Semi-Supervised Modified-UNet for Lung Infection Image Segmentation

Automatic lung infection segmentation in computed tomography (CT) scans can offer great assistance in radiological diagnosis by improving accuracy and reducing time required for diagnosis. The biggest challenges for deep learning (DL) models in segmenting infection region are the high variances in infection characteristics, fuzzy boundaries between infected and normal tissues, and the troubles in getting large number of annotated data for training. To resolve such issues, we propose a Modified U-Net (Mod-UNet) model with minor architectural changes and significant modifications in the training process of vanilla 2D UNet. As part of these modifications, we updated the loss function, optimization function, and regularization methods, added a learning rate scheduler and applied advanced data augmentation techniques. Segmentation results on two Covid-19 Lung CT segmentation datasets show that the performance of Mod-UNet is considerably better than the baseline U-Net. Furthermore, to mitigate the issue of lack of annotated data, the Mod-UNet is used in a semi-supervised framework (Semi-Mod-UNet) which works on a random sampling approach to progressively enlarge the training dataset from a large pool of unannotated CT slices. Exhaustive experiments on the two Covid-19 CT segmentation datasets and on a real lung CT volume show that the Mod-UNet and Semi-Mod-UNet significantly outperform other state-of-theart approaches in automated lung infection segmentation. IEEE

A Freehand 3D Ultrasound Carotid Scanning System for Point-of-Care Ultrasonography

Stroke is a leading cause of morbidity and mortality throughout the world. Three-dimensional ultrasound (3DUS) imaging was shown to be more sensitive to treatment effect and more accurate in stratifying stroke risk than two-dimensional ultrasound (2DUS) imaging. Point-of-care ultrasound screening (POCUS) is important for patients with limited mobility and at times when the patients have limited access to the ultrasound scanning room, such as in the COVID-19 era. We used an optical tracking system to track the 3D position and orientation of the 2DUS frames acquired by a commercial wireless ultrasound system and subsequently reconstructed a 3DUS image from these frames. The tracking requires spatial and temporal calibrations. Spatial calibration is required to determine the spatial relationship between the 2DUS machine and the tracking system. Spatial calibration was achieved by localizing the landmarks with known coordinates in a custom-designed Z-fiducial phantom in an 2DUS image. Temporal calibration is needed to synchronize the clock of the wireless ultrasound system and the optical tracking system so that position and orientation detected by the optical tracking system can be registered to the corresponding 2DUS frame. Temporal calibration was achieved by initiating the scanning by an abrupt motion that can be readily detected in both systems. This abrupt motion establishes a common reference time point, thereby synchronizing the clock in both systems. We demonstrated that the system can be used to visualize the three-dimensional structure of a carotid phantom. The error rate of the measurements is 2.3%. Upon in-vivo validation, this system will allow POCUS carotid scanning in clinical research and practices. © 2023 SPIE.

The Use of Metaverse in the Healthcare Sector: Analysis and Applications

With the use of blockchain, Internet of Things, virtual platform/telecommunications network, artificial intelligence and the fourth industrial revolution, the essential demand for digital transition within the health care settings has increased as an outcome of the 2019 coronavirus illness outbreak and the fourth industrial revolution. The evolution of virtual environments with three-dimensional (3D) spaces and avatars, known as metaverse, has slowly gained acceptance in the field of health care. These environments may be especially useful for patient-facing platforms (such as platforms for telemedicine), functional uses (such as meeting management), digital education (such as modeled medical and surgical learning), treatments and diagnoses. This chapter offers the most recent state-of-the-art metaverse services and applications and a growing problem when it comes to using it in the healthcare sector. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Towards Augmented Reality Guiding Systems: An Engineering Design of an Immersive System for Complex 3D Printing Repair Process

Over the past decade, additive manufacturing (AM) has become widely adopted for both prototyping and, more recently, end-use products. In particular, fused deposition modeling (FDM) is the most widespread form of additive manufacturing due to its low cost, ease of use, and versatility. While additive processes are relatively automated, many steps in their operation and repair require trained human operators. Finding such operators can be difficult, as highlighted during the recent COVID-19 pandemic. Augmented reality (AR) systems could significantly help address this challenge by automating the training for 3D printer operators. Given multidimensional design choices, however, a research gap exists in the system requirements for such immersive guidance. To address this need, we explore the applicability of AR to guide users through a repair process. In that context, we report on the system design as well as the results of the AR system assessment in a qualitative study with experts. © 2023 IEEE.

