Linear Combination Model Research Articles

Development of multivariate ionospheric TEC forecasting algorithm using linear time series model and ARMA over low-latitude GNSS station

Forecasting of ionospheric time delays has become a significant importance in satellite based navigation and communication system applications. Several researchers have been developed and implemented univariate Total Electron Content (TEC) forecasting models successfully over low, mid and high latitude regions. Therefore, identifying an effective multivariate forecasting technique is very essential to alert the Global Navigation Satellite System (GNSS) users under various space weather conditions. In this paper, a new multivariate ionospheric TEC forecasting model based on linear time series model in combination with Autoregressive and Moving Average (ARMA) is proposed and implemented using Bengaluru International GNSS Service (IGS) station data (geographic lat. −13.02°N, long. 77.57°E; geomagnetic latitude: 4.4°N) during the period of 8 years (2009–2016) in the 24th solar cycle. The major factors, namely, geomagnetic activity (Ap), solar Extreme Ultraviolet (EUV) irradiance (F10.7p), periodic oscillations (annual, semi-annual, terannual and biennial oscillations) and long-term trend are considered in the model as input parameters along with real time TEC observations. The proposed model is twofold: first, the impact of solar, geomagnetic, trend and periodic factors on TEC has been investigated from linear model. Second, ARMA method is applied for forecasting each factor. The forecasted individual factors are combined to obtain the forecasted TEC values. The estimated TEC from the proposed model has good agreement with the observed Global Positioning System (GPS) – TEC. It is noticed that the magnitudes of semi-annual variation have been reflected to be high during the High Solar Activity (HSA) period. It is also found that the geomagnetic effect on TEC is relatively low. The proposed multivariate ionospheric TEC forecasting model would be useful for characterizing the low-latitude ionospheric variations under various space weather conditions.

A Novel Nonlinear Combined Forecasting System for Short-Term Load Forecasting

Short-term load forecasting plays an indispensable role in electric power systems, which is not only an extremely challenging task but also a concerning issue for all society due to complex nonlinearity characteristics. However, most previous combined forecasting models were based on optimizing weight coefficients to develop a linear combined forecasting model, while ignoring that the linear combined model only considers the contribution of the linear terms to improving the model’s performance, which will lead to poor forecasting results because of the significance of the neglected and potential nonlinear terms. In this paper, a novel nonlinear combined forecasting system, which consists of three modules (improved data pre-processing module, forecasting module and the evaluation module) is developed for short-term load forecasting. Different from the simple data pre-processing of most previous studies, the improved data pre-processing module based on longitudinal data selection is successfully developed in this system, which further improves the effectiveness of data pre-processing and then enhances the final forecasting performance. Furthermore, the modified support vector machine is developed to integrate all the individual predictors and obtain the final prediction, which successfully overcomes the upper drawbacks of the linear combined model. Moreover, the evaluation module is incorporated to perform a scientific evaluation for the developed system. The half-hourly electrical load data from New South Wales are employed to verify the effectiveness of the developed forecasting system, and the results reveal that the developed nonlinear forecasting system can be employed in the dispatching and planning for smart grids.

A MULTI-OBJECTIVE APPROACH FOR LAND CONSERVATION CAPABILITY EVALUATION USING MULTI-CRITERION EVALUATION MODELS

In order to protect ecosystem functions and structures, it is essential to identify areas with high conservation potentials. The present study investigates the use of Multi-Criteria Evaluation (MCE) Models and effects of trade-off levels on the results of land conservation capability in the Pelasjan subbasin, western part of the Gavkhooni watershed and Gavkhooni wetland water source in central Iran. Delphi survey method was used to gather, condense and use experts’ knowledge in determining and ranking the evaluation criteria. Analytic Hierarchy Process (AHP) was then used to estimate the criteria weights. Subsequently, to create a single ranked map of potential conservation area, a weighted linear combination (WLC) model and ordered weighted averaging (OWA) model with medium risk and tradeoff were used. Conservation capability maps, were then classified into 6 classes based on conservation capability values ranges. The comparison analysis has shown that designed OWA model with lower trade-off level leads to relate areas as very high and high capability classes. Models accuracy assessments have shown that OWA model was more accurate and realistic, but with regard to Plasjan’s importance as a habitat for fauna and flora, producing water, and high quality soil and biodiversity, it is highly necessary to consider WLC results.

