ACEP: an adaptive strategy for proactive and elastic processing of complex events

The Journal of Supercomputing - The execution of complex event processing (CEP) applications on a set of clustered homogenous computing nodes is latency-sensitive, especially when workload...     

Understanding how social media imagery empowers caregivers: an analysis of microcephaly in Latin America

Personal and Ubiquitous Computing - This paper reports the results of a 2-year-long imagery analysis about microcephaly in Latin America from the perspective of caregivers. The content analysis...     

An iterative stochastic algorithm based on distributed learning automata for finding the stochastic shortest path in stochastic graphs

The Journal of Supercomputing - In this paper, we study the problem of finding the shortest path in stochastic graphs and propose an iterative algorithm for solving it. This algorithm is based on...     

Non‐Isothermal Kinetic Study of the Thermal Decomposition of Melamine 3‐Nitro‐1,2,4‐triazol‐5‐one Salt

Study on thermal behavior of 3‐nitro‐1,2,4‐triazol‐5‐one (NTO) salts was required to obtain important data for application purposes. These compounds have been shown to be useful intermediates for gun propellant ingredients, high energetic ballistic modifiers for solid propellants and other potential applications. In this paper, thermal decomposition and non‐isothermal kinetics of melamine 3‐nitro‐1,2,4‐triazol‐5‐one salt (MNTO) were studied under non‐isothermal conditions by DSC and TG methods. The kinetic parameters were obtained from analysis of the DSC and TG curves by Kissinger and Ozawa methods. The critical temperature of thermal explosion (T_b) was 574 K. The results show that MNTO is thermally more stable than NTO when compared in terms of the critical temperature of thermal explosion. Finally, the values of ΔS^#, ΔH^#, and ΔG^# of its decomposition reaction were calculated.     

Microstructure and mechanical properties of Al2O3–20 wt%Al2TiO5 composite prepared from alumina and titania nanopowders

In the present work, Al₂O₃–20 wt%Al₂TiO₅ composite was prepared from reaction sintering of alumina and titania nanopowders. The nano-sized raw powders were reconstituted into nanostructured particles by ball milling. Then, the nanostructured reconstituted powders were pressed and pressureless-sintered into bulk ceramics at 1300, 1400, 1500 °C for 2 h. The phase composition and microstructures of reconstituted powders and as-prepared ceramic composites were characterized by using X-ray diffractometer (XRD), scanning electron microscope (SEM), transmission electron microscope and energy-dispersive spectrometer (EDS). The microstructural analysis of the ceramic showed that the average grain size of the alumina–aluminium titanate composite increases with increasing the temperature. Also, SEM proved the existence of a proper interface between Al₂TiO₅ and Al₂O₃ grains and preferential distribution of aluminium titanate particles in the grain boundaries. XRD analysis indicated the absence of rutile titania in the sintered composite ensuring complete formation of aluminium titanate. The hardness of the samples sintered at 1300, 1400, 1500 °C were 4.8, 6.2 and 8.5 GPa, respectively.     

Quantum dot-pseudopolyrotaxane supramolecules as anticancer drug delivery systems

Pseudopolyrotaxanes (Ps-PR) consisting of α-cyclodextrin rings, polyethylene glycol axes and end triazine groups were synthesized and characterized. Dissociation of the α-cyclodextrin rings from the polyethylene glycol axes was avoided by the host-guest relationship between its end triazine groups and β-cyclodextrins conjugated onto the surface of quantum dots (β-CD-graft-QDs), leading to a new type of the dynamic polyrotaxanes in which QDs play the role of stoppers noncovalently. Stability of the synthesized supramolecules was depended on the efficiency of the host-guest relationships between the end triazine groups of Ps-PR and β-CD-graft-QDs through which release of α-cyclodextrin rings from the polyethylene glycol axes was controlled.To prove the efficacy of the synthesized supramolecules as drug delivery systems (DDSs) cisplatin (Cis-Diamminedichloroplatinum (CDDP) a platinum-based chemotherapy drug) and folic acid as a tumor-recognition module were conjugated to their stoppers and they were subjected to the receptor-mediated endocytosis and release inside the cancer cells, murine colon adenocarcinoma tumor C26. Then, it was proved that these tumor-targeting DDSs are promising systems for future cancer therapy. Rate of the release of the drugs, conjugated to the functional groups of stoppers was also investigated.     

