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Based on the perspective of technology supply chain, this study explores the effect of macroeconomic uncertainty regarding the spatiotemporal evolution of urban innovation networks to establish causality.

It collects patent trading data for 283 cities in China (2005–2017) and employs the spatial econometric model to investigate the causal relationship.

The regional transfer of advanced technology in China is rising sharply, and the innovation network based on patent trading is typically high-density, multi-direction and wide-spreading. Further, macroeconomic uncertainty has a negative effect on the scale of innovation flows and the absorptive capacity in eastern cities. However, it has no significant impact on the innovation network characteristics in developed cities. In contrast, macroeconomic uncertainty is detrimental for the absorptive capacity and node importance in inland and undeveloped cities.

As macroeconomic uncertainty increases, it is important to improve the quality of the urban innovation network with a better understanding of heterogeneity to promote further suitability innovation at the region-level.

This study highlights a clear and distinctive view that macroeconomic uncertainty not only directly affects the evolution of the urban innovation network but also indirectly affects the characteristics of other city nodes via the spatial spillover mechanism.

While the bring-your-own-device (BYOD) trend provides benefits for employees, it also poses security risks to organizations. This study explores whether and how employees decide to adopt BYOD practices when they encounter information security–related conflict.

Using survey data from 235 employees of Chinese enterprises and applying partial least squares based structural equation modeling (PLS-SEM), we test a series of hypotheses.

The results suggest that information security–related conflict elicits information security fatigue among employees. As their information security fatigue increases, employees become less likely to adopt BYOD practices. In addition, information security–related conflict has an indirect effect on employee's BYOD adoption through the full mediation of information security fatigue.

This study provides practical implications to adopt BYOD in the workplace through conflict management measures and emotion management strategies. Conflict management measures focused on the reducing of four facets of information security–related conflict, such as improve organization's privacy policies and help employees to build security habits. Emotion management strategies highlighted the solutions to reduce fatigue through easing conflict, such as involving employees in the development or update of information security policies to voice their demands of privacy and other rights.

Our study extends knowledge by focusing on the barriers to employees' BYOD adoption when considering information security in the workplace. Specifically, this study takes a conflict perspective and builds a multi-faceted construct of information security–related conflict. Our study also extends information security behavior research by revealing an emotion-based mediation effect, that of information security fatigue, to explore the mechanism underlying the influence of information security–related conflict on employee behavior.

This paper aims to reduce environment pollution caused by benzotriazole. The authors chose one of the best inhibitors from 2-aminobenzimidazole, 2-methylbenzimidazol, 2-mercaptobenzimidazole and benzimidazole in combination with benzotriazole.

The electrochemical measurement indicated that 2-methylbenzimidazol had the best inhibition behavior. Then, it was mixed with benzotriazole. Techniques such as field emission scanning electron microscopy, atomic force microscopy, Raman spectroscopy and optical contact angle measurements were used.

The results showed that the inhibition efficiency was up to 99.98%, when the mixture concentration was 20 mmol/L and the molar ratio 1:1.

1-benzotriazole was mixed with 2-methylbenzimidazol for the first time. During the exist of methyl, 2-methylbenzimidazol has the better inhibition; this point was ignored by researchers.

Current modellings of granular collapse are lack of considering the effect of soil density. This paper aims to present a numerical method to analyse the collapse of granular column based on the critical-state soil mechanics.

In the proposed method, a simple critical-state based constitutive model is first adopted and implemented into a finite element code using the coupled Eulerian–Lagrangian technique for large deformation analysis. Simulations of column collapse with various aspect ratios are then conducted for a given initial soil density. The effect of aspect ratio on the final size of deposit morphology, dynamical collapse profiles and the stable region is discussed comparing to experimental results. Moreover, complementary simulations with various initial soil densities on each aspect ratio are conducted.

Simulations show that a lower value of initial density leads to a lower final deposit height and a longer run-out distance. The simulated evolutions of kinetic energy and collapsing profile with time by the proposed numerical approach also show clearly a soil density-dependent collapse process.

