Study of Adsorption/Desorption Effect of 2-Mercaptobenzothiazole as Sweet Corrosion Inhibitor on API-5L X60 Steel

Protection of Metals and Physical Chemistry of Surfaces - In this study, the inhibitive performance of 2-mercaptobenzothiazole (2MBT) and 2-aminobenzothiazole (2ABT) were investigated on API-5L X60...     

Drag coefficient of flow around a sphere: Matching asymptotically the wide trend

An empirical relationship of drag coefficient of flow around a sphere is developed for the entire range of Particle Reynolds numbers reported in the literature from Stokes regime to the condition when turbulent boundary layer prevails. The relationship is obtained using an approach to match asymptotically the wide trend of drag coefficient. The matching approach, which relies on dividing the wide trend into smaller segments that can be combined into an overall relationship, employs regression techniques and thus warrants the best-fit accuracy results. The relationship is calibrated with experimental data available in the literature covering the entire range for Reynolds numbers up to ~ 10⁶. For Reynolds values greater than 10⁶, the relationship renders a drag coefficient of 0.2. The performance of the relationship is tested and compared with other suitable models found in the literature. This relationship is also transformed into an explicit expression for settling velocity calculations.     

Intramuscular ceftriaxone compared with oral amoxicillin-clavulanate for treatment of acute otitis media in children

Two hundred and fifteen children aged 4 months 6 years with acute otitis media (AOM) were randomized to be treated either by a single i.m. injection of ceftriaxone, 50 mg/kg, with a second dose in the event of unsatisfactory response after 48 h or a history of recurrent AOM (109 patients) or amoxicillin clavulanate 12.5 mg tid (106 patients). The failure rate was similar in children treated by ceftriaxone and amoxicillin clavulanate, 4.6% and 4.7%, respectively (standard error for intergroup difference -2.87%, 95% confidence interval -5.62% to 5.87%). No significant differences between the groups were found in the dynamics of the resolution of the acute symptomatology, otoscopy findings, relapse rate at 30 days or tympanographic evidence of middle ear effusion at the scheduled visits on days 30, 60 and 90. Recurrence of AOM between days 31 and 90 was observed significantly in more children treated with amoxicillin clavulanate than with ceftriaxone--25 out of 84 (29.4%) versus 11 out of 81 (13.6%) (P = 0.012). Conclusion: Ceftriaxone injection(s) is as efficient at least as 10-day oral amoxicillin clavulanate for treatment of acute otitis media in children. Although not recommended as routine, ceftriaxone can be considered in the management of acute otitis media under special circumstances, particularly in cases when the ability to tolerate or absorb oral drugs is compromised, in children refusing or unable to take oral therapy or when the compliance is questionable.     

Luminescent Amphiphilic Aminoglycoside Probes to Study Transfection

We report the characterization of amphiphilic aminoglycoside conjugates containing luminophores with aggregation-induced emission properties as transfection reagents. These inherently luminescent transfection vectors are capable of binding plasmid DNA through electrostatic interactions; this binding results in an emission “on” signal due to restriction of intramolecular motion of the luminophore core. The luminescent cationic amphiphiles effectively transferred plasmid DNA into mammalian cells (HeLa, HEK 293T), as proven by expression of a red fluorescent protein marker. The morphologies of the aggregates were investigated by microscopy as well as ζ-potential and dynamic light-scattering measurements. The transfection efficiencies using luminescent cationic amphiphiles were similar to that of the gold-standard transfection reagent Lipofectamine® 2000.     

An economic and sensitive method for extracting chromium speciation in airborne inhalable dust,using a green sample treatment coupled with electrothermal atomic absorption

ABSTRACT

A new approach of cloud point extraction CPE procedure is optimized for hexavalent chromium determination in airborne dusts. Triton X-114 is used as a surfactant and 1-(2-pyridylazo)-2-naphthol as a specific complexing agent for the trivalent chromium’s removal from the aqueous phase to isolate hexavalent chromium compounds. The parameters influencing the extraction protocol (pH, surfactant concentration, and temperature are optimized. The obtained detection and quantification limits are 0.1 and 0.4 μg/L, respectively. The linearity is verified, with a regression coefficient close to 0.999 and the extraction recovery exceeds 99%. The method was successfully applied to analyze airborne samples collected from workplaces.     

