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Voice assistant technology represents one of the most radical artificial intelligence innovations. Drawing on the processing fluency theory and consumer learning literature, this study aims to explore how consumer acceptance of new products is influenced by voice assistant function (VAF), along with the impacts of role clarity and learning modality.

Four between-subjects experimental studies were conducted. Study 1 tested the main effect of VAF on consumer acceptance. Study 2 included role clarity as a mediator between VAF and consumer acceptance. Study 3 examined the moderation effect of learning modality and contrasted the effectiveness of experiential and verbal learning in helping increase consumer acceptance. Study 4, as a post hoc study, tested serial mediations to validate whether processing fluency was indeed the mechanism explaining the indirect relationship between VAF and consumer acceptance via role clarity.

The negative impact of VAF on consumer acceptance was demonstrated in all four studies. Studies 2 and 3 showed VAF decreased role clarity which further influenced consumer acceptance. Moreover, Study 3 evidenced that experiential learning was more effective than verbal learning in increasing consumer acceptance of voice-assisted products via role clarity. Study 4 demonstrated that VAF decreased role clarity, which in turn decreased processing fluency, leading to lower consumer acceptance.

This research views the usage of voice-assisted products as a coproduction process between consumers and the VAF. Accordingly, findings provide novel insights into processing fluency of tasks assisted by VAF through the lens of role clarity and learning modality, which enriches the understanding of potential barriers and opportunities for consumers to accept voice-assisted products.

The purpose of this study is to increasing the gauge factor, reducing the hysteresis error and improving the stability over cyclic deformations of a conductive polylactic acid (CPLA)-based 3D-printed strain sensor by modifying the sensing element geometry.

Five different configurations, namely, linear, serpentine, square, triangular and trapezoidal, of CPLA sensing elements are printed on the thermoplastic polyurethane substrate material individually. The resistance change ratio of the printed sensors, when loaded to a predefined percentage of the maximum strain values over multiple cycles, is recorded. Finally, the thickness of substrate and CPLA and the included angle of the triangular strain sensor are evaluated for their influences on the sensitivity.

The triangular configuration yields the least hysteresis error with high accuracy over repeated loading conditions, because of its uniform stress distribution, whereas the conventional linear configuration produces the maximum sensitivity with low accuracy. The thickness of the substrate and sensing element has more influence over the included angle, in enhancing the sensitivity of the triangular configuration. The sensitivity of the triangular configuration exceeds the linear configuration when printed at ideal sensor dimensional values.

The 3D printing parameters are kept constant for all the configurations; rather it can be varied for improving the performance of the sensor. Furthermore, the influences of stretching rate and nozzle temperature of the sensing material are not considered in this work.

The sensitivity and accuracy of CPLA-based strain sensor are evaluated for modification in its geometry, and the performance metrics are enhanced using the regression modelling.

This research aims to identify lean tools and techniques that are needed to be implemented to improve the performance in the fabrication industry. The objective is to find the wastes in manufacturing processes using value stream mapping (VSM) and prioritize the lean tools suitable to enable the attainment of leanness and streamline the processes.

VSM tool is applied in the industry to construct the current state map, identify improvement proposals and implement in future state. Fuzzy technique for order performance by similarity to ideal solution (TOPSIS), a multi-criteria decision-making technique (MCDM), is used to prioritize the identified improvement proposals. This study observed that mistake-proof processing and layout organization are the two techniques with the top priority that needs further improvement to enhance the leanness of the organization.

Upon successful implementation, the cycle time is reduced by 14.97%, and total inventory is reduced by 45.67% which leads to the improvement of value addition from 5.88 to 9.21%. Although lean has been adopted for many years, implementation of lean in the fabrication industry has been limited.

This study addresses the challenges in terms of implementing lean in fabrication industries and practical implications of lean tools and techniques and the prioritization of lean concepts against various lean criteria to enable leanness.

The deployment of improvement prioritization tool integrated with VSM in the context of a fabrication industry is the original contribution of the authors.

The purpose of this paper is to create a numerical technique to tackle the challenge of selecting software reliability growth models (SRGMs).

A real-time case study with five SRGMs tested against a set of four selection indexes were utilised to show the functionality of TOPSIS approach. As a result of the current research, rating of the different SRGMs is generated based on their comparative closeness.

An innovative approach has been developed to generate the current SRGMs selection under TOPSIS environment by blending the entropy technique and the distance-based approach.

