Regulatory Demands Research Articles

Rethinking Natural Gas Infrastructure: How Non‐Pipe Alternatives are Shaping Utility Planning

As greenhouse gas (GHG) emission reduction efforts accelerate, utility regulators nationwide are rethinking the role of natural gas in reducing GHG emissions over current levels. A key strategy emerging in several states is the mandatory consideration of non‐pipe alternatives (NPAs); solutions that avoid the need for new natural gas hook‐ups. California, Massachusetts, New York, and Colorado are leading the effort, and other states like Maryland, Hawaii, Oregon, Minnesota, and New Jersey are not far behind. Before gaining approval for traditional new pipeline investments as opposed to pipeline replacement, regulators are requiring utilities to thoroughly analyze the costs and benefits of NPAs. This is compelling natural gas utilities to rethink their business models and find the most effective ways to present NPAs to regulators and customers. With this enhanced scrutiny and regulatory mandates that are increasingly focusing on sustainability, natural gas distribution utilities are at a crossroads. The challenge lies in adapting to these expectations and innovating scalable investments that will meet regulatory demands while driving the transition to a cleaner energy future.

Comprehensive framework for data fusion in distributed ledger technologies to enhance supply chain sustainability

This review proposes a comprehensive framework that integrates data fusion with Distributed Ledger Technologies (DLT) to enhance sustainability in supply chain management. In today’s global supply chains, ensuring transparency, efficiency, and environmental responsibility is critical, yet the lack of real-time visibility and data fragmentation presents significant challenges. The framework addresses these issues by merging data from multiple sources, including IoT devices, operational databases, and external environmental factors, using advanced data fusion algorithms. DLT, with its decentralized, immutable, and transparent nature, ensures the integrity and security of the data, allowing all stakeholders to access accurate and tamper-proof information. The fusion of data within a DLT infrastructure not only improves traceability and accountability but also enables the automation of sustainability checks via smart contracts. These contracts can trigger actions based on predefined sustainability metrics such as carbon emissions, energy consumption, and resource efficiency. Furthermore, predictive analytics and machine learning algorithms integrated into the system provide real-time monitoring and optimization of sustainability performance throughout the supply chain. The proposed review offers numerous benefits, including enhanced transparency, reduced operational costs, improved sustainability outcomes, and risk mitigation. It also addresses challenges such as scalability, data privacy, and regulatory compliance, offering solutions to overcome these hurdles. By exploring case studies of successful implementations, this review demonstrates the practical applications and future potential of combining DLT and data fusion for sustainable supply chain management, positioning it as a critical tool for organizations aiming to meet environmental and regulatory demands in an increasingly digital and eco-conscious world.

A SYSTEMATIC LITERATURE REVIEW OF PREDICTIVE MODELS AND ANALYTICS IN AI-DRIVEN CREDIT SCORING

This systematic review examines the transformative role of AI-driven models in credit scoring, highlighting their advances over traditional statistical methods in terms of predictive accuracy, adaptability, and inclusivity. By synthesizing findings from 70 studies, this review demonstrates that machine learning techniques, particularly ensemble models such as random forests and gradient boosting, effectively capture complex, non-linear relationships in borrower data, providing more accurate risk assessments across diverse demographics. Deep learning models, especially convolutional and recurrent neural networks, extend credit scoring capabilities to unstructured and alternative data sources, supporting financial inclusion by enabling assessments of individuals without traditional credit histories. Hybrid models that integrate logistic regression with neural networks offer an optimal balance between interpretability and predictive power, addressing regulatory demands for transparency while maintaining robust accuracy. Ensemble techniques like stacking and blending enhance model adaptability, allowing credit scoring systems to integrate multiple perspectives and improve prediction accuracy in varied borrower contexts. Despite these advancements, challenges remain in the form of ethical concerns and the need for model interpretability, particularly with complex deep-learning architectures. The review underscores the importance of developing fairness-aware and explainable AI frameworks to ensure that as AI-driven credit scoring evolves, it remains both transparent and equitable. These insights suggest that with careful attention to ethics and transparency, AI has the potential to create a more inclusive and resilient credit scoring landscape, accommodating the needs of an increasingly diverse global population.

