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With the development of earthquake disaster reduction efforts in China, the content of earthquake disaster reduction policies has become increasingly enriched and improved. Particularly, multiple provincial governments have proposed earthquake disaster reduction planning policies. It is important to explore whether these policies can affect disaster mitigation. Therefore, this paper summarizes the earthquake disaster reduction plans and factors influencing seismic resilience. Panel data from 24 provinces between 2012 and 2021 were collected, and a difference-in-differences approach was used to construct an econometric model to evaluate the policy effects and analyze the enhancement of seismic resilience. The results show that the implementation of earthquake disaster reduction policies has a positive impact on earthquake monitoring, evacuation, and emergency relief capabilities, and the estimated policy effects are statistically significant. Moreover, a series of tests were conducted. The conclusions are as follows: (1) Earthquake disaster reduction policies have a positive impact on the improvement of seismic resilience in provinces. (2) Provinces with a higher number of earthquakes experience more significant effects from earthquake disaster reduction policies. (3) Provinces with higher seismic peak ground acceleration values exhibit more pronounced improvements in seismic resilience. [ABSTRACT FROM AUTHOR]

In the field of microbiome studies, it is of interest to infer correlations between abundances of different microbes (here referred to as operational taxonomic units, OTUs). Several methods taking the compositional nature of the sequencing data into account exist. However, these methods cannot infer correlations between OTU abundances and other variables. In this paper we introduce the novel methods SparCEV (Sparse Correlations with External Variables) and SparXCC (Sparse Cross-Correlations between Compositional data) for quantifying correlations between OTU abundances and either continuous phenotypic variables or components of other compositional datasets, such as transcriptomic data. SparCEV and SparXCC both assume that the average correlation in the dataset is zero. Iterative versions of SparCEV and SparXCC are proposed to alleviate bias resulting from deviations from this assumption. We compare these new methods to empirical Pearson cross-correlations after applying naive transformations of the data (log and log-TSS). Additionally, we test the centered log ratio transformation (CLR) and the variance stabilising transformation (VST). We find that CLR and VST outperform naive transformations, except when the correlation matrix is dense. SparCEV and SparXCC outperform CLR and VST when the number of OTUs is small and perform similarly to CLR and VST for large numbers of OTUs. Adding the iterative procedure increases accuracy for SparCEV and SparXCC for all cases, except when the average correlation in the dataset is close to zero or the correlation matrix is dense. These results are consistent with our theoretical considerations. [ABSTRACT FROM AUTHOR]

Using the panel data of 31 provinces in China between 2000 and 2018, this study theoretically and empirically analyses the impact of environmental regulations on the adjustment of the agriculture industrial structure from the perspectives of rationalisation and optimisation. Overall, variability in the impact of environmental regulation on the adjustment of the agricultural industrial structure is identified: a negative influence on the rationalisation of agricultural industrial structure and a positive influence on the optimisation of agricultural industrial structure. The impact of environmental regulations on the adjustment of agricultural industrial structure also reflects "large country characteristics": environmental regulations are more significant at the medium and low industrial structure levels. Environmental regulation significantly impacts the rationalisation of agricultural industrial structure in central and western regions and its optimisation in eastern and central regions. Through the panel threshold model test, this paper further finds that the effect of environmental regulations on the adjustment of agricultural industrial structure is not invariant. However, there is a non-linear relationship with significant threshold characteristics. Based on the above results, some countermeasures are suggested. [ABSTRACT FROM AUTHOR]

Formalistic tasks are widely utilized in modern companies due to their ability to increase productivity and contribute to the achievement of corporate goals at a lower cost. However, these tasks are often meet with resistance from individuals because they do not provide direct short-term rewards for their efforts. Drawing on social cognitive theory, this study examined the influence of individual quality and organizational attachment on the completion of formalistic tasks. To address this, the study conducted a questionnaire survey to collect data from 602 Chinese respondents and built a structural equation model for data analysis. Through empirical research, the study confirmed the positive role of individual quality, including knowledge and personality, in the completion of formalistic tasks. Furthermore, the study proved that avoidant attachment could significantly weaken the effect of some components of individual quality on formalistic task completion. This paper is the first to reveal the influence of individual and environmental factors on individuals' completion of formalistic tasks, progressing from bottom to top. The implications of these results are discussed. [ABSTRACT FROM AUTHOR]

