Search results for: variable reduction

Authors: Nasser Ghassembaglou, Armin Rahmatfam, Faramarz Ranjbar

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Using of cold EGR method with variable venturi and turbocharger has a very significant affection on the reduction of NOX and grime simultaneously. EGR cooler is one of the most important parts in the cold EGR circuit. In this paper optimum design of cooler for working in different percents of EGR and for determining of optimum temperature of exhausted gases, growth of efficiency, reduction of weight, reduction of dimension and expenditures, and reduction of sediment and optimum performance by using gas oil which has significant amounts of brimstone are investigated and optimized.

Keywords: cold EGR, NOX, cooler, gas oil

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Authors: Bakur Gulua

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In this work, we consider some boundary value problems for the plate. The plate is the elastic material with voids. The state of plate equilibrium is described by the system of differential equations that is derived from three-dimensional equations of equilibrium of an elastic material with voids (Cowin-Nunziato model) by Vekua's reduction method. Its general solution is represented by means of analytic functions of a complex variable and solutions of Helmholtz equations. The problem is solved analytically by the method of the theory of functions of a complex variable.

Keywords: the elastic material with voids, boundary value problems, Vekua's reduction method, a complex variable

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Authors: Fengxia Li, Lufeng Zhang, Haibo Wang

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The tight sandstone gas reservoir in the Xujiahe Formation of the Sichuan Basin has huge reserves, but its utilization rate is low. Fracturing and stimulation are indispensable technologies to unlock their potential and achieve commercial exploitation. Slickwater is the most widely used fracturing fluid system in the fracturing and renovation of tight reservoirs. However, its viscosity is low, its sand-carrying performance is poor, and the risk of sand blockage is high. Increasing the sand carrying capacity by increasing the displacement will increase the frictional resistance of the pipe string, affecting the resistance reduction performance. The variable viscosity slickwater can flexibly switch between different viscosities in real-time online, effectively overcoming problems such as sand carrying and resistance reduction. Based on a self-developed indoor loop friction testing system, a visualization device for proppant transport, and a HAAKE MARS III rheometer, a comprehensive evaluation was conducted on the performance of variable viscosity slickwater, including resistance reduction, rheology, and sand carrying. The indoor experimental results show that: 1. by changing the concentration of drag-reducing agents, the viscosity of the slippery water can be changed between 2~30mPa. s; 2. the drag reduction rate of the variable viscosity slickwater is above 80%, and the shear rate will not reduce the drag reduction rate of the liquid; under indoor experimental conditions, 15mPa. s of variable viscosity and slickwater can basically achieve effective carrying and uniform placement of proppant. The layered fracturing effect of the JiangX well in the dense sandstone of the Xujiahe Formation shows that the drag reduction rate of the variable viscosity slickwater is 80.42%, and the daily production of the single layer after fracturing is over 50000 cubic meters. This study provides theoretical support and on-site experience for promoting the application of variable viscosity slickwater in tight sandstone gas reservoirs.

Keywords: slickwater, hydraulic fracturing, dynamic sand laying, drag reduction rate, rheological properties

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Authors: Jan Doutreloigne

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This paper describes two methods for the reduction of the peak input current during the boosting of Dickson charge pumps. Both methods are implemented in the fully integrated Dickson charge pumps of a high-voltage display driver chip for smart-card applications. Experimental results reveal good correspondence with Spice simulations and show a reduction of the peak input current by a factor of 6 during boosting

Keywords: bi-stable display driver, Dickson charge pump, high-voltage generator, peak current reduction, sub-pump boosting, variable frequency boosting

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Authors: M. G. M. Khan, V. D. Prasad, D. K. Rao

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In this manuscript, we discuss the problem of determining the optimum stratification of a study (or main) variable based on the auxiliary variable that follows a uniform distribution. If the stratification of survey variable is made using the auxiliary variable it may lead to substantial gains in precision of the estimates. This problem is formulated as a Nonlinear Programming Problem (NLPP), which turn out to multistage decision problem and is solved using dynamic programming technique.

