Does Commuting Lead to Migration?

This paper investigates the interaction between commuting and migration within a local labor market, focusing especially on the question of whether commuting can lead to migration over time. Using Virginia data from 2000 to 2006, the study shows that the commuting flow between two locations has a positive and significant effect on the migration flow in the same direction in subsequent years. The underlying reasons are that increased commuting costs or reduced migration costs can induce commuters to become migrants. These results may have useful implications for urban communities in their revitalization efforts, as cities can explore ways of attracting daily commuters to their cities to become permanent residents, reversing the trends of declining urban population

Do Economic Development Efforts Benefit All? Business Attraction and Income Inequality

This paper extends the current literature on county-level income distribution in the United States by explicitly exploring the effect of business-attraction efforts by state governments. Using county-level job attraction and retention data from 2000 to 2005 in Virginia to explain the income distribution from 2006 to 2010, while controlling for demographic and socioeconomic conditions of local communities, this study shows that bringing in manufacturing jobs can reduce income inequality at the local level while attracting jobs in professional and business services tends to increase local income inequality. The results indicate that state and local governments’ efforts to attract and retain manufacturing jobs help improve local income distribution

Will Specialization Continue Forever? A Case Study of Interactions between Industry Specialization and Diversity

This paper studies the interactions between industry specialization and diversity. Several studies have shown that competitive industries in a region grew faster, thus expanding their shares in overall employment. The implication is that a region will become more specialized in its competitive industries and the process will continue forever barring external intervention. Utilizing an econometric model on county level employment growth in Virginia, this study confirms that competitive industries experience faster employment growth, reinforcing specialization. However, as specialization proceeds, it reduces economic diversity. That will hurt job creation, as economic diversity also stimulates employment growth. The interactions between specialization and diversity can lead to complex patterns of industry structural change. This study concludes that if a locality starts with low economic diversity, specialization will continue to deepen and the region may be trapped with limited economic diversity. However, when an economy starts with high diversity, specialization and diversity tend to offset each other, resulting a more consistent industry structure

Are Social Networks a Double-Edged Sword? A Case Study of Defense Contractors

Utilizing a survey of defense contractors in the New England region, this study explores the effect of social networks on business performance—measured by annual employment growth and market diversification—during a time when defense spending in the United States was contracting. In contrast to prevailing literature focusing on entrepreneurial firms, this study offers insights on how social networks function in defense contractors, which tend to be mature firms. The main conclusion is that having more network connections is associated with faster short-term employment growth (from 2014 to 2015) for defense contractors, but there is a limit to that benefit. The analysis also shows that social networks do not aid market diversification for defense contractors. This poses an interesting challenge for defense contractors, as they need to balance the priorities of short-term growth and long-term success

The Effect of State Corporate Income Tax Rate Cuts on Job Creation

This paper compares the employment growth of states that enacted corporate income tax rate cuts in the past 23 years with those making no changes. Overall employment comparisons from 1990 to 2012 suggest that a reduction in the corporate income tax rate is associated with faster job creation. The states that cut corporate income tax rates started with slower employment growth than the states that made no changes. However, the growth gaps between the two groups of states disappeared in about five years after the tax cuts were made. Regression results confirm the observation that lower corporate tax rates have a significant and positive effect on employment growth. The enactment of a tax rate cut also has the additional but temporary benefit of promoting job creation as businesses adjust to the new tax rate. However, this benefit is temporary and only occurs during first year of the enactment of a tax cut

Who Benefits from Job Creation at County Level? An Analysis of Leakage and Spillover of New Employment Opportunities in Virginia

Using an econometric model system built on county level labor market data, this study allocates new employments in Virginia from 1990 to 2000 into various demographic segments: commuters, residents, and new immigrants. The study finds significant leakage of new employment opportunities in Virginia. 52% of new jobs created in the 1990s in a locality were taken by outside commuters. However, Virginia’s localities also benefit from spillover benefits from job creation elsewhere. Economists need to account for employment leakage and spillover to accurately evaluate the fiscal impacts of potential economic development projects

