Uncertainties Analysis of Collapse Susceptibility Prediction Based on Remote Sensing and GIS: Influences of Different Data-Based Models and Connections between Collapses and Environmental Factors

To study the uncertainties of a collapse susceptibility prediction (CSP) under the coupled conditions of different data-based models and different connection methods between collapses and environmental factors, An’yuan County in China with 108 collapses is used as the study case, and 11 environmental factors are acquired by data analysis of Landsat TM 8 and high-resolution aerial images, using a hydrological and topographical spatial analysis of Digital Elevation Modeling in ArcGIS 10.2 software. Accordingly, 20 coupled conditions are proposed for CSP with five different connection methods (Probability Statistics (PSs), Frequency Ratio (FR), Information Value (IV), Index of Entropy (IOE) and Weight of Evidence (WOE)) and four data-based models (Analytic Hierarchy Process (AHP), Multiple Linear Regression (MLR), C5.0 Decision Tree (C5.0 DT) and Random Forest (RF)). Finally, the CSP uncertainties are assessed using the area under receiver operation curve (AUC), mean value, standard deviation and significance test, respectively. Results show that: (1) the WOE-based models have the highest AUC accuracy, lowest mean values and average rank, and a relatively large standard deviation; the mean values and average rank of all the FR-, IV- and IOE-based models are relatively large with low standard deviations; meanwhile, the AUC accuracies of FR-, IV- and IOE-based models are consistent but higher than those of the PS-based model. Hence, the WOE exhibits a greater spatial correlation performance than the other four methods. (2) Among all the data-based models, the RF model has the highest AUC accuracy, lowest mean value and mean rank, and a relatively large standard deviation. The CSP performance of the RF model is followed by the C5.0 DT, MLR and AHP models, respectively. (3) Under the coupled conditions, the WOE-RF model has the highest AUC accuracy, a relatively low mean value and average rank, and a high standard deviation. The PS-AHP model is opposite to the WOE-RF model. (4) In addition, the coupled models show slightly better CSP performances than those of the single data-based models not considering connect methods. The CSP performance of the other models falls somewhere in between. It is concluded that the WOE-RF is the most appropriate coupled condition for CSP than the other models.

A Benchmark of Popular Indoor 3D Reconstruction Technologies: Comparison of ARCore and RTAB-Map

The fast evolution in computational and sensor technologies brings previously niche solutions to a wider userbase. As such, 3D reconstruction technologies are reaching new use-cases in scientific and everyday areas where they were not present before. Cost-effective and easy-to-use solutions include camera-based 3D scanning techniques, such as photogrammetry. This paper provides an overview of the available solutions and discusses in detail the depth-image based Real-time Appearance-based Mapping (RTAB-Map) technique as well as a smartphone-based solution that utilises ARCore, the Augmented Reality (AR) framework of Google. To qualitatively compare the two 3D reconstruction technologies, a simple length measurement-based method was applied with a purpose-designed reference object. The captured data were then analysed by a processing algorithm. In addition to the experimental results, specific case studies are briefly discussed, evaluating the applicability based on the capabilities of the technologies. As such, the paper presents the use-case of interior surveying in an automated laboratory as well as an example for using the discussed techniques for landmark surveying. The major findings are that point clouds created with these technologies provide a direction- and shape-accurate model, but those contain mesh continuity errors, and the estimated scale factor has a large standard deviation.

An option contract model for leasing containers in the shipping industry

We propose an option contract model for the leasing of containers. In an option contract, the shipping company commits to order a quantity of containers from the leasing company and has the right to modify its order at a later stage, according to its actual requirement. Under this scheme, the shipping company is allowed to request a smaller or larger number of containers than the agreed initial order. This is done by buying an option premium in advance from the container leasing company. We present numerical results for different scenarios based on information provided by experts in the industry. For the purposes of comparison, a nonoption contract scheme is also evaluated. According to our numerical results, an option contract is better under a scenario where demand is normally distributed with a large standard deviation. This scenario is commonly observed in practice due to the dynamism and volatility of the shipping industry. We conclude that, under an option contract scheme, the shipping company has more flexibility to adjust its demand for containers and to be requested from the leasing company, and this adjustment is compensated by an option price determined according to variations in demand.

Renaissance Distribution for Statistically Failed Experiments

Much of the experimental data, especially in life sciences, is considered to be useless if it demonstrates a large standard deviation from the mean value. The Renaissance distribution, as presented in this study, allows one to extract true values from such statistical data with large noise. To obtain proof of the Renaissance distribution, high-throughput synthesis of deep substitutions for a target amino acid sequence was performed, and the known epitope was identified in assay with human serum antibodies. In addition, the Renaissance distribution was shown to approach the epitope affinity maturation by the deep alanine substitution. The Renaissance distribution may have an impact in the development of novel specific drugs.

