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The spatial information of rockhead is crucial for the design and construction of tunneling or underground excavation. Although the conventional site investigation methods (i.e. borehole drilling) could provide local engineering geological information, the accurate prediction of the rockhead position with limited borehole data is still challenging due to its spatial variation and great uncertainties involved. With the development of computer science, machine learning (ML) has been proved to be a promising way to avoid subjective judgments by human beings and to establish complex relationships with mega data automatically. However, few studies have been reported on the adoption of ML models for the prediction of the rockhead position. In this paper, we proposed a robust probabilistic ML model for predicting the rockhead distribution using the spatial geographic information. The framework of the natural gradient boosting (NGBoost) algorithm combined with the extreme gradient boosting (XGBoost) is used as the basic learner. The XGBoost model was also compared with some other ML models such as the gradient boosting regression tree (GBRT), the light gradient boosting machine (LightGBM), the multivariate linear regression (MLR), the artificial neural network (ANN), and the support vector machine (SVM). The results demonstrate that the XGBoost algorithm, the core algorithm of the probabilistic N-XGBoost model, outperformed the other conventional ML models with a coefficient of determination (R2) of 0.89 and a root mean squared error (RMSE) of 5.8 m for the prediction of rockhead position based on limited borehole data. The probabilistic N-XGBoost model not only achieved a higher prediction accuracy, but also provided a predictive estimation of the uncertainty. Thus, the proposed N-XGBoost probabilistic model has the potential to be used as a reliable and effective ML algorithm for the prediction of rockhead position in rock and geotechnical engineering.

Non-structural elements (NSEs) are not designed to be part of the structural load-bearing system of a building but are subjected to the same dynamic environment as the supporting structure during an earthquake. Non-structural damage and ensuing losses have exceeded structural damage and losses in many buildings during recent earthquakes that have occurred in urban regions around the world. Damage to NSEs is strongly influenced by the amplitude, the frequency content and the duration of floor motions. Although the effects of amplitude and frequency content of floor motions are well captured by floor response spectra, little attention has been given on the characterisation of floor motion duration and how it can impact damage to NSEs, particularly for those responding in the inelastic range. To contribute to a better understanding of these issues, this paper develops a probabilistic strong floor motion duration model through multiple stripes non-linear time-history analyses of a population of 100 code-compliant reinforced concrete frame building models designed for a medium-to-high seismicity site in Italy. The resulting probabilistic model can be used when selecting historical or generating synthetic floor motions for the seismic response assessment of NSEs or when generating shake-table test records for the seismic qualification of NSEs.

Crowdsourcing is a cost-effective method that gathers crowd wisdom to solve machine-hard problems. In crowdsourcing systems, requesters post tasks for obtaining reliable solutions. Nevertheless, since workers have various expertise and knowledge background, they probably deliver low-quality and ambiguous submissions. A task aggregation scheme is generally employed in crowdsourcing systems, to deal with this problem. Existing methods mainly focus on structured submissions and also do not consider the cost incurred for completing a task. We exploit features of submissions to improve the task aggregation for proposing a method which is applicable to both structured and unstructured tasks. Moreover, existing probabilistic methods for answer aggregation are sensitive to sparsity. Our approach uses a generative probabilistic model that incorporates similarity in answers along with worker and task features. Thereafter, we present a method for minimizing the cost of tasks, that eventually leverages the quality of answers. We conduct experiments on empirical data that demonstrates the effectiveness of our method compared to state-of-the-art approaches.

