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JavaScript is the most popular programming language for web applications. Static analysis of JavaScript applications is highly challenging due to its dynamic language constructs and event-driven asynchronous executions, which also give rise to many security-related bugs. Several static analysis tools to detect such bugs exist, however, research has not yet reported much on the precision and scalability trade-off of these analyzers. As a further obstacle, JavaScript programs structured in Node. js modules need to be collected for analysis, but existing bundlers are either specific to their respective analysis tools or not particularly suitable for static analysis.
As a potentially toxic agent on nervous system and bone, the safety of aluminium exposure from adjuvants in vaccines and subcutaneous immune therapy (SCIT) products has to be continuously reevaluated, especially regarding concomitant administrations. For this purpose, knowledge on absorption and disposition of aluminium in plasma and tissues is essential. Pharmacokinetic data after vaccination in humans, however, are not available, and for methodological and ethical reasons difficult to obtain. To overcome these limitations, we discuss the possibility of an in vitro-in silico approach combining a toxicokinetic model for aluminium disposition with biorelevant kinetic absorption parameters from adjuvants. We critically review available kinetic aluminium-26 data for model building and, on the basis of a reparameterized toxicokinetic model (Nolte et al., 2001), we identify main modelling gaps. The potential of in vitro dissolution experiments for the prediction of intramuscular absorption kinetics of aluminium after vaccination is explored. It becomes apparent that there is need for detailed in vitro dissolution and in vivo absorption data to establish an in vitro-in vivo correlation (IVIVC) for aluminium adjuvants. We conclude that a combination of new experimental data and further refinement of the Nolte model has the potential to fill a gap in aluminium risk assessment. (C) 2017 Elsevier Inc. All rights reserved.
Previous work has shown that surface modification with orthophosphoric acid can significantly enhance the charge stability on polypropylene (PP) surface by generating deeper traps. In the present study, thermally stimulated potential-decay measurements revealed that the chemical treatment may also significantly increase the number of available trapping sites on the surface. Thus, as a consequence, the so-called "cross-over" phenomenon, which is observed on as-received and thermally treated PP electrets, may be overcome in a certain range of initial charge densities. Furthermore, the discharge behavior of chemically modified samples indicates that charges can be injected from the treated surface into the bulk, and/or charges of opposite polarity can be pulled from the rear electrode into the bulk at elevated temperatures and at the high electric fields that are caused by the deposited charges. In the bulk, a lack of deep traps causes rapid charge decay already in the temperature range around 95 degrees C.
The maximum entropy method is used to predict flows on water distribution networks. This analysis extends the water distribution network formulation of Waldrip et al. (2016) Journal of Hydraulic Engineering (ASCE), by the use of a continuous relative entropy defined on a reduced parameter set. This reduction in the parameters that the entropy is defined over ensures consistency between different representations of the same network. The performance of the proposed reduced parameter method is demonstrated with a one-loop network case study.
The maximum entropy method is used to derive an alternative gravity model for a transport network. The proposed method builds on previous methods which assign the discrete value of a maximum entropy distribution to equal the traffic flow rate. The proposed method however, uses a distribution to represent each flow rate. The proposed method is shown to be able to handle uncertainty in a more elegant way and give similar results to traditional methods. It is able to incorporate more of the observed data through the entropy function, prior distribution and integration limits potentially allowing better inferences to be made.
The nature restoration project ‘Lenzener Elbtalaue’, realised from 2002 to 2011 at the river Elbe, included the first large scale dike relocation in Germany (420 ha). Its aim was to initiate the development of endangered natural wetland habitats and processes, accompanied by greater biodiversity in the former grassland dominated area. The monitoring of spatial and temporal variations of soil moisture in this dike relocation area is therefore particularly important for estimating the restoration success. The topsoil moisture monitoring from 1990 to 2017 is based on the Soil Moisture Index (SMI)1 derived with the triangle method2 by use of optical remotely sensed data: land surface temperature and Normalized Differnce Vegetation Index are calculated from Landsat 4/5/7/8 data and atmospheric corrected by use of MODIS data. Spatial and temporal soil moisture variations in the restored area of the dike relocation are compared to the agricultural and pasture area behind the new dike. Ground truth data in the dike relocation area was obtained from field measurements in October 2017 with a FDR device. Additionally, data from a TERENO soil moisture sensor network (SoilNet) and mobile cosmic ray neutron sensing (CRNS) rover measurements are compared to the results of the triangle method for a region in the Harz Mountains (Germany). The SMI time series illustrates, that the dike relocation area has become significantly wetter between 1990 and 2017, due to restructuring measurements. Whereas the SMI of the dike hinterland reflects constant and drier conditions. An influence of climate is unlikely. However, validation of the dimensionless index with ground truth measurements is very difficult, mostly due to large differences in scale.
