Inversion of diffraction data for amorphous materials

The general and practical inversion of diffraction data-producing a computer model correctly representing the material explored - is an important unsolved problem for disordered materials. Such modeling should proceed by using our full knowledge base, both from experiment and theory. In this paper, we describe a robust method to jointly exploit the power of ab initio atomistic simulation along with the information carried by diffraction data. The method is applied to two very different systems: amorphous silicon and two compositions of a solid electrolyte memory material silver-doped GeSe3 . The technique is easy to implement, is faster and yields results much improved over conventional simulation methods for the materials explored. By direct calculation, we show that the method works for both poor and excellent glass forming materials. It offers a means to add a priori information in first principles modeling of materials, and represents a significant step toward the computational design of non-crystalline materials using accurate interatomic interactions and experimental information

Realistic inversion of diffraction data for an amorphous solid: the case of amorphous silicon

We apply a new method "force enhanced atomic refinement" (FEAR) to create a computer model of amorphous silicon (a-Si), based upon the highly precise X-ray diffraction experiments of Laaziri et al. The logic underlying our calculation is to estimate the structure of a real sample a-Si using experimental data and chemical information included in a non-biased way, starting from random coordinates. The model is in close agreement with experiment and also sits at a suitable minimum energy according to density functional calculations. In agreement with experiments, we find a small concentration of coordination defects that we discuss, including their electronic consequences. The gap states in the FEAR model are delocalized compared to a continuous random network model. The method is more efficient and accurate, in the sense of fitting the diffraction data than conventional melt quench methods. We compute the vibrational density of states and the specific heat, and find that both compare favorably to experiments.Comment: 7 pages and 10 figure

Inversion of Diffraction Data for Amorphous Materials

The general and practical inversion of diffraction data–producing a computer model correctly representing the material explored–is an important unsolved problem for disordered materials. Such modeling should proceed by using our full knowledge base, both from experiment and theory. In this paper, we describe a robust method to jointly exploit the power of ab initio atomistic simulation along with the information carried by diffraction data. The method is applied to two very different systems: amorphous silicon and two compositions of a solid electrolyte memory material silver-doped GeSe3. The technique is easy to implement, is faster and yields results much improved over conventional simulation methods for the materials explored. By direct calculation, we show that the method works for both poor and excellent glass forming materials. It offers a means to add a priori information in first-principles modeling of materials and represents a significant step toward the computational design of non-crystalline materials using accurate interatomic interactions and experimental information

Essential Palatal Myoclonus

Introduction: Palatal myoclonus is a rare condition presenting with clicking sound in ear or muscle tremor in pharynx. There are two varieties: essential and symptomatic. Various treatment options exists ranging from watchful observation to botulinum toxin injection. We have not found any reported case of palatal myoclonus from our country. Here we present a case of essential palatal myoclonus managed with clonazepam. Case report: A young female presented in Ear Nose and Throat clinic with complain of auditory click and spontaneous rhythmic movement of throat muscles for eight months. On examination, there was involuntary, rhythmic contraction of bilateral soft-palate, uvula, and base of tongue. Neurological, eye, and peripheral examination were normal. A diagnosis of essential palatal myoclonus was made. It was managed successfully with clonazepam; patient was still on low dose clonazepam at the time of making this report. Conclusion: Essential palatal myoclonus can be clinically diagnosed and managed even in settings where MRI is not available or affordable

Effectiveness of Same-Day Human Ear Wax Removal as an Office Procedure and Factors Associated with its Successful Removal

