Search results

With the development of digitalization and interconnection, there is a growing need for enterprise customers to ensure the compatibility of the third-party components they are using in the manufacturing process, thus raising the integration requirements for the Industrial Internet platform and its third-party developers. Therefore, our study investigates the optimal integration decision of the Industrial Internet platform while considering its access price, the integration cost, and the net utility derived by enterprise customers from the third-party components.

We model a two-sided Industrial Internet platform that connects customers on the demand side to the developers on the supply side. We then explore the integration decision of the Industrial Internet platform and its important factors by solving the optimal profit function.

First, despite the high integration cost of third-party developers, the platform still chooses to integrate when enterprise customers derive high utility from the third-party components. Second, due to the compatibility effect, charging the enterprise customers a higher price may reduce the platform profits when these customers derive low utility from the third-party components. Third, the platform profits will increase along with the integration cost of third-party developers when it is low in the case where enterprise customers derive low utility from third-party components.

Our findings offer insightful takeaways for the Industrial Internet platform when making integration decisions.

Considering behavior-based pricing strategy, we explore the choice of instant logistics service model and pricing strategy of Quick-commerce channel.

We adopt the Hotelling framework to develop a two-period game in which two horizontally differentiated suppliers sell repetitively purchased products through a traditional e-commerce channel and an instant e-commerce channel in two periods.

The results show that: (1) when consumer’s mismatch cost and instant logistics cost coefficient are moderate or relatively high or low, platform-operated logistics (PL) is more beneficial to traditional supplier and traditional e-platform, while Q-commerce platform may prefer self-operated logistics. However, for Q-commerce suppliers, as his/her instant logistics cost coefficient increases, he/she tends to prefer PL. (2) If the instant logistics cost coefficient is moderate, traditional suppliers may always earn more than Q-commerce suppliers in both models, despite the higher commission rate of traditional e-platform. (3) When the instant logistics cost coefficient for Q-commerce suppliers is low, traditional suppliers should significantly reduce price for new customers under PL.

Our research constructs a competition between traditional and quick commerce channels, using game theory methods to examine the impact of different instant logistics models on the dynamic pricing strategies, profitability and instant logistics efficiency of these two online channels.

As an important form of the e-commerce industry, online group buying is under the spotlight from with two sides: cheaper price but longer waiting time. The purpose of this paper is to adequately investigate the interaction between saving and waiting time of group buying comprehensively.

To fill the research gap, the authors elaborate a dual-channel supply chain (SC) with regular retail (individual buying) and group-buying channel, and formulate the demand based on the consumer utility with the positive effect of saving money and the negative effect of wasting time.

The authors find that power structure only changes the optimal prices, instead of the waiting time. The selling price mainly influences consumer demands, instead of the price discount of group buying. The SC profits are only positive to the channel preference, and it is the decisive parameter of consumers' choice. The price sensitivity lays a more remarkable impact on the SC compared to the time sensitivity. Above all, the price is the main factor of group buying, instead of time.

These results underscore the improvement for the dual-channel SC of group buying, providing managerial insights for the group-buying industry.

The brake pipe system was an essential braking component of the railway freight trains, but the existing E-type sealing rings had problems such as insufficient low-temperature resistance, poor heat stability and short service life. To address these issues, low-phenyl silicone rubber was prepared and tested, and the finite element analysis and experimental studies on the sealing performance of its sealing rings were carried out.

The low-temperature resistance and thermal stability of the prepared low-phenyl silicone rubber were studied using low-temperature tensile testing, differential scanning calorimetry, dynamic thermomechanical analysis and thermogravimetric analysis. The sealing performance of the low-phenyl silicone rubber sealing ring was studied by using finite element analysis software abaqus and experiments.

