Economic Assimilation of Chinese Immigrants in the United States: Is There Wage Convergence with Natives?

Asian Americans are often referred to as the “model minority” due to perceptions of their high income and educational attainment; yet relatively little is known about their economic assimilation experience. The purpose of this study is to determine economic assimilation of Chinese immigrants over time. This research follows a cohort of Chinese immigrants from 1994 to 2011 and compares their earnings performance with natives that have similar educational attainment. Multiple regression analysis is used to analyze data from the Current Population Survey. Results show that, although the cohort of Chinese immigrants initially has earnings substantially lower than the natives, it is only about 10 years before they reach income parity. By 2011, Chinese immigrants’ earnings exceed natives’ earnings by about 4 percent. The study concludes that despite the language and adjustment challenges, Chinese immigrants do show rapid economic assimilation in the United States

Free Boundary Stable Minimal Hypersurfaces in Positively Curved 4-Manifolds

We show that the combination of nonnegative 2-intermediate Ricci Curvature and strict positivity of scalar curvature forces rigidity of two-sided free boundary stable minimal hypersurface in a 4-manifold with bounded geometry and weakly convex boundary. This extends the method of Chodosh-Li-Stryker to free boundary minimal hypersurfaces in ambient manifolds with boundary.Comment: 21 pages, comments welcom

USING THE AUTOMATED RANDOM FOREST APPROACH FOR OBTAINING THE COMPRESSIVE STRENGTH PREDICTION OF RCA

The intricate relationships and cohesiveness among numerous components make the task of designing mixture proportions for high-performance concrete (HPC) a challenging endeavour. Machine learning (ML) algorithms are indeed efficacious in mitigating this predicament. However, their lack of an explicit correlation between mixture proportions and compressive strength renders them opaque black box models. To surpass this constraint, the present research puts forward a semi-empirical methodology that involves the utilization of tactics such as non-dimensionalization and optimization. The methodology proposed exhibits a remarkable level of accuracy in predicting compressive strength across various datasets, exemplifying its all-encompassing applicability to diverse datasets.Furthermore, the exact association furnished by semi-empirical equations is a valuable asset for engineers and researchers operating in this domain, especially concerning their prognostic capabilities. The compressive strength of concrete holds significant importance in designing high-performance concrete, and achieving an optimal mixture proportion necessitates a comprehensive comprehension of the complex interplay among diverse factors, including the type and proportion of cement, water-cement ratio, size and type of aggregate, curing conditions, and admixtures. The semi-empirical approach put forth in this study presents a potential remedy to the intricate undertaking by establishing a more unequivocal correlation between mixture ratios and compressive strength

Key Developmental Opportunities for Long-term Organization Development of G Commercial Bank China : A Mixed Method Research

This research-based article employs needs assessment research. The research site is at G Commercial Bank in China. The study comprises two objectives: 1) to examine the current and expected situations of the five performance-related factors, consisting of training & development, work-life balance, supervision, organizational commitment, and perceived AI utilization, and 2) to propose an OD intervention for improvement of the aforementioned performance-related factors. This study employs a semi-structured questionnaire containing fixed choices and open-ended questions for data collection from the respondents. The data analysis and synthesis procedures include descriptive statistics, PNIModified, and contents analysis. The actual sample is 138 respondents who completed the questionnaire, and 35 out of 138 respondents provide the qualitative suggestions in the open-ended question section. The key findings based on the quantitative and qualitative data revealed that the priority needs for Organization Development Interventions for improvement comprise work-life balance as the 1st priority need, perceived AI utilization as the 2nd priority needs, training & development as the 3rd priority need, the supervision as the 4th priority, and organizational commitment as the 5th priority. A set of OD interventions for improvements proposed, where the work-life balance, perceived AI utilization, training &development, and supervision were regarded as the interdependent factors for the total improvement program of their employees’ performance. At the same time, the current organization continuously nurtured the current condition of organizational commitment

Research on Industry Alliance Knowledge Transfer Network Modeling and Simulation Based on Complex Networks

The booming of the complex network research provides new avenues of research and support for various types of complex systems. And a large number of studies have shown that industrial technology innovation coalition belongs to the scope of complex system, so it is available to use the complex network theory to study it. This paper first describes the theory of complex networks. Second, the use of complex network theory in the industry alliance knowledge transfer is probed in terms of the overall network structure, network node centrality and network subgroup. Finally, the industry alliances knowledge transfer network model is constructed and the quantitative analysis of the simulation example is done with the network analysis tool, reflecting the effectiveness of analyzing knowledge transfer from a complex network perspective

Higher-order Graph Convolutional Network with Flower-Petals Laplacians on Simplicial Complexes

Despite the recent successes of vanilla Graph Neural Networks (GNNs) on many tasks, their foundation on pairwise interaction networks inherently limits their capacity to discern latent higher-order interactions in complex systems. To bridge this capability gap, we propose a novel approach exploiting the rich mathematical theory of simplicial complexes (SCs) - a robust tool for modeling higher-order interactions. Current SC-based GNNs are burdened by high complexity and rigidity, and quantifying higher-order interaction strengths remains challenging. Innovatively, we present a higher-order Flower-Petals (FP) model, incorporating FP Laplacians into SCs. Further, we introduce a Higher-order Graph Convolutional Network (HiGCN) grounded in FP Laplacians, capable of discerning intrinsic features across varying topological scales. By employing learnable graph filters, a parameter group within each FP Laplacian domain, we can identify diverse patterns where the filters' weights serve as a quantifiable measure of higher-order interaction strengths. The theoretical underpinnings of HiGCN's advanced expressiveness are rigorously demonstrated. Additionally, our empirical investigations reveal that the proposed model accomplishes state-of-the-art (SOTA) performance on a range of graph tasks and provides a scalable and flexible solution to explore higher-order interactions in graphs