ABSTRACT
The Medical Imaging and Data Resource Center (MIDRC) is a multi-institutional effort to accelerate medical imaging machine intelligence research and create a publicly available data commons as well as a sequestered commons for performance evaluation of algorithms. This work sought to evaluate the currently implemented methodology for apportioning data to the public and sequestered data commons by investigating the resulting distributions of joint demographic characteristics between the public and sequestered commons. 54,185 patients whose de-identified imaging studies and metadata had been submitted to MIDRC were previously separated into public and sequestered commons using a multi-dimensional stratified sampling method, resulting in 41,556 patients (77%) in the public commons and 12,629 patients (23%) in the sequestered commons. To compare the balance obtained in the joint distributions of patient characteristics from use of the developed sequestration method, patients from each commons were separated into bins, representing a unique combination of the demographic variables of COVID-19 status, age, race, and sex assigned at birth. The joint distributions of patients were visualized, and the absolute and percent difference in each bin from an exact 77:23 split of the data were calculated. Results indicated 75.9% of bins obtained differences of less than 15 patients, with a median difference of 3.6 from the total data for both public and sequestered commons. Joint distributions of patient characteristics in both the public and sequestered commons closely matched each other as well as that of the total data, indicating the sequestration by stratified sampling method has operated as intended. © 2023 SPIE.
ABSTRACT
The current COVID-19 pandemic has highlighted the importance of health safety assessment in various indoor scenarios. Computational fluid dynamics (CFD) combined with a modified Wells-Riley equation provides a powerful tool to analyse local infection probability in an indoor space. Compared to a single infection probability characterising the space in the traditional Wells-Riley model, the coupled approach provides a distribution of infection probability within the space. Furthermore, this approach avoids assuming a well-mixed state, usually related to Wells-Riley equation. This study compares displacement and mixing ventilation strategies with four different ventilation rates to assess the local quanta concentrations modelled using passive scalar transport approach. The simulation results are processed to also account for the effect of wearing masks and vaccinations. The result show that a well-designed displacement ventilation system can significantly reduce infection probability compared to mixing ventilation system at similar airflow rate. Additionally, the results emphasised the importance of wearing mask and getting vaccinated as a means of reducing infection probability. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.
ABSTRACT
The second phase of Johan Sverdrup came on stream in December 2022. This paper focuses on the execution of Johan Sverdrup phase 2 and describes the assessments and investments for improved oil recovery (IOR) from one of the largest oil fields in Norway. The Johan Sverdrup field development has been called Equinor's ‘digital flagship', and this paper includes the proof of concept for the digital initiatives after more than three years of operation. Despite the Covid-19 pandemic Johan Sverdrup phase 2 has been able to deliver on schedule, under budget, and with an excellent safety record. The paper includes experiences from the concept development and engineering phase to the global contracting strategy, through the construction on multiple building sites in Norway and globally, and until the end of the completion phase offshore Norway. Johan Sverdrup is the third largest oil field on the Norwegian Continental Shelf (NCS), and with recoverable reserves estimated at 2.7 billion barrels of oil equivalents, has the resources to be a North Sea Giant. Start-up of the Johan Sverdrup phase 2 extends and accelerates oil and gas production from the NCS for another five decades. This paper aims to highlight what it took to make Johan Sverdrup a true North Sea Giant, fit for the 21st century: a safe and successful execution of a mega-project, with next-generation facilities adapted to a more digital way of working, with an ambition to profitably recover more than 70% of the resources, while limiting carbon emissions from production to a minimum. In many ways the Johan Sverdrup development has set a new standard for project execution in Equinor. The impact of different variables made during the execution of the project, such as the Covid-19 pandemic, market effects, procurement strategies, value improvement initiatives, execution performance and reservoir characteristics is addressed, as well as describing assessments and investments for improved oil recovery (IOR). Data acquisition, Permanent Reservoir Monitoring (PRM), fibre-optic monitoring of wells, innovative technologies, and digitalization, as well as new ways of working are included. Equinor ´s digital strategy was established in 2017, and Johan Sverdrup was highlighted as a digital flagship at that time and a frontrunner in applying digital solutions to improve safety and efficiency from the development to the operational phase. What has been implemented so far together with experiences will be shared. © 2023, Offshore Technology Conference.
