Effect Of Accelerating Aging On Retention And The Release Period Of Clips In A Bar Retained Maxillary Implant Overdenture

Objectives: This study was made to detect the effect of accelerating aging on retention and measuring the release period of clips in a 2 and 3 bar retained maxillary implant overdenture. Materials and Methods: Four implants were placed in two maxillary edentulous epoxy models. One model had two bar attachments with two clips overdenture while the other model had three bar attachments with three clips in the overdenture. Retention and release period of the clips were checked before applying insertion removal cycles. Retention was measured using universal testing machine after 540 cycles (6 months) and 1080 cycles (1 year) of insertion removal on a chewing simulator.Results and Conclusions: There was a significant difference in retention and release period between two bars and three bars implant retained maxillary overdentures. A significant difference was also seen in each group after accelerated aging. Therefore, the three bar implant retained overdenture had higher retention values than two bar. Retention loss occurred in both groups after the insertion removal cycles. Release period value was lower in two bar overdenture than three bar overdenture.

Observations on Ion Interaction Chromatographic System: Reversed Phase Column—H3BO3/TBAOH Mobile Phase and the Effect of Temperature

New ion interaction chromatographic (IIC) system with RP column and boric acid plus tetra-n-butylammonium hydroxide (TBAOH) mobile phase was investigated. In the system: CPhenylHexyl—10 mM H3BO3/4 mM TBAOH, a large group of anions viz. F−,IO3−, Cl−, ClO2−, BrO3−, NO2−, Br−, NO3−, ClO3−, I−, HPO42−, SO42−, CrO42−, S2O32−, benzoate−, SCN−, ClO4− and phthalate2−, could be isocratically resolved. A study on the effect of temperature revealed that some ions added to ion exchange are also held in the stationary phase by the hydrophobic adsorption. The retention loss for all anions with time was observed. This effect however, was quite slow and good separations could be obtained even after the column stayed a few hundred hours in the mobile phase.

Urbanization—Its Hidden Impact on Water Losses: Prądnik River Basin, Lesser Poland

Urban development causes multiple water losses. Some of them may be ignored but some could have a huge influence on the whole catchment, including soil drought. As urban sprawl rises, space for unaffected infiltration and retention is increasingly limited. The objective of this study was to backcast and to estimate water-retention loss due to urbanization during the period of 1990–2018. We used landcover data, meteorological and hydrological data and data on soil water-holding capacity. Water-retention loss was expressed as soil water retention capacity loss, net precipitation loss and total sum of precipitation loss. Historical change in urban extension has led to large impacts on the hydrological cycle of the study area. Progressive urban development caused water-retention losses which range from 3.380 to 14.182 millions of cubic meters—depending on the methodology used. Hydrological analysis showed the lack of a significant trend (decrease trend) of low flow which is caused by the high percentage of natural land use in the upper part of catchment. Our results show that backcasting of water retention change using CLC data (a) brings new and plausible data on retention loss, (b) is possible to replicate and (c) data used are common and easy-to-get.

Similarity Retention Loss (SRL) Based on Deep Metric Learning for Remote Sensing Image Retrieval

Recently, with the rapid growth of the number of datasets with remote sensing images, it is urgent to propose an effective image retrieval method to manage and use such image data. In this paper, we propose a deep metric learning strategy based on Similarity Retention Loss (SRL) for content-based remote sensing image retrieval. We have improved the current metric learning methods from the following aspects—sample mining, network model structure and metric loss function. On the basis of redefining the hard samples and easy samples, we mine the positive and negative samples according to the size and spatial distribution of the dataset classes. At the same time, Similarity Retention Loss is proposed and the ratio of easy samples to hard samples in the class is used to assign dynamic weights to the hard samples selected in the experiment to learn the sample structure characteristics within the class. For negative samples, different weights are set based on the spatial distribution of the surrounding samples to maintain the consistency of similar structures among classes. Finally, we conduct a large number of comprehensive experiments on two remote sensing datasets with the fine-tuning network. The experiment results show that the method used in this paper achieves the state-of-the-art performance.