COVID‑19 and tourism sector stock price in Spain:medium‑term relationship through dynamic regression models

The global pandemic,coronavirus disease 2019(COVID-19),has significantly affected tourism,especially in Spain,as it was among the first countries to be affected by the pandemic and is among the world’s biggest tourist destinations.Stock market values are responding to the evolution of the pandemic,especially in the case of tourist companies.Therefore,being able to quantify this relationship allows us to predict the effect of the pandemic on shares in the tourism sector,thereby improving the response to the crisis by policymakers and investors.Accordingly,a dynamic regression model was developed to predict the behavior of shares in the Spanish tourism sector according to the evolution of the COVID-19 pandemic in the medium term.It has been confirmed that both the number of deaths and cases are good predictors of abnormal stock prices in the tourism sector.

Time Series Analysis and Prediction of COVID-19 Pandemic Using Dynamic Harmonic Regression Models

Rapidly spreading COVID-19 virus and its variants, especially in metropolitan areas around the world, became a major health public concern. The tendency of COVID-19 pandemic and statistical modelling represents an urgent challenge in the United States for which there are few solutions. In this paper, we demonstrate combining Fourier terms for capturing seasonality with ARIMA errors and other dynamics in the data. Therefore, we have analyzed 156 weeks COVID-19 dataset on national level using Dynamic Harmonic Regression model, including simulation analysis and accuracy improvement from 2020 to 2023. Most importantly, we provide new advanced pathways which may serve as targets for developing new solutions and approaches.

Dynamic soft sensor development based on Gaussian mixture regression for fermentation processes

The dynamic soft sensor based on a single Gaussian process regression(GPR) model has been developed in fermentation processes.However,limitations of single regression models,for multiphase/multimode fermentation processes,may result in large prediction errors and complexity of the soft sensor.Therefore,a dynamic soft sensor based on Gaussian mixture regression(GMR) was proposed to overcome the problems.Two structure parameters,the number of Gaussian components and the order of the model,are crucial to the soft sensor model.To achieve a simple and effective soft sensor,an iterative strategy was proposed to optimize the two structure parameters synchronously.For the aim of comparisons,the proposed dynamic GMR soft sensor and the existing dynamic GPR soft sensor were both investigated to estimate biomass concentration in a Penicillin simulation process and an industrial Erythromycin fermentation process.Results show that the proposed dynamic GMR soft sensor has higher prediction accuracy and is more suitable for dynamic multiphase/multimode fermentation processes.

A dynamic logistic regression for network link prediction

In social network analysis, link prediction is a problem of fundamental importance. How to conduct a comprehensive and principled link prediction, by taking various network structure information into consideration,is of great interest. To this end, we propose here a dynamic logistic regression method. Specifically, we assume that one has observed a time series of network structure. Then the proposed model dynamically predicts future links by studying the network structure in the past. To estimate the model, we find that the standard maximum likelihood estimation(MLE) is computationally forbidden. To solve the problem, we introduce a novel conditional maximum likelihood estimation(CMLE) method, which is computationally feasible for large-scale networks. We demonstrate the performance of the proposed method by extensive numerical studies.

ClimateAP: an application for dynamic local downscaling of historical and future climate data in Asia Pacific

While low-to-moderate resolution gridded climate data are suitable for climate-impact modeling at global and ecosystems levels, spatial analyses conducted at local scales require climate data with increased spatial accuracy. This is particularly true for research focused on the evaluation of adaptive forest management strategies. In this study, we developed an application, Climate AP, to generate scale-free(i.e., specific to point locations) climate data for historical(1901–2015) and future(2011–2100)years and periods. Climate AP uses the best available interpolated climate data for the reference period 1961–1990 as baseline data. It downscales the baseline data from a moderate spatial resolution to scale-free point data through dynamic local elevation adjustments. It also integrates and downscales the historical and future climate data using a delta approach. In the case of future climate data, two greenhouse gas representative concentration pathways(RCP 4.5 and 8.5) and 15 general circulation models are included to allow for the assessment of alternative climate scenarios. In addition, Climate AP generates a large number of biologically relevant climate variables derived from primary monthly variables. The effectiveness of the local downscaling was determined based on the strength of the local linear regression for the estimate of lapse rate. The accuracy of the Climate AP output was evaluated through comparisons of Climate AP output against observations from 1805 weather stations in the Asia Pacific region. The local linear regression explained 70%–80% and 0%–50% of the total variation in monthly temperatures and precipitation, respectively, in most cases. Climate AP reduced prediction error by up to27% and 60% for monthly temperature and precipitation,respectively, relative to the original baselines data. The improvements for baseline portions of historical and futurewere more substantial. Applications and limitations of the software are discussed.

