scholarly journals Model-Based Probabilistic Inversion Using Magnetic Data: A Case Study on the Kevitsa Deposit

Geosciences ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 150
Author(s):  
Nilgün Güdük ◽  
Miguel de la Varga ◽  
Janne Kaukolinna ◽  
Florian Wellmann
Keyword(s):  
Structural Parameters ◽  
Rock Properties ◽  
Magnetic Data ◽  
Geological Model ◽  
Borehole Data ◽  
Likelihood Functions ◽  
Different Types ◽  
Geophysical Information ◽  
Different Sources

Structural geological models are widely used to represent relevant geological interfaces and property distributions in the subsurface. Considering the inherent uncertainty of these models, the non-uniqueness of geophysical inverse problems, and the growing availability of data, there is a need for methods that integrate different types of data consistently and consider the uncertainties quantitatively. Probabilistic inference provides a suitable tool for this purpose. Using a Bayesian framework, geological modeling can be considered as an integral part of the inversion and thereby naturally constrain geophysical inversion procedures. This integration prevents geologically unrealistic results and provides the opportunity to include geological and geophysical information in the inversion. This information can be from different sources and is added to the framework through likelihood functions. We applied this methodology to the structurally complex Kevitsa deposit in Finland. We started with an interpretation-based 3D geological model and defined the uncertainties in our geological model through probability density functions. Airborne magnetic data and geological interpretations of borehole data were used to define geophysical and geological likelihoods, respectively. The geophysical data were linked to the uncertain structural parameters through the rock properties. The result of the inverse problem was an ensemble of realized models. These structural models and their uncertainties are visualized using information entropy, which allows for quantitative analysis. Our results show that with our methodology, we can use well-defined likelihood functions to add meaningful information to our initial model without requiring a computationally-heavy full grid inversion, discrepancies between model and data are spotted more easily, and the complementary strength of different types of data can be integrated into one framework.

Participatory Mapping in Browns Canyon National Monument, Colorado (USA)

2017 ◽  
Vol 1 (1) ◽  
pp. 1-16
Author(s):  
John Harner ◽  
Lee Cerveny ◽  
Rebecca Gronewold
Keyword(s):  
Land Management ◽  
Planning Process ◽  
Management Plan ◽  
Bureau Of Land Management ◽  
National Monument ◽  
Resource Managers ◽  
Different Types ◽  
Us Forest Service ◽  
Natural Resource Managers

Natural resource managers need up-to-date information about how people interact with public lands and the meanings these places hold for use in planning and decision-making. This case study explains the use of public participatory Geographic Information System (GIS) to generate and analyze spatial patterns of the uses and values people hold for the Browns Canyon National Monument in Colorado. Participants drew on maps and answered questions at both live community meetings and online sessions to develop a series of maps showing detailed responses to different types of resource uses and landscape values. Results can be disaggregated by interaction types, different meaningful values, respondent characteristics, seasonality, or frequency of visit. The study was a test for the Bureau of Land Management and US Forest Service, who jointly manage the monument as they prepare their land management plan. If the information generated is as helpful throughout the entire planning process as initial responses seem, this protocol could become a component of the Bureau’s planning tool kit.

Effective Altruism

2019 ◽  
Keyword(s):  
Animal Welfare ◽  
Poverty Alleviation ◽  
Future Generations ◽  
Global Poverty ◽  
Global Community ◽  
Helping Others ◽  
The World ◽  
Different Types ◽  
Effective Altruism

The effective altruism movement consists of a growing global community of people who organize significant parts of their lives around two key ideas, represented in its name. Altruism: If we use a significant portion of the resources in our possession—whether money, time, or talents—with a view to helping others, we can improve the world considerably. Effectiveness: When we do put such resources to altruistic use, it is crucial to focus on how much good this or that intervention is reasonably expected to do per unit of resource expended (for example, per dollar donated). While global poverty is a widely used case study in introducing and motivating effective altruism, if the ultimate aim is to do the most good one can with the resources expended, it is far from obvious that global poverty alleviation is highest priority cause area. In addition to ranking possible poverty-alleviation interventions against one another, we can also try to rank interventions aimed at very different types of outcome against one another. This includes, for example, interventions focusing on animal welfare or future generations. The scale and organization of the effective altruism movement encourage careful dialogue on questions that have perhaps long been there, throwing them into new and sharper relief, and giving rise to previously unnoticed questions. In the present volume, the first of its kind, a group of internationally recognized philosophers, economists, and political theorists contribute in-depth explorations of issues that arise once one takes seriously the twin ideas of altruistic commitment and effectiveness.

