A Case-control Study of Haemorrhagic Septicaemia in Buffaloes and Cattle in Karachi, Pakistan, in 2012

A retrospective epidemiological case–control study was performed in Karachi, Pakistan, from January to April 2013. The owners of 217 dairy cattle and buffalo farms from six different locations in Karachi were interviewed. The aim of the study was to identify risk factors associated with the presence of haemorrhagic septicaemia (HS). Farms with a history of at least one instance of sudden death in a dairy animal during 2012 and a positive clinical HS diagnosis (made by local veterinarians) were defined as cases. Farms having no history of sudden deaths in 2012 were defined as controls. Univariable analyses were initially conducted, and factors with P ≤ 0.25 were offered to a multivariable logistic regression model to identify putative risk factors. The final multivariable logistic model contained five factors. Vaccination was found to be a protective factor (OR = 0.22) along with the length of time cattle were kept on farm (months). For every extra month cattle were kept, the odds of HS disease were reduced by a factor of 0.9. In contrast, for every extra animal in a herd, the risk of infection increased by a factor of 1.01. Supplying underground water and the presence of foot and mouth disease on the farm increased the risk by 2.90 and 2.37, respectively. To understand the epidemiology of HS in Karachi dairy herds, more in-depth research is required to study the risk and protective factors identified in this survey and to evaluate risk mitigation strategies, where possible

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

Concept and benchmark results for Big Data energy forecasting based on Apache Spark

The present article describes a concept for the creation and application of energy forecasting models in a distributed environment. Additionally, a benchmark comparing the time required for the training and application of data-driven forecasting models on a single computer and a computing cluster is presented. This comparison is based on a simulated dataset and both R and Apache Spark are used. Furthermore, the obtained results show certain points in which the utilization of distributed computing based on Spark may be advantageous

Limits of the seismogenic zone in the epicentral region of the 26 December 2004 great Sumatra-Andaman earthquake: Results from seismic refraction and wide-angle reflection surveys and thermal modeling

The 26 December 2004 Sumatra earthquake (Mw = 9.1) initiated around 30 km depth and ruptured 1300 km of the Indo-Australian Sunda plate boundary. During the Sumatra OBS (ocean bottom seismometer) survey, a wide angle seismic profile was acquired across the epicentral region. A seismic velocity model was obtained from combined travel time tomography and forward modeling. Together with reflection seismic data from the SeaCause II cruise, the deep structure of the source region of the great earthquake is revealed. Four to five kilometers of sediments overlie the oceanic crust at the trench, and the subducting slab can be imaged down to a depth of 35 km. We find a crystalline backstop 120 km from the trench axis, below the fore arc basin. A high velocity zone at the lower landward limit of the raycovered domain, at 22 km depth, marks a shallow continental Moho, 170 km from the trench. The deep structure obtained from the seismic data was used to construct a thermal model of the fore arc in order to predict the limits of the seismogenic zone along the plate boundary fault. Assuming 100C-150C as its updip limit, the seismogenic zone is predicted to begin 530 km from the trench. The downdip limit of the 2004 rupture as inferred from aftershocks is within the 350C 450C temperature range, but this limit is 210-250 km from the trench axis and is much deeper than the fore arc Moho. The deeper part of the rupture occurred along the contact between the mantle wedge and the downgoing plate

Model confidence sets and forecast combination: an application to age-specific mortality

Background: Model averaging combines forecasts obtained from a range of models, and it often produces more accurate forecasts than a forecast from a single model. Objective: The crucial part of forecast accuracy improvement in using the model averaging lies in the determination of optimal weights from a finite sample. If the weights are selected sub-optimally, this can affect the accuracy of the model-averaged forecasts. Instead of choosing the optimal weights, we consider trimming a set of models before equally averaging forecasts from the selected superior models. Motivated by Hansen et al. (2011), we apply and evaluate the model confidence set procedure when combining mortality forecasts. Data & Methods: The proposed model averaging procedure is motivated by Samuels and Sekkel (2017) based on the concept of model confidence sets as proposed by Hansen et al. (2011) that incorporates the statistical significance of the forecasting performance. As the model confidence level increases, the set of superior models generally decreases. The proposed model averaging procedure is demonstrated via national and sub-national Japanese mortality for retirement ages between 60 and 100+. Results: Illustrated by national and sub-national Japanese mortality for ages between 60 and 100+, the proposed model-average procedure gives the smallest interval forecast errors, especially for males. Conclusion: We find that robust out-of-sample point and interval forecasts may be obtained from the trimming method. By robust, we mean robustness against model misspecification

Multiple shades of grey: Opening the black box of public sector executives' hybrid role identities

Public sector reforms of recent decades in Europe have promoted managerialism and aimed at introducing private sector thinking and practices. However, with regard to public sector executives' self-understanding, managerial role identities have not replaced bureaucratic ones; rather, components from both paradigms have combined. In this article, we introduce a bi-dimensional approach (attitudes and practices) that allows for different combinations and forms of hybridity. Empirically, we explore the role identities of public sector executives across Europe, building on survey data from over 7,000 top public officials in 19 countries (COCOPS survey). We identify country-level profiles, as well as patterns across countries, and find that administrative traditions can account for these profiles and patterns only to a limited extent. Rather, they have to be complemented by factors such as stability of the institutional environment (indicating lower shares of hybrid combinations) or extent of reform pressures (indicating higher shares of hybrid combinations)