in the built up area in the study area during the period of 1998 to 2018 was quite paltry with a general dispersed type of built up. The increase varied from moderate to nominal with the entropy value decreasing from 0.84 to 0.71 for the study area in general.

The study seeks to provide a deeper insight and strategies into the carbon trading market, the internal mechanism, and the linkage mechanism for emission reduction in the carbon trading market right from its inception. The study differs from most prior research on carbon trading, as this current paper incorporates the carbon market with the dissipative structure theory. Based on the dissipative structure theory, this study adapts the entropy generation principle as the base for the carbon trading market. The study captures the following key dimensions: the change of carbon market value, the entropy generation principle of carbon quota trading process, and the replacement process involved in production factors in carbon trading. The study revealed that the entropy generation of the carbon trading market is mainly caused by the cost gradient formed as a result of the cost difference of emission reduction and the increase of production factor input required by economic development. Moreover, the study revealed clearly the dynamics of the entropy index and the carbon market emission reduction efficiency. Thus, the lower the entropy value of the carbon market, the higher the emission reduction efficiency.

A challenge emerging from the literature on measuring and modelling segregation at multiple scales is how to map the results. This Featured Graphic uses a method based on the CMY colour model, allowing each of the C, M and Y values to represent one of the micro-, meso- and macro-levels of analysis separately, then combining them in an overall map. It presents the method using a new index, a multilevel index of diversity, with a case study looking at the ethnic diversity of state-school pupils living in London’s neighbourhoods.

An attempt has been made to assess the status and trends of ecosystems of differenttype reservoirs in Europe and Asia (the Saratov and Volgograd ones, closing the Volga cascade, and the Iriklinsky one, which is the largest on the Ural river) on the basis of the relationship of basic hydrochemical parameters. The ecological status of water bodies was considered from the position of the stability of their ecosystems, which was evaluated using some statistical approaches based on the data of long-term monitoring. The proportion of variability of the main components, the index of plasticity (reliability) corresponding to the average module of the correlation coefficients of hydrochemical parameters were calculated. The desirability function method for qualitative assessment of aquatic ecosystems, characterized by a large number of hydrochemical parameters, was applied. This method allowed minimizing a large amount of information and overcoming the dimension problem. The results of the application of the calculated statistical indices allow us to consider the volume of water flow and summer water temperature as the main factors affecting the content of basic hydrochemical ingredients in the Volgograd and Iriklin reservoirs. The hydrochemical regime of the Saratov reservoir, due to its high flow rate, is determined mainly by the waters coming from the Kuibyshev storage reservoir, and therefore such dependencies were not established. During the study period, the entropy index in the Volga reservoirs fluctuated at the same level, but significantly increased in the Iriklinsky one. The highest values of plasticity are characteristic for the Iriklinsky reservoir, which has the greatest spatial and seasonal heterogeneity of its aquatic environment. During the study period, there have been trends of increasing plasticity in the Saratov reservoir and decreasing in the Iriklinsky one. A decrease in the desirability function indicates some destabilization of their ecosystems.

The paper presents results of a descriptive analysis of income distributions as well as top income inequality among women and men in Poland. The analysis is based on the dataset provided by the Council for Social Monitoring (2019). Throughout 2003–2015 their panel survey included, for example, a question on individual net monthly income in the past three months. In order to reduce differences associated with the age of entering and exiting the labour market on declared income levels (especially pensions), the calculations include only women and men aged 25–60 years. The analysis of income distributions of women and men in Poland is based on standard measures such as mean income, median income and related measures, as well as the Gini coefficient, Theil index and entropy index. It is supplemented by kernel density estimates and results of simultaneous quantile regressions that demonstrate differences between women and men across income groups. The analysis of top income inequality includes comparisons of subsamples consisting of top 3% earners in each group. The share of women in the top percentiles is then calculated and discussed. The analysis shows different dynamics related to the incomes of women and men, which provides support for including business cycle considerations in the analysis of income inequalities and their gender aspects.

Increasing land use land cover changes, especially urban growth has put a negative impact on biodiversity and ecological process. As a consequences, they are creating a major impact on the global climate change. There is a recent concern on the necessity of exploring the cause of urban growth with its prediction in future and consequences caused by this for sustainable development. This can be achieved by using multitemporal remote sensing imagery analysis, spatial metrics, and modeling. In this study, spatio-temporal urban change analysis and modeling were performed for Biratnagar City and its surrounding area in Nepal. Land use land cover map of 2004, 2010, and 2016 were prepared using Landsat TM imagery using supervised classification based on support vector machine classifier. Urban change dynamics, in term of quantity, and pattern was measured and analyzed using selected spatial metrics and using Shannon’s entropy index. The result showed that there is increasing trend of urban sprawl and showed infill characteristics of urban expansion. Projected land use land cover map of 2020 was modeled using cellular automata-based approach. The predictive power of the model was validated using kappa statistics. Spatial distribution of urban expansion in projected land use land cover map showed that there is increasing threat of urban expansion on agricultural land.