Internal control is the key to achieve high-quality development of enterprises, but internal control failure cases frequently occur at home and abroad. Therefore, it is particularly important to explore ways to improve the quality of internal control and promote high-quality development of enterprises. Taking non-financial listed enterprises in China from 2015 to 2020 as research samples, this paper adopts empirical research methods to research the impact and mechanism of employee welfare on the quality of internal control of enterprises from the perspective of all-employee governance, aiming to explore ways for enterprises to improve the quality of internal control.The results showed that employee welfare can improve the quality of internal controls significantly. Furthermore, the effect is more significant in non-employee-intensive industries and high-marketization areas. Based on the analysis of internal control elements, the research found that employee welfare affects the quality of internal control mainly through two elements, risk assessment and control activities. However, the effects of employee welfare on the three other elements are insignificant. This paper enriched the relevant research on the economic consequences of employee welfare and the factors affecting the quality of internal control, and has certain enlightenment significance for the popularization of employee welfare, especially supplementary pension, and also provides a reference path for the improvement of the quality of internal control. [ABSTRACT FROM AUTHOR]

With the rapid development of technologies such as cloud computing and big data, various levels of government departments in the country have successively introduced digital subsidy policies to promote enterprises' digital transformation. However, the effectiveness of these policies and their ability to truly achieve policy objectives have become pressing concerns across society. Against this backdrop, this paper employs a moderated mediation effects model to empirically analyze the incentive effects of financial subsidies on the digital transformation of A-share listed manufacturing companies in the Shanghai and Shenzhen stock markets from 2013 to 2022. The research findings indicate a significant promotion effect of financial subsidies on the digital transformation of manufacturing enterprises, especially demonstrating a notable incentive impact on the digital transformation of large enterprises, non-asset-intensive enterprises, technology-intensive enterprises, and non-labor-intensive enterprises. However, the incentive effect on the digital transformation of small and medium-sized enterprises (SMEs), asset-intensive enterprises, non-technology-intensive enterprises, and labor-intensive enterprises is not significant. Notably, the expansion of financial subsidies positively influences the augmentation of R&D investment within manufacturing enterprises, subsequently providing indirect encouragement for their digital transformation. Additionally, the incorporation of the degree of marketization implies its potential to moderate both the direct and indirect impacts of financial subsidies on enterprise digital transformation. This study enriches the research on the mechanism of the role of financial subsidies in digital transformation and provides empirical evidence on how market participation influences the effects of financial subsidies, thereby assisting policymakers in comprehensively understanding the impact of financial subsidy policies on different types of enterprises. [ABSTRACT FROM AUTHOR]

Public environmental satisfaction is related to the healthy living standard of human beings and sustainable development of an economic society. In the context of the continuous updating of Internet technologies, it is necessary to study the correlation between Internet use and public environmental satisfaction, but few studies have focused on the effect of the orientation of Internet use on public environmental satisfaction, and its mediating mechanisms. This study considered survey data from the China Social Survey 2019 which were conducted in a sample of 5,112 residents, SPSS 16.0 and ordinary least squares regression model was used to analyze the relationship between orientation of Internet use, government trust and public environmental satisfaction. The results showed politically-oriented Internet use was positively correlated with public environmental satisfaction, and entertainment-oriented Internet use did not correlate with public environmental satisfaction. Furthermore, government trust partially mediated the relation between politically-oriented Internet use and public environmental satisfaction. Several implication policies are suggested for improving public environmental satisfaction. This study takes into account individual initiative and government's control of information during Internet use, emphasizes the impact of different individual Internet use preferences on environmental satisfaction in the web environment with strong government control. By incorporating individual factors at the micro level and social factors at the macro level, this paper is in order to improve public environmental satisfaction. [ABSTRACT FROM AUTHOR]

