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The surface energy per unit area of material is known to be proportional to the thermal energy at the melting point of the material. The purpose of this paper is to employ the values of the melting points of metals to develop a model that estimates the average surface energies of metals. Average surface energy estimator (ASEE) was developed with the aid of computational intelligence technique on the platform of support vector regression (SVR) using the values of the melting point of the materials as the descriptor.

The development of ASEE which involves 12 data set was conducted by training and testing SVR model using test-set-cross-validation technique. The developed model (ASEE) was used to estimate average surface energies of 3d, 4d, 5d and other selected metals in the periodic table. The average surface energies obtained from ASEE are in good agreement with the experimental values and with the values from other theoretical models.

The accuracy of this developed model coupled with its adoption of descriptor that can be easily obtained makes it a viable alternative in circumventing the difficulty experienced in experimental determination of average surface energies of materials.

Modeling of ASEE has never been reported in the literature. Meanwhile, the use of ASEE will help circumvent the difficulties involved in the experimental determination of average surface energies of materials.

The analysis of error estimation is addressed in the framework of viscoplasticity problems, this is to say, of incompressible and non‐linear materials. Firstly, Zienkiewicz—Zhu (Z²) type error estimators are studied. They are based on the comparison between the finite element solution and a continuous solution which is computed by smoothing technique. From numerical examples, it is shown that the choice of a finite difference smoothing method (Orkisz’ method) improves the precision and the efficiency of this type of estimator. Then a Δ estimator is introduced. It makes it possible to take into account the fact that the smoothed solution does not verify the balance equations. On the other hand, it leads us to introduce estimators for the velocity error according to the L² and L^∞norms, since in metal forming this error is as important as the energy error. These estimators are applied to an industrial problem of extrusion, demonstrating all the potential of the adaptive remeshing method for forming processes.

Gives introductory remarks about chapter 1 of this group of 31 papers, from ISEF 1999 Proceedings, in the methodologies for field analysis, in the electromagnetic community. Observes that computer package implementation theory contributes to clarification. Discusses the areas covered by some of the papers ‐ such as artificial intelligence using fuzzy logic. Includes applications such as permanent magnets and looks at eddy current problems. States the finite element method is currently the most popular method used for field computation. Closes by pointing out the amalgam of topics.

During the production process of steel strip, some defects may appear on the surface, that is, traditional manual inspection could not meet the requirements of low-cost and high-efficiency production. The purpose of this paper is to propose a method of feature selection based on filter methods combined with hidden Bayesian classifier for improving the efficiency of defect recognition and reduce the complexity of calculation. The method can select the optimal hybrid model for realizing the accurate classification of steel strip surface defects.

A large image feature set was initially obtained based on the discrete wavelet transform feature extraction method. Three feature selection methods (including correlation-based feature selection, consistency subset evaluator [CSE] and information gain) were then used to optimize the feature space. Parameters for the feature selection methods were based on the classification accuracy results of hidden Naive Bayes (HNB) algorithm. The selected feature subset was then applied to the traditional NB classifier and leading extended NB classifiers.

The experimental results demonstrated that the HNB model combined with feature selection approaches has better classification performance than other models of defect recognition. Among the results of this study, the proposed hybrid model of CSE + HNB is the most robust and effective and of highest classification accuracy in identifying the optimal subset of the surface defect database.

The main contribution of this paper is the development of a hybrid model combining feature selection and multi-class classification algorithms for steel strip surface inspection. The proposed hybrid model is primarily robust and effective for steel strip surface inspection.

Several a posteriori error indicators and error estimators for frictionless contact problems are compared. In detail, residual based error estimators, error indicators relying on superconvergence properties and error estimators based on duality principles are investigated. Applications are to 2D solids under the hypothesis of nonlinear elastic material behaviour associated with finite deformations. A penalization technique is applied to enforce multilateral boundary conditions due to contact. The approximate solution of the problem is obtained by using the finite element method. Several numerical results are reported to show the applicability of the adaptive algorithm to the considered problems.

Transient climate sensitivity relates total climate forcings from anthropogenic and other sources to surface temperature. Global transient climate sensitivity is well studied, as are the related concepts of equilibrium climate sensitivity (ECS) and transient climate response (TCR), but spatially disaggregated local climate sensitivity (LCS) is less so. An energy balance model (EBM) and an easily implemented semiparametric statistical approach are proposed to estimate LCS using the historical record and to assess its contribution to global transient climate sensitivity. Results suggest that areas dominated by ocean tend to import energy, they are relatively more sensitive to forcings, but they warm more slowly than areas dominated by land. Economic implications are discussed.

Sub-Saharan Africa is a region that is highly vulnerable to the effects of climate change. Renewable energy consumption could play a major role in mitigating the effects of climate change by improving environmental quality in the region. The purpose of this paper is to examine the effect of renewable energy consumption on environmental quality in sub-Saharan African countries.

The empirical investigation is based on the estimation of an augmented Green Solow model through the defactored instrumental variables approach on a sample of 34 countries over the period 1996 to 2018.

The results of two-stage defactored instrumental variables estimator show that renewable energy consumption improves environmental quality. Indeed, renewable energies have a significant negative influence on CO₂ emissions. This result is robust when using the ecological footprint as an indicator of environmental quality.

In terms of implications, governments in Sub-Saharan Africa need to pursue policies to encourage investment in the renewable energy sector. This will promote renewable energy consumption, change the structure of the energy mix in favour of renewable energy, improve environmental quality and effectively combat climate change.

The originality of this research in relation to the existing literature lies at several levels. Firstly, the analysis is carried out using a unified framework combining the environmental Kuznets curve and the environmental convergence hypotheses. Secondly, this research uses a very recent econometric method. Finally, environmental quality is measured using two indicators.

Discusses the 27 papers in ISEF 1999 Proceedings on the subject of electromagnetisms. States the groups of papers cover such subjects within the discipline as: induction machines; reluctance motors; PM motors; transformers and reactors; and special problems and applications. Debates all of these in great detail and itemizes each with greater in‐depth discussion of the various technical applications and areas. Concludes that the recommendations made should be adhered to.

The malfunction variables of power stations are related to the areas of weather, physical structure, control and load behaviour. To predict temporal power failure is difficult due to their unpredictable characteristics. As high accuracy is normally required, the estimation of failures of short-term temporal prediction is highly difficult. This study presents a method for converting stochastic behaviour into a stable pattern, which can subsequently be used in a short-term estimator. For this conversion, K-means clustering is employed, followed by Long-Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms are used to perform the Short-term estimation. The environment, the operation and the generated signal factors are all simulated using mathematical models. Weather parameters and load samples have been collected as part of a data set. Monte-Carlo simulation using MATLAB programming has been used to conduct experimental estimation of failures. The estimated failures of the experiment are then compared with the actual system temporal failures and found to be in good match. Therefore, for any future power grid, there is a testbed ready to estimate the future failures.

“It should also be noted that the objective of convergence and equal distribution, including across under-performing areas, can hinder efforts to generate growth. Contrariwise, the objective of competitiveness can exacerbate regional and social inequalities, by targeting efforts on zones of excellence where projects achieve greater returns (dynamic major cities, higher levels of general education, the most advanced projects, infrastructures with the heaviest traffic, and so on). If cohesion policy and the Lisbon Strategy come into conflict, it must be borne in mind that the former, for the moment, is founded on a rather more solid legal foundation than the latter” European Commission (2005, p. 9)Adaptation of Cohesion Policy to the Enlarged Europe and the Lisbon and Gothenburg Objectives.