Influence of Different Additives on the Interaction of Quinolone Antibiotic Drug with Surfactant: Conductivity and Cloud Point Measurement Study

Conductometric and cloud point (CP) measurement studies have been performed to investigate the interaction of tetradecyltrimethylammonium bromide (TTAB) and Triton® X-100 (TX-100) with ciprofloxacin hydrochloride (CFH) in different solvents over the temperature range of 295.15–315.15 K. CFH is used for the treatment of various bacterial infections. The observed critical micelle concentration (CMC) values of TTAB were found to be reduced in the presence of electrolytes (Na₂SO₄/Na₃PO₄), and this reduction proceeds with the elevation of salt concentration. The order of the CMC of TTAB follows the trend: > >. The observed CMC values of TTAB were found to increase with increasing temperature and decrease with increasing concentration of CFH in aqueous medium. The values of Gibbs free energy of micellization () for the TTAB/TTAB + CFH mixture were found to be negative, implying spontaneous micellization. The estimated CP of TX-100 decreases with increasing concentration of TX-100 in aqueous medium. The CP values first decrease with increasing concentration of CFH and then increase at higher concentration of CFH almost in all cases investigated. The values of free energy of clouding were found to be positive in all cases studied implying that phase separation of TX-100 was nonspontaneous. The other thermodynamic parameters associated with the micellization of TTAB and the phase separation of TX-100 were estimated and explained.     

A new SEAIRD pandemic prediction model with clinical and epidemiological data analysis on COVID-19 outbreak

Applied Intelligence - Measuring the spread of disease during a pandemic is critically important for accurately and promptly applying various lockdown strategies, so to prevent the collapse of the...     

Parastrephia quadrangularis: A Possible Alternative to Inhibit the Microbial Effect on the Generation of Acid Mine Drainage

Mine Water and the Environment - The role of iron- and sulfur- oxidizing microorganisms in the generation of acid mine drainage (AMD) from sulfide ores and tailings is widely recognized. The...     

Design of an aquatic biomonitoring network for an environmental specimen bank

The aim of the present study was to establish an aquatic biomonitoring network for the Galician Environmental Specimen Bank (BEAG) (NW Spain). For this, a sampling system was designed that comprised of 121 points distributed throughout Galician rivers, from which samples of water and of three species of bryophytes were collected. The results obtained allowed selection of 74 sampling points and 2 species (Fontinalis antipyretica Hedw. and F. squamosa Hedw.) as the most suitable for use in future BEAG sampling surveys. The two species selected showed a strong similarity in their capacity to accumulate the 17 elements determined (Al, As, Ca, Cd, Co, Cr, Cu, Fe, Hg, K, Mg, Mn, Na, Ni, Pb, Se and Zn), as well as in their ecological preferences. Furthermore, the levels of contamination of epicontinental waters were lower than those observed in previous surveys.     

Rapid Quantification of Live Cell Receptors Using Bioluminescence in a Flow‐Based Microfluidic Device

The number of receptors expressed by cells plays an important role in controlling cell signaling events, thus determining its behaviour, state and fate. Current methods of quantifying receptors on cells are either laborious or do not maintain the cells in their native form. Here, a method integrating highly sensitive bioluminescence, high precision microfluidics and small footprint of lensfree optics is developed to quantify cell surface receptors. This method is safe to use, less laborious, and faster than the conventional radiolabelling and near field scanning methods. It is also more sensitive than fluorescence based assays and is ideal for high throughput screening. In quantifying β₁ adrenergic receptors expressed on the surface of H9c2 cardiomyocytes, this method yields receptor numbers from 3.12 × 10⁵ to 9.36 × 10⁵ receptors/cell which are comparable with current methods. This can serve as a very good platform for rapid quantification of receptor numbers in ligand/drug binding and receptor characterization studies, which is an important part of pharmaceutical and biological research.     

Process Capability Analysis for NonLinear Profiles Using Depth Functions

There are practical situations in which the quality of a process or product can be better characterized by a functional relationship between a response variable and one or more explanatory variables, which is called profile. Such profiles frequently can be represented adequately using linear or nonlinear models. While there are several studies in monitoring profiles, there are few studies to evaluate the capability of a process with profile quality characteristic; specifically, there is no method in the literature to analyze process capability characterized by nonlinear profiles. In this paper, we propose two methods to measure the capability of these processes, based on the concept of functional depth. These methods do not have distributional assumptions and extend to functional data the Process Capability Indexes proposed by Clements 1 to measure the capability of a process characterized by a random variable. Performance of the proposed methods is evaluated through simulation studies. An example illustrates the applicability of these methods. Copyright © 2013 John Wiley & Sons, Ltd.     

A Non-Invasive Technique for Online Defect Detection on Steel Strip Surfaces

During the production of steel strips, a large amount of surface defects can be generated, due to harsh environmental conditions. A high number of surface defects can lead to rejection by the customer, which represents significant economic losses to the production plant. Thus, it is very important to detect the presence and type of the defects generated during the production of each steel strip. Using this information, it is possible to determine whether a strip is suitable for sale, and it may also be useful to determine the origin of defects and, if possible, prevent them from being generated in subsequent strips. To perform these tasks, non-invasive inspection techniques are usually used, carried out automatically by artificial vision systems. Although the inspection conducted by humans is more accurate, they become fatigued quickly, or may even be unable to carry out the inspection correctly when the forward speed of the strip is high. In this paper, a new detection technique is proposed, based on the division of an image into a set of overlapping areas. The optimum values for the configuration parameters of the detection technique are automatically determined using a genetic algorithm. After the detection phase, all the defects are classified using a neural network. A very satisfactory success rate has been achieved in both detection and classification phases.     

Statistical analysis of a multi-objective optimization algorithm based on a model of particles with vorticity behavior

In this paper, a strategy for multi-objective optimization based upon the behavior of a particle swarm with rotational and linear motion is presented. The strategy for multi-objective optimization is based upon the emulation of the linear and circular movements of a swarm (flock). Thus emerges the physical basis for the cognitive model, which in conjunction with exploration–exploitation results in the proposal of a cognitive algorithm, which is tested through several multi-objective optimization functions. The algorithm proposed is compared with standard particle swarm optimization multi-objective via statistical analysis.