Using GIS-Based Weighted Linear Combination Analysis and Remote Sensing Techniques to Select Optimum Solid Waste Disposal Sites within Mafraq City, Jordan

Landfill siting was determined within Mafraq City, Jordan, through the integration of geographic information system (GIS), weighted linear combination (WLC) analysis, and remote sensing techniques. Several parameters were collected from various sources in vector and raster GIS formats, and then, used within the GIS-based WLC analysis to select optimum solid waste disposal sites. Namely, urban areas, agricultural lands, access roads, surface aquifers, groundwater table, fault system, water wells, streams, and land slope were considered in this research. Also, the trend of urban expansion within the study area was monitored using the Landsat data of 1989, 1999, and 2009 to support the selection process of disposal sites. It is found that about 84% of the study area was within “most suitable” to “moderately suitable” classes for landfill sites, while the rest of the study area was within “poorly suitable” and “unsuitable” classes. Based on the analysis of Landsat satellite data the urban area was expanded of more than 240% during the last three decades, mainly toward south, and southwest, except the villages near the existing disposal site, where the trend was toward east and northeast. Finally, three sites were suggested as alternatives to the existing disposal site taking into the consideration the environmental, biophysical, and economical variables applied in the GIS-based WLC analysis.

Quadratic Stabilization of Switched Uncertain Linear Systems: A Convex Combination Approach

We consider quadratic stabilization for a class of switched systems which are composed of a finite set of continuoustime linear subsystems with norm bounded uncertainties. Under the assumption that there is no single quadratically stable subsystem, if a convex combination of subsystems is quadratically stable, then we propose a state-dependent switching law, based on the convex combination of subsystems, such that the entire switched linear system is quadratically stable. When the state information is not available, we extend the discussion to designing an outputdependent switching law by constructing a robust Luenberger observer for each subsystem.

Combinations of Right Half-Plane Mappings and Vertical Strip Mappings Convex in the Vertical Direction

In this paper, we explore the linear combinations of right half-plane mappings and vertical strip mappings. We demonstrate that the combinations of these harmonic mappings are convex in the vertical direction provided they are locally univalent and sense-preserving. Furthermore, we extend this analysis to a more general case by setting specific conditions. Additionally, we take some common parameters such as as the dilatation of these harmonic mappings, and prove the sufficient conditions that their combinations are locally univalent and convex in the vertical direction. Several examples are constructed by the Mathematica software to demonstrate our main results.

A Linear Combination of Superderivations on Prime Superalgebras

In the paper, we give some results about a linear combination of superderivations on prime superalgebras and generalize a result of Niu Feng-wen.

Quantitative analysis of glutamate compounds in the swine brain following central analgesics nasal spray using proton magnetic resonance spectroscopy and linear combination model

BACKGROUND： In localized brain proton magnetic resonance spectroscopy （^1H-MRS）, metabolite levels are often expressed as ratios, rather than absolute concentrations. Frequently, the denominator is creatine, which is assumed to be stable in normal, as well as many pathological, states. However, in vivo creatine levels do not remain constant. Therefore, absolute metabolite measurements, which provide the precise concentrations of certain chemical compounds, are superior to metabolite ratios for determining pathological and evolutional changes. OBJECTIVE： To investigate the feasibility of quantification analysis of brain metabolite changes caused by central analgesics nasal spray using the ^1H-MRS and linear combination model （LCModel） methods. DESIGN, TIME AND SETTING： This neuroimaging, observational, animal study was performed at the Laboratory of the Department of Medical Imaging, Second Affiliated Hospital, Medical College, Shantou University, China from July to December 2007. MATERIALS： Butorphanol tartrate nasal spray, as a mixed agonist-antagonist opioid analgesic, was purchased from Shanghai Hengrui Pharmacy, China. A General Electric Signa 1.5T System （General Electric Medical Systems, Milwaukee, WI, USA） and LCModel software （Stephen Provencher, Oakville, Ontario, Canada） were used in this study. METHODS： MRS images were acquired in ten healthy swine aged 2 weeks using single-voxel point-resolved spectroscopic sequence. A region of interest （2 cm × 2 cm × 2 cm） was placed in the image centers of maximum brain parenchyma. Repeated MRS scanning was performed 15-20 minutes after intranasal administration of 1 mg of butorphanol tartrate. Three settings of repetition time/echo time were selected before and after nasal spray administration 3 000 ms/30 ms,1 500 ms/30 ms, and 3 000 ms/50 ms. Metabolite concentrations were estimated by LCModel software. MAIN OUTCOME MEASURES： ^1H-MRS spectra was obtained using various repetition time/echo time settings. Concentrations of glutamate compounds （glutamate ＋ glutamine）, N-acetyl aspartate, and choline were detected in swine brain prior to and following nasal spray treatment. RESULTS： The glutamate compounds curve was consistent with original spectra, when a repetition time/echo time of 3 000 ms/30 ms was adopted. Concentrations of glutamate compounds, N-acetyl aspartate, and choline decreased following administration. The most significant reduction was observed in glutamate compound concentrations from （9.28 ± 0.54） mmol/kg to （7.28 ± 0.54） mmol/kg （P 〈 0.05）. CONCLUSION： ^1H-MRS and LCModel software were effectively utilized to quantitatively analyze and measure brain metabolites. Glutamate compounds might be an important neurotransmitter in central analgesia.

