Regulatory Compliance Issues Research Articles

Development and implementation of cost control strategies in oil and gas engineering projects

This review delves into the development and implementation of cost control strategies in oil and gas engineering projects, an area critical for maintaining project viability and profitability amidst fluctuating market conditions. Effective cost control is essential in the oil and gas sector, where projects are often capital-intensive and subject to economic and operational uncertainties. This review examines various cost control methodologies and their application throughout the project lifecycle, from initial planning and budgeting to execution and closure. The paper highlights key cost control strategies, including detailed project cost estimation, budget forecasting, and variance analysis. It discusses the use of advanced project management tools and technologies, such as software for real-time cost tracking and predictive analytics, which enhance decision-making and financial oversight. The review also covers the importance of risk management practices in identifying and mitigating potential cost overruns, emphasizing the role of contingency planning and risk assessment in maintaining financial control. Case studies demonstrate how successful implementation of these strategies can lead to significant cost savings, improved project efficiency, and enhanced financial performance. The challenges associated with cost control, including dealing with unexpected price fluctuations, scope changes, and regulatory compliance issues, are also addressed. Solutions to these challenges, such as adopting flexible budgeting techniques and leveraging data-driven insights, are discussed. The review concludes with recommendations for refining cost control practices in oil and gas engineering projects, including the need for continuous monitoring and adjustment of cost control measures, integrating cost management with overall project strategy, and fostering collaboration among project stakeholders to ensure alignment and accountability.

FDA Warning Letter Trends: A 15-Year Analysis

The U.S. Food and Drug Administration (FDA) plays a crucial role in safeguarding public health by regulating pharmaceutical and medical device industries. Warning letters issued by the FDA serve as a critical tool for enforcing regulatory compliance and highlighting significant violations that require prompt correction. Aim: The article analyzes trends in FDA warning letters issued to pharmaceutical companies from 2005 to 2021, identifying common violations, geographic distribution of recipients, and emerging regulatory concerns. The research is driven by the critical need to understand evolving FDA enforcement priorities and persistent industry challenges. Study Design: Retrospective analysis of publicly available FDA warning letters and related literature. Methodology: We reviewed 13 studies analyzing FDA warning letters, covering a total of 1,569 warning letters issued during the study period. Data were extracted on the frequency of warning letters, types of violations, geographic distribution of recipients, and emerging trends. Quantitative and qualitative analyses were performed to identify patterns and changes over time. Results: The annual number of warning letters increased significantly from an average of 17 in the early 2000s to 304 in 2020. Quality system issues were the most common violation, accounting for 34% of all citations from 2014-2016. Data integrity breaches emerged as a major concern, rising from negligible levels pre-2014 to 24% of violations by 2016. The proportion of warning letters issued to foreign manufacturers increased from 22.9% in 2019 to 33% in 2020. COVID-19 related violations accounted for 42.1% of all warning letters in 2020. Conclusion: FDA enforcement actions have intensified over the study period, with a shift towards systemic quality issues and data integrity concerns. The globalization of pharmaceutical manufacturing has led to increased scrutiny of foreign facilities. These trends highlight the need for robust quality management systems and proactive compliance strategies in the pharmaceutical industry. By synthesizing data from various periods and studies, this paper offers a longitudinal perspective on regulatory compliance issues to prioritize areas for improvement in quality management systems.

Impact of Human Resources in Indian Oil Companies: Evolution, Challenges, and Future Trends

This study analyses the evolution, current challenges, and future human resource (HR) management trends within Indian oil companies. It seeks to provide a comprehensive understanding of how HR practices have developed over time, the obstacles faced by HR departments today, and the strategic directions necessary for future growth and adaptation. The research employs a mixed-methods approach, combining qualitative and quantitative data. Primary data were collected through surveys and interviews with HR professionals and employees in major Indian oil companies. Secondary data were drawn from industry reports, academic literature, and company records. The study utilizes thematic analysis to identify critical trends and challenges and statistical analysis to quantify the impact of various factors on HR practices. The study finds that HR practices in Indian oil companies have evolved from traditional administrative roles to strategic partners in business development. Current challenges include managing a diverse and technologically evolving workforce, adapting to global economic fluctuations, and addressing regulatory compliance issues. Future trends suggest an increased focus on digital transformation, diversity and inclusion, and strategic workforce planning. The findings offer valuable insights for HR practitioners and policymakers in Indian oil companies. They highlight the need for innovative HR strategies to address emerging challenges, such as integrating advanced technologies and fostering an inclusive work environment. The study also recommends improving HR practices to enhance organizational performance and employee satisfaction.

