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The formation and corrosion processes of a conversion film on the AZ80 Mg alloy with different second phases were compared to clarify the effect of microstructure on the quality of protective coatings.

The size and distribution of second phases in the edge and central regions of the AZ80 cast ingot exhibit a great difference. The film growth processes and their corrosion resistance on the edge and central regions of the AZ80 cast ingot were investigated by scanning electron microscope observations, immersion tests and electrochemical measurements.

The results indicate that second phases act as micro-cathodes and hydrogen evolution reaction occurs on their surface, which is not beneficial for the deposition of the conversion film.

The conversion film formed on the central regions of AZ80 cast ingot with a low volume fraction of second phases exhibits a more uniform surface and higher corrosion resistance than that formed on the edge regions of the sample with a higher volume fraction of second phases.

The essential investments in new product development (NPD) made by industrial companies entail effective management of NPD activities. In this context, performance measurement is one of the means that can be employed in the pursuit of effectiveness.

This paper aims to present a novel numerical technique for solving the incompressible multiphase mixture model.

The multiphase mixture model contains a set of momentum and continuity equations for the mixture phase, a second phase continuity equation and the algebraic equation for the relative velocity. For solving continuity equation for the second phase and advection term of momentum, an improved approach fine grid advection-multiphase mixture flow (FGA-MMF) is developed. In the FGA-MMF method, the continuity equation for the second phase is solved with higher-order schemes in a two times finer grid. To solve the advection term of the momentum equation, the advection fluxes of the volume fraction in the continuity equation for the second phase are used.

This approach has been used in various tests to simulate unsteady flow problems. Comparison between numerical results and experimental data demonstrates a satisfactory performance. Numerical examples show that this approach increases the accuracy and stability of the solution and decreases non-monotonic results.

The solver for the multi-phase mixture model can only be adopted to solve the incompressible fluid flow.

The paper developed an innovative solution (FGA-MMF) to find multi-phase flow field value in the multi-phase mixture model. Advantages of the FGA-MMF technique are the ability to accurately determine the phases interpenetrating, decreasing the numerical diffusion of the interface and preventing instability and non-monotonicity in solution of large density variation problems.

In this chapter, six cases are presented, four from Poland and two from Hungary. The Polish cases are Partec Rockwool, PLM, Bulten Tools, and Vattenfall, while Svedala and Getinge belong to Hungary.

The cases have been described in different phases following the conceptual framework, developed in chapter six. All cases we present in three phases except Svedala where there are two phases. In the later case, neither the alliance nor the partners could be traced. Among the cases, level of performance varied. Getinge is the only case where the partners continued with the same alliance and the ownership structure remained unchanged. In Partec, the foreign partner acquired the local shares to establish a wholly owned subsidiary, and in Bulten Tool, the foreign partner became the major owner to have control over the company. Partec Rockwool and Vattenfall had been sold to other companies after amicable settlement between the partners.

Methods to optimize lattice structure design, such as ground structure optimization, have been shown to be useful when generating efficient design concepts with complex truss-like cellular structures. Unfortunately, designs suggested by lattice structure optimization methods are often infeasible because the obtained cross-sectional parameter values cannot be fabricated by additive manufacturing (AM) processes, and it is often very difficult to transform a design proposal into one that can be additively designed. This paper aims to propose an improved, two-phase lattice structure optimization framework that considers manufacturing constraints for the AM process.

The proposed framework uses a conventional ground structure optimization method in the first phase. In the second phase, the results from the ground structure optimization are modified according to the pre-determined manufacturing constraints using a second optimization procedure. To decrease the computational cost of the optimization process, an efficient gradient-based optimization algorithm, namely, the method of feasible directions (MFDs), is integrated into this framework. The developed framework is applied to three different design examples. The efficacy of the framework is compared to that of existing lattice structure optimization methods.

The proposed optimization framework provided designs more efficiently and with better performance than the existing optimization methods.

The proposed framework can be used effectively for optimizing complex lattice-based structures.

An improved optimization framework that efficiently considers the AM constraints was reported for the design of lattice-based structures.

This paper aims to propose a new valuation method for income producing properties. The model originally called cyclical dividend discount models (d’Amato, 2003) has been recently proposed as a family of income approach methodologies called cyclical capitalization (d’Amato, 2013; d’Amato, 2015; d’Amato, 2017).

The proposed methodology tries to integrate real estate market cycle analysis and forecast inside the valuation process allowing the appraiser to deal with real estate market phases analysis and their consequence in the local real estate market.

The findings consist in the creation of a methodology proposed for market value and in particular for mortgage lending determination, as the model may have the capability to reach prudent opinion of value in all the real estate market phase.

Research limitation consists mainly in a limited number of sample of time series of rent and in the forecast of more than a cap rate or yield rate even if it is quite commonly accepted the cyclical nature of the real estate market.

The implication of the proposed methodology is a modified approach to direct capitalization finding more flexible approaches to appraise income producing properties sensitive to the upturn and downturn of the real estate market.

The model proposed can be considered useful for the valuation process of those property affected by the property market cycle, both in the mortgage lending and market value determination.

These methodologies try to integrate in the appraisal process the role of property market cycles. Cyclical capitalization modelling includes in the traditional dividend discount model more than one g-factor to plot property market cycle dealing with the future in a different way. It must be stressed the countercyclical nature of the cyclical capitalization that may be helpful in the determination of mortgage lending value. This is a very important characteristic of such models.

The purpose of this paper is to propose a novel improved teaching and learning-based algorithm (TLBO) to enhance its convergence ability and solution accuracy, making it more suitable for solving large-scale optimization issues.