The authors investigate the risks of computer attacks on automated banking systems. The relevance of the study is due to the need to revise the approaches to risk assessment based on the technical components of banking business processes and the consequences of cyber-attacks aimed at banking automated systems in credit institutions. The aim of the study is to describe the developed methods for assessing cyber risks in a commercial bank and provide an option for assessing the risks of information security violations in electronic banking technologies. The methodology of the article includes the analysis of domestic and foreign literature on the research topic, the theoretical and probabilistic method of calculation, computer programming and graphic interpretation of information. The authors analysed the operational risk of a commercial bank to develop components of the operational risk management system in the context of developing electronic banking technologies. They designed a computer program to quantify risk probabilities of cyberattacks on electronic banking technologies (by means of Borland Delphi). The work presents a formalised probabilistic model for determining the most vulnerable segment of risk management techniques used by information security structures. The conclusion is that it is possible to develop a software package based on a mathematical model that reduces the number of checks of risk factors by several times. The research results may be of further use for the development of risk divisions in credit institutions using electronic banking technologies.

Underwater acoustics that is the study of the phenomenon of underwater wave propagation and its interaction with boundaries, has mainly been applied to the fields of underwater communication, target detection, marine resources, marine environment, and underwater sound sources. Based on the scientific and engineering understanding of acoustic signals/data, recent studies combining traditional and data-driven machine learning methods have shown continuous progress. Machine learning, represented by deep learning, has shown unprecedented success in a variety of fields, owing to big data, graphical processor unit computing, and advances in algorithms. Although machine learning has not yet been implemented in every single field of underwater acoustics, it will be used more actively in the future in line with the ongoing development and overwhelming achievements of this method. To understand the research trends of machine learning applications in underwater acoustics, the general theoretical backgroundof several related machine learning techniques is introduced in this paper.

This paper will present an overview of indoor and outdoor drone localization methods. Outdoor scenarios almost always use a GPS with IMU. Indoor systems are using short-range sensors that are sensitive to the external conditions of the environment. Mostly used methods are optical flow and stereovision, while an ultrasonic transceiver system optimizes and provides high precision and orientation of the drone. An ultrasonic preceptor is integrated into a listener/beacon and can be used with referenced beacons inside a WSN. The Crossbow Cricket hardware platform, which is based on TDOA and RSS principle, is used for simulations and code development. The researched direction is the localization of referent nodes (beacons) concerning the listener which is mounted on a flying drone. For that purpose, a probabilistic approach is used, based on a Bayes filter, where the positions of the beacon can be observed like random variables. Considering that these distributions significantly vary from a Gauss distribution, it is appropriate to use a particle filter.

Surge arresters are usually used to protect distribution feeders against randomly distributed lightning overvoltages. However, there is a probability of surge arrester failure due to an increase in its absorbed energy over the withstand capability. This paper experimentally and theoretically evaluates the application of a compound overvoltage protection scheme to avoid this failure. The compound overvoltage protection consists of an installed spark gap in parallel with an existing surge arrester to limit the risk of surge arrester failure. The experimental evaluation is performed by applying standard lightning impulse voltage waveform, 1.2/ $50~\mu \text{s}$ according to IEC Standard 60060-1. The evaluation is carried out without and with sparkgap adjusted at different lengths. The energy stress of the surge arrester with and without sparkgap is theoretically carried out using the ATP/EMTP software considering an overhead distribution feeder. The statistical evaluation for the failure risk assessment is performed using the MATLAB software. The results indicate that the installation of the spark gap in parallel with the surge arrester relieves the thermal stress on the surge arrester, and thus eliminates any risk of surge arrester failure using the proposed coordination.

Pyrrolizidine alkaloids (PAs) are a large group of botanical toxins of concern, as they are considered genotoxic carcinogens, with long-term dietary exposure presenting an elevated risk of liver cancer. PAs can contaminate honey through honeybees visiting the flowers of PA-containing plant species. A program of monitoring New Zealand honey has been undertaken over several years to build a comprehensive dataset on the concentration, regional and seasonal distribution, and botanical origin of 18 PAs and PA N-oxides. A bespoke probabilistic exposure model has then been used to assess the averaged lifetime dietary risk to honey consumers, with exposures at each percentile of the model characterized for risk using a margin of exposure from the Joint World Health Organization and United Nations Food and Agriculture Organization Expert Committee on Food Additives (JECFA) Benchmark Dose. Survey findings identify the typical PA types for New Zealand honey as lycopsamine, echimidine, retrorsine and senecionine. Regional and seasonal variation is evident in the types and levels of total PAs, linked to the ranges and flowering times of certain plants. Over a lifetime basis, the average exposure an individual will receive through honey consumption is considered within tolerable levels, although there are uncertainties over high and brand-loyal consumers, and other dietary contributors. An average lifetime risk to the general population from PAs in honey is not expected. However, given the uncertainties in the assessment, risk management approaches to limit or reduce exposures through honey are still of value.