The role of data governance in enhancing cybersecurity resilience for global enterprises

Data governance plays a critical role in enhancing cybersecurity resilience for global enterprises. In the face of increasingly sophisticated cyber threats, coupled with rising regulatory demands such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), enterprises must implement robust data governance frameworks to safeguard data integrity, availability, and confidentiality (Ramirez et al., 2008). Effective data governance involves a combination of policies, procedures, and technologies that align with an organization’s overall cybersecurity strategy (Khatri & Brown, 2010). This paper provides a comprehensive analysis of the critical intersection between data governance and cybersecurity, emphasizing the link between governance structures, risk management processes, and technological innovations in mitigating evolving cyber threats (Weber et al., 2009). Additionally, we explore the essential role of data stewardship, stakeholder responsibilities, and the deployment of advanced technologies, such as artificial intelligence (AI) and machine learning (ML), in fostering organizational resilience (Arora & Pedersen, 2017). Through case studies and best practices, the study presents a conceptual framework that enables global enterprises to adapt to the fast-changing cybersecurity landscape while maintaining compliance with international data regulations. By addressing key elements such as access control, data classification, and real-time monitoring, this research underscores how comprehensive data governance frameworks serve as a foundation for enhanced cybersecurity resilience in today’s digital economy (Da Veiga & Eloff, 2007).

Brazilian natural gas market dynamics: A data panel analysis of the Brazilian market compared with Argentina, Colombia, Mexico, and India using Porter's five forces framework

This study offers a comparative analysis of natural gas markets in Brazil, Mexico, Colombia, Argentina, and India, spanning from 2000 to 2020. Leveraging panel data and historical time series analysis, coupled with Porter's Five Forces framework, it examines industry dynamics across upstream, downstream, midstream, and upstream dimensions. Insights into competitive landscapes are derived, considering regulatory frameworks, resource availability, infrastructure development, and demand patterns. Despite Brazil's efforts to enhance domestic production, infrastructure, and regulatory reforms, challenges persist, hindering sectoral growth. Comparative studies underscore the need for policy improvements to align with investment commitments. This study contributes novel insights into the competitive positioning of natural gas markets, offering actionable recommendations for stakeholders. Identifying potential bottlenecks informs strict decision-making in the global energy landscape.

Technological Innovation for Sustainable Supply Chain Management in the Food Industry

This study explores the critical role of technological innovations—specifically blockchain, Internet of Things (IoT), and artificial intelligence (AI)—in enhancing sustainable supply chain management within Malaysia's food industry. As the industry faces increasing pressure to adopt sustainable practices due to environmental concerns, regulatory demands, and consumer expectations, these technologies offer significant potential for improving traceability, reducing waste, and optimizing operational efficiency. The research highlights how blockchain enhances transparency and ethical sourcing, IoT enables real-time monitoring to minimize waste, and AI optimizes demand forecasting and inventory management, reducing overproduction and resource waste. However, the study also identifies substantial barriers to the adoption of these technologies, particularly for small and medium-sized enterprises (SMEs). These challenges include high implementation costs, a shortage of skilled labor, and concerns over data security and privacy. To address these issues, the study recommends starting with pilot projects to mitigate risks, investing in continuous workforce training, enhancing data security measures, and leveraging government support and industry collaborations. By overcoming these challenges, Malaysia's food industry can effectively integrate these innovations, leading to more sustainable, efficient, and resilient supply chains. This research underscores the importance of strategic planning and continuous improvement in fully realizing the benefits of technological advancements, thereby positioning Malaysia’s food industry as a leader in sustainable practices within the global market.