The increasing availability of remote sensing data allows the quantification of biodiversity in space and time. In particular, spectral diversity, defined as the variability of electromagnetic radiation reflected from plants, can be assessed with remote sensing. Plant traits vary diurnally and seasonally due to plant phenology and land management. This results in strong temporal variation of spectral diversity, which cannot be accurately represented by remotely sensed data collected at a single point in time. However, knowledge of how datasets sampled at multiple points in time should best be used to quantify spectral diversity is scarce. To address this issue, we first introduced a new approach using spatio-temporal spectral diversity based on the dissimilarity measure Rao's quadratic entropy index (RaoQ). Thereby, we demonstrated how RaoQ can be used to partition the total spectral diversity of a region (γSD) into additive alpha (αSD, within communities) and spatio-temporal beta (βSD; between communities) components, allowing the calculation of βSD from community mean spectral features, independent from αSD. Second, we illustrated our methodological approach with a case study in which βSD is calculated from Sentinel-2 satellite data at high temporal resolution for managed grasslands which differ across a large gradient of environmental properties. We were able to show differences in βSD and separate its components into phenological and management effects. Furthermore, the contribution of different plant communities to βSD was assessed, and the results were validated against a dataset of in-situ measured β diversity from plant surveys. Compared to spatial dissimilarities from distinct stages of the growing season, using spatio-temporal dissimilarities between communities produced a more accurate estimation of the uniqueness of a community. This study shows how to account for temporal variations in the spectral diversity of plant communities and demonstrates that this improves the estimation of plant biodiversity through remote sensing. Spectral diversity in space and time makes it possible to assess mechanisms that drive biodiversity and identify plant communities relevant for conservation purposes.

Affect plays a major role in the individual’s daily life, driving the sensemaking of experience, psychopathological conditions, social representations of phenomena, and ways of coping with others. The characteristics of affect have been traditionally investigated through physiological, self-report, and behavioral measures. The present article proposes a text-based measure to detect affect intensity: the Affective Saturation Index (ASI). The ASI rationale and the conceptualization of affect are overviewed, and an initial validation study on the ASI’s convergent and concurrent validity is presented. Forty individuals completed a non-clinical semi-structured interview. For each interview transcript, the ASI was esteemed and compared to the individual’s physiological index of propensity to affective arousal (measured by heart rate variability (HRV)), transcript semantic complexity (measured through the Semantic Entropy Index (SEI)), and lexical syntactic complexity (measured through the Flesch–Vacca Index (FVI)). ANOVAs and bi-variate correlations estimated the size of the relationships between indexes and sample characteristics (age, gender), then a set of multiple linear regressions tested the ASI’s association with HRV, the SEI, and the FVI. Results support the ASI construct and criteria validity. The ASI proved able to detect affective saturation in interview transcripts (SEI and FVI, adjusted R2 = 0.428 and adjusted R2 = 0.241, respectively) and the way the text’s affective saturation reflected the intensity of the individual’s affective state (HRV, adjusted R2 = 0.428). In conclusion, although the specificity of the sample (psychology students) limits the findings’ generalizability, the ASI provides the chance to use written texts to measure affect in accordance with a dynamic approach, independent of the spatio-temporal setting in which they were produced. In doing so, the ASI provides a way to empower the empirical analysis of fields such as psychotherapy and social group dynamics.

