Probabilistic Treatment Research Articles

Virtual rehabilitation system for fine motor skills using artificial neural networks

This work presents a rehabilitation system for upper limbs performed by the acquisition of electromyographic signals (EMG) of a patient with movement limitation after stroke. To determine the physical gestures of the arm, the MYO Armband is used, and to increase the default number of movements (4 gestures), a recognition algorithm is performed using artificial neural networks. MATLAB has been used for the extraction of a vector containing the characteristics through probabilistic signals treatment and to determinate the angles of rotation. In addition, an immersive environment is developed in Unity 3D software allowing the user to experience a more motivating therapy. As a peripheral output, HTC VIVE glasses and binaural hearing aids were chosen. To validate this proposal, five users with an age range of 49 to 69 years have made 200 tests in total with the proposed physical gestures (7). In addition, the qualitative validation of the system is done through the SUS usability test and as a result it is obtained (55.25 ± 0.18), demonstrating good acceptance.

Self-sustained oscillations in the potential of a CO-poisoned PEM fuel cell: A model based on physical principles

A dynamic 1-D model for the simulation of the oscillatory behavior in the anode potential of a PEM fuel cell under constant current conditions and the presence of CO impurities in the H2 inlet is presented. The model is built upon electrostatic, chemical kinetic and thermodynamic principles, featuring a probabilistic treatment of dipole interactions between several chemical species in the compact layer. Model predictions are validated with available experimental data in the literature. The model is able to capture the phenomenon of the oscillatory behavior. This phenomenon occurs when the CO molar fraction in the feed exceeds 8 × 10−5. A parametric sensitivity study for CO-electrooxidation and OH-adsorption kinetic rates is also performed. The CO-electrooxidation rate has a noticeable effect on the oscillation frequency, and damped oscillations are observed for a kinetic rate of 6 × 10−3 mol m−2s−1, while OH-adsorption and desorption rates have a moderate effect on system dynamics.

Prevalence and antimicrobial susceptibility profile of Mycoplasma hominis and Ureaplasma urealyticum in female population, Gabon

Mycoplasma hominis and Ureaplasma urealyticum are microorganisms involved in many pathologies and intrauterine infections. Vaginal wall swabs were taken from 278 women for mycoplasma detection and to study the antimicrobial susceptibility using the Mycoplasma IST2 Kit (bioMerieux). We were able to document in 278 women enrolled in the study, 64.7% have infections caused by U. urealyticum, 22.7% by M. hominis, and 19.4 % of coinfections involving both germs. Such infections were observed in age groups between 18 and 42 years. Other socio-demographic characteristics studies have been conducted, which allowed to document that single women were the majority population with 68.7% against 16.5% who were living in couple. In response to our questionnaire, 131 single women enrolled in the study never protected themselves during sexual intercourse compared with 52.9% who reported using at least condom. A total of 260 (93.5%) of women reported having only one partner versus 6.5% having more. The study of strains susceptibility to antibiotics revealed a resistance of approximately 80% for macrolides and 100% for fluoroquinolones. All our isolated strains had a sensitivity of about 90% for Pristinamycin and 70% for Josamycin. This study shows that carriage of mycoplasma infection is high in female population in Franceville and vicinities, what should be an element of cautious use of antibiotic during probabilistic therapy treatment of mycoplasma.

A randomized approach to sensor placement with observability assurance

Given a linear dynamical system, we provide a probabilistic treatment to the classic problem of placing sensors in a set of candidate locations such that the observability Gramian of the resulting placement is sufficiently non-singular. Our contributions are as follows: First, we present a randomized algorithm that samples the sensor locations with replacement as per a specified distribution. Second, we derive a high probability bound on two measures of non-singularity, viz. the minimum eigenvalue and the trace of the inverse of the observability Gramian of the resulting placement, relative to that of placing one sensor at every location. Our analysis yields upper and lower bounds on any eigenvalue-based metric used in sensor placement and characterizes the tradeoff between the number of samples required by the algorithm and the two measures of the observability Gramian. We supplement the claims with insightful numerical studies and comparisons with multiple competing approaches.

