bluuebunny/crossref_metadata_2025_split huggingface dataset:
Integrated, Remote, Digital – Confined Space Monitoring in IR4
Authors: Daher Elie , Schoeib Sameh 
Published: September 2024

Abstract
Confined space work is one of the most hazardous jobs in a turnaround. In recent years, confined space monitoring has been revolutionized. Safety watch personnel positioned outside the confined spaces have been replaced by qualified technicians who remotely monitor multiple confined spaces. These technicians can directly communicate with the confined space workers and have clear A/V communications with them.
While the current remote monitoring system is a step in the right direction, there are certain safety challenges that persist. One such challenge, for example, is that you can currently only monitor workers through A/V cameras. While workers may look fine physically, in emergencies like gas leaks, tracking internal vitals like heart rate and breathing rate can alert the command center to a potential medical emergency. The affected worker can be taken out of the confined space for appropriate treatment and rest preventing an incident within the Confined Space.
Another challenge with the current Confined Space Monitoring systems is that they are monitored by technicians who are monitoring multiple confined spaces at the same time. No matter how experienced and well trained the technicians are, they are still human and might miss something. This is where AI comes in. Enhancing the current confined space monitoring solutions with AI technology like Computer Vision enables improved PPE compliance, fall alerts etc enhancing safety of the workers within confined spaces.
Lastly, one other challenge of the current confined space monitoring systems is that they require a command center which relies on the power and network connectivity available at the plant it is situated in. This results in certain limitations where there are space constraints and/ or connectivity issues. For these situations, the new confined space monitoring solution has a ‘light’ version which does not require a full command center set up. The ‘light’ version can be installed on a laptop and managed by a technician from anywhere on or off the site. This reduces its footprint and can work independently on battery power.
This paper looks in detail at the technical advances of the new digital confined spaces monitoring technology and how it maximizes worker safety and efficiency.
A Comprehensive Life Cycle Assessment of Hydraulic Fracturing
Authors: Khan Abdul Muqtadir , Anderson Christine , Prabhu Rasika , Beletskaya Anna , Kuan Pearl , Danican Samuel , Mostowfi Farshid 
Published: September 2024

Abstract
The oil and gas industry has been taking steps to achieve sustainable growth. Most E&P companies have declared their commitment to a carbon-neutral future. The first step in realizing this goal is to identify high-intensity operations, such as unconventional hydraulic fracturing and reduce those emissions. Therefore, the primary objective of this work is to quantify the main sources of emissions in the hydraulic fracturing value chain and identify technologies that could drastically reduce those emissions.
The main purpose of this work is to identify nearly all sources of emissions and the scale of their impact. Once the main emission sources are identified in the entire unconventional fracturing chain, we then focus on technologies that enable significant emission reduction. We employed qualitative and quantitative analysis to assess the emissions impact of diesel engines, material transportation, and water utilization. Our observations yielded a high-level view of emission intensity across the value chain of hydraulic fracturing. Wellsite execution related fuel consumption during pumping was the highest contributor to emissions at 36%, followed closely by the flaring for fracture cleanup at 29%. Total transportation contributed 24% to the total emissions. While the emissions of the fracturing fleets are often very visible due to their large footprint and duration, surprisingly, the nonroutine flaring and transportation are also impactful. In fact, nonroutine flaring could have much higher impact depending on how fast the cleanup process could take place, which may not be easy to predict.
It is reported that using natural gas in internal combustion engines reduced the CO2 emissions. However, the methane leak from engines would offset the CO2 savings and render the benefits marginal. We also observed that the reuse of produced water to be a feasible way to reduce the well stimulation footprint on water resources, which could be enabled by improved water treatment techniques at scale, advances in hydraulic fracturing fluid composition, and appropriate infrastructure. We quantified the upstream emissions (embodied carbon) of commonly used chemicals. Our calculations show that the embodied carbon of commonly used chemicals in unconventional fracturing is rather small. Our conclusions emphasize the importance of adopting ecofriendly technologies to address the challenges posed by hydraulic fracturing. They can impact up to 35% of emissions reduction.
This study contributes a high-level, yet accurate, perspective on emissions of the hydraulic fracturing process. We highlight the high-emitting steps in the process and identify technology gaps that could reduce the emission footprint of the industry. This research underscores the urgency of adopting responsible practices in hydraulic fracturing for a harmonious coexistence with global sustainability objectives.
Data Structures and Integrations for Environmental Reporting and Analytics
Authors: Hill Travis 
Published: September 2024

