Enhanced Ion-Selective Diffusion Achieved by Supramolecular Interaction for High Thermovoltage and Thermal Stability

Thermoelectric(TE)generators capable of converting thermal energy into applicable electricity have gained great popularity among emerging energy conversion technologies.Biopolymer-based ionic thermoelectric(i-TE)materials are promising candidates for energy conversion systems because of their wide sources,innocuity,and low manufacturing cost.However,common physically crosslinked biopolymer gels induced by single hydrogen bonding or hydrophobic interaction suffer from low differential thermal voltage and poor thermodynamic stability.Here,we develop a novel i-TE gel with supramolecular structures through multiple noncovalent interactions between ionic liquids(ILs)and gelatin molecular chains.The thermopower and thermoelectric power factor of the ionic gels are as high as 2.83 mV K-1 and 18.33μW m^(-1)K^(-2),respectively.The quasi-solid-state gelatin-[EMIM]DCA i-TE cells achieve ultrahigh 2 h output energy density(E_(2h)=9.9 mJ m^(-2))under an optimal temperature range.Meanwhile,the remarkable stability of the supramolecular structure provides the i-TE hydrogels with a thermal stability of up to 80℃.It breaks the limitation that biopolymer-based i-TE gels can only be applied in the low temperature range and enables biopolymer-based i-TE materials to pursue better performance in a higher temperature range.

Genotype×year interaction of pod and seed mass and stability of Pongamia pinnata families in a semi-arid region

Sixteen pongamia families were evaluated in a field experiment for eight consecutive years in dryland conditions to identify stable,high-yielding families.The trial was conducted in a randomized complete block design with three replications.Each family,consisting of nine trees per replication,was planted at a spacing of3 m x 3 m.Yield stability was analyzed using(1)Eberhart and Russel’s regression coefficient(β_i)and deviation from regression(S_d^2),(2)Wrike’s ecovalence(W_i);(3)Shukla stability variance(σ_i^2);and(4)Piepho and Lotito’s stability index(L_i).Families were also analyzed for adaptability and stability using AMMI and GGE biplots graphical methods.The study revealed significant variances due to family and family x year interaction for pod and seed yield.Families performed differently and ranked differently across years.The performance of families was influenced by both genetic factor and environmental conditions in different years.Among families tested,TNMP20,Acc14,TNMP14 and Acc30 were high yielders for pods,and Acc14,Acc30,TNMP6,RAK19 and TNMP14 were high for seed yield.According to the Eberhart and Russell model,Acc30,TNMP14 and TNMP3 were stable across years.In the graphical view of family x year interaction based on AMMI methods,TNMP3,TNMP4 and TNMP14 had greater stability with moderate seed yield,and Acc14 and Acc30 had moderate stability with high seed yield.On the other hand,GGE biplots revealed Acc14,Acc30 and TNMP14 as high yielders with moderate stability.AMMI and GGE biplots were able to capture nonlinear parts of the family x year interaction that were not be captured by the Eberhart and Russel model while also identifying stable families.Based on different methodologies,Acc14,Acc30 and TNMP14 were identified as high yielding and stable families for promoting pongamia cultivation as a biofuel crop for semi-arid regions.

Multi-Environment Evaluation and Genotype ×Environment Interaction Analysis of Sorghum [<i>Sorghum bicolor</i>(L.) Moench] Genotypes in Highland Areas of Ethiopia

Sorghum [Sorghum bicolor (L.) Moench] is a high-yielding, nutrient-use efficient, and drought tolerant crop that can be cultivated on over 80 per cent of the world’s agricultural land. However, a number of biotic and abiotic factors are limiting grain yield increase. Diseases (leaf and grain) are considered as one of the major biotic factors hindering sorghum productivity in the highland and intermediate altitude sorghum growing areas of Ethiopia. In addition, the yield performance of crop varieties is highly influenced by genotype × environment (G × E) interaction which is the major focus of researchers while generating improved varieties. In Ethiopia, high yielding and stable varieties that withstand biotic stress in the highland areas are limited. In line with this, the yield performance of 21 sorghum genotypes and one standard check were evaluated across 14 environments with the objectives of estimating magnitude G × E interaction for grain yield and to identify high yielder and stable genotypes across environments. The experiment was laid out using Randomized Complete Block Design with three replications in all environments. The combined analysis of variance across environments revealed highly significant differences among environments, genotypes and G × E interactions of grain yield suggesting further analysis of the G × E interaction. The results of the combined AMMI analysis of variance indicated that the total variation in grain yield was attributed to environments effects 71.21%, genotypes effects 4.52% and G × E interactions effects 24.27% indicating the major sources of variation. Genotypes 2006AN7010 and 2006AN7011 were high yielder and they were stable across environments and one variety has been released for commercial production and can be used as parental lines for genetic improvement in the sorghum improvement program. In general, this research study revealed the importance of evaluating sorghum genotypes for their yield and stability across diverse highland areas of Ethiopia before releasing for commercial production.

