998 resultados para Earthquake prediction.
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We predict here from first-principle calculations that finite-length (n,0) single walled carbon nanotubes (SWCNTs) with H-termination at the open ends displaying antiferromagnetic coupling when n is greater than 6. An opposite local gating effect of the spin states, i.e., half metallicity, is found under the influence of an external electric field along the direction of tube axis. Remarkably, boron doping of unpassivated SWCNTs at both zigzag edges is found to favor a ferromagnetic ground state, with the B-doped tubes displaying half-metallic behavior even in the absence of an electric field. Aside of the intrinsic interest of these results, an important avenue for development of CNT-based spintronic is suggested.
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Recent literature has focused on realized volatility models to predict financial risk. This paper studies the benefit of explicitly modeling jumps in this class of models for value at risk (VaR) prediction. Several popular realized volatility models are compared in terms of their VaR forecasting performances through a Monte Carlo study and an analysis based on empirical data of eight Chinese stocks. The results suggest that careful modeling of jumps in realized volatility models can largely improve VaR prediction, especially for emerging markets where jumps play a stronger role than those in developed markets.
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This paper proposes a practical prediction procedure for vertical displacement of a Rotarywing Unmanned Aerial Vehicle (RUAV) landing deck in the presence of stochastic sea state disturbances. A proper time series model tending to capture characteristics of the dynamic relationship between an observer and a landing deck is constructed, with model orders determined by a novel principle based on Bayes Information Criterion (BIC) and coefficients identified using the Forgetting Factor Recursive Least Square (FFRLS) method. In addition, a fast-converging online multi-step predictor is developed, which can be implemented more rapidly than the Auto-Regressive (AR) predictor as it requires less memory allocations when updating coefficients. Simulation results demonstrate that the proposed prediction approach exhibits satisfactory prediction performance, making it suitable for integration into ship-helicopter approach and landing guidance systems in consideration of computational capacity of the flight computer.
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A method for prediction of the radiation pattern of N strongly coupled antennas with mismatched sources is presented. The method facilitates fast and accurate design of compact arrays. The prediction is based on the measured N-port S parameters of the coupled antennas and the N active element patterns measured in a 50 ω environment. By introducing equivalent power sources, the radiation pattern with excitation by sources with arbitrary impedances and various decoupling and matching networks (DMN) can be accurately predicted without the need for additional measurements. Two experiments were carried out for verification: pattern prediction for parasitic antennas with different loads and for antennas with DMN. The difference between measured and predicted patterns was within 1 to 2 dB.
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Organisations are constantly seeking efficiency gains for their business processes in terms of time and cost. Management accounting enables detailed cost reporting of business operations for decision making purposes, although significant effort is required to gather accurate operational data. Process mining, on the other hand, may provide valuable insight into processes through analysis of events recorded in logs by IT systems, but its primary focus is not on cost implications. In this paper, a framework is proposed which aims to exploit the strengths of both fields in order to better support management decisions on cost control. This is achieved by automatically merging cost data with historical data from event logs for the purposes of monitoring, predicting, and reporting process-related costs. The on-demand generation of accurate, relevant and timely cost reports, in a style akin to reports in the area of management accounting, will also be illustrated. This is achieved through extending the open-source process mining framework ProM.
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Travel time prediction has long been the topic of transportation research. But most relevant prediction models in the literature are limited to motorways. Travel time prediction on arterial networks is challenging due to involving traffic signals and significant variability of individual vehicle travel time. The limited availability of traffic data from arterial networks makes travel time prediction even more challenging. Recently, there has been significant interest of exploiting Bluetooth data for travel time estimation. This research analysed the real travel time data collected by the Brisbane City Council using the Bluetooth technology on arterials. Databases, including experienced average daily travel time are created and classified for approximately 8 months. Thereafter, based on data characteristics, Seasonal Auto Regressive Integrated Moving Average (SARIMA) modelling is applied on the database for short-term travel time prediction. The SARMIA model not only takes the previous continuous lags into account, but also uses the values from the same time of previous days for travel time prediction. This is carried out by defining a seasonality coefficient which improves the accuracy of travel time prediction in linear models. The accuracy, robustness and transferability of the model are evaluated through comparing the real and predicted values on three sites within Brisbane network. The results contain the detailed validation for different prediction horizons (5 min to 90 minutes). The model performance is evaluated mainly on congested periods and compared to the naive technique of considering the historical average.
