LASER SHOCK IMPRINTING OF METALLIC NANOSTRUCTURES AND SHOCK PROCESSING OF LOW-DIMENSIONAL MATERIALS

Laser shock imprinting (LSI) is proposed and developed as a novel ultrafast room-temperature top-down technique for fabricating and tuning of plasmonic nanostructures, and processing of one-dimensional semiconductor nanowires and two-dimensional crystals. The technique utilizes a shock pressure generated by laser ablation of sacrificial materials. Compared with conventional technologies, LSI features ambient condition, good scalability, low cost and high efficiency

Health and economic benefits of building ventilation interventions for reducing indoor PM2.5 exposure from both indoor and outdoor origins in urban Beijing, China

China is confronted with serious PM2.5 pollution, especially in the capital city of Beijing. Exposure to PM2.5 could lead to various negative health impacts including premature mortality. As people spend most of their time indoors, the indoor exposure to PM2.5 from both indoor and outdoor origins constitutes the majority of personal exposure to PM2.5 pollution. Different building interventions have been introduced to mitigate indoor PM2.5 exposure, but always at the cost of energy expenditure. In this study, the health and economic benefits of different ventilation intervention strategies for reducing indoor PM2.5 exposure are modelled using a representative urban residence in Beijing, with consideration of different indoor PM2.5 emission strengths and outdoor pollution. Our modelling results show that the increase of envelope air-tightness can achieve significant economic benefits when indoor PM2.5 emissions are absent; however, if an indoor PM2.5 source is present, the benefits only increase slightly in mechanically ventilated buildings, but may show negative benefit without mechanical ventilation. Installing mechanical ventilation in Beijing can achieve annual economic benefits ranging from 200yuan/capita to 800yuan/capita if indoor PM2.5 sources exist. If there is no indoor emission, the annual benefits above 200yuan/capita can be achieved only when the PM2.5 filtration efficiency is no less than 90% and the envelope air-tightness is above Chinese National Standard Level 7. Introducing mechanical ventilation with low PM2.5 filtration efficiency to current residences in urban Beijing will increase the indoor PM2.5 exposure and result in excess costs to the resident

Information Systems-based Real Estate Macrocontrol Systems

With the continuous increase of marketization and normalization in the Chinese real estate market, the market mechanism now plays an important role in market regulation. The existing macro-control system for the real estate market, however, appears to lack the ability to regulate it. Thus, an effective and efficient information-oriented tool is needed to guide the development of China’s real estate market. The research reported herein constructs a new macro-control system for this market that is based on information systems, specifically, a real estate warning system, a confidence index system, and a simulation system. This paper first presents the framework of the new information systems-based macro-control system, and its functions are analyzed. The methods of constructing the system are then discussed. Based on these methods, the index systems of the respective information systems are established, and the main models are presented. Finally, a case study that is based on survey data from the Shenzhen real estate market is described to demonstrate the applicability of the new macrocontrol system.Real estate; Macro-control system; Warning system; Confidence index; System simulation

Ensemble methods for testing a global null

Testing a global null is a canonical problem in statistics and has a wide range of applications. In view of the fact that no uniformly most powerful test exists, prior and/or domain knowledge are commonly used to focus on a certain class of alternatives to improve the testing power. However, it is generally challenging to develop tests that are particularly powerful against a certain class of alternatives. In this paper, motivated by the success of ensemble learning methods for prediction or classification, we propose an ensemble framework for testing that mimics the spirit of random forests to deal with the challenges. Our ensemble testing framework aggregates a collection of weak base tests to form a final ensemble test that maintains strong and robust power for global nulls. We apply the framework to four problems about global testing in different classes of alternatives arising from Whole Genome Sequencing (WGS) association studies. Specific ensemble tests are proposed for each of these problems, and their theoretical optimality is established in terms of Bahadur efficiency. Extensive simulations and an analysis of a real WGS dataset are conducted to demonstrate the type I error control and/or power gain of the proposed ensemble tests