Entanglement Entropy Signature of Quantum Phase Transitions in a Multiple Spin Interactions Model

Through the Jordan Wigner transformation, the entanglement entropy and ground state phase diagrams of exactly solvable spin model with alternating and multiple spin exchange interactions are investigated by means of Green＇s function theory. In the absence of four-spin interactions, the ground state presents plentiful quantum phases due to the multiple spin interactions and magnetic fields. It is shown that the two-site entanglement entropy is a good indicator of quantum phase transition （QPT）. In addition, the alternating interactions can destroy the magnetization plateau and wash out the spin-gap of low-lying excitations. However, in the presence of four-spin interactions, apart from the second order QPTs, the system manifests the first order OPT at the tricritical point and an additional new phase called ＂spin waves＂, which is due to the collapse of the continuous tower-like low-lying excitations modulated by the four-spin interactions for large three-spin couplings.

An interacting multiple model-based two-stage Kalman filter for vehicle positioning

To address the problem that a general augmented state Kalman filter or a two-stage Kalman filter cannot achieve satisfactory positioning performance when facing uncertain noise of the micro-electro-mechanical system（MEMS） inertial sensors, a novel interacting multiple model-based two-stage Kalman filter（IMM-TSKF） is proposed to adapt to the uncertain inertial sensor noise. Three bias filters are developed based on different noise characteristics to cover a wide range of noise levels. Then, an accurate estimation of biases is calculated by the interacting multiple model algorithm to correct the bias-free filter. Thus, the vehicle positioning system can achieve good performance when suffering from uncertain inertial sensor noise. The experimental results indicate that the average position error of the proposed IMMTSKF is 25% lower than that of the general TSKF.

One compound approach combining factor-analytic model with AMMI and GGE biplot to improve multi-environment trials analysis

To improve multi-environmental trial(MET)analysis,a compound method—which combines factor analytic(FA)model with additive main effect and multiplicative interaction(AMMI)and genotype main effect plus genotype-by-environment interaction(GGE)biplot—was conducted in this study.The diameter at breast height of 36 open-pollinated(OP)families of Pinus taeda at six sites in South China was used as a raw dataset.The best linear unbiased prediction(BLUP)data of all individual trees in each site was obtained by fitting the spatial effects with the FA method from raw data.The raw data and BLUP data were analyzed and compared by using the AMMI and GGE biplot.BLUP results showed that the six sites were heterogeneous and spatial variation could be effectively fitted by spatial analysis with the FA method.AMMI analysis identified that two datasets had highly significant effects on the site,family,and their interactions,while BLUP data had a smaller residual error,but higher variation explaining ability and more credible stability than raw data.GGE biplot results revealed that raw data and BLUP data had different results in mega-environment delineation,test-environment evaluation,and genotype evaluation.In addition,BLUP data results were more reasonable due to the stronger analytical ability of the first two principal components.Our study suggests that the compound method combing the FA method with the AMMI and GGE biplot could improve the analysis result of MET data in Pinus teada as it was more reliable than direct AMMI and GGE biplot analysis on raw data.

Detection and Analysis of High Temperature Sensitivity of TGMS Lines in Rice Using AMMI Model

With the AMMI (additive main effects and multiplicative interaction) analysis model, thedetermination of the sensitivity to temperature among different TGMS (thermo-sensitivegenic male sterile) lines was performed. To assess the genetic differences due to hightemperature stress at the fertility-sensitive stage (10-20d before heading), sevengenotypes (six TGMS lines and the control Pei-Ai64S) were grown from May 4 at sevendifferent stages with 10d intervals. The temperatures at the fertility-sensitive stagesinvolved twelve levels from<20 to>℃ under the regime natural conditions in Hangzhou,China. There was considerable variation in pollen fertility among genotypes in responseto high temperature. Five genotypes identified as TGMS lines as their percentages offertile pollens were lower than or close to that of the control except for the unstableline RTS19 (V6). When the temperatures at the fertility-sensitive stage were at Ⅰ-Ⅳ,Ⅴ-Ⅵ and Ⅶ-Ⅻ, the percentages of fertile pollens varied in the ranges of 46.46-48.49%,19.62-22.79% and 3.49-5.87%, respectively. The critical temperatures of sterility andfertility in the five TGMS lines were 25.1 and 23.0℃, respectively. Considering theamounts and directions of main effect and their IPCA (interaction principal componentsanalysis), we can classify the lines and temperature levels into different groups, anddescribe the characteristics of genotypetemperature interaction, offering the informationand tools for the development and utility of thermo-sensitive male sterile lines.Several TGMS rice lines with their reproductive sensitivity to high temperature that canbe screened using the AMMI model may add valuable germplasm to the breeding program ofhybrid rice.

