Survey of Deep-Learning Techniques in Big-Data Analytics

Big-data research studies relying upon Deep-learning methods are revitalized the decision-making mechanism in the business sectors and the enterprise domains. The firms’ operational parameters also have the dependency of the Big-data analytics phase, their way of managing the data, and to evolve the outcomes of Big-data implementation by using the Deep-learning algorithms. Deep-learning approaches enhancements in Big-data applications facilitate the decision-making process such as the information-processing to the employees, analytical potentials augmentation, and in the transition of more innovative work. In this DL-approach, the robust-patterns of the data-predictions resulted from the unstructured information by conceptualizing the Decision-making methods. Hence this paper reviewed the impact of the Deep-learning process utilizing the Big-data in the enterprise and Business sectors. Also this study provides a comprehensive survey of all the Deep-learning techniques illustrating the efficiency of Big-Data processing and their impacts of operational parameters. Further it concentrating the data-dimensionality factors and the Big-data complications rectifying by utilizing the DL-algorithms, usage of Machine-learning or deep-learning process for the decision-making mechanism in the Enterprise sectors and business sectors. This research discussed the predictions of the Big-data analytics resulting to the decision parameters within the organisations, and in the management of larger scale of datasets in Big-data analytics processing by utilizing the Deep-learning implementations. The comparative analysis of the reviewed studies has also been described by comparing existing approaches of Deep-learning methodologies in employing Big-data analytics.

     

Sensitivity Analysis of Calibration Methods and Factors Effecting the Statistical Nature of Radiation Measurement

The scintillator detectors are recalibrated against the datasheet given by the manufacturer. Optimal and mutual dependent values of (a) high voltage at PMT (Photomultiplier Tube), (b) amplifier gain, (c) average time to count the radiation particles (set by operator), and (d) number of instances/sample number are estimated. Total 5: two versions of Central Limit Theorem (CLT), (3) industry preferred Pulse Width Saturation, (4) calibration based on MPPC coupled Gamma-ray detector, and (5) gross method are used. It is shown that the CLT method is the most optimal method to calibrate the detector and its respective electronics couple. An inverse modeling-based Computerized Tomography method is used for verification. It is shown that statistically averaging results are more accurate and precise data than mode and median if the data is not skewed and a random number of samples are used during the calibration process. It is also shown that the average time to count the radiation particle is the most important parameter affecting the optimal calibration setting for precision and accurate measurements of gamma radiation.

     

Tuning of shear thickening behavior and elastic strength of polyvinylidene fluoride via doping of ZnO-graphene

ZnO rice like nonarchitects are grafted on the graphene carbon core via a rapid microwave synthesis route. The prepared grafted systems are characterized via XRD, SEM, RAMAN, and XPS to examined the structural and morphological parameters. Zinc oxide grafted graphene sheets (ZnO-G) are further doped in β-phase of polyvinylidene fluoride (PVDF) to prepare the polymer nanocomposites (PNCs) via mixed solvent approach (THF/DMF). β-phase confirmation of PVDF PNCs is done by FTIR studies. It is observed that ZnO-G filler enhances the β-phase content in the PNCs. Non-doped PVDF and PNCs are further studied for rheological behavior under the shear rate of 1–100 s⁻¹. Doping of ZnO-G dopant to the PVDF matrix changes its discontinuous shear thickening (DST) behavior to continues shear thickening behavior (CST). Hydrocluster formation and their interaction with the dopant could be the reason for this striking DST to CST behavioral change. Strain amplitude sweep (10⁻³% -10%) oscillatory test reveals that the PNCs shows extended linear viscoelastic region with high elastic modulus and lower viscous modulus. Effective shear thickening behavior and strong elastic strength of these PNCs present their candidature for various fields including mechanical and soft body armor applications.     

Isomeric Carborane Neuromuscular Blocking Agents

We synthesized a family of neuromuscular blocking agents (NMB) based on decamethonium, but containing a carborane cluster in the methylene chain between the two quaternary ammonium groups. The carborane cluster isomers o-NMB, m-NMB, and p-NMB were tested in animals for neuromuscular block and compared with agents used clinically: rocuronium and decamethonium. All three isomers caused reversible muscle weakness in mice as determined by grip strength and inverted screen tests, with a potency rank of p-NMB > rocuronium > decamethonium > m-NMB > o-NMB. The mechanism of action of the compounds was determined by using the in vitro rat phrenic nerve hemi-diaphragm preparation and electrophysiologic measurements in cells. Neostigmine reversed hemi-diaphragm weakness caused by the three isomers and rocuronium, but not succinylcholine. In electrophysiologic recordings of currents through acetylcholine receptor channels, the carborane compounds did not activate channel activity but did inhibit channel activation by acetylcholine. These results demonstrate that the carborane neuromuscular blocking agents are non-depolarizers in contrast to the depolarizing action of the parent compound.     

