Reservoir Operation Optimization: A Nonstructural Solution for Control of Seepage from Lar Reservoir in Iran

Abstract

In this paper, the results of utilizing a deterministic dynamic programming model for operation of Lar Reservoir in Iran are discussed. This reservoir has experienced extensive seepage from the start of its operation. The optimization model consists of a three-step cycle, which began with the optimization of reservoir operation for a given set of streamflows. The optimal policies are then analyzed in a regression procedure to obtain a set of operating rules. After the first run, operating rules from the previous run were placed as a new constraint on the water releases with some pre-assigned tolerance and the cycle continues. The model also consisted of mathematical functions for modeling the seepage from Lar Reservoir as a function of storage head in the reservoir. The loss function in the model was also modified in order to incorporate parameters that reduce the seepage. Results of different scenarios showed the significant effect of optimal policies on reduction of seepage and increasing the reliability of water supply to Tehran Metropolitan Area. A pumping station was also proposed to utilize the inactive part of the reservoir, in access of over 100 MCM, in order to reduce the seepage. The effectiveness of different pumping capacities to reduce the seepage was also investigated.     

A stable indium-phosphide diffused junction field-effect transistorwith high gain and low leakage

The fabrication and performance of a JFET which is made by diffusing zinc into an epitaxial channel of indium phosphide grown by MOCVD on a semi-insulating InP(Fe) substrate are presented. The total gate length is 2.4 μm. At 0-V gate bias the transconductance is 140 mS/mm, the gate-source capacitance is 3.0 pF/mm, and the output conductance is less than 0.5 mS/mm. At -2-V gate bias the leakage from gate to source is 4 nA/mm. The drift in drain-source current is less than ±1% after 10⁶ s under continual DC bias     

Spatial Scale Resolution of Prognostic Hydrological Models: Simulation Performance and Application in Climate Change Impact Assessment

Water Resources Management - In this paper, long-term hydrological response of a watershed to climate change was investigated taking into account the spatial scale effect on the performance of...     

Radiation dose to neonates undergoing X-ray imaging in special care baby units in Iran

Radiographic imaging has a significant role in the timely diagnosis of the diseases of neonates in intensive care units. The estimation of the dose received by the infants undergoing radiographic examination is of great importance, due to greater more radiosensitivity and longer life expectancy of the neonates and premature babies. In this study, the values of entrance skin dose (ESD), dose area products (DAPs), energy imparted (EI), whole-body dose, effective dose and risk of childhood cancer were estimated using three methods including direct method [using thermoluminescence dosimetry (TLD) chips], indirect method (using tube output) and Monte Carlo (MC) method (using MCNP4C code). In the first step, the ESD of the neonates was directly measured using TLD-100 chips. Fifty neonates, mostly premature, with different weights and gestational ages in five hospitals mostly suffering from respiratory distress syndrome and pneumonia were involved in this study. In the second step, the values of ESD to neonates were indirectly obtained from the tube output in different imaging techniques. The imaging room, incubator, neonates and other components were then simulated in order to obtain the ESD values using the MCNP4C code. Finally, the values of ESD assessed by the three methods were used for calculation of DAP, EI, whole-body dose, effective dose and risk of childhood cancer. The results indicate that the mean ESD per radiograph estimated by the direct, indirect and MC methods are 56.6±4.1, 50.1±3.1 and 54.5±3.3 μGy, respectively. The mean risk of childhood cancer estimated in this study varied between 4.21×10(-7) and 2.72×10(-6).     

Peanut and cardiovascular disease risk factors: A systematic review and meta-analysis

Abstract

Several studies have been conducted on the effects of peanut consumption on cardiovascular diseases (CVD) risk factors. However, the findings are conflicting and appear inconsistent. The aim of this review is to summarize the findings on the effect of peanut consumption on the risk factors of CVDs. We used relevant keywords and searched through PubMed, Scopus and Web of Science for articles published studies up to November 2018. Randomized controlled trials (RCTs) were included in this meta-analysis. Random or fixed-effects meta-analysis method depending on the results of heterogeneity tests was used to estimate the effect size. Between-study heterogeneity was assessed by Q test and I² index. Subgroup analysis was conducted to find any excess relationship. Publication bias was checked by Egger’s test and funnel plot. Quality of studies was assessed by the Cochrane criteria. According to the results of 13 RCTs, peanuts has no significant effect on weight (WMD: ?0.11?kg, P?=?0.773), waist circumference (WMD: ?1.41?cm, P?=?0.139), body mass index (WMD: ?0.14?kg/m², P?=?0.428), systolic and diastolic blood pressure (WMD: ?0.09?mmHg, P?=?0.939 and WMD: 0.60?mmHg, P?=?0.652, respectively), low-density lipoprotein cholesterol (WMD: ?3.31?mg/dl, P?=?0.472), triglyceride (WMD: ?7.59?mg/dl, P?=?0.180), total cholesterol (WMD: 3.15?mg/dl, P?=?0.171), fasting blood sugar (WMD: 0.57?mg/dl, P?=?0.604) and serum insulin (WMD: ?0.40, P?=?0.582). Also, this meta-analysis showed that peanut had a positive significant effect on high-density lipoprotein cholesterol (HDL) (WMD: 2.72?mg/dl, P?=?0.001). Peanuts consumption has a positive significant effect on HDL especially at the type of peanut oil, high-oleic peanut and peanut sprout and in healthy subjects and for consumption more than 12?weeks, while has no significant effect on other CVD risk factors.     

