Genotype × environmental interactions for analyzing adaptability and stability in different clones of Dalbergia sissoo Roxb.

In total 36 superior clones of Dalbergia sissoo Roxb., screened from 300 selections conducted in natural and growing range of India and Nepal, were multiplied using single nodal cuttings and estab- lished to evaluate genotypexenvironmental interactions for adaptability and stability at the age of 30 months in three geographical locations in the state of Punjab, India. Clone 124 had maximum adaptability and stability （bi = 1.04） to perform exceedingly well over the locations. Clones 36 and 1 were stable with mean regression coefficient of 0.84 and 1.22, respectively. Nonetheless, clone 4 1 performed exceedingly well for all the characters to attain maximum population mean, and the perform- ance varied substantially across the locations. Therefore, clone 41 was considered as productive but non-adaptive clone. Though some of the clones were sensitive to sites, 14 clones for height, 16 for collar diameter, 12 for DBH and 7 for volume were relatively un-sensitive with higher regression coefficient. Nonetheless, clone 124 was the most Stable with average bi value of 1.04 and productive, which could play an important role in future breeding and commercial deployment of stable and produc- tive planting stock of Dalbergia sissoo.

Quantitatively probing interactions between membrane with adaptable wettability and oil phase in oil/water separation

The membrane method based on adaptive wettability shows great advantages in oil-water separation.At present,researches focus on the excellent application performance of the membrane material,while the quantitative analysis of interactions in oil-water separation is rarely recognized.Herein,we constructed an adaptable wettability membrane with multiple polymer networks by polydopamine(PDA)and mussel-inspired amphiphilic polymer.Based on the Owens three-probe liquid method,the surface energy of the modified membrane was verified to meet the adaptive wettability conditions,with surface energies(γ-8)of 147.6 mJ m^(−2)(superhydrophilic/underwater superoleophobic)and 49.87 mJ m^(−2)(superhydrophobic/superoleophobic),respectively.The adhesion or repulsion of the membrane to the oil phase under different conditions during the separation process was quantified by the chemical probe AFM technique.In addition,the oil-water selective separation mechanism was further analyzed in a simplified membrane microchannel model.The results show that the different wetting produces capillary additional pressure in opposite directions,resulting in different energies to be overcome when the oil or water passes through the microchannels,thus achieving selective separation.

ADAPTIVE FINITE ELEMENT METHOD FOR HIGH-SPEED FLOW-STRUCTURE INTERACTION

An adaptive finite element method for high-speed flow-structure interaction is pre- sented.The cell-centered finite element method is combined with an adaptive meshing technique to solve the Navier-Stokes equations for high-speed compressible flow behavior.The energy equation and the quasi-static structural equations for aerodynamically heated structures are solved by applying the Galerkin finite element method.The finite element formulation and computational procedure are de- scribed.Interactions between the high-speed flow,structural heat transfer,and deformation are studied by two applications of Mach 10 flow over an inclined plate,and Mach 4 flow in a channel.

OpenIFEM:A High Performance Modular Open-Source Software of the Immersed Finite Element Method for Fluid-Structure Interactions

We present a high performance modularly-built open-source software-OpenIFEM.OpenIFEM is a C++implementation of the modified immersed finite element method(mIFEM)to solve fluid-structure interaction(FSI)problems.This software is modularly built to perform multiple tasks including fluid dynamics(incompressible and slightly compressible fluid models),linear and nonlinear solid mechanics,and fully coupled fluid-structure interactions.Most of open-source software packages are restricted to certain discretization methods;some are under-tested,under-documented,and lack modularity as well as extensibility.OpenIFEM is designed and built to include a set of generic classes for users to adapt so that any fluid and solid solvers can be coupled through the FSI algorithm.In addition,the package utilizes well-developed and tested libraries.It also comes with standard test cases that serve as software and algorithm validation.The software can be built on cross-platform,i.e.,Linux,Windows,and Mac OS,using CMake.Efficient parallelization is also implemented for high-performance computing for large-sized problems.OpenIFEM is documented using Doxygen and publicly available to download on GitHub.It is expected to benefit the future development of FSI algorithms and be applied to a variety of FSI applications.

