The collapse of shallow coal mine workings

The present study was -undertaken to investigate the mechanism of void migration and the collapse characteristics of old shallow surface (< 50m), pillar and stall coal mine workings. Simple stereo-photographic techniques have been employed to record these structures where they occur in the high walls of NCB and private opencast coal sites. Several relationships have been identified from this data, and the investigation concludes that the crushing of coal pillars at depth is rare and that the principal mechanism of failure involves the collapse of the roof material into the working. A classification of failure mechanisms based on the frequency and spacing of horizontal and vertical discontinuities relative to the span of the working is proposed. Two distinct situations for analysis are recognised. The first involves the stability of the immediate roof, while the second is concerned with the stability of the 'arch' that develops when the immediate roof beam collapses. Continuous roof beams have been found to be rare in Coal Measures rocks and therefore simple beam analysis is considered to be of little use. Where discontinuities are present Voussoir beam analysis may be appropriate, and Voussoir beam theory has been corrected and extended to overcome some of the problems recognised with the technique. Bulking and arching have been recognised as the 'normal' limiting factors on the height of collapse and are considered as complimentary failure mechanisms. For a 'typical coal mine collapse' situation arching is shown to be the dominant control. However, a review of arching has shown that in general all the theories underestimate the height of collapse. Thus, a statistically derived relationship of (collapse height = 2.68 X span of working) has been proposed as the limiting height for arching situations. Existing bulking relationships have been shown to be rather simplistic and appropriate corrections to the theories are suggested. An analysis of bulking factors derived from colliery discard has shown that a regional variation in this parameter is likely

Beyond advocacy: making space for conservation scientists in public debate

The topic of advocacy by scientists has been debated for decades, yet there is little agreement about whether scientists can or should be advocates. The fear of crossing a line into advocacy continues to hold many scientists back from contributing to public discourse, impoverishing public debate about important issues. We believe that progress in this debate is limited by a misconception about the relationship between scientific integrity and objectivity. We begin by unpacking this relationship and debunking three common misconceptions about advocacy by scientists: namely, that advocacy is harmful to scientific credibility, beyond the scope of science, and incompatible with science, which is value-free. We propose new ways of thinking about responsible advocacy by conservation scientists, drawing on practices from the health sciences, where researchers and professional bodies are empowered to act as health advocates.In so doing, we hope to open further space for conservation scientists to actively and legitimately engage in public debate about conservation issues

Nonlinear Dynamic Modeling and Controls Development for Supersonic Propulsion System Research

This paper covers the propulsion system component modeling and controls development of an integrated nonlinear dynamic simulation for an inlet and engine that can be used for an overall vehicle (APSE) model. The focus here is on developing a methodology for the propulsion model integration, which allows for controls design that prevents inlet instabilities and minimizes the thrust oscillation experienced by the vehicle. Limiting thrust oscillations will be critical to avoid exciting vehicle aeroelastic modes. Model development includes both inlet normal shock position control and engine rotor speed control for a potential supersonic commercial transport. A loop shaping control design process is used that has previously been developed for the engine and verified on linear models, while a simpler approach is used for the inlet control design. Verification of the modeling approach is conducted by simulating a two-dimensional bifurcated inlet and a representative J-85 jet engine previously used in a NASA supersonics project. Preliminary results are presented for the current supersonics project concept variable cycle turbofan engine design

Calibrating Mini-Mental State Examination Scores to Predict Misdiagnosed Dementia Patients

Mini-Mental State Examination (MMSE) is used as a diagnostic test for dementia to screen a patient’s cognitive assessment and disease severity. However, these examinations are often inaccurate and unreliable either due to human error or due to patients’ physical disability to correctly interpret the questions as well as motor deficit. Erroneous data may lead to a wrong assessment of a specific patient. Therefore, other clinical factors (e.g., gender and comorbidities) existing in electronic health records, can also play a significant role, while reporting her examination results. This work considers various clinical attributes of dementia patients to accurately determine their cognitive status in terms of the Mini-Mental State Examination (MMSE) Score. We employ machine learning models to calibrate MMSE score and classify the correctness of diagnosis among patients, in order to assist clinicians in a better understanding of the progression of cognitive impairment and subsequent treatment. For this purpose, we utilize a curated real-world ageing study data. A random forest prediction model is employed to estimate the Mini-Mental State Examination score, related to the diagnostic classification of patients.This model uses various clinical attributes to provide accurate MMSE predictions, succeeding in correcting an important percentage of cases that contain previously identified miscalculated scores in our dataset. Furthermore, we provide an effective classification mechanism for automatically identifying patient episodes with inaccurate MMSE values with high confidence. These tools can be combined to assist clinicians in automatically finding episodes within patient medical records where the MMSE score is probably miscalculated and estimating what the correct value should be. This provides valuable support in the decision making process for diagnosing potential dementia patients

Identifying the presence and severity of dementia by applying interpretable machine learning techniques on structured clinical records.

