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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.

This study aims to propose a cloud platform architecture considering information sharing based on blockchain to realize the security and convenience of enterprise information sharing in the automotive supply chain.

A bilateral matching model considering enterprises information contribution stimulates information sharing and improves the efficiency and quality of supply and demand matching. Three smart contracts are used to complete the information sharing process and match supply and demand in the automotive supply chain.

The system is tested on the local Ganache private chain, and the decentralized web page is designed based on the architecture prototype.

Solve the problem of information island in automobile supply chain.

Depression has become one of the most serious and prevalent mental health problems worldwide. The rise and popularity of social networks such as microblogs provides a wealth of psychological data for early depression detection. Language use patterns reflect emotional states and psychological traits. Differences in language use between depressed and general users may help predict and diagnose early depression. Existing work focuses on depression detection using users' social textual emotion expressions, with less psychology-related knowledge.

In this paper, we propose an RNN-capsule-based depression detection method for microblog users that improves depression detection accuracy in social texts by combining textual emotional information with knowledge related to depression pathology. Specifically, we design a multi-classification RNN capsule that enhances emotion expression features in utterances and improves classification performance of depression-related emotional features. Based on user emotion annotations over time, we use integrated learning to detect depression in a user’s social text by combining the analysis results with components such as emotion change vector, emotion causality analysis, depression lexicon and the presence of surprising emotions.

In our experiments, we test the accuracy of RNN capsules for emotion classification tasks and then validate the effectiveness of different depression detection components. Finally, we achieved 83% depression detection accuracy on real datasets.

The paper overcomes the limitations of social text-based depression detection by incorporating more psychological background knowledge to enhance the early detection success rate of depression.

By seamlessly integrating physical laws, physics-informed neural networks (PINNs) have flexibly solved a wide variety of partial differential equations (PDEs). However, encoding PDEs and constraints as soft penalties in the loss function can cause gradient imbalances, leading to training and accuracy issues. This study aims to introduce the augmented Lagrangian method (ALM) and transfer learning to address these challenges and enhance the effectiveness of PINNs for hydrodynamic lubrication analysis.

The loss function was reformatted by ALM, adaptively adjusting the loss weights during training. Transfer learning was used to accelerate the convergence of PINNs under similar conditions. Additionally, the iterative process for load balancing was reframed as an inverse problem by extending film thickness as a trainable variable.

ALM-PINNs significantly reduced the maximum absolute boundary error by almost 80%. Transfer learning accelerated PINNs for solving the Reynolds equation, reducing training epochs by an order of magnitude. The iterative process for load balancing was effectively eliminated by extending the thickness as a trainable parameter, achieving a maximum percentage error of 2.31%. These outcomes demonstrated strong agreement with FDM results, analytical solutions and experimental data.

This study proposes a PINN-based approach for hydrodynamic lubrication analysis that significantly improves boundary accuracy and the training process. Additionally, it effectively replaces the load balancing procedure. This methodology demonstrates considerable potential for broader applications across various boundary value problems and iterative processes.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-07-2024-0277/

This study longitudinally investigated the predictors and mediators of adolescent smartphone addiction by examining the impact of parental smartphone addiction at T1 on adolescent smartphone addiction at T3, as well as the separate and sequential role of adolescent self-esteem and depression at T2 as mediating factors.

This study used a hierarchical regression and the PROCESS macro (Model 6) to investigate research model by collecting 3,904 parent-adolescent pairs. Panel data were collected from three waves of the Korean Children and Youth Panel Survey (KCYPS).

First, the result showed that parental smartphone addiction at T1 significantly and positively predicted adolescent smartphone addiction at T3. Second, the serial mediation analysis revealed that the impact of parental smartphone addiction at T1 on adolescent smartphone addiction at T3 was mediated by adolescent self-esteem and depression at T2 independently and serially.

The findings enhance our comprehension of the impact of parental smartphone addiction, adolescent self-esteem and depression, on adolescent smartphone addiction.

This study aims to investigate the relationship between inventory flexibility, digital transformation, supply chain concentration, and productivity in the context of Chinese manufacturing enterprises.

Empirical analysis was conducted using data from listed Chinese manufacturing firms spanning from 2013 to 2022. The study employs a moderated model to examine how digital transformation influences the connection between inventory flexibility and productivity. Additionally, a moderated moderation model is utilized to explore the role of supply chain concentration in moderating the relationship among inventory flexibility, digital transformation, and productivity.

The study reveals a significant positive correlation between inventory flexibility and productivity, underlining the importance of flexible inventory management. Digital transformation moderates this relationship, with digital transformation enhancing the impact of inventory flexibility on productivity. Supplier and customer concentration also positively moderate this connection, suggesting a complementary relationship with digital transformation.

These findings offer valuable insights for managers and policymakers, emphasizing the need for a flexible approach to inventory management that considers the evolving digital landscape and supply chain dynamics.

This study contributes to the literature by providing empirical evidence of the nuanced relationship between inventory flexibility, digital transformation, supply chain concentration, and productivity in Chinese manufacturing enterprises. It underscores the importance of integrating digital transformation and supply chain concentration initiatives with flexible inventory management to optimize productivity in the business landscape.

Using signaling theory, the overarching purpose of this study is to provide an insight into how age-inclusive HR practices (AIHRP) influence older workers' voice behavior through job crafting toward strengths (JCS) and how negative age-based metastereotypes (NABM) moderate these relationships.

