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Task scheduling problem in a cloud computing environment is one of the most important issues in this field. Because task schedulers should be aware of underlying platform heterogeneity, task interdependencies, and virtual machine (VM) variable configurations. An efficient task scheduling algorithm can potentially increase the efficiency of cloud computing, by choosing the appropriate virtual machine to do a specific task-scheduling problem, for dependent tasks on heterogeneous resources which is a well-known NP-Hard problem. To reduce minimum total execution time, makespan, in cloud computing many heuristics were presented in literature but lots of them suffer from low efficiency. In this article, a new task priority strategy and applying task duplication methods are proposed, for solving the task scheduling problem of the dependent tasks in heterogeneous cloud computing systems. The novelty of the current paper is to present a new list scheduling algorithm with a new task priority strategy and applying pertinent task duplication techniques. This paper uses optimistic cost table downward (OCTd) and optimistic cost table upward (OCTu) procedures to prioritize tasks in an efficient ordered list; then, it utilizes Heterogeneous Earliest Finish Time (HEFT)-duplication method for performing task duplication technique which significantly reduces makespan. To validate the proposal, we experimentally analyzed the proposed scheduling algorithm with different scientific workflows such as Molecular, LU-Like, FFT, and Montage datasets. The performance comparison of the novel heuristic scheduling algorithm against other existing approaches proved the superiority of proposed algorithm in terms of makespan, speedup, SLR, and efficiency which are prominent scheduling evaluation metrics.

In recent years, the use of directional sensor networks (DSNs) has continued to rise increasingly, which is due to their extensive use in many situations. In such networks, the main problem is how to cover the targets distributed in a defined area and simultaneously prolong the network lifetime as much as possible. The reason of this problem is the limitation of both sensing angle and energy resource of sensors in such networks. This problem gets more complex in cases where targets need to be covered by more than one sensor direction (i.e., each target needs to be monitored for at least k times). This problem is generally known as target k -coverage problem which its NP-completeness has been already proved in the literature. The k -coverage problem can be considered in over-provisioned and under-provisioned environments. In both of these environments, especially in the latter, it is important to create a balanced coverage, as these environments do not have enough sensors to monitor all targets for k times. Thus, in this paper, we proposed a genetic-based algorithm to solve the problem in over-provisioned environments, then developed the proposed algorithm in another way to solve the problem in under-provisioned networks so that it uses the minimum number of sensors. many experiments were performed to test the efficiency of the proposed algorithm, and the obtained results showed the high capacity of the proposed algorithm in solving the k -coverage problem in both environments.

The major objective of the current review is to highlight the prime importance of bone cements particularly monetite cement as filler to treat various bone defects which may result due to osteoporosis, some accidents, some trauma disease or any other orthopedic surgical disorders. Previous studies showed that polymethyl methacrylate (PMMA), calcium phosphate cements (CPCs), dicalcium phosphate (DCP) cement and acrylic polymer cements have been employed to improve the bone defects, but these materials have a certain issue of in-situ setting. To overcome these problems, concentration was swiftly diverted towards monetite cement which revealed better results. Therefore, in this review, more focus has been given to the monetite cement. In this work, a brief but very productive discussion has also been inducted about the various synthetic routes to synthesize monetite cement and its properties which will help the readers to get key information about the growing significance of monetite cement as a bone filler and its future use and importance. The main theme of this review is to highlight the tremendous achievements achieved in the monetite cementing materials and their further scope in the near future as to upgrade their properties and use in the biomedical field.

Gene selection is the process of selecting the optimal feature subset in an arbitrary dataset. The significance of gene selection is in high dimensional datasets in which the number of samples and features are low and high respectively. The major goals of gene selection are increasing the accuracy, finding the minimal effective feature subset, and increasing the performance of evaluations. This paper proposed two heuristic methods for gene selection, namely, Xvariance against Mutual Congestion. Xvariance tries to classify labels using internal attributes of features however Mutual Congestion is frequency based. The proposed methods have been conducted on eight binary medical datasets. Results reveal that Xvariance works well with standard datasets, however Mutual Congestion improves the accuracy of high dimensional datasets considerably.

