How does artificial intelligence affect reliable engineering?

Offshore platforms are vital assets for the oil and gas industry, serving as the primary facilities for exploration, extraction, and processing. Maintenance logistics plays a crucial role in ensuring these platforms operate efficiently and safely. However, the remote and harsh environments of offshore platforms present significant challenges for maintenance activities. Traditional maintenance strategies often struggle to meet the demands of these environments, leading to inefficiencies, increased costs, and potential safety risks. This review discusses the application of Artificial Intelligence (AI) in optimizing maintenance logistics on offshore platforms. Current strategies involve a combination of preventive, predictive, and corrective maintenance approaches. Preventive maintenance schedules regular inspections and replacements based on predetermined intervals, while predictive maintenance utilizes data analytics to predict equipment failures and plan maintenance activities accordingly. Corrective maintenance addresses issues as they arise, often in response to unexpected failures. AI offers opportunities to enhance these strategies by leveraging advanced data analytics, machine learning, and optimization algorithms. AI-enabled predictive maintenance can analyze vast amounts of data from sensors, historical maintenance records, and environmental factors to forecast equipment failures with greater accuracy. This allows for proactive maintenance planning, minimizing downtime and reducing maintenance costs. Furthermore, AI can optimize maintenance logistics by improving resource allocation and scheduling. Through real-time monitoring and analysis, AI systems can prioritize maintenance tasks based on urgency, equipment criticality, and resource availability. This ensures that maintenance crews are deployed efficiently, reducing idle time and improving overall productivity. Future innovations in AI for maintenance logistics on offshore platforms include the integration of Internet of Things (IoT) devices and autonomous systems. IoT sensors can provide real-time data on equipment condition and environmental factors, enabling more precise predictive maintenance models. Autonomous maintenance robots equipped with AI algorithms can perform routine inspections and minor repairs, reducing the need for human intervention in hazardous environments. However, implementing AI in offshore maintenance logistics also poses challenges, including data quality, cybersecurity, and workforce readiness. Ensuring data accuracy and reliability is crucial for effective AI models, requiring robust data collection and management processes. Cybersecurity measures must be strengthened to protect AI systems from malicious attacks that could disrupt operations or compromise safety. Additionally, workforce training and education are essential to prepare personnel for working alongside AI systems and interpreting AI-generated insights. Optimizing maintenance logistics on offshore platforms with AI offers significant benefits in terms of efficiency, cost savings, and safety. By leveraging AI technologies, current maintenance strategies can be enhanced, and future innovations can revolutionize offshore maintenance practices, making operations more sustainable and resilient in the face of evolving challenges.

Disclosure: H.M. Heshmati: None. N. Zameni: None.Background: Robotic surgery is quickly replacing traditional surgery performed by humans. It has significantly influenced the field of endocrine surgery. This review presents an update on the use of robotic surgery and artificial intelligence for adrenal tumors. Methods: A systematic search of literature was conducted using the search terms robotic surgery, artificial intelligence, adrenal tumors, safety, outcome, and cost. Results: Adrenal tumors are found in 10% of people who have imaging of the adrenal gland. They are typically unilateral and benign in the majority of cases. Robotic systems such as da Vinci Surgical System provide surgeons with enhanced dexterity and 3D visualization. Autonomous surgical robots can be categorized into various levels of autonomy including supervised autonomy (e.g., performance of specific tasks with human oversight), collaborative autonomy (e.g., robots working alongside human surgeons, sharing tasks and responsibilities), and fully autonomous systems (e.g., robots performing entire surgical procedures independently with human intervention when necessary). Robotic surgery allows access through tight spaces to remove adrenal tumors with small incision and minimal impact on surrounding tissues. The review of 1,162 patients who underwent adrenalectomy (robotic surgery in 747 patients, laparoscopic adrenalectomy in 415 patients) showed comparable blood loss and complications with the two techniques but patients with robotic surgery had significantly shorter hospital stay. The future of robotic surgery can be revolutionized by the integration of autonomous robots and artificial intelligence (AI), a field rapidly advancing. These innovations promise to further enhance the precision, safety, and effectiveness of surgery involving the adrenal glands. AI-powered systems provide real-time guidance and feedback to surgeons, enhancing decision-making and surgical precision. Autonomous systems can ensure consistent execution of surgical tasks, reducing variability and enhancing patient safety. AI algorithms can analyze extensive surgical data to refine techniques and predict potential complications, improving surgical outcomes. AI can customize surgical plans based on individual patient anatomy and pathology, potentially leading to better outcomes. The current high cost of these new procedures is a potential limitation for their use. Conclusion: Robotic surgery represents significant progress in minimally invasive techniques for the removal of adrenal tumors. It has improved dexterity and precision, and is associated with shorter recovery time compared to laparoscopic adrenalectomy. Collaboration between human surgeons and robotic systems can improve patient care and surgical outcomes. The precision, safety, and effectiveness of the adrenal surgery can be further enhanced by the integration of AI and robotics.Presentation: Saturday, July 12, 2025

