Finite-element analysis for magnetic resonance image artifact evaluation

The static magnetic field of a magnetic resonance imaging scanner can be distorted by the presence of materials, perturbing the spatial encoding process in magnetic resonance imaging and often resulting in image artifacts. The relationship between the image artifact size and magnetic susceptibility of the material specimen is of interest to engineers for the design of devices that are to be compatible with the imaging volume of the scanner. In this study, a finite-element method was used to simulate the distorted magnetic field of samples with different susceptibilities. With the knowledge of the external- and self- magnetic field interactions, a Lorentz correction was applied to compute the magnetic field deviation. The simulated results were then validated by the corresponding experimental magnetic resonance images.     

Restoration of heavily polluted branches of the Shatt Al-Arab river, Iraq

In view of the desire to improve the water quality of the heavily polluted branches of the Shatt al-Arab River at the City of Basrah, it was proposed to maintain effective flushing as well as contracting sewerage system. The present study was conducted in order to examine the water quality of these branches in an attempt to evaluate the effectiveness of the proposed flushing system. It has been found that their waters contained very low levels of dissolved oxygen and relatively high amounts of both COD and BOD₅. The annual average water quality parameters for Basrah Branches were: dissolved oxygen 3.4 ppm; pH 7.67; hydrogen sulphide 1.4 ppm; ammonia 97 μg-at. N l⁻¹; COD 15.9 mg l⁻¹; BOD₅ 12.7 mg l⁻¹; dissolved silicates 202 μg-at. Si l⁻¹; dissolved reactive phosphate 13.4 μg-at. P-PO₄³⁻ l⁻¹; nitrate 10.4 μg-at. N-NO₃⁻ l⁻¹; nitrite 2.1 μg-at. N-NO₂⁻ l⁻¹ and chlorophyll-α 14.3 mg m⁻³. Based on our calculations, it has been concluded that the proposed system is effective, thus within a flushing cycle all of the above mentioned parameters will become within the acceptable values of the Shatt al-Arab water quality. Moreover, this system has no appreciable effect upon the water quality characteristics of the Shatt al-Arab River due to the fact that it discharges a high volume of water annually. However, It has been recommended to dredge the deposited sludge to a minimum depth of 50 cm.     

Metal-Bound Methisazone; Novel Drugs Targeting Prophylaxis and Treatment of SARS-CoV-2, a Molecular Docking Study

SARS-CoV-2 currently lacks effective first-line drug treatment. We present promising data from in silico docking studies of new Methisazone compounds (modified with calcium, Ca; iron, Fe; magnesium, Mg; manganese, Mn; or zinc, Zn) designed to bind more strongly to key proteins involved in replication of SARS-CoV-2. In this in silico molecular docking study, we investigated the inhibiting role of Methisazone and the modified drugs against SARS-CoV-2 proteins: ribonucleic acid (RNA)-dependent RNA polymerase (RdRp), spike protein, papain-like protease (PlPr), and main protease (MPro). We found that the highest binding interactions were found with the spike protein (6VYB), with the highest overall binding being observed with Mn-bound Methisazone at −8.3 kcal/mol, followed by Zn and Ca at −8.0 kcal/mol, and Fe and Mg at −7.9 kcal/mol. We also found that the metal-modified Methisazone had higher affinity for PlPr and MPro. In addition, we identified multiple binding pockets that could be singly or multiply occupied on all proteins tested. The best binding energy was with Mn–Methisazone versus spike protein, and the largest cumulative increases in binding energies were found with PlPr. We suggest that further studies are warranted to identify whether these compounds may be effective for treatment and/or prophylaxis.     

Electrocatalytic decomposition of hydrogen sulfide

Cu/ZSM-5 catalysts prepared by impregnation of Cu acetate are active for NO reduction. In mixtures containing both NO and O₂, reductants such as CO or CH₄ preferentially react with O₂, but propane reacts preferentially with NO. Small amounts of O₂ actually increase the reduction of NO by C₃H₈. The N₂ yield reaches a maximum near an O₂ /C₃H₈ ratio=5, i.e. the stoichiometry of total C₃H₈ oxidation. At much higher than stoichiometric O₂ contents for total C₃H₈ oxidation the N₂ yield with C₃H₈ (54%) is still substantially higher than, for instance, with CO of the same or larger concentration. The hypothesis that an intermediate of C₃H₈ oxidation is responsible for the enhanced NO reduction is discussed. Temperature-programmed reduction shows that after reaction with NO+C₃H₈ and sub-stoichiometric amounts of O₂ Cu in the catalyst is mainly metallic, but CuO particles appear to be formed in the presence of an excess of O₂.     

Evaluation of Cost Drivers for Field Canals Improvement Projects

Field Canals Improvement Project (FCIP) aims to conserve fresh water. Several methods are existed to predict project preliminary cost. However, identification of model inputs remains a challenging task during model development. This study utilizes two procedures, Traditional Delphi Method (TDM) and Fuzzy Delphi Method (FDM), which are used for collecting and initially ranking cost drivers. According to the second approach, Fuzzy Analytic Hierarchy Process (FAHP) is used for finally ranking the screened parameters by FDM. Delphi rounds and Likert scale are conducted to determine the most important factors from the viewpoint of consultant engineers and contractors. Regression analysis and R square is used as a comparison criteria between traditional technique and fuzzy techniques. The study suggests using Fuzzy theory and Delphi method with Analytic Hierarchy Process in order to identify cost drivers efficiently. Paper contribution is proposing a methodology to evaluate cost drivers of FCIP using qualitative data such as experts’ opinions.     

