Linear Scale Transformation Research Articles

The impact of public health efficiency on well-being in Italian provinces

PurposeThis work aims to analyse the relationship between public health efficiency and well-being considering a panel of 102 Italian provinces from 2000 to 2016 and evaluates if there are omitted variable biases and endogeneity biases and also evaluates if there are heterogeneous effects among provinces with different income levels.Design/methodology/approachWe use a multi-input and output bootstrap data envelopment analysis to assess public health efficiency. Then, we measure well-being indices using the min-max linear scaling transformation technique. A two-stage least squares model is used to identify the causal effect of improving public health efficiency on well-being to account for time-invariant heterogeneity, omitted variable bias and endogeneity bias.FindingsAfter controlling for important economic factors, the results show a significant effect of an accountable and efficient public health system on well-being. Those effects are concentrated in the North, the most economically, geographically and environmentally advantageous areas.Research limitations/implicationsThe use of the sample mean, probably the oldest and most used method for aggregating the indicators, could be affected by variable compensation, with consequent misleading results in the process of constructing the well-being index. Another limitation is the use of lagged values of the main predictor as an instrument in the instrumental variables setting because it could lead to information loss. Finally, the availability of data over a long period of time.Practical implicationsThe findings could help policymakers adopt measures to strengthen the public health system, encourage private providers and inspire countries worldwide.Social implicationsThese results draw the attention of local authorities, who play an important role in designing and implementing policies to stimulate local public health efficiency, which puts individuals in the conditions of achieving overall well-being in their communities.Originality/valueFor the first time in Italy, a panel of well-being indices was constructed by developing new methodologies based on microeconomic theory. Furthermore, for the first time, the assessment of the relationship between public health efficiency and well-being is carried out using a panel of 102 Italian provinces.

Assessing the impact of external determinants on the social performance of Islamic banks

PurposeExplaining the sources of the differences in social performance among Islamic banks (IBs) is the motivation for this research. Consequently, the purpose of this paper is to investigate the relationship between the development of Islamic finance regulation, the development of an Islamic financial system, the proportions of affected Muslim populations and the level of competition, on the one hand, and the social performance of IBs, on the other. To the best of the authors' knowledge, this is the first study that investigates the impact of the development of regulation and the Islamic financial system on the social performance of IBs.Design/methodology/approachA balanced panel of annual data for 40 banks from 13 countries is applied, spanning 2012–2018. A social performance index with eight dimensions is constructed and measures the social performance of IBs. The index based on qualitative and quantitative data derives from IBs’ annual reports and financial statements. The linear scaling transformation method articulates the quantitative dimensions of the index. In hypotheses testing, the authors use OLS, LSDV, FEM and Random Effect Model to estimate Model (1) and panel-corrected standard errors with Prais–Winsten transformation to estimate Model (2).FindingsThis unique research confirms the positive impact of the development of Islamic finance regulation on the social performance of IBs. The results show that the development of Islamic finance regulation is consistently significant on all standard significance levels. IBs’ age and the presence of Muslim populations in the country are also significant in most estimators.Research limitations/implicationsThe results of this research highlight a significant value for regulators, shareholders and the management of IBs. Without proper regulation, these banks can hardly operate under the principles and expectations of the Islamic moral economy.Originality/valueThis is pioneering research that explores the development of Islamic finance regulation and market concentration as a determinant of social performance of IBs. Development of Islamic finance regulation has proved significant in all estimated models, which confirms that a new variable has been discovered among determinants of the social performance of IBs.

Ranking and selection of optimum alternatives based on linear scale transformation for cylindrical roller bearing

Bearing is the most precautious element in rotating machinery. Defects produced in any part of the bearing may lead to a hazardous environment that increases the noise and vibration. It can be diminished with appropriate loading combinations. The selection of optimum loading conditions requires developing an algorithm. Multiple Attribute Decision Making (MADM) techniques are utilized to pick up a unique and ideal solution or ranking of choices from the available alternatives. The MADM techniques have not been used for bearing fault diagnosis so far. This paper offers a novel methodology for the Ranking and Selection of Optimum Alternative based on Linear Scale Transformation (RSOA-LST), having combined benefit and cost criteria that rank the choices and determine the percentage influence of various alternatives. This methodology has been performed with several experimental signals of cylindrical roller bearing, acquired from twenty-four bearing conditions having several speeds and loading combinations. The normalization of attributes (statistical features) considering combined benefit-cost criteria, mean of various parameters (speeds, attributes, and bearing conditions), and Percentage Influencing (PI) are intended simultaneously to obtain the ranking of alternatives (loading conditions). Local Defect Analysis (LDA) and Global Defect Analysis (GDA) are carried out in this research. In LDA, the ranking of alternatives based on the PI for each defect and the ranking of bearings based on fluctuation in PI of various alternatives are attained. In GDA, the sequence of alternatives is obtained globally. The effectiveness of the suggested methodology has been verified over other MADM techniques. The outcomes state that the suggested technique effectively bifurcates the influence of alternatives locally as well as globally over other techniques.

