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This study aims to improve the detection and quantification of cardiac issues, which are a leading cause of mortality globally. By leveraging past data and using knowledge mining strategies, this study seeks to develop a technique that could assess and predict the onset of cardiac sickness in real time. The use of a triple algorithm, combining particle swarm optimization (PSO), artificial bee colony (ABC) and support vector machine (SVM), is proposed to enhance the accuracy of predictions. The purpose is to contribute to the existing body of knowledge on cardiac disease prognosis and improve overall performance in health care.

This research uses a knowledge-mining strategy to enhance the detection and quantification of cardiac issues. Decision trees are used to form predictions of cardiovascular disorders, and these predictions are evaluated using training data and test results. The study has also introduced a novel triple algorithm that combines three different combination processes: PSO, ABC and SVM to process and merge the data. A neural network is then used to classify the data based on these three approaches. Real data on various aspects of cardiac disease are incorporated into the simulation.

The results of this study suggest that the proposed triple algorithm, using the combination of PSO, ABC and SVM, significantly improves the accuracy of predictions for cardiac disease. By processing and merging data using the triple algorithm, the neural network was able to effectively classify the data. The incorporation of real data on various aspects of cardiac disease in the simulation further enhanced the findings. This research contributes to the existing knowledge on cardiac disease prognosis and highlights the potential of leveraging past data for strategic forecasting in the health-care sector.

The originality of this research lies in the development of the triple algorithm, which combines multiple data mining strategies to improve prognosis accuracy for cardiac diseases. This approach differs from existing methods by using a combination of PSO, ABC, SVM, information gain, genetic algorithms and bacterial foraging optimization with the Gray Wolf Optimizer. The proposed technique offers a novel and valuable contribution to the field, enhancing the competitive position and overall performance of businesses in the health-care sector.

This study aims to propose a moderated mediation model to investigate the moderating effects of environmental, social and governance (ESG) performance on the relationship between inefficient investment and firm performance and the mediating effect of firms that participate in institutional research on the relationship between investment efficiency and performance. This study also analyses the heterogeneity of the corporate nature, intensity of industrial research and development (R&D), industrial competition and regional marketization.

This study uses a panel data fixed-effects model to conduct a regression analysis of 1,918 Chinese listed firms from 2016 to 2020. A Fisher’s permutation test is used to examine the differences between state-owned and nonstate-owned firms.

Inefficient investment negatively impacts corporate performance and higher ESG performance exacerbates this effect by attracting more institutional research which reveals more problems. State-owned enterprises perform significantly better than nonstate-owned enterprises in terms of ESG transformation. Industrial R&D intensity, competition and regional marketization also mitigate the negative effects of inefficient investment on corporate performance.

This study suggests that companies should consider inefficient investments that arise from agency issues in corporate ESG transformation. In addition, state-owned enterprises in ESG transformation should take the lead to achieve sustainable development more efficiently. China should balance regional marketization, encourage enterprises to increase R&D intensity, reduce industry concentration, encourage healthy competition and prevent market monopolies.

This study combines the agency and stakeholder theories to reveal how inefficient investments that arise from agency issues inhibit value creation in ESG initiatives.

This paper aims to appraise current challenges in adopting generative AI by reviewers to evaluate the readability and quality of submissions. The paper discusses how to make the AI-powered peer-review process immune to unethical practices, such as the proliferation of AI-generated poor-quality or fake reviews that could harm the value of peer review.

This paper examines the potential roles of AI in peer review, the challenges it raises and their mitigation. It critically appraises current opinions and practices while acknowledging the lack of consensus about best practices in AI for peer review.

The adoption of generative AI by the peer review process seems inevitable, but this has to happen (1) gradually, (2) under human supervision, (3) by raising awareness among stakeholders about all its ramifications, (4) through improving transparency and accountability, (5) while ensuring confidentiality through the use of locally hosted AI systems, (6) by acknowledging its limitations such as its inherent bias and unawareness of up-to-date knowledge, (7) by putting in place robust safeguards to maximize its benefits and lessen its potential harm and (8) by implementing a robust quality assurance to assess its impact on overall research quality.

In the current race for more AI in scholarly communication, this paper advocates for a human-centered oversight of AI-powered peer review. Eroding the role of humans will create an undesirable situation where peer review will gradually metamorphose into an awkward conversation between an AI writing a paper and an AI evaluating that paper.

This study aims to explore which relational factors are crucial for accelerator-based start-ups to achieve high financial performance and whether innovation levels influence this relationship. Utilizing fsQCA and drawing from the resource-based view (RBV), we analyze 128 start-ups in a Spanish accelerator, split by innovativeness, to understand the impact of relational and human capital factors on performance.

The study uses fuzzy-set qualitative comparative analysis (fsQCA) to investigate conditions leading to high financial performance among 128 start-ups in a Spanish accelerator, divided by innovativeness. Four key factors are analyzed: social capital, social competence, resource mobilization and entrepreneurial ecosystem support. fsQCA examines complex relationships between these factors and financial performance.

Relational and human capital factors significantly impact start-up financial performance, varying with innovativeness. Highly innovative start-ups benefit from social competence and networked support, while less innovative but profitable start-ups rely on resource mobilization skills. The study highlights the contingent value of these factors, showing that unique configurations drive financial success.

The paper enhances the RBV in entrepreneurial contexts by highlighting the critical role of relational resources and their configurations. It suggests social competence and networked support are crucial for highly innovative start-ups, while resource mobilization is key for less innovative ones. These findings encourage nuanced theorizing of start-up success strategies, considering varying innovativeness levels and their impact on performance.

This study enhances understanding of the relationship between relational factors and financial performance in accelerator-based start-ups, considering innovation levels. It provides insights into how different configurations of social capital, competence, resource mobilization and ecosystem support lead to success. It underscores the importance of considering the contingent value of relational factors for start-up growth.