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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.

Social networking services (SNS) empower users with a robust capability to connect with others and manage their social relationships. However, as the size of users’ social networks increases, coupled with the inherent boundary-spanning technical features of SNS, users are faced with unprecedented role stresses. This, in turn, leads to maladaptive lurking decisions. This study delves into the mechanism of this technology-induced decision-making process among SNS users.

Survey data were collected from 491 Chinese WeChat Moment users. The model and hypotheses testing were conducted using SmartPLS 4.0.

Our findings indicate that both social network size and boundary spanning have a positive influence on role conflict and role overload. Both role conflict and role overload significantly contribute to SNS fatigue, which further intensifies users’ lurking intention. Furthermore, SNS fatigue fully mediated the relationship between role conflict and lurking intention, and partially mediated the relationship between role overload and lurking intention.

Our study offers a fresh viewpoint for comprehending lurking behaviors on SNS, furnishing practical insights for platform providers. Additionally, it paves the way for future research into the deeper mechanisms driving SNS lurking behaviors, by providing a novel construct (i.e. boundary spanning) to distinguish and measure the unique social environment of SNS.

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.

Agriculture commodity price forecasts have long been important for a variety of market players. The study we conducted aims to address this difficulty by examining the weekly wholesale price index of green grams in the Chinese market. The index covers a ten-year period, from January 1, 2010, to January 3, 2020, and has significant economic implications.

In order to address the nonlinear patterns present in the price time series, we investigate the nonlinear auto-regressive neural network as the forecast model. This modeling technique is able to combine a variety of basic nonlinear functions to approximate more complex nonlinear characteristics. Specifically, we examine prediction performance that corresponds to several configurations across data splitting ratios, hidden neuron and delay counts, and model estimation approaches.

Our model turns out to be rather simple and yields forecasts with good stability and accuracy. Relative root mean square errors throughout training, validation and testing are specifically 4.34, 4.71 and 3.98%, respectively. The results of benchmark research show that the neural network produces statistically considerably better performance when compared to other machine learning models and classic time-series econometric methods.

Utilizing our findings as independent technical price forecasts would be one use. Alternatively, policy research and fresh insights into price patterns might be achieved by combining them with other (basic) prediction outputs.