To the end, this study can improve the understanding of column collapse in different aspect ratios and soil densities, and provide a computational tool for the analysis of real scale granular flow.

The originality of this paper is proposed in a numerical approach to model granular column collapse considering the influences of aspect ratio and initial void ratio. The proposed approach is based on the finite element platform with coupled Eulerian–Lagrangian technique for large deformation analysis and implementing the critical-state based model accounting for the effect of soil density.

Introduction: Governance is the management of various actions to improve human capacities and boost the efficiency with which services are delivered to the general public. The study analyses the relationship between two variables: geographic location and desire to switch to an E-governance system. The study also aims to explore the relevance of artificial intelligence (AI) in supporting E-Governance.

Objectives of study: (1) To investigate the notion of e-governance and the many approaches available. (2) To research various government E-Governance initiatives and raise awareness about the difficulties and opportunities facing India’s e-government system. (3) To study the acceptance of E-governance by the public from rural and urban districts.

Methodology: This study will use a descriptive research approach as its research strategy. Primary data was collected to check for the preference for an acceptance rate of E-Governance based on geographic location (URBAN and RURAL). The current investigation is conducted on 200 respondents from Northern India’s selected urban and rural districts.

Finding and implications: The report summarises the significance of e-governing system adoption in India and offers ways to improve the operation of these systems in the future. The results of the test show that both factors are highly significant. The study recommends that future research integrate our search for scientific studies with a search for non-scientific publications, as journal and conference publications may lag behind the most recent breakthroughs in the implications of AI use in public administration.

Prediction of excess pore water pressure and estimation of soil parameters are the two key interests for consolidation problems, which can be mathematically quantified by a set of partial differential equations (PDEs). Generally, there are challenges in solving these two issues using traditional numerical algorithms, while the conventional data-driven methods require massive data sets for training and exhibit negative generalization potential. This paper aims to employ the physics-informed neural networks (PINNs) for solving both the forward and inverse problems.

A typical consolidation problem with continuous drainage boundary conditions is firstly considered. The PINNs, analytical, and finite difference method (FDM) solutions are compared for the forward problem, and the estimation of the interface parameters involved in the problem is discussed for the inverse problem. Furthermore, the authors also explore the effects of hyperparameters and noisy data on the performance of forward and inverse problems, respectively. Finally, the PINNs method is applied to the more complex consolidation problems.

The overall results indicate the excellent performance of the PINNs method in solving consolidation problems with various drainage conditions. The PINNs can provide new ideas with a broad application prospect to solve PDEs in the field of geotechnical engineering, and also exhibit a certain degree of noise resistance for estimating the soil parameters.

This study presents the potential application of PINNs for the consolidation of soils. Such a machine learning algorithm helps to obtain remarkably accurate solutions and reliable parameter estimations with fewer and average-quality data, which is beneficial in engineering practice.

The purpose of this paper was to attempt to confirm the root cause of wafer damage issue by heavy Al wire wedge bonding and propose some permanent solutions for it.

The infra red–optical beam-induced resistance change (IR-OBIRCH) analysis defines the position of an abnormal hotspot. A cross section and an scanning electron microscope (SEM) confirmed the wafer damage issue and its position. Based on the position of wafer damage, the wedge tool with different life and Al buildup was checked found to be on the wedge tool. Finite element analysis (FEA) modeling analysis and simulation experiment guarantee the Al buildup, and low wedge deformation thickness (WDT) can cause the wafer damage issue. Finally, design of experiment (DOE) experiments are designed to optimize wedge tool dimension and wedge-bond parameters to eliminate wafer damage issue.

Wafer damage issue caused the Vpwr-OUTPUT leakage issue by IR-OBIRCH analysis. Al buildup was found on wedge tool with different life and its size gets larger along with the increase in wedge tool life. Low WDT and bigger Al buildup can cause the wafer damage. Designing new wedge tool and parameters optimization can increase WDT.