Leaving the area under the receiving operating characteristic curve behind: An evaluation method for species distribution modelling applications based on presence‐only data

Abstract The area under the curve (AUC) of the receiving‐operating characteristic (or certain modifications of it) is almost universally used to assess the performance of species distribution models (SDMs), despite the well‐recognized problems encountered with this approach, mainly present when dealing with presence‐only data. We present a probabilistic treatment of the presence‐only problem and derive a method to assess the performance of SDMs based on the analysis of an area‐presence plot and the SDM outputs represented in both geographic and environmental spaces. We show how our method is useful to solve the two main tasks for which the AUC is used: assessing the performance of an SDM and comparing the performance of different SDMs. Our results build on previous work and constitute a rigorous method for assessing the performance of SDMs in relation to a random classifier. We establish comparisons with two of the most popular approaches used to assess the performance of an SDM, the AUC and the Boyce index, and identified cases in which our method has advantages over these two approaches. We suggest that the performance of an algorithm that classifies presence‐only data can be assessed by two factors: (a) the degree of non‐randomness of the classification at every step in the accumulation curve of presences, and (b) the amount of uninformative niche space used for the classification. The method we developed can be applied to any SDM output by using the R functions available at: https://github.com/LauraJim/SDM‐hyperTest.

Derivation of soil water retention curve incorporating electrochemical effects

Water retention of clayey soils with wide particle size distributions involves a combination of capillary and adsorbed layers effects that result into suction–saturation relations spanning over multiple decades of matric suction values. The present study provides a physics-based analysis to reproduce the water retention curve of such soils based solely on particle size distribution and porosity. The distribution of inter-particle pore sizes is inferred through a probabilistic treatment of the particle size distribution, which is then used, together with an assigned pore entry pressure, to estimate the inter-particle water volume at a given suction. The contribution to water content from adsorbed layers is also taken into account by considering the balance of electrochemical forces between water and clay material. The total water content is therefore found by summing up the contribution of inter-particle water, as well as adsorbed layers that form around clay particles and around the individual clay platelets. Comparisons with experimental results on nine different soil samples verify the capability of the model in accurately predicting the wide water retention curves without any prior calibration. Additional to capturing the essential features of the water retention curve with remarkable detail, the analytical model also provides insights into the relative contributions of capillary and adsorbed waters to the overall saturation at different suction regimes. Being based upon easily accessible information such as particle size distribution and void ratio, the model can therefore be considered as a substitute for costly and lengthy laboratory and in situ measurements of water retention curve.

Hemophagocytic Lymphohistiocytosis During HIV Infection in Cayenne Hospital 2012-2015: First Think Histoplasmosis.

Introduction: Haemophagocytic Lymphohistiocytosis (HLH), during HIV infection is a rare complication with a poor prognosis. There are few data on HLH within the Amazon region. The objective was to describe epidemiological, clinical and therapeutic features of HIV-related HLH in French Guiana.Methods: A retrospective analysis of adult HIV patients at Cayenne hospital with HLH between 2012 and 2015. A diagnosis of HLH was given if the patient presented at least 3 of 8 criteria of the HLH-2004 classification.Results: Fourteen cases of HLH were tallied during the study period. The mean age was 46 years with a sex ratio of 1.8. The most frequent etiology of HLH was an associated infection (12/14). Confirmed disseminated histoplasmosis, was found in 10 of 14 cases, and it was suspected in 2 other cases. The CD4 count was below 200/mm3 in 13/14 cases. An HIV viral load >100,000 copies/ml was observed in 13/14 cases. An early treatment with liposomal amphotericin B was initiated in 12/14 cases. The outcome was favorable in 12/14 of all cases and in 10/12 cases involving histoplasmosis. Case fatality was 2/14 among all cases (14.3%) et 1/10 among confirmed disseminated histoplasmosis with HLH (10%). During the study period 1 in 5 cases of known HIV-associated disseminated histoplasmosis in French Guiana was HLH.Conclusion: Histoplasmosis was the most frequent etiology associated with HLH in HIV-infected patients in French Guiana. The prognosis of HLH remains severe. However, a probabilistic empirical first line treatment with liposomal amphotericin B seemed to have a favorable impact on patient survival.

