Framework for Evaluating Sustainability of Transport System in Megalopolis and its Application

AbstractIt has been acknowledged that megalopolises are playing a leading role in the processes of both economic development and culture change. Thereupon, the new emphases on sustainability of transportation system in megalopolis are creating new demands for adequate approach to measure its performance and diagnosis potential drawbacks. By examining the descriptions of sustainable transport system as well as its evaluating approach, a framework with the general applicability and easily accessible data resource for evaluating sustainability of transport system in megalopolis is developed based on nature of regional structure and the feature transport demand in megalopolis. The proposed framework is applied in the analysis and comparison of Jing-Jin-Ji and Yangtze River Delta.

The hairiness of worsted wool and cashmere yarns and the impact of fiber curvature on hairiness

In this study, a range of carefully selected wool and cashmere yarns as well as their blends were used to examine the effects of fiber curvature and blend ratio on yarn hairiness. The results indicate that yarns spun from wool fibers with a higher curvature have lower yarn hairiness than yarns spun from similar wool of a lower curvature. For blend yarns made from wool and cashmere of similar diameter, yarn hairiness increases with the increase in the cashmere content in the yarn. This is probably due to the presence of increased proportion of the shorter cashmere fibers in the surface regions of the yarn, leading to increased yarn hairiness. A modified hairiness composition model is used to explain these results and the likely origin of leading and trailing hairs. This model highlights the importance of yarn surface composition on yarn hairiness.<br /

Structure, diversity and spatial distribution pattern of significant tree species in Hefei city

In order to further tap the ecological potential of urban trees, improve the diversity of tree species in urban environment, and promote the sustainable development of urban trees, this paper further analyzes the structure, diversity and spatial distribution law of the prominent tree species in Hefei city on the basis of on–the–spot identification of the prominent trees distributed in the main city. The results show that: 1) 528 significant trees are identified in the three districts, belonging to 40 species, 36 genera and 27 families, with 23 trees evaluated by qualitative index and 505 trees by quantitative index. In quantity distribution, the number of significant trees in the three districts is ranked as Yaohai District&gt; firstringroad City&gt; Government Affairs District. The top four tree species in relative abundance are Platanus acerifolia, Cinnamomum camphora, Ginkgo biloba and Cedar. 2) In terms of the tree age structure, most trees are 20~40 years old, and only 22 trees are over 100 years old; the ratio of evergreen to deciduous tree species is 1: 3, and the ratio of plant to tree is about 7: 18; there are 23 native tree species accounting for 57.50%, but only 171 trees. 3) In terms of area distribution, the average tree height and crown area in Yaohai District are the largest, the average DBH, Shannon–Wiener index and Simpson index in the first–ring city area are the highest, and the species evenness index in Government District is the highest. 4) In terms of site types, most significant trees are distributed in urban streets, residential areas and parks, and only 11.74% of them are distributed in government organizations, schools, public facilities, scenic spots and religious land

Deep Feature Screening: Feature Selection for Ultra High-Dimensional Data via Deep Neural Networks

The applications of traditional statistical feature selection methods to high-dimension, low sample-size data often struggle and encounter challenging problems, such as overfitting, curse of dimensionality, computational infeasibility, and strong model assumption. In this paper, we propose a novel two-step nonparametric approach called Deep Feature Screening (DeepFS) that can overcome these problems and identify significant features with high precision for ultra high-dimensional, low-sample-size data. This approach first extracts a low-dimensional representation of input data and then applies feature screening based on multivariate rank distance correlation recently developed by Deb and Sen (2021). This approach combines the strengths of both deep neural networks and feature screening, and thereby has the following appealing features in addition to its ability of handling ultra high-dimensional data with small number of samples: (1) it is model free and distribution free; (2) it can be used for both supervised and unsupervised feature selection; and (3) it is capable of recovering the original input data. The superiority of DeepFS is demonstrated via extensive simulation studies and real data analyses

AMG: Automated Efficient Approximate Multiplier Generator for FPGAs via Bayesian Optimization

Approximate computing is a promising approach to reduce the power, delay, and area in hardware design for many error-resilient applications such as machine learning (ML) and digital signal processing (DSP) systems, in which multipliers usually are key arithmetic units. Due to the underlying architectural differences between ASICs and FPGAs, existing ASIC-based approximate multipliers do not offer symmetrical gains when they are implemented by FPGA resources. In this paper, we propose AMG, an open-source automated approximate multiplier generator for FPGAs driven by Bayesian optimization (BO) with parallel evaluation. The proposed method simplifies the exact half adders (HAs) for the initial partial product (PP) compression in a multiplier while preserving coarse-grained additions for the following accumulation. The generated multipliers can be effectively mapped to lookup tables (LUTs) and carry chains provided by modern FPGAs, reducing hardware costs with acceptable errors. Compared with 1167 multipliers from previous works, our generated multipliers can form a Pareto front with 28.70%-38.47% improvements in terms of the product of hardware cost and error on average. All source codes, reproduced multipliers, and our generated multipliers are available at https://github.com/phyzhenli/AMG.Comment: 7 pages, 2023 IEEE International Conference on Field-Programmable Technology (ICFPT