Risks and Opportunities Brought by Artificial Intelligence Empowering the Financial Industry

Intelligent risk management and trade optimization play pivotal roles in the realm of investment banking. Leveraging AI's capabilities, institutions harness vast datasets and machine learning algorithms to detect potential risks and fine-tune trading strategies. This results in more accurate decision-making processes and better risk mitigation measures. Furthermore, AI enables personalized customer service and precise market forecasting, elevating client satisfaction levels and enabling the anticipation of market trends with greater precision. Tailored services foster stronger client relationships, driving long-term partnerships and loyalty. In addition to enhancing operational efficiency and client satisfaction, AI drives innovation and development within investment banking. This includes the introduction of intelligent advisory services and the implementation of digital perception systems, revolutionizing traditional banking practices. However, the integration of AI also presents challenges such as data privacy concerns, regulatory compliance issues, the need for talent cultivation, and organizational transformation. Looking ahead, the future of investment banking is expected to witness increased AI adoption, leading to higher levels of intelligence, more sophisticated learning techniques, and collaborative innovation across the industry. This trajectory promises to reshape the landscape of investment banking, creating new opportunities for growth and advancement.

Enhancing Cybersecurity with Blockchain-based Identity Management

In the digital age, the rapid increase in cyber threats has highlighted the need for more robust and secure identity management systems. Traditional centralized identity management systems are often vulnerable to data breaches, unauthorized access, and identity theft, posing significant risks to individuals and organizations. This paper explores the potential of blockchain technology to enhance cybersecurity through decentralized identity management. By leveraging the inherent properties of blockchain—such as immutability, transparency, and decentralization—blockchain-based identity management systems offer a more secure and efficient alternative to traditional methods. The paper provides an in-depth analysis of how blockchain can be utilized to store and manage digital identities securely, reducing the risk of identity fraud and data breaches. It examines various blockchain-based identity management frameworks, highlighting their advantages and potential challenges in implementation. Furthermore, the study discusses the integration of blockchain with other emerging technologies, such as cryptographic algorithms and smart contracts, to create a more resilient cybersecurity infrastructure. Through a review of current literature and case studies, the paper demonstrates the effectiveness of blockchain in enhancing data security and privacy while promoting user control over personal information. The findings suggest that adopting blockchain-based identity management could significantly mitigate cybersecurity threats and enhance trust in digital transactions. However, the paper also acknowledges the need for addressing scalability, regulatory compliance, and interoperability issues to fully realize the potential of blockchain in cybersecurity. This research contributes to the growing body of knowledge on innovative cybersecurity solutions and provides a foundation for future studies in the field.

Blockchain and smart contracts for supply chain transparency and vendor management

Blockchain technology and smart contracts are emerging as transformative tools for enhancing transparency and efficiency in supply chain management and vendor relations. Traditional supply chains often face challenges such as inefficiencies, lack of transparency, and susceptibility to fraud (Kouhizadeh & Sarkis, 2018). Blockchain, a decentralized ledger technology, coupled with smart contracts, which are self-executing contracts with the terms directly written into code, offers promising solutions to these challenges (Wang, Han, & Beynon-Davies, 2019). This paper explores the application of blockchain and smart contracts in these domains, examining their potential to provide immutable records, streamline processes, and mitigate risks (Casado-Vara et al., 2018). Through a comprehensive analysis of current literature and case studies, we identify key benefits including improved transparency, enhanced efficiency, and better risk management (Christidis & Devetsikiotis, 2016; Tian, 2016). Our findings suggest that blockchain and smart contracts can significantly improve supply chain transparency and vendor management, though their implementation requires careful consideration of technical, regulatory, and organizational factors (Saberi et al., 2019). Notable case studies, such as Walmart’s blockchain pilot for food safety and De Beers' diamond tracking initiative, illustrate the practical benefits and challenges of adopting these technologies in real-world scenarios (Casino, Dasaklis, & Patsakis, 2019; Kshetri, 2018). Despite the promising outlook, further research is needed to address scalability, interoperability, and regulatory compliance issues to fully realize the potential of blockchain and smart contracts in supply chain management (Hughes et al., 2019).

