Spillover Effect of the Internet on Trade Performance Based on a Vision of the Public's Sleep Health: A Spatial Study of the Global Network

This is the first study to analyze the spatial spillover effect of the internet on trade performance based on a vision of the public's sleep health. The internet's effect on trade performance has been enhanced in a new economy consisting of larger global markets. An overall improvement in health gradually impacts economic development. In this study, hierarchical modeling is applied to reveal the effect of the internet on trade performance at a fundamental level, and the effect of sleep health on trade performance at general level. The global network is structured by a spatial weight matrix based on the Mahalanobis distance of the internet and sleep health. Furthermore, spatial autoregressive modeling is applied to study the effect of the spatial weight matrix based on the Mahalanobis distance matrix of the internet and sleep health on trade performance. The spatial Durbin modeling is applied to further analyze the interaction effect of the spatial weight matrix and countries' factors on trade performance. It was found that the internet has a positive effect on trade performance, and good sleep health can be helpful to the spillover effect of the internet on trade performance. The interaction of the spatial weight matrix and gross domestic product (GDP) can further enhance the effect. This research can assist global managers to further understand the spatial spillover effect of the internet on trade performance based on a vision of the public's sleep health.

Green Credit, Environmental Pollution and High-Quality Development of Green Economy

Based on the panel data of 30 provinces (cities and districts) in China from 2003 to 2019, this paper uses the Green Development Index System jointly formulated and released by the National Development and Reform Commission, the National Bureau of Statistics, the Ministry of Environmental Protection and the Central Organization Department to construct a comprehensive index system which can calculate the high-quality development index of green economy, and research the impact of green credit, environmental pollution and high-quality development of green economy. The results show that: (1) The improvement of green credit is conducive to promoting the high-quality development of green economy. Considering the high autocorrelation of the high-quality development of green economy, the impact of green credit on the high-quality development of green economy is still robust and does not depend on the specific metrology. (2) With Moran Index, it is found that the high-quality development of green economy has spatial characteristics. By using Spatial Dobbin Model (SDM), it is found that under both (0,1) weight matrix and geographical distance weight matrix, the impact of green credit on the high-quality development of green economy is positive, forming a positive spatial spillover effect on the high-quality development of green economy in surrounding areas. (3) By using the Intermediary Effect Model,it can be seen that environmental pollution plays a partial intermediary effect between green credit and high-quality development of green economy. There is a transmission channel of "green credit → environmental pollution → high-quality development of green economy". (4) By using Panel Quantile Regression Model, it is found, with the improvement of high-quality development of green economy, that the promotional effect brought by green credit increased.

A Weighted EFOR Algorithm for Dynamic Parametrical Model Identification of the Nonlinear System

In this paper, the Nonlinear Auto-Regressive with exogenous inputs (NARX) model with parameters of interest for design (NARX-M-for-D), where the design parameter of the system is connected to the coefficients of the NARX model by a predefined polynomial function is studied. For the NARX-M-for-D of nonlinear systems, in practice, to predict the output by design parameter values are often difficult due to the uncertain relationship between the design parameter and the coefficients of the NARX model. To solve this issue and conduct the analysis and design, an improved algorithm, defined as the Weighted Extended Forward Orthogonal Regression (WEFOR), is proposed. Firstly, the initial NARX-M-for-D is obtained through the traditional Extended Forward Orthogonal Regression (EFOR) algorithm. Then a weight matrix is introduced to modify the polynomial functions with respect to the design parameter, and then an improved model, which is referred to as the final NARX-M-for-D is established. The genetic algorithm (GA) is used for deriving the weight matrix by minimizing the normalized mean square error (NMSE) over the data sets corresponding to the design parameter values used for modeling and first prediction. Finally, both the numerical and experimental studies are conducted to demonstrate the application of the WEFOR algorithm. The results indicate that the final NARX-M-for-D can accurately predict the system output of a nonlinear system. The new algorithm is expected to provide a reliable model for dynamic analysis and design of the nonlinear system.

Post-Alignment Adjustment and Its Automation

Multiple sequence alignment (MSA) is the basis for almost all sequence comparison and molecular phylogenetic inferences. Large-scale genomic analyses are typically associated with automated progressive MSA without subsequent manual adjustment, which itself is often error-prone because of the lack of a consistent and explicit criterion. Here, I outlined several commonly encountered alignment errors that cannot be avoided by progressive MSA for nucleotide, amino acid, and codon sequences. Methods that could be automated to fix such alignment errors were then presented. I emphasized the utility of position weight matrix as a new tool for MSA refinement and illustrated its usage by refining the MSA of nucleotide and amino acid sequences. The main advantages of the position weight matrix approach include (1) its use of information from all sequences, in contrast to other commonly used methods based on pairwise alignment scores and inconsistency measures, and (2) its speedy computation, making it suitable for a large number of long viral genomic sequences.

The Generalized STAR Modelling with Minimum Spanning Tree Approach of Weight Matrix for COVID-19 Case in Java Island

The ongoing global Coronavirus 2019 (COVID-19) pandemic poses a major threat. The spread of the COVID-19 virus is likely to occur from one location to another location due to the mobility of people. Many efforts and policies have been made by each country to reduce the spread of the COVID-19 outbreak. The imposition of lockdown and large-scale social restrictions or social distancing has been widely applied to limit the transmission of this virus among the community and provincial levels. Both policies have proven effective in reducing the spread of the COVID-19 virus. To obtain the overview of this case, many researchers were conducted. Here, the Generalized STAR (GSTAR) model was applied to model the increasing number of COVID-19 positive cases per day in six provinces in Java Island. The data was recorded simultaneously in six locations, namely in the Provinces of Banten, Jakarta, West Java, Central Java, Yogyakarta Special Region, and East Java. This paper proposes a new approach in constructing the weight matrix required to build the GSTAR model, namely Minimum Spanning Tree (MST). The weight matrix represents the relationship among observed locations. By using the MST, a topological (undirected graph) network model could be created to show the correlation, centrality, and relationship on the increase of COVID-19 positive cases among the provinces in Java Island. The GSTAR(1;1) with the inverse distance weight matrix using MST presents a good ability to predict the COVID-19 increasing cases of Java island. This is indicated by the final MAPE average score of 19.55.