Dynamic Adaptive Finite Element Analysis of Acoustic Wave Propagation Due to Underwater Explosion for Fluid-structure Interaction Problems

In this paper, an investigation into the propagation of far field explosion waves in water and their effects on nearby structures are carried out. For the far field structure, the motion of the fluid surrounding the structure may be assumed small, allowing linearization of the governing fluid equations. A complete analysis of the problem must involve simultaneous solution of the dynamic response of the structure and the propagation of explosion wave in the surrounding fluid. In this study, a dynamic adaptive finite element procedure is proposed. Its application to the solution of a 2D fluid-structure interaction is investigated in the time domain. The research includes：a） calculation of the far-field scatter wave due to underwater explosion including solution of the time-depended acoustic wave equation, b） fluid-structure interaction analysis using coupled Euler-Lagrangian approach, and c） adaptive finite element procedures employing error estimates, and re-meshing. The temporal mesh adaptation is achieved by local regeneration of the grid using a time-dependent error indicator based on curvature of pressure function. As a result, the overall response is better predicted by a moving mesh than an equivalent uniform mesh. In addition, the cost of computation for large problems is reduced while the accuracy is improved.

An Algorithm of the Adaptive Grid and Fuzzy Interacting Multiple Model

This paper studies the algorithm of the adaptive grid and fuzzy interacting multiple model （AGFIMM） for maneuvering target tracking, while focusing on the problems of the fixed structure multiple model （FSMM） algorithm＇s cost-efficiency ratio being not high and the Markov transition probability of the interacting multiple model （IMM） algorithm being difficult to determine exactly. This algorithm realizes the adaptive model set by adaptive grid adjustment, and obtains each model matching degree in the model set by fuzzy logic inference. The simulation results show that the AGFIMM algorithm can effectively improve the accuracy and cost-efficiency ratio of the multiple model algorithm, and as a result is suitable for enineering apolications.

基于Adaptive Interaction理论的PID控制器的设计及应用

本文在Adaptive Interaction理论的基础上,提出了一种新的自调整 PID 控制器。这种新的控制器根据输入及其误差信号进行在线训练,通过误差评价函数的最小化,在模型未知的情况下能很好地调整比例、积分、微分三个参数。对于被控对象的变化具有鲁棒性,很大程度上解决了传统的 PID 控制器对于非线性、不稳定系统控制效果不佳及在线调整困难的问题。通过仿真实例,验证了应用 Adaptive Interaction 理论的 PID 控制器的有效性和实用性。

Identifying key traits in high-yielding rice cultivars for adaptability to both temperate and tropical environments

Increasing rice yield potential is a continuous challenge posed by world population growth.To increase yield potential,favorable alleles of valuable genes need to be accumulated in promising germplasm.We conducted comparative yield trials for two years in Tsukuba,Japan,in a temperate region and at the International Rice Research Institute(IRRI),Philippines,in a tropical region using five high-yielding rice cultivars:Takanari and Hokuriku193,developed in Japan,and IR64,NSIC Rc158,and YTH183,developed in the Philippines.Genotype plus genotype×environment interaction(GGE)biplot analysis across four environments(two regions×two seasons)classified the five cultivars into four categories:Takanari and YTH183 showed high adaptability to both tropical and temperate regions,Hokuriku193 was suitable for temperate regions,NSIC Rc158 was suitable for the tropics,and IR64 was inferior to the other cultivars in both regions.The high yield and adaptability in Takanari and YTH183 were attributed to their large sink capacity with good grain filling.The plant type for high yield was different,however,between the two cultivars;Takanari was a panicle-weight type,whereas YTH183 was a panicle-number type.Evaluations of F2 progeny of a cross between Takanari and YTH183 showed transgressive segregation for number of panicles per plant as well as number of spikelets per panicle,leading some F2 plants to produce more spikelets per plant(corresponding to larger sink size)than the parental cultivars in both regions.These results suggest the possibility of developing rice cultivars with high yield potential in both temperate and tropical regions by crossing temperate with tropical high-yielding cultivars.

