Relevant Probability Research Articles

Setting course for a translational pharmacology and a predictive toxicology based on the numerical probability of clinical relevance

For a significant share of the chemicals, current bioassays mispredicted the outcomes in the reference methods they simulate. For any drug or chemical, and depending on the regulatory or corporate situation, three different approaches calculate the numerical probability by which agreement (or discrepancy) can be statistically expected between (1) the result of a predictive bioassay, and (2) the outcome on its reference method. If such concordance is expected with enough confidence based on a sufficient percentage probability, then specific results from that bioassay can be considered as correctly predictive. The statistical approaches analyzed in this article assist in valuable tasks, including (1) a better translation of the clinical relevance (or insignificance) of specific preclinical findings; (2) waiving unnecessary animal testing (or any other unpredictive testing; e.g., a given in vitro bioassay), and (3) in advancing only the most promising candidates in the pharmaceutical, pesticide, or chemical development process.

Gaussian mixture models for diatomic gas−surface interactions under thermal non-equilibrium conditions

Scattering kernels are of paramount importance in modeling gas–surface interactions for rarefied gas flows. However, most existing empirical models need one or several accommodation coefficients (ACs) to be determined before applications. In this paper, an unsupervised machine learning technique, known as the Gaussian mixture (GM) model, is applied to establish a new scattering kernel based on the simulated data collected by molecular dynamics (MD) simulations. The main work is devoted to the scattering of diatomic molecules under thermal non-equilibrium conditions. Correspondingly, different MD simulations on the scattering process of nitrogen molecules from a platinum surface have been performed involving rotational and translational excitation. Here, we evaluate the performance of the GM and Cercignani–Lampis–Lord models against the MD approach by comparing the velocity correlation distributions and the relevant outgoing velocity probability density function as well as the computed ACs. The presented comparisons have demonstrated the superiority of the GM model in matching with MD results. Therefore, in the case of diatomic gases, the GM model can be employed as a promising strategy to derive the generalized boundary conditions.

Study on Fault Diagnosis Method and Application of Automobile Power Supply Based on Fault Tree-Bayesian Network

Among various fault types of automotive power faults, power supply UB + faults have the most complex relationship between fault signs and fault points and are difficult to diagnose. So this paper proposes a Bayesian network fault diagnosis model of automobile power supply based on fault tree. Firstly, based on the in-depth analysis of the principle of automobile power supply fault, the UB + fault tree model is constructed. The fuzzy Bayesian network model of UB + fault is constructed through the mapping relationship between fault tree and Bayesian network. Then, the prior probability of UB + fault points are obtained according to the five-year fault dataset of FAW Volkswagen Reck system, and the relevant conditional probabilities are determined by fuzzy set theory due to the lack of data and the uncertainty in expert scoring. Finally, the relevant fault point probability values are determined according to the Bayesian network inference algorithm in the case of single or parallel UB + fault sign occurring, and the fault diagnosis sequence is guided, further improving fault diagnosis efficiency.

Partition of free energy for a Brownian quantum oscillator: Effect of dissipation and magnetic field

Recently, the quantum counterpart of energy equipartition theorem has drawn considerable attention. Motivated by this, we formulate and investigate an analogous statement for the free energy of a quantum oscillator linearly coupled to a passive heat bath consisting of an infinite number of independent harmonic oscillators. We explicitly demonstrate that the free energy of the Brownian oscillator can be expressed in the form F(T)=〈f(ω,T)〉 where f(ω,T) is the free energy of an individual bath oscillator. The overall averaging process involves two distinct averages: the first one is over the canonical ensemble for the bath oscillators, whereas the second one signifies averaging over the entire bath spectrum of frequencies from zero to infinity. The latter is performed over a relevant probability distribution function P(ω) which can be derived from the knowledge of the generalized susceptibility encountered in linear response theory. The effect of different dissipation mechanisms is exhibited. We find two remarkable consequences of our results. First, the quantum counterpart of energy equipartition theorem follows naturally from our analysis. The second corollary we obtain is a natural derivation of the third law of thermodynamics for open quantum systems. Finally, we generalize the formalism to three spatial dimensions in the presence of an external magnetic field.

