Suitability of Natural Geomedia for Radwaste Storage

1982 ◽  
Vol 15 ◽  
Author(s):  
W. S. Fyfe
Keyword(s):  
Rock Properties ◽  
Fracture Flow ◽  
Time Prediction ◽  
Sedimentary Sequences ◽  
Rock Types ◽  
Direct Observations ◽  
Long Time ◽  
Mining Disturbance ◽  
Selection Of

ABSTRACTSelection of the best rock types for radwaste disposal will depend on their having minimal permeability, maximal flow dispersion, minimal chance of forming new wide aperture fractures, maximal ion retention, and minimal thermal and mining disturbance. While no rock is perfect, thinly bedded complex sedimentary sequences may have good properties, either as repository rocks, or as cover to a repository.Long time prediction of such favorable properties of a rock at a given site may be best modelled from studies of in situ rock properties. Fracture flow, dispersion history, and geological stability can be derived from direct observations of rocks themselves, and can provide the parameters needed for convincing demonstration of repository security for appropriate times.

Direct Observations of the Epitaxial Growth of Sputtered Ag on Si

1973 ◽  
Vol 31 ◽  
pp. 122-123
Author(s):  
J. S. Maa ◽  
Thos. E. Hutchinson
Keyword(s):  
Epitaxial Growth ◽  
Film Growth ◽  
Ion Beam ◽  
Ion Beam Sputtering ◽  
Microscopy Technique ◽  
Direct Observations ◽  
In Situ Electron Microscopy ◽  
Beam Sputtering ◽  
The Subject

The growth of Ag films deposited on various substrate materials such as MoS2, mica, graphite, and MgO has been investigated extensively using the in situ electron microscopy technique. The three stages of film growth, namely, the nucleation, growth of islands followed by liquid-like coalescence have been observed in both the vacuum vapor deposited and ion beam sputtered thin films. The mechanisms of nucleation and growth of silver films formed by ion beam sputtering on the (111) plane of silicon comprise the subject of this paper. A novel mode of epitaxial growth is observed to that seen previously.The experimental arrangement for the present study is the same as previous experiments, and the preparation procedure for obtaining thin silicon substrate is presented in a separate paper.

SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN KEPALA CABANG TERBAIK BANK SUMUT DENGAN MENERAPKAN METODE VIKOR

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Betrik J Hutapea ◽  
Mesran Mesran ◽  
Siti Nurhabibah
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Development Bank ◽  
Vikor Method ◽  
The North ◽  
Long Time ◽  
North Sumatra ◽  
Regional Development Bank ◽  
Selection Of

SUMUT Bank is one of the Banks in Indonesia with the name of the company PT. Regional Development Bank of North Sumatra. The North Sumatra Bank has branches in each region in North Sumatra both in the district and in the sub-district, and each of these branches is led by a branch leader or branch head. The head of this branch is responsible for the reversal of the Bank being led. The best and most accomplished branch heads deserve more and more awards. The selection of the best branch heads is selected transparently and structured in the hope that it can be a motivation for all branch heads to be able to further improve the quality and service of the Bank they lead. Making the best branch head selection done manually will take a long time and tends to be less transparent and structured. One solution so that the implementation of the selection can be carried out easily and quickly, it requires a Decision Support System that can provide consistency of assessment. In this study the method used is the VIKOR method (Visekriterijumsko Kompromisno Rangiranje). This method makes cracking on alternatives based on criteria that have been determined with an ideal compromise solution or the best solution, so that this system can later be beneficial for the SUMUT Bank to get the title in determining the best branch head.Keywords: Decision Support System, North Sumatra Bank, Branch Head, Vikor

scholarly journals Internet page content analysis of north European Sea ports

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Olli-Pekka Hilmola ◽  
Andres Tolli ◽  
Ain Kiisler
Keyword(s):  
Content Analysis ◽  
Environmental Issues ◽  
Basic Design ◽  
Time Period ◽  
Long Time ◽  
Sea Ports ◽  
Selection Of

Abstract This study analyses 98 Internet pages of sea ports located in Sweden, Finland and Estonia during years 2017–2019. Aim of the study is to find, how website basic design is completed (colours and languages), how slogans, environmental issues, statistics and hinterland transports are reported. Based on the analysis, it appears as rather common that sea ports follow conservative selection of colours in their websites, where blue and white are clearly most popular. Typically, English and Swedish are as the most common used language, followed by Finnish, Russian and Estonian. In some rare cases, websites are offered in Chinese or German. Larger sea ports do have clear “slogans”, where smaller ones are just having lengthy justification for their existence. Environmental issues are increasing concern among sea ports, and these are mostly mentioned in details within Swedish actors. Providing statistics varies among companies, and in some sea ports these are provided from very long time period, where in others from just previous years or then only from last year (or even at all). It is common for companies to report that they have sustainable hinterland access, railway available.

