Holevo bound independent of weight matrices for estimating two parameters of a qubit

Holevo bound plays an important role in quantum metrology as it sets the ultimate limit for multi-parameter estimations,which can be asymptotically achieved.Except for some trivial cases,the Holevo bound is implicitly defined and formulated with the help of weight matrices.Here we report the first instance of an intrinsic Holevo bound,namely,without any reference to weight matrices,in a nontrivial case.Specifically,we prove that the Holevo bound for estimating two parameters of a qubit is equivalent to the joint constraint imposed by two quantum Cramér–Rao bounds corresponding to symmetric and right logarithmic derivatives.This weightless form of Holevo bound enables us to determine the precise range of independent entries of the mean-square error matrix,i.e.,two variances and one covariance that quantify the precisions of the estimation,as illustrated by different estimation models.Our result sheds some new light on the relations between the Holevo bound and quantum Cramer–Rao bounds.Possible generalizations are discussed.

Estimating wheat spike-leaf composite indicator(SLI)dynamics by coupling spectral indices and machine learning

The contribution of spike photosynthesis to grain yield(GY)has been overlooked in the accurate spectral prediction of yield.Thus,it’s essential to construct and estimate a yield-related phenotypic trait considering spike photosynthesis.Based on field and spectral reflectance data from 19 wheat cultivars under two nitrogen fertilization conditions in two years,our objectives were to(i)construct a yield-related phenotypic trait(spike–leaf composite indicator,SLI)accounting for the contribution of the spike to photosynthesis,(ii)develop a novel spectral index(enhanced triangle vegetation index,ETVI3)sensitive to SLI,and(iii)establish and evaluate SLI estimation models by integrating spectral indices and machine learning algorithms.The results showed that SLI was sensitive to nitrogen fertilizer and wheat cultivar variation as well as a better predictor of yield than the leaf area index.ETVI3 maintained a strong correlation with SLI throughout the growth stage,whereas the correlations of other spectral indices with SLI were poor after spike emergence.Integrating spectral indices and machine learning algorithms improved the estimation accuracy of SLI,with the most accurate estimates of SLI showing coefficient of determination,root mean square error(RMSE),and relative RMSE values of 0.71,0.047,and 26.93%,respectively.These results provide new insights into the role of fruiting organs for the accurate spectral prediction of GY.This high-throughput SLI estimation approach can be applied for wheat yield prediction at whole growth stages and may be assisted with agronomical practices and variety selection.

Estimating the Input Power of a Power Plant Using the Efficiency of the Inverter

The study focuses on estimating the input power of a power plant from available data, using the theoretical inverter efficiency as the key parameter. The paper addresses the problem of missing data in power generation systems and proposes an approach based on the efficiency formula widely documented in the literature. In the absence of input data, this method makes it possible to estimate the plant’s input power using data extracted from the site, in particular that provided by the Ministry of the Environment. The importance of this study lies in the need to accurately determine the input power in order to assess the overall performance of the energy system.

Comparison on Winter Wheat Yield Estimating Models Based on Radarsat-2 and HJ Satellite in Huaihe River Region

The establishment of crop yield estimating model based on microwave and optical satellite images can conduct the mutual verification of the accuracy of the reported crop yield and the precision of the estimating model. With Shou County and Huaiyuan County of Anhui Province as the experimental fields of winter wheat producing areas, the linear winter wheat yield estimating models were established by adopting backscattering coefficient and Normalized Difference Vegetation Index（NDVI） based on images from the synthetic aperture radar（SAR）—RDARSAT-2 and HJ satellite photographed in mid-April and early May, 2014, and then comparisons were conducted on the accuracy of the yield estimating models. The accuracies of the yield estimating models established using co-polarized（HH） and cross-polarized（HV） modes of SAR in Jiangou Town, Shou County were 68.37% and 74.01%, respectively, while the accuracies in Longkang Town, Huaiyuan County were 63.10%and 69.10%, respectively. Accuracies of yield estimating models established by HJ satellite data were 69.52% and 66.43% in Shou County and Huaiyuan County, respectively. Accuracies of winter yield estimating model based on HJ satellite data and that based on SAR were closed, and the yield difference of winter wheat in the lodging region was analyzed in detail. The model results laid the foundation and accumulated experience for the verification, parameters correction and promotion of the winter wheat yield estimating model.

