scholarly journals Multi-Level Sensing Technologies in Landslide Research—Hrvatska Kostajnica Case Study, Croatia

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 177
Author(s):  
Laszlo Podolszki ◽  
Ivan Kosović ◽  
Tomislav Novosel ◽  
Tomislav Kurečić
Keyword(s):  
Ground Surface ◽  
Surface Displacement ◽  
Data Sets ◽  
Level Data ◽  
Landslide Body ◽  
Aerial Vehicle ◽  
Multi Level ◽  
Geochemical Analyses ◽  
Landslide Model

In March 2018, a landslide in Hrvatska Kostajnica completely destroyed multiple households. The damage was extensive, and lives were endangered. The question remains: Can it happen again? To enhance the knowledge and understanding of the soil and rock behaviour before, during, and after this geo-hazard event, multi-level sensing technologies in landslide research were applied. Day after the event field mapping and unmanned aerial vehicle (UAV) data were collected with the inspection of available orthophoto and “geo” data. For the landslide, a new geological column was developed with mineralogical and geochemical analyses. The application of differential interferometric synthetic aperture radar (DInSAR) for detecting ground surface displacement was undertaken in order to determine pre-failure behaviour and to give indications about post-failure deformations. In 2020, electrical resistivity tomography (ERT) in the landslide body was undertaken to determine the depth of the landslide surface, and in 2021 ERT measurements in the vicinity of the landslide area were performed to obtain undisturbed material properties. Moreover, in 2021, detailed light detection and ranging (LIDAR) data were acquired for the area. All these different level data sets are being analyzed in order to develop a reliable landslide model as a first step towards answering the aforementioned question. Based on applied multi-level sensing technologies and acquired data, the landslide model is taking shape. However, further detailed research is still recommended.

scholarly journals Quantitative data analysis of ESAR data

2013 ◽  
Vol 11 ◽  
pp. 291-295
Author(s):  
N. Phruksahiran ◽  
M. Chandra
Keyword(s):  
Ground Surface ◽  
Quantitative Information ◽  
Polarization Property ◽  
Data Sets ◽  
Program Code ◽  
Quantitative Data Analysis ◽  
Linear Depolarization Ratio ◽  
German Aerospace ◽  
Differential Reflectivity

Abstract. A synthetic aperture radar (SAR) data processing uses the backscattered electromagnetic wave to map radar reflectivity of the ground surface. The polarization property in radar remote sensing was used successfully in many applications, especially in target decomposition. This paper presents a case study to the experiments which are performed on ESAR L-Band full polarized data sets from German Aerospace Center (DLR) to demonstrate the potential of coherent target decomposition and the possibility of using the weather radar measurement parameter, such as the differential reflectivity and the linear depolarization ratio to obtain the quantitative information of the ground surface. The raw data of ESAR has been processed by the SAR simulator developed using MATLAB program code with Range-Doppler algorithm.

scholarly journals No more aggregate NAEP studies? [editorial].

2006 ◽  
Vol 14 ◽  
pp. 31 ◽  
Author(s):  
Sherman Dorn
Keyword(s):  
State Policies ◽  
Data Sets ◽  
Educational Progress ◽  
National Assessment ◽  
Individual Level ◽  
Point Estimates ◽  
Level Data ◽  
Policy Analysts ◽  
Multi Level ◽  
The Individual

This editorial reviews recent studies of accountability policies using National Assessment of Educational Progress (NAEP) data and compares the use of aggregate NAEP data to the availability of individual-level data from NAEP. While the individual-level NAEP data sets are restricted-access and do not give accurate point-estimates of achievement, they nonetheless provide greater opportunity to conduct more appropriate multi-level analyses with state policies as one set of variables. Policy analysts using NAEP data should still look at exclusion rates and the non-longitudinal nature of the NAEP data sets.

scholarly journals Segment-Based Approach for Assessing Hazard Risk of Coastal Highways in Hawai‘i

2019 ◽  
Vol 2673 (1) ◽  
pp. 83-91 ◽  
Author(s):  
Harrison Togia ◽  
Oceana P. Francis ◽  
Karl Kim ◽  
Guohui Zhang
Keyword(s):  
Qualitative Method ◽  
Data Sets ◽  
Regional Environment ◽  
The Road ◽  
Hazard Exposure ◽  
Multiple Indicators ◽  
System Data ◽  
Rural Highway ◽  
Geographic Information System Data

Hazards to roadways and travelers can be drastically different because hazards are largely dependent on the regional environment and climate. This paper describes the development of a qualitative method for assessing infrastructure importance and hazard exposure for rural highway segments in Hawai‘i under different conditions. Multiple indicators of roadway importance are considered, including traffic volume, population served, accessibility, connectivity, reliability, land use, and roadway connection to critical infrastructures, such as hospitals and police stations. The method of evaluating roadway hazards and importance can be tailored to fit different regional hazard scenarios. It assimilates data from diverse sources to estimate risks of disruption. A case study for Highway HI83 in Hawai‘i, which is exposed to multiple hazards, is conducted. Weakening of the road by coastal erosion, inundation from sea level rise, and rockfall hazards require adaptation solutions. By analyzing the risk of disruption to highway segments, adaptation approaches can be prioritized. Using readily available geographic information system data sets for the exposure and impacts of potential hazards, this method could be adapted not only for emergency management but also for planning, design, and engineering of resilient highways.

