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Influencer marketing mediated by social media is prevalent in social commerce. Micro-, meso- and macro-influencers all play an irreplaceable role in marketing. The purpose of this paper is to explore how companies with limited budgets choose influencers according to products' various levels of brand familiarity.

This study constructs an evolutionary game model of influencer marketing based on evolutionary game theory on complex networks. This model initiates various networks to demonstrate how influencers disseminate information and constructs update mechanisms to depict how individuals react to this information based on individuals' information utility and friends' strategies.

Simulation results suggest that companies should invest more in macro-influencers than in meso-influencers, however investing all in macro-influencers is not a good choice. The investment in meso-influencers will increase as brand familiarity decreases, whereas it will not exceed investment in macro-influencers. Furthermore, the accumulation of micro-influencers can accelerate the marketing process.

This study examines the combined effects of micro-influencers, meso-influencers and macro-influencers in marketing by simulating the marketing process initiated by influencers on social media.

The purpose of this paper is to present an up-to-date survey on the non-orthogonal multiple access (NOMA) technique with co-operative strategy, a fast-evolving fifth-generation (5 G) technology. NOMA is used for serving many mobile users, both in power and code domains. This paper considers the power-domain NOMA, which is now discussed as NOMA.

The first part of the paper discusses NOMA-based cooperative relay systems using different relay strategies over different channel models. In various research works, the analytical expressions of many performance metrics were derived, measured and simulated for better performance of the NOMA systems. In the second part, a brief introduction to diversity techniques is discussed. The multiple input and multiple output system merged with cooperative NOMA technology, and its future challenges were also presented in this part. In the third part, the paper surveys some new conceptions such as cognitive radio, index modulation multiple access, space-shift keying and reconfigurable intelligent surface that can be combined with NOMA systems for better performance.

The paper presents a brief survey of diverse research projects being carried out in the field of NOMA. The paper also surveyed two different relaying strategies that were implemented in cooperative NOMA over different channels and compared several performance parameters that were evaluated and derived in these implementations.

The paper provides a scope for recognizable future work and presents a brief idea of the new techniques that can be united with NOMA for better performance in wireless systems.

The purpose of this paper is to design an improved parallel regenerative braking system (IPRBS) for electric vehicles (EVs) that increases energy recovery with a constant brake pedal feel (BPF).

The conventional hydro-mechanical braking system is redesigned by incorporating a reversing linear solenoid (RLS) and allowed to work in parallel with a regenerative brake. A braking algorithm is proposed, and correspondingly, a control system is designed for the IPRBS for its proper functioning, and a mathematical model is formulated considering vehicle drive during braking. The effectiveness of IPRBS is studied by analyzing two aspects of regenerative braking (BPF and regenerative efficiency) and the impact of regenerative braking contribution to range extension and energy consumption reduction under European Union Urban Driving Cycle (ECE).

IPRBS is found to maintain a constant BPF in terms of deceleration rate vs pedal displacement during the entire braking period irrespective of speed change and deceleration rate. The regenerative ratio of IPRBS is found to be high compared with conventional parallel regenerative braking, but it is quite the same at high deceleration.

A constant BPF is achieved by introducing an RLS between the input pushrod and booster input rod with appropriate controller design. Comparative analysis of energy regenerated under different regenerative conditions establishes the originality of IPRBS. An average contribution ratio to energy consumption reduction and driving range extension of IPRBS in ECE are obtained as 18.38 and 22.76, respectively.

Every day, small and big incidents happen all over the world, and given the human, financial and spiritual damage they cause, proper planning should be sought to deal with them so they can be appropriately managed in times of crisis. This study aims to examine humanitarian supply chain models.

A new model is developed to pursue the necessary relations in an optimal way that will minimize human, financial and moral losses. In this developed model, in order to optimize the problem and minimize the amount of human and financial losses, the following subjects have been applied: magnitude of the areas in which an accident may occur as obtained by multiple attribute decision-making methods, the distances between relief centers, the number of available rescuers, the number of rescuers required and the risk level of each patient which is determined using previous data and machine learning (ML) algorithms.

