Modeling of Zinc Bromide Energy Storage for Vehicular Applications

Energy storage devices such as lithium-ion and nickel-metal hydrate batteries and ultracapacitors have been considered for utilization in plug-in hybrid electric vehicles (HEVs) and HEVs to improve efficiency and performance and reduce gas mileage. In this paper, we analyze and model an advanced energy storage device, namely, zinc bromide, for vehicular applications. This system has high energy and power density, high efficiency, and long life. A series of tests has been conducted on the storage to create an electrical model of the system. The modeling results show that the open-circuit voltage of the battery is a direct function of the battery's state of charge (SOC). In addition, the battery internal resistance is also a function of SOC at constant temperature. A Kalman filtering technique is also designed to adjust the estimated SOC according to battery current.      

Cooperative transmission based on decode-and-forward relaying with partial repetition coding

We propose a novel half-duplex decode-and-forward relaying scheme based on partial repetition coding at the relay. In the proposed scheme, if the relay decodes the received message successfully, it re-encodes the message using the same channel code as the one used at the source, but retransmits only a fraction of the codeword. We analyze the proposed scheme and optimize the cooperation level (i.e., the fraction of the message that the relay should transmit). We compare our scheme with conventional repetition in which the relay retransmits the entire decoded message, with parallel coding, and additionally with dynamic decode-and-forward (DDF). We provide a finite-SNR analysis for all the collaborative schemes. The analysis reveals that the proposed partial repetition method can provide a gain of several dB over conventional repetition. It also shows that in general, power allocation is less important provided that one optimally allocates bandwidth. Surprisingly, the proposed scheme is able to achieve the same performance as that of parallel coding for some relay network configurations, but at a much lower complexity.     

A new multi-level trust management framework (MLTM) for solving the invalidity and sparse problems of user feedback ratings in cloud environments

Choosing a trusted cloud service provider (CSP) is a major challenge for cloud users (CUs) in the cloud environment, as many CSPs offer cloud services (CSs) with the same functionality. Trust evaluation of CSPs is often based on information from quality of service (QoS) monitoring and CUs’ feedback ratings. Despite the volume of feedback ratings received in trust management systems, the quality of feedback storage is very low, as many CUs do not send their feedback ratings when using CSs. Additionally, a percentage of existing feedback ratings may not be valid, since some malicious CUs send unfair feedback ratings to change the trust evaluation results. As these lead to poor data quality, the accuracy of trust evaluation results might be affected. To overcome these limitations, this paper proposes a new multi-level trust management framework, which completes previous frameworks by defining new components to improve the data quality of feedback storage. In our framework, new components were defined to solve the invalidity and sparse problems of feedback storage. Certainly, the trust assessment of CSP would be more accurate based on high-quality feedback ratings. The performance of the MLTM was evaluated using two different datasets based on a real Quality of Web Services dataset (QWS) and an artificial data set (Cloud-Armor), whose quality was reduced for the purpose of this study. Analytical values revealed that our proposed approach significantly outperformed other approaches even with the poor data quality of feedback storage.

     

Time Delay Compensation for Hardware-in-the-loop Simulation of a Turbojet Engine Fuel Control Unit Using Neural NARX Smith Predictor

Hardware-in-the-loop (HIL) simulation is an effective technique that is used for development and testing of control systems while some of the control loop components are simulated in a proper environment and the other components are real hardware. In a conventional HIL simulation, the hardware is an electronic control unit which electronic control signals are communicated between the hardware and the software. But, HIL simulation of a mechanical component requires additional transfer systems to connect the software and hardware. The HIL simulation can achieve unstable behavior or inaccurate results due to unwanted time-delay dynamic of the transfer system. This paper presents the use of Smith predictor for time-delay compensation of transfer system in the HIL simulation of an electro-hydraulic fuel control unit (FCU) for a turbojet engine. A nonlinear auto regressive with exogenous input (NARX) neural network model is used for modeling and predicting the FCU behavior. The neural model is trained by Levenberg-Marquardt algorithm and the training and validation sets are generated using the amplitude modulated pseudo random binary sequence (APRBS). The consistency of the experimental real-time simulation and off-line simulation shows the applicability of the presented method for mitigating the effect of unwanted dynamic of the transfer system in the HIL simulation.

     

A GES/TS algorithm for the job shop scheduling

The job shop scheduling problem is a difficult combinatorial optimization problem. This paper presents a hybrid algorithm which combines global equilibrium search, path relinking and tabu search to solve the job shop scheduling problem. The proposed algorithm used biased random sampling to have a better covering of the solution space. In addition, a new version of N6 neighborhood is applied in a tabu search framework. In order to evaluate the algorithm, comprehensive tests are applied to it using various standard benchmark sets. Computational results confirm the effectiveness of the algorithm and its high speed. Besides, 19 new upper bounds among the unsolved problems are found.     

Deriving support threshold values and membership functions using the multiple-level cluster-based master–slave IFG approach

Today, development of e-commerce has provided many transaction databases with useful information for investigators exploring dependencies among the items. In data mining, the dependencies among different items can be shown using an association rule. The new fuzzy-genetic (FG) approach is designed to mine fuzzy association rules from a quantitative transaction database. Three important advantages are associated with using the FG approach: (1) the association rules can be extracted from the transaction database with a quantitative value; (2) extracting proper membership functions and support threshold values with the genetic algorithm will exert a positive effect on the mining process results; (3) expressing the association rules in a fuzzy representation is more understandable for humans. In this paper, we design a comprehensive and fast algorithm that mines level-crossing fuzzy association rules on multiple concept levels with learning support threshold values and membership functions using the cluster-based master–slave integrated FG approach. Mining the fuzzy association rules on multiple concept levels helps find more important, useful, accurate, and practical information.     

