Lead and uranium group abundances in cosmic rays

The importance of Lead and Uranium group abundances in cosmic rays is discussed in understanding their evolution and propagation. The electronic detectors can provide good charge resolution but poor data statistics. The plastic detectors can provide somewhat better statistics but charge resolution deteriorates. The extraterrestrial crystals can provide good statistics but with poor charge resolution. Recent studies of extraterrestrial crystals regarding their calibration to accelerated uranium ion beam and track etch kinetics are discussed. It is hoped that a charge resolution of two charge units can be achieved provided an additional parameter is taken into account. The prospects to study abundances of Lead group, Uranium group and superheavy element in extraterrestrial crystals are discussed, and usefulness of these studies in the light of studies with electronic and plastic detectors is assessed

EBS: decentralised slot synchronisation for broadcast messaging for low-power wireless embedded systems

In this paper, we present a decentralised scheme that facili- tates reliable network wide broadcast messaging without the requirement of strict time synchronisation, for duty-cycled low-power wireless embedded systems. In this emergent broadcast slot (EBS) scheme, devices coordinate their wake- up periods with their neighbours to exchange schedule infor- mation locally. This leads to the emergence of local slot syn- chronisation without the need for either network-wide syn- chronisation or a centralised time synchronisation element. We theoretically show that this scheme converges faster than similar emergent and gradient-based approaches, which we confirm by evaluation on real test-beds. We also show that our scheme exhibits lower overheads while being more tol- erant to disturbances caused by faulty nodes, wireless link failures, contention and interference in presence of deter- ministic propagation delays

Acute Malnutrition and Under-5 Mortality, Northeastern Part of India.

We assessed the prevalence of childhood acute malnutrition and under-five mortality rate (U5MR) in Darbhanga district, India, using a two-stage 49-cluster household survey. A total of 1379 households comprising 8473 people were interviewed. During a 90-day recall period, U5MR was 0.5 [95% confidence interval (CI), 0.2-1.4] per 10 000 per day. The prevalence of global acute malnutrition among 1405 children aged 6-59 months was 15.4% (NCHS) and 19.4% (2006 WHO references). This survey suggests that in Darbhanga district, the population is in a borderline food crisis with few food resources. Appropriate strategies should be developed to improve the overall nutritional and health status of children

Transport and Magnetic Properties of FexVse2 (x = 0 - 0.33)

We present our results of the effect of Fe intercalation on the structural, transport and magnetic properties of 1T-VSe2. Intercalation of iron, suppresses the 110K charge density wave (CDW) transition of the 1T-VSe2. For the higher concentration of iron, formation of a new kind of first order transition at 160K takes place, which go on stronger for the 33% Fe intercalation. Thermopower of the FexVSe2 compounds (x = 0 - 0.33), however do not show any anomaly around the transition. The intercalation of Fe does not trigger any magnetism in the weak paramagnetic 1T-VSe2, and Fe is the low spin state of Fe3+.Comment: 7 pages, 8 figures, 2 table

Higher-Order Gravitational Lensing Reconstruction using Feynman Diagrams

We develop a method for calculating the correlation structure of the Cosmic Microwave Background (CMB) using Feynman diagrams, when the CMB has been modified by gravitational lensing, Faraday rotation, patchy reionization, or other distorting effects. This method is used to calculate the bias of the Hu-Okamoto quadratic estimator in reconstructing the lensing power spectrum up to O(\phi^4) in the lensing potential $\phi$. We consider both the diagonal noise TTTT, EBEB, etc. and, for the first time, the off-diagonal noise TTTE, TBEB, etc. The previously noted large O(\phi^4) term in the second order noise is identified to come from a particular class of diagrams. It can be significantly reduced by a reorganization of the $\phi$ expansion. These improved estimators have almost no bias for the off-diagonal case involving only one $B$ component of the CMB, such as EEEB.Comment: 17 pages, 17 figure

An Iterative Cyclic Algorithm for Designing Vaccine Distribution Networks in Low and Middle-Income Countries

The World Health Organization's Expanded Programme on Immunization (WHO-EPI) was developed to ensure that all children have access to common childhood vaccinations. Unfortunately, because of inefficient distribution networks and cost constraints, millions of children in many low and middle-income countries still go without being vaccinated. In this paper, we formulate a mathematical programming model for the design of a typical WHO-EPI network with the goal of minimizing costs while providing the opportunity for universal coverage. Since it is only possible to solve small versions of the model optimally, we describe an iterative heuristic that cycles between solving restrictions of the original problem and show that it can find very good solutions in reasonable time for larger problems that are not directly solvable.Comment: International Joint Conference on Industrial Engineering and Operations Management- ABEPRO-ADINGOR-IISE-AIM-ASEM (IJCIEOM 2019). Novi Sad, Serbia, July 15-17t

Gravitational Lensing of the CMB: a Feynman Diagram Approach

We develop a Feynman diagram approach to calculating correlations of the Cosmic Microwave Background (CMB) in the presence of distortions. As one application, we focus on CMB distortions due to gravitational lensing by Large Scale Structure (LSS). We study the Hu-Okamoto quadratic estimator for extracting lensing from the CMB and derive the noise of the estimator up to ${\mathcal O}(\phi^4)$ in the lensing potential $\phi$. The previously noted large ${\mathcal O}(\phi^4)$ term can be significantly reduced by a reorganization of the $\phi$ expansion. Our approach makes it simple to obtain expressions for quadratic estimators based on any CMB channel. We briefly discuss other applications to cosmology of this diagrammatic approach, such as distortions of the CMB due to patchy reionization, or due to Faraday rotation from primordial axion fields.Comment: 5 pages, 8 figures, v2: journal versio

An artificial intelligence approach to predicting personality types in dogs

Canine personality and behavioural characteristics have a significant influence on relationships between domestic dogs and humans as well as determining the suitability of dogs for specific working roles. As a result, many researchers have attempted to develop reliable personality assessment tools for dogs. Most previous work has analysed dogs’ behavioural patterns collected via questionnaires using traditional statistical analytic approaches. Artificial Intelligence has been widely and successfully used for predicting human personality types. However, similar approaches have not been applied to data on canine personality. In this research, machine learning techniques were applied to the classification of canine personality types using behavioural data derived from the C-BARQ project. As the dataset was not labelled, in the first step, an unsupervised learning approach was adopted and K-Means algorithm was used to perform clustering and labelling of the data. Five distinct categories of dogs emerged from the K-Means clustering analysis of behavioural data, corresponding to five different personality types. Feature importance analysis was then conducted to identify the relative importance of each behavioural variable’s contribution to each cluster and descriptive labels were generated for each of the personality traits based on these associations. The five personality types identified in this paper were labelled: “Excitable/Hyperattached”, “Anxious/Fearful”, “Aloof/Predatory”, “Reactive/Assertive”, and “Calm/Agreeable”. Four machine learning models including Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Naïve Bayes, and Decision Tree were implemented to predict the personality traits of dogs based on the labelled data. The performance of the models was evaluated using fivefold cross validation method and the results demonstrated that the Decision Tree model provided the best performance with a substantial accuracy of 99%. The novel AI-based methodology in this research may be useful in the future to enhance the selection and training of dogs for specific working and non-working roles