Ground substrates classification and adaptive walking through interaction dynamics for legged robots

Adaptive locomotion in different types of surfaces is of critical importance for legged robots.The knowledge of various ground substrates,especially some geological properties,plays an essential role in ensuring the legged robots'safety.In this paper,the interaction between the robots and the environments is investigated through interaction dynamics with the closed-loop system model,the compliant contact model,and the friction model,which unveil the influence of environment's geological characteristics for legged robots'locomotion.The proposed method to classify substrates is based on the interaction dynamics and the sensory-motor coordination.The foot contact forces,joint position errors,and joint motor currents,which reflect body dynamics,are measured as the sensing variables.We train and classify the features extracted from the raw data with a multilevel weighted k-Nearest Neighbor(kNN) algorithm.According to the interaction dynamics,the strategy of adaptive walking is developed by adjusting the touchdown angles and foot trajectories while lifting up and dropping down the foot.Experiments are conducted on five different substrates with quadruped robot FROG-I.The comparison with other classification methods and adaptive walking between different substrates demonstrate the effectiveness of our approach.

Approaches to Detecting Gene-Environment Interactions in Human Variation Using Genetic Engineering, Remote Sensing and GIS

Elucidation of the relationships between genetic polymorphisms and environmental exposures can provide insights into the pathways and mechanisms underlying complex traits. A new approach was used to detect G×E （gene-environment） interactions involved in human skin pigmentation variation to better understand the adaptive evolution of skin pigmentation. Specifically, we used genetic engineering, remote UVR （ultraviolet radiation） sensing and GIS （geographic information systems） to integrate the analysis of genetic and environmental factors into a coherent biological framework. Since we expected to generate large datasets for this multidimensional analysis, we used PCA （principal components analysis） as a spatial statistical analysis technique for analyzing the G×E interactions. The results suggest that skin pigmentation may be affected by mutations induced by UVR and support the hypothesis that global variation in skin pigmentation may be the result of localized adaptation to different UVR conditions via natural selection. Analyzing the relationships between heterozygous frequencies for SNP （single nucleotide polymorphism） loci and seasonal UVR levels as the environment changes will help elucidate the selective mechanisms involved in the UVR-induced evolution of skin pigmentation. Skin pigmentation fulfills the criteria for a successful evolutionary G×E interactions model.

Interactive-Adaptive Combination Rule

Aiming at the invalidation of DS theory dealing with the evidence in a high conflict and reducing confidence level of DSm theory processing a low conflict,this paper proposes an interactive-adaptive combination rule. Adopting the angle similarity based on hyper-power set as the weight of generalized Dempster rule and PCR rule,the new rule adaptively processes various evidence combination issues. In this way,the rule can obtain not only the better fusion of decision making effect in a low conflict,but also the solution to the synthesis in a high conflict. Simulation analysis demonstrates the validity and applicability from this rule of combination.

Cyber-Physical-Social System Between a Humanoid Robot and a Virtual Human Through a Shared Platform for Adaptive Agent Ecology

Two artificial agents(a humanoid robot and a virtual human) are enriched with various similar intelligence,autonomy, functionalities and interaction modalities. The agents are integrated in the form of a cyber-physical-social system(CPSS) through a shared communication platform to create a social ecology. In the ecology, the agents collaborate(assist each other) to perform a real-world task(search for a hidden object)for the benefits of humans. A robot-virtual human bilateral trust model is derived and a real-time trust measurement method is developed. The role of taking initiative in the collaboration is switched between the agents following a finite state machine model triggered by bilateral trust, which results in a mixedinitiative collaboration. A scheme is developed to evaluate the performance of the agents in the ecology through the CPSS.The results show that the robot and the virtual human perform satisfactorily in the collaboration through the CPSS. The results thus prove the effectiveness of the real-world ecology between artificial agents of heterogeneous realities through a shared platform based on trust-triggered mixed-initiatives. The results can help develop adaptive social ecology comprising intelligent agents of heterogeneous realities to assist humans in various tasks through collaboration between the agents in the form of a CPSS.

