Process principle for the production of sintered dynamic component-inherent data storage

This paper presents a method for the magnetic data storage on the surface of gentelligent components. One key issue is the possibility to store customers?? data on components dynamically and to read it out later on. The data storage is carried out magnetically and is demonstrated on a high-speed milling head. As such components are mostly made of light materials without magnetic properties; areas for the data storage on the surface must first be defined. Sintered components made of Mg and of hard magnetic particles have the potential to meet these requirements.     

Characterization of the produced electrospun fish gelatin nanofiber containing fucoxanthin

Food Science and Biotechnology - This study aims to prepare fish gelatin nanofibers extracted from fish waste by using electrospinning method and its encapsulation with fucoxanthin extracted from...     

Microstructural and mechanical investigation of high alumina refractory castables containing nano-titania

In this study, mechanical and structural properties of two high alumina refractory castables, i.e. without nano-titania and with 0.4?wt% TiO₂, are investigated. Bulk density, apparent porosity, cold compressive strength, cold modulus of rupture, permanent linear changes, hot modulus of rupture and shock resistance tests were performed on the samples. The microstructure and phase analyses were conducted by field emission scanning electron microscope equipped with EDX analyzer and X-ray diffraction, respectively. Results showed that hibonite phase formation in refractory is dependent on the temperature which could be decreased by addition of nano-titania, leading to the improvement of refractory strength. On the other hand, the decrease of hot modulus of rupture and shock resistance was observed for the refractory castable containing nano-titania. Also, Ca₃Ti₈Al₁₂O₃₇ phase was produced after firing at 1000?°C in the presence of nano-titania. Probable mechanisms are proposed for phase and microstructural changes in high alumina refractory castable containing nano-titania.     

Optimization of Propofol Dose Estimated During Anesthesia Through Artificial Intelligence by Genetic Algorithm: Design and Clinical Assessment

Neural Processing Letters - This paper addresses the application of an adaptive neuro-fuzzy inference system (ANFIS) to assign the optimal dose of propofol as a vital anesthetic drug considering...     

Backstepping design for a class of coupled parabolic PDEs with spatially varying coefficient

In this paper, we concentrate on the state feedback stabilization of a class of coupled parabolic partial differential equations with space dependent diffusion coefficients. To stabilize the system, we design a state feedback controller using the backstepping technique. Namely, we convert the original system into a stable desired system called the target system to obtain a backstepping feedback controller. We prove that the feedback controller causes the main system to be exponential stable. Also, the numerical simulations show that validity and efficiency of theoretical results.     

Double-diffusive natural convection of Al<Subscript>2</Subscript>O<Subscript>3</Subscript>-water nanofluid in an enclosure with partially active side walls using variable properties

Natural convection heat and mass transfer characteristics in a square enclosure using variable thermal conductivity and variable viscosity are numerically studied. The fluid in the enclosure is a water-based nanofluid containing Al₂O₃ nanoparticles. The top and bottom horizontal walls are insulated, while a source (T_h, C_h) and a sink (T_c, C_c) are located at the vertical left and right walls as active parts, respectively, with T_h>T_c and C_h>C_c. The governing equations in the two-dimensional space are discretized using the control volume method. A proper upwinding scheme is employed to obtain stabilized solutions. The study has been carried out for the Rayleigh numbers of 10⁴ to 10⁶, the buoyancy ratios of ?5～5, and different configurations of the source and sink. Results are presented in the form of the streamlines, isotherms and iso-concentraions as well as the average Nusselt and Sherwood numbers. It is observed that average Nusselt number is increased by adding the nanoparticles, while average Sherwood number is reduced. Moreover, both Nusselt and Sherwood number are increased as absolute value of the buoyancy ratio or Rayleigh number is increased.     

Waste Load Allocation in Rivers using Fallback Bargaining

In this paper, bargaining process between different stakeholders involved in a waste load allocation problem is simulated using the Fallback Bargaining (FB) concept. The paper considers two main parties in a waste load allocation problem. On the one hand, there are wastewater dischargers intending to minimize their treatment costs and on the other hand, there is an environmental protection agency which monitors the river water quality at a checkpoint downstream of the location of dischargers. In this paper, different alternatives which are combinations of dischargers’ treatment scenarios are defined. A water quality simulation model is utilized to estimate the concentration of the water quality indicator along the river based on a selected alternative. If the concentration of water quality indicator in the selected checkpoint violates the water quality standards, a penalty function is used to calculate the amount of penalty assigned to dischargers. The allocated cost to each discharger is computed considering his treatment scenario as well as the penalty allocated to him. Two kinds of Fallback bargaining procedure termed as Unanimity Fallback Bargaining (UFB) and Fallback bargaining with Impasse (FBI), which both aim at minimizing the maximum dissatisfaction of bargainers in a negotiation problem, are utilized for finding a Compromise Set (CS) of alternatives. In this paper, the best alternative (alternatives) among CS members is (are) selected using a social choice theory namely Condorcet winner. The results of these two approaches are compared and the final alternative is selected which shows the initial Tradable Discharge Permits (TDPs) allocated to dischargers. Finally, in order to decrease the total allocated cost to dischargers, initial allocated TDPs are exchanged between them using the Extended Trading Ratio System (ETRS) developed by Mesbah et al. (Environ Model Software 24:238–246, 2009). The applicability and efficiency of the proposed methodology is investigated by applying it to a case study of the Zarjub River in the northern part of Iran.     

Water Quality Zoning Using Probabilistic Support Vector Machines and Self-Organizing Maps

Water quality zoning is an important step for studying and evaluating surface and groundwater quality variations with time and space. It can also provide important information for developing efficient water quality management strategies. Most common methods for water quality zoning do not consider the uncertainties associated with water resources systems. Also, these methods only categorize the water quality monitoring stations into some classes based on existing water quality but do not provide any information about the quality of water which is categorized in a class. In this paper, a new methodology for probabilistic water quality zoning is developed which utilizes the capabilities of Probabilistic Support Vector Machines (PSVMs) and Fuzzy Inference System (FIS). The required data for training the PSVM is generated by using the FIS in a Monte Carlo Analysis. The trained PSVM-based water quality index provides the probabilities of belonging quality of water to different water quality classes. The applicability of the proposed methodology is investigated by applying it to two surface and groundwater resources systems in Iran. Also, for more evaluating the efficiency of the methodology, the results are compared with those obtained from two clustering techniques, namely Fuzzy Clustering Technique (FCT) and Self-Organizing Map (SOM). The results of surface and ground water quality zoning are depicted in maps by utilizing the Geographic Information System (GIS).     

Time series prediction using PSO-optimized neural network and hybrid feature selection algorithm for IEEE load data

Artificial neural networks have been widely used in time series prediction. In this paper, multi-layer feedforward neural networks with optimized structures, using particle swarm optimization (PSO) algorithm, are used for hourly load prediction based on load time series of IEEE Reliability Test System. To have a small and appropriate feature subset, a hybrid method is used for feature selection in this paper. This hybrid method uses the combination of genetic algorithm (GA) and ant colony optimization (ACO) algorithm. The season, day of the week, time of the day and history load are considered as load influencing factors in this study based on the mentioned standard load dataset. The optimized number of neurons in the hidden layers of multi-layer perceptron (MLP) is determined using PSO algorithm. Experimental results show that the proposed hybrid model offers superior performance, in terms of mean absolute percentage error (MAPE), in time series prediction as compared to some recent researches in this field.