Determination of Ideal Mild Steel Weld Properties Using the Simple Additive Weighting (SAW) Method

Weld deformation which directly contributes to failures of welded components, has been a real time challenge in the manufacturing industry. To resolve this challenges, subtle manipulation of input process parameters is recommended by Researchers. This manipulation of input process parameters is done through the application of optimization methods hence this study.
 In this study, the Simple Additive Weighting (SAW) method was used. This method was categorized into four groups, which are namely, linear scale transformation-Max method; linear scale transformation-Sum method; Vector normalization method and linear scale transformation-Max Min method. Each of these SAW methods was used to optimize the output parameters, which were classified as maximum criteria. It was noticed from the analysis, that the higher the values of the mechanical properties, the better the weldquality obtained and from using the linear scale transformation maximum method, weldment 7 was found to possess the best mechanical properties with ultimate tensile strength (UTS) of 395MPa, Impact energy (CVN) of 250J, Bead Height(BH) of 1.98 mm and Bead Width (BW) of4.82mmwas found to possess the best input and output parameters.

Substitution potential of rare earth catalysts in ethanol steam reforming

This study suggests the possibility of substituting rare earths containing catalysts in ethanol steam reforming by means of sustainability assessment. Four Ni-catalysts are assessed; two of the Ni-catalysts are rare earths containing catalysts Ni/Al2O3-CeO2 and Ni/La2O3 while the other two are non-rare earths containing catalysts Ni/Al2O3 and Ni/ZnO. The sustainability assessment tool used covers environmental, health and safety and economic indicators in conjunction with a linear scale transformation (Max) normalization technique and an analytical hierarchy method to evaluate the sustainability performance of the catalysts. The sustainability assessment results obtained demonstrated that Ni/Al2O3 is the best performing catalysts in terms of the overall sustainability of the ethanol steam reforming reaction. The rare earths containing catalyst supports, i.e. cerium and lanthanum oxides can be successfully substituted with aluminum oxide catalyst support whilst still maintaining the overall sustainability performance of the reaction.

Selection of software requirements using TOPSIS under fuzzy environment

The goal of the decision making process is to find out the most appropriate alternative(s) from the set of the alternatives based on different criteria. Software requirements selection is a multi-criteria decision making (MCDM) problem whose objective is to choose the software requirements (SRs) from the set of the SRs so that the need of the stakeholders can be implemented during the development of software. Due to the increasing complexity of the socio-economic environment, multiple decision makers are involved during SRs selection process because it is difficult for single decision maker to deal with all the SRs. During the SRs selection process, decision makers use linguistic terminologies to specify the preferences of the SRs. Therefore, this paper presents a decision making method for the selection of the SRs using fuzzy TOPSIS method in which linear scale transformation is used to construct the fuzzy decision matrix based on the triangular fuzzy numbers. Finally, the applicability of the proposed method is demonstrated with the help of an example.

Identifying new quarries as a method for expanding research: A GIS case study from the Mesa Verde region in the American Southwest

The study of stone-tool raw material acquisition to evaluate temporal changes in regional sociopolitical organization and economic-related behaviors in the American Southwest is relatively rare when compared to pottery studies for the same purpose. To promote and conduct more meaningful study of these activities using lithic remains, we contend that the location of additional local raw material quarries must be identified and documented. Because of known data gaps in regional quarry datasets, we develop a method to identify additional quarries in the central Mesa Verde region of the American Southwest using a multi-step GIS approach. Our exploratory analysis of new quarry sites uses community-center site locations coupled with a site suitability analysis (a Linear Scale Transformation of geological attributes) to identify future survey target areas. This method of identifying areas that are highly suitable for local raw materials seeks to both optimize our time in the field, while also providing us with additional data points to conduct more sophisticated GIS analyses. Our analysis has identified areas of western Papoose and Ruin Canyons that will serve as the target of future surveys, with the goal of ground truthing our model and discovering, and documenting, new local quarries.

Identifying the Disadvantaged Regions for Concentrated State Support Using the DEA Method

Abstract The governmental approach to the selection of the Czech regions (NUTS 4) for the state support distribution is analyzed and the Data Envelopment Analysis (DEA) model is proposed. A set of used indicators, their dependence or independence, and their availability in the statistical databases of the Czech Statistical Office are examined. The results of the selection method used by the Czech government (Simple Additive Weighting method with the linear scale transformation procedure based on reference variant) and the results provided by the proposed DEA model (covering both the used indicators and a proposed set of indicators) are compared. All results indicate that the DEA method is a useful tool for the ranking of the regions and for the selection of the regions intended for the concentrated state support. Its advantage is that the weights of the indicators (inputs and outputs) should not be estimated subjectively before computation

Parameter adjustment based on improved genetic algorithm for cognitive radio networks

Multi-objective parameter adjustment plays an important role in improving the performance of the cognitive radio (CR) system. Current research focus on the genetic algorithm (GA) to achieve parameter optimization in CR, while general GA always fall into premature convergence. Thereafter, this paper proposed a linear scale transformation to the fitness of individual chromosome, which can reduce the impact of extraordinary individuals exiting in the early evolution iterations, and ensure competition between individuals in the latter evolution iterations. This paper also introduces an adaptive crossover and mutation probability algorithm into parameter adjustment, which can ensure the diversity and convergence of the population. Two applications are applied in the parameter adjustment of CR, one application prefers the bit error rate and another prefers the bandwidth. Simulation results show that the improved parameter adjustment algorithm can converge to the global optimal solution fast without falling into premature convergence.

