Influences of time delay and connection topology on a multi-delay inertial neural system.

Multiple delays and connection topology are the key parameters for the realistic modeling of networks. This paper discusses the influences of time delays and connection weight on multi-delay artificial neural models with inertial couplings. Firstly, sufficient conditions of some singularities involving static bifurcation, Hopf bifurcation, and pitchfork-Hopf bifurcation are presented by analyzing the transcendental characteristic equation.

Research on information leakage in time series prediction based on empirical mode decomposition.

Time series analysis predicts the future based on existing historical data and has a wide range of applications in finance, economics, meteorology, biology, engineering, and other fields. Although the combination of decomposition techniques and machine learning algorithms can effectively solve the problem of predicting nonstationary sequences, this kind of decomposition-integration-prediction strategy of the prediction method has serious defects. After the decomposition of the division of the training set and the test set, the information of the test set in the process of decomposition of the information leakage ultimately shows a high accuracy of the prediction of the illusionary.

Navigating Ghana's economic waters: Exploring the impact of Fiscal and Monetary policies on stock market performance.

This study leverages the Autoregressive Distributed Lag (ARDL) and Exponential Generalized Conditional Heteroscedasticity (EGARCH) models to conduct a thorough examination of the impact of fiscal and monetary policies on the Ghanaian stock market from 1990 to 2022. Key findings indicate that government spending and tax revenue, as components of fiscal policy, are positively associated with stock returns, contrasting with the negative influence of the industrial production index. On the monetary policy front, interest rates are found to negatively affect stock performance, while exchange rates and the money supply exert positive influences.

Innovative talent agglomeration, spatial spillover effects and regional innovation performance-Analyzing the threshold effect of government support.

As the primary source of innovation in science and technology, the gathering of creative talent plays a significant role in fostering regional innovation and providing the impetus needed to realize the Chinese people's aspiration of a great national rejuvenation. Using panel data from 31 provinces over a 12-year period between 2009 and 2020, the spatial Durbin model is constructed to examine the spatial spillover impact of talent agglomeration on the advancement of regional innovation performance, and the panel threshold model is identified and set up to consider whether the nonlinear effect between talent agglomeration and regional achievements in innovation is significant. The analysis demonstrates that: talent pooling has a non-linear effect on the level of innovation performance development, within a certain scale, talent pooling produces an increasing marginal contribution to innovation performance, but after exceeding the limit, it produces a diminishing marginal contribution; the double threshold effect of talent pooling on regional innovation performance is more significant, and government support as a moderating variable confirms that there is a structural mutation between talent pooling and innovation capability.

Psychological needs and social comparison: a dual analysis of the life satisfaction of local workers with agricultural hukou.

Background: Based on their place of employment, workers with agricultural hukou (WAH) are categorized into local workers with agricultural hukou (LWAH) and migrant workers with agricultural hukou (MWAH). Research shows that although LWAH often find themselves at a disadvantage in terms of labour income and access to public services, the significant growth of this group in recent years has drawn attention to their choice of workplace and the factors influencing it. Guided by the theories of self-determination and social comparison, this study aims to explore the impact of the differences between LWAH and MWAH in psychological needs and social comparisons on life satisfaction, as well as the underlying mechanisms.

Multimodal contrastive learning for spatial gene expression prediction using histology images.

In recent years, the advent of spatial transcriptomics (ST) technology has unlocked unprecedented opportunities for delving into the complexities of gene expression patterns within intricate biological systems. Despite its transformative potential, the prohibitive cost of ST technology remains a significant barrier to its widespread adoption in large-scale studies. An alternative, more cost-effective strategy involves employing artificial intelligence to predict gene expression levels using readily accessible whole-slide images stained with Hematoxylin and Eosin (H&E).

Complement Receptor 1 Is a Potential Extracerebral Factor Promoting α-Synuclein Pathology.

The deposition of pathological α-synuclein (α-Syn) in the central nervous system (CNS) is a hallmark of Parkinson's disease (PD). Notably, pathological α-Syn exists not only in the CNS but also in peripheral organs and body fluids in PD patients. Emerging evidence has shown the transmission of α-Syn pathology from the body to the brain.

Design and Evaluation of a Novel Variable Stiffness Hip Joint Exoskeleton.

An exoskeleton is a wearable device with human-machine interaction characteristics. An ideal exoskeleton should have kinematic and kinetic characteristics similar to those of the wearer. Most traditional exoskeletons are driven by rigid actuators based on joint torque or position control algorithms.

Social Media Use and Academic Performance in Chinese Children and Adolescents: A Moderated Chain Mediation Model.

Based on data from the 2022 Chinese Minors' Digital Life and Online Protection Survey, this study investigated the status quo of social media use and its influencing mechanism on academic performance among Chinese children and adolescents. The statistical results indicate that the average level of Chinese students' social media use was generally low, with their academic performance varying across socio-demographic and schooling characteristics. After controlling for other variables, it was found that the frequency of social media use could exert a significant positive impact on students' academic performance.

Digital economy and green transformation of regional industries: New insights from sustainability.

Studying the impact of the digital economy on carbon emissions in the distribution industry is of great significance for realizing sustainable development goals and coping with climate change. This study finds that increasing the level of digital economy development can reduce the carbon emission intensity of the circulation industry through fixed-effects modeling. Moreover, the effect is different in different geographic regions, and the improvement of the digital economy development level in the east and central regions can significantly reduce the carbon emission intensity of the distribution industry.

Role of supply chain digitalization and global supply chain in decarbonization of natural resources sector supply chain.

