Planning a national park in lower Yangtze Delta, China

Chen Zhi's National Taihu Lake Park published in 1929, is the first planning of our country’s national park. This article attempts to analyze the beautification and recreation of Chen Zhi's conception. The author starts with the development of the national park at that time and the practice experience of Chen Zhi, combing the Taihu Lake watershed’s natural and social conditions. What’s more, the author interprets the planning text of Taihu Lake from four aspects, including landscape resources, traffic system, supporting facilities, and construction of scenic forests. Based on this, this article analyzes Chen Zhi’s considerations of drawing lessons from abroad and integrating them into the local culture, pursues the relevance of its design concept with America and Japan, and presents the spread of national park’s concept in our country in the same period

Development of a graphical numerical accuracy debugger based on an FPGA computing system

In scientific computing, the number of floating point operations are increasing along with the higher performance of computers, as well as the larger problem size. Due to the finite representation of real numbers in computers, the calculated results are rounded into the representative numbers, which results in round-off errors. The round-off errors might be propagated as the program runs longer and in the end leads to an unreliable result. Discrete Stochastic Arithmetic (DSA) provides a method to evaluate the accuracy of computed results and detect numerical instabilities during execution of the program. The DSA has been implemented on an FPGA-based hardware system. The FPGA-based hardware system has N parallel processing blocks so that it can run the same piece of code N times in parallel in different round-off error propagations, which is required by DSA. In this thesis, based on this hardware architecture, a graphical numerical accuracy debugger is developed. Using this graphical numerical accuracy debugger, the user can debug same piece of code in both PowerPC processors synchronously, without any modification to source codes. In order to implement the proposed debugging flow, a script has been written to substitute the original underlying debugging engine of SDK. Within the script, a series of functionalities are achieved: GDB input commands catching/forwarding, process calling, GDB output messages catching/forwarding etc. Moreover, with the substitution, it’s able to collect results from all processing blocks and then the number of significant bits can be calculated and presented to users

Exploring human mobility for multi-pattern passenger prediction : a graph learning framework

Traffic flow prediction is an integral part of an intelligent transportation system and thus fundamental for various traffic-related applications. Buses are an indispensable way of moving for urban residents with fixed routes and schedules, which leads to latent travel regularity. However, human mobility patterns, specifically the complex relationships between bus passengers, are deeply hidden in this fixed mobility mode. Although many models exist to predict traffic flow, human mobility patterns have not been well explored in this regard. To address this research gap and learn human mobility knowledge from this fixed travel behaviors, we propose a multi-pattern passenger flow prediction framework, MPGCN, based on Graph Convolutional Network (GCN). Firstly, we construct a novel sharing-stop network to model relationships between passengers based on bus record data. Then, we employ GCN to extract features from the graph by learning useful topology information and introduce a deep clustering method to recognize mobility patterns hidden in bus passengers. Furthermore, to fully utilize spatio-temporal information, we propose GCN2Flow to predict passenger flow based on various mobility patterns. To the best of our knowledge, this paper is the first work to adopt a multi-pattern approach to predict the bus passenger flow by taking advantage of graph learning. We design a case study for optimizing routes. Extensive experiments upon a real-world bus dataset demonstrate that MPGCN has potential efficacy in passenger flow prediction and route optimization. © 2000-2011 IEEE

Light Auditor: Power Measurement Can Tell Private Data Leakage Through IoT Covert Channels

Despite many conveniences of using IoT devices, they have suffered from various attacks due to their weak security. Besides well-known botnet attacks, IoT devices are vulnerable to recent covert-channel attacks. However, no study to date has considered these IoT covert-channel attacks. Among these attacks, researchers have demonstrated exfiltrating users\u27 private data by exploiting the smart bulb\u27s capability of infrared emission. In this paper, we propose a power-auditing-based system that defends the data exfiltration attack on the smart bulb as a case study. We first implement this infrared-based attack in a lab environment. With a newly-collected power consumption dataset, we pre-process the data and transform them into two-dimensional images through Continous Wavelet Transformation (CWT). Next, we design a two-dimensional convolutional neural network (2D-CNN) model to identify the CWT images generated by malicious behavior. Our experiment results show that the proposed design is efficient in identifying infrared-based anomalies: 1) With much fewer parameters than transfer-learning classifiers, it achieves an accuracy of 88% in identifying the attacks, including unseen patterns. The results are similarly accurate as the sophisticated transfer-learning CNNs, such as AlexNet and GoogLeNet; 2) We validate that our system can classify the CWT images in real time

Is A 15-minute City within Reach in the United States? An Investigation of Activity-Based Mobility Flows in the 12 Most Populous US Cities