From the perspective of news topic modeling, this paper investigated how the Communist Youth League of China (CYLC) uses organizational information communication to serve organizational goals—"Keep the Party Assured and the Youth Satisfied" ("让党放心, 让青年满意"). Using the Latent Dirichlet allocation (LDA) algorithm, we performed a topic analysis on 1898 news articles published on the CYLC website. We discovered that nearly all of the CYLC's news centered on the achievement of its organizational goals, reflecting the characteristics of information dissemination that is highly supportive of organizational objectives. We discovered distinct differences in the dissemination of organizational information between the central, provincial, municipal, county, and school league committees through cluster analysis. The various league organizations have distinct positioning and distinguishing characteristics. In addition, correlation analysis reveals that higher-level league organizations prioritize the dissemination of "Keep the Party Assured" information. While lower-level organizations gradually implement "Keep the Youth Satisfied" initiatives. This paper fills a gap in research on mass organizations in the field of information dissemination and serves as a resource for other political organizations involved in public information dissemination. [ABSTRACT FROM AUTHOR]

The problem of high stress conditions is a critical challenge for mining activity in deep metal mines, and the difficulty and economic cost of maintaining stope stability also increases. In order to ensure stope stability in the mining process, it is necessary to evaluate stope stability for a more stable stope structure. However, there are many kinds of methods for rock stability assessment, and most of them are based on empirical and analytical methods, which lead to uncertain results. In this paper, a comprehensive evaluation method including factors of stope stability, self-stabilization time, maximum safety span, and support measure is determined, which is more likely to provide a comprehensive result. Finally, it was verified by the field application in Jiaojia Gold Mine. Results show that it can accurately evaluate the stope stability from different aspects compared with the traditional single evaluation method, and it leads to a more comprehensive and reliable result. [ABSTRACT FROM AUTHOR]

Urban infrastructure resilience is an important perspective for measuring the development quality of resilient cities and an important way to measure the level of infrastructure development. This paper uses the kernel density estimation, exploratory spatial data analysis, and spatial econometric models to analyze the characteristics of dynamic evolution and the spillover effects of the infrastructure resilience levels in 283 prefecture-level and above cities in China from 2010 to 2019. Our results are as follows. (1) The overall level of urban infrastructure resilience increased. The eastern region had a higher level than the national average. In contrast, the central, western and north-eastern regions had a slightly lower level than the national average. (2) The areas with high and higher resilience levels were mostly cities with more developed economic and social conditions in Eastern China. The areas below moderate resilience levels show a certain degree of clustering and mainly include some cities in Central, Western, and Northeast China. (3) The national level of urban infrastructure resilience shows significant spatial clustering characteristics, and the spatial pattern from coastal to inland regions presents a hotspot-subhotspot-subcoldspot-coldspot distribution. (4) There is a differential spatial spillover effect of national urban infrastructure resilience, which is gradually strengthened under the role of the economy, financial development, population agglomeration and government funding and weakened under the role of urbanization, market consumption and infrastructure investment. By exploring the dynamic evolution of infrastructure resilience in cities at the prefecture level and above and its spatial spillover effects, we provide a scientific basis for avoiding the siphoning effect among cities, improving the level of infrastructure resilience, and guiding the construction and development of resilient cities. [ABSTRACT FROM AUTHOR]

This paper addresses the impact of bank liquidity on risk-taking behaviour of Chinese banks, and provides evidence for a risk-taking channel of monetary policy operating through bank liquidity. By using bank-level panel data from 123 Chinese commercial banks during 2003–2018, it is found that banks facing lower liquidity risk will be encouraged to take more risk. Moreover, loose monetary policy leads to more aggressive risk-taking by reducing the bank liquidity risk, namely a liquidity risk-taking channel of monetary policy. These findings suggest that authorities should give full consideration to the influence of the monetary policy on bank risk-taking through bank liquidity channels. [ABSTRACT FROM AUTHOR]