Metal/semiconductor interfacial reactions

Vapor phase deposition of thin metal films on semiconductors followed by annealing frequently leads to the nucleation and growth of one or more reaction products in the vicinity of the metal/ semiconductor interface. Often, these products are intermetallic compounds that may be distributed as precipitates in the interfacial region. The composition, size, shape, and areal density of these precipitates may be the major factors in determining the electrical properties of the interface. The semiconductors studied were GaAs, SiC, and semiconducting diamond, while the metals were principally the refractory metals, although several other metals were also investigated. The interfaces of representative samples were examined using Auger electron spectroscopy (AES), secondary ion mass spectroscopy (SIMS), X-ray diffraction (XRD), Rutherford backscattering spectroscopy (RBS), scanning electron microscopy (SEM), transmission electron microscopy (TEM), including selected area electron diffraction (SAD), and optical metallography. The electrical properties were evaluated by current-potential (I-V) and Hall effect measurements. This paper is based on a presentation made in the “G. Marshall Pound Memorial Symposium on the Kinetics of Phase Transformations” presented as part of the 1990 fall meeting of TMS, October 8–12, 1990, in Detroit, MI, under the auspices of the ASM/MSD Phase Transformations Committee.     

A Scale and Translation Invariant Approach for Early Classification of Spatio-Temporal Patterns Using Spiking Neural Networks

This paper addresses the problem of encoding and classifying spatio-temporal patterns, which are typical for human actions or gestures. The proposed method has the following main contributions: (i) it requires a very small number of training examples, (ii) it accepts variable sized input patterns, (iii) it is invariant to scale and translation, and (iv) it enables early recognition, from only partial information of the pattern. The underlying representation employed is a spiking neural network with axonal conductance delay. We designed a novel approach for mapping spatio-temporal patterns to spike trains, which are used to stimulate the network. The pattern features emerge in the network as a result of this stimulation in the form of polychronous neuronal groups, which are used for classification. The proposed method is validated on a set of gestures representing the digits from \(0\) to \(9\), extracted from video data of a human drawing the corresponding digits. The paper presents a comparison with several other standard pattern recognition approaches. The results show that the proposed approach significantly outperforms these methods, it is invariant to scale and translation, and it has the ability to recognize patterns from only partial information.     

A Multi-criteria Decision-making Optimization Model for Flood Management in Reservoirs

Water Resources Management - Flood management in a reservoir-outlet system is a multi-criterion decision-making (MCDM) issue, in which preventing flood damage and flood overtopping, as well as...     

Design and microfabrication of a polymer membrane-based submicron scale electrophoretic flow detector for biomedical applications

This study demonstrates the design and microfabrication of single cylindrical submicron-sized pores in 1 μm-thick PMMA membranes, and their integration and assembly into all-polymeric electrophoretic detectors. Pore sizes vary from 200 to 600 nm. Fabrication includes electron beam lithography of the pore and mechanical microfabrication and assembly of the remaining detector system, using UV-curing glues and a silicon sacrificial substrate wafer. Initial electrophoretic translocation experiments have been performed for various potassium chloride (KCl) electrolyte concentrations between 0.1 and 1 M. Experiments prove that the detector system is hermetically sealed, that the pores are capable of sustaining an open pore current, and that they respond with a steady and low-noise signal. The same experiments have also been applied to analyze the pore metrology, and revealed that submicron pore sizes have been underestimated by roughly 150 nm.     

PROMETHEE with Precedence Order in the Criteria (PPOC) as a New Group Decision Making Aid: An Application in Urban Water Supply Management

In this paper, a new group Multi-Criteria-Decision-Making (MCDM) method is introduced by combining two ??Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE)?? and ??Multi-attribute decision making with dominance in the criteria?? methods. PROMETHEE family of outranking methods is among the recently developed MCDM methods which have received lots of attention in the recent years because of its capacity in ranking finite set of alternative actions based on conflicting criteria. The second method helps the decision makers to consider ambiguity and imprecision of relative importance of each objective (criterion) without allocating importance weights to them. The proposed method of PROMETHEE with Precedence Order in the Criteria (PPOC) not only can address capabilities of PROMETHEE method just with determination of precedence order of criteria, but also can make it possible to have a group decision making environment with conflicting objectives. Operational management of an urban water supply system is a good example of a set of decision making problems with several objectives and Decision Makers (DMs). In this paper, PPOC method has been applied to the case study of Melbourne water supply system, previously analyzed in the literature, to assess a number of operation rules with respect to eight criteria evaluated under single or group decision-making situations. The satisfaction degree of each DM and the overall group ranking results have also been provided in the paper. The proposed method is applicable for different decision making problems in urban water supply management.