The impact of Chinese airport infrastructure on airline pollutant emissions: A hybrid stochastic-neural network approach based on utility functions.

With China being the world's largest emitter of greenhouse gases and its aviation sector burgeoning, the environmental performance of Chinese airlines has global significance. Amidst rising demands for eco-friendly practices from both customers and regulators, the interplay between airport infrastructure and environmental performance becomes pivotal. This research offers an innovative methodology to gauge the environmental performance of Chinese airlines, emphasizing the distance traveled between airports using weighted additive utility functions. Leveraging neural networks, the study investigates the impact of various airport infrastructural characteristics on environmental performance. Noteworthy findings indicate that ground control measures, automatic information services at origin airports, surface concrete on runways at both ends, and a centerline lighting system in destination airports positively influence environmental performance. In contrast, longer and wider runways at origin airports, increased distances to control towers, and asphalt runways at destination airports adversely affect it. These insights not only underscore the importance of strategic infrastructure enhancements for reducing carbon footprints but also hold profound policy implications. As global climate change remains at the forefront, fostering sustainable airport infrastructure in China can significantly contribute to worldwide mitigation efforts.

The Impact of School Infrastructure and Teachers' Human Capital on Academic Performance in Brazil.

To improve one of the lowest rates of literacy and numeracy in the world, the government of Brazil has targeted public education reform, given the strong link between an educated population and economic growth. This study examines the academic performance of the Brazilian public primary school system. It addresses the empirical shortcomings of prior research to examine the dynamics of the relationship between academic performance scores and several demographic and institutional variables, such as socioeconomic characteristics, variations in school infrastructure and school complexity, and teachers' human capital. We employed quantile regression to explore the determinants of academic performance across 35,490 schools in rural and urban environments in Brazil. The dependent variable in our analysis captures the academic performance score, as measured by Brazil's education authorities, of each school in our dataset. The model includes several education-related indices used in prior research and, as explanatory factors, measures of teachers' human capital and the students' socioeconomic level, which synthesizes information on parents' education and household income. The results suggest that several institutional variables, including access to school libraries, computer facilities, projectors, and televisions, are positively and significantly related to the academic performance of primary students in Brazil's system of public education. Furthermore, students' socioeconomic level is positively associated with their academic performance.

Trends in road accidents on Brazil's highways: Evidence of persistence using fractional integration.

This paper deals with the analysis of trends in road accidents on highways in Brazil. We use time series techniques based on fractional integration that allow us to determine if exogenous shocks in the data have transitory or permanent effects depending on the order of integration of the series. Our results indicate that a low degree of long memory was detected in the series with shocks having thus transitory effects over time. We further find that the number of accidents have been reducing over time, though in the presence of negative shocks, the recovery is not going to be immediate due to the long memory nature of the data. Despite the absence of relevant investment relating to infrastructure expansion, it is worth mentioning the consolidation of a nationwide tolled road system in Brazil involving concessions to private administrators, alongside more severe traffic laws that can impose limitations on driving licences.

How, When, & Where temporary hospitals fit in turbulent times: A hybrid MADM optimization in the middle east.

Governments have been challenged to provide temporary hospitals and other types of facilities to face the COVID-19 pandemic. This research proposes a novel multi-attribute decision-making (MADM) model to help determine how, when, and where these temporary facilities should be installed based on a set of critical success factors (CSFs) mapped in an uncertain environment. We portray the available facilities for temporary hospitals based on the CSFs that must be considered to make critical decisions regarding the optimal position based on the government's strategic decision-making process, thus indirectly providing better services and maximizing resources. In relation to earlier work, this research builds upon hybrid Pythagorean fuzzy numbers to find weights in Best-Worst Methods and rank temporary facilities based on evaluation by an area-based method for ranking. Policy implications and future directions are derived.

Impacts of the COVID-19 on all aircraft emissions of international routes in South America.

The COVID-19 pandemic has had a significant impact on South America's economic development, as well as its international civil aviation industry. This paper seeks to calculate the emissions of six pollutions (CO2, CO, HC, NOx, SO2, and PM2.5) from the international routes in South America during 2019-2021 and discusses the impacts of COVID-19 on the emission change. The modified BFFM2-FOA-FPM method is proposed to unify the CO2 and non-CO2 calculations. The calculated results' average error rate is about 5.12%. The results showed that COVID-19 affected all emissions, including the number of routes, average flight distance, aircraft configuration, the proportion of CCD phase emissions, average emissions, etc. In addition, some airlines increased the number of flights and aircraft types during the pandemic, increasing emissions. The results give a reasonable data basis for the aviation industry in South America to formulate emission reduction policies.

