Markov chain model probability of dry, wet of rainy wet weeks during moonson period over Andhra Pradesh

For agricultural planning, it is important to know the sequence of dry, wet periods. For this purpose a week period may be taken as the optimum length of time. The success or failure of crops particularly under rainfed conditions is closely linked with the rainfall patterns. In this study the Markov chain model method has been applied to know the probability of having a dry or a wet week and consecutive dry or wet periods of 2 or 3 weeks during monsoon period over Andhra Pradesh.

Empirical Investigation of the Verbal Cues Involved in Delivering Experiential Metaphors

Delivering metaphors experientially has been emphasized in several psychotherapies, such as acceptance and commitment therapy. However, few research has analyzed the variables involved in the efficacy of metaphors. This experimental analog study aims to advance in this topic by analyzing the effect of two components involved in the experiential delivery of metaphors in psychotherapy. The first component is presenting the metaphor by asking the individual to imagine herself as the protagonist of the story versus presenting the metaphor in the third person (Self vs. Other). The second component is the inclusion of verbal cues prompting the relational elaboration of the rules derived from the metaphor content versus not including these prompts (Elaboration vs. No Elaboration). The effect of these components was tested in a double-blind, randomized, 2 × 2 factorial experiment that used the cold pressor task (CPT). Eighty-four participants were exposed to the CPT at the pretest. Afterward, participants were randomly assigned to four experimental protocols. The protocols were audiotaped and consisted of the same metaphor presented in four slightly different ways. Specifically, the protocol of Condition A involved a metaphor with Self and Elaboration, Condition B involved Self and No Elaboration, Condition C involved Other and Elaboration, and Condition D involved Other and No Elaboration. Then, participants were re-exposed to the CPT in the posttest. We hypothesized that Condition A (Self and Elaboration) would show a higher mean increase in pain tolerance than the remaining conditions, which would show similar results. The results were consistent with this hypothesis because Condition A showed a higher percentual increase in pain tolerance (Condition A: M = 268.21, SD = 167.47; Condition B: M = 180.86, SD = 73.01; Condition C: M = 204.81, SD = 100.19; Condition D: M = 175.41, SD = 76.00). A Bayesian informative hypothesis evaluation showed that this hypothesis obtained the highest posterior model probability. Thus, the results indicate that introducing metaphors by asking the individual to imagine herself as the protagonist of the story and providing prompts for relational elaboration might increase the therapeutic effect of the metaphor.

A Multi-Model Probability Based Two-Layer Fusion Modeling Approach of Supercapacitor for Electric Vehicles

The improvement of the supercapacitor model redundancy is a significant method to guarantee the reliability of the power system in electric vehicle application. In order to enhance the accuracy of the supercapacitor model, eight conventional supercapacitor models were selected for parameter identification by genetic algorithm, and the model accuracies based on standard diving cycle are further discussed. Then, three fusion modeling approaches including Bayesian fusion, residual normalization fusion, and state of charge (SOC) fragment fusion are presented and compared. In order to further improve the accuracy of these models, a two-layer fusion model based on SOC fragments is proposed in this paper. Compared with other fusion models, the root mean square error (RMSE), maximum error, and mean error of the two-layer fusion model can be reduced by at least 23.04%, 8.70%, and 30.13%, respectively. Moreover, the two-layer fusion model is further verified at 10, 25, and 40 °C, and the RMSE can be correspondingly reduced by 60.41%, 47.26%, 23.04%. The results indicate that the two-layer fusion model proposed in this paper achieves better robustness and accuracy.

Uncertainty of prior and posterior model probability: Implications for interpreting Bayes factors

In most applications of Bayesian model comparison or Bayesian hypothesis testing, the results are reported in terms of the Bayes factor only, not in terms of the posterior probabilities of the models. Posterior model probabilities are not reported because researchers are reluctant to declare prior model probabilities, which in turn stems from uncertainty in the prior. Fortunately, Bayesian formalisms are designed to embrace prior uncertainty, not ignore it. This article provides a novel derivation of the posterior distribution of model probability, and shows many examples. The posterior distribution is useful for making decisions taking into account the uncertainty of the posterior model probability. Benchmark Bayes factors are provided for a spectrum of priors on model probability. R code is posted at https://osf.io/36527/. This framework and tools will improve interpretation and usefulness of Bayes factors in all their applications.

An efficient uncertainty propagation analysis method for problems involving non-parameterized probability-boxes

As a kind of imprecise probabilistic model, probability-box (P-box) model can deal with both aleatory and epistemic uncertainties in parameters effectively. The P-box can generally be categorized into two classes, namely, parameterized P-box and non-parameterized P-box. Currently, the researches involving P-boxes mainly aim at the parameterized P-boxes while the works handling the non-parameterized P-boxes are relatively inadequate. This paper proposes an efficient uncertainty propagation analysis method based on cumulative distribution function discretization (CDFD) for problems with non-parameterized P-boxes, through which the bounds of statistical moments and the cumulative distribution function (CDF) of a response function with non-parameterized P-box variables can be obtained. Firstly, a series of linear programming models are established for acquiring the lower and upper bounds of the first four origin moments of the response function. Secondly, based on the bounds of the origin moments, the CDF bounds for the response function can be obtained using Johnson distributions fitting and an optimization approach based on percentiles. Finally, the accuracy and efficiency of the proposed method are verified by investigating two numerical examples.

