scholarly journals A Novel Approach to Harmonize Vulnerability Assessment in Carbonate and Detrital Aquifers at Basin Scale

Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 2971 ◽  
Author(s):  
Leticia Baena-Ruiz ◽  
David Pulido-Velazquez
Keyword(s):  
Vulnerability Assessment ◽  
Optimal Solution ◽  
Precipitation Method ◽  
Carbonate Aquifers ◽  
Novel Approach ◽  
Reliable Assessment ◽  
Basin Scale ◽  
Guadiana Basin ◽  
Soil Media ◽  
Karst Systems

The DRASTIC (D: Depth to water; R: Net recharge; A: Aquifer media; S: Soil media; T: Topography; I: Impact of vadose zone; C: Hydraulic conductivity) index is usually applied to assess intrinsic vulnerability in detrital and carbonate aquifers, although it does not take into account the particularities of karst systems as the COP (C: Concentration of flow; O: Overlying layers above water table; P: precipitation) method does. In this paper we aim to find a reasonable correspondence between the vulnerability maps obtained using these two methods. We adapt the DRASTIC index in order to obtain reliable assessments in carbonate aquifers while maintaining its original conceptual formulation. This approach is analogous to the hypothesis of “equivalent porous medium”, which applies to karstic aquifers the numerical solution developed for detrital aquifers. We applied our novel method to the Upper Guadiana Basin, which contains both carbonate and detrital aquifers. Validation analysis demonstrated a higher confidence in the vulnerability assessment provided by the COP method in the carbonate aquifers. The proposed method solves an optimization problem to minimize the differences between the assessments provided by the modified DRASTIC and COP methods. Decision trees and spatial statistics analyses were combined to identify the ranges and weights of DRASTIC parameters to produce an optimal solution that matches the COP vulnerability classification for carbonate aquifers in 75% of the area, while maintaining a reliable assessment of the detrital aquifers in the Basin.

Acalypha Indica mediated MgO NPs: A novel approach in greener route with its antibacterial activity against pathogens

2020 ◽  
Vol 3 (2) ◽  
Author(s):  
Kavipriya K C ◽  
Sudha A P ◽  
Sujatha K ◽  
Sowmya Lakshmi K
Keyword(s):  
Antibacterial Activity ◽  
Metal Oxides ◽  
Bacterial Species ◽  
Magnesium Nitrate ◽  
Precipitation Method ◽  
Average Crystalline Size ◽  
Face Centered Cubic ◽  
X Ray ◽  
Novel Approach ◽  
Acalypha Indica

The interest in miniaturization of particles revealed the hidden applications of metal oxides. The potential applications of the particles may vary when the size of the particle is reduced. One of the alternative routes to the conventional approach is the use of plant extract for the synthesis of metal oxides NPs. In the framework of this study, the ecofriendly MgO nanoparticles were synthesized using Acalypha Indica leaf extract,functioning as reducing and capping agent by co-precipitation method. The predecessor taken here was Magnesium Nitrate. The biologically synthesized MgO NPs were characterized by various techniques like X ray diffraction(XRD), Fourier Transform infrared spectroscopy(FTIR), Scanning electron microscope (SEM) with Energy Dispersive X-ray spectroscopy(EDX) profile and its antibacterial activity is evaluated against causative organisms. XRD studies confirmed the face centered cubic crystalline structure of MgO NPs and the average crystalline size of MgO NPs calculated using Scherer’s formula was found to be 13 nm. FTIR spectrum shows a significant Mg-O vibrational band. Purity, surface morphology and chemical composition of elements were confirmed by SEM with EDX. The SEM result shows the fine spherical morphology with the grain size range between 43nm to 62nm. Antimicrobial assay of MgO NPs was examined against gram positive and negative bacteria. Appreciated activity was observed on the Staphylococcus aureus bacterial species. In general, the renewed attempt of this facile approach gave the optimum results of multifunctional MgO NPs.

scholarly journals A Novel Approach for Solving Nonsmooth Optimization Problems with Application to Nonsmooth Equations

2013 ◽  
Vol 2013 ◽  
pp. 1-10
Author(s):  
Hamid Reza Erfanian ◽  
M. H. Noori Skandari ◽  
A. V. Kamyad
Keyword(s):  
Nonsmooth Optimization ◽  
Optimization Problems ◽  
Piecewise Linear ◽  
Linear Optimization ◽  
Optimal Solution ◽  
Piecewise Linear Function ◽  
Nonsmooth Equations ◽  
Smooth Functions ◽  
Generalized Derivative ◽  
Novel Approach

We present a new approach for solving nonsmooth optimization problems and a system of nonsmooth equations which is based on generalized derivative. For this purpose, we introduce the first order of generalized Taylor expansion of nonsmooth functions and replace it with smooth functions. In other words, nonsmooth function is approximated by a piecewise linear function based on generalized derivative. In the next step, we solve smooth linear optimization problem whose optimal solution is an approximate solution of main problem. Then, we apply the results for solving system of nonsmooth equations. Finally, for efficiency of our approach some numerical examples have been presented.

