A fast response cadmium-selective polymeric membrane electrode based on N,N'-(4-methyl-1,2-phenylene)diquinoline-2-carboxamide as a new neutral carrier

A new polyvinylchloride membrane sensor for Cd(2+) ions based on N,N'-(4-methyl-1,2-phenylene)diquinoline-2-carboxamide (Mebqb) as a new and excellent neutral ionophore has been prepared. The sensor shows a Nernestian response for cadmium ions over a wide concentration range (1.0 x 10(-6) to 1.0 x 10(-1) M) with the determination coefficient of 0.9964 and slope of 29.9 +/- 0.5 mV decade(-1). The limit of detection is 8 x 10(-7) M. It has a fast response time of 3-8s and can be used for at least 8 weeks without any divergence in potential. The electrode can be used in the pH range from 4.0 to 9.0. The proposed sensor shows a very good discriminating ability towards Cd(2+) ion in comparison to some alkali, alkaline earth, transition and heavy metal ions. It was successfully applied for the direct determination of Cd(2+) in standard and real sample solutions.     

Investigating liquid-fronts during spontaneous imbibition of liquids in industrial wicks. Part II: Validation by DNS

To validate the experimental results of Part-1, we conducted a two-phase flow simulation of imbibition of a wetting liquid through 2D microstructures made of ellipses of varying aspect ratios. The flow simulation in the particulate microstructures, characterized by low (ellipse) aspect ratio, produced somewhat even micro-fronts, thus replicating the sharp fronts at the visual (macroscopic) scale observed in Part-1. Whereas simulations in the fibrous microstructures produced highly uneven micro-fronts, suggesting the formation of semi-sharp or diffuse visual fronts. Increasing the porosity from 50% to 70% resulted in solid-phase clustering and led to further increase in the unevenness of micro-fronts, pointing to purely diffuse visual fronts. The evolution of the saturation plots along the flow direction, obtained from area-averaging of fluid-distribution plots, pointed to diffusing of sharp fronts with time. The predictions matched our previous experimental and numerical observations, that is, the particulate media create sharp fronts while the fibrous media create semi-sharp/diffuse fronts.     

Electrospun poly (N-isopropylacrylamide-co-acrylic acid)/cellulose laurate blend nanofibers containing adapalene: Morphology,drug release,and cell culture studies

In this work, a thermo- and pH-sensitive polymer based on poly (N-isopropylacrylamide-co-acrylic acid)/cellulose laurate blend [P(NIPAAm-co-AAc)/CL] was electrospun to produce nanofibers containing adapalene. The synthesized P(NIPAAm-co-AAc), and CL were characterized by ATR-FTIR and ¹H NMR spectroscopies. The thermal behavior of the electrospun nanofibrous samples were determined by a DSC. Afterward, the drug release in different temperatures and pHs from the nanofibers was investigated by UV-vis spectrophotometry and then the LCST behavior of the samples containing adapalene was observed by an ESEM. Moreover, the fibroblast cells culture was carried out with and without adapalene and the cell cytotoxicity of the samples was evaluated via MTT assay. The results from (H&E) and DAPI staining of the viable cells onto the samples surfaces revealed the adapalene has a remarkable short-term potential for the fibroblast cells mortality. It was found that the adapalene loaded electrospun nanofibrous samples enabled to minimize the adverse side effects of the drug by controlling release during three days of cell culture.     

Systematic image processing for diagnosing brain tumors: A Type-II fuzzy expert system approach

This paper presents a systematic Type-II fuzzy expert system for diagnosing the human brain tumors (Astrocytoma tumors) using T₁-weighted Magnetic Resonance Images with contrast. The proposed Type-II fuzzy image processing method has four distinct modules: Pre-processing, Segmentation, Feature Extraction, and Approximate Reasoning. We develop a fuzzy rule base by aggregating the existing filtering methods for Pre-processing step. For Segmentation step, we extend the Possibilistic C-Mean (PCM) method by using the Type-II fuzzy concepts, Mahalanobis distance, and Kwon validity index. Feature Extraction is done by Thresholding method. Finally, we develop a Type-II Approximate Reasoning method to recognize the tumor grade in brain MRI. The proposed Type-II expert system has been tested and validated to show its accuracy in the real world. The results show that the proposed system is superior in recognizing the brain tumor and its grade than Type-I fuzzy expert systems.     