Integrating Environmental, Social and Governance (Esg) Principles into Organisations

The term ESG, which stands for Environmental, Social and Governance, has gained significant traction in the business world. A company’s dedication to ESG reflects its awareness of both the positive and negative impacts it has on society and the environment, influencing not only the business itself but the entire ecosystem around it. Organisations that embrace the ESG framework recognise that adopting best governance practices leads to positive outcomes across all sectors. This approach helps minimise the environmental impact and fosters a more equitable world, while maintaining sound administrative processes and sustaining profitability. Incorporating ESG factors into operations is essential for ensuring long-term sustainability, compliance and competitiveness. By focusing on environmental stewardship, fostering positive social impacts and maintaining strong governance structures, organisations can minimise their negative externalities, meet regulatory demands, and appeal to a growing number of ESG-conscious stakeholders. The main aim of this article is to analyse the ESG principles, and present examples of integrating ESG in large and SMEs companies. These organisations were chosen as examples of good practice: Maersk, Nestlé, Toyota, Baltic Marine Bunker and Klaipėdos Nafta. The companies in this article were chosen to show a diverse range of industries and approaches to ESG implementation.

Implementing data governance in financial systems: Strategies for ensuring compliance and security in multi-source data integration projects

This study explores the complexities of implementing data governance within financial systems, focusing on strategies to ensure compliance and security in multi-source data integration projects. The primary aim was to identify challenges and establish a comprehensive framework to enhance data integrity, confidentiality, and regulatory adherence in the financial sector. Using a mixed-methods approach, the research included an extensive literature review, case studies, and expert interviews to gain insights into current practices, challenges, and emerging trends in data governance. Key findings reveal that while multi-source data integration offers significant opportunities for enhanced decision-making, it also presents substantial risks, including data breaches and regulatory non-compliance. The study underscores the pivotal role of data governance frameworks in mitigating these risks through the establishment of clear data policies, the implementation of robust cybersecurity measures, and the promotion of a compliance-focused organizational culture. Additionally, the analysis highlights the necessity for harmonized global data privacy laws to address the diverse regulatory environments that financial institutions operate within. The research concludes that effective data governance is crucial for financial institutions to safeguard their data assets, ensure regulatory compliance, and maintain stakeholder trust. Recommendations include the adoption of advanced technologies like artificial intelligence and blockchain to enhance data governance capabilities, the provision of ongoing employee training to foster a culture of compliance, and the implementation of dynamic compliance monitoring systems to keep pace with evolving regulatory demands. By embracing these strategies, financial institutions can develop a resilient data governance framework that not only ensures compliance and security but also drives operational excellence, competitive advantage, and sustained trust in the increasingly digital financial landscape. Keywords: Data Governance, Financial Systems, Multi-Source Data Integration, Regulatory Compliance, Cybersecurity, Data Privacy.

Backtest overfitting in the machine learning era: A comparison of out-of-sample testing methods in a synthetic controlled environment

We present a comprehensive framework to assess these methods, considering the unique characteristics of financial data like non-stationarity, autocorrelation, and regime shifts. Through our analysis, we unveil the marked superiority of the Combinatorial Purged (CPCV) method in mitigating overfitting risks, outperforming traditional methods as evidenced by its lower Probability of Backtest Overfitting (PBO) and superior Deflated Sharpe Ratio (DSR) test statistic. Walk-Forward, by contrast, exhibits notable shortcomings in false discovery prevention, characterized by increased temporal variability and weaker stationarity. This contrasts with CPCV’s demonstrable stability and efficiency. We introduce novel variants of CPCV, including Bagged CPCV and Adaptive CPCV, which enhance robustness through ensemble approaches and dynamic adjustments based on market conditions. Our empirical validation using historical SP 500 data confirms these advanced cross-validation methods’ practical applicability and resilience. The analysis also suggests that choosing between Purged K-Fold and K-Fold necessitates caution due to their comparable performance and potential impact on the robustness of training data in out-of-sample testing. Our investigation utilizes a Synthetic Controlled Environment incorporating advanced models like the Heston Stochastic Volatility, Merton Jump Diffusion, and Drift-Burst Hypothesis alongside regime-switching models. This approach provides a nuanced simulation of market conditions, offering new insights into evaluating cross-validation techniques. We also address the computational aspects of these methods, demonstrating that parallelization significantly improves efficiency, making them feasible for large-scale financial datasets. Our study underscores the necessity of specialized validation methods in financial modeling, especially in the face of growing regulatory demands and complex market dynamics.