Data-Driven Human-Robot Interaction Without Velocity Measurement Using Off-Policy Reinforcement Learning

In this paper,we present a novel data-driven design method for the human-robot interaction(HRI)system,where a given task is achieved by cooperation between the human and the robot.The presented HRI controller design is a two-level control design approach consisting of a task-oriented performance optimization design and a plant-oriented impedance controller design.The task-oriented design minimizes the human effort and guarantees the perfect task tracking in the outer-loop,while the plant-oriented achieves the desired impedance from the human to the robot manipulator end-effector in the inner-loop.Data-driven reinforcement learning techniques are used for performance optimization in the outer-loop to assign the optimal impedance parameters.In the inner-loop,a velocity-free filter is designed to avoid the requirement of end-effector velocity measurement.On this basis,an adaptive controller is designed to achieve the desired impedance of the robot manipulator in the task space.The simulation and experiment of a robot manipulator are conducted to verify the efficacy of the presented HRI design framework.

Interaction of genotype and environment effects on important traits of cassava(Manihot esculenta Crantz)

General and specific environmental adaptation of genotypes is the main goal of breeders.However, genotype-by-environment(G x E) interaction complicates the identification of genotypes for release. This study aimed at analyzing the effects of G x E interaction on the expression of important cassava traits using two multivariate analyses: additive main effects and multiplicative interaction(AMMI) and genotype stability index(GSI). Total carotene content(TCC), postharvest physiological deterioration(PPD), and reaction to viral diseases were significantly affected by G x E interaction effects. The low percent(%)variation due to genotype for cassava brown streak disease(GBSD) explained the influence of environment on CBSD expression. The % variation due to genotype for TCC was higher(96%) than variation due to environment(1.7%) and G x E interaction(2.4%) indicating a low interaction effect of environment on TCC accumulation. The % variation due to genotype was higher than % variation due to environment for all traits but CBSD root necrosis and CBSD on stems, indicating the influence of environment on the severity of the viral diseases. These findings indicate that screening for disease resistance requires multi-environment trials, whereas a single-environment trial suffices to screen for total carotene content.

Traffic Signals Control with Adaptive Fuzzy Controller in Urban Road Network

An adaptive fuzzy logic controller (AFC) is presented for the signal control of the urban traffic network. The AFC is composed of the signal control system-oriented control level and the signal controller-oriented fuzzy rules regulation level. The control level decides the signal timings in an intersection with a fuzzy logic controller. The regulation level optimizes the fuzzy rules by the Adaptive Rule Module in AFC according to both the system performance index in current control period and the traffic flows in the last one. Consequently the system performances are improved. A weight coefficient controller (WCC) is also developed to describe the interactions of traffic flow among the adjacent intersections. So the AFC combined with the WCC can be applied in a road network for signal timings. Simulations of the AFC on a real traffic scenario have been conducted. Simulation results indicate that the adaptive controller for traffic control shows better performance than the actuated one.

ADAPTIVE MULTIPLE MODEL FILTER USING IMM AND STF

In fault identification, the Strong Tracking Filter (STF) has strong ability to track the change of some parameters by whitening filtering innovation. In this paper, the authors give out a modified STF by searching the fading factor based on the Least Squared Estimation. In hybrid estimation, the well known Interacting Multiple Model (IMM) Technique can model the change of the system modes. So one can design a new adaptive filter — SIMM. In this filter, our modified STF is a parameter adaptive part and IMM is a mode adaptive part. The benefit of the new filter is that the number of models can be reduced considerably. The simulations show that SIMM greatly improves accuracy of velocity and acceleration compared with the standard IMM to track the maneuvering target when 2 model conditional estimators are used in both filters. And the computation burden of SIMM increases only 6% compared with IMM.

