From patterned response dependency to structured covariate dependency: categorical-pattern-matching

Data generated from a system of interest typically consists of measurements from an ensemble of subjects across multiple response and covariate features, and is naturally represented by one response-matrix against one covariate-matrix. Likely each of these two matrices simultaneously embraces heterogeneous data types: continuous, discrete and categorical. Here a matrix is used as a practical platform to ideally keep hidden dependency among/between subjects and features intact on its lattice. Response and covariate dependency is individually computed and expressed through mutliscale blocks via a newly developed computing paradigm named Data Mechanics. We propose a categorical pattern matching approach to establish causal linkages in a form of information flows from patterned response dependency to structured covariate dependency. The strength of an information flow is evaluated by applying the combinatorial information theory. This unified platform for system knowledge discovery is illustrated through five data sets. In each illustrative case, an information flow is demonstrated as an organization of discovered knowledge loci via emergent visible and readable heterogeneity. This unified approach fundamentally resolves many long standing issues, including statistical modeling, multiple response, renormalization and feature selections, in data analysis, but without involving man-made structures and distribution assumptions. The results reported here enhance the idea that linking patterns of response dependency to structures of covariate dependency is the true philosophical foundation underlying data-driven computing and learning in sciences.Comment: 32 pages, 10 figures, 3 box picture

Size distribution and diffuse pollution impacts of PAHs in street dust in urban streams in the Yangtze River Delta

Particles of dust washed off streets by stormwater are an important pathway of polyaromatic hydrocarbons (PAHs) into urban streams. This article presented a comprehensive assessment of the size distribution of PAHs in street dust particles, the potential risks of the particles in urban streams, and the sources and sinks of PAHs in the stream network. This assessment was based on measurements of 16 PAHs from the USEPA priority list in street dust particles and river sediments in Xincheng, China. The content of total PAHs ranged from 1629 to 8986 μg/kg in street dust particles, where smaller particles have a higher concentrations. Approximately 55% of the total PAHs were associated with particles less than 250 μm which accounted for 40% of the total mass of street dust. The PAH quantities increased from 2.41 to 46.86 μg/m2 in the sequence of new residential, rising through main roads, old town residential, commercial and industrial areas. The sediments in stream reaches in town were found to be sinks for street dust particle PAHs. The research findings suggested that particle size, land use and the hydrological conditions in the stream network were the factors which most influenced the total loads of PAH in the receiving water bodies.<br/

Simulating Public Administration Crisis: A Novel Generative Agent-Based Simulation System to Lower Technology Barriers in Social Science Research

This article proposes a social simulation paradigm based on the GPT-3.5 large language model. It involves constructing Generative Agents that emulate human cognition, memory, and decision-making frameworks, along with establishing a virtual social system capable of stable operation and an insertion mechanism for standardized public events. The project focuses on simulating a township water pollution incident, enabling the comprehensive examination of a virtual government's response to a specific public administration event. Controlled variable experiments demonstrate that the stored memory in generative agents significantly influences both individual decision-making and social networks. The Generative Agent-Based Simulation System introduces a novel approach to social science and public administration research. Agents exhibit personalized customization, and public events are seamlessly incorporated through natural language processing. Its high flexibility and extensive social interaction render it highly applicable in social science investigations. The system effectively reduces the complexity associated with building intricate social simulations while enhancing its interpretability.Comment: 12 Pages, 14 figures. This paper was submitted to IEEE TCSS on November 12, 202

Therapeutic angiogenesis for cardiovascular disease

The identification of angiogenic growth factors, such as vascular endothelial growth factor and fibroblast growth factor, has fueled interest in using such factors to induce therapeutic angiogenesis. The results of numerous animal studies and clinical trials have offered promise for new treatment strategies for various ischemic diseases. Increased understanding of the cellular and molecular biology of vessel growth has, however, prompted investigators and clinicians alike to reconsider the complexity of therapeutic angiogenesis. The realization that formation of a stable vessel is a complex, multistep process may provide useful insights into the design of the next generation of angiogenesis therapy

Variation of Oriental Oak (Quercus variabilis) Leaf δ13C across Temperate and Subtropical China: Spatial Patterns and Sensitivity to Precipitation

The concentration of the carbon-13 isotope (leaf δ13C) in leaves is negatively correlated with the mean annual precipitation (MAP) atlarge geographical scales. In this paper, we explain the spatial pattern of leaf δ13C variation for deciduous oriental oak (Quercus variabilis Bl.) across temperate and subtropical biomes and its sensitivity to climate factors such as MAP. There was a 6‰ variation in the leaf δ13C values of oak with a significant positive correlation with latitude and negative correlations with the mean annual temperature (MAT) and MAP. There was no correlation between leaf δ13C and altitude or longitude. Stepwise multiple regression analyses showed that leaf δ13C decreased 0.3‰ per 100 mm increase in MAP. MAP alone could account for 68% of the observed variation in leaf δ13C. These results can be used to improve predictions for plant responses to climate change and particularly lower rainfall