Responsible Environmental Management: Sustainable Strategy Models for the Future

The aim of this study is to explore the relationship between responsible en- vironmental management and business performance, emphasizing sustainable strategy models that enhance both environmental sustainability and profitability in modern organizations. Using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS, data were collected from 150 medium to large organizations across multiple industries, including manufacturing, retail, and energy sectors, all of which have implemented formal environmental man- agement initiatives. The findings reveal a statistically significant positive impact of responsible environmental management on both environmental sustainability (β = 0.72, p < 0.01) and business performance (β = 0.65, p < 0.01). Addi- tionally, environmental sustainability was found to positively influence business performance (β = 0.54, p < 0.01). These results validate the relevance of models such as the Triple Bottom Line and Circular Economy, providing action- able insights for companies aiming to enhance competitiveness while achieving sustainability goals. This study highlights the practical implications for busi- ness leaders and policymakers, particularly in fostering sustainable practices that align with both regulatory demands and long-term profitability. Future research is recommended to explore the longitudinal impacts of these strategies across different sectors and regulatory environments.

Challenges and Alternatives to Evaluation Methods and Regulation Approaches for Medical Apps as Mobile Medical Devices: International and Multidisciplinary Focus Group Discussion.

The rapid proliferation of medical apps has transformed the health care landscape by giving patients and health care providers unprecedented access to personalized health information and services. However, concerns regarding the effectiveness and safety of medical apps have raised questions regarding the efficacy of randomized controlled trials (RCTs) in the evaluation of such apps and as a requirement for their regulation as mobile medical devices. This study aims to address this issue by investigating alternative methods, apart from RCTs, for evaluating and regulating medical apps. Using a qualitative approach, a focus group study with 46 international and multidisciplinary public health experts was conducted at the 17th World Congress on Public Health in May 2023 in Rome, Italy. The group was split into 3 subgroups to gather in-depth insights into alternative approaches for evaluating and regulating medical apps. We conducted a policy analysis on the current regulation of medical apps as mobile medical devices for the 4 most represented countries in the workshop: Italy, Germany, Canada, and Australia. We developed a logic model that combines the evaluation and regulation domains on the basis of these findings. The focus group discussions explored the strengths and limitations of the current evaluation and regulation methods and identified potential alternatives that could enhance the quality and safety of medical apps. Although RCTs were only explicitly mentioned in the German regulatory system as one of many options, an analysis of chosen evaluation methods for German apps on prescription pointed toward a "scientific reflex" where RCTs are always the chosen evaluation method. However, this method has substantial limitations when used to evaluate digital interventions such as medical apps. Comparable results were observed during the focus group discussions, where participants expressed similar experiences with their own evaluation approaches. In addition, the participants highlighted numerous alternatives to RCTs. These alternatives can be used at different points during the life cycle of a digital intervention to assess its efficacy and potential harm to users. It is crucial to recognize that unlike analog tools, digital interventions constantly evolve, posing challenges to inflexible evaluation methods such as RCTs. Potential risks include high dropout rates, decreased adherence, and nonsignificant results. However, existing regulations do not explicitly advocate for other evaluation methodologies. Our research highlighted the necessity of overcoming the gap between regulatory demands to demonstrate safety and efficacy of medical apps and evolving scientific practices, ensuring that digital health innovation is evaluated and regulated in a way that considers the unique characteristics of mobile medical devices.

Advancing Maternal Transfer of Organic Pollutants across Reptiles for Conservation and Risk Assessment Purposes.

