Improving the multi-objective evolutionary optimization algorithm for hydropower reservoir operations in the California Oroville-Thermalito complex

This study demonstrates the application of an improved Evolutionary optimization Algorithm (EA), titled Multi-Objective Complex Evolution Global Optimization Method with Principal Component Analysis and Crowding Distance Operator (MOSPD), for the hydropower reservoir operation of the Oroville-Thermalito Complex (OTC) - a crucial head-water resource for the California State Water Project (SWP). In the OTC's water-hydropower joint management study, the nonlinearity of hydropower generation and the reservoir's water elevation-storage relationship are explicitly formulated by polynomial function in order to closely match realistic situations and reduce linearization approximation errors. Comparison among different curve-fitting methods is conducted to understand the impact of the simplification of reservoir topography. In the optimization algorithm development, techniques of crowding distance and principal component analysis are implemented to improve the diversity and convergence of the optimal solutions towards and along the Pareto optimal set in the objective space. A comparative evaluation among the new algorithm MOSPD, the original Multi-Objective Complex Evolution Global Optimization Method (MOCOM), the Multi-Objective Differential Evolution method (MODE), the Multi-Objective Genetic Algorithm (MOGA), the Multi-Objective Simulated Annealing approach (MOSA), and the Multi-Objective Particle Swarm Optimization scheme (MOPSO) is conducted using the benchmark functions. The results show that best the MOSPD algorithm demonstrated the best and most consistent performance when compared with other algorithms on the test problems. The newly developed algorithm (MOSPD) is further applied to the OTC reservoir releasing problem during the snow melting season in 1998 (wet year), 2000 (normal year) and 2001 (dry year), in which the more spreading and converged non-dominated solutions of MOSPD provide decision makers with better operational alternatives for effectively and efficiently managing the OTC reservoirs in response to the different climates, especially drought, which has become more and more severe and frequent in California

Calibration of probabilistic quantitative precipitation forecasts with an artificial neural network

A feed-forward neural network is configured to calibrate the bias of a high-resolution probabilistic quantitative precipitation forecast (PQPF) produced by a 12-km version of the NCEP Regional Spectral Model (RSM) ensemble forecast system. Twice-daily forecasts during the 2002-2003 cool season (1 November-31 March, inclusive) are run over four U.S. Geological Survey (USGS) hydrologic unit regions of the southwest United States. Calibration is performed via a cross-validation procedure, where four months are used for training and the excluded month is used for testing. The PQPFs before and after the calibration over a hydrological unit region are evaluated by comparing the joint probability distribution of forecasts and observations. Verification is performed on the 4-km stage IV grid, which is used as "truth." The calibration procedure improves the Brier score (BrS), conditional bias (reliability) and forecast skill, such as the Brier skill score (BrSS) and the ranked probability skill score (RPSS), relative to the sample frequency for all geographic regions and most precipitation thresholds. However, the procedure degrades the resolution of the PQPFs by systematically producing more forecasts with low nonzero forecast probabilities that drive the forecast distribution closer to the climatology of the training sample. The problem of degrading the resolution is most severe over the Colorado River basin and the Great Basin for relatively high precipitation thresholds where the sample of observed events is relatively small. © 2007 American Meteorological Society

Short-range probabilistic quantitative precipitation forecasts over the southwest United States by the RSM ensemble system

The National Centers for Environmental Prediction (NCEP) Regional Spectral Model (RSM) is used to produce twice-daily (0000 and 1200 UTC), high-resolution ensemble forecasts to 24 h. The forecasts are performed at an equivalent horizontal grid spacing of 12 km for the period 1 November 2002 to 31 March 2003 over the southwest United States. The performance of 6-h accumulated precipitation is assessed for 32 U.S. Geological Survey hydrologic catchments. Multiple accuracy and skill measures are used to evaluate probabilistic quantitative precipitation forecasts. NCEP stage-IV precipitation analyses are used as "truth," with verification performed on the stage-IV 4-km grid. The RSM ensemble exhibits a ubiquitous wet bias. The bias manifests itself in areal coverage, frequency of occurrence, and total accumulated precipitation over every region and during every 6-h period. The biases become particularly acute starting with the 1800-0000 UTC interval, which leads to a spurious diurnal cycle and the 1200 UTC cycle being more adversely affected than the 0000 UTC cycle. Forecast quality and value exhibit marked variability over different hydrologic regions. The forecasts are highly skillful along coastal California and the windward slopes of the Sierra Nevada Mountains, but they generally lack skill over the Great Basin and the Colorado basin except over mountain peaks. The RSM ensemble is able to discriminate precipitation events and provide useful guidance to a wide range of users over most regions of California, which suggests that mitigation of the conditional biases through statistical postprocessing would produce major improvements in skill. © 2007 American Meteorological Society

Fast Deep Matting for Portrait Animation on Mobile Phone

Image matting plays an important role in image and video editing. However, the formulation of image matting is inherently ill-posed. Traditional methods usually employ interaction to deal with the image matting problem with trimaps and strokes, and cannot run on the mobile phone in real-time. In this paper, we propose a real-time automatic deep matting approach for mobile devices. By leveraging the densely connected blocks and the dilated convolution, a light full convolutional network is designed to predict a coarse binary mask for portrait images. And a feathering block, which is edge-preserving and matting adaptive, is further developed to learn the guided filter and transform the binary mask into alpha matte. Finally, an automatic portrait animation system based on fast deep matting is built on mobile devices, which does not need any interaction and can realize real-time matting with 15 fps. The experiments show that the proposed approach achieves comparable results with the state-of-the-art matting solvers.Comment: ACM Multimedia Conference (MM) 2017 camera-read