ADAPTIVE ESTIMATING DETAIL PRESERVING FILTER FOR IMAGE PROCESSING

A new adaptive detail preserving filter for image processing is presented.By comparing the difference of the values evaluated in the different directions or regions,this filter can decide which region (homogeneous region or detail region) the filtering pixels belong to and then apply different filtering schemes.This filter has better performance of noise filtering and detail preserving than the multistage median filter (MMF).It can be applied especially to the images simultaneously corrupted by Gaussian noise and impulsive noise,and is simple in computation and implementation.

A Novel Estimating Method for Steering Efficiency of the Driver with Electromyography Signals

The existing research of steering efficiency mainly focuses on the mechanism efficiency of steering system, aiming at designing and optimizing the mechanism of steering system. In the development of assist steering system especially the evaluation of its comfort, the steering efficiency of driver physiological output usually are not considered, because this physiological output is difficult to measure or to estimate, and the objective evaluation of steering comfort therefore cannot be conducted with movement efficiency perspective. In order to take a further step to the objective evaluation of steering comfort, an estimating method for the steering efficiency of the driver was developed based on the research of the relationship between the steering force and muscle activity. First, the steering forces in the steering wheel plane and the electromyography （EMG） signals of the primary muscles were measured. These primary muscles are the muscles in shoulder and upper ann which mainly produced the steering torque, and their functions in steering maneuver were identified previously. Next, based on the multiple regressions of the steering force and EMG signals, both the effective steering force and the total force capacity of driver in steering maneuver were calculated. Finally, the steering efficiency of driver was estimated by means of the estimated effective force and the total force capacity, which represented the information of driver physiological output of the primary muscles. This research develops a novel estimating method for driver steering efficiency of driver physiological output, including the estimation of both steering force and the force capacity of primary muscles with EMG signals, and will benefit to evaluate the steering comfort with an objective perspective.

Approach to estimating non-point pollutant load removal rates based on water environmental capacity: a case study in Shenzhen

An innovative approach based on water environmental capacity for non-point source NPS pollution removal rate estimation was discussed by using both univariate and multivariate data analysis.Taking Shenzhen city as the study case a 67% to 74% NPS pollutant load removal rate can lead to meeting the chemical oxygen demand COD pollution control target for most watersheds.In contrast it is hardly to achieve the ammonia nitrogen NH4-N total phosphorus TP and biological oxygen demand BOD5 pollution control target by simply removing NPS pollutants. This highlights that the pollution control strategies should be taken according to different pollutant species and sources in different watersheds rather than one-size-fits-all .

Estimating Synechococcus spp. growth rates and grazingpressure by heterotrophic nanoplankton in theEnglish Channel and the Celtic Sea

-Marine chroococcoid phycoerythrin - containing Synechococcus spp. recently have been implicated as a substantial component of the photosynthetic picoplankton in the ocean. Although the importance of Synechococcus as food sources for heterotrophic nanoplankton are now recognized, the information about its cycling of biomass and diel patterns is limited and the methodology used varies according to different authors. A selective metabolic inhibitor method was used to allow simultanous estimation of both growth rates and grazing disappearance rates of Synechococcus. Results obtained in the English Channel show growth rates ranging from 0. 25 to 0. 72 d-1 with an average value of 0. 51 d -1and grazing disappearance rates ranged from 0. 21 to 0. 64 d-1 (mean = 0. 44 d-1). Offshore in the Celtic Sea of the Northeast Atlantic Ocean, both rates were lower than in the channel. The similarity between average growth and grazing rates suggests a rapid recycling of Synechococcus biomass. In diel pattern, Synechococcus grazing mortality rates were higher during the day (mean = 0. 61 d-1) than during the night (mean = 0. 21d-1) in all the experiments. A positive correlation was observed between growth rates and in situ temperature ranged from 9 to 20 ℃. Sire-fractionated experiments demonstrate that up to about 7 0 % of Synechococcus disappearance could be attributed to the grazer smaller than 2 μm in diameter. The variations of Synechococcus cell characteristics such as size and phycoerythin contents in the growth and grazing experiments were determined with a flow cytometer. The methodology recently used on estimating dynamics of Synechococcus population is reviewed.