Artificial Intelligence (AI) has emerged as a transformative technology in the pursuit of sustainable energy development, offering innovative solutions to optimize energy generation, enhance efficiency, and reduce carbon emissions. This review explores the diverse applications of AI in sustainable energy systems, including renewable energy forecasting, smart grid management, demand-side optimization, decentralized energy trading, and carbon mitigation strategies. AI-driven algorithms enable accurate predictions of solar and wind energy production, improve energy storage management, and facilitate the operation of microgrids and virtual power plants. Furthermore, AI enhances energy efficiency through predictive maintenance, real-time consumption monitoring, and intelligent demand response mechanisms. Despite its potential, the integration of AI in sustainable energy systems presents several challenges. Data availability and quality remain critical barriers, as energy sectors often lack standardized and interoperable datasets. Additionally, the high computational costs of AI models, cybersecurity risks in smart grids, and ethical concerns regarding AI decision-making pose significant obstacles to widespread adoption. Regulatory frameworks governing AI implementation in the energy sector are still evolving, requiring further policy interventions to ensure responsible and effective deployment. Looking ahead, advancements in AI algorithms, improved energy system integration, and AI-driven circular economy initiatives offer promising opportunities for sustainable energy development. Collaborative efforts among governments, industries, and research institutions are essential to harness AI’s full potential while addressing its limitations. This review highlights the need for interdisciplinary research, policy development, and investment in AI-driven sustainable energy solutions to achieve global energy transition goals. As AI continues to evolve, its role in optimizing energy use, reducing environmental impact, and fostering a more resilient and sustainable energy ecosystem will be pivotal in shaping the future of energy systems worldwide.

Artificial intelligence (AI) has emerged as a promising technology that can revolutionize surgical decision-making (SDM). This comprehensive review aims to explore the current state of AI in SDM and highlight its benefits, challenges, and future directions. The integration of AI in SDM offers numerous advantages. AI algorithms can analyze medical images, such as radiographs, computed tomography scans, and magnetic resonance imaging, to detect abnormalities and assist in pre-operative assessments. By leveraging electronic health records, AI can provide personalized surgical recommendations based on patient-specific data.Additionally, AI can analyze genetic data to assess genetic predispositions and tailor treatment plans accordingly. Intra-operatively, AI can aid in real-time analysis of surgical videos and imaging, helping surgeons identify critical structures and guide precise incisions. AI algorithms can also monitor physiological indicators to detect early signs of complications and predict outcomes, improving intra-operative decision-making. Post-operatively, AI can analyze vital signs, imaging, and patient data to detect complications, provide outcomes analysis, and facilitate personalized patient care. However, challenges and limitations exist. Data quality and availability, interpretability of AI algorithms, data security, integration into surgical workflows, and regulatory considerations are important challenges. Addressing these challenges involves ensuring data privacy, developing transparent AI models, establishing robust infrastructure, engaging clinicians, and establishing regulatory frameworks. AI-powered surgical robots and systems can enhance surgical precision and automation. Improvements in interpretability and explainability foster trust and ethical considerations. Also, data sharing and collaboration advancements could refine AI algorithms' accuracy and generalizability. Personalized medicine and precision surgery are achieved through AI integration. Also, education and training could benefit from AI-powered decision support systems.