Antecedents of Perceived Service Quality of Online Education During a Pandemic: Configuration Analysis Based on Fuzzy-Set Qualitative Comparative Analysis.

BACKGROUND: During COVID-19 lockdown worldwide, classroom education continues remotely through online. The question remains, comparing with the face-to-face education, does online education has a similar satisfaction level among the students? There are only a few studies that examine the perceived service quality of online education. OBJECTIVE: The study aims to analyze the factors of perceived service quality of online education during a pandemic. RESEARCH DESIGN: A structured questionnaire elicits information from 147 students from different study backgrounds of various universities worldwide. The fuzzy-set qualitative comparative analysis (fsQCA) is used for data analysis and model design. Research constructs evaluation for reliability and internal consistency are subsequently performed. A snowball random sampling method is applied for data collection. RESULTS: Findings from the fsQCA analysis identify four core factors that underpin student satisfaction through positive perceived service quality of online education. Alternative paths are determined based on gender, students' current education status, and their loyalty toward online education. We also introduce two topologies of perceived quality regarding online education and student satisfaction. ORIGINALITY: Because of the primary nature of the data, this is firsthand experience gathered from different universities around the world who have willingly or unwillingly experienced online learning during the pandemic. The fsQCA technique for examining perceived service quality of online education. CONCLUSIONS: The findings contain a number of contributions, illustrating different topologies of the student from different backgrounds and their intention, satisfaction and loyalty towards e-learning, and identifying causal factors that influence willingness to recommend online education.

Portfolio Optimization with a Mean-Entropy-Mutual Information Model.

This paper describes a new model for portfolio optimization (PO), using entropy and mutual information instead of variance and covariance as measurements of risk. We also compare the performance in and out of sample of the original Markowitz model against the proposed model and against other state of the art shrinkage methods. It was found that ME (mean-entropy) models do not always outperform their MV (mean-variance) and robust counterparts, although presenting an edge in terms of portfolio diversity measures, especially for portfolio weight entropy. It further shows that when increasing return constraints on portfolio optimization, ME models were more stable overall, showing dampened responses in cumulative returns and Sharpe indexes in comparison to MV and robust methods, but concentrated their portfolios more rapidly as they were more evenly spread initially. Finally, the results suggest that it was also shown that, depending on the market, increasing return constraints may have positive or negative impacts on the out-of-sample performance.

The impact of social welfare and COVID-19 stringency on the perceived utility of food apps: A hybrid MCDM approach.

The COVID-19 pandemic has created enormous challenges for society due to the various ways of impacting health. This paper focuses on the impact of the COVID-19 pandemic on people's food consumption patterns in the online environment. We investigate food app reviews and examine whether countries with a high rate of success with COVID-19 control consume more unhealthy food through mobile apps. We also investigate whether the population of countries with low social welfare eat more unhealthy food during the COVID-19 pandemic compared to countries with high social welfare. We take a hybrid multi-criteria decision making (MCDM) approach to calculate indexes based on the technique for order of preference by similarity to an ideal solution, complex proportional assessment, and VlseKriterijuska Optimizacija I Komoromisno Resenje. Results show that country social welfare and success in COVID-19 control negatively affect the perceived utility of the apps. Also, success in COVID-19 control and the perceived utility of food apps positively affect the proportion of unhealthy reviews, whereas social welfare has a negative impact. The results have important implications for public health policymakers, showing that the online food environment can be an important setting for interventions that seek to incentivize healthy eating.

Inventory pooling decisions under demand scenarios in times of COVID-19.

Governments have been challenged to provide timely medical care to face the COVID-19 pandemic. The aim of this research is to propose a novel inventory pooling model to help determine order sizes and safety inventories in local hospital warehouses. The current study attempts to portray the availability of pharmaceutical items in public hospitals facing COVID-19 challenges. Different from previous studies, this research builds upon the consecrated theory of inventory pooling, extending it to pandemic circumstances where the intractability of kurtosis and skewness in inventory models are critical issues for making sure that medicines have high availability at a low cost. These effects on the total cost of inventory are explored and compared to a supply system with no consolidation. A continuous-review model is assumed with allocation rules for centralization and regular transshipment given different skewness and kurtosis structures for the demand, describing them by the copula method. This method models a multivariate demand considering that the marginal distributions of the demand can be specified by the Generalized Additive Model for Location, Scale and Shape, which offers advantages to model demands considering virtually any marginal statistical distribution. Numerical simulations and an illustrative example show that distributions of demands with more negative skewness and high kurtosis favor to a greater extent obtaining lower total costs with regular supply transshipment systems. Our study points out important considerations for supply chain decision makers when having demands with skewness and kurtosis patterns.