Over time, coal has been an important input for power generation, being used for basic purposes such as cooking food and also contributing to technological development. This study evaluated the regional concentration of charcoal production in the state of Pará, Brazil. Data used in this analysis were obtained from the Data Recovery System (SIDRA) of the Brazilian Institute of Geography and Statistics (IBGE) from 1990 to 2017. The indicators used were: Concentration Ratio (CR(k)), Herfindahl-Hirschman Index (HHI), Theil Entropy Index (EI), and Gini Index (G). Main results indicate that most charcoal production is from Southeast and Northeast Pará mesoregions. The CR(k) showed an extremely high concentration for both municipalities and microregions, HHI and EI tended toward high concentration and monopolized markets, G showed that inequality ranges from very strong to absolute in the municipalities and, for most of the period, in mesoregions and microregions, except for 2004. Therefore, we inferred that the concentration of charcoal production at regional levels is concentrated throughout the state., Ao longo do tempo o carvão tem sido um importante insumo para geração de energia, sendo utilizado para fins básicos como cocção de alimentos e contribuindo até para o desenvolvimento tecnológico. Deste modo, este trabalho analisou a concentração regional da produção de carvão vegetal no estado do Pará. Os dados para a análise foram obtidos no Sistema de Recuperação de Dados (SIDRA) do Instituto Brasileiro de Geografia e Estatística (IBGE), no período de 1990 a 2017. Os indicadores utilizados foram a Razão de Concentração [CR(k)], o Índice de Herfindahl-Hirschman (IHH), o índice de entropia de Theil (E) e o Índice de Gini (G). Os principais resultados mostram que as mesorregiões da Sudeste e Nordeste do Pará concentram a maior parte da produção de carvão vegetal. O CR(k) tanto para os municípios quanto para as microrregiões apresentaram concentração extrema; o IHH e E mostraram tendências de concentração apresentando mercados monopolizados; o G mostrou que a desigualdade tende de muito forte a absoluta para os municípios e na maioria do período para as mesorregiões e microrregiões, salvo o ano de 2004. Nesse sentido, infere-se que a concentração da produção do carvão vegetal nos níveis regionais é concentrada na sCua totalidade

Longevity risk is a major concern for governments around the world as they have to address social benefits, whether in the form of pensions, healthcare, or caring for dependents and providing long-term care, and so forth, which directly impact countries’ budgets. This paper uses a single entropy index to measure this type of risk. This methodology is clearly different from the one traditionally used in the literature, which is nearly entirely based on measuring the evolution of mathematical life expectancy. The authors used the longest-living populations in the world, Japan and Spain, to create a database in order to analyse the virtue of the indicator. The aim was to establish whether the longevity of those populations is accelerating or decelerating, compared by sex, and whether that occurs at the same intensity at different stages of a person’s life in each case. If the indicator showed differences in intensity, it would be a benchmark for the insurance and financial industry, providing it with information to market different products.

This paper examines human capital inequality and how it relates to earnings inequality in Portugal using data from Quadros de Pessoal for the period 1986–2017. The objective is threefold: (i) show how the distribution of human capital has evolved over time; (ii) investigate the association between human capital inequality and earnings inequality; and (iii) analyse the role of returns to schooling, together with human capital inequality, in the explanation of earnings inequality. Our findings suggest that human capital inequality, computed based on the distribution of average years of schooling of employees working in the Portuguese private labour market, records a positive trend until 2007 and decreases from this year onwards, suggesting the existence of a Kuznets curve of education relating educational attainment levels and education inequality. Based on the decomposition of a Generalized Entropy index (Theil N) for earnings inequality, we observe that inequality in the distribution of human capital plays an important role in the explanation of earnings inequality, although this role has become less important over the last decade. Using Mincerian earnings regressions to estimate the returns to schooling together with the Blinder-Oaxaca decomposition of real hourly earnings we confirm that there are two important forces associated with the observed decrease in earnings inequality: a reduction in education inequality and compressed returns to schooling, mainly in tertiary education.

One of the requirements for planning and decision-making to develop the infrastructures is to prepare the landslide occurrence hazard map. For this purpose, in this article, the Shannon entropy and Dempster–Shafer intuition theory methods were used to prepare the hazard map and applying the data uncertainty in the Tutkabon region, Guilan Province. In this study, parameters of the slope, elevation, geomorphological conditions, the curvature of the earth, proximity to the river and proximity to faults were used as the affecting factors on the landslide occurrence. By using these parameters, the map of the landslide occurrence hazard was prepared using the entropy index; besides, the belief values were calculated by the Dempster–Shafer method. To investigate the uncertainty, the disbelief and uncertainty values were calculated by the Dempster–Shafer method. Besides, in the Shannon entropy method the maps were compared before and after applying the entropy. Finally, by evaluating the results using comparing the landslide occurrence places in the study area and modeled hazard map, the value of 0.69 was obtained for the area under the prediction rate curve (as the parameter of prediction total precision) in the status with entropy and the value of 0.54 was obtained for the status without entropy. Similarly, the evaluation of the hazard belief map by the Dempster–Shafer method indicates that 65% of the landslide occurrence places occur in the classes of high and very high hazard and the value of 0.74 was obtained for the area under the prediction rate curve in the belief map.