ABSTRACT
The fifth ACM International Workshop on Multimedia Content Analysis in Sports (ACM MMSports'22) is part of the ACM International Conference on Multimedia 2022 (ACM Multimedia 2022). After two years of pure virtual MMSports workshops due to COVID-19, MMSports'22 is held on-site again. The goal of this workshop is to bring together researchers and practitioners from academia and industry to address challenges and report progress in mining, analyzing, understanding, and visualizing multimedia/multimodal data in sports, sports broadcasts, sports games and sports medicine. The combination of sports and modern technology offers a novel and intriguing field of research with promising approaches for visual broadcast augmentation and understanding, for statistical analysis and evaluation, and for sensor fusion during workouts as well as competitions. There is a lack of research communities focusing on the fusion of multiple modalities. We are helping to close this research gap with this workshop series on multimedia content analysis in sports. Related Workshop Proceedings are available in the ACM DL at: https://dl.acm.org/doi/proceedings/10.1145/3552437. © 2022 Owner/Author.
ABSTRACT
In a modern era of unprecedented events, such as the COVID-19 pandemic, energy matters now more than ever. What was previously impossible is now a challenge that should be met with measured risk and a mitigation plan. In early 2020, a service company pursued a solution to provide a compact surface well test (SWT) package for an extended well test (EWT) on an offshore production platform in the Zafaraana field with extremely high concentrations of H2S. The solution involved proper treatment and safe delivery of well effluent within acceptable H2S limits to a floating production storage and offloading (FPSO) facility. The EWT was to be installed for a long period to allow production from the reservoir, treatment of the effluent using H2S scavenger, and delivery to the FPSO facility by means of electric transfer pumps. This was the only way to produce because the FPSO facility could not accommodate the high H2S concentrations from the reservoir. A further challenge was that it was a simultaneous operation (SIMOPs) wherein the rig was engaged in drilling and completion activities of other wells on the same offshore platform;operational conflicts were identified during the HAZOP/HAZID meeting to help mitigate potential issues. Copyright © 2023, Society of Petroleum Engineers.
ABSTRACT
In the early phase of the Covid-19 outbreak, all aspects of life, including the culinary sector, were impacted very hard by this outbreak in addition to the existing tight competition. Many businesses were in danger of going bankrupt. However, there was a culinary brand that continued to make an expansion in Bandung, Indonesia. There are seven branches opened by this brand until 2021. Hence, it is interesting to study the phenomena. This study aims to determine the effect of product innovation on the competitive advantage of this culinary brand. Besides this, the customers' evaluation regarding product innovation and competitive advantage is revealed by this study as well. This research uses a quantitative method by surveying with the purposive sampling technique. The questionnaires were distributed to 100 customers who had made transactions more than once within the last six months before the survey began. The descriptive data analysis was carried out to investigate the customers' evaluation. It is examined that product innovation has a positive impact on the competitive advantage after a simple linear regression analysis was conducted. This result is strengthened by the t-test that shows the acceptance of the hypothesis. Moreover, the coefficient of determination test presents that the impact of product innovation on competitive advantage is 51 percent. It means that the product innovation has provided 51 percent of the information needed to predict the competitive advantage in this small culinary business. From the overall results of the descriptive analysis, the consumer assessment scores obtained fall into the medium to high category range. This result proves that the response given to the company's competitive advantage and product innovation is still quite good and positive. The result of this study is expected can inspire other culinary companies to survive their businesses during a difficult situation like the Covid-19 outbreak and to sustain for the future. Further study can compare the condition during and after the Covid-19 outbreak. © 2022, Academic Conferences and Publishing International Limited. All right reserved.
