Comparison of Vs and SPT Soil Liquefaction Assessments of NCEER: Including Hypothesis Testing

Soil liquefaction is one of the complex research topics in geotechnical engineering and engineering geology. Especially after the 1964 Niigata earthquake (Japan) induced many soil liquefaction incidents, a variety of soil liquefaction studies were conducted and reported, including the liquefaction potential assessment methods utilizing the shear wave velocity (Vs) or SPT-N profiles (SPT: standard penetration test). This study used the Vs and SPT methods recommended by the National Center for Earthquake Engineering Research (NCEER) to examine which is more conservative according to the assessment results on 41 liquefiable soil layers at sites in two major cities in Taiwan. Statistical hypothesis testing was used to make the analysis more quantitative and objective. Based on three sets of hypothesis tests, it shows that the hypothesis—the SPT method is more conservative than the Vs method—was not rejected on a 5% level of significance.

Exploring the Abnormal Brain Regions and Abnormal Functional Connections in SZ by Multiple Hypothesis Testing Technique

Schizophrenia(SZ)is one of the most common mental diseases.Its main characteristics are abnormal social behavior and inability to correctly understand real things.In recent years,the magnetic resonance imaging(MRI)technique has been popularly utilized to study SZ.However,it is still a great challenge to reveal the essential information contained in the MRI data.In this paper,we proposed a biomarker selection approach based on the multiple hypothesis testing techniques to explore the difference between SZ and healthy controls by using both functional and structural MRI data,in which biomarkers represent both abnormal brain functional connectivity and abnormal brain regions.By implementing the biomarker selection approach,six abnormal brain regions and twenty-three abnormal functional connectivity in the brains of SZ are explored.It is discovered that compared with healthy controls,the significantly reduced gray matter volumes are mainly distributed in the limbic lobe and the basal ganglia,and the significantly increased gray matter volumes are distributed in the frontal gyrus.Meanwhile,it is revealed that the significantly strengthened connections are those between the middle frontal gyrus and the superior occipital gyrus,the superior occipital gyrus and the middle occipital gyrus as well as the middle occipital gyrus and the fusiform gyrus,and the rest connections are significantly weakened.

A New Deghosting Algorithm with Hypothesis Testing Data Fusion

Eliminating the false intersection (deghosting) is a difficult problem in a passive cross location system. Using a decentralized decision fusion topology, a new deghosting algorithm derived from hypothesis testing theory is developed. It uses the difference between ghosts and true targets in the statistical error, which occurs between their projection angles on a deghosting sensor and is measured from a deghosting sensor, and constructs a corresponding test statistic. Under the Gaussian assumption, ghosts and true targets are decided and discriminated by Chi-square distribution. Simulation results show the feasibility of the algorithm.

Hypothesis Testing of Population Percentiles via the Wald Test with Bootstrap Variance Estimates

Testing the equality of percentiles (quantiles) between populations is an effective method for robust, nonparametric comparison, especially when the distributions are asymmetric or irregularly shaped. Unlike global nonparametric tests for homogeneity such as the Kolmogorv-Smirnov test, testing the equality of a set of percentiles (i.e., a percentile profile) yields an estimate of the location and extent of the differences between the populations along the entire domain. The Wald test using bootstrap estimates of variance of the order statistics provides a unified method for hypothesis testing of functions of the population percentiles. Simulation studies are conducted to show performance of the method under various scenarios and to give suggestions on its use. Several examples are given to illustrate some useful applications to real data.

Sensitivity and Specificity Analysis Relation to Statistical Hypothesis Testing and Its Errors: Application to Cryptosporidium Detection Techniques

The use of Statistical Hypothesis Testing procedure to determine type I and type II errors was linked to the measurement of sensitivity and specificity in clinical trial test and experimental pathogen detection techniques. A theoretical analysis of establishing these types of errors was made and compared to determination of False Positive, False Negative, True Positive and True Negative. Experimental laboratory detection methods used to detect Cryptosporidium spp. were used to highlight the relationship between hypothesis testing, sensitivity, specificity and predicted values. The study finds that, sensitivity and specificity for the two laboratory methods used for Cryptosporidium detection were low hence lowering the probability of detecting a “false null hypothesis” for the presence of cryptosporidium in the water samples using either Microscopic or PCR. Nevertheless, both procedures for cryptosporidium detection had higher “true negatives” increasing its probability of failing to reject a “true null hypothesis” with specificity of 1.00 for both Microscopic and PCR laboratory detection methods.