MLCNN-COV: A multilabel convolutional neural network-based framework to identify negative COVID medicine responses from the chemical three-dimensional conformer

To treat the novel COronaVIrus Disease (COVID), comparatively fewer medicines have been approved. Due to the global pandemic status of COVID, several medicines are being developed to treat patients. The modern COVID medicines development process has various challenges, including predicting and detecting hazardous COVID medicine responses. Moreover, correctly predicting harmful COVID medicine reactions is essential for health safety. Significant developments in computational models in medicine development can make it possible to identify adverse COVID medicine reactions. Since the beginning of the COVID pandemic, there has been significant demand for developing COVID medicines. Therefore, this paper presents the transfer-learning methodology and a multilabel convolutional neural network for COVID (MLCNN-COV) medicines development model to identify negative responses of COVID medicines. For analysis, a framework is proposed with five multilabel transfer-learning models, namely, MobileNetv2, ResNet50, VGG19, DenseNet201, and Inceptionv3, and an MLCNN-COV model is designed with an image augmentation (IA) technique and validated through experiments on the image of three-dimensional chemical conformer of 17 number of COVID medicines. The RGB color channel is utilized to represent the feature of the image, and image features are extracted by employing the Convolution2D and MaxPooling2D layer. The findings of the current MLCNN-COV are promising, and it can identify individual adverse reactions of medicines, with the accuracy ranging from 88.24% to 100%, which outperformed the transfer-learning model's performance. It shows that three-dimensional conformers adequately identify negative COVID medicine responses. 1225-6463/$ © 2023 ETRI.

Fabrication, Testing, and 3D Comprehensive Analysis of Swept-Tip Tiltrotor Blades

This paper covers the design, fabrication, testing, and modeling of a family of Froude-scale tiltrotor blades. They are designed with the objective of gaining a fundamental understanding of the impact of a swept tip on tiltrotor whirl flutter. The goal of this paper is to describe the development of the blades needed for this purpose. The rotor is three bladed with a diameter of 4.75 ft. The blades have a VR-7 profile, chord of 3.15 inches, and linear twist of −37◦ per span. The swept-tip blades have a sweep of 20◦ starting at 80%R. The blade properties are loosely based on the XV-15 design. A CATIA and Cubit-based high-fidelity three-dimensional (3D) finite element model is developed. It accurately represents the fabricated blade and is analyzed with X3D. Experiments in a vacuum chamber were carried out to demonstrate the structural integrity of the blades. Measured frequencies and strains were validated with X3D predictions proving the fidelity of the 3D model. Thus, even though the wind tunnel facilities were closed due to COVID-19, hover and forward flight calculations for the blade stress could be performed using the high-fidelity 3D structural model. The results prove the blades have sufficient structural integrity and stress margins to allow for wind tunnel testing. © 2023 Vertical Flight Society.

The preclinical and clinical progress of cell sheet engineering in regenerative medicine.

Cell therapy is an accessible method for curing damaged organs or tissues. Yet, this approach is limited by the delivery efficiency of cell suspension injection. Over recent years, biological scaffolds have emerged as carriers of delivering therapeutic cells to the target sites. Although they can be regarded as revolutionary research output and promote the development of tissue engineering, the defect of biological scaffolds in repairing cell-dense tissues is apparent. Cell sheet engineering (CSE) is a novel technique that supports enzyme-free cell detachment in the shape of a sheet-like structure. Compared with the traditional method of enzymatic digestion, products harvested by this technique retain extracellular matrix (ECM) secreted by cells as well as cell-matrix and intercellular junctions established during in vitro culture. Herein, we discussed the current status and recent progress of CSE in basic research and clinical application by reviewing relevant articles that have been published, hoping to provide a reference for the development of CSE in the field of stem cells and regenerative medicine.

Three-dimensional printing in cardiopulmonary disease

Three-dimensional (3D) printing has gained increasing interests and recognition in the medical domain with studies confirming its clinical value and applications in many areas. 3D-printed personalized models provide information that cannot be obtained by traditional visualization tools, and this is especially apparent in the cardiopulmonary disease due to the complexity of anatomical structures and pathologies that are seen in the cardiopulmonary system. This chapter provides an overview of the application and usefulness of 3D-printed models in cardiopulmonary disease, including 3D printing in heart and cardiovascular disease, and 3D printing in pulmonary disease. Emerging applications of 3D printing in coronavirus disease 2019 patients, in particular, the 3D-printed ventilators, and 3D printing in cardiopulmonary bypass are also presented, while limitations and future research of 3D printing in cardiopulmonary disease are highlighted. It is expected that this chapter presents an update of current research on 3D printing in cardiopulmonary disease and possible research directions along this pathway. © 2023 Elsevier Inc. All rights reserved.