Maneuvering target tracking using threshold interacting multiple model algorithm

To avoid missing track caused by the target maneuvers in automatic target tracking system, a new maneuvering target tracking technique called threshold interacting multiple model （TIMM） is proposed. This algorithm is based on the interacting multiple model （IMM） method and applies a threshold controller to improve tracking accuracy. It is also applicable to other advanced algorithms of IMM. In this research, we also compare the position and velocity root mean square （RMS） errors of TIMM and IMM algorithms with two different examples. Simulation results show that the TIMM algorithm is superior to the traditional IMM alzorithm in estimation accuracy.

Target Tracking Using the Interactive Multiple Model Method

Aim To develop a practical target tracking algorithm for different motion modes. Methods After creation of the new model, it was implemented by computer simulation to prove its performance and compared with the often-used current statistical model. Results The simulation results show that the new IMM (interactive multiple model) have low tracking error in both maneuVering segment and non^Inaneuwi segment while the current statistical model bas muCh higher tracking error in non-maneuvering segment. Conclusion In the point of trackintaccuracy, the new IMM method is much better than the current acceleration method. It can develop into a practical target hacking method.

The Influence of Solonetz Soil Limited Growth Conditions on Bread Wheat Yield

The wheat yield variation on solonetz and chernozem soil in six environments was in study in order to obtain information for use of genetic variability and for building strategy in plant breeding for less productive and marginal environments. The sample of eight bread wheat varieties： Rcnesansa, Pobeda, Rapsodija, Dragana, Cipovka, Evropa 90, NSR-5 and Nevesinjka, which are characterized by tolerance to stressful growing conditions and broader adaptability, was selected for the study. The trial was established by Randomized Complete Block Design in three replications at two locations in the Pannonian Plain, Northern Serbia in two vegetation periods 2004/2005 and 2008/2009. Locations differed in a soil type, primarily. The tested locality was on solonetz, while control locality was on chernozem soil type. Additive Main and Multiplicative Interaction model （AMMI） grouped varieties that exhibited strong reaction to environmental improvement （Nevesinjka and Evropa 90）, varieties showing fairly small GE interaction （Renesansa, Cipovka and Pobeda） and varieties having the ability for maximum use of less productive soil in better meteorological conditions （Dragana, Rapsodija and NSR-5）. Meteorological conditions significantly influenced the effect of soil quality variation on grain yield in trial. Varieties have interacted differently with the environment, depending on their genetic background.

Using interacting multiple model particle filter to track airborne targets hidden in blind Doppler

In airborne tracking,the blind Doppler makes the target undetectable,resulting in tracking difficulties. In this paper,we studied most possible blind-Doppler cases and summed them up into two types:targets' intentional tangential flying to radar and unintentional flying with large tangential speed. We proposed an interacting multiple model(IMM) particle filter which combines a constant velocity model and an acceleration model to handle maneuvering motions. We compared the IMM particle filter with a previous particle filter solution. Simulation results showed that the IMM particle filter outperforms the method in previous works in terms of tracking accuracy and continuity.

Interacting Multiple Model Algorithm with the Unscented Particle Filter (UPF)

Combining interacting multiple model （IMM） and unscented particle filter （UPF）, a new multiple model filtering algorithm is presented. Multiple models can be adapted to targets＇ high maneu- vering. Particle filter can be used to deal with the nonlinear or non-Gaussian problems and the unscented Kalman filter （UKF） can improve the approximate accuracy. Compared with other interacting multiple model algorithms in the simulations, the results demonstrate the validity of the new filtering method.

Method for Underwater Target Tracking Based on an Interacting Multiple Model

According to the requirements of real-time performance and reliability in underwater maneuvering target tracking as well as clarifying motion features of the underwater target, an interacting multiple model algorithm based on fuzzy logic inference （FIMM） is proposed. Maneuvering patterns of the target are represented by model sets, including the constant velocity model （CA）, the Singer mode~, and the nearly constant speed horizontal-turn model （HT） in FIMM technology. The simulation results show that compared to conventional IMM, the reliability and real-time performance of underwater target tracking can be improved by FIMM algorithm.

