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The purpose of this study is to analyze influence of airfoil profile on lateral-directional flying quality of flying wing aircraft. The lateral-directional stability is always insufficient for aircraft with the layout due to the absence of vertical stabilizer. A flying wing aircraft with double-swept wing is used as research object in the paper.

The 3D model is established for the aircraft with flying wing layout, and parametric modeling is carried out for airfoil mean camber line of the aircraft to analyze lateral-directional stability of the aircraft with different camber line parameters. To increase computational efficiency, vortex lattice method is adopted to calculate aerodynamic coefficients and aerodynamic derivatives of the aircraft.

It is found from the research results that roll mode and spiral mode have a little effect on lateral-directional stability of the aircraft but Dutch roll mode is the critical factor affecting flying quality level of such aircraft. Even though changes of airfoil mean line parameters can greatly change assessment parameters of aircraft lateral-directional flying quality, that is kind of change cannot have a fundamental impact on level of flying quality of the aircraft. In case flat shape parameters are determined, the airfoil profile has a limited impact on Dutch roll mode.

Influences of airfoil profile on lateral-directional flying quality of aircraft with double-swept flying wing layout are revealed in the thesis and some important rules and characteristics are also summarized to lay a theoretical basis for design of airfoil and flight control system of aircraft with the layout.

The purpose of this paper is to develop a flexible design-oriented development cost method for commercial aviation aircraft based on small sample and poor information.

To predict the development cost of commercial aviation aircraft accurately, the methodology is based on the collected cost data and actual technical, and then the cost prediction relationships derived from an exhaustive statistical and filtered from regression analysis are incorporated. A series of regression equations with high regression coefficient are yielded after the cost driving factors of the development cost are fixed. Next, several sets of equations with high regression coefficient are selected for final integration. It is a flexible method that can be used efficiently to predict the cost of commercial aviation aircraft.

The development of commercial aviation aircraft has relatively a late start and no cost prediction model has been suitable for small sample, the proposed method is expected and is rather desirable.

By comparing the approach with the ordinary regression model and back propagation (BP) neural network, the scheme in this work is more efficient and convenient.

The results obtained in this paper show that the proposed method not only has a certain degree of versatility, but also can provide a preliminary prediction of the development cost of commercial aviation aircraft.

The purpose of this paper is to propose a robust optimization strategy to deal with the aerodynamic optimization issue, which does not need a large sum of information on the uncertainty of input parameters.

Interval numbers were adopted to describe the uncertain input, which only requires bounds and does not necessarily need probability distributions. Based on the method, model outputs were also regarded as intervals. To identify a better solution, an order relation was used to rank interval numbers.

Based on intervals analysis method, the uncertain optimization problem was transformed into nested optimization. The outer optimization was used to optimize the design vector, and inner optimization was used to compute the interval of model outputs. A flying wing aircraft was used as a basis for uncertainty optimization through the suggested optimization strategy, and optimization results demonstrated the validity of the method.

In aircraft conceptual design, the uncertain information of design parameters are often insufficient. Interval number programming method used for uncertainty analysis is effective for aerodynamic robust optimization for aircraft conceptual design.

This study aims to explore the relationship between corporate environmental information disclosure and digital finance.

The authors used Chinese listed enterprises as the sample. Data on digital finance are from the China Digital Inclusive Finance Index published by the Institute of Digital Finance of Peking University, and corporate environmental information disclosure is collected from the China Securities Market and Accounting Research database. Multivariate regression and Stata software were used for data analysis.

The findings suggest that digital finance significantly inhibits firms’ environmental information disclosure. Digital finance increases firms’ motivation to meet low-income customers’ demands, which is achieved at the expense of environmental performance, leading to deteriorated environmental information disclosure. Furthermore, this inhibitory effect is exacerbated by managerial power but mitigated by institutional shareholdings and political connections.

The findings have important implications for policymakers and managers when formulating relevant policies regarding the co-development of digital finance and corporate environmental information disclosure.

Environmental information disclosure is a crucial element in CSR disclosure quality.

This study enriches the literature on the environmental influences of digital finance by examining the relationship between digital finance and corporate environmental information disclosure, a crucial way through which external stakeholders obtain information about corporate environmental behaviours.

The purpose of this paper is to calculate the fuel consumption and emissions of carbon monoxide (CO), nitrogen oxide (NO_x) and hydrocarbons (HC) in the taxi-out period of aircraft at the International Diyarbakir Airport in 2018 and 2019.

Calculations were performed by determining the engine operating times in the taxi-out period with the flight data obtained from the airport authority. In the analyses, aircraft series and aircraft engine types were determined, and the Engine Exhaust Emission Databank of the International Civil Aviation Authority (ICAO) were used for the calculation.

Total fuel consumption in the taxi-out period in 2018 and 2019 was calculated as 525.64 and 463.69 tons, respectively. In 2018, HC, CO and NO_x emissions caused by fuel consumption were found to be 1,109, 10,668 and 2,339 kg, respectively. In 2019, the total HC, CO and NO_x emissions released to the atmosphere during the taxi-out phase are 966, 9,391 and 2,126 kg, respectively. B737 Series aircraft have the largest share in total fuel consumption and pollutant emissions.

This study explains the importance of determining fuel consumption and pollutant emissions by considering engine operating times in the taxi-out period. The study provides aviation authorities with scientific methods to follow in calculating fuel consumption and emissions from aircraft operations.

