Exploring the Design Space of Immersive Urban Analytics

Recent years have witnessed the rapid development and wide adoption of immersive head-mounted devices, such as HTC VIVE, Oculus Rift, and Microsoft HoloLens. These immersive devices have the potential to significantly extend the methodology of urban visual analytics by providing critical 3D context information and creating a sense of presence. In this paper, we propose an theoretical model to characterize the visualizations in immersive urban analytics. Further more, based on our comprehensive and concise model, we contribute a typology of combination methods of 2D and 3D visualizations that distinguish between linked views, embedded views, and mixed views. We also propose a supporting guideline to assist users in selecting a proper view under certain circumstances by considering visual geometry and spatial distribution of the 2D and 3D visualizations. Finally, based on existing works, possible future research opportunities are explored and discussed.Comment: 23 pages,11 figure

Quantum-inspired variational algorithms for partial differential equations: Application to financial derivative pricing

Variational quantum Monte Carlo (VMC) combined with neural-network quantum states offers a novel angle of attack on the curse-of-dimensionality encountered in a particular class of partial differential equations (PDEs); namely, the real- and imaginary time-dependent Schr\"odinger equation. In this paper, we present a simple generalization of VMC applicable to arbitrary time-dependent PDEs, showcasing the technique in the multi-asset Black-Scholes PDE for pricing European options contingent on many correlated underlying assets

DriveSceneGen: Generating Diverse and Realistic Driving Scenarios from Scratch

Realistic and diverse traffic scenarios in large quantities are crucial for the development and validation of autonomous driving systems. However, owing to numerous difficulties in the data collection process and the reliance on intensive annotations, real-world datasets lack sufficient quantity and diversity to support the increasing demand for data. This work introduces DriveSceneGen, a data-driven driving scenario generation method that learns from the real-world driving dataset and generates entire dynamic driving scenarios from scratch. DriveSceneGen is able to generate novel driving scenarios that align with real-world data distributions with high fidelity and diversity. Experimental results on 5k generated scenarios highlight the generation quality, diversity, and scalability compared to real-world datasets. To the best of our knowledge, DriveSceneGen is the first method that generates novel driving scenarios involving both static map elements and dynamic traffic participants from scratch.Comment: 7 pages, 5 figures, 2 table

Radiofrequency ablation for papillary thyroid microcarcinoma close to the thyroid capsule versus far from the thyroid capsule: a retrospective study

Introduction: The aim of this study was to evaluate the safety and efficacy of ultrasound-guided radiofrequency ablation (RFA) for the management of papillary thyroid microcarcinoma (PTMC) close to the thyroid capsule. Material and methods: This was a retrospective study of 202 patients with PTMC who underwent RFA close to the thyroid capsule and 80 patients with PTMC who underwent RFA far from the thyroid capsule between June 2015 and December 2022. The follow-up time after RFA, change in size of tumour, location, thyroid function, the rates of PTMC disappearance, and complications were evaluated. Results: A total of 202 patients with PTMC close to the thyroid capsule and 80 patients with PTMC far from the thyroid capsule successfully treated with RFA were studied. The thyroid function including free triiodothyronine (fT3), free thyroxine (fT4), triiodothyronine (T3), thyroxine (T4), and thyroid-stimulating hormone (TSH) showed no changes after RFA for one months in both groups. The tumour size was increased at 1, 3, and 6 months after RFA compared with pre-operative RFA in both groups. The tumour size was decreased at 12 and 24 months after RFA compared with pre-operative RFA both in both group. Seventy-nine PTMC close to the thyroid capsule and 30 PTMC far from the thyroid capsule completely disappeared as assessed by ultrasound examination. Eighty-four PTMC patients close to the thyroid capsule and 34 PTMC patients far from the thyroid capsule had minor complications after RFA treatment. The complication rates between the 2 groups were similar. Conclusion: Ultrasound-guided RFA seems to be an effective and safe method for patients with PTMC close to the thyroid capsule