BACKGROUND: Dementia develops as cognitive abilities deteriorate, and early detection is critical for effective preventive interventions. However, mainstream diagnostic tests and screening tools, such as CAMCOG and MMSE, often fail to detect dementia accurately. Various graph-based or feature-dependent prediction and progression models have been proposed. Whenever these models exploit information in the patients' Electronic Medical Records, they represent promising options to identify the presence and severity of dementia more precisely. METHODS: The methods presented in this paper aim to address two problems related to dementia: (a) Basic diagnosis: identifying the presence of dementia in individuals, and (b) Severity diagnosis: predicting the presence of dementia, as well as the severity of the disease. We formulate these two tasks as classification problems and address them using machine learning models based on random forests and decision tree, analysing structured clinical data from an elderly population cohort. We perform a hybrid data curation strategy in which a dementia expert is involved to verify that curation decisions are meaningful. We then employ the machine learning algorithms that classify individual episodes into a specific dementia class. Decision trees are also used for enhancing the explainability of decisions made by prediction models, allowing medical experts to identify the most crucial patient features and their threshold values for the classification of dementia. RESULTS: Our experiment results prove that baseline arithmetic or cognitive tests, along with demographic features, can predict dementia and its severity with high accuracy. In specific, our prediction models have reached an average f1-score of 0.93 and 0.81 for problems (a) and (b), respectively. Moreover, the decision trees produced for the two issues empower the interpretability of the prediction models. CONCLUSIONS: This study proves that there can be an accurate estimation of the existence and severity of dementia disease by analysing various electronic medical record features and cognitive tests from the episodes of the elderly population. Moreover, a set of decision rules may comprise the building blocks for an efficient patient classification. Relevant clinical and screening test features (e.g. simple arithmetic or animal fluency tasks) represent precise predictors without calculating the scores of mainstream cognitive tests such as MMSE and CAMCOG. Such predictive model can identify not only meaningful features, but also justifications of classification. As a result, the predictive power of machine learning models over curated clinical data is proved, paving the path for a more accurate diagnosis of dementia

Galactic Cosmic Rays from Supernova Remnants: II Shock Acceleration of Gas and Dust

This is the second paper (the first was astro-ph/9704267) of a series analysing the Galactic Cosmic Ray (GCR) composition and origin. In this we present a quantitative model of GCR origin and acceleration based on the acceleration of a mixture of interstellar and/or circumstellar gas and dust by supernova remnant blast waves. We present results from a nonlinear shock model which includes (i) the direct acceleration of interstellar gas-phase ions, (ii) a simplified model for the direct acceleration of weakly charged dust grains to energies of order 100keV/amu simultaneously with the gas ions, (iii) frictional energy losses of the grains colliding with the gas, (iv) sputtering of ions of refractory elements from the accelerated grains and (v) the further shock acceleration of the sputtered ions to cosmic ray energies. The calculated GCR composition and spectra are in good agreement with observations.Comment: to appear in ApJ, 51 pages, LaTeX with AAS macros, 9 postscript figures, also available from ftp://wonka.physics.ncsu.edu/pub/elliso