2-Aminobenzothiazoles Inhibit Virulence Gene Expression and Block Polymyxin Resistance in Salmonella enterica

One promising strategy to combat antibiotic-resistant bacteria is to develop compounds that block bacterial defenses against antibacterial conditions produced by the innate immune system. Salmonella enterica, which causes food-borne gastroenteritis and typhoid fever, requires histidine kinases (HKs) to resist innate immune defenses such as cationic antimicrobial peptides (CAMPs). Herein, we report that 2-aminobenzothiazoles block histidine kinase-dependent phenotypes in Salmonella enterica serotype Typhimurium. We found that 2-aminobenzothiazoles inhibited growth under low Mg²⁺, a stressful condition that requires histidine kinase-mediated responses, and decreased expression of the virulence genes pagC and pagK. Furthermore, we discovered that 2-aminobenzothiazoles weaken Salmonella’s resistance to polymyxin B and polymyxin E, which are last-line antibiotics and models for host defense CAMPs. These findings raise the possibilities that 2-aminobenzothiazoles can block HK-mediated bacterial defenses and can be used in combination with polymyxins to treat infections caused by Salmonella.     

Comparative evaluation of extraction of tetra- and hexa-valent neptunium ions by CMPO in a room temperature ionic liquid

ABSTRACT

Solvent extraction studies were performed to understand the extraction behavior of Np⁴⁺ and NpO₂ ²⁺ from acidic feeds with CMPO (octyl (phenyl)-N,N-diisobutyl carbamoyl methyl phosphine oxide) dissolved in 1-butyl-3-methylimidazolium bis(trifluoromethane sulfonyl)imide, a water immiscible ionic liquid. Slope analyses on the distribution data revealed the extraction of ML₂ type species, where M = Np⁴⁺ or NpO₂ ²⁺, and L = CMPO. Studies were also carried out with Pu⁴⁺ and UO₂ ²⁺ under identical conditions. The nature of the extracted species was found to vary with the nature of the ionic species.     

Multi-objective particle swarm optimisation based integrated production inventory routing planning for efficient perishable food logistics operations

Sustainable and efficient food supply chain has become an essential component of one’s life. The model proposed in this paper is deeply linked to people's quality of life as a result of which there is a large incentive to fulfil customer demands through it. This proposed model can enhance food quality by making the best possible food quality accessible to customers, construct a sustainable logistics system considering its environmental impact and ensure the customer demand to be fulfilled as fast as possible. In this paper, an extended model is examined that builds a unified planning problem for efficient food logistics operations where four important objectives are viewed: minimising the total expense of the system, maximising the average food quality along with the minimisation of the amount of CO₂ emissions in transportation along with production and total weighted delivery lead time minimisation. A four objective mixed integer linear programming model for intelligent food logistics system is developed in the paper. The optimisation of the formulated mathematical model is proposed using a modified multi-objective particle swarm optimisation algorithm with multiple social structures: MO-GLNPSO (Multi-Objective Global Local Near-Neighbour Particle Swarm Optimisation). Computational results of a case study on a given dataset as well as on multiple small, medium and large-scale datasets followed by sensitivity analysis show the potency and effectiveness of the introduced method. Lastly, there has been a scope for future study displayed which would lead to the further progress of these types of models.     

Synthesis and characterization of UV photocrosslinkable hydrogels with poly(N-vinyl-2-pyrrolidone): Determination of the network mesh size distribution

Hydrogels of poly(n-vinyl-2-pyrrolidone) were produced by UV irradiation of aqueous solutions of the polymer in presence of hydrogen peroxide, used as initiator. The mechanical and the nanostructural properties of the gels were characterized by a combination of experimental techniques including rheology, low field nuclear magnetic resonance spectroscopy (LF-NMR), and small angle X-ray scattering. Different irradiation doses as well as polymer and initiator concentrations were tested in the characterization. The study elucidates the relationship between different methods to estimate the mesh size of the gel polymeric network. Moreover, a novel correlation model was developed based on Chui and Scherer theories for the interpretation of LF-NMR dataset of polymer solutions and networks.