Consecutive tracking for ballistic missile based on bearings-only during boost phase

This paper proposes a modified centralized shifted Rayleigh filter（MCSRF） algorithm for tracking boost phase of ballistic missile（BM） trajectory with a highly nonlinear dynamical model based on bearings-only.This paper contributes three folds.Firstly,the mathematical model of an MCSRF for multiple passive sensors is derived.Then,minimum entropy based onedimensional optimization search to adaptively adjust the probability of the different filters for real time state estimation is deployed.Finally,the unscented transform（UT） is introduced to resolve the asymmetric state estimation problem.Simulation results show that the proposed algorithm can consecutively track the BM precisely during the boost phase.In comparison with the unscented Kalman filter（UKF） algorithm,the proposed algorithm effectively reduces the tracking position and velocity root mean square（RMS） errors,which will make more sense for early precision interception.

An unscented particle filter for ground maneuvering target tracking

In this study, an unscented particle filtering method based on an interacting multiple model (IMM) frame for a Markovian switching system is presented. The method integrates the multiple model (MM) filter with an unscented particle filter (UPF) by an interaction step at the beginning. The framework (interaction/mixing, filtering, and combination) is similar to that in a standard IMM filter, but an UPF is adopted in each model. Therefore, the filtering performance and degeneracy phenomenon of particles are improved. The filtering method addresses nonlinear and/or non-Gaussian tracking problems. Simulation results show that the method has better tracking performance compared with the standard IMM-type filter and IMM particle filter.

Novel sensor selection strategy for LPI based on an improved IMMPF tracking method

Sensor platforms with active sensing equipment such as radar may betray their existence, by emitting energy that will be intercepted by enemy surveillance sensors. The radar with less emission has more excellent performance of the low probability of intercept（LPI）. In order to reduce the emission times of the radar, a novel sensor selection strategy based on an improved interacting multiple model particle filter（IMMPF） tracking method is presented. Firstly the IMMPF tracking method is improved by increasing the weight of the particle which is close to the system state and updating the model probability of every particle. Then a sensor selection approach for LPI takes use of both the target＇s maneuverability and the state＇s uncertainty to decide the radar＇s radiation time. The radar will work only when the target＇s maneuverability and the state＇s uncertainty exceed the control capability of the passive sensors. Tracking accuracy and LPI performance are demonstrated in the Monte Carlo simulations.

Application of Road Information in Ground Moving Target Tracking

A new algorithm is developed to achieve accurate state estimation in ground moving target tracking by means of using road information. It is an adaptive variable structure interacting multiple model estimator with dynamic models modification （DMM VS-IMM for short）. Firstly, road information is employed to modify the target dynamic models used by filter, including modification of state transition matrix and process noise. Secondly, road information is applied to update the model set of a VS-IMM estimator. Predicted state estimation and road information are used to locate the target in the road network on which the model set is updated and finally IMM filtering is implemented. As compared with traditional methods, the accuracy of state estimation is improved for target moving not only on a single road, but also through an intersection. Monte Carlo simulation demonstrates the efficiency and robustness of the proposed algorithm with moderate computational loads.

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.

Maneuvering target tracking algorithm based on cubature Kalman filter with observation iterated update

Reasonable selection and optimization of a filter used in model estimation for a multiple model structure is the key to improve tracking accuracy of maneuvering target.Combining with the cubature Kalman filter with iterated observation update and the interacting multiple model method,a novel interacting multiple model algorithm based on the cubature Kalman filter with observation iterated update is proposed.Firstly,aiming to the structural features of cubature Kalman filter,the cubature Kalman filter with observation iterated update is constructed by the mechanism of iterated observation update.Secondly,the improved cubature Kalman filter is used as the model filter of interacting multiple model,and the stability and reliability of model identification and state estimation are effectively promoted by the optimization of model filtering step.In the simulations,compared with classic improved interacting multiple model algorithms,the theoretical analysis and experimental results show the feasibility and validity of the proposed algorithm.

Modified switched IMM estimator based on autoregressive extended Viterbi method for maneuvering target tracking

In this paper, a new approach of maneuvering target tracking algorithm based on the autoregressive extended Viterbi(AREV) model is proposed. In contrast to weakness of traditional constant velocity(CV) and constant acceleration(CA) models to noise effect reduction, the autoregressive(AR) part of the new model which changes the structure of state space equations is proposed. Also using a dynamic form of the state transition matrix leads to improving the rate of convergence and decreasing the noise effects. Since AR will impose the load of overmodeling to the computations, the extended Viterbi(EV) method is incorporated to AR in two cases of EV1 and EV2. According to most probable paths in the interacting multiple model(IMM) during nonmaneuvering and maneuvering parts of estimation, EV1 and EV2 respectively can decrease load of overmodeling computations and improve the AR performance. This new method is coupled with proposed detection schemes for maneuver occurrence and termination as well as for switching initializations. Appropriate design parameter values are derived for the detection schemes of maneuver occurrences and terminations. Finally, simulations demonstrate that the performance of the proposed model is better than the other older linear and also nonlinear algorithms in constant velocity motions and also in various types of maneuvers.