Evaluation of Hand Washing Procedure Using Vision-Based Frame Level and Spatio-Temporal Level Data Models

Hand hygiene is obligatory for all healthcare workers and vital for patient care. During COVID-19, adequate hand washing was among recommended measures for preventing virus transmission. A general hand-washing procedure consisting several steps is recommended by World Health Organization for ensuring hand hygiene. This process can vary from person to person and human supervision for inspection would be impractical. In this study, we propose computer vision-based new methods using 12 different neural network models and 4 different data models (RGB, Point Cloud, Point Gesture Map, Projection) for the classification of 8 universally accepted hand-washing steps. These methods can also perform well under situations where the order of steps is not observed or the duration of steps are varied. Using a custom dataset, we achieved 100% accuracy with one of the models, and 94.23% average accuracy for all models. We also developed a real-time robust data acquisition technique where RGB and depth streams from Kinect 2.0 camera were utilized. Results showed that with the proposed methods and data models, efficient hand hygiene control is possible.

THREE-DIMENSIONAL ANALYTICAL SOLUTION OF THE ADVECTION-DIFFUSION EQUATION FOR AIR POLLUTION DISPERSION

We develop a new analytical solution of a three-dimensional atmospheric pollutant dispersion. The main idea is to subdivide vertically the planetary boundary layer into sub-layers, where the wind speed and eddy diffusivity assume average values for each sub-layer. Basically, the model is assessed and validated using data obtained from the Copenhagen diffusion and Prairie Grass experiments. Our findings show that there is a good agreement between the predicted and observed crosswind-integrated concentrations. Moreover, the calculated statistical indices are within the range of acceptable model performance.

Augmented Reality for medical practice: A comparative study of Deep learning models for Ct-scan segmentation

The coronavirus disease has hardly affected medical healthcare systems worldwide. Physicians use radiological examinations as a primary clinical tool for diagnosing patients with suspected COVID-19 infection. Recently, deep learning approaches have further enhanced medical image processing and analysis, reduced the workload of radiologists, and improved the performance of radiology systems. This paper addresses medical image segmentation;we present a comparative performance study of four neural networks 'NN' models, U-Net, 3D-Unet, KiU-Net and SegNet, for aid diagnosis. Additionally, we present his 3D reconstruction of COVID-19 lesions and lungs and his AR platform with augmented reality, including AR visualization and interaction. Quantitative and qualitative assessments are provided for both contributions. The NN model performed well in the AI-COVID-19 diagnostic process. The AR-COVID-19 platform can be viewed as an ancillary diagnostic tool for medical practice. It serves as a tool to support radiologist visualization and reading. © 2022 IEEE.

Serodominant SARS-CoV-2 Nucleocapsid Peptides Map to Unstructured Protein Regions.

The SARS-CoV-2 nucleocapsid (N) protein is highly immunogenic, and anti-N antibodies are commonly used as markers for prior infection. While several studies have examined or predicted the antigenic regions of N, these have lacked consensus and structural context. Using COVID-19 patient sera to probe an overlapping peptide array, we identified six public and four private epitope regions across N, some of which are unique to this study. We further report the first deposited X-ray structure of the stable dimerization domain at 2.05 Å as similar to all other reported structures. Structural mapping revealed that most epitopes are derived from surface-exposed loops on the stable domains or from the unstructured linker regions. An antibody response to an epitope in the stable RNA binding domain was found more frequently in sera from patients requiring intensive care. Since emerging amino acid variations in N map to immunogenic peptides, N protein variation could impact detection of seroconversion for variants of concern. IMPORTANCE As SARS-CoV-2 continues to evolve, a structural and genetic understanding of key viral epitopes will be essential to the development of next-generation diagnostics and vaccines. This study uses structural biology and epitope mapping to define the antigenic regions of the viral nucleocapsid protein in sera from a cohort of COVID-19 patients with diverse clinical outcomes. These results are interpreted in the context of prior structural and epitope mapping studies as well as in the context of emergent viral variants. This report serves as a resource for synthesizing the current state of the field toward improving strategies for future diagnostic and therapeutic design.

