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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.

Aim to the limitations of grey relational analysis of interval grey number, based on the generalized greyness of interval grey number, this paper tries to construct a grey angle cosine relational degree model from the perspective of proximity and similarity.

Firstly, the algorithms of the generalized greyness of interval grey number and interval grey number vector are given, and its properties are analyzed. Then, based on the grey relational theory, the grey angle cosine relational model is proposed based on the generalized greyness of interval grey number, and the relationship between the classical cosine similarity model and the grey angle cosine relational model is analyzed. Finally, the validity of the model in this paper is illustrated by the calculation examples and an application example of related factor analysis of maize yield.

The results show that the grey angle cosine relational degree model has strict theoretical basis, convenient calculation and is easy to program, which can not only fully utilize the information of interval grey numbers but also overcome the shortcomings of greyness relational degree model. The grey angle cosine relational degree is an extended form of cosine similarity degree of real numbers. The calculation examples and the related factor analysis of maize yield show that the model proposed in this paper is feasible and valid.

The research results not only further enrich the grey system theory and method but also provide a basis for the grey relational analysis of the sequences in which the interval grey numbers coexist with the real numbers.

The paper succeeds in realizing the algorithms of the generalized greyness of interval grey number and interval grey number vector, and the grey angle cosine relational degree, which provide a new method for grey relational analysis.

Research efforts aiming to improve understanding of how various organisational relationships contribute to better food quality (FQ) in a constantly changing business environment are limited. This study examines the effects of supply chain (SC) organisations on the quality of food products across multi-tiered segments with dynamic business situations.

Guided by a conceptual research framework based on contingency theory and netchain analysis, moderation-based partial least squares structural equation modelling (PLS-SEM) was used to analyse multi-tiered data from 405 shrimpers and 238 women processors in Akwa-Ibom, Lagos and Ondo states in Nigeria.

The authors' findings show that unpredictable business environments such as market turbulence (MT), power asymmetry (PA) and distrust (DT) not only directly influence SC organisations but also moderate how organisational networks contribute to improved FQ. Further results reveal that closer vertical ties such as relational contracts are prerequisites for small-scale actors to guarantee improved FQ along multiple nodes of the food system.

This is the first study to examine, from a contingency and multi-tiered perspective, how small-scale actors can maintain FQ across interdependent nodes of a food chain in a developing country context and to explore the complex interplay between SC networks and the quality of highly perishable food products in unpredictable business environments. Relevant theoretical and policy implications are discussed.

This study aims to examine the association between risk tolerance and risky investment intention with financial literacy as a moderating variable. The proposed relationship was explored specifically for millennials.

The questionnaire was divided into three segments to assess millennials' financial literacy, risk tolerance and risky investment intention. This study uses survey data from 402 millennial investors residing in Delhi-NCR region. The authors exploited PLS-SEM for the analysis because the model involved higher-order constructs.

The findings revealed that financial literacy has a negative impact on risky investment intention. Further, risk tolerance had a positive and significant influence on risky investment intention; however, when financial literacy was added as a moderating variable in this relationship, it had a negative impact on risky investment intention.

Every generation has its quirks, and millennials are no exception. Given their age and sheer number, leading to their dominance in the global workforce, millennials will bring about a generational shift. Awareness of Gen Y's financial literacy and risk behavior enhances their ability to make informed financial decisions, thus proving beneficial not only to them, but also to the whole economy. This will also help policymakers and institutions to introduce financial literacy programs and financial products in alignment with their needs and preferences.

This study aims to understand how abusive supervision influences employees’ unethical pro-organizational behavior (UPB). In particular, the mediating effect of moral disengagement and moderating role of traditionality on this relationship were examined.

This study conducted a two-wave questionnaire survey using data collected from 629 employees from different companies in China. Moderated mediation analysis was conducted to test the hypotheses.

The results show that moral disengagement mediates the relationship between abusive supervision and employees’ UPB. Employee traditionality enhances the relationship between abusive supervision and moral disengagement as well as the indirect effect of abusive supervision on employees’ UPB via moral disengagement.

First, by exploring the positive impact of abusive supervision on UPB, the authors enhance the current understanding of the role of negative leadership in the development of UPB and enrich the research on the antecedents of UPB and outcomes of abusive supervision. Second, based on social cognitive theory, this study enriches the literature on abusive supervision and employee UPB by identifying moral disengagement as a mediator. Third, to the best of the authors’ knowledge, the authors are among the first to incorporate traditionality to tell a Chinese story about how traditional employees respond to the effects of abusive supervision on UPB, providing a new lens for the cultural boundary condition in the occurrence mechanism of UPB.

Based on social influence theory, this paper aims to explore the influence of academic entrepreneurs on team innovation activities. The innovation behavior of academic team members is the key behavior in academic entrepreneurial activities. As a special entrepreneurial group, academic entrepreneurs' political skills play an important role in stimulating team innovative behaviors.

This paper adopts a multi-level study design and takes as samples the paired data of 91 academic entrepreneurial teams (n = 475). Based on team cognition, it constructs a model of the influence mechanism of academic entrepreneurs' political skills on team innovation behavior and explores the mechanism of transactive memory system in this influence effect. The authors use HLM and PROCESS macro to test our multilevel model.

