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The aging population, a higher proportion of older adults (aged 65+), is considered a global and severe problem, while the information systems (IS) literature on detecting the relationship between the aging population and the development of electronic commerce (e-commerce) is limited and insufficient. Hence, the main objective of this paper is to examine whether an aging population can moderate the effect of infrastructure constructions on e-commerce sales and whether an aging population can affect e-commerce sales.

To investigate the relationship between the aging population and e-commerce sales, this study proposes two potential influential mechanisms: moderating the effects of infrastructure development on e-commerce sales and direct influence. Subsequently, a sample of 31 Chinese provinces from 2013 to 2019 is utilized to conduct regression analyses in order to examine these hypotheses.

The findings suggest that the development of urban transportation infrastructure and network constructions can significantly contribute to the enhancement of e-commerce sales, and the influence cannot be affected by aging population. Furthermore, it is noteworthy that an aging population can have a positive effect on e-commerce sales.

The findings can inform future infrastructure constructions by assessing the potential of infrastructure projects to boost e-commerce sales and examining whether this effect varies in an aging population context.

The findings substantiate the pivotal role of older adults in the e-commerce industry. Moreover, the obtained results establish a positive relationship between an aging population and e-commerce sales, thereby offering diverse perspectives on existing theories.

Fueled by the soaring popularity of the digital medium, consumers are increasingly relying on dynamic images to inform their decisions. However, little is known about how changes in the presentation of movement impacts these decisions. The purpose of this paper is to document whether and how movement speed–a fundamental characteristic of dynamic images in the digital medium–influences consumers' risk judgments and subsequent decisions.

Three experimental studies investigate the impact of movement speed displayed in the digital medium, focusing on different risk-laden domains including health (pilot study), gambling (Study 1) and stock market decisions (Study 2).

The authors find that faster movement speed displayed in the digital medium elevates consumers’ feelings of risk and elicits cautionary actions in response. The authors reveal a mechanism for this effect, showing that faster movement reduces feelings of control over outcomes, which predicts greater feelings of risk.

Future work could expand upon these findings by systematically examining whether certain individuals are more susceptible to movement speed effects in the digital medium. Research could also investigate whether different ways of experiencing movement speed (e.g. physical movement) similarly influence risk judgments and whether movement speed can have positive connotations outside of risky domains.

The authors offer important insights to marketing practitioners and public policymakers seeking to guide consumers’ judgments and decisions in risk-laden contexts through the digital medium.

By showing how movement speed alters judgments in risk-laden contexts, the authors contribute to literature on risk perception and the growing body of literature examining how moving images shape consumers’ behaviors.

This paper aims to address the safety concerns of path-planning algorithms in dynamic obstacle warehouse environments. It proposes a method that uses improved artificial potential fields (IAPF) as expert knowledge for an improved deep deterministic policy gradient (IDDPG) and designs a hierarchical strategy for robots through obstacle detection methods.

The IAPF algorithm is used as the expert experience of reinforcement learning (RL) to reduce the useless exploration in the early stage of RL training. A strategy-switching mechanism is introduced during training to adapt to various scenarios and overcome challenges related to sparse rewards. Sensor inputs, including light detection and ranging data, are integrated to detect obstacles around waypoints, guiding the robot toward the target point.

Simulation experiments demonstrate that the integrated use of IDDPG and the IAPF method significantly enhances the safety and training efficiency of path planning for mobile robots.

This method enhances safety by applying safety domain judgment rules to improve APF’s security and designing an obstacle detection method for better danger anticipation. It also boosts training efficiency through using IAPF as expert experience for DDPG and the classification storage and sampling design for the RL experience pool. Additionally, adjustments to the actor network’s update frequency expedite convergence.

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 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 purpose of this study is to enhance the functional properties of Hessian fabric through resin finishing. Hessian bags made of lignocellulosic jute fiber are commonly used to pack, store and transport agro-commodities, including horticultural crops such as rice, potato, onion and wheat. However, because of high water affinity, these bags undergo degradation in properties due to moisture release by the stored commodities themselves. Exposure to natural elements, e.g. rain and dew, also causes moisture absorption in hessian bags. Once the bag gets moistened, degradation of jute bags starts due to microbial attack, leading to loss in tensile strength and change in extensibility, leading to ultimate breakage in warp and weft directions of the fabric.

