A Wafer-Bonded, Floating Element Shear-Stress Sensor Using a Geometric Moire Optical Transduction Technique

This paper presents a geometric Moir optical-based floating-element shear stress sensor for wind tunnel turbulence measurements. The sensor was fabricated using an aligned wafer-bond/thin-back process producing optical gratings on the backside of a floating element and on the top surface of the support wafer. Measured results indicate a static sensitivity of 0.26 microns/Pa, a resonant frequency of 1.7 kHz, and a noise floor of 6.2 mPa/(square root)Hz

Free-standing graphene films embedded in epoxy resin with enhanced thermal properties

The poor thermal conductivity of polymer composites has long been a deterrent to their increased use in high-end aerospace or defence applications. This study describes a new approach for the incorporation of graphene in an epoxy resin, through the addition of graphene as free-standing film in the polymeric matrix. The electrical and thermal conductivity of composites embedding two different free-standing graphene films was compared to composites with embedded carbon nanotube buckypapers (CNT-BP). Considerably higher thermal conductivity values than those achieved with conventional dispersing methods of graphene or CNTs in epoxy resins were obtained. The characterisation was complemented with a study of the structure at the microscale by cross-sectional scanning electron microscopy (SEM) images and a thermogravimetric analysis (TGA). The films are preconditioned in order to incorporate them into the composites, and the complete manufacturing process proposed allows the production and processing of these materials in large batches. The high thermal conductivity obtained for the composites opens the way for their use in demanding thermal management applications, such as electronic enclosures or platforms facing critical temperature loads.European Defence Agency tender No 17.ESI.OP.066. Study on the Impact of Graphene on Defence Application

The Extracellular Matrix and Blood Vessel Formation: Not Just a Scaffold

The extracellular matrix plays a number of important roles, among them providing structural support and information to cellular structures such as blood vessels imbedded within it. As more complex organisms have evolved, the matrix ability to direct signalling towards the vasculature and remodel in response to signalling from the vasculature has assumed progressively greater importance. This review will focus on the molecules of the extracellular matrix, specifically relating to vessel formation and their ability to signal to the surrounding cells to initiate or terminate processes involved in blood vessel formation

Large-scale unit commitment under uncertainty: an updated literature survey

The Unit Commitment problem in energy management aims at finding the optimal production schedule of a set of generation units, while meeting various system-wide constraints. It has always been a large-scale, non-convex, difficult problem, especially in view of the fact that, due to operational requirements, it has to be solved in an unreasonably small time for its size. Recently, growing renewable energy shares have strongly increased the level of uncertainty in the system, making the (ideal) Unit Commitment model a large-scale, non-convex and uncertain (stochastic, robust, chance-constrained) program. We provide a survey of the literature on methods for the Uncertain Unit Commitment problem, in all its variants. We start with a review of the main contributions on solution methods for the deterministic versions of the problem, focussing on those based on mathematical programming techniques that are more relevant for the uncertain versions of the problem. We then present and categorize the approaches to the latter, while providing entry points to the relevant literature on optimization under uncertainty. This is an updated version of the paper "Large-scale Unit Commitment under uncertainty: a literature survey" that appeared in 4OR 13(2), 115--171 (2015); this version has over 170 more citations, most of which appeared in the last three years, proving how fast the literature on uncertain Unit Commitment evolves, and therefore the interest in this subject

Progressive hemorrhage and myotoxicity induced by echis carinatus venom in murine model: neutralization by inhibitor cocktail of n,n,n `,n `-tetrakis (2-pyridylmethyl) ethane-1,2-diamine and silymarin

Viperbite is often associated with severe local toxicity, including progressive hemorrhage and myotoxicity, persistent even after the administration of anti-snake venom (ASV). In the recent past, investigations have revealed the orchestrated actions of Zn2+ metalloproteases (Zn(2+)MPs), phospholipase A(2)s (PLA(2)s) and hyaluronidases (HYs) in the onset and progression of local toxicity from the bitten site. As a consequence, venom researchers and medical practitioners are in deliberate quest of potent molecules alongside ASV to tackle the brutal local manifestations induced by aforesaid venom toxins. Based on these facts, we have demonstrated the protective efficacy of inhibitor cocktail containing equal ratios of N,N,N', N'-tetrakis (2-pyridylmethyl) ethane-1,2-diamine (TPEN) and silymarin (SLN) against progressive local toxicity induced by Echis carinatus venom (ECV). In our previous study we have shown the inhibitory potentials of TPEN towards Zn(2+)MPs of ECV (IC50: 6.7 mu M). In this study we have evaluated in vitro inhibitory potentials of SLN towards PLA(2)s (IC50: 12.5 mu M) and HYs (IC50: 8 mu M) of ECV in addition to docking studies. Further, we have demonstrated the protection of ECV induced local toxicity with 10 mM inhibitor cocktail following 15, 30 min (for hemorrhage and myotoxicity); 60 min (for hemorrhage alone) of ECV injection in murine model. The histological examination of skin and thigh muscle sections taken out from the site of ECV injection substantiated the overall protection offered by inhibitor cocktail. In conclusion, the protective efficacy of inhibitor cocktail is of high interest and can be administered locally alongside ASV to treat severe local toxicity

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency–Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe