Disaster displacement and zoonotic disease dynamics: The impact of structural and chronic drivers in Sindh, Pakistan

Projected increases in human and animal displacement driven by climate change, disasters and related environmental degradation will have significant implications to global health. Pathways for infectious disease transmission including zoonoses, diseases transmitted between animals and humans, are complex and non-linear. While forced migration is considered an important driver for the spread of zoonoses, actual disease dynamics remain under researched. This paper presents the findings of a case study investigating how disaster displacement affected zoonotic disease transmission risk following the 2010 ‘superfloods’ in Sindh province, Pakistan. We interviewed 30 key informants and 17 household members across 6 rural communities between March and November 2019, supported by observational studies and a review of secondary data. Results were analysed using the ecosocial theoretical framework. Buffalo, cattle and goats were often the only moveable asset, therefore livestock was an important consideration in determining displacement modality and destination location, and crowded locations were avoided to protect human and animal health. Meanwhile however, livestock was rarely included in the humanitarian response, resulting in communities and households fragmenting according to the availability of livestock provisions. We found that rather than a driver for disease, displacement acted as a process affecting community, household and individual zoonotic disease risk dynamics, based on available resources and social networks before, during and after displacement, rooted in the historical, political and socio-economic context. We conclude that in rural Sindh, disaster displaced populations’ risk of zoonoses is the result of changes in dynamics rooted in pre-existing structural and chronic inequalities, making people more or less vulnerable to disease through multiple interlinked pathways. Our findings have implications for policy makers and humanitarian responders assisting displaced populations dependent on livestock, with a call to integrate livestock support in humanitarian policies and responses for health, survival and recovery

Developed single-phase turbulent flow through a square-pitch rod cluster for an extended range of reynolds numbers.

The mean velocity profiles wall shear stress distribution and all components of the Reynolds stress tensor have been determined from measurements for developed single-phase flow through a square-pitch rod cluster. For a rod pitch-to-diameter ratio of 1.107 four Reynolds numbers in the range 22.6 x 103 to 207.6 x 103 were investigated. The experimental technique which involved a rotatable inclined hot-wire anemometer probe allowed the measurement of secondary flow components of the order of 1 per cent of the local velocity. No evidence was found for secondary flows in the open rod gap area. The highly anisotropic nature of the turbulence particularly for the interconnecting rod gap region was shown by the level of the azimuthal turbulent shear stress. The mean velocity profiles were generally consistent with the logarithmic region of the universal velocity profile using the Patel values for the profile constants. The wall shear stress distribution measured by Preston tubes was shown to be symmetrical around the central rods of the array

Data bank of developed single-phase flow through a square-pitch rod cluster for four reynolds numbers.

Complete tabulated data of the mean velocity profiles wall shear stress distribution and all components of the Reynolds stresses are presented for developed single-phase flow through a square-pitch rod array (p/d = 1.107) at Reynolds numbers of 22.6 x 10 346.3 x 103 and 207.6 x 103

Forecasting Player Behavioral Data and Simulating in-Game Events

Understanding player behavior is fundamental in game data science. Video games evolve as players interact with the game, so being able to foresee player experience would help to ensure a successful game development. In particular, game developers need to evaluate beforehand the impact of in-game events. Simulation optimization of these events is crucial to increase player engagement and maximize monetization. We present an experimental analysis of several methods to forecast game-related variables, with two main aims: to obtain accurate predictions of in-app purchases and playtime in an operational production environment, and to perform simulations of in-game events in order to maximize sales and playtime. Our ultimate purpose is to take a step towards the data-driven development of games. The results suggest that, even though the performance of traditional approaches such as ARIMA is still better, the outcomes of state-of-the-art techniques like deep learning are promising. Deep learning comes up as a well-suited general model that could be used to forecast a variety of time series with different dynamic behaviors

Does cardiac rehabilitation meet minimum standards: an observational study using UK national audit?

Objective: To assess the extent by which programmes meet national minimum standards for the delivery of cardiac rehabilitation (CR) as part of the National Certification Programme for Cardiovascular Rehabilitation (NCP_CR). Methods: The analysis used UK National Audit of Cardiac Rehabilitation (NACR) data extracted and validated for the period 2013–2014 set against six NCP_CR measures deemed as important for the delivery of high-quality CR programmes. Each programme that achieved a single minimum standard was given a score of 1. The range of the scoring for meeting the minimum standards is between 1 and 6. The performance of CR programmes was categorised into three groups: high (score of 5–6), middle (scores of 3–4) and low (scores of 1–2). If a programme did not meet any of the six criteria, they were considered to have failed. Results: Data from 170 CR programmes revealed statistically significant differences among UK CR programmes. The principal findings were that, based on NCP_CR criteria, 30.6% were assessed as high performance with 45.9% as midlevel performance programmes, 18.2% were in the lower-level and 5.3% failed to meet any of the minimum criteria. Conclusions: This study shows that high levels of performance is achievable in the era of modern cardiology and that many CR programmes are close to meeting high performance standards. However, substantial variation, below the recommended minimum standards, exists throughout the UK. National certification should be seen as a positive step to ensure that patients, irrespective of where they live, are accessing quality services

How early can myocardial iron overload occur in Beta thalassemia major?

