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This paper aims to investigate the effects of different volume fractions of Al₂O₃-water nanofluid on flow and heat transfer under chaotic convection conditions in an L-shaped channel, comparing the difference of numerical simulation results between single-phase and Eulerian–Lagrangian models.

The correctness and accuracy of the two calculation models were verified by comparing with the experimental values in literature. An experimental model of the L-shaped channel was processed, and the laser Doppler velocimeter was used to measure the velocities of special positions in the channel. The simulated values were compared with the experimental results, and the correctness and accuracy of the simulation method were verified.

The calculated results using the two models are basically consistent. Under the condition of Reynolds number is 500, when the volume fractions of nanofluid range from 1% to 4%, the heat transfer coefficients simulated by single-phase model are 1.49%–25.80% higher than that of pure water, and simulated by Eulerian–Lagrangian model are 3.19%–27.48% higher than that of pure water. Meanwhile, the friction coefficients are barely affected. Besides, there are obvious secondary flow caused by lateral oscillations on the cross sections, and the appearance of secondary flow makes the temperature distributions uniform on the cross section and takes more heat away, thus the heat transfer performance is enhanced.

The originality of this work is to reveal the differences between single-phase and two-phase numerical simulations under different flow states. The combination of chaotic convection and nanofluid indicates the direction for further improving the heat transfer threshold.

The purpose of this paper is to add to the small but growing body of research examining the influence of founder gender on new venture access to venture development programs.

Hypotheses were tested utilizing a sample of 482 nascent technology ventures which applied for admittance into a venture development organization headquartered in the southern region of the United States from March 2004 through February 2016.

Findings suggest that female-founded applicant ventures experience a higher likelihood of acceptance into venture development programs than male-founded applicant ventures. Results further suggest that social attention to gender equality reduces this effect for female-founded applicant ventures. Findings extend the understanding of the gendered nature of high-technology venturing and venture development organizations.

The findings of this study may not generalize to new ventures operating in other contexts (e.g., non-U.S., low-tech, and other venture development programs). Additionally, this study's design and data limitations do not allow for the establishment of causality or address founder motivations to apply for acceptance into venture development programs.

This study adds to empirical findings regarding the influence of founder gender on new venture acceptance into venture development programs by developing and testing competing hypotheses. This study also extends extant research by examining the moderating effect of social attention to gender equality on the hypothesized relationships between founder gender and acceptance into venture development programs.

During the coronavirus disease 2019 (COVID-19) pandemic, ubiquitous social media has become a primary channel for information dissemination, social interactions and recreational activities. However, it remains unclear how social media usage influences nonpharmaceutical preventive behavior of individuals in response to the pandemic. This paper aims to explore the impacts of social media on COVID-19 preventive behaviors based on the theoretical lens of empowerment.

In this paper, survey data has been collected from 739 social media users in China to conduct structural equation modeling (SEM) analysis.

The results indicate that social media empowers individuals in terms of knowledge seeking, knowledge sharing, socializing and entertainment to promote preventive behaviors at the individual level by increasing each person's perception of collective efficacy and social cohesion. Meanwhile, social cohesion negatively impacts the relationship between collective efficacy and individual preventive behavior.

This study provides insights regarding the role of social media in crisis response and examines the role of collective beliefs in the influencing mechanism of social media. The results presented herein can be used to guide government agencies seeking to control the COVID-19 pandemic.

Consumers’ digital engagement can bring various benefits to both brands and consumers. Besides, few studies investigated the outcomes of engagement with restaurant brands on Instagram. Therefore, this study aims to examine the effect of consumer engagement (CE) with restaurant brands on consumer-related factors (namely, consumer’s brand knowledge, perceived enjoyment and consumer social interaction) and brand-related factors (namely, e-WOM and brand reputation), as well as the mediating role of consumer-related factors.

The sample consisted of 394 Instagram followers of restaurant/coffee shop brands, and covariance-based structural equation modeling and bootstrapping were used to assess the hypothesized relationships.

The results show that CE with restaurant brands on Instagram enhances brand-related outcomes as well as consumer-related outcomes. Moreover, consumer-related factors partially mediate these relationships.