An uncertain Business Process Management System (BPMS) capability is Business Processes (BPs) management in the presence of uncertain factors. This ability should be defined by different uncertain computer-based components inside the classic BPMSs operations. This study proposed autonomous and combinatorial optimal process management architecture to increase the ability, flexibility, and accuracy of uncertain processes management. The autonomous architecture based on the bi-level optimization approach has been constructed inward a meta-model of multi-agent system technology, optimal Neural Network and Cellular Learning Automata in different agents. A case study of an uncertain business process evolving the closed loop supply chain was studied. The results of the simulated case and the statistical evaluation of it, have been demonstrated the robustness and accuracy of this new proposed architecture.

There are many processes in the business world which are dependent on Critical Infrastructure Systems (CISs) to provide important services. Hence, undesirable behaviors of these processes can provide cascading disturbances and might cause high impacts on criticality infrastructure in Automatic Closed-Loop Supply Chain (A-CLSC). Thus, this level of vulnerability needs to monitor and control the disturbances related to uncertain parameters. In order to develop the controller component inward uncertain Business Process Management System (BPMS), the authors provided adaptive controller considering uncertainty, which could be tuned with an intelligent algorithm called Improved Particle Swarm Optimization (IPSO). In addition to multi-objective optimization, the novel controller has been used to control the two most important activities includes inventory and transportation evolving the criticality infrastructure in A-CLSC at the desired behavior as a simulation and practical case study. Furthermore, the proposed IPSO algorithm has been compared against legendary methods such as adaptive PSO (APSO) and classic PSO. Also, the paper has been provided some metrics to statistical evaluation of proposed method performance. The results indicated the better set response of the new controller to control the A-CLSC as well as effective control of CISs.

In role-based access control (RBAC), users gain access to predetermined roles and permissions. Thus, desired results are not achieved in emergency situations through policy in RBAC. In emergency situations, users should sometimes gain access to resources not authorized in normal situations. To increase the flexibility of access control, Break the Glass (BTG) policy was proposed. It allows users to break or override access controls, while every operation is documented to create maximum responsibility for users. Users with BTG access have maximum freedom to override the access controls and constraints of the model. In this paper, the flexibility of RBAC is enhanced by proposing an Emergency RBAC (E-RBAC), which uses BTG policy for managing the system in emergency situation. However, separation of duty (SOD) constraint is included to control and limit user access in this situation. Then, an administrative model is proposed to manage large E-RBAC systems. An administrative model reduces excessive burden for an administrator in large E-RBAC systems. At the next stage, E-RBAC is illustrated with medical and drug-dispensation scenarios and is then implemented through Alloy (the first logic language) so as to analyze the validity of model specifications.

In recent years, the directional sensor networks have been attractive to researchers due to their wide and different applications. These networks normally contain a number of self-configurable directional sensors holding adjustable spherical sectors with limited angle. One of the most significant problems in such networks is how to monitor the targets scattered in these networks using sensors with adjustable sensing range and, at the same time, maximize the network lifetime. This problem is recognized as Maximum Network Lifetime With Adjustable Ranges; it has been already proved as an NP-complete problem. As an efficient solution to this problem, the present paper proposes a target-oriented GA-based algorithm that can form cover sets comprising sensors with appropriate directions and sensing ranges in a way to desirably monitor all targets in the network. We examined the efficiency of the proposed algorithm by comparing its obtained results with those of a greedy-based one introduced recently in literature. The comparative results confirmed the efficient performance of the proposed algorithm and also its superiority over the greedy-based algorithm in terms of extending the network lifetime.