scholarly journals The Problem of Success and Failure in Public-private Innovation Partnerships

2021 ◽  
pp. 1-21
Author(s):  
JONATHAN HAMMOND ◽  
SIMON BAILEY ◽  
OZ GORE ◽  
KATH CHECKLAND ◽  
SARAH DARLEY ◽  
...  
Keyword(s):  
Public Sector ◽  
Health Service ◽  
Policy Maker ◽  
Policy And Practice ◽  
Policy Success ◽  
Political Effects ◽  
Innovation Project ◽  
Multi Level ◽  
Test Beds

Abstract Public-Private Innovation Partnerships (PPIPs) are increasingly used as a tool for addressing ‘wicked’ public sector challenges. ‘Innovation’ is, however, frequently treated as a ‘magic’ concept: used unreflexively, taken to be axiomatically ‘good’, and left undefined within policy programmes. Using McConnell’s framework of policy success and failure and a case study of a multi-level PPIP in the English health service (NHS Test Beds), this paper critically explores the implications of the mobilisation of innovation in PPIP policy and practice. We highlight how the interplay between levels (macro/micro and policy maker/recipient) can shape both emerging policies and their prospects for success or failure. The paper contributes to an understanding of PPIP success and failure by extending McConnell’s framework to explore inter-level effects between policy and innovation project, and demonstrating how the success of PPIP policy cannot be understood without recognising the particular political effects of ‘innovation’ on formulation and implementation.

scholarly journals Queue Length Forecasting in Complex Manufacturing Job Shops

Forecasting ◽  
2021 ◽  
Vol 3 (2) ◽  
pp. 322-338
Author(s):  
Marvin Carl May ◽  
Alexander Albers ◽  
Marc David Fischer ◽  
Florian Mayerhofer ◽  
Louis Schäfer ◽  
...  
Keyword(s):  
Operations Management ◽  
Queue Length ◽  
Manufacturing Systems ◽  
Production Control ◽  
Data Sets ◽  
Job Shops ◽  
Static Data ◽  
Queue Lengths ◽  
Operational Excellence

Currently, manufacturing is characterized by increasing complexity both on the technical and organizational levels. Thus, more complex and intelligent production control methods are developed in order to remain competitive and achieve operational excellence. Operations management described early on the influence among target metrics, such as queuing times, queue length, and production speed. However, accurate predictions of queue lengths have long been overlooked as a means to better understanding manufacturing systems. In order to provide queue length forecasts, this paper introduced a methodology to identify queue lengths in retrospect based on transitional data, as well as a comparison of easy-to-deploy machine learning-based queue forecasting models. Forecasting, based on static data sets, as well as time series models can be shown to be successfully applied in an exemplary semiconductor case study. The main findings concluded that accurate queue length prediction, even with minimal available data, is feasible by applying a variety of techniques, which can enable further research and predictions.

scholarly journals Generalizing Treatment Effect Estimates From Sample to Population: A Case Study in the Difficulties of Finding Sufficient Data

2016 ◽  
Vol 41 (4) ◽  
pp. 357-388 ◽  
Author(s):  
Elizabeth A. Stuart ◽  
Anna Rhodes
Keyword(s):  
Head Start ◽  
Randomized Trial ◽  
Treatment Effect ◽  
External Validity ◽  
Randomized Trials ◽  
Target Population ◽  
Data Sets ◽  
Common Data Elements ◽  
Ex Post

Background: Given increasing concerns about the relevance of research to policy and practice, there is growing interest in assessing and enhancing the external validity of randomized trials: determining how useful a given randomized trial is for informing a policy question for a specific target population. Objectives: This article highlights recent advances in assessing and enhancing external validity, with a focus on the data needed to make ex post statistical adjustments to enhance the applicability of experimental findings to populations potentially different from their study sample. Research design: We use a case study to illustrate how to generalize treatment effect estimates from a randomized trial sample to a target population, in particular comparing the sample of children in a randomized trial of a supplemental program for Head Start centers (the Research-Based, Developmentally Informed study) to the national population of children eligible for Head Start, as represented in the Head Start Impact Study. Results: For this case study, common data elements between the trial sample and population were limited, making reliable generalization from the trial sample to the population challenging. Conclusions: To answer important questions about external validity, more publicly available data are needed. In addition, future studies should make an effort to collect measures similar to those in other data sets. Measure comparability between population data sets and randomized trials that use samples of convenience will greatly enhance the range of research and policy relevant questions that can be answered.

scholarly journals Empirical Underidentification with the Bifactor Model: A Case Study

2017 ◽  
Vol 78 (5) ◽  
pp. 717-736 ◽  
Author(s):  
Samuel Green ◽  
Yanyun Yang
Keyword(s):  
Structural Equation Model ◽  
Structural Equation ◽  
Equation Model ◽  
Bifactor Model ◽  
Data Sets ◽  
Bifactor Models

Bifactor models are commonly used to assess whether psychological and educational constructs underlie a set of measures. We consider empirical underidentification problems that are encountered when fitting particular types of bifactor models to certain types of data sets. The objective of the article was fourfold: (a) to allow readers to gain a better general understanding of issues surrounding empirical identification, (b) to offer insights into empirical underidentification with bifactor models, (c) to inform methodologists who explore bifactor models about empirical underidentification with these models, and (d) to propose strategies for structural equation model users to deal with underidentification problems that can emerge when applying bifactor models.

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