Keywords: auxiliary variable, dynamic programming technique, nonlinear programming problem, optimum stratification, uniform distribution

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Authors: C. A. Akaash Emmanuel Raj, V. R. Sanal Kumar

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In this paper using smart materials we have proposed a specially manufactured variable volume spin tub for loading clothes for negating the vibration to a certain extent for getting better operating performance. Additionally, we have recommended a variable spinning speed rotor for handling varieties of garments for an efficient washing, aiming for increasing the life span of both the garments and the machine. As a part of the conflicting dynamic constraints and demands of the customer friendly design optimization of a lucrative and cosmetic washing machine we have proposed a drier and a desalination system capable to supply desirable heat and a pleasing fragrance to the garments. We thus concluded that while incorporating variable volume and variable spinning speed tub integrated with a drier and desalination system, the washing machine could meet the varieties of domestic requirements of the customers cost-effectively.

Keywords: customer friendly washing machine, drier design, quick cloth cleaning, variable tub volume washing machine, variable spinning speed washing machine

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Authors: Adji Achmad Rinaldo Fernandes

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The research was conducted to apply semiparametric SEM modeling to the timeliness of paying credit. Semiparametric SEM is structural modeling in which two combined approaches of parametric and nonparametric approaches are used. The analysis method in this research is semiparametric SEM with a nonparametric approach using a truncated spline. The data in the study were obtained through questionnaires distributed to Bank X mortgage debtors and are confidential. The study used 3 variables consisting of one exogenous variable, one intervening endogenous variable, and one endogenous variable. The results showed that (1) the effect of capacity and willingness to pay variables on timeliness of payment is significant, (2) modeling the capacity variable on willingness to pay also produces a significant estimate, (3) the effect of the capacity variable on the timeliness of payment variable is not influenced by the willingness to pay variable as an intervening variable, (4) the R^2 value of 0.763 or 76.33% indicates that the model has good predictive relevance.

Keywords: structural equation modeling semiparametric, credit bank, accuracy of payment time, willingness to pay

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Authors: Vishnu S. Prasad, Preeti Aghalayam

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The major harmful automobile exhausts are nitric oxide (NO) and unburned hydrocarbon (HC). Reduction of NO using unburned fuel HC as a reductant is the technique used in hydrocarbon-selective catalytic reduction (HC-SCR). In this work, we study the microkinetic modelling of NO reduction using propene as a reductant on Pt catalysts. The selectivity of NO reduction to N₂O is detected in some ranges of operating conditions, whereas the effect of inlet O₂% causes a number of changes in the feasible regimes of operation.

Keywords: microkinetic modelling, NOx, platinum on alumina catalysts, selective catalytic reduction

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Authors: Elvis Adam Alhassan, Kaiyu Tian, Akos Konadu, Ernest Zamanah, Michael Jackson Adjabui, Ibrahim Justice Musah, Esther Agyeiwaa Owusu, Emmanuel K. A. Agyeman

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In this paper, how to solve simultaneous equations using the Elvis improved method is shown. The Elvis improved method says; to make one variable in the first equation the subject; make the same variable in the second equation the subject; equate the results and simplify to obtain the value of the unknown variable; put the value of the variable found into one equation from the first or second steps and simplify for the remaining unknown variable. The difference between our Elvis improved method and the substitution method is that: with Elvis improved method, the same variable is made the subject in both equations, and the two resulting equations equated, unlike the substitution method where one variable is made the subject of only one equation and substituted into the other equation. After describing the Elvis improved method, findings from 100 secondary students and the views of 5 secondary tutors to demonstrate the effectiveness of the method are presented. The study's purpose is proved by hypothetical examples.