Abstract
Data structure and integrations are vital components to a successful implementation of environmental software used for tracking, reporting, and data analytics. Defining the expected data outputs at the beginning of a software implementation results in an efficient and effective reporting process, which meets stakeholders’ expectations, and is the foundation for advanced analytics. A robust evaluation of the expected reporting, whether it is required for regulatory, voluntary, or internal reporting, will set the requirements for source system data. If the data in the identified source systems is not available or reliable, the outputs of the environmental system should be adjusted, or the project should be postponed until the source system data is readily available.
This paper explains how the project team and stakeholders defined the requirements, data fields, calculation methodologies, necessary reports, and document approvals. Documentation is necessary to effectively communicate the requirements and expectations for developers, while also allowing appropriate stakeholders to agree on the project deliverables. Without proper documentation and approvals, the project outputs are likely to miss the intended outputs and require rework.
During the project planning phase, the implementation teams created workflows or diagrams tracing components for each desired output back to source systems. The diagrams identified data points such as source system fields, frequency of updates, unit of measure, unique identifies, Global Positioning System (GPS) coordinates, or run-time information. The data point definitions show the availability of the data from the source system and frequency of updates of source system data to influence integration design.
Digital Reporting Solution Delivers a New HSE Observation & Intervention Every Minute!
Authors: Pamnani Amit , Bhushan Bharat , Albanna Mohammed Abdulsalam , Dey Dhiman 
Published: September 2024

Abstract
ADNOC Offshore has developed a robust HSE Observation & Intervention program, leveraging digital solutions to enhance reporting culture and safety performance. This initiative has led to a significant decline in incident and injury rates, reinforcing our commitment to 100% HSE.
ADNOC Offshore collects HSE observation data from HSE Observation & Intervention Mobile application through which all our employees and contractors reports safe or unsafe acts or conditions by selecting various dropdown options and attaching photos when and where possible and applicable. However, the observation description captured in these reports is often incomplete, inconsistent, and unstructured, making it difficult to analyze and derive insights. Due to varied level of literacy and educational background the description typed in English may not be grammatically correct or even misspelled. The company wants to use machine learning to improve the data quality, risk rank, identify patterns and trends, and enhance the decision-making process for HSE management. The aim is to do real-time intervention at the time of reporting an event based on AI tool, further analysis for these unsafe acts & conditions and prevent those from becoming incidents. Due to the large volume of data, it was very challenging to analyze and identify trends and common hazards.
Transforming Safety and Operational Excellence: ADNOC's Implementation of an Integrated Work Management System
Authors: Saleem Juzar 
Published: September 2024

In the ever-evolving landscape of the global oil and gas industry, ensuring safety, maximizing efficiency, and achieving operational excellence are paramount objectives. The Abu Dhabi National Oil Company (ADNOC), a global leader in energy production, has taken a groundbreaking step towards realizing these goals through the implementation of its innovative Work Management System (WMS). This comprehensive digital solution represents a paradigm shift in how work is conceptualized, managed, controlled, and executed across ADNOC's vast and diverse operations, effectively setting new benchmarks for the entire industry.
The scale and scope of the WMS implementation are truly remarkable, making it one of the largest and most ambitious projects of its kind in the oil and gas sector. With over 50000 trained and active users, the system processes more than 2 million permits annually and manages in excess of 1.2 million certificates and risk assessments. These figures alone underscore the transformative impact of this initiative on ADNOC's operations.
This comprehensive overview delves deep into the intricacies of ADNOC's Work Management System, meticulously detailing its key components, chronicling the implementation journey, and analyzing the profound impact it has had on the organization's safety culture, operational efficiency, and overall performance. This thorough exploration aims to provide a detailed understanding of how ADNOC has managed to create a safer, more efficient, and high-performing operational environment through the strategic implementation of the WMS.
Estimating Probability Distribution for the Random Time Variable in the Continuous Compounding Formula: Implication to Time Value of Money in Investment Decisions
Authors: Hesse Christian Akrong , Boyetey Dominic Buer , Nyarko Eunice Stella , Ashiagbor Albert Ayi 
Published: September 2024

Abstract

This paper treats the time “t” variable in the continuous compounding formula of the time value of money as a random variable and derives a probability distribution for it. It formulates a mathematical model that shows the probability associated with the time of recouping the present value of the investment at a specified time and estimates the time at which a given asset changes, holding other factors constant. The study finds that there is an indirect relationship between the investment holding period and the probability of obtaining an investment return on the future value of an asset. It also finds that there is a maximum threshold of time that maintains the present value of an asset after which the probability decreases. This paper differs from previous studies by modeling time as a random variable.
Recent development of a laser interferometer for Taiji space gravitational wave detection
Authors: Luo Ziren , Liu He Shan , Wang Juan , Tao Wei , Qi Ke Qi , Wang Shao Xin , Gao Rui Hong , Li Pan , Dong Peng , Sha Wei , Hu Wen Rui 
Published: September 2024

