Blowing Away

Nature has always been playing one of the positive protagonists in the architecture field. Despite the disparate milieux and the cognitions discrepancy of nature in different eras, people keep exploring the interrelationships of nature, architecture, and h. In this case, “natural air” as an extensive component of nature, is an integration of sustainability foundation, architectural system enquiry, technological methods intervention, and research of human perception. In the process of exploring how to rebuild the “connectedness” between nature and humans in the architectural context, researching the human-sensible means that architecture interacts with the natural air and will be a primary “natural-representative” prototype

Large-Scale Unmanned Aerial Systems Traffic Density Prediction and Management

In recent years, the applications of Unmanned Aerial Systems (UAS) has become more and more popular. We envision that in the near future, the complicated and high density UAS traffic will impose significant burden to air traffic management. Lot of works focus on the application development of individual Small Unmanned Aerial Systems (sUAS) or sUAS management Policy, however, the study of the UAS cluster behaviors such as forecasting and managing of the UAS traffic has generally not been addressed. In order to address the above issue, there is an urgent need to investigate three research directions. The first direction is to develop a high fidelity simulator for the UAS cluster behavior evaluation. The second direction to study real time trajectory planning algorithms to mitigate the high dense UAS traffic. The last direction is to investigate techniques that rapidly and accurately forecast the UAS traffic pattern in the future. In this thesis, we elaborate these three research topics and present a universal paradigm to predict and manage the traffic for the large-scale unmanned aerial systems. To enable the research in UAS traffic management and prediction, a Java based Multi-Agent Air Traffic and Resource Usage Simulation (MATRUS) framework is first developed. We use two types of UAS trajectories, Point-to-Point (P2P) and Man- hattan, as the case study to describe the capability of presented framework. Various communication and propagation models (i.e. log-distance-path loss) can be integrated with the framework to model the communication between UASs and base stations. The results show that MATRUS has the ability to evaluate different sUAS traffic management policies, and can provide insights on the relationships between air traf- fic and communication resource usage for further studies. Moreover, the framework can be extended to study the effect of sUAS Detect-and-Avoid (DAA) mechanisms, implement additional traffic management policies, and handle more complex traffic demands and geographical distributions. Based on the MATRUS framework, we propose a Sparse Represented Temporal- Spatial (SRTS) UAS trajectory planning algorithm. The SRTS algorithm allows the sUAS to avoid static no-fly areas (i.e. static obstacles) or other areas that have congested air traffic or communication traffic. The core functionality of the routing algorithm supports the instant refresh of the in-flight environment making it appropri- ate for highly dynamic air traffic scenarios. The characterization of the routing time and memory usage demonstrate that the SRTS algorithm outperforms a traditional Temporal-Spatial routing algorithm. The deep learning based approach has shown an outstanding success in many areas, we first investigated the possibility of applying the deep neural network in predicting the trajectory of a single vehicle in a given traffic scene. A new trajectory prediction model is developed, which allows information sharing among vehicles using a graph neural network. The prediction is based on the embedding feature, which is derived from multi-dimensional input sequences including the historical trajectory of target and neighboring vehicles, and their relative positions. Compared to other existing trajectory prediction methods, the proposed approach can reduce the pre- diction error by up to 50.00%. Then, we present a deep neural network model that extracts the features from both spatial and temporal domains to predict the UAS traffic density. In addition, a novel input representation of the future sUAS mission information is proposed. The pre-scheduled missions are categorized into 3 types according to their launching times. The results show that our presented model out- performs all of the baseline models. Meanwhile, the qualitative results demonstrate that our model can accurately predict the hot spot in the future traffic map

4d4d steady gradient Ricci solitons with nonnegative curvature away from a compact set

In the paper, we analysis the asymptotic behavior of noncompact $\kappa$-noncollapsed steady gradient Ricci soliton $(M, g)$ with nonnegative curvature operator away from a compact set $K$ of $M$. In particular, we prove: any $4d$ noncompact $\kappa$-noncollapsed steady gradient Ricci soliton $(M^4, g)$ with nonnegative sectional curvature must be a Bryant Ricci soliton up to scaling if it admits a sequence of rescaled flows of $(M^4, g)$, which converges subsequently to a family of shrinking quotient cylinders.Comment: Proof of Proposition 4.1 has been modified. Also some typos are correcte

Mastering Efficiency: Leveraging Multihoming Boundary Resources for Mobile App Development

Platform complementors (third-party software developers) play a critical role in enriching platform ecosystems. As app development becomes more costly and time-consuming, complementors must strategically allocate scarce resources, which includes selecting the right platforms to target and identifying appropriate boundary resources, such as software development kits (SDKs). Although complementors may aim to maximize market reach by developing apps for different platforms (a practice known as multihoming), multihoming can potentially spread resources thinly across different app versions and compromise app quality. Multihoming SDKs offer a solution by enabling app deployment across multiple platforms using a single codebase. However, this approach can compromise app quality due to insufficient platform specificity. This research examines the impact of adopting multihoming SDKs on app quality, providing theoretical insights at the intersection of technical design and platform governance. In addition, it provides practical guidance for complementors to navigate trade-offs when aligning boundary resource selection with strategic goals

A Simulation Framework for Fast Design Space Exploration of Unmanned Air System Traffic Management Policies

The number of daily small Unmanned Aircraft Systems (sUAS) operations in uncontrolled low altitude airspace is expected to reach into the millions. UAS Traffic Management (UTM) is an emerging concept aiming at the safe and efficient management of such very dense traffic, but few studies are addressing the policies to accommodate such demand and the required ground infrastructure in suburban or urban environments. Searching for the optimal air traffic management policy is a combinatorial optimization problem with intractable complexity when the number of sUAS and the constraints increases. As the demands on the airspace increase and traffic patterns get complicated, it is difficult to forecast the potential low altitude airspace hotspots and the corresponding ground resource requirements. This work presents a Multi-agent Air Traffic and Resource Usage Simulation (MATRUS) framework that aims for fast evaluation of different air traffic management policies and the relationship between policy, environment and resulting traffic patterns. It can also be used as a tool to decide the resource distribution and launch site location in the planning of a next-generation smart city. As a case study, detailed comparisons are provided for the sUAS flight time, conflict ratio, cellular communication resource usage, for a managed (centrally coordinated) and unmanaged (free flight) traffic scenario.Comment: The Integrated Communications Navigation and Surveillance (ICNS) Conference in 201