Introduction: There are various methods of ear wax extraction and there are no specific guidelines on this subject. Many times we ask patients to instil some wax softening product for a few days and revisit for wax removal. These revisits result in increased cost, discomfort and loss of time. We conducted this study to determine the effectiveness of same-day ear wax removal as an office procedure with one or more techniques. Our secondary objective was to find the association between various factors and successful wax removal. Methods: During the study period, all patients with ear wax managed by a single ENT surgeon were included. History and examination were done and findings noted. One or more methods including probe, forceps, hooks, curette, suction, wax softening with wax softening agents, syringing were applied for wax removal. Complete wax removal was noted as success. Results: There were a total of 63 cases of ear wax among 34 participants. Wax was successfully removed in 52 (82.5%) cases in the same day. Presence of ear ache, narrow canal, complete obstruction and hard dry wax were adversely associated with successful wax removal. Presence of ear fullness, ear discharge, or use of ear drops in home was not significantly associated with successful ear wax removal. Conclusion: We were able to extract wax from a large proportion of patients on the same day of visit, thereby reducing their cost of revisit, however there were 17.5% of cases who could not be treated successfully on the same day

Tobacco Use during Pregnancy and Its Associated Factors in a Mountain District of Eastern Nepal: A Cross-Sectional Questionnaire Survey

BackgroundTobacco using among women is more prevalent in Nepal as compared to other South-East Asian countries. The effect of its use is seen not only on the pregnant women, but also health of the growing fetus is compromised. Currently, little is known about the tobacco use among women especially during pregnancy in Nepal. This study explored the tobacco use prevalence and its associated factors during pregnancy.Materials and methodsA cross-sectional study was conducted in Sankhuwasabha, a mountain district of eastern Nepal. Representative sample of 436 women of reproductive age group with infant were selected by stratified simple random sampling. Data were collected by face-to-face interviews of selected participants. Data were analyzed with SPSS version 16.0. Binary logistic regression was used to analyze the relationship among variables.ResultsThe study revealed that the prevalence of tobacco use during pregnancy was 17.2%. Only one fifth of the research participants were asked to quit tobacco by health workers during last pregnancy. Multivariable analyses revealed that illiteracy (AOR: 2.31, CI: 1.18–4.52), more than two parity (AOR: 2.45, CI: 1.19–5.07), alcohol use during last pregnancy (AOR: 3.99, CI: 1.65–9.68), and having tobacco user within family (AOR: 2.05, CI: 1.11–3.78) are more likely to use tobacco during pregnancy.ConclusionTobacco use during pregnancy was widely prevalent. Tobacco-focused interventions are required for antenatal women to promote cessation among user and prevent initiation with focus on overcoming problems like illiteracy, high parity, alcohol use, and having other tobacco user family members in family

6G Connected Vehicle Framework to Support Intelligent Road Maintenance using Deep Learning Data Fusion

The growth of IoT, edge and mobile Artificial Intelligence (AI) is supporting urban authorities exploit the wealth of information collected by Connected and Autonomous Vehicles (CAV), to drive the development of transformative intelligent transport applications for addressing smart city challenges. A critical challenge is timely and efficient road infrastructure maintenance. This paper proposes an intelligent hierarchical framework for road infrastructure maintenance that exploits the latest developments in 6G communication technologies, deep learning techniques, and mobile edge AI training approaches. The proposed framework abides with the stringent requirements of training efficient machine learning applications for CAV, and is able to exploit the vast numbers of CAVs forecasted to be present on future road networks. At the core of our framework is a novel Convolution Neural Networks (CNN) model which fuses imagery and sensory data to perform pothole detection. Experiments show the proposed model can achieve state of the art performance in comparison to existing approaches while being simple, cost- effective and computationally efficient to deploy. The proposed system can form part of a federated learning framework for facilitating large scale real-time road surface condition monitoring and support adaptive resource allocation for road infrastructure maintenance

An Analysis of Private School Closings

We add to the small literature on private school supply by exploring exits of K-12 private schools. We find that the closure of private schools is not an infrequent event, and use national survey data from the National Center for Education Statistics to study closures of private schools. We assume that the probability of an exit is a function of excess supply of private schools over the demand, as well as the school's characteristics such as age, size, and religious affiliation. Our empirical results generally support the implications of the model. Working Paper 07-0