Using time-lagged data were obtained from 321 Chinese older workers. PROCESS MACRO and Bootstrapping were used to test theoretical hypotheses.

Our results revealed the positive effects of AIHRP on both JCS and voice behavior, and the positive effect of JCS on voice behavior, as well as the mediating role of JCS in the relationship between AIHRP and voice behavior. Besides, results also found that NABM negatively moderate the effect of AIHRP on voice behavior, and the effect of AIHRP on JCS. Additionally, significant moderated mediation effect indicates that the indirect effect of AIHRP on voice via JCS will be weaker for employees with higher NABM.

First, we take the lead in linking AIHRP and employees' voice behavior. This complements voice literature by identifying an important new factor in motivating older workers' voice behavior. Second, by exploring the mediating role of JCS, we reveal the “black box” of how AIHRP affect older workers' voice behavior. Third, this study responds to the call for more studies exploring the boundary conditions of AIHRP and expands the theoretical research framework of the relationship between AIHRP, JCS and voice, deepens our understanding of the mechanism of voice behavior.

Our findings have several practical implications. First, the leadership personnel throughout the firm should be conscious of the crucial role of AIHRP. Second, managers should provide older workers with opportunities to craft their jobs to use their strengths and achieve a better person-job fit, which will result in a series of positive outcomes. Third, organizations should blur intergenerational boundaries within the organization and provide older workers with mentoring opportunities to motivate their voice.

Our findings have some social implications. Firstly, the results of this study are beneficial in demonstrating to society that older workers still have significant strengths and value. With reasonable methods, older workers can continue to contribute to the development of organizations and society, which in turn is conducive to changing society's perceived bias toward older workers, reducing age discrimination and promoting social harmony. Secondly, this study provides theoretical guidance for organizations and society to manage older workers, which is conducive to alleviating social problems such as youth labor shortage and increased burden of retirement.

This study is innovative as it first explores the influence, mechanism and boundary conditions of AIHRP on older workers' voice behavior, which not only deepens our understanding of older workers voice, but also enriches the research on AIHRP and JCS.

The purpose of this paper is to investigate the fault estimation issue of nonlinear dynamical systems via distributed sensor networks. Furthermore, based on the communication topology of sensor networks, the nonfragile design strategy considering the gain fluctuation is also adopted for distributed fault estimators.

By means of intensive dynamical model transformation, sufficient conditions with disturbance attenuation performance are established to design desired fault estimator gains with the help of convex optimization.

A novel distributed fault estimation framework for a class of nonlinear dynamical systems is established over a set of distributed sensor networks, where sampled data of sensor nodes via local information exchanges can be used for more efficiency.

The proposed distributed fault estimator gain fluctuations are taken into account for the nonfragile strategy, such that the distributed fault estimators are more applicable for practical sensor networks implementations. In addition, an illustrative example with simulation results are provided to validate the effectiveness and applicableness of the developed distributed fault estimation technique.

Artificial intelligence (AI) carries the risk of widening gender inequalities due to the digital divide, while simultaneously promising to equalise the situation for women through the gender digital dividend. The conflicting findings from previous studies justify the need to investigate the gendered aspects of Artificial Intelligence (AI) diffusion. Specifically, the aim of this chapter is to understand the relationship between female entrepreneurship and the adoption of AI technologies within business contexts at the macroeconomic level. To achieve this, cluster analyses are conducted for the European Union (EU) countries. The results indicate an inverted U-shaped pattern in the relationship between the level of female entrepreneurship and the diffusion of AI technology in business. In the EU countries belonging to clusters with the highest level of AI diffusion, female entrepreneurship is at a moderate level, while in the EU countries with the lowest level of intelligent transformation, both extremes are observed: the highest and the lowest levels of female entrepreneurship. The variety of patterns in female entrepreneurship and AI technology spread in the EU countries implies the complex and multidimensional nature of the interrelationship, and, thus, it indicates the need for diverse, country-specific policies and practices to reach the intelligent transformation with respect to more equal society.

In this chapter, we explore the dark side of the metaverse and the need for user protection. While the metaverse offers multiple opportunities it also poses significant risks for users, such as privacy concerns, addiction, harassment, and cyberbullying. First, we discuss the various threats that users may encounter such as online harassment, assaults, cyberbullying, hate speech, identity theft, and virtual property theft. As per the Center for Countering Digital Hate, an incident of violation occurs every seven minutes within VRChat, a popular virtual reality game. The level of misconduct in the metaverse can surpass the extent of internet harassment. Virtual reality gaming has been associated with various health issues like sleep deprivation, and insomnia as well as mental health concerns such as depression, anger, and anxiety. We examine how these issues may impact user’s physical and mental health. The sensors and devices used in the metaverse collect a vast amount of user biometric data and spatial data. Interactions between users and metaverse could be leaked. We examine different methods that improve user protection, including everyone from enhanced security protocols via the application of privacy-enhanced technology to several avatars, two-factor authentication, and user educational and awareness programs. Moreover, we explore how the newest technologies, like blockchain and artificial intelligence, play a role in making user safety more important. We finished the course with the study of the case of Second Life, the virtual reality gaming platform, and pointing out some of the problems that exist within it.