Hardware impairments aware full-duplex non-orthogonal multiple access networks over Nakagami-m channels

Non-orthogonal multiple access (NOMA) and full-duplex (FD) relaying communications are promising candidates for 5G cellular networks. In this paper, by exploiting the impact of hardware impairment, we study FD NOMA communications with a downlink scheme. In a group of two users, we find that the target rates and power allocation strategies are main factors affecting the system performance metric. We derive the closed-form formula of outage probability for two users. As main contribution, numerical results are considered to illustrate the performance of the FD NOMA. We also study the base station (BS) can adjust its transmit signal to noise ratio (SNR) to achieve relevant outage probability in several scenarios.

Development and Validation of a Prognostic Nomogram for Predicting Overall Survival for T1 High-Grade Patients After Radical Cystectomy: A Study Based on SEER.

ObjectiveTo construct a prognostic model that estimates the probability of overall survival for T1 high-grade bladder cancer patients after radical cystectomy.Patients and MethodsWe enrolled 801 patients diagnosed with T1 high grade and received radical cystectomy from the Surveillance, Epidemiology, and End Results (SEER) database (2004–2015). All patients were randomly divided into the development group (n = 561) and validation group (n = 240) with the ratio of 7:3. Cox proportional hazards regression analyses were used to filter variables and the Kaplan–Meier method to evaluate survival outcomes. The results of sensitivity analysis determined the variables in the final model. The performance of the model was internally validated by calibration curves, the receiver operating characteristic (ROC) curves, and the concordance index (C-index).ResultsThe mean survival months were 56.086 in the development group and 58.21 in the validation group. Six variables including age, marital status, tumour size, tumour sites, region nodes examined, and N stage were incorporated in the final nomogram. The accuracy of the nomogram for prediction of overall survival was estimated by C-index (0.732; 0.712–0.752) and AUC (0.771 for 3-year; 0.766 for 5-year) in the development group. In the validation group, the C-index of the nomogram was 0.752 (0.723–0.781), and AUC was 0.761 for 3-year as well as 0.793 for 5-year. These results all showed better performance than the AJCC stage. Calibration plots for 3- and 5-year overall survival presented good concordance in both the development and validation group.ConclusionWe have established a prognostic nomogram that provides a more accurate and relevant individualized probability of overall survival for patients with T1HG bladder transitional cell carcinoma after radical cystectomy. It can contribute to improving patient counselling and treatment selection.

A dynamic condition-based maintenance optimization model for mission-oriented system based on inverse Gaussian degradation process

An effective maintenance policy optimization model can reduce maintenance cost and system operation risk. For mission-oriented systems, the degradation process changes dynamically and is monotonous and irreversible. Meanwhile, the risk of early failure is high. Therefore, this paper proposes a dynamic condition-based maintenance (CBM) optimization model for mission-oriented system based on inverse Gaussian (IG) degradation process. Firstly, the IG process with random drift coefficient is used to describe the degradation process and the relevant probability distributions are obtained. Secondly, the dynamic preventive maintenance threshold (DPMT) function is used to control the early failure risk of the mission-oriented system, and the influence of imperfect preventive maintenance (PM) on the degradation amount and degradation rate is analysed comprehensively. Thirdly, according to the mission availability requirement, the probability formulas of different types of renewal policies are obtained, and the CBM optimization model is constructed. Finally, a numerical example is presented to verify the proposed model. The comparison with the fixed PM threshold model and the sensitivity analysis show the effectiveness and application value of the optimization model.

