Optimum speed controller structure utilizing the MCA approach

In this paper, an optimal speed controller for dc motor is considered using a PID controller and tuned its parameters of gain to offer an optimal solution by using a modified camel algorithm MCA approach. The proposed MCA scheme was applied to solve the difficulty of getting the optimum gains of PID parameters. The MCA has good evolutionary speed with the simple construction of optimization depend on camel searching performance. The characteristics of the MCA algorithm were confirmed by optimizing the gains parameters of proportional, integral, derivative PID controller. The performance of PID-MCA is comparing with a classic PID controller enhanced with GA genetic algorithm optimization method to tune the gain parameters of the speed controller system. It was shown that the utilize of optimization processes indicated better performance for the MCA procedure in term of speed of execution and the size of memory compared with the GA method by applying computer simulations analysis. The proposed scheme has an efficient feature that includes the ease of implementation, good efficiency of computational performances with stable convergence characteristics. The results indicated that the proposed MCA scheme is a useful tool for search ability, produced efficient outcomes compared with the GA optimized method when applied in the proposed system.

Mechanical influences on in silico tumor evolution

Experimental insight and conceptual understanding of tumor growth are steadily growing and leading to new therapeutic interventions. Experiments and clinical studies are able to link single-cell properties to macroscopic tumor attributes. The development of cellular subpopulations in heterogeneous tumors can be understood as an evolutionary system with different cell types competing over both space and nutrients. However, to predict the growth trajectory and development of a tumor, fitness and trade-offs of cell properties in the context of the surroundings are required and often inaccessible. The optimum of the evolutionary trajectory provides a target for intervention, but can mostly not be identified. We propose that the optimal value of cellular properties is influenced by the tumor surrounding. Computational multiscale-modeling of tissue enables the observation of the trajectory of each cell while modeling the tumor surrounding. We model a 3D spheroid tumor and the fitness of individual cells and the evolutionary behavior of the tumor are quantified and linked to global parameters. Cell–cell adhesion and cell motility are two important mechanical properties for cell development and used as free parameters. Mechanical properties alone are able to drive the tumor towards low adhesion.We implement a dynamically changing nutrient surrounding representing the fluctuating blood-supply through blood vessel collapse and angiogenesis. We find that the evolutionary speed depends on the frequency of the fluctuations. We identify a frequency domain in which the evolutionary speed is significantly increased over a tumor with constant nutrient supply. The findings suggest that mechanically-induced fluctuations can accelerate tumor evolution.Author summaryLimited space and nutrients together with competing cell types drive an evolutionary process inside tumors. This process selects for the fittest cell types and optimizes the growing behavior for its local surroundings. An expanding tumor exerts mechanical forces on its cells and its surroundings, leading to a fluctuating nutrient supply through collapsing blood vessels. Here, we observe the influence of a dynamically changing surrounding on the evolutionary behavior of heterogeneous tumors in a high-resolution computational model. We find that the evolutionary speed depends on the frequency of the fluctuations and a fitness advantage of low-adhesion cells.

An Improved Gravity Search Algorithm and Its Application in Planar Array Synthesis

An improved gravity search algorithm, adaptive gravity search algorithm (AGSA), is proposed to solve the problem that the gravity neutralization caused by the cumulative effect of particle inertia mass at the end of iteration, which will affect the optimization performance. An adaptive decay factor is designed, which can produce different gravitation values at different iteration stages of the algorithm and accelerate the mining ability of the algorithm at the later iteration stage. In order to enhance the memory ability of the algorithm, the influence of elite particles is added to the realization of the speed to expand the exploration ability. The improved algorithm is used to optimize uniform concentric ring array, the main lobe width optimized by the AGSA is 6.7°narrower and the side lobe level is 5.1 dB and 1.8 dB lower than the algorithm in the literature. It is clear that the pattern obtained by AGSA meets the desired pattern very well. Moreover, when the number of iterations is 2 000, the fitness value of the improved algorithm is increased by 30%. It can be seen that AGSA outperforms the algorithm in the literature in evolutionary speed and accuracy. Sparse concentric ring array also has the same optimization results. The effectiveness of the proposed improved algorithm in solving the array pattern synthesis is proved.

The Phylogeography of MERS-CoV in Hospital Outbreak-Associated Cases Compared to Sporadic Cases in Saudi Arabia

This study compared the phylogeography of MERS-CoV between hospital outbreak-associated cases and sporadic cases in Saudi Arabia. We collected complete genome sequences from human samples in Saudi Arabia and data on the multiple risk factors of human MERS-CoV in Saudi Arabia reported from 2012 to 2018. By matching each sequence to human cases, we identified isolates as hospital outbreak-associated cases or sporadic cases. We used Bayesian phylogenetic methods including temporal, discrete trait analysis and phylogeography to uncover transmission routes of MERS-CoV isolates between hospital outbreaks and sporadic cases. Of the 120 sequences collected between 19 June 2012 and 23 January 2017, there were 64 isolates from hospital outbreak-associated cases and 56 from sporadic cases. Overall, MERS-CoV is fast evolving at 7.43 × 10−4 substitutions per site per year. Isolates from hospital outbreaks showed unusually fast evolutionary speed in a shorter time-frame than sporadic cases. Multiple introductions of different MERS-CoV strains occurred in three separate hospital outbreaks. MERS-CoV appears to be mutating in humans. The impact of mutations on viruses transmissibility in humans is unknown.