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A qualitative analysis of the expected dilatation strain field in the vicinity of an array of grain-boundary (GB) dislocations is presented. The analysis provides a basis for the prediction of the critical current densities (jc) across low-angle YBa2Cu3O7- (YBCO) GBs as a function of their energy. The introduction of the GB energy allows the extension of the analysis to high-angle GBs using established models which predict the GB energy as a function of misorientation angle. The results are compared to published data for jc across [001]-tilt YBCO GBs for the full range of misorientations, showing a good fit. Since the GB energy is directly related to the GB structure, the analysis may allow a generalization of the scaling behavior of jc with the GB energy. © 1995 The American Physical Society.
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Objectives The purpose of the study was to establish regression equations that could be used to predict muscle thickness and pennation angle at different intensities from electromyography (EMG) based measures of muscle activation during isometric contractions. Design Cross-sectional study. Methods Simultaneous ultrasonography and EMG were used to measure pennation angle, muscle thickness and muscle activity of the rectus femoris and vastus lateralis muscles, respectively, during graded isometric knee extension contractions performed on a Cybex dynamometer. Data form fifteen male soccer players were collected in increments of approximately 25% intensity of the maximum voluntary contraction (MVC) ranging from rest to MVC. Results There was a significant correlation (P < 0.05) between ultrasound predictors and EMG measures for the muscle thickness of rectus femoris with an R2 value of 0.68. There was no significant correlation (P > 0.05) between ultrasound pennation angle for the vastus lateralis predictors for EMG muscle activity with an R2 value of 0.40. Conclusions The regression equations can be used to characterise muscle thickness more accurately and to determine how it changes with contraction intensity, this provides improved estimates of muscle force when using musculoskeletal models.
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We advocate for the use of predictive techniques in interactive computer music systems. We suggest that the inclusion of prediction can assist in the design of proactive rather than reactive computational performance partners. We summarize the significant role prediction plays in human musical decisions, and the only modest use of prediction in interactive music systems to date. After describing how we are working toward employing predictive processes in our own metacreation software we reflect on future extensions to these approaches.
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Lean body mass (LBM) and muscle mass remains difficult to quantify in large epidemiological studies due to non-availability of inexpensive methods. We therefore developed anthropometric prediction equations to estimate the LBM and appendicular lean soft tissue (ALST) using dual energy X-ray absorptiometry (DXA) as a reference method. Healthy volunteers (n= 2220; 36% females; age 18-79 y) representing a wide range of body mass index (14-44 kg/m2) participated in this study. Their LBM including ALST was assessed by DXA along with anthropometric measurements. The sample was divided into prediction (60%) and validation (40%) sets. In the prediction set, a number of prediction models were constructed using DXA measured LBM and ALST estimates as dependent variables and a combination of anthropometric indices as independent variables. These equations were cross-validated in the validation set. Simple equations using age, height and weight explained > 90% variation in the LBM and ALST in both men and women. Additional variables (hip and limb circumferences and sum of SFTs) increased the explained variation by 5-8% in the fully adjusted models predicting LBM and ALST. More complex equations using all the above anthropometric variables could predict the DXA measured LBM and ALST accurately as indicated by low standard error of the estimate (LBM: 1.47 kg and 1.63 kg for men and women, respectively) as well as good agreement by Bland Altman analyses. These equations could be a valuable tool in large epidemiological studies assessing these body compartments in Indians and other population groups with similar body composition.
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In this paper, a refined classic noise prediction method based on the VISSIM and FHWA noise prediction model is formulated to analyze the sound level contributed by traffic on the Nanjing Lukou airport connecting freeway before and after widening. The aim of this research is to (i) assess the traffic noise impact on the Nanjing University of Aeronautics and Astronautics (NUAA) campus before and after freeway widening, (ii) compare the prediction results with field data to test the accuracy of this method, (iii) analyze the relationship between traffic characteristics and sound level. The results indicate that the mean difference between model predictions and field measurements is acceptable. The traffic composition impact study indicates that buses (including mid-sizedtrucks) and heavy goods vehicles contribute a significant proportion of total noise power despite their low traffic volume. In addition, speed analysis offers an explanation for the minor differences in noise level across time periods. Future work will aim at reducing model error, by focusing on noise barrier analysis using the FEM/BEM method and modifying the vehicle noise emission equation by conducting field experimentation.