Viscoelastic modeling of the contact interaction between a tactile sensor and an atrial tissue

Modeling and parameter identification of soft tissue are essential in establishing an accurate contact model for tool-tissue interaction, which can be used in the development of high-fidelity surgical instruments. This paper discusses the interaction between a tissue and a tactile sensor in minimally invasive surgery, the focus being a novel technique for robotic-assisted mitral valve repair, in which tactile sensors are used to distinguish between different kinds of tissue by their relative softness. A discrete viscoelastic model is selected to represent the tissue behavior. To populate the model of the tissue with actual data, a set of tissue-testing experiments is designed and implemented on the atrial tissue of a swine heart by analyzing its dynamic response. By means of a genetic algorithm, data of the complex compliance are extracted and used to find the coefficients of the model. Further, a viscoelastic contact model is developed to model the interaction between the tissue and the tactile sensor with annular shape. Finally, the relation among the indentation displacement, the ratio of the radii, and the applied force is established parametrically.     

CLA-DE: a hybrid model based on cellular learning automata for numerical optimization

This paper presents a hybrid model named: CLA-DE for global numerical optimization. This model is based on cellular learning automata (CLA) and differential evolution algorithm. The main idea is to learn the most promising regions of the search space using cellular learning automata. Learning automata in the CLA iteratively partition the search dimensions of a problem and learn the most admissible partitions. In order to facilitate incorporation among the CLA cells and improve their impact on each other, differential evolution algorithm is incorporated, by which communication and information exchange among neighboring cells are speeded up. The proposed model is compared with some evolutionary algorithms to demonstrate its effectiveness. Experiments are conducted on a group of benchmark functions which are commonly used in the literature. The results show that the proposed algorithm can achieve near optimal solutions in all cases which are highly competitive with the ones from the compared algorithms.     

A new real-coded stochastic Bayesian optimization algorithm for continuous global optimization

Estimation of distribution algorithms are considered to be a new class of evolutionary algorithms which are applied as an alternative to genetic algorithms. Such algorithms sample the new generation from a probabilistic model of promising solutions. The search space of the optimization problem is improved by such probabilistic models. In the Bayesian optimization algorithm (BOA), the set of promising solutions forms a Bayesian network and the new solutions are sampled from the built Bayesian network. This paper proposes a novel real-coded stochastic BOA for continuous global optimization by utilizing a stochastic Bayesian network. In the proposed algorithm, the new Bayesian network takes advantage of using a stochastic structure (that there is a probability distribution function for each edge in the network) and the new generation is sampled from the stochastic structure. In order to generate a new solution, some new structure, and therefore a new Bayesian network is sampled from the current stochastic structure and the new solution will be produced from the sampled Bayesian network. Due to the stochastic structure used in the sampling phase, each sample can be generated based on a different structure. Therefore the different dependency structures can be preserved. Before the new generation is generated, the stochastic network’s probability distributions are updated according to the fitness evaluation of the current generation. The proposed method is able to take advantage of using different dependency structures through the sampling phase just by using one stochastic structure. The experimental results reported in this paper show that the proposed algorithm increases the quality of the solutions on the general optimization benchmark problems.     

Congestion avoidance in cognitive wireless sensor networks using TOPSIS and response surface methodology

Congestion in wireless sensor networks degrades the quality of the channel and network throughput. This leads to packet loss and energy dissipation. To cope with this problem, a two-stage cognitive network congestion control approach is presented in this paper. In the first stage of the proposed strategy, initially downstream nodes calculate their buffer occupancy ratio and estimate congestion degree in the MAC layer. Then, they send the estimated value to both network and transport layers of their upstream nodes. The network layer of the upstream node uses TOPSIS in order to rank all neighbors to select the best one as the next relay node. In the second stage, transport layer of the given node adjusts the transmission rate using an optimized regression analysis by RSM. Extensive simulations demonstrated that the proposed method not only decreases packet loss, but also significantly improves throughput and energy efficiency under different traffic conditions, especially in heavy traffic areas. Also, Tukey test is used to compare performance of algorithms as well as to demonstrate that the proposed method is significantly better than other methods.