Effects of a high-dose 24-h infusion of tranexamic acid on death and thromboembolic events in patients with acute gastrointestinal bleeding (HALT-IT): an international randomised, double-blind, placebo-controlled trial

Background: Tranexamic acid reduces surgical bleeding and reduces death due to bleeding in patients with trauma. Meta-analyses of small trials show that tranexamic acid might decrease deaths from gastrointestinal bleeding. We aimed to assess the effects of tranexamic acid in patients with gastrointestinal bleeding. Methods: We did an international, multicentre, randomised, placebo-controlled trial in 164 hospitals in 15 countries. Patients were enrolled if the responsible clinician was uncertain whether to use tranexamic acid, were aged above the minimum age considered an adult in their country (either aged 16 years and older or aged 18 years and older), and had significant (defined as at risk of bleeding to death) upper or lower gastrointestinal bleeding. Patients were randomly assigned by selection of a numbered treatment pack from a box containing eight packs that were identical apart from the pack number. Patients received either a loading dose of 1 g tranexamic acid, which was added to 100 mL infusion bag of 0·9% sodium chloride and infused by slow intravenous injection over 10 min, followed by a maintenance dose of 3 g tranexamic acid added to 1 L of any isotonic intravenous solution and infused at 125 mg/h for 24 h, or placebo (sodium chloride 0·9%). Patients, caregivers, and those assessing outcomes were masked to allocation. The primary outcome was death due to bleeding within 5 days of randomisation; analysis excluded patients who received neither dose of the allocated treatment and those for whom outcome data on death were unavailable. This trial was registered with Current Controlled Trials, ISRCTN11225767, and ClinicalTrials.gov, NCT01658124. Findings: Between July 4, 2013, and June 21, 2019, we randomly allocated 12 009 patients to receive tranexamic acid (5994, 49·9%) or matching placebo (6015, 50·1%), of whom 11 952 (99·5%) received the first dose of the allocated treatment. Death due to bleeding within 5 days of randomisation occurred in 222 (4%) of 5956 patients in the tranexamic acid group and in 226 (4%) of 5981 patients in the placebo group (risk ratio [RR] 0·99, 95% CI 0·82–1·18). Arterial thromboembolic events (myocardial infarction or stroke) were similar in the tranexamic acid group and placebo group (42 [0·7%] of 5952 vs 46 [0·8%] of 5977; 0·92; 0·60 to 1·39). Venous thromboembolic events (deep vein thrombosis or pulmonary embolism) were higher in tranexamic acid group than in the placebo group (48 [0·8%] of 5952 vs 26 [0·4%] of 5977; RR 1·85; 95% CI 1·15 to 2·98). Interpretation: We found that tranexamic acid did not reduce death from gastrointestinal bleeding. On the basis of our results, tranexamic acid should not be used for the treatment of gastrointestinal bleeding outside the context of a randomised trial

Modelling Techniques Used in The Analysis of Stratified Thermal Energy Storage: A Review

Thermal energy storage plays an important role in the energy management and has got great attention for many decades; stratification is a key parameter to be responsible for the performance of the stratified thermal energy storage tank. In this paper detailed study of modelling techniques used to analyse thermal energy storage has been conducted. The division of literature has been made as numerical, analytical, and artificial neural network-based. Numerical modelling being very physical based and required more specific software’s tools remain costly and computationally very complex at the same time it provides the detailed insights into the system, analytical model provide the exact solutions but need some assumptions which make the system unrealistic in some cases but is easy and flexible in terms of computational requirements, ANN though recently used modelling technique is a black box model which merely needs the data rather than any physical based complex calculations is attracting the scientific community

Application of Machine Learning Approaches to Prediction of Corrosion Defects in Energy Pipelines

