Management and Technology Life Cycle: Bulgarian Case Study on the Technology of Counter-pressure Casting

At IIASA, several researchers have studied and described the cumulative nature of development of technologies and their substitution, using global and macro-economic data. Those processes have their "fine micro-structure" which is interesting and valuable for one country as a whole or for individual companies. Studying this micro-structure can permit us to connect global theory with processes taking place on the micro-level and, based on that, to make recommendations to decision-makers to permit them to select instruments for analyzing and synthesizing their strategy. Small countries often have limited resources (either natural or financial or even both). However, they always have limited human resources which should be used effectively and purposefully. Today, technological developments even outside the sphere of so-called high-tech are very intensive scientifically and intellectually. This once more increases the necessity for small countries to concentrate their scientific human potential in areas in which they can make break-throughs with high economic efficiency. From this point, positioning technological innovations correctly in the international market and forecasting their competitiveness are very important. A picture of the possible future development of a technological innovation gives the small countries and their companies the opportunity to spot market niches and to develop effective strategies for their fulfillment. The application of life cycle theory and use of substitution curves as possible management instruments for strategy development on company level is one of the main goals of the research currently being carried out in Bulgaria under the contract with IIASA's "Management of the Technological Life Cycle" (MTL) activity, part of the "Technology-Economy-Society" (TES) program. The research in Bulgaria is being conducted by the Problem Center "Management of Technological Development" through the Institute for Social Management and has broader goals in the area. These goals are directed towards enhancing instruments for strategic management on company level and methods for accelerating technological development. The Bulgarian study is directed to three main groups of technologies (irrespective of branch of industry): a) original Bulgarian technologies with possibilities on international market; b) new technologies transferred from other countries; and c) traditional mature technologies. Structuring the research in this way not only avoids certain drawbacks inherent in research based on particular characteristics of industrial branches (namely the questionable validity of results and lack of transferability of those results to other branches of industry). It also permits researchers to study the dynamics of these technologies and the dynamics of organizational and management characteristics of the companies independent of branch specification, according to the type of technology described and the degree of its development. In the paper presented, some results of the first stage of the study are discussed. The objects of this first stage are several original Bulgarian technologies. The case study presented here concerns the technology of counter-pressure casting. This original Bulgarian technology is part of a group of technologies based on the method of casting with counter-pressure developed by the Bulgarian Academy of Sciences. The company under study is an interesting integration of a basic research institute, with applied research and production functions. Preliminary results based only on aluminum casting technology are presented in this paper. This method is also being applied to plastic and steel casting technologies which will be addressed in the second stage of the study. Variables and indicators through which technology is studied are developed within the MTL activity, but for the purposes of national study have been adapted, increased in number, and developed according to the specific requirements of a centrally planned economy by the Bulgarian national team

Redefining the research hospital

Introduction All medicine was innovation, once. Yet the contemporary notion of medical research is remarkably narrow. While every clinician is encouraged to be aware of the latest advances, only a few are expected to contribute to them. Anyone may be a patient, yet clinical practice is determined by the minority included in research studies. The aim of medicine is to improve the lives of patients, yet knowledge of disease is arbitrarily prioritised as its primary means. The agents of medicine are clinicians, yet new interventions are mostly created by others, within corporate enterprise deliberately kept at arm’s length. We treat the specific, individual patient in front of us, now, yet most research is addressed to faceless, generic groups, to be realised deep into an ill-defined, hypothetical future

The Frontal Control of Stopping

Stopping is a critical aspect of brain function. Like other voluntary actions, it is defined by its context as much as by its execution. Its neural substrate must therefore reflect both. Here, we distinguish those elements of the underlying brain circuit that preferentially reflect contextual aspects of stopping from those related to its execution. Contextual complexity of stopping was modulated using a novel "Stop/Change-signal" task, which also allowed us to parameterize the duration of the stopping process. Human magnetoencephalographic activity and behavioral responses were simultaneously recorded. Whereas theta/alpha frequency activity in the right inferior frontal gyrus was most closely associated with the duration of the stopping process, earlier gamma frequency activity in the pre-supplementary motor area was unique in showing contextual modulation. These results differentiate the roles of 2 key frontal regions involved in stopping, a crucial aspect of behavioral control

The Value of Data: Applying a Public Value Model to the English National Health Service

Research and innovation in biomedicine and health care increasingly depend on electronic data. The emergence of data-driven technologies and associated digital transformations has focused attention on the value of such data. Despite the broad consensus of the value of health data, there is less consensus on the basis for that value; thus, the nature and extent of health data value remain unclear. Much of the existing literature presupposes that the value of data is to be understood primarily in financial terms, and assumes that a single financial value can be assigned. We here argue that the value of a dataset is instead relational; that is, the value depends on who wants to use it and for what purposes. Moreover, data are valued for both nonfinancial and financial reasons. Thus, it may be more accurate to discuss the values (plural) of a dataset rather than the singular value. This plurality of values opens up an important set of questions about how health data should be valued for the purposes of public policy. We argue that public value models provide a useful approach in this regard. According to public value theory, public value is created, or captured, to the extent that public sector institutions further their democratically established goals, and their impact on improving the lives of citizens. This article outlines how adopting such an approach might be operationalized within existing health care systems such as the English National Health Service, with particular focus on actionable conclusions