Blockchain-Based MCS Detection Framework of Abnormal Spectrum Usage for Satellite Spectrum Sharing Scenario

In this paper, the problem of abnormal spectrum usage between satellite spectrum sharing systems is investigated to support multi-satellite spectrum coexistence. Given the cost of monitoring, the mobility of low-orbit satellites, and the directional nature of their signals, traditional monitoring methods are no longer suitable, especially in the case of multiple power level. Mobile crowdsensing(MCS), as a new technology, can make full use of idle resources to complete a variety of perceptual tasks. However, traditional MCS heavily relies on a centralized server and is vulnerable to single point of failure attacks. Therefore, we replace the original centralized server with a blockchain-based distributed service provider to enable its security. Therefore, in this work, we propose a blockchain-based MCS framework, in which we explain in detail how this framework can achieve abnormal frequency behavior monitoring in an inter-satellite spectrum sharing system. Then, under certain false alarm probability, we propose an abnormal spectrum detection algorithm based on mixed hypothesis test to maximize detection probability in single power level and multiple power level scenarios, respectively. Finally, a Bad out of Good(BooG) detector is proposed to ease the computational pressure on the blockchain nodes. Simulation results show the effectiveness of the proposed framework.

On Testing Equality of K Multiple Correlation Matrices

Coutsourides derived an ad hoc nuisance paratmeter removal test for testing equality of two multiple correlation matrices of two independent p variate normal populations under the assumption that a sample of size n is available from each population. This paper presents a likelihood ratio test criterion for testing equality of K multiple correlation matrices and extends the results to the testing of equality of K partial correlation matrices.

Hypothesis testing of Poisson rates in COVID-19 offspring distributions

In the present study,we undertake the task of hypothesis testing in the context of Poissondistributed data.The primary objective of our investigation is to ascertain whether two distinct sets of discrete data share the same Poisson rate.We delve into a comprehensive review and comparative analysis of various frequentist and Bayesian methodologies specifically designed to address this problem.Among these are the conditional test,the likelihood ratio test,and the Bayes factor.Additionally,we employ the posterior predictive p-value in our analysis,coupled with its corresponding calibration procedures.As the culmination of our investigation,we apply these diverse methodologies to test both simulated datasets and real-world data.The latter consists of the offspring distributions linked to COVID-19 cases in two disparate geographies-Hong Kong and Rwanda.This allows us to provide a practical demonstration of the methodologies’applications and their potential implications in the field of epidemiology.

Post-Processing of InSAR Deformation Time Series Using Clustering-Based Pattern Identification

Multi-temporal synthetic aperture radar interferometry(MT-InSAR)is a standard technique for mapping clustering and wide-scale deformation.A linear model is often used in phase unwrapping to overcome the underdetermination.It’s difficult to identify different types of nonlinear deformation.However,the interpretation of nonlinear deformation is very important in monitoring potential risk.This paper introduces a comprehensive approach for identifying and interpreting different types of deformation within InSAR datasets,integrating initial clustering and classification simplification.Initial classification is performed using the K-means clustering method to cluster the collected InSAR deformation time-series data.Then we use F test and Anderson-Darling test(AD test)to simplify the clusters after initial classification.This technique distinctly discerns the changing trends of deformation signals,thereby providing robust support for interpreting potential deformation scenarios within observed InSAR regions.

Effect size in papers published by the Journal of Forestry Research:A missing treasure?

P values based on standard hypothesis testing are commonly reported in articles published by the Journal of Forestry Research(JFR).However,effect sizes are barely used and reported,even if they are of direct relevance to the primary questions of many of the published studies.The incorporation of effect sizes in studies published by JFR should be encouraged and promoted.Inclusion of effect sizes as a requirement in the journal guidelines will facilitate a major change in the way data are tested and interpreted,with the ultimate goal to exempt researchers from the custom of drawing conclusions merely based upon a dichotomous statistical result(P value).Such a policy can also lead to more informed decisions of whether identified effects are of practical relevance to the forestry.

The Deformation Analysis of the 3D Alignment Control Network Based on the Multiple Congruence Models

In the construction and maintenance of particle accelerators,all the accelerator elements should be installed in the same coordinate system,only in this way could the devices in the actual world be consistent with the design drawings.However,with the occurrence of the movements of the reinforced concrete cover plates at short notice or building deformations in the long term,the control points upon the engineering structure will be displaced,and the fitness between the subnetwork and the global control network may be irresponsible.Therefore,it is necessary to evaluate the deformations of the 3D alignment control network.Different from the extant investigations,in this paper,to characterize the deformations of the control network,all of the congruent models between the points measured in different epochs have been identified,and the congruence model with the most control points is considered as the primary or fundamental model,the remaining models are recognized as the additional ones.Furthermore,the discrepancies between the primary S-transformation parameters and the additional S-transformation parameters can reflect the relative movements of the additional congruence models.Both the iterative GCT method and the iterative combinatorial theory are proposed to detect multiple congruence models in the control network.Considering the actual work of the alignment,it is essential to identify the competitive models in the monitoring network,which can provide us a hint that,even the fitness between the subnetwork and the global control network is good,there are still deformations which may be ignored.The numerical experiments show that the suggested approaches can describe the deformation of the 3D alignment control network roundly.