Assessment of record keeping management in General Hospital, Ikot Abasi, Akwa Ibom State

Introduction: This study examines records management at Ikot Abasi General Hospital. Four specific objectives were established and four research questions were formulated to guide the development of the study. The research focused on current records management practices at the hospital, assessing the existing infrastructure and technology used to maintain these records.Methods: A descriptive survey design with a qualitative approach was used. Data collection was conducted by interviewing a total of 11 participants, which included 2 physicians, 5 nurses, and 4 records managers at Ikot Abasi General Hospital. An interview guide was designed to elicit information on the participants' perspectives on records management practices in the hospital. The data collected were analyzed using thematic analysis.Results: The study revealed a varying level of understanding among respondents about current records management practices, with concerns about accuracy and completeness of documentation. Despite these challenges, evaluation of existing infrastructure and technology showed a positive impact, particularly with the implementation of electronic health record (EHR) systems, which has streamlined processes and reduced reliance on paper documentation.Conclusions: The study identified several challenges faced by healthcare professionals and administrators, including inconsistent documentation practices, lack of adequate storage facilities, limited access to technology and resources, and regulatory compliance issues. It was recommended that healthcare and related organizations implement standardized documentation protocols and provide regular training sessions for healthcare professionals to ensure consistency, accuracy, and completeness in records management. In addition, it was suggested that governments at all levels allocate resources to improve records management infrastructure and invest in modern technological solutions, such as electronic health record (EHR) systems.

Challenges and way forward in the handling and disposal of battery waste: Towards sustainable practices

<p>The management and disposal of battery waste, particularly lead-acid batteries, present numerous challenges that must be addressed to ensure environmental protection, public health, and sustainable resource management. This thesis examines the multifaceted nature of these challenges and explores potential solutions for sustainable battery management. Drawing on insights from prior research, field observations, stakeholder interviews, and literature reviews, the study synthesizes existing knowledge to inform strategies for mitigating the environmental, safety, and economic impacts associated with battery waste. The current scenario on battery waste management highlights significant challenges, including environmental pollution, health risks, safety hazards, recycling challenges, regulatory compliance issues, technological limitations, infrastructure constraints, and the need for public awareness and education. Countermeasures to address these challenges encompass regulatory interventions, technological innovations, infrastructure development, capacity building, and public engagement initiatives. By integrating findings from diverse sources, this thesis aims to contribute to the existing knowledge base on battery waste management and propel strategies for sustainable resource management.<strong></strong></p>

On responsible machine learning datasets emphasizing fairness, privacy and regulatory norms with examples in biometrics and healthcare

Artificial Intelligence (AI) has seamlessly integrated into numerous scientific domains, catalysing unparalleled enhancements across a broad spectrum of tasks; however, its integrity and trustworthiness have emerged as notable concerns. The scientific community has focused on the development of trustworthy AI algorithms; however, machine learning and deep learning algorithms, popular in the AI community today, intrinsically rely on the quality of their training data. These algorithms are designed to detect patterns within the data, thereby learning the intended behavioural objectives. Any inadequacy in the data has the potential to translate directly into algorithms. In this study we discuss the importance of responsible machine learning datasets through the lens of fairness, privacy and regulatory compliance, and present a large audit of computer vision datasets. Despite the ubiquity of fairness and privacy challenges across diverse data domains, current regulatory frameworks primarily address human-centric data concerns. We therefore focus our discussion on biometric and healthcare datasets, although the principles we outline are broadly applicable across various domains. The audit is conducted through evaluation of the proposed responsible rubric. After surveying over 100 datasets, our detailed analysis of 60 distinct datasets highlights a universal susceptibility to fairness, privacy and regulatory compliance issues. This finding emphasizes the urgent need for revising dataset creation methodologies within the scientific community, especially in light of global advancements in data protection legislation. We assert that our study is critically relevant in the contemporary AI context, offering insights and recommendations that are both timely and essential for the ongoing evolution of AI technologies.