Because of the limitation of time and resources, finite element method (FEM) modeling and wedge tool dimension could not be studied more deeply.

This paper sets an example on how to find out the root cause of wafer damage by a step-by-step analysis and put forward a quick solution accordingly for the issue.

With the rapid development of the economy, carbon emissions have also risen sharply. This study explores the relationship between the two by combining the literature of relevant fields and maps the analytical framework from the knowledge base to the research frontier model using CiteSpace.

Using CiteSpace and data statistical tools, we conducted a bibliometric and visual analysis of nearly ten thousand research papers on carbon emissions and economic development published in the Web of Science (WOS) and China National Knowledge Infrastructure (CNKI) databases from 1991 to 2021.

It shows that research on economic development and carbon emissions is developing steadily and involves a wide range of fields. Notably, keywords such as “carbon emissions,” “economic growth,” and “energy consumption” had high frequency, centrality, and persistence. “carbon emissions,” “economic growth,” and “energy consumption” had high frequency, centrality, and persistence. Research institutions in the USA and China have made great contributions to research on economic development and carbon emissions. The authors should continue to enrich and improve research on related subjects and concerns to reasonably plan the path of carbon emission reduction and economic development.

The study analyzes the evolution of the relationship between carbon emissions and economic growth to provide scholars a more comprehensive and in-depth understanding of the relationship from an international perspective.

The purpose of this paper is to investigate problems in performing stable lane changes and to find a solution to reduce energy consumption of autonomous electric vehicles.

An optimization algorithm, model predictive control (MPC) and Karush–Kuhn–Tucker (KKT) conditions are adopted to resolve the problems of obtaining optimal lane time, tracking dynamic reference and energy-efficient allocation. In this paper, the dynamic constraints of vehicles during lane change are first established based on the longitudinal and lateral force coupling characteristics and the nominal reference trajectory. Then, by optimizing the lane change time, the yaw rate and lateral acceleration that connect with the lane change time are limed. Furthermore, to assure the dynamic properties of autonomous vehicles, the real system inputs under the restraints are obtained by using the MPC method. Based on the gained inputs and the efficient map of brushless direct-current in-wheel motors (BLDC IWMs), the nonlinear cost function which combines vehicle dynamic and energy consumption is given and the KKT-based method is adopted.

The effectiveness of the proposed control system is verified by numerical simulations. Consequently, the proposed control system can successfully achieve stable trajectory planning, which means that the yaw rate and longitudinal and lateral acceleration of vehicle are within stability boundaries, which accomplishes accurate tracking control and decreases obvious energy consumption.

This paper proposes a solution to simultaneously satisfy stable lane change maneuvering and reduction of energy consumption for autonomous electric vehicles. Different from previous path planning researches in which only the geometric constraints are involved, this paper considers vehicle dynamics, and stability boundaries are established in path planning to ensure the feasibility of the generated reference path.

The rising uncertainties in the macroeconomic environment exacerbate the challenges firms face in the export market. This study aims to explore which strategy is suitable for export enterprises to develop sustainably under COVID-19.

Based on the sample data of China’s A-stock listed manufacturing firms from 2010 to 2020, this study applies a survival analysis method to explore the impact of strategic flexibility on export firm survival. Furthermore, this study uses the difference-in-difference model to test the relationship between strategic flexibility and firms’ profits in the context of the pandemic.

The results show that strategic flexibility can increase firms’ survival time, improving dynamic production and innovation capabilities, which is favorable for their sustainable development. Meanwhile, after the spread of COVID-19, firms with strategic flexibility have higher profits than those without. This influence mechanism mainly involves exploring new markets that can improve the company revenue and the coordination capabilities of the supply chain; this reduces corporate costs.

This study expands relevant research on the factors affecting the survival of export enterprises and supplements research on the economic consequences of firms’ strategic flexibility; this also enriches the dynamic capability theory. Additionally, it provides important implications for firms to enhance strategic flexibility and recommends government implementation of policies that encourage the domestic sales of commodities originally produced for exports under COVID-19.