Hazard Assessment for Manufacture of Combustible Cartridge Cases using Picrite

A systematic study of the effect of impact, friction, flame and electric spark sensitivity was carried out on the samples combustible cartridge case (CCC) withdrawn at different stages of manufacture. These are Stage I dried felted CCC; stage II-CCC from stage III Coated with nitrocellulose coating. based on the results obtained from various experiments, the CCC can be classified for handling storage and transportation as Group 3, for safety distance category as UN 1.3 and for fire fighting as class 2. further it is concluded from hazard analysis study that the CCCs are safe to handle but these should be protected from naked flame

The Effects of Vision Impairment on Balance in Athletes and Non-Athletes

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Increased Executive Functioning, Attention, and Cortical Thickness in White-Collar Criminals

Very little is known on white collar crime and how it differs to other forms of offending. This study tests the hypothesis that white collar criminals have better executive functioning, enhanced information processing, and structural brain superiorities compared to offender controls. Using a case-control design, executive functioning, orienting, and cortical thickness was assessed in 21 white collar criminals matched with 21 controls on age, gender, ethnicity, and general level of criminal offending. White collar criminals had significantly better executive functioning, increased electrodermal orienting, increased arousal, and increased cortical gray matter thickness in the ventromedial prefrontal cortex, inferior frontal gyrus, somatosensory cortex, and the temporal-parietal junction compared to controls. Results, while initial, constitute the first findings on neurobiological characteristics of white-collar criminals It is hypothesized that white collar criminals have information-processing and brain superiorities that give them an advantage in perpetrating criminal offenses in occupational settings

EAIMS: Emergency Analysis Identification and Management System

Social media has great potential as a means to enable civil protection and law enforcement agencies to more effectively tackle disasters and emergencies. However, there is currently a lack of tools that enable civil protection agencies to easily make use of social media. The Emergency Analysis Identification and Management System (EAIMS) is a prototype service that provides real-time detection of emergency events, related information finding and credibility analysis tools for use over social media during emergencies. This system exploits machine learning over data gathered from past emergencies and disasters to build effective models for identifying new events as they occur, tracking developments within those events and analyzing those developments for the purposes of enhancing the decision making processes of emergency response agencies

Commission des Communautes Europeennes: Groupe du Porte-Parole = Commission of European Communities: Spokesman Group. Spokesman Service Note to National Offices Bio No. (81) 276, 8 July 1981

This paper presents a novel approach for multi-lingual sentiment classification in short texts. This is a challenging task as the amount of training data in languages other than English is very limited. Previously proposed multi-lingual approaches typically require to establish a correspondence to English for which powerful classifiers are already available. In contrast, our method does not require such supervision. We leverage large amounts of weakly-supervised data in various languages to train a multi-layer convolutional network and demonstrate the importance of using pre-training of such networks. We thoroughly evaluate our approach on various multi-lingual datasets, including the recent SemEval-2016 sentiment prediction benchmark (Task 4), where we achieved state-of-the-art performance. We also compare the performance of our model trained individually for each language to a variant trained for all languages at once. We show that the latter model reaches slightly worse – but still acceptable – performance when compared to the single language model, while benefiting from better generalization properties across languages

Early structural brain development in infants exposed to HIV and antiretroviral therapy in utero in a South African birth cohort

INTRODUCTION: There is a growing population of children who are HIV-exposed and uninfected (HEU) with the successful expansion of antiretroviral therapy (ART) use in pregnancy. Children who are HEU are at risk of delayed neurodevelopment; however, there is limited research on early brain growth and maturation. We aimed to investigate the effects of in utero exposure to HIV/ART on brain structure of infants who are HEU compared to HIV-unexposed (HU). METHODS: Magnetic resonance imaging using a T2-weighted sequence was undertaken in a subgroup of infants aged 2–6 weeks enrolled in the Drakenstein Child Health Study birth cohort, South Africa, between 2012 and 2015. Mother–child pairs received antenatal and postnatal HIV testing and ART per local guidelines. We compared subcortical and total grey matter volumes between HEU and HU groups using multivariable linear regression adjusting for infant age, sex, intracranial volume and socio-economic variables. We further assessed associations between brain volumes with maternal CD4 cell count and ART exposure. RESULTS: One hundred forty-six infants (40 HEU; 106 HU) with high-resolution images were included in this analysis (mean age 3 weeks; 50.7% male). All infants who were HEU were exposed to ART (88% maternal triple ART). Infants who were HEU had smaller caudate volumes bilaterally (5.4% reduction, p 0.2). Total grey matter volume was also reduced in infants who were HEU (2.1% reduction, p < 0.05). Exploratory analyses showed that low maternal CD4 cell count (<350 cells/mm3) was associated with decreased infant grey matter volumes. There was no relationship between timing of ART exposure and grey matter volumes. CONCLUSIONS: Lower caudate and total grey matter volumes were found in infants who were HEU compared to HU in the first weeks of life, and maternal immunosuppression was associated with reduced volumes. These findings suggest that antenatal HIV exposure may impact early structural brain development and improved antenatal HIV management may have the potential to optimize neurodevelopmental outcomes of children who are HEU