Environmental management and decarbonization are becoming increasingly critical as industries seek sustainable development paths that align with global climate targets. As businesses aim to reduce their carbon footprint, integrating effective environmental strategies with technological advancements is essential. This necessitates a clear understanding of digital technologies and environmental policies to drive decarbonization efforts.

Digital technology application on corporate internationalization: Co-moderating effect of centralizing decision power and internationalization experience.

Digital technologies are a significant additional powerhouse for corporate internationalization and competitiveness. The existing research has not effectively distinguished the breadth and depth of internationalization, and there are also inadequate context considerations of how much digital technology applications influence enterprise internationalization. To bridge this gap, this paper has selected 203 valid questionnaires from manufacturing enterprises in Zhejiang Province, China, then elaborates and constructs a theoretical model based on existing studies, and using multiple linear regression analysis, finally to empirically assess and further explain the mechanisms of digital technologies' influence on corporate internationalization.

Horizontal ecological compensation and urban inclusive green growth: evidence from China.

Introduction: Horizontal ecological compensation (HEC) has the potential to incentivize inclusive green growth in cities.

Methods: Using the multi-stage difference-in-differences (DID) method, this study examines the impact of HEC policies as a quasi-natural experiment. Panel data are analyzed; the data pertain to 87 cities in the Yangtze River Basin, from 2007 to 2020.

Does outpatient mutual-aid reduce oncology patients' utilization of inpatient services?-An empirical study of outpatient mutual-aid policy in Wuhan, China.

Background: The escalating costs of healthcare had prompted countries to undertake reforms, and in recent years China had focused on overhauling its outpatient healthcare system. China implemented the outpatient mutual-aid policy which had led to a change in the costs associated with outpatient treatment from being fully self-paid by the patient to being partially self-paid.

Purpose: This study aimed to assess the impact of the outpatient mutual-aid policy on inpatient services for oncology patients in Wuhan, China, exploring the impact that the cumbersome administration of health insurance would have on patient welfare.

Detecting anomalous anatomic regions in spatial transcriptomics with STANDS.

Detection and Dissection of Anomalous Tissue Domains (DDATD) from multi-sample spatial transcriptomics (ST) data provides unprecedented opportunities to characterize anomalous tissue domains (ATDs), revealing both population-level and individual-specific pathogenic factors for understanding pathogenic heterogeneities behind diseases. However, no current methods can perform de novo DDATD from ST data, especially in the multi-sample context. Here, we introduce STANDS, an innovative framework based on Generative Adversarial Networks which integrates three core tasks in multi-sample DDATD: detecting, aligning, and subtyping ATDs.

Forecasting health financing sustainability under the unified pool reform: evidence from China's Urban Employee Basic Medical Insurance.

Objective: The integration of the health insurance fund pool may threaten the sustainability of the fund by increasing its expenditures through the exacerbation of the moral hazard of participations. The purpose of this paper is to assess and predict the impact of the single pool reform of China's Urban Employee Basic Medical Insurance (UEBMI) on the expenditure and sustainability of the health insurance fund.

Methods: In this paper, we consider the pilot implementation of the single pool reform in some provinces of China as a quasi-natural experiment, and develop a staggered DID model to assess the impact of the single pool reform on medical reimbursement expenditure.

RV-FELM: Futures commodity price forecasting based on RIME-VMD algorithm coupled with FA-ELM.

Commodity futures are an important hedging tool in material trade, and by accurately predicting prices, countries and firms are able to make informed production and consumption decisions. This paper introduces a novel machine learning ensemble method that combines decomposition algorithms and physical optimization algorithms to predict commodity futures prices. First, the VMD(Variational mode decomposition) is optimized by the RIME algorithm (Rime optimization algorithm) to obtain the optimal modal decomposition results, and the trend and seasonal terms are predicted using the ELM (Extreme Learning Machines) and FA (Fourier Attention) models, respectively, and the results are finally synthesized.

Bipartite secure synchronization criteria for coupled quaternion-valued neural networks with signed graph.

This study explores the bipartite secure synchronization problem of coupled quaternion-valued neural networks (QVNNs), in which variable sampled communications and random deception attacks are considered. Firstly, by employing the signed graph theory, the mathematical model of coupled QVNNs with structurally-balanced cooperative-competitive interactions is established. Secondly, by adopting non-decomposition method and constructing a suitable unitary Lyapunov functional, the bipartite secure synchronization (BSS) criteria for coupled QVNNs are obtained in the form of quaternion-valued LMIs.

Energy-consuming right transaction and low-carbon technology innovation: Evidence from Chinese listed enterprises.

To decarbonize the economy and spur high-quality development, formulating effective environmental policies to encourage low-carbon technology innovation is increasingly critical. While the energy-consuming right transaction represents a significant institutional breakthrough, its potential to motivate enterprises towards low-carbon technology innovation remains underexplored. To address this gap, our study utilizes panel data from Chinese listed enterprises between 2009 and 2020, employing the energy-consuming right transaction pilot policy to develop a difference-in-difference model that assesses the policy's impact on low-carbon innovation.

Circular economy, economic growth, and e-waste generation in EU27 countries: Further evidence from the novel circular economy index and threshold effect.

The circular economy has been identified as a critical keyword for achieving the Sustainable Development Goals. Nevertheless, there is a lack of in-depth empirical literature on the impact mechanisms of the circular economy (CE) and economic growth (GDP) in mitigating e-waste generation (waste electrical and electronic equipment - WEEE). Given Europe's leading position in e-waste generation per capita, the study aims to scrutinize the interplay between CE, GDP, and WEEE for 2010-2020.