Climate Change Perceptions , Impacts and Adaptation Strategies of F arm Households in Potohar Region of Punjab, Pakistan

Climate change has become a global phenomenon and is adversely affecting agricultural development across the globe.Developing countries like Pakistan where 18.9%of the GDP(gross domestic product)came from the agriculture sector and also 42%of the labor force involved in agriculture.They are directly and indirectly affected by climate change due to an increase in the frequency and intensity of climatic extreme events such as floods,droughts and extreme weather events.In this paper,we have focused on the impact of climate change on farm households and their adaptation strategies to cope up the climatic extremes.For this purpose,we have selected farm households by using multistage stratified random sampling from four districts of the Potohar region i.e.Attock,Rawalpindi,Jhelum and Chakwal.These districts were selected by dividing the Potohar region into rain-fed areas.We have employed logistic regression to assess the determinants of adaptation to climate change and its impact.We have also calculated the marginal effect of each independent variable of the logistic regression to measure the immediate rate of change in the model.In order to check the significance of our suggested model,we have used hypothesis testing.

小麦品种感染赤霉病的独立性检验(英文)

The relationship between wheat varieties and occurrence of head blight was discussed, the independence test was carried out by using contingency table.

Testing the mantle plume hypothesis： an IODP effort to drill into the Kamchatka-Okhotsk Sea basement

The great mantle plume debate(GPD) has been going on for ~15 years(Foulger and Natland, 2003;Anderson, 2004; Niu, 2005; Davies, 2005; Foulger, 2005; Campbell, 2005; Campbell and Davies, 2006),centered on whether mantle plumes exist as a result of Earth's cooling or whether their existence is purely required for convenience in explaining certain Earth phenomena(Niu, 2005). Despite the mounting evidence that many of the so-called plumes may be localized melting anomalies, the debate is likely to continue. We recognize that the slow progress of the debate results from communication difficulties.Many debaters may not truly appreciate(1) what the mantle plume hypothesis actually is, and(2) none of the petrological, geochemical and geophysical methods widely used can actually provide smoking-gun evidence for or against mantle plume hypothesis. In this short paper, we clarify these issues, and elaborate a geologically effective approach to test the hypothesis. According to the mantle plume hypothesis, a thermal mantle plume must originate from the thermal boundary layer at the core-mantle boundary(CMB), and a large mantle plume head is required to carry the material from the deep mantle to the surface. The plume head product in ocean basins is the oceanic plateau, which is a lithospheric terrane that is large(1000's km across), thick(>200 km), shallow(2–4 km high above the surrounding seafloors), buoyant(~1% less dense than the surrounding lithosphere), and thus must be preserved in the surface geology(Niu et al., 2003). The Hawaiian volcanism has been considered as the surface expression of a type mantle plume, but it does not seem to have a(known) plume head product. If this is true, the Hawaiian mantle plume in particular and the mantle plume hypothesis in general must be questioned. Therefore, whether there is an oceanic plateau-like product for the Hawaiian volcanism is key to testing the mantle plume hypothesis, and the Kamchatka-Okhotsk Sea basement is the best candidate to find out if it is indeed the Hawaiian mantle plume head product or not(Niu et al., 2003; Niu, 2004).

Gearbox Deterioration Detection under Steady State,Variable Load, and Variable Speed Conditions

Multiple dominant gear meshing frequencies are present in the vibration signals collected from gearboxes and the conventional spiky features that represent initial gear fault conditions are usually difficult to detect. In order to solve this problem, we propose a new gearbox deterioration detection technique based on autoregressive modeling and hypothesis testing in this paper. A stationary autoregressive model was built by using a normal vibration signal from each shaft. The established autoregressive model was then applied to process fault signals from each shaft of a two-stage gearbox. What this paper investigated is a combined technique which unites a time-varying autoregressive model and a two sample Kolmogorov-Smimov goodness-of-fit test, to detect the deterioration of gearing system with simultaneously variable shaft speed and variable load. The time-varying autoregressive model residuals representing both healthy and faulty gear conditions were compared with the original healthy time-synchronous average signals. Compared with the traditional kurtosis statistic, this technique for gearbox deterioration detection has shown significant advantages in highlighting the presence of incipient gear fault in all different speed shafts involved in the meshing motion under variable conditions.

