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Three‐dimensional photonic bandgap (PBG) structures using alumina (Al₂O₃) as the high permittivity material were modeled and then the structures were fabricated by Fused Deposition of Multi‐materials (FDMM) technology. A finite element method and a real‐time electromagnetic wave propagation software were used to simulate and design the layered PBG structures for applications in the microwave frequency range. The modeling predicted a 3‐D photonic bandgap in the 16.5–23.5 GHz range. FDMM provides a computer‐controlled process to generate 3‐D structures, allowing high fabrication flexibility and efficiency. Electromagnetic measurements displayed the presence of a bandgap between 17.1–23.3 GHz, showing a good agreement with the predicted values. These PBG structures are potential candidates for applications in advanced communication systems.

Compared to conventional manufacturing processes, fused deposition (FD)‐based layered manufacturing processes have dramatically reduced the part design and manufacturing time. However, it is necessary to further enhance their process productivity. To this end, improvement of FD process efficiency is studied in this paper. A build time analysis is conducted and the deposition parameters that can be used to speed up fabrication processes are identified. The tool path‐based deposition planning approach is extended for ensuring layer quality when the build process is expedited under adjusted deposition parameters. A test part was built to demonstrate the proposed approach.

To fabricate high quality parts, and to make the development generic yet compatible with the in‐house hardware, a virtual simulation system has been developed, and an in‐house intelligent multi‐material toolpath generation system has been under development. This new development includes the issues such as multiple fill‐toolpaths for the same material, interface mismatch between adjacent materials and the intelligent toolpath features for machine control. After the multi‐material toolpath file generated by the in‐house software, the existing virtual graphical simulation as well as well selected part fabrication experiments were used to validate it. Based on the authors’ ongoing research about multi‐material layered manufacturing, it was determined that the build characteristic was heavily dependent on the material being used. Therefore, it was important to develop the hardware/software that will accommodate this requirement.

Human resources are one of the most important and effective elements for companies. In other words, employees are a competitive advantage. This issue is more vital in the supply chains and production systems, because of high need for manpower in the different specification. Therefore, manpower planning is an important, essential and complex task. The purpose of this paper is to present a manpower planning model for production departments. The authors consider workforce with individual and hierarchical skills with skill substitution in the planning. Assuming workforce demand as a factor of uncertainty, a two-stage stochastic model is proposed.

To solve the proposed mixed-integer model in the real-world cases and large-scale problems, a Benders’ decomposition algorithm is introduced. Some test instances are solved, with scenarios generated by Monte Carlo method. For some test instances, to find the number of suitable scenarios, the authors use the sample average approximation method and to generate scenarios, the authors use Latin hypercube sampling method.

The results show a reasonable performance in terms of both quality and solution time. Finally, the paper concludes with some analysis of the results and suggestions for further research.

Researchers have attracted to other uncertainty factors such as costs and products demand in the literature, and have little attention to workforce demand as an uncertainty factor. Furthermore, most of the time, researchers assume that there is no difference between the education level and skill, while they are not necessarily equivalent. Hence, this paper enters these elements into decision making.

A parabolic trough solar collector is an advanced concentrated solar power technology that significantly captures radiant energy. Solar power will help different sectors reach their energy needs in areas where traditional fuels are in use. This study aims to examine the sensitivity analysis for optimizing the heat transfer and entropy generation in the Jeffrey magnetohydrodynamic hybrid nanofluid flow under the influence of motile gyrotactic microorganisms with solar radiation in the parabolic trough solar collectors. The influences of viscous dissipation and Ohmic heating are also considered in this investigation.

Governing partial differential equations are derived via boundary layer assumptions and nondimensionalized with the help of suitable similarity transformations. The resulting higher-order coupled ordinary differential equations are numerically investigated using the Runga-Kutta fourth-order numerical approach with the shooting technique in the computational MATLAB tool.

The numerical outcomes of influential parameters are presented graphically for velocity, temperature, entropy generation, Bejan number, drag coefficient and Nusselt number. It is observed that escalating the values of melting heat parameter and the Prandl number enhances the Nusselt number, while reverse effect is observed with an enhancement in the magnetic field parameter and bioconvection Lewis number. Increasing the magnetic field and bioconvection diffusion parameter improves the entropy and Bejan number.

Nanotechnology has captured the interest of researchers due to its engrossing performance and wide range of applications in heat transfer and solar energy storage. There are numerous advantages of hybrid nanofluids over traditional heat transfer fluids. In addition, the upswing suspension of the motile gyrotactic microorganisms improves the hybrid nanofluid stability, enhancing the performance of the solar collector. The use of solar energy reduces the industry’s dependency on fossil fuels.

One of the problems in post-earthquake disaster management in developing countries, such as Iran, is the prediction of the residual network available for disaster relief operations. Therefore, it is important to use methods that are executable in such countries given the limited amount of accurate data. The purpose of this paper is to present a multi-objective model that seeks to determine the set of roads of a transportation network that should preserve its role in carrying out disaster relief operations (i.e. known as “emergency road network” (ERN)) in the aftermath of earthquakes.

In this paper, the total travel time of emergency trips, the total length of network and the provision of coverage to the emergency demand/supply points have been incorporated as three important metrics of ERN into a multi-objective mixed integer linear programming model. The proposed model has been solved by adopting the e-constraint method.

The results of applying the model to Tehran’s highway network indicated that the least possible length for the emergency transportation network is about half the total length of its major roads (freeways and major arterials).