Keywords: simultaneous equations, substitution method, elimination method, graphical method, Elvis improved method

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Authors: Justin Pargeter, Metin Eren

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Lithic miniaturization, the systematic production and use of small tools from small cores, was a consequential development in Pleistocene lithic technology. The bipolar reduction is an important, but often overlooked and misidentified, strategy for lithic miniaturization. This experiment addresses the role of axial bipolar reduction in processes of lithic miniaturization. The experiments answer two questions: what benefits does axial bipolar reduction provide, and can we distinguish axial bipolar reduction from freehand reduction? Our experiments demonstrate the numerous advantages of bipolar reduction in contexts of lithic miniaturization. Bipolar reduction produces more cutting edge per gram and is more economical than freehand reduction. Our cutting edge to mass values exceeds even those obtained with pressure blade production on high-quality obsidian. The experimental results show that bipolar reduction produces cutting edge quicker and is more efficient than freehand reduction. We show that bipolar reduction can be distinguished from freehand reduction with a high degree of confidence using the quantitative criteria in these experiments. These observations overturn long-held perceptions about bipolar reduction. We conclude by discussing the role of bipolar reduction in lithic miniaturization and Stone Age economics more broadly.

Keywords: lithic miniaturization, bipolar reduction, late Pleistocene, Southern Africa

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Authors: Michel Han, Christophe Besson, Alain Savary, Yvan Becher

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The variable flux permanent magnet synchronous motor (VF-PMSM), also called "Memory Motor", is a new generation of motor capable of modifying the magnetization state with short pulses of current during operation or standstill. The impact of such operation is the expansion of the operating range in the torque-speed characteristic and an improvement in energy efficiency at high-speed in comparison to conventional permanent magnet synchronous machines (PMSMs). This paper reviews the operating principle and the unique features of the proposed memory motor. The benefits of this concept are highlighted by comparing the performance of the rotor of the VF-PMSM to that of two PM rotors that are typically found in the industry. The investigation emphasizes the properties of the variable magnetization and presents the comparison of the torque-speed characteristic with the capability of loss reduction in a VF-PMSM by means of experimental results, especially when tests are conducted under identical conditions for each rotor (same stator, same inverter and same experimental setup). The experimental results demonstrated that the VF-PMSM gives an additional degree of freedom to optimize the efficiency over a wide speed range. Thus, with a design easy to manufacture and with the possibility of controlling the magnetization and the demagnetization of the magnets during operations, the VF-PMSM can be interesting for various applications.

Keywords: efficiency, magnetization state, memory motors, performances, permanent-magnet, synchronous machine, variable-flux, variable magnetization, wide speed application

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Authors: Carlos A. Domínguez Torres, Antonio Domínguez Delgado

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In this work we study the thermodynamic behavior of some ventilated facades under summer operating conditions in Southern Spain. Under these climatic conditions, indoor comfort implies a high energetic demand due to high temperatures that usually are reached in this season in the considered geographical area. The aim of this work is to determine if during summer operating conditions in Southern Spain, ventilated façades provide some energy saving compared to the non-ventilated façades and to deduce their behavior patterns in terms of energy efficiency. The modeling of the air flow in the channel has been performed by using Navier-Stokes equations for thermodynamic flows. Numerical simulations have been carried out with a 2D Finite Element approach. This way, we analyze the behavior of ventilated façades under different weather conditions as variable wind, variable temperature and different levels of solar irradiation. CFD computations show that the combined effect of the shading of the external wall and the ventilation by the natural convection into the air gap achieve a reduction of the heat load during the summer period. This reduction has been evaluated by comparing the thermodynamic performances of two ventilated and two unventilated façades with the same geometry and thermophysical characteristics.

