Power law scaling of early-stage forces during granular impact

We experimentally and computationally study the early-stage forces during intruder impacts with granular beds in the regime where the impact velocity approaches the granular force propagation speed. Experiments use 2D assemblies of photoelastic disks of varying stiffness, and complimentary discrete-element simulations are performed in 2D and 3D. The peak force during the initial stages of impact and the time at which it occurs depend only on the impact speed, the intruder diameter, the mass density of the grains, and the elastic modulus of the grains according to power-law scaling forms that are not consistent with Poncelet models, granular shock theory, or added-mass models. The insensitivity of our results to many system details suggest that they may also apply to impacts into similar materials like foams and emulsions.Comment: 5 pages, 4 figure

Surface Polaron Formation in the Holstein model

The effect of a solid-vacuum interface on the properties of a strongly coupled electron-phonon system is analyzed using dynamical mean-field theory to solve the Holstein model in a semi-infinite cubic lattice. Polaron formation is found to occur more easily (i.e., for a weaker electron-phonon coupling) on the surface than in the bulk. On the other hand, the metal-insulator transition associated to the binding of polarons takes place at a unique critical strength in the bulk and at the surface.Comment: 5 pages, 3 figure

Current Trends of fMRI in Vision Science: A Review

Studying brain functional activities is an area that is experiencing rapid interest in the field of neuroimaging. Functional magnetic resonance imaging (fMRI) has provided vision science researchers a powerful and noninvasive tool to understand eye function and correlate it with brain activities. In this chapter, we focus on the physiological aspects followed by a literature review. More specifically, to motivate and appreciate the complexity of the visual system, we will begin with a description of specific stages the visual pathway, beginning from the distal stimulus and ending in the visual cortex. More importantly, the development of ascending visual pathway will be discussed in order to help in understanding various disorders associated with it such as monochromacy, albinism, amblyopia (refractive, strabismic). In doing so we will divide the first half into two main sections, the visual pathway and the development of the ascending pathway. The first of these sections will be mostly an anatomy review and the latter will discuss the development of this anatomy with specific examples of disorders as a result of abnormal development. We will then discuss fMRI studies with focus on vision science applications. The remaining sections of this chapter will be highlighting the work done on mainly oculomotor function, some perception and visual dysfunction with fMRI and investigate the differences and similarities in their findings. We will then conclude with a discussion on how this relates to neurologists, neuroscientists, ophthalmologists and other specialists

An Enhanced Source Location Privacy based on Data Dissemination in Wireless Sensor Networks (DeLP)

open access articleWireless Sensor Network is a network of large number of nodes with limited power and computational capabilities. It has the potential of event monitoring in unattended locations where there is a chance of unauthorized access. The work that is presented here identifies and addresses the problem of eavesdropping in the exposed environment of the sensor network, which makes it easy for the adversary to trace the packets to find the originator source node, hence compromising the contextual privacy. Our scheme provides an enhanced three-level security system for source location privacy. The base station is at the center of square grid of four quadrants and it is surrounded by a ring of flooding nodes, which act as a first step in confusing the adversary. The fake node is deployed in the opposite quadrant of actual source and start reporting base station. The selection of phantom node using our algorithm in another quadrant provides the third level of confusion. The results show that Dissemination in Wireless Sensor Networks (DeLP) has reduced the energy utilization by 50% percent, increased the safety period by 26%, while providing a six times more packet delivery ratio along with a further 15% decrease in the packet delivery delay as compared to the tree-based scheme. It also provides 334% more safety period than the phantom routing, while it lags behind in other parameters due to the simplicity of phantom scheme. This work illustrates the privacy protection of the source node and the designed procedure may be useful in designing more robust algorithms for location privac

Latent consequences of early-life lead (Pb) exposure and the future: Addressing the Pb crisis