Transdiagnostic Approaches With Children

2018 ◽  
Author(s):  
Andrea B. Temkin ◽  
Mina Yadegar ◽  
Christine Cho ◽  
Brian C. Chu
Keyword(s):  
Mental Disorders ◽  
Externalizing Symptoms ◽  
Case Conceptualization ◽  
Common Elements ◽  
Transdiagnostic Treatment ◽  
Treatment Models ◽  
Different Types ◽  
Internalizing And Externalizing ◽  
Internalizing And Externalizing Symptoms

In recent years, the field of clinical psychology has seen a growing movement toward the research and development of transdiagnostic treatments. Transdiagnostic approaches have the potential to address numerous issues related to the development and treatment of mental disorders. Among these are the high rates of comorbidity across disorders, the increasing need for efficient protocols, and the call for treatments that can be more easily disseminated. This chapter provides a review of the current transdiagnostic treatment approaches for the treatment of youth mental disorders. Three different types of transdiagnostic protocols are examined: mechanism-based protocols, common elements treatments, and general treatment models that originated from single-disorder approaches to have broader reach. A case study illuminates how a mechanism-based approach would inform case conceptualization for a client presenting with internalizing and externalizing symptoms and how a transdiagnostic framework translates into practice.

scholarly journals How to measure uncertainty in uncertainty sampling for active learning

2021 ◽  
Author(s):  
Vu-Linh Nguyen ◽  
Mohammad Hossein Shaker ◽  
Eyke Hüllermeier
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Sampling Strategies ◽  
Total Uncertainty ◽  
Uncertainty Sampling ◽  
Different Types ◽  
Alternative Approaches ◽  
Active Learner ◽  
Probabilistic Nature ◽  
Different Sources

AbstractVarious strategies for active learning have been proposed in the machine learning literature. In uncertainty sampling, which is among the most popular approaches, the active learner sequentially queries the label of those instances for which its current prediction is maximally uncertain. The predictions as well as the measures used to quantify the degree of uncertainty, such as entropy, are traditionally of a probabilistic nature. Yet, alternative approaches to capturing uncertainty in machine learning, alongside with corresponding uncertainty measures, have been proposed in recent years. In particular, some of these measures seek to distinguish different sources and to separate different types of uncertainty, such as the reducible (epistemic) and the irreducible (aleatoric) part of the total uncertainty in a prediction. The goal of this paper is to elaborate on the usefulness of such measures for uncertainty sampling, and to compare their performance in active learning. To this end, we instantiate uncertainty sampling with different measures, analyze the properties of the sampling strategies thus obtained, and compare them in an experimental study.

scholarly journals Game-Theoretic Decision Support for Cyber Forensic Investigations

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5300
Author(s):  
Antonia Nisioti ◽  
George Loukas ◽  
Stefan Rass ◽  
Emmanouil Panaousis
Keyword(s):  
Cyber Security ◽  
Strategic Decisions ◽  
Bayesian Games ◽  
Bayesian Game ◽  
Cyber Forensics ◽  
Detection And Attribution ◽  
Different Types ◽  
Game Theoretic ◽  
The Cost

The use of anti-forensic techniques is a very common practice that stealthy adversaries may deploy to minimise their traces and make the investigation of an incident harder by evading detection and attribution. In this paper, we study the interaction between a cyber forensic Investigator and a strategic Attacker using a game-theoretic framework. This is based on a Bayesian game of incomplete information played on a multi-host cyber forensics investigation graph of actions traversed by both players. The edges of the graph represent players’ actions across different hosts in a network. In alignment with the concept of Bayesian games, we define two Attacker types to represent their ability of deploying anti-forensic techniques to conceal their activities. In this way, our model allows the Investigator to identify the optimal investigating policy taking into consideration the cost and impact of the available actions, while coping with the uncertainty of the Attacker’s type and strategic decisions. To evaluate our model, we construct a realistic case study based on threat reports and data extracted from the MITRE ATT&CK STIX repository, Common Vulnerability Scoring System (CVSS), and interviews with cyber-security practitioners. We use the case study to compare the performance of the proposed method against two other investigative methods and three different types of Attackers.

scholarly journals Case Study on Fire Resistance of Sandwiches for Means of Transport

Coatings ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 207
Author(s):  
Pavel Koštial ◽  
Zora Koštialová Jančíková ◽  
Robert Frischer
Keyword(s):  
Fire Resistance ◽  
Fire Protection ◽  
Public Transport ◽  
Hybrid Vehicles ◽  
Safety Requirements ◽  
Different Types ◽  
Aluminum Honeycomb ◽  
Heat Loads

These days there are undeniably unique materials that, however, must also meet demanding safety requirements. In the case of vehicles, these are undoubtedly excellent fire protection characteristics. The aim of the work is to experimentally verify the proposed material compositions for long-term heat loads and the effect of thickness, the number of laminating layers (prepregs) as well as structures with different types of cores (primarily honeycomb made of Nomex paper type T722 of different densities, aluminum honeycomb and PET foam) and composite coating based on a glass-reinforced phenolic matrix. The selected materials are suitable candidates for intelligent sandwich structures, usable especially for interior cladding applications in the industry for the production of means of public transport (e.g., train units, trams, buses, hybrid vehicles).

scholarly journals Accessible Routes Integrating Data from Multiple Sources

2020 ◽  
Vol 10 (1) ◽  
pp. 7
Author(s):  
Miguel R. Luaces ◽  
Jesús A. Fisteus ◽  
Luis Sánchez-Fernández ◽  
Mario Munoz-Organero ◽  
Jesús Balado ◽  
...  
Keyword(s):  
Information System ◽  
Data Model ◽  
Large Scale ◽  
Heterogeneous Data ◽  
Multiple Sources ◽  
Heterogeneous Data Sources ◽  
Different Types ◽  
Software Sensors ◽  
The City

