Edge caching with mobility prediction in virtualized LTE mobile networks

Abstract Mobile Edge Computing enables the deployment of services, applications, content storage and processing in close proximity to mobile end users. This highly distributed computing environment can be used to provide ultra-low latency, precise positional awareness and agile applications, which could significantly improve user experience. In order to achieve this, it is necessary to consider next-generation paradigms such as Information-Centric Networking and Cloud Computing, integrated with the upcoming 5th Generation networking access. A cohesive end-to-end architecture is proposed, fully exploiting Information-Centric Networking together with the Mobile Follow-Me Cloud approach, for enhancing the migration of content-caches located at the edge of cloudified mobile networks. The chosen content-relocation algorithm attains content-availability improvements of up to 500 when a mobile user performs a request and compared against other existing solutions. The performed evaluation considers a realistic core-network, with functional and non-functional measurements, including the deployment of the entire system, computation and allocation/migration of resources. The achieved results reveal that the proposed architecture is beneficial not only from the users’ perspective but also from the providers point-of-view, which may be able to optimize their resources and reach significant bandwidth savings.

Ensemble prediction distribution maps of macroalgae for current conditions and four climate change scenarios and high resultion bathymetry for Potter Cove, WAP, Antarctica

Species distribution models (SDM) predict species occurrence based on statistical relationships with environmental conditions. The R-package biomod2 which includes 10 different SDM techniques and 10 different evaluation methods was used in this study. Macroalgae are the main biomass producers in Potter Cove, King George Island (Isla 25 de Mayo), Antarctica, and they are sensitive to climate change factors such as suspended particulate matter (SPM). Macroalgae presence and absence data were used to test SDMs suitability and, simultaneously, to assess the environmental response of macroalgae as well as to model four scenarios of distribution shifts by varying SPM conditions due to climate change. According to the averaged evaluation scores of Relative Operating Characteristics (ROC) and True scale statistics (TSS) by models, those methods based on a multitude of decision trees such as Random Forest and Classification Tree Analysis, reached the highest predictive power followed by generalized boosted models (GBM) and maximum-entropy approaches (Maxent). The final ensemble model used 135 of 200 calculated models (TSS > 0.7) and identified hard substrate and SPM as the most influencing parameters followed by distance to glacier, total organic carbon (TOC), bathymetry and slope. The climate change scenarios show an invasive reaction of the macroalgae in case of less SPM and a retreat of the macroalgae in case of higher assumed SPM values.

Rail-highway crossing hazard prediction research results. Final report.

Transportation Department, Washington, D.C.

Highway noise measurements for verification of prediction models. Final report.

Transportation Department, Office of the Assistant Secretary for Systems Development and Technology, Washington, D.C.

A 50-megawatt arc heater: scaling parameters and performance prediction

"Project No. 1426, Task No. 142612."

Prediction of subclinicial left ventricular dysfunction with strain rate imaging in patients with asymptomatic severe mitral regurgitation

This study sought the ability of strain rate imaging to detect subclinical left ventricular dysfunction, as evidenced by reduced contractile reserve (CR) in 32 asymptomatic patients with isolated severe mitral regurgitation. Compared with CR- patients (n = 10), CR+ patients (n = 22) had significantly higher end-systolic strain and peak systolic strain rate, but these parameters were not significantly different between CR+ patients and matched normal controls. (C) 2004 by Excerpta Medica, Inc.

Quantitative myocardial contrast echocardiography for prediction of thrombolysis in myocardial infarction flow in acute myocardial infarction

Clinical evaluation of arterial potency in acute ST-elevation myocardial infarction (STEMI) is unreliable. We sought to identify infarction and predict infarct-related artery potency measured by the Thrombolysis In Myocardial Infarction (TIMI) score with qualitative and quantitative intravenous myocardial contrast echocardiography (MCE). Thirty-four patients with suspected STEMI underwent MCE before emergency angiography and planned angioplasty. MCE was performed with harmonic imaging and variable triggering intervals during intravenous administration of Optison. Myocardial perfusion was quantified offline, fitting an exponential function to contrast intensity at various pulsing intervals. Plateau myocardial contrast intensity (A), rate of rise (beta), and myocardial flow (Q = A x beta) were assessed in 6 segments. Qualitative assessment of perfusion defects was sensitive for the diagnosis of infarction (sensitivity 93%) and did not differ between anterior and inferior infarctions. However, qualitative assessment had only moderate specificity (50%), and perfusion defects were unrelated to TIMI flow. In patients with STEMI, quantitatively derived myocardial blood flow Q (A x beta) was significantly lower in territories subtended by an artery with impaired (TIMI 0 to 2) flow than those territories supplied by a reperfused artery with TIMI 3 flow (10.2 +/- 9.1 vs 44.3 +/- 50.4, p = 0.03). Quantitative flow was also lower in segments with impaired flow in the subtending artery compared with normal patients with TIMI 3 flow (42.8 +/- 36.6, p = 0.006) and all segments with TIMI 3 flow (35.3 +/- 32.9, p = 0.018). An receiver-operator characteristic curve derived cut-off Q value of