This research examines residential segregation in the hillside residential areas (HRAs) of Seoul, South Korea, by applying a newly classified geomorphons -- a synthetic land classification system -- created from 30 × 30 m2 resolution digital elevation model data to redefine Seoul's hillside residential areas. The multi-group Entropy Index explores segregation within and among the dimensions of socio-economic and built-environment characteristics in HRAs and non-HRAs. We apply the Local Indicator of Spatial Association to confirm strong spatial autocorrelation, and the clusters and outliers within the HRAs. This is rectified by the Spatial Error (regression) Model which affirms that the residential communities are most strongly segregated along dimensions of household composition, housing-built year, and educational attainments, and have significant relationship with the economic status of HRA residents. The newly classified geomorphons also suggest that segregation along the dimensions of socio-economic and built-environment characteristics are higher in HRAs compared to the non-HRAs.

The Remote Sensing (R/ S) and Geographical Information System (GIS) play a vital role to evaluate and study the urban expansion pattern. In this study, the Chennai city was selected to perform the urban sprawl study. Five different periods of satellite imageries for the time elapsed between 1994 and 2016 were used. The main aim of this paper was to identify the urban sprawl of Chennai as a patterning process. The extended areas of urban in the period of 1994, 2001, 2006, 2011 and 2016 were extracted by the sub-pixel classification method from the satellite imageries. Furthermore, Shannon’s entropy index was used for assessing urban expansion. The findings of this study proved that Chennai city has sprawled by urban expansion during the period between 1994 and 2016. Likewise, the dispersion rate of urban sprawl for the periods of 1994, 2001, 2006, 2011 and 2016 were 0.671, 0.679, 0.688, 0.693 and 0.695 respectively. Consequently, this uncontrolled dispersed urban development had resulted in the study area losses their green space.

Flooding is one of the main natural hazards in Northern Europe and several areas of the Northern Boreal Hemisphere, where during intense rainfall events, several river basins are affected by a fast water level rise that may cause severe damage to human lives and properties. For these reasons, the development of flood models to identify susceptible areas is essential for decision-makers. Geographic Information Systems (GIS) are currently accurate and valuable support tools for defining flood susceptibility maps at different spatial scales. In this study, the prediction accuracy of different GIS-based procedures in the identification of flooding susceptibility is tested and compared. These procedures include the frequency ratio, a combination of the frequency ratio and logistic regression, a combination of the frequency ratio and Shannon’s entropy index, and the statistical index. Ten conditioning parameters of flooding susceptibility are considered: elevation, slope, curvature, land use, Topographic Wetness Index, Stream Power Index, hydrogeology, stream distance, flow direction and average annual rainfall. The comparison analysis is carried out by applying these methods to the study area of Devon County in Southwest England. A total of 225 flood events are used to define the models. For model validation, 1000 randomly selected training and testing sub-datasets have been used in the definition of the receiver operating characteristic (ROC) curves. The results show that the procedure based on the statistical index provides the highest accuracy and reliability in flood susceptibility predictions.

This paper proposed a new multi-objective approach to find the optimal set of weight's combination of forecasts that were jointly efficient with respect to various performance and precision metrics. For this, the residues' series of each previously selected forecasts methods were calculated and, to combine them through of a weighted average, several sets of weights were obtained using Simplex - Lattice Design {m,q}. Then, several metrics were calculated for each combined residues' series. After, Principal Components Factor Analysis (PCFA) was used for extracting a small number series' factor scores to represent the metrics selected with minimal loss of information. The extracted series' factor scores were mathematically modeled with Mixture Design of Experiments (DOE-M). Normal Boundary Intersection method (NBI) was applied to perform joint optimization of these objective functions, allowing to obtain different optimal weights set and the Pareto frontier construction. As selection criteria of the best optimal weights' set were used the Shannon's Entropy Index and Global Percentage Error (GPE). Here, these steps were successfully applied to predict coffee demand in Brazil as a case study. In order to test the applicability and feasibility of the proposed method based on distinct time series, the coffee's Brazilian production and exportation were also foreseen by new method. Besides, the simulated series available in Montgomery et al. (2008) were also used to test the viability of the new method. The results showed that the proposed approach, named of FA-NBI combination method, can be successfully employed to find the optimal weights of a forecasts' combination.

Background and Objectives Nursing homes remain subjected to institutional racial segregation in the United States. However, a standardized approach to measure segregation in nursing homes does not appear to be established. A systematic review was conducted to identify all formal measurement approaches to evaluate racial segregation among nursing home facilities, and to then identify the association between segregation and quality of care in this context. Research Design and Methods PubMed, Scopus, and Web of Science databases were searched (January 2018) for publications relating to nursing home segregation. Following the PRISMA guidelines, studies were included that formally measured racial segregation of nursing homes residents across facilities with regional-level data. Results Eight studies met the inclusion criteria. Formal segregation measures included the Dissimilarity Index, Disparities Quality Index, Modified Thiel’s Entropy Index, Gini coefficient, and adapted models. The most common data sources were the Minimum Data Set (MDS; resident-level), the Certification and Survey Provider Enhanced Reporting data (CASPER; facility-level), and the Area Resource File/ U.S. Census Data (regional-level). Most studies showed evidence of racial segregation among U.S. nursing home facilities and documented a negative impact of segregation on racial minorities and facility-level quality outcomes. Discussion and Implications The measurement of racial segregation among nursing homes is heterogeneous. While there are limitations to each methodology, this review can be used as a reference when trying to determine the best approach to measure racial segregation in future studies. Moreover, racial segregation among nursing homes remains a problem and should be further evaluated.

