Prediction of landing gear loads using machine learning techniques

This article investigates the feasibility of using machine learning algorithms to predict the loads experienced by a landing gear during landing. For this purpose, the results on drop test data and flight test data will be examined. This article will focus on the use of Gaussian process regression for the prediction of loads on the components of a landing gear. For the learning task, comprehensive measurement data from drop tests are available. These include measurements of strains at key locations, such as on the side-stay and torque link, as well as acceleration measurements of the drop carriage and the gear itself, measurements of shock absorber travel, tyre closure, shock absorber pressure and wheel speed. Ground-to-tyre loads are also available through measurements made with a drop test ground reaction platform. The aim is to train the Gaussian process to predict load at a particular location from other available measurements, such as accelerations, or measurements of the shock absorber. If models can be successfully trained, then future load patterns may be predicted using only these measurements. The ultimate aim is to produce an accurate model that can predict the load at a number of locations across the landing gear using measurements that are readily available or may be measured more easily than directly measuring strain on the gear itself (for example, these may be measurements already available on the aircraft, or from a small number of sensors attached to the gear). The drop test data models provide a positive feasibility test which is the basis for moving on to the critical task of prediction on flight test data. For this, a wide range of available flight test measurements are considered for potential model inputs (excluding strain measurements themselves), before attempting to refine the model or use a smaller number of measurements for the prediction

Machine-learning of atomic-scale properties based on physical principles

We briefly summarize the kernel regression approach, as used recently in materials modelling, to fitting functions, particularly potential energy surfaces, and highlight how the linear algebra framework can be used to both predict and train from linear functionals of the potential energy, such as the total energy and atomic forces. We then give a detailed account of the Smooth Overlap of Atomic Positions (SOAP) representation and kernel, showing how it arises from an abstract representation of smooth atomic densities, and how it is related to several popular density-based representations of atomic structure. We also discuss recent generalisations that allow fine control of correlations between different atomic species, prediction and fitting of tensorial properties, and also how to construct structural kernels---applicable to comparing entire molecules or periodic systems---that go beyond an additive combination of local environments

Facing danger: exploring personality and reactions of European hedgehogs (Erinaceus europaeus) towards robotic lawn mowers

The populations of European hedgehog (Erinaceus europaeus) are in decline, and it is essential that research identifies and mitigates the factors causing this. Hedgehogs are increasingly sharing habitats with humans, being exposed to a range of dangers in our backyards. Previous research has documented that some models of robotic lawn mowers can cause harm to hedgehogs. This study explored the personality and behaviour of 50 live hedgehogs when facing an approaching, disarmed robotic lawn mower. By combining a novel arena and novel object test, we found that 27 hedgehogs could be categorised as “shy” and 23 as “bold”, independently of sex and age. The encounter tests with a robotic lawn mower showed that the hedgehogs positioned themselves in seven different ways. Personality did not affect their reactions. Adult hedgehogs tended to react in a shyer manner, and the hedgehogs, generally, acted less boldly during their second encounter with the robotic lawn mower. Additionally, our results show that bold individuals reacted in a more unpredictable way, being more behaviourally unstable compared to the shy individuals. This knowledge will be applied in the design of a standardised hedgehog safety test, eventually serving to produce and approve hedgehog-friendly robotic lawn mowers

Are isomeric alkenes used in species recognition among neo-tropical stingless bees (Melipona spp)

