ChevOpt: Continuous-time State Estimation by Chebyshev Polynomial Optimization

In this paper, a new framework for continuous-time maximum a posteriori estimation based on the Chebyshev polynomial optimization (ChevOpt) is proposed, which transforms the nonlinear continuous-time state estimation into a problem of constant parameter optimization. Specifically, the time-varying system state is represented by a Chebyshev polynomial and the unknown Chebyshev coefficients are optimized by minimizing the weighted sum of the prior, dynamics and measurements. The proposed ChevOpt is an optimal continuous-time estimation in the least squares sense and needs a batch processing. A recursive sliding-window version is proposed as well to meet the requirement of real-time applications. Comparing with the well-known Gaussian filters, the ChevOpt better resolves the nonlinearities in both dynamics and measurements. Numerical results of demonstrative examples show that the proposed ChevOpt achieves remarkably improved accuracy over the extended/unscented Kalman filters and extended batch/fixed-lag smoother, closes to the Cramer-Rao lower bound.Comment: 12 pages, 16 figure

Dietary shift brings non-ignorable cross-border health and environment change

Dietary shifts are key for enhancing the sustainability of current national food systems but need to account for potential human health and environmental spillover effects as well. Employing the Global Trade Analysis Project (GTAP), we examine the direct health and environmental effects of countries adopting national recommended dietary patterns on their own health and environment, as well as spillovers through food trade. We find that when countries shift to their National food based dietary guidelines it could make great improvements in human health (e.g., changes in obesity rates) but positive and negative impacts on the environment (e.g., changes in blue water use). It will have spillover effects on the health and environment of other countries through the price-income mechanism of food trade, so that the dietary shift does not necessarily result in universally beneficial outcomes for global health and the environment. In light of these findings, we explore alternative policy solutions, such as technical assistance, to enhance the potential for win-win outcomes for both health and the environment during dietary shifts

Sustainable strategy for corporate governance based on the sentiment analysis of financial reports with CSR

Abstract Focusing only on shareholders’ financial return is not consistent with the concept of sustainable corporate governance. In contrast to financial performance, corporate social responsibility (CSR) is a non-financial performance index. Financial reports consist of both financial and non-financial disclosures. These disclosures help investors make decisions. This paper characterizes the interaction between the sentiment analysis of financial reports and CSR scores. The classification accuracy through SVM exceeds 86%. The empirical study shows that the financial report sentiment based on the PESTEL model, Porter’s Five Forces model, and Value Chain (Primary and Support Activities) significantly correlates to the CSR score

Stochastic Drone Fleet Deployment and Planning Problem Considering Multiple-Type Delivery Service

Drone delivery has a great potential to change the traditional parcel delivery service in consideration of cost reduction, resource conservation, and environmental protection. This paper introduces a novel drone fleet deployment and planning problem with uncertain delivery demand, where the delivery routes are fixed and couriers work in collaboration with drones to deliver surplus parcels with a relatively higher labor cost. The problem involves the following two-stage decision process: (i) The first stage determines the drone fleet deployment (i.e., the numbers and types of drones) and the drone delivery service module (i.e., the time segment between two consecutive departures) on a tactical level, and (ii) the second stage decides the numbers of parcels delivered by drones and couriers on an operational level. The purpose is to minimize the total cost, including (i) drone deployment and operating cost and (ii) expected labor cost. For the problem, a two-stage stochastic programming formulation is proposed. A classic sample average approximation method is first applied. To achieve computational efficiency, a hybrid genetic algorithm is further developed. The computational results show the efficiency of the proposed approaches

Liner ship bunkering and sailing speed planning with uncertain demand

International audienceLiner shipping is an important branch of maritime transportation. As bunker fuel consumption causes high operating cost and harmful gas emissions, bunker fuel management is a great challenge and a hot research topic in liner shipping. Bunker charging can be achieved at ports with diverse prices, and it is recognized that appropriately managing bunker fuel and sailing speed can improve liner shipping performance and reduce environmental pollution. Most existing works assume that the container demand is deterministic. However, in practice, it is usually difficult to exactly estimate the volume of containers to be shipped due to various factors. This paper studies a liner ship bunkering and speed optimization problem under uncertain container demand. For the problem, a two-stage stochastic and non-linear programming formulation is proposed. To split the complexity of the problem, the complicated bunker consumption function is approximated by piecewise linear ones. To solve the problem, a classic sample average approximation (SAA) method, and the SAA based on scenario reduction, and an L-shaped method are developed and compared. Numerical results show that the L-shaped method outperforms the two SAA methods, in terms of solution quality and computational time