Best-Estimate Plus Uncertainty Analysis of CANDU Fuel Reliability Using Manufacturing and Simulated Core Data

Abstract A novel method of assessing the reliability of 37-element Canada deuterium uranium (reactor) (CANDU) fuel bundle was explored. The method implements a “best-estimate plus uncertainty” (BEPU) approach where a probabilistic treatment of manufacturing and operating inputs is used to predict fuel performance. The fuel performance was predicted using the Canadian industry standard codes for fuel performance, ELESTRESS and ELOCA, which, respectively, model fuel behaviors during normal and transient conditions. The outputs of the codes were compared against failure criteria from industry norms to determine the probability of failure. A Monte Carlo simulation method was applied to analyze this problem. Probability distributions of manufacturing input variables were estimated from real data, which were then randomly sampled. The inputs for fuel burnup and power were simulated using core-following data generated using a three-dimensional diffusion code, the Reactor Fuelling Simulation Program (RFSP), which were also then randomly sampled. The results of the simulations predict significant improvements in margins to limits for all performance parameters. An average improvement of 500 °C in centerline temperature, 10 °C in sheath temperature, 12 MPa in element internal pressure, and 0.8% in pellet end sheath hoop strain was predicted for the highest-powered region of the core, during normal operations, in comparison with the limit-of-envelope (LOE) benchmark. An 80% reactor overhead break (ROH) transient simulation was also simulated, and an average improvement of 500 °C in centerline temperature, 150 °C in sheath temperature, 6.5 MPa in internal pressure, and 2% in sheath hoop strain was predicted.

Impact of Gaussian uncertainty assumptions on probabilistic optimization in particle therapy

Range and setup uncertainties in charged particle therapy may induce a discrepancy between the planned and the delivered dose. Countermeasures based on probabilistic (stochastic) optimization usually assume a Gaussian probability density to model the underlying range and setup error. While this standard assumption is generally taken for granted, this study explicitly investigates the dosimetric consequences if the actual range and setup errors obey a different probability density function (PDF) over the course of treatment to the one used during the probabilistic treatment plan optimization. Discrete random sampling was performed for conventionally and probabilistically optimized proton and carbon ion treatment plans utilizing various PDFs that modeled the setup and range error. This method allowed us to assess the treatment plan robustness against different PDFs of conventional and probabilistic plans, which both explicitly assume Gaussian uncertainties. The induced uncertainty in dose was quantified by estimating the expectation value and standard deviation of the RBE-weighted dose for each PDF on the basis of 2500-5000 random dose samples. Probabilistic dose metrics and standard deviation volume histograms were computed to quantify treatment plan robustness of both optimization approaches. It was shown that the classical planning target volume margin extension concept did not compensate the influence of range and setup errors and consequently resulted in a non-negligible average standard deviation in dose of 7.3% throughout the clinical target volume (CTV). In contrast, probabilistic optimization on normally distributed errors yielded treatment plans that not only entailed a lower standard deviation against normally distributed errors accounted for during optimization, but also lower standard deviations for other symmetric PDFs. It was shown that the impact of an incorrect probability distribution assumption is of lower importance after probabilistic optimization as the average uncertainty in the CTV drops to 3.9%. Probabilistic optimization is an effective tool to create robust particle treatment plans. Normally distributed range and setup error assumptions for probabilistic optimization are a reasonable first approximation and yield treatment plans that are also robust against other PDFs.

