An Iterative Algebraic Geometric Approach for Identification of Switched ARX Models with Noise

The algebraic geometric (AG) approach has been used to identify switched auto regressive exogenous (SARX) models in hybrid systems, and it has several advantages over other SARX identification methods. This paper is focused on improving the estimation accuracy of the AG approach for systems corrupted with indispensable noises. A stochastic hybrid decoupling polynomial (SHDP) is constructed by reformulating the hybrid decoupling polynomial (HDP) used in the original AG method. An iterative scheme is developed to estimate parameters of the SHDP, which are used to calculate the SARX model parameters. This estimation involves linear regression with multiplicative noises, therefore a novel approach is proposed to solve this regression problem. Then, the parameters are recovered from the SHDP. Finally, all these steps for SARX model identification are summarized in an algorithm called the iterative algebraic geometric (IAG) approach. Simulations and experimental validation results are shown to demonstrate the effectiveness of and the improvement made by the proposed IAG method.     

Physical distribution,logistics, supply chain management,and the material flow theory: a historical perspective

The evolution from physical distribution to logistics, and then to supply chain management has invoked many interesting studies in recent years. By applying the material flow theory that expands attributes of logistics to coordination of environment, economy, and society, this paper examines the evolution from a unique perspective: sustainable development. The purpose of this paper is to provide a summary review of the evolution of physical distribution, logistics, supply chain management, and their new stage-material flow theory. The paper aims at tracing how concepts and findings in physical distribution, logistics, supply chain management, and material flow have been developed and extended continually. The results show that the requirements for material flow services vary with the economic development level in different countries and that material flow service is required to be compatible with a country’s development stage. The paper concludes that the MF theory is the option for dealing with the overwhelming complexity of material flow systems in the global economy of the twenty-first century.     

Chattering and stability analysis of the sliding mode control using inverse hyperbolic function

Chattering is a known issue in sliding mode control (SMC) and much research has been carried out to mitigate the chattering and its effects. In this work the chattering and stability analysis of the inverse hyperbolic function (IHF) based reaching law (RL) has been carried out. The work mathematically formulates the reaching time and calculates the describing function (DF) for the IHF based RL, which is used to formulate the conditions for chattering avoidance. The bound for the gain of RL has been calculated that will result in the chattering avoidance. Finally the proposed scheme has been used to control the speed of DC motor. Experimental results show avoidance of chattering and reduction in the reaching time.     

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.