The findings of this study provide insights for restaurant managers and digital marketers to stimulate consumer-brand engagement.

To the best of the authors’ knowledge, this study is among the first that examines the effect of CE with restaurant brands on consumer- and brand-related outcomes on Instagram. The context of the study is Iran, which adds to the literature on CE that mainly focuses on developed countries.

A novel method has been proposed to reduce the false alarm rate of arrhythmia patients regarding life-threatening conditions in the intensive care unit. In this purpose, the atrial blood pressure, photoplethysmogram (PLETH), electrocardiogram (ECG) and respiratory (RESP) signals are considered as input signals.

Three machine learning approaches feed-forward artificial neural network (ANN), ensemble learning method and k-nearest neighbors searching methods are used to detect the false alarm. The proposed method has been implemented using Arduino and MATLAB/SIMULINK for real-time ICU-arrhythmia patients' monitoring data.

The proposed method detects the false alarm with an accuracy of 99.4 per cent during asystole, 100 per cent during ventricular flutter, 98.5 per cent during ventricular tachycardia, 99.6 per cent during bradycardia and 100 per cent during tachycardia. The proposed framework is adaptive in many scenarios, easy to implement, computationally friendly and highly accurate and robust with overfitting issue.

As ECG signals consisting with PQRST wave, any deviation from the normal pattern may signify some alarming conditions. These deviations can be utilized as input to classifiers for the detection of false alarms; hence, there is no need for other feature extraction techniques. Feed-forward ANN with the Lavenberg–Marquardt algorithm has shown higher rate of convergence than other neural network algorithms which helps provide better accuracy with no overfitting.

The purpose of this study was conducted at an enterprise that produces fasteners and is one of the leading companies in the sector in terms of market share. Possible defects in the coating of bolts and nuts either lead to products being scrapped or all of the coating process being repeated from beginning to end. In both cases, the enterprise faces a waste of time and excessive costs. Through this project, the six sigma theory and its means were effectively used to improve the efficiency and quality management of the company. The selection of the six sigma project has also contributed to the creation of various documents to be used for project screening and evaluation of financial results.

Six sigma is an optimization strategy that is used to improve the profitability of businesses, avoid waste, scrap and losses, reduce costs and improve the effectiveness of all activities to meet or exceed customers’ needs and expectations. Six sigma’s process improvement model, known as Definition-Measurement-Analysis-Improvement-Control, contributes to the economic and technical achievements of businesses. The normal distribution of a process should be within ±3 sigma of the mean. This represents a scale of 99.7% certainty. However, improving the process through the utilization of the six sigma rule, which accepts normal variabilities of processes twice as strict, will result in an error rate of 3.4 per million instead of 2,700 per million for each product or service.

Using six sigma practices to reduce the costs associated with low quality and to increase economic added value became a cultural practice. With this, the continuation of six sigma practices throughout the Company was intended. The annual cost reduction achieved with the utilization of six sigma practices can be up to $21,780. When time savings are also considered, a loss reduction of about $30,000 each year can be achieved. The coating thickness efficiency increased from 85% to 95% after the improvements made through the six sigma project. There is a significant increase in the efficiency of coating thickness. In addition, the coating thickness efficiency is also close to the target value of 95%–97%.

The results of the study were optimized with the help of deep learning. The performance of the model created in deep learning was quite close to the actual performance. This result implicates the validity of the improvement work. The results may act as a guide for the use of deep learning in new projects.

Although mechanical behavior of rigid frame pier has been clearly recognized, their time-varying seismic performance are yet to be well characterized due to some offshore piers that are eroded by chloride ion and located in earthquake-prone area. In this study, the time-variant seismic fragility analysis was conducted to evaluate seismic performance of rigid frame pier under four damage states with considering the time-varying characteristics of the material.

This paper establishes the nonlinear finite element model for the investigated offshore reinforcement concrete (RC) pier with considering the time-varying durability damage of the materials and defines the damage state, damage position and damaged index of the offshore RC pier. It also analyzes the time-varying seismic fragility of the offshore RC pier by using the capacity demand ratio method in the whole life cycle.

The results show that chloride induced corrosion has a significant effect on the rigid frame pier and bending capacity of top section is less than that of bottom section. The rate of decline accelerates after the service life reaching 30 years under the coupling of the earthquake and the environmental erosion. In the early years of service, the seismic fragility of the structure changed slowly.

This paper analyzes the influencing factors of seismic performance of rigid structure pier, and analyzes the seismic capacity and seismic performance of rigid structure pier under different service periods.

This study aims to improve the lubrication performance of molybdenum disulfide powders at textured surface of cemented carbide materials, a squeeze motion of vibration assistance method was introduced and investigated.

Surface texture was fabricated on YT15 cemented carbide samples using a laser marking machine. After that, a tribological experiment was carried out on a self-built friction testing machine under different amplitude and frequency of squeeze motion conditions. Moreover, a simulation model was also established to verify the principle of squeeze motion on the lubrication performance improving of MoS₂ particles at textured interfaces.

Analysis results indicated that surface texture on test sample can increase the storage ability of solid lubrication particles, and the lubrication film at the contact interface is more easily formed due to the reciprocating action. Squeeze motion can improve the storage ability of it due to an intermittent contact, which provides an opportunity for MoS₂ particles infiltration, and then a more uniform distribution and load-bearing properties of force chain are also established and formed simultaneously. Thus, a better tribological performance at the contact interface is obtained.

The main contribution of this work is to provide a reference for the molybdenum disulfide powder lubrication with textured surface of cemented carbide materials.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2024-0166/

Surface roughness has a serious impact on the fatigue strength, wear resistance and life of mechanical products. Realizing the evolution of surface quality through theoretical modeling takes a lot of effort. To predict the surface roughness of milling processing, this paper aims to construct a neural network based on deep learning and data augmentation.

This study proposes a method consisting of three steps. Firstly, the machine tool multisource data acquisition platform is established, which combines sensor monitoring with machine tool communication to collect processing signals. Secondly, the feature parameters are extracted to reduce the interference and improve the model generalization ability. Thirdly, for different expectations, the parameters of the deep belief network (DBN) model are optimized by the tent-SSA algorithm to achieve more accurate roughness classification and regression prediction.

The adaptive synthetic sampling (ADASYN) algorithm can improve the classification prediction accuracy of DBN from 80.67% to 94.23%. After the DBN parameters were optimized by Tent-SSA, the roughness prediction accuracy was significantly improved. For the classification model, the prediction accuracy is improved by 5.77% based on ADASYN optimization. For regression models, different objective functions can be set according to production requirements, such as root-mean-square error (RMSE) or MaxAE, and the error is reduced by more than 40% compared to the original model.

A roughness prediction model based on multiple monitoring signals is proposed, which reduces the dependence on the acquisition of environmental variables and enhances the model's applicability. Furthermore, with the ADASYN algorithm, the Tent-SSA intelligent optimization algorithm is introduced to optimize the hyperparameters of the DBN model and improve the optimization performance.

The purpose of this study is to present a finite element (FE) implementation of phenomenological three-dimensional viscoelastic and viscoplastic constitutive models for long term behaviour prediction of polymers.

The method is based on the small strain assumption but is extended to large deformation for materials in which the stress-strain relation is nonlinear and the concept of incompressibility is governing. An empirical approach is used for determining material parameters in the constitutive equations, based on measured material properties. The modelling process uses a spring and dash-pot and a power-law approximation function method for viscoelastic and viscoplastic nonlinear behaviour, respectively. The model improvement for long term behaviour prediction is done by modifying the material parameters in such a way that they account for the current test time. The determination of material properties is based on the non-separable type of relations for nonlinear materials in which the material properties change with stress coupled with time.

The proposed viscoelastic and viscoplastic models are implemented in a user material algorithm of the FE general-purpose program ABAQUS and the validity of the models is assessed by comparisons with experimental observations from tests on high-density polyethylene samples in one-dimensional tensile loading. Comparisons show that the proposed constitutive model can satisfactorily represent the time-dependent mechanical behaviour of polymers even for long term predictions.

The study provides a new approach in long term investigation of material behaviour using FE analysis.