Assessing the effect of reducing agents on the selective catalytic reduction of NO<inf>x</inf> over Ag/Al<inf>2</inf>O<inf>3</inf> catalysts

The selective catalytic reduction (SCR) of NOx in the presence of different reducing agents over Ag/Al2O3 prepared by wet impregnation was investigated by probing catalyst activity and using NMR relaxation time analysis.We gratefully acknowledge funding for this work from the EPSRC CASTech grant (EP/G012156/1). Carmine D’Agostino would like to acknowledge Wolfson College, Cambridge, for supporting his research activities. The authors would also like to thank Dr Jonathan Mitchell for useful discussions.This is the final version of the article. It first appeared from RSC via http://dx.doi.org/10.1039/C5CY01508

Erosion and Accretion on a Mudflat: The Importance of Very Shallow-Water Effects

Understanding erosion and accretion dynamics during an entire tidal cycle is important for assessing their impacts on the habitats of biological communities and the long‐term morphological evolution of intertidal mudflats. However, previous studies often omitted erosion and accretion during very shallow‐water stages (VSWS, water depths 0.2 m (i.e., probe submerged) are considered. These findings suggest that the magnitude of bed‐level changes during VSWS should not be neglected when modeling morphodynamic processes. Our results are useful in understanding the mechanisms of micro‐topography formation and destruction that often occur at VSWS, and also improve our understanding and modeling ability of coastal morphological changes

Species composition, plant cover and diversity of recently reforested wild lands near Dabao Highway in Longitudinal Range-Gorge Region of Yunnan Province, China

Deforestation, over-cultivation and rural growth have severely damaged native vegetation of woodlands along roadsides in the Longitudinal Range-Gorge Region of Yunnan Province. This study wasconducted to evaluate the effect of different reforestation practices, which consisted of natural restoration or planting with tree seedlings that varied in species composition, coverage and diversity,on damaged roadside woodlands. Three randomly selected 10 m x 10 m plots in each reforestation practice were investigated. The results showed that the species composition, plant cover and speciesdiversity of the planted communities varied with reforestation strategies and time since planting. A higher number of species, proportion of native species and woody plants, canopy cover and speciesdiversity were found in naturally restored plots and in 3 - 4 year old plots that were planted with native plants. In the early stages of reforestation, herbs dominated the plant community in most plots, andwoody plants became more important with time after reforestation. Preliminary results suggest that plant height can be used an auxiliary indicator of plant cover to assess ecosystem function status ofthe restoration project. Also, evenness may be easier to restore than species richness. Natural restoration or reforestation with native dominant plants is a good management strategy for vegetationrestoration or improvement

Ori-Finder: A web-based system for finding oriCs in unannotated bacterial genomes

<p>Abstract</p> <p>Background</p> <p>Chromosomal replication is the central event in the bacterial cell cycle. Identification of replication origins (<it>oriC</it>s) is necessary for almost all newly sequenced bacterial genomes. Given the increasing pace of genome sequencing, the current available software for predicting <it>oriC</it>s, however, still leaves much to be desired. Therefore, the increasing availability of genome sequences calls for improved software to identify <it>oriC</it>s in newly sequenced and unannotated bacterial genomes.</p> <p>Results</p> <p>We have developed Ori-Finder, an online system for finding <it>oriC</it>s in bacterial genomes based on an integrated method comprising the analysis of base composition asymmetry using the <it>Z</it>-curve method, distribution of DnaA boxes, and the occurrence of genes frequently close to <it>oriC</it>s. The program can also deal with unannotated genome sequences by integrating the gene-finding program ZCURVE 1.02. Output of the predicted results is exported to an HTML report, which offers convenient views on the results in both graphical and tabular formats.</p> <p>Conclusion</p> <p>A web-based system to predict replication origins of bacterial genomes has been presented here. Based on this system, <it>oriC </it>regions have been predicted for the bacterial genomes available in GenBank currently. It is hoped that Ori-Finder will become a useful tool for the identification and analysis of <it>oriC</it>s in both bacterial and archaeal genomes.</p

Association of Mineralocorticoid Receptor Antagonists With the Mortality and Cardiovascular Effects in Dialysis Patients: A Meta-analysis

Whether Mineralocorticoid receptor antagonists (MRA) reduce mortality and cardiovascular effects of dialysis patients remains unclear. A meta-analysis was designed to investigate whether MRA reduce mortality and cardiovascular effects of dialysis patients, with a registration in INPLASY (INPLASY2020120143). The meta-analysis revealed that MRA significantly reduced all-cause mortality (ACM) and cardiovascular mortality (CVM). Patients receiving MRA presented improved left ventricular mass index (LVMI) and left ventricular ejection fraction (LVEF), decreased systolic blood pressure (SBP) and diastolic blood pressure (DBP). There was no significant difference in the serum potassium level between the MRA group and the placebo group. MRA vs. control exerts definite survival and cardiovascular benefits in dialysis patients, including reducing all-cause mortality and cardiovascular mortality, LVMI, and arterial blood pressure, and improving LVEF. In terms of safety, MRA did not increase serum potassium levels for dialysis patients with safety. Systematic Review Registration: (https://inplasy.com/inplasy-protocol-1239-2/), identifier (INPLASY2020120143)