BACKGROUND: Myocardial siderosis is the most common cause of death in patients with beta thalassemia major(TM). This study aimed at investigating the occurrence, prevalence and severity of cardiac iron overload in a young Chinese population with beta TM. METHODS AND RESULTS: We analyzed T2* cardiac magnetic resonance (CMR), left ventricular ejection fraction (LVEF) and serum ferritin (SF) in 201 beta TM patients. The median age was 9 years old. Patients received an average of 13 units of blood per year. The median SF level was 4536 ng/ml and 165 patients (82.1%) had SF>2500 ng/ml. Myocardial iron overload was detected in 68 patients (33.8%) and severe myocardial iron overload was detected in 26 patients (12.6%). Twenty-two patients ≤10 years old had myocardial iron overload, three of whom were only 6 years old. No myocardial iron overload was detected under the age of 6 years. Median LVEF was 64% (measured by CMR in 175 patients). Five of 6 patients with a LVEF<56% and 8 of 10 patients with cardiac disease had myocardial iron overload. CONCLUSIONS: The TM patients under follow-up at this regional centre in China patients are younger than other reported cohorts, more poorly-chelated, and have a high burden of iron overload. Myocardial siderosis occurred in patients younger than previously reported, and was strongly associated with impaired LVEF and cardiac disease. For such poorly-chelated TM patients, our data shows that the first assessment of cardiac T2* should be performed as early as 6 years old

Liquid-Solid Phase Transition of the System with Two particles in a Rectangular Box

We study the statistical properties of two hard spheres in a two dimensional rectangular box. In this system, the relation like Van der Waals equation loop is obtained between the width of the box and the pressure working on side walls. The auto-correlation function of each particle's position is calculated numerically. By this calculation near the critical width, the time at which the correlation become zero gets longer according to the increase of the height of the box. Moreover, fast and slow relaxation processes like $\alpha$ and $\beta$ relaxations observed in supper cooled liquid are observed when the height of the box is sufficiently large. These relaxation processes are discussed with the probability distribution of relative position of two particles.Comment: 6 figure

sCompile: Critical path identification and analysis for smart contracts

Ethereum smart contracts are an innovation built on top of the blockchain technology, which provides a platform for automatically executing contracts in an anonymous, distributed, and trusted way. The problem is magnified by the fact that smart contracts, unlike ordinary programs, cannot be patched easily once deployed. It is important for smart contracts to be checked against potential vulnerabilities. In this work, we propose an alternative approach to automatically identify critical program paths (with multiple function calls including inter-contract function calls) in a smart contract, rank the paths according to their criticalness, discard them if they are infeasible or otherwise present them with user friendly warnings for user inspection. We identify paths which involve monetary transaction as critical paths, and prioritize those which potentially violate important properties. For scalability, symbolic execution techniques are only applied to top ranked critical paths. Our approach has been implemented in a tool called sCompile, which has been applied to 36,099 smart contracts. The experiment results show that sCompile is efficient, i.e., 5 seconds on average for one smart contract. Furthermore, we show that many known vulnerabilities can be captured if user inspects as few as 10 program paths generated by sCompile. Lastly, sCompile discovered 224 unknown vulnerabilities with a false positive rate of 15.4% before user inspection.Comment: Accepted by ICFEM 201

LMI based stability analysis and controller design for a class of 2D discrete linear systems

Discrete linear repetitive processes are a distinct class of 2D linear systems with applications in areas ranging from long-wall coal cutting through to iterative learning control schemes. The main feature which makes them distinct from other classes of 2D linear systems is that information propagation in one of the two distinct directions only occurs over a finite duration. In this paper we give an LMI based interpretation of stability for the sub-class of so-called discrete linear repetitive processes, both open loop and closed loop under a well defined practically relevant control law, and then apply this theory to solve currently open problems relating to robustness and stability margins for these processes. Also it is shown that the LMI approach to the computation of the stability margins for these processes can be combined with the recently developed concept of a pole for them to link these margins to expected performance - a key feature which is missing from the analysis of stability margins currently available in the 2D systems literature.published_or_final_versio