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State agencies responsible for wildlife management and habitat preservation have historically relied on revenues generated from traditional sources, such as hunting and fishing licenses (consumptive users). This conventional funding model tends to overlook the shift in demographics and preferences toward non-consumptive activities like wildlife watching and nature tourism, as well as the indirect benefits from such activities. To address this disproportionate representation, innovative funding mechanisms are being explored throughout the Rocky Mountain West to provide avenues for inclusive conservation funding that incorporates non-consumptive users, such as recreational fees, conservation-oriented merchandizing, and co-branding partnerships with small businesses. Inspired by this methodology, initiatives like the University of Wyoming’s, “WYldlife for Tomorrow” (WFT) have been developed as an innovative approach that fosters collaboration between state agencies, businesses, educational institutions, and local communities for the purpose of creating sustainable funding streams for wildlife and habitat conservation. By responding to the evolving trends in hunting and fishing interests, this collaborative effort holds the potential to establish a sustainable model for wildlife management programs.

Software defect prediction (SDP) is a critical aspect of software quality assurance, aiming to identify and manage potential defects in software systems. In this paper, we have proposed a novel hybrid approach that combines Grey Wolf Optimization with Feature Selection (GWOFS) and multilayer perceptron (MLP) for SDP. The GWOFS-MLP hybrid model is designed to optimize feature selection, ultimately enhancing the accuracy and efficiency of SDP. Grey Wolf Optimization, inspired by the social hierarchy and hunting behavior of grey wolves, is employed to select a subset of relevant features from an extensive pool of potential predictors. This study investigates the key challenges that traditional SDP approaches encounter and proposes promising solutions to overcome time complexity and the curse of the dimensionality reduction problem.

The integration of GWOFS and MLP results in a robust hybrid model that can adapt to diverse software datasets. This feature selection process harnesses the cooperative hunting behavior of wolves, allowing for the exploration of critical feature combinations. The selected features are then fed into an MLP, a powerful artificial neural network (ANN) known for its capability to learn intricate patterns within software metrics. MLP serves as the predictive engine, utilizing the curated feature set to model and classify software defects accurately.

The performance evaluation of the GWOFS-MLP hybrid model on a real-world software defect dataset demonstrates its effectiveness. The model achieves a remarkable training accuracy of 97.69% and a testing accuracy of 97.99%. Additionally, the receiver operating characteristic area under the curve (ROC-AUC) score of 0.89 highlights the model’s ability to discriminate between defective and defect-free software components.

Experimental implementations using machine learning-based techniques with feature reduction are conducted to validate the proposed solutions. The goal is to enhance SDP’s accuracy, relevance and efficiency, ultimately improving software quality assurance processes. The confusion matrix further illustrates the model’s performance, with only a small number of false positives and false negatives.

The purpose of this paper is to investigate the role of institutional investors in the cost of equity for Chinese firms, especially state-owned enterprises (SOEs).

By using data from Chinese firms with a unique state ownership structure, we provide empirical evidence on whether institutional investors can help reduce the cost of equity for SOEs and non-SOEs, respectively, and if so, identify the underlying channels.

We find that an increase in the shareholdings of institutions, especially independent institutions, can lead to a reduction in the cost of equity. This effect is particularly prominent in SOEs compared to non-SOEs. Moreover, institutional investors promote corporate social responsibility activities and innovation activities of invested firms, thereby reducing the cost of equity.

This paper contributes to a comprehensive understanding of the effects of institutional shareholdings with heterogeneity on the cost of equity and their influential mechanisms in the process of mixed ownership reform.

This paper aims to propose a stable and precise recognition and pose estimation method to deal with the difficulties that industrial parts often present, such as incomplete point cloud data due to surface reflections, lack of color texture features and limited availability of effective three-dimensional geometric information. These challenges lead to less-than-ideal performance of existing object recognition and pose estimation methods based on two-dimensional images or three-dimensional point cloud features.

In this paper, an image-guided depth map completion method is proposed to improve the algorithm's adaptability to noise and incomplete point cloud scenes. Furthermore, this paper also proposes a pose estimation method based on contour feature matching.

Through experimental testing on real-world and virtual scene dataset, it has been verified that the image-guided depth map completion method exhibits higher accuracy in estimating depth values for depth map hole pixels. The pose estimation method proposed in this paper was applied to conduct pose estimation experiments on various parts. The average recognition accuracy in real-world scenes was 88.17%, whereas in virtual scenes, the average recognition accuracy reached 95%.

