McDermott International (United States)

Abstract The objective of the paper is to retrospectively analyze the suction pile design methodologies based on suction pile installations recently carried out offshore East India, at water depths ranging from 380 m to 805 m. The scope of the study includes evaluating horizontal holding capacity, self-penetration, suction pressure, retrieval pressure, penetration, and retrieval rates of the installed suction piles in very soft clay. Challenges associated with deepwater suction pile installation are also discussed briefly in this paper. The ultimate capacity of the suction piles was initially estimated using limit equilibrium approaches based on the works of Randolph et al. (1998), Taiebat & Carter (2005). A more detailed verification was subsequently carried out using finite element analysis. Monitoring data from the installation of suction piles were evaluated against these predictions. Soil sensitivity values (remoulding effect) of 2 to 3 were considered for self-penetration prediction across the site. The suction pile installations were completed smoothly. Field installation data indicate that many of the actual suction pile self-penetration values fall between the low and high estimate ranges. The maximum suction pressures observed during the installation were within the predicted values. A Self-penetration rate between 0.10 m/min and 0.9 m/min was observed, considering ~0.7% top vent hatch opening and gradual release of tension. The over-pressure required for the suction pile retrieval increased by up to 60% compared to the installation suction pressure, accounting for a setup time between 10 and 94 days and the application of constant tension. All minimum lateral holding capacity requirements were successfully met, as demonstrated by the actual pipeline initiation operations, confirming the reliability of the analytical approach. The paper discusses the installation experiences of thirty-five suction piles in deep water clay. The field data and analysis methods undertaken in this study enhance the reliability of future design and installation predictions, particularly for this site and other locations with similar soil conditions. The marine operations and monitoring procedures that address challenges associated with deepwater installation are briefly presented, providing useful insights for future installations.

The extraction of structured metadata from Piping and Instrumentation Diagrams (P&IDs) is a major bottleneck for digitalization in the process industries. Existing methods, based on Optical Character Recognition (OCR), stop at raw text extraction, failing to interpret critical engineering information encoded within variable-format identifiers like pipeline numbers. This paper bridges this semantic gap by introducing a system for the format-aware interpretation of P&ID pipeline metadata. Our hybrid system architecture integrates deep learning for text recognition with domain interpretation rules that allow the system to adapt to new project formats without model retraining. These rules perform validation, error correction, and semantic mapping of raw text to structured data. We validated our system on a challenging dataset of real-world P&IDs from four distinct industrial projects, each with a unique and complex pipeline number format. Our method achieved 91.1% end-to-end accuracy, demonstrating a significant leap in performance over standard OCR tools, which proved insufficient for the task. This work presents a robust solution that unlocks valuable data from non-standardized engineering documents, providing a practical pathway for creating reliable digital twins and supporting plant lifecycle management in the chemical engineering sector.

Objective: The Industry 4.0 era brings challenges and opportunities in education and entrepreneurship, particularly in technopreneurship, which combines technological innovation and entrepreneurship. However, the integration of Islamic values such as honesty, responsibility, and sustainability into technopreneurship remains limited, both in research and curriculum. This study aims to develop a learning approach that integrates technology, entrepreneurship, and Islamic values to shape ethical technopreneurs who contribute to sustainable development. Method: This study employed a qualitative approach. Participants were selected using purposive sampling. Data were collected through in-depth interviews, participatory observation, and document analysis, and analyzed using thematic analysis to identify key patterns. Results: The study reveals that Islamic values such as honesty, responsibility, and sustainability play a significant role in supporting ethical and spirituality-based technopreneurship. Their integration into the technopreneurship curriculum helps students develop both technical and moral competencies, supported by academic environments and external communities. Novelty: This study offers a distinctive framework that formally embeds Islamic values into technopreneurship education, positioning them as essential competencies rather than complementary ethics. In contrast to prior research that treats spirituality as external motivation, this model integrates values like amanah and maslahah within structured academic, mentorship, and community systems. It highlights the transformative potential of value-based innovation, despite persisting institutional challenges.

Manual quality checks of isometric pipe system drawings in the Engineering, Procurement and Construction (EPC) industry are time consuming, prone to human error, and require significant expertise. The high cost of design errors, combined with the complexity of these congested drawings, makes automation a critical challenge. To address this, we propose a deep learning-based approach to automate the detection of design errors in isometric drawings. Our solution utilizes an object detection framework trained on a diverse dataset from various EPC projects, incorporating expert engineering knowledge from McDermott. By identifying design mistakes represented by specific object assemblies, the model assists engineers in document verification, freeing them to focus on higher value tasks. The model is validated on real-world datasets and overcomes challenges such as small object detection, class imbalance, symbol variability, and high-resolution image processing. Techniques like tiling and data augmentation significantly improve detection accuracy. Our results demonstrate high precision in identifying design errors, showcasing the adaptability of the model for similar engineering diagram applications. This research advances automated drawing analysis and provides a scalable AI-driven solution to capture engineering expertise and integrate it into quality control workflows.

Repercussions from the COVID pandemic and a depleted nursing workforce demand more robust support to assure successful new graduate nurse transitions. Clinical learning in the real-world patient care setting is widely regarded as the essential element for preparation to practice. An academic-practice partnership established an apprenticeship program offering student nurse's greater exposure to real-world clinical experience to evaluate the value of a student nurse apprenticeship on new graduate nurse readiness for practice. Students complete 180 apprenticeship hours over 3 semesters. Satisfaction with the apprenticeship program was expressed by 100% of leaders/mentors and 93% of students. Most students also expressed an interest in a wider variety of clinical settings. This apprenticeship program is a demonstration of academic and practice partners leveraging their combined resources to invest in nursing student preparation to accelerate readiness to practice. An apprenticeship program offered in partnership with academia and practice can support the nursing workforce and may decrease the time required to prepare novice nurses for independent practice.

