Development of natural and synthetic polymer-based semi-interpenetrating polymer network for controlled drug delivery: optimization and in vitro evaluation studies

This research paper describes the development, optimization and in vitro characterization of chemically cross-linked pectin–polyvinyl alcohol-co-poly(2-Acrylamido-2-methylpropane sulfonic acid) semi-interpenetrating polymer network hydrogel [pectin–PVA-co-poly(AMPS) semi-IPN hydrogel] for controlled delivery of model drug tramadol HCl. Response surface methodology based on 3² factorial design was used for optimization and investigating the effect of independent factors: polymer-blend ratio (pectin:PVA = X ₁) and monomer (AMPS = X ₂) concentration on the dependent variables, swelling ratio (q _18th), percent drug release (R _18th, %), time required for 80 % drug release (t _{80 %}, h), drug encapsulation efficiency (DEE, %) and drug loaded contents (DLC, mg/g) in pectin-PVA-co-poly(AMPS) gels prepared by free radical polymerization. The optimized semi-IPN gel (FPP-10) showed controlled in vitro drug release (R _18th) of 56.34 % in 18 h, t _{80 %} of 30 h, and DEE of 23.40 %. These semi-IPN hydrogels were also characterized through SEM, FTIR, sol–gel analysis, swelling studies and drug release characteristics. Therefore, this newly synthesized polymeric network could be a potential polymeric system for controlled drug delivery of tramadol HCl for prolonged drug release.     

Probabilistic opposition-based particle swarm optimization with velocity clamping

A probabilistic opposition-based Particle Swarm Optimization algorithm with Velocity Clamping and inertia weights (OvcPSO) is designed for function optimization—to accelerate the convergence speed and to optimize solution’s accuracy on standard benchmark functions. In this work, probabilistic opposition-based learning for particles is incorporated with PSO to enhance the convergence rate—it uses velocity clamping and inertia weights to control the position, speed and direction of particles to avoid premature convergence. A comprehensive set of 58 complex benchmark functions including a wide range of dimensions have been used for experimental verification. It is evident from the results that OvcPSO can deal with complex optimization problems effectively and efficiently. A series of experiments have been performed to investigate the influence of population size and dimensions upon the performance of different PSO variants. It also outperforms FDR-PSO, CLPSO, FIPS, CPSO-H and GOPSO on various benchmark functions. Last but not the least, OvcPSO has also been compared with opposition-based differential evolution (ODE); it outperforms ODE on lower swarm population and higher-dimensional functions.     

Inverse Design of Cooling Arrays of Micro Pin-Fins Subject to Specified Coolant Inlet Temperature and Hot Spot Temperature

Given a micro pin-fin array cooling scheme with these constraints: (a) given maximum allowable temperature of the material (the hot spot temperature), (b) given inlet cooling fluid temperature, (c) given total pressure loss (pumping power affordable), and (d) given overall thickness of the entire micro pin-fin cooling array, find the maximum possible average heat flux on the hot surface and find the maximum possible heat flux at the hot spot under the condition that the entire amount of the inputted heat is removed by the cooling fluid. The goal was to create an optimum performance map for a cooling micro array having specified inlet coolant temperature and maximum temperature. Fully 3D conjugate heat transfer analysis was performed on each of the randomly created candidate configurations. Response surfaces based on Radial Basis Functions were coupled with a genetic algorithm to arrive at a Pareto set of best trade-off solutions. These Pareto optimized configurations indicate the maximum physically possible heat fluxes for specified material and constraints. Detailed off-design performance maps of such Pareto-optimized cooling arrays of micro pin-fins were calculated that demonstrate superior on-design and off-design performance of pin-fins having symmetric convex cross sections as opposed to the commonly used circular cross sections.     

Development of paracetamol-caffeine co-crystals to improve compressional,formulation and in vivo performance

Paracetamol, a frequently used antipyretic and analgesic drug, has poor compression moldability owing to its low plasticity. In this study, new co-crystals of paracetamol (PCM) with caffeine (as a co-former) were prepared and delineated. Co-crystals exhibited improved compaction and mechanical behavior. A screening study was performed by utilizing a number of methods namely dry grinding, liquid assisted grinding (LAG), solvent evaporation (SE), and anti-solvent addition using various weight ratios of starting materials. LAG and SE were found successful in the screening study. Powders at 1:1 and 2:1 weight ratio of PCM/CAF by LAG and SE, respectively, resulted in the formation of co-crystals. Samples were characterized by PXRD, DSC, and ATR-FTIR techniques. Compressional properties of PCM and developed co-crystals were analyzed by in-die heckle model. Mean yield pressure (Py), an inverse measure of plasticity, obtained from the heckle plots decreased significantly (p?In vitro dissolution studies on tablets also showed enhanced dissolution profiles (～90–97%) in comparison to the tablets of PCM prepared by direct compression (～55%) and wet granulation (～85%) methods. In a single dose sheep model study, co-crystals showed up to twofold increase in AUC and C_max. A significant (p?in vitro and in vivo profile. Enhancement in AUC and C_max of PCM by co-crystallization might suggest the dose reduction and avoidance of side effects.     

