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Artificial Intelligent Models for Detection and Prediction of Lost Circulation Events: A Review
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Lost circulation or losses in drilling fluid is one of the most important problems in the oil and gas industry, and it appeared at the beginning of this industry, which caused many problems during the drilling process, which may lead to closing the well and stopping the drilling process. The drilling muds are relatively expensive, especially the muds that contain oil-based mud or that contain special additives, so it is not economically beneficial to waste and lose these muds. The treatment of drilling fluid losses is also somewhat expensive as a result of the wasted time that it caused, as well as the high cost of materials used in the treatment such as heavy materials, cement, and others. The best way to deal with drilling fluid losses is to prevent them. Drilling fluid loss is a complex problem that is difficult to predict using simple and traditional methods. Artificial intelligence represents a modern and accurate technology for solving complex problems such as drilling fluid loss. Artificial intelligence through supervised machine learning provides the possibility of predicting these losses before they occur based on field data such as drilling fluid properties, drilling parameters, rock properties, and geomechanical parameters that are related to the loss of circulation of the wells suffered from losses problem located in the same area.

   In this paper, several supervised machine learning models have been reviewed that were used for detecting and predicting of loss of drilling fluids during the drilling process. The paper provides an inclusive review of drilling fluid prediction and detection from simplest to more complected intelligent models.

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Publication Date
Thu Aug 01 2019
Journal Name
Journal Of Economics And Administrative Sciences
Some Estimation methods for the two models SPSEM and SPSAR for spatially dependent data
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ABSTRUCT

In This Paper, some semi- parametric spatial models were estimated, these models are, the semi – parametric spatial error model (SPSEM), which suffer from the problem of spatial errors dependence, and the semi – parametric spatial auto regressive model (SPSAR). Where the method of maximum likelihood was used in estimating the parameter of spatial error          ( λ ) in the model (SPSEM), estimated  the parameter of spatial dependence ( ρ ) in the model ( SPSAR ), and using the non-parametric method in estimating the smoothing function m(x) for these two models, these non-parametric methods are; the local linear estimator (LLE) which require finding the smoo

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Publication Date
Mon Jul 27 2026
Journal Name
Al-kindy College Medical Journal (kcmj)
Review Article Emerging Markers in Osteoporosis, A Review
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Osteoporosis is a global health concern with bone frailty and high fracture risk. Existing diagnostic paradigms largely rely on bone scanning and bone mineral density evaluation which are hindered by the delayed prediction of fractures, especially in high-risk groups. This review assesses existing and novel Osteoporosis biomarkers, their mechanisms, clinical efficacy, drawbacks, and discusses the best biomarkers in Osteoporosis risk stratification and management, to convert them into better patient care. An online search was conducted, including PubMed, Web of Science, Embase, and Google Scholar up to June 2026. Passed studies were reviewed critically and organized biomarkers into five different panels: traditional and bone turnover

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Publication Date
Fri Mar 29 2024
Journal Name
Iraqi Journal Of Science
Evaluating the Performance and Behavior of CNN, LSTM, and GRU for Classification and Prediction Tasks
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     Deep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning mod

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Publication Date
Mon Apr 04 2022
Journal Name
Journal Of Educational And Psychological Researches
Stressful Life Events and their Relationship to Life Skills in Middle School Students
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The current research aims to identify the stressful life events among middle school students in terms of gender (male-female), academic branch (scientific-literary), and the Life Skills of the students of the preparatory stage in terms of gender (male-female), and academic branch (scientific-literary). Additionally, the study aims to identify the relationship between stressful life events and life skills in middle school students. A sample of (200) students who were selected randomly from the Directorate of education of Baghdad Karkh/ III was used in this study. To achieve the objectives of the current research, the researcher has adopted two scales, the stressful life events scale for the researcher Al-Sultan (2008), and the life skills

