MRI brain scan classification using novel 3-D statistical features

The paper presents an automated algorithm for detecting and classifying magnetic resonance brain slices into normal and abnormal based on a novel three-dimensional modified grey level co-occurrence matrix approach that is used for extracting texture features from MRI brain scans. This approach is used to analyze and measure asymmetry between the two brain hemispheres, based on the prior-knowledge that the two hemispheres of a healthy brain have approximately a bilateral symmetry. The experimental results demonstrate the efficacy of our proposed algorithm in detecting brain abnormalities with high accuracy and low computational time. The dataset used in the experiment comprises 165 patients with 88 having different brain abnormalities whilst the remaining do not exhibit any detectable pathology. The algorithm was tested using a ten-fold cross-validation technique with 10 repetitions to avoid the result depending on the sample order. The maximum accuracy achieved for the brain tumors detection was 93.3% using a Multi-Layer Perceptron Neural Network.

MRI brain classification using the quantum entropy LBP and deep-learning-based features

Brain tumor detection at early stages can increase the chances of the patient’s recovery after treatment. In the last decade, we have noticed a substantial development in the medical imaging technologies, and they are now becoming an integral part in the diagnosis and treatment processes. In this study, we generalize the concept of entropy di erence defined in terms of Marsaglia formula (usually used to describe two di erent figures, statues, etc.) by using the quantum calculus. Then we employ the result to extend the local binary patterns (LBP) to get the quantum entropy LBP (QELBP). The proposed study consists of two approaches of features extractions of MRI brain scans, namely, the QELBP and the deep learning DL features. The classification of MRI brain scan is improved by exploiting the excellent performance of the QELBP–DL feature extraction of the brain in MRI brain scans. The combining all of the extracted features increase the classification accuracy of long short-term memory network when using it as the brain tumor classifier. The maximum accuracy achieved for classifying a dataset comprising 154 MRI brain scan is 98.80%. The experimental results demonstrate that combining the extracted features improves the performance of MRI brain tumor classification.N/

Calcite Scale Inhibition Using Environmental-Friendly Amino Acid Inhibitors: DFT Investigation

Scale prevention is a long-term challenge. It is essential for ensuring the optimum utilization of oil and gas wells and minimizing economic losses due to disruptions in the hydrocarbon flow. Among the commonly precipitated scales is calcite, especially in oilfield production facilities. Previous studies on scale inhibitors have focused on investigating the performance of several phosphonates and carboxylates. However, the increased environmental awareness has pushed toward investigating environmental-friendly inhibitors. Research studies demonstrated the potential of using amino acids as standalone inhibitors or as inhibitor-modifying reagents. In this study, 10 amino acids for calcite inhibitors have been investigated using molecular simulations. Eco-toxicity, quantum chemical calculations, binding energy, geometrical, and charge analyses were all evaluated to gain a holistic view of the behavior and interaction of these inhibitors with the calcite {1 0 4} surface. According to the DFT simulation, alanine, aspartic acid, phenylalanine, and tyrosine amino acids have the best inhibitor features. The results revealed that the binding energies were -2.16, -1.75, -2.24, and -2.66 eV for alanine, aspartic acid, phenylalanine, and tyrosine, respectively. Therefore, this study predicted an inhibition efficiency of the order tyrosine > phenylalanine > alanine > aspartic acid. The predicted inhibition efficiency order reveals agreement with the reported experimental results. Finally, the geometrical and charge analyses illustrated that the adsorption onto calcite is physisorption in the acquired adsorption energy range.This publication was made possible by Qatar University Grant No. QUCP-CENG-2021-3. The statements made herein are solely the responsibility of the authors. Gas Processing Center at Qatar University is acknowledged for providing the support and facilities. In addition, the authors would like to acknowledge the use of computational resources provided by Texas A&M University in Qatar.Scopu

Review of Iron Sulfide Scale Removal and Inhibition in Oil and Gas Wells: Current Status and Perspectives

Iron sulfide scale is one of the main types of inorganic scales that block oil and gas wells. Iron sulfide has polymorph crystallinity structures, which complicate its dissolving and inhibition. Hydrochloric acid (HCl) is used conventionally to remove iron sulfide scale; however, toxic hydrogen sulfide (H2S) is released. Consequently, tubular corrosion and formation damage are accelerated. This review highlights the major role of understanding iron sulfide chemistry in developing chemicals for removing or inhibiting the different iron sulfide polymorphs. Furthermore, the physical features of iron sulfide types and the optimum conditions for their formation in terms of depth, temperature, pressure, and well types are explored. The article also emphasizes the mechanisms and introduces the new green formulations used to dissolve and inhibit iron sulfide. Moreover, recent theoretical work on molecular simulation efforts proved to be significant in identifying potential dissolvers and inhibitors and insightful on the reaction mechanisms. Furthermore, the field practice in removing this type of scale is illustrated with field case studies to present the obstacles facing the efficient implementation of the lab-scale techniques. Finally, the environmental impact and economic assessment are reviewed to identify the most efficient chemicals.The authors acknowledge the support of the Qatar National Research Fund under the grant NPRP 9-084-2-041. The authors would also like to acknowledge support from Qatar University Grant # QUCP-CENG-3. The findings made herein are solely the responsibility of the authors.Scopu

Adsorption of organic pollutants by natural and modified clays: A comprehensive review