Abstract

In this study, we developed and constructed the first-generation ultrastable optical bench (OB) for the Taiji program. The OB uses optical bonding technology, and for the first time, noise testing and calibration were performed. A comprehensive analysis was carried out on the various noise sources impacting the interferometer system; these sources included laser frequency noise, laser power noise, thermal drift noise, and angular jitter noise. When the environmental conditions fell short of our stringent requirements, an advanced post-data processing technique was employed to effectively reduce both the thermal drift noise and angular coupling noise. The results revealed that the noise level of the interferometer system reached 1.1 pm/Hz1/2 at 1 Hz, 5 pm/Hz1/2 at 0.1 Hz, and 8 pm/Hz1/2 at 0.01 Hz; these levels met the requirements of the Taiji pathfinder mission. Furthermore, a future research proposal was provided, with the aim to meet the full frequency range requirements of the Taiji program.
Development and validation of prediction model for prolonged mechanical ventilation after total thoracoscopic valve replacement: a retrospective cohort study
Authors: Lin Zhiqin , Xu Zheng , Chen Liangwan , Dai Xiaofu 
Published: September 2024

Abstract

Background
Total thoracoscopic valve replacement (TTVR) is a minimally invasive alternative to traditional open-heart surgery. However, some patients undergoing TTVR experience prolonged mechanical ventilation (PMV). Predicting PMV risk is crucial for optimizing perioperative management and improving outcomes.
Methods
We conducted a retrospective cohort study of 2,319 adult patients who underwent TTVR at a tertiary care center between January 2017 and May 2024. PMV was defined as mechanical ventilation exceeding 72 hours post-surgery. A Fine-Gray competing risks regression model was developed and validated to identify predictors of PMV.
Results
Significant predictors of PMV included cardiopulmonary bypass time, ejection fraction, New York Heart Association grading, serum albumin, atelectasis, pulmonary infection, pulmonary edema, age, need for postoperative dialysis, hemoglobin levels, and PaO2/FiO2. The model demonstrated good discriminative ability, with areas under the receiver operating characteristic curves of 0.747 in the training set and 0.833 in the validation set. Calibration curves showed strong agreement between predicted and observed PMV probabilities. Decision curve analysis indicated clinical utility across a range of threshold probabilities.
Conclusions
Our predictive model for PMV following TTVR demonstrates strong performance and clinical utility. It helps identify high-risk patients and tailor perioperative management to reduce PMV risk and improve outcomes. Further validation in diverse settings is recommended.
Accurate estimation of suspended sediment concentration integrated remote sensing information and a novel stacking machine learning model
Authors: Fang Xiaotian , Zhang Jiahua , Yu Xiang , Zhang Shichao , Kong Delong , Wang Xiaopeng , Ali Shawkat , Ullah Hidayat , Xu Nuo 
Published: September 2024

Abstract

Suspended sediment concentration (SSC) in rivers significantly impacts the preservation of the ecological environment and the exploitation of water resources. The advancement of remote sensing technique offers a robust approach for monitoring SSC. However, the complexity of watersheds and the surrounding environment present a new challenge for accurate estimation of SSC. To address this limitation, this study proposes a new stacking model considering Multilayer Perceptron and Light Gradient Boosting Machine with Elastic Net algorithm (MLEN), and integrates remote sensing information for precise estimating SSC. The Tree-structured Parzen Estimator method was adopted to optimize hyperparameters, the MLEN model was trained by reconstructed datasets combining surface reflectance from high-quality Landsat remotely-sensed images over 30 years, with environmental factors including precipitation, temperature, wind, and surface pressure from ERA5 dataset, as well as discharge and SSC data from USGS five hydrographic stations of the Middle Rio Grande River Basin in the United States. Those stations were selected with over 30 years of available data and nearby gauged stream widths of at least 90 meters to ensure local characteristics and reliable satellite sampling. Moreover, the contribution of features on estimating SSC was also discussed in detail. The results show that compared with the individual models, the MLEN model achieved best accuracy in estimating SSC. Furthermore, the MLEN model also outperformed the other five machine learning algorithms (R2 = 0.80, RMSE = 0.44, and MAPE = 0.30). It indicates the MLEN model can effectively predict SSC in complex, long-term, and time-varying watersheds with readily available hydrographic data.
Rack1 Contributes to the Upregulation of Embryonic Genes in a Model of Cardiac Hypertrophy
Authors: Ceci Marcello , Bonvissuto Davide , Papetti Flavia , Silvestri Federica , Sette Claudio , Catalani Elisabetta , Cervia Davide , Gornati Rosalba , Romano Nicla 
Published: September 2024