Gathering detailed data about origin-destination pair of emergency trips and network characteristics have a direct effect on designing a suitable emergency network in pre-disaster phase.

To become solvable in a reasonable time, especially in large-scale cases, the problem has been modeled based on a decomposing technique. The model has been solved successfully for the emergency roads of Tehran within about 10 min of CPU time.

The purpose of this research paper is to report about an investigation on the relationship between knowledge acquisition and knowledge creation to find out whether knowledge acquisition can predict knowledge creation. The study measures the concept of knowledge acquisition through the faculty use of knowledge acquisition tools and reading knowledge sources while measuring the concept of knowledge creation through the faculty use of knowledge creation tools and publishing knowledge sources.

The population of the study is faculty members in the United Arab Emirates (UAE). The sample of the population consisted of 300 faculty members affiliated with 26 universities and colleges. Data was collected from the sample through questionnaire instrument. Stated hypotheses and Mathew’s theory of knowledge consumption–production correlation are tested and verified through correlation matrix and regression analysis.

Findings of the study revealed that the use of knowledge acquisition tools by faculty members has a positive effect on the use of knowledge creation tools and on publishing knowledge sources. Likewise, reading knowledge sources appeared to have a positive impact on the use of knowledge creation tools and publishing knowledge sources. Accordingly, the study confirmed the stated four hypotheses. Moreover, the results of the study supported the theory of knowledge consumption–production correlation and strongly confirmed the prediction of knowledge creation through the use of information and communication technology (ICT) tools for knowledge acquisition and reading knowledge sources.

Findings of the study appeal to the decision-makers and stakeholders of academic institutions to make effective investment in ICT facilities and knowledge sources to improve knowledge creation among faculty members.

Not many studies have investigated how knowledge acquisition can predict knowledge creation in the academic environment. This paper contributes to the understanding of the relationship between knowledge acquisition and knowledge creation in academic settings. Findings of the study can be an important reference for providing and improving knowledge sources, knowledge acquisition tools and knowledge creation tools in the academic environment.

With US$ 320 bn spent on luxury fashion in the Middle East and a growing digital consumer presence, local start-ups must form an integrated online relationship with millennials in order to recruit and retain a viable customer base. Nonetheless, these elements are yet to be extensively and properly researched as the literature is still scarce vis-à-vis this area.

A mixed qualitative approach was adopted using both in-depth interviews and focus groups. Two qualitative studies were conducted, with a total of 13 elite respondents and 28 consumer respondents from Saudi Arabia and the United Arab Emirates (UAE) using semi-structured interviews. Four focus groups were also conducted in both countries with six participants each group for triangulation of the findings.

The findings enhance current understanding pertaining to Gen Ys' motivations when selecting and engaging online with a luxury fashion start-up brand. The study suggests a detailed strategic framework that can be used in an integrated omni-channel approach. It also discusses the different touchpoints that play a role in influencing luxury consumption across different motivation stages.

The literature relating to digital strategies for luxury fashion start-up brands in the Middle East is still nascent. This study fills a considerable gap in the literature related to such brands that are aiming to stay relevant amidst the growing impact of the digital landscape on luxury fashion brand shoppers in the Middle East.

Despite significant progress in Iran's immunization programs, vaccine policymaking in the country still faces various challenges and shortcomings. To address these issues and ensure sustained progress toward achieving comprehensive vaccination policies, it is essential to identify the critical factors influencing vaccine policies in Iran. Our study aims to provide evidence-based insights that can inform the development of effective and equitable vaccine strategies, leading to a more sustainable and efficient approach to vaccination in the country.

This mixed-method study aimed to analyze the factors influencing the future of human vaccine policy using Cross Impact Analysis. Firstly, a scoping review was conducted to identify the factors affecting the future of human vaccine development. Secondly, a semi-structured interview was conducted with experts in this field to add more factors and confirm the identified factors within the Iranian context. Finally, a Cross-Impact Analysis (CIA) approach was applied to comprehend the complex relationships between the identified factors. Thematic analysis was used for the qualitative data, and MICMAC analysis was applied to characterize the relationships between the factors.

Seventeen key driving force factors were identified through comprehensive review and interviews. These factors were assigned weighted values ranging from zero to three and subsequently analyzed using MICMAC software. Employing the Cross-Impact Analysis (CIA) technique, the study characterized the impact of each factor on vaccine policy and elucidated the intricate interactions between them. The findings underscored that robust leadership and governance, an innovative ecosystem, and well-established immunization information systems emerged as pivotal driving forces shaping vaccine policy in Iran.

While this study contributes valuable insights into the driving factors influencing vaccine policy in Iran, it is important to acknowledge several limitations. The results rely on the subjective perceptions of a diverse group of specialists, and future research could delve into additional factors in other countries to identify common themes and differences.

This study provides evidence to assist policymakers in making informed decisions regarding vaccines in Iran. The findings suggest that enhancing access to vaccines, fostering trust in the healthcare system, and prioritizing equity in distribution can contribute to increased vaccination rates and a reduction in vaccine-preventable diseases.

This study provides a unique contribution to the field of vaccine policy by utilizing the cross-impact analysis to examine the complex interactions among various factors. The results of this analysis demonstrate that these interactions can significantly impact the overall system, highlighting the need for policymakers to consider multiple factors when formulating effective strategies. By revealing the significance of these interactions, this research offers valuable insights into the development of successful policies that can shape a desirable future for vaccine policy in Iran. Future studies could ratify the findings from this research by applying other methodological approaches.