Background. The lead (Pb) exposure crisis in Flint, Michigan has passed from well-publicized event to a footnote, while its biological and social impact will linger for lifetimes. Interest in the “water crisis” has dropped to pre-event levels, which is neither appropriate nor safe. Flint’s exposure was severe, but it was not unique. Problematic Pb levels have also been found in schools and daycares in 42 states in the USA. The enormity of Pb exposure via municipal water systems requires multiple responses. Herein, we focus on addressing a possible answer to long-term sequelae of Pb exposure. We propose “4R’s” (remediation, renovation, reallocation, and research) against the Pb crisis that goes beyond a short-term fix. Remediation for affected individuals must continue to provide clean water and deal with both short and long-term effects of Pb exposure. Renovation of current water delivery systems, at both system-wide and individual site levels, is necessary. Reallocation of resources is needed to ensure these two responses occur and to get communities ready for potential sequelae of Pb exposure. Finally, properly focused research can track exposed individuals and illuminate latent (presumably epigenetic) results of Pb exposure and inform further resource reallocation. Conclusion. Motivation to act by not only the general public but also by scientific and medical leaders must be maintained beyond initial news cycle spikes and an annual follow-up story. Environmental impact of Pb contamination of drinking water goes beyond one exposure incident in an impoverished and forgotten Michigan city. Population effects must be addressed long-term and nationwide

Machine Learning for Diabetes and Mortality Risk Prediction From Electronic Health Records

Data science can provide invaluable tools to better exploit healthcare data to improve patient outcomes and increase cost-effectiveness. Today, electronic health records (EHR) systems provide a fascinating array of data that data science applications can use to revolutionise the healthcare industry. Utilising EHR data to improve the early diagnosis of a variety of medical conditions/events is a rapidly developing area that, if successful, can help to improve healthcare services across the board. Specifically, as Type-2 Diabetes Mellitus (T2DM) represents one of the most serious threats to health across the globe, analysing the huge volumes of data provided by EHR systems to investigate approaches for early accurately predicting the onset of T2DM, and medical events such as in-hospital mortality, are two of the most important challenges data science currently faces. The present thesis addresses these challenges by examining the research gaps in the existing literature, pinpointing the un-investigated areas, and proposing a novel machine learning modelling given the difficulties inherent in EHR data. To achieve these aims, the present thesis firstly introduces a unique and large EHR dataset collected from Saudi Arabia. Then we investigate the use of a state-of-the-art machine learning predictive models that exploits this dataset for diabetes diagnosis and the early identification of patients with pre-diabetes by predicting the blood levels of one of the main indicators of diabetes and pre-diabetes: elevated Glycated Haemoglobin (HbA1c) levels. A novel collaborative denoising autoencoder (Col-DAE) framework is adopted to predict the diabetes (high) HbA1c levels. We also employ several machine learning approaches (random forest, logistic regression, support vector machine, and multilayer perceptron) for the identification of patients with pre-diabetes (elevated HbA1c levels). The models employed demonstrate that a patient's risk of diabetes/pre-diabetes can be reliably predicted from EHR records. We then extend this work to include pioneering adoption of recent technologies to investigate the outcomes of the predictive models employed by using recent explainable methods. This work also investigates the effect of using longitudinal data and more of the features available in the EHR systems on the performance and features ranking of the employed machine learning models for predicting elevated HbA1c levels in non-diabetic patients. This work demonstrates that longitudinal data and available EHR features can improve the performance of the machine learning models and can affect the relative order of importance of the features. Secondly, we develop a machine learning model for the early and accurate prediction all in-hospital mortality events for such patients utilising EHR data. This work investigates a novel application of the Stacked Denoising Autoencoder (SDA) to predict in-hospital patient mortality risk. In doing so, we demonstrate how our approach uniquely overcomes the issues associated with imbalanced datasets to which existing solutions are subject. The proposed model –– using clinical patient data on a variety of health conditions and without intensive feature engineering –– is demonstrated to achieve robust and promising results using EHR patient data recorded during the first 24 hours after admission