ABSTRACT
The outbreak of COVID-2019 has resulted in the adaptation of the teaching and learning style in schools to become more online, conducting teaching and learning from any places without classroom meeting. Systems such as School Management, Online Meeting, and Online library, have been deployed to support all school members including students, teachers, parents, and administrators. These systems need to be properly managed. For business enterprises, this job falls on the shoulders of the IT department, which is usually well-staffed and well-equipped as companies realize their competitive edge depends on it. For educational institutions, especially in small schools, only 1 or 2 "computer specialists"assume the responsibility of the whole IT department. This can be overwhelming for them and, when IT tasks are poorly managed, dissatisfaction and productivity loss among school members ensue. This paper describes a system that we have designed and developed called Admin Task Management Center (ATMC). It aims to significantly reduce the manual workload of IT staff in small schools in document management, system monitoring, and other IT-related tasks. Our ATMC is currently being deployed at Satit Kaset IP (Kasetsart University Laboratory School, Center for Educational Research and Development, International Program). Our evaluation shows that the ATMC considerably raises the productivity level of IT staff, as well as other members of the school. We have released version 1 of our ATMC tool as open-source software. It is available on Github. © 2022 IEEE.
ABSTRACT
We investigate the dispersal of droplet nuclei inside a canonical room of size 10×10×3.2m3 with a four-way cassette air-conditioning unit placed at the center of the ceiling. We use Reynolds averaged Navier–Stokes (RANS) simulations with three flow rates corresponding to air changes per hour (ACH) values of 2.5, 5, and 10. The room setup as well as the operating conditions are chosen to match those of a recent high-fidelity large eddy simulation (LES) study. We use statistical overloading with a total of one million droplet nuclei being initially distributed randomly with uniform probability within the room. Six nuclei sizes are considered ranging in radius from 0.1 to 10μm (166,667 nuclei per size). The simulations are one-way coupled and employ the Langevin equations to model sub-grid motion. The flow and particle statistics are compared against the reference LES simulations, and we find that the RANS k−ɛ realizable model may be used as a computationally cheaper alternative to LES for predicting pathogen concentration in confined spaces albeit, with potentially increased statistical discrepancy. © 2023 Elsevier Ltd
ABSTRACT
The digital transformation of the IT consulting domain recently gained momentum due to the Covid-19 pandemic. However, the range of IT consulting services that are fully digital is still very limited. Plus, there are no standardized and established methods for describing digital IT consulting services, nor there is any suitable tooling for digital IT consulting service provisioning. The present work aims to reduce this gap by contributing to establishing a well-defined approach to formally describing digital IT consulting services that could possibly be a candidate for standardization. Building upon (i) the ontology DITCOS-O, which provides the semantic basis for our approach, and (ii) the YAML-based description notation DITCOS-DN, which we leverage to describe digital IT consulting service models, we propose a graphical, web-based editor (called DITCOS-ModEd) to simplify service model maintenance. Following a design science based research process, we developed a prototype and empirically evaluated its applicability with the help of IT consultants. This first evaluation allowed us to identify some limitations and to plan specific improvements, both to the underlying artifacts DITCOS-O and DITCOS-DN, as well as to DITCOS-ModEd itself. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Much of today's data are represented as graphs, ranging from social networks to bibliographic citations. Nodes in such graphs correspond to records that generally represent entities, while edges represent relationships between these entities. Both nodes and edges in a graph can have attributes that characterize the entities and their relationships. Relationships are either explicitly known (like friends in a social network), or they are inferred using link prediction (such as two babies are siblings because they have the same mother). Any graph representing real-world data likely contains nodes and edges that are abnormal, and identifying these can be important for outlier detection in applications ranging from crime and fraud detection to viral marketing. We propose a novel approach to the unsupervised detection of abnormal nodes and edges in graphs. We first characterize nodes and edges using a set of features, and then employ a one-class classifier to identify abnormal nodes and edges. We extract patterns of features from these abnormal nodes and edges, and apply clustering to identify groups of patterns with similar characteristics. We finally visualize these abnormal patterns to show co-occurrences of features and relationships between those features that mostly influence the abnormality of nodes and edges. We evaluate our approach on datasets from diverse domains, including historical birth certificates, COVID patient records, e-mails, books, and movies. This evaluation demonstrates that our approach is well suited to identify both abnormal nodes and edges in graphs in an unsupervised way, and it can outperform several baseline anomaly detection techniques. © 2022 Copyright held by the owner/author(s).