Domain Consistency Regularization for Unsupervised Multi-source Domain Adaptive Classification

Deep learning-based multi-source unsupervised domain adaptation (MUDA) has been actively studied in recent years. Compared with single-source unsupervised domain adaptation (SUDA), domain shift in MUDA exists not only between the source and target domains but also among multiple source domains. Most existing MUDA algorithms focus on extracting domain-invariant representations among all domains whereas the task-specific decision boundaries among classes are largely neglected. In this paper, we propose an end-to-end trainable network that exploits domain Consistency Regularization for unsupervised Multi-source domain Adaptive classification (CRMA). CRMA aligns not only the distributions of each pair of source and target domains but also that of all domains. For each pair of source and target domains, we employ an intra-domain consistency to regularize a pair of domain-specific classifiers to achieve intra-domain alignment. In addition, we design an inter-domain consistency that targets joint inter-domain alignment among all domains. To address different similarities between multiple source domains and the target domain, we design an authorization strategy that assigns different authorities to domain-specific classifiers adaptively for optimal pseudo label prediction and self-training. Extensive experiments show that CRMA tackles unsupervised domain adaptation effectively under a multi-source setup and achieves superior adaptation consistently across multiple MUDA datasets

New RNN Algorithms for Different Time-Variant Matrix Inequalities Solving Under Discrete-Time Framework

Abstract A series of discrete time-variant matrix inequalities is generally regarded as one of the challenging problems in science and engineering fields. As a discrete time-variant problem, the existing solving schemes generally need the theoretical support under the continuous-time framework, and there is no independent solving scheme under the discrete-time framework. The theoretical deficiency of solving scheme greatly limits the theoretical research and practical application of discrete time-variant matrix inequalities. In this article, new discrete-time recurrent neural network (RNN) algorithms are proposed, analyzed, and investigated for solving different time-variant matrix inequalities under the discrete-time framework, including discrete time-variant matrix vector inequality (discrete time-variant MVI), discrete time-variant generalized matrix inequality (discrete time-variant GMI), discrete time-variant generalized-Sylvester matrix inequality (discrete time-variant GSMI), and discrete time-variant complicated-Sylvester matrix inequality (discrete time-variant CSMI), and all solving processes are based on the direct discretization thought. Specifically, first of all, four discrete time-variant matrix inequalities are presented as the target problems of these researches. Second, for solving such problems, we propose corresponding discrete-time recurrent neural network (RNN) (DT-RNN) algorithms (termed DT-RNN-MVI algorithm, DT-RNN-GMI algorithm, DT-RNN-GSMI algorithm, and DT-RNN-CSMI algorithm), which are different from the traditional DT-RNN design thought because second-order Taylor expansion is applied to derive the DT-RNN algorithms. This creative process avoids the intervention of continuous-time framework. Then, theoretical analyses are presented, which show the convergence and precision of the DT-RNN algorithms. Abundant numerical experiments are further carried out, which further confirm the excellent properties of the DT-RNN algorithms.Abstract A series of discrete time-variant matrix inequalities is generally regarded as one of the challenging problems in science and engineering fields. As a discrete time-variant problem, the existing solving schemes generally need the theoretical support under the continuous-time framework, and there is no independent solving scheme under the discrete-time framework. The theoretical deficiency of solving scheme greatly limits the theoretical research and practical application of discrete time-variant matrix inequalities. In this article, new discrete-time recurrent neural network (RNN) algorithms are proposed, analyzed, and investigated for solving different time-variant matrix inequalities under the discrete-time framework, including discrete time-variant matrix vector inequality (discrete time-variant MVI), discrete time-variant generalized matrix inequality (discrete time-variant GMI), discrete time-variant generalized-Sylvester matrix inequality (discrete time-variant GSMI), and discrete time-variant complicated-Sylvester matrix inequality (discrete time-variant CSMI), and all solving processes are based on the direct discretization thought. Specifically, first of all, four discrete time-variant matrix inequalities are presented as the target problems of these researches. Second, for solving such problems, we propose corresponding discrete-time recurrent neural network (RNN) (DT-RNN) algorithms (termed DT-RNN-MVI algorithm, DT-RNN-GMI algorithm, DT-RNN-GSMI algorithm, and DT-RNN-CSMI algorithm), which are different from the traditional DT-RNN design thought because second-order Taylor expansion is applied to derive the DT-RNN algorithms. This creative process avoids the intervention of continuous-time framework. Then, theoretical analyses are presented, which show the convergence and precision of the DT-RNN algorithms. Abundant numerical experiments are further carried out, which further confirm the excellent properties of the DT-RNN algorithms