Business analytics: an example of integration of TD-ABC and the balanced scorecard

PurposeThe purpose of this paper is twofold. First, to combine a holistic model – in our case the balanced scorecard – with the time-driven activity-based costing model. The inspiration for this stems both from Kaplan and Norton and from the intense discussions and use of business analytics (BA) and performance management (PM). Second, to use numerical experiments – more specifically Monte Carlo simulation – to design and explore four hypothetical scenarios within such a holistic model. The paper also aims to emphasise the role played by statistics in increasing the confidence in using such a framework.Design/methodology/approachThe author runs four numerical experiments using different assumptions to show how a decision-maker can improve the outcome by making small changes in the key performance indicator (KPI) input variables.FindingsThe paper gives recommendations for the assumptions that each decision-maker has to consider when setting out to conduct this kind of analysis. Small changes in some input variables may completely change the output and hence the decision result.Practical implicationsThe paper shows why practitioners and researchers need to better understand the limitations of deterministic analysis to make realistic models when combining more accounting models. To choose the relevant probability distributions for the input resources is an important issue for the decision-maker as they have a very large impact on the result.Originality/valueThe real value of the paper lies in making students and practitioners as well as researchers aware of the opportunities for stochastic modelling and also to point at the problems and limitations of combining elements from BA with performance measurement and management.

SMOTE-RkNN: A hybrid re-sampling method based on SMOTE and reverse k-nearest neighbors

In recent years, class imbalance learning (CIL) has become an important branch of machine learning. The Synthetic Minority Oversampling TEchnique (SMOTE) is considered to be a benchmark algorithm among CIL techniques. Although the SMOTE algorithm performs well on the vast majority of class-imbalance tasks, it also has the inherent drawback of noise propagation. Many SMOTE-variants have been proposed to address this problem. Generally, the improved solutions conduct a hybrid sampling procedure, i.e., carrying out an undersampling process after SMOTE to remove noises. However, owing to the complexity of data distribution, it is sometimes difficult to accurately identify real instances of noise, resulting in low modeling quality. In this paper, we propose a more robust and universal SMOTE hybrid variant algorithm named SMOTE-reverse k-nearest neighbors (SMOTE-RkNN). The proposed algorithm identifies noise based on probability density but not local neighborhood information. Specifically, the probability density information of each instance is provided by RkNN, a well-known KNN variant. Noisy instances are found and deleted according to their relevant probability density. In experiments on 46 class-imbalanced data sets, SMOTE-RkNN showed promising results in comparison with several popular SMOTE hybrid variant algorithms.

A Novel Method for the Determination of the Lateral Dimensions of 2D Rectangular Flakes.

We present a novel method for the determination of the lateral dimensions of thin rectangular flakes, as they exist randomly dispersed in flake composites. Knowledge of flake size and shape is essential for the correct prediction of the mechanical, electrical, thermal and barrier properties of flake composites. The required information is the distribution function of lengths of the lines representing the intersection of flakes with a sectioning plane, as seen in cross-sections of composite samples used in optical or electron microscopy or obtained using tomographic imaging techniques. The key observation is that the major peak of the distribution function coincides with the short dimension S of the flake while a secondary peak corresponds to its long dimension W. These observations are explained using Monte-Carlo simulations, as well as deterministic, geometry-based modeling and probability analysis. Since the strength of the secondary peak diminishes with increasing flake aspect ratio , we develop two additional methods for the determination of W. The first finds W from the maximum intersection length; this procedure is justified by computing the relevant probability fields through Monte-Carlo simulations. The second method finds r from the average intersection length and is valid in the range . The performance of these techniques is tested and found to be very good using blind experiments in numerically sectioned specimens.

A retrieval model family based on the probability ranking principle for ad hoc retrieval

AbstractMany successful retrieval models are derived based on or conform to the probability ranking principle (PRP). We present a new derivation of a document ranking function given by the probability of relevance of a document, conforming to the PRP. Our formulation yields a family of retrieval models, called probabilistic binary relevance (PBR) models, with various instantiations obtained by different probability estimations. By extensive experiments on a range of TREC collections, improvement of the PBR models over some established baselines with statistical significance is observed, especially in the large Clueweb09 Cat‐B collection.