Firey linear combinations of convex bodies

For convex bodies, the Firey linear combinations were introduced and studied in several papers. In this paper the mean width of the Firey linear combinations of convex bodies is studied, and the lower bound of the mean width of the Firey linear combinations of convex body and its polar body is given.

Determination of Light Rare Earths by a Spectrophoto-metry Based on the Linear Combination of Multiwavelength Data

The present paper proposes a new method of spectrophotometry based on linear combination of multiwavelength data by means of selecting a set of properly weighted coefficients and combination methods. It is clear that the weighted combination absorbance attained is only in direct proportion to the concentration of the analysed component and independent of coexisting interferents.The accuracy of the analytical results is improved greatly for the analysis of light rare earths with the coexistence of heavy rare earths.The analyti- cal error from the reagent blank and co-coloration of light and heavy rare earths have also been overcome. The greatly improved linearity and additivity of absorbance are obtained.

Potential Hazard Map for Snow Disaster Prevention Using GIS-Based Weighted Linear Combination Analysis and Remote Sensing Techniques: A Case Study in Northern Xinjiang, China

Snow disaster is one of the top ten natural disasters worldwide. Almost every year, there will be snow disasters in north Xinjiang, northwestern China. Since the accumulated heavy snow in winter season will seriously threaten people’s lives, the main object of this study is to produce a potential hazard map for snow avalanche prevention. Taking three snow seasons from November to March of year 2008 to 2010, potential hazard areas were estimated, based on snow volume products and terrain features. Remote sensing (RS) techniques and geographical information system (GIS) based weighted linear combination (WLC) approach were applied, taking into consideration multiple criteria. Snow avalanche risks were analyzed using physical exposure and vulnerability indexes. The analysis indicates that: the areas at high-risk of avalanches are located in the north and south part of the counties of Altay, Bortala and Ili prefectures;the areas at medium-risk of avalanches are found in the certain part of Altay prefecture and Urumqi, Changji, Tacheng prefectures;the avalanche risk is generally low throughout the large area to the certain part of the study area and the region on the border of the eastern north Xinjiang. Overall, the risks of snow avalanche in Altay and Ili prefectures are higher than that other regions;those areas should be allocated correspondingly more salvage materials.

The Average Errors for Linear Combinations of Bernstein Operators on the Wiener Space

In this paper, we discuss the average errors of function approximation by linear combinations of Bernstein operators. The strongly asymptotic orders for the average errors of the combinations of Bernstein operators sequence are determined on the Wiener space.

Stechkin-Marchaud Type Inequalities in Lp for Linear Combination of Bernstein-Durrmeyer Operators

In this paper, we use the equivalence relation between K-functional and modulus of smoothness, and give the Stechkin-Marchaud-type inequalities for linear combination of Bernstein-Durrmeyer operators . Moreover, we obtain the inverse result of approximation for linear combination of Bernstein-Durrmeyer operators with . Meanwhile we unify and extend some previous results.

Study on the Linear Combination of a Class ofBivariate Exponential Distribution

Making use of an easier method, this paper derives the probability density function （pdf） of the linear combination of randomvariableZ =αX＋βY. Furthermore, cumulative distribution function （cdf） of Z=αX＋βY, where X and Y have the bivariateexponential distributions, has also been proposed in this paper. Results from this paper would have important role in the reliabilitystudy.

EXPLICIT VORONOVSKAJA-TYPE RESULTS FOR LINEAR COMBINATIONS OF BDT-OPERATORS

In this paper me prove an explicit Vcionovskvja-type result for linear combmations of the so-called BDT -operators.

Combined model based on optimized multi-variable grey model and multiple linear regression

The construction method of background value is improved in the original multi-variable grey model （MGM（1,m）） from its source of construction errors. The MGM（1,m） with optimized background value is used to eliminate the random fluctuations or errors of the observational data of all variables, and the combined prediction model together with the multiple linear regression is established in order to improve the simulation and prediction accuracy of the combined model. Finally, a combined model of the MGM（1,2） with optimized background value and the binary linear regression is constructed by an example. The results show that the model has good effects for simulation and prediction.