MICROABSTRACT: Integration of Next Generation Sequencing (NGS) information for use in distinguishing between Multiple Primary Lung Cancer and intrapulmonary metastasis was evaluated. We used a probabilistic model, comprehensive histologic assessment and NGS to classify patients. Integrating NGS data confirmed initial diagnosis (n = 41), revised the diagnosis (n = 12), while resulted in non-informative data (n = 8). Accuracy of diagnosis can be significantly improved with integration of NGS data.BACKGROUND: Distinguishing between multiple primary lung cancers (MPLC) and intrapulmonary metastases (IPM) is challenging. The goal of this study was to evaluate how Next Generation Sequencing (NGS) information may be integrated in the diagnostic strategy.PATIENTS AND METHODS: Patients with multiple lung adenocarcinomas were classified using both the comprehensive histologic assessment and NGS. We computed the joint probability of each pair having independent mutations by chance (thus being classified as MPLC). These probabilities were computed using the marginal mutation rates of each mutation, and the known negative dependencies between driver genes and different gene loci. With these NGS-driven data, cases were re-classified as MPLC or IPM.RESULTS: We analyzed 61 patients with a total of 131 tumors. The most frequent mutation was KRAS (57.3%) which occured at a rate higher than expected (p < 0.001) in lung cancer. No mutation was detected in 25/131 tumors (19.1%). Discordant molecular findings between tumor sites were found in 46 patients (75.4%); 11 patients (18.0%) had concordant molecular findings, and 4 patients (6.6%) had concordant molecular findings at 2 of the 3 sites. After integration of the NGS data, the initial diagnosis was confirmed for 41 patients (67.2%), the diagnosis was revised for 12 patients (19.7%) or was considered as non-informative for 8 patients (13.1%).CONCLUSION: Integrating the information of NGS data may significantly improve accuracy of diagnosis and staging.

A probabilistic model is devised for predicting the plastic hinge lengths (PHLs) of RC columns. Seven existing parametric models are evaluated first using a comprehensive database comprising PHL measurements from 133 RC column tests. It is observed that the performances of these seven models are fair (as opposed to strong), and their predictions bear significant uncertainties. A novel technique is devised to combine them into a weighted-average supermodel wherein the weights are determined via Bayesian inference. This approach naturally produces the weights' statistical moments, and thus, the resulting model is a probabilistic one that is amenable for performance-based seismic design and assessment analyses. Prediction comparisons indicate that the proposed supermodel has a higher performance than all prior models. The new model is easily expandable should more test data become available.

International audience; The consumption of red meat has been associated with colorectal cancer (CRC) and cardiovascular disease (CVD) worldwide. The objective of this study was to assess quantitatively the burden of disease of CRC and CVD due to the consumption of red meat in France. A probabilistic risk assessment model quantifying the risk, deaths and disability adjusted life years (DALY) of both outcomes was built. In the model, uncertainty and variability were propagated separately. The model used data on current CRC and CVD incidence and red meat consumption in France, as well as dose-response from epidemiological studies. Results were given by age class and gender. A total of 19 [95% CI = 8-33] DALY per 100,000 people per year for CRC associated with red meat consumption was estimated. For CVD, 21 [95% CI = 12-32) DALY per 100,000 people per year was estimated. The uncertainty was mainly due to the dose-response, as revealed by a sensitivity analysis. A scenario analysis, performed on red meat intake, highlighted that consumption of less than 65 g per day could limit the risk of CRC and CVD in the most affected sub-populations.