The paper evaluates the competitiveness of Bangladesh Readymade Garments (RMG) sector in the global RMG market through Market share analysis using two-digit classifications of Harmonized System (HS)data for the period covering 2012 to 2016 to understand the actual condition of sector’s export comparison with its competitors. While Trade Entropy index has also been calculated to make an attempt to understand the geographical dispersion of export market scenario using two-digit classifications of HS data for the time period between 2006 and 2016. The findings of the paper based on market share analysis illustrate that Bangladesh’s RMG export share in the global market reached 7.50percentin 2016, a shot up by 2.73 percentage points, indicating an increase in export competitiveness, whereas China’s share declined to 33.75 percentin 2016, with an exception to 2013. During this period, Vietnam and India’s market share increased by 2.24, and 0.72 percentage points respectively, making them the 3rd and 6th largest RMG exporter in the world. Trade Entropyindex calculationindicates that the geographical diversification of Bangladesh’s RMG export has increased considering the value of the index, it has increased more than two times during this time period. But, in spite of increasing geographical diversification of the RMG sector market, itsexport is still heavily concentrated on two markets: the European UnionEU (28), and the United States of America (USA). The paper also provides a set of policyrecommendations that wouldbe helpful to both RMG sector stakeholders and policymakers to move forward the sector towards more export competitiveness.

En este artículo se va a valorar la heterogeneidad en los procesos de transición hacia la vida adulta comparando dos periodos de diferente coyuntura económica: el periodo previo la crisis y en los años en los que es posible considerar como el comienzo de la salida de la crisis económica. A través del índice de entropía se medirá el grado de complejidad, superposición o simultaneidad de los diferentes estatus o estados que estructuran el proceso de transición hacia la vida adulta (estudiante, activo, emancipado, en pareja y ser padres) y atendiendo a diversos factores o variables como la edad, sexo y riesgo de pobreza. Con este indicador será posible construir secuencias donde constatar el final de la transición, situado por la bibliografía especializada en el ápice de entropía o edad a la que se observa la máxima heterogeneidad.In this article we will assess the heterogeneity in the processes of transition to adult life by comparing two periods of different economic conjuncture: the period prior to the crisis and in the years in which it is possible to consider the beginning of the exit from the economic crisis. Through the entropy index, the degree of complexity will be measured, overlap or simultaneity of the different status or states that structure the process of transition to adult life (student, active, emancipated, as a couple and to be parents) and attending to various factors or variables such as age, sex and risk of poverty. With this indicator it will be possible to build sequences to verify the end of the transition, located by specialized bibliography at the apex of entropy or age at which the maximum heterogeneity is observed.

Debris flow is a destructive natural phenomenon that normally occurs in mountainous regions or steeply sloping areas. Its destructive force is so high that whatever comes in its way is obliterated. It is therefore difficult to collect field observations of debris flow. This study developed a model of the vertical distribution of sediment concentration in debris flow by maximizing Renyi entropy. The model contains a parameter, called entropy parameter, which is a combination of Lagrange multipliers and has a physical justification. An expression for the equilibrium sediment concentration is also developed, and is found to be a function of the entropy parameter and an entropy index. The proposed model is validated with experimental data and is compared with other entropy-based models reported in the literature. It is observed that the prediction accuracy of the proposed models is superior to that of the other models. Errors, namely relative error and relative root-mean-squared error, are computed and are reported in the paper in support of the derived model.