The originality of this study is the calculation of fuel consumption and pollutant emissions by determining real-time engine running times in the taxi-out period. In addition, calculations were made with real engine operating times determined in the taxi-out period using real flight data.

The purpose of this study is to present the pollutant gas produced by hydrocarbons (HC), carbon monoxide (CO) and nitrogen oxides (NO_x) and the quantity of fuel burned from commercial aircraft at Ordu-Giresun International Airport, Turkey during the landing and take-off (LTO) cycles in 2017.

The flight data recorded by the General Directorate of State Airports Authority and the aircraft engine emission data from International Civil Aviation Organization (ICAO) Engine Exhaust Emission Databank were used for calculation. The aircraft and engine types used by the airlines for flight at Ordu-Giresun International Airport were determined. To evaluate the effect of taxi time on emission amounts, analysis and evaluations were made by taking different taxi times into consideration.

As a result of the emission analysis, the amount of fuel consumed by the aircraft were calculated as 6,551.52 t/y, and the emission amounts for CO, HC and NO_x were estimated as 66.81, 4.20 and 79.97 t/y, respectively.

This study is aimed to reveal the effect and contribution of taxi time on the emitted emission at the airport during the LTO phase of the aircraft.

This study helps aviation authorities explain the importance of developing procedures that ensure the delivery of aircraft to flights in minimum time by raising awareness of the impact of taxi time on emitted emissions, and contributes to the determination of an aircraft emission inventory at Ordu-Giresun International Airport.

This paper aims to investigate the environmental impact of various pollutant emissions including carbon monoxide (CO), carbon dioxide (CO₂), nitrogen oxide (NO_x) and hydrocarbon (HC) from aircraft exhaust gases during the landing and take-off (LTO) cycles at Eskisehir Hasan Polatkan Airport, Turkey, between 2017 and 2018.

The methodology approach used to calculate the emissions from aircrafts is based on the ICAO databank and the actual data records taken from Presidency of The Republic of Turkey Directorate of Communications (DoC).

The maximum amount of total fuel burnt during the two years is 80.898 and 70.168 tons in 2017 and 2018, respectively, while the average fuel burnt per year from 2017 to 2018 is approximately 369.773 tons. The highest CO, CO₂, NO_x and HC emissions are found to be 248.3 kg in 2017, 261.380 tons, 1.708 tons and 22.15 kg, during the 2018 year, respectively. Average CO, HC, NO_x and CO₂ emissions amount per year are observed to be 1.392 tons, 135 kg, 6.909 tons and 1,143 tons, respectively. Considering the average of total emission amount as an environmental factor, as expected, CO₂ emissions contributed the most to the total emissions while HC emissions contributed the least to the total emissions from the airport.

The study presents the approach in determining the amounts of emissions released into the interannual atmosphere and it explicitly provides researchers and policymakers how to follow emissions from commercial aircraft activities at different airports.

The value of the study lies in the transparent computation of the amounts of pollutants by providing the data directly from the first hand-DoC.

Internet marketing underground industry users refer to people who use technology means to simulate a large number of real consumer behaviors to obtain marketing activities rewards illegally, which leads to increased cost of enterprises and reduced effect of marketing. Therefore, this paper aims to construct a user risk assessment model to identify potential underground industry users to protect the interests of real consumers and reduce the marketing costs of enterprises.

Method feature extraction is based on two aspects. The first aspect is based on traditional statistical characteristics, using density-based spatial clustering of applications with noise clustering method to obtain user-dense regions. According to the total number of users in the region, the corresponding risk level of the receiving address is assigned. So that high-quality address information can be extracted. The second aspect is based on the time period during which users participate in activities, using frequent item set mining to find multiple users with similar operations within the same time period. Extract the behavior flow chart according to the user participation, so that the model can mine the deep relationship between the participating behavior and the underground industry users.

Based on the real underground industry user data set, the features of the data set are extracted by the proposed method. The features are experimentally verified by different models such as random forest, fully-connected layer network, SVM and XGBOST, and the proposed method is comprehensively evaluated. Experimental results show that in the best case, our method can improve the F1-score of traditional models by 55.37%.

This paper investigates the relative importance of static information and dynamic behavior characteristics of users in predicting underground industry users, and whether the absence of features of these categories affects the prediction results. This investigation can go a long way in aiding further research on this subject and found the features which improved the accuracy of predicting underground industry users.

The aim of this study is to determine the preferences to participate in diabetes screening program of women with gestational diabetes mellitus (GDM) in postpartum period.

The data of retrospective and descriptive study were collected using “Individual Identification Form” and “Information Form for the Screening of Diabetes in the Postpartum Period” from 151 women in referred to obstetrics and gynecology clinic of a university hospital in Turkey.

Only 21.9% of women had diabetes screening in postpartum period and 21.2% of the participants were diagnosed with type 2 diabetes. It was determined that the participants mostly participated in screening because of the diabetes history in their family (30.3%). Women who had diabetes screening in postpartum period had lower level of education than those who did not and their level of knowledge about the screening in postpartum and the history of abortion were higher (p < 0.01).

The rate of participation in the screening for diabetes in the postpartum period is very low in pregnant women diagnosed with GDM. It was determined that the educational status, history of previous abortion and knowledge level of the women were factors that prevented participation in diabetes screening. This research is original because there are inadequacy of studies examining determining the participation status of pregnant women with GDM to diabetes screening in the literature. This study will contribute to health professionals in order to improve preventive factors and increase the participation of pregnant women with GDM in diabetes screening in the postpartum period.