The prepared low-phenyl silicone rubber sealing ring possessed excellent low-temperature resistance and thermal stability. According to the finite element analysis results, the finish of the flange sealing surface and groove outer edge should be ensured, and extrusion damage should be avoided. The sealing rings were more susceptible to damage in high compression ratio and/or low-temperature environments. When the sealing effect was ensured, a small compression ratio should be selected, and rubbers with hardness and elasticity less affected by temperature should be selected. The prepared low-phenyl silicone rubber sealing ring had zero leakage at both room temperature (RT) and −50 °C.

The innovation of this study is that it provides valuable data and experience for the future development of the sealing rings used in the brake pipe flange joints of the railway freight cars in China.

Given that a prerequisite for COVID-19 transmission is the interaction between individuals, it is reasonable to suspect that transportation networks may have contributed to the spread of COVID-19. This study uses the air transportation network to quantify the risk of COVID-19 spread in the United States. The proposed model is applied at the county level and identifies the risk of importing COVID-19-infected passengers into a given county. We also undertake an examination of the factors influencing the spread of COVID-19 in relation to air travel. Utilizing an extensive dataset encompassing various socioeconomic, demographic, and healthcare-related variables, our results indicate a positive relationship between these factors and the relative risk of COVID-19 spread, highlighting the pronounced impact of population density, air travel volume, and larger household sizes on increasing travel-related risk. Conversely, greater healthcare capacity, particularly in terms of hospital and intensive care unit (ICU) beds, is associated with reduced risk. We provide estimates of expected relative risk for each county and a ranking that can be useful for informing public health policies to stem the spread of the virus by devoting resources such as screening and enhanced travel protocols to airports located in at-risk counties.

The purpose of this paper is to study the relationship between high temperature oxidation and temperature rise rate of engine oil attempted to explore a new indicator to evaluate oil degradation.

Accelerated oxidation test combined with molecular simulation and road test is carried out in this paper. The temperature rise characteristics of mineral oil and synthetic oil under different oxidation temperatures (140°C, 155°C and 170°C) and time (50 h, 100 h, 150 h and 200 h) were determined by accelerated oxidation. The mechanism of temperature change characteristics of used oils was analyzed with molecular simulation. Two experimental vehicles carried six road tests with synthetic and mineral oil.

The results of this study show that the temperature rise rate of oxidized mineral and synthetic oil is higher than the new oil. The temperature rise rate is proportional to the oxidation time and oxidation temperature. The synthetic engine oil temperature rise rate is lower than that of the mineral engine oil. The same result was obtained in road tests. Molecular simulation verifies that small molecules were generated after oil oxidation which results in intermolecular friction and increased heat generation.

This paper indicates that temperature rise rate has potential to be taken as an indicator to evaluate oil oxidation which provides a new way for engine oil analysis.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2024-0177/

This study aims to improve detection efficiency of fluorescence biosensor or a graphene field-effect transistor biosensor. Graphene field-effect transistor biosensing and fluorescent biosensing were integrated and combined with magnetic nanoparticles to construct a multi-sensor integrated microfluidic biochip for detecting single-stranded DNA. Multi-sensor integrated biochip demonstrated higher detection reliability for a single target and could simultaneously detect different targets.

In this study, the authors integrated graphene field-effect transistor biosensing and fluorescent biosensing, combined with magnetic nanoparticles, to fabricate a multi-sensor integrated microfluidic biochip for the detection of single-stranded deoxyribonucleic acid (DNA). Graphene films synthesized through chemical vapor deposition were transferred onto a glass substrate featuring two indium tin oxide electrodes, thus establishing conductive channels for the graphene field-effect transistor. Using π-π stacking, 1-pyrenebutanoic acid succinimidyl ester was immobilized onto the graphene film to serve as a medium for anchoring the probe aptamer. The fluorophore-labeled target DNA subsequently underwent hybridization with the probe aptamer, thereby forming a fluorescence detection channel.

This paper presents a novel approach using three channels of light, electricity and magnetism for the detection of single-stranded DNA, accompanied by the design of a microfluidic detection platform integrating biosensor chips. Remarkably, the detection limit achieved is 10 pm, with an impressively low relative standard deviation of 1.007%.