Research background: This article investigates the connection between consumer’s budget growth and diversification of household spending. The main question of research is “are there new drivers of modern processes of consumer spending's diversification, at a time when spending on food has reached the minimum share in the consumer budget. Purpose of the article: The objective of the article is to clarify the hypothesis about the existence of a certain limit of income (and consumer spending) after which the growing of consumer’s purchasing capacity loses power of influence on spending diversity. Methods: Theil entropy index was used for measuring the diversity of household spending. This index was defined as a sum of within-group and between-group entropy, which allows for comparing the diversification of household spending in two aggregate groups of expenditure, which were formed by the authors. The Workings’ equation was used for modeling the spending entropy’s dependence on their absolute value. Two categories of household spending were regrouped (consolidated) by us through forming a group more related to the development of human economic potential (SMRHD) and less related to these processes (SLRHD). The research was done on the basis of ICP (2011) data, which covers 178 countries and refers to 2011 year — the latest available on the moment of the article was completed. Findings & Value added: The results obtained in this research confirmed that there is a limit of household spending’s size, beyond which further increasing of consumers’ economic opportunities loses a significant impact on the diversity of consumption spending. However, the weakening of the link between size of spending and its entropy reflects impact of two qualitative differenced factors. The first is relatively much more radical decrease of spending growth influence on within-group entropy for SLRHD. The second — is relatively much less significant decrease of entropy’s sensitivity to spending growth for SMRHD. Such results reflect the increase in the importance of "non-functional demand components", which reduces the capacity of data on functional distribution of household expenditures to characterize the extent of their diversification.

When analyzing and describing the statistical and topological characteristics of complex networks, the heterogeneity can provide profound and systematical recognition to illustrate the difference of individuals, and many node significance indices have been investigated to describe heterogeneity in different perspectives. In this paper a new node heterogeneity index based on the von Neumann entropy is proposed, which allows us to investigate the differences of nodes features in the view of spectrum eigenvalues distribution, and examples in reality networks present its great performance in selecting crucial individuals. Then to lower down the computational complexity, an approximation calculation to this index is given which only depends on its first and second neighbors. Furthermore, in reducing the network heterogeneity index by Estrada, this entropy heterogeneity presents excellent efficiency in Erdos–Renyi and scale-free networks compared to other node significance measurements; in reducing the average clustering coefficient, this node entropy index could break down the cluster structures efficiently in random geometric graphs, even faster than clustering coefficient itself. This new methodology reveals the node heterogeneity and significance in the perspective of spectrum, which provides a new insight into networks research and performs great potentials to discover essential structural features in networks.

The present study tested the associations of housing type diversity, neighborhood disadvantage, and employment-related community supportive services with new job placements in 257 HOPE VI projects. This study used a merged data from quarterly reports by HOPE VI revitalization grantees, 2010–2014 American Community Survey, and the entropy index on housing type diversity of each HOPE VI site. This study first described the patterns of housing type diversity and the surrounding neighborhoods of the HOPE VI sites. Then, a negative binomial regression analysis showed that higher housing type diversity and a higher number of education programs were significantly associated with a higher number of new job placements. The results suggest that mixing residents with diverse housing types could yield positive employment outcomes. None of the neighborhood-level predictors was statistically significant. This paper is one of the first to apply the entropy index to diverse housing types of mixed-income developments.

The automatic and accurate determination of the epileptogenic area can assist doctors in presurgical evaluation by providing higher security and quality of life. Visual inspection of electroencephalogram (EEG) signals is expensive, time-consuming and prone to errors. Several numbers of automated seizure detection frameworks were proposed to replace the traditional methods and to assist neurophysiologists in identifying epileptic seizures accurately. However, these systems lagged in achieving high performance due to the anti-noise ability of feature extraction techniques, while EEG signals are highly susceptible to noise during acquisition. The present study put forwards a new entropy index Permutation Fuzzy Entropy (PFEN), which may delineate between ictal and interictal state of epileptic seizure using different machine learning classifiers. 10-fold cross-validation has been used to avoid the over-fitting of the classification model to achieve unbiased, stable, and reliable performance. The proposed index correctly distinguishes ictal and interictal states with an average accuracy of 98.72%, sensitivity of 98.82% and a specificity of 98.63%, across 21 patients with six epileptic seizure origins. The proposed system manifests the fact that lower PFEN characterizes the EEG during seizure state than in the Interictal seizure state. The study also helps us to investigate the more profound enactment of different classifiers in term of their distance metrics, learning rate, distance, weights, multiple scales, etc. rather than the conventional methods in the literature. Compared to other state of art entropy-based feature extraction methods, PFEN showed its potential to be a promising non-linear feature for achieving high accuracy and efficiency in seizure detection. It also show's its feasibility towards the development of a real-time EEG-based brain monitoring system for epileptic seizure detection.