Image data are considered as significant data in medical systems. The amount of medical image data available for analysis keeps increasing with the modernization of image devices and biomedical image processing techniques. To prevent from being hacked over an insecure network, medical images should be encrypted safely. This study aims at proposing a medical image encryption method based on a hybrid model of the modified genetic algorithm (MGA) and coupled map lattices. First, the proposed method employs coupled map lattice to generate the number of secure cipher-images as initial population of MGA. Next, it applies the MGA to both increase the entropy of the cipher-images and decrease the algorithm computational time. Experimental results and computer simulations both indicate that the proposed method that includes a hybrid algorithm not only performs excellent encryption but also is able to resist to various typical attacks.

Streaming video in peer-to-peer(P2P) networks is always faced with many challenges. In most of video streaming methods, service quality and observing the video integration are less concentrated; which is resulted from the loss of suitable algorithms to identify optimized route for sending information between a pair of nodes. The dynamic structure of such networks needs an algorithm which beside its high speed and accuracy can find suitable path for sending data pieces that provide service quality parameters. Using the algorithm of improved particle swarm optimization(IPSO), the present paper provides a method for video streaming. The suggested method is based on the principle that in every moment, every particle adjusts its location in the search space according to the best location that has been placed in it so far as well as the best place in its whole neighborhood. For validity check of the suggested method, a simulation with MATLAB has been done. The results show that the suggested method is more efficient comparing to other methods as well as other meta-heuristic methods such as Genetic Algorithm(GA).

Feature selection refers to selecting optimal feature subset with effective data preprocessing policy in making high dimensional data for diverse pattern recognition problems. The aims of feature selection are enhancing accuracy, improving the evaluation performance, and finding the smallest effective feature subset. In this study, ensemble feature selection method is adopted based on an assumption indicating that a combination of several feature selection methods obtains more robust results than any individual feature selection method. Accordingly, when carrying out ensemble feature selection, a combinational method should be used to combine rankings of features from diverse algorithms into an individual rank for each feature. It is also required to set a threshold to acquire a functional subset of features. In this work, a three-step ensemble feature selection technique called Automatic Thresholding Feature Selection (ATFS) is proposed. The first step involves diversity generation where multiple rankers are applied to each dataset to generate different feature rankings. Second, output rankings of individual selectors are combined using fast non-dominated sorting that is a combinational method empowering the proposed ensemble with automatic thresholding capability. Third, feature sets are generated to obtain the optimal feature set. Additionally, a new filter method called Sorted Label Interference (SLI) is proposed based on interference between class labels. Both SLI and ATFS are applicable to binary datasets. The performance of SLI and ATFS is at least comparable and often better than the performance of individual rankers and existing ensemble methods. The obtained results also show that the use of ATFS-generated threshold improves not only the performance of ATFS and SLI, but also the performance of other filters and combinational methods.

Dispersion and agglomeration control the macroscopic properties of nanocomposites; thus, quantitative characterization of particle dispersion and agglomeration is crucial. So far, methods such as image analysis or measurement of macroscopic properties have been insufficient. So, a novel methodology is introduced to predict particle dispersion and agglomeration degree quantitatively through the measurement of effective dielectric constant. This is done by taking into account the impact of inter-particle interactions in respect to microstructure for the calculation of effective dielectric constant. Inter-particle interaction is calculated as a function of inter-particle distance for random dispersion of particles in composites and as a function of agglomerate size and packing density for nanocomposites with agglomerated microstructure. The inter-particle interactions increase with increase in filler content and agglomeration. This increment is abrupt at percolation where inter-particle distance is significantly reduced. Experimental data from ZnO/PS composites with controlled inter-particle distance and agglomeration confirm the theoretical results.

