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This study aims to build on social comparison theory to develop a theoretical model of leader–member exchange (LMX) relationship to workplace ostracism through perceived organizational status by coworkers and envy. This study further proposes that warmth and competence may potentially moderate these two indirect effects.

This study tested the hypotheses in a battery manufacturing company located in South China by a survey of 216 employees organized in 55 work teams, using different sources. Additionally, the authors conduct two online vignette experiments to test this study’s mediation, proving the causality.

The authors found that high-level LMX leads to both envy and perceived organizational status by coworkers, which results in a mixed blessing on workplace ostracism toward the employee with high-level LMX. The focal employee’s warmth and competence moderate these indirect relationships.

The authors use LMX to explore antecedents of workplace ostracism and explain how and when these focal employees suffer workplace ostracism from their coworkers. The authors extend the research on LMX by examining the interpersonal risk of being a focal employee. The authors discover two critical boundary conditions – warmth and competence.

This study suggests that it is important to balance the level of the differential LMX; appropriately endorsing other members is a good way to avoid eliciting envy and opposition. Meanwhile, person-oriented citizenship behaviors such as demonstrations of concern or help may shortly build up an employee’s warm impression on their coworkers.

By discovering the bright and dark sides of LMX, this paper has the potential to advance theories on LMX and workplace ostracism. Therefore, the authors believe the current research will have an important impact on relevant research in the future.

This study aims to improve the stability and obstacle surmounting ability of the traditional wall-climbing robot on the surface of the ship, a wheel-track composite magnetic adsorption wall-climbing robot is proposed in this paper.

The robot adopts a front and rear obstacle-crossing mechanism to achieve a smooth crossover. The robot is composed of two passive obstacle-crossing mechanisms and a frame, which is composed of two obstacle-crossing magnetic wheels and a set of tracks. The obstacle-crossing is realized by the telescopic expansion of the obstacle-crossing mechanism. Three static failure models are established to determine the minimum adsorption force for the robot to achieve stable motion. The Halbach array is used to construct the track magnetic circuit, and the influence of gap, contact area and magnet thickness on the adsorption force is analyzed by parameter simulation.

The prototype was designed and manufactured by the authors for static failure and obstacle crossing tests. The prototype test results show that the robot can cross the obstacle of 10 mm height under the condition of 20 kg load.

A new structure of wall-climbing robot is proposed and verified. According to the test results, the wall-climbing robot can stably climb over the obstacle of 10 mm height under the condition of 20 kg load, which provides a new idea for future robot design.

This paper adds risk perception and personalized human-computer interaction to the technology acceptance model, and further analyzes the impact of personalized unmanned ride hailing on users' behavior intention.

This study model was tested using a sample of 299 social media users from China and we apply structural equation modeling (SEM) to build the theoretical framework.

Our results show that perceived ease of use has a greater positive impact on behavior intention compared to perceived usefulness. In addition, we find that the impact of risk perception on behavior intention is manifested in a number of ways, including people’s risk perception of the new technology, people’s risk perception of data leakage, and so on. Finally, we find that users’ personalized human-computer interaction has a positive effect on their perceived ease of use, perceived usefulness, and behavior intention.

Our study contributes to illuminate the pivotal role of tailoring the human-computer interface to individual preferences and needs for ride-hailing platforms from the perspective of behavior intention.

Current multi-source image fusion methods frequently overlook the issue of detailed features when employing deep learning technology, resulting in inadequate target feature information. In real-world mission scenarios, such as military information acquisition or medical image enhancement, the prominence of target feature information is of paramount importance. To address these challenges, this paper introduces a novel infrared-visible light fusion model.

Leveraging the foundational architecture of the traditional DenseFuse model, this paper optimizes the backbone network structure and incorporates a Unique Feature Encoder (UFE) to meticulously extract the distinctive features inherent in the two images. Furthermore, it integrates the Convolutional Block Attention Module (CBAM) and the Squeeze and Excitation Network (SE) to enhance and replace the original spatial and channel attention mechanisms.

Compared to other methods such as IFCNN, NestFuse, DenseFuse, etc., the values of entropy, standard deviation, and mutual information index of the method presented in this paper can reach 6.9985, 82.6652, and 13.6022, respectively, which are significantly improved compared with other methods.