Embryonic exposure through maternally transferred pollutants can affect embryo vitality, survival, and health. Reptiles face global declines and are sensitive to embryonic pollutant exposure. Yet, they are often neglected in pollution risk assessment and conservation. We analyzed maternal transfer of organic pollutants in reptiles through a systematic extraction, homogenization, and integration of published data on organic pollutants measured in mother-egg pairs into a comprehensive database (DOI:10.5281/zenodo.10900226), complemented with molecular physical-chemical properties of the pollutants. Over four decades, 17 publications provided 19,955 data points shifting from legacy to emerging contaminants although research on newer contaminants lags regulatory and societal demands. Challenges including taxonomic bias, heterogeneity in sampled tissues, and 73% of censored data complicate comparative analyses. However, significant opportunities were identified including the use of the turtle Malachlemys terrapin and snake Enhydris chinensis as flagship species where a large amount of data is available across tissues (allowing investigation into physiological relations) and compounds (allowing insights into maternal transfer across the chemical universe). Data on other freshwater and marine turtles provide the possibility of exploring taxonomic patterns in this subgroup. The analysis, integrated database, and discussion present opportunities for research in an era where science needs to achieve more with limited wildlife data.

A method for selecting the type of energy storage for power systems with high penetration of renewable energy with multi-application scenarios

Energy storage (ES) configurations effectively relieve regulatory pressure on power systems with a high penetration of renewable energy. However, it is difficult for a single ES type to satisfy the complex regulatory demands of a power system. There has been little research on the selection methods for multiple types of ES that meet the demands of multiple application scenarios of power systems. This study introduces a method for the selection of ES types for power systems with a high penetration of renewable energy to determine the optimal ES types for multi-application scenarios. First, a generation method for ES schemes is proposed based on K-means clustering and combination theory. Then, considering the demands of peak shaving, frequency regulation and reserve of power systems with high penetration of renewable energy, an optimisation model of the expected indices of the ES is constructed to meet the demands of multi-application scenarios. Finally, the comprehensive evaluation method for ES schemes based on the error mean and the selection method for optimal ES types based on projection technology are proposed. Numerical studies show that for the power system in the example whose demand coefficients for peak shaving, frequency regulation and reserve are 30.59 %, 26.29 % and 43.11 %, respectively, a combination of pumped storage and colloidal battery, lithium iron phosphate battery, or lithium titanate battery is the most suitable ES type for the power system. This study offers a reference for the selection of ES types when an ES is configured.

Investigating Data Protection Compliance Challenges

In today's landscape, safeguarding sensitive data is crucial for Organizations, but navigating data protection regulations and ensuring compliance is increasingly challenging. This research project explores Organizations' hurdles in achieving data protection compliance, offering insights to develop more effective strategies. A survey via Google Forms gathered insights from data protection experts and professionals, revealing key challenges such as difficulty understanding complex regulations, limited resources, and obstacles in implementing compliance measures. The study also reviewed the existing data protection regulatory framework and relevant literature, uncovering a common theme of confusion and a gap between regulatory requirements and practical application across Organizations. The research recognises that data protection extends beyond regulatory compliance, reflecting the evolving expectations of individuals and customers regarding the ethical handling of their data. This underscores the importance of data protection as both a legal and ethical responsibility closely tied to organisational reputation and public trust. The findings highlight the need for more precise, accessible guidelines and support mechanisms to bridge the gap between regulatory demands and organisational implementation. By addressing these challenges, Organizations can strengthen their data protection measures, foster trust, and ensure the security of sensitive information.

Determination of DEET, Icaridin, and IR3535 in insect repellents using excitation-emission matrix (EEM) fluorescence spectroscopy and multiway calibration

Due to rising temperatures in recent years, there has been an increase in outbreaks of arboviral diseases like Dengue, chikungunya, Zika, and yellow fever, which are primarily transmitted by two Aedes mosquitos. The outbreak has sparked significant concern among the population and prompted countries to heighten their precautions, specially by increasing the use of insects repellents. Regarding the regulatory demands of these products, this study develops a novel analytical method for the determination of DEET, Icaridin, and IR3535 in insect repellents combining excitation-emission matrix (EEM) fluorescence spectroscopy with higher-order multivariate calibration techniques. For this purpose, a dataset comprising 21 samples containing the studied active ingredients was used to construct Parallel Factor Analysis (PARAFAC) and Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) models. As a result, DEET, Icaridin, and IR3535 were quantified with a RMSEP lower than 1.84% for PARAFAC and below 11.30% for MCR-ALS. The EJCR test confirmed the accuracy of the proposed methods, which agreed with the high-performance liquid chromatography reference method at a 95% confidence level. These findings suggest that developing multiway calibration models using EEM proves to be accurate and cost-effective for quality control in repellent-based products.