Application of combined model with DGM(1,1) and linear regression in grain yield prediction

PurposeThe purpose of this paper is to address and overcome the problem that a single prediction model cannot accurately fit a data sequence with large fluctuations.Design/methodology/approachInitially, the grey linear regression combination model was put forward. The Discrete Grey Model (DGM)(1,1) model and the multiple linear regression model were then combined using the entropy weight method. The grain yield from 2010 to 2015 was forecasted using DGM(1,1), a multiple linear regression model, the combined model and a GM(1,N) model. The predicted values were then compared against the actual values.FindingsThe results reveal that the combination model used in this paper offers greater simulation precision. The combination model can be applied to the series with fluctuations and the weights of influencing factors in the model can be objectively evaluated. The simulation accuracy of GM(1,N) model fluctuates greatly in this prediction.Practical implicationsThe combined model adopted in this paper can be applied to grain forecasting to improve the accuracy of grain prediction. This is important as data on grain yield are typically characterised by large fluctuation and some information is often missed.Originality/valueThis paper puts the grey linear regression combination model which combines the DGM(1,1) model and the multiple linear regression model using the entropy weight method to determine the results weighting of the two models. It is intended that prediction accuracy can be improved through the combination of models used within this paper.

Evidence for regional hippocampal damage in patients with schizophrenia.

Schizophrenia patients show cognitive and mood impairments, including memory loss and depression, suggesting damage in the brain regions. The hippocampus is a brain structure that is significantly involved in memory and mood function and shows impairment in schizophrenia. In the present study, we examined the regional hippocampal changes in schizophrenia patients using voxel-based morphometry (VBM), Freesurfer, and proton magnetic resonance spectroscopy (1H MRS) procedures. 1H MRS and high-resolution T1-weighted magnetic resonance imaging were collected in both healthy control subjects (N = 28) and schizophrenia patients (N = 28) using 3-Tesla whole body MRI system. Regional hippocampal volume was analyzed using VBM and Freesufer procedures. The relative ratios of the neurometabolites were calculated using linear combination model (LCModel). Compared to controls, schizophrenia patients showed significantly decreased gray matter volume in the hippocampus. Schizophrenia patients also showed significantly reduced glutamate (Glu) and myo-inositol (mI) ratios in the hippocampus. Additionally, significant positive correlation between gray matter volume and Glu/tCr was also observed in the hippocampus in schizophrenia. Our findings provide an evidence for a possible association between structural deficits and metabolic alterations in schizophrenia patients.

T–S Fuzzy Logic Based Modeling and Robust Control for Burning-Through Point in Sintering Process

Burning-through point (BTP) control is the key to guranteeing the sintering process and sinter quality. Due to an opening combustion and multiple input conditions, the combustion process of the sintering process shows a typical characteristic of time-varying operation conditions. This paper analyzes the influence factors resulting in time-varying operation conditions and then constructs an improved Takagi–Sugeno (T–S) fuzzy model of BTP based on the linear parameter varying (LPV-TS-BTP model), in which the BTP temperature and the central bellows temperature are taken as time-varying parameters. The model explains how the different working conditions change the systems dynamics and describes this time-varying nonlinear model in the form of a linear combination model. Then, a fuzzy robust control method based on the LPV-TS-BTP model is designed by using the parallel distributed compensation principle to realize the BTP control of complex process, in which the controller parameters are achieved by using the linear matrix inequality (LMI) method. The simulations about modeling and control, which are carried out on a semiphysical simulation platform for the sintering process, verify that the model has a high prediction accuracy, and the fuzzy robust control makes BTP stable around a given bellows.

