ISSN 2512-6660 (Online) ISSN 1478-9906 (Print)
CN 10-1192/N
CN 10-1192/N
Driven by different promotion pressures, different decisions made by government officials may change the development path of cities and directly affect the ability to cope with crises, thus playing an all-encompassing and sustained role in urban economic resilience (UER). Considering that the COVID-19 pandemic that occurred at the end of 2019 is a large external shock, which may cause a large disturbance to economic resilience, this article tests the impact of official promotion pressure (OPP) on UER using data from 265 cities in China from 2004 to 2019. This paper also explores the role of the “National Civilized City” (NCC) selection mechanism in the process. The findings indicate a positive correlation and spatial spillover effect between OPP and UER. Moreover, the impact of both civilization status and civilization intensity on OPP is negative, which means that obtaining the title weakens OPP, and the positive effect on UER is weakened. And this effect becomes increasingly obvious with the increase in the duration of the title of NCC. Furthermore, the heterogeneity analysis yields rich findings, which provide new perspectives for the policy recommendations in this paper.
Interval-valued functional principal component analysis (IFPCA) is a comprehensive evaluation method that can effectively handle continuous high-frequency data. However, most existing IFPCA methods assume that samples within intervals follow a uniform distribution, which may overlook the actual distribution of samples within intervals. This assumption may result in the omission of key features in samples, thereby affecting the accuracy of analyses. To address this issue, this study considers the internal distributional information of intervals using means and standard deviations to reflect the centralized location and discrete changes of intervals under the general distribution. The current time-varying distance function does not fully utilize this distributional information, necessitating an extension to accommodate the general distribution. Building on this, an IFPCA based on the time-varying distance function under the general distribution is proposed. This new IFPCA better utilizes the known internal information within intervals, uncovering intrinsic features of data. Simulation studies demonstrate the effectiveness of the IFPCA under the general distribution. An empirical application further confirms that the new IFPCA is superior to existing IFPCA methods.
Recent research has indicated that urban renewal can positively impact residents’ happiness. However, the reciprocal influence of residents’ happiness on urban renewal requires further exploration. Employing an inter-provincial panel dataset spanning from 2006 to 2020 and considering spatial dynamics, this study employs a spatial simultaneous equation model to analyze the mutual interaction and spatial spillover effects between residents’ happiness and urban renewal. The findings reveal a bidirectional promotion mechanism between residents’ happiness and urban renewal. Specifically, urban renewal contributes to heightened residents’ happiness, while residents’ happiness also fosters urban renewal. Moreover, a notable spatial interaction spillover effect is observed between residents’ happiness and urban renewal. The linkage between residents’ happiness and urban renewal in the focal region is intricately intertwined with the same factors in surrounding areas.
In the contemporary globalized business environment, organizations face intense competition and significant pressure to navigate uncertainties. Strategic decision-making, particularly in allocating scarce resources to innovation endeavors, is a crucial yet complex task for organizational leaders. This study addresses the gap in the existing literature by proposing a decision-making framework grounded in multi-criteria decision making (MCDM), specifically utilizing the analytic hierarchy process (AHP), to enhance strategic decision-making capabilities. The framework aims to improve resource allocation and organizational performance by integrating cognitive and affective factors influencing decision-makers. The analysis presented in this study has successfully computed the final rankings of the strategic alternatives, scanning ability, interpretation ability, and action ability within the organization. By integrating the weights assigned to each criterion and alternative, it was determined that scanning ability holds the highest value at 50.75%, followed by interpretation at 26.65%, and action at 22.58%. Additionally, the factors influencing these alternatives were ranked, with sentiment being the most significant at 0.3607, followed by emotion at 0.2123, attention at 0.2011, ideation at 0.1271, and memory at0.0986. This outcome highlights the significance of scanning ability and sentiment in strategic decision-making. This research contributes to the field by providing a model influencing strategic decision-making, offering valuable insights for managers and policymakers aiming to optimize resource allocation and drive sustainable growth.
In the modern economy, startups are not only significant drivers of innovation and technological progress but also key players in addressing employment issues and promoting economic diversification. However, startups often face substantial operational risks and uncertainties in their early stages, especially regarding financing. To uncover the impact of different resource allocations and strategic choices on financing success, this study proposes a predictive method based on the latent Dirichlet allocation (LDA) topic model and deep neural networks through an in-depth analysis of startup financing cases. We systematically collected description text data from 2,000 startups and extracted text features from these descriptions using the LDA topic model. These features, combined with several traditional numerical indicators such as industry, product type, technology type, number of employees, and company size, were used to train a deep neural network to predict startup financing outcomes. The experimental results show that the prediction performance based on the LDA topic model is significantly better than that of traditional models relying solely on numerical data. This highlights the importance of text features in predicting the success of startup financing.
Network intrusion detection plays a critical role in safeguarding network security; however, traditional detection methods often struggle with complex attacks and large-scale data. To address these challenges, we propose a novel network intrusion detection model named GCM-CSDNN, which integrates the group cloud model (GCM) with a depthwise separable convolutional neural network (CSDNN). The model introduces group cloud transformation to reduce data dimensionality and employs 3D channel fusion technology to enhance feature extraction capabilities, thereby improving both accuracy and computational efficiency. We conducted extensive experiments on multiple benchmark datasets — including UNSW-NB15, KDD99, WSN-DS, and WADI — which cover diverse network environments and attack types. Experimental results demonstrate that GCM-CSDNN significantly outperforms traditional machine learning models and deep learning models in terms of accuracy and F1-score, achieving 98.79% and98.81% respectively, and surpassing the next-best model, SSG-DCNN. Moreover, GCM-CSDNN exhibits excellent performance on high-dimensional and large-scale datasets, significantly reducing training and testing times while demonstrating strong robustness and generalization capabilities. These findings indicate that GCM-CSDNN can efficiently and accurately detect network intrusions, making it suitable for real-time network security environments requiring the processing of large volumes of data.
Recidivism among ex-offenders is a complicated socioeconomic issue that now significantly affects social security and stability. This article’s theoretical foundations are primarily life story theory and identity label theory. It also builds a conceptual model of the effects of reoffending on social stability and social security using structure equation modelling (SEM) and trajectory analysis techniques, based on data from 355 questionnaires in 10 Chinese provinces. There was an empirical test of the model. The study’s findings indicate that: 1) There is a strong negative association between social stability and social security and recidivism; 2) Income status, education level, legal awareness, prior prison experience, social recognition, and other factors are closely associated with the likelihood of reoffending; 3) Reoffending risk may significantly affect public safety through intervention crimes, such as those that immediately compromise public safety or morality.
Green production is an effective approach to achieve sustainable development. In this paper, the government determines the optimal subsidy policy under a finite budget, and then a manufacturer and a retailer play a Stackelberg game for selling green products. First, the case of subsidy for the manufacturer is discussed. It is shown that the government subsidy for per product generated by green production and the cost coefficient of the green production technology are positively correlated. Second, the case of subsidy for the retailer is discussed. By comparing the two cases, it proves that subsidy for the manufacturer generates a higher green level. Nevertheless, in some situations, subsidy for the retailer is optimal for the sales volume. Some numerical illustrations are designed to analyze the sensitivity of each subsidy policy with respect to the cost coefficient of the green production technology and the cost coefficient of the blockchain technology, and to examine the dominant region of each subsidy policy.
