Nuclear Matter in Intense Magnetic Field and Weak Processes

We study the effect of magnetic field on the dominant neutrino emission processes in neutron stars.The processes are first calculated for the case when the magnetic field does not exceed the critical value to confine electrons to the lowest Landau state.We then consider the more important case of intense magnetic field to evaluate the direct URCA and the neutronisation processes. In order to estimate the effect we derive the composition of cold nuclear matter at high densities and in beta equilibrium, a situation appropriate for neutron stars. The hadronic interactions are incorporated through the exchange of scalar and vector mesons in the frame work of relativistic mean field theory. In addition the effects of anomalous magnetic moments of nucleons are also considered.Comment: 29 pages (LaTeX) including 7 figure

Measuring patient-perceived quality of care in US hospitals using Twitter

BACKGROUND: Patients routinely use Twitter to share feedback about their experience receiving healthcare. Identifying and analysing the content of posts sent to hospitals may provide a novel real-time measure of quality, supplementing traditional, survey-based approaches. OBJECTIVE: To assess the use of Twitter as a supplemental data stream for measuring patient-perceived quality of care in US hospitals and compare patient sentiments about hospitals with established quality measures. DESIGN: 404 065 tweets directed to 2349 US hospitals over a 1-year period were classified as having to do with patient experience using a machine learning approach. Sentiment was calculated for these tweets using natural language processing. 11 602 tweets were manually categorised into patient experience topics. Finally, hospitals with ≥50 patient experience tweets were surveyed to understand how they use Twitter to interact with patients. KEY RESULTS: Roughly half of the hospitals in the US have a presence on Twitter. Of the tweets directed toward these hospitals, 34 725 (9.4%) were related to patient experience and covered diverse topics. Analyses limited to hospitals with ≥50 patient experience tweets revealed that they were more active on Twitter, more likely to be below the national median of Medicare patients (p<0.001) and above the national median for nurse/patient ratio (p=0.006), and to be a non-profit hospital (p<0.001). After adjusting for hospital characteristics, we found that Twitter sentiment was not associated with Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) ratings (but having a Twitter account was), although there was a weak association with 30-day hospital readmission rates (p=0.003). CONCLUSIONS: Tweets describing patient experiences in hospitals cover a wide range of patient care aspects and can be identified using automated approaches. These tweets represent a potentially untapped indicator of quality and may be valuable to patients, researchers, policy makers and hospital administrators

Decay modes of 250No

The Fragment Mass Analyzer at the ATLAS facility has been used to unambiguously identify the mass number associated with different decay modes of the nobelium isotopes produced via 204Pb(48Ca,xn)(252-x)No reactions. Isotopically pure (>99.7%) 204Pb targets were used to reduce background from more favored reactions on heavier lead isotopes. Two spontaneous fission half-lives (t_1/2 = 3.7+1.1-0.8 us and 43+22-15 us) were deduced from a total of 158 fission events. Both decays originate from 250No rather than from neighboring isotopes as previously suggested. The longer activity most likely corresponds to a K-isomer in this nucleus. No conclusive evidence for an alpha branch was observed, resulting in upper limits of 2.1% for the shorter lifetime and 3.4% for the longer activity.Comment: RevTex4, 10 pages, 5 figures, submitted to PR

AI augmented Edge and Fog computing: trends and challenges

In recent years, the landscape of computing paradigms has witnessed a gradual yet remarkable shift from monolithic computing to distributed and decentralized paradigms such as Internet of Things (IoT), Edge, Fog, Cloud, and Serverless. The frontiers of these computing technologies have been boosted by shift from manually encoded algorithms to Artificial Intelligence (AI)-driven autonomous systems for optimum and reliable management of distributed computing resources. Prior work focuses on improving existing systems using AI across a wide range of domains, such as efficient resource provisioning, application deployment, task placement, and service management. This survey reviews the evolution of data-driven AI-augmented technologies and their impact on computing systems. We demystify new techniques and draw key insights in Edge, Fog and Cloud resource management-related uses of AI methods and also look at how AI can innovate traditional applications for enhanced Quality of Service (QoS) in the presence of a continuum of resources. We present the latest trends and impact areas such as optimizing AI models that are deployed on or for computing systems. We layout a roadmap for future research directions in areas such as resource management for QoS optimization and service reliability. Finally, we discuss blue-sky ideas and envision this work as an anchor point for future research on AI-driven computing systems