Keywords: passive cooling, ventilated façades, energy-efficient building, CFD, FEM

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Authors: Buddhi Arachchige, Hessam Ghasemnejad

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In this study, the post impact behavior of curved composite plates subjected to low velocity impact was studied analytically and numerically. Approaches to damage modelling are proposed through the degradation of stiffness in the damaged region by reduction of thickness in the damage region. Spring-mass models were used to model the impact response of the plate and impactor. The study involved designing two damage models to compare and contrast the model best fitted with the numerical results. The theoretical force-time responses were compared with the numerical results obtained through a detailed study carried out in LS-DYNA. The modified damage model established a good prediction with the analytical force-time response for different layups and geometry. This study provides a gateway in selecting the most effective layups for variable stiffness curved composite panels able to withstand a higher impact damage.

Keywords: analytical modelling, composite damage, impact, variable stiffness

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Authors: Yong-Jun Kim, Jae-Hyun Ro, Chang-Bin Ha, Hyoung-Kyu Song

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Because current wireless communication requires high reliability in a limited bandwidth environment, this paper proposes the variable modulation scheme based on the codebook. The variable modulation scheme adjusts transmission power using the codebook in accordance with hannel state. Also, if the codebook is composed of many bits, the reliability is more improved by the proposed scheme. The simulation results show that the performance of proposed scheme has better reliability than the the performance of conventional scheme.

Keywords: MIMO-OFDM, variable modulation, codebook, channel state

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Authors: S. A. Alotaibi, M. A. Al-Ajmi

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This work introduces a new concept for controlling the mechanical vibrations via variable stiffness coil spring. The concept relies on fitting a screw though the spring to change the number of active spring coils. A prototype has been built and tested with promising results toward an innovation in the field of vibration control.

Keywords: variable stiffness, coil spring, vibration control, computer science

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Authors: I. Algul, G. Akgun, H. Kurtaran

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Dynamic analysis of composite doubly curved panels with variable thickness subjected to different pulse types using Generalized Differential Quadrature method (GDQ) is presented in this study. Panels with variable thickness are used in the construction of aerospace and marine industry. Giving variable thickness to panels can allow the designer to get optimum structural efficiency. For this reason, estimating the response of variable thickness panels is very important to design more reliable structures under dynamic loads. Dynamic equations for composite panels with variable thickness are obtained using virtual work principle. Partial derivatives in the equation of motion are expressed with GDQ and Newmark average acceleration scheme is used for temporal discretization. Several examples are used to highlight the effectiveness of the proposed method. Results are compared with finite element method. Effects of taper ratios, boundary conditions and loading type on the response of composite panel are investigated.

Keywords: differential quadrature method, doubly curved panels, laminated composite materials, small displacement

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Authors: Daniel E. Martin, Chenghai Yang

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Variable-rate aerial application systems are becoming more readily available; however, aerial applicators typically only use the systems for constant-rate application of materials, allowing the systems to compensate for upwind and downwind ground speed variations. Much of the resistance to variable-rate aerial application system adoption in the U.S. pertains to applicator’s trust in the systems to turn on and off automatically as desired. The objectives of this study were to evaluate a commercially available variable-rate aerial application system under field conditions to demonstrate both the response and accuracy of the system to desired application rate inputs. This study involved planting oats in a 35-acre fallow field during the winter months to establish a uniform green backdrop in early spring. A binary (on/off) prescription application map was generated and a variable-rate aerial application of glyphosate was made to the field. Airborne multispectral imagery taken before and two weeks after the application documented actual field deposition and efficacy of the glyphosate. When compared to the prescription application map, these data provided application system response and accuracy information. The results of this study will be useful for quantifying and documenting the response and accuracy of a commercially available variable-rate aerial application system so that aerial applicators can be more confident in their capabilities and the use of these systems can increase, taking advantage of all that aerial variable-rate technologies have to offer.

