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The purpose of this paper is to present a new efficient method for the tolerance–reliability analysis and quality control of complex nonlinear assemblies where explicit assembly functions are difficult or impossible to extract based on Bayesian modeling.

In the proposed method, first, tolerances are modelled as the random uncertain variables. Then, based on the assembly data, the explicit assembly function can be expressed by the Bayesian model in terms of manufacturing and assembly tolerances. According to the obtained assembly tolerance, reliability of the mechanical assembly to meet the assembly requirement can be estimated by a proper first-order reliability method.

The Bayesian modeling leads to an appropriate assembly function for the tolerance and reliability analysis of mechanical assemblies for assessment of the assembly quality, by evaluation of the assembly requirement(s) at the key characteristics in the assembly process. The efficiency of the proposed method by considering a case study has been illustrated and validated by comparison to Monte Carlo simulations.

The method is practically easy to be automated for use within CAD/CAM software for the assembly quality control in industrial applications.

Bayesian modeling for tolerance–reliability analysis of mechanical assemblies, which has not been previously considered in the literature, is a potentially interesting concept that can be extended to other corresponding fields of the tolerance design and the quality control.

The purpose of this paper is to present an integrated algorithm composed of data envelopment analysis (DEA), corrected ordinary least squares (COLS) and principal component analysis (PCA) to estimate efficiency scores of electricity distribution units.

Several DEA and COLS models are prescribed and their results are verified and validated by the algorithm. To calculate efficiency scores, three standard internal consistency conditions between DEA and COLS results are checked by the algorithm. If these conditions are satisfied, DEA is chosen as the superior model because it could be used for optimization as well. Otherwise, the geometric mean of DEA and COLS model is used as the final efficiency scores.

The algorithm of this paper may be easily applied to decision‐making units because of its robustness (combined DEA‐COLS input and output) and validity gained through PCA.

The integrated approach has several unique features which are: verification and validation mechanism by PCA, consideration of internal consistency conditions between DEA and COLS and consolidation of DEA and COLS for improved ranking given consistency conditions are violated.

Protecting households against financial risks of healthcare services is one of the main functions of health systems. The purpose of this paper is to provide a pooled estimate of the prevalence of catastrophic healthcare expenditures (CHE) among households in Iran.

Both international (PubMed, Scopus and Clarivate Analytics (previously known as the Institute for Scientific Information)) and Iranian (Scientific Information Database, Iranmedex and Magiran) scientific databases were searched for published studies on CHE among Iranian households. The following keywords in Persian and English language were used as keywords for the search: “catastrophic healthcare costs,” “catastrophic health costs,” “impoverishment due to health costs,” “fair financial contribution,” “prevalence,” “frequency” and “Iran” with and without “health system”. The I²-test and χ²-based Q-test suggested heterogeneity in the reported prevalence among the qualified studies; thus, a random-effects model was used to estimate the overall prevalence of CHE among households in Iran.

A total of 24 studies with a cumulative sample of 301,097 households were included in the study. The estimated pooled prevalence of CHE among households was 7 percent (95 percent confidence interval: 6–8 percent). Meta-regression analysis indicated that the prevalence of CHE was inversely related to the sample size (p<0.05). The results did not suggest a significant association between the prevalence of CHE and the year of data collection.

The findings revealed that the prevalence of CHE among Iranian households is significantly higher than 1 percent, which is the goal set out in Iran’s fourth five-year development plan. This warrants further policy interventions to protect households from incurring CHE in Iran.

Disruption due to a crisis or disaster is a constant threat for the tourism industry, unfortunately the frameworks designed to prepare leaders for these events are inadequate. Most frameworks are designed to assess and enhance resilience and recovery and minimally prepare leaders for the complexities that emerge before, during and after these events. The purpose of this paper is to offer a leadership development framework that integrates context, competence and a complexity mindset.

This general review examines the literature focused on crises and disasters in the tourism industry for the purpose of understanding the circumstances surrounding several kinds of disruptive events, the competencies needed to address them. It also explores the usefulness of three forms of leadership development inputs. The result is a framework that builds capacity while ensuring organizational alignment.

The preparation of tourism industry leaders who address implications of crises and disaster should involve an understanding of the crisis processes and factors that can be known, and the development of a mindset that allows the leader to address those factors which cannot be known beforehand.

This paper offers a framework for tourism leaders and developers that moves beyond static and linear approaches to crisis and disaster training. It encourages the acquisition of contextual knowledge and adaptive processes through leadership-focused education, exposure to leaders and experiences.