Sustainability drivers in road transportation system: Evidence from China.

This research explores the sustainability drivers of the Chinese road transportation system in terms of its cargo and environmental productivity levels. A novel Fuzzy Double-Frontier Network Data Envelopment Analysis (FDFNDEA) model is proposed to investigate the relationship between desirable (freight and passenger turnovers) and undesirable (CO2 and NOx emission levels) outputs against the respective power consumed in each one of the 29 Chinese provinces (municipalities and autonomous regions) between 1985 and 2017. The power consumption emerges spatially and temporally as a consequence of the evolution of the road system's productive resources (employees, highway length, number of vehicles, and fuel consumed) at the province level over the course of time. Shannon's entropy is used as the cornerstone to quantify input and output vagueness of this evolution in terms of triangular fuzzy numbers (TFN), thus allowing the building of alternative optimistic and pessimistic double efficiency frontiers. Respective Malmquist Productivity Indexes (MPI) for overall and each stage productivity are regressed against contextual variables related to demography, economic activity, competitor infrastructure, and highway quality using bootstrapped Cauchy regressions. Results confirm the disruptive evolution of the Chinese road transport system over the course of the years and different expansion strategies at the regions. The energy and environmental efficiency of the Chinese road transportation system is affected by these contextual variables.

Longitudinal bibliometric analysis applied to home care services.

The aim of this study is to present a new methodology to explore a field of research and exercise this technique to find good mathematical models to solve the problem of territorial alignment applied to health services. For this purpose we show a methodology that combines three methods of analysis: social network analysis, longitudinal analysis, and mapping change analysis. In this paper, we applied the mapping change method, originally used in large networks, to small and medium ones, and used the Tabu search scheme instead of simulated annealing. Finally, to highlight the significant changes over time of keywords networks, an alluvial diagram is used to show the significance clusterings through the subperiods studied. The work reports on the most relevant authors on the subject and the most widely used mathematical models applied to solve the problem.

Hotel Performance in the UK: The Role of Information Entropy in a Novel Slack-Based Data Envelopment Analysis.

Previous hotel performance studies neglected the role of information entropy in feedback processes between input and output management. This paper focuses on this gap by exploring the relationship between hotel performance at the industry level and the capability of learning by doing and adopting best practices using a sample of 153 UK hotels over a 10-year period between 2008-2017. Besides, this research also fills a literature gap by addressing the issues of measuring hotel performance in light of negative outputs. In order to achieve this, we apply a novel Modified slack-based model for the efficiency analysis and Least Absolute Shrinkage and Selection Operator to examine the influence of entropy related variable on efficiency score. The Results indicate that less can be learnt from inputs than from outputs to improve efficiency levels and resource allocation is more balanced than cash flow and liquidity. The findings suggest that market dynamics explains the cash flow generation potential and liquidity. We find that market conditions are increasingly offering the opportunities for learning and improving hotel efficiency. The results report that the distinctive characteristic of superior performance in hotel operations is the capability to match the cash flow generation potential with market opportunities.

Modelling stock market data in China: Crisis and Coronavirus.

Global financial markets experienced distinct collapses during the global financial crisis in 2008 and the COVID-19 pandemic in 2020, and similarity in the underlying nature is still a hot topic to be investigated. This paper investigates their degree of persistence in order to detect whether the shocks affecting them have temporary or permanent effects by examining the closing prices of the Shanghai and Shenzhen Composite Indices from 1991 to 2020. The results before the coronavirus indicate large degrees of persistence with shocks having permanent effects, while during the coronavirus the results indicate a mean reversion with shocks having temporary effects.

Sustainability efficiency and carbon inequality of the Chinese transportation system: A Robust Bayesian Stochastic Frontier Analysis.