Adsorption process has been widely used for treatment of wastewaters due to its simplicity and lower costs as compared to other traditional technologies. Among the alternative sorbent materials, the use of abundantly available clays for adsorption of organic pollutants has garnered increasing attention worldwide. Clays, in its natural and modified forms, have been extensively employed for the removal of organic contaminants from different wastewaters. The current review appraises the sorption performance of natural and modified clays for environmental remediation applications. The adsorption capacity of phenolic compounds, aromatic compounds, pesticides and herbicides, and other organic contaminants are comprehensively reviewed. The effect of the experimental conditions (pH, initial concentration (Co), surfactant loading, etc.) on the adsorption capacity is also appraised. Furthermore, the adsorption mechanisms, structures, and adsorptive characteristics of natural and modified clay sorbents are included. A statistical analysis of the adsorption isotherms reveals that Langmuir and Freundlich are the most examined models in fitting the experimental adsorption data. In addition, the adsorption kinetics is predominantly based on the pseudo-second-order model. The current review is an attempt to draw a prior knowledge about the technical viability of clay sorption process by assessing outcomes of the studies published between 2000 and 2018. - 2019 Elsevier B.V.Scopu

An empirical determination of the whole-life cost of FO-based open-loop wastewater reclamation technologies

Over the past 5–10 years it has become apparent that the significant energy benefit provided by forward osmosis (FO) for desalination arises only when direct recovery of the permeate product from the solution used to transfer the water through the membrane (the draw solution) is obviated. These circumstances occur specifically when wastewater purification is combined with saline water desalination. It has been suggested that, for such an “open loop” system, the FO technology offers a lower-cost water reclamation option than the conventional process based on reverse osmosis (RO). An analysis is presented of the costs incurred by this combined treatment objective. Three process schemes are considered combining the FO or RO technologies with membrane bioreactors (MBRs): MBR-RO, MBR–FO–RO and osmotic MBR (OMBR)-RO. Calculation of the normalised net present value (NPV/permeate flow) proceeded through developing a series of empirical equations based on available individual capital and operating cost data. Cost curves (cost vs. flow capacity) were generated for each option using literature MBR and RO data, making appropriate assumptions regarding the design and operation of the novel FO and OMBR technologies. Calculations revealed the MBR–FO–RO and OMBR-RO schemes to respectively offer a ∼20% and ∼30% NPV benefit over the classical MBR-RO scheme at a permeate flow of 10,000 m3  d−1, provided the respective schemes are applied to high and low salinity wastewaters. Outcomes are highly sensitive to the FO or OMBR flux sustained: the relative NPV benefit (compared to the classical system) of the OMBR-RO scheme declined from 30% to ∼4% on halving the OMBR flux from a value of 6 L m−2. h−1.This work was made possible by the support of a National Priorities Research Programme (NPRP) grant from the Qatar National Research Fund ( QNRF ), grant reference number NPRP10-0118-170191 . The statements made herein are solely the responsibility of the authors. The authors would like to thank Dan Jerry Cortes from Qatar University and Arnold Janson from ConocoPhillips, Qatar for providing useful information for this paper.Scopu

Investigation of thin-film composite hollow fiber forward osmosis membrane for osmotic concentration: A pilot-scale study

The current study applied the forward osmosis (FO) based osmotic concentration (OC) process at the pilot-scale for concentrating synthetic feed solution (FS). The process water (PW) salinity represents effluents from the gas industry, while the draw solution (DS) mimics seawater. Besides, the performance of a hollow fiber (HF) membrane manufactured from polyamide thin film composite (PA-TFC) was evaluated. The effect of operation with various feed recovery rates, flowrates and temperatures on the OC performance was examined. Outcomes reveal that the tested membrane succeeded in recovering up to 90% of FS at water flux of 6.40 LMH. The stability of OC plant was successfully demonstrated for 48 hours long-term run at 75% feed recovery as an optimum condition, where the TFC membrane achieved average water flux of 6.00 LMH, respectively. Higher DS flowrate improved the OC performance by inducing higher water permeation and FS recovery; however, it increased the undesirable reverse solute diffusion. Lastly, the permeability coefficient of the HF membrane was estimated by 2.69 LMH/bar at 25 �C, which significantly enhanced at higher temperatures.This work was made possible by the support of a National Priorities Research Program (NPRP) grant from the Qatar National Research Fund (QNRF), grant reference number NPRP10-0118170191. The statements made herein are solely the responsibility of the authors. The authors would like to thank Dan Jerry Cortes from Qatar University and Arnold Janson from ConocoPhillips, Qatar for providing useful information for this paper.Scopu

A New Local Fractional Entropy-Based Model for Kidney MRI Image Enhancement

Kidney image enhancement is challenging due to the unpredictable quality of MRI images, as well as the nature of kidney diseases. The focus of this work is on kidney images enhancement by proposing a new Local Fractional Entropy (LFE)-based model. The proposed model estimates the probability of pixels that represent edges based on the entropy of the neighboring pixels, which results in local fractional entropy. When there is a small change in the intensity values (indicating the presence of edge in the image), the local fractional entropy gives fine image details. Similarly, when no change in intensity values is present (indicating smooth texture), the LFE does not provide fine details, based on the fact that there is no edge information. Tests were conducted on a large dataset of different, poor-quality kidney images to show that the proposed model is useful and effective. A comparative study with the classical methods, coupled with the latest enhancement methods, shows that the proposed model outperforms the existing methods