Statistical study of the influence of building architectural properties on scattered fields in urban environment

Statistical studies on the reflection coefficient of concrete–glass building facades are conducted using a fast and an accurate method based on the Green’s functions. The variation of different architectural parameters, such as concrete permitting and distribution, size and type of windows, are studied. The influence of selected parameters on the total reflection coefficient of the building is quantified for different incidence and observation angles as well as in different diffraction zones.     

β-Carotene and Ascorbic Acid Retention in Fresh and Processed Vegetables

Broccoli, carrots, and green beans (grown in 2 consecutive years) were randomly divided into 3 treatments: fresh-refrigerated (F-R), frozen (FZ) or canned (C) (carrots only). FZ or C vegetables were processed within 24 h and stored for up to 1 yr. F-R vegetables were held at 4 °C for 3 wk (broccoli and green beans) or 6 mo (carrots). Trans b-carotene (Tb-C) and total ascorbic acid (AA) were determined at specified times, before and after microwave cooking. Vitamin content differed between years due to environmental conditions. Blanching resulted in AA loss, but retention remained stable after freezing broccoli and green beans. F-R green beans lost >90% AA after 16 d storage. Linear decreases in AAwere found in most F-R or FZ vegetables. Tb-C decreased slightly during freezer storage. Reductions in Tb-C occurred in canned carrots. Microwave cooking had minimal effects on AA or Tb-C.     

eFeed-Hungers.com: Mitigating global hunger crisis using next generation technologies

Hunger is a global crisis and impacting the world for a very long time. Significant efforts have been made in the past by various organizations employing various means to address this persisting problem. Despite this, it still remains far from being resolved. Recently, the United Nations has published a report on global hunger, which claims that the global food production is fairly enough to feed the entire world with a population of about 7.3 billion. However, a major quantity of the food grown is not channeled appropriately and effectively to reach the needy and thus gets wasted unfortunately. Using the advanced computer technologies, we devise and develop a web-based computational framework (eFeed-Hungers.com) that serves as a bridge between the food waste and the hunger to mitigate the hunger issue; the food waste is the excess, unused, edible food, which otherwise is destined to the dumpster unfortunately. The eFeed-Hungers.com encourages and assists the food waste donation announcements with the least minimal efforts with the best possible outreach. Through the eFeed-Hungers.com, the food donations are globally searchable by the needy, with enough additional information imparted for quick decisions making to fetch the appropriate donated food. The eFeed-Hungers.com is envisioned to be a fully functional organization eventually with global outreach.     

Encrypted image classification based on multilayer extreme learning machine

Nowadays, numerous corporations (such as Google, Baidu, etc.) require an efficient and effective search algorithm to crawl out the images with queried objects from databases. Moreover, privacy protection is a significant issue such that confidential images must be encrypted in corporations. Nevertheless, decrypting and then classifying millions of encrypted images becomes a heavy burden to computation. In this paper, we proposed an encrypted image classification framework based on multi-layer extreme learning machine that is able to directly classify encrypted images without decryption. Experiments were conducted on popular handwritten digits and letters databases. Results demonstrate that the proposed framework is secure, efficient and accurate for classifying encrypted images.     

Monte Carlo cluster refinement for noise robust image segmentation

This paper explores a stochastic approach to refining clustering results for data with spatial-feature context such as images under the presence of noise. We formulate the clustering problem as a maximum a posteriori (MAP) problem, and refine clustering results using importance-weighted Monte Carlo posterior estimates based on between-neighborhood error statistics to account for local spatial-feature context within a global framework. This cluster refinement approach is non-iterative and can be integrated with existing clustering methods to achieve improved clustering performance for image segmentation under high noise scenarios. Experiments on synthetic gray-level images, real-world natural images, and real-world satellite synthetic aperture radar imagery illustrate the proposed method’s potential for improving clustering performance of existing clustering algorithms for image segmentation under high noise situations.     

An effective bilayer cathode buffer for highly efficient small molecule organic solar cells

Novel organic/ultrathin low work function metal bilayer cathode buffers for small molecule organic solar cells are proposed. Ultrathin low work function metal layers possess a high built-in electric field for effective carrier extraction and a high cathode reflectivity for maximum absorption in the photoactive layers. This leads to a significant increase of short circuit current density and fill factor of cells. By integrating this bilayer cathode buffer with DTDCTB:C₆₀ small molecular heterojunction, the device exhibits a high power conversion efficiency of up to 5.28%, which is an improvement of 22% compared to a device with a traditional single organic layer buffer.     

Automated segmentation, classification, and tracking of cancer cell nuclei in time-lapse microscopy

Quantitative measurement of cell cycle progression in individual cells over time is important in understanding drug treatment effects on cancer cells. Recent advances in time-lapse fluorescence microscopy imaging have provided an important tool to study the cell cycle process under different conditions of perturbation. However, existing computational imaging methods are rather limited in analyzing and tracking such time-lapse datasets, and manual analysis is unreasonably time-consuming and subject to observer variances. This paper presents an automated system that integrates a series of advanced analysis methods to fill this gap. The cellular image analysis methods can be used to segment, classify, and track individual cells in a living cell population over a few days. Experimental results show that the proposed method is efficient and effective in cell tracking and phase identification.     

Optimization of photomask design for reducing aberration-induced placement error

In semiconductor manufacturing, the accurate placement of circuit components ensures the proper functioning of microelectronic circuits. This is often subject to photolithography, an optical technique that transfers circuit patterns from photomasks to silicon wafers. Sources of placement error include aberration and misalignment between different levels, and we focus on the former. Aberration is an optical phenomenon that often degrades imaging system performance. Since aberration differs from one imaging system to another, a photomask design that minimizes the aberration-induced placement error is desired. In this paper, we discuss the optimization process of a general one-dimensional mask pattern under a general illumination condition. The constraint is a known population mean of the root mean square aberrations for the imaging systems under consideration. To apply the theory, we search for the optimal parameters for two common mask designs: alternating phase-shifting masks (PSMs) and attenuated PSMs. The theoretical results are compared with those from a Monte Carlo analysis on a large set of imaging systems. These results are indicative to mask manufacturers and circuit designers of increasing manufacturability of circuits.