Purpose of the research. The research of influence of the system of professional education on parameters of development of digital economy in Russian Federation regions can be conducted in different directions: identification of the professional education system status as the institute, providing the digital economy of the region with human resources; to identify the needs of the separate industries of economy for the specialists, having the corresponding competences for the work in the field of the digital economy. The purpose of this research is justification of the mathematical model, allowing creating evidential basic statistical sampling for the evaluation of the levels of mastering digital competences by lecturers of educational institutions of professional education.Materials and methods. In this work the estimation methods, based on soft computing is offered. This approach allows correlating a quality indicator of mastering digital competences and quantitative category, to create basic statistical sampling for the analysis of personnel potential in the field of professional education and assessment of digital competences development in the explored area. The competence-based approach is used for the assessment of readiness of lecturers of the professional education system to carry out the professional activity, aimed at providing development of digital economy of the region. The received values of levels of mastering different digital competences are aggregated on each indicator of a linguistic variable in summary values, which can be used as basic statistical sampling.Results. On the basis of this model statistical analysis of the human resources of the region in the aspect of formation of knowledge and abilities in the field of information and computer technologies can be carried out. This model can be used for information processing about testing of different groups: pedagogical employees, public and municipal officers. The results will allow to diagnose an initial status of levels of mastering digital competences of the employees of the regional industry or the studied organization and to carry out monitoring of development of human resources of the region within the Digital Economy project. Statistically the data obtained on the basis of the offered model are well interpreted with the use of standard graphic means (for example, diagrams and histograms).Conclusion. The developed mathematical model is tested on the basis of real data and accepted as the basic one for evaluating the level of mastering digital competences of lecturers by the Ministry of Education and Youth Policy of the Ryazan region. The offered model has characteristic of universality and can be applied to receive basic statistical samplings of the level of mastering digital competences of areas of the real sector of economy. Further researches are planned to be conducted in the sphere of automation of process of the statistical data analysis on digitalization of the population of the region, first of all in the sphere of professional education. On the basis of the mathematical model the algorithm of analytical processing of statistical data on monitoring of digital competences is developed.

International audience; Human activity recognition plays an important role especially in medical applications. This paper proposes a formal approach to model such activities, taking into account possible variations in human behavior. This approach is based on discrete-time Markov chains enriched with event occurrence probabilities. We use the PRISM and Storm frameworks and their model checking facilities to express and check interesting temporal logic properties concerning the dynamic evolution of activities. We illustrate our approach on two serious games used by clinicians to monitor Alzheimer patients. This paper focuses on the suitability of such a formal approach to model patients' behavior, to check behavioral properties of medical interest, and on the respective advantages of the PRISM and Storm frameworks. Our goal is to provide a new tool for doctors to evaluate patients.; La reconnaissance d'activités humaines joue un rôle important en particulier dans les applications médicales. Cet article propose une approche formelle pour modéliser ces activités en prenant en compte les variations possibles du comportement humain. Cette approche est fondée sur les chaînes de Markov probabilistes à temps discret. Nous utilisons les outils PRISM et Storm avec leur système de vérification de modèles pour exprimer en logique temporelle et vérifier des propriétés intéressantes concernant l'évolution dynamique des activités. Nous illustrons notre approche avec deux jeux sérieux utilisés par les cliniciens pour évaluer les patients Alzheimer. Cet article se concentre sur la pertinence d'une telle approche formelle pour modéliser les comportements de patients, vérifier des propriétés de comportement ayant un intérêt médical et sur les avantages respectifs des outils de PRISM et de Storm. Notre but est de fournir un nouvel outil aux médecins pour évaluer leurs patients.