Fuzzy community detection on the basis of similarities in structural/attribute in large-scale social networks

Artificial Intelligence Review - Community detection aims to partition a set of nodes with more similarities in the set than out of it based on different criteria like neighborhood similarity or...     

Effect of the Spectrum Sensing Period on the TCP Performance Over Cognitive Radio Ad-hoc Networks

Wireless Personal Communications - Modeling of spectrum occupancy by both primary and secondary users in the Cognitive Radio Ad-hoc Network (CRAHN) can be helpful to improve spectrum usage...     

Evolutionary fuzzy intelligent system for multi-objective supply chain network designs: an agent-based optimization state of the art

Supply chain network designing and programming is a momentous issue that many practitioners have focused on and contributed numerous novelties for this prompt. This paper puts forward a fuzzy multi-agent system according to which compatible with the decision makers’ interests and environmental survey, identifies the parameters of the mathematical model. An embedded optimization party including evolutionary-based optimizer intelligent agents, obtains non-dominated potential solutions. The output of these optimizer agents during the calibration process is an underpinning for evaluating the performance of the party. The system makes the policy of optimization complying with the results evaluation as well as the decision makers’ elaborated desires. Afterwards, in step with this policy, it sets a pool from obtained Pareto Fronts and aggregates them to extract a set of the best individuals. It interactively represents this set to the decision makers and catches their desired circumstance amongst these optional solutions. Proposing the network graph and program—which its generic morphography is determined—for decision makers is contrived as the system last stage. The main competencies of this system could be contemplated regarding the facts that it interactively fulfills the decision makers’ utilities relying on its robustness in optimization, self-tuning, training loop, ambient intelligence and consciousness toward the changes in environment.     

HYSTERY: a hybrid scheduling and mapping approach to optimize temperature,energy consumption and lifetime reliability of heterogeneous multiprocessor systems

In this paper, a hybrid scheduling and mapping approach to jointly optimize performance, lifetime reliability, energy consumption and temperature of heterogeneous multiprocessor systems on chip (MPSoCs), called “HYSTERY,” is proposed. Due to the growth of dynamic behavior in modern applications of embedded systems, along with necessity of performing complicated computations to jointly optimize the main design challenges of MPSoCs, we propose a hybrid scheduling approach in this paper. The proposed approach deals with the optimization of the mentioned design challenges at the design-time through solving an optimization problem and considering load balancing in task assignment. Moreover, at the runtime, the derived static solution is applied to the system and the design metrics monitored periodically and controlled, if required, to adapt the static scheduling decisions at the runtime. Several experiments with synthetic and real-life applications demonstrate that the proposed approach can effectively optimize the design challenges and manage dynamism of execution environment. In comparison with the uncontrolled runtime scheduling approach, HYSTERY shows 20% improvement in temperature averagely, which subsequently enhance lifetime reliability and power consumption. Furthermore, HYSTERY improves the main design parameters of MPSOCs about 21% in average compared to the existing scheduling approaches.     

A general fuzzy-statistical clustering approach for estimating the time of change in variable sampling control charts

Despite their capability in monitoring the variability of the processes, control charts are not effective tools for identifying the real time of such changes. Identifying the real time of the change in a process is recognized as change-point estimation problem. Most of the change-point models in the literature are limited to fixed sampling control charts which are only a special case of more effective charts known as variable sampling charts. In this paper, we develop a general fuzzy-statistical clustering approach for estimating change-points in different types of control charts with either fixed or variable sampling strategy. For this purpose, we devise and evaluate a new similarity measure based on the definition of operation characteristics and power functions. We also develop and examine a new objective function and discuss its relation with maximum-likelihood estimator. Finally, we conduct extensive simulation studies to evaluate the performance of the proposed approach for different types of control charts with different sampling strategies.     

Two-machine robotic cell scheduling problem with sequence-dependent setup times

In this paper, we introduce a new and practical two-machine robotic cell scheduling problem with sequence-dependent setup times (2RCSDST) along with different loading/unloading times for each part. Our objective is to simultaneously determine the sequence of robot moves and the sequence of parts that minimize the total cycle time. The proposed problem is proven to be strongly NP-hard. Using the Gilmore and Gomory (GnG) algorithm, a polynomial-time computable lower bound is provided.