In any multi-criteria decision-making process, ambiguity is a crucial issue. To deal with the uncertain environment of decision-making, various devices and methodologies have been explained. Pythagorean fuzzy sets (PFSs) are perhaps the most contemporary device for dealing with ambiguity. This article addresses novel tangent distance-entropy measures under PFSs. Additionally, numerical illustration is utilized to ascertain the strength and authenticity of the suggested measures.

The final properties of ductile iron are decided by the inoculant processing while pouring the melt. The shape and size of nodules generated during solidification are of paramount importance in solidification of ductile cast iron. The purpose of this study is to examine the effect of different inoculant addition on the solidification of ductile cast iron melt through thermal analysis.

Thermal analysis has recently grown as a tool for modeling the solidification behavior of ductile cast irons. Iron properties will be predicted by analyzing the cooling curve patterns of the melts and predicting the related effectiveness of inoculant processing. In this study, thermal analysis is used to evaluate the need for inoculation.

The amount and type of inoculation will affect the amount of undercooling during the solidification of ductile cast iron. It is found that the addition of 0.1 to 0.4 Wt.% inoculant lowers the austenite dendrite formation starting temperature while increasing the eutectic freezing temperature. Microstructure analysis revealed that the addition of inoculation increases the nodule count from 103 to 242 nodules. The beneficial effects of inoculation are sustained by an improved graphitization factor, which shows the formation of graphite nodules in the second phase of the eutectic reaction.

The inoculation treatment has improved metallurgical occurrences such as carbide to graphite conversion, graphite microstructure control, graphite nodule count at the start of solidification and the last stage of solidification, which determines the soundness of casting. The foundry industry can follow these steps for monitoring the solidification of ductile iron castings.

The purpose of this paper is to explain how emotional intelligence (EI) influences intercultural competence (IC), which in turn may influence online instruction. The authors further explored the varying attributes of EI and the extent to which it intersects with IC in the workplace.

Literature on EI and IC from the fields of education, business and leadership was reviewed. The search entailed articles related to EI and IC using the following databases: Business Search Premier, ERIC, JSTOR and ProQuest. The authors used the following key search terms in researching the articles: EI, IC, learning and online instruction. Title and abstract analyses judged each article’s suitability for the study.

To better perceive, understand and appreciate others and their cultures, we need to understand our own emotions and the way we interact with others. EI is thus the foundation on which IC can be built. It takes a higher level of EI to develop higher IC quotient. An online instructor should be cognizant about the emotional issues involved in the online learning and suitably modify the instruction to improve learner engagement to ensure better and improved student learning.

Findings of this study should provide useful information for theory building and practice. Further, it is hoped the findings of this study will stimulate more scholarly interest in this relatively untapped research area exploring how EI can influence IC and ultimately influence online instruction and improve student learning.

The findings will serve as useful pointers for instructors and scholars who strive to improve ICs and appreciate the nuances that enable an emotionally intelligent instructor to perform better and connect with learners from a different culture.

Based on empirical literature reviewed, EI is the ability to perceive, understand and control our own emotions to better connect and relate with other individuals. It is the ability to recognize the emotional cues and change our behavior accordingly. IC is the ability to understand and appreciate the cultural differences to better function in a culture different from our own. The two constructs are therefore interrelated and have a significant overlap. However, while EI has been studied exclusively in different contexts, surprisingly, the researchers have not given adequate attention to the important theme of using EI in improving IC or even the role EI can play in improving instructors’ IC. Moreover, the interrelationship between EI, IC and online learning has not been explored previously. This paper seeks to address this gap.

Continuum robots modeling, be it from a hard or soft class, is giving rise to several challenges compared with rigid robots. These challenges are mainly due to kinematic redundancy, dynamic nonlinearity and high flexibility. This paper aims initially at designing a hard class of continuum robots, namely, cable-driven continuum robot (CDCR) and equally at developing their kinematic and dynamic models.

First, the CDCR prototype is constructed, and its description is made. Second, kinematic models are established based on the constant curvature assumption and inextensible bending section. Third, by using the Lagrange method, the dynamic model is derived under some simplifications and based on the kinematic equations, in which the flexible backbone’s elasticity modulus was identified experimentally. Finally, the static model of the CDCR is also derived based on the dynamic model.

Numerical examples are carried out using Matlab software to verify the static and dynamic models. Moreover, the static model is validated by comparing the simulation’s results to the real measurements that have been provided with satisfactory results.

