Effects of double layer porous asphalt pavement of urban streets on noise reduction

AbstractRoad traffic is the major noise source that impacts the largest numbers of city dwellers. Urban traffic noise control at the source typically involves providing quieter i.e. low noise pavement and regular maintenance. The aim of this paper is to propose a double-layer porous asphalt pavement for keeping the traffic noise at a low level with good durability. It contains the top layer of fine aggregates and bottom layer of course aggregates. The noise-absorption performance of this asphalt pavement is evaluated by adjusting the parameters of the pavement structure simulated in air–solid coupled numerical models. The reduction of noise by using the newly proposed asphalt pavement is compared with those of the traditional pavements such as the thin surfacing (TSF) with small aggregates and rubberized asphalt pavement (RAP). The results from the outdoor noise tests for the double-layer porous asphalt pavement verifies the virtual pavement models and noise reduction effects in practice. This asphalt pavement is designated to lower the noise level of urban road traffic and boost the living environments of the city dwellers

Enhancing thermoelectric figure-of-merit by low-dimensional electrical transport in phonon-glass crystals

Low-dimensional electronic and glassy phononic transport are two important ingredients of highly-efficient thermoelectric material, from which two branches of the thermoelectric research emerge. One focuses on controlling electronic transport in the low dimension, while the other on multiscale phonon engineering in the bulk. Recent work has benefited much from combining these two approaches, e.g., phonon engineering in low-dimensional materials. Here, we propose to employ the low-dimensional electronic structure in bulk phonon-glass crystal as an alternative way to increase the thermoelectric efficiency. Through first-principles electronic structure calculation and classical molecular dynamics simulation, we show that the $\pi$-$\pi$ stacking Bis-Dithienothiophene molecular crystal is a natural candidate for such an approach. This is determined by the nature of its chemical bonding. Without any optimization of the material parameter, we obtain a maximum room-temperature figure of merit, $ZT$, of $1.48$ at optimal doping, thus validating our idea.Comment: Nano Lett.201

HYPRO: A Hybridly Normalized Probabilistic Model for Long-Horizon Prediction of Event Sequences

In this paper, we tackle the important yet under-investigated problem of making long-horizon prediction of event sequences. Existing state-of-the-art models do not perform well at this task due to their autoregressive structure. We propose HYPRO, a hybridly normalized probabilistic model that naturally fits this task: its first part is an autoregressive base model that learns to propose predictions; its second part is an energy function that learns to reweight the proposals such that more realistic predictions end up with higher probabilities. We also propose efficient training and inference algorithms for this model. Experiments on multiple real-world datasets demonstrate that our proposed HYPRO model can significantly outperform previous models at making long-horizon predictions of future events. We also conduct a range of ablation studies to investigate the effectiveness of each component of our proposed methods.Comment: NeurIPS 2022 camera-read

Rank-Based Learning and Local Model Based Evolutionary Algorithm for High-Dimensional Expensive Multi-Objective Problems

Surrogate-assisted evolutionary algorithms have been widely developed to solve complex and computationally expensive multi-objective optimization problems in recent years. However, when dealing with high-dimensional optimization problems, the performance of these surrogate-assisted multi-objective evolutionary algorithms deteriorate drastically. In this work, a novel Classifier-assisted rank-based learning and Local Model based multi-objective Evolutionary Algorithm (CLMEA) is proposed for high-dimensional expensive multi-objective optimization problems. The proposed algorithm consists of three parts: classifier-assisted rank-based learning, hypervolume-based non-dominated search, and local search in the relatively sparse objective space. Specifically, a probabilistic neural network is built as classifier to divide the offspring into a number of ranks. The offspring in different ranks uses rank-based learning strategy to generate more promising and informative candidates for real function evaluations. Then, radial basis function networks are built as surrogates to approximate the objective functions. After searching non-dominated solutions assisted by the surrogate model, the candidates with higher hypervolume improvement are selected for real evaluations. Subsequently, in order to maintain the diversity of solutions, the most uncertain sample point from the non-dominated solutions measured by the crowding distance is selected as the guided parent to further infill in the uncertain region of the front. The experimental results of benchmark problems and a real-world application on geothermal reservoir heat extraction optimization demonstrate that the proposed algorithm shows superior performance compared with the state-of-the-art surrogate-assisted multi-objective evolutionary algorithms. The source code for this work is available at https://github.com/JellyChen7/CLMEA