Keywords: variable-rate, aerial application, remote sensing, precision application

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Authors: Yuanxiaoyue Yang

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Emissions trading, or ‘cap-and-trade', has been long promoted by economists as a more cost-effective pollution control approach than traditional performance standard approaches. While there is a large body of empirical evidence for the overall effectiveness of emissions trading, relatively little attention has been paid to other unintended consequences brought by emissions trading. One important consequence is that cap-and-trade could introduce the risk of creating high-level emission concentrations in areas where emitting facilities purchase a large number of emission allowances, which may cause an unequal distribution of environmental benefits. This study will contribute to the current environmental policy literature by linking trading activity with environmental injustice concerns and empirically analyzing the causal relationship between trading activity and emissions reduction under a cap-and-trade program for the first time. To investigate the potential environmental injustice concern in cap-and-trade, this paper uses a differences-in-differences (DID) with instrumental variable method to identify the causal effect of allowance trading behaviors on emission reduction levels under the clean air interstate rule (CAIR), a cap-and-trade program targeting on the power sector in the eastern US. The major data source is the facility-year level emissions and allowance transaction data collected from US EPA air market databases. While polluting facilities from CAIR are the treatment group under our DID identification, we use non-CAIR facilities from the Acid Rain Program - another NOx control program without a trading scheme – as the control group. To isolate the causal effects of trading behaviors on emissions reduction, we also use eligibility for CAIR participation as the instrumental variable. The DID results indicate that the CAIR program was able to reduce NOx emissions from affected facilities by about 10% more than facilities who did not participate in the CAIR program. Therefore, CAIR achieves excellent overall performance in emissions reduction. The IV regression results also indicate that compared with non-CAIR facilities, purchasing emission permits still decreases a CAIR participating facility’s emissions level significantly. This result implies that even buyers under the cap-and-trade program have achieved a great amount of emissions reduction. Therefore, we conclude little evidence of environmental injustice from the CAIR program.

Keywords: air pollution, cap-and-trade, emissions trading, environmental justice

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Authors: Salisu ibrahim, Abdalla Rababah

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In this research, we use Weighted G2-Multi-degree reduction of Bezier curve of degree n to a Bezier curve of degree m, m < n. The degree reduction of Bezier curves is used to represent a given Bezier curve of n by a Bezier curve of degree m, m < n. Exact degree reduction is not possible, and degree reduction is approximate process in nature. We derive a weighted degree reducing method that is geometrically continuous at the end points. Different norms will be considered, several error minimizations will be given. The proposed methods produce error function that are less than the errors of existing methods.

Keywords: Bezier curves, multiple degree reduction, geometric continuity, error function

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Authors: Kimiyo Shimomai, Mihoko Harada

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This study is a literature review of the psychological reactions that occur in end-of-life cancer patients who are nearing death. It searched electronic databases and selected literature related to psychological studies of end-of-life patients. There was no limit on the search period, and the search was conducted until the second week of December 2021. The keywords were specified as “death and dying”, “terminal illness”, “end-of-life”, “palliative care”, “psycho-oncology” and “research”. These literatures referred to Holly (2017): Comprehensive Systematic Review for Advanced Practice Nursing, P268 Figure 10.3 to ensure quality. These literatures were selected with a dissertation score of 4 or 5. The review was conducted in two stages with reference to the procedure of George (2002). First, these references were searched for keywords in the database, and then relevant references were selected from the psychology and nursing studies of end-of-life patients. The number of literatures analyzed was 76 for overseas and 17 for domestic. As for the independent variables, "physical variable" was the most common in 36 literatures (66.7%), followed by "psychological variable" in 35 literatures (64.8%), "spiritual variable" in 21 literatures (38%), and "social variable" in 17 literatures. (31.5%), "Variables related to medical care / treatment" were 16 literatures (29.6%). To summarize the relationship between these independent variables and the dependent variable, when the dependent variable is "psychological variable", the independent variables are "psychological variable", "social variable", and "physical variable". Among the independent variables, the physical variables were the most common. The psychological responses that occur in end-stage cancer patients who are nearing death are mutually influenced by psychological, social, and physical variables. Therefore, it supported the "total pain" advocated by Cicely Saunders.