Risk assessment framework for low-altitude UAV traffic management

To provide real-time safety assessment for low-altitude unmanned aerial vehicle (UAV) air traffic management, and to ensure the UAVs safe operation in low-altitude airspace, a risk assessment framework is proposed. It considers the accidents probability and the accidents hazards. Firstly, accidents probability model based on the System Theoretic Process Analysis-Bayesian Network (STPA-BN) algorithm is built. Potential system hazards are effectively identified and analyzed through the STPA process. The accidents cause identified based on the STPA process is taken as the root node. The relevant failure probability table is given respectively. It constitutes the BN used to analyze the system accidents probability. This method uses a combination of qualitative and quantitative methods to calculate the accidents probability. Then, based on the UAV fall model, considering the uncertainty of the UAV operation process, the UAV fall point distribution is determined based on the Monte-Carlo method, and the impact area of the fall is calculated. Thus the system risk value is obtained. Finally, through case analysis, the validity and rationality of the proposed risk assessment framework are verified.

Second-look surgery after pediatric brain tumor resection – Single center analysis of morbidity and volumetric efficacy

IntroductionPostoperative residual tumor can occur for intentional or unintentional reasons. Decision-making regarding second-look surgery has to weigh molecular biology, probability of total resection and prognostic relevance against potential additional morbidity. In interdisciplinary tumor boards the neurosurgeon has to estimate risk and efficacy of second-look surgery in individual cases, based on precise data. Research questionAim of this study was to provide such data by analyzing morbidity and volumetric efficacy of second-look surgery at a designated pediatric neuro-oncology unit. Material and methodsChildren who received second-look surgery in 2007–2018 after incomplete resections were analyzed retrospectively. Measurements were performed on early postoperative magnetic resonance imaging, comparing axial diameter-based measurement as well as computer-assisted volumetric analysis. Results59 patients (37% of the overall cohort; 21 female; mean age: 8 ​± ​5 years) received a subtotal (n ​= ​35) or near total (n ​= ​24) resection. After interdisciplinary case review, 12 of these patients received second-look surgery mainly for residual ependymoma. This led to further tumor volume reduction in all cases (new degrees of resection: subtotal ​= ​2, near total ​= ​6, gross total ​= ​4). No new permanent morbidity or perioperative mortality was observed. Discussion and conclusionSecond-look surgery did not increase mortality and permanent morbidity, had an 8% rate of transient morbidity and achieved tumor volume reduction above 95% in 75% of selected cases, with 4 additional gross total resections. Second-look surgery is safe and effective with regard to volumetric outcome parameters even in cases with good initial resections, although the role of second-look surgery regarding oncological outcome has to be further investigated in times of personalized molecular medicine.

Basket Credit Default Swap Pricing with Two Defaultable Counterparties

In this paper, we study the basket CDS pricing with two defaultable counterparties based on the reduced‐form model. The default jump intensities of the reference firms and counterparties are all assumed to follow the mean‐reverting constant elasticity of variance (CEV) processes. Taking the Vasicek process which is a special case of CEV process as an example, the approximate analytic solutions of the joint survival probability density, the probability densities of the first default and the first two defaults can be solved by using PDE method. In addition, we also extend the Vasciek process to the Vasciek process with cojumps and obtain the approximate closed‐form solutions of the relevant default probability densities. Then with the expressions of the probability densities, we can get the formula of the basket CDS price with two defaultable counterparties. In the numerical analysis, we do sensitivity analysis and compare the basket CDS prices under our model with that with only one defaultable counterparty. The numerical results show that our model can be applied into practice.

