Coastal Hazard Modeling from Radar Data

The effects of wave force or the effects of the coastal engineering structures induce coastal hazards such as erosion. Coastal engineering structures such as jetties could trap a sediment transport along the coastline. This could induce erosion in the downstream. The aim of this study is to model the effects of shoreline changes to jetties located along coastal water of Chendering, Malaysia. The numerical model will be based on the change of wave spectra extracted from ERS-I data. For this purpose, two-dimensional Fourier Transform was applied on window size of 200 x 200. The quasi-linear model was used to model significant wave height. The significant wave height was used to model the volume of sediment transport and shoreline evaluation along jetties. The result shows that the erosion occurred in the south of Chendering with rate of change of 4 m/month. The prediction shows that the rate of erosion would increase within 10 years. This study shows the location of jetty decreases the rate of sediment transport along the south of Chendering. It can be said that ERS-l data are able to predict shoreline evaluation along the coastal structures. The jetty induced an equilibrium beach profile along jetty. This is due to that jetties-trap sediment in the north of Chendering, which lead to erosion in the south of Chendering

Predicting the duration of leveraged buyouts.

We employ newly developed split hazard modeling to estimate the conditional probability that a firm eventually return to public status following a leveraged buyout (LBO), and the conditional probability of reversion to public status in a given year for a firm that eventually may reverse. Our results, based on 343 LBO transactions, imply that not all LBO firms expect eventual reversion to public status. In addition, we find that those LBO decisions that are expected to enhance value the most are less likely to reverse eventually. We also find that eventual reversal probabilities and the timing of reversals for divisional LBOs are not significantly different from full-firm LBOs.Leveraged buyouts;

An example of debris-flows hazard modeling using GIS

International audienceWe present a GIS-based model for predicting debris-flows occurrence. The availability of two different digital datasets and the use of a Digital Elevation Model (at a given scale) have greatly enhanced our ability to quantify and to analyse the topography in relation to debris-flows. In particular, analysing the relationship between debris-flows and the various causative factors provides new understanding of the mechanisms. We studied the contact zone between the calcareous basement and the fluvial-lacustrine infill adjacent northern area of the Terni basin (Umbria, Italy), and identified eleven basins and corresponding alluvial fans. We suggest that accumulations of colluvium in topographic hollows, whatever the sources might be, should be considered potential debris-flow source areas. In order to develop a susceptibility map for the entire area, an index was calculated from the number of initiation locations in each causative factor unit divided by the areal extent of that unit within the study area. This index identifies those units that produce the most debris-flows in each Representative Elementary Area (REA). Finally, the results are presented with the advantages and the disadvantages of the approach, and the need for further research

Predicting time to graduation at a large enrollment American university

The time it takes a student to graduate with a university degree is mitigated by a variety of factors such as their background, the academic performance at university, and their integration into the social communities of the university they attend. Different universities have different populations, student services, instruction styles, and degree programs, however, they all collect institutional data. This study presents data for 160,933 students attending a large American research university. The data includes performance, enrollment, demographics, and preparation features. Discrete time hazard models for the time-to-graduation are presented in the context of Tinto's Theory of Drop Out. Additionally, a novel machine learning method: gradient boosted trees, is applied and compared to the typical maximum likelihood method. We demonstrate that enrollment factors (such as changing a major) lead to greater increases in model predictive performance of when a student graduates than performance factors (such as grades) or preparation (such as high school GPA).Comment: 28 pages, 11 figure

A framework for probabilistic seismic risk assessment of NG distribution networks

Lifelines are essential infrastructures for human activities and the economic developm ent of a region. Lifelines vulnerability reduction is an actual question, particularly with reference to NaTech events, like earthquakes. In this regard, worldwide past seismic experiences revealed heavy damages to NG distribution networks. It is therefore essential to perform seismic risk assessment of NG buried pipelines systems with the aim to identify potential criticalities and avoid significant consequences. For such reasons, this work illustrates the proposal of a probabilistic framework for seismic risk assessment of NG lifelines. The proposed procedure is subsequently applied to a specific case study in Italy to highlight its feasibility

Intratumor Heterogeneity of the Estrogen Receptor and the Long-term Risk of Fatal Breast Cancer.