Keywords: cancer patient, end-of-life, literature review, psychological process

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Authors: Priya Kedia, Kiranmoy Das

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There is a rich literature on variable selection in regression setting. However, most of these methods assume normality for the response variable under consideration for implementing the methodology and establishing the statistical properties of the estimates. In many real applications, the distribution for the response variable may be non-Gaussian, and one might be interested in finding the best subset of covariates at some predetermined quantile level. We develop dynamic Bayesian approach for variable selection in quantile regression framework. We use a zero-inflated mixture prior for the regression coefficients, and consider the asymmetric Laplace distribution for the response variable for modeling different quantiles of its distribution. An efficient Gibbs sampler is developed for our computation. Our proposed approach is assessed through extensive simulation studies, and real application of the proposed approach is also illustrated. We consider the data from health and retirement study conducted by the University of Michigan, and select the important predictors when the outcome of interest is out-of-pocket medical cost, which is considered as an important measure for financial risk. Our analysis finds important predictors at different quantiles of the outcome, and thus enhance our understanding on the effects of different predictors on the out-of-pocket medical cost.

Keywords: variable selection, quantile regression, Gibbs sampler, asymmetric Laplace distribution

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Authors: Ehsan Motamedian

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Finding all optimal flux distributions of a metabolic model is an important challenge in systems biology. In this paper, a new algorithm is introduced to identify all alternate optimal solutions of a large scale metabolic network. The algorithm reduces the model to decrease computations for finding optimal solutions. The algorithm was implemented on the Escherichia coli metabolic model to find all optimal solutions for lactate and acetate production. There were more optimal flux distributions when acetate production was optimized. The model was reduced from 1076 to 80 variable fluxes for lactate while it was reduced to 91 variable fluxes for acetate. These 11 more variable fluxes resulted in about three times more optimal flux distributions. Variable fluxes were from 12 various metabolic pathways and most of them belonged to nucleotide salvage and extra cellular transport pathways.

Keywords: flux variability, metabolic network, mixed-integer linear programming, multiple optimal solutions

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Authors: Jinho Hur, Minjung Shin, Heekyu Kim

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In many places, new railway constructions in the city are being used to build a viaduct station to take advantage of the space below the line, for difficulty of securing railway site and disconnections of areas. The space under the viaduct has limited to use by noise and vibration. In order to use it for various purposes, reducing noise and vibration is required. The vibration reduction method for new structures is recently developed enough to use as accommodation, but the reduction method for existing structures is still far-off. In this study, it suggests vibration reduction method by filling vibration reduction material to column members which is path of structure-bone-noise from trains run. Because most of railroad stations are reinforced concrete structures. It compares vibration reduction of station applied the method and original station by FEM analysis. As a result, reduction of vibration acceleration level in bandwidth 15~30Hz can be reduced. Therefore, using this method for viaduct railroad station, vibration of station is expected to be reduced.

Keywords: structure borne noise, TPU, viaduct rail station, vibration reduction method

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Authors: Nguyen J. M., Lucas G., Brunner M., Ruan S., Antonioli D.

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Deep learning based on convolutional neural networks (CNN) is a very powerful technique for classifying information from an image. We propose a new method, DeepNic, to transform each variable of a tabular dataset into an image where each pixel represents a set of conditions that allow the variable to make an error-free prediction. The contrast of each pixel is proportional to its prediction performance and the color of each pixel corresponds to a sub-family of NICs. NICs are probabilities that depend on the number of inputs to each neuron and the range of coefficients of the inputs. Each variable can therefore be expressed as a function of a matrix of 2 vectors corresponding to an image whose pixels express predictive capabilities. Our objective is to transform each variable of tabular data into images into an image that can be analysed by CNNs, unlike other methods which use all the variables to construct an image. We analyse the NIC information of each variable and express it as a function of the number of neurons and the range of coefficients used. The predictive value and the category of the NIC are expressed by the contrast and the color of the pixel. We have developed a pipeline to implement this technology and have successfully applied it to genomic expressions on an Affymetrix chip.

