Concentrations of railway metal theft and the locations of scrap-metal dealers

Metal theft has become a substantial crime problem in many areas. In response, several countries have introduced legislation to regulate scrap-metal recycling yards. However, at present there is little evidence to support this use of the market reduction approach (MRA) in preventing metal theft. The present study sought to test the underlying assumption of the MRA that the presence of a market for stolen property (in this case provided by scrap yards) drives thefts in a local area. This study tested for a spatial association between the locations of scrap yards and those of metal thefts. The density of industry, local burglary rate and road-accessibility of an area were controlled for. Metal thefts from railway lines in England were shown to be significantly more common in areas with more scrap-metal yards, high road accessibility and high population density. The results support the use of the MRA in relation to metal theft

The when and where of an emerging crime type: the example of metal theft from the railway network of Great Britain

Metal theft has become an increasingly common crime in recent years, but lack of data has limited research into it. The present study used police-recorded crime data to study the spatial and temporal concentration of metal theft from the railway network of Great Britain. Metal theft was found to exhibit only weak seasonality, to be concentrated at night and to cluster in a few locations close to – but not in – major cities. Repeat-victimisation risk continued for longer than has been found for other crime types. These and other features appear to point to metal theft being a planned, rather than opportunistic, offence and to the role of scrap-metal dealers as facilitators

A probabilistic interpretation of PID controllers using active inference

In the past few decades, probabilistic interpretations of brain functions have become widespread in cognitive science and neuroscience. The Bayesian brain hypothesis, predictive coding, the free energy principle and active inference are increasingly popular theories of cognitive functions that claim to unify understandings of life and cognition within general mathematical frameworks derived from information and control theory, statistical physics and machine learning. The connections between information and control theory have been discussed since the 1950’s by scientists like Shannon and Kalman and have recently risen to prominence in modern stochastic optimal control theory. However, the implications of the confluence of these two theoretical frameworks for the biological sciences have been slow to emerge. Here we argue that if the active inference proposal is to be taken as a general process theory for biological systems, we need to consider how existing control theoretical approaches to biological systems relate to it. In this work we will focus on PID (Proportional-Integral-Derivative) controllers, one of the most common types of regulators employed in engineering and more recently used to explain behaviour in biological systems, e.g. chemotaxis in bacteria and amoebae or robust adaptation in biochemical networks. Using active inference, we derive a probabilistic interpretation of PID controllers, showing how they can fit a more general theory of life and cognition under the principle of (variational) free energy minimisation under simple linear generative models.most common types of regulators employed in engineering and more recently used to explain behaviour in biological systems, e.g. chemotaxis in bacteria and amoebae or robust adaptation in biochemical networks. Using active inference, we derive a probabilistic interpretation of PID controllers, showing how they can fit a more general theory of life and cognition under the principle of (variational) free energy minimisation under simple linear generative models

Reducing the Risk of Cognitive Decline and Dementia: WHO Recommendations

With population ageing worldwide, dementia poses one of the greatest global challenges for health and social care in the 21st century. In 2019, around 55 million people were affected by dementia, with the majority living in low- and middle-income countries. Dementia leads to increased costs for governments, communities, families and individuals. Dementia is overwhelming for the family and caregivers of the person with dementia, who are the cornerstone of care and support systems throughout the world. To assist countries in addressing the global burden of dementia, the World Health Organisation (WHO) developed the Global Action Plan on the Public Health Response to Dementia 2017–2025. It proposes actions to be taken by governments, civil society, and other global and regional partners across seven action areas, one of which is dementia risk reduction. This paper is based on WHO Guidelines on risk reduction of cognitive decline and dementia and presents recommendations on evidence-based, multisectoral interventions for reducing dementia risks, considerations for their implementation and policy actions. These global evidence-informed recommendations were developed by WHO, following a rigorous guideline development methodology and involved a panel of academicians and clinicians with multidisciplinary expertise and representing geographical diversity. The recommendations are considered under three broad headings: lifestyle and behaviour interventions, interventions for physical health conditions and specific interventions. By supporting health and social care professionals, particularly by improving their capacity to provide gender and culturally appropriate interventions to the general population, the risk of developing dementia can be potentially reduced, or its progression delayed

Derivation and internal validation of an equation for albumin-adjusted calcium

YesFunding provided by the Open Access Authors Fund

The Dreyfus model of clinical problem-solving skills acquisition: a critical perspective

