Search results

Airline scheduling is an extremely complex process. Moreover, disruption in a single flight may damage the entire schedule tremendously. Using an efficient recovery scheduling strategy is vital for a commercial airline. The purpose of this paper is to present an integrated aircraft and crew recovery plans to reduce delay and prevent delay propagation on airline schedule with the minimum cost.

A mixed-integer linear programming model is proposed to formulate an integrated aircraft and crew recovery problem. The main contribution of the model is that recovery model is formulated based on individual flight legs instead of strings. This leads to a more accurate schedule and better solution. Also, some important issues such as crew swapping, reassignment of aircraft to other flights as well as ground and sit time requirements are considered in the model. Benders’ decomposition approach is used to solve the proposed model.

The model performance is also tested by a case including 227 flights, 64 crew, 56 aircraft and 40 different airports from American Airlines data for a 24-h horizon. The solution achieved the minimum cost value in 35 min. The results show that the model has a great performance to recover the entire schedule when disruption happens for random flights and propagation delay is successfully limited.

The authors confirm that this is an original paper and has not been published or under consideration in any other journal.

This study aims to introduce a novel rank aggregation algorithm that leverages graph theory and deep-learning to improve the accuracy and relevance of aggregated rankings in metasearch scenarios, particularly when faced with inconsistent and low-quality rank lists. By strategically selecting a subset of base rankers, the algorithm enhances the quality of the aggregated ranking while using only a subset of base rankers.

The proposed algorithm leverages a graph-based model to represent the interrelationships between base rankers. By applying Spectral clustering, the algorithm identifies a subset of top-performing base rankers based on their retrieval effectiveness. These selected rankers are then integrated into a sequential deep-learning model to estimate relevance labels for query-document pairs.

Empirical evaluation on the MQ2007-agg and MQ2008-agg data sets demonstrates the substantial performance gains achieved by the proposed algorithm compared to baseline methods, with an average improvement of 8.7% in MAP and 11.9% in NDCG@1. The algorithm’s effectiveness can be attributed to its ability to effectively integrate diverse perspectives from base rankers and capture complex relationships within the data.

This research presents a novel approach to rank aggregation that integrates graph theory and deep-learning. The author proposes a graph-based model to select the most effective subset for metasearch applications by constructing a similarity graph of base rankers. This innovative method addresses the challenges posed by inconsistent and low-quality rank lists, offering a unique solution to the problem.

This paper aims to explore the extent to which the internet has created new opportunities for Iranian women in Tehran. It analyses both challenges and opportunities offered to Iranian women by the internet as a means of economic empowerment.

This paper adopts a qualitative approach and based on 13 semi-structured interviews with female internet users between the ages of 20 and 55 years. The qualitative data was collected through open-ended questions in face-to-face interviews. This study uses ethnography as a research tool to explore the question of whether the internet has made a difference in the economic lives of Iranian women.

Result reveals that the internet and working online have significant impact on the economic lives of Tehrani women by enabling them to engage in new forms of online business. This technology is being used for online advertising to attract more clients, to establish business contacts with peers and to manage households positively.

The result of the research cannot be regarded as applicable to all women in Iran, as the opportunity to access online economic activities is only available to those women who are highly trained and well-educated. In addition, the result of the research may not reflect the barriers that women from different social classes and ethnic groups have faced in the achievement of economic empowerment online.

The study highlights that due to a generally lack of computer proficiency, women in these areas are unable to effectively maximise their participation in the online economic sphere. This barrier must be removed by enhancing women’s computer literacy and ICT (information, communication and technologies) and establishing development networking programme centres for internet skills training.

The internet has created opportunity for Iranian women to expand their participation in the online economic sphere. However, research in the field of online economic activities in Iran, especially concerning women working online, is scant. The key contribution of this paper is to fill the gap in this area of study, in particular offering insights into the ways in which women use the internet to overcome the boundaries of physical space and become empowered.

This study aims to investigate the effect of high temperature (600°C) on the compressive strength of concrete covered with a mixture of polypropylene fiber and gypsum plaster (PFGP).

To study the compressive strength of concrete specimens exposed to temperature, 16 cubic specimens (size: 150 mm × 150 mm × 150 mm) were made. After 28 days of processing and gaining the required strength of specimens, first, polypropylene fiber was mixed with gypsum plaster (CaSO4.2H2O) and then the concrete specimens were covered with this mixture. To cover the concrete specimens with the PFGP, the used PFGP thickness was 15 mm or 25 mm. The polypropylene rates mixed with the gypsum plaster were 1, 3 and 5 per cent. A total of 14 specimens, 12 of which were covered with PFGP, were exposed to high temperature in two target times of 90 and 180 min.

