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The composition of author teams is a significant factor affecting the novelty of academic papers. Existing research lacks studies focusing on institutional types and measures of novelty remained at a general level, making it difficult to analyse the types of novelty in papers and to provide a detailed explanation of novelty. This study aims to take the field of natural language processing (NLP) as an example to analyse the relationship between team institutional composition and the fine-grained novelty of academic papers.

Firstly, author teams are categorized into three types: academic institutions, industrial institutions and mixed academic and industrial institutions. Next, the authors extract four types of entities from the full paper: methods, data sets, tools and metric. The novelty of papers is evaluated using entity combination measurement methods. Additionally, pairwise combinations of different types of fine-grained entities are analysed to assess their contributions to novel papers.

The results of the study found that in the field of NLP, for industrial institutions, collaboration with academic institutions has a higher probability of producing novel papers. From the contribution rate of different types of fine-grained knowledge entities, the mixed academic and industrial institutions pay more attention to the novelty of the combination of method indicators, and the industrial institutions pay more attention to the novelty of the combination of method tools.

This paper explores the relationship between the team institutional composition and the novelty of academic papers and reveals the importance of cooperation between industry and academia through fine-grained novelty measurement, which provides key guidance for improving the quality of papers and promoting industry–university–research cooperation.

This paper aims to present a set of processes for obtaining the global spraying trajectory of a cold spraying robot on a complex surface.

The complex workpiece surfaces in the project are first divided by triangular meshing. Then, the geodesic curve method is applied for local path planning. Finally, the subsurface trajectory combination optimization problem is modeled as a GTSP problem and solved by the ant colony algorithm, where the evaluation scores and the uniform design method are used to determine the optimal parameter combination of the algorithm. A global optimized spraying trajectory is thus obtained.

The simulation results show that the proposed processes can achieve the shortest global spraying trajectory. Moreover, the cold spraying experiment on the IRB4600 six-joint robot verifies that the spraying trajectory obtained by the processes can ensure a uniform coating thickness.

The proposed processes address the issue of different parameter combinations, leading to different results when using the ant colony algorithm. The two methods for obtaining the optimal parameter combinations can solve this problem quickly and effectively, and guarantee that the processes obtain the optimal global spraying trajectory.

In everyday life, people generally wear two layers of clothes (a knitted vest and a knitted t-shirt) during the summer. It is essential to understand which types of innerwear and outerwear maximize comfort. The primary objective of this research is to investigate the influence of layering outerwear on innerwear, as well as the air gap between two layers, on thermal comfort properties.

In this study, a total of 12 combinations were created from four vest fabrics and three T-shirt fabrics. The thermal properties (thermal conductivity, thermal resistance, thermal absorptivity, thermal diffusion and peak heat flow) were evaluated for the individual inner and outer layers. Each inner layer was layered with an outer layer to observe the effect of layering on the thermal properties. An air gap of 2 mm was introduced between the inner and outer layers to study the effect of air gap on thermal properties.

Tencel fibre exhibits higher thermal conductivity and absorptivity than cotton and polyester. Upon layering an outer layer on an inner layer, the thermal conductivity and thermal absorptivity increase to a slight extent, thermal resistance and diffusion increase drastically and the peak heat flow reduces. With an air gap between the two layers, the thermal conductivity did not improve, the difference in thermal resistance among all the combinations reduced, the thermal absorptivity of the combination textiles was lower than that of the innerwear alone, the thermal diffusion increased and the peak heat flow diminished for all the combinations.

In practice, this comprehensive thermal comfort analysis provides specific combinations of inner and outer articles of clothing that are most appropriate for enhancing comfort during the summer season.

Though there are many studies on the effect of multilayer fabrics on thermal properties, no extensive research analyses the influence of innerwear and outerwear combinations on thermal comfort properties.

SPACs are reshaping the world of digital entrepreneurial finance. Firms in the digital sector often need access to public markets for long-term competitiveness. SPACs offer a viable solution for these entities to collect capital and transition to public ownership quicker than IPOs. In this context, the paper aims to analyse and compare the performance of SPACs with those of IPOs in the post-business combination phase. The objective is to provide novel insights into the determinants of SPAC operating and market performance by considering firm-specific and deal-specific characteristics and the broader implications of market uncertainty.