Abstract

Receptors for activated C kinase (RACKs) have been shown to coordinate PKC-mediated hypertrophic signaling in the mouse. However, rare information is available on its participation in embryonic gene expression. This study investigated the involvement of RACK1 in the expression of embryonic genes in a zebrafish (ZF) ex vivo heart’s culture model by using Phenylephrine (PE) or a growth factors cocktail (GFs) as pro-hypertrophic/regeneration stimuli. Blebbistatin (BL) inhibitory action treatment was also studied for its involvement in blocking some of PE’s signal transduction actions. qRT-PCR and immunoblot analyses confirmed the upregulation of RACK1 in the PE- and GFs-treated groups. BL administration counteracted PE-induced hypertrophy and downregulated RACK1’s expression. Immunohistochemical analyses of the heart showed the co-localization of RACK1 and embryonic genes, namely, Gata4, Wt1, and Nfat2, under stimulation treatment, whereas resulted less expressed in the BL treatment. Cultured ZF heart cells activated via GFs treatment the enhanced expression of RACK1. Overexpression of RACK1 induced by transfection of recombinant RACK1 cDNA in ZF heart cells, enhances the expression of embryonic genes, especially after a week of GFs treatment. In summary, these results support the involvement of RACK1 in the induction of embryonic genes during cardiac hypertrophy/GFs stimulation in a fish heart model, which can be used as a mammal's alternative study model.
Oakland Score to identify low-risk patients with lower gastrointestinal bleeding performs well among emergency department patients
Authors: DiLena Daniel D. , Bouvet Sean C. , Somers Madeline J. , Merchant Maqdooda A. , Levin Theodore R. , Rauchwerger Adina S. , Sax Dana R. 
Published: September 2024

Abstract

Background: The Oakland Score predicts risk of 30-day adverse events among hospitalized patients with lower gastrointestinal bleeding (LGIB) possibly identifying patients who may be safe for discharge. The Oakland Score has not been studied among emergency department (ED) patients with LGIB. The Oakland Score composite outcome includes re-bleeding, defined as additional blood transfusion requirements and/or a further decrease in hematocrit (Hct) >/= 20% after 24 hours in clinical stability; red blood cell transfusion; therapeutic intervention to control bleeding, including surgery, mesenteric embolization, or endoscopic hemostasis; in-hospital death, all cause; and re-admission with further LGIB within 28 days. Prediction variables include age, sex, previous LGIB admission, systolic blood pressure, heart rate, and hemoglobin concentration, and scores range from 0 to 35 points, with higher scores indicating greater risk.
Methods: Retrospective cohort study of adult (≥18 years old) patients with a primary ED diagnosis of LGIB across 21 EDs from March 1st, 2018, through March 21st, 2020. We excluded patients who were more likely to have upper gastrointestinal bleeding (esophago-gastroduodenoscopy without LGIB evaluation), patients who left against medical advice or prior to ED provider evaluation, ED patients without active health plan membership, and patients with incomplete Oakland Score variables. We assessed predictive accuracy by reporting the area under the receiver operator curve (AUROC) and sensitivity, specificity, positive and negative predictive values, and positive and negative likelihood ratios at multiple clinically relevant thresholds.
Results: We identified 8,283 patients with LGIB, 52% were female, mean age was 68, 49% were non-white, and 27% had an adverse event. The AUROC for predicting an adverse event was 0.85 (95% CI: 0.84 to 0.86). There were 1,358 patients with an Oakland Score of </=8; 4.9% had an adverse event, and sensitivity of the Oakland Score at this threshold was 97% (95% CI 0.96 to 0.98).
Conclusion: The Oakland Score had high predictive accuracy among ED patients with LGIB. Prospective evaluation is needed to understand if the risk score could augment ED decision-making and improve outcomes and resource utilization.
Harnessing Industry 4.0 Technologies for Human Resource Development in Iraqi Higher Education: Challenges and Opportunities
Authors: Jubouri Hassan Abed Ali Alwan 
Published: September 2024

Abstract

This research delves into the transformative potential of Industry 4.0 technologies, such as artificial intelligence (AI), the Internet of Things (IoT), big data analytics, and robotics, in advancing human resource development within Iraqi higher education. Anchored in the principles of digital transformation and organizational change management, this study illustrates how AI can personalize learning experiences and streamline administrative tasks, thus enhancing efficiency and effectiveness. IoT supports the real-time monitoring and upkeep of educational infrastructure, while big data analytics offer critical insights into student performance and institutional operations. Additionally, robotics enrich practical training and research opportunities. By integrating these theoretical frameworks, the study examines the comprehensive impact of Industry 4.0 technologies on developing human resources in the educational sector.