Optimal Deep Learning Based Intruder Identification in Industrial Internet of Things Environment

With the increased advancements of smart industries, cybersecurity has become a vital growth factor in the success of industrial transformation. The Industrial Internet of Things (IIoT) or Industry 4.0 has revolutionized the concepts of manufacturing and production altogether. In industry 4.0, powerful Intrusion Detection Systems (IDS) play a significant role in ensuring network security. Though various intrusion detection techniques have been developed so far, it is challenging to protect the intricate data of networks. This is because conventional Machine Learning (ML) approaches are inadequate and insufficient to address the demands of dynamic IIoT networks. Further, the existing Deep Learning (DL) can be employed to identify anonymous intrusions. Therefore, the current study proposes a Hunger Games Search Optimization with Deep Learning-Driven Intrusion Detection (HGSODL-ID) model for the IIoT environment. The presented HGSODL-ID model exploits the linear normalization approach to transform the input data into a useful format. The HGSO algorithm is employed for Feature Selection (HGSO-FS) to reduce the curse of dimensionality. Moreover, Sparrow Search Optimization (SSO) is utilized with a Graph Convolutional Network (GCN) to classify and identify intrusions in the network. Finally, the SSO technique is exploited to fine-tune the hyper-parameters involved in the GCN model. The proposed HGSODL-ID model was experimentally validated using a benchmark dataset, and the results confirmed the superiority of the proposed HGSODL-ID method over recent approaches.     

Stacked Gated Recurrent Unit Classifier with CT Images for Liver Cancer Classification

Liver cancer is one of the major diseases with increased mortality in recent years, across the globe. Manual detection of liver cancer is a tedious and laborious task due to which Computer Aided Diagnosis (CAD) models have been developed to detect the presence of liver cancer accurately and classify its stages. Besides, liver cancer segmentation outcome, using medical images, is employed in the assessment of tumor volume, further treatment plans, and response monitoring. Hence, there is a need exists to develop automated tools for liver cancer detection in a precise manner. With this motivation, the current study introduces an Intelligent Artificial Intelligence with Equilibrium Optimizer based Liver cancer Classification (IAIEO-LCC) model. The proposed IAIEO-LCC technique initially performs Median Filtering (MF)-based pre-processing and data augmentation process. Besides, Kapur’s entropy-based segmentation technique is used to identify the affected regions in liver. Moreover, VGG-19 based feature extractor and Equilibrium Optimizer (EO)-based hyperparameter tuning processes are also involved to derive the feature vectors. At last, Stacked Gated Recurrent Unit (SGRU) classifier is exploited to detect and classify the liver cancer effectively. In order to demonstrate the superiority of the proposed IAIEO-LCC technique in terms of performance, a wide range of simulations was conducted and the results were inspected under different measures. The comparison study results infer that the proposed IAIEO-LCC technique achieved an improved accuracy of 98.52%.     

Political Optimizer with Deep Learning-Enabled Tongue Color Image Analysis Model

Biomedical image processing is widely utilized for disease detection and classification of biomedical images. Tongue color image analysis is an effective and non-invasive tool for carrying out secondary detection at anytime and anywhere. For removing the qualitative aspect, tongue images are quantitatively inspected, proposing a novel disease classification model in an automated way is preferable. This article introduces a novel political optimizer with deep learning enabled tongue color image analysis (PODL-TCIA) technique. The presented PODL-TCIA model purposes to detect the occurrence of the disease by examining the color of the tongue. To attain this, the PODL-TCIA model initially performs image pre-processing to enhance medical image quality. Followed by, Inception with ResNet-v2 model is employed for feature extraction. Besides, political optimizer (PO) with twin support vector machine (TSVM) model is exploited for image classification process, shows the novelty of the work. The design of PO algorithm assists in the optimal parameter selection of the TSVM model. For ensuring the enhanced outcomes of the PODL-TCIA model, a wide-ranging experimental analysis was applied and the outcomes reported the betterment of the PODL-TCIA model over the recent approaches.