Context: The Dreyfus model describes how individuals progress through various levels in their acquisition of skills and subsumes ideas with regard to how individuals learn. Such a model is being accepted almost without debate from physicians to explain the ‘acquisition’ of clinical skills. Objectives: This paper reviews such a model, discusses several controversial points, clarifies what kind of knowledge the model is about, and examines its coherence in terms of problem-solving skills. Dreyfus’ main idea that intuition is a major aspect of expertise is also discussed in some detail. Relevant scientific evidence from cognitive science, psychology, and neuroscience is reviewed to accomplish these aims. Conclusions: Although the Dreyfus model may partially explain the ‘acquisition’ of some skills, it is debatable if it can explain the acquisition of clinical skills. The complex nature of clinical problem-solving skills and the rich interplay between the implicit and explicit forms of knowledge must be taken into consideration when we want to explain ‘acquisition’ of clinical skills. The idea that experts work from intuition, not from reason, should be evaluated carefully

Measurement of triple gauge boson couplings from W⁺W⁻ production at LEP energies up to 189 GeV

A measurement of triple gauge boson couplings is presented, based on W-pair data recorded by the OPAL detector at LEP during 1998 at a centre-of-mass energy of 189 GeV with an integrated luminosity of 183 pb⁻¹. After combining with our previous measurements at centre-of-mass energies of 161–183 GeV we obtain κ = 0.97_{-0.16}^{+0.20}, g_{1}^{z} = 0.991_{-0.057}^{+0.060} and λ = -0.110_{-0.055}^{+0.058}, where the errors include both statistical and systematic uncertainties and each coupling is determined by setting the other two couplings to their Standard Model values. These results are consistent with the Standard Model expectations

A dimensional summation account of polymorphous category learning

This is the author accepted manuscript. The final version is available from Springer via the DOI in this record.Data and code availaibility: The data and code for all analyses for all experiments are available at the OSF addresses given in each Results section. The stimuli are available at the same locations.Polymorphous concepts are hard to learn, and this is perhaps surprising because they, like many natural concepts, have an overall similarity structure. However, the dimensional summation hypothesis (Milton & Wills, 2004) predicts this difficulty. It also makes a number of other predictions about polymorphous concept formation, which are tested here. In Experiment 1 we confirm the theory’s prediction that polymorphous concept formation should be facilitated by deterministic pretraining on the constituent features of the stimulus. This facilitation is relative to an equivalent amount of training on the polymorphous concept itself. In Experiments 2–4, the dimensional summation account of this single feature pretraining effect is contrasted with some other accounts, including a more general strategic account (Experiment 2), seriality of training and stimulus decomposition accounts (Experiment 3), and the role of errors (Experiment 4). The dimensional summation hypothesis provides the best account of these data. In Experiment 5, a further prediction is confirmed — the single feature pretraining effect is eliminated by a concurrent counting task. The current experiments suggest the hypothesis that natural concepts might be acquired by the deliberate serial summation of evidence. This idea has testable implications for classroom learning.Biotechnology and Biological Sciences Research Council (BBSRC

Surprised at All the Entropy: Hippocampal, Caudate and Midbrain Contributions to Learning from Prediction Errors

Influential concepts in neuroscientific research cast the brain a predictive machine that revises its predictions when they are violated by sensory input. This relates to the predictive coding account of perception, but also to learning. Learning from prediction errors has been suggested for take place in the hippocampal memory system as well as in the basal ganglia. The present fMRI study used an action-observation paradigm to investigate the contributions of the hippocampus, caudate nucleus and midbrain dopaminergic system to different types of learning: learning in the absence of prediction errors, learning from prediction errors, and responding to the accumulation of prediction errors in unpredictable stimulus configurations. We conducted analyses of the regions of interests' BOLD response towards these different types of learning, implementing a bootstrapping procedure to correct for false positives. We found both, caudate nucleus and the hippocampus to be activated by perceptual prediction errors. The hippocampal responses seemed to relate to the associative mismatch between a stored representation and current sensory input. Moreover, its response was significantly influenced by the average information, or Shannon entropy of the stimulus material. In accordance with earlier results, the habenula was activated by perceptual prediction errors. Lastly, we found that the substantia nigra was activated by the novelty of sensory input. In sum, we established that the midbrain dopaminergic system, the hippocampus, and the caudate nucleus were to different degrees significantly involved in the three different types of learning: acquisition of new information, learning from prediction errors and responding to unpredictable stimulus developments. We relate learning from perceptual prediction errors to the concept of predictive coding and related information theoretic accounts