The analysis applies univariate and multivariate OLS regressions to a sample of 96 SPACs to investigate the drivers affecting SPACs' performance vis-a-vis IPOs.

The study finds that SPACs underperform the matched group of IPOs on both operating and stock market performance (buy-and-hold strategy). The time to execute a business combination negatively correlates with SPAC performance, and proximity to the 80% deal threshold negatively affects share price performance and EBITDA margin.

The objective is to offer insights for institutional investors to effectively select prime targets within the SPAC framework.

This study strengthens the findings related to the drivers influencing the long-term performance of SPACs that were previously identified in prior research.

The research paper comprehensively investigates the gear tooth deflection of standard thermoplastic gears with steel gear as the driver and driven companions. An accurate mapping of characteristic contact regions between the meshing gears was done, and the behaviour of the gear tooth in the premature and prolonged contact zones was studied.

The study employs the finite element method to conduct a quasi-static 2D analysis of meshing gear teeth. The finite element model was created in AutoCAD and analysed using the ANSYS 19.1 simulation package.

In the polymer-polymer gear combinations, premature and prolonged contact primarily occurs along the addendum radii of meshing gears, whereas a novel contact phenomenon was observed in the coast side for polymer-metal and metal-polymer combinations, exhibiting a path perpendicular to the standard drive side contact. As well, the deflection of the tooth alters the load distribution across the interlocking gears, leading to a decrement in the root stresses.

The Lewis bending equation demonstrates that bending stresses depend solely on the applied load and the geometry of the tooth. It does not consider the effects of deflection. However, the computational results showed that the gear tooth deflection caused by different gear pair combinations also affects the bending stresses. The contact stresses observed in the polymer-polymer gear combination were observed to be within the material’s proportional limit. However, when a steel gear is paired with a polymer gear, the contact stresses exceed the proportional limit due to coast side contact.

The survival of companies today hinges on their adaptability and flexibility, with digital transformation (DT) and organizational slack (OS) playing crucial roles. Despite their recognized importance, these factors are often studied separately. This study aims to explore how OS facilitates DT and evaluate their synergies and trade-offs to improve performance.

Using data from the European Manufacturing Survey, structural equation modeling (PLS-SEM) and fuzzy set qualitative comparative analysis (fsQCA), we investigate causal relationships and possible combinations between different dimensions of OS and DT that contribute to business performance.

We confirmed the positive effect of OS and DT on business performance, highlighting the importance of organizational over technological factors. While not definitively establishing OS as a precursor to DT, our findings underscore the need for human and operational slack to improve performance, especially in less technology-intensive contexts.

Our findings evidence that decision-makers should integrate OS with DT initiatives to improve the firm’s competitiveness. However, it is worth noting that while OS seems essential in low-tech shopfloors, its importance is lower in high-tech environments. Furthermore, within the possible combinations, managers should promote operational slack and digitalization, as it seems fundamental to improve business performance.

This article contributes to the management field in three ways. First, it clarifies controversies by providing evidence of the positive roles of DT and OS as drivers of competitiveness for manufacturing firms. Second, we verify that OS is not directly linked to DT, challenging existing assumptions. Third, it investigates the combinations of OS and DT that drive business performance improvement, emphasizing their synergies and trade-offs.

This study aims to investigate the impact of ex-ante regulatory interventions on emerging digital markets related to data sharing and combination practices. Specifically, it evaluates how such interventions influence market contestability by considering data network effects and the economic value of data.

The research uses agent-based modeling and simulations to analyze the dynamics of value generation and market competition related to the regulatory obligations on data sharing and combination practices.

Results show that while the promotion of data sharing through data portability and interoperability has a positive impact on the market, restricting data combination may damage value generation or, at best, have no positive impact even when it is imposed only on those platforms with very large market shares. More generally, the results emphasize the role of regulators in enabling the market through interoperability and service multihoming. Data sharing through portability fosters competition, while the usage of complementary data enhances platform value without necessarily harming the market. Service provider multihoming complements these efforts.