Voxel-level biological optimisation of prostate IMRT using patient-specific tumour location and clonogen density derived from mpMRI

AimsThis study aimed to develop a framework for optimising prostate intensity-modulated radiotherapy (IMRT) based on patient-specific tumour biology, derived from multiparametric MRI (mpMRI). The framework included a probabilistic treatment planning technique in the effort to yield dose distributions with an improved expected treatment outcome compared with uniform-dose planning approaches.MethodsIMRT plans were generated for five prostate cancer patients using two inverse planning methods: uniform-dose to the planning target volume and probabilistic biological optimisation for clinical target volume tumour control probability (TCP) maximisation. Patient-specific tumour location and clonogen density information were derived from mpMRI and geometric uncertainties were incorporated in the TCP calculation. Potential reduction in dose to sensitive structures was assessed by comparing dose metrics of uniform-dose plans with biologically-optimised plans of an equivalent level of expected tumour control.ResultsThe planning study demonstrated biological optimisation has the potential to reduce expected normal tissue toxicity without sacrificing local control by shaping the dose distribution to the spatial distribution of tumour characteristics. On average, biologically-optimised plans achieved 38.6% (p-value: < 0.01) and 51.2% (p-value: < 0.01) reduction in expected rectum and bladder equivalent uniform dose, respectively, when compared with uniform-dose planning.ConclusionsIt was concluded that varying the dose distribution within the prostate to take account for each patient’s clonogen distribution was feasible. Lower doses to normal structures compared to uniform-dose plans was possible whilst providing robust plans against geometric uncertainties. Further validation in a larger cohort is warranted along with considerations for adaptive therapy and limiting urethral dose.

Bayesian Model-Updating Using Features of Modal Data: Application to the Metsovo Bridge

A Bayesian framework is presented for finite element model-updating using experimental modal data. A novel likelihood formulation is proposed regarding the inclusion of the mode shapes, based on a probabilistic treatment of the MAC value between the model predicted and experimental mode shapes. The framework is demonstrated by performing model-updating for the Metsovo bridge using a reduced high-fidelity finite element model. Experimental modal identification methods are used in order to extract the modal characteristics of the bridge from ambient acceleration time histories obtained from field measurements exploiting a network of reference and roving sensors. The Transitional Markov Chain Monte Carlo algorithm is used to perform the model updating by drawing samples from the posterior distribution of the model parameters. The proposed framework yields reasonable uncertainty bounds for the model parameters, insensitive to the redundant information contained in the measured data due to closely spaced sensors. In contrast, conventional Bayesian formulations which use probabilistic models to characterize the components of the discrepancy vector between the measured and model-predicted mode shapes result in unrealistically thin uncertainty bounds for the model parameters for a large number of sensors.

Liquefaction case studies of gravelly soils during the 2008 Wenchuan earthquake

The 2008 Wenchuan earthquake caused widespread liquefaction of gravelly soils (i.e., gravelly sands and sandy gravels), which phenomena show many differences compared to those of typical sands. To study the liquefaction mechanism and triggering evaluation of gravelly soils during this event, comprehensive case studies with measurement of shear wave velocity on both liquefied and non-liquefied sites were carried out according to a suite of recommended procedures for site investigation, field testing and data processing. Three typical cases were provided to illustrate the distinct characteristics of ground motion, liquefaction manifestation, soil profiles and field testing, and explore the possible mechanisms of liquefaction. Eighty-one well identified case histories were obtained, which feature the updated estimation of ground motion parameters from the USGS ShakeMap and the probabilistic treatment of the datasets. The preliminary analyses on liquefaction triggering boundary curves, shear wave velocities of these case histories and the applicability of the Ishihara criterion (1985) indicate that the gravel content plays a key role in the liquefaction mechanism and the consequences of gravelly soil deposits, and the liquefaction evaluation of gravelly sands and sandy gravels should be treated separately.

Expert-validated estimation of diagnostic uncertainty for deep neural networks in diabetic retinopathy detection.

Deep learning-based systems can achieve a diagnostic performance comparable to physicians in a variety of medical use cases including the diagnosis of diabetic retinopathy. To be useful in clinical practice, it is necessary to have well calibrated measures of the uncertainty with which these systems report their decisions. However, deep neural networks (DNNs) are being often overconfident in their predictions, and are not amenable to a straightforward probabilistic treatment. Here, we describe an intuitive framework based on test-time data augmentation for quantifying the diagnostic uncertainty of a state-of-the-art DNN for diagnosing diabetic retinopathy. We show that the derived measure of uncertainty is well-calibrated and that experienced physicians likewise find cases with uncertain diagnosis difficult to evaluate. This paves the way for an integrated treatment of uncertainty in DNN-based diagnostic systems.