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Publication Date
Fri Feb 19 2021
Journal Name
Annals Of Telecommunications
Challenges and limits of fractal and slot antennas for WLAN, LTE, ISM, and 5G communication: a review paper
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Abstract Portable communication devices such as WLAN, WiMAX, LTE, ISM, and 5G utilize one or more of the triple bands at (2.32.7 GHz,3.4–3.6GHz,and5–6GHz)andsufferfromtheeffectofmultipathproblemsbecausetheyareusedinurbanregions.To date, no one has performed a review of the antennas used for these types of wireless communications. This study reviewed two types of microstrip antennas (slot and fractal) that have been reported by researchers (as a single element) using a survey that included the evaluation of several important specifications of the antennas in previous research, such as operating bandwidth, gain, efficiency, axial ratio bandwidth (ARBW), and size. The weaknesses in the design of all antennas were carefully identified to de

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Publication Date
Fri Jun 01 2007
Journal Name
Al-khwarizmi Engineering Journal
Correlation for fitting multicomponent vapor-liquid equilibria data and prediction of azeotropic behavior
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Correlation equations for expressing the boiling temperature as direct function of liquid composition have been tested successfully and applied for predicting azeotropic behavior of multicomponent mixtures and the kind of azeotrope (minimum, maximum and saddle type) using modified correlation of Gibbs-Konovalov theorem. Also, the binary and ternary azeotropic point have been detected experimentally using graphical determination on the basis of experimental binary and ternary vapor-liquid equilibrium data.

            In this study, isobaric vapor-liquid equilibrium for two ternary systems: “1-Propanol – Hexane – Benzene” and its binaries “1-Propanol –

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Publication Date
Tue Jul 01 2025
Journal Name
Mastering The Minds Of Machines
Deep Reinforcement Learning: Bridging Learning and Control in Intelligent Systems
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Publication Date
Wed Sep 01 2021
Journal Name
International Journal Of Nonlinear Analysis And Application
Suggested methods for prediction using semiparametric regression function
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Ferritin is a key organizer of protected deregulation, particularly below risky hyperferritinemia, by straight immune-suppressive and pro-inflammatory things. , We conclude that there is a significant association between levels of ferritin and the harshness of COVID-19. In this paper we introduce a semi- parametric method for prediction by making a combination between NN and regression models. So, two methodologies are adopted, Neural Network (NN) and regression model in design the model; the data were collected from مستشفى دار التمريض الخاص for period 11/7/2021- 23/7/2021, we have 100 person, With COVID 12 Female & 38 Male out of 50, while 26 Female & 24 Male non COVID out of 50. The input variables of the NN m

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Publication Date
Wed Mar 29 2017
Journal Name
Iraqi Journal Of Pharmaceutical Sciences ( P-issn 1683 - 3597 E-issn 2521 - 3512)
Role of the Clinical Pharmacist in Reducing Preventable Adverse Drug Events
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According to so many previous studies, lack of sufficient information during prescribing steps may lead to medication errors. Thus, the presence of the clinical pharmacist during routine rounding process in the ward with intervention of patient care plan may reduce the probability of adverse drug events (ADEs).This study evaluate role of the clinical pharmacists, as a member of medical team with the physician, on ADEs and report their interventions in the internal medicine unit. This study was designed to compare between two groups of patients, those receiving care from a rounding team (physician, nurse, and clinical pharmacist) (study or intervention group with 51 patient); and those receiving c

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Publication Date
Tue Aug 25 2026
Journal Name
Al–bahith Al–a'alami
SPECIALIZED JOURNALISM IN IRAQ : (After The Events of 09-04-2003)
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The specialized Journalism plays an important role in our daily lives using all of its different ways, including news, caricature, commentary and dialogue. The specialized Journalism deserves vigilance and interest for it is caring about having a new media system. It is meant by “specialization”, first: identifying the areas of work in which the person concerned possesses great knowledge in the specialty resulting from a long experience, it also means being able to continuously develop required skills in that specialty. The specialization is not only a feature of the press, but also a feature of human development; thousands of years ago, the primitive society consisted of people from different specializations: fishermen, farmers, her

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