Enhancing anti-money laundering capabilities: The strategic use of AI and cloud technologies in financial crime prevention

Integrating Artificial Intelligence (AI) and Cloud Technologies is transforming the fight against money laundering in the financial sector. As digital financial platforms become more prevalent, traditional Anti-Money Laundering (AML) methods are proving inadequate against increasingly sophisticated criminal tactics. This study examines how AI and Cloud Technologies can enhance AML capabilities by improving real-time threat detection, reducing false positives, and ensuring compliance with global regulatory standards. Through a qualitative analysis of recent literature and case studies, the research highlights the effectiveness of AI in identifying complex, suspicious activity patterns that traditional systems often miss. Cloud Technologies complement AI by offering scalable, flexible, and secure solutions, enabling financial institutions to manage evolving risks and regulatory demands better. The findings suggest that the strategic use of these technologies represents a significant advancement in financial crime prevention. However, the study also notes challenges, including concerns about data privacy and the need for specialized skills to manage AI systems. The conclusion emphasizes that while AI and Cloud Technologies provide powerful tools for enhancing AML frameworks, ongoing research, and development are needed to address these challenges and fully realize their potential in safeguarding the financial system. This strategic adoption of technology marks a critical shift in combating financial crimes, ensuring that financial institutions remain resilient against the dynamic threats from money laundering.

The Moderating Role of Entrepreneurial Narrative in the Impact of Environmental Regulation on Migrant Workers’ Entrepreneurial Legitimacy from a Green Entrepreneurship Perspective

Globally, environmental regulatory pressures are mounting, eliciting concern for their effects on migrant workers who return home to found businesses. These entrepreneurial migrants contribute to rural economic growth and urbanization, yet concurrently confront the challenge of stringent environmental rules. The study aims to dissect the environmental regulatory pressure’s influence on the legitimacy of these entrepreneurial migrants and the underlying mechanisms. It further investigates the role of a green entrepreneurial orientation as a mediator and the moderating influence of entrepreneurial narratives on this relationship. Utilizing quantitative research methodologies, the analysis is grounded in extensive, firsthand data from an empirical study of migrant entrepreneurs. The findings corroborate a direct link between environmental regulatory pressure and the legitimacy of migrant entrepreneurs while highlighting the mitigating impact of green orientation and the moderating role of narratives. Specifically, environmental regulatory pressure significantly enhances the legitimacy of migrant entrepreneurs. Green entrepreneurial orientation buffers this impact, while entrepreneurial narratives moderate the relationship. This research offers a novel theoretical framework for comprehending the legitimacy dynamics of migrant entrepreneurs amidst environmental regulation and provides actionable guidance for these entrepreneurs to pursue green entrepreneurship in compliance with regulatory demands.

Inconsistent Transcriptomic Responses to Hexabromocyclododecane in Japanese Quail: A Comparative Analysis of Results From Four Different Study Designs.

Efforts to use transcriptomics for toxicity testing have classically relied on the assumption that chemicals consistently produce characteristic transcriptomic signatures that are reflective of their mechanism of action. However, the degree to which transcriptomic responses are conserved across different test methodologies has seldom been explored. With increasing regulatory demand for New Approach Methods (NAMs) that use alternatives to animal models and high-content approaches such as transcriptomics, this type of comparative analysis is needed. We examined whether common genes are dysregulated in Japanese quail (Coturnix japonica) liver following sublethal exposure to the flame retardant hexabromocyclododecane (HBCD), when life stage and test methodologies differ. The four exposure scenarios included one NAM: Study 1-early-life stage (ELS) exposure via a single egg injection, and three more traditional approaches; Study 2-adult exposure using a single oral gavage; Study 3-ELS exposure via maternal deposition after adults were exposed through their diet for 7 weeks; and Study 4-ELS exposure via maternal deposition and re-exposure of nestlings through their diet for 17 weeks. The total number of differentially expressed genes (DEGs) detected in each study was variable (Study 1, 550; Study 2, 192; Study 3, 1; Study 4, 3) with only 19 DEGs shared between Studies 1 and 2. Factors contributing to this lack of concordance are discussed and include differences in dose, but also quail strain, exposure route, sampling time, and HBCD stereoisomer composition. The results provide a detailed overview of the transcriptomic responses to HBCD at different life stages and routes of exposure in a model avian species and highlight certain challenges and limits of comparing transcriptomics across different test methodologies. Environ Toxicol Chem 2024;00:1-11. © 2024 The Author(s). Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Improving the performance of cutting fluids by using ZnO and ZrO2 nanoparticles