Increasing evidence shows that many infections of COVID-19 are asymptomatic, becoming a global challenge, since asymptomatic infections have the same infectivity as symptomatic infections. We developed a probabilistic model for estimating the proportion of undetected asymptomatic COVID-19 patients in the country. We considered two scenarios: one is conservative and the other is nonconservative. By combining the above two scenarios, we gave an interval estimation of 0.0001–0.0027 and in terms of the population, 5200–139,900 is the number of undetected asymptomatic cases in South Korea as of 2 February 2021. In addition, we provide estimates for total cases of COVID-19 in South Korea. Combination of undetected asymptomatic cases and undetected symptomatic cases to the number of confirmed cases (78,844 cases on 2 February 2021) shows that 0.17–0.42% (89,244–218,744) of the population have COVID-19. In conclusion, to control and understand the true ongoing reality of the pandemic, it is of outermost importance to focus on the ratio of undetected asymptomatic cases in the total population.

Accurately modeling local surface properties of objects is crucial to many robotic applications, from grasping to material recognition. Surface properties like friction are however difficult to estimate, as visual observation of the object does not convey enough information over these properties. In contrast, haptic exploration is time consuming as it only provides information relevant to the explored parts of the object. In this work, we propose a joint visuo-haptic object model that enables the estimation of surface friction coefficient over an entire object by exploiting the correlation of visual and haptic information, together with a limited haptic exploration by a robotic arm. We demonstrate the validity of the proposed method by showing its ability to estimate varying friction coefficients on a range of real multi-material objects. Furthermore, we illustrate how the estimated friction coefficients can improve grasping success rate by guiding a grasp planner toward high friction areas., Published in RA-L and ICRA2021. 8 pages, 8 figures. Tin IEEE Robotics and Automation Letters, 2021

Evapotranspiration is often expressed in terms of reference crop evapotranspiration (ETo), actual evapotranspiration (ETa), or surface water evaporation (Esw), and their reliable predictions are critical for groundwater, irrigation, and aquatic ecosystem management in semi-arid regions. We demonstrated that a newly developed probabilistic machine learning (ML) model, using a hybridized “boosting” framework, can simultaneously predict the daily ETo, Esw, &amp, ETa from local hydroclimate data with high accuracy. The probabilistic approach exhibited great potential to overcome data uncertainties, in which 100% of the ETo, 89.9% of the Esw, and 93% of the ETa test data at three watersheds were within the models’ 95% prediction intervals. The modeling results revealed that the hybrid boosting framework can be used as a reliable computational tool to predict ETo while bypassing net solar radiation calculations, estimate Esw while overcoming uncertainties associated with pan evaporation &amp, pan coefficients, and predict ETa while offsetting high capital &amp, operational costs of EC towers. In addition, using the Shapley analysis built on a coalition game theory, we identified the order of importance and interactions between the hydroclimatic variables to enhance the models’ transparency and trustworthiness.

Given that the integration of renewable energy generation systems may have the potential to satisfy the electrical requirements of remote areas, this paper consists of a feasibility study with the focus on integrating renewable generation into the energy system of Popova Island. It adopts an analytical approach that consists in applying the Monte Carlo method, an energy systems model, and then evaluating the financial outcomes. In so doing, the approach takes into account the inherent uncertainties and risks of various input parameters. Thus, Multiple Monte Carlo-based parameterisations define scenarios that are fed into the Hybrid Optimization of Multiple Energy Resources (HOMER) energy model. The broad set of result scenarios can provide a valuable reference for stakeholders, and effectively communicate its potential for increased renewables, the resulting system configurations, and reduced electricity costs.

Recent advances in the manufacturing industry have paved the way for a systematical deployment and implementation of systems, including Cyber-Physical Systems (CPS) and smart tools, aimed at reaching Zero Defect Manufacturing (ZDM). Nowadays, ZDM strategies are easier to be implemented due to the availability of the required amount of data coming from the distributed sensor network architectures of the production systems. Such a trend is transforming the manufacturing industry to the next generation, namely Industry 4.0. In this context, there is an urgent need to identify and adopt effective quality controls to be performed on the products. This paper proposes a probabilistic model that can be used to evaluate quality inspection effectiveness, i.e., the ability to identify product defects. This model, which integrates perfectly within the current modern manufacturing context, can act as a support tool for decision making and guide designers toward zero-defect strategies.