Background:Breast cancer patients with estrogen receptor (ER)-positive disease have a continuous long-term risk for fatal breast cancer, but the biological factors influencing this risk are unknown. We aimed to determine whether high intratumor heterogeneity of ER predicts an increased long-term risk (25 years) of fatal breast cancer. Methods:The STO-3 trial enrolled 1780 postmenopausal lymph node-negative breast cancer patients randomly assigned to receive adjuvant tamoxifen vs not. The fraction of cancer cells for each ER intensity level was scored by breast cancer pathologists, and intratumor heterogeneity of ER was calculated using Rao's quadratic entropy and categorized into high and low heterogeneity using a predefined cutoff at the second tertile (67%). Long-term breast cancer-specific survival analyses by intra-tumor heterogeneity of ER were performed using Kaplan-Meier and multivariable Cox proportional hazard modeling adjusting for patient and tumor characteristics. Results:A statistically significant difference in long-term survival by high vs low intratumor heterogeneity of ER was seen for all ER-positive patients (P < .001) and for patients with luminal A subtype tumors (P = .01). In multivariable analyses, patients with high intratumor heterogeneity of ER had a twofold increased long-term risk as compared with patients with low intratumor heterogeneity (ER-positive: hazard ratio [HR] = 1.98, 95% confidence interval [CI] = 1.31 to 3.00; luminal A subtype tumors: HR = 2.43, 95% CI = 1.18 to 4.99). Conclusions:Patients with high intratumor heterogeneity of ER had an increased long-term risk of fatal breast cancer. Interestingly, a similar long-term risk increase was seen in patients with luminal A subtype tumors. Our findings suggest that intratumor heterogeneity of ER is an independent long-term prognosticator with potential to change clinical management, especially for patients with luminal A tumors

TSUNAMI HAZARD MODELING IN THE COASTAL AREA OF KULON PROGO REGENCY

Kulon Progo Regency is located in the southern part of Java Island, one of Indonesia's areas that is prone to tsunami disasters. Kulon Progo Regency is prone to tsunamis because it faces a subduction zone in the Indian Ocean. Therefore, it is necessary to model tsunami inundation and map the tsunami hazard zone in the Kulon Progo coastal area. This study aims to model tsunami inundation and produce a tsunami hazard map with a tsunami height scenario of 5 meters and 10 meters. The method used in modeling tsunami inundation is using a mathematical calculation developed by Berryman-2006 using the parameters of the coefficient of surface roughness, slope, and the height of the tsunami at the coastline. The estimated tsunami inundation area is classified into a tsunami hazard index using the fuzzy logic method resulting in an index of 0 – 1, which is then divided into three hazard classes. The results of the tsunami hazard mapping with the 5 meters scenario are 15 villages in 4 sub-districts included in the hazard zone with a total area of 20672,34 Ha affected. The results of the tsunami hazard mapping with a 10 meters scenario are 26 villages in 4 sub-districts with a total area of 53042,66 Ha affected. The results of this research can be used as basic information for disaster mitigation

A Remote Sensing-Based Tool for Assessing Rainfall-Driven Hazards

RainyDay is a Python-based platform that couples rainfall remote sensing data with Stochastic Storm Transposition (SST) for modeling rainfall-driven hazards such as floods and landslides. SST effectively lengthens the extreme rainfall record through temporal resampling and spatial transposition of observed storms from the surrounding region to create many extreme rainfall scenarios. Intensity-Duration-Frequency (IDF) curves are often used for hazard modeling but require long records to describe the distribution of rainfall depth and duration and do not provide information regarding rainfall space-time structure, limiting their usefulness to small scales. In contrast, Rainy Day can be used for many hazard applications with 1-2 decades of data, and output rainfall scenarios incorporate detailed space-time structure from remote sensing. Thanks to global satellite coverage, Rainy Day can be used in inaccessible areas and developing countries lacking ground measurements, though results are impacted by remote sensing errors. Rainy Day can be useful for hazard modeling under nonstationary conditions