Ultrahigh Mass Activity Pt Entities Consisting of Pt Single atoms,Clusters, and Nanoparticles for Improved Hydrogen Evolution Reaction

Platinum is one of the best-performing catalysts for the hydrogen evolution reaction (HER). However, high cost and scarcity severely hinder the large-scale application of Pt electrocatalysts. Constructing highly dispersed ultrasmall Platinum entities is thereby a very effective strategy to increase Pt utilization and mass activities, and reduce costs. Herein, highly dispersed Pt entities composed of a mixture of Pt single atoms, clusters, and nanoparticles are synthesized on mesoporous N-doped carbon nanospheres. The presence of Pt single atoms, clusters, and nanoparticles is demonstrated by combining among others aberration-corrected annular dark-field scanning transmission electron microscopy, X-ray absorption spectroscopy, and electrochemical CO stripping. The best catalyst exhibits excellent geometric and Pt HER mass activity, respectively ≈4 and 26 times higher than that of a commercial Pt/C reference and a Pt catalyst supported on nonporous N-doped carbon nanofibers with similar Pt loadings. Noteworthily, after optimization of the geometrical Pt electrode loading, the best catalyst exhibits ultrahigh Pt and catalyst mass activities (56 ± 3 A mg⁻¹_Pt and 11.7 ± 0.6 A mg⁻¹_Cat at −50 mV vs. reversible hydrogen electrode), which are respectively ≈1.5 and 58 times higher than the highest Pt and catalyst mass activities for Pt single-atom and cluster-based catalysts reported so far.     

Synthesis of magnetic chelating resins functionalized with tetraethylenepentamine for adsorption of molybdate anions from aqueous solutions

Magnetic resins were synthesized through polymerization of glycidyl methacrylate (GMA) in the presence of divinylbenzene (DVB) or N,N′-methylenebisacrylamide (MBA) as hydrophobic or hydrophilic crosslinker, respectively and in presence of suspended magnetite particles. The resins containing (DVB or MBA) as crosslinker were immobilized with tetraethylenepentamine (TEP) to give the amino resins, GMA/DVB/TEP (R1-en) and GMA/MBA/TEP (R2-en), respectively. The uptake behavior of the two resins was studied towards molybdate anions and uptake capacities of 4.24 and 6.18 mmol/g [as (Mo(VI)] were obtained using (R1-en) and (R2-en). Kinetic studies showed that the adsorption followed the pseudo-second-order model pointing the influence of the textural properties of the resin on the rate of adsorption. Thermodynamic data indicated an endothermic adsorption process. The uptake of Mo(VI) and regeneration of the resins were also studied using the column method. Regeneration efficiency up to 90–96% was reached using ammonia buffer.     

Machine learning as a service for enabling Internet of Things and People

The future Internet is expected to connect billions of people, things and services having the potential to deliver a new set of applications by deriving new insights from the data generated from these diverse data sources. This highly interconnected global network brings new types of challenges in analysing and making sense of data. This is why machine learning is expected to be a crucial technology in the future, in making sense of data, in improving business and decision making, and in doing so, providing the potential to solve a wide range of problems in health care, telecommunications, urban computing, and others. Machine learning algorithms can learn how to perform certain tasks by generalizing examples from a range of sampling. This is a totally different paradigm than traditional programming language approaches, which are based on writing programs that process data to produce an output. However, choosing a suitable machine learning algorithm for a particular application requires a substantial amount of time and effort that is hard to undertake even with excellent research papers and textbooks. In order to reduce the time and effort, this paper introduces the TCDC (train, compare, decide, and change) approach, which can be thought as a ‘Machine Learning as a Service’ approach, to aid machine learning researchers and practitioners to choose the optimum machine learning model to use for achieving the best trade-off between accuracy and interpretability, computational complexity, and ease of implementation. The paper includes the results of testing and evaluating the recommenders based on the TCDC approach (in comparison with the traditional default approach) applied to 12 datasets that are available as open-source datasets drawn from diverse domains including health care, agriculture, aerodynamics and others. Our results indicate that the proposed approach selects the best model in terms of predictive accuracy in 62.5 % for regression tests performed and 75 % for classification tests.     

From the User Interface to the Consumer Interface

Business success requires that advances in technology be designed and applied within a human context. Too often technological advance has occurred without reference to human behavior. While research appreciates that electronic consumers are computer users, and vice-versa, few understand exactly how this is transforming consumer behavior. In other words, technological imagination has surpassed our knowledge of media-based consumer behavior. This paper seeks to re-dress the balance of understanding. By (i) integrating themes in human-computer interaction (HCI) with consumer behavior models and (ii) placing the results within a framework of relevant research issues, we present the conceptual foundations for a consumer interface.