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In particle-strengthened metallic alloys, fatigue damage incubates at inclusion particles near the surface or at the change of geometries. Micromechanical simulation of inclusions such that the fatigue damage incubation mechanisms can be categorized. As micro-plasticity gradient field around different inclusions is different, a novel concept for nonlocal evaluation of micro-plasticity intensity is introduced. The effects of void aspects ration and spatial distributions are quantified for fatigue incubation life in the high-cycle fatigue regime. At last, these effects are integrated based on the statistical facts of inclusions to predict the fatigue life of structural components.
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Purpose – Traumatic events can cause post-traumatic stress disorder due to the severity of the often unexpected events. The purpose of this paper is to reveal how conversations around lived experiences of traumatic events, such as the Christchurch earthquake in February 2011, can work as a strategy for people to come to terms with their experiences collaboratively. By encouraging young children to recall and tell of their earthquake stories with their early childhood teachers they can begin to respond, renew, and recover (Brown, 2012), and prevent or minimise more stress being developed. Design/methodology/approach – The study involved collecting data of the participating children taking turns to wear a wireless microphone where their interactions with each other and with teachers were video recorded over one week in November 2011. A total of eight hours and 21 minutes of footage was collected; four minutes and 19 seconds of that footage are presented and analysed in this paper. The footage was watched repeatedly and transcribed using conversation analysis methods (Sacks, 1995). Findings – Through analysing the detailed turn-taking utterances between teachers and children, the orderliness of the co-production of remembering is revealed to demonstrate that each member orients to being in agreement about what actually happened. These episodes of story telling between the teachers and children demonstrate how the teachers encourage the children to tell about their experiences through actively engaging in conversations with them about the earthquake. Originality/value – The conversation analysis approach used in this research was found to be useful in investigating aspects of disasters that the participants themselves remember as important and real. This approach offers a unique insight into understanding how the earthquake event was experienced and reflected on by young children and their teachers, and so can inform future policy and provision in post-disaster situations.
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The therapeutic value of play can be shown in spontaneous play situations following children’s experiences of traumatic events. Following the events of the Christchurch earthquakes in New Zealand in 2010 and 2011, an investigation was conducted of how children used the earthquake event as a catalyst in pretend play with peers and in discussions with teachers. Supporting children’s well-being is a focus area in New Zealand early childhood education as it is a strand of the national curriculum, Te Whāriki (Ministry of Education [MoE], 1996). In this article, children are observed engaging in pretend play episodes and with Learning Story books to explore personal reflections of the earthquake, prompting the children to make reference to things being ‘broken’ and needing 'fixing.' Analysis shows how the content of the pretend play experiences helped the children to come to terms with their experiences. Affording children time and interactional opportunities to play out and discuss traumatic experiences contributes to the psychological well-being of participants following a traumatic event.
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BACKGROUND: The objective of this study was to determine whether it is possible to predict driving safety in individuals with homonymous hemianopia or quadrantanopia based upon a clinical review of neuro-images that are routinely available in clinical practice. METHODS: Two experienced neuro-ophthalmologists viewed a summary report of the CT/MRI scans of 16 participants with homonymous hemianopic or quadrantanopic field defects which provided information regarding the site and extent of the lesion and made predictions regarding whether they would be safe/unsafe to drive. Driving safety was defined using two independent measures: (1) The potential for safe driving was defined based upon whether the participant was rated as having the potential for safe driving, determined through a standardized on-road driving assessment by a certified driving rehabilitation specialist conducted just prior and (2) state recorded motor vehicle crashes (all crashes and at-fault). Driving safety was independently defined at the time of the study by state recorded motor vehicle crashes (all crashes and at-fault) recorded over the previous 5 years, as well as whether the participant was rated as having the potential for safe driving, determined through a standardized on-road driving assessment by a certified driving rehabilitation specialist. RESULTS: The ability to predict driving safety was highly variable regardless of the driving outcome measure, ranging from 31% to 63% (kappa levels ranged from -0.29 to 0.04). The level of agreement between the neuro-ophthalmologists was also only fair (kappa =0.28). CONCLUSIONS: The findings suggest that clinical evaluation of summary reports currently available neuro-images by neuro-ophthalmologists is not predictive of driving safety. Future research should be directed at identifying and/or developing alternative tests or strategies to better enable clinicians to make these predictions.