To reduce the complexity of the dynamic model’s expressions and avoid the numerical singularity when the bending angle is close to zero, some simplifications have been taken, especially for the kinetic energy terms, by using the nonlinear functions approximation. Hence, the main advantage of this analytical-approximate solution is that it can be applied in the bending angle that ranges up to 2p with reasonable errors, unlike the previously proposed techniques. Furthermore, the resulting dynamic model has, to some extent, the proprieties of simplicity, accuracy and fast computation time. Ultimately, the obtained results from the simulations and real measurements demonstrate that the considered CDCR’s static and dynamic models are feasible.

The purpose of this paper is to investigate how customers’ upgrading/downgrading _(t−1) behavior may be predictive of future spending. Further, this paper also investigates how customers’ post-consumption evaluations of upgrades and downgrades [satisfaction_(t−1) and perceived value_(t−1)] may moderate the relationship between upgrades/downgrades and future spending.

The predictions are tested using a large longitudinal data set of river cruise purchases (N = 48,103) and largely replicated using a data set of zoo membership purchases (N = 2,469).

Satisfaction_(t−1) mitigates the positive relationship between prior upgrades_(t−1) and future spending_(t). In contrast, perceived value_(t−1) magnifies the positive relationship between prior upgrades_(t−1) and future spending_(t). However, no positively moderating effects are observed to alleviate the negative relationship between prior downgrades_(t−1) and future spending_(t).

This research suggests that managers should work hard early in customer–firm relationships because of an asymmetric difficultly in altering the trajectory of an established relationship. Specifically, relationships that are trending downward (as consecutive downgrades would suggest) are difficult to repair – a mechanism to alter this trajectory is not observed. In contrast, relationships that are trending upward (as consecutive upgrades would suggest) can be improved with high perceived value evaluations but also degraded with high satisfaction evaluations.

This research should recast marketers’ understanding of the value of customers’ upgrade and downgrade decisions. Instead of using customers’ upgrade or downgrade decisions as the dependent variable, or final outcome in buyer behavior, this study shows how the accumulation of prior upgrades and prior downgrades, over time, acts as a bellwether of the customer–firm relationship. Further, to the best of the authors’ knowledge, this study is the first to connect these upgrade/downgrade decisions to customers’ evaluations of those purchases to understand how individual purchases can impact the overall customer–firm relationship.

The purpose of this paper is to investigate the impact of near-wall treatment approaches, which are crucial parameters in predicting the flow characteristics of open channels, and the influence of different vegetation covers in different layers.

Ansys Fluent, a computational fluid dynamics software, was used to calculate the flow and turbulence characteristics using a three-dimensional, turbulent (k-e realizable), incompressible and steady-flow assumption, along with various near-wall treatment approaches (standard, scalable, non-equilibrium and enhanced) in the vegetated channel. The numerical study was validated concerning an experimental study conducted in the existing literature.

The numerical model successfully predicted experimental results with relative error rates below 10%. It was determined that nonequilibrium wall functions exhibited the highest predictive success in experiment Run 1, standard wall functions in experiment Run 2 and enhanced wall treatments in experiment Run 3. This study has found that plant growth significantly alters open channel flow. In the contact zones, the velocities and the eddy viscosity are low, while in the free zones they are high. On the other hand, the turbulence kinetic energy and turbulence eddy dissipation are maximum at the solid–liquid interface, while they are minimum at free zones.

This is the first study, to the best of the author’s knowledge, concerning the performance of different near-wall treatment approaches on the prediction of vegetation-covered open channel flow characteristics. And this study provides valuable insights to improve the hydraulic performance of open-channel systems.

The soliton solutions are obtained by using extended rational sin/cos and sinh-cosh method. The methods are powerful and have ease of use. Applying wave transformation to the nonlinear partial differential equations (NLPDEs) and the considered equation turns into a nonlinear differential equation (NODE). According to the methods, the solution sets of the NODE are supposed to the form of the rational terms as sinh/cosh and sin/cos and the trial solutions are substituted into the NODE. Collecting the same power of the trigonometric functions, a set of algebraic equations is derived.

The main purpose of this paper is to obtain soliton solutions of the modified equal width (MEW) equation. MEW is a form of regularized-long-wave (RLW) equation that represents one-dimensional wave propagation in nonlinear media with dispersion processes. This is also used to simulate the undular bore in a long shallow water canal.

Thus, the solution of the main PDE is reduced to the solution of a set of algebraic equations. In this paper, the kink, singular and singular periodic solitons have been successfully obtained.

Illustrative plots of the solutions have been presented for physical interpretation of the obtained solutions. The methods are powerful and might be used to solve a broad class of differential equations in real-life problems.