Base-rate neglect is a pervasive bias in judgment that is conceptualized as underweighting of prior information and can have serious consequences in real-world scenarios. This bias is thought to reflect variability in inferential processes but empirical support for a cohesive theory of base-rate neglect with sufficient explanatory power to account for longer-term and real-world beliefs is lacking. A Bayesian formalization of base-rate neglect in the context of sequential belief updating predicts that belief trajectories should exhibit dynamic patterns of dependence on the order in which evidence is presented and its consistency with prior beliefs. To test this, we developed a novel 'urn-and-beads' task that systematically manipulated the order of colored bead sequences and elicited beliefs via an incentive-compatible procedure. Our results in two independent online studies confirmed the predictions of the sequential base-rate neglect model: people exhibited beliefs that are more influenced by recent evidence and by evidence inconsistent with prior beliefs. We further found support for a noisy-sampling inference model whereby base-rate neglect results from rational discounting of noisy internal representations of prior beliefs. Finally, we found that model-derived indices of base-rate neglect—including noisier prior representation—correlated with propensity for unusual beliefs outside the laboratory. Our work supports the relevance of Bayesian accounts of sequential base-rate neglect to real-world beliefs and hints at strategies to minimize deleterious consequences of this pervasive bias. Author summary: Base-rate neglect is a common bias in judgment, a bias defined by a tendency to underuse older information when forming a new belief. This bias can have serious consequences in the real world. Base-rate neglect is often cited as a source of errors in medical and legal decisions, and in many other socially relevant contexts. Despite its broad societal relevance, it is unclear whether current theories capture the expression of base-rate neglect in sequential belief formation, and perhaps more crucially why people have this bias in the first place. In this paper, we find support for a model that describes how base-rate neglect influences belief formation over time, showing that people behave in a way that matches theoretical predictions. Knowing how base-rate neglect influences beliefs over time suggests possible strategies that could be implemented in the future to minimize its impact. We also find support for a model which may explain why people exhibit base-rate neglect in the first place. This model suggests that people's representation of older information in the brain is noisy and that it is therefore rational to underuse this older information to some extent depending on how noisy or unreliable its representation is. Finally, we show that our measures of base-rate neglect and noise in the representation of older information correlate with variation in real-world belief oddity, suggesting that these models capture belief-formation processes likely to dictate functioning in real-world settings. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a neurodegenerative condition that progresses over decades. Early detection of individuals at high risk of future progression toward AD is likely to be of critical significance for the successful treatment and/or prevention of this devastating disease. In this paper, we present an empirical study to characterize how predictable an individual subjects' future AD trajectory is, several years in advance, based on rich multi-modal data, and using modern deep learning methods. Crucially, the machine learning strategy we propose can handle different future time horizons and can be trained with heterogeneous data that exhibit missingness and non-uniform follow-up visit times. Our experiments demonstrate that our strategy yields predictions that are more accurate than a model trained on a single time horizon (e.g. 3 years), which is common practice in prior literature. We also provide a comparison between linear and nonlinear models, verifying the well-established insight that the latter can offer a boost in performance. Our results also confirm that predicting future decline for cognitively normal (CN) individuals is more challenging than for individuals with mild cognitive impairment (MCI). Intriguingly, however, we discover that prediction accuracy decreases with increasing time horizon for CN subjects, but the trend is in the opposite direction for MCI subjects. Additionally, we quantify the contribution of different data types in prediction, which yields novel insights into the utility of different biomarkers. We find that molecular biomarkers are not as helpful for CN individuals as they are for MCI individuals, whereas magnetic resonance imaging biomarkers (hippocampus volume, specifically) offer a significant boost in prediction accuracy for CN individuals. Finally, we show how our model's prediction reveals the evolution of individual-level progression risk over a five-year time horizon. Our code is available at https://github.com/batuhankmkaraman/mlbasedad. [ABSTRACT FROM AUTHOR]

