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Considering the importance of green knowledge in firms' sustainability, this study investigates the mediating mechanism of green knowledge acquisition (GKA) and the moderating role of resource orchestration capability (ROC) in the relationship between green entrepreneurial orientation (GEO) and corporate sustainable performance (CSP).

Using a sample of 388 executives from 195 small and medium-sized enterprises (SMEs) in the UAE, this study used partial least squares structural equation modelling to examine the proposed relationships among the constructs.

The research shows that GEO affects CSP's environmental, economic, and social aspects of CSP. This study also highlights the mediating role of GKA in the relationship between GEO and CSP. The moderated mediation analysis results indicate that when ROC is elevated, GEO's indirect influence on environmental and economic performance through GKA is more pronounced.

This study provides useful insights and a novel approach for manufacturing industries and authoritative bodies to alleviate environmental deterioration and improve CSP by encouraging GKA through green entrepreneurship.

This study enriches the existing literature on GEO, GKA, and CSP by focusing on environmental challenges and applying the resource-based view (RBV) framework. The study's findings broaden the theoretical basis for green entrepreneurship, provide guidance on enhancing CSP in manufacturing firms, and advance green entrepreneurship research.

In view of the significance of intangible organizational resources and firm sustainability, this study investigates the mediating role of ambidextrous green innovation and the moderating effects of resource orchestration capability in the relationship between green entrepreneurial orientation and green performance.

The research employed a quantitative analysis technique using hierarchical linear regression and a moderated mediation approach on a sample of 409 managers from UAE manufacturing firms to investigate the proposed relationships among the variables.

The research results show that a firm’s green performance is influenced by its green entrepreneurial orientation. Green innovation, both exploratory and exploitative, mediates the link between green entrepreneurial orientation and green performance. Moreover, the association between green entrepreneurial orientation and exploitative green innovation, as well as between exploitative green innovation and a firm's green performance, is strengthened by resource orchestration capability. The findings of the moderated mediation show that when resource orchestration capacity is high, exploitative green innovation has a greater mediating effect on green entrepreneurial orientation and green performance.

This study provides valuable insights for manufacturing firms to achieve sustainable performance and reduce their environmental impact. Firms should adopt proactive environmental strategies and innovative approaches to achieve sustainable green performance by adopting green entrepreneurship and establishing ambidextrous green innovation.

This study contributes to the literature on GEO, ambidextrous green innovation, resource orchestration capability, and green performance. These results provide insight into fostering green innovation in the manufacturing industry, deepen the theoretical foundation for green entrepreneurship, and advance the field of green entrepreneurship study.

Amidst the increasing global emphasis on environmental sustainability, manufacturing firms seek to integrate eco-conscious practices into their innovation processes. This study aims to explore the intricate relationships between green learning orientation (GLO), knowledge management capability (KMC), resource orchestration capability (ROC) and two dimensions of green innovation (GI): green product innovation (GPDI) and green process innovation.

Partial least squares structural equation modelling (PLS-SEM) and moderated mediation techniques were used to investigate the relationships among the constructs using data gathered from a survey of 167 manufacturing firms in the United Arab Emirates.

This study indicates that GLO significantly influences GPDI and green process innovation. Although KMC mediates the relationship between GLO and process innovation, it does not mediate the GPDI relationship. Moreover, ROC significantly strengthens the links between GLO, KMC and both the aspects of GI.

This study emphasises the importance of fostering a green learning culture and integrating it into product development without complex knowledge management systems. This study also highlighted the role of effective resource allocation in maximising environmental learning benefits for sustainable innovation. Organisations can achieve environmental progress by integrating green knowledge into product and process development and by investing in sustainable practices.

By examining various mechanisms involving moderation and mediation, this study has made a notable contribution to advancing the field of knowledge-based view theory. This study also offers enhanced insights into the interconnections among GLO, knowledge management capability, ROC and a firm’s capacity for GI.

This study explores the complex impact of COVID-19 on India's financial sector, moving beyond simplistic public health vs. economy views. We assess market vulnerabilities and analyze how public sentiment, measured through Google Trends, can predict stock market fluctuations. We propose a novel framework using Google Trends for financial sentiment analysis, aiming to improve understanding and preparedness for future crises.

Hybrid approach leverages Google Trends as sentiment tool, market data, and momentum indicators like Rate of Change, Average Directional Index and Stochastic Oscillator, to deliver accurate, market insights for informed investment decisions during pandemic.

Our study reveals that the pandemic significantly impacted the Indian financial sector, highlighting its vulnerabilities. Capitalizing on this insight, we built a ground-breaking predictive model with an impressive 98.95% maximum accuracy in forecasting stock market values during such events.