The bone defects arising as a result of trauma should be filled to provide a framework to support and encourage the growth of new and living bone tissues. Among the many synthetic bone graft substitutes, self-hardening calcium phosphate (CP) cements have been widely used to repair hard tissue defects. In this study, pure dicalcium phosphate (DCP) and lanthanum (La) modified dicalcium phosphate (La-DCP) bone cements were prepared based on acid/base reaction between β- tricalcium phosphate (βTCP) (or La- βTCP) and monocalcium phosphate monohydrate (MCPM) in the presence of water. The prepared bone cements were characterized using XRD, FTIR and SEM techniques to verify both La doping and to explore the alterations in the structural and molecular properties upon doping. With minimum addition of La3+ ions (0.090 mol), the pure phase of brushite transformed into monetite and the plate-like crystals of brushite turned into spheroid particles. The setting times of DCP bone cement declined gradually upon boosting amount of La3+ ions in DCP lattice. As the La amount in DCP cements rose from 0 to 0.225 mol, the compressive strength also increased from 7.90 ± 0.8 to 9.64 ± 1.47 MPa. The dissolution rate of DCP cements improved with addition of La3+ ions. Adsorption/desorption of Fetal bovine serum (FBS) on/from the prepared DCP bone cements showed higher protein loading of La-DCP cements than pure DCP. In vitro experiments on proliferation, adhesion, and osteogenic differentiation of Sarcoma osteogenic (Saos-2) cells indicated that addition 0.225 mol of La3+ ions promoted these properties compared to pure DCP. Results suggested that La3+ (0.225 mol) incorporated DCP bone cement (2La-DCP) has a potential to be used as a bone filler material.

Control of cell structure during pressure drop is essential for the investigation of cell formation mechanism. In this work, a one step batch foaming setup was totally redesigned to stabilize foam structure at initial stages of cell forming during and after depressurization as a novel idea. The saturation pressure, duration of pressure drop and foaming temperatures were 18.5 MPa, 100 ms and 70, 90, 110 °C, respectively. The shortest stabilized foaming time was 5 ms and the longest was 5000 ms. For comparison purposes non-stabilized samples were also produced. It was concluded that the effect of stabilization on foam morphology is dependent on the foaming temperature. Foam structure at foaming temperatures of 70 °C did not change with change of foaming time significantly. Foam structure at foaming temperatures of 110 °C for the shortest and the longest foaming times changed from 90 μm to 170 μm and 7E + 06 cell/cm 3 to 8E + 06 cell/cm 3 .

The understanding of the effect of cell structure on the electromagnetic absorption properties can help to optimize foam structures for different applications. In this research, foams with different air volume percent, spherical and polygonal cell shapes, cell densities in the range of 104–1010 cell/cm3 and cell sizes ranging from 2.8 to 370.3 μm are produced using an automatic and controllable supercritical CO2 foaming system. The results showed that foaming of PMMA/MWCNT composites resulted in a 60% reduction in electromagnetic reflection and a 96% increase in specific electromagnetic absorption. The percolation threshold of foams with polygonal cell shape is found to occur at higher MWCNT loading than spherical cell shapes. Electromagnetic reflection is independent of cell density and cell size, while absorption was improved for about 34% with increasing cell density and decreasing cell size, indicating an enhancement of the multiple reflection mechanism. Keywords: Electromagnetic absorber, Cell structure, Supercritical CO2 foaming

In order to find a solution for the target coverage problem in directional sensor networks (DSNs), some researchers have recently introduced several efficient algorithms. These sensors are conventionally supposed to have a single power level and a single coverage is just needed for targets. In other words, there are various sensing ranges and power consumptions for these sensors under real conditions and at least k times monitoring is required for each target. The present paper addresses this issue as the target k -coverage with adjustable sensing range, which has not been already studied in DSNs. To solve this problem, two learning automata-based algorithms (Algorithms 1 and 2) are proposed and equipped with a strong pruning rule that facilitates the selection of appropriate sensor directions capable of providing the targets with k -coverage. After evaluating the efficiency of the algorithms’ performance by conducting several experiments, the results were compared to those ones obtained by a greedy-based algorithm, which is discussed in the literature. Finding indicated that algorithms have superiority over their rivals regarding the prolonged lifetime of their network.