Culture and Immunomodulation of Equine Muscle-Derived Mesenchymal Stromal Cells: A Comparative Study of Innovative 2D versus 3D Models Using Equine Platelet Lysate.

Muscle-derived mesenchymal stromal cells (mdMSCs) hold great promise in regenerative medicine due to their immunomodulatory properties, multipotent differentiation capacity and ease of collection. However, traditional in vitro expansion methods use fetal bovine serum (FBS) and have numerous limitations including ethical concerns, batch-to-batch variability, immunogenicity, xenogenic contamination and regulatory compliance issues. This study investigates the use of 10% equine platelet lysate (ePL) obtained by plasmapheresis as a substitute for FBS in the culture of mdMSCs in innovative 2D and 3D models. Using muscle microbiopsies as the primary cell source in both models showed promising results. Initial investigations indicated that small variations in heparin concentration in 2D cultures strongly influenced medium coagulation with an optimal proliferation observed at final heparin concentrations of 1.44 IU/mL. The two novel models investigated showed that expansion of mdMSCs is achievable. At the end of expansion, the 3D model revealed a higher total number of cells harvested (64.60 ± 5.32 million) compared to the 2D culture (57.20 ± 7.66 million). Trilineage differentiation assays confirmed the multipotency (osteoblasts, chondroblasts and adipocytes) of the mdMSCs generated in both models with no significant difference observed. Immunophenotyping confirmed the expression of the mesenchymal stem cell (MSC) markers CD-90 and CD-44, with low expression of CD-45 and MHCII markers for mdMSCs derived from the two models. The generated mdMSCs also had great immunomodulatory properties. Specific immunological extraction followed by enzymatic detection (SIEFED) analysis demonstrated that mdMSCs from both models inhibited myeloperoxidase (MPO) activity in a strong dose-dependent manner. Moreover, they were also able to reduce reactive oxygen species (ROS) activity, with mdMSCs from the 3D model showing significantly higher dose-dependent inhibition compared to the 2D model. These results highlighted for the first time the feasibility and efficacy of using 10% ePL for mdMSC expansion in novel 2D and 3D approaches and also that mdMSCs have strong immunomodulatory properties that can be exploited to advance the field of regenerative medicine and cell therapy instead of using FBS with all its drawbacks.

Challenges and way forward in the handling and disposal of battery waste: Towards sustainable practices

The management and disposal of battery waste, particularly lead-acid batteries, present numerous challenges that must be addressed to ensure environmental protection, public health, and sustainable resource management. This thesis examines the multifaceted nature of these challenges and explores potential solutions for sustainable battery management. Drawing on insights from prior research, field observations, stakeholder interviews, and literature reviews, the study synthesizes existing knowledge to inform strategies for mitigating the environmental, safety, and economic impacts associated with battery waste. The current scenario on battery waste management highlights significant challenges, including environmental pollution, health risks, safety hazards, recycling challenges, regulatory compliance issues, technological limitations, infrastructure constraints, and the need for public awareness and education. Countermeasures to address these challenges encompass regulatory interventions, technological innovations, infrastructure development, capacity building, and public engagement initiatives. By integrating findings from diverse sources, this thesis aims to contribute to the existing knowledge base on battery waste management and propel strategies for sustainable resource management.