GIS-based multi-criteria earthquake hazards evaluation using analytic hierarchy process for a nuclear power plant site, west Alexandria, Egypt

Nuclear power plants are designed to prevent the hazardous effects of the earthquakes and any external events to keep the safety of the plant. Ninety-one shallow seismic refraction profiles were performed to determine shear wave velocity of the engineering layers at the site of El Dabaa area that is situated to the northern coastline of Egypt for seismic hazard microzonation evaluation according to hazard index values. A microzonation is a procedure of delineating an area into individual zones having different ranks of numerous seismic hazards. This will aid in classifying areas of high seismic risk which is vigorous for industrial design of nuclear structures. The site response analysis requires the characterization of subsurface materials considering local subsurface profiles of the site. Site classification of the area under investigation was undertaken using P- and S-waves and available borehole data. The studied nuclear power plant site has been characterized as per NEHRP site classification using an average velocity of transverse wave (V s 30 ) of depth 30 m which acquired from seismic survey. This site was categorized into two site classes: the major one is “site class B,” and the minor one is “site class A.” The attenuation coefficient, the damping ratio and the liquefaction potential are geotechnical parameters which were derived from P- and S-waves, and have their major effects on the seismic hazard contribution. 1D ground response analysis was carried out in the places of seismic profiles inside the site for estimating the amount of ground quaking using peak ground acceleration (PGA), site amplification, predominant frequency and spectral accelerations on the surface of ground by the DEEPSOIL software package. Seven factors (criteria) deliberated to assess the earthquake hazard index map are: (1) the peak ground acceleration at the bedrock, (2) the amplification of the site, (3) the liquefaction potential, (4) the main frequency of the earthquake signal, (5) the average V s of the first 30 m from the ground surface, (6) the depth to the groundwater and (7) the depth to the bedrock. These features were exemplified in normalized maps after uniting them to 0–1 scores according to some criteria by the minimum and maximum values as linear scaling points. Multi-criteria evaluation is an application of multi-criteria decision analysis theory that used for developing a seismic hazard index map for a nuclear power plant site at El Dabaa area in ArcGIS 10.1 software. Two models of decision making were used in this work for seismic hazard microzonation. The analytic hierarchy process model was applied to conduct the relative weights of the criteria by pairwise comparison using Expert Choice Software. An earthquake hazard index map was combined using Weighted Linear Combination model of the raster weighted overlay tool of ArcGIS 10.1. The results indicated that most of the study site of the nuclear power plant is a region of low to moderate hazard; its values are ranging between 0.2 and 0.4.

Striatal structure and its association with N-Acetylaspartate and glutamate in autism spectrum disorder and obsessive compulsive disorder

Autism spectrum disorders (ASD) and obsessive compulsive disorder (OCD) are often comorbid and are associated with changes in striatal volumes and N-Acetylaspartate (NAA) and glutamate levels. Here, we investigated the relation between dorsal striatal volume and NAA and glutamate levels. We additionally compared striatal volume and shape between ASD, OCD and controls. T1-weighted magnetic resonance (MR) images, proton spectra (1H-MRS) in the left striatum, and phenotypic information were collected from 54 children with ASD, 32 with OCD, and 56 controls (aged 8–13 years) in a four-site study. Dorsal striatal volume and shape were determined using the FMRIB integrated registration and segmentation tool (FIRST). Spectra were processed with Linear Combination Model. The relationship of left striatal volume with NAA and glutamate was investigated, and group comparisons were performed for NAA levels and for bilateral striatal volume and shape. NAA levels were lower in subjects with ASD compared with controls (t=2.86, p=0.005) and were associated with striatal volume (β=0.37, t=2.78, p=0.008). Glutamate levels were also associated with volume in the ASD group (β=0.38, t=2.46, p=0.018). No group differences were found for striatal volume or shape, but a post-hoc diagnosis-by-hemisphere interaction (F(2,129)=3.86, p=0.024) revealed greater asymmetry (right>left) in striatal volume for the disorder-groups compared with controls. Our findings show involvement of NAA and glutamate in striatal volume in ASD and suggest greater asymmetry in paediatric ASD and OCD compared with controls, pointing to overlapping subcortical abnormalities. The lower NAA in ASD reflects reduced neuronal integrity or impaired neuronal functioning.