By detecting target DNA, the photo-electro-magnetic multi-sensor graphene field-effect transistor biosensor not only enhances the reliability and efficiency of detection but also exhibits additional advantages such as compact size, affordability, portability and straightforward automation. Real-time display of detection outcomes on the host facilitates a deeper comprehension of biochemical reaction dynamics. Moreover, besides detecting the same target, the sensor can also identify diverse targets, primarily leveraging the penetrative and noninvasive nature of light.

As China is on an inevitable march into the digital era, firms have accumulated abundant digital assets, such as algorithms and data. Facing the possibility of using digital assets as a new type input, besides traditional inputs such as capital and labor, would powerful managers perform better? Would managerial power help managers increase the efficiency of how a firm combines traditional and digital inputs and converts them into outputs? Thus, the purpose of this study is to investigate whether powerful managers promotes corporate productivity by using digital assets as a new input.

Using data from listed Chinese firms between 2008 and 2020, the authors constructed panel regressions with three-way fixed effects to examine whether and how managerial power influences corporate productivity in the current digital context, particularly under market uncertainty.

The findings reveal no consistent relationship between managerial power and corporate productivity. The results explain this from two contrasting effects: while managerial power promotes technological change it hinders technical efficiency – two components of total productivity. Moreover, this study identifies market uncertainty as a significant external contingency. In uncertain markets, strong managerial power positively impacts corporate productivity.

The results extend extant theoretical insights in the literature on how managerial power might influence corporate productivity.

The rise of digitalization brings new opportunities and challenges to female entrepreneurship (FE). This paper aims to systematically review the intellectual landscape of FE research, identifying gaps and proposing future directions.

A systematic literature review was conducted on 1,918 highly relevant articles on FE, which were retrieved from the Scopus and Web of Science up to 2023.

While cyberfeminism is a rising perspective, the impact of digitalization on FE remains underexplored. Antecedents of FE are predominantly examined at the individual, family, organizational and environmental levels, overlooking team-level factors. Studies predominantly focus on outcomes at a single level and within a country, neglecting FE’s cross-level and cross-national complexity and diversity. The static analysis of mediating mechanisms in existing research fails to capture the dynamic process of FE. Furthermore, contextualization research often isolates family, institutional, social and mixed embeddedness, neglecting the simultaneous multiple contexts in which female entrepreneurs operate.

To bridge gaps in existing literature, this paper introduces an integrated research framework for FE. It suggests future research directions encompassing the impact of digital intelligence, heterogeneity of different groups of female entrepreneurs, the multiple contextual embedded nature of FE, dynamic process mechanisms for FE and cross-cultural comparisons. The proposed framework aims to inspire new insights and contribute to the evolving field of FE research.

Experts may adjust their assessments through communication and mutual influence, and this dynamic evolution relies on the spread of internal trust relationships. Due to differences in educational backgrounds and knowledge experiences, trust relationships among experts are often incomplete. To address such issues and reduce decision biases, this paper proposes a probabilistic linguistic multi-attribute group decision consensus model based on an incomplete social trust network (InSTN).

In this paper, we first define the new trust propagation operators based on the operations of Probability Language Term Set (PLTS) with algebraic t-conorm and t-norm, which are combined with trust aggregation operators to estimate InSTN. The adjustment coefficients are then determined through trust relations to quantify their impact on expert evaluation. Finally, the particle swarm algorithm (PSO) is used to optimize the expert evaluation to meet the consensus threshold.

This study demonstrates the feasibility of the method through the selection of treatment plans for complex cases. The proposed consensus model exhibits greater robustness and effectiveness compared to traditional methods, mainly due to the effective regulation of trust relations in the decision-making process, which reduces decision bias and inconsistencies.

This paper introduces a novel probabilistic linguistic multi-attribute swarm decision consensus model based on an InSTN. It proposes a redefined trust propagation and aggregation approach to estimate the InSTN. Moreover, the computational efficiency and decision consensus accuracy of the proposed model are enhanced by using PSO optimization.