Flexible poly(styrene-ethylene-butadiene-styrene) hybrid nanofibers for bioengineering and water filtration applications

Among the thermoplastic elastomers that play important roles in the polymer industry due to their superior properties, styrene-based species and polyurethane block copolymers are of great interest. Poly(styrene-ethylene-butadiene-styrene) (SEBS) as a triblock copolymer seems to have the potential to meet many demands in different applications due to various industrial requirements where durability, biocompatibility, breaking elongation, and interfacial adhesion are important. In this study, the SEBS triblock copolymer was functionalized with natural (Satureja hortensis, SH) and synthetic (nanopowder, TiO₂) agents to obtain composite nanofibers by electrospinning and electrospraying methods for use in biomedical and water filtration applications. The results were compared with thermoplastic polyurethane (TPU) composite nanofibers, which are commonly used in these fields. Here, functionalized SEBS nanofibers exhibited antibacterial effect while at the same time improving cell viability. In addition, because of successful water filtration by using the SEBS composite nanofibers, the material may have a good potential to be used comparably to TPU for the application.     

Reliability Study of Loss of Off-Site Power Supply

Probabilistic parameters related to the total loss of off-site power at the Chalk River Nuclear Laboratories (CRNL) are derived from site specific data. Because of weather related seasonal dependency a periodic power failure frequency model has been adopted.     

The ChaLearn gesture dataset (CGD 2011)

This paper describes the data used in the ChaLearn gesture challenges that took place in 2011/2012, whose results were discussed at the CVPR 2012 and ICPR 2012 conferences. The task can be described as: user-dependent, small vocabulary, fixed camera, one-shot-learning. The data include 54,000 hand and arm gestures recorded with an RGB-D \(\hbox {Kinect}^\mathrm{TM}\) camera. The data are organized into batches of 100 gestures pertaining to a small gesture vocabulary of 8–12 gestures, recorded by the same user. Short continuous sequences of 1–5 randomly selected gestures are recorded. We provide man-made annotations (temporal segmentation into individual gestures, alignment of RGB and depth images, and body part location) and a library of function to preprocess and automatically annotate data. We also provide a subset of batches in which the user’s horizontal position is randomly shifted or scaled. We report on the results of the challenge and distribute sample code to facilitate developing new solutions. The data, datacollection software and the gesture vocabularies are downloadable from http://gesture.chalearn.org. We set up a forum for researchers working on these data http://groups.google.com/group/gesturechallenge.     

Executive Perspectives on Top Risks for 2015

This article summarizes the results of Executive Perspectives on Top Risks for 2015, a study conducted by North Carolina State University’s ERM Initiative and Protiviti. For more information, visit www.protiviti.com/TopRisks.     

Multimodal Intelligent Eye-Gaze Tracking System

This article presents a series of user studies to develop a new eye-gaze tracking–based pointing system. We developed a new target prediction model that works for different input modalities and combined the eye-gaze tracking–based pointing with a joystick controller that can reduce pointing and selection times. The system finds important applications in cockpit of combat aircraft and for computer novice users. User studies confirmed that users can perform significantly faster using this new eye-gaze tracking–based system for both military and everyday computing tasks compared to existing input devices. As part of the study it was also found that the amplitude of maximum power component obtained through Fourier Transform of pupil signal significantly correlates with selection times and perceived cognitive load of users in terms of Task Load Index scores.     

Technology transfer in digital mammography. Report of the Joint National Cancer Institute-National Aeronautics and Space Administration workshop of May 19-20, 1993

Digital mammography is one of the most promising novel technologies for further improvement of early detection of breast cancer, offering important potential advantages: 1) improved image quality; 2) digital image processing for improved lesion contrast; 3) computer-aided diagnosis for enhanced radiologic interpretation; and 4) teleradiology for facilitated radiologic consultation. The Diagnostic Imaging Research Branch of the National Cancer Institute (NCI) recently funded an international, multidisciplinary, multi-institutional Digital Mammography Development Group for collaborations between NCI, the academic community, and industry to facilitate the integrated development and implementation of digital mammographic systems. Currently, however, digital mammography faces a number of fundamental technological roadblocks: 1) cost-effective digital detectors and displays for imaging systems; 2) the need for novel algorithms for image processing and computer-aided diagnosis; and 3) high performance, low cost digital networks to provide an "information superhighway" for teleradiology. To solve some of these technological problems, the Diagnostic Imaging Research Branch of NCI joined efforts with the Technology Transfer Division of the National Aeronautics and Space Administration to pursue a federal technology transfer program in digital mammography. The authors discuss the findings and recommendations of the workshop entitled "Technology Transfer in Digital Mammography," which was organized and held jointly by the NCI and the National Aeronautics and Space Administration in May, 1993. Numerous innovative technologies of varying degree of promise for digital mammography were presented at the conference. In this article, specific technologies presented at the workshop by the federal and federally-supported laboratories are described, and critiques of these technologies by the leaders of the medical imaging community are presented.     

Principal motion components for one-shot gesture recognition

This paper introduces principal motion components (PMC), a new method for one-shot gesture recognition. In the considered scenario a single training video is available for each gesture to be recognized, which limits the application of traditional techniques (e.g., HMMs). In PMC, a 2D map of motion energy is obtained per each pair of consecutive frames in a video. Motion maps associated to a video are processed to obtain a PCA model, which is used for recognition under a reconstruction-error approach. The main benefits of the proposed approach are its simplicity, easiness of implementation, competitive performance and efficiency. We report experimental results in one-shot gesture recognition using the ChaLearn Gesture Dataset; a benchmark comprising more than 50,000 gestures, recorded as both RGB and depth video with a Kinect?camera. Results obtained with PMC are competitive with alternative methods proposed for the same data set.