scholarly journals Seawater Absorption and Adhesion Properties of Hydrophobic and Superhydrophobic Thermoset Epoxy Nanocomposite Coatings

Nanomaterials ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 272
Author(s):  
Ayman M. Atta ◽  
Mohamed H. El-Newehy ◽  
Meera Moydeen Abdulhameed ◽  
Mohamed H. Wahby ◽  
Ahmed I. Hashem
Keyword(s):  
Thermal Stability ◽  
Oleic Acid ◽  
Salt Spray ◽  
Adhesion Properties ◽  
In Situ Modification ◽  
Long Time ◽  
Seawater Environment ◽  
Thermoset Epoxy ◽  
The Cost

The enhancement of both thermal and mechanical properties of epoxy materials using nanomaterials becomes a target in coating of the steel to protect it from aggressive environmental conditions for a long time, with reducing the cost. In this respect, the adhesion properties of the epoxy with the steel surfaces, and its proper superhyrophobicity to repel the seawater humidity, can be optimized via addition of green nanoparticles (NPs). In-situ modification of silver (Ag) and calcium carbonate (CaCO3) NPs with oleic acid (OA) was carried out during the formation of Ag−OA and CaCO3−OA, respectively. The epoxide oleic acid (EOA) was also used as capping for Ca−O3 NPs by in-situ method and epoxidation of Ag−OA NPs, too. The morphology, thermal stability, and the diameters of NPs, as well as their dispersion in organic solvent, were investigated. The effects of the prepared NPs on the exothermic curing of the epoxy resins in the presence of polyamines, flexibility or rigidity of epoxy coatings, wettability, and coatings durability in aggressive seawater environment were studied. The obtained results confirmed that the proper superhyrophobicity, coating adhesion, and thermal stability of the epoxy were improved after exposure to salt spray fog for 2000 h at 36 °C.

scholarly journals Snow Depth Fusion Based on Machine Learning Methods for the Northern Hemisphere

2021 ◽  
Vol 13 (7) ◽  
pp. 1250
Author(s):  
Yanxing Hu ◽  
Tao Che ◽  
Liyun Dai ◽  
Lin Xiao
Keyword(s):  
Machine Learning ◽  
Northern Hemisphere ◽  
Snow Depth ◽  
Learning Algorithm ◽  
Random Forest Regression ◽  
Machine Learning Methods ◽  
Long Time ◽  
In Situ Observations ◽  
Input Variables

In this study, a machine learning algorithm was introduced to fuse gridded snow depth datasets. The input variables of the machine learning method included geolocation (latitude and longitude), topographic data (elevation), gridded snow depth datasets and in situ observations. A total of 29,565 in situ observations were used to train and optimize the machine learning algorithm. A total of five gridded snow depth datasets—Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) snow depth, Global Snow Monitoring for Climate Research (GlobSnow) snow depth, Long time series of daily snow depth over the Northern Hemisphere (NHSD) snow depth, ERA-Interim snow depth and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) snow depth—were used as input variables. The first three snow depth datasets are retrieved from passive microwave brightness temperature or assimilation with in situ observations, while the last two are snow depth datasets obtained from meteorological reanalysis data with a land surface model and data assimilation system. Then, three machine learning methods, i.e., Artificial Neural Networks (ANN), Support Vector Regression (SVR), and Random Forest Regression (RFR), were used to produce a fused snow depth dataset from 2002 to 2004. The RFR model performed best and was thus used to produce a new snow depth product from the fusion of the five snow depth datasets and auxiliary data over the Northern Hemisphere from 2002 to 2011. The fused snow-depth product was verified at five well-known snow observation sites. The R2 of Sodankylä, Old Aspen, and Reynolds Mountains East were 0.88, 0.69, and 0.63, respectively. At the Swamp Angel Study Plot and Weissfluhjoch observation sites, which have an average snow depth exceeding 200 cm, the fused snow depth did not perform well. The spatial patterns of the average snow depth were analyzed seasonally, and the average snow depths of autumn, winter, and spring were 5.7, 25.8, and 21.5 cm, respectively. In the future, random forest regression will be used to produce a long time series of a fused snow depth dataset over the Northern Hemisphere or other specific regions.