This paper reviews the synergy of Industry 4.0 and additive manufacturing (AM) and discusses the integration of data-driven manufacturing systems and product service systems as a key component of the Industry 4.0 revolution. This paper aims to highlight the potential effects of Industry 4.0 on AM via tools such as digitalisation, data transfer, tagging technology, information in Industry 4.0 and intelligent features.

In successive phases of industrialisation, there has been a rise in the use of, and dependence on, data in manufacturing. In this review of Industry 4.0 and AM, the five pillars of success that could see the Internet of Things (IoT), artificial intelligence, robotics and materials science enabling new levels of interactivity and interdependence between suppliers, producers and users are discussed. The unique effects of AM capabilities, in particular mass customisation and light-weighting, combined with the integration of data and IoT in Industry 4.0, are studied for their potential to support higher efficiencies, greater utility and more ecologically friendly production. This research also illustrates how the digitalisation of manufacturing for Industry 4.0, through the use of IoT and AM, enables new business models and production practices.

The discussion illustrates the potential of combining IoT and AM to provide an escape from the constraints and limitations of conventional mass production whilst achieving economic and ecological savings. It should also be noted that this extends to the agile design and fabrication of increasingly complex parts enabled by simulations of complex production processes and operating systems. This paper also discusses the relationship between Industry 4.0 and AM with respect to improving the quality and robustness of product outcomes, based on real-time data/feedback.

This research shows how a combined approach to research into IoT and AM can create a step change in practice that alters the production and supply paradigm, potentially reducing the ecological impact of industrial systems and product life cycle. This paper demonstrates how the integration of Industry 4.0 and AM could reshape the future of manufacturing and discusses the challenges involved.

The purpose of this study is, to compare laser-based additive manufacturing and subtractive methods. Laser-based manufacturing is a widely used, noncontact, advanced manufacturing technique, which can be applied to a very wide range of materials, with particular emphasis on metals. In this paper, the governing principles of both laser-based subtractive of metals (LB-SM) and laser-based powder bed fusion (LB-PBF) of metallic materials are discussed and evaluated in terms of performance and capabilities. Using the principles of both laser-based methods, some new potential hybrid additive manufacturing options are discussed.

Production characteristics, such as surface quality, dimensional accuracy, material range, mechanical properties and applications, are reviewed and discussed. The process parameters for both LB-PBF and LB-SM were identified, and different factors that caused defects in both processes are explored. Advantages, disadvantages and limitations are explained and analyzed to shed light on the process selection for both additive and subtractive processes.

The performance of subtractive and additive processes is highly related to the material properties, such as diffusivity, reflectivity, thermal conductivity as well as laser parameters. LB-PBF has more influential factors affecting the quality of produced parts and is a more complex process. Both LB-SM and LB-PBF are flexible manufacturing methods that can be applied to a wide range of materials; however, they both suffer from low energy efficiency and production rate. These may be useful when producing highly innovative parts detailed, hollow products, such as medical implants.

This paper reviews the literature for both LB-PBF and LB-SM; nevertheless, the main contributions of this paper are twofold. To the best of the authors’ knowledge, this paper is one of the first to discuss the effect of the production process (both additive and subtractive) on the quality of the produced components. Also, some options for the hybrid capability of both LB-PBF and LB-SM are suggested to produce complex components with the desired macro- and microscale features.

This paper aims to study multiobjective genetic algorithm ability in determining the process parameter and postprocess condition that leads to maximum relative density (RD) and minimum surface roughness (Ra) simultaneously in the case of a Ti6Al4V sample process by laser beam powder bed fusion.

In this research, the nondominated sorting genetic algorithm II is used to achieve situations that correspond to the highest RD and the lowest Ra together.

The results show that several situations cause achieving the best RD and optimum Ra. According to the Pareto frontal diagram, there are several choices in a close neighborhood, so that the best setup conditions found to be 102–105 watt for laser power followed by scanning speed of 623–630 mm/s, hatch space of 76–73 µm, scanning patter angle of 35°–45° and heat treatment temperature of 638–640°C.

Suitable selection of process parameters and postprocessing treatments lead to a significant reduction in time and cost.

In this paper, it can be seen from AFM images of the as-deposited ZnO and CZO films, and the particle size and shape are not clear, while by increasing annealing temperature, they become distinguishable. By increasing temperature to 600°C, ZnO and CZO, CAZO and aluminum-doped zinc oxide (AZO) films particles became almost spherical. Due to high content of Cu in CZO target, and of Al in AZO target which was 5% weight ratio, doping plays a great role in the subject. Therefore, the annealing processing strongly affect the size and the shape of nanoparticles.