Modeling Students’ Procrastination in Higher Education: Causes, Outcomes, and Prediction

Thesis (Ph.D.)--University of Washington, 2023Students spend little time completing tasks when deadlines are far off; however, theytend to increase their work amounts as deadline approaches. This phenomenon, which is called deadline rush, can be modeled by exponential distributions. Deadline reactivity, represented by a rate parameter of the exponential distribution, parameterizes individual differences in procrastination. That is, an individual with high reactivity to deadlines procrastinates more than an individual with low deadline reactivity. While the phenomenon and parametric models of individual differences in procrastination have been investigated, practical applications in the classroom setting have garnered little attention from researchers. Past research on procrastination has not much considered its relationships with learning environment factors and academic performance, with a lack of objective measurements and heavy reliance on self-reported questionnaires. My dissertation will respond to this gap in the research by modeling students’ individual procrastination in university classroom settings, paying close attention to factors influencing the students’ procrastination as well as the effects of procrastination on performance. In particular, the dissertation will answer the following three research questions: (1) Do learning environments (i.e., online learning, task complexity, and time in the academic term) affect students’ procrastination? (2) Does procrastination affect individual and team performance? and (3) How can procrastination be predicted through physiological responses (i.e., eye movement, heart rate, electrodermal activity, and skin temperature)? The first two research questions have been answered by longitudinal field studies, while controlled laboratory experiments are conducted to answer the third research question. My findings shed light on how objective modeling and prediction of procrastination can be applied in the classroom setting. In particular, the findings will provide instructors, researchers, and online learning platforms with practical strategies to better design classes and interventions of procrastination for improvements in students’ performance

Does Environmental Regulation Promote Corporate Green Innovation? Empirical Evidence from Chinese Carbon Capture Companies

The proposal of the “double carbon” goal of “carbon peak, carbon neutralization” highlights the determination of China’s green and low-carbon development. Carbon capture is one of the essential ways to reduce carbon dioxide (CO2) emissions and cope with climate change. Then, how to improve the green innovation capability of organizations and promote the transformation and upgrading of enterprises with green development is a practical problem that needs to be dealt with quickly. This paper uses multiple linear regression to investigate the impact of environmental regulation on corporate green innovation and explores the mediating effect of corporate environmental investment and the moderating effect of corporate digital transformation. The analysis results show that government environmental regulation can effectively enhance the green innovation of enterprises and environmental investments play an intermediary role. However, the development of environmental regulation in China is still relatively backward, and its positive incentive role needs to be further played. As a result, the government should strengthen environmental legislation while also accelerating system development, increasing corporate investment in environmental protection, and raising protection awareness among companies using digital network technology

Design, analysis and experimental validation of high static and low dynamic stiffness mounts based on target force curves

In order to improve vibration isolation, soft components can be used in engineering applications, but this can lead to excessive static deflection. An ideal vibration isolator should have a high static stiffness to ensure that it has sufficient load carrying capacity; at the same time, it should have a low dynamic stiffness to maximize the vibration isolation frequency range. Recently, high static and low dynamic stiffness (HSLDS) mounts have been increasingly shown to have significant benefits for various engineering applications. This paper proposes a method for designing HSLDS mounts based on target force curves. In the design method, the HSLDS mount is obtained by placing a negative stiffness structure in parallel with a positive stiffness linear spring. The negative stiffness structure is achieved by using a roller-slider curve which can be designed according to the requirements to achieve the target force curve. HSLDS mounts are proposed with nth-order stiffness behavior​ which are designed using the method presented here. The results show that, compared with lower order HSLDS mounts based on the same static stiffness, higher order HSLDS mounts have lower dynamic stiffness near the equilibrium position. The Average Method is used to analyze the dynamics of a system based on the nth-order HSLDS mounts, and the displacement transmissibility under harmonic excitation is obtained. The effects of different parameters on the transmissibility are studied. The results show that appropriately increasing the damping ratio is beneficial for the isolation performance of the HSLDS mount. Finally, an experimental prototype is designed and manufactured. The proposed design method and the vibration isolation performance of the HSLDS mount are verified by constant-frequency excitation experiments.</p