Use of tissue Doppler imaging to facilitate the prediction of events in patients with abnormal left ventricular function dobutamine echocardiography

The extent of abnormality in patients with positive do-butamine echocardiography (DE) is predictive of risk, but the wall motion score (WMS) has low concordance among observers. We sought whether quantifying the extent of abnormal wall motion using tissue Doppler (TD) could guide risk assessment in patients with abnormal DE in 576 patients with known or suspected coronary artery disease; standard DE was combined with color TD imaging at peak dose. WMS was assessed by an expert observer and studies were identified as abnormal in the presence of 2:1 segments with resting or stress-induced wall motion abnormalities. Patients with abnormal DE had peak systolic velocity measured in each segment. Tissue tracking was used to measure myocardial displacement. Follow-up for death or infarction was per-formed after. 16 +/- 12 months. Of 251 patients with abnormal DE, 22 patients died (20 from cardiac causes) and 7 had nonfatal myocardial infarctionis. The average WMS in patients with events was 1.8 +/- 0.5, compared with 1.7 +/- 0.5 in patients without events (p = NS). The average systolic velocity in patients with events was 4.9 +/- 1.7 cm/s and 6.4 +/- 6.5 cm/s in the patients without events (p <0.001). The average tissue tracking in patients with events was 4.5 +/- 1.5 mm and was significant. (5.7 +/- 3.1 mm),in those,without events (p <0.001). Thus, TD is an alternative to WMS for quantifying the total extent of abnormal left ventricular function-at DE, and appears to be superior for predicting adverse outcomes. (C) 2004 by Excerpta Medica, Inc.

How preferred ear for listening moderates emotional cognitions in the prediction of personality

Two studies investigate how cognitions of aurally presented information interact with aural preference (self-reported preferred ear for listening) in the prediction of personality. In Study 1, participants provided attractiveness cognitions of various statements after listening to aurally presented material. Aural preference x attractiveness interactions significantly predicted Extraversion and Neuroticism. In Study 2, participants provided cognitions of pleasantness from various scenarios. An aural preference x pleasantness interaction significantly predicted Neuroticism. Although other interpretations are possible, I conclude that these findings support the idea of aural preference as a useful measure of hemispheric asymmetry, such that the right hemisphere (left aural preference) provides facilitation of emotional expression, whereas the left hemisphere (right aural preference) provides suppression. My findings support a more historical view of emotional asymmetry than the more modem approach-avoidance perspective and suggest that moderating effects of hemispheric asymmetry are important to include in studies investigating emotions associated with personality.

Efffluent flux prediction in variably saturated soil zones within a septic tank-soil absorption trench

The treatment and hydraulic mechanisms in a septic tank-soil absorption system ( SAS) are highly influenced by the clogging layer or biomat zone which develops on bottom and lower sidewall surfaces within the trench. Flow rates through the biomat and sub-biomat zones are governed largely by the biomat hydraulic properties (resistance and hydraulic conductivity) and the unsaturated hydraulic conductivity of the underlying soil. One- and 2-dimensional models were used to investigate the relative importance of sidewall and vertical flow rates and pathways in SAS. Results of 1-dimensional modelling show that several orders of magnitude variation in saturated hydraulic conductivity (Ks) reduce to a 1 order of magnitude variation in long-term flow rates. To increase the reliability of prediction of septic trench hydrology, HYDRUS-2D was used to model 2-dimensional flow. In the permeable soils, under high trench loading, effluent preferentially flowed in the upper region of the trench where no resistant biomat was present (the exfiltration zone). By comparison, flow was more evenly partitioned between the biomat zones and the exfiltration zones of the low permeability soil. An increase in effluent infiltration corresponded with a greater availability of exfiltration zone, rather than a lower resistance of biomat. Results of modelling simulations demonstrated the important role that a permeable A horizon may play in limiting surface surcharge of effluent under high trench hydraulic loading.

Prediction of functional capacity in chronic heart failure patients using myocardial tissue Doppler

