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Since the biomedical implants with an improved compressive strength, near bone elastic modulus, controlled porosity, and sufficient surface roughness, can assist in long term implantation. Therefore, the fine process tuning plays its crucial role to develop optimal settings to achieve these desired properties. This paper aims to find applications for fine process tuning in laser powder bed fusion of biomedical Ti alloys for load-bearing implants.

In this work, the parametric porosity simulations were initially performed to simulate the process-induced porosity for selective laser-melted Ti₆Al₄V as per full factorial design. Continually, the experiments were performed to validate the simulation results and perform multiresponse optimization to fine-tune the processing parameters. Three levels of each control variable, namely, laser power – Pl (180, 190, 200) W, scanning speed – Vs (1500, 1600, 1700) mm/s and scan orientation – ϴ{1(0,0), 2(0,67°), 3(0,90°)} were used to investigate the processing performance. The measured properties from this study include compressive yield strength, elastic modulus, process-induced porosity and surface roughness. Finally, confirmatory experiments and comparisons with the already published works were also performed to validate the research results.

The results of porosity parametric simulation and experiments in selective laser melting of Ti₆Al₄V were found close to each other with overall porosity (less than 10%). The fine process tuning was resulted in optimal settings [Pl (200 W), Vs (1500 mm/s), ϴ (0,90°)], [Pl (200 W), Vs (1500 mm/s), ϴ (0,67°)], [Pl (200 W), Vs (1500 mm/s), ϴ (0,0)] and [Pl (200 W), Vs (1500 mm/s), ϴ (0,0)] with higher compressive strength (672.78 MPa), near cortical bone elastic modulus (12.932 GPa), process-induced porosity (0.751%) and minimum surface roughness (2.72 µm). The morphology of the selective laser melted (SLMed) surface indicated that the lack of fusion pores was prominent because of low laser energy density among the laser and powder bed. Confirmatory experimentation revealed that an overall percent improvement of around 15% was found between predicted and the experimental values.

Since no significant works are available on the collaborative optimization and fine process tuning in laser powder bed fusion of biomedical Ti alloys for different load bearing implants. Therefore, this work involves the comprehensive investigation and multi-objective optimization to determine optimal parametric settings for better mechanical and physical properties. Another novel aspect is the parametric porosity simulation using Ansys Additive to assist in process parameters and their levels selection. As a result, selective laser melted Ti alloys at optimal settings may help in examining the possibility for manufacturing metallic implants for load-bearing applications.

The efficiency of execution of public works contracts is usually defined in terms of the capacity to complete works within the costs and the time agreed on in the contract. Therefore, it has been traditionally measured considering either costs overruns or delays. Our purpose is to consider both measures simultaneously, so as to develop a measure of overall efficiency of public works contracts execution. We compute this measure, through a benchmark procedure, using a non-parametric approach (DEA - Data Envelopment Analysis). The analysis is carried out employing a detailed data set of Italian public contracts for roads and highways, in the period 2000- 2005.

While crop insurance ratemaking has been studied for many decades, it is still faced with many challenges. Crop insurance premium rates (PRs) are traditionally determined only by point estimation, and this approach may lead to uncertainty because it is sensitive to the underwriter’s assumptions regarding the trend, yield distribution, and other issues such as data scarcity and credibility. Thus, the purpose of this paper is to obtain the interval estimate for the PR, which can provide additional information about the accuracy of the point estimate.

A bootstrap method based on the loss cost ratio ratemaking approach is proposed. Using Monte Carlo experiments, the performance of this method is tested against several popular methods. To measure the efficiency of the confidence interval (CI) estimators, the actual coverage probabilities and the average widths of these intervals are calculated.

The proposed method is shown to be as efficient as the non-parametric kernel method, and has the features of flexibility and robustness, and can provide insight for underwriters regarding uncertainty based on the width of the CI.

Comprehensive comparisons are conducted to show the advantage and the efficiency of the proposed method. In addition, a significant empirical example is given to show how to use the CIs to support ratemaking.

The paper aims to provide an overview of the area of digital pattern developing for customized apparel.

The paper outlines several methods of digital pattern developing for customized apparel, and discusses the principles, characters and applications. Digital pattern developing process has two paths. One path develops apparel according to traditional 2D pattern‐making technology. There are three methods: parametric design, traditional grading technique, and pattern generating based on artificial intelligence (AI). Another path develops pattern through surface flattening directly from individual 3D apparel model.

For parametric method, it can improve greatly the efficiency of pattern design or pattern alteration. However, the development and application of parametric Computer‐Aided‐Design (CAD) systems in apparel industry are difficult, because apparel pattern has fewer laws in graphical structure. For grading technique, it is the most practical method because of its simple theory, with which pattern masters are familiar. But these methods require users with higher experience. Creating expert pattern system based on AI can reduce the experience requirements. Meanwhile, a great deal of experiments should be conducted for each garment with different style to create their knowledge databases. For 3D CAD technology, two methods of surface flattening have been outlined, namely geometry flattening and physical flattening. But many improvements should be done if the 3D CAD systems are applied in apparel mass customization.

The paper provides information of value to the future research on developing a practical made‐to‐measure apparel pattern system.

The purpose of this paper is to offer the theory of a parametrical regulation of market economy development, and the results of the theory development and usage.

Theoretical results of the abstract have been obtained by way of applying the theory of ordinary differential equations, geometrical methods in variation tasks and the theory of dynamic systems. These results have been used for solving a number of practical tasks.

