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The objective of this study was to empirically examine how family financial socialization affects individuals' financial outcomes, including financial literacy, financial behavior and financial well-being, based on the family financial socialization theory (FFST).

Using a national representative sample of 6,311 US respondents from the 2016 National Financial Well-Being Survey, structural equation modeling (SEM) was conducted to test the hypotheses in this study. Sampling weights were incorporated into the structural model using the maximum likelihood estimation with robust standard errors and a Satorra-Bentler scaled test statistic (MLM estimation).

This study concludes the effectiveness of family financial socialization by showing that parental financial socialization has significant positive impacts on financial literacy, financial behavior and financial well-being. In addition, parents' education can significantly influence the quality of parental financial socialization.

The result underscores the importance of financial socialization in the family context and encourages parents to discuss financial matters with their children at home. Detailed implications have been provided to financial educators, practitioners and policymakers to incorporate parental involvement in the design of financial education programs, as well as financial services providers to improve marketing strategies for their banking services.

This research is amongst the first to empirically explore the relationships among parental financial socialization, financial literacy, financial behavior and financial well-being based on the FFST. The study also contributes to the literature by confirming the effects of parental socialization received in childhood on adults' later financial outcomes.

This study, which is based on the extended hierarchy of effects (HOE) model, aims to examine whether social media interactions with one or more luxury brands can affect consumers' knowledge of, affection for and purchases of as well as loyalty to luxury fashion brands as a category.

SoJump (a leading data collection company in China) launched an online survey to collect data from Chinese luxury fashion brand consumers. Structural equation modeling (SEM) was conducted to analyze data from the random sample of 308 Chinese luxury fashion brand consumers.

The findings of this study demonstrated that social media interaction had direct positive influences on three stages of the luxury fashion brand decision-making process – knowledge, affection and loyalty – but not purchases. The results also empirically confirmed that consumers' response to social media interaction follows the cognition-affect-conation sequential process presented in the HOE model.

This study not only provides a new perspective for researchers to investigate the impacts of interactive social media marketing on purchase decision-making in the luxury fashion brand category but also underpins the importance of building interactive alliances for luxury brands to increase consumers' knowledge of, affection for, purchases in and loyalty to the luxury fashion brand category.

This study is among the first to investigate whether social media interactions with luxury fashion brands as a category influence consumers' knowledge of, affection for and loyalty to that category. In addition, this study is the first attempt to explore whether social media interactions can directly influence consumers' luxury fashion brand purchases.

The purpose of this study was to investigate how financial literacy and investment experience impact cryptocurrency investment behavior and explore which factor is more influential in cryptocurrency investment.

Using a sample of US individual investors from the 2018 National Financial Capability Study Investor Survey, a three-step hierarchical logistic regression was conducted following a model-comparison approach. In addition, a mediation analysis was conducted using the Karlson−Holm−Breen (KHB) method to further explore the mediating effect of investment experience between financial literacy and cryptocurrency investment.

This study found that while both financial literacy and investment experience were positively associated with investing in cryptocurrencies, investment experience was more influential in cryptocurrency investment. The findings also demonstrated that investment experience, especially risky asset holding, had a significant mediating effect between subjective financial knowledge and cryptocurrency investment behavior.

The findings of this study offer insight to researchers by providing a deeper understanding of the determinants of cryptocurrency investment in the United States. This study also provides detailed implications for financial institutions, financial professionals and policymakers to guide rational cryptocurrency investment behavior.

This study is one of the initial attempts to explore the determinant factors in cryptocurrency investment, an area that has rarely been studied in the literature.

The purpose of this paper is to investigate the effects of financial incentives on consumers’ intention to adopt near field communication (NFC) mobile payment.

An online experiment was conducted crossing two levels of incentive types (cash back and discount), two levels of incentive amounts (5 and 10 percent), and two levels of incentive promotion periods (one and three months). A total of eight treatment conditions plus one control group comprised the 2×2×2 factorial design. A sample of 463 subjects with no prior experience with NFC mobile payment was recruited using a Qualtrics panel.

This study found that: the availability of financial incentives had a positive effect on intention to adopt NFC mobile payment; financial incentives indirectly affected intention through perceived risk; and while different types, amounts or promotion periods did not seem to matter for those in the low perceived risk group, the main effect of promotion period and the interaction effect between amount and promotion period were significant for those in the high perceived risk group.

The study sample was limited from 18 to 35 age group, which could have affected the varied effect of the different attributes of incentives that were examined.

This study is the first to give some empirical evidence about the impact of financial incentives on NFC mobile payment adoption. The results provide insight to providers as well as retailers offering the incentive payment option.

A new deviation propagation model considering the form defects in compliant assembly process is proposed. The purpose of this paper is to analyze the deviation propagation by using the basic deviation fields. In particular, each basic deviation field is defined with a generalized compliance matrix of part.

First, the form defects of parts may be decomposed into a linear combination of basic deviation fields, which are constructed by the eigen-decomposition of the structure stiffness of parts with ideal dimensions. Each basic deviation field is defined with a generalized compliance of part. Moreover, by analyzing the relationship between the basic deviation fields before and after assembling process, a new sensitive matrix is obtained in which each value expresses the correlation of a basic deviation field between the parts and the assembly.

