Increasing coastal populations and urban development have led to the loss of estuarine habitats for fish and wildlife. Specifically, a decline in complexity and heterogeneity of tidal marshes and creeks is thought to negatively impact fish communities by altering the function of nursery grounds, including predator refuge and prey resources. To offset these impacts, numerous agencies are restoring degraded habitats while also creating new ones where habitat has been lost. To improve understanding of what contributes to a successful restoration, six quarterly sampling events using two gear types to collect small- and large-bodied fishes were conducted to compare the fish community structure and habitat characteristics at three natural, three restored, and three impacted (i.e. ditched) areas along the coast of Tampa Bay, Florida. Overall, impacted sites had significantly lower small-bodied and juvenile fish diversity than natural and restored areas, while restored sites harbored a greater number of fish species than impacted sites for both large- and small-bodied fish. Habitat features such as shoreline slope differentiated impacted and restored from natural areas. Although we did not find a direct correlation, habitat heterogeneity likely played a role in structuring fish communities. These findings provide guidance for future coastal restoration or modification of existing projects. Specifically, the habitat mosaic approach of creating a geographically compact network of heterogenous habitat characteristics is likely to support fish diversity, while decreasing shoreline slope in a greater amount of area within coastal wetland restorations would more closely mimic natural areas.Color-based image segmentation classifies pixels of digital images in numerous groups for further analysis in computer vision, pattern recognition, image understanding, and image processing applications. Various algorithms have been developed for image segmentation, but clustering algorithms play an important role in the segmentation of digital images. This paper presents a novel and adaptive initialization approach to determine the number of clusters and find the initial central points of clusters for the standard K-means algorithm to solve the segmentation problem of color images. The presented scheme uses a scanning procedure of the paired Red, Green, and Blue (RGB) color-channel histograms for determining the most salient modes in every histogram. Next, the histogram thresholding is applied and a search in every histogram mode is performed to accomplish RGB pairs. These RGB pairs are used as the initial cluster centers and cluster numbers that clustered each pixel into the appropriate region for generating the homogeneous regions. The proposed technique determines the best initialization parameters for the conventional K-means clustering technique. In this paper, the proposed approach was compared with various unsupervised image segmentation techniques on various image segmentation benchmarks. Furthermore, we made use of a ranking approach inspired by the Evaluation Based on Distance from Average Solution (EDAS) method to account for segmentation integrity. The experimental results show that the proposed technique outperforms the other existing clustering techniques by optimizing the segmentation quality and possibly reducing the classification error.Shedding of DNA of pathogenic Leptospira spp. has been documented in naturally infected cats in several countries, but urinary shedding of infectious Leptospira spp. has only recently been proven. The climate in Southern Chile is temperate rainy with high annual precipitations which represents ideal preconditions for survival of Leptospira spp., especially during spring and summer. The aims of this study were to investigate shedding of pathogenic Leptospira spp. in outdoor cats in Southern Chile, to perform molecular characterization of isolates growing in culture, and to assess potential risk factors associated with shedding. Urine samples of 231 outdoor cats from rural and urban areas in southern Chile were collected. Urine samples were investigated for pathogenic Leptospira spp. by 4 techniques qPCR targeting the lipL32 gene, immunomagnetic separation (IMS)-coupled qPCR (IMS-qPCR), direct culture and IMS-coupled culture. Positive urine cultures were additionally confirmed by PCR. Multilocus sequence typingcribed worldwide.Incineration has emerged as one of the acceptable ways to treat municipal solid waste (MSW) due to its potential in reducing the mass and volume of the waste. However, it produces two major by-product residues, namely MSW-bottom ash (MSW-BA) and MSW-fly ash (MSW-FA). These residues have gained great attention to their hazardous nature and potential to be reused and recycled. In this paper, the physicochemical characterizations of the MSW-BA and the MSW-FA were performed, followed by a systematic investigation of metals extraction from MSW-BA and MSW-FA. Various extracting agents were used to investigate the possibility to extract 21 metals including cadmium (Cd), vanadium (V), chromium (Cr), and lead (Pb). It was revealed that some metals were present in a high amount in the MSW-BA while other metals were higher in the MSW-FA. Moreover, the energy-dispersive X-ray spectroscopy results revealed that the MSW-BA was dominated by oxygen (O) 55.4 ±0.6 wt%, silicon (Si) 22.5 ±0.3 wt%, and calcium (Ca) 18.5 ±0.2 wt%sustainable option to utilize MSW.The running of high-speed electrically driven feed pump has a direct impact on the safety of personnel equipment and economic benefits of power plant, as the result, intelligent condition monitoring and fault diagnosis of electrically driven feed pump becomes an urgent need. https://www.selleckchem.com/products/tp-1454.html In the practical process of electrically driven feed pump fault diagnosis, the running of the equipment is in normal state for a long time, occasionally, with faults, which makes the fault data very rare in a large number of monitoring data, and makes it difficult to extract the internal fault features behind the original time series data, When the deep learning theory is used in practice, the imbalance between the fault data and the normal data occurs in the operation data set. In order to solve the problem of data imbalance, this paper proposes a fault diagnosis method of GAN-SAE. This method first makes compensation for the imbalance of sample data based on the Generative Adversarial Network (GAN), and then uses the Stacked Auto Encoder (SAE) method to extract the signal features.