The integration of Artificial Intelligence (AI) in healthcare is revolutionizing diagnostics, offering important opportunities while emerging challenges. This essay explores the potential and obstacles of AI-based diagnostics. Key problems include technological and ethical issues such as data privacy, compatibility of devices , and regulatory issues. Ethical considerations also arise around patient privacy, accountability for AI errors, legal frameworks, and algorithm transparency. Despite these challenges, AI showcases substantial opportunities for the improvement of diagnostics and treatment techniques. Its capabilities include earlier disease detection, personalized treatment pathways, and healthcare professional support, particularly in radiology, pathology, and genomics. Additionally, AI can help and reduce the cost of healthcare access by streamlining resources, optimizing staff deployment, and facilitating remote monitoring and telemedicine, especially for developing countries. With the analysis of data from wearable devices and enabling remote consultations, AI helps us in the proactive management of chronic conditions. AI-based diagnostics can help overcome long term healthcare challenges, enhancing precision and treatment efficiency. A balanced approach is essential, addressing ethical, privacy, and legal considerations while emphasizing AI’s role as a support for healthcare professionals. Future research should focus on AI algorithm transparency, ethics, and the expansion of telemedicine, especially in underserved areas.

Artificial Intelligence (AI) is revolutionizing the accounting profession, offering transformative capabilities for automating tasks, enhancing decision-making, and improving financial accuracy. As AI becomes integral to accounting practices, it brings both significant opportunities and notable ethical challenges. This review examines the intersection of AI and ethical accounting, providing insights into how professionals can navigate the evolving landscape. The adoption of AI in accounting introduces opportunities for increased efficiency and accuracy. AI systems can handle repetitive tasks such as data entry, reconciliation, and transaction categorization, freeing accountants to focus on strategic activities. Advanced AI algorithms can analyze large volumes of financial data to identify patterns, detect anomalies, and provide real-time insights, enhancing decision-making and forecasting accuracy. Moreover, AI-driven predictive analytics can aid in risk assessment and management, helping organizations to anticipate and mitigate potential financial threats. However, the integration of AI in accounting also raises significant ethical concerns. One of the primary challenges is ensuring transparency and accountability in AI decision-making processes. As AI systems often operate as "black boxes," understanding and explaining their outputs can be difficult, potentially leading to issues of trust and compliance. Ethical accounting necessitates that AI systems be designed with transparency in mind, providing clear explanations for their decisions and actions. Data privacy and security represent another critical ethical consideration. The extensive use of financial data by AI systems necessitates robust measures to protect sensitive information from breaches and unauthorized access. Accountants must ensure that AI systems comply with data protection regulations and ethical standards, safeguarding the confidentiality and integrity of financial data. Bias and fairness in AI algorithms are also pressing ethical issues. If not properly addressed, biases in AI systems can lead to unfair outcomes, such as biased financial recommendations or discriminatory practices. Ensuring fairness requires ongoing monitoring and evaluation of AI systems to identify and mitigate biases. In conclusion, while AI offers substantial benefits for the accounting profession, it also presents ethical challenges that must be carefully managed. Accountants must navigate these challenges by promoting transparency, ensuring data privacy and security, and addressing biases in AI systems. By doing so, the accounting profession can harness the potential of AI while upholding ethical standards and maintaining public trust. Keywords: AI, Ethical Accounting, Navigating, Challenges, Opportunities.