For this purpose, a case study in the east of Tehran has been conducted. According to the results obtained from the algorithms, problem modeling and case study, the accuracy of the proposed model is evaluated very well.

Obtaining each injured person's priority using ML techniques and each area's importance or risk level, besides developing a bi-objective mathematical model and using multiple attribute decision-making methods, make this study unique among very few studies that concern ML in the humanitarian supply chain. Moreover, the findings validate the results and the model's functionality very well.

316 L stainless steel alloy is potentially the most used material in the selective laser melting (SLM) process because of its versatility and broad fields of applications (e.g. medical devices, tooling, automotive, etc.). That is why producing fully functional parts through optimal printing configuration is still a key issue to be addressed. This paper aims to present an entirely new framework for simultaneously reducing surface roughness (SR) while increasing the material processing rate in the SLM process of 316L stainless steel, keeping fundamental mechanical properties within their allowable range.

Considering the nonlinear relationship between the printing parameters and features analyzed in the entire experimental space, machine learning and statistical modeling methods were defined to describe the behavior of the selected variables in the as-built conditions. First, the Box–Behnken design was adopted and corresponding experimental planning was conducted to measure the required variables. Second, the relationship between the laser power, scanning speed, hatch distance, layer thickness and selected responses was modeled using empirical methods. Subsequently, three heuristic algorithms (nonsorting genetic algorithm, multi-objective particle swarm optimization and cross-entropy method) were used and compared to search for the Pareto solutions of the formulated multi-objective problem.

A minimum SR value of approximately 12.83 μm and a maximum material processing rate of 2.35 mm³/s were achieved. Finally, some verification experiments recommended by the decision-making system implemented strongly confirmed the reliability of the proposed optimization methodology by providing the ultimate part qualities and their mechanical properties nearly identical to those defined in the literature, with only approximately 10% of error at the maximum.

To the best of the authors’ knowledge, this is the first study dealing with an entirely different and more comprehensive approach for optimizing the 316 L SLM process, embedding it in a unique framework of mechanical and surface properties and material processing rate.

This research aims to examine the impact of virtual learning platforms and instructor presence (IP) on learner satisfaction (LS). Further, this study examines the role of learner engagement (LE) in order to improve the LS.

This research uses both primary and secondary data sources to compile the research's findings. The primary source of data includes 610 responses from various higher education institutes in India. The collected data were analysed using the partial least square structural equation modelling (PLS-SEM) technique.

This research provides evidence that the theoretical model is accurate with the gathered data sample. In the model, online platform (OP) is an independent variable, whereas LS is a dependent variable, and IP and LE are the mediating variables. The outcomes demonstrated that OP has a positive impact on IP and LE. Also, the relationships between IP and LE, IP and LS and LE and LS are significantly positive. The mediation analysis validates the importance of the IP and LE for relationships.

This investigation presents a comprehensive model, which demonstrates the relationship between OP, IP, LE and LS. The study makes a unique reference to several theories in order to boost interaction and IP in virtual learning, the learner's learning experience can be enhanced. The model helps teachers and educational institutions formalise strategies to boost interaction and examine the institutions' pedagogy to enhance satisfaction.

The present study investigates the mechanical properties of three types of Ti6Al4V ELI bone screws realized using the laser powder bed fusion (LPBF) process: a fully threaded screw and two groups containing differently arranged sectors made of lattice-based Voronoi (LBV) structure in a longitudinal and transversal position, respectively. This study aims to explore the potentialities related to the introduction of LBV structure and assess its impact on the implant’s primary stability and mechanical performance.

The optimized bone screw designs were realized using the LPBF process. The quality and integrity of the specimens were assessed by scanning electron microscopy and micro-computed tomography. Primary stability was experimentally verified by the insertion and removal of the screws in standard polyurethane foam blocks. Finally, torsional tests were carried out to compare and assess the mechanical strength of the different designs.