Objectives: Schizophrenia is a predominant product of pathological alterations distributed throughout interconnected neural systems. Designing new objectively diagnostic methods are burning questions. Dynamical functional connectivity (DFCs) methodology based on fMRI data is an effective lever to investigate changeability evolution in macroscopic neural activity patterns underlying critical aspects of cognition and behavior. However, region properties of brain architecture have been less investigated by special indexes of dynamical graph in general mental disorders. Methods: Embracing the network dynamics concept, we introduce topology entropy index (TE-scores) which is focused on time-varying aspects of FCs, hence develop a new framework for researching the dysfunctional roots of schizophrenia in holism significance. In this work, the important process is to uncover noticeable regions endowed with antagonistic stance in TE-scores of between morbid and normal DFCs of 63 healthy controls (HCs) and 57 chronic schizophrenia patients (SZs). Results: For the whole brain region levels, right olfactory, right hippocampus, left parahippocampal gyrus, right parahippocampal gyrus, left amygdala, and left cuneus in SZs are endowed with significantly different TE-scores. At brain subsystems level, TE-scores in DMN are abnormal in the SZs. Comparison with existing method(s): Topology entropy in DFCs is introduced to explore the dynamical information organization of diverse regions and their abnormal changes in mental illness. Several classical graph indexes (such as degree strength, betweenness, centrality) in the static brain network measure the region importance of FCs under senses of information integration and separation process. Although highly related to degree strength by comparing the corresponding values, topology entropy further explores the regions’ aberrant adaptability of functional contact and function switching. Conclusion: TE-scores of abnormal regions in SZs are associated to the passive apathetic social withdrawal, unusual thought content, disturbance of volition, preoccupation, active social avoidance and hallucinatory symptoms. Thought the strict contrastive study, it is worth stressing that topology entropy is a meaningful biological marker to excavating schizophrenic psychopathology.

The implementation of the New Economic Policy (NEP) had taken place in peninsular Malaysia for 20 years (1970-1990). It was formulated as a social re-engineering in the aftermath of the 13th of May 1969 Incident. The NEP was “as simple as two eyes” because it targeted to eradicate poverty and at the same time to restructure society in order to eliminate the identification of race with economic function. The objectives reflected that the NEP was focusing on demographic aspects, particularly targeted at eyeing the division of races based on economic function. In conjunction with that, this article aims to study the changes in racial segregation in peninsular Malaysia from 1980 until 2010. To strengthen the findings, this article introduces spatial elements to geo-visualize the long-term changes in the spatial pattern of racial segregation in peninsular Malaysia. In terms of statistical analysis, racial segregation was calculated by using the Entropy Index. In the course of spatial analysis, the results were then being mapped by applying Geographic Information Systems (GIS) according to each district in peninsular Malaysia. Generally, the results divulge that from the year 1980 to 2010, districts in peninsular Malaysia experienced medium segregation of H value between 0.6144-0.7339. However, in detail, the value varied across each district. It was observed that the majority of districts on the East Coast experienced a high degree of racial segregation compared to the majority of districts on the West Coast. Keywords : Entropy index, geographic information systems, new economic policy, race, spatial demography and segregation

Environmental pollution is a negative externality of urbanization and is of great concern due to the fact that it poses serious problems to human health. Pollutants, such as heavy metals, have been found in urban road dust, however, it is unclear whether the urban form has a role in its accumulation, mainly in cases where there is no dominant unique source. We collected 482 samples of road dust, we determined the concentrations of five heavy metals (Cr, Cu, Pb, Zn, and Ni) using inductively coupled plasma optical emission spectrometry (ICP-OES), and then we derived the pollution load index (PLI). After estimating the mostly anthropogenic origin of these pollutants based on global levels of reference, there were two main aims of this study. Firstly, to analyze the spatial correlation of heavy metals, and secondly, to identify the main factors that influenced the heavy metal concentrations in the road dust of Mexico City. We did this by using a spatial autocorrelation indicator (Global Moran’s I) and applying ordinary least squares (OLS) and spatial regression models. The results indicated low levels of positive spatial autocorrelation for all heavy metals. Most variables failed to detect any relationship with heavy metals. The median strip area in the roads had a weak (significance level of 90%) but consistent positive relationship with Cr, Cu, Ni, Pb, and the PLI. The distance to the airport had a weak (significance level of 90%) and inverse relationship with Pb. Manufacturing units were associated with an increase in Cu (significance level of 95%), while the entropy index was associated with an increase in Ni (significance level of 95%).

