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With the booming of the unconventional oil and gas industry, its inevitable damage to the environment and human health has attracted public attention. We applied text mining on a total 6057 the type of Environmental Health and Safety compliance reports from 2008 to 2018 lunched by the Department of Environmental Protection in Pennsylvania, USA, to discover the intern mechanism of environmental violations., Comment: 23 pages

Additional file 1. Neural networks training characteristics. Table A.1. Neural networks design and training characteristics

Supplemental material, sj-pdf-1-jtr-10.1177_00472875211036194 for Decomposition Methods for Tourism Demand Forecasting: A Comparative Study by Chengyuan Zhang, Mingchen Li, Shaolong Sun, Ling Tang and Shouyang Wang in Journal of Travel Research

Supplemental material, sj-pdf-1-jtr-10.1177_00472875211036194 for Decomposition Methods for Tourism Demand Forecasting: A Comparative Study by Chengyuan Zhang, Mingchen Li, Shaolong Sun, Ling Tang and Shouyang Wang in Journal of Travel Research

The Asian-pacific region is the major international tourism demand market in the world, and its tourism demand is deeply affected by various factors. Previous studies have shown that different market factors influence the tourism market demand at different timescales. Accordingly, the decomposition ensemble learning approach is proposed to analyze the impact of different market factors on market demand, and the potential advantages of the proposed method on forecasting tourism demand in the Asia-pacific region are further explored. This study carefully explores the multi-scale relationship between tourist destinations and the major source countries, by decomposing the corresponding monthly tourist arrivals with noise-assisted multivariate empirical mode decomposition. With the China and Malaysia as case studies, their respective empirical results show that decomposition ensemble approach significantly better than the benchmarks which include statistical model, machine learning and deep learning model, in terms of the level forecasting accuracy and directional forecasting accuracy.

The accurate seasonal and trend forecasting of tourist arrivals is a very challenging task. In the view of the importance of seasonal and trend forecasting of tourist arrivals, and limited research work paid attention to these previously. In this study, a new adaptive multiscale ensemble (AME) learning approach incorporating variational mode decomposition (VMD) and least square support vector regression (LSSVR) is developed for short-, medium-, and long-term seasonal and trend forecasting of tourist arrivals. In the formulation of our developed AME learning approach, the original tourist arrivals series are first decomposed into the trend, seasonal and remainders volatility components. Then, the ARIMA is used to forecast the trend component, the SARIMA is used to forecast seasonal component with a 12-month cycle, while the LSSVR is used to forecast remainder volatility components. Finally, the forecasting results of the three components are aggregated to generate an ensemble forecasting of tourist arrivals by the LSSVR based nonlinear ensemble approach. Furthermore, a direct strategy is used to implement multi-step-ahead forecasting. Taking two accuracy measures and the Diebold-Mariano test, the empirical results demonstrate that our proposed AME learning approach can achieve higher level and directional forecasting accuracy compared with other benchmarks used in this study, indicating that our proposed approach is a promising model for forecasting tourist arrivals with high seasonality and volatility.

In this paper, we are concerned with global efficiency in multiobjective optimization. After exposing a property of a cone-subconvexlike function, we prove that a local weakly efficient solution, a local efficient solution and a local properly efficient solution are respectively a global weakly efficient solution, a global efficient solution and a global properly efficient solution of a multiobjective programming problem if cone- subconvexlikeness or cone-pre-invexity is assumed

We study the mechanism that controls the shape of the bear market through an information diffusion perspective, and establish a frontier of market decline, in terms of a trade-off between amplitude, duration and the rate of information diffusion. Empirical analysis using data from 15 stock markets confirms the existence of this trade-off relationship. An algorithm for generating the frontier using real data is proposed and applied in several market scenarios. The results suggest that the behaviour of international stock markets during the current US credit crunch is similar to that in previous bear markets in terms of the trivariate trade-off.

t Approximating the Pareto fronts of nonlinear multiobjective optimization problems is considered and a property called inherent nondominance is proposed for such approximations. It is shown that an approximation having the above property can be explored by interactively solving a multiobjective optimization problem related to it. This exploration can be performed with available interactive multiobjective optimization methods. The ideas presented are especially useful in solving computationally expensive multiobjective optimization problems with costly function value evaluations. peerReviewed