The majority of our understanding of the role of cuticular hydrocarbons (CHC) in recognition is based largely on temperate ant species and honey bees. The stingless bees remain relatively poorly studied, despite being the largest group of eusocial bees, comprising more than 400 species in some 60 genera. The Meliponini and Apini diverged between 80-130 Myr B.P. so the evolutionary trajectories that shaped the chemical communication systems in ants, honeybees and stingless bees may be very different. Therefore, the main aim of this study was to study if a unique species CHC signal existed in Neotropical stingless bees, as shown for many temperate species, and if so what compounds are involved. This was achieved by collecting CHC data from 24 colonies belonging to six species of Melipona from North-eastern Brazil and comparing this new data with all previously published CHC studies on Melipona. We found that each of the eleven Melipona species studied so far each produced a unique species CHC signal based around their alkene isomer production. A remarkable number of alkene isomers, up to 25 in M. asilvai, indicated the diversification of alkene positional isomers among the stingless bees. The only other group to have really diversified in alkene isomer production are the primitively eusocial Bumblebees (Bombus spp), which are the sister group of the stingless bees. Furthermore, among the eleven Neotropical Melipona species we could detect no effect of the environment on the proportion of alkane production as has been suggested for some other species

Technology transfer offices as boundary spanners in the pre-spin-off process: the case of a hybrid model

Over the past decades, universities have increasingly become ambidextrous organizations reconciling scientific and commercial missions. In order to manage this ambidexterity, technology transfer offices (TTOs) were established in most universities. This paper studies a specific, often implemented, but rather understudied type of TTO, namely a hybrid TTO model uniting centralized and decentralized levels. Employing a qualitative research design, we examine how and why the two TTO levels engage in diverse boundary spanning activities to help nascent spin-off companies move through the pre-spin-off process. Our research identifies differences in the types of boundary spanning activities that centralized and decentralized TTOs perform and in the parties they engage with. We find geographical, technological and organizational proximity to be important antecedents of the TTOs’ engagement in external and internal boundary spanning activities. These results have important implications for both academics and practitioners interested in university technology transfer through spin-off creation

Using combined diagnostic test results to hindcast trends of infection from cross-sectional data

Infectious disease surveillance is key to limiting the consequences from infectious pathogens and maintaining animal and public health. Following the detection of a disease outbreak, a response in proportion to the severity of the outbreak is required. It is thus critical to obtain accurate information concerning the origin of the outbreak and its forward trajectory. However, there is often a lack of situational awareness that may lead to over- or under-reaction. There is a widening range of tests available for detecting pathogens, with typically different temporal characteristics, e.g. in terms of when peak test response occurs relative to time of exposure. We have developed a statistical framework that combines response level data from multiple diagnostic tests and is able to ‘hindcast’ (infer the historical trend of) an infectious disease epidemic. Assuming diagnostic test data from a cross-sectional sample of individuals infected with a pathogen during an outbreak, we use a Bayesian Markov Chain Monte Carlo (MCMC) approach to estimate time of exposure, and the overall epidemic trend in the population prior to the time of sampling. We evaluate the performance of this statistical framework on simulated data from epidemic trend curves and show that we can recover the parameter values of those trends. We also apply the framework to epidemic trend curves taken from two historical outbreaks: a bluetongue outbreak in cattle, and a whooping cough outbreak in humans. Together, these results show that hindcasting can estimate the time since infection for individuals and provide accurate estimates of epidemic trends, and can be used to distinguish whether an outbreak is increasing or past its peak. We conclude that if temporal characteristics of diagnostics are known, it is possible to recover epidemic trends of both human and animal pathogens from cross-sectional data collected at a single point in time

Somatostatin and dopamine receptors as targets for medical treatment of Cushing's Syndrome