Keywords: tabular data, deep learning, perfect trees, NICS

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Authors: Amitha Puranik, V. S. Binu, Seena Biju

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Missing data is a common problem in spatial analysis especially at the aggregate level. Missing can either occur in covariate or in response variable or in both in a given location. Many missing data techniques are available to estimate the missing data values but not all of these methods can be applied on spatial data since the data are autocorrelated. Hence there is a need to develop a method that estimates the missing values in both response variable and covariates in spatial data by taking account of the spatial autocorrelation. The present study aims to develop a model to estimate the missing data points at the aggregate level in spatial data by accounting for (a) Spatial autocorrelation of the response variable (b) Spatial autocorrelation of covariates and (c) Correlation between covariates and the response variable. Estimating the missing values of spatial data requires a model that explicitly account for the spatial autocorrelation. The proposed model not only accounts for spatial autocorrelation but also utilizes the correlation that exists between covariates, within covariates and between a response variable and covariates. The precise estimation of the missing data points in spatial data will result in an increased precision of the estimated effects of independent variables on the response variable in spatial regression analysis.

Keywords: spatial regression, missing data estimation, spatial autocorrelation, simulation analysis

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Authors: Chee Sun Won

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This paper presents the size reduction of images for machine-to-machine communications. Here, the salient image regions to be preserved include the image patches of the key-points such as corners and blobs. Based on a saliency image map from the key-points and their image patches, an axis-aligned grid-size optimization is proposed for the reduction of image size. To increase the size-reduction efficiency the aspect ratio constraint is relaxed in the constraint optimization framework. The proposed method yields higher matching accuracy after the size reduction than the conventional content-aware image size-reduction methods.

Keywords: image compression, image matching, key-point detection and description, machine-to-machine communication

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Authors: E. Tchandao Mangamana, V. Cariou, E. Vigneau, R. Glele Kakai, E. M. Qannari

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A new definition of a latent variable associated with a dataset makes it possible to propose variants of the PLS2 regression and the multi-block PLS (MB-PLS). We shall refer to these variants as Rd-PLS regression and Rd-MB-PLS respectively because they are inspired by both Redundancy analysis and PLS regression. Usually, a latent variable t associated with a dataset Z is defined as a linear combination of the variables of Z with the constraint that the length of the loading weights vector equals 1. Formally, t=Zw with ‖w‖=1. Denoting by Z' the transpose of Z, we define herein, a latent variable by t=ZZ’q with the constraint that the auxiliary variable q has a norm equal to 1. This new definition of a latent variable entails that, as previously, t is a linear combination of the variables in Z and, in addition, the loading vector w=Z’q is constrained to be a linear combination of the rows of Z. More importantly, t could be interpreted as a kind of projection of the auxiliary variable q onto the space generated by the variables in Z, since it is collinear to the first PLS1 component of q onto Z. Consider the situation in which we aim to predict a dataset Y from another dataset X. These two datasets relate to the same individuals and are assumed to be centered. Let us consider a latent variable u=YY’q to which we associate the variable t= XX’YY’q. Rd-PLS consists in seeking q (and therefore u and t) so that the covariance between t and u is maximum. The solution to this problem is straightforward and consists in setting q to the eigenvector of YY’XX’YY’ associated with the largest eigenvalue. For the determination of higher order components, we deflate X and Y with respect to the latent variable t. Extending Rd-PLS to the context of multi-block data is relatively easy. Starting from a latent variable u=YY’q, we consider its ‘projection’ on the space generated by the variables of each block Xk (k=1, ..., K) namely, tk= XkXk'YY’q. Thereafter, Rd-MB-PLS seeks q in order to maximize the average of the covariances of u with tk (k=1, ..., K). The solution to this problem is given by q, eigenvector of YY’XX’YY’, where X is the dataset obtained by horizontally merging datasets Xk (k=1, ..., K). For the determination of latent variables of order higher than 1, we use a deflation of Y and Xk with respect to the variable t= XX’YY’q. In the same vein, extending Rd-MB-PLS to the path modeling setting is straightforward. Methods are illustrated on the basis of case studies and performance of Rd-PLS and Rd-MB-PLS in terms of prediction is compared to that of PLS2 and MB-PLS.