Neuroprotective effects of bavachalcone in a mouse model of Parkinson’s disease: linking the gut-brain axis and systemic metabolism

BackgroundParkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by motor dysfunction and dopaminergic neuronal loss. Emerging evidence suggests that gut microbiota dysbiosis and systemic metabolic disturbances contribute to the pathogenesis of PD. This study aimed to investigate the neuroprotective effects of bavachalcone, a prenylated chalcone isolated from Psoralea corylifolia, in an MPTP-induced mouse model of PD, with a particular focus on its effects on motor function, inflammation, gut microbiota, and serum metabolism.MethodsMale C57BL/6 mice were divided into Control, MPTP, Bavac-L (low-dose bavachalcone), and Bavac-H (high-dose bavachalcone) groups. Bavachalcone was administered by gavage, followed by MPTP injection to induce PD. Behavioral assessments (open field test, pole test, and rotarod test), western blotting, immunohistochemistry, immunofluorescence, 16S rDNA sequencing of fecal microbiota, and untargeted metabolomics of serum were performed to evaluate the effects of bavachalcone.ResultsBavachalcone significantly alleviated MPTP-induced motor impairment, preserved dopaminergic neurons in the substantia nigra and striatum, and reduced systemic inflammation and glial activation. Gut microbiota analysis showed that bavachalcone improved microbial richness and diversity, enriched beneficial genera, such as Allobaculum, and suppressed harmful taxa, such as Ligilactobacillus and Helicobacter. Metabolomic profiling revealed that bavachalcone modulated pathways, including pyruvate metabolism, folate biosynthesis, and phenylalanine metabolism.ConclusionBavachalcone exerts neuroprotective effects in mice with PD by improving motor function, preserving dopaminergic neurons, reducing inflammation, modulating gut microbiota composition, and remodeling systemic metabolism. These findings highlight bavachalcone as a promising therapeutic candidate for PD

Histone H3 proline 16 hydroxylation regulates mammalian gene expression

Histone post-translational modifications (PTMs) are important forregulating various DNA-templated processes. Here, we report theexistence of a histone PTM in mammalian cells, namely histone H3 withhydroxylation of proline at residue 16 (H3P16oh), which is catalyzed by theproline hydroxylase EGLN2. We show that H3P16oh enhances direct bindingof KDM5A to its substrate, histone H3 with trimethylation at the fourthlysine residue (H3K4me3), resulting in enhanced chromatin recruitmentof KDM5A and a corresponding decrease of H3K4me3 at target genes.Genome- and transcriptome-wide analyses show that the EGLN2–KDM5Aaxis regulates target gene expression in mammalian cells. Specifically, ourdata demonstrate repression of the WNT pathway negative regulator DKK1through the EGLN2-H3P16oh-KDM5A pathway to promote WNT/β-cateninsignaling in triple-negative breast cancer (TNBC). This study characterizesa regulatory mark in the histone code and reveals a role for H3P16oh inregulating mammalian gene expressio