Dietary intake of fish,n-3 polyunsaturated fatty acids and risk of hip fracture: A systematic review and meta-analysis on observational studies

Previous studies have shown that fish consumption and dietary intake of n-3 polyunsaturated fatty acids (n-3 PUFAs) are associated with hip fracture; however, findings were conflicting. The present review aimed to summarize the current evidence on the association of fish consumption and dietary intake of n-3 PUFAs with hip fracture. The online databases of PubMed, ISI Web of Science, Scopus, ProQuest, Science Direct and Embase were searched until August 2017 for related publications using relevant keywords. To pool data, either a fixed-effects model or random-effects models were used. Cochran's Q tests were used to assess heterogeneity between studies. In total, 10 studies (7 prospective and 3 case-control studies) were included in this systematic review, and 9 studies with total sample size of 292657 participants were included in the meta-analysis. The age of participants was 20 years or older. Combining 8 effect sizes from 4 prospective studies and 2 case-control studies revealed a significant inverse association between fish consumption and risk of hip fracture (pooled effect size: 0.88, 95% CI: 0.79-0.98, P = 0.02). Although this relationship became non-significant in prospective studies, a significant inverse association was found in prospective studies with sample size of 10000 individuals or more, and studies that considered body mass index as a covariate. Furthermore, dietary intake of n-3 PUFAs was inversely associated with risk of hip fracture (pooled effect size: 0.89, 95% CI: 0.80-0.99, P = 0.02). Also, such relationship was seen after excluding one case-control study and combining effect sizes only from prospective studies (pooled effect size: 0.88, 95% CI: 0.80-0.98, P = 0.02). In conclusion, we found that fish consumption and dietary intake of n-3 PUFAs might have protective effects on bone health and decline the risk of hip fracture.     

Exploring contraction–expansion inertial microfluidic-based particle separation devices integrated with curved channels

Separation of particles or cells has various applications in biotechnology, pharmaceutical and chemical industry. Inertial cell separation, in particular, has been gaining a great attention in the recent years since it has exhibited a label-free, high-throughput and efficient performance. In this work, first, an inertial contraction–expansion array microchannel device, capable of passively separating two particles with diameters of 4 and 10 μm, was numerically studied. Then, the validated model was combined with curved geometries in order to investigate the effect of curve features on the separation process. The overall purpose was to investigate the interaction between the two different separation methods (separation with curved channels and with contraction–expansion arrays) to find an ideal model that can enjoy the merits of both contraction–expansion and curved channel based methods. Moreover, the relation between separation strength and the aspect ratio in the contraction and expansion zones of the simulated model as well as its height were examined. Then, a new model that combines the curved and the contraction–expansion geometries was tested for its efficiency. This new geometry showed that separation could be achieved with shorter lengths compared with straight contraction–expansion geometries.     

Multistage adaptive optimization using hybrid scenario and decision rule formulation

Scenario-based stochastic programming and linear decision rule (LDR)-based robust optimization are prevalent methods for solving multistage adaptive optimization (MSAP) problems. In practical applications such as capacity expansion planning of chemical processes, often multiple sources of uncertainty affect the problem which introduces challenges to traditional stochastic optimization methods. While a large number of uncertain parameters exist in the problem, using scenario-based method results in very large problem size and the solution becomes computationally expensive. In addition, when the constraints include multiplication of uncertain parameters and adaptive variables, the constraints are not linear with respect to uncertain parameters when the LDR method is used. In order to address these challenges, we propose two different hybrid methods where scenario and decision rule methods are combined to solve the MSAP problem. The article demonstrates the computational performance of the proposed hybrid methods using two chemical process planning examples.     

Microfluidic‐Based Approaches in Targeted Cell/Particle Separation Based on Physical Properties: Fundamentals and Applications

Cell separation is a key step in many biomedical research areas including biotechnology, cancer research, regenerative medicine, and drug discovery. While conventional cell sorting approaches have led to high‐efficiency sorting by exploiting the cell's specific properties, microfluidics has shown great promise in cell separation by exploiting different physical principles and using different properties of the cells. In particular, label‐free cell separation techniques are highly recommended to minimize cell damage and avoid costly and labor‐intensive steps of labeling molecular signatures of cells. In general, microfluidic‐based cell sorting approaches can separate cells using “intrinsic” (e.g., fluid dynamic forces) versus “extrinsic” external forces (e.g., magnetic, electric field, etc.) and by using different properties of cells including size, density, deformability, shape, as well as electrical, magnetic, and compressibility/acoustic properties to select target cells from a heterogeneous cell population. In this work, principles and applications of the most commonly used label‐free microfluidic‐based cell separation methods are described. In particular, applications of microfluidic methods for the separation of circulating tumor cells, blood cells, immune cells, stem cells, and other biological cells are summarized. Computational approaches complementing such microfluidic methods are also explained. Finally, challenges and perspectives to further develop microfluidic‐based cell separation methods are discussed.