Keywords: multiblock data analysis, partial least squares regression, path modeling, redundancy analysis

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Authors: Hamid Sharif, Nazish Saleem Abbas, Muhammad Haris Jamil

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An adaptive filter is a filter that self-adjusts its transfer function according to an optimization algorithm driven by an error signal. Because of the complexity of the optimization algorithms, most adaptive filters are digital filters. Adaptive filtering constitutes one of the core technologies in digital signal processing and finds numerous application areas in science as well as in industry. Adaptive filtering techniques are used in a wide range of applications, including adaptive noise cancellation and echo cancellation. Acoustic echo cancellation is a common occurrence in today’s telecommunication systems. The signal interference caused by acoustic echo is distracting to both users and causes a reduction in the quality of the communication. In this paper, we review different techniques of adaptive filtering to reduce this unwanted echo. In this paper, we see the behavior of techniques and algorithms of adaptive filtering like Least Mean Square (LMS), Normalized Least Mean Square (NLMS), Variable Step-Size Least Mean Square (VSLMS), Variable Step-Size Normalized Least Mean Square (VSNLMS), New Varying Step Size LMS Algorithm (NVSSLMS) and Recursive Least Square (RLS) algorithms to reduce this unwanted echo, to increase communication quality.

Keywords: adaptive acoustic, echo cancellation, LMS algorithm, adaptive filter, normalized least mean square (NLMS), variable step-size least mean square (VSLMS)

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Authors: Farid Saeidi, Serkan Dag

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In this study, fracture analysis of a fibrous composite laminate with variable fiber spacing is carried out using Jk-integral method. The laminate is assumed to be under thermal loading. Jk-integral is formulated by using the constitutive relations of plane orthotropic thermoelasticity. Developed domain independent form of the Jk-integral is then integrated into the general purpose finite element analysis software ANSYS. Numerical results are generated so as to assess the influence of variable fiber spacing on mode I and II stress intensity factors, energy release rate, and T-stress. For verification, some of the results are compared to those obtained using displacement correlation technique (DCT).

Keywords: Jk-integral, Variable Fiber Spacing, Thermoelasticity, T-stress, Finite Element Method, Fibrous Composite.

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Authors: Georgiana Onicescu, Yuqian Shen

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Due to the complex nature of geo-referenced data, multicollinearity of the risk factors in public health spatial studies is a commonly encountered issue, which leads to low parameter estimation accuracy because it inflates the variance in the regression analysis. To address this issue, we proposed a two-stage variable selection method by extending the least absolute shrinkage and selection operator (Lasso) to the Bayesian spatial setting, investigating the impact of risk factors to health outcomes. Specifically, in stage I, we performed the variable selection using Bayesian Lasso and several other variable selection approaches. Then, in stage II, we performed the model selection with only the selected variables from stage I and compared again the methods. To evaluate the performance of the two-stage variable selection methods, we conducted a simulation study with different distributions for the risk factors, using geo-referenced count data as the outcome and Michigan as the research region. We considered the cases when all candidate risk factors are independently normally distributed, or follow a multivariate normal distribution with different correlation levels. Two other Bayesian variable selection methods, Binary indicator, and the combination of Binary indicator and Lasso were considered and compared as alternative methods. The simulation results indicated that the proposed two-stage Bayesian Lasso variable selection method has the best performance for both independent and dependent cases considered. When compared with the one-stage approach, and the other two alternative methods, the two-stage Bayesian Lasso approach provides the highest estimation accuracy in all scenarios considered.

Keywords: Lasso, Bayesian analysis, spatial analysis, variable selection

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