Somatostatin (SS) and dopamine (DA) receptors are widely expressed in neuroendocrine tumours that cause Cushing's Syndrome (CS). Increasing knowledge of specific subtype expression within these tumours and the ability to target these receptor subtypes with high-affinity compounds, has driven the search for new SS- or DA-based medical therapies for the various forms of CS. In Cushing's disease, corticotroph adenomas mainly express dopamine receptor subtype 2 (D2) and somatostatin receptor subtype 5 (sst5), whereas sst2is expressed at lower levels. Activation of these receptors can inhibit ACTH-release in primary cultured corticotroph adenomas and compounds that target either sst5(pasireotide, or SOM230) or D2(cabergoline) have shown significant efficacy in subsets of patients in recent clinical studies. Combination therapy, either by administration of both types of compounds separately or by treatment with novel somatostatin-dopamine chimeric molecules (e.g. BIM-23A760), appears to be a promising approach in this respect. In selected cases of Ectopic ACTH-producing Syndrome (EAS), the sst2-preferring compound octreotide is able to reduce cortisol levels effectively. A recent study showed that D2receptors are also significantly expressed in the majority of EAS and that cabergoline may decrease cortisol levels in subsets of these patients. In both normal adrenal tissue as well as in adrenal adenomas and carcinomas that cause CS, sst and DA receptor expression has been demonstrated. Although selected cases of adrenal CS may benefit from sst or DA-targeted treatment, its total contribution to the treatment of these patients is likely to be low as surgery is effective in most cases

Regulating STING in health and disease.

The presence of cytosolic double-stranded DNA molecules can trigger multiple innate immune signalling pathways which converge on the activation of an ER-resident innate immune adaptor named "STimulator of INterferon Genes (STING)". STING has been found to mediate type I interferon response downstream of cyclic dinucleotides and a number of DNA and RNA inducing signalling pathway. In addition to its physiological function, a rapidly increasing body of literature highlights the role for STING in human disease where variants of the STING proteins, as well as dysregulated STING signalling, have been implicated in a number of inflammatory diseases. This review will summarise the recent structural and functional findings of STING, and discuss how STING research has promoted the development of novel therapeutic approaches and experimental tools to improve treatment of tumour and autoimmune diseases

The influence of organizational culture and climate on entrepreneurial intentions among research scientists

Over the past decades, universities have increasingly become involved in entrepreneurial activities. Despite efforts to embrace their ‘third mission’, universities still demonstrate great heterogeneity in terms of their involvement in academic entrepreneurship. This papers adopts an institutional perspective to understand how organizational characteristics affect research scientists’ entrepreneurial intentions. Specifically, we study the impact of university culture and climate on entrepreneurial intentions, including intentions to spin off a company, to engage in patenting or licensing and to interact with industry through contract research or consulting. Using a sample of 437 research scientists from Swedish and German universities, our results reveal that the extent to which universities articulate entrepreneurship as a fundamental element of their mission fosters research scientists’ intentions to engage in spin-off creation and intellectual property rights, but not industry-science interaction. Furthermore, the presence of university role models positively affects research scientists’ propensity to engage in entrepreneurial activities, both directly and indirectly through entrepreneurial self-efficacy. Finally, research scientists working at universities which explicitly reward people for ‘third mission’ related output show higher levels of spin-off and patenting or licensing intentions. This study has implications for both academics and practitioners, including university managers and policy makers

Early lineage restriction in temporally distinct populations of Mesp1 progenitors during mammalian heart development.

Cardiac development arises from two sources of mesoderm progenitors, the first heart field (FHF) and the second (SHF). Mesp1 has been proposed to mark the most primitive multipotent cardiac progenitors common for both heart fields. Here, using clonal analysis of the earliest prospective cardiovascular progenitors in a temporally controlled manner during early gastrulation, we found that Mesp1 progenitors consist of two temporally distinct pools of progenitors restricted to either the FHF or the SHF. FHF progenitors were unipotent, whereas SHF progenitors were either unipotent or bipotent. Microarray and single-cell PCR with reverse transcription analysis of Mesp1 progenitors revealed the existence of molecularly distinct populations of Mesp1 progenitors, consistent with their lineage and regional contribution. Together, these results provide evidence that heart development arises from distinct populations of unipotent and bipotent cardiac progenitors that independently express Mesp1 at different time points during their specification, revealing that the regional segregation and lineage restriction of cardiac progenitors occur very early during gastrulation.This is the author's accepted manuscript and will be under embargo until the 24th of February 2015. The final version is published by NPG in Nature Cell Biology here: http://www.nature.com/ncb/journal/v16/n9/full/ncb3024.html