Probabilistic corrosion condition assessment of a tunnel structure

AbstractReinforced concrete structures constitute the basis of our infrastructure. Their functionality and structural safety is essential to maintain our mobility and the industrial performance of our societies. Unexpected maintenance can lead to serious economic consequences. Most industrial countries are faced with the fact that the reliability of their infrastructure has decreased over the past decades due to ageing and associated durability issues. Most often reinforcement corrosion is the major problem with respect to ageing phenomena observed. Therefore, data from corrosion inspection and their implementation in probabilistic corrosion condition assessment are considered to be of major importance to assist engineers in the maintenance process. Reliable information about the corrosion condition state can be derived through half‐cell potential measurements. The half‐cell potential measurement method is an easy to apply nondestructive measurement method to detect ongoing corrosion. The procedure on how to update the service life based on a probabilistic treatment of measured half‐cell potentials is presented in a case study of a chloride‐contaminated reinforced concrete structure. Besides the chosen probabilistic model, major impact on the accuracy of the updated service life has the evaluation of the half‐cell potential measurements.

Simulating a residential building stock to support regional efficiency policy

Building simulation tools are seeing growing use in the residential sector due to a combination of increased interest in building energy performance, more user-friendly software packages, and decreasing computing power requirements. These tools are now being applied in batch simulations, where large numbers of homes are simulated at once to understand existing building stock energy consumption and potential energy efficiency improvements to make to those stocks. As these types of applications grow, attention is needed to ensure that simulation inputs and outputs are handled with an appropriate understanding of the uncertainties involved.In this paper, we use a batch simulation tool to translate energy audit records of 296 existing single-family homes in Pennsylvania into EnergyPlus building simulation models. We compare the results of these simulations to actual metered electricity use data from these homes, representative device-level data, and local utility data to better understand the abilities and limits of such modeling practices.Results show that while batch simulations are capable of producing accurate estimates of the energy consumption of a large number of homes, many more homes had their energy consumption overestimated than underestimated, resulting in a significant net overestimate for the sample as a whole. When results are scaled up to the utility level, simulation accuracy varies by utility with net overestimates ranging from around 15% to 25%. Comparisons of individual simulation results to metered consumption identify electric space heating systems as the source of much of this error. This highlights the importance of gathering accurate, detailed information on these systems to generate reliable simulation results.NREL has recently released a batch simulation tool called ResStock that takes a more statistically robust approach to simulating representative archetype homes as opposed to actual existing homes. The results of this study suggest that the type of probabilistic treatment of building characteristics underlying that tool will provide more meaningful results than trying to simulate actual buildings. With additional validation measures similar to those used in this study, ResStock can likely be used to reliably estimate baseline energy consumption and quantify efficiency savings opportunities and can serve as a valuable decision-making tool for state, local, and utility efficiency program designers.

On Collision Risk Assessment for Autonomous Ships Using Scenario-Based MPC

Collision Avoidance (COLAV) for autonomous ships is challenging since it relies on track estimates of nearby obstacles which are inherently uncertain in both state and intent. This uncertainty must be accounted for in the COLAV system in order to ensure both safe and efficient operation of the vessel in accordance with the traffic rules. Here, a COLAV system built on the Scenario-based Model Predictive Control (SB-MPC) with dynamic probabilistic risk treatment is presented. The system estimates the probability of collision with all nearby obstacles using a combination of Monte Carlo simulation (MCS) and a Kalman Filter (KF), taking the uncertainty in both position and velocity into account. A probabilistic collision cost is then used in the MPC to penalize risk-taking maneuvers. Simulation results show that the proposed method may provide increased robustness due to increased situational awareness, while also being able to efficiently follow the nominal path and adhere to the traffic rules.