IMPLEMENTATION OF GIS-BASED MULTICRITERIA DECISION ANALYSIS WITH VB IN ArcGIS

This article focuses on the integration of multicriteria decision analysis (MCDA) and geographical information systems (GIS) and introduces a tool, GIS–MCDA, written in visual basic in ArcGIS for GIS-based MCDA. The GIS–MCDA deals with raster-based data sets and includes standardization, weighting and decision analysis methods, and sensitivity analysis. Simple additive weighting, weighted product method, technique for order preference by similarity to ideal solution, compromise programming, analytic hierarchy process, and ordered weighted average for decision analysis; ranking, rating, and pairwise comparison for weighting and linear scale transformation for standardization can be applied by using this tool. The maximum score and score range procedures can be used for linear scale transformation. In this article also an application of the GIS–MCDA to determine the flood vulnerability of the South Marmara Basin in Turkey is examined. To check the validity and reliability of the results, the flood vulnerability layer is compared with flood-affected areas.

Virtual age of non-repairable objects

Two definitions of virtual age are considered. The first one is based on the fact that a system's deterioration depends on an environment. In a more severe environment deterioration is more intensive and, therefore, the corresponding virtual age is larger than the calendar age. The second approach defines virtual age at the moment of switching from one regime to another. It is shown that both definitions coincide only for the linear scale transformation in the lifetime distribution functions.

A new parameterization of Johnson's SB distribution with application to fitting forest tree diameter data

The SB distributional model of Johnson's 1949 paper was introduced by a transformation to normality, that is, z ~ N(0, 1), consisting of a linear scaling to the range (0, 1), a logit transformation, and an affine transformation, z = γ + δu. The model, in its original parameterization, has often been used in forest diameter distribution modelling. In this paper, we define the SB distribution in terms of the inverse transformation from normality, including an initial linear scaling transformation, u = γ′ + δ′z (δ′ = 1/δ and γ′ = –γ/δ). The SB model in terms of the new parameterization is derived, and maximum likelihood estimation schema are presented for both model parameterizations. The statistical properties of the two alternative parameterizations are compared empirically on 20 data sets of diameter distributions of Changbai larch (Larix olgensis Henry). The new parameterization is shown to be statistically better than Johnson's original parameterization for the data sets considered here.

A Comparison of Bootstrap Standard Errors of IRT Equating Methods for the Common-Item Nonequivalent Groups Design

The primary purpose of this study was to compare bootstrap standard errors of 5 item response theory (IRT) equating methods for the common-item nonequivalent groups design. For true-score (Method 1) and observed-score (Method 2) equating, IRT parameters were estimated separately, and a linear scaling transformation method was used to rescale the IRT parameter estimates for Form X onto the Form Y scale. For IRT chained true-score equating (Method 3), IRT parameters for Form X and Form Y were estimated separately, and then IRT chained true-score equating was performed. For the last 2 methods, IRT parameters for both forms were estimated simultaneously. Using the simultaneously estimated parameter estimates, IRT true-score (Method 4) and observed-score (Method 5) equatings were performed. For each method, the standard deviation was computed over 500 bootstrap replications to obtain the standard error of IRT equating at each raw score point for the new form. The estimated bootstrap standard errors for Methods 4 and 5 were slightly less than those for Methods 1 and 2. Method 3 produced the greatest standard errors. However, the standard errors for all 5 methods were small enough to suggest that standard errors of equating less than 0.1 standard deviation units could be obtained with any method, even with sample sizes of 500.

An application of data envelopment analysis in telephone officesevaluation with partial data

The presence of partial data motivates the need to investigate how such factors can be incorporated into the existing measurement models. In this paper, a procedure is proposed for incorporating a set of factors with partial data into the DEA structure and restricting factor weights. The first DEA formulation is a complicated non-linear model issued from the set of partial data. In order to transform the first formulation into an ordinary linear programming model, both a linear scale transformation and variable change technique are used. The resulting linear programming model is then applied to the efficiency evaluation of telephone offices. Scope and purpose A procedure for handling both linear partial data on factor values and its weight preferences in data envelopment analysis (DEA) structure is presented. DEA is a methodology for driving the relative efficiencies of organizations or decision making units (DMUs) where there are multiple incommensurate inputs and outputs. The usual setting for many DEA applications involves a set of similar DMUs, for each of which there is an observable and measurable set of inputs and outputs. In some applications, however, a number of factors may be measurable only on partial data such as ordinal rankings and ratio bounds, owing mainly to intangible attributes to reflect social and environmental impacts. A model dealing with these partial data is presented and applied to the efficiency of telephone offices.