The integrity of energy pipelines is crucial for assuring the safe and reliable transportation of resources. Corrosion defects significantly threaten pipeline infrastructure, necessitating advanced predictive maintenance strategies. The energy industry grapples with significant financial losses attributed to corrosion, prompting a crucial need for accurate prediction and prevention measures. This book chapter amalgamates diverse studies that delve into the transformative role of artificial intelligence (AI) and machine learning (ML) in the oil and gas sector, specifically focusing on the energy pipelines sector. Furthermore, the chapter analyzes how artificial intelligence (AI) reshapes the oil and gas industry, particularly in the oil and gas pipeline domain. It discusses AI applications, algorithms, and data considerations, highlighting trends and potential future scenarios over the next few years. Non-technical challenges related to data, human factors, and collaborative models are also examined. The chapter culminates with a focus on artificial intelligence (AI) applications in oil and gas assets, including pipeline infrastructure development, elucidating its potential in predicting production dynamics, optimizing development plans, identifying residual oil, recognizing fractures, and enhancing oil recovery. A comprehensive literature review provides insights into existing AI algorithms, their pros and cons, and concludes with suggestions and potential directions for future AI applications in oil and gas development. This collective exploration underscores the transformative impact of artificial intelligence (AI) across multiple facets of the oil and gas industry, heralding a new era of efficiency, risk mitigation, and strategic decision-making. Overall, this chapter contributes to the advancement of predictive maintenance in energy pipelines, offers accuracy and precision, and identifies corrosion defects using advanced predictive analytics like machine learning (ML) and artificial intelligence (AI). Such ML-based corrosion prediction models can improve pipeline safety, reduce operational risks, and optimize maintenance schedules, ultimately contributing to energy transportation infrastructure\u27s overall reliability and sustainability

Enhancing pipeline integrity: a comprehensive review of deep learning-enabled finite element analysis for stress corrosion cracking prediction

Pipelines are crucial for transporting energy sources, yet corrosion especially stress corrosion cracking (SCC) poses a complex and potentially catastrophic form of material degradation. Traditional techniques like finite element analysis (FEA) have been utilized for SCC prediction, but it suffers from high computational cost and limited scalability. Deep learning (DL) with integration of FEA leverages large-scale data and learn complex nonlinear patterns for SCC prediction. Currently, literature on deep learning-enabled finite element analysis for pipelines SCC prediction is scarce, offering limited insights into this emerging approach and lack of comprehensive review. This paper reviews and investigates the current research directions and applications of DL-enabled FEA methodologies for simulation of SCC prediction. The importance of DL, technique type and network are also outlined in this review paper. This paper delves into integration of DL algorithms with FEA and their ability to grab complex interactions between mechanical stress, material properties, and environmental factors. Based on this comprehensive review, it was found that DL and FEA have proven to be strong prediction tools with high accuracy and lower training cost. DL-enabled FEA techniques are also being utilized to replace time-consuming methods and conventional codes. Furthermore, article discusses potential of this integrated approach for enhancing accuracy and efficiency of SCC prediction, leading to improved pipeline integrity management practices.Abbreviations: SCC: Stress corrosion cracking; FE: Finite element; FEA: Finite element analysis; FEM: Finite element method; ML: Machine learning; DL: Deep learning; EGIG: European gas pipeline incident data group; PHMSA: Pipeline and hazardous materials safety administration; DNNs: Deep neural networks; HpHSCC: High pH stress corrosion cracking; SMYS: Specified minimum yield strength; HE: Hydrogen embrittlement; NNpHSCC: Near-neutral pH stress corrosion cracking; ASME: American society of mechanical engineers; DNV: Det norske veritas; AI-FEM: Advanced iterative finite element method; CGR: Crack growth rate; ME: Mechanoelectrochemical; XGB: XGBoost; CAT: Catboost; CP: Cathodic Protection; AI: Artificial intelligence; ANNs: Artificial neural networks; CNNs: Convolutional neural networks; RNNs: Recurrent neural networks; ReLU: Rectified linear uni

Machine Learning Approach to Predict the Performance of a Stratified Thermal Energy Storage Tank at a District Cooling Plant Using Sensor Data

In the energy management of district cooling plants, the thermal energy storage tank is critical. As a result, it is essential to keep track of TES results. The performance of the TES has been measured using a variety of methodologies, both numerical and analytical. In this study, the performance of the TES tank in terms of thermocline thickness is predicted using an artificial neural network, support vector machine, and k-nearest neighbor, which has remained unexplored. One year of data was collected from a district cooling plant. Fourteen sensors were used to measure the temperature at different points. With engineering judgement, 263 rows of data were selected and used to develop the prediction models. A total of 70% of the data were used for training, whereas 30% were used for testing. K-fold cross-validation were used. Sensor temperature data was used as the model input, whereas thermocline thickness was used as the model output. The data were normalized, and in addition to this, moving average filter and median filter data smoothing techniques were applied while developing KNN and SVM prediction models to carry out a comparison. The hyperparameters for the three machine learning models were chosen at optimal condition, and the trial-and-error method was used to select the best hyperparameter value: based on this, the optimum architecture of ANN was 14-10-1, which gives the maximum R-Squared value, i.e., 0.9, and minimum mean square error. Finally, the prediction accuracy of three different techniques and results were compared, and the accuracy of ANN is 0.92%, SVM is 89%, and KNN is 96.3%, concluding that KNN has better performance than others