Mathematical Modeling the Biology of Single Nucleotide Polymorphisms (SNPs) in Whole Genome Adaptation

As a living information and communications system, the genome encodes patterns in single nucleotide polymorphisms (SNPs) reflecting human adaptation that optimizes population survival in differing environments. This paper mathematically models environmentally induced adaptive forces that quantify changes in the distribution of SNP frequencies between populations. We make direct connections between biophysical methods (e.g. minimizing genomic free energy) and concepts in population genetics. Our unbiased computer program scanned a large set of SNPs in the major histocompatibility complex region and flagged an altitude dependency on a SNP associated with response to oxygen deprivation. The statistical power of our double-blind approach is demonstrated in the flagging of mathematical functional correlations of SNP information-based potentials in multiple populations with specific environmental parameters. Furthermore, our approach provides insights for new discoveries on the biology of common variants. This paper demonstrates the power of biophysical modeling of population diversity for better understanding genome-environment interactions in biological phenomenon.

Information Dynamics of Whole Genome Adaptation

The human genome is a complex, dynamic information system that encodes principles of life and living systems. These principles are incorporated in the structure of human genome sequence variation and are foundational for the continuity of life and human survival. Using first principles of thermodynamics and statistical physics, we have developed analogous “genodynamic tools” for population genomic studies. Characterizing genomic information through the lens of physics has allowed us to develop energy measures for modeling genome-environment interactions. In developing biophysical parameters for genome-environment homeostasis, we found that stable genomic free energy trades off low genomic energy (genomic conservation and increased order) and high genomic entropy (genomic variation) with an environmental potential that drives the variation. In our approach, we assert that common variants are dynamic sites in the genome of a population and that the stability of whole genome adaptation is reflected in the frequencies of maintained diversity in common variants for the population in its environment. In this paper, we address the relativity of whole genome adaptation towards homeostasis. By this we mean that adaptive forces are directly reflected in the frequency distribution of alleles and/or haplotypes of the population relative to its environment, with adaptive forces driving the genome towards homeostasis. The use of genomic energy units as a biophysical metric in DNA sequence variation analyses provides new insights into the foundations of population biology and diversity. Using our biophysical tools, population differences directly reflect the adaptive influences of the environment on populations.

Mobile Devices Interface Adaptivity Using Ontologies

Currently,many mobile devices provide various interaction styles and modes which create complexity in the usage of interfaces.The context offers the information base for the development of Adaptive user interface(AUI)frameworks to overcome the heterogeneity.For this purpose,the ontological modeling has been made for specific context and environment.This type of philosophy states to the relationship among elements(e.g.,classes,relations,or capacities etc.)with understandable satisfied representation.The contextmechanisms can be examined and understood by anymachine or computational framework with these formal definitions expressed in Web ontology language(WOL)/Resource description frame work(RDF).The Protégéis used to create taxonomy in which system is framed based on four contexts such as user,device,task and environment.Some competency questions and use-cases are utilized for knowledge obtaining while the information is refined through the instances of concerned parts of context tree.The consistency of the model has been verified through the reasoning software while SPARQL querying ensured the data availability in the models for defined use-cases.The semantic context model is focused to bring in the usage of adaptive environment.This exploration has finished up with a versatile,scalable and semantically verified context learning system.This model can be mapped to individual User interface(UI)display through smart calculations for versatile UIs.

ADAPTIVE UPDATE RATE FOR PHASED ARRAY RADAR BASED ON IMMK-PF

Interacting Multiple Model Kalman-Particle Filter (IMMK-PF) has the advantages of particle filter and Kalman filter and good computation efficiency compared with Interacting Multiple Model Particle Filter (IMMPF). Based on IMMK-PF, an adaptive sampling target tracking algorithm for Phased Array Radar (PAR) is proposed. This algorithm first predicts Posterior Cramer-Rao Bound Matrix (PCRBM) of the target state, then updates the sample interval in accordance with change of the target dynamics by comparing the trace of the predicted PCRBM with a certain threshold. Simulation results demonstrate that this algorithm could solve the nonlinear motion and the nonlinear relationship between radar measurement and target motion state and decrease computation load.