Cytokinesis is the last step in the eukaryotic cell cycle, which physically separates a mitotic cell into 2 daughter cells. A few days after birth in mouse cardiomyocytes, DNA synthesis occurs without cytokinesis, leading to the majority of cardiomyocytes becoming binucleated instead of generating 2 daughter cells with 1 nucleus each. This results in cell cycle arrest of cardiomyocytes, and the mouse heart is no longer able to regenerate. A longstanding unanswered question is whether binucleation of cardiomyocytes is a result of cytokinesis failure. To address this, we generated several transgenic mouse models to determine whether forced induction of cardiomyocyte cytokinesis generates mononucleated cardiomyocytes and restores the endogenous regenerative properties of the myocardium. We focused on 2 complementary regulators of cytokinesis: Plk1 (polo-like kinase 1) and Ect2 (epithelial cell-transformation sequence 2). We found that cardiomyocyte-specific transgenic overexpression of constitutively active Plk1(T210D) promotes mitosis and cytokinesis in adult hearts, whereas overexpression of Ect2 alone promotes only cytokinesis. Cardiomyocyte-specific overexpression of both Plk1(T210D) and Ect2 concomitantly (double transgenic) prevents binucleation of cardiomyocytes postnatally and results in widespread cardiomyocyte mitosis, cardiac enlargement, contractile failure, and death before 2 weeks of age. In contrast, doxycycline-inducible cardiomyocyte-specific overexpression of both genes (inducible double transgenic) in the adult heart results in cardiomyocyte mitosis and transient contractile dysfunction. Importantly, this transient induction of cytokinesis in adult mice improves left ventricular systolic function after myocardial infarction. These results collectively demonstrate that cytokinesis failure mediates cardiomyocyte multinucleation and cell cycle exit of postnatal cardiomyocytes, but may be a protective mechanism to preserve the contractile function of the myocardium.

Carbon Capture, Usage and Storage – in short CCUS, is not a new term in the current industrial environment. It is being discussed widely at all industrial forums, across all countries, big or small, to control pollution to ensure a healthy and happy life for all. There have been attempts and studies over last 50 years to capture carbon dioxide from the environment to avoid pollution and control global warming scenario. Many technologies have been in use for carbon capture, across the globe, some in large scale and others confined to local areas. It is expensive but very much essential – it is a goal set by WHO/UNO/European Commission – to clean the environment off CO2 gases, use it for alternative purposes or store it for future action. According to International Energy Agency (IEA), we must capture around 100 billion tons of CO2 by 2060 to meet International Global Warming and Energy goals. It is a huge goal and is a very expensive goal to achieve. All big countries who impact the environment in a huge way through their industries shall come forward for a collaboration to prepare a robust plan, pump-in money, monitor and control the processes, to achieve the goal. There is an estimate that US emitted around 5,1 mln t of CO2 in 2019 whereas Global estimate emitted around 33 mln t. These are very huge numbers and hence Global monitoring is very essential to control emission and safe storage and disposal. It is important to understand that Carbon Capture (CC), Carbon Capture and Use (CCU) and Carbon Capture and Storage (CCS) are three different terms. CC, CCU and CCS are combinedly termed as CCUS. CC is to capture carbon from various sources – fuel gas, fossil fuels, petroleum products, flares from refineries and power plants etc. Usage of the captured carbon in the form of carbon dioxide (CO2) is CCU and this is an important long-term vision by any government to ensure it is being used meaningful to avoid negative impact on climate. CCS is storage of the captured carbon quickly so that it does not impact the environment as a greenhouse gas and at the same time, it can be used later. Another term which is frequently used is Net Zero CO2. It is defined as follows: Any CO2 released into the atmosphere from a human activity shall be balanced by an equivalent amount being removed, either by nature-based solutions (including afforestation, reforestation or other changes in land use) or technological solutions that stores CO2 captured directly or indirectly, permanently. Net Zero CO2 is very important, unless this is achieved, CCUS goal cannot be achieved to control climate change. This write-up provides an insight into the following topics:  What is CCUS?  Why is it important?  Steps to CCUS;  CAPEX and OPEX involved.

In the Engineering, Procurement, and Construction (EPC) sector, accurate cost estimations during the tendering phase are crucial for maintaining competitiveness, especially with constrained project schedules and rising labor expenses. Typically, these estimations are labor-intensive, relying heavily on manual evaluations of engineering drawings, which are often shared in PDF format due to intellectual property concerns. This study introduces an innovative solution tailored for the energy industry, utilizing Artificial Intelligence (AI) - primarily deep learning (DL) and machine learning (ML) techniques - to streamline material quantity estimation, thereby saving engineering time and costs. Built on empirical data from a large EPC company operating in the energy sector, AI-based product development experiences, and academic research, our approach aims to enhance the efficiency and accuracy of engineering work, promoting better decision-making and resource distribution. While our focus is on enhancing a particular activity within the case company using AI, the method's broader applicability in the EPC sector potentially benefits both industry professionals and researchers. This study not only advances a practical application but also provides valuable insights for those seeking to develop AI -driven solutions across various engineering disciplines.