Predictive value of neutrophil-to-lymphocyte ratio and other inflammatory indicators in estimating clinical severity of coronavirus disease

Dear editor,The recent outbreak of coronavirus disease(COVID-19)has become a major public health issue caused by 2019 novel coronavirus(2019-nCoV).[1]Severe COVID-19 patients may reveal a dysregulated immune response that allows the development of v iral hyperinfl ammation.[2]In the fight against COVID-19,inflammatory parameters towards illness severity should be identified to improve the prognosis of patients.In this study,we aimed to assess the discriminative ability of several infl ammation indicators in severe COVID-19 infection.

Estimating HIV Incidence Rates among MSM in an Urban Area of Chongqing Using Three Approaches

To evaluate the HIV pandemic in Chongqing, the pooled PCR, IgG-capture BED enzyme immunoassay （BED-CEIA）, and cohort observations were used to estimate the HIV incidences among men who have sex with men （MSM）. 617 MSM subjects completed the survey at a voluntary counseling and testing （VCT） site. The observed HIV incidence was 12.5 per 100 P-Ys （95% CI = 9.1-15.7）.

Application of Monte-Carlo statistical experiments in design of ocean engineering - Estimating the parameters, models and probabilities

Recently, some results have been acquired with the Monte- Carlo statistical experiments in the design of ocean en gineering. The results show that Monte-Carlo statistical experiments can be widely used in estimating the parameters of wave statistical distributions, checking the probability model of the long- term wave extreme value distribution under a typhoon condition and calculating the failure probability of the ocean platforms.

Improving Accuracy of Estimating Two-Qubit States with Hedged Maximum Likelihood

As a widely used reconstruction algorithm in quantum state tomography, maximum likelihood estimation tends to assign a rank-deficient matrix, which decreases estimation accuracy for certain quantum states. Fortunately, hedged maximum likelihood estimation （HMLE） [Phys. Rev. Lett. 105 （2010）200504] was proposed to avoid this problem. Here we study more details about this proposal in the two-qubit case and further improve its performance. We ameliorate the HMLE method by updating the hedging function based on the purity of the estimated state. Both performances of HMLE and ameliorated HMLE are demonstrated by numerical simulation and experimental implementation on the Werner states of polarization-entangled photons.

Application of Bit Error Rate Information Based on Weighted Error Estimating Code

Error Estimating Code (EEC) is a new channel coding method to estimate the Bit Error Rate (BER) information of the transmitted sequence. However, the estimated BER is not precise enough if the practical value of BER is high. A weighted EEC estimation method is proposed to improve the accuracy performance of BER estimation by classifying the raw estimation results into intervals and multiplying them by different coefficients separately. The applications of weighted EEC in modulation selection scheme and distributed video coding are discussed. Simulation results show that the EEC-based modulation selection method can achieve better performance at a cost of little redundancy and computation, and the EEC-based rate estimation method in distributed video coding can save the decoding time.

Estimating Canopy Nitrogen Contents of an Apple Tree Using Hyperspectral Remote Sensing

Estimating an apple tree canopy nitrogen contents using hyperspectral techniques is important in theoretical and realistic significance for fertilization and management.Taking 80 Fuji apple trees at the early fruiting stage as the research objects,the hyperspectral characteristics of the apple canopy were analyzed systematically.The apple canopy hyperspectral and the canopy nitrogen contents were measured respectively.The canopy hyperspectral characteristics under different nitrogen contents were analyzed and selected the sensitive wave bands.The apple canopy nitrogen content monitoring models were established by using multiple regression method,robust regression and BP neural network method.The results showed that the canopy hyperspectral reflectance had obvious differences under different nitrogen contents.The sensitive bands concentrate on 724~1136 nm.Estimation models based on hyperspectral indices are not ideal.Models based on robust regression(M regression)and BP neural network are better than multiple statistical model,and the accuracy of the BP neural network monitoring model is the best.The results of the study provide a certain reference for estimating apple nutrition using hyperspectral technology.