ABSTRACT
We conduct the analysis of the Twitter discourse related to the anti-lockdown and anti-vaccination protests during the so-called 4th wave of COVID-19 infections in Austria (particularly in Vienna). We focus on predicting users' protest activity by leveraging machine learning methods and individual driving factors such as language features of users supporting/opposing Corona protests. For evaluation of our methods we utilize novel datasets, collected from discussions about a series of protests on Twitter (40488 tweets related to 20.11.2021;7639 from 15.01.2022 – the two biggest protests as well as 192 from 22.01.2022;8412 from 11.12.2021;3945 from 11.02.2022). We clustered users via the Louvain community detection algorithm on a retweet network into pro- and anti-protest classes. We show that the number of users engaged in the discourse and the share of users classified as pro-protest are decreasing with time. We have created language-based classifiers for single tweets of the two protest sides – random forest, neural networks and a regression-based approach. To gain insights into language-related differences between clusters we also investigated variable importance for a word-list-based modeling approach. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
A resilient city includes multiple energy carriers, high-efficiency infrastructure, lower resource demand to decarbonize and sustain the urban system in accordance with the Paris Agreement, the United Nations 2030 Agenda for Sustainable Development, and the various recovery plans following the COVID-19 pandemic period. To achieve these goals, a key role is played by all urban sectors, which can reduce environmental impacts and accelerate the green transition at larger scale. Intervening on a district scale obviously requires the evaluation of different aspects, taking into account both economic and non-economic criteria, as well as different points of view, involving all stakeholders. This paper proposes a multi-step evaluation procedure that extends the European manual-based Cost-Benefit Analysis (CBA) to include the extra-economic benefits and the stakeholders' opinion in the evaluation, according to the COmpoSIte Model for Assessment (COSIMA) method. This is the first application of COSIMA in the urban design sectors (i.e. buildings, water, public lighting, transportation and waste management) where different sustainable measures for a real case study located in Turin (Italy) were compared to define the most suitable transformation scenario according to multiple criteria. The results have shown how invasive scenarios allow achieving the greatest benefits, despite the huge initial costs of realization. © 2022 Elsevier Ltd
ABSTRACT
The first ever CO2 foam fracturing new technology in Kuwait Oil Company (KOC) history was executed flawlessly in late 2021. Three treatments were executed. Co2 Foam Fracturing proved its significant added value of improving productivity in deep depleted tight carbonate Jurassic reservoirs, enhance flow back, reduce water consumption and carbon emission, and enable early production plus improving operation efficiency and cost saving. The stimulation operation has proven to be a huge success for all multidisciplinary teams involved as preliminary results showed over 50-70% production increase compared to offset wells. The main challenges of acid fracturing stimulation in depleted reservoirs are the need for extended formation cleanup to flow back the injected fluids via prolonging Nitrogen lift that add higher operational costs and intervention operations. Therefore, energetic high foam efficiency frac fluid becomes essential to assist flowback and retrieve pumped frac fluids from reservoir. To tackle these challenges, Carbon Dioxide CO2 is pumped in liquid phase as energetic fluid together with normal frac fluids. Due to CO2 liquid nature, high foam efficiency can be reached (40 – 50%) at much lower friction losses. So, it enables achieving pumping frac at high rates and high foam efficiency. The main benefits of CO2 Foam frac are better fracture cleanup due to expansion of the stored compressed gas in the liquid CO2, fluid loss control that is provided by foam, minimized fracture conductivity damage, and the increase in hydrostatic pressure while pumping that translates to lower surface pressures during injection. The selected pilot well is in depleted deep tight carbonate reservoir area of North Kuwait Jurassic gas fields. The executed acid fracturing operation required close planning starting from Q1-2021. Many challenges faced from logistical issues, lack of infrastructure and CO2 resources for the multi-faceted operation due to COVID-19 pandemic limitations. These challenges were tackled ahead with the integration of technical and operations teams to bridge the knowledge gap and to enable executing the operation safely. The pilot well's net incremental production gain is estimated at 50-70% compared to offset wells, with improved flowback and formation cleanup with less well intervention. The resulting time and cost savings as well as the incremental well productivity and better operation efficiency confirmed high perspectives for the implemented foam acid fracturing approach. Another two CO2 Foam acid fracturing wells were executed with good results too. This paper will demonstrate the value of CO2 foam fracturing in depleted reservoir and KOC experience post first application and its plans to expand CO2 Foam Fracturing application across KOC different fields. © Society of Petroleum Engineers - HFTC 2023. All rights reserved.
