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.     

Supramolecular π–π assembly of a neutral [Cu(salen)] complex via the templating effect of an ionic inorganic complex Na2[Cu(mnt)2] forming a framework type material having well-defined channels

A classical ionic inorganic complex Na₂[Cu(mnt)₂] (mnt²⁻ = maleonitriledithiolate = 1,2-dicyanoethylenedithiolate), that acts as a template in assembling neutral [Cu(salen)] (salen = bis(salicylidene)ethylenediamine) complexes forming a framework type arrangement, is accommodated in the channel formed in the crystal structure of a new type of host–guest compound [Cu(salen)]₄ · Na₂[Cu(mnt)₂] (1). The non-covalent supramolecular interactions among [Cu(salen)] complexes and between [Cu(salen)] and [Cu(mnt)₂]²⁻ complexes in the crystal lattice of 1 result in weak antiferromagnetic coupling.     

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.     

Effectiveness of Deep Learning Models for Brain Tumor Classification and Segmentation

A brain tumor is a mass or growth of abnormal cells in the brain. In children and adults, brain tumor is considered one of the leading causes of death. There are several types of brain tumors, including benign (non-cancerous) and malignant (cancerous) tumors. Diagnosing brain tumors as early as possible is essential, as this can improve the chances of successful treatment and survival. Considering this problem, we bring forth a hybrid intelligent deep learning technique that uses several pre-trained models (Resnet50, Vgg16, Vgg19, U-Net) and their integration for computer-aided detection and localization systems in brain tumors. These pre-trained and integrated deep learning models have been used on the publicly available dataset from The Cancer Genome Atlas. The dataset consists of 120 patients. The pre-trained models have been used to classify tumor or no tumor images, while integrated models are applied to segment the tumor region correctly. We have evaluated their performance in terms of loss, accuracy, intersection over union, Jaccard distance, dice coefficient, and dice coefficient loss. From pre-trained models, the U-Net model achieves higher performance than other models by obtaining 95% accuracy. In contrast, U-Net with ResNet-50 outperforms all other models from integrated pre-trained models and correctly classified and segmented the tumor region.     

A study of the impact of the willingness-to-learn of CAD novice users on their competence development

The notion that ‘attitude drives behavior’ manifests itself in a variety of ways in educational and occupational settings. As applied to CAD competence development, industrial training of novice CAD users on their way to becoming competent CAD users consume a lot of corporate resources. This paper is the third paper in the line of research that attempts to answer the question having to do with what it takes to make a competent CAD user. Specifically, we examine the CAD-specific factors revolving around the trainees’ willingness-to-learn CAD.These factors are analyzed in two stages. At the start of the training, trainees’ initial attitude towards CAD is established by means of a short questionnaire. Afterwards, throughout the training, trainees’ behavior (online and offline practice) is gauged and, in turn, a relation is established to illustrate how this practice leads to the development of CAD-specific skills. For this purpose, another short questionnaire was utilized. Strong correlations were established relating the trainees’ CAD-specific behavior with the CAD-specific outcomes of learning CAD syntax.Furthermore, and in order to assess the quality of the trainees’ learning of CAD, overall competence was monitored throughout the study via performance measures that describe the time it took the trainees to build test models (speed), which reflects upon the ability to learn the syntax of the CAD tool (declarative knowledge). The sophistication of the models is also used as another measure. Correlating the trainees’ character attributes with these assessed measures, it was found that the stronger is the trainees’ will to learn CAD, the stronger is the likelihood to learn faster. Perhaps more importantly, trainees with initial favorable attitude toward CAD were shown to develop increasingly positive behavior that manifested through additional practice and other forms of visible effort.