Editorial
(2019)
Background: Evidence that home telemonitoring (HTM) for patients with chronic heart failure (CHF) offers clinical benefit over usual care is controversial as is evidence of a health economic advantage. Therefore the CardioBBEAT trial was designed to prospectively assess the health economic impact of a dedicated home monitoring system for patients with CHF based on actual costs directly obtained from patients’ health care providers.
Methods: Between January 2010 and June 2013, 621 patients (mean age 63,0 ± 11,5 years, 88 % male) with a confirmed diagnosis of CHF (LVEF ≤ 40 %) were enrolled and randomly assigned to two study groups comprising usual care with and without an interactive bi-directional HTM (Motiva®). The primary endpoint was the Incremental Cost-Effectiveness Ratio (ICER) established by the groups’ difference in total cost and in the combined clinical endpoint “days alive and not in hospital nor inpatient care per potential days in study” within the follow up of 12 months. Secondary outcome measures were total mortality and health related quality of life (SF-36, WHO-5 and KCCQ).
Results: In the intention-to-treat analysis, total mortality (HR 0.81; 95% CI 0.45 – 1.45) and days alive and not in hospital (343.3 ± 55.4 vs. 347.2 ± 43.9; p = 0.909) were not significantly different between HTM and usual care. While the resulting primary endpoint ICER was not positive (-181.9; 95% CI −1626.2 ± 1628.9), quality of life assessed by SF-36, WHO-5 and KCCQ as a secondary endpoint was significantly higher in the HTW group at 6 and 12 months of follow-up.
Conclusions: The first simultaneous assessment of clinical and economic outcome of HTM in patients with CHF did not demonstrate superior incremental cost effectiveness compared to usual care. On the other hand, quality of life was improved. It remains open whether the tested HTM solution represents a useful innovative approach in the recent health care setting.
The keynote article (Mayberry & Kluender, 2017) makes an important contribution to questions concerning the existence and characteristics of sensitive periods in language acquisition. Specifically, by comparing groups of non-native L1 and L2 signers, the authors have been able to ingeniously disentangle the effects of maturation from those of early language exposure. Based on L1 versus L2 contrasts, the paper convincingly argues that L2 learning is a less clear test of sensitive periods. Nevertheless, we believe Mayberry and Kluender underestimate the evidence for maturational factors in L2 learning, especially that coming from recent research.
The Gradient Symbolic Computation (GSC) model presented in the keynote article (Goldrick, Putnam & Schwarz) constitutes a significant theoretical development, not only as a model of bilingual code-mixing, but also as a general framework that brings together symbolic grammars and graded representations. The authors are to be commended for successfully integrating a theory of grammatical knowledge with the voluminous research on lexical co-activation in bilinguals. It is, however, unfortunate that a certain conception of bilingualism was inherited from this latter research tradition, one in which the contrast between native and non-native language takes a back seat.
Audit - and then what?
(2019)
Current trends such as digital transformation, Internet of Things, or Industry 4.0 are challenging the majority of learning factories. Regardless of whether a conventional learning factory, a model factory, or a digital learning factory, traditional approaches such as the monotonous execution of specific instructions don‘t suffice the learner’s needs, market requirements as well as especially current technological developments. Contemporary teaching environments need a clear strategy, a road to follow for being able to successfully cope with the changes and develop towards digitized learning factories. This demand driven necessity of transformation leads to another obstacle: Assessing the status quo and developing and implementing adequate action plans. Within this paper, details of a maturity-based audit of the hybrid learning factory in the Research and Application Centre Industry 4.0 and a thereof derived roadmap for the digitization of a learning factory are presented.