Application of GSTAR(1,1) model for layer peat soil predicted based on resistivity log data

In this study, GSTAR modeling was carried out with the inverse of distance weight matrix obtained from Geoelectrical Resistivity data at several peatland locations around the Universitas Tanjungpura, Pontianak. This data can identify the subsurface layer of the soil through the electric current that binds into the soil. However, due to the limitation of the tool to measure the resistivity value, it can only measure 1/5 of the depth of the observation length. To overcome this problem, predictions are made at the next depth using the GSTAR model. The study began by measuring the resistivity value of the land using the geoelectric method and mapping it. Through this GSTAR modeling, predictions are made for the unobserved subsurface to determine the type of soil layer. Knowing the type of deeper soil layer can help contractors build plant concrete stakes to keep buildings safe on peatland. The results of the GSTAR(1.1) model are not accurate enough to estimate the resistivity value data. This is possible because the correlation between rock ages is not the same, so further analysis is required.

Research on the Spatial Correlation and Spatial Lag of COVID-19 Infection Based on Spatial Analysis

COVID-19 has spread throughout the world since the virus was discovered in 2019. Thus, this study aimed to identify the global transmission trend of the COVID-19 from the perspective of the spatial correlation and spatial lag. The research used primary data collected of daily increases in the amount of COVID-19 in 14 countries, confirmed diagnosis, recovered numbers, and deaths. Findings of the Moran index showed that the propagation of infection was aggregated between 9 May and 21 May based on the composite spatial weight matrix. The results from the Lagrange multiplier test indicated the COVID-19 patients can infect others with a lag.

Optimization of Roasting Parameters for Recovery of Vanadium and Tungsten from Spent SCR Catalyst with Composite Roasting

Every year, large amounts of selective catalytic reduction (SCR) catalysts with losing catalytic activity and failing to be regenerated need to be regenerated, which will result in acute pollution. Recycling valuable metals from spent SCR catalysts can not only solve environmental problems, but also save resources. The process of sodium roasting and water leaching is able to effectively extract vanadium (V) and tungsten (W) from spent SCR catalysts. To improve the efficiencies of V and W, different sodium additives were first investigated in the roasting process. The results revealed that the process of NaCl-NaOH composite roasting and water leaching showed superior leaching efficiencies of V and W, which can reach 91.39% and 98.26%, respectively, and simultaneously, it can be found that adding low melting point NaOH promoted mass transfer as compared with the melting points of different sodium additives. Next, a single-factor experiment was conducted to investigate different roasting conditions, such as roasting temperature, roasting time, mass ratio of sodium additive and catalyst, and mass ratio of NaCl and NaOH, on the leaching efficiencies of V and W. Then, a three-level and four-factor orthogonal experiment and a weight matrix analysis were used to optimize the roasting parameters. The results showed that roasting temperature had the most significant effect on the leaching efficiencies of V and W, and the optimal roasting conditions were as follows: the roasting temperature was 750 °C, the roasting time was 2.5 h, the mass ratio of sodium additive and catalyst was 2.5, and the mass ratio of NaCl and NaOH was 1.5. Under the optimal roasting conditions, the leaching efficiencies of V and W were 93.25% and 99.17%, respectively. The results of XRD analysis inferred that VO2 coming from the decomposition of VOSO4 in spent SCR catalysts may first oxidize into V2O5 and then react with sodium additives to produce NaVO3. The formation of titanium-vanadium oxide ((Ti0.5V0.5)2O3) was a part reason of hindering the leaching of vanadium. With the increase of roasting temperature, TiO2 converted into Na2Ti3O7, which indicated that the main structure of the catalyst was destroyed, and simultaneously, more characteristic peaks of sodium metavanadate and sodium tungstate appeared, thus enhancing the leaching of V and W. Finally, it can be seen that the process of NaCl-NaOH roasting and water leaching remained higher leaching efficiencies of V and W and lower roasting temperature by comparing with leaching efficiencies of V and W in different processes of recycling SCR catalyst. The process of NaCl-NaOH composite roasting and water leaching provided a strategy with a highly efficient and clean route to leach V and W from spent SCR catalyst. The orthogonal experiment and weight matrix analysis in our study can be used as a reference to optimize the reaction conditions of a multiple indexes experiment.

Geospatial Least Squares Support Vector Regression Fused with Spatial Weight Matrix

Due to the increasingly complex objects and massive information involved in spatial statistics analysis, least squares support vector regression (LS-SVR) with a good stability and high calculation speed is widely applied in regression problems of geospatial objects. According to Tobler’s First Law of Geography, near things are more related than distant things. However, very few studies have focused on the spatial dependence between geospatial objects via SVR. To comprehensively consider the spatial and attribute characteristics of geospatial objects, a geospatial LS-SVR model for geospatial data regression prediction is proposed in this paper. The 0–1 type and numeric-type spatial weight matrices are introduced as dependence measures between geospatial objects and fused into a single regression function of the LS-SVR model. Comparisons of the results obtained with the proposed and conventional models and other traditional models indicate that fusion of the spatial weight matrix can improve the prediction accuracy. The proposed model is more suitable for geospatial data regression prediction and enhances the ability of geospatial phenomena to explain geospatial data.