scholarly journals A novel causality-centrality-based method for the analysis of the impacts of air pollutants on PM2.5 concentrations in China

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Bocheng Wang
Keyword(s):  
Air Pollutants ◽  
Regression Models ◽  
Large Population ◽  
Vital Role ◽  
Superior Performance ◽  
Causal Direction ◽  
Northern Cities ◽  
Main Components ◽  
Surrounding Areas ◽  
Centrality Analysis

AbstractIn this paper, we analyzed the spatial and temporal causality and graph-based centrality relationship between air pollutants and PM2.5 concentrations in China from 2013 to 2017. NO2, SO2, CO and O3 were considered the main components of pollution that affected the health of people; thus, various joint regression models were built to reveal the causal direction from these individual pollutants to PM2.5 concentrations. In this causal centrality analysis, Beijing was the most important area in the Jing-Jin-Ji region because of its developed economy and large population. Pollutants in Beijing and peripheral cities were studied. The results showed that NO2 pollutants play a vital role in the PM2.5 concentrations in Beijing and its surrounding areas. An obvious causality direction and betweenness centrality were observed in the northern cities compared with others, demonstrating the fact that the more developed cities were most seriously polluted. Superior performance with causal centrality characteristics in the recognition of PM2.5 concentrations has been achieved.

Assessing the Unacquainted: Inferred Reviewer Personality and Review Helpfulness

MIS Quarterly ◽  
2021 ◽  
Vol 45 (3) ◽  
pp. 1113-1148
Author(s):  
Angela Xia Liu ◽  
◽  
Yilin Li ◽  
Sean Xu ◽  
◽  
...  
Keyword(s):  
Personality Traits ◽  
Supervised Classification ◽  
Large Population ◽  
Superior Performance ◽  
Product Reviews ◽  
Review Helpfulness ◽  
Personality Theories ◽  
Deep Learning Model ◽  
Practical Implications

This work examines the question of who is more likely to provide future helpful reviews in the context of online product reviews by synergistically using personality theories and data analytics. It trains a deep learning model to infer a reviewer’s personality traits. This enables analyses to reveal the role of personality traits in review helpfulness among a large population of reviewers. We develop hypotheses on how personality traits are associated with review helpfulness, followed by hypotheses testing that confirms that higher review helpfulness is related to higher openness, conscientiousness, extraversion, and agreeableness and to lower emotional stability. These results suggest the appropriateness of using these five personality traits as inputs for developing a model for predicting future review helpfulness. Based on an ensemble model using supervised classification algorithms, we develop a predictive model and demonstrate its superior performance. Theoretical and practical implications are discussed.

Multilevel Regression and Poststratification Versus Survey Sample Weighting for Estimating Population Quantities in Large Population Health Studies

2020 ◽  
Vol 189 (7) ◽  
pp. 717-725 ◽  
Author(s):  
Marnie Downes ◽  
John B Carlin
Keyword(s):  
Population Health ◽  
Large Scale ◽  
Census Data ◽  
Large Population ◽  
Population Data ◽  
Superior Performance ◽  
Population Parameter ◽  
Multilevel Regression ◽  
Sample Weighting ◽  
Survey Weighting

Abstract Multilevel regression and poststratification (MRP) is a model-based approach for estimating a population parameter of interest, generally from large-scale surveys. It has been shown to be effective in highly selected samples, which is particularly relevant to investigators of large-scale population health and epidemiologic surveys facing increasing difficulties in recruiting representative samples of participants. We aimed to further examine the accuracy and precision of MRP in a context where census data provided reasonable proxies for true population quantities of interest. We considered 2 outcomes from the baseline wave of the Ten to Men study (Australia, 2013–2014) and obtained relevant population data from the 2011 Australian Census. MRP was found to achieve generally superior performance relative to conventional survey weighting methods for the population as a whole and for population subsets of varying sizes. MRP resulted in less variability among estimates across population subsets relative to sample weighting, and there was some evidence of small gains in precision when using MRP, particularly for smaller population subsets. These findings offer further support for MRP as a promising analytical approach for addressing participation bias in the estimation of population descriptive quantities from large-scale health surveys and cohort studies.

scholarly journals Urban Accounting and Welfare

2013 ◽  
Vol 103 (6) ◽  
pp. 2296-2327 ◽  
Author(s):  
Klaus Desmet ◽  
Esteban Rossi-Hansberg
Keyword(s):  
Large Population ◽  
The United States ◽  
Welfare Effects ◽  
City Size ◽  
Negative Effects ◽  
The Us ◽  
City Size Distribution ◽  
Using Data ◽  
Main Components ◽  
The City