This study aims to evaluate the relationship between urban built environment and hospital admissions from cardiovascular diseases in Kuala Lumpur, Malaysia. Hospital admission data from 2004 to 2016 for cardiovascular diseases were used with patient residential postcodes as the unit of analysis. Data was split into 2004–2009 (12,551 cases) and 2010–2016 (17,154 cases) periods corresponding to land use data. We used generalized linear mixed model to analyse population density, property value, entropy index, and the kernel density (800 m) of specific land use, bus and rail stations, and road junctions, with time period and postcodes as the random effects to generate incidence rate ratios (IRRs). Results indicated that entropy index and recreational area density were associated with fewer hypertensive disease and ischemic heart disease hospital admissions (IRR range: 0.49–0.68, 95%CI: 0.27, 0.97). Population density and property value were associated with fewer cerebrovascular disease hospital admissions (IRR range: 0.29–0.34, 95%CI: 0.11, 0.75). Contrarily, density of road junctions was associated with 2.5–6.3 times more hospital admissions for cardiovascular disease hospital admissions (IRR range: 2.53–6.34, 95%CI: 1.07,17.91). There were no significant association between hospital admission and density of residential area, undeveloped land, rail and bus stations. The shapes of relationships for all attributes were non-linear, and changed markedly at the third quartile except for recreational area density. The findings suggest that land use attributes have some protective effects on the cardiovascular disease admission cases as compared to the transport attributes. These findings have important merits for integrating health into urban planning.

The paper suggests a new generalized variance concept for measuring multidimensional inequality of a stratified society, based on multivariate statistical methods, where the members of society form a cloud in the oblique space of dimensions of inequality, such as income, expenditure and property. The cloud presents the multidimensional inequality capsulized in the cloud. The goal is to condense all the inequality information embodied by the cloud into a composite compact metric characterizing both the shape and the inner structure of the cloud. Contrary to the conventional literature that considers multidimensionality as a unidimensional weighted combination of the dimensions, our new composite index measures the inequality of the configuration of the points in the cloud. Our aim is twofold. First, we introduce the Inequality Covariance Matrix (ICM) assigned to the cloud, with elements measuring the correlations among dimensions. Having ICM, we propose the Generalized Variance (GV) of ICM to measure the composite Generalized Variance Inequality (GVI) level. Second, to evaluate the stratum-specific structure of the overall inequality, we suggest a new two-stage procedure. In the first stage, we divide the total GVI into between-groups and within-groups effects. Then, in the second stage the contributions of the strata to the within-groups inequality and, the contributions of the dimensions to the between-groups inequality are calculated. This GVI approach is sensitive to the correlation system, decomposable into stratum effects and, the number of dimensions is not limited. Moreover, including the log-dimensions in the analysis, GVI yields an Entropy Covariance Matrix giving a new Generalized Variance Entropy index. Finally, the GVI of censored poverty indicators means multidimensional poverty measurement. This special complex task is not yet solved in the traditional literature so far.

In the travel insurance industry, cancel-for-any-reason insurance, also known as a cancellation protection service (CPS), is a recent attempt to strike a balance between customer satisfaction and service provider (SP) profits. However, some exceptional circumstances, particularly the COVID-19 pandemic, have led to a dramatic decrease in SP revenues, especially for non-refundable tickets purchased early with CPS. This paper begins by presenting a risk group segmentation of customers in an online ticket reservation system. Then, a CPS fee is recommended depending on the different customer risk groups provided by the cluster segmentation via different clustering algorithms such as centroid-based K-means, hierarchical agglomerative, DBSCAN, and artificial neural network-based SOM algorithms. According to the implemented cluster metrics, which include the Silhouette index, Davies-Bouldin index, Entropy index, and DBCV index, the SOM algorithm presents the most appropriate result. After predicting the new customer cluster, a CPS fee will be calculated with the proposed adaptive CPS method based on the cluster segmentation weights. Determining the weight of each cluster is related to the total CPS revenue threshold for all clusters defined by the SP. Therefore, to avoid a loss for SPs, the total CPS revenue will be kept constant with the threshold that the SP has been adjusted. The experimental results based on real-world data show that the risk group segmentation of customers helps to maintain a balance between CPS fees and SP profits. Finally, according to the calculated weights, the proposed model pegs the SP gain/loss variation with a 0.00012 exchange ratio.

Methods based on vibration analysis are currently regarded as the most conclusive means for fault diagnosis and health prognostics in rotary machinery. However, changing working conditions mean that the vibration signals originating from rotary machinery exhibit different levels of complexity. This complexity leads to increased difficulty in constructing health indicators (HIs). In this paper, we propose a multiscale Tsallis permutation entropy (MTPE) to construct the HIs of rotary machinery under different working conditions. MTPE values are a function of an entropy index and scale, which have the universality for handling the complexity of a permutated time series. The health condition of the rotary machinery was effectively represented by the MTPEs in conditional monitoring; the initial point of the unhealthy stage was found using the 3 σ interval. This was set as the alarm threshold according to the varying HI trend. Once this was established, dividing the stages into two-stage health stages (HS) was straightforward. Using a rolling bearing, a run-to-failure experiment was conducted and results suggested that the proposed method effectively assessed the status of the rotary machinery. Taken together, this study provided a novel complexity measure based on a methodology for constructing the HIs of rotary machinery and enriches conditional monitoring theory.