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Recent studies suggested the ratio of option to stock volume reflected the private information. Informed traders were drawn to the options market for its leverage effect and relatively low transaction costs. Informed traders use different intervals of option moneyness to execute their strategies. The question is which types of option moneyness were traded by informed traders and what information was reflected in the market. In this study, the authors focused on this question and constructed a method for capturing the activity of informed traders in the options and stock markets.

The authors constructed the daily measure, moneyness option trading volume to stock trading volume ratio (MOS), to capture the activity of informed traders in the market. The authors formed quintile portfolios sorted with respect to the moneyness option to stock trading volume ratio and provided the capital asset pricing model and Fama–French five-factor alphas. To determine whether MOS had predictive ability on future stock returns after controlling for company characteristic effects, the authors formed double-sorted portfolios and performed Fama–Macbeth regressions.

The authors found that the firms in the lowest moneyness option trading volume to stock trading volume ratio for put quintile outperform the highest quintile by 0.698% per week (approximately 36% per year). The firms in the highest moneyness option trading volume to stock trading volume ratio for call quintile outperform the lowest quintile by 0.575% per week (approximately 30% per year).

The authors first propose the measures, moneyness option trading volume to stock trading volume ratio, that combined with the trading volume and option moneyness. The authors provide evidence that the measures have the predictive ability to the future stock returns.

The purpose of this review is to comprehensively consider the material properties and processing of additive titanium alloy and provide a new perspective for the robotic grinding and polishing of additive titanium alloy blades to ensure the surface integrity and machining accuracy of the blades.

At present, robot grinding and polishing are mainstream processing methods in blade automatic processing. This review systematically summarizes the processing characteristics and processing methods of additive manufacturing (AM) titanium alloy blades. On the one hand, the unique manufacturing process and thermal effect of AM have created the unique processing characteristics of additive titanium alloy blades. On the other hand, the robot grinding and polishing process needs to incorporate the material removal model into the traditional processing flow according to the processing characteristics of the additive titanium alloy.

Robot belt grinding can solve the processing problem of additive titanium alloy blades. The complex surface of the blade generates a robot grinding trajectory through trajectory planning. The trajectory planning of the robot profoundly affects the machining accuracy and surface quality of the blade. Subsequent research is needed to solve the problems of high machining accuracy of blade profiles, complex surface material removal models and uneven distribution of blade machining allowance. In the process parameters of the robot, the grinding parameters, trajectory planning and error compensation affect the surface quality of the blade through the material removal method, grinding force and grinding temperature. The machining accuracy of the blade surface is affected by robot vibration and stiffness.

This review systematically summarizes the processing characteristics and processing methods of aviation titanium alloy blades manufactured by AM. Combined with the material properties of additive titanium alloy, it provides a new idea for robot grinding and polishing of aviation titanium alloy blades manufactured by AM.

In the final step, the trust model is applied to the on-demand federated multipath distance vector routing protocol (AOMDV) to introduce path trust as a foundation for routing selection in the route discovery phase, construct a trusted path, and implement a path warning mechanism to detect malicious nodes in the route maintenance phase, respectively.

A trust-based on-demand multipath distance vector routing protocol is being developed to address the problem of flying ad-hoc network being subjected to internal attacks and experiencing frequent connection interruptions. Following the construction of the node trust assessment model and the presentation of trust evaluation criteria, the data packet forwarding rate, trusted interaction degree and detection packet receipt rate are discussed. In the next step, the direct trust degree of the adaptive fuzzy trust aggregation network compute node is constructed. After then, rely on the indirect trust degree of neighbouring nodes to calculate the trust degree of the node in the network. Design a trust fluctuation penalty mechanism, as a second step, to defend against the switch attack in the trust model.

When compared to the lightweight trust-enhanced routing protocol (TEAOMDV), it significantly improves the data packet delivery rate and throughput of the network significantly.

Additionally, it reduces the amount of routing overhead and the average end-to-end delay.

The purpose of this study is to investigate and demonstrate the advancements achieved in the field of chest X-ray image captioning through the utilization of dynamic convolutional encoder–decoder networks (DyCNN). Typical convolutional neural networks (CNNs) are unable to capture both local and global contextual information effectively and apply a uniform operation to all pixels in an image. To address this, we propose an innovative approach that integrates a dynamic convolution operation at the encoder stage, improving image encoding quality and disease detection. In addition, a decoder based on the gated recurrent unit (GRU) is used for language modeling, and an attention network is incorporated to enhance consistency. This novel combination allows for improved feature extraction, mimicking the expertise of radiologists by selectively focusing on important areas and producing coherent captions with valuable clinical information.