Background. Exercise therapy improves functional capacity in CHF, but selection and individualization of training would be helped by a simple non-invasive marker of peak VO2. Peak VO2 in these pts is difficult to predict without direct measurement, and LV ejection fraction is a poor predictor. Myocardial tissue velocities are less load-dependent, and may be predictive of the exercise response in CHF pts. We sought to use tissue velocity as a predictor of peak VO2 in CHF pts. Methods. Resting 2D-echocardiography and tissue Doppler imaging were performed in 182 CHF pts (159 male, age 62±10 years) before and after metabolic exercise testing. The majority of these patients (129, 71%) had an ischemic cardiomyopathy, with resting EF of 35±13% and a peak VO2 of 13.5±4.7 ml/kg/min. Results. Neither resting EF (r=0.15) nor peak EF (r=0.18, both p=NS) were correlated with peak VO2. However, peak VO2 correlated with peak systolic velocity in septal (Vss, r=0.31) and lateral walls (Vsl, r=0.26, both p=0.01). In a general linear model (r2 = 0.25), peak VO2 was calculated from the following equation: 9.6 + 0.68*Vss - 0.09*age + 0.06*maximum HR. This model proved to be a superior predictor of peak VO2 (r=0.51, p=0.01) than the standard prediction equations of Wasserman (r= -0.12, p=0.01). Conclusions. Resting tissue Doppler, age and maximum heart rate may be used to predict functional capacity in CHF patients. This may be of use in selecting and following the response to therapy, including for exercise training.

Customer information system data pre-processing with feature selection techniques for non-technical losses prediction in an electricity market

Non-technical losses (NTL) identification and prediction are important tasks for many utilities. Data from customer information system (CIS) can be used for NTL analysis. However, in order to accurately and efficiently perform NTL analysis, the original data from CIS need to be pre-processed before any detailed NTL analysis can be carried out. In this paper, we propose a feature selection based method for CIS data pre-processing in order to extract the most relevant information for further analysis such as clustering and classifications. By removing irrelevant and redundant features, feature selection is an essential step in data mining process in finding optimal subset of features to improve the quality of result by giving faster time processing, higher accuracy and simpler results with fewer features. Detailed feature selection analysis is presented in the paper. Both time-domain and load shape data are compared based on the accuracy, consistency and statistical dependencies between features.

Wear-type rail corrugation prediction: Field study

Rail corrugation consists of undesirable periodic fluctuations in wear on railway track and costs the railway industry substantially for it's removal by regrinding. Much research has been performed on this problem, particularly over the past two decades, however, a reliable cure remains elusive for wear-type corrugations. Recently the growth behaviour of wear-type rail corrugation-has been investigated using theoretical and experimental models as part of the RailCRC Project (#18). A critical part of this work is the tuning and validation of these models via an extensive field testing program. Rail corrugations have been monitored for 2 years on sites throughout Australia. Measured rail surface profiles are used to determine corrugation growth rates on each site. Growth rates and other characteristics are compared with theoretical predictions from a computer model for validation. The results from several pertinent sites are presented and discussed.

Towards a Modeling Environment for Earth Systems Simulations

The developments of models in Earth Sciences, e.g. for earthquake prediction and for the simulation of mantel convection, are fare from being finalized. Therefore there is a need for a modelling environment that allows scientist to implement and test new models in an easy but flexible way. After been verified, the models should be easy to apply within its scope, typically by setting input parameters through a GUI or web services. It should be possible to link certain parameters to external data sources, such as databases and other simulation codes. Moreover, as typically large-scale meshes have to be used to achieve appropriate resolutions, the computational efficiency of the underlying numerical methods is important. Conceptional this leads to a software system with three major layers: the application layer, the mathematical layer, and the numerical algorithm layer. The latter is implemented as a C/C++ library to solve a basic, computational intensive linear problem, such as a linear partial differential equation. The mathematical layer allows the model developer to define his model and to implement high level solution algorithms (e.g. Newton-Raphson scheme, Crank-Nicholson scheme) or choose these algorithms form an algorithm library. The kernels of the model are generic, typically linear, solvers provided through the numerical algorithm layer. Finally, to provide an easy-to-use application environment, a web interface is (semi-automatically) built to edit the XML input file for the modelling code. In the talk, we will discuss the advantages and disadvantages of this concept in more details. We will also present the modelling environment escript which is a prototype implementation toward such a software system in Python (see www.python.org). Key components of escript are the Data class and the PDE class. Objects of the Data class allow generating, holding, accessing, and manipulating data, in such a way that the actual, in the particular context best, representation is transparent to the user. They are also the key to establish connections with external data sources. PDE class objects are describing (linear) partial differential equation objects to be solved by a numerical library. The current implementation of escript has been linked to the finite element code Finley to solve general linear partial differential equations. We will give a few simple examples which will illustrate the usage escript. Moreover, we show the usage of escript together with Finley for the modelling of interacting fault systems and for the simulation of mantel convection.

Conditional distribution modeling for estimating and exploiting uncertainty in control systems

This paper presents a general methodology for estimating and incorporating uncertainty in the controller and forward models for noisy nonlinear control problems. Conditional distribution modeling in a neural network context is used to estimate uncertainty around the prediction of neural network outputs. The developed methodology circumvents the dynamic programming problem by using the predicted neural network uncertainty to localize the possible control solutions to consider. A nonlinear multivariable system with different delays between the input-output pairs is used to demonstrate the successful application of the developed control algorithm. The proposed method is suitable for redundant control systems and allows us to model strongly non Gaussian distributions of control signal as well as processes with hysteresis.