The introduction of the LBV structure decreases the elastic modulus of the implant. Longitudinal LBV type screws demonstrated the lowest insertion torque (associated with lower bone damage) while still displaying promising torsional strength and removal force compared with full-thread screws. The use of LBV structure can promote improved functional performances with respect to the reference thread, enabling the use of lattice structures in the biomedical sector.

The paper fulfils an identified interest in designing customized implants with improved primary stability and promising features for secondary stability.

In this paper, based on the density functional theory (DFT) and finite element method (FEM), the elastic, buckling and vibrational behaviors of the monolayer bismuthene are studied.

The computed elastic properties based on DFT are used to develop a finite element (FE) model for the monolayer bismuthene in which the Bi-Bi bonds are simulated by beam elements. Furthermore, mass elements are used to model the Bi atoms. The developed FE model is used to compute Young's modulus of monolayer bismuthene. The model is then used to evaluate the buckling force and fundamental natural frequency of the monolayer bismuthene with different geometrical parameters.

Comparing the results of the FEM and DFT, it is shown that the proposed model can predict Young's modulus of the monolayer bismuthene with an acceptable accuracy. It is also shown that the influence of the vertical side length on the fundamental natural frequency of the monolayer bismuthene is not significant. However, vibrational characteristics of the bismuthene are significantly affected by the horizontal side length.

DFT and FEM are used to study the elastic, vibrational and buckling properties of the monolayer bismuthene. The developed model can be used to predict Young's modulus of the monolayer bismuthene accurately. Effect of the vertical side length on the fundamental natural frequency is negligible. However, vibrational characteristics are significantly affected by the horizontal side length.

Available research on mobile learning all stand on the viewpoint of teachers. However, as mobile learning is learner-centric, learners' roles must be understood clearly from the learners' viewpoint, particularly considering its self-directed learning feature. It is well-known learners for self-directed learning must bear some teachers' core responsibilities. The knowledge gap on this important issue inspires the present work which tries to answer two questions: which “teacher roles” can learners play and how will learners play their “teacher roles” in a mobile learning context. This study aims to discuss the aforementioned objectives.

A novel research approach integrating an action research model and a teaching presence scale analysis is proposed to answer the questions. The mobile learning courses conducted by the present authors for engineering undergraduate students, during the COVID-19 pandemic, provided the experimental data for this study.

The experimental results reveal that the learners could play a number of “teacher roles” actively for mobile learning. Some of this research studies are consistent with available studies but a discrepancy is also observed. Discussion is conducted for such discrepancy.

The findings will contribute to improve the pedagogy of mobile learning theoretically and practically.

As online learning is the embryonic form of education in the metaverse, it is extremely important to explore the behavioral preferences of users. The aim is to explore the impact of interactive features on continuous use in online learning and to further explore what kind of interaction mode should be constructed for different types of students to obtain the best educational experience.

The study developed an empirical model and used a real-world dataset to test hypotheses. Specifically, the interaction in online learning is analyzed from different dimensions, including the interaction intensity of multiple subjects, the immersion of interactive technology, the timeliness of interactive feedback, and the fun in interaction.

The authors found that the intensity of interaction, immersion, timeliness of feedback and fun in the interaction all had significant positive effects on continuous use. Among them, the most important is the interaction between teachers and students. With the growth of user grades, the role of parents in the interaction is getting smaller and smaller, and the fun in the interaction is gradually becoming unnecessary. For high school students, gamified interactions can even have a negative impact. In addition, from the perspective of gender, males prefer immersive interaction, while females pay more attention to themselves and have negative feedback on fees.

The authors deepened the interaction and summarized the impact of different interactive features on continuous use in online learning platforms. The authors focused on the impact of the immersive experience brought by the application of interactive technology, which can confirm the user behavior preferences of online learning in the context of the metaverse. The research also provides a reference for online learning institutions to set up course interaction modes and targeted marketing programs.