Although agent-based modeling and simulations describe the dynamics of data markets and platform competition, they do not provide accurate forecasts of possible market outcomes.

This paper presents a novel approach to understanding the dynamics of data value generation and the effects of related regulatory interventions. In the absence of real-world data, agent-based modeling provides a means to understand the general dynamics of data markets under different regulatory decisions that have yet to be implemented. This analysis is timely given the emergence of regulatory concerns on how to stimulate a competitive digital market and a shift toward ex-ante regulation, such as the regulatory obligations to large gatekeepers set in the Digital Markets Act.

Single-shot multi-category clothing recognition and retrieval play a crucial role in online searching and offline settlement scenarios. Existing clothing recognition methods based on RGBD clothing images often suffer from high-dimensional feature representations, leading to compromised performance and efficiency.

To address this issue, this paper proposes a novel method called Manifold Embedded Discriminative Feature Selection (MEDFS) to select global and local features, thereby reducing the dimensionality of the feature representation and improving performance. Specifically, by combining three global features and three local features, a low-dimensional embedding is constructed to capture the correlations between features and categories. The MEDFS method designs an optimization framework utilizing manifold mapping and sparse regularization to achieve feature selection. The optimization objective is solved using an alternating iterative strategy, ensuring convergence.

Empirical studies conducted on a publicly available RGBD clothing image dataset demonstrate that the proposed MEDFS method achieves highly competitive clothing classification performance while maintaining efficiency in clothing recognition and retrieval.

This paper introduces a novel approach for multi-category clothing recognition and retrieval, incorporating the selection of global and local features. The proposed method holds potential for practical applications in real-world clothing scenarios.

The purpose of this paper is to ascertain whether the combination of anti-corruption measures, with simultaneous impact on the institutional, economic and cultural dimensions, is associated with lower levels of corruption.

The authors have developed a research that seeks to find statistical evidence of the relationship between levels of corruption and combinations of anti-corruption measures. To test the hypotheses, the authors performed panel regression analysis using different panel estimation methods, namely, fixed effects (FE) models.

The results do not fully support the idea that the combination of anti-corruption measures with simultaneous impact on the institutional, economic and cultural dimensions has more impact on corruption levels than other measures taken singly, but rather that the most determining factor in preventing corruption lies in government effectiveness.

The relevance of this study lies in the shortcomings of this type of research applied to anti-corruption measures. Moreover, the development of panel data analysis makes it possible to approach the phenomenon from a double perspective, over time and in comparative terms between different countries, incorporating non-immediate effects and possible interactive effects between different anti-corruption measures.

This study strives to better understand resistance to chatbots in the banking sector. To achieve this, it proposes a model based on the paradigm of resistance to innovation and the complexity theory. In addition, it explores the role of gender in relation to chatbot resistance.

Data are collected in France using a snowball sampling technique. The sample is composed of 385 participants. FsQCA is used to identify all possible combinations of usage, value, risk, tradition and image barriers, as well as two gender conditions that predict resistance to chatbots.

The results reveal that the sample provides four possible solutions/combinations that may explain resistance to chatbots. These are: (i) a combination of usage, value, risk and tradition barriers, (ii) a combination of value, risk, tradition and image barriers, (iii) a combination of usage, value, risk and image barriers, along with the male gender and (iv) a combination of usage, value, tradition and image barriers, along with the female gender.

This study provides valuable and straightforward theoretical and managerial implications. The proposed solutions suggest a deep understanding of chatbot resistance. Chatbot developers and marketers can highly benefit from these findings to enhance user acceptance.

In this study, barriers are envisioned within the larger context of innovation resistance. The interactions among barriers causing resistance to chatbots are examined through the lens of the complexity theory, while the data analysis employs the fsQCA approach. Furthermore, this study sheds light on the role of gender in explaining chatbot resistance in the banking sector.