Providing citizens with the ability to move around in an accessible way is a requirement for all cities today. However, modeling city infrastructures so that accessible routes can be computed is a challenge because it involves collecting information from multiple, large-scale and heterogeneous data sources. In this paper, we propose and validate the architecture of an information system that creates an accessibility data model for cities by ingesting data from different types of sources and provides an application that can be used by people with different abilities to compute accessible routes. The article describes the processes that allow building a network of pedestrian infrastructures from the OpenStreetMap information (i.e., sidewalks and pedestrian crossings), improving the network with information extracted obtained from mobile-sensed LiDAR data (i.e., ramps, steps, and pedestrian crossings), detecting obstacles using volunteered information collected from the hardware sensors of the mobile devices of the citizens (i.e., ramps and steps), and detecting accessibility problems with software sensors in social networks (i.e., Twitter). The information system is validated through its application in a case study in the city of Vigo (Spain).

scholarly journals Blended and more: instructors organize sensemaking opportunities for mathematical equations in different ways when teaching the same scientific phenomenon

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Fang Fang Zhao ◽  
Linh Chau ◽  
Anita Schuchardt
Keyword(s):  
Scientific Understanding ◽  
Biological Phenomenon ◽  
Study Approach ◽  
Different Types ◽  
Multiple Case ◽  
Mathematical Equations ◽  
Scientific Phenomenon ◽  
Student Problem ◽  
Mathematics And Science

Abstract Background Many students solving quantitative problems in science struggle to apply mathematical instruction they have received to novel problems. The few students who succeed often draw on both their mathematical understanding of the equation and their scientific understanding of the phenomenon. Understanding the sensemaking opportunities provided during instruction is necessary to develop strategies for improving student outcomes. However, few studies have examined the types of sensemaking opportunities provided during instruction of mathematical equations in science classrooms and whether they are organized in ways that facilitate integration of mathematical and scientific understanding. This study uses a multiple case study approach to examine the sensemaking opportunities provided by four different instructors when teaching the same biological phenomenon, population growth. Two questions are addressed: (1) What types of sensemaking opportunities are provided by instructors, and (2) How are those sensemaking opportunities organized? The Sci-Math Sensemaking Framework, previously developed by the authors, was used to identify the types of sensemaking. Types and organization of sensemaking opportunities were compared across the four instructors. Results The instructors provided different opportunities for sensemaking of equations, even though they were covering the same scientific phenomenon. Sensemaking opportunities were organized in three ways, blended (previously described in studies of student problem solving as integration of mathematics and science resources), and two novel patterns, coordinated and adjacent. In coordinated sensemaking, two types of sensemaking in the same dimension (either mathematics or science) are explicitly connected. In adjacent sensemaking, two different sensemaking opportunities are provided within the same activity but not explicitly connected. Adjacent sensemaking was observed in three instructors’ lessons, but only two instructors provided opportunities for students to engage in blended sensemaking. Conclusions Instructors provide different types of sensemaking opportunities when teaching the same biological phenomenon, making different resources available to students. The organization of sensemaking also differed with only two instructors providing blended sensemaking opportunities. This result may explain why few students engage in the successful strategy of integrating mathematics and science resources when solving quantitative problems. Documentation of these instructional differences in types and organization of sensemaking provides guidance for future studies investigating the effect of instruction on student sensemaking.

scholarly journals Assessment of the mercury contamination of landfilled and recovered foundry waste – a case study

2021 ◽  
Vol 19 (1) ◽  
pp. 462-470
Author(s):  
Marta Bożym ◽  
Beata Klojzy-Karczmarczyk
Keyword(s):  
Negative Impact ◽  
Mercury Content ◽  
Mercury Contamination ◽  
Reference Level ◽  
Organic Carbon Content ◽  
Foundry Sands ◽  
Industrial Sites ◽  
Different Types ◽  
Main Component

Abstract Environmental pollution by mercury is a local problem in Poland and concerns mainly industrial sites. Foundry waste are usually characterized by low mercury content compared to other heavy metals. Spent foundry sands with low content of Hg are the main component of foundry waste. However, Hg may be present in foundry dust, which may also be landfilled. Due to Hg toxicity, even a minimal content may have a negative impact on biota. This study focuses on assessing the mercury content of landfilled foundry waste (LFW), to assess its toxicity. Currently tested waste is recovered and reused as a road aggregate. The results were compared with the mercury content of local soils as the reference level. Waste samples were taken from foundry landfill. The mercury content, fractional composition, organic matter (OM) and total organic carbon content, pH and elementary composition of waste were analysed. It was found that the mercury content in LFW was very low, at the level of natural content in soils and did not pose a threat to the environment. The statistical analysis shows that mercury was not associated with OM of the waste, in contrast to soils, probably due to different types of OM in both materials.

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