The results show that the PFGP as covering materials can improve the compressive strength lost because of the heating of the concrete specimens. The results also show that the presence of polypropylene fiber in gypsum plaster has the effect on the compressive strength lost because of the heating of the PFGP-covered concrete. The cover of PFGP having 3 per cent polypropylene fiber had the best effect on remained strength of the specimens.

The cover of PFGP having 3 per cent polypropylene fiber has the best effect on remained strength of the PFGP covered specimens exposed to temperature.

The purpose of this study is to explore the intricate relationship between oil prices, house prices in the UK and Norway, and the mediating role of gold and stock prices in both the short- and long-term, unraveling these complex linkages by employing an empirical approach.

This study benefits from a comprehensive set of econometric tools, including a multiequation vector autoregressive (VAR) system, Granger causality test, impulse response function, variance decomposition and a single-equation autoregressive distributed lag (ARDL) system. This rigorous approach enables to identify both short- and long-run dynamics to unravel the intricate linkages between Brent oil prices, housing prices, gold prices and stock prices in the UK and Norway over the period from 2005:Q1 to 2022:Q2.

The findings indicate that rising oil prices negatively impact house prices, whereas the positive influence of stock market performance on housing is more pronounced. A two-way causal relationship exists between stock market indices and house prices, whereas a one-way causal relationship exists from crude oil prices to house prices in both countries. The VAR model reveals that past housing prices, stock market indices in each country and Brent oil prices are the primary determinants of current housing prices. The single-equation ARDL results for housing prices demonstrate the existence of a long-run cointegrating relationship between real estate and stock prices. The variance decomposition analysis indicates that oil prices have a more pronounced impact on housing prices compared with stock prices. The findings reveal that shocks in stock markets have a greater influence on housing market prices than those in oil or gold prices. Consequently, house prices exhibit a stronger reaction to general financial market indicators than to commodity prices.

This study may have several limitations. First, the model does not include all relevant macroeconomic variables, such as interest rates, unemployment rates and gross domestic product growth. This omission may affect the accuracy of the model’s predictions and lead to inefficiencies in the real estate market. Second, this study does not consider alternative explanations for market inefficiencies, such as behavioral finance factors, information asymmetry or market microstructure effects. Third, the models have limitations in revealing how predictors react to positive and negative shocks. Therefore, the results of this study should be interpreted with caution.

These findings hold significant implications for formulating dynamic policies aimed at stabilizing the housing markets of these two oil-producing nations. The practical implications of this study extend to academics, investors and policymakers, particularly in light of the volatility characterizing both housing and commodity markets. The findings reveal that shocks in stock markets have a more profound impact on housing market prices compared with those in oil or gold prices. Consequently, house prices exhibit a stronger reaction to general financial market indicators than to commodity prices.

These findings could also serve as valuable insights for future research endeavors aimed at constructing models that link real estate market dynamics to macroeconomic indicators.

Using a variety of econometric approaches, this paper presents an innovative empirical analysis of the intricate relationship between euro property prices, stock prices, gold prices and oil prices in the UK and Norway from 2005:Q1 to 2022:Q2. Expanding upon the existing literature on housing market price determinants, this study delves into the role of gold and oil prices, considering their impact on industrial production and overall economic growth. This paper provides valuable policy insights for effectively managing the impact of oil price shocks on the housing market.

Since 2017, China's digital economy has accounted for more than 30% of the country's GDP. The digital economy has become the main driving force of China's economic development. Moreover, the digital economy has also changed the traditional modes of production and distribution between urban and rural areas. This paper aims to explore the influential mechanism of digital economy infrastructure (DEI) on the urban-rural income gap (URIG).

By analyzing the theoretical model of the URIG, this paper constructs a theoretical analysis framework and clarifies the key roles of rural land circulation (RLC) and resident population urbanization (RPU) in the relationship between DEI and the URIG.

The DEI can effectively reduce the URIG; the regression coefficient (RC) was −0.109. The reduction effect is mainly reflected in: 1) the wage income gap between urban and rural residents (RC = −0.128) and 2) the net property income gap of urban and rural residents (RC = −0.321). Also, for the spatial spillover effect, the path effect of “DEI – RLC – URIG” is almost equal to the path effect of “DEI – RPU – URIG”; for the local effect, the path effect of the former is far smaller than the latter. Moreover, when the RPU reaches the threshold of 86.29%, the DEI will expand the URIG (RC = 0.201).

This paper proposes a theoretical framework for the impact of DEI on the URIG, explores the mechanism of RLC and RPU in the DEI and URIG and enriches the theory of traditional research on URIG.