A model needs to make verifiable predictions to have any scientific value. In opinion dynamics, the study of how individuals exchange opinions with one another, there are many theoretical models which attempt to model opinion exchange, one of which is the Martins model, which differs from other models by using a parameter that is easier to control for in an experiment. In this paper, we have designed an experiment to verify the Martins model and contribute to the experimental design in opinion dynamic with our novel method. [ABSTRACT FROM AUTHOR]

The special width approach lane (SWAL) is a newly proposed unconventional design, whereby a wide approach lane is divided into two narrower lanes. The design entails the use of a single lane by two passenger cars or one heavy vehicle. Such design has been applicated at signalized intersections of Karlsruhe, Germany. This paper focuses on the saturation flow rate analysis since most existing studies on such design rely on the default highway capacity manual (HCM) values. Saturation flow rate data was collected at four SWAL design based signalized intersections with procedural steps of the HCM 2010 using the video camera. The two-sample t-test was performed to explore the potential influencing factors, and then the non-linear regression analysis was conducted to estimate the saturation flow rate of SWAL. The proposed model can effectively depict the saturation flow rate with lane marking, presence of cyclists, and rainfall being the influencing factors. The overall accuracy of the proposed model is about 95%. The results indicate that the three influencing factors are independent of each other. The existence of cyclists and rainfall lead to a decrease in the saturation flow rate, while the lane markings can improve the saturation flow rate. Moreover, the SWAL works well in Karlsruhe, Germany. The model predicts a base saturation flow rate value of 1652 pcu/h/ln, which is plausible with comparison of the base saturation flow rate recommended in the German Highway Capacity Manual. [ABSTRACT FROM AUTHOR]

The direct observation method is commonly used for data collection in family business case studies. Nevertheless, in research on family business succession that is mainly based on retrospective data, it is difficult to directly observe an event or situation at a given time. This paper thus first explores the application of observation data in the published research results of some family business case studies through a literature review. It then describes our observation methodology, i.e., sampling process, method, observation process, and reevaluation of our interview data, through a case study. Finally, the conclusion offers suggestions for using these observation methods, i.e., employing different types of observation, by considering running time and financial cost, familiarizing observers with various observation occasions, and seeking the assistance of relevant professionals for a research topic. [ABSTRACT FROM AUTHOR]

It has great significance for improving the logistics service ability of the Yangtze River economic belt, optimizing the industrial structure of manufacturing industry, and realizing the integrated development of the Yangtze River economic belt to explore the collaborative evolution of logistics industry and manufacturing industry in the Yangtze River economic belt, and identify the leading position of the collaborative development of the two industries, so as to. Based on the Haken Model, this paper summarizes the coevolution law of logistics industry and manufacturing industry in the Yangtze River economic belt through two-stage empirical analysis, and identifies the order parameters of the co-development of logistics industry and manufacturing industry. The results show that the overall degree of coordination between the logistics industry and the manufacturing industry in the Yangtze River economic belt is high. And the order parameter has been changed from manufacturing industry in 2003–2009 to logistics industry in 2010–2017. The gap between regions has been reduced, and the western region has the advantage of post development. [ABSTRACT FROM AUTHOR]

Emotions at work have long been identified as critical signals of work motivations, status, and attitudes, and as predictors of various work-related outcomes. When more and more employees work remotely, these emotional signals of workers become harder to observe through daily, face-to-face communications. The use of online platforms to communicate and collaborate at work provides an alternative channel to monitor the emotions of workers. This paper studies how emojis, as non-verbal cues in online communications, can be used for such purposes and how the emotional signals in emoji usage can be used to predict future behavior of workers. In particular, we present how the developers on GitHub use emojis in their work-related activities. We show that developers have diverse patterns of emoji usage, which can be related to their working status including activity levels, types of work, types of communications, time management, and other behavioral patterns. Developers who use emojis in their posts are significantly less likely to dropout from the online work platform. Surprisingly, solely using emoji usage as features, standard machine learning models can predict future dropouts of developers at a satisfactory accuracy. Features related to the general use and the emotions of emojis appear to be important factors, while they do not rule out paths through other purposes of emoji use. [ABSTRACT FROM AUTHOR]