Propagating fragility curve for RC buildings via HAZUS methodology

In this era where sustainability plays a key role, to design structures for resisting the earthquake in best probable manner is an inspiration for all designers. The recent trend for structural engineers is evolving in the direction of NSA (Nonlinear Static Analysis) in order to generate its ultimate building capacity. Frame structures are evaluated using NSA i.e. push-over analysis to interpret the phenomenon of plastic yielding being experienced within the structure. ATC-40 has well illustrated various performance levels of the buildings. When seismic loads are active due to shaking of ground, it is essential to assess the conditional probability of structural response. This can be done using conditional probability as a function and plotting fragility curves for structural response due to application of seismic activity or loading as a function. These fragility curves are useful not only for calamities before earthquake but also after earthquake to evaluate the seismic losses. The probability of whichever damage state being exceeded can be plotted using fragility curves which can be derived using empirical or analytical methods which rely on both the source of the data along with type of analysis. This paper addresses the analytical method using the guidelines given by the HAZUS MH technical manual. The work represented here is compiled by means of procedure for establishing the fragility curves for three typical Reinforced Concrete (RC) frame structures having variations resembling 3 storey intended for short-period structures, 6 storey used for medium-period structures and 12 storey representing long-period structures. For analyzing structures, FEM based software SAP2000 has been used as a tool. The present study contributes towards quantification of seismic fragility; henceforth fragility curves are co-related and plotted which provides a rational and consistent probabilistic treatment of damage or loss. The fragility curves are figured out to assess and interpret the results.

Incorporating cost uncertainty and path dependence into treatment selection for pavement networks

This paper proposes a new probabilistic treatment path dependence (PTPD) model for budget allocation within pavement network management problems. During the evaluation of treatment alternatives for a segment in a pavement network, the model considers benefits of both the evaluated treatment and its following actions. It also incorporates the influence of treatment cost and deterioration uncertainties. Treatments are selected for each segment in the pavement network using a risk-based optimization model. Three case studies are presented to illustrate the application and benefits of this new model. Results of the first two cases show that the risk-aversion coefficient in the model influences segment-level treatment selections and pavement network performance. The third case shows that the PTPD model performs better than a benefit-cost ratio model due to the incorporation of uncertainties and treatment path dependence. To obtain a similar performance level, the conventional model requires a 10.4% higher annual budget for the given case study. The results presented here suggest that elements of this model – notably consideration of uncertainty in deterioration and cost, treatment path dependency, and explicit risk trade-offs – could be incorporated into asset management tools to improve the cost-effectiveness of pavement network planning.

État actuel de la résistance en France d’E. coli au triméthoprime dans les infections urinaires aiguës simples

The objective of this study was to assess the level of resistance of trimethoprim alone (TMP) with respect to E.coli strains isolated from the urines of women with simple acute cystitis in community. Prospective study realized for 9 months in 2017-18. A total of 351 urine samples were analyzed. Culture has been made according to the usual techniques and antibiogram was carried out according to the recommendations of the CA-SFM. The rate of resistance to TMP was 16.5% (58/351). Only 11 strains of E.coli (3%) producing ESBL were found, 5 of which were sensitive to TMP. The resistance rate of E.coli to TMP remains below 20%, the threshold for choosing a probabilistic treatment of a non-serious infection. Considering the good tolerance of TMP and its weak effect on the microbiota during a short treatment, one can propose TMP alone in the probabilistic treatment of simple acute cystitis.

Three Concepts of Probabilistic Causation in the Law

Frederick Schauer and Barbara Spellman have recently argued that the probabilistic treatment of causality is widespread in legal practice. Although judicial decisions are focused on single events, there are many aspects of the law in which the relationship between cause and effect is interpreted in probabilistic terms. As in social and natural sciences, also in the law what matters is often to predict ex ante the class of consequences flowing from a class of acts, and this kind of predictions govern legal judgments more than we usually believe. The view just outlined captures some important aspects of legal practice, that have been insufficiently explored by the philosophy of law. However, in the paper I elaborate on the points made by Schauer and Spellman and propose some refinements of them. Probabilistic causal statements in the law often presuppose different concepts of probability. In other words, the expression “probabilistic causation” is ambiguous and needs to be clarified. Distinguishing between different concepts of probability may help to evaluate the use of probabilistic causation in legal practice and to better understand its function in legal proceedings.