This study focuses on the sustainability efficiency of the Chinese transportation system by investigating the relationship between CO2 emission levels and the respective freight and passenger turnovers for each transportation mode from January 1999 to December 2017. A novel Robust Bayesian Stochastic Frontier Analysis (RBSFA) is developed by taking carbon inequality into account. In this model, the aggregated variance/covariance matrix for the three classical distributional assumptions of the inefficiency term-Gamma, Exponential, and Half-Normal-is minimized, yielding lower Deviance Information Criteria when compared to each classical assumption separately. Results are controlled for the impact of major macro-economic variables related to fiscal policy, monetary policy, inflationary pressure, and economic activity. Results indicate that the Chinese transportation system shows high sustainability efficiency with relatively small random fluctuations explained by macro-economic policies. Waterway, railway, and roadway transportation modes improved sustainability efficiency of freight traffic while only the railway transportation mode improved sustainability efficiency of passenger traffic. However, the air transportation mode decreased sustainability efficiency of both freight and passenger traffic. The present research helps in reaching governmental policies based not only on the internal dynamics of carbon inequality among different transportation modes, but also in terms of macro-economic impacts on the Chinese transportation sector.

Managing slow-moving item: a zero-inflated truncated normal approach for modeling demand.

This paper proposes a slow-moving management method for a system using of intermittent demand per unit time and lead time demand of items in service enterprise inventory models. Our method uses zero-inflated truncated normal statistical distribution, which makes it possible to model intermittent demand per unit time using mixed statistical distribution. We conducted numerical experiments based on an algorithm used to forecast intermittent demand over fixed lead time to show that our proposed distributions improved the performance of the continuous review inventory model with shortages. We evaluated multi-criteria elements (total cost, fill-rate, shortage of quantity per cycle, and the adequacy of the statistical distribution of the lead time demand) for decision analysis using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). We confirmed that our method improved the performance of the inventory model in comparison to other commonly used approaches such as simple exponential smoothing and Croston's method. We found an interesting association between the intermittency of demand per unit of time, the square root of this same parameter and reorder point decisions, that could be explained using classical multiple linear regression model. We confirmed that the parameter of variability of the zero-inflated truncated normal statistical distribution used to model intermittent demand was positively related to the decision of reorder points. Our study examined a decision analysis using illustrative example. Our suggested approach is original, valuable, and, in the case of slow-moving item management for service companies, allows for the verification of decision-making using multiple criteria.

Modeling lot-size with time-dependent demand based on stochastic programming and case study of drug supply in Chile.

The objective of this paper is to propose a lot-sizing methodology for an inventory system that faces time-dependent random demands and that seeks to minimize total cost as a function of order, purchase, holding and shortage costs. A two-stage stochastic programming framework is derived to optimize lot-sizing decisions over a time horizon. To this end, we simulate a demand time-series by using a generalized autoregressive moving average structure. The modeling includes covariates of the demand, which are used as predictors of this. We describe an algorithm that summarizes the methodology and we discuss its computational framework. A case study with unpublished real-world data is presented to illustrate the potential of this methodology. We report that the accuracy of the demand variance estimator improves when a temporal structure is considered, instead of assuming time-independent demand. The methodology is useful in decisions related to inventory logistics management when the demand shows patterns of temporal dependence.

Job satisfaction and intention to quit: an empirical analysis of nurses in Turkey.

The aim of this study was to identify the facets influencing job satisfaction and intention to quit of nurses employed in Turkey. Using a non-probability sampling technique, 417 nurses from six large private hospitals were surveyed from March 2014 to June 2014. The nurses' demographic data, their job-related satisfaction and turnover intentions were recorded through a self-administered questionnaire. In this study, descriptive and bivariate analyses were used to explore data, and multivariate analysis was performed using logistic regression. Nurses' job satisfaction was found at a moderate level with 61% of the nurses intended to quit. Nevertheless, nurses reported a high satisfaction level with work environment, supervisor support, and co-workers among the selected nine facets of job satisfaction. They also reported a low satisfaction level with contingent reward, fringe benefits, and pay. The impact of demographic characteristics on job satisfaction and intention to quit was also examined. The study revealed a negative relationship between job satisfaction and intention to quit the existing employment. Moreover, satisfaction with supervisor support was the only facet that significantly explained turnover intent when controlling for gender, age, marital status, education, and experience. The implications for nurse management were also described for increasing nurses' job satisfaction and retention. This study is beneficial for hospital management to ensure proper nursing care that would lead to a better quality healthcare service.

Efficiency determinants and capacity issues in Brazilian for-profit hospitals.

This paper reports on the use of different approaches for assessing efficiency of a sample of major Brazilian for-profit hospitals. Starting out with the bootstrapping technique, several DEA estimates were generated, allowing the use of confidence intervals and bias correction in central estimates to test for significant differences in efficiency levels and input-decreasing/output-increasing potentials. The findings indicate that efficiency is mixed in Brazilian for-profit hospitals. Opportunities for accommodating future demand appear to be scarce and strongly dependent on particular conditions related to the accreditation and specialization of a given hospital.