Optimal Planning of Multiple Renewable Energy-Integrated Distribution System With Uncertainties Using Artificial Hummingbird Algorithm

Appropriate installation of renewable energy-based distributed generation units (RDGs) is one of the most important challenges and current topics of interest in the optimal functioning of modern power networks. Due to the intermittent nature of renewable energy sources, optimal allocation and sizing of RDGs, particularly photovoltaic (PV) and wind turbine (WT), remains a critical task. Based on a new metaheuristic known as the Artificial hummingbird algorithm (AHA), this paper provides a novel approach for addressing the problem of RDG planning optimization. Considering various operational constraints, the optimization problem is developed with multiple objectives including power loss reduction, voltage stability margin (VSM) enhancement, voltage deviation minimization, and yearly economic savings. Furthermore, using relevant probability distribution functions, the ambiguities related with the stochastic nature of PV and WT output powers are evaluated. The proposed algorithm was compared to two of the recent metaheuristics applied in this domain known as improved harris hawks and particle swarm optimization algorithm (HHO-PSO) and hybrid of phasor particle swarm and gravitational search algorithm (PPSOGSA). The IEEE 33-bus and 69-bus systems are assessed as the test systems in this study. According to the findings, AHA delivers superior solutions and enhances the techno-economic benefits of distribution systems in all the scenarios evaluated.

Exact stochastic constraint optimisation with applications in network analysis

We present an extensive study of methods for exactly solving stochastic constraint (optimisation) problems (SCPs) in network analysis. These problems are prevalent in science, governance and industry. The first method we study is generic and decomposes stochastic constraints into a multitude of smaller local constraints that are solved using a constraint programming (CP) or mixed-integer programming (MIP) solver. However, many SCPs are formulated on probability distributions with a monotonic property, meaning that adding a positive decision to a partial solution to the problem cannot cause a decrease in solution quality. The second method is specifically designed for solving global stochastic constraints on monotonic probability distributions (SCMDs) in CP. Both methods use knowledge compilation to obtain a decision diagram encoding of the relevant probability distributions, where we focus on ordered binary decision diagrams (OBDDs). We discuss theoretical advantages and disadvantages of these methods and evaluate them experimentally. We observed that global approaches to solving SCMDs outperform decomposition approaches from CP, and perform complementarily to MIP-based decomposition approaches, while scaling much more favourably with instance size. Both methods have many alternative design choices, as both knowledge compilation and constraint solvers are used in a single pipeline. To identify which configurations work best, we apply programming by optimisation. Specifically, we show how an automated algorithm configurator can be used to find optimised configurations of our pipeline. After configuration, our global SCMD solving pipeline outperforms its closest competitor (a MIP-based decomposition pipeline) on all test sets we considered by up to two orders of magnitude in terms of PAR10 scores.

Data requirements for improving the Quantitative Risk Assessment of liquid hydrogen storage systems

Quantitative Risk Assessment (QRA) supports the development of risk-informed safety codes and standards which are employed to enable the safe deployment of hydrogen technologies essential to decarbonize the transportation sector. System reliability data is a necessary input for rigorous QRA. The lack of reliability data for bulk liquid hydrogen (LH2) storage systems located on site at fueling stations limits the use of QRAs. In turn, this hinders the ability to develop the necessary safety codes and standards that enable worldwide deployment of these stations. Through a QRA-based analysis of a LH2 storage system, this work focuses on identifying relevant scenario and probability data currently available and ascertaining future data collection requirements regarding risks specific to liquid hydrogen releases. The work developed consists of the analysis of a general bulk LH2 storage system design located at a hydrogen fueling station. Failure Mode and Effect Analysis (FMEA) and traditional QRA modeling tools such as Event Sequence Diagrams (ESD) and Fault Tree Analysis (FTA) are employed to identify, rank, and model risk scenarios related to the release of LH2. Based on this analysis, scenario and reliability data needs to add LH2-related components to QRA are identified with the purpose of improving the future safety and risk assessment of these systems.