In this paper, the authors tried to study, in detail, nobel optical characterizations of ZnO films doped by transition metals in different annealing temperature. The authors found that the values of skin depth, optical density, electron–phonon interaction, steepness parameter, band tail width, direct and indirect carriers transitions and the dissipation factor, free carriers density and roughness of films affect the optical properties, especially the optical absorptions of ZnO films doped by transition metals. Also these properties were affected by annealing temperatures. The authors also found that topography characterizations strongly were affected by these parameters.

The CZO films have maximum value of coordination number ß, with considering N_C = 4, Z_a = 2, N_e = 8. The CZO films annealed at 500 °C have maximum value of optical density. The as-deposited CAZO films have maximum value of steepness parameters in about of 0.13 eV. The as-deposited AZO films have maximum value of dispersion energy Ed in about of 5.75 eV. Optical gap and disordering energy plots of films can be fitted by linear relationships E_g = 0.49 + 0.2 EU and E_g = 0.52 + 0.5 EU, respectively.

With considering N_c = 4, Z_a = 2, N_e = 8 for ZnO films, coordination number ß has maximum value of 0.198. CZO nanocomposites films annealed at 500°C have maximum value of optical density. Different linear fitting of ln (α) for films were obtained as y = A_x + B where 5<A < 17 and 5<B < 12. As-deposited CAZO nanocomposites films have minimum value of electron phonon interaction in about of 4.91 eV. Optical gap and disordering energy plots can be fitted by linear relationships E_g = 0.49 + 0.2 EU and E_g = 0.52 + 0.5 EU for as-deposited films and films annealed at 500°C, respectively. Steepness parameters of as-deposited CAZO nanocomposites films have maximum value of 0.13 eV. Dispersion energy Ed for as-deposited AZO nanocomposites films has maximum value of 5.75 eV.

This study evaluates the efficiency and productivity change of 39 electricity distribution companies in Iran over the period 2005-2014. For purposes of electricity management and utilization of scarce resources, Iran’s 33 provinces have been classified into five regions by the Ministry of the Interior. Analyzing the efficiency of distribution companies across these regions yields significant understanding of these resources and helps policymakers to generate more informed decisions.

The proposed method of this study develops nonparametric data envelopment analysis (DEA) with the consideration of geographic classification, size and type of company. At the first stage, a DEA model is used to estimate the relative technical efficiency and productivity change of these companies. At the second stage, distributions of efficiency improvements are examined based on geographic classification, size and type of the company type. A stability test is also conducted to verify the proposed model’s robustness.

The results demonstrate that the average technical efficiency of the companies increased during the years 2006-2009, but decreased during 2010-2014. The productivity measurement reveals that low efficiency change was the largest contributor to the small increase in productivity change rather than technology change. In addition, testing the hypothesis that the large and small companies have statistically the same efficiency scores revealed no statistical difference among them. Moreover, another test did not detect a difference among companies at the urban and provincial levels.

By applying this approach, policymakers and practitioners in the power industry at the country and corporate level can effectively compare the efficiency and productivity changes among electricity distribution companies, and therefore generate more informed decisions.

The paper’s novel concept applies DEA to Iran’s electricity distribution companies and analyzes them by examining geographic classification, size and the type of the companies. In addition, a stability test is conducted and productivity changes are estimated.

Boutique hotels reflect a nascent phenomenon in the literature and in the accommodation sector. As a result, they are void of governmental classification and regulation. Concurrently, they lack any form of operational definition. The purpose of this paper is to address these limitations by identifying the core attributes found to define boutique hotels.

To fulfil the purpose of this paper, the authors conducted a systematic literature review on Web of Science. This resulted in an analysis of 33 peer-review articles published from 1994 to 2022.

The findings of this review revealed that boutique hotels are defined by eight core attributes; these are (1) multiple ownership structures, (2) situatedness in a historic premises, (3) strategic locations, (4) thematized internal décor, (5) experience design, (6) high levels of personalization, (7) novelty in service provisions and (8) niche marketing strategies.

For policymakers, this paper may be used as a reference point to establish designated classification systems for boutique hotels. For practitioners, this paper may be used as a source of inspiration and benchmark to establish boutique hotels which align to the criteria highlighted in this paper. For scholars, this paper proposes an operational description of boutique hotels and novel areas for future research.

This paper represents one of the initial efforts undertaken by researchers to unify the literature on boutique hotels.