Utilizing multiple cooperation mechanisms in teaching and learning processes, an improved TBLO named CTLBO (collectivism teaching-learning-based optimization) is developed. This algorithm introduces a new preparation phase before the teaching and learning phases and applies multiple teacher–learner cooperation strategies in teaching and learning processes. Applying modularization idea, based on the configuration structure of operators of CTLBO, six variants of CTLBO are constructed. For identifying the best configuration, 30 general benchmark functions are tested. Then, three experiments using CEC2020 (2020 IEEE Conference on Evolutionary Computation)-constrained optimization problems are conducted to compare CTLBO with other algorithms. At last, a large-scale industrial engineering problem is taken as the application case.

Experiment with 30 general unconstrained benchmark functions indicates that CTLBO-c is the best configuration of all variants of CTLBO. Three experiments using CEC2020-constrained optimization problems show that CTLBO is one powerful algorithm for solving large-scale constrained optimization problems. The application case of industrial engineering problem shows that CTLBO and its variant CTLBO-c can effectively solve the large-scale real problem, while the accuracies of TLBO and other meta-heuristic algorithm are far lower than CLTBO and CTLBO-c, revealing that CTLBO and its variants can far outperform other algorithms. CTLBO is an excellent algorithm for solving large-scale complex optimization issues.

The innovation of this paper lies in the improvement strategies in changing the original TLBO with two-phase teaching–learning mechanism to a new algorithm CTLBO with three-phase multiple cooperation teaching–learning mechanism, self-learning mechanism in teaching and group teaching mechanism. CTLBO has important application value in solving large-scale optimization problems.

The purpose of this paper is to propose an integrative approach for improving failure modes and effects analysis (FMEA).

An extensive literature review on FMEA has been performed. Then, an integrative approach has been proposed based on literature review. The proposed approach is an integration of FMEA and quality function deployment (QFD). The proposed approach includes a two-phase QFD. In the first phase, failure modes are prioritized based on failure effects and in the second phase, failure causes are prioritized based on failure modes. The proposed approach has been examined in a case example at the blast furnace operation of a steel-manufacturing company.

Results of the case example indicated that stove shell crack in hot blast blower, pump failure in cooling water supply pump and bleeder valves failed to operate are the first three important failure modes. In addition, fire and explosion are the most important failure effects. Also, improper maintenance, over pressure and excess temperature are the most important failure causes. Findings also indicated that the proposed approach with the consideration of interrelationships among failure effects, failure mode and failure causes can influence and adjust risk priority number (RPN) in FMEA.

As manufacturing departments are mostly dealing with failure effects and modes of machinery and maintenance departments are mostly dealing with causes of failures, the proposed model can support better coordination and integration between the two departments. Such support seems to be more important in firms with continuous production lines wherein line interruption influences response to customers more seriously. A wide range of future study opportunities indicates the attractiveness and contribution of the subject to the knowledge of FMEA.

Although the literature indicates that in most of studies the outcomes of QFD were entered into FMEA and in some studies the RPN of FMEA was entered into QFD as importance rating, the proposed approach is a true type of the so-called “integration of FMEA and QFD” because the three main elements of FMEA formed the structure of QFD. In other words, the proposed approach can be considered as an innovation in the FMEA structure, not as a data provider prior to it or a data receiver after it.

The purpose of this paper is to provide a dating system for the Italian residential real estate market from 1927 to 2019 and investigate its interaction with credit and business cycles.

To detect the local turning point of the Italian residential real estate market, the authors apply the honeycomb cycle developed by Janssen et al. (1994) based on the joint analysis of house prices and the number of transactions. To this end, the authors use a unique historical reconstruction of house price levels by Baffigi and Piselli (2019) in addition to data on transactions.

This study confirms the validity of the honeycomb model for the last four decades of the Italian housing market. In addition, the results show that the severe downsizing of the housing market is largely associated with business and credit contraction, certainly contributing to exacerbating the severity of the recession. Finally, preliminary evidence suggests that whenever a price bubble occurs, it is coincident with the start of phase 2 of the honeycomb cycle.

To the best of the authors’ knowledge, this is the first time that the honeycomb approach has been tested over such a long historical period and compared to the cyclic features of financial and real aggregates. In addition, even if the honeycomb cycle is not a model for detecting booms and busts in the housing market, the preliminary evidence might suggest a role for volume/transactions in detecting housing market bubbles.

As eyewitnesses provide the most valuable information for criminal investigations, it is important to further develop and test techniques for collecting eyewitness testimony so that they meet the major objective of a police interview: obtaining details pertaining to criminal actions. The purpose of this paper is to test a new instruction – the re-enactment investigative instruction – formulated to collect the most fine-grained details of a criminal event as accurately as possible. It leads the interviewee to decompose all directly recollected actions into the most minimal actions so that the event can be accurately re-enacted.

In all, 40 participants individually viewed a video depicting a robbery, were randomly assigned to a re-enactment or structured interview (SI) group and then interviewed face-to-face. Each interview was comprised of two free recall phases and a questioning phase. Manipulation of the re-enactment instruction took place in the second free recall phase of the re-enactment interviews (RIs).

The RI elicited more correct information compared to the SI (d=1.14), and slightly but not significantly less incorrect information (d=0.09). Participants in the RI condition reported significantly more details pertaining to general and specific actions.

The re-enactment instruction shows the potential to increase witness recall in a way that promotes recall of both additional correct information and investigative-relevant information.

The instruction provides witnesses a retrieval strategy that facilitates overcoming both the gap between memory availability and accessibility and the gap between memory availability and output regulation, eliciting more details with no significant increase of errors.