Landslide Forecast by Time Series Modeling and Analysis of High-Dimensional and Non-Stationary Ground Motion Data

High-dimensional, non-stationary vector time-series data are often seen in ground motion monitoring of geo-hazard events, e.g., landslides. For timely and reliable forecasts from such data, we developed a new statistical approach based on two advanced econometric methods, i.e., error-correction cointegration (ECC) and vector autoregression (VAR), and a newly developed dimension reduction technique named empirical dynamic quantiles (EDQ). Our ECC–VAR–EDQ method was born by analyzing a big landslide dataset, comprising interferometric synthetic-aperture radar (InSAR) measurements of ground displacement that were observed at 5090 time states and 1803 locations on a slope. The aim was to develop an early warning system for reliably forecasting any impending slope failure whenever a precursory slope deformation is on the horizon. Specifically, we first reduced the spatial dimension of the observed landslide data by representing them as a small set of EDQ series with negligible loss of information. We then used the ECC–VAR model to optimally fit these EDQ series, from which forecasts of future ground motion can be efficiently computed. Moreover, our method is able to assess the future landslide risk by computing the relevant probability of ground motion to exceed a red-alert threshold level at each future time state and location. Applying the ECC–VAR–EDQ method to the motivating landslide data gives a prediction of the incoming slope failure more than 8 days in advance.

Can AI learn to identify systematic reviews on the effectiveness of public health interventions?

Abstract Issue/problem Health Evidence™ aims to make it easier for public health professionals and decision-makers to use evidence in their programs and policies. We provide access to over 7,000 critically appraised systematic reviews on the effectiveness of public health interventions. On average, 8,000-10,000 records are screened each month to identify around 50 relevant reviews that are critically appraised and uploaded to the registry. As the number of published reviews continues to grow each year, maintaining an up-to-date Registry is becoming increasingly resource intensive. Description of the problem Artificial intelligence (AI) may be one way to ensure maintenance of this Registry continues to be feasible. It is important that the use of AI is accurate and efficient to support monthly relevance screening for the Health Evidence ™ Registry. To assess if the use of AI is appropriate in this context, the team uploaded a large, labelled training set (n = 43,273) of relevant and non-relevant records to DistillerSR and used the AI Preview and Rank function to predict the probability of relevance to the Registry. We identified an optimal threshold (0.17) that automatically removes the greatest number of records with minimal classification errors. We then tested this threshold on one year of manually screened records (n = 89,832) from May 2019 to June 2020 to ensure continued accuracy. Results Using AI to support our monthly relevance screening has reduced our monthly manual screening burden by over 77% (from ∼9,000 references manually screened per month to ∼2,000) saving upwards of 10 hours staff time per month! Lessons The use of AI shows promise to help improve the feasibility of maintaining a large Registry of quality appraised synthesis level evidence relevant for public health decision making. Key messages Health Evidence™ provides easy access to high quality synthesis evidence relevant for public health. The use of AI shows promise for helping to automate the Health Evidence™ monthly update and improve the feasibility of maintaining a public health registry of quality appraised synthesis evidence.

Design of an Interactive Web Application for Teaching Uncertainty Interpretations of Clinical Tests.

Assessing the uncertainty of diagnostic findings is essential for advising patients. Previous research has demonstrated the difficulty of computing the expected correctness of positive or negative results, although clinical decision support (CDS) tools promise to facilitate adequate interpretations. To teach the potential utility of CDS tools to medical students, we designed an interactive software module that computes and visualizes relevant probabilities from typical inputs. We reviewed the literature on recommended graphical approaches and decided to support contingency tables, plain table formats, tree diagrams, and icon arrays. We implemented these functions in a single-page web application, which was configured to complement our local learning management system where students also access interpretation tasks. Our technical choices promoted a rapid implementation. We intend to explore the utility of the tool during some upcoming courses. Future developments could also model a more complex clinical reality where the likelihood of alternative diagnoses is estimated from sets of clinical investigations.