To overcome the degradation in the functional properties of hessian fabric due to exposure to moisture and microbial attack, the application of semi-synthetic polymeric materials was carried out.

Tenacity, bursting strength, puncture resistance, tear strength and breaking load, as well as life cycle of resin-treated jute fabric was found to be better than control jute.

To the best of the authors’ knowledge, no recent reports of resin finishing on jute (hessian) fabric with semi-synthetic resins are presently available, other than coating with rubber.

The stress concentration factor (SCF) is commonly utilized to assess the fatigue life of a tubular T-joint in offshore structures. Parametric equations derived from experimental testing and finite element analysis (FEA) are utilized to estimate the SCF efficiently. The mathematical equations provide the SCF at the crown and saddle of tubular T-joints for various load scenarios. Offshore structures are subjected to a wide range of stresses from all directions, and the hotspot stress might occur anywhere along the brace. It is critical to incorporate stress distribution since using the single-point SCF equation can lead to inaccurate hotspot stress and fatigue life estimates. As far as we know, there are no equations available to determine the SCF around the axis of the brace.

A mathematical model based on the training weights and biases of artificial neural networks (ANNs) is presented to predict SCF. 625 FEA simulations were conducted to obtain SCF data to train the ANN.

Using real data, this ANN was used to create mathematical formulas for determining the SCF. The equations can calculate the SCF with a percentage error of less than 6%.

Engineers in practice can use the equations to compute the hotspot stress precisely and rapidly, thereby minimizing risks linked to fatigue failure of offshore structures and assuring their longevity and reliability. Our research contributes to enhancing the safety and reliability of offshore structures by facilitating more precise assessments of stress distribution.

Precisely determining the SCF for the fatigue life of offshore structures reduces the potential hazards associated with fatigue failure, thereby guaranteeing their longevity and reliability. The present study offers a systematic approach for using FEA and ANN to calculate the stress distribution along the weld toe and the SCF in T-joints since ANNs are better at approximating complex phenomena than standard data fitting techniques. Once a database of parametric equations is available, it can be used to rapidly approximate the SCF, unlike experimentation, which is costly and FEA, which is time consuming.

The purpose of this study is to solve two unique but difficult partial differential equations: the foam drainage equation and the nonlinear time-fractional fisher’s equation. Through our methods, we aim to provide accurate solutions and gain a deeper understanding of the intricate behaviors exhibited by these systems.

In this study, we use a dual technique that combines the Aboodh residual power series method and the Aboodh transform iteration method, both of which are combined with the Caputo operator.

We develop exact and efficient solutions by merging these unique methodologies. Our results, presented through illustrative figures and data, demonstrate the efficacy and versatility of the Aboodh methods in tackling such complex mathematical models.

Owing to their fractional derivatives and nonlinear behavior, these equations are crucial in modeling complex processes and confront analytical complications in various scientific and engineering contexts.

Endorsement marketing has been widely used to generate consumer attention, interest and purchase decisions among targeted audiences. Internet celebrities who become famous on the Internet are dependent on strategic intimacy to appeal to their followers. Our study aims to examine how multiple exposures to Internet celebrity endorsements influence consumers’ click and purchase decisions in the context of influencer marketing.

Based on a unique and representative dataset, the authors first model consumers’ choices for clicks and purchases with two panel fixed-effect logit models linking clicks and purchases with the frequency of exposure to Internet celebrity endorsement. To further control the endogeneity produced by the intercorrelation between the click and purchase models, the authors also adopt the two-stage Heckman probit structure to jointly estimate the two models using Maximum Likelihood Estimation. Robustness checks confirm the effectiveness of the models.

The results suggest that Internet celebrity endorsement plays a significant role in bringing referral traffic to e-commerce sites but is less helpful in affecting conversion to sales. The impact of repetitive Internet celebrity endorsements on consumers’ click decisions is U-shaped, but the role of Internet celebrities as online retailers will “shape-flip” this relationship to a negative linear relation.

Our study is the first to investigate the repetitive exposure effect of Internet celebrity endorsement. The results show a contradictory pattern with a wear-out effect of repetition in the advertising literature. This is the first study to show how the endorsing self, which is a common business model in influencer marketing, moderates the effectiveness of influencer marketing.