Enhanced efforts in the transportation sector should be implemented to mitigate the adverse effects of CO2 emissions resulting from zoning-based planning paradigms. The innovative concept of the 15-minute city, with a focus on proximity-based planning, holds promise in minimizing unnecessary travel and advancing the progress toward achieving carbon neutrality. However, an important research question that remains insufficiently explored is: to what extent is a 15-minute city concept within reach for US cities? This paper establishes a comprehensive framework to evaluate the 15-minute city concept using SafeGraph Point of Interest (POI) check-in data in the 12 most populous US cities. The results reveal that residents are more likely to rely on cars due to the fact that most of their essential activities are located beyond convenient walking, cycling, and public transit distances. However, there is significant potential for the implementation of the 15-minute city concept, as most residents' current activities can be accommodated within a 15-minute radius by the aforementioned low-emission modes of transportation. Our findings can offer policymakers insight into how far US cities are away from the 15-minute city and the potential CO2 emission reduction they can expect if the concept is successfully implemented

Modeling Link-level Road Traffic Resilience to Extreme Weather Events Using Crowdsourced Data

Climate changes lead to more frequent and intense weather events, posing escalating risks to road traffic. Crowdsourced data offer new opportunities to monitor and investigate changes in road traffic flow during extreme weather. This study utilizes diverse crowdsourced data from mobile devices and the community-driven navigation app, Waze, to examine the impact of three weather events (i.e., floods, winter storms, and fog) on road traffic. Three metrics, speed change, event duration, and area under the curve (AUC), are employed to assess link-level traffic change and recovery. In addition, a user's perceived severity is computed to evaluate link-level weather impact based on crowdsourced reports. This study evaluates a range of new data sources, and provides insights into the resilience of road traffic to extreme weather, which are crucial for disaster preparedness, response, and recovery in road transportation systems

API : an index for quantifying a scholar's academic potential

In the context of big scholarly data, various metrics and indicators have been widely applied to evaluate the impact of scholars from different perspectives, such as publication counts, citations, ${h}$-index, and their variants. However, these indicators have limited capacity in characterizing prospective impacts or achievements of scholars. To solve this problem, we propose the Academic Potential Index (API) to quantify scholar's academic potential. Furthermore, an algorithm is devised to calculate the value of API. It should be noted that API is a dynamic index throughout scholar's academic career. By applying API to rank scholars, we can identify scholars who show their academic potentials during the early academic careers. With extensive experiments conducted based on the Microsoft Academic Graph dataset, it can be found that the proposed index evaluates scholars' academic potentials effectively and captures the variation tendency of their academic impacts. Besides, we also apply this index to identify rising stars in academia. Experimental results show that the proposed API can achieve superior performance in identifying potential scholars compared with three baseline methods. © 2019 IEEE

Deciphering the Factors Associated With Adoption of Alternative Fuel Vehicles in California: An Investigation of Latent Attitudes, Socio-Demographics, and Neighborhood Effects

This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license https://creativecommons.org/licenses/by/4.0/. Please cite this article as:Xiatian Iogansen, Kailai Wang, David Bunch, Grant Matson, Giovanni Circella, Deciphering the factors associated with adoption of alternative fuel vehicles in California: An investigation of latent attitudes, socio-demographics, and neighborhood effects, Transportation Research Part A: Policy and Practice, Volume 168, 2023, 103535, ISSN 0965-8564, https://doi.org/10.1016/j.tra.2022.10.012.Promoting the use of alternative fuel vehicles (AFVs) has become a long-term transportation strategy in California, which can bring a broad range of social, economic, and environmental benefits. Based on a sample of 3260 California residents from the 2018 California Panel Survey, this study explores the impacts of latent attitudes, socio-demographic characteristics, and neighborhood effects on consumers\u2019 current vehicle fuel type choice and their interest in purchasing or leasing an AFV in the future

Electrochemical Oxidation of Sodium Metabisulfite for Sensing Zinc Oxide Nanoparticles Deposited on Graphite Electrode

A novel concept was successfully evaluated for the electrochemical quantitative analysis of zinc oxide nanoparticles originally in aqueous suspension. An aliquot of the suspension was first placed on the working area of a graphite screen-printed electrode and the water was evaporated to form a dry deposit of ZnO nanoparticles. Deposition of ZnO nanoparticles on the electrode was confirmed by energy-dispersive X-ray spectroscopy. A probe solution containing KCl and sodium metabisulfite was added on top of the deposit for electrochemical analysis by cyclic voltammetry. The anodic peak current (Ipa) for metabisulfite, measured at +1.2 V vs. Ag/AgCl, afforded a lower detection limit of 3 µg and exhibited a linear dependence on the mass of deposited ZnO nanoparticles up to 15 μg. Further, the current increased nonlinearly until it reached a saturation level beyond 60 μg of ZnO nanoparticles. The diffusion coefficient of metabisulfite anions through the electrical double layer was determined to be 4.16 × 10−5 cm2/s. Apparently the surface reactivity of ZnO originated from the oxide anion rather than the superoxide anion or the hydroxyl radical. Enhancement of the metabisulfite oxidation peak current can be developed into a sensitive method for the quantitation of ZnO nanoparticles