Application and Impact Analysis of Intelligent Financial Systems in Small and Medium-Sized Manufacturing Industries

This paper explores how smart financial systems can empower small and medium-sized manufacturing industries by integrating artificial intelligence, big data analytics and cloud computing technologies to provide customised financial service solutions for the sector. The study outlines the core challenges faced by small and medium-sized manufacturing industries, such as financing difficulties, lagging risk management and operational inefficiencies. The core components of the smart financial system are detailed, including functional modules such as automated credit assessment, supply chain finance optimisation, cost prediction models and personalised insurance product recommendations. This paper proposes strategies to address data security, technology acceptance, and regulatory compliance issues that may be encountered during the implementation process, and emphasises the importance of building a good ecosystem between the government, financial institutions, and technology enterprises to comprehensively support the intelligent transformation and sustainable development of the small and medium-sized manufacturing industry.

SOCIAL AND ENVIRONMENTAL ANALYSIS CONTRIBUTIONS TO FLORICULTURE IN THE HYDRANGEA SECTOR THROUGH A LIFE CYCLE ASSESSMENT APPROACH

In La Paz Province (Eastern Antioquia, Colombia), hydrangea floriculture has emerged as a significant driver of economic and social development following the post-conflict situation, offering high value-added contributions to the territory. The objective was to identify both the social and environmental aspects of hydrangea cultivation by small and medium-sized growers, assessing the environmental impacts and externalities across the crop’s life cycle. This initiative aimed to foster sustainable growth in floriculture production under eco-friendly conditions, safeguarding human health, and benefiting local stakeholders. To establish a methodological framework, the study adhered to the requirements and guidelines outlined in ISO 14040 and 14044 standards for environmental life cycle assessment, in addition to utilizing the UNEP Methodological Guide. The research revealed significant findings, highlighting the key environmental impacts affecting hydrangea production throughout its life cycle. These impacts primarily stem from the excessive use of agrochemicals, which pose risks to human health and ecosystems due to alterations in soil properties. Moreover, the reliance on fossil resources (such as plastics and fuels) at various stages of the cultivation process contributes to these impacts. From a social perspective, the study identified stakeholders and critical aspects requiring improvement, including integration, safety, occupational health, proper agrochemical handling, regulatory compliance, and system replacement issues. These challenges underscore the complexity of achieving sustainability, emphasizing the need for specialized technical support and new knowledge for growers. Implementing cost-effective technologies is crucial to mitigate environmental impacts, along with fostering associativity models among hydrangea crop producers, which significantly contribute to enhancing growing conditions.

An insight into the Application of AI in maritime and Logistics toward Sustainable Transportation

This review article looks at the developing field of artificial intelligence and machine learning in maritime and marine environment management. The marine industry is increasingly interested in applying advanced AI and ML technologies to solve sustainability, efficiency, and regulatory compliance issues. This paper examines maritime and marine AI and ML applications using a deep literature review and case study analysis. Modeling ship fuel consumption, which impacts the environment and operating expenses, is a top responsibility. The study demonstrates that ML approaches such as Random Forest and Tweedie models can estimate ship fuel use. Statistical analysis demonstrates that the Random Forest model beats the Tweedie model regarding accuracy and consistency. For the training and testing datasets, the Random Forest model has high R2 values of 0.9997 and 0.9926, indicating a solid match. Low Root Mean Square Error (RMSE) and average absolute relative deviation (AARD) suggest that the model accurately reflects fuel use variability. While still performing well, the Tweedie model has lower R2 values and higher RMSE and AARD values, suggesting reduced accuracy and precision in fuel consumption prediction. These findings provide light on the potential applications of artificial intelligence and machine learning in maritime and marine environment management. Advanced analytics enables decision-makers to analyze fuel consumption patterns better, increase operational efficiency, and decrease environmental impact, thus improving maritime sustainability.