A Novel Approach to Designing Surrogate-assisted Genetic Algorithms by Combining Efficient Learning of Walsh Coefficients and Dependencies

2021 ◽  
Vol 1 (2) ◽  
pp. 1-23
Author(s):  
Arkadiy Dushatskiy ◽  
Tanja Alderliesten ◽  
Peter A. N. Bosman
Keyword(s):  
Boolean Functions ◽  
Surrogate Model ◽  
Optimization Problems ◽  
State Of The Art ◽  
Fitness Function ◽  
Optimal Solution ◽  
Problem Structure ◽  
Novel Approach ◽  
Efficient Learning ◽  
Nk Landscapes

Surrogate-assisted evolutionary algorithms have the potential to be of high value for real-world optimization problems when fitness evaluations are expensive, limiting the number of evaluations that can be performed. In this article, we consider the domain of pseudo-Boolean functions in a black-box setting. Moreover, instead of using a surrogate model as an approximation of a fitness function, we propose to precisely learn the coefficients of the Walsh decomposition of a fitness function and use the Walsh decomposition as a surrogate. If the coefficients are learned correctly, then the Walsh decomposition values perfectly match with the fitness function, and, thus, the optimal solution to the problem can be found by optimizing the surrogate without any additional evaluations of the original fitness function. It is known that the Walsh coefficients can be efficiently learned for pseudo-Boolean functions with k -bounded epistasis and known problem structure. We propose to learn dependencies between variables first and, therefore, substantially reduce the number of Walsh coefficients to be calculated. After the accurate Walsh decomposition is obtained, the surrogate model is optimized using GOMEA, which is considered to be a state-of-the-art binary optimization algorithm. We compare the proposed approach with standard GOMEA and two other Walsh decomposition-based algorithms. The benchmark functions in the experiments are well-known trap functions, NK-landscapes, MaxCut, and MAX3SAT problems. The experimental results demonstrate that the proposed approach is scalable at the supposed complexity of O (ℓ log ℓ) function evaluations when the number of subfunctions is O (ℓ) and all subfunctions are k -bounded, outperforming all considered algorithms.

Local Optima Avoidance in GA Biclustering using Map Reduce

2016 ◽  
Vol 6 (1) ◽  
pp. 37-47
Author(s):  
Gowri R. ◽  
Rathipriya R.
Keyword(s):  
Gene Expression ◽  
Genetic Algorithm ◽  
Population Size ◽  
Optimal Solution ◽  
Search Space ◽  
Gene Expression Dataset ◽  
Map Reduce ◽  
Mapreduce Framework ◽  
Local Optima ◽  
Novel Approach

One of the prominent issues in Genetic Algorithm (GA) is premature convergence on local optima. This restricts the enhanced optimal solution searching in the entire search space. Population size is one of the influencing factors in Genetic Algorithm. Increasing the population size will improvise the randomized searching and maintains the diversity in the population. It also increases its computational complexity. Especially in GA Biclustering (GABiC), the search should be randomized to find more optimal patterns. In this paper, a novel approach for population setup in MapReduce framework is proposed. The maximal population is split into population sets, and these groups will proceed searching in parallel using MapReduce framework. This approach is attempted for biclustering the gene expression dataset in this paper. The performance of this proposed work seems promising on comparing its results with those obtained from previous hybridized optimization approaches. This approach will also handle data scalability issues and applicable to the big data biclustering problems.

scholarly journals Intensity-Modulated Radiation Therapy Optimization for Acceptable and Remaining-One Unacceptable Dose-Volume and Mean-Dose Constraint Planning

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Ryosei Nakada ◽  
Omar M. Abou Al-Ola ◽  
Tetsuya Yoshinaga
Keyword(s):  
Radiation Therapy ◽  
Planning Process ◽  
Intensity Modulated Radiation Therapy ◽  
Optimal Solution ◽  
Intensity Modulated ◽  
Novel Approach ◽  
Dose Volume ◽  
Dose Constraint ◽  
Imrt Planning ◽  
Dose Constraints

We give a novel approach for obtaining an intensity-modulated radiation therapy (IMRT) optimization solution based on the idea of continuous dynamical methods. The proposed method, which is an iterative algorithm derived from the discretization of a continuous-time dynamical system, can handle not only dose-volume but also mean-dose constraints directly in IMRT treatment planning. A theoretical proof for the convergence to an equilibrium corresponding to the desired IMRT planning is given by using the Lyapunov stability theorem. By introducing the concept of “acceptable,” which means the existence of a nonempty set of beam weights satisfying the given dose-volume and mean-dose constraints, and by using the proposed method for an acceptable IMRT planning, one can resolve the issue that the objective and evaluation are different in the conventional planning process. Moreover, in the case where the target planning is totally unacceptable and partly acceptable except for one group of dose constraints, we give a procedure that enables us to obtain a nearly optimal solution close to the desired solution for unacceptable planning. The performance of the proposed approach for an acceptable or unacceptable planning is confirmed through numerical experiments simulating a clinical setup.