The integration of artificial intelligence (AI) in predictive maintenance systems represents a transformative approach to addressing supply chain inefficiencies in utility spare-parts logistics. This research investigates how AI-driven predictive grid maintenance can substantially reduce supply chain delays through intelligent forecasting of equipment failures and optimized spare-parts inventory management. Drawing from recent advances in explainable AI and smart maintenance conceptualization, this study develops a comprehensive framework integrating machine learning algorithms, IoT sensor data, and decision-tree based prediction models. The proposed system leverages Long Short-Term Memory (LSTM) neural networks combined with Bayesian inference to predict equipment failures with 92% accuracy, enabling proactive spare-parts procurement. Through empirical analysis of utility maintenance operations, the research demonstrates that AI-driven predictive maintenance reduces supply chain lead times by 43%, decreases emergency spare-parts orders by 67%, and improves overall equipment effectiveness (OEE) by 31%. The framework incorporates explainable AI techniques to enhance stakeholder trust and decision transparency. Validation through case studies in semiconductor manufacturing and industrial IoT environments confirms the scalability and effectiveness of the approach. This research contributes to the growing body of knowledge on Industry 5.0 technologies by demonstrating how AI-driven maintenance systems can transform utility operations, reduce operational costs, and enhance grid reliability. The findings have significant implications for utility operators seeking to modernize maintenance practices and supply chain managers aiming to optimize spare-parts logistics in critical infrastructure contexts.

The integration of artificial intelligence (AI) in education has the potential to revolutionize teaching and learning, particularly in the development of students’ critical thinking skills. This study explores science instructors' familiarity, perceptions, and experiences with using AI to enhance students' critical thinking skills, as well as the level of institutional support for AI integration in teaching. A quantitative survey was conducted among 20 science instructors from higher education institutions in Isabela, Philippines. The findings reveal that while instructors acknowledge AI's potential to improve educational outcomes, there is a significant gap in formal AI training and literacy among educators. Positive correlations were found between AI literacy, AI integration, and critical thinking development, suggesting that as AI literacy increases, AI integration and enhancement of critical thinking skills also increase. Regression analysis identified AI integration as a significant predictor of critical thinking development. Challenges remain in the effective implementation of AI, including concerns about overreliance on AI-generated responses and the need for clear assessment guidelines. Interestingly, years of teaching experience did not significantly influence participants’ AI literacy, perceptions, or integration. This study highlights the importance of developing comprehensive AI literacy programs for educators and integrating AI into curriculum structures to balance AI-enhanced learning with human-centered pedagogy. These findings emphasize the need for thoughtful implementation and ongoing research to effectively leverage AI in promoting critical thinking skills in science education.

Introduction: The integration of artificial intelligence (AI) into higher education is swiftly revolutionizing pedagogical methodologies, in conjunction with learning processes and research paradigms. The interdisciplinary potential of AI within academic settings was examined in this study, employing a case study conducted at the University of Tirana. Through the utilization of bibliometric analysis and survey-based research, this study comprehensively investigates the swiftly emerging trends in AI applications, students' significant familiarity with AI technologies, as well as the substantial challenges impeding broader adoption. The bibliometric analysis highlights significant exponential growth in AI research, particularly within pivotal domains such as finance and accounting, thereby emphasizing the swiftly increasing relevance of blockchain and automation. The survey indicates a robust enthusiasm among students for AI in educational settings. More than 90 percent of students actively incorporate AI tools in their project work. Nonetheless, resource limitations and ethical considerations, including privacy, data security, and algorithmic bias, pose considerable challenges to the widespread adoption of AI, despite the prevailing enthusiasm. Objective: The aim of this research is to examine the incorporation of Artificial Intelligence (AI) within the realm of higher education. This analysis concentrates on the applications, advantages, and challenges associated with AI, with the intent of advancing interdisciplinary research and educational methodologies at the University of Tirana. Theoretical Framework: This research expands upon the principles of AI adoption within the educational sector, alongside an examination of ethical considerations and multidisciplinary collaboration. Theories pertaining to technological integration and adaptive learning systems serve as the foundational framework for comprehending the implications of AI in the realm of education. Method: The methodology employs bibliometric analysis to examine AI-related research trends utilizing data from SCOPUS and conducts a survey to assess students' familiarity with and perceptions of AI. Data collection was facilitated through bibliometric instruments and an online survey incorporating Likert-scale and open-ended questions. Results and Discussion: The results underscore an increased focus on artificial intelligence (AI) and blockchain within scholarly research, wherein students exhibit considerable engagement and interest in AI applications. Nevertheless, limitations in resources and ethical issues, including privacy and bias, persist as primary challenges. The discourse underscores the imperative for investment in infrastructure and the incorporation of ethical education. Research Implications: This research highlights the imperative for higher education institutions to integrate artificial intelligence tools, cultivate adaptive curricula, and address ethical considerations in order to adequately prepare students for a future shaped by AI. The implications of these findings also pertain to educational policy and the formulation of interdisciplinary research strategies. Originality/Value: The study contributes by delivering a comprehensive bibliometric analysis and gives insights into student engagement with artificial intelligence. Its significance resides in presenting actionable recommendations to enhance the integration of artificial intelligence in higher education.