Persistent luminescent nanoparticles for super-long time in vivo and in situ imaging with repeatable excitation

2014 ◽  
Vol 145 ◽  
pp. 838-842 ◽  
Author(s):  
Meng Sun ◽  
Zhan-Jun Li ◽  
Chun-Lin Liu ◽  
Hai-Xia Fu ◽  
Jiang-Shan Shen ◽  
...  
Keyword(s):  
Long Time ◽  
Luminescent Nanoparticles ◽  
In Situ Imaging

scholarly journals Radiomorphology of the Habib Sealer-Induced Resection Plane during Long-Time Followup:A Longitudinal Single Center Experience after 64 Radiofrequency-AssistedLiver Resections

HPB Surgery ◽  
2010 ◽  
Vol 2010 ◽  
pp. 1-7 ◽  
Author(s):  
Robert Kleinert ◽  
Roger Wahba ◽  
Christoph Bangard ◽  
Klaus Prenzel ◽  
Arnulf H. Hölscher ◽  
...  
Keyword(s):  
Liver Resection ◽  
Morphological Characteristics ◽  
Postoperative Mortality ◽  
Coagulation Necrosis ◽  
Time Interval ◽  
Safe Alternative ◽  
Time Points ◽  
Single Center Experience ◽  
Long Time

Background. Radiofrequency (RF-) assisted liver resection devices like the Habib sealer induce a necrotic resection plane from which a small margin of necrotic liver tissue remains in situ. The aim of the present paper was to report our long-time experience with the new resection method and the morphological characteristics of the remaining necrotic resection plane. Methods. 64 RF-assisted liver resections were performed using the Habib sealer. Followup was assessed at defined time points. Results. The postoperative mortality was 3,6% and morbidity was 18%. The followup revealed that the necrotic zone was detectable in all analyzed CT and MRI images as a hypodense structure without any contrast enhancement at all time points, irrespectively of the time interval between resection and examination. Conclusion. Liver resection utilizing radiofrequency-induced resection plane coagulation is a safe alternative to the established resection techniques. The residual zone of coagulation necrosis remains basically unchanged during a followup of three years. This has to be kept in mind when evaluating the follow up imaging of these patients.

Digital Rock Typing DRT Algorithm Formulation with Optimal Supervised Semantic Segmentation

2022 ◽  
Author(s):  
Omar Alfarisi ◽  
Djamel Ouzzane ◽  
Mohamed Sassi ◽  
TieJun Zhang
Keyword(s):  
Carbonate Rock ◽  
Chemical Properties ◽  
Rock Physics ◽  
Semantic Segmentation ◽  
Rock Properties ◽  
Digital Rock Physics ◽  
Digital Rock ◽  
Rock Types ◽  
Fluid Saturation ◽  
Rock Typing

<p><a></a>Each grid block in a 3D geological model requires a rock type that represents all physical and chemical properties of that block. The properties that classify rock types are lithology, permeability, and capillary pressure. Scientists and engineers determined these properties using conventional laboratory measurements, which embedded destructive methods to the sample or altered some of its properties (i.e., wettability, permeability, and porosity) because the measurements process includes sample crushing, fluid flow, or fluid saturation. Lately, Digital Rock Physics (DRT) has emerged to quantify these properties from micro-Computerized Tomography (uCT) and Magnetic Resonance Imaging (MRI) images. However, the literature did not attempt rock typing in a wholly digital context. We propose performing Digital Rock Typing (DRT) by: (1) integrating the latest DRP advances in a novel process that honors digital rock properties determination, while; (2) digitalizing the latest rock typing approaches in carbonate, and (3) introducing a novel carbonate rock typing process that utilizes computer vision capabilities to provide more insight about the heterogeneous carbonate rock texture.<br></p>

scholarly journals Self-Adaptive K-Means Based on a Covering Algorithm

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Yiwen Zhang ◽  
Yuanyuan Zhou ◽  
Xing Guo ◽  
Jintao Wu ◽  
Qiang He ◽  
...  
Keyword(s):  
Large Scale ◽  
Clustering Algorithm ◽  
Real Data ◽  
Second Phase ◽  
Data Sets ◽  
Number Of Clusters ◽  
Large Scale Data ◽  
Long Time ◽  
Two Phases ◽  
Selection Of

The K-means algorithm is one of the ten classic algorithms in the area of data mining and has been studied by researchers in numerous fields for a long time. However, the value of the clustering number k in the K-means algorithm is not always easy to be determined, and the selection of the initial centers is vulnerable to outliers. This paper proposes an improved K-means clustering algorithm called the covering K-means algorithm (C-K-means). The C-K-means algorithm can not only acquire efficient and accurate clustering results but also self-adaptively provide a reasonable numbers of clusters based on the data features. It includes two phases: the initialization of the covering algorithm (CA) and the Lloyd iteration of the K-means. The first phase executes the CA. CA self-organizes and recognizes the number of clusters k based on the similarities in the data, and it requires neither the number of clusters to be prespecified nor the initial centers to be manually selected. Therefore, it has a “blind” feature, that is, k is not preselected. The second phase performs the Lloyd iteration based on the results of the first phase. The C-K-means algorithm combines the advantages of CA and K-means. Experiments are carried out on the Spark platform, and the results verify the good scalability of the C-K-means algorithm. This algorithm can effectively solve the problem of large-scale data clustering. Extensive experiments on real data sets show that the accuracy and efficiency of the C-K-means algorithm outperforms the existing algorithms under both sequential and parallel conditions.

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