Research on ricochet and its regularity of projectiles obliquely penetrating into concrete target

To address the ricochet problem in penetration process, the mathematical model of projectile penetrating into concrete target is established according to the basic kinetic equation and surface layer mechanism. The motion trajectory of projectile nose is obtained. Experimental studies on projectiles with different nose penetrating into concrete targets are conducted to explain the ricochet problem. These studies analyze fifty-four penetration conditions under different initial velocities and oblique angles when the projectiles have flat, hemispherical, ogive noses and conical noses. The regularity and critical angles of ricochet are analyzed with different nose shapes at different velocities. Results show that the ricochet angle increases depending on nose sharp and penetration velocity. The factors affecting the ricochet from big to small were analyzed via orthogonal test. The results show that with increasing the velocity from 652 m/s to 1022 m/s, the critical angle increases from 44° to 66°. The order of factors affecting the ricochet from big to small is the shape of the nose, the material of the projectiles and the penetrating velocity respectively

Smaller Genetic Risk in Catabolic Process Explains Lower Energy Expenditure, More Athletic Capability and Higher Prevalence of Obesity in Africans

Lower energy expenditure (EE) for physical activity was observed in Africans than in Europeans, which might contribute to the higher prevalence of obesity and more athletic capability in Africans. But it is still unclear why EE is lower among African populations. In this study we tried to explore the genetic mechanism underlying lower EE in Africans. We screened 231 common variants with possibly harmful impact on 182 genes in the catabolic process. The genetic risk, including the total number of mutations and the sum of harmful probabilities, was calculated and analyzed for the screened variants at a population level. Results of the genetic risk among human groups showed that most Africans (3 out of 4 groups) had a significantly smaller genetic risk in the catabolic process than Europeans and Asians, which might result in higher efficiency of generating energy among Africans. In sport competitions, athletes need massive amounts of energy expenditure in a short period of time, so higher efficiency of energy generation might help make African-descendent athletes more powerful. On the other hand, higher efficiency of generating energy might also result in consuming smaller volumes of body mass. As a result, Africans might be more vulnerable to obesity compared to the other races when under the same or similar conditions. Therefore, the smaller genetic risk in the catabolic process might be at the core of understanding lower EE, more athletic capability and higher prevalence of obesity in Africans

A Scalable Test Problem Generator for Sequential Transfer Optimization

Sequential transfer optimization (STO), which aims to improve optimization performance by exploiting knowledge captured from previously-solved optimization tasks stored in a database, has been gaining increasing research attention in recent years. However, despite significant advancements in algorithm design, the test problems in STO are not well designed. Oftentimes, they are either randomly assembled by other benchmark functions that have identical optima or are generated from practical problems that exhibit limited variations. The relationships between the optimal solutions of source and target tasks in these problems are manually configured and thus monotonous, limiting their ability to represent the diverse relationships of real-world problems. Consequently, the promising results achieved by many algorithms on these problems are highly biased and difficult to be generalized to other problems. In light of this, we first introduce a few rudimentary concepts for characterizing STO problems (STOPs) and present an important problem feature overlooked in previous studies, namely similarity distribution, which quantitatively delineates the relationship between the optima of source and target tasks. Then, we propose general design guidelines and a problem generator with superior extendibility. Specifically, the similarity distribution of a problem can be systematically customized by modifying a parameterized density function, enabling a broad spectrum of representation for the diverse similarity relationships of real-world problems. Lastly, a benchmark suite with 12 individual STOPs is developed using the proposed generator, which can serve as an arena for comparing different STO algorithms. The source code of the benchmark suite is available at https://github.com/XmingHsueh/STOP