MULTILEVEL SYNTHESIS OF FINITE STATE MACHINES BASED ON SYMBOLIC FUNCTIONAL DECOMPOSITION

2006 ◽  
Vol 06 (02) ◽  
pp. 257-271 ◽  
Author(s):  
MARIUSZ RAWSKI ◽  
HENRY SELVARAJ ◽  
TADEUSZ ŁUBA ◽  
PIOTR SZOTKOWSKI
Keyword(s):  
Internal State ◽  
Logic Synthesis ◽  
Optimal Solution ◽  
Good Method ◽  
Functional Decomposition ◽  
Novel Approach ◽  
Target Architecture ◽  
Finite State ◽  
Field Programmable ◽  
State Encoding

This paper presents a Finite State Machine (FSM) implementation method based on symbolic functional decomposition. This novel approach to multilevel logic synthesis of FSMs targets Field Programmable Gate Array (FPGA) architectures. Traditional methods consist of two steps: internal state encoding and then mapping the encoded state transition table into target architecture. In the case of FPGAs, functional decomposition is recognized as the most efficient method of implementing digital circuits. However, none of the known state encoding algorithms can be considered as a good method to be used with functional decomposition. In this paper, the concept of symbolic functional decomposition is applied to obtain a multilevel structure that is suitable for implementation in FPGA architectures. The symbolic functional decomposition does not require a separate encoding step. It accepts FSM description with symbolic states and performs decomposition, producing such a state encoding that guarantees the optimal or near-optimal solution.

scholarly journals A novel approach reveals high zooplankton standing stock deep in the sea

2016 ◽  
Author(s):  
Alexander Vereshchaka ◽  
Galina Abyzova ◽  
Anastasia Lunina ◽  
Eteri Musaeva ◽  
Tracey T. Sutton
Keyword(s):  
Standing Stock ◽  
World Ocean ◽  
Deep Ocean ◽  
Biogeochemical Cycling ◽  
Ocean Basin ◽  
Zooplankton Biomass ◽  
Ocean Dynamics ◽  
Novel Approach ◽  
Basin Scale

Abstract. In a changing ocean there is a critical need to understand global biogeochemical cycling, particularly regarding carbon. We have made strides in understanding upper ocean dynamics, but the deep ocean interior (> 1000 m) is still largely unknown, despite representing the overwhelming majority of Earth's biosphere. Here we present a method for estimating deep-pelagic zooplankton biomass on an ocean-basin scale. In so doing we have made several new discoveries about the Atlantic, which likely apply to the World Ocean. First, zooplankton biomass in the upper bathypelagic domain is higher than expected, representing an inverted biomass pyramid. Second, the majority of this biomass comprises macroplanktonic shrimps, which have been historically underestimated. These findings, coupled with recent findings of increased global deep-pelagic fish biomass, revise our perspective on the role of the deep-pelagic fauna in oceanic biogeochemical cycling.

scholarly journals A Novel Zero-Sum Polymatrix Game Theory Bidding Strategy for Power Supply Market

2019 ◽  
Vol 8 (2) ◽  
pp. 5669-5675
Keyword(s):  
Game Theory ◽  
Financial Risk ◽  
Optimal Solution ◽  
Theoretic Approach ◽  
Bidding Strategies ◽  
Load Demand ◽  
Novel Approach ◽  
Game Theoretic ◽  
Player Game ◽  
Zero Sum

The competitive power system market involves very high financial risk due to the essential requirements of real-time bidding decision making. Decisions once taken cannot be altered easily because multiple generators participate in bidding process while simultaneously dispatching to meet the load demand most economically. In order to avoid such risks it becomes pertinent to re-structure the bidding strategies from time to time to meet upcoming techno-economical challenges. In this paper, three generating units are studied using Matrix Laboratory software with a novel approach for deciding the best strategy from the most economical strategy viewpoint. A scenario of different formulations is created for muti-player game, which then is solved with the help of zero-sum polymatrix game theory. A systematic tabular layout of revenues pertaining to each formulation in terms of mixed strategies is developed. The minimax and maximin revenues, identified using Game theoretic approach, gave the most economical strategy. Thus exact and self-enforcing generalized method for best bidding strategies of all three generators are logically derived for the most optimal solution.

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