The results show that academic entrepreneurs' political skills positively impact team innovation behavior, and a transactive memory system plays a mediating role between them. Team psychological safety significantly enhances the positive relationship of both academic entrepreneurs' political skills and a transactive memory system with team innovation behavior. Moreover, with enhanced perceptions of team psychological safety, academic entrepreneurs' political skills are more likely to improve team innovation behavior through the transactive memory system.

The study explores the influence of transactive memory system on the relationship between academic entrepreneurs' political skills and team innovation behavior, with the team cognitive perspective derived from social influence theory. This provides authors with new insights on the complex dynamics at place in the team innovation process and offers implications for how we can fruitfully manage this process.

In recent decades, the field of robotic mapping has witnessed widespread research and development in light detection and ranging (LiDAR)-based simultaneous localization and mapping (SLAM) techniques. This paper aims to provide a significant reference for researchers and engineers in robotic mapping.

This paper focused on the research state of LiDAR-based SLAM for robotic mapping as well as a literature survey from the perspective of various LiDAR types and configurations.

This paper conducted a comprehensive literature review of the LiDAR-based SLAM system based on three distinct LiDAR forms and configurations. The authors concluded that multi-robot collaborative mapping and multi-source fusion SLAM systems based on 3D LiDAR with deep learning will be new trends in the future.

To the best of the authors’ knowledge, this is the first thorough survey of robotic mapping from the perspective of various LiDAR types and configurations. It can serve as a theoretical and practical guide for the advancement of academic and industrial robot mapping.

Although idea implementation is a praised useful resource, the psychological and behavioral costs that employees may pay for idea implementation are rarely discussed. This study aims to examine the buffer effect of intrinsic interest on dark side of idea implementation.

Based on the conservation of resources theory, this study tested hypotheses with a multi-wave survey study of four information technology companies in China.

First, idea implementation increased emotional exhaustion. Second, emotional exhaustion mediated the relationship between employee idea implementation and negative workplace gossip about a leader. Third, intrinsic interest negatively moderated the relationship between idea implementation and emotional exhaustion. Fourth, idea implementation increased workplace negative gossip about a leader as a result of increased emotional exhaustion when intrinsic interest was low.

These findings are conducive to further understanding of the psychological mechanism and boundary condition of the negative impact of idea implementation. It provides practical guidance for buffering the dark side of idea implementation and effectively controlling the workplace negative gossip in the workplace.

This study aims to examine the risk-resistant role of environmental, social and governance (ESG) performance in the capital market, focusing on an organizational standpoint. Furthermore, it aims to offer management decision advice to companies seeking protection against stock market risks. Conclusions obtained through this research have the potential to enrich the economic consequences of ESG performance, provide practical implications for enhancing corporate ESG performance, improving corporate information quality and stabilizing capital market development.

Based on the data of Chinese A-share listed companies from 2009 to 2020, this study examines the risk-resistant function of ESG performance in the capital market. The impact of ESG performance on management behavior is analyzed from the perspective of organizational management and the three mechanisms of pre-event, during the event and post-event.

This paper demonstrates that companies that effectively implement ESG practices are capable of effectively mitigating risks associated with stock price crashes. Heterogeneity analysis reveals that the inhibitory effect of ESG performance on stock price crash risk is more pronounced in nonstate-owned enterprises and enterprises with higher levels of marketization. After controlling for issues such as endogeneity, the conclusions of this paper are still valid. The mechanism analysis indicates that ESG performance reduces the risk of stock price crash through three paths of organizational management: pre-event, during the event and post-event. That is, ESG performance plays the role of restraining managers’ opportunistic behavior, reducing information asymmetry and boosting investor sentiment.

This paper provides new insights into the relationship between ESG performance and stock price crash risk from an organizational management perspective. This study establishes three impact mechanisms (governance effect, information effect and insurance effect), offering a theoretical basis for strategic corporate decisions of risk management. Additionally, it comprehensively examines the contextual differences in the role of ESG performance, shedding light on the specific domains where ESG practices are influential. These findings offer valuable insights for promoting stable development in the capital market and fostering the healthy growth of the real economy.

This study investigates the relationships among digital transformation, technological innovation, industry–university–research collaborations and labor income share in manufacturing firms.

The relationships are tested using an empirical method, constructing regression models, by collecting 1,240 manufacturing firms and 9,029 items listed on the A-share market in China from 2013 to 2020.

The results indicate that digital transformation has a positive effect on manufacturing companies’ labor income share. Technological innovation can mediate the effect of digital transformation on labor income share. Industry–university–research cooperation can positively moderate the promotion effect of digital transformation on labor income share but cannot moderate the mediating effect of technological innovation. Heterogeneity analysis also found that firms without service-based transformation and nonstate-owned firms are better able to increase their labor income share through digital transformation.

This study provides a new path to increase the labor income share of enterprises to achieve common prosperity, which is important for manufacturing enterprises to better transform and upgrade to achieve high-quality development.