Bright betatron x-ray radiation from a laser-driven-clustering gas target

Hard X-ray sources from femtosecond (fs) laser-produced plasmas, including the betatron X-rays from laser wakefield-accelerated electrons, have compact sizes, fs pulse duration and fs pump-probe capability, making it promising for wide use in material and biological sciences. Currently the main problem with such betatron X-ray sources is the limited average flux even with ultra-intense laser pulses. Here, we report ultra-bright betatron X-rays can be generated using a clustering gas jet target irradiated with a small size laser, where a ten-fold enhancement of the X-ray yield is achieved compared to the results obtained using a gas target. We suggest the increased X-ray photon is due to the existence of clusters in the gas, which results in increased total electron charge trapped for acceleration and larger wiggling amplitudes during the acceleration. This observation opens a route to produce high betatron average flux using small but high repetition rate laser facilities for applications

Compressing web Geodata for real-time environmental applications

The advent of connected mobile devices has caused an unprecedented availability of geo-referenced user-generated content, which can be exploited for environment monitoring. In particular, Augmented Reality (AR) mobile applications can be designed to enable citizens collect observations, by overlaying relevant meta-data on their current view. This class of applications rely on multiple meta-data, which must be properly compressed for transmission and real-time usage. This paper presents a two-stage approach for the compression of Digital Elevation Model (DEM) data and geographic entities for a mountain environment monitoring mobile AR application. The proposed method is generic and could be applied to other types of geographical data

Learning curves and long-term outcome of simulation-based thoracentesis training for medical students

<p>Abstract</p> <p>Background</p> <p>Simulation-based medical education has been widely used in medical skills training; however, the effectiveness and long-term outcome of simulation-based training in thoracentesis requires further investigation. The purpose of this study was to assess the learning curve of simulation-based thoracentesis training, study skills retention and transfer of knowledge to a clinical setting following simulation-based education intervention in thoracentesis procedures.</p> <p>Methods</p> <p>Fifty-two medical students were enrolled in this study. Each participant performed five supervised trials on the simulator. Participant's performance was assessed by performance score (PS), procedure time (PT), and participant's confidence (PC). Learning curves for each variable were generated. Long-term outcome of the training was measured by the retesting and clinical performance evaluation 6 months and 1 year, respectively, after initial training on the simulator.</p> <p>Results</p> <p>Significant improvements in PS, PT, and PC were noted among the first 3 to 4 test trials (p < 0.05). A plateau for PS, PT, and PC in the learning curves occurred in trial 4. Retesting 6 months after training yielded similar scores to trial 5 (p > 0.05). Clinical competency in thoracentesis was improved in participants who received simulation training relative to that of first year medical residents without such experience (p < 0.05).</p> <p>Conclusions</p> <p>This study demonstrates that simulation-based thoracentesis training can significantly improve an individual's performance. The saturation of learning from the simulator can be achieved after four practice sessions. Simulation-based training can assist in long-term retention of skills and can be partially transferred to clinical practice.</p

Quick and sensitive determination of gene expression of fatty acid synthase in vitro by using real-time polymerase chain reaction amplification (PCR)

Obesity results from an imbalance between energy intake and energy expenditure, which leads to a pathological accumulation of adipose tissue, but the underlying mechanism at gene level, is far from being elucidated. The objective of this study was to investigate the correlation between mRNA express from fatty acid synthase (FAS) with a different glucose level in primary adipocytes by real-time polymerase chain reaction amplification (PCR), which can aid in the understanding of the mechanism of obesity in vitro. By using the following formula, this study was able to quantify the mRNA expression of FAS of unknown samples: Y = -3.156X + 41.21 (Y = threshold cycle, X = log starting quantity). The high concentrations of glucose group significantly improved the mRNA expression of FAS (P &lt; 0.01) rather than 0.25 and 0% concentrations of glucose. These results provide significant data that confirm an association between different glucose level and FAS expression in preadipocytes. The glucose concentration of the high group substantially augmented the mRNA expression of FAS.Key words: Expression, fatty acid synthase, lipid deposition, real-time polymerase chain reaction amplification (PCR)

Uncorking the potential of wine language for young wine tourists

Effective communication with consumers underpins growth in wine knowledge that, in turn, contributes to growth in wine consumption. Indeed, tasting notes may enhance consumers’ experiences of wine. Yet wine language is full of fuzzy concepts. In this chapter, we consider the language used to talk about wine, specifically the humanlike features of wine (e.g., wine is described as honest, sexy, shy, or brooding). We demonstrate that metaphoric language is integral to the experience of wine and influences consumer behaviour. We discuss practical implications for the cellar door experience, and for effective and ethical wine communication. We conclude that metaphoric language is a pedagogical and cultural platform for engaging younger wine tourists in the cellar door experience, which is a significant revenue source for micro, small, and medium wineries