Apply advanced management techniques in demolition, renovation and construction projects to improve building material recycling efficiency and reduce carbon emissions

This research delves into the complicated issues associated with demolition, renovation, and construction of buildings to find viable, sustainable practices that will relieve some strain on nature. Identifying major components like governmental policies, cooperative work of stakeholders, assimilation of technology, and the possible transformative capacity brought by Building Information Modeling (BIM), this research attempts to unveil how a more ecologically mindful construction industry can be achieved. The methodology used in this research has a multi-dimensional approach that is aimed at thoroughly understanding and clearing the challenges within the construction industry. The strengths like government support, stakeholder involvement, technological innovations, and adoption of BIM assist in building a strong base for sustainability. The research identified opportunities for technological advancements, special solutions, and government incentives. Technological advancement is a constantly changing environment, which gives the building sector an opportunity to adapt and enhance its recycling strategies. Customized solutions highlight the fact that such "place-based" strategies are needed because different urban, suburban, and rural landscapes present distinct challenges. Most property owners would be convinced to swap their regular buildings with sustainable building techniques through incentives from the government, such as tax benefits and subsidies. Furthermore, regulatory compliance issues and resistance to change cement the fact that societies need not only solid guidelines but also constant efforts to redefine standards in every industry. Effective negotiation between stakeholders becomes essential. Therefore, it can be stated that the findings give weight as catalysts of change in terms of action within the construction industry.

Reliability of AI Algorithms in Safety Applications

The integration of Artificial Intelligence (AI) algorithms into safety-critical applications has become increasingly prevalent across various domains, including autonomous vehicles, medical diagnosis, industrial automation, and aerospace systems. These applications rely heavily on AI to make decisions that directly affect human safety, economic stability, and operational efficiency. Given the critical nature of these tasks, it is essential to rigorously assess the reliability of AI algorithms to ensure they perform consistently and accurately under all conditions. Reliability, in this context, refers to the AI system's ability to function without failure over a specific period, under defined operational conditions. In safety-critical domains, even minor errors or inconsistencies in AI decision-making can lead to catastrophic outcomes, such as traffic accidents involving autonomous vehicles, incorrect medical diagnoses leading to improper treatments, or failures in industrial processes that may cause costly downtime or even human casualties. The increasing complexity and deployment of AI technologies in these domains highlight the urgent need for a comprehensive understanding and evaluation of AI reliability. This paper provides a detailed analysis of the design considerations and methodologies for enhancing the reliability of AI algorithms. The discussion begins by exploring the underlying principles of reliability in AI systems, focusing on both theoretical and practical perspectives. We examine key factors that influence reliability, including data quality, algorithmic robustness, model interpretability, and system integration. The paper then delves into various reliability assessment techniques, such as fault tolerance mechanisms, error detection and correction methods, redundancy, and validation processes. To provide a deeper understanding of reliability in AI, we introduce mathematical models and statistical evaluation techniques that quantify reliability metrics. For instance, reliability modeling using exponential distribution, Monte Carlo simulations for probabilistic reliability analysis, and error propagation studies using Jacobian matrices are presented. We also explore the use of machine learning-specific reliability metrics, such as the Area Under the Curve (AUC) in Receiver Operating Characteristic (ROC) analysis, which helps evaluate the performance of AI in critical decision-making contexts. Furthermore, this paper addresses the current challenges and limitations in ensuring AI reliability, including computational complexity, ethical considerations, and regulatory compliance issues. We highlight the difficulties in developing AI models that can maintain their reliability across diverse and unpredictable real-world scenarios. The potential for bias, lack of transparency in AI decision-making, and difficulties in explaining complex AI models also present significant hurdles that need to be addressed to enhance reliability. The findings and methodologies discussed in this paper aim to contribute to a deeper understanding of the complex landscape of AI reliability, providing a framework for researchers, practitioners, and policymakers to develop safer, more reliable AI systems that can be trusted to operate in environments where safety is paramount.

Securing Blockchain Enterprises: Legal Due Diligence Amidst Rising Cyber Threats

This study aims to understand the vulnerabilities faced by enterprises operating on token-based blockchain businesses and the role of legal, due diligence procedures in mitigating such risks. It employed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses method and sourced data from DeFillama, a platform tracking decentralized finance developments, to categorize hacking incidents into five major groups: Ecosystem, Infrastructure, Protocol Logic, Rugpull, and Smart Contract Language. The findings highlight that Infrastructure attacks, mainly through Private Key Compromise, are the most damaging. They cause losses of over 800 million dollars between 2020 and 2023. It necessitates comprehensive and adaptable legal, due diligence strategies focusing on jurisdictional legal frameworks, platform usage terms, regulatory compliance, and potential legal issues. The study underscores the importance of further research to evaluate and enhance the effectiveness of these measures in addressing the unique challenges of blockchain technology, which are crucial for enhancing the resilience and sustainability of blockchain enterprises, thereby promoting global trust in this emerging field.