ABSTRACT
The COVID-19 pandemic resulted in the hurried adoption of e-learning with no proper need analysis to inform the design and subsequent evaluation of students' performance in e-learning in medical education. Consequently, several studies evaluating performance in e-learning in medical education do so by conducting pre-Test and post-Test with no defined framework or model to guide the evaluation. This makes the findings from these studies subjective and biased since factors that possibly impact students' performance were neither considered in the design of the course nor measured and reported in the evaluation studies. We, therefore, introduce an essential pedagogical e-learning concept by developing a framework to inform the design and evaluation of students' performance in e-learning in medical education via the thoughtful fusion of the Task-Technology Fit Model and the Kirkpatrick Evaluation Model. Our hybrid framework was piloted at the University of KwaZulu-Natal, Durban, South Africa and findings emphasize the need for alignment between learning tasks, technology infrastructures, individual traits, and contextual limitations of students as key factors in determining how well students perform in the classroom and their clinical practices at work. This study advances the body of knowledge by providing a well-brainstormed and intricately designed framework to guide the design of courses and evaluation of student's performance in an e-learning context in medical education. © 2022 IEEE.
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Non-contact heart rate measurement reaches a higher level in scientific research;this field presents important advantages in our life, such as human-machine interaction, medical applications, especially in the current situation of the world suffering from COVID-19 pandemic. In recent years, several techniques of extracting the imaging photoplethysmography (ippg) signals from facial videos have been proposed and developed. Based on this, we performed a study and evaluation of the four most well known heart rate estimation methods such as Green, ICA, POS and CHROM in two accessible public datasets MAHNOB-HCI and UBFC-Phys under two different facial regions to enable researchers to develop and use them in real applications. The results show that the video imaging condition and the correct face region detection step play an important role in the accuracy of heart rate estimation. © 2022 IEEE.
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Investments should be decisions made by companies at a strategic level, aiming to maximize the return on invested capital. However, the evaluation required to obtain the best results demand, in addition to information about available assets, the ability of decision-makers to relate and weigh all the criteria that must be maximized or minimized to achieve the expected goal. This article plays an important role in supporting the decision making of a micro company, located in the state of Rio de Janeiro, which needs to invest the available amount in investment. In order to obtain the investment alternatives, as well as the evaluation criteria, the Value-Focused Thinking (VFT) was applied. After obtaining all necessary data, the CRITIC-GRA-3N method was used as a Multicriteria Decision Support technique, with the CRiteria Importance Through Intercriteria Correlation (CRITIC) method to generate the criteria weights and the Grey Relational Analysis (GRA) method, with three normalizations, to order the alternatives. With this, five ordinations were established, being the first three ordinations performed with the help of two normalizations and the last two ordinations being the arithmetic and geometric averages of the first three ordinations normalized with the third normalization. In the end it brought positive results to the microenterprise. © 2022 The Authors. Published by Elsevier B.V.