Investigation of ionospheric TEC changes related to the 2008 Wenchuan earthquake based on statistic analysis and signal detection

Ionospheric TEC （total electron content） time series are derived from GPS measurements at 13 stations around the epicenter of the 2008 Wenchuan earthquake. Defining anomaly bounds for a sliding window by quartile and 2-standard deviation of TEC values, this paper analyzed the characteristics of ionospheric changes before and after the destructive event. The Neyman-Pearson signal detection method is employed to compute the probabilities of TEC abnormalities. Result shows that one week before the Wenchuan earthquake, ionospheric TEC over the epicenter and its vicinities displays obvious abnormal disturbances, most of which are positive anomalies. The largest TEC abnormal changes appeared on May 9, three days prior to the seismic event. Signal detection shows that the largest possibility ofTEC abnormity on May 9 is 50.74%, indicating that ionospheric abnormities three days before the main shock are likely related to the preparation process of the Ms8.0 Wenchuan earthquake.

Robust cubature Kalman filter method for the nonlinear alignment of SINS

Nonlinear initial alignment is a significant research topic for strapdown inertial navigation system(SINS).Cubature Kalman filter(CKF)is a popular tool for nonlinear initial alignment.Standard CKF assumes that the statics of the observation noise are pre-given before the filtering process.Therefore,any unpredicted outliers in observation noise will decrease the stability of the filter.In view of this problem,improved CKF method with robustness is proposed.Multiple fading factors are introduced to rescale the observation noise covariance.Then the update stage of the filter can be autonomously tuned,and if there are outliers exist in the observations,the update should be less weighted.Under the Gaussian assumption of KF,the Mahalanobis distance of the innovation vector is supposed to be Chi-square distributed.Therefore a judging index based on Chi-square test is designed to detect the noise outliers,determining whether the fading tune are required.The proposed method is applied in the nonlinear alignment of SINS,and vehicle experiment proves the effective of the proposed method.

New deghosting method based on generalized triangulation

A new deghosting method based on the generalized triangulation is presented. First, two intersection points corresponding to the emitter position are obtained by utilizing two azimuth angles and two elevation angles from two jammed 3-D radars （or 2-D passive sensors）. Then, hypothesis testing based deghosting method in the multiple target scenarios is proposed using the two intersection points. In order to analyze the performance of the proposed method, the correct association probability of the true targets and the incorrect association probability of the ghost targets are defined. Finally, the Monte Carlo simulations are given for the proposed method compared with the hinge angle method in the cases of both two and three radars. The simulation results show that the proposed method has better performance than the hinge angle method in three radars case.

Calorific values and ash contents of different parts of Masson pine trees in southern China

Calorific value of plants is an important parameter for evalu- ating and indexing material cycles and energy conversion in forest eco- systems. Based on mensuration data of 150 sample sets, we analyzed the calorific value （CV） and ash content （AC） of different parts of Masson pine （Pinus massoniana） trees in southern China using hypothesis testing and regression analysis. CV and AC of different tree parts were almost significantly different （P〈0.05）. In descending order, ash-free calorific value （AFCV） ranked as foliage 〉 branch 〉 stem bark 〉 root 〉 stem wood, and AC ranked as foliage 〉 stem bark 〉 root 〉 branch 〉 stem wood. CV and AC of stem wood from the top, middle and lower sections of trees differed significantly. CV increased from the top to the lower sections of the tnmk while AC decreased. Mean gross calorific value （GCV） and AFCV of aboveground parts were significantly higher than those of belowground parts （roots）. The mean GCV, AFCV and AC of a whole tree of Masson pine were 21.54 kJ/g, 21.74 kJ/g and 0.90%, re- spectively. CV and AC of different tree parts were, to some extent, cor- related with tree diameter, height and origin.

Model update mechanism for mean-shift tracking

In order to solve the model update problem in mean-shift based tracker, a novel mechanism is proposed. Kalman filter is employed to update object model by filtering object kernel-histogram using previous model and current candidate. A self-tuning method is used for adaptively adjust all the parameters of the filters under the analysis of the filtering residuals. In addition, hypothesis testing servers as the criterion for determining whether to accept filtering result. Therefore, the tracker has the ability to handle occlusion so as to avoid over-update. The experimental results show that our method can not only keep up with the object appearance and scale changes but also be robust to occlusion.