Critical Discourse Analysis of the RIA Novosti News

This study is situated within corpus-based discourse analysis and provides a critical discussion on China in the Russian mainstream media RIA Novosti during the COVID-19 epidemic. The paper analyzes RIA Novosti's reports on China during the pandemic COVID-19. The authors use Fairclough's Three-Dimensional Model to explore the discourse representations of RIA Novosti's reports on China during the epidemic and thus uncover the attitudes and stances of the Russian media and social cognition. The authors come to conclusion that RIA Novosti shows great concern about China during the pandemic by focusing on the epidemic itself and its impact. Additionally, Russian reports reflect the stages of China's fight against the pandemic objectively, truthfully, and comprehensively. RIA Novosti holds a positive attitude towards China's efforts to fight the epidemic. The study broadens the perspective of academic study of COVID-19 pandemic coverage in China from foreign media and enriches empirical research in Russian.

FV-Seg-Net: Fully Volumetric Network for Accurate Segmentation of COVID-19 Lesions From Chest CT Scans

Automated and precise pneumonia segmentation of COVID-19 extends the view of medical supply chains and offers crucial medical supplies to fight the COVID-19 pandemic. Deep learning plays a vital role in improving the COVID-19 segmentation from computed tomography (CT) scans. However, the literature lacks a precise segmentation approach on small-size lesions because they often split the CT scan into 2-D slices or 3-D patches, leading to the loss of contextual and/or global information. In order to address this, this article proposes a novel fully volumetric segmentation network, called FV-Seg-Net, that effectively exploits the local and global spatial information and enables the entire CT volume processing at once. The decoder is designed using a computationally efficient recalibrated anisotropic convolution module that can acquire the 3-D semantic representation of the CT volumes with anisotropic resolution. To avoid losing information during down-sampling, we reconstruct the skip-connection using a multilevel multiscale pyramid aggregation module and ensure more effective context fusion that improves the reconstruction capability of the decoder. Finally, stacked data augmentation (StackAug) is presented to magnify the training data and improve the generalizability of FV-Seg-Net. Proof of concept experiments on two public datasets demonstrates that the FV-Seg-Net achieves excellent segmentation performance (Dice score: 85.69 and a surface-dice: 84.79%), outperforming the current cutting-edge studies.

CNN approach for predicting survival outcome of patients with COVID-19

Coronavirus disease 2019 (COVID-19) has been challenged specifically with the new variant. The number of patients seeking treatment has increased significantly, putting tremendous pressure on hospitals and healthcare systems. With the potential of artificial intelligence (AI) to leverage clinicians to improve personalized medicine for COVID-19, we propose a deep learning model based on 1D and 3D convolutional neural networks (CNNs) to predict the survival outcome of COVID-19 patients. Our model consists of two CNN channels that operate with CT scans and the corresponding clinical variables. Specifically, each patient data set consists of CT images and the corresponding 44 clinical variables used in the 3D CNN and 1D CNN input, respectively. This model aims to combine imaging and clinical features to predict short-term from long-term survival. Our models demonstrate higher performance metrics compared to state-of-the-art models with AUC-ROC of 91.44 –91.60% versus 84.36 –88.10% and Accuracy of 83.39 –84.47% versus 79.06 –81.94% in predicting the survival groups of patients with COVID-19. Based on the findings, the combined clinical and imaging features in the deep CNN model can be used as a prognostic tool and help to distinguish censored and uncensored cases of COVID-19. IEEE

Overcoming Chip Shortages: Low-Cost Open-Source Parametric 3-D Printable Solderless SOIC to DIP Breakout Adapters

