A fast airplane boarding strategy using online seat assignment based on passenger classification

The minimization of the turnaround time, the duration which an aircraft must remain parked at the gate, is an important goal of airlines to increase their profitability. This work introduces a procedure to minimize of the turnaround time by speeding up the boarding time in passenger aircrafts. This is realized by allocating the seat numbers adaptively to passengers when they pass the boarding gate and not before. Using optical sensors, an agility measure is assigned to each person and also a measure to characterize the size of her/his hand-luggage. Based on these two values per passenger and taking into account additional constraints, like reserved seats and the belonging to a group, a novel seat allocation algorithm is introduced to minimize the boarding time. Extensive simulations show that a mean reduction of the boarding time with approximately 15% is achieved compared to existing boarding strategies. The costs of introducing the proposed procedure are negligible, while the savings of reducing the turnaround time are enormous, considering that the costs generated by inactive planes on an airport are estimated to be about 30 $ per minute.     

Climate change versus land-use change—What affects the mountain landscapes more?

Land use and climate change are both strong drivers of landscape transformation. Using a representative valley of the Central Alps (Stubai Valley, Tyrol, Austria) we assess (1) the historical and likely future spatial patterns of land use/land cover (LULC), (2) the influence of temperature increase on the LULC distribution, and (3) the speed at which these changes will occur. Based on the historical landscape development and spatially explicit models, the effects of various land use and climate scenarios were modelled. Employing a pan-Alpine model, we were able to detect the temporal trajectory of spatial reforestation. The results show that land-use changes that already occurred during the last decades are responsible for the main future LULC changes (by secondary succession). Only an extreme land abandonment scenario and extreme climate scenarios (5 K temperature increase) would bring about similar changes in LULC distribution and expansion of the forested areas. While alpine grasslands, alpine pioneer formation and glaciers would shrink drastically, especially deciduous forests would spread. To a considerable degree, such changes might take place over the next 300 years. By contrast, the increase in forest areas triggered by temperature changes would be slower and longer termed (up to 700–800 years).The effects and intensity of land-use change in the investigated valley, that is comparable to many regions in the Alps, will be at least equally severe and responsible for transformation of the landscape as those of a projected temperature increase.     

FROM DISCRETE TO CONTINUOUS‐TIME TRANSITION MATRICES IN INTRA‐DISTRIBUTION DYNAMICS ANALYSIS: AN APPLICATION TO PER CAPITA WEALTH IN EUROPE

Previous studies focusing on the intra‐distribution dynamics analysis have usually computed, in a Markov chain framework, discrete‐time transition matrices. Such an approach, however, can involve some limitations, especially when using stock variables. In order to illustrate the importance of the time‐scale issue when estimating transition matrices, this paper applies both discrete and continuous‐time approaches to a set of cross‐national European data on per capita wealth for the period 2000–10. The results reveal, on the one hand, that the continuous‐time estimation provides a most accurate estimation of transition probabilities and, on the other, that the differences between both approaches are especially remarkable in the long‐term equilibrium distribution.     

解用工计划管控之殇--契合国企战略的人力资源需求预测体系研究

文章以传统国有企业为研究对象，从其用工计划管控存在的实际问题出发，结合多种人力资源需求预测方法的特点和适用范围，探索构建国有企业人力资源总量、结构、素质需求预测模型体系，并设计具体建模方法与建模步骤，以回答企业未来“需要多少人”、“需要的人如何分布”和“需要什么样的人”的三个核心问题，以帮助国有企业最终实现“控总量、优结构、提素质”的管理目的。     

A continuous time Bayesian network classifier for intraday FX prediction

Abstract

Prediction of foreign exchange (FX) rates is addressed as a binary classification problem in which a continuous time Bayesian network classifier (CTBNC) is developed and used to solve it. An exact algorithm for inference on CTBNC is introduced. The performance of an instance of these classifiers is analysed and compared to that of dynamic Bayesian network by using real tick by tick FX rates. Performance analysis and comparison, based on different metrics such as accuracy, precision, recall and Brier score, evince a predictive power of these models for FX rates at high frequencies. The achieved results also show that the proposed CTBNC is more effective and more efficient than dynamic Bayesian network classifier. In particular, it allows to perform high frequency prediction of FX rates in cases where dynamic Bayesian networks-based models are computationally intractable.     