The integration of artificial intelligence (AI) in healthcare presents significant promise to enhance clinical procedures and patient outcomes. This research examines the setting, methodology, conclusions, and issues associated with AI in healthcare. The swift proliferation of digital health data, encompassing medical imaging and clinical records, has generated substantial prospects for AI applications. Artificial intelligence methodologies, including machine learning, natural language processing, and computer vision, facilitate the derivation of significant insights from intricate datasets, hence improving clinical decision-making. A thorough literature review examines the practical applications of AI, encompassing its roles in medical diagnostics, treatment planning, and patient outcome prediction. The report also examines ethical issues, data protection, and legal frameworks, which are crucial for the responsible application of AI in healthcare. The results illustrate AI's capacity to enhance diagnostic precision, facilitate administrative efficiency, and optimise resource distribution, resulting in tailored therapies and improved healthcare administration. Nonetheless, obstacles persist, such as data integrity, algorithm transparency, and ethical considerations, which must be resolved to guarantee the secure and efficient deployment of AI. Continuous research, cooperation between healthcare and AI experts, and the establishment of comprehensive regulatory frameworks are essential for optimising the advantages of AI while minimising hazards. This research highlights AI's capacity to transform healthcare, stressing the necessity for a multidisciplinary strategy to effectively harness its benefits and tackle the associated ethical and regulatory dilemmas.

Educational technologies leverage artificial intelligence and adaptive learning algorithms to analyze learners' performance and provide personalized feedback and recommendations. This helps identify areas of improvement and tailor learning experiences accordingly. Adaptive learning and artificial intelligence (AI) have emerged as powerful tools in the field of education, revolutionizing traditional teaching methods. This article explores the integration of adaptive learning technologies with AI algorithms to personalize and enhance the learning experience for students. The use of AI allows for the collection and analysis of vast amounts of data, enabling the system to adapt and tailor the content and delivery of educational materials to meet the individual needs of each student. By continuously monitoring and evaluating student performance, AI algorithms can identify areas of weakness and provide targeted interventions to address them. This personalized approach not only improves learning outcomes but also promotes student engagement and motivation. Additionally, AI-powered adaptive learning systems have the potential to assist teachers by automating administrative tasks, providing real-time feedback, and generating comprehensive progress reports. However, challenges such as privacy concerns, ethical considerations, and the need for teacher training in utilizing these technologies must be addressed. Overall, the integration of adaptive learning and AI holds great promise in transforming education by creating personalized and effective learning experiences for students.

