Under the Umbrella: Promoting Public Access to the Law

People need to know the law and have access to the law. Allowing copyright claims in “the law” can lead to severe restrictions on public knowledge and access. This article reviews court decisions spanning three centuries that have upheld the people’s needs over the proprietary rights of copyright holders. The review includes a discussion and analysis of three recent decisions that are under the umbrella of the principle that members of the public need unfettered access to the law. The Supreme Court in Georgia v. Public.Resource.Org reaffirmed and further refined the government edicts doctrine which holds that government edicts are not copyrightable. While this decision was important for ensuring access to the law, it did not address all circumstances. The decision in International Code Council v. UpCodes considered the status of model codes adopted into law. It was the first decision to consider the application of the holding in Georgia v. Public.Resource.Org to privately created works adopted into law. Meanwhile, the decision in American Society for Testing and Materials v. Public.Resource.Org addressed how fair use could apply to privately drafted standards incorporated into law. In addition to exploring and assessing these recent decisions, this article undertakes a deeper review of historical decisions that supported access to the law by overriding copyright claims. Included is a review of documents that reveal a hidden player, as well as additional facts, in the Banks v. Manchester litigation. The article also discusses open issues and concerns regarding whether “the law” can be copyrighted. Supporting unfettered access to the law and the public benefits that flow therefrom should outweigh copyright concerns

Inductive queries for a drug designing robot scientist

It is increasingly clear that machine learning algorithms need to be integrated in an iterative scientific discovery loop, in which data is queried repeatedly by means of inductive queries and where the computer provides guidance to the experiments that are being performed. In this chapter, we summarise several key challenges in achieving this integration of machine learning and data mining algorithms in methods for the discovery of Quantitative Structure Activity Relationships (QSARs). We introduce the concept of a robot scientist, in which all steps of the discovery process are automated; we discuss the representation of molecular data such that knowledge discovery tools can analyse it, and we discuss the adaptation of machine learning and data mining algorithms to guide QSAR experiments

Bayesian optimization using sequential Monte Carlo

We consider the problem of optimizing a real-valued continuous function $f$ using a Bayesian approach, where the evaluations of $f$ are chosen sequentially by combining prior information about $f$, which is described by a random process model, and past evaluation results. The main difficulty with this approach is to be able to compute the posterior distributions of quantities of interest which are used to choose evaluation points. In this article, we decide to use a Sequential Monte Carlo (SMC) approach

The effectiveness of conceptual airport terminal designs

It is forecast that there will be a large growth in air traffic over the next decade or so and to accommodate this will require investment in airport infrastructure including terminals. These buildings represent large, lumpy investments so it is important to provide the capacity to accommodate the forecast traffic. However, this depends on at least two factors; the accuracy of the forecast of future demand and the process of translating these forecasts into designs. Errors in either of these can be financially catastrophic. The latter of these two factors depend on “rules of thumb” formulae that convert design hour flows into area requirements for each terminal facility. This paper will look in detail at the process of translating demand forecasts into conceptual terminal designs. The basic methods that are used will be outlined and how they affect the conceptual terminal design process will be revealed. It will be shown that even if demand forecasts can be taken to be completely accurate, there can still be errors in terminal design and size resulting from the use of these “rules of thumb.

The Effective Potential, the Renormalisation Group and Vacuum Stability

We review the calculation of the the effective potential with particular emphasis on cases when the tree potential or the renormalisation-group-improved, radiatively corrected potential exhibits non-convex behaviour. We illustrate this in a simple Yukawa model which exhibits a novel kind of dimensional transmutation. We also review briefly earlier work on the Standard Model. We conclude that, despite some recent claims to the contrary, it can be possible to infer reliably that the tree vacuum does not represent the true ground state of the theory.Comment: 23 pages; 5 figures; v2 includes minor changes in text and additional reference

Grid simulation services for the medical community

The first part of this paper presents a selection of medical simulation applications, including image reconstruction, near real-time registration for neuro-surgery, enhanced dose distribution calculation for radio-therapy, inhaled drug delivery prediction, plastic surgery planning and cardio-vascular system simulation. The latter two topics are discussed in some detail. In the second part, we show how such services can be made available to the clinical practitioner using Grid technology. We discuss the developments and experience made during the EU project GEMSS, which provides reliable, efficient, secure and lawful medical Grid services

