Multi-classifier ensemble based on dynamic weights

In this study, a novel multi-classifier ensemble method based on dynamic weights is proposed to reduce the interference of unreliable decision information and improve the accuracy of fusion decision. The algorithm defines decision credibility to describe the real-time importance of the classifier to the current target, combines this credibility with the reliability calculated by the classifier on the training data set and dynamically assigns the fusion weight to the classifier. Compared with other methods, the contribution of different classifiers to fusion decision in acquiring weights is fully evaluated in consideration of the capability of the classifier to not only identify different sample regions but also output decision information when identifying specific targets. Experimental results on public face databases show that the proposed method can obtain higher classification accuracy than that of single classifier and some popular fusion algorithms. The feasibility and effectiveness of the proposed method are verified

Spatio-temporal Incentives Optimization for Ride-hailing Services with Offline Deep Reinforcement Learning

A fundamental question in any peer-to-peer ride-sharing system is how to, both effectively and efficiently, meet the request of passengers to balance the supply and demand in real time. On the passenger side, traditional approaches focus on pricing strategies by increasing the probability of users' call to adjust the distribution of demand. However, previous methods do not take into account the impact of changes in strategy on future supply and demand changes, which means drivers are repositioned to different destinations due to passengers' calls, which will affect the driver's income for a period of time in the future. Motivated by this observation, we make an attempt to optimize the distribution of demand to handle this problem by learning the long-term spatio-temporal values as a guideline for pricing strategy. In this study, we propose an offline deep reinforcement learning based method focusing on the demand side to improve the utilization of transportation resources and customer satisfaction. We adopt a spatio-temporal learning method to learn the value of different time and location, then incentivize the ride requests of passengers to adjust the distribution of demand to balance the supply and demand in the system. In particular, we model the problem as a Markov Decision Process (MDP)

Effect of rosuvastatin and benazepril on matrix metalloproteinase-2, matrix metalloproteinase-9 and leukotriene B4 of patients with acute myocardial infarction

Purpose: To investigate the effects of rosuvastatin and benazepril on matrix metalloproteinase-2 (MMP-2), MMP-9 and leukotriene B4 (LTB4) of patients with acute myocardial infarction (AMI). Methods: Fifty-six patients with AMI were selected. They were randomly divided into control and study groups. Thirty healthy people were used in the normal group. On the basis of conventional therapy, patients in the control group were given rosuvastatin orally, while those in the study group received rosuvastatin and benazepril orally. The duration of treatment in both groups was 3 months. Serum levels of MMP-2, MMP-9 and LTB4, and incidence of left ventricular remodelling and recurrence of cardiovascular events were determined before and after treatment for both groups. Results: MMP-2, MMP-9 and LTB4 levels in serum were significantly lower for the two groups after treatment, when compared to pre-treatment values, and significantly lower in the study group (p < 0.05). Left ventricular remodelling was lower in the study group than in the control group (p < 0.05). Recurrence of cardiovascular events declined significantly in the study group, relative to control (p > 0.05). Conclusion: Rosuvastatin and benazepril significantly reduce serum levels of MMP-2, MMP-9 and LTB4 in AMI patients, and thus can potentially prevent ventricular remodelling, improve prognosis and reduce recurrence rate

Pattern formation of a Schnakenberg-type plant root hair initiation model

This paper concentrates on the diversity of patterns in a quite general Schnakenberg-type model. We discuss existence and nonexistence of nonconstant positive steady state solutions as well as their bounds. By means of investigating Turing, steady state and Hopf bifurcations, pattern formation, including Turing patterns, nonconstant spatial patterns or time periodic orbits, is shown. Also, the global dynamics analysis is carried out

Pattern formation of a Schnakenberg-type plant root hair initiation model

This paper concentrates on the diversity of patterns in a quite general Schnakenberg-type model. We discuss existence and nonexistence of nonconstant positive steady state solutions as well as their bounds. By means of investigating Turing, steady state and Hopf bifurcations, pattern formation, including Turing patterns, nonconstant spatial patterns or time periodic orbits, is shown. Also, the global dynamics analysis is carried out

A method on estimating time-varying vertical eddy viscosity for an Ekman layer model with data assimilation

Author Posting. © American Meteorological Society, 2019. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Atmospheric and Oceanic Technology 36(9), (2019): 1789-1812, doi:10.1175/JTECH-D-18-0223.1.Temporal vertical eddy viscosity coefficient (VEVC) in an Ekman layer model is estimated using an adjoint method. Twin experiments are carried out to investigate the influences of several factors on inversion results, and the conclusions of twin experiments are 1) the adjoint method is a capable method to estimate different kinds of temporal distributions of VEVCs; 2) the gradient descent algorithm is better than CONMIN and L-BFGS for the present problem, although the posterior two algorithms perform better on convergence efficiency; 3) inversion results are sensitive to initial guesses; 4) the model is applicable to different wind conditions; 5) the inversion result with thick boundary layer depth (BLD) is slightly better than thin BLD; 6) inversion results are more sensitive to observations in upper layers than those in lower layers; 7) inversion results are still acceptable when data noise exists, indicating the method can sustain noise to a certain degree; 8) a regularization method is proved to be useful to improve the results for present problem; and 9) the present method can tolerate the existence of balance errors due to the imperfection of governing equations. The methodology is further validated in practical experiments where Ekman currents are derived from Bermuda Testbed Mooring data and assimilated. Modeled Ekman currents coincide well with observed ones, especially for upper layers. The results demonstrate that the assumptions of depth dependence and time dependence are equally important for VEVCs. The feasibility of the typical Ekman model, the imperfection of Ekman balance equations, and the deficiencies of the present method are discussed. This method provides a potential way to realize the time variations of VEVCs in ocean models.The authors thank the seven reviewers for the constructive suggestions which have greatly improved the manuscript. Financial support is provided by the National Key Research and Development Plan of China (Grants 2017YFA0604100 and 2017YFC1404000), the National Natural Science Foundation of China (Grants 41876086 and 41806012), Scientific Research Fund of the Second Institute of Oceanography, MNR (Grant JG1819), and the Fundamental Research Funds for the Central Universities of China. Jicai thanks the support of China Scholarship Council for the visiting research in WHOI, and he also thanks the host of WHOI. BTM data are provided by Ocean Physics Laboratory, University of California, Santa Barbara (http://opl.ucsb.edu).2020-03-1

Editorial Special Issue on Enhancement Algorithms, Methodologies and Technology for Spectral Sensing

The paper is an editorial issue on enhancement algorithms, methodologies and technology for spectral sensing and serves as a valuable and useful reference for researchers and technologists interested in the evolving state-of-the-art and/or the emerging science and technology base associated with spectral-based sensing and monitoring problem. This issue is particularly relevant to those seeking new and improved solutions for detecting chemical, biological, radiological and explosive threats on the land, sea, and in the air