geoTangle : Interactive Design of Geodesic Tangle Patterns on Surfaces

Tangles are complex patterns, which are often used to decorate the surface of real-world artisanal objects. They consist of arrangements of simple shapes organized into nested hierarchies, obtained by recursively splitting regions to add progressively finer details. In this article, we show that 3D digital shapes can be decorated with tangles by working interactively in the intrinsic metric of the surface. Our tangles are generated by the recursive application of only four operators, which are derived from tracing the isolines or the integral curves of geodesics fields generated from selected seeds on the surface. Based on this formulation, we present an interactive application that lets designers model complex recursive patterns directly on the object surface without relying on parametrization. We reach interactive speed on meshes of a few million triangles by relying on an efficient approximate graph-based geodesic solver.

Flexible, non-parametric modeling using regularized neural networks

Non-parametric, additive models are able to capture complex data dependencies in a flexible, yet interpretable way. However, choosing the format of the additive components often requires non-trivial data exploration. Here, as an alternative, we propose PrAda-net, a one-hidden-layer neural network, trained with proximal gradient descent and adaptive lasso. PrAda-net automatically adjusts the size and architecture of the neural network to reflect the complexity and structure of the data. The compact network obtained by PrAda-net can be translated to additive model components, making it suitable for non-parametric statistical modelling with automatic model selection. We demonstrate PrAda-net on simulated data, where we compare the test error performance, variable importance and variable subset identification properties of PrAda-net to other lasso-based regularization approaches for neural networks. We also apply PrAda-net to the massive U.K. black smoke data set, to demonstrate how PrAda-net can be used to model complex and heterogeneous data with spatial and temporal components. In contrast to classical, statistical non-parametric approaches, PrAda-net requires no preliminary modeling to select the functional forms of the additive components, yet still results in an interpretable model representation.

Performance of graphene-zinc oxide nanocomposite coated-glassy carbon electrode in the sensitive determination of para-nitrophenol

Graphene: zinc oxide nanocomposite (GN:ZnO NC) platform was tried for the sensitive determination of para-nitrophenol (p-NP) through the electrochemical method. ZnO nanoparticles (NPs) were synthesized by the modified wet-chemical method where in potassium hydroxide and zinc nitrate were used as precursors and starch as a stabilizing agent. A green and facile approach was applied to synthesize GN:ZnO NC in which glucose was employed as a reductant to reduce graphene-oxide to graphene in the presence of ZnO NPs. The synthesized NC was characterized using scanning and high-resolution transmission electron microscopy, energy dispersive x-ray analysis, X-ray diffraction and Raman spectroscopic techniques to examine the crystal phase, crystallinity, morphology, chemical composition and phase structure. GN:ZnO NC layer deposited over the glassy carbon electrode (GCE) was initially probed for its electrochemical performance using the standard 1 mM K3[Fe(CN)6] model complex. GN:ZnO NC modified GCE was monitored based on p-NP concentration. An enhanced current response was observed in 0.1 M phosphate buffer of pH 6.8 for the determination of p-NP in a linear working range of 0.09 × 10–6 to 21.80 × 10–6 M with a lower detection limit of 8.8 × 10–9 M employing square wave adsorptive stripping voltammetric technique at a deposition-potential and deposition-time of − 1.0 V and 300 s, respectively. This electrochemical sensor displayed very high specificity for p-NP with no observed interference from some other possible interfering substances such as 2, 4-di-NP, ortho-NP, and meta-NP. The developed strategy was useful for sensitive detection of p-NP quantity in canals/rivers and ground H2O samples with good recoveries.

Graph Entropy Based on Strong Coloring of Uniform Hypergraphs

The classical graph entropy based on the vertex coloring proposed by Mowshowitz depends on a graph. In fact, a hypergraph, as a generalization of a graph, can express complex and high-order relations such that it is often used to model complex systems. Being different from the classical graph entropy, we extend this concept to a hypergraph. Then, we define the graph entropy based on the vertex strong coloring of a hypergraph. Moreover, some tightly upper and lower bounds of such graph entropies as well as the corresponding extremal hypergraphs are obtained.

METHODICAL ASPECTS OF DEVELOPING TOOLKIT MODELING AND INFORMATION SUPPORT FOR THE FORMATION OF FORECASTS OF THE DEMOGRAPHIC DEVELOPMENT OF REGIONS

According to Rosstat for 2020, the population of Russia as a result of its natural movement decreased by 688.7 thousand people. If the birth rate in relation to 2019 decreased by 3.0%, then mortality increased by 17.9%. For many regions of the country (oddly enough, in the first place of its European part), the situation turned out to be even more difficult. At the same time, heterogeneous factors had an impact on each other - a change in the age structure of the population, the COVID-19 pandemic, a decrease in real income, etc. Under these conditions, the problem of obtaining accurate predictive assessments of the situation development in order to develop government policies to improve it is updated. Objectively necessary is the introduction of relevant information systems built on the basis of integrated economic and mathematical models. In this regard, the article discusses the development and application of modern tools for analyzing and predicting the development of territorial systems, including demographic aspects. It is indicated that a significant factor is the development of the social infrastructure of the territory. A system of criteria and indicators are proposed to assess the impact of its level of development on demographic processes. In particular, areas such as health care, education, culture and leisure, housing, trade and services are considered. An approach to the formation of integral indicators in various areas of life of society and an example of developing regression equations based on them is presented. It is noted that in different regions of the country, the degree of influence of the level of development of social infrastructure on demographic processes may differ significantly, which requires accounting within the framework of the model being formed. The possibility and need to build a decision support system based on the obtained model complex and is defined by such a toolkit in the strategic development management system of the region. The key stages of developing tools are described. The results obtained can be used as part of modeling changes in the demographic potential of regions in the context of the transformation of the territorial settlement system.

On rough convergence of complex uncertain sequences

Complex uncertain variables are measurable functions from an uncertainty space to the set of complex numbers and are used to model complex uncertain quantities. The main purpose of this paper is to introduce rough convergence of complex uncertain sequences and study some convergence concepts namely rough convergence in measure, rough convergence in mean, rough convergence in distribution of complex uncertain sequences. Lastly some relationship between them have been investigated.

Exploring the potential of machine learning for simulations of urban ozone variability

Machine learning (ML) has emerged as a powerful technique in the Earth system science, nevertheless, its potential to model complex atmospheric chemistry remains largely unexplored. Here, we applied ML to simulate the variability in urban ozone (O3) over Doon valley of the Himalaya. The ML model, trained with past variations in O3 and meteorological conditions, successfully reproduced the independent O3 data (r2 ~ 0.7). Model performance is found to be similar when the variation in major precursors (CO and NOx) were included in the model, instead of the meteorology. Further the inclusion of both precursors and meteorology improved the performance significantly (r2 = 0.86) and the model could also capture the outliers, which are crucial for air quality assessments. We suggest that in absence of high-resolution measurements, ML modeling has profound implications for unraveling the feedback between pollution and meteorology in the fragile Himalayan ecosystem.