Simulation of the radio signal from ultrahigh energy neutrino-initiated showers

PeV neutrinos produce particle showers when they interact with the atomic nuclei in ice. We briefly describe characteristics of these showers and the radio Cherenkov signal produced by the showers. We study pulses from electromagnetic (em), hadronic, and combined em-hadronic showers and propose extrapolations to EeV energies.Comment: Made changes in figure captions and reference 6. 3 pages, to appear in "Lake Louise Winter Institute 2004" conference proceeding

Modelling commercial vehicle handling and rolling stability

YesThis paper presents a multi-degrees-of-freedom non-linear multibody dynamic model of a three-axle heavy commercial vehicle tractor unit, comprising a subchassis, front and rear leaf spring suspensions, steering system, and ten wheels/tyres, with a semi-trailer comprising two axles and eight wheels/tyres. The investigation is mainly concerned with the rollover stability of the articulated vehicle. The models incorporate all sources of compliance, stiffness, and damping, all with non-linear characteristics, and are constructed and simulated using automatic dynamic analysis of mechanical systems formulation. A constant radius turn test and a single lane change test (according to the ISO Standard) are simulated. The constant radius turn test shows the understeer behaviour of the vehicle, and the single lane change manoeuvre was conducted to show the transient behaviour of the vehicle. Non-stable roll and yaw behaviour of the vehicle is predicted at test speeds .90 km/h. Rollover stability of the vehicle is also investigated using a constant radius turn test with increasing speed. The articulated laden vehicle model predicted increased understeer behaviour, due to higher load acting on the wheels of the middle and rear axles of the tractor and the influence of the semi-trailer, as shown by the reduced yaw rate and the steering angle variation during the constant radius turn. The rollover test predicted a critical lateral acceleration value where complete rollover occurs. Unstable behaviour of the articulated vehicle is also predicted in the single lane change manoeuvre

Productivity and performance of irrigated wheat farms across canal commands in the Lower Indus Basin

Irrigated farmingWheatProductivityPerformance evaluationWater managementCropping systemsWater supplySoil propertiesModels

The quarter-point quadratic isoparametric element as a singular element for crack problems

The quadratic isoparametric elements which embody the inverse square root singularity are used for calculating the stress intensity factors at tips of cracks. The strain singularity at a point or an edge is obtained in a simple manner by placing the mid-side nodes at quarter points in the vicinity of the crack tip or an edge. These elements are implemented in NASTRAN as dummy elements. The method eliminates the use of special crack tip elements and in addition, these elements satisfy the constant strain and rigid body modes required for convergence

Health risks of irrigation with untreated urban wastewater in the southern Punjab, Pakistan

Irrigation water / Water quality / Water reuse / Waste waters / Risks / Public health / Diseases / Farmers / Pakistan / Southern Punjab / Haroonabad

Risk-based framework for SLA violation abatement from the cloud service provider's perspective

© The British Computer Society 2018. The constant increase in the growth of the cloud market creates new challenges for cloud service providers. One such challenge is the need to avoid possible service level agreement (SLA) violations and their consequences through good SLA management. Researchers have proposed various frameworks and have made significant advances in managing SLAs from the perspective of both cloud users and providers. However, none of these approaches guides the service provider on the necessary steps to take for SLA violation abatement; that is, the prediction of possible SLA violations, the process to follow when the system identifies the threat of SLA violation, and the recommended action to take to avoid SLA violation. In this paper, we approach this process of SLA violation detection and abatement from a risk management perspective. We propose a Risk Management-based Framework for SLA violation abatement (RMF-SLA) following the formation of an SLA which comprises SLA monitoring, violation prediction and decision recommendation. Through experiments, we validate and demonstrate the suitability of the proposed framework for assisting cloud providers to minimize possible service violations and penalties

Comparing time series with machine learning-based prediction approaches for violation management in cloud SLAs

© 2018 In cloud computing, service level agreements (SLAs) are legal agreements between a service provider and consumer that contain a list of obligations and commitments which need to be satisfied by both parties during the transaction. From a service provider's perspective, a violation of such a commitment leads to penalties in terms of money and reputation and thus has to be effectively managed. In the literature, this problem has been studied under the domain of cloud service management. One aspect required to manage cloud services after the formation of SLAs is to predict the future Quality of Service (QoS) of cloud parameters to ascertain if they lead to violations. Various approaches in the literature perform this task using different prediction approaches however none of them study the accuracy of each. However, it is important to do this as the results of each prediction approach vary according to the pattern of the input data and selecting an incorrect choice of a prediction algorithm could lead to service violation and penalties. In this paper, we test and report the accuracy of time series and machine learning-based prediction approaches. In each category, we test many different techniques and rank them according to their order of accuracy in predicting future QoS. Our analysis helps the cloud service provider to choose an appropriate prediction approach (whether time series or machine learning based) and further to utilize the best method depending on input data patterns to obtain an accurate prediction result and better manage their SLAs to avoid violation penalties