POWER AND SIZE OF NORMAL DISTRIBUTION AND ITS APPLICATIONS

. The research studied power and size of normal distribution and its applications on linear regression model. The power and size formulas are derived, and the unrestricted test (UT), restrcited test (RT) and pre-test test (PTT) are used. The recommendation of the test is given by choosing maximum power and minimum size, and also graphical analysis. The result showed that the power and size for large standard deviation () tend to be identical and flat. In simulation study, the graphs of the UT, RT, and PTT are still similar to the previous research (Pratikno, 2012), where the PTT tend to lie between UT and RT.

Age determination in dogs using ocular light reflection, dental abrasion and tartar

SummaryObjective: The knowledge of an animal’s age is important for disease probability, prognoses, or epidemiological questions, but unfortunately, it is often unknown for dogs in animal shelters. A simple estimating procedure is preferable being quick and easy to perform, even for nonveterinarians. Material and methods: In 295 dogs the dimension of light reflection (diameter in millimetres), visible on the posterior lens capsule using a penlight, the grade of dental abrasion and dental tartar were documented photographically and the exact weight and age in days were obtained. These photographs were evaluated blinded. The dogs were divided randomly into two groups. The first group was used to establish a model for age determination using linear and logistic regression models considering the documented parameters, which was then validated with the data of the second group. Results: The size of ocular light reflection and age correlated significantly (r = 0.781; p < 0.001; sy,x = 2.45 years [SD of y for given x]). The linear regression model gave the final equation: Estimated age [months] = 13.954 + 33.400 × lens reflection [mm] + 8.406 × dental abrasion [grade] + 8.871 × tartar [grade] with a standard error of estimation of 2.26 years. Conclusion and clinical relevance: Age determination, even based on three parameters results in a large standard deviation making age estimation in dogs very crude.

Four Coptic Textiles from the Louvre Collection 14C Redated after 55 Years

Dating of Coptic textiles performed in the early days of the radiocarbon dating method was revisited. In 1957–1958, Louvre curator and art historian P du Bourguet had 4 Coptic textiles 14C dated by the Saclay laboratory. The results were rejected, not because of the large standard deviation (>100 yr), but because their ages did not support his chronological framework based on typological comparison. Furthermore, textiles with comparable ages were dated several centuries apart. As a result of this investigation, for many decades art historians rejected 14C as a dating tool for Coptic textiles. Re-examination of the old data and new 14C analyses revealed that mistakes were made, both in the reporting as in the interpretation of the data and that the textiles are much older than presumed.

Toxicity of perfluorinated carboxylic acids for aquatic organisms

Toxicity of perfluorinated carboxylic acids for aquatic organismsToxicity of perfluorinated carboxylic acids with carbon chain C8to C12were tested with oligochaetaTubifex tubifex.Toxicity was evaluated as the exposure time ET50from onset of damage of the oligochaeta in saturated aqueous solutions. The ET50fluctuated between 25 and 257 minutes. No statistically significant difference was found among the C8, C9and C12acids (ET50between 143 and 257 minutes with large standard deviation). The acids with carbon chain C10and C11induced the effect significantly quicker (25 to 47 minutes). No acute toxicity measured in the three-minute test was observed in any case.

Observations of Precipitation Size and Fall Speed Characteristics within Coexisting Rain and Wet Snow

Ground-based measurements of particle size and fall speed distributions using a Particle Size and Velocity (PARSIVEL) disdrometer are compared among samples obtained in mixed precipitation (rain and wet snow) and rain in the Oregon Cascade Mountains and in dry snow in the Rocky Mountains of Colorado. Coexisting rain and snow particles are distinguished using a classification method based on their size and fall speed properties. The bimodal distribution of the particles’ joint fall speed–size characteristics at air temperatures from 0.5° to 0°C suggests that wet-snow particles quickly make a transition to rain once melting has progressed sufficiently. As air temperatures increase to 1.5°C, the reduction in the number of very large aggregates with a diameter &gt; 10 mm coincides with the appearance of rain particles larger than 6 mm. In this setting, very large raindrops appear to be the result of aggregrates melting with minimal breakup rather than formation by coalescence. In contrast to dry snow and rain, the fall speed for wet snow has a much weaker correlation between increasing size and increasing fall speed. Wet snow has a larger standard deviation of fall speed (120%–230% relative to dry snow) for a given particle size. The average fall speed for observed wet-snow particles with a diameter ≥ 2.4 mm is 2 m s−1 with a standard deviation of 0.8 m s−1. The large standard deviation is likely related to the coexistence of particles of similar physical size with different percentages of melting. These results suggest that different particle sizes are not required for aggregation since wet-snow particles of the same size can have different fall speeds. Given the large standard deviation of fall speeds in wet snow, the collision efficiency for wet snow is likely larger than that of dry snow. For particle sizes between 1 and 10 mm in diameter within mixed precipitation, rain constituted 1% of the particles by volume within the isothermal layer at 0°C and 4% of the particles by volume for the region just below the isothermal layer where air temperatures rise from 0° to 0.5°C. As air temperatures increased above 0.5°C, the relative proportions of rain versus snow particles shift dramatically and raindrops become dominant. The value of 0.5°C for the sharp transition in volume fraction from snow to rain is slightly lower than the range from 1.1° to 1.7°C often used in hydrological models.