L'umano nell'uomo: Vasilij Grossman tra ideologie e domande eterne

Vasilij Grossman (1905-1964) s'impone solo ora, con il recente successo delle ripubblicazioni in diverse lingue del suo capolavoro postumo Vita e destino, come una delle figure artistiche e filosofiche pi\uf9 interessanti del XX secolo. La dimensione letteraria di Grossman che affonda le radici nella pi\uf9 alta tradizione russa - \ue8 votata ad un realismo classico, aperto all'universale, costantemente teso all'espressione di domande ultime ed eterne che affermano l'uomo e la sua libert\ue0 contro il potere dell'ideologia. L'umano nell'uomo \ue8 questo nucleo originale presente in ogni uomo che impedisce al potere di schiacciare il singolo nella morsa dell'omologazione. Tra i meandri oscuri della storia del Novecento, la risposta di Grossman non \ue8 da intendersi solo in senso intellettuale: \ue8 una strada reale che la letteratura, nella sua bellezza "incarnata", manifesta come possibilit\ue0 aperta all'esperienza di ciascuno. I saggi che compongono questo volume il secondo di studi collettanei su Grossman - rappresentano un passo decisivo verso una conoscenza completa della vita e dell'opera del grande autore russo e ne documentano l'appartenenza ai classici della letteratura di ogni tempo

Machine learning methods applied to genotyping data capture interactions between single nucleotide variants in late onset Alzheimer's disease

Introduction Genome-wide association studies (GWAS) in late onset Alzheimer's disease (LOAD) provide lists of individual genetic determinants. However, GWAS do not capture the synergistic effects among multiple genetic variants and lack good specificity. Methods We applied tree-based machine learning algorithms (MLs) to discriminate LOAD (>700 individuals) and age-matched unaffected subjects in UK Biobank with single nucleotide variants (SNVs) from Alzheimer's disease (AD) studies, obtaining specific genomic profiles with the prioritized SNVs. Results MLs prioritized a set of SNVs located in genes PVRL2, TOMM40, APOE, and APOC1, also influencing gene expression and splicing. The genomic profiles in this region showed interaction patterns involving rs405509 and rs1160985, also present in the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. rs405509 located in APOE promoter interacts with rs429358 among others, seemingly neutralizing their predisposing effect. Discussion Our approach efficiently discriminates LOAD from controls, capturing genomic profiles defined by interactions among SNVs in a hot-spot region

Counter-current chromatography for the separation of terpenoids: A comprehensive review with respect to the solvent systems employed

Copyright @ 2014 The Authors.This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.Natural products extracts are commonly highly complex mixtures of active compounds and consequently their purification becomes a particularly challenging task. The development of a purification protocol to extract a single active component from the many hundreds that are often present in the mixture is something that can take months or even years to achieve, thus it is important for the natural product chemist to have, at their disposal, a broad range of diverse purification techniques. Counter-current chromatography (CCC) is one such separation technique utilising two immiscible phases, one as the stationary phase (retained in a spinning coil by centrifugal forces) and the second as the mobile phase. The method benefits from a number of advantages when compared with the more traditional liquid-solid separation methods, such as no irreversible adsorption, total recovery of the injected sample, minimal tailing of peaks, low risk of sample denaturation, the ability to accept particulates, and a low solvent consumption. The selection of an appropriate two-phase solvent system is critical to the running of CCC since this is both the mobile and the stationary phase of the system. However, this is also by far the most time consuming aspect of the technique and the one that most inhibits its general take-up. In recent years, numerous natural product purifications have been published using CCC from almost every country across the globe. Many of these papers are devoted to terpenoids-one of the most diverse groups. Naturally occurring terpenoids provide opportunities to discover new drugs but many of them are available at very low levels in nature and a huge number of them still remain unexplored. The collective knowledge on performing successful CCC separations of terpenoids has been gathered and reviewed by the authors, in order to create a comprehensive document that will be of great assistance in performing future purifications. © 2014 The Author(s)

The acute mania of King George III: A computational linguistic analysis.

We used a computational linguistic approach, exploiting machine learning techniques, to examine the letters written by King George III during mentally healthy and apparently mentally ill periods of his life. The aims of the study were: first, to establish the existence of alterations in the King's written language at the onset of his first manic episode; and secondly to identify salient sources of variation contributing to the changes. Effects on language were sought in two control conditions (politically stressful vs. politically tranquil periods and seasonal variation). We found clear differences in the letter corpus, across a range of different features, in association with the onset of mental derangement, which were driven by a combination of linguistic and information theory features that appeared to be specific to the contrast between acute mania and mental stability. The paucity of existing data relevant to changes in written language in the presence of acute mania suggests that lexical, syntactic and stylometric descriptions of written discourse produced by a cohort of patients with a diagnosis of acute mania will be necessary to support the diagnosis independently and to look for other periods of mental illness of the course of the King's life, and in other historically significant figures with similarly large archives of handwritten documents