Thermal apoptosis analysis considering injection behavior optimization and mass diffusion during magnetic hyperthermia

Thermally induced apoptosis for tumors depends mainly on the intrinsic characteristics of biological tissues as well as treatment temperature profile during magnetic hyperthermia.Further,treatment temperature distribution inside tumor depends on the injection behavior of irregular tumors,such as the injection dose and the injection location of nanofluids.In order to improve the treatment effect,the simulated annealing algorithm is adopted in this work to optimize the nanofluid injection behavior,and the improved Arrhenius model is used to evaluate the malignant ablations for three typical malignant tumor cell models.In addition,both the injection behavior optimization and the mass diffusion of nanofluid are both taken into consideration in order to improve the treatment effect.The simulation results demonstrate that the injection behavior can be optimized effectively by the proposed optimization method before therapy,the result of which can also conduce to improving the thermal apoptosis possibility for proposed typical malignant cells.Furthermore,an effective approach is also employed by considering longer diffusion duration and correct power dissipation at the same time.The results show that a better result can then be obtained than those in other cases when the power dissipation of MNPs is set to be QMNP=5.4×10^(7)W·m^(3) and the diffusion time is 16 h.

Self-deception can evolve under appropriate costs

Apparent biases in decision making by animals, including humans, seem to present an evolutionary puzzle, since one would expect decisions based on biased （unrealistic） information to be suboptimal. Although cognitive biases are hard to diag- nose in real animals （Marshall et al., 2013b）, we investigate Trivers＇ proposal that individuals should self-deceive first in order to better deceive others （Trivers, 2011）. Although this proposal has been scrutinized extensively （Bandura et al., 2011） it has not been formally modelled. We present the first model designed to investigate Trivers＇ proposal. We introduce an extension to a re- cent model of the evolution of self-deception （Johnson and Fowler, 2011）. In the extended model individuals make decisions by taking directly into account the benefits and costs of each outcome and by choosing the course of action that can be estimated as the best with the information available. It is shown that in certain circumstances self-deceiving decision-makers are the most evolutionarily successful, even when there is no deception between these. In a further extension of this model individuals addi- tionally exhibit deception biases and Trivers＇ premise （that effective deception is less physiologically costly with the aid of self-deception） is incorporated. It is shown that under Trivers＇ hypothesis natural selection favors individuals that self-deceive as they deceive others .

Reactive Navigation of Underwater Mobile Robot Using ANFIS Approach in a Manifold Manner

Learning and self-adaptation ability is highly required to be integrated in path planning algorithm for underwater robot during navigation through an unspecified underwater environment. High frequency oscillations during underwater motion are responsible for nonlinearities in dynamic behavior of underwater robot as well as uncertainties in hydrodynamic coefficients. Reactive behaviors of underwater robot are designed considering the position and orientation of both target and nearest obstacle from robot s current position. Human like reasoning power and approximation based learning skill of neural based adaptive fuzzy inference system（ANFIS）has been found to be effective for underwater multivariable motion control. More than one ANFIS models are used here for achieving goal and obstacle avoidance while avoiding local minima situation in both horizontal and vertical plane of three dimensional workspace.An error gradient approach based on input-output training patterns for learning purpose has been promoted to spawn trajectory of underwater robot optimizing path length as well as time taken. The simulation and experimental results endorse sturdiness and viability of the proposed method in comparison with other navigational methodologies to negotiate with hectic conditions during motion of underwater mobile robot.

Adv Paxos: making classical Paxos more efficient

Paxos is a well-known distributed algorithm that provides strong consistency.However,the original Paxos has several shortcomings,including those of slow elections,redundant communications and excessive traffic of the coordinator node.In order to tackle the above deficiencies,the design of advanced edition of Paxos(Adv Paxos)was proposed,which is a new distributed consensus algorithm that is derived from Basic Paxos.This paper analyzes the behavior of each character of the original algorithm during each of its phases.By optimizing the behavior of the proposer and acceptor,a series of behavioral optimization measures was proposed,which included distance related waiting mechanisms,optimization of the number of proposals,self-learning and a reduction in broadcast communications.Through theoretical analysis and experimentation,it is demonstrated that the new algorithm has a lower probability of livelock without a reduction in reliability,faster agreement reaching speeds,lower communication costs among server clusters and higher percentage of successful proposals.

Suboptimal Auditory Dominance in Audiovisual Integration of Temporal Cues

The present study examined whether audiovisual integration of temporal stimulus features in humans can be predicted by the maximum likelihood estimation （MLE） model which is based on the weighting of unisensory cues by their relative reliabilities. In an audiovisual temporal order judgment paradigm, the reliability of the auditory signal was manipulated by Gaussian volume envelopes, introducing varying degrees of temporal uncertainty. While statistically optimal weighting according to the MLE rule was found in half of the participants, the other half consistently overweighted the auditory signal. The results are discussed in terms of a general auditory bias in time perception, interindividual differences, as well as in terms of the conditions and limits of statistically optimal multisensory integration.