Optimization of Cavity Combination for 20 MA LTD-Based Accelerators

Based on a transmission line code, a circuit model is proposed that could serve as the basic method for the analysis of linear transformer driver （LTD）-based accelerators. By using 1 MA, 100 kV LTD cavities, the peak load current is optimized for a total of N cavities between 500 and 1200. The simulation results suggest that, with the same number of cavities, the peak current changes obviously with the types of combinations, and the maximum change can be as large as 1.2 MA. The results also show that, for the cases considered, the optimized peak current as a function of the total number of cavities agrees with the exponential associate, and the peak current for one level LTD cannot be enhanced infinitely. Furthermore, it is found that, to obtain a 20 MA peak load current, at least 1029 LTD cavities （49 in series and 21 in parallel connection） are needed. Finally, the typical parameters of the optimized design are compared to those of the existing Z accelerator.

Short-Term Scheduling of Combined Cycle Units Using Mixed Integer Linear Programming Solution

Combined cycle plants (CCs) are broadly used all over the world. The inclusion of CCs into the optimal resource scheduling causes difficulties because they can be operated in different operating configuration modes based on the number of combustion and steam turbines. In this paper a model CCs based on a mixed integer linear programming approach to be included into an optimal short term resource optimization problem is presented. The proposed method allows modeling of CCs in different modes of operation taking into account the non convex operating costs for the different combined cycle mode of operation.

Revisiting ENSO impacts on the Indian Ocean SST based on a combined linear regression method

The El Nino-Southern Oscillation(ENSO)has great impacts on the Indian Ocean sea surface temperature(SST).In fact,two major modes of the Indian Ocean SST namely the Indian Ocean Basin(IOB)and the Indian Ocean Dipole(IOD)modes,exerting strong influences on the Indian Ocean rim countries,are both influenced by the ENSO.Based on a combined linear regression method,this study quantifies the ENSO impacts on the IOB and the IOD during ENSO concurrent,developing,and decaying stages.After removing the ENSO impacts,the spring peak of the IOB disappears along with significant decrease in number of events,while the number of events is only slightly reduced and the autumn peak remains for the IOD.By isolating the ENSO impacts during each stage,this study reveals that the leading impacts of ENSO contribute to the IOD development,while the delayed impacts facilitate the IOD phase switch and prompt the IOB development.Besides,the decadal variations of ENSO impacts are various during each stage and over different regions.These imply that merely removing the concurrent ENSO impacts would not be sufficient to investigate intrinsic climate variability of the Indian Ocean,and the present method may be useful to study climate variabilities independent of ENSO.

Generation of Linear Isolated Sub-60 Attosecond Pulses by Combining a Circularly Polarized Pulse with an Elliptically Polarized Pulse

The two-color circularly polarized pulses scheme was proposed to generate isolated attosecond pulses in our previous work [Phys. Rev. A 87 （2013） 043406], while the polarization of the attosecond pulse was not investigated. We show a supplementary explanation of this scheme and present another scheme to generate linear isolated attosecond pulses by combining a circularly polarized pulse with an elliptically polarized pulse. High-order harmonic generation and quantum path control are investigated to compare these two schemes. Both schemes can obtain supercontinuum spectra plateau from about 200eV to 550eV, which belong to the water window region. It is found that the latter scheme can clearly eliminate the short quantum path and extend the harmonic plateau. A linear isolated attosecond pulse with a duration of sub-6Oas can be generated by superposing a bandwidth of 70eV.

DECISION OF OPTIMAL HOPFIELD NEURAL NETWORK MODEL OF COMBINATIONAL CIRCUITS

By use of Hopfield model and basis solution of homogeneous linear equations which are established in accordance with consistent state, a practical decision method for the existence of optimal Hopfield model of combinational circuits is provided. Finally, an example is given.

A New Multi-Method Combination Forecasting Model for ESDD Predicting

Equal Salt Deposit Density (ESDD) is a main factor to classify contamination severity and draw pollution distribution map. The precise ESDD forecasting plays an important role in the safety, economy and reliability of power system. To cope with the problems existing in the ESDD predicting by multivariate linear regression (MLR), back propagation (BP) neural network and least squares support vector machines (LSSVM), a nonlinear combination forecasting model based on wavelet neural network (WNN) for ESDD is proposed. The model is a WNN with three layers, whose input layer has three neurons and output layer has one neuron, namely, regarding the ESDD forecasting results of MLR, BP and LSSVM as the inputs of the model and the observed value as the output. In the interest of better reflection of the influence of each single forecasting model on ESDD and increase of the accuracy of ESDD prediction, Morlet wavelet is used to con-struct WNN, error backpropagation algorithm is adopted to train the network and genetic algorithm is used to determine the initials of the parameters. Simulation results show that the accuracy of the proposed combina-tion ESDD forecasting model is higher than that of any single model and that of traditional linear combina-tion forecasting (LCF) model. The model provides a new feasible way to increase the accuracy of pollution distribution map of power network.