This paper presents a UFEFusion framework that synergizes with the CBAM attention mechanism to markedly augment the extraction of detailed features relative to other methods. Moreover, the framework adeptly extracts and amplifies unique features from disparate images, thereby elevating the overall feature representation capability.

Political factors play a crucial role in China's initial public offering (IPO) market due to its distinctive institutional context (i.e. “economic decentralization” and “political centralization”). Given the significant level of IPO underpricing in China, we examine the impact of local political uncertainty (measured by prefecture-level city official turnover rate) on IPO underpricing.

Using 2,259 IPOs of A-share listed companies from 2001 to 2019, we employ a structural equation model (SEM) to examine the channel (voluntarily lower the issuance price vs aftermarket trading) through which political uncertainty affects IPO underpricing. We check the robustness of the results using bootstrap tests, adopting alternative proxies for political uncertainty and IPO underpricing and employing subsample analysis.

Local official turnover-induced political uncertainty increases IPO underpricing by IPO firms voluntarily reducing the issuance price rather than by affecting investor sentiment in aftermarket trading. These relations are stronger in firms with pre-IPO political connections. The effect of political uncertainty on IPO underpricing is also contingent upon the industry and the growth phase of an IPO firm, more pronounced in politically sensitive industries and firms listed on the growth enterprise market board.

Local government officials in China usually have a short tenure and Chinese firms witness significantly severe IPO underpricing. By introducing the SEM model in studying China IPO underpricing, this study identifies the channel through which local government official turnover to political uncertainty on IPO underpricing.

This paper proposes a new intra-layer partition adaptive slicing algorithm for FDM 3D printing, aiming to further improve forming efficiency based on the adaptive slicing algorithm while preserving the surface finish quality of the formed model.

This method initially applies a large layer thickness for primary slicing, then refines layer thickness in layer height ranges with significant cross-sectional contour changes. Refined layers are partitioned: the internal region uses the large layer thickness for efficiency, while the external region uses a smaller layer thickness for surface quality. A thickness ratio and transition zone between regions prevent overlaps and gaps in printing paths.

The experimental results show that, compared to traditional adaptive slicing algorithms, the intra-layer partition adaptive slicing algorithm can effectively improve forming efficiency for most models while ensuring the model’s surface finish, with minimal impact on the bonding strength of the model.

The intra-layer partition adaptive slicing algorithm is a novel algorithm improved upon the traditional adaptive slicing algorithm, enabling models to achieve higher printing efficiency while maintaining the surface finish provided by the conventional adaptive slicing algorithm. This algorithm is of significant importance to vendors and individual users who provide printing services for large-sized fused deposition modeling models, as it can greatly enhance their production efficiency.

Metadiscourse is an important dialogue technique used in productive knowledge building to help a group evaluate and advance their knowledge progress. Previous studies have identified and defined various types of metadiscourse. However, there is scant knowledge about how different metadiscourse types emerge among different groups or what implicit correlations lie between progressive discourse and metadiscourse. Moreover, research on how different types of metadiscourse influence groups' knowledge advancement and artifacts is still inadequate. Therefore, this study aims to further examine the roles that different types of metadiscourse play in the collaborative knowledge building community on both a fine-grained (i.e. progressive discourse) and coarse-grained (i.e. group knowledge advancement and group artifacts) level.

Data for this study are drawn from the behaviour of undergraduate students participating in a 12-week course at a key university in China. On the fine-grained level, epistemic network analysis (ENA) is applied to illustrate how metadiscourse promotes the development of progressive discourse. On the coarse-grained level, two different chi-square tests are conducted to examine the roles of different types of metadiscourse in groups' knowledge advancement and artifacts.

The analysis allowed several conclusions to be drawn. First, the types of metadiscourse that students most often adopted were reflecting on ideas development (RD) and commenting on ideas (CI); they less frequently adopted setting group goals (SG) and making group plans (MP). Second, most types of metadiscourse correlated with developments in progressive discourse, particularly RD and CI. Third, the metadiscourse types RD, CI and coordinating group efforts (CE) played essential roles in knowledge advancement. Fourth, higher-quality artifacts could be created by using the metadiscourse type reviewing the state of knowledge building progress (RP).

A more profound comprehension of the role that metadiscourse plays in the collaborative knowledge building community not only contributes to the literature in the knowledge building field but also carries a significant meaning in regulating community, promoting learner agency and sustained knowledge, and consequently improving collaborative learning performance.