Severe hyponatremia due to water intoxication in a child with sickle cell disease: A case report

Water intoxication is a potentially fatal hypo-osmolar syndrome with brain function impairment. Isolated symptomatic excessive ingestion of free water is very rare in childhood. We report a case of acute hyponatremia due to water intoxication without Antidiuretic Hormone (ADH) excess in a child with sickle cell disease. The boy was admitted to our Emergency Department because of new-onset prolonged generalized seizures. Blood test showed hyponatremia, and elevated creatine kinase value; neuroimaging was negative. His recent medical history revealed that on the day before he had drunk about 4 liters of water in 2 hours to prevent sickling, because of back pain. He was treated with mild i.v. hydration with normal saline solution and showed progressive clinical improvement and normalization of laboratory test. Rhabdomyolysis is a rare complication of hyponatremia whose underlying mechanism is still unclear

ThermoSim: Deep Learning based Framework for Modeling and Simulation of Thermal-aware Resource Management for Cloud Computing Environments

Current cloud computing frameworks host millions of physical servers that utilize cloud computing resources in the form of different virtual machines. Cloud Data Center (CDC) infrastructures require significant amounts of energy to deliver large scale computational services. Moreover, computing nodes generate large volumes of heat, requiring cooling units in turn to eliminate the effect of this heat. Thus, overall energy consumption of the CDC increases tremendously for servers as well as for cooling units. However, current workload allocation policies do not take into account effect on temperature and it is challenging to simulate the thermal behavior of CDCs. There is a need for a thermal-aware framework to simulate and model the behavior of nodes and measure the important performance parameters which can be affected by its temperature. In this paper, we propose a lightweight framework, ThermoSim, for modeling and simulation of thermal-aware resource management for cloud computing environments. This work presents a Recurrent Neural Network based deep learning temperature predictor for CDCs which is utilized by ThermoSim for lightweight resource management in constrained cloud environments. ThermoSim extends the CloudSim toolkit helping to analyze the performance of various key parameters such as energy consumption, service level agreement violation rate, number of virtual machine migrations and temperature during the management of cloud resources for execution of workloads. Further, different energy-aware and thermal-aware resource management techniques are tested using the proposed ThermoSim framework in order to validate it against the existing framework (Thas). The experimental results demonstrate the proposed framework is capable of modeling and simulating the thermal behavior of a CDC and ThermoSim framework is better than Thas in terms of energy consumption, cost, time, memory usage and prediction accuracy

A New Portfolio Formation Approach to Mispricing of Marketing Performance Indicators: an Application to Customer Satisfaction

There has been a recent debate in the marketing literature concerning the possible mispricing of customer satisfaction. While earlier studies claim that portfolios with attractive out-of-sample properties can be formed by loading on stocks whose firms enjoy high customer satisfaction, later studies challenge this finding. A large part of the disagreement stems from the difficulty of how to actually evaluate mispricing based on the observed portfolio returns. In particular, any portfolio formation method that requires the use of a risk model is open to the criticism of time-varying risk factor loadings due to the changing composition of the portfolio over time. As an alternative, we construct portfolios that are neutral with respect to the desired risk factors a priori. Consequently, no risk model is needed when analyzing the observed returns of our portfolios. Using various ways of measuring customer satisfaction, we do not find any convincing evidence that portfolios that load on high customer satisfaction lead to abnormal returns