ABSTRACT
Reservoir surveillance and production optimization will remain at the forefront of company strategies in the new post-COVID19 environment. We anticipate that companies will focus more on producing assets and go the route of production enhancement rather than exploration. Accordingly, production logging will remain an important surveillance method in evaluating and strategizing production-optimization schemes pertaining to flow-characterization from reservoir-to-wellbore. This work is culmination of operational and technical excellence that enabled the revival of a loaded-up well through simultaneous lifting-and-logging technique. Conventionally, wireline is the preferred mode of conveyance for production-logging;however, well must be continuously flowing throughout acquisition timeframe. Kicking-off the well using nitrogen-lift and then bringing in wireline-unit for production-logging in Well A-4 was not feasible as previous attempts confirmed well to load-up in few hours post-offloading. Therefore, success of this project was heavily dependent on initial planning stage, which accounted for all available data including production-history, well-events, intervention-details, fluid analysis and well load-up behavior. Next, a multi-domain approach was adopted while bringing-out each domain from its silos and strategize collectively to simultaneously kickoff the well with nitrogen and acquire real-time downhole production-logging data through smart-coiled-tubing (CT). This was first implementation of concurrent lifting and logging operation in Pakistan. By deploying the approach mentioned above through smart CT (using optical-telemetry-link inside the CT-string coupled with downhole-assembly), synchronized lifting-and-logging operation was carried-out successfully. Well was observed to swiftly go back to load-up conditions post-kickoff;however, continuous well dynamics monitoring downhole enabled us to log perforated interval across multiple time domains. Well was activated through CT nitrogen-injection but depicted continuous loading tendency, which was captured downhole in form of flow-transients. Real-time job optimization ensured vigilant monitoring and selection of right-time to acquire meaningful zonal-contribution data for evaluation and diagnostic solutions. Finally, operational excellence was complemented through technical data analysis and interpretation, integrating passes data with transients and stationary measurements. Ultimately, acquired data analyzed using an integrated lens involving fluid velocities, downhole density, temperature, and water hold up data. Consequently, enabling us to decipher gas and water-entries on a zonal-basis across perforated sandstone reservoir. Copyright © 2022, Society of Petroleum Engineers.
ABSTRACT
The Medical Imaging and Data Resource Center (MIDRC) is a multi-institutional effort to accelerate medical imaging machine intelligence research and create a publicly available image repository/commons as well as a sequestered database for performance evaluation and benchmarking of algorithms. After de-identification, approximately 80% of the medical images and associated meta-data will become part of the open repository and 20% will be sequestered and kept separate from the open commons. To ensure that both the public, open dataset and the sequestered dataset are representative of the population available, demographic characteristics across the two datasets must be balanced. Our method uses multidimensional stratified sampling where several demographic variables of interest are sequentially used to separate the data into individual strata, each representing a unique combination of variables. Within each stratum, patients are randomly assigned to the open set (80%) or the sequestered set (20%). Thus, for p variables of interest, the balance of the pdimensional distribution of variable combinations can be controlled. This algorithm was used on an example COVID-19 dataset containing image exams of 4662 patients using the variables of race, age, sex at birth, and ethnicity, each containing 8, 8, 2, and 4 categories, respectively. After stratification of this dataset into the two subsets, resulting distributions of each variable matched the distribution from the original dataset with a maximum percent difference from its original fraction of 0.4%. These results demonstrate that the implemented process of multi-dimensional sequential stratified sampling can partition a large database while maintaining balance across several variables. © 2022 SPIE. All rights reserved.
ABSTRACT
Animal Assisted Therapy (AAT) has demonstrated to provide numerous benefits, such as alleviating stress, improving patients' wellbeing or being a motivational source that could foster and facilitate psychosocial activities in people with functional diversity. In-person AAT is limited to specific times per week, is sometimes not feasible due to safety limitations, and has been disrupted by the COVID-19 pandemic. This paper proposes the definition and design of a technological intervention that would use the human-animal bond as a motivator to improve daily activities at home for children with functional diversity that participate regularly in AAT. We present the participatory design process of the system, potential case studies as well as future lines of research and evaluation of this on-going work. This manuscript aims to demonstrate the potential of the proposed solution in scenarios where the animal could not be physically present, helping to extend the benefits of AAT beyond in-person interactions. © 2021 ACM.
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Since the discovery of corona virus (nCOV-19), and its subsequent progression into a global pandemic, an enormous hurdle faced by hospitals and their healthcare staff has been to streamline, and look after the huge flow of cases. It has become increasingly difficult to consult a Covid specialist when the first wave occurred in rural and areas not connected as well to modern amenities. Thus, it has become obvious that an interactive Chatbot with efficient execution can help patients living in such areas by educating on the appropriate preventive measures, news on virus strains, reducing the psychological damage caused by the fear of the virus and mental effects of solitary isolation. This study displays and discusses the schematics of an artificial intelligence (AI) chatbot for the purpose of evaluation, diagnosis and recommending immediate preventive as well as safety measures for patients who have been exposed to nCOV-19, and doubles as a virtual assistant to aid in measuring the severity of the infection via symptom analysis and connects with the authorised medical facilities when it progresses to a serious stage. © 2022 IEEE.