In this study, we have presented a new report generation approach that utilizes dynamic convolution applied Resnet-101 (DyCNN) as an encoder (Verelst and Tuytelaars, 2019) and GRU as a decoder (Dey and Salemt, 2017; Pan et al., 2020), along with an attention network (see Figure 1). This integration innovatively extends the capabilities of image encoding and sequential caption generation, representing a shift from conventional CNN architectures. With its ability to dynamically adapt receptive fields, the DyCNN excels at capturing features of varying scales within the CXR images. This dynamic adaptability significantly enhances the granularity of feature extraction, enabling precise representation of localized abnormalities and structural intricacies. By incorporating this flexibility into the encoding process, our model can distil meaningful and contextually rich features from the radiographic data. While the attention mechanism enables the model to selectively focus on different regions of the image during caption generation. The attention mechanism enhances the report generation process by allowing the model to assign different importance weights to different regions of the image, mimicking human perception. In parallel, the GRU-based decoder adds a critical dimension to the process by ensuring a smooth, sequential generation of captions.

The findings of this study highlight the significant advancements achieved in chest X-ray image captioning through the utilization of dynamic convolutional encoder–decoder networks (DyCNN). Experiments conducted using the IU-Chest X-ray datasets showed that the proposed model outperformed other state-of-the-art approaches. The model achieved notable scores, including a BLEU_1 score of 0.591, a BLEU_2 score of 0.347, a BLEU_3 score of 0.277 and a BLEU_4 score of 0.155. These results highlight the efficiency and efficacy of the model in producing precise radiology reports, enhancing image interpretation and clinical decision-making.

This work is the first of its kind, which employs DyCNN as an encoder to extract features from CXR images. In addition, GRU as the decoder for language modeling was utilized and the attention mechanisms into the model architecture were incorporated.

This study aims to discuss the influences of surface severe plastic deformation (S²PD) on the electrochemical corrosion, pitting corrosion, intergranular corrosion, stress corrosion cracking of aluminum (Al) alloys and attempt to correlate the microstructural/compositional changes with the performances.

This study provides a novel gradient design of structure/composition caused by S²PD for the purpose of enhancing Al alloys’ corrosion resistance.

S²PD has a significant effect on corrosion behavior of Al alloys through tuning the grain size, residual stress, composition, grain boundary phase and second phase particle distribution.

Although Al alloys are known to form a protective Al₂O₃ film, corrosion is a major challenge for the longevity of Al structures across numerous industries, especially for the infrastructures made of high-strength Al alloys. Traditional strategies of improving corrosion resistance of Al alloys heavily relied on alloying and coatings. In this review, gradient design of structure/composition caused by S²PD provides a novel strategy for corrosion protection of Al alloys, especially in the enhancement of localized corrosion resistance.

Sustainability continues to be put forth as a strategic priority. However, sustainability efforts are often deemphasized for short-term profitability. This study explores the nuances in managerial decision-making related to adopting sustainability initiatives within food supply chains in an emerging economy. We identify a complex interaction between sustainability efforts and risk mitigation. We derive a model to explain conflicting company goals, managerial decisions and system design.

We followed an exploratory research design with an inductive approach. We analyzed data from semi-structured interviews with 29 companies representing different tiers in Turkish food supply chains. We refined and validated the interview findings through a focus group with nine senior managers. We conducted open, focused and theoretical coding in an iterative and reflective manner to analyze the data and derive our results.

From the data, three themes emerged, indicating that managers are pursuing different, often conflicting, goals concerning value creation, risk management and sustainability performance. Managers identified and commented on new risks brought on by sustainability initiatives. These sustainability-induced risks were seen as a threat to operational performance, a driver of increased costs and a negative impact on product quality and delivery performance. Trade-offs across operating, sustainability and risk management systems create transformational tension that confounds the sustainability adoption decision-making process.

The data from the study was contrasted with a theoretical framework derived from systems theory, goal-setting theory of motivation and the theory of planned behavior. We identified four distinct decision paths that managers pursue. Increased awareness of transformational tension and how it influences managerial decision-making can enhance strategic sustainability system design and initiative success.

Globalization and increased outsourcing have contributed to increased supply chain complexity, exposing firms to greater vulnerability in the areas of product safety and supply chain security. Meanwhile, stakeholders pressure firms to ensure that their products are safe, and their supply chains are secure. Drawing from stakeholder theory, this paper aims to explore how the supply chain characteristics of distance and power affect the adoption of consumer protection (CP) practices, which ensure product safety and supply chain security.

Using primary survey data from a sample of Canadian manufacturing firms, this research examines the relationships among supply chain characteristics, adoption of CP practices and firm performance.