Grain Science and Technology Policies and Food Security in China:An Empirical Study Based on a Provincial Dynamic Panel Model

China’s grain science and technology policies have played an important role in the development of China’s food industry.This paper aims to examine the effects of China’s grain science and technology policies on food security.It quantitatively assesses China’s food security by analyzing the main contents and development trends of China’s food science technology policies through the text metrology method,and then investigates the effects of grain science and technology policies on food security by employing a provincial dynamic panel model.The results show that food security in China is all-round developed,and that the release frequency and cumulative effect of grain science and technology policies play a significant role in promoting food security.Powerful grain science and technology policies can effectively guarantee China’s food security.

Transient simulation of regression rate on thrust regulation process in hybrid rocket motor

The main goal of this paper is to study the characteristics of regression rate of solid grain during thrust regulation process. For this purpose, an unsteady numerical model of regression rate is established. Gas–solid coupling is considered between the solid grain surface and combustion gas.Dynamic mesh is used to simulate the regression process of the solid fuel surface. Based on this model, numerical simulations on a H2O2/HTPB（hydroxyl-terminated polybutadiene） hybrid motor have been performed in the flow control process. The simulation results show that under the step change of the oxidizer mass flow rate condition, the regression rate cannot reach a stable value instantly because the flow field requires a short time period to adjust. The regression rate increases with the linear gain of oxidizer mass flow rate, and has a higher slope than the relative inlet function of oxidizer flow rate. A shorter regulation time can cause a higher regression rate during regulation process. The results also show that transient calculation can better simulate the instantaneous regression rate in the operation process.

Obstacle-circumventing adaptive control of a four-wheeled mobile robot subjected to motion uncertainties

To achieve the collision-free trajectory tracking of the four-wheeled mobile robot(FMR),existing methods resolve the tracking control and obstacle avoidance separately.Guaranteeing the synergistic robustness and smooth navigation of mobile robots subjected to motion uncertainties in a dynamic environment using this non-cooperative processing method is difficult.To address this challenge,this paper proposes an obstacle-circumventing adaptive control(OCAC)framework.Specifically,a novel anti-disturbance terminal slide mode control with adaptive gains is formulated,incorporating specified control laws for different stages.This formulation guarantees rapid convergence and simultaneous chattering elimination.By introducing sub-target points,a new sub-target dynamic tracking regression obstacle avoidance strategy is presented to transfer the obstacle avoidance problem into a dynamic tracking one,thereby reducing the burden of local path searching while ensuring system stability during obstacle circumvention.Comparative experiments demonstrate that the proposed OCAC method can strengthen the convergence and obstacle avoidance efficiency of the concerned FMR system.

Estimating the effect of minimum wage on firm profitability in China

Using the firm-level panel datasets and hand-collected data on county level minimum wage,this paper estimates the effect of minimum wage on firm profitability.As firms may take time to adjust in response to changes in minimum wage,this paper estimates a dynamic panel model with lagged minimum wage.To capture the heterogeneous effect of minimum wage on profitability,this paper further estimates quantile regression dynamic panel model.The estimation results suggest that the effect on firm profitability of minimum wage in the current year is negative across the whole conditional distribution of profitability and it exhibits an inverted-U shape across conditional quantiles.The effect on profitability of lagged minimum wage is positive at the 5th,10th,15th quantiles,negative at the 90th and 95th quantiles,and not significant at other quantiles.Turning to the overall effect on profitability of minimum wage,we find that minimum wage exerts significantly negative effect on profitability at the 5th quantile and quantiles higher than 40th and the absolute value of the effect of minimum wage increases with these quantiles.For other quantiles,the overall effect of minimum wage on profitability is negligible.