The COVID-19 pandemic exposed the vulnerability of global supply chains of many products. One area that requires improved supply chain resilience and that is of particular importance to electronic designers is the shortage of basic dual in-line package (DIP) electronic components commonly used for prototyping. This anecdotal observation was investigated as a case study of using additive manufacturing to enforce contact between premade, off-the-shelf conductors to allow for electrical continuity between two arbitrary points by examining data relating to the stock quantity of electronic components, extracted from Digi-Key Electronics. This study applies this concept using an open hardware approach for the design, testing, and use of a simple, parametric, 3-D printable invention that allows for small outline integrated circuit (SOIC) components to be used in DIP package circuits (i.e., breadboards, protoboards, etc.). The additive manufacture breakout board (AMBB) design was developed using two different open-source modelers, OpenSCAD and FreeCAD, to provide reliable and consistent electrical contact between the component and the rest of the circuit and was demonstrated with reusable 8-SOIC to DIP breakout adapters. The three-part design was optimized for manufacturing with RepRap-class fused filament 3-D printers, making the AMBB a prime candidate for use in distributed manufacturing models. The AMBB offers increased flexibility during circuit prototyping by allowing arbitrary connections between the component and prototyping interface as well as superior organization through the ability to color-code different component types. The cost of the AMBB is CAD $0.066/unit, which is a 94% saving compared to conventional PCB-based breakout boards. Use of the AMBB device can provide electronics designers with an increased selection of components for through-hole use by more than a factor of seven. Future development of AMBB devices to allow for low-cost conversion between arbitrary package types provides a path towards more accessible and inclusive electronics design as well as faster prototyping and technical innovation.

Augmented reality visualisation and interaction for COVID-19 Ct-scan NN automated segmentation: A validation study

Although medical imaging technology has persisted in evolving over the last decades, the techniques and technologies used for analytical and visualisation purposes have remained constant. Manual or semi-automatic segmentation is, in many cases, complicated. It requires the intervention of a specialist and is time-consuming, especially during the Coronavirus disease (COVID-19) pandemic, which has had devastating medical and economic consequences. Processing and visualising medical images with advanced techniques represent medical professionals’breakthroughs. This paper studies how augmented reality (AR) and artificial intelligence (AI) can transform medical practice during COVID-19 and post-COVID-19 pandemic. Here we report an augmented reality visualisation and interaction platform;it covers the whole process from uploading chest Ct-scan images to automatic segmentation-based deep learning, 3D reconstruction, 3D visualisation, and manipulation. AR provides a more realistic 3D visualisation system, allowing doctors to effectively interact with the generated 3D model of segmented lungs and COVID-19 lesions. We use the U-Net Neural Network (NN) for automated segmentation. The statistical measures obtained using the Dice score, pixel accuracy, sensitivity, G-mean, and specificity are 0.749, 0.949, 0.956, 0.955, and 0.954, respectively. The user-friendliness and usability are objectified by a formal user study that compared our augmented reality-assisted design to the standard diagnosis setup. One hundred and six doctors and medical students, including eight senior medical lecturers, volunteered to assess our platform. The platform could be used as an aid diagnosis tool to identify and analyse the COVID-19 infectious or as a training tool for residents and medical students. The prototype can be extended to other pulmonary pathologies. IEEE

Problems of Regulation and Prospective Development of E-commerce Systems in the Post-coronavirus Era