Application of Information Gain to Estimating the Seismic Tendency

Considering two seismic parameters,energy and the frequency of an earthquake as a whole from the definition of information gain in entropy,we study the information gain of M≥6.0 earthquakes from the world earthquake catalogue during 1900-1992.The results show that the information gain decreases before strong earthquakes.Our study of the recent seismic tendency of large earthquakes shows that the probability of earthquakes with M≥8.5 is low for the near future around the world.The information gain technique provides a new approach to tracing and predicting earthquakes from the data of moderate and small earthquakes.

Determining the time and space domains forestimating b value in seismic zoning

To take the seismic zone that includes the great shock with M S8.5 as the statistical unit of estimating b value can often lead to more large variance, because the seismogenic zone of the great shock with M S8.5 are larger than that delineated in general seismic zone. Two-level statistical units are considered in this paper. The seismic province is the first level unit that is suitable for group of earthquakes including the great shock of M S8.5. A seismic province can be divided into several seismic zones. They can be taken as the second level unit for group of quakes in which the super magnitude of the greatest shock do not exceed 8. Because of the nonstationarity in time of seismic activity, the unbalancedness of data and differential of seismic temporal series feature in different areas need to be considered when we select the time period for estimating b value. According to local conditions, the time period is selected at one′s discretion in order to reflect seismicity level of this statistical unit in future 100 years.

An Artificial Neural Network for Estimating Sizes of Spot Welding Nuggets

Sizes of nuggets are often used to evaluate spot weld quality in production. This paper presents a neural estimator used to carry out non-destructive on-line analysis of spot weld quality in which trained ANN functions to map dynamic resistance characteristics into sizes of spot weld nuggets and results confirm the validity of neural network for this type of application.

Critical assessment of a wide-spread method for estimating energy expenditure during accelerated running based on positioning tracking systems

1.Introduction An innovative method for estimating energy cost is to consider accelerated/decelerated running on flat terrain metabolically equivalent to uphill/downhill running at constant speed. In this method,the authors estimated an acceleration-dependent body slope.That slope corresponded to a given terrain slope during constant speed running,for which energy costs were known. Then they used accelerations(transformed into equivalent slopes)in a previously reported regression equation for energy cost estimation during uphill and downhill running at constant speed. This method allows researchers to estimate energy cost via player accelerations,thus making energy estimations feasible in team sports matches.

Estimating Topology of Discrete Dynamical Networks

In this paper,by applying Lasalle's in variance principle and some results about the trace of a matrix,we propose a method for estimating the topological structure of a discrete dynamical network based on the dynamicalevolution of the network.The network concerned can be directed or undirected,weighted or unweighted,and the localdynamics of each node can be nonidentical.The connections among the nodes can be all unknown or partially known.Finally,two examples,including a Henon map and a central network,are illustrated to verify the theoretical results.

Analysis of KCSE Performance in Nakuru County: A Generalized Estimating Equations Approach

In Kenya, the Ministry of Education has set the pass mark for university entry examination as C plus and above. Using publicly available data for 2006-2010, differences in KCSE performance amongst the three types of schools in Kenya—boys only, girls only, and mixed schools—was assessed. A generalized estimating equations marginal model was applied in order to account for association between scores within a school in the five year period. To account for the missing data, multiple imputation was performed followed by estimation and inference. Results indicated that there was a significant difference between the three type of schools in their candidates’ odds of attaining the stipulated minimum university entry grade. However, the odds of success in KCSE did not depend on the year under review as was evident in the slope parameters which was not statistically significant. Although it was clear that same-gender schools perform better than mixed gender schools, there is need to weigh the social benefits of mixed-schools against respective performance in KCSE. This should guide the policy makers on the way forward with regards to the education policy in Nakuru County.