To the best of authors knowledge this model's originality lies in its focus on short-term impact, novel data fusion and methodology, and high accuracy.• Focus on short-term impact: Our model uniquely identifies and quantifies the fleeting effects of COVID-19 on market behavior.• Novel data fusion and framework: A novel framework of sentiment analysis was introduced in the form of Trend Popularity Index. Combining trend popularity index with momentum offers a comprehensive and dynamic approach to predicting market movements during volatile periods.• High predictive accuracy: Achieving the prediction accuracy (98.93%) sets this model apart from existing solutions, making it a valuable tool for informed decision-making.

Grey relational analysis (GRA) has already proved itself as an efficient tool for multi-objective optimization of many of the machining processes. In GRA, the distinguishing coefficient (ξ) plays an important role in identifying the optimal parametric combinations of the machining processes and almost all the past researchers have considered its value as 0.5. In this paper, based on past experimental data, the application of GRA is extended to dynamic GRA (DGRA) to optimize two electrochemical machining (ECM) processes.

Instead of a static distinguishing coefficient, this paper considers dynamic distinguishing coefficient for each of the responses for both the ECM processes under consideration. Based on these coefficients, the application of DGRA leads to determination of the dynamic grey relational grade (DGRG) and grey relational standard deviation (GRSD), helping in initial ranking of the alternative experimental trials. Considering the ranks obtained by DGRG and GRSD, a composite rank in terms of rank product score is obtained, aiding in final rankings of the experimental trials for both the ECM processes.

In the first example, the maximum material removal rate (MRR) would be obtained at an optimal combination of ECM parameters as electrolyte concentration = 2 mol/l, voltage = 16V and current = 4A, while another parametric intermix as electrolyte concentration = 2 mol/l, voltage = 14V and current = 2A would result in minimum radial overcut and delamination. For the second example, an optimal combination of ECM parameters as electrode temperature = 30°C, voltage = 12V, duty cycle = 90% and electrolyte concentration = 15 g/l would simultaneously maximize MRR and minimize surface roughness and conicity.

In this paper, two ECM operations are optimized using a newly developed but yet to be popular multi-objective optimization tool in the form of the DGRA technique. For both the examples, the derived rankings of the ECM experiments exactly match with those obtained by the past researchers. Thus, DGRA can be effectively adopted to solve parametric optimization problems in any of the machining processes.

Coronary artery disease is one of the most common cardiovascular disorders in the world, and it can be deadly. Traditional diagnostic approaches are based on angiography, which is an interventional procedure having side effects such as contrast nephropathy or radio exposure as well as significant expenses. The purpose of this paper is to propose a novel artificial intelligence (AI) approach for the diagnosis of coronary artery disease as an effective alternative to traditional diagnostic methods.

In this study, a novel ensemble AI approach based on optimization and classification is proposed. The proposed ensemble structure consists of three stages: feature selection, classification and combining. In the first stage, important features for each classification method are identified using the binary particle swarm optimization algorithm (BPSO). In the second stage, individual classification methods are used. In the final stage, the prediction results obtained from the individual methods are combined in an optimized way using the particle swarm optimization (PSO) algorithm to achieve better predictions.

The proposed method has been tested using an up-to-date real dataset collected at Basaksehir Çam and Sakura City Hospital. The data of disease prediction are unbalanced. Hence, the proposed ensemble approach improves majorly the F-measure and ROC area which are more prominent measures in case of unbalanced classification. The comparison shows that the proposed approach improves the F-measure and ROC area results of the individual classification methods around 14.5% in average and diagnoses with an accuracy rate of 96%.

This study presents a low-cost and low-risk AI-based approach for diagnosing heart disease compared to traditional diagnostic methods. Most of the existing research studies focus on base classification methods. In this study, we mainly investigate an effective ensemble method that uses optimization approaches for feature selection and combining stages for the medical diagnostic domain. Furthermore, the approaches in the literature are commonly tested on open-access dataset in heart disease diagnoses, whereas we apply our approach on a real and up-to-date dataset.

The geopolitical significance of the Mediterranean Sea transcends regional security and energy supply, profoundly impacting global security dynamics. Daily headlines underscore the plight of migrants from the Middle East and North Africa crossing the Mediterranean, exacerbating humanitarian crises and European identity challenges. Environmental concerns are further heightened by the abundance of global ports facilitating oil and goods transportation, alongside the staggering number of tourists flocking to the Mediterranean coast annually. This chapter serves as a gateway to the book, exploring the concept of “geopolitics” and delineating its characteristics. It specifically delves into the political economy of the Eastern Mediterranean and the geopolitical obstacles to energy security in the region. The chapter strategically selects four primary issues to dissect the region’s conflict complexity: the Syrian crisis, the Israeli–Palestinian conflict, the unresolved Cyprus dispute, and the Lebanon–Israel conflict over water border demarcation.