Equitable modelling of brain imaging by counterfactual augmentation with morphologically constrained 3D deep generative models

We describe CounterSynth, a conditional generative model of diffeomorphic deformations that induce label-driven, biologically plausible changes in volumetric brain images. The model is intended to synthesise counterfactual training data augmentations for downstream discriminative modelling tasks where fidelity is limited by data imbalance, distributional instability, confounding, or underspecification, and exhibits inequitable performance across distinct subpopulations. Focusing on demographic attributes, we evaluate the quality of synthesised counterfactuals with voxel-based morphometry, classification and regression of the conditioning attributes, and the Fréchet inception distance. Examining downstream discriminative performance in the context of engineered demographic imbalance and confounding, we use UK Biobank and OASIS magnetic resonance imaging data to benchmark CounterSynth augmentation against current solutions to these problems. We achieve state-of-the-art improvements, both in overall fidelity and equity. The source code for CounterSynth is available at https://github.com/guilherme-pombo/CounterSynth

Lost in translation

Translation in cognitive neuroscience remains beyond the horizon, brought no closer by supposed major advances in our understanding of the brain. Unless our explanatory models descend to the individual level-a cardinal requirement for any intervention-their real-world applications will always be limited. Drawing on an analysis of the informational properties of the brain, here we argue that adequate individualisation needs models of far greater dimensionality than has been usual in the field. This necessity arises from the widely distributed causality of neural systems, a consequence of the fundamentally adaptive nature of their developmental and physiological mechanisms. We discuss how recent advances in high-performance computing, combined with collections of large-scale data, enable the high-dimensional modelling we argue is critical to successful translation, and urge its adoption if the ultimate goal of impact on the lives of patients is to be achieved

An MRF-UNet Product of Experts for Image Segmentation

While convolutional neural networks (CNNs) trained by back-propagation have seen unprecedented success at semantic segmentation tasks, they are known to struggle on out-of-distribution data. Markov random fields (MRFs) on the other hand, encode simpler distributions over labels that, although less flexible than UNets, are less prone to over-fitting. In this paper, we propose to fuse both strategies by computing the product of distributions of a UNet and an MRF. As this product is intractable, we solve for an approximate distribution using an iterative mean-field approach. The resulting MRF-UNet is trained jointly by back-propagation. Compared to other works using conditional random fields (CRFs), the MRF has no dependency on the imaging data, which should allow for less over-fitting. We show on 3D neuroimaging data that this novel network improves generalisation to out-of-distribution samples. Furthermore, it allows the overall number of parameters to be reduced while preserving high accuracy. These results suggest that a classic MRF smoothness prior can allow for less over-fitting when principally integrated into a CNN model. Our implementation is available at https://github.com/balbasty/nitorch

Enrolment in clinical research at UCLH and geographically distributed indices of deprivation [version 1; peer review: awaiting peer review]

Healthcare should be judged by its equity as well as its quality. Both aspects depend not only on the characteristics of service delivery but also on the research and innovation that ultimately shape them. Conducting a fully-inclusive evaluation of the relationship between enrolment in primary research studies at University College London Hospitals NHS Trust and indices of deprivation, here we demonstrate a quantitative approach to evaluating equity in healthcare research and innovation. We surveyed the geographical locations, aggregated into Lower Layer Super Output Areas (LSOAs), of all England-resident UCLH patients registered as enrolled in primary clinical research studies. We compared the distributions of ten established indices of deprivation across enrolled and non-enrolled areas within Greater London and within a distance-matched subset across England. Bayesian Poisson regression models were used to examine the relation between deprivation and the volume of enrolment standardized by population density and local disease prevalence. A total of 54593 enrolments covered 4401 LSOAs in Greater London and 10150 in England, revealing wide geographical reach. The distributions of deprivation indices were similar between enrolled and non-enrolled areas, exhibiting median differences from 0.26% to 8.73%. Across Greater London, enrolled areas were significantly more deprived on most indices, including the Index of Multiple Deprivation; across England, a more balanced relationship to deprivation emerged. Regression analyses of enrolment volumes yielded weak biases, in favour of greater deprivation for most indices, with little modulation by local disease prevalence. Primary clinical research at UCLH has wide geographical reach. Areas with enrolled patients show similar distributions of established indices of deprivation to those without, both within Greater London, and across distance-matched areas of England. We illustrate a robust approach to quantifying an important aspect of equity in clinical research and provide a flexible set of tools for replicating it across other institutions