Enhanced modelling of planar radial-loaded deep groove ball bearings with smooth-contact formulation

AbstractBearings are mechanical components designed to restrict the relative rotary motion between moving parts and transmit loads with low friction. Their performance directly impacts the durability, efficiency and reliability of various machinery. Therefore, bearing failures can lead to economic costs, repair/stoppage times, accidents and regulatory compliance issues. In the context of Industry 4.0, the development of detailed and reliable computational models for simulating bearings’ dynamics plays a crucial role in establishing digital twins and implementing advanced predictive maintenance strategies.This work focuses on modelling radial-loaded deep groove ball bearings under the multibody systems dynamics framework and the components of the bearing (inner and outer rings, rolling elements, and cage) are treated as separate bodies. A smooth contact approach is utilised to characterise the contact/impact phenomena, providing flexibility and efficiency in monitoring the whole contact event. In this sense, suitable normal and friction contact force models are used to describe those interactions between the contacting bodies. The main contribution of this work relies on the modelling strategies to represent the cage/rolling element interaction.Having that in mind, several multibody models of radial-loaded deep groove ball bearings are developed considering different modelling assumptions, resulting in dynamic analyses with various levels of complexity. The underlying simplifications are described, and their main advantages and shortcomings are discussed. The simulation results demonstrated the significant impact of accurately selecting the modelling parameters. The promising results of this study pave the way for future investigations, extending to other geometries of rolling contact bearings and working conditions.

Digitalization in Global Trade: Opportunities and Challenges for Investment

This paper explores the evolving landscape of global trade and customs in the context of digitalization and the opportunities and challenges it presents for investment. The rapid advancement of technology and the digital revolution have significantly transformed the way businesses engage in international trade, leading to increased efficiency, streamlined processes, and enhanced global connectivity. However, these advancements also bring forth new complexities, such as cybersecurity risks, data privacy concerns, and regulatory compliance issues. We examine the key areas where investment in digital technologies can drive innovation, efficiency, and competitiveness in global trade and customs. It discusses the potential benefits of implementing technologies such as blockchain, artificial intelligence (AI), Internet of Things (IoT), and cloud computing in areas like supply chain management, customs compliance, trade finance, and risk management. The paper also explores case studies and real-world examples of successful digital transformation initiatives in the global trade and customs domain. The study emphasizes the importance of strategic investment in digital technologies as a means to unlock the full potential of global trade and customs. It provides insights and recommendations for policymakers, industry stakeholders, and investors on navigating the evolving landscape, seizing opportunities, and mitigating risks associated with digitalization in the realm of international trade and customs. Global trade, Customs, Digitalization, Investment, Blockchain, Artificial Intelligence, Supply chain management, Risk management, Cybersecurity

Analysing Effective and Ineffective Impacts of Maintenance Strategies on Electric Power Plants: A Comprehensive Approach

The maintenance strategy used in an electric power plant plays a crucial role in its overall performance and operational efficiency. An effective maintenance strategy describes the approach to exploiting various forms of maintenance (corrective, preventive, predictive, proactive, etc.) in an electric power plant. In this paper, the effective and ineffective impacts of maintenance strategies on power plants were examined. Also, the distinction between corrective, preventive, and aggressive maintenance was considered. In terms of effective impacts, a well-designed and executed maintenance strategy enhances the reliability and availability of the electric power plant by minimising unplanned downtime. It extends the lifespan of critical equipment, improves safety measures, increases energy efficiency, and contributes to long-term cost savings. However, in terms of ineffective impacts, poorly planned or executed maintenance strategies can result in increased downtime, higher repair costs, safety risks, decreased efficiency, and regulatory compliance issues. Neglecting maintenance can lead to equipment failures, reduced productivity, and potential environmental incidents.