Since 2010, the eastern Mediterranean has witnessed a transformative narrative with the discovery of natural gas reserves off the coasts of Cyprus and Israel. This pivotal development has drawn attention to the region, where Egypt, Israel, Cyprus, Turkey, and Greece share maritime borders. The emergence of natural gas has reshaped geopolitical dynamics, and Western countries assume to reduce their reliance on Russia for energy supplies. This chapter explores the magnitude of natural gas discoveries and production in Cyprus and Israel, examining the interconnection of their fields and the ambitious endeavor of laying a 1,900-km underwater pipeline to the Greek island of Crete. Additionally, it highlights the pivotal roles played by key regional actors such as Israel, Turkey, and Egypt in shaping security and energy negotiations. However, Turkey has a significant position in the eastern Mediterranean and the Middle East, but tensions have arisen as neighboring countries seek to limit Turkey’s involvement in regional energy discussions, viewing its policies as a potential threat, thereby exacerbating Turkey’s regional interventions, particularly in Cyprus. Each of these countries in the Middle East is struggling to get more of the cake. Above all, Israel has been a gas importer throughout its history and now dreams of becoming a natural gas exporter to Europe.

The purpose of this study is to determine propeller damage based on acoustic recordings taken from unmanned aerial vehicle (UAV) propellers operated at different thrust conditions on a test bench. Propeller damage is especially critical for fixed-wing UAVs to sustain a safe flight. The acoustic characteristics of the propeller vary with different propeller damages.

For the research, feature extraction methods and machine learning techniques were used during damage detection from propeller acoustic data. First of all, sound recordings were obtained by operating five different damaged propellers and undamaged propellers under three different thrusts. Afterwards, the harmonic-to-noise ratio (HNR) feature extraction technique was applied to these audio recordings. Finally, model training and validation were performed by applying the Gaussian Naive Bayes machine learning technique to create a diagnostic approach.

A high recall value of 96.19% was obtained in the performance results of the model trained according to damaged and undamaged propeller acoustic data. The precision value was 73.92% as moderate. The overall accuracy value of the model, which can be considered as general performance, was obtained as 81.24%. The F1 score has been found as 83.76% which provides a balanced measure of the model’s precision and recall values.

This study include provides solid method to diagnose UAV propeller damage using acoustic data obtain from the microphone and allows identification of differently damaged propellers. Using that, the risk of in-flight failures can be reduced and maintenance costs can be lowered with addressing the occurred problems with UAV propeller before they worsen.

This study introduces a novel method to diagnose damaged UAV propellers using the HNR feature extraction technique and Gaussian Naive Bayes classification method. The study is a pioneer in the use of HNR and the Gaussian Naive Bayes and demonstrates its effectiveness in augmenting UAV safety by means of propeller damages. Furthermore, this approach contributes to UAV operational reliability by bridging the acoustic signal processing and machine learning.

This study aims to present a model for detecting and predicting creative accounting in companies listed on the Tehran Stock Exchange (TSE).

The authors conduct this research in three stages. First, the authors review the literature to determine the dimensions, components, indicators and techniques of creative accounting. Second, the authors conduct semi-structured interviews with experts using the fuzzy Delphi technique to obtain screening and reach a consensus. Finally, the authors develop a model to predict creative accounting by classifying the financial statements of the sample companies into two groups based on the use or non-use of creative accounting techniques, measuring the indicators determined in the previous stage, running various machine learning algorithms and choosing the superior algorithm.

The results indicate the usefulness of accounting information for detecting and predicting creative accounting and the relevance of several financial attributes as important predictors. The results also indicate the superiority of extremely randomized trees over other algorithms in predicting creative accounting and suggest that the primary purpose of creative accounting in Iran is earnings management. Contrary to the political cost hypothesis, large Iranian companies use creative accounting to inflate profits.

The present research also has several limitations that must be considered, and caution must be exercised in interpreting and generalizing the findings as specified in the revised manuscript.

This study’s implications are significant for policymakers, standard-setters and practitioners. By recognizing the detrimental effects of creative accounting on financial transparency within companies, policymakers can address existing gaps in accounting standards to minimize the potential for earnings manipulation. Consequently, strengthening internal and external mechanisms related to a firm’s financial performance becomes achievable. The study provides evidence of the need for audit firms to recognize the importance of creative accounting and consider creative accounting in their audit plans to prevent insufficient or even misleading disclosure by companies that extensively use creative accounting practices in their financial reporting. Moreover, knowledge of creative accounting techniques can help auditors assess audit and detection risks and serve as a valuable guide for reducing audit costs and improving audit quality.

Given that creative accounting practices distort the true or real accounting results, curbing creative accounting practices reduces corporate failures and could lead to the reduction of job losses and other social consequences.

This study uses a unique database in Iran to determine a model for predicting creative accounting using a mixed-method methodology, qualitative and quantitative, to identify creative accounting techniques and run various machine learning algorithms.