The market economy development parametrical regulation theory structure has been offered. The approach to parametrical regulation of a nonlinear dynamic system's development has been suggested. An assumption about the existence of solution to the task of calculus of variations on the choice of the optimum laws of parametrical regulation within the given finite set of algorithms has been set forward. An assumption about the conditions sufficient for the existence of an extremal's bifurcation point of the task of calculus of variations on the choice of the optimum laws of parametrical regulation within the given finite set of algorithms is presented, formulated and proved. Theory application samples have been provided.

Future papers would be focused on studies of rigidness of other mathematic models of economic systems.

The research findings could be applied to the choice and realization of an effective budget and tax as well as monetary and loan state policy.

The market economy development parametrical regulation theory has been offered for consideration for the first time.

This paper examines whether earnings and its components are relevant and sufficient to bridge the gap between banks' market and book values, and also considers if bank efficiency is “value relevant” for banks valuation.

This paper follows the value relevance literature methodology which tests for the difference between book and market values using a variety of indicators including net income and its components as well as bank efficiency (derived using DEA) and risk indicators. The regression models are estimated using OLS, random and fixed effects approaches for a sample of listed Jordanian banks between 1993 and 2004.

The main findings of this paper are twofold. First, it is found that earnings (and its components) are value relevant and explain the gap between market and book values. Secondly, cost efficiency, as an economic performance measure, provides incremental information, not contained directly in banks financial statements, to the market. Overall it is found that the components of net income are more important than aggregate net income in explaining bank value. Furthermore, bank operational efficiency adds incremental information in explaining the gap between market and book value. These results support the view that stock prices aggregate signals received by the market as well as from firm's accounting systems.

The study shows that bank efficiency indicators (along with more traditional accounting measures) help explain market values.

This is one of only a limited number of studies that link bank efficiency to market valuation. It is the first, we believe, to do this for banks operating in an emerging economy.

Conventional wisdom dictates that there is a positive relationship between governance and growth. This article reexamines this empirical relationship using nonparametric quantile methods. We apply these methods on different levels of countries' growth and governance measures as defined in World Governance Indicators provided by the World Bank. We concentrate our analysis on three of the six measures: voice and accountability, political stability, and rule of law that were found to be significantly correlated with economic growth. To illustrate the nonparametric quantile analysis we use growth profile curves as a visual device. We find that the empirical relationship between voice and accountability, political stability, and growth are highly nonlinear at different quantiles. We also find heterogeneity in these effects across indicators, regions, time, and quantiles. These results are a cautionary tale to practitioners using parametric quantile methods.

The purpose of this paper is to study the production and efficiency of rice growers in drought prone areas with special attention given to economic and financial factors.

The authors use a parametric stochastic frontier approach and a non-parametric data envelopment analysis.

The study found that financial and liquidity constraints negatively influence production efficiency while off-farm work positively influences efficiency in drought prone areas.

Many biotic and abiotic factors affect the production efficiency of rice growers. Among abiotic stress, drought is the strongest constraint affecting nearly one third of the total rice area in Asia and causing significant economic losses. Farmers’ economic conditions and financial constraints further exacerbate the situation. However, very few studies have analyzed the efficiency in drought prone areas and the influence of economic and financial factors. This study contributes to in this regard by augmenting economic and financial factors in the efficiency estimation of drought prone areas using parametric and non-parametric approach.

The purpose of this paper is to introduce a novel measure of access to credit suited to estimate the relative change in credit reserves.

A debt possibility frontier is estimated using data envelopment analysis and the Malmquist index is calculated. The Malmquist index is redubbed the Debt Development index and decomposed into “change in debt capacity” and “change in debt capacity utilization”. Bootstrapping is applied for statistical inference. The method is applied to an unbalanced panel of 92,000 Danish farm accounts from 1996 to 2009.

The paper finds that credit capacity roughly doubled for Danish farmers over the period, and that utilization of credit capacity generally was proportional to capacity change, utilization being higher for dairy and pig farms, than for crop farms.

Changes in credit reserves may have important implications for risk management practice, investment and technology adoption and related policy issues. The method is limited by the possibility of strategic behavior of lenders during credit cycle busts. In credit cycle booms, the method gives a good basis for the estimates of change in credit reserves.

In a period of increasing credit reserves, risk management institutions are unlikely to develop. Like agricultural policy, access to credit may crowd out market-based risk management.

The study represents a novel application and interpretation of a well-known method.

The purpose of this paper is to propose a novel contribution to adaptive sampling strategies for non‐intrusive reduced order models based on Proper Orthogonal Decomposition (POD). These strategies aim at reducing the cost of optimization by improving the efficiency and accuracy of POD data‐fitting surrogate models to be used in an online surrogate‐assisted optimization framework for industrial design.

The effect of the strategies on the model accuracy is investigated considering the snapshot scaling, the design of experiment size and the truncation level of the POD basis and compared to a state‐of‐the‐art radial basis function network surrogate model on objectives and constraints. The selected test case is a Mach number and angle of attack domain exploration of the well‐known RAE2822 airfoil. Preliminary airfoil shape optimization results are also shown.

The numerical results demonstrate the potential of the capture/recapture schemes proposed for adequately filling the parametric space and maximizing the surrogates relevance at minimum computational cost.

The proposed approaches help in building POD‐based surrogate models more efficiently.