Controlling the speed and trajectory of evolution with counterdiabatic driving

ABSTRACTThe pace and unpredictability of evolution are critically relevant in a variety of modern challenges: combating drug resistance in pathogens and cancer, understanding how species respond to environmental perturbations like climate change, and developing artificial selection approaches for agriculture. Great progress has been made in quantitative modeling of evolution using fitness landscapes, allowing a degree of prediction for future evolutionary histories. Yet fine-grained control of the speed and the distributions of these trajectories remains elusive. We propose an approach to achieve this using ideas originally developed in a completely different context – counterdiabatic driving to control the behavior of quantum states for applications like quantum computing and manipulating ultra-cold atoms. Implementing these ideas for the first time in a biological context, we show how a set of external control parameters (i.e. varying drug concentrations / types, temperature, nutrients) can guide the probability distribution of genotypes in a population along a specified path and time interval. This level of control, allowing empirical optimization of evolutionary speed and trajectories, has myriad potential applications, from enhancing adaptive therapies for diseases, to the development of thermotolerant crops in preparation for climate change, to accelerating bioengineering methods built on evolutionary models, like directed evolution of biomolecules.

Ecological constraints coupled with deep-time habitat dynamics predict the latitudinal diversity gradient in reef fishes

We develop a spatially explicit model of diversification based on palaeohabitat to explore the predictions of four major hypotheses potentially explaining the latitudinal diversity gradient (LDG), namely, the ‘time-area’, ‘tropical niche conservatism’, ‘ecological limits’ and ‘evolutionary speed’ hypotheses. We compare simulation outputs to observed diversity gradients in the global reef fish fauna. Our simulations show that these hypotheses are non-mutually exclusive and that their relative influence depends on the time scale considered. Simulations suggest that reef habitat dynamics produced the LDG during deep geological time, while ecological constraints shaped the modern LDG, with a strong influence of the reduction in the latitudinal extent of tropical reefs during the Neogene. Overall, this study illustrates how mechanistic models in ecology and evolution can provide a temporal and spatial understanding of the role of speciation, extinction and dispersal in generating biodiversity patterns.

Industrial Symbiosis Systems: Promoting Carbon Emission Reduction Activities

The carbon emission problem in China needs to be solved urgently. Industrial symbiosis, as an effective means to improve resource efficiency, can better alleviate the carbon emission problem. Under such a circumstance, this paper regards an industrial symbiosis system as a collection of producers, consumers and decomposers, and analyzes the strategic selections and behavioral characteristics of their carbon emission reduction activities through a tripartite evolutionary game model, and then the effects of related parameters on the evolutionary stable strategies of stakeholders are discussed. The results demonstrate that: (1) the regular return and the rate of return determine the ability of stakeholders to undertake carbon reduction activities; (2) the initial willingness of stakeholders to participate will affect the evolutionary speed of the strategies; (3) a high opportunity cost reduces the inertia of stakeholders to carry out carbon emission reductions; (4) producers, consumers and decomposers can avoid “free rides” by signing agreements or adopting punitive measures.

Support for the evolutionary speed hypothesis from intraspecific population genetic data in the non-biting midge Chironomus riparius

The evolutionary speed hypothesis (ESH) proposes a causal mechanism for the latitudinal diversity gradient. The central idea of the ESH is that warmer temperatures lead to shorter generation times and increased mutation rates. On an absolute time scale, both should lead to an acceleration of selection and drift. Based on the ESH, we developed predictions regarding the distribution of intraspecific genetic diversity: populations of ectothermic species with more generations per year owing to warmer ambient temperatures should be more differentiated from each other, accumulate more mutations and show evidence for increased mutation rates compared with populations in colder regions. We used the multivoltine insect species Chironomus riparius to test these predictions with cytochrome oxidase I (COI) sequence data and found that populations from warmer regions are indeed significantly more differentiated and have significantly more derived haplotypes than populations from colder regions. We also found a significant correlation of the annual mean temperature with the population mutation parameter θ that serves as a proxy for the per generation mutation rate under certain assumptions. This pattern could be corroborated with two nuclear loci. Overall, our results support the ESH and indicate that the thermal regime experienced may be crucially driving the evolution of ectotherms and may thus ultimately govern their speciation rate.