The continued spread of electric vehicles raises new challenges for the supporting digital infrastructure. For example, long-distance route planning for such vehicles relies on the prediction of both the expected travel time as well as energy use. We envision a two-Tier architecture to produce such predictions. First, a routing and travel-Time-prediction subsystem generates a suggested route and predicts how the speed will vary along the route. Next, the expected energy use is predicted from the speed profile and other contextual characteristics, such as weather information and slope. To this end, the paper proposes deep-learning models that are built from EV tracking data. First, as the speed profile of a route is one of the main predictors for energy use, different simple ways to build speed profiles are explored. Next, eight different deep-learning models for energy-use prediction are proposed. Four of the models are probabilistic in that they predict not a single-point estimate but parameters of a probability distribution of energy use on the route. This is particularly relevant when predicting EV energy use, which is highly sensitive to many input characteristics and, thus, can hardly be predicted precisely. Extensive experiments with two real-world EV tracking datasets validate the proposed methods. The code for this research has been made available on GitHub.

When metal structures are subjected to long-term cyclic loading at high temperature, simultaneous creep and fatigue damage may occur. In this paper probabilistic life models, described by hold times in tension and total strain range at elevated temperature have been derived based on the creeprupture behavior of 316FR austenitic stainless steel, which is one of the candidate structural materials for fast reactors and future Generation IV nuclear power plants operating at high temperatures. The parameters of the proposed creepfatigue model were estimated using a standard Bayesian regression approach. This approach has been performed using the WinBUGS software tool, which is an open source Bayesian analysis software tool that uses the Markov Chain Monte Carlo sampling method. The results have shown a reasonable fit between the experimental data and the proposed probabilistic creep-fatigue life assessment models. The models are useful for predicting expended life of the critical structures in advanced high temperature reactors when performing structural health management.

Robotic manipulation has been generally applied to particular settings and a limited number of known objects. In order to manipulate novel objects, robots need to be capable of discovering the physical properties of objects, such as the center of mass, and reorienting objects to the desired pose required for subsequent actions. In this work, we proposed a computationally efficient 2-stage framework for planar pushing, allowing a robot to push novel objects to a specified pose with a small amount of pushing steps. We developed three modules: Coarse Action Predictor (CAP), Forward Dynamic Estimator (FDE), and Physical Property Estimator (PPE). The CAP module predicts a mixture of Gaussian distribution of actions. FDE learns the causality between action and successive object state. PPE based on Recurrent Neural Network predicts the physical center of mass (PCOM) from the robot-object interaction. Our preliminary experiments show promising results to meet the practical application requirements of manipulating novel objects.

Drinking water and farm-to-fork pathways have been identified as the predominant environmental pathways associated with human exposure (HE) to Pb. This study integrates a GIS-based survey of metal concentrations in soil and a probabilistic quantitative risk assessment of Pb through the food chain. The case study area was selected in the east of Ireland. A step-wise exposure assessment collated the data for Pb concentration in soil and water media, bioaccumulation of Pb in unprocessed food products, such as potatoes, carrots, green vegetables, and salad vegetables. The daily mean HE to Pb through selected food products was found to be 0.073 mg day-1, where a mean weekly exposure was estimated as 0.0065 mg kg body weight-1 week-1. Multiple risk estimates were used. Hazard Quotient (HQ), Daily Dietary Index (DDI), Daily Intake of Metal (DIM), Health Risk Index (HRI), Target Hazard Quotient (THQ) and Cancer Risk (CR) were found as 0.234 to 0.669, 0.002, 0.0002, 0.020 to 0.057, 0.234 to 