Sustainability is nowadays a global research priority, especially in machining, where optimizing production processes for increased productivity, profits, and efficiency is key. Addressing this need, the adoption of nanofluids in minimum quantity lubrication machining has surged, aligning with environmental concerns and regulatory demands. In this study, sustainable zinc oxide (ZnO) and zirconium dioxide (ZrO2) nanoparticles fabricated using plant extracts have been incorporated into conventional cutting fluids to enhance their machinability performance under minimum quantity lubrication for turning process. The microstructural analysis confirms the successful synthesis of the targeted nanoparticles with excellent purity and size distribution. The addition of nanoparticles significantly enhanced thermal conductivity from 0.5916 W/(m⋅K) for the base fluid to 0.6286 W/(m⋅K) for ZnO and to 0.6242 W/(m⋅K) for ZrO2. Further, nanofluids exhibited an increased dynamic viscosity, 1.435 mPa.s for ZrO2 and 1.125 mPa.s for ZnO as compare to 0.7644 mPa.s of base fluid, attributed to the nanoparticle confinement effect whereas contact angle measurements indicated an improved wettability for all nanofluids. Machining experiments validate the efficacy of nanofluids, demonstrating reduced cutting temperatures and enhanced surface finish. Notably, ZrO2-based nanofluids exhibit improved tribological response, while ZnO-based nanofluids showcase exceptional heat transfer ability, offering promising solutions to key technical challenges in machining processes. In conclusion, this study underscores the potential of green, sustainable ZnO and ZrO2 nanoparticles as additives in cutting fluids, poised to revolutionize metalworking and manufacturing processes, thereby enhancing product quality and sustainability.

The moderating role of board gender diversity on the association between audit committee attributes and integrated reporting quality

PurposeThe main objective of the research is to examine the influence of audit committee attributes on the integrated reporting quality (IRQ) and to investigate whether this association is moderated by board gender diversity in the manufacturing and non-manufacturing sectors. The study specifically investigates the association between audit committee independence, meetings frequency, financial expertise, audit committee size, and integrated reporting quality.Design/methodology/approachThe data collection was from the financial statements and corporate governance reports of 54 Egyptian firms listed on the stock market [EGX100] for the period of 2018–2022. The study used pooled effect modelling and performed data analysis using the STATA software.FindingsFor the manufacturing sector, the results showed a significant impact of audit committee independence and meetings frequency on IRQ, while audit committee size and financial expertise showed insignificant association with IRQ. Board gender diversity moderates the association between audit committee meetings only and IRQ. In contrast, audit committee financial expertise and meetings frequency significantly impact IRQ, while audit committee independence and size showed an insignificant impact on IRQ in the non-manufacturing sector. Also, board gender diversity moderates the association between audit committee and IRQ. The findings highlight the significance of AC attributes and provide guidance to firms to strengthen their audit committees in order to provide high quality of integrated reports to increase investor trust in the Egyptian business environment as it is now mandatory by the government.Originality/valueThis research offers unique perspectives on the association between variables in Egypt. This study is one of the first investigations on the impact of audit committee attributes and board diversity on integrated reporting quality within the context of Egyptian listed companies. Comprehending the ways in which audit committee attributes impact the integrated reporting quality in the manufacturing and non-manufacturing sectors can enable organizations to devise focused approaches that comply to the unique operational, regulatory, and investor demands in each sector.