The proposed recognition and pose estimation method can stably and precisely deal with the difficulties that industrial parts present and improve the algorithm's adaptability to noise and incomplete point cloud scenes.

We attempt to construct a grey three-way conflict analysis model with constraints to deal with correlated conflict problems with uncertain information.

In order to address these correlated conflict problems with uncertain information, considering the interactive influence and mutual restraints among agents and portraying their attitudes toward the conflict issues, we utilize grey numbers and three-way decisions to propose a grey three-way conflict analysis model with constraints. Firstly, based on the collected information, we introduced grey theory, calculated the degree of conflict between agents and then analyzed the conflict alliance based on the three-way decision theory. Finally, we designed a feedback mechanism to identify key agents and key conflict issues. A case verifies the effectiveness and practicability of the proposed model.

The results show that the proposed model can portray their attitudes toward conflict issues and effectively extract conflict-related information.

By employing this approach, we can provide the answers to Deja’s fundamental questions regarding Pawlak’s conflict analysis: “what are the underlying causes of conflict?” and “how can a viable consensus strategy be identified?”

This study aims at proposing and testing a model delineating how and when the quality of a special B2B professional service, investment relations (IR), would drive corporate intangible value.

This study employs a proprietary dataset on voting records of an annual investment relations (IR) awards event and the corresponding company-level archival data for analysis. Regression analysis is used to test hypotheses.

IR service quality not only directly enhances corporate intangible value, but also indirectly boosts it via information transparency. While competitive intensity does not moderate the relationship between IR service quality and corporate intangible value, its moderating effect on the relationship between information transparency and this value is negative.

The findings advance academic understanding of the mechanism and boundary conditions underlying the complex and dynamic relationships among IR service quality, information transparency, corporate intangible value and competitive intensity. Future research endeavors to verify the present findings in other service and/or geographic settings would help establish their external validity.

The findings advise companies to expand the traditional role of IR by taking it as a powerful communication and relationship marketing tool to improve their visibility and attract investors.

The findings suggest that superior IR service would strengthen the company’s social bonding with institutional investors and effectively signal to them its commitment to good corporate governance practices.

Matching a proprietary dataset on IR voting records with the corresponding company-level archival data over a five-year period to investigate the performance implications of IR service quality within the Hong Kong context rectifies methodological limitation and geographic confinement of prior IR research.

This chapter presents a COVID-19 case study from the perspective of a library director’s workplace dynamics during the COVID-19 response implemented by the Blount County Public Library (BCPL) in Maryville, Tennessee, US. Prior to COVID-19, the BCPL had a robust program offering extensive use of the library’s resources and spaces. When the library shutdown in mid-March 2020 in response to the global COVID lockdown, staff continued to provide services to the public via online resources and curbside delivery of library materials. The library reopened to the public in July 2020 with extreme spatial protocols addressing staff and visitor safety. This case study looks at the COVID-driven changes and their impacts on the library staff, the library board, and the community at large, as well as evaluates the resulting permanent changes occurring due to deficits in funding, staffing levels, and the restructuring of library board procedures for handling library staff and working with the library director. This work reflects on the mental and behavioral responses to these issues and their impact on the operation of a public library during the pandemic.

This study aims to analyse the motivations and underlying assumptions of decision makers driving the adoption of sustainability-oriented targets in executive compensation (SCTs) to better understand SCTs’ impact on sustainability performance.

Through a qualitative approach, 15 in-depth interviews are conducted in a two-tier governance setting. Participants include management and supervisory board members, compensation consultants and other stakeholders involved in proxy voting.

SCT implementation is primarily determined by meeting shareholders’ expectations rather than those of other stakeholders. Decision makers react in a differentiated way to increased expectations by implementing either primarily symbolic or substantive measures and encounter different implementation challenges like insufficient data quality and a lack of experience within supervisory boards, both of which potentially contribute to decoupling.

The study offers valuable insights for companies in designing SCTs and emphasises the significance of addressing decoupling to effectively enhance sustainability performance through SCTs and provides a foundation for future studies aimed at analysing this phenomenon.

Using a neo-institutional theory lens, this study marks one of the first interview-based investigations to distinguish between symbolic and substantial SCTs. It delves deeply into the role of decoupling and the associated challenges, offering fresh perspectives within the under-researched framework of a two-tier corporate governance structure. Moreover, this study aims to meticulously capture the real-world design practices and implementation processes of SCTs through experts, an aspect that was emphasised as a limitation in previous studies.