This study aims to explore the influence of Fourier-feature enhanced physics-informed neural networks (PINNs) on effectively solving two-dimensional local time-fractional anomalous diffusion equations with nonlinear thermal diffusivity. By tackling the shortcomings of conventional numerical methods in managing fractional derivatives and nonlinearities, this research addresses a significant gap in the literature regarding efficient solution strategies for complex diffusion processes.

This study uses a quantitative methodology featuring a feed-forward neural network architecture combined with a Fourier feature layer. Automatic differentiation is implemented to ensure precise gradient calculations for fractional derivatives. The effectiveness of the proposed approach is showcased through numerical simulations across various sub-diffusion and super-diffusion scenarios, with fractal space parameters adjusted to examine behavior. In addition, the training process is assessed using the Fisher information matrix to analyze the loss landscape.

The results demonstrate that the Fourier-feature enhanced PINNs effectively capture the dynamics of the anomalous diffusion equation, achieving greater solution accuracy than traditional methods. The analysis using the Fisher information matrix underscores the importance of hyperparameter tuning in optimizing network performance. These findings support the hypothesis that Fourier features improve the model’s capacity to represent complex solution behaviors, providing the relationship between model architecture and diffusion dynamics.

This research presents a novel approach to solving fractional anomalous diffusion equations through Fourier-feature enhanced PINNs. The results contribute to the advancement of computational methods in areas such as thermal engineering, materials science and biological diffusion modeling, while also providing a foundation for future investigations into training dynamics within neural networks.

This paper examines the moderating role of firms’ litigation environment on the association between gender diversity and financial reporting quality.

This study draws on a sample of US firms to examine the moderating role of firms’ litigation environment on the association between gender diversity and financial reporting quality. Firm-specific financial data come from Compustat. To measure the firms’ litigation environment, we use state-level datasets from the Lawsuit Climate Survey conducted for the US Chamber Institute for Legal Reform by the Harris Poll.

Findings suggest that firm litigation environment moderates gender diversity, as defined by female members on the board to subdue our first proxy for financial reporting quality (accruals-based earnings management), but our second proxy for financial reporting quality (real-activities manipulations) increases in a firm’s litigation environment. To the extent that our results hold after controlling for firms’ reputation indicates that female members on the board are sensitive to reputational loss and protect firms’ reputation in a litigation environment.

The study is based on a specific country, limiting the generalizability of the findings.

The findings provide support for promoters and advocates of gender diversity in corporate boards. Specifically, it shows the importance of gender diversity policies in business and society.

This study is the first to examine the moderating role of firms’ litigation environment on the association between gender diversity and financial reporting quality. The study provides novel evidence and shows that the litigation environment moderates gender diversity to improve financial reporting quality in the short-term (by decreasing accruals manipulation). In firms’ litigation environment, when female members on the board are restrained from engaging in accruals earnings management, they shift to value-destroying and costly real activities to maintain reputation and firm performance. To the extent that we control for the potential effects of firms’ reputation and financial performance, our findings suggest that ethical concerns are likely to drive female members on the board to produce high-quality financial reports.

This research examines the complex relationship between usage of Chat Generative Pre-Trained Transformer (ChatGPT) amongst student and their creativity, learning and assessment using empirical data collected from postgraduate students. In addition, the study explores the student’s intrinsic motivation for usage to understand student categories. This research seeks to provide further insights into this artificial intelligence tool in enhancing the educational ecosystem for all stakeholders concerned.

The target population of this research – the students of post-graduation in diverse fields of science and management. A five-point Likert scale-structured questionnaire adapted from earlier literature relevant to the research questions was adopted for data collection. The data were collected for two months, resulted in 403 usable responses. Ethical considerations of assurance of confidentiality to the participants were strictly adhered to. Structured equation modelling (SEM) was employed to explore the relationships between the constructs of the study for the assessment of latent relationships. SmartPLS 4 was used to explore these relationships.

Usage has a negative impact on a student’s creativity, but increased usage of ChatGPT encourages a student’s adoption due to its perceived usability. Pedagogical applications of ChatGPT aid students as a learning tool but require controlled usage under supervision.

This study is innovative in the context of postgraduate students, where very little evidence of creativity exists. Through this research, the authors illuminate how ChatGPT use affects academic performance, benefiting educators as a tool but for evaluation and assessment, policymakers and students. The findings of the study provide implications that help to create effective digital education strategies for stakeholders.