Analysis supported the use of two practices related to product safety (consumer education and product design) and three practices for supply chain security (packaging, tracking and authenticity). Greater cultural distance between the focal firm and its suppliers was positively associated with investments in safer design practices, while increased geographical distance between the focal firm and the customer was significantly related to increased consumer education. Moreover, as power of a focal firm relative to its suppliers increased, so too did investments in supply chain security. Finally, CP practices were related to improved operational performance along multiple dimensions.

This research focuses on the critical role of two key stakeholder groups in improving product safety and supply chain security: suppliers and customers. The authors add to the theoretical discussion of product safety and supply chain security by identifying critical differences between suppliers and customers for the focal firm. Second, the research informs the managerial community of the potential benefits of investments in CP practices.

Growing social concerns and ecological issues accelerate firms’ environmental, social and governance (ESG) engagement. Hence, this study aims to advance the existing literature by focusing on the interplay between institutional and firm governance mechanisms for greater ESG engagement. More specifically, the authors investigate whether public governance stimulates excessive ESG engagement and whether corporate governance moderates this relationship.

Using a sample of 43,803 firm-year observations affiliated with 41 countries and 9 industries, the authors adopt a country, industry and year fixed-effects regression analysis.

The authors find that public governance strength via its six dimensions stimulates excessive ESG engagement. This implies that firms in countries with strong voice and accountability, political stability, government effectiveness, regulatory quality, rule of law and control of corruption are more motivated for ESG engagement. Furthermore, corporate governance negatively moderates the relationship between all public governance dimensions (except political stability) and excessive ESG engagement. This implies that public governance and corporate governance are substitutes for encouraging firms to commit to ESG. Further tests reveal that whereas these results in the baseline analyses are valid for developed countries, they are not valid in emerging markets.

The findings support the interplay between institutional and agency theories. In countries with strong (weak) institutional mechanisms, corporate governance becomes weak (strong) in inciting greater stakeholder engagement. This implies that the public governance mechanism alleviates agency costs, rendering internal mechanisms of corporate governance noncompulsory for ESG engagement.

The findings suggest that emerging countries need to reinforce their institutions for greater accountability, regulatory quality and control of corruption, which will have a domino effect on firms in addressing stakeholder expectations. The results also advise emerging country firms to augment their internal monitoring mechanisms for greater stakeholder engagement, such as structuring boards and establishing corporate social responsibility mechanisms, committees and policies.

This study contributes to the recent literature investigating the role of corporate governance mechanisms in excessive ESG engagement. The study also explores whether public governance is associated with greater ESG involvement and provides a comprehensive analysis of the association between six indicators of public governance quality and excessive ESG practices in developed and emerging economies.

The transformation towards the Green Economy (GE) in Poland is a relatively new topic for researchers, policymakers and business practitioners. A comprehensive picture of the shift towards the GE can help mentioned groups translate theoretical assumptions into practice.

This chapter presents the assessment of Poland's shift towards the GE, measured by the proposed Green Transformation Index (GTI).

The set of GE indicators was elaborated in Structured Literature Review (SLR) variation method. Then, this set of indicators was compared with the Statistics Poland (GUS) secondary data and employed in the taxonometric calculation methods.

In the result, the GTI for the Polish economy was proposed and calculated between 2007 and 2020. The GTI allowed us to present a dynamic analysis of the transformation towards GE in Poland.

Extant research shows mixed results on the impact of expressed negative emotions on donations in online charitable crowdfunding. This study solves the puzzle by examining how different types of negative emotions (i.e. sadness, anxiety and fear) expressed in crowdfunding project descriptions affect donations.

Data on 15,653 projects across four categories (medical assistance, education assistance, disaster assistance and poverty assistance) from September 2013 to May 2019 come from a leading online crowdfunding platform in China. Text analysis and regression models serve to test the hypotheses.

In the medical assistance category, the expression of sadness has an inverted U-shaped effect on donations, while the expression of anxiety has a negative effect. An appropriate number of sadness words is helpful but should not exceed five times. In the education assistance and disaster assistance categories, the expression of sadness has a positive effect on donations, but disclosure of anxiety and fear has no influence on donations. Expressions of sadness, anxiety and fear have no impact on donations in the poverty assistance category.

This work has important implications for fundraisers on how to regulate the fundraisers' expressions of negative emotions in a project's description to attract donations. These insights are also relevant for online crowdfunding platforms.

Online crowdfunding research often studies negative emotions as a whole and does not differentiate project types. The current work contributes by empirically testing the impact of three types of negative emotions on donations across four major online crowdfunding categories.