Low-Temperature Plasma-Assisted Nitrogen Fixation for Corn Plant Growth and Development

Nitrogen fixation is crucial for plants as it is utilized for the biosynthesis of almost all biomolecules. Most of our atmosphere consists of nitrogen, but plants cannot straightforwardly assimilate this from the air, and natural nitrogen fixation is inadequate to meet the extreme necessities of global nutrition. In this study, nitrogen fixation in water was achieved by an AC-driven non-thermal atmospheric pressure nitrogen plasma jet. In addition, Mg, Al, or Zn was immersed in the water, which neutralized the plasma-treated water and increased the rate of nitrogen reduction to ammonia due to the additional hydrogen generated by the reaction between the plasma-generated acid and metal. The effect of the plasma-activated water, with and without metal ions, on germination and growth in corn plants (Zea Mays) was investigated. The germination rate was found to be higher with plasma-treated water and more efficient in the presence of metal ions. Stem lengths and germination rates were significantly increased with respect to those produced by DI water irrigation. The plants responded to the abundance of nitrogen by producing intensely green leaves because of their increased chlorophyll and protein contents. Based on this report, non-thermal plasma reactors could be used to substantially enhance seed germination and seedling growth.     

Edge Metric Dimension of Honeycomb and Hexagonal Networks for IoT

Wireless Sensor Network (WSN) is considered to be one of the fundamental technologies employed in the Internet of things (IoT); hence, enabling diverse applications for carrying out real-time observations. Robot navigation in such networks was the main motivation for the introduction of the concept of landmarks. A robot can identify its own location by sending signals to obtain the distances between itself and the landmarks. Considering networks to be a type of graph, this concept was redefined as metric dimension of a graph which is the minimum number of nodes needed to identify all the nodes of the graph. This idea was extended to the concept of edge metric dimension of a graph G, which is the minimum number of nodes needed in a graph to uniquely identify each edge of the network. Regular plane networks can be easily constructed by repeating regular polygons. This design is of extreme importance as it yields high overall performance; hence, it can be used in various networking and IoT domains. The honeycomb and the hexagonal networks are two such popular mesh-derived parallel networks. In this paper, it is proved that the minimum landmarks required for the honeycomb network HC(n), and the hexagonal network HX(n) are 3 and 6 respectively. The bounds for the landmarks required for the hex-derived network HDN1(n) are also proposed.     

Classification of M-QAM and M-PSK signals using genetic programming (GP)

With the popularity of software-defined radio and cognitive radio-technologies in wireless communication, radio frequency devices have to adapt to changing conditions and adjust its transmitting parameters such as transmitting power, operating frequency, and modulation schemes. Thus, automatic modulation classification becomes an essential feature for such scenarios where the receiver has a little or no knowledge about the transmitter parameters. This paper presents kth nearest neighbor (KNN)-based classification of M-QAM and M-PSK modulation schemes using higher-order cumulants as input features set. Genetic programming is used to enhance the performance of the KNN classifier by creating super features from the data set. Simulation result shows improved accuracy at comparatively lower signal-to-noise ratio for all the considered modulations.

     

An inter-temporal resource emergency management model

One of the most important ways for promoting the service level during emergency management in public sectors is through enhancing management of strategic resources. From the perspective of vertical integration of operational process in emergency management, emergency resources can be classified as those resources used during the response period as well as those resources that are consumed during the recovery period. An inter-temporal integrated single-period resource model for solving optimal order quantity is proposed that meets the characteristics during the demand for the recovery resources depending on the stock shortage of the response resources. In the light of the classification of the dependent relationship of the two kinds of resources, namely deterministic or stochastic, the research investigates the analytical properties of the model, based on which, a genetic algorithm-based simulation approach is proposed. Finally, a real case with numerical example is provided to assess and validate our model, as well as managerial insights are obtained through key parameters sensitivity analysis.     

Mechanical fatigue in aluminium at elevated temperature and remaining life prediction based on natural frequency evolution

Fatigue is one of the principle damage mechanisms in materials especially at elevated temperatures. It possesses a great influence on the material properties and the possible working life. In this work, a novel technique for the analysis of fatigue at elevated temperature is suggested. Material fundamental frequency and its drop are used as the key parameters to predict the remaining useful life of a selected shape specimen, which is operated under low‐cyclic fatigue loading at elevated temperature. Experiments are performed on aluminium 1050 with two different lengths of a non‐prismatic cantilever beam at room temperature, 100, 200 and 300 °C, respectively. The experimental data is further transformed in to an empirical correlation that can predict specimen useful life. The error in this life prediction is less than 5%, and it reduces more once the frequency drop is increased.     

A vector for double epitope tagging with a recyclable marker

Multimeric protein complexes play diverse and vital roles in the cell, but following the composition of these complexes under varying growth conditions can be challenging. Toward that goal, we have designed a vector that permits the double epitope tagging of a protein at its carboxy terminus. One 'universal' tag, a triple repeat of the HA1 epitope, is fused with every protein to be studied, allowing the composition and stoichiometry of the proteins in a complex to be detected with a single antibody. Each protein also can be tagged with a second epitope specific for that protein. This 'specific' tag can be used to immunoprecipitate complexes containing that protein of interest. Any epitope to which a specific antibody is available can be used for this second tag. Because there are a limited number of selection markers for cloning in yeast, the kanamycin cassette, flanked by loxP sites, was incorporated into the vector to permit marker recycling using Cre-lox recombinase. This vector was used to tag 4 proteins involved in ribosome biogenesis-Ytm1, Cic1, Brx1 and Drs1. An anti-HA1 antibody could detect all four proteins in crude lysates and yielded the relative abundance of these four proteins, of which Drs1 is reproducibly less abundant than any of the others, which may have implications for the control of ribosome biogenesis. The Ytm1 protein was also tagged with the VSV epitope and can be specifically detected using an anti-VSV antibody. This vector may prove useful for exploring other protein complexes.