Efficient generative modeling of protein sequences using simple autoregressive models

Generative models emerge as promising candidates for novel sequence-data driven approaches to protein design, and for the extraction of structural and functional information about proteins deeply hidden in rapidly growing sequence databases. Here we propose simple autoregressive models as highly accurate but computationally extremely efficient generative sequence models. We show that they perform similarly to existing approaches based on Boltzmann machines or deep generative models, but at a substantially lower computational cost. Furthermore, the simple structure of our models has distinctive mathematical advantages, which translate into an improved applicability in sequence generation and evaluation. Using these models, we can easily estimate both the model probability of a given sequence, and the size of the functional sequence space related to a specific protein family. In the case of response regulators, we find a huge number of ca. 1068 sequences, which nevertheless constitute only the astronomically small fraction 10-80 of all amino-acid sequences of the same length. These findings illustrate the potential and the difficulty in exploring sequence space via generative sequence models.

Abstract P529: Optimal Transport Scenario for Access to Endovascular Therapy With Consideration of Patient Outcomes and Cost

Introduction: For an ischemic stroke patient whose onset geographically occurs outside of the catchment area of an EVT enabled facility and whose stroke is suspected to be caused by an occlusion in a large vessel of the brain, a transportation dilemma exists. Bypassing the nearest stroke hospital will delay tPA but expedite EVT. Not bypassing allows for confirmation of an LVO diagnosis before transfer to a CSC, but ultimately delays EVT. Air transportation can reduce a patient’s overall time to treatment. However, air transportation is costly. Methods: In a previously published model probability functions were developed to predict the outcome of a patient who screened positive for an LVO in the field based on how the patient was transported, Drip and Ship (PSC first, then CSC) or Mothership (direct to CSC). The addition of rotary wing transportation was conditionally applied to inter-facility transfer scenarios where it provided a time advantage. Transportation cost functions were created to include both fixed and variable costs as well as probabilities that model the likelihood of air transport providing a time advantage, air-worthy weather, and air resource availability. Both outcome and cost functions were developed for Mothership scenarios and for Drip and Ship scenarios including transfers via either ground or air depending on the conditional probabilities. Results: The figure shows the results of the model for location scenarios with 60 and 90 minutes between the thrombolysis only center and EVT capable center. Three efficiency scenarios are also shown in the figure: 1) both hospitals are efficient; 2) thrombolysis center is inefficient; and 3) both hospitals are inefficient. Conclusions: In some scenarios, both outcome and cost can be optimized to indicate whether Drip and Ship or Mothership is preferred. However, scenarios exist where outcome and cost are divergent. In divergent scenarios cost can be minimized at the expense of patient outcomes.

MM-PHD filter-based sensor control for tracking multiple maneuvering targets hidden in the DBZ

<a></a><a></a><a>To improve the performance of tracking multiple maneuvering targets hidden in the Doppler blind zone (DBZ), we put forward the idea of using sensor control technique to suppress the DBZ masking problem for the first time, by utilizing the principle that the absolute Doppler of a target with respect to a sensor is affected by the target-to-sensor relative geometry and extending multi model probability hypothesis density (MM-PHD) filter for DBZ masking to the partially observable Markov decision process (POMDP) framework. First, the process flow of sensor control is systematically constructed based on our existing work. Second, in the core sensor controller module, we devise three objective functions (including a new safety indicator ensuring sensor safety, a novel reward rule for the DBZ avoidance, and the Cauchy-Schwarz divergence (CSD) compatible with the multi-maneuvering-target tracking) and a decision-making logic for the selection of control commands. Finally, the feasibility and effectiveness of the proposed control scheme are verified through numerical examples, and it is demonstrated that it is obviously superior to the random control strategy and the earlier work without using the control technology.</a>

MM-PHD filter-based sensor control for tracking multiple maneuvering targets hidden in the DBZ

<a></a><a></a><a>To improve the performance of tracking multiple maneuvering targets hidden in the Doppler blind zone (DBZ), we put forward the idea of using sensor control technique to suppress the DBZ masking problem for the first time, by utilizing the principle that the absolute Doppler of a target with respect to a sensor is affected by the target-to-sensor relative geometry and extending multi model probability hypothesis density (MM-PHD) filter for DBZ masking to the partially observable Markov decision process (POMDP) framework. First, the process flow of sensor control is systematically constructed based on our existing work. Second, in the core sensor controller module, we devise three objective functions (including a new safety indicator ensuring sensor safety, a novel reward rule for the DBZ avoidance, and the Cauchy-Schwarz divergence (CSD) compatible with the multi-maneuvering-target tracking) and a decision-making logic for the selection of control commands. Finally, the feasibility and effectiveness of the proposed control scheme are verified through numerical examples, and it is demonstrated that it is obviously superior to the random control strategy and the earlier work without using the control technology.</a>

Tracking multiple maneuvering targets hidden in the DBZ based on the MM-PHD filter

<p><a></a><a></a><a>For a ground moving target indication (GMTI) radar, the presence of </a><a></a><a></a><a></a><a>Doppler blind zone (DBZ)</a> results in many short tracks with frequent label switching, which seriously deteriorates the tracking performance. When the DBZ masking is coupled with targets maneuvering, tracking multiple maneuvering targets hidden in the DBZ becomes very challenging, which is reflected in the fact that there is no public research on this issue. To overcome this complicated problem, we propose a practical and fully functional GMTI multi-maneuvering-target tracker based on the multiple model probability hypothesis density (MM-PHD) filter. Unlike the standard MM-PHD filter, the proposed tracker utilizes the Doppler information and incorporates the minimum detectable velocity (MDV) to suppress the DBZ masking. Furthermore, to cope with the problems of the fixed initiation and no label output of the standard MM-PHD filter, the resulting MM-PHD filter with the Doppler and MDV information is augmented with measurement-driven adaptive track initiation and track label propagation, which are necessary for a practical tracker and also required for evaluating the overall GMTI tracking performance. Finally, numerical examples show that the proposed tracker outperforms significantly the existing ones, thus verifying its effectiveness.</p> <p> </p>