Soil water transformation regularity of farmland for typical crop in Beijing-Tianjin-Hebei region: Experimental and simulating analyses

The transformation process of soil water plays an important role in the hydrological cycle, and is a link to other water processes. Study on the regularity of soil water transformation under agricultural plantation is favorable to understanding the influence of human activities on soil water conversion. Typical crop was selected in Beijing-Tianjin-Hebei(BTH) region and the study on regularity of field-scale soil water transformation was carried out by means of crop-soil water field experimental observation combined with model simulation. In the field experiment, testing and observation of irrigated and rainfed maize were simultaneously carried out in the adjacent fields respectively to form a comparative experimental study. The experimental observation data were used to establish the soil water model, which is calibrated in many aspects, such as field water content change during the maize growth period, the soil profile water content distribution at different moments, maize leaf area index and plant height. The results show that this model has an efficient simulation effect. Quantitative study on field evapotranspiration regularity, field soil water flux under irrigated and rainfed modes, impact mechanism of soil water deep seepage during maize growth period was achieved through the simulation of soil water process, and related reference conclusions were also proposed for water resources management and conservation in BTH

Estimation of Aerosol Optical Depth at 30 m Resolution Using Landsat Imagery and Machine Learning

Current remote sensing-based aerosol optical depth (AOD) products have coarse spatial resolutions, which are useful for studies at continental and global scales, but unsatisfactory for local scale applications, such as urban air pollution monitoring. In this study, we investigated the possibility of using Landsat imagery to develop high-resolution AOD estimations at 30 m based on machine learning algorithms. We assessed the performance of six machine learning algorithms, including Extreme Gradient Boosting, Random Forest, Cascade Random Forest, Gradient Boosted Decision Trees, Extremely Randomized Trees, and Multiple Linear Regression. To obtain accurate AOD estimations, we used prior knowledge from multiple sources as inputs to the machine learning models, including the Global Land Surface Satellite (GLASS) albedo, the 1-km AOD product from MODIS data using the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm, and meteorological and surface elevation data. A total of 13,624 AOD measurements from Aerosol Robotic Network (AERONET) sites were used for model training and validation. We found that all six algorithms exhibited good performance, with R2 values ranging from 0.73 to 0.78 and AOD root-mean-square errors (RMSE) ranging from 0.089 to 0.098. The extremely randomized trees algorithm, however, demonstrated marginally superior performance as compared to the other algorithms; hence, it was used to produce AOD estimates at a 30 m resolution for one Landsat scene coving Beijing in 2013&ndash;2019. Through a comparison with overlapping AERONET observations, a high level of accuracy was achieved, with an R2 = 0.889 and an RMSE = 0.156. Our method can be potentially used to generate a global high-resolution AOD dataset based on Landsat imagery

Soil water transformation regularity of farmland for typical crop in Beijing-Tianjin-Hebei region: Experimental and simulating analyses

The transformation process of soil water plays an important role in the hydrological cycle, and is a link to other water processes. Study on the regularity of soil water transformation under agricultural plantation is favorable to understanding the influence of human activities on soil water conversion. Typical crop was selected in Beijing-Tianjin-Hebei(BTH) region and the study on regularity of field-scale soil water transformation was carried out by means of crop-soil water field experimental observation combined with model simulation. In the field experiment, testing and observation of irrigated and rainfed maize were simultaneously carried out in the adjacent fields respectively to form a comparative experimental study. The experimental observation data were used to establish the soil water model, which is calibrated in many aspects, such as field water content change during the maize growth period, the soil profile water content distribution at different moments, maize leaf area index and plant height. The results show that this model has an efficient simulation effect. Quantitative study on field evapotranspiration regularity, field soil water flux under irrigated and rainfed modes, impact mechanism of soil water deep seepage during maize growth period was achieved through the simulation of soil water process, and related reference conclusions were also proposed for water resources management and conservation in BTH