The trade-off between taxi time and fuel consumption in airport ground movement

Environmental impact is a very important agenda item in many sectors nowadays, which the air transportation sector is also trying to reduce as much as possible. One area which has remained relatively unexplored in this context is the ground movement problem for aircraft on the airport’s surface. Aircraft have to be routed from a gate to a runway and vice versa and it is still unknown whether fuel burn and environmental impact reductions will best result from purely minimising the taxi times or whether it is also important to avoid multiple acceleration phases. This paper presents a newly developed multi-objective approach for analysing the trade-off between taxi time and fuel consumption during taxiing. The approach consists of a combination of a graph-based routing algorithm and a population adaptive immune algorithm to discover different speed profiles of aircraft. Analysis with data from a European hub airport has highlighted the impressive performance of the new approach. Furthermore, it is shown that the trade-off between taxi time and fuel consumption is very sensitive to the fuel-related objective function which is used

Comparison of techniques for handling missing covariate data within prognostic modelling studies: a simulation study

Background: There is no consensus on the most appropriate approach to handle missing covariate data within prognostic modelling studies. Therefore a simulation study was performed to assess the effects of different missing data techniques on the performance of a prognostic model. Methods: Datasets were generated to resemble the skewed distributions seen in a motivating breast cancer example. Multivariate missing data were imposed on four covariates using four different mechanisms; missing completely at random (MCAR), missing at random (MAR), missing not at random (MNAR) and a combination of all three mechanisms. Five amounts of incomplete cases from 5% to 75% were considered. Complete case analysis (CC), single imputation (SI) and five multiple imputation (MI) techniques available within the R statistical software were investigated: a) data augmentation (DA) approach assuming a multivariate normal distribution, b) DA assuming a general location model, c) regression switching imputation, d) regression switching with predictive mean matching (MICE-PMM) and e) flexible additive imputation models. A Cox proportional hazards model was fitted and appropriate estimates for the regression coefficients and model performance measures were obtained. Results: Performing a CC analysis produced unbiased regression estimates, but inflated standard errors, which affected the significance of the covariates in the model with 25% or more missingness. Using SI, underestimated the variability; resulting in poor coverage even with 10% missingness. Of the MI approaches, applying MICE-PMM produced, in general, the least biased estimates and better coverage for the incomplete covariates and better model performance for all mechanisms. However, this MI approach still produced biased regression coefficient estimates for the incomplete skewed continuous covariates when 50% or more cases had missing data imposed with a MCAR, MAR or combined mechanism. When the missingness depended on the incomplete covariates, i.e. MNAR, estimates were biased with more than 10% incomplete cases for all MI approaches. Conclusion: The results from this simulation study suggest that performing MICE-PMM may be the preferred MI approach provided that less than 50% of the cases have missing data and the missing data are not MNAR

Shape-induced force fields in optical trapping

Advances in optical tweezers, coupled with the proliferation of two-photon polymerization systems, mean that it is now becoming routine to fabricate and trap non-spherical particles. The shaping of both light beams and particles allows fine control over the flow of momentum from the optical to mechanical regimes. However, understanding and predicting the behaviour of such systems is highly complex in comparison with the traditional optically trapped microsphere. In this Article, we present a conceptually new and simple approach based on the nature of the optical force density. We illustrate the method through the design and fabrication of a shaped particle capable of acting as a passive force clamp, and we demonstrate its use as an optically trapped probe for imaging surface topography. Further applications of the design rules highlighted here may lead to new sensors for probing biomolecule mechanics, as well as to the development of optically actuated micromachines

Refractive index in holographic superconductors

With the probe limit, we investigate the behavior of the electric permittivity and effective magnetic permeability and related optical properties in the s-wave holographic superconductors. In particular, our result shows that unlike the strong coupled systems which admit a gravity dual of charged black holes in the bulk, the electric permittivity and effective magnetic permeability are unable to conspire to bring about the negative Depine-Lakhtakia index at low frequencies, which implies that the negative phase velocity does not appear in the holographic superconductors under such a situation.Comment: JHEP style, 1+15 pages, 11 figures, version to appear in JHE

An Intelligent Context Aware Recommender System for Real-Estate

Finding products and items in large online space that meet user needs is difficult. Time spent searching before finding a relevant item can be a significant time sink for users. As with other economic branches, growing Internet usage also changed user behavior in the real-estate market. Advancements in virtual reality offer virtual tours and interactive map and floor plans which make an online rental websites very popular among users. With the abundance of information, recommender systems become more important than ever to give the user relevant property suggestions and reduce search time. A sophisticated recommender in this domain can help reduce the need of a real-estate agent. Session-based user behavior and lack of user profiles leads to the use of traditional recommendation methods. In this research, we propose an approach for real-estate recommendation based on Gated Orthogonal Recurrent Unit (GORU) and Weighted Cosine Similarity. GORU captures the user search context and weighted cosine similarity improves the rank of pertinent property. We have used the data of an online public real estate web portal (AARZ.PK). The data represents the original behavior of the user on an online portal. We have used Recall, User coverage and Mean Reciprocal Rank (MRR) metrics for the evaluation of our system against other state-of-the-art techniques. The proposed solution outperforms various baselines and state-of-the-art RNN based solutions