The article examines the application of e-commerce systems and technologies that have a positive impact on the development of the economy of the post-coronavirus period and the formation of appropriate technical and technological infrastructure for it, as well as promising features and directions of e-commerce. The physical and virtual opportunities created by e-commerce technologies for buyers and sellers are explained. The advantages of e-commerce in the international economic space have been identified. The functions of e-business models in accordance with the commercial stages of enterprises are explained. It was noted that the development of ICT has accelerated the process of transition from traditional commerce to e-commerce, led to the emergence of new global trends in e-commerce. These innovations have raised the issue of the application of modern ICT in the development of e-commerce on the platform of the 4.0 Industrial Revolution. Taking into account these factors, the presented article discusses the application of modern technologies in e-commerce systems, such as 3D modeling, the Internet of Things, artificial intelligence, big data. Features of application and regulation mechanisms of E-commerce systems in real economic sectors, which have a direct stimulating effect on economic growth in Azerbaijan, have been studied. Recommendations were given for the modernization and use of e-commerce systems with the application of the latest ICT technologies.The purpose of the research. The main goal of the scientific research carried out in the article was to develop the scientific-methodological basis for the regulation of the application of e-commerce systems and the study of perspective development problems in the so-called post-coronavirus period after 2020. In the article, attention was paid to the problems of regulation of the application of e-commerce systems and the development of recommendations on increasing the efficiency of prospective development directions.Taking into account the characteristics of the relevant electronic business models, applying them in accordance with the commercial stages of the enterprises' activities and obtaining effective results were among the main goals. Attempts have been made to implement e-commerce systems based on the developing technologies of the Industry 4.0 platform. An attempt was made to solve the issue of using modern ICT in the development of trade processes, which corresponds to the 4.0 Industrial revolution platform. The main stages of application of modern technologies such as 3D modeling, the Internet of Things, artificial intelligence, and Big Data in electronic commerce systems are described.The following are included among the goals of the conducted scientific research: investigation of the application features and regulation mechanisms of e-commerce systems that have a stimulating effect on the economic development of Azerbaijan in real economic sectors, development of recommendations on increasing the efficiency of electronic commerce systems using modern ICT technologies, etc.Research methods used. In the post-coronavirus period, the following research methods were used in the study of the problems of regulation of the application of e-commerce systems and prospective development directions and in the development of their scientific and methodological bases: a systematic analysis, correlation, and regression analysis, mathematical and econometric modeling methods, expert evaluation method, measurement theory, algorithmization, ICT tools, and technologies, etc.Achievements of the author. Achievements of the author. In the so-called post-coronavirus period after 2020, a special approach was taken to the application of e-commerce systems and technologies, which have a positive impact on the development of the economy as an innovative element, and to the study of its prospective development features and directions. By providing scientific support to ensure the effective formation of the digital economy and its sustainability, the researcher offered relev nt recommendations to achieve the solution to some of the goals set before the country. It should be noted that the development of e-commerce systems based on technologies relevant to the Industry 4.0 platform can give a serious impetus to the development of the sustainability of the digital economy.Due to the fact that e-commerce technologies create new additional physical and virtual opportunities for buyers and sellers, the scientific-methodological approaches proposed by the author develop them as a special tool for ensuring the stability of both e-commerce systems and the digital economy in general. The proposals presented will lead to more effective results for the economy to be more cyber resilient through the application of e-commerce systems in the so-called post-coronavirus era. The researcher showed that the effective application of electronic business models in the activities of enterprises can help to achieve effective results. In the development of e-commerce, solutions to the issues of application of 4.0 Industrial technologies such as 3D modeling, Internet of Things, artificial intelligence, and Big Data can be considered as a contribution to the investigation of solutions to existing problems in economic development. For this reason, the means and mechanisms proposed by the author for solving the problems of regulation of the application of e-commerce systems in the post-coronavirus era can be considered one of the main ways to ensure the stability and development of the digital economy.

Region-of-Interest Aware 3D ResNet for Classification of COVID-19 Chest Computerised Tomography Scans

Coronavirus disease 2019, commonly known as COVID-19, is an extremely contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Computerised Tomography (CT) scans based diagnosis and progression analysis of COVID-19 have recently received academic interest. Most algorithms include two-stage analysis where a slice-level analysis is followed by the patient-level analysis. However, such an analysis requires labels for individual slices in the training data. In this paper, we propose a single-stage 3D approach that does not require slice-wise labels. Our proposed method comprises volumetric data pre-processing and 3D ResNet transfer learning. The pre-processing includes pulmonary segmentation to identify the regions of interest, volume resampling and a novel approach for extracting salient slices. This is followed by proposing a region-of-interest aware 3D ResNet for feature learning. The backbone networks utilised in this study include 3D ResNet-18, 3D ResNet-50 and 3D ResNet-101. Our proposed method employing 3D ResNet-101 has outperformed the existing methods by yielding an overall accuracy of 90%. The sensitivity for correctly predicting COVID-19, Community Acquired Pneumonia (CAP) and Normal class labels in the dataset is 88.2%, 96.4% and 96.1%, respectively. © 2013 IEEE.

CRITICAL SUCCESS FACTORS FOR LARGE HYDROCARBON PROJECTS

According to World Energy Outlook 2020, investment of near about USD 1.2 Trillion is required every year to meet global energy demand for the period of 2020-30. Out of this, substantial portion of investment is expected in the hydrocarbon industry. Like many other industries, hydrocarbon industry is hit hard by Covid-19 pandemic with decrease in demand though recovery in demand picked up gradually from the latter half of year 2021. Timely execution of Large Hydrocarbon (LHC) projects within budgeted estimates is necessary to keep the faith of investors in this sector and to attract further investment. The LHC projects are more complex in nature due to various stakeholders' involvement, which may typically involve process technology licensors, owners, project management consultants, contractors, government agencies etc. The purpose of this study is to find out the Critical Success Factors (CSFs) for LHC projects. The study commenced with comprehensive literature review for identification of reported CSFs for various industries. Thereafter, data collected from expert interviews and questionnaire survey are analyzed to find out CSFs for LHC projects. The study provides a list of CSFs which may be referred as guiding tool by industry practitioners and may help in reducing the schedule and cost overruns. © 2022 ISEC Press.