In this study, mesoporous particles of β-tricalcium phosphate (βTCP, βCa3(PO4)2) and lanthanum (La) doped βTCP were synthesized using wet precipitation method attached with microwave refluxing system. The obtained materials were characterized and analysed using different sort of techniques such; X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), Scanning electron microscope (SEM), Inductively coupled plasma optical emission spectroscopy (ICP-OES), Brunauer–Emmett–Teller (BET) and helium pycnometer. With incorporation of La3+ ions, relevant expansion in lattice parameters of βTCP crystal was observed along with inhibition growth rate was observed. βTCP and La- βTCP materials were mesoporous in nature, pore diameter and pore volume were expanded with incorporation of La3+ ions. The loading /release on βTCP and La- βTCP particles surfaces were evaluated using fetal bovine serum (FBS) proteins. In vitro cell culture studies were performed with human osteosarcoma cell line (Saos-2). The outcomes approved that all La-βTCP materials were cytocompatible and strict dose dependent effect of La3+ ions was observed on cell viability and alkaline phosphatase (ALP) activity. These results strongly suggest that La-βTCP materials have a potential application in bone tissue engineering.

In recent years, the security of digital images has constantly been a major topic discussed by researchers. In this paper, we proposed a new structure for image encryption using Deoxyribonucleic Acid (DNA) and Recursive Cellular Automata (RCA). Image encryption takes place in two separate phases. At the permutation phase, a logistic map is employed for cellular shift in the image rows and columns. Then, the DNA and RCA at the diffusion phase are used to change the gray level of pixels to new values. The optimum entropy value obtained in this method was 7.9994 while the correlation coefficient was 0.0001, which indicated good results. Moreover, the newly proposed technique was demonstrated to enhance resistance against a variety of attacks through desirable image encryption.

Tender coconut water (TCW) is a natural plant product rich in phytochemicals and protects against toxic liver injury. However, the mechanism by which TCW inhibits inflammation and tissue damage is unknown. We examined the effect of TCW on primary rat hepatocyte viability, cytokine-induced gene expression and proinflammatory signaling in an in vitro model of sepsis. We observed that TCW improved hepatocyte viability and protected hepatocytes against cytokine-mediated cell death. TCW suppressed IL-1β-mediated increases in Nos2, Tnf, and Il6 mRNA and increased heme oxygenase 1 (HMOX1) protein. TCW inhibited iNOS expression through activation of AKT and JNK pathways since inhibition of PI3K and JNK signaling reduced TCW’s effect on iNOS protein expression and activity. These results demonstrate that TCW reduces proinflammatory gene expression and hepatocyte injury produced by elevated inflammatory cytokines and nitric oxide production.

Consensus clustering has been recently applied as a solution to the clustering problem. This combines multiple clusterings of a set of objects into a single integrated clustering. Consensus clustering algorithms attempt to find stable and robust results by composing calculated results from the base clustering algorithms. However, finding a consensus clustering algorithm capable of obtaining the final clusters automatically has remained a challenge. Furthermore, most of them are affected by an outlier. In the present paper, a cluster-based consensus clustering algorithm is proposed based on partitioning similarity graph in which each vertex is a cluster composed of a set of points. Within the proposed algorithm, the Cosine, Jaccard, and Dice similarity measures are used to measure the similarity between two vertices. The proposed algorithm operates in three steps. First, outlier clusters are automatically obtained by pruning similarity graph. Then, meta-clusters are obtained by splitting graph and merging sub-graphs. Finally, based on the meta-clusters, the consensus solution and outlier points are obtained using majority voting. The proposed algorithm has linear time complexity in terms of the number of points. The number of clusters also is obtained automatically in a consensus solution. To evaluate the performance of the algorithm, real and artificial datasets were used. The obtained results showed a dramatic improvement in the accuracy of the final clusters.