The rise of artificial intelligence (AI) heralds a significant revolution in healthcare, particularly in mental health.AI's potential spans diagnostic algorithms, data analysis from diverse sources, and real-time patient monitoring. It is essential for clinicians to remain informed about AI's progress and limitations. The inherent complexity of mental disorders, limited objective data, and retrospective studies pose challenges to the application of AI. Privacy concerns, bias, and the risk of AI replacing human care also loom. Regulatory oversight and physician involvement are needed for equitable AI implementation. AI integration and use in psychotherapy and other services are on the horizon. Patient trust, feasibility, clinical efficacy, and clinician acceptance are prerequisites. In the future, governing bodies must decide on AI ownership, governance, and integration approaches. While AI can enhance clinical decision-making and efficiency, it might also exacerbate moral dilemmas, autonomy loss, and issues regarding the scope of practice. Striking a balance between AI's strengths and limitations involves utilizing AI as a validated clinical supplement under medical supervision, necessitating active clinician involvement in AI research, ethics, and regulation. AI's trajectory must align with optimizing mental health treatment and upholding compassionate care.

Real-World Surveillance of FDA-Cleared Artificial Intelligence Models: Rationale and Logistics.

The rapid advancement of artificial intelligence (AI) technologies has profoundly transformed virtually all areas of human activity, and public administration is no exception. The integration of AI into public management systems opens up unprecedented opportunities to increase efficiency, transparency, and responsiveness in the provision of public services. This article examines the potential, challenges, and prospects of using artificial intelligence in public administration, focusing on ethical, legal, and organizational aspects. It emphasizes that AI tools—from data analytics and machine learning to natural language processing and automated decision-making systems—can significantly contribute to improving the quality of political planning, resource allocation, and citizen participation. Through predictive modeling, intelligent data processing, and real-time monitoring, AI enables evidence-based decision-making and strengthens the adaptability of public institutions to a dynamic socioeconomic environment. At the same time, the implementation of AI in public administration presents several complex challenges. Key issues include algorithmic bias, data protection violations, a lack of transparency regarding automated decisions, and insufficient digital literacy among public sector employees. The article emphasizes that without a clear ethical framework and appropriate regulatory mechanisms, the use of AI could exacerbate social inequalities and undermine citizens’ trust in government institutions. Therefore, the development of clear governance standards for the use of AI in the public sector is essential to ensure accountability, fairness, and human oversight in all decision-making processes. The study also analyzes international experiences in AI governance, particularly in the European Union, the United States, and leading Asian countries, and identifies best practices that can be transferred to national contexts. It argues that the strategic implementation of AI should be based on the principles of open government, inclusivity, and human-centered digital transformation. The article also emphasizes the need for continuous training and development measures for public sector employees to ensure competent use of AI-based tools and the interpretation of algorithmic results. In conclusion, it is concluded that artificial intelligence represents both a challenge and an opportunity for modern public administration. It can optimize administrative processes and strengthen democratic governance, but at the same time requires new legal safeguards, ethical standards, and institutional competencies. The success of integrating AI into public administration depends crucially on the balance between technological innovation and the protection of human rights, transparency, and accountability. The future of AI in the public sector therefore lies not solely in technological progress, but in the development of responsible and sustainable political strategies that align innovation with the public interest and democratic values.

Artificial Intelligence (AI) is currently one of the most talked-about technologies, both among scientists and in public media. Several factors have contributed to its development in recent years. The first is access to vast quantities of data, such as in the industrial field, the advent of Industry 4.0, which promotes automation and data sharing in several technologies. Another factor is the continuous improvement in computing power thanks to the development of ever more powerful processors and the optimization of algorithms. With these two limitations removed, the focus of most AI developments is on the quality of predictions. The integration of AI into the industrial domain represents an exciting new frontier for innovation. Just as AI has transformed many other sectors, its application to mechanical technologies enables significant improvements in design, manufacturing and quality control processes: from computer-aided design (CAD) to printing parameter optimization, defect detection and real-time monitoring. This type of technology requires computer systems, data with management systems and advanced algorithms which can be used by AIs. In mechanical engineering, AI offers many possibilities in mechanical construction, predictive maintenance, plant monitoring, robotics, additive manufacturing, materials, vibration, etc. Methods and Applications of Artificial Intelligence is dedicated to the methods and applications of AI in mechanical engineering. Each chapter clearly sets out the techniques used and developed and accompanies them with illustrative examples. The book is aimed at students but is also a valuable resource for practicing engineers and research lecturers.