The effect of low altitude on blood count parameters

BACKGROUND AND OBJECTIVESHigh altitude has an effect on blood count parameters, but low altitude (especially below sea level) has not been studied.DESIGN AND SETTINGA cross-sectional study of aymptomatic subjects aged between 18 to 35 years of age who had reported to the blood bank at the King Abdullah University Hospital (KAUH)/Irbid and Ministry of Health, Jordan, during the period between January 2010 to June 2011 for blood donation.METHODSHematological values were compared in healthy adult blood donors living in areas 200 to 300 meters below sea level and areas 500 to 1500 meters above sea level. The study population consisted of 800 females and 666 males aged between 18 to 35 years.RESULTSThe mean values for hemoglobin level, mean corpuscular volume and leukocyte counts were significantly higher in people living above sea level than in people living below sea level (P<.0001), whereas platelet count and red cell distribution width were significantly higher in people living below sea level than in people living above sea level (P<.0001).CONCLUSIONWe found a significant difference in hematological parameters in healthy adults living above and below sea level. The hematological values presented here are from a large, representative population sample and the first report of people living below sea level

Demographic Characteristics and Review of Patients with Locally Advanced Breast Cancer in Sudan

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Design of GCSC Stabilizing Controller for Damping Low Frequency Oscillations

This paper presents a systematic procedure for modeling and simulation of a power system equipped with FACTS type Gate Controlled Series Compensator (GCSC) based stabilizer controller. Single Machine Infinite Bus (SMIB) power system was investigated for evaluation of GCSC stabilizing controller for enhancing the overall dynamic system performance. PSO algorithm is employed to compute the optimal parameters of damping controller. Eigenvalues of system under various operating condition and nonlinear time domain simulation is employed to verify the effectiveness and robustness of GCSC stabilizing controller in damping low frequency oscillations (LFO) modes

An artificial lift selection approach using machine learning: a case study in Sudan.

This article presents a machine learning (ML) application to examine artificial lift (AL) selection, using only field production datasets from a Sudanese oil field. Five ML algorithms were used to develop a selection model, and the results demonstrated the ML capabilities in the optimum selection, with accuracy reaching 93%. Moreover, the predicted AL has a better production performance than the actual ones in the field. The research shows the significant production parameters to consider in AL type and size selection. The top six critical factors affecting AL selection are gas, cumulatively produced fluid, wellhead pressure, GOR, produced water, and the implemented EOR. This article contributes significantly to the literature and proposes a new and efficient approach to selecting the optimum AL to maximize oil production and profitability, reducing the analysis time and production losses associated with inconsistency in selection and frequent AL replacement. This study offers a universal model that can be applied to any oil field with different parameters and lifting methods

Artificial lift selection methods in conventional and unconventional wells: a summary and review from old techniques to machine learning applications.

Artificial lift (AL) selection is an important process in enhancing oil and gas production from reservoirs. This article explores the old and current states of AL selection in conventional and unconventional wells, identifying the challenges faced in the process. The role of various factors such as production and reservoir data and economic and environmental considerations is highlighted. The article also examines the use of machine learning (ML) techniques in the AL selection process, emphasising their potential to increase the accuracy of selection and reduce data analysis time. The findings of this article provide valuable insights for researchers and practitioners in the oil and gas industry, as well as for those interested in the development of AL selection methods

A Predictive Technique using Random Forest Classifier for Phishing Malicious Attack

A person with an email account is always vulnerable to fraud. An email account can be exploited by using a type of social engineering attack where the attackers trick the victims to steal user credentials by masquerading as a trusted entity. Email phishing has become the major action performed in various sectors such as banking, business, any enterprise or social media etc. While the action of phishing, the attackers make use of another technique called email spoofing. Email spoofing is not much different from email phishing since the email spoofing involves the usage of forged email header pretending as an entity created by a person of a trusted source. Phishing always has a malicious intent which means the person behaves knowingly or purposefully to cause them harm without a legal reasoning. Since the globe has more victims, we come across a large dataset. The major objective of the study is to determine the performance factors based on the phishing using random forest classifiers. For analysing a predictive model, we need a proper technique or an algorithm. In this case the random forest algorithm is accurate because it is built with many decision trees that produce a predictive model about the error rate

A summary of artificial lift failure, remedies and run life improvements in conventional and unconventional wells.

Artificial lift (AL) systems are crucial for enhancing oil and gas production from reservoirs. However, the failure of these systems can lead to significant losses in production and revenue. This paper explores the different types of AL failures and the causes behind them. The article discusses the traditional methods of identifying and mitigating these failures and highlights the need for new designs and technologies to improve the run life of AL systems. Advances in AL system design and materials, as well as new methods for monitoring and predicting failures using data analytics and machine learning techniques, have been discussed. The findings of this work provide valuable insights for researchers and practitioners in the development of more reliable and efficient AL systems

Feature selection using information gain for improved structural-based alert correlation

Grouping and clustering alerts for intrusion detection based on the similarity of features is referred to as structurally base alert correlation and can discover a list of attack steps. Previous researchers selected different features and data sources manually based on their knowledge and experience, which lead to the less accurate identification of attack steps and inconsistent performance of clustering accuracy. Furthermore, the existing alert correlation systems deal with a huge amount of data that contains null values, incomplete information, and irrelevant features causing the analysis of the alerts to be tedious, time-consuming and error-prone. Therefore, this paper focuses on selecting accurate and significant features of alerts that are appropriate to represent the attack steps, thus, enhancing the structural-based alert correlation model. A two-tier feature selection method is proposed to obtain the significant features. The first tier aims at ranking the subset of features based on high information gain entropy in decreasing order. The second tier extends additional features with a better discriminative ability than the initially ranked features. Performance analysis results show the significance of the selected features in terms of the clustering accuracy using 2000 DARPA intrusion detection scenario-specific dataset

Prediction Model for Construction Cost and Duration in Jordan

Risk is mitigated in the course of reliable prediction. A probabilistic model is proposed to predict the risk effects on time and cost of construction projects. Project managers and consultants can use the model in estimating project cost and duration based on historic data. Statistical regression models and sample tests are developed using real data of 140 projects. The research objective is to develop a model to predict project cost and duration based on historic data of similar projects. The model result can be used by project managers in the planning phase to validate the schedule critical path time and project budget. Research methodology is steered per the following progression: i) Conduct nonparametric test for project cost and time performance. ii) Develop generic multiple-regression models to predict project cost and duration using historic performance data. iii) The percent prediction error is statistically analyzed; and found to be substantial; thus, iv) Custom multiple regression models are developed for each project type to obtain statistically reliable results. In conclusion, the 95% point estimation of error margin= ±0.035%. Therefore, at a probability of 95%, the proposed model predicts the project cost and duration with a precision of ±0.035% of the mean cost and time