Development of crash frequency models for safety promotion of urban collector streets

The merits for development and application of crash frequency prediction models for safety promotion on any road type, with a focus on urban collector streets, are presented in this article. The city of Yazd, a medium-sized city in the middle of Iran, was selected as a case study and the data required for modelling crash frequencies along five collector streets comprising 31 street sections were collected. Six models including Poisson and negative binomial models and their deviations along with a hybrid artificial neural networks (ANN) model were developed to predict crash frequency along each street section. The overfitting problem was addressed using appropriate sensitivity analysis methods which were also used to identify the input variables with significant impact on the model performance. The results indicated that the developed hybrid ANN model provided the best performance in terms of accuracy and the number of input variables. The application of hybrid ANN model to evaluate the safety impacts of four different strategies, each resembled by one of the input variables of this model, indicated that these models can successfully be used for this purpose.     

Predicting demand for air taxi urban aviation services using machine learning algorithms

This research focuses on predicting the demand for air taxi urban air mobility (UAM) services during different times of the day in various geographic regions of New York City using machine learning algorithms (MLAs). Several ride-related factors (such as month of the year, day of the week and time of the day) and weather-related variables (such as temperature, weather conditions and visibility) are used as predictors for four popular MLAs, namely, logistic regression, artificial neural networks, random forests, and gradient boosting. Experimental results suggest gradient boosting to consistently provide higher prediction performance. Specific locations, certain time periods and weekdays consistently emerged as critical predictors.     

深部“孤岛”工作面通防保障技术研究

由于矿井深部煤层具有"三高一扰动"的特点,进而在一定程度上使得深部安全开采问题逐步突出。受深部煤层煤质松软的影响和制约,在一定程度上为了防止生产和连续工作面之间产生干扰,通常情况下,通过"孤岛"布置方式确保生产的安全进行。采空区以及相邻采空区因"孤岛"布置进而导致容易发生通防事故。因此,通过对深部松软煤层"孤岛"综放面通防保障技术进行研究分析,进而在一定程度上对于安全开采具有重要的现实意义。     

Unemployment and the shadow economy

Current research links the shadow economy (SE) and the unemployment rate either indirectly or by means of a preliminary estimate. This article establishes and empirically implements a methodology for estimating the size of the SE as a direct function of the tax and unemployment rates. This link is found to be extremely relevant in countries with high unemployment rates (such as Greece and Spain) and less relevant in countries with moderate unemployment rates (such as Germany and Italy). Unemployment’s contribution to the SE is shown to be significant, especially in the years following the economic downturn of 2008. The calculation of the variance and distribution of these estimates is another significant contribution. The common criticism that SE estimates are unreliable is addressed by calculating the variance and the distribution of the estimates, and the large size of the SE in Greece and Spain is once again confirmed.     

The art of wasting time: sociability,friendship, community and holidays

Slow tourism is motivated by the desire for personal and communal well-being. It emerged as an antidote to the fast-paced imperatives of global capitalism that urge the entrepreneurial self to speed up and work harder to achieve and demonstrate desired social status. The entrepreneurial self can be understood in the contexts of neoliberalism and the class- and gender-based histories of time-thrift and rational recreation; the entrepreneurial self uses leisure time purposively in the pursuit of status, avoids idle pursuits and has restricted capacity to experience leisurely social relationships. In this article, it is argued that leisurely social relations can be reclaimed by letting go, even temporarily, of time-thrift and the compulsion to use leisure time purposively. Data drawn from in-depth interviews with repeat visitors at two Australian caravan parks revealed that for the period of their holiday the tourists relax, refuse to be driven by schedules, socialise with other tourists and feel no compulsion to use time purposively. The key reasons the tourists return to the parks each year were for the friendships and the sense of community they experience as part of the holiday. Slow tourism by its very nature rejects time-thrift, however, as the movement is harnessed by global capitalism, slow tourism risks becoming a source of conspicuous consumption. The findings of this study suggest that friendship and community thrive more readily in conditions where the need to achieve and demonstrate social status is discarded along with time-thrift.