We use a simple theory of a system of cities to decompose the determinants of the city size distribution into three main components: efficiency, amenities, and frictions. Higher efficiency and better amenities lead to larger cities but also to greater frictions through congestion and other negative effects of agglomeration. Using data on MSAs in the United States, we estimate these city characteristics. Eliminating variation in any of them leads to large population reallocations, but modest welfare effects. We apply the same methodology to Chinese cities and find welfare effects that are many times larger than those in the US. (JEL H71, O18, P25, R11, R23, R41)

scholarly journals Robust Statistical Inference in Generalized Linear Models Based on Minimum Renyi’s Pseudodistance Estimators

Entropy ◽  
2022 ◽  
Vol 24 (1) ◽  
pp. 123
Author(s):  
María Jaenada ◽  
Leandro Pardo
Keyword(s):  
Generalized Linear Models ◽  
Influence Function ◽  
Regression Models ◽  
Linear Models ◽  
Asymptotic Properties ◽  
Significant Loss ◽  
Superior Performance ◽  
Test Statistics ◽  
Linear Regression Models ◽  
Type Test

Minimum Renyi’s pseudodistance estimators (MRPEs) enjoy good robustness properties without a significant loss of efficiency in general statistical models, and, in particular, for linear regression models (LRMs). In this line, Castilla et al. considered robust Wald-type test statistics in LRMs based on these MRPEs. In this paper, we extend the theory of MRPEs to Generalized Linear Models (GLMs) using independent and nonidentically distributed observations (INIDO). We derive asymptotic properties of the proposed estimators and analyze their influence function to asses their robustness properties. Additionally, we define robust Wald-type test statistics for testing linear hypothesis and theoretically study their asymptotic distribution, as well as their influence function. The performance of the proposed MRPEs and Wald-type test statistics are empirically examined for the Poisson Regression models through a simulation study, focusing on their robustness properties. We finally test the proposed methods in a real dataset related to the treatment of epilepsy, illustrating the superior performance of the robust MRPEs as well as Wald-type tests.

scholarly journals Durian Rind Micro Composter Model: A Case of Kampung Durian, Ngrogung, Ponorogo, Indonesia

2021 ◽  
Vol 226 ◽  
pp. 00021
Author(s):  
Haris Setyaningrum ◽  
Atika Rukminastiti Masrifah ◽  
Adib Susilo ◽  
Imam Haryadi
Keyword(s):  
Air Pollution ◽  
Organic Fertilizer ◽  
Dry Season ◽  
Organic Fertilizers ◽  
Direct Impact ◽  
The Third ◽  
Main Components ◽  
Surrounding Areas ◽  
Durian Rind

The case of waste from durian rind in Ngrogung, Ngebel, Ponorogo, Indonesia increased significantly per year. Agglomerations and decomposition of durian rind surrounding agro park caused air pollution and bad view in park areas. The decomposition of durian rind is very important to reduce those impacts. The micro composter model in Kampung Durian consisted of three main components. The first component was drying area with cemented ground. The drying area was set to accommodate 1 t fresh durian rind per day. The materials come from surrounding areas of agro park. The second was a machine house with one ton material capacity per day. The third was a composter house consisted of composter tube and packing area. The result of the micro composter house showed a cleaning agglomeration of durian rind from Ngebel lake and surrounding areas. In addition, the model produced liquid organic fertilizer and compost. A cycle of composting was 30 d in the dry season. Amount 1 t of durian rind decomposition resulted of 100 L liquid organic fertilizers and 50 kg compost. The result of composter had a direct impact to control waste problems in Ngebel and surrounding areas.

Search Engine

The Dark Web ◽  
2018 ◽  
pp. 359-374
Author(s):  
Dilip Kumar Sharma ◽  
A. K. Sharma
Keyword(s):  
Computer Networks ◽  
Search Engines ◽  
Web Search ◽  
Relevant Information ◽  
Vital Role ◽  
Deep Web ◽  
Telecommunication Networks ◽  
Web Pages ◽  
Web Crawler ◽  
Main Components

ICT plays a vital role in human development through information extraction and includes computer networks and telecommunication networks. One of the important modules of ICT is computer networks, which are the backbone of the World Wide Web (WWW). Search engines are computer programs that browse and extract information from the WWW in a systematic and automatic manner. This paper examines the three main components of search engines: Extractor, a web crawler which starts with a URL; Analyzer, an indexer that processes words on the web page and stores the resulting index in a database; and Interface Generator, a query handler that understands the need and preferences of the user. This paper concentrates on the information available on the surface web through general web pages and the hidden information behind the query interface, called deep web. This paper emphasizes the Extraction of relevant information to generate the preferred content for the user as the first result of his or her search query. This paper discusses the aspect of deep web with analysis of a few existing deep web search engines.

