Optimal surface profile design of deployable mesh reflectors via a force density strategy

Based on a force density method coupled with optimal design of node positions, a novel approach for optimal surface profile design of mesh reflectors is presented. Uniform tension is achieved by iterations on coefficients of force density. The positions of net nodes are recalculated in each iteration so that the faceting RMS error of the reflector surface is minimized. Applications of both prime focus and offset configurations are demonstrated. The simulation results show the effectiveness of the proposed approach

From anionic porous organic frameworks with phosphate functional groups to a mesoporous aluminosilica material with crystalline pore walls

Porous materials are of expanding scientific interest because of their ability to interact with atoms, ions, and molecules at their large interior pore surfaces at nanoscale throughout the bulk of the material. In this dissertation, a series of microporous materials and a mesoporous material are discussed.In the first part, the synthesis of new anionic porous organic frameworks with phosphate groups (PA-POFs) through Yamamoto coupling reactions is presented. These materials have high adsorption capacity and exceptional adsorption kinetics towards bisphenol A (BPA) in water, a wide-spread organic pollutant and a known endocrine disruptor. The maximum adsorption capacity of BPA at equilibrium is found as high as 3,366 mg g-1 by Langmuir adsorption model, which is more than ten times greater than peer materials. The polymer also rapidly removes various other organic micropollutants with more than 90% removal efficiencies. In addition, the PA-POF material can be regenerated at least five times by mild washing using methanol without significant loss in removal efficiency. The CO2 adsorption capacity of these materials were also studied. The carbon dioxide uptake capacities of the PA-POFs reach up to 103 cc g-1 (1.0 bar and 273 K), making them promising candidates for CO2 capture.In the second part, the crystallization of an amorphous Al-SBA-15 material under a strong flux of Li+ with the retention of mesostructure is discussed. The crystalline lithium aluminosilicate (LAS) material was fully characterized by XRD, solid state NMR, TEM, SAXS, ICP-OES and EDS. The acidity and ion-exchange properties are studied

Why People Search for Images using Web Search Engines

What are the intents or goals behind human interactions with image search engines? Knowing why people search for images is of major concern to Web image search engines because user satisfaction may vary as intent varies. Previous analyses of image search behavior have mostly been query-based, focusing on what images people search for, rather than intent-based, that is, why people search for images. To date, there is no thorough investigation of how different image search intents affect users' search behavior. In this paper, we address the following questions: (1)Why do people search for images in text-based Web image search systems? (2)How does image search behavior change with user intent? (3)Can we predict user intent effectively from interactions during the early stages of a search session? To this end, we conduct both a lab-based user study and a commercial search log analysis. We show that user intents in image search can be grouped into three classes: Explore/Learn, Entertain, and Locate/Acquire. Our lab-based user study reveals different user behavior patterns under these three intents, such as first click time, query reformulation, dwell time and mouse movement on the result page. Based on user interaction features during the early stages of an image search session, that is, before mouse scroll, we develop an intent classifier that is able to achieve promising results for classifying intents into our three intent classes. Given that all features can be obtained online and unobtrusively, the predicted intents can provide guidance for choosing ranking methods immediately after scrolling