This model may solve the deviation propagation problems of compliant assembly with considering form defects. Here, one case is used to illustrate the deviation propagation in the assembly process. The results indicate that the proposed method has higher accuracy than the method of influence coefficient when the entire deviation fields of parts are considered. Moreover, the numerical results with the proposed method basically agree with the experimental measurements.

Owing to the hypothesis of linear superposition of basic deviation fields, the research in this paper is limited to the parts with linear elastic deformation. However, the entire form defects of parts are considered rather than the deviations of the local feature points. It may be extended to analyze the three-dimensional deviations of complex thin-walled parts.

A deviation propagation model considering parts form defects is developed to achieve more accurate predictions of assembly deviation by using the basic deviation fields.

Thin-walled structures inevitably always have manufacturing deviations, which affects the assembly quality of mechanical products. The assembly quality directly determines the performances, reliability and service life of the products. To achieve the automatic assembly of large-scale thin-walled structures, the sizing force of the structures with deviations should be calculated, and its assembling ability should be studied before assembly process. The purpose of this study is to establish a precise model to describe the deviations of structures and to study the variation propagation during assembly process.

Curved thin-walled structures are modeled by using the shell element via the absolute nodal coordinate formulation. Two typical deviation modes of the structure are defined. The generalized elastic force of shell elements with anisotropic materials is deduced based on a continuum mechanics approach to account for the geometric non-linearity. The quasi-static method is introduced to describe the assembly process. The effects of the deviation forms, geometrical parameters of the thin-walled structures and material properties on assembly quality are investigated numerically.

The geometric non-linearity of structure and anisotropy of materials strongly affect the variation propagation and the assembly quality. The transformation and accumulation effects of the deviations are apparent in the multiple assembly process. The constraints on the structures during assembly can reduce assembly deviation.

The plate element via the absolute nodal coordinate formulation is first introduced to the variation propagation analysis. Two typical shape deviation modes are defined. The elastic force of structures with anisotropic materials is deduced. The variation propagation during the assembly of structures with various geometrical and material parameters is investigated.

This paper aims to propose an optimization method to automatically adjust the spatial route of multibend pipes to meet the assembly demands in constrained space.

The compact geometric parameters that uniquely determine the pipe route are analyzed. Besides, the relationship between these parameters and the end pose is revealed based on the exponential product formula. Mathematical representations for the engineering constraints, including the end pose restriction, collision interference, manufacture ability and geometric limitations, are further established. On this basis, the adjustment of the spatial route is formulated as a multiconstraint optimization problem. A modified particle swarm optimization method based on the combination of gradient projection and swarm intelligence is designed to find the near-optimal pipe that meets the required assembly demands.

The experimental results show that the proposed method can effectively find the feasible pipe route that satisfies the engineering constraints and the end pose requirement is highly guaranteed.

The proposed method can automate the geometric adjustment of multi-bend pipes to meet the actual assembly demands, which significantly reduces manual efforts and guarantees high accuracy. The results demonstrate the possibility of further applications in the pipe assembly or design process, especially in ships, aerospace products or pressure vessels.

This paper aims to examine the effects of bonding temperature, bonding time, bonding pressure and the presence of a Pt catalyst on the bonding strength of Cu/SB/P-Cu/SB/Cu joints by transient liquid phase bonding (TLPB).

TLPB is promising to assemble die-attaching packaging for power devices. In this study, porous Cu (P-Cu) foil with a distinctive porous structure and Sn-58Bi solder (SB) serve as the bonding materials for TLPB under a formic acid atmosphere (FA). The high surface area of P-Cu enables efficient diffusion of the liquid phase of SB, stimulating the wetting, spreading and formation of intermetallic compounds (IMCs).

The higher bonding temperature decreased strength due to the coarsening of IMCs. The longer bonding time reduced the bonding strength owing to the coarsened Bi and thickened IMC. Applying optimal bonding pressure improved bonding strength, whereas excessive pressure caused damage. The presence of a Pt catalyst enhanced bonding efficiency and strength by facilitating reduction–oxidation reactions and oxide film removal.

Overall, this study demonstrates the feasibility of low-temperature TLPB for Cu/SB/P-Cu/SB/Cu joints and provides insights into optimizing bonding strength for the interconnecting materials in the applications of power devices.

This paper aims to provide a better understanding of the challenges and potential solutions in Visual Simultaneous Localization and Mapping (SLAM), laying the foundation for its applications in autonomous navigation, intelligent driving and other related domains.

In analyzing the latest research, the review presents representative achievements, including methods to enhance efficiency, robustness and accuracy. Additionally, the review provides insights into the future development direction of Visual SLAM, emphasizing the importance of improving system robustness when dealing with dynamic environments. The research methodology of this review involves a literature review and data set analysis, enabling a comprehensive understanding of the current status and prospects in the field of Visual SLAM.

This review aims to comprehensively evaluate the latest advances and challenges in the field of Visual SLAM. By collecting and analyzing relevant research papers and classic data sets, it reveals the current issues faced by Visual SLAM in complex environments and proposes potential solutions. The review begins by introducing the fundamental principles and application areas of Visual SLAM, followed by an in-depth discussion of the challenges encountered when dealing with dynamic objects and complex environments. To enhance the performance of SLAM algorithms, researchers have made progress by integrating different sensor modalities, improving feature extraction and incorporating deep learning techniques, driving advancements in the field.

To the best of the authors’ knowledge, the originality of this review lies in its in-depth analysis of current research hotspots and predictions for future development, providing valuable references for researchers in this field.