A Relational Based-View of Intellectual Capital in High-Tech Firms

2010 ◽  
pp. 179-191
Author(s):  
G. Martín De Castro ◽  
P. López Sáez ◽  
J.E. Navas López ◽  
M. Delgado-Verde
Keyword(s):  
Intellectual Capital ◽  
Strategic Alliances ◽  
Superior Performance ◽  
High Tech ◽  
Knowledge Stocks ◽  
Sustained Competitive Advantage ◽  
Strategic Resources ◽  
High Technology Firms ◽  
Range Theory ◽  
Main Components

The Resource-Based View (RBV) has tried to test the role of strategic resources on sustained competitive advantage and superior performance. Although this theory has found several flaws in order to reach its objective effectively (Priem & Butler, 2001), recent proposals have suggested that these problems can be overcome (Peteraf & Barney, 2003). This solution requires paying a greater attention to the analysis of knowledge stocks, developing a mid-range theory: the Intellectual Capital-Based View (Reed, Lubatkin & Srinivasan, 2006). This mid-range and pracmatic theory allows the hypotheses development and empirical testing in a more effective way that the RBV. There is a certain degree of general agreement about the presence of human capital and organizational capital as the main components of intellectual capital, as well as about the fact that the configuration of knowledge stocks will vary from one industry and firm to another one. Taking these assumptions as a starting point, this paper explores the configuration of intellectual capital that can be empirically found on a sample of high-technology firms. Our findings highlight the importance of relational capital, which must be divided into business and alliance capital, so the strategic alliances play a relevance role in the type of firms that have been included in our research.

Search Engine

2011 ◽  
Vol 3 (2) ◽  
pp. 38-51 ◽  
Author(s):  
Dilip Kumar Sharma ◽  
A. K. Sharma
Keyword(s):  
Computer Networks ◽  
Search Engines ◽  
Web Search ◽  
Relevant Information ◽  
Vital Role ◽  
Deep Web ◽  
Telecommunication Networks ◽  
Web Pages ◽  
Web Crawler ◽  
Main Components

ICT plays a vital role in human development through information extraction and includes computer networks and telecommunication networks. One of the important modules of ICT is computer networks, which are the backbone of the World Wide Web (WWW). Search engines are computer programs that browse and extract information from the WWW in a systematic and automatic manner. This paper examines the three main components of search engines: Extractor, a web crawler which starts with a URL; Analyzer, an indexer that processes words on the web page and stores the resulting index in a database; and Interface Generator, a query handler that understands the need and preferences of the user. This paper concentrates on the information available on the surface web through general web pages and the hidden information behind the query interface, called deep web. This paper emphasizes the Extraction of relevant information to generate the preferred content for the user as the first result of his or her search query. This paper discusses the aspect of deep web with analysis of a few existing deep web search engines.

scholarly journals Associations between COVID-19 Incidence Rates and the Exposure to PM2.5 and NO2: A Nationwide Observational Study in Italy

2020 ◽  
Vol 17 (24) ◽  
pp. 9318
Author(s):  
Fabiana Fiasca ◽  
Mauro Minelli ◽  
Dominga Maio ◽  
Martina Minelli ◽  
Ilaria Vergallo ◽  
...  
Keyword(s):  
Population Density ◽  
Observational Study ◽  
Air Pollutants ◽  
Regression Models ◽  
Positive Relationship ◽  
The Elderly ◽  
Incidence Rates ◽  
Multiple Factors ◽  
Epidemic Period ◽  
Nationwide Observational Study

The COVID-19 outbreak disproportionately affected the elderly and areas with higher population density. Among the multiple factors possibly involved, a role for air pollution has also been hypothesized. This nationwide observational study demonstrated the significant positive relationship between COVID-19 incidence rates and PM2.5 and NO2 levels in Italy, both considering the period 2016–2020 and the months of the epidemic, through univariate regression models, after logarithmic transformation of the variables, as the data were not normally distributed. That relationship was confirmed by a multivariate analysis showing the combined effect of the two pollutants, adjusted for the old-age index and population density. An increase in PM2.5 and NO2 concentrations by one unit (1 µg/m3) corresponded to an increase in incidence rates of 1.56 and 1.24 × 104 people, respectively, taking into account the average levels of air pollutants in the period 2016–2020, and 2.79 and 1.24 × 104 people during March–May 2020. Considering the entire epidemic period (March–October 2020), these increases were 1.05 and 1.01 × 104 people, respectively, and could explain 59% of the variance in COVID-19 incidence rates (R2 = 0.59). This evidence could support the implementation of targeted responses by focusing on areas with low air quality to mitigate the spread of the disease.

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