A review on Bayesian modeling approach to quantify failure risk assessment of oil and gas pipelines due to corrosion

Funding Information: The authors would like to thank Universiti Teknologi PETRONAS (UTP) Malaysia for giving the opportunity to conduct research under grant number 015LC0-381 for the project “Failure Prediction Model for Stress Corrosion Cracking Using Deep Learning Approach." Publisher Copyright: © 2022 Elsevier LtdTo forecast safety and security measures, it is vital to evaluate the integrity of a pipeline used to carry oil and gas that has been subjected to corrosion. Corrosion is unavoidable, yet neglecting it might have serious personal, economic, and environmental repercussions. To predict the unanticipated behavior of corrosion, most of the research relies on probabilistic models (petri net, markov chain, monte carlo simulation, fault tree, and bowtie), even though such models have significant drawbacks, such as spatial state explosion, dependence on unrealistic assumptions, and static nature. For deteriorating oil and gas pipelines, machine learning-based models such as supervised learning models are preferred. Nevertheless, these models are incapable of simulating corrosion parameter uncertainties and the dynamic nature of the process. In this case, Bayesian network approaches proved to be a preferable choice for evaluating the integrity of oil and gas pipeline models that have been corroded. The literature has no compilations of Bayesian modeling approaches for evaluating the integrity of hydrocarbon pipelines subjected to corrosion. Therefore, the objective of this study is to evaluate the current state of the Bayesian network approach, which includes methodology, influential parameters, and datasets for risk analysis, and to provide industry experts and academics with suggestions for future enhancements using content analysis. Although the study focuses on corroded oil and gas pipelines, the acquired knowledge may be applied to several other sectors.Peer reviewe

Insights into modern machine learning approaches for bearing fault classification: A systematic literature review

Rolling bearings are essential components in a wide range of equipment, such as aeroplanes, trains, and wind turbines. Bearing failure has the potential to result in complete system failure, and it accounts for approximately 45 %–50 % of failures in rotating machinery. Hence, it is imperative to establish a thorough and accurate predictive maintenance program that can efficiently foresee and prevent mishaps or malfunctions. The literature has employed a variety of techniques and approaches, from conventional methods to contemporary machine learning (ML) and ML-integrated IoT-based solutions, to categorise bearing faults. This article provides an overview of the most recent research and models used in the classification of bearing faults. The literature summary highlights various significant challenges in current models, such as issues with the classification function, complexities in the neural network structure, unrealistic datasets, dynamic working conditions of rotating machines, noise in the dataset, limited data availability, and imbalanced datasets. In order to tackle the problems, researchers have endeavored to improve and apply different methods, such as convolutional neural networks, deep belief neural networks, and LiNet, among others. Researchers have primarily developed these approaches using datasets from publicly accessible sources. This study also identified research gaps and deficiencies, including limited data availability, data imbalance, and difficulties in data integration. The nascent technologies in the field of problem diagnosis and predictive maintenance are acknowledged as Internet of Things-based ML and vision-based deep learning techniques, which are currently in their initial phases of advancement. Ultimately, the study puts forth several prospective suggestions and recommendations

Water Stress Affects the Some Morpho-Physiological Traits of Twenty Wheat (<i>Triticum aestivum</i> L.) Genotypes under Field Condition

Water stress has become one of the foremost constraints to agricultural development, mostly in areas that are deficient in water. A field trial has been conducted to evaluate the performance of different twenty wheat genotypes under three stress treatments viz., control (T0) = normal watering, stress-1 (T1) = water stress from tillering up to maturity, and stress-2 (T2) = water stress from anthesis to maturity were used as treatments. The results revealed that a highly significant (p −1, spikeletspike−1, and relative water content. In the early days, 50% flowering was noted in Anmole-91 (64.33 days) under (T0), while Anmol-91 showed a relative decrease (RD-1) (−2.34 days) at days 50% flowering in (T1). The TJ-83 genotype showed an early response (−8.34 day) at days to 50% flowering under stress-2 (T2), but TD-I (−3.34) was observed to be relatively tolerant. Underwater stress from tillering to maturity (T1) SKD-1 was found more susceptible (−36.7 days) than other cultivars. Wheat cultivar Soghat-90 showed maximum RD-1 (−24.7) for grain yield plant−1 in stress-1 (T1) from tillering to maturity. Anmole-91, NIA-Sarang, and TD-I observed minimum was (−6) in the same water stress for various traits. Therefore, the findings of present work revealed that the best performing genotypes can be recommended for effective cultivation in future breeding programs