Nutrient transport in bioreactors for bone tissue growth : why do hollow fiber membrane bioreactors work

One of the main aims of bone tissue engineering is to produce three-dimensional soft bone tissue constructs of acceptable clinical size and shape in bioreactors. The tissue constructs have been proposed as possible replacements for diseased or dysfunctional bones in the human body through surgical transplantations. However, because of certain restrictions to the design and operation of the bioreactors, the size of the tissue constructs attained are currently below clinical standards. We believe that understanding the fluid flow and nutrient transport behaviour in the bioreactors is critical in achieving clinically viable constructs. Nevertheless, characterization of transport behaviour in these bioreactors is not trivial. As they are very small in size and operate under stringent conditions, in-situ measurements of nutrients are almost impossible. This issue has been somewhat resolved using computational modelling in previous studies. However, there is still a lack of certainty on the suitability of bioreactors. To address this issue we systematically compare the suitability of three bioreactors for growing bone tissues using mathematical modelling tools. We show how nutrient transport may be improved in these bioreactors by varying the operating conditions and suggest which bioreactor may be best suited for operating at high cell densities in order to achieve soft bone tissues of clinical size. The governing equations defined in our mathematical frameworks are solved through finite element method. The results show that the hollow fiber membrane bioreactor (HFMB) is able to maintain higher nutrient concentration during operation at high cell densities compared to the other two bioreactors, namely suspended tube and confined profusion type bioreactor. Our results show that by varying the operating conditions nutrient transport may be enhanced and the nutrient gradient can be substantially reduced. These are consistent with previous claims suggesting that the HFMB is suited for bone tissue growth at high cell densities

The four-loop DRED gauge beta-function and fermion mass anomalous dimension for general gauge groups

We present four-loop results for the gauge beta-function and the fermion mass anomalous dimension for a gauge theory with a general gauge group and a multiplet of fermions transforming according to an arbitrary representation, calculated using the dimensional reduction scheme. In the special case of a supersymmetric theory we confirm previous calculations of both the gauge beta-function and the gaugino mass beta-function.Comment: 44 pages, added references (v2) minor changes (v3

Characterising the within-field scale spatial variation of nitrogen in a grassland soil to inform the efficient design of in-situ nitrogen sensor networks for precision agriculture

The use of in-situ sensors capable of real-time monitoring of soil nitrogen (N) may facilitate improvements in agricultural N-use efficiency (NUE) through better fertiliser management. The optimal design of such sensor networks, consisting of clusters of sensors each attached to a data logger, depends upon the spatial variation of soil N and the relative cost of the data loggers and sensors. The primary objective of this study was to demonstrate how in-situ networks of N sensors could be optimally designed to enable the cost-efficient monitoring of soil N within a grassland field (1.9 ha). In the summer of 2014, two nested sampling campaigns (June & July) were undertaken to assess spatial variation in soil amino acids, ammonium (NH4+) and nitrate (NO3−) at a range of scales that represented the within (less than 2 m) and between (greater than 2 m) data logger/sensor cluster variability. Variance at short range (less than 2 m) was found to be dominant for all N forms. Variation at larger scales (greater than 2 m) was not as large but was still considered an important spatial component for all N forms, especially NO3−. The variance components derived from the nested sampling were used to inform the efficient design of theoretical in-situ networks of NH4+ and NO3− sensors based on the costs of a commercially available data logger and ion-selective electrodes (ISEs). Based on the spatial variance observed in the June nested sampling, and given a budget of £5000, the NO3− field mean could be estimated with a 95% confidence interval width of 1.70 μg N g−1 using 2 randomly positioned data loggers each with 5 sensors. Further investigation into “aggregate-scale” (less than 1 cm) spatial variance revealed further large variation at the sub 1-cm scale for all N forms. Sensors, for which the measurement represents an integration over a sensor-soil contact area of diameter less than 1 cm, would be subject to this aggregate-scale variability. As such, local replication at scales less than 1 cm would be needed to maintain the precision of the resulting field mean estimation. Adoption of in-situ sensor networks will depend upon the development of suitable low‐cost sensors, demonstration of the cost-benefit and the construction of a decision support system that utilises the generated data to improve the NUE of fertiliser N management

Exploitation of lettuce waste flour to increase bread functionality: effect on physical, nutritional, sensory properties and on consumer response

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