ABCD Neurocognitive Prediction Challenge 2019: Predicting individual residual fluid intelligence scores from cortical grey matter morphology

We predicted residual fluid intelligence scores from T1-weighted MRI data available as part of the ABCD NP Challenge 2019, using morphological similarity of grey-matter regions across the cortex. Individual structural covariance networks (SCN) were abstracted into graph-theory metrics averaged over nodes across the brain and in data-driven communities/modules. Metrics included degree, path length, clustering coefficient, centrality, rich club coefficient, and small-worldness. These features derived from the training set were used to build various regression models for predicting residual fluid intelligence scores, with performance evaluated both using cross-validation within the training set and using the held-out validation set. Our predictions on the test set were generated with a support vector regression model trained on the training set. We found minimal improvement over predicting a zero residual fluid intelligence score across the sample population, implying that structural covariance networks calculated from T1-weighted MR imaging data provide little information about residual fluid intelligence.Comment: 8 pages plus references, 3 figures, 2 tables. Submission to the ABCD Neurocognitive Prediction Challenge at MICCAI 201

ABCD Neurocognitive Prediction Challenge 2019: Predicting individual fluid intelligence scores from structural MRI using probabilistic segmentation and kernel ridge regression

We applied several regression and deep learning methods to predict fluid intelligence scores from T1-weighted MRI scans as part of the ABCD Neurocognitive Prediction Challenge (ABCD-NP-Challenge) 2019. We used voxel intensities and probabilistic tissue-type labels derived from these as features to train the models. The best predictive performance (lowest mean-squared error) came from Kernel Ridge Regression (KRR; $\lambda=10$), which produced a mean-squared error of 69.7204 on the validation set and 92.1298 on the test set. This placed our group in the fifth position on the validation leader board and first place on the final (test) leader board.Comment: Winning entry in the ABCD Neurocognitive Prediction Challenge at MICCAI 2019. 7 pages plus references, 3 figures, 1 tabl

Broad white matter impairment in multiple system atrophy.

Multiple system atrophy (MSA) is a rare neurodegenerative disorder characterized by the widespread aberrant accumulation of α-synuclein (α-syn). MSA differs from other synucleinopathies such as Parkinson's disease (PD) in that α-syn accumulates primarily in oligodendrocytes, the only source of white matter myelination in the brain. Previous MSA imaging studies have uncovered focal differences in white matter. Here, we sought to build on this work by taking a global perspective on whole brain white matter. In order to do this, in vivo structural imaging and diffusion magnetic resonance imaging were acquired on 26 MSA patients, 26 healthy controls, and 23 PD patients. A refined whole brain approach encompassing the major fiber tracts and the superficial white matter located at the boundary of the cortical mantle was applied. The primary observation was that MSA but not PD patients had whole brain deep and superficial white matter diffusivity abnormalities (p < .001). In addition, in MSA patients, these abnormalities were associated with motor (Unified MSA Rating Scale, Part II) and cognitive functions (Mini-Mental State Examination). The pervasive whole brain abnormalities we observe suggest that there is widespread white matter damage in MSA patients which mirrors the widespread aggregation of α-syn in oligodendrocytes. Importantly, whole brain white matter abnormalities were associated with clinical symptoms, suggesting that white matter impairment may be more central to MSA than previously thought

SentiBench - a benchmark comparison of state-of-the-practice sentiment analysis methods

In the last few years thousands of scientific papers have investigated sentiment analysis, several startups that measure opinions on real data have emerged and a number of innovative products related to this theme have been developed. There are multiple methods for measuring sentiments, including lexical-based and supervised machine learning methods. Despite the vast interest on the theme and wide popularity of some methods, it is unclear which one is better for identifying the polarity (i.e., positive or negative) of a message. Accordingly, there is a strong need to conduct a thorough apple-to-apple comparison of sentiment analysis methods, \textit{as they are used in practice}, across multiple datasets originated from different data sources. Such a comparison is key for understanding the potential limitations, advantages, and disadvantages of popular methods. This article aims at filling this gap by presenting a benchmark comparison of twenty-four popular sentiment analysis methods (which we call the state-of-the-practice methods). Our evaluation is based on a benchmark of eighteen labeled datasets, covering messages posted on social networks, movie and product reviews, as well as opinions and comments in news articles. Our results highlight the extent to which the prediction performance of these methods varies considerably across datasets. Aiming at boosting the development of this research area, we open the methods' codes and datasets used in this article, deploying them in a benchmark system, which provides an open API for accessing and comparing sentence-level sentiment analysis methods