Variable-Weighted Linear Combination Model for Landslide Susceptibility Mapping: Case Study in the Shennongjia Forestry District, China

A landslide susceptibility map plays an essential role in urban and rural planning. The main purpose of this study is to establish a variable-weighted linear combination model (VWLC) and assess its potential for landslide susceptibility mapping. Firstly, different objective methods are employed for data processing rather than the frequently-used subjective judgments: K-means clustering is used for classification; binarization is introduced to determine buffer length thresholds for locational elements (road, river, and fault); landslide area density is adopted as the contribution index; and a correlation analysis is conducted for suitable factor selection. Secondly, considering the dimension changes of the preference matrix varying with the different locations of the mapping cells, the variable weights of each optimal factor are determined based on the improved analytic hierarchy process (AHP). On this basis, the VWLC model is established and applied to regional landslide susceptibility mapping for the Shennongjia Forestry District, China, where shallow landslides frequently occur. The obtained map is then compared with a map using the traditional WLC, and the results of the comparison show that VWLC is more reasonable, with a higher accuracy, and can be used anywhere that has the same or similar geological and topographical conditions.

English

English

Autofluorescence imaging identifies tumor cell-cycle status on a single-cell level.

The goal of this study is to validate fluorescence intensity and lifetime imaging of metabolic co-enzymes NAD(P)H and FAD (optical metabolic imaging, or OMI) as a method to quantify cell-cycle status of tumor cells. Heterogeneity in tumor cell-cycle status (e. g. proliferation, quiescence, apoptosis) increases drug resistance and tumor recurrence. Cell-cycle status is closely linked to cellular metabolism. Thus, this study applies cell-level metabolic imaging to distinguish proliferating, quiescent, and apoptotic populations. Two-photon microscopy and time-correlated single photon counting are used to measure optical redox ratio (NAD(P)H fluorescence intensity divided by FAD intensity), NAD(P)H and FAD fluorescence lifetime parameters. Redox ratio, NAD(P)H and FAD lifetime parameters alone exhibit significant differences (p<0.05) between population means. To improve separation between populations, linear combination models derived from partial least squares - discriminant analysis (PLS-DA) are used to exploit all measurements together. Leave-one-out cross validation of the model yielded high classification accuracies (92.4 and 90.1 % for two and three populations, respectively). OMI and PLS-DA also identifies each sub-population within heterogeneous samples. These results establish single-cell analysis with OMI and PLS-DA as a label-free method to distinguish cell-cycle status within intact samples. This approach could be used to incorporate cell-level tumor heterogeneity in cancer drug development.

Output Prediction of Photovoltaic Power Station Based on Nonlinear Combined Model

The output prediction of photovoltaic (PV) power station is necessary because the weather, environment and seasonal factors lead to the unstable PV power generation, which will affect the planning and scheduling of power system. Compared with the single model method, the linear combination method could improve the prediction accuracy of the output of PV power station. However, the linear combination forecast method is a simple convex combination of different prediction methods and is lack of general applicability. This paper presents a nonlinear combination method based on BP neural network and ARMA model to predict the output of PV power plant. This method based on the nonlinear relationship between the results of two single prediction models and the actual value, and utilize the nonlinear fitting ability of BP neural network, predicted the power generation capacity of PV power station. The nonlinear prediction theory and algorithm are given at the end of the article, and also compare nonlinear combined model with linear combined model of the power plant output prediction, the results show that the proposed method has a high accuration and an extensive applicability.

Fronto-Striatal Glutamate in Autism Spectrum Disorder and Obsessive Compulsive Disorder.

Autism spectrum disorders (ASDs) and obsessive compulsive disorder (OCD) are often comorbid with the overlap based on compulsive behaviors. Although previous studies suggest glutamatergic deficits in fronto-striatal brain areas in both disorders, this is the first study to directly compare the glutamate concentrations across the two disorders with those in healthy control participants using both categorical and dimensional approaches. In the current multi-center study (four centers), we used proton magnetic resonance spectroscopy in 51 children with ASD, 29 with OCD, and 53 healthy controls (aged 8-13 years) to investigate glutamate (Glu) concentrations in two regions of the fronto-striatal circuit: midline anterior cingulate cortex (ACC) and left dorsal striatum. Spectra were processed with Linear Combination Model. Group comparisons were performed with one-way analyses of variance including sex, medication use, and scanner site as covariates. In addition, a dimensional analysis was performed, linking glutamate with a continuous measure of compulsivity across disorders. There was a main group effect for ACC glutamate (p=0.019). Contrast analyses showed increased glutamate both in children with ASD and OCD compared with controls (p=0.007), but no differences between the two disorders (p=0.770). Dimensional analyses revealed a positive correlation between compulsive behavior (measured with the Repetitive Behavior Scale) and ACC glutamate (rho=0.24, p=0.03). These findings were robust across sites. No differences were found in the striatum. The current findings confirm overlap between ASD and OCD in terms of glutamate involvement. Glutamate concentration in ACC seems to be associated with the severity of compulsive behavior.

Hippocampal Neurometabolite Changes in Hypothyroidism: An In Vivo (1) H Magnetic Resonance Spectroscopy Study Before and After Thyroxine Treatment.

The hippocampus is a thyroid hormone receptor-rich region of the brain. A change in thyroid hormone levels may be responsible for an alteration in hippocampal-associated function, such as learning, memory and attention. Neuroimaging studies have shown functional and structural changes in the hippocampus as a result of hypothyroidism. However, the underlying process responsible for this dysfunction remains unclear. Therefore, the present study aimed to investigate the metabolic changes in the brain of adult hypothyroid patients during pre- and post-thyroxine treatment using invivo proton magnetic resonance spectroscopy ((1) H MRS). (1) H MRS was performed in both healthy control subjects (n=15) and hypothyroid patients (n=15) (before and after thyroxine treatment). The relative ratios of the neurometabolites were calculated using the linear combination model (LCModel). Our results revealed a significant decrease of glutamate (Glu) (P=0.045) and myo-inositol (mI) (P=0.002) levels in the hippocampus of hypothyroid patients compared to controls. No significant changes in metabolite ratios were observed in the hypothyroid patients after thyroxine treatment. The findings of the present study reveal decreased Glu/tCr and mI/tCr ratios in the hippocampus of hypothyroid patients and these metabolite alterations persisted even after the patients became clinically euthyroid subsequent to thyroxine treatment.

Dispersion and the electron–phonon interaction in a single heterostructure

We investigate the electron–phonon interaction in a polar–polar single heterostructure through the use of the linear combination of hybrid phonon modes, considering the role of longitudinal optical, transverse optical and interface modes, using a continuum model that accounts for both mechanical and electrical continuity over a heterostructure interface. We discuss the use of other models for such systems, such as the bulk phonon (3DP) and dielectric continuum (DC) models, using previously developed sum-rules to explain the limitations on their validity. We find that our linear combination (LC) model gives an excellent agreement with scattering rates previously derived using the 3DP and DC models when the lattice dispersion is weak enough to be ignored, however, when there is a noticeable lattice dispersion, the LC model returns a different answer, suggesting that interface modes play a much greater part in the scattering characteristics of the system under certain conditions. We also discuss the remote phonon effect in polar/polar heterostructures.

Assessment of mountain slope instability in the Lish River basin of Eastern Darjeeling Himalaya using frequency ratio model (FRM)

Landslides are the most destructive natural hazards that undermine the economic and cultural development in Darjeeling Himalaya. To prepare landslide susceptibility map of the Lish River basin of Eastern Darjeeling Himalaya frequency ratio model (FRM) was applied based on remote sensing and GIS tools and an integration of ten landslide triggering parameters or factors like Geomorphology, lithology, slope angle, slope aspect, slope curvature, drainage density, NDVI, Relative Relief, land use and land cover (LULC) and soli map were made. The frequency ratio model (FRM) was used to derive class frequency ratio or class weight incorporating both pixels with and without landslides and to determine the relative importance of individual classes. All the data layers were prepared in consultation with SOI Topo-sheet (78B/9), Google earth image, Landsat 8 OLI (operational land imager), GDEM (Global Digital Elevation Model) image with the help of Arc View and ARC GIS Software (10.1). A Weighted linear combination model was performed to combine frequency ratio/class rating values and to determine the landslide susceptibility index (LSI) value on GIS software tools. Greater the value of ‘LSI’, higher is the propensity of landslide susceptibility over the space. Then, the Lish River basin was classified into six landslide susceptibility zones, i.e., very low, low, moderate, moderately high, high and very high considering the ranges of LSI.

Landslide vulnerability mapping (LVM) using weighted linear combination (WLC) model through remote sensing and GIS techniques

Weighted linear combination (WLC) method was used to assess landslides vulnerability of the Simbu Province, Papua New Guinea within the GIS environment of ArcGIS. This multi-criteria evaluation method allows flexibility and tradeoffs amongst all parameters used. Ranks and weights are assigned depending on their influence on the occurrence of landslides. Parameters selected for the study include slope angle, elevation, rainfall, vegetation cover, land use/land cover, landform, proximity to roads, proximity to rivers and proximity to lineaments. Restricted in some sense in terms of data, WLC was appropriate in using existing metadata of the country; Papua New Guinea Resource Information System and Forest Information Management System. The landslide susceptibility map provides valuable information of the risk at hand in the province and district levels to better manage and plan mitigation measures. The slope factor was assigned a weighted of 4 as having greater influence on landslides in the region followed by rainfall weighted of 2 and the other having uniform influence of 1. The study area shows the distribution of the five vulnerability/susceptibility classes ranking from very low (1) to very high (5). Areas with very high landslide vulnerability zones are found in the northern and western parts of Simbu Province. Comparatively, southern parts have very low vulnerability areas. From the calculations done, 6.21 % of area is at very low risk, 20.24 % at low risk, 32.27 % of moderate risk, 26.88 % of high risk and 14.41 % of very high risk area coverage.

Word Sense Disambiguation Based on Perceptron Model

Word sense disambiguation (WSD) is an important research topic in natural language processing field, which is very useful for machine translation and information retrieval. In this paper, a linear combination model based on multiple discriminative features is proposed to determine correct sense of an ambiguous word, in which morphology and part of speech in left and right words around ambiguous word are used as features. Then, perceptron algorithm is applied to optimize the WSD model. Experiments show that the WSD performance is improved after the proposed method is applied.

Multiscale thermomechanical modeling of short fiber-reinforced composites

Abstract A study of the micromechanical behavior to predict the overall response of short fiber-reinforced composites under cyclic mechanical and thermal loading is presented. The instantaneous average over a “representative volume” of the material is considered. The influence of the short fiber’s aspect ratio, volume fraction, and spatial orientation has been investigated. The linear combined hardening model is used to describe the cyclic hardening effects in the case of metal matrix. A numerical procedure is used to predict the response of composites under mechanical and thermal conditions. The results of the numerical procedure have been compared to the results of three different models and to published experimental data.

Assessment of WLC and Fuzzy Logic Methods for Site Selection of Water Reservoirs in Malaysia

The purpose of this study is to compare the weighted linear combination (WLC) and fuzzy logic (FL) models in identifying the most suitable location for a water reservoir in the area of Batu Pahat, Johor, Malaysia. First of all, parameters important to a water reservoir for the studied area were identified. Then maps of the study area were prepared and integrated. The main criteria selected for this study are pipe line, elevation, rive, land use, road network, water supply network, and slope. Suitable locations for the water reservoir were selected using each model. The results of this study indicated that the FL method in the early stages had better decision-making powers for locating water reservoir sites when compared to the WLC method. The appropriate places for water reservoir construction based on two models were located to the northwest and west of the study area. The two models are validated using the Kappa index. According to the results of the validation method, the map produced by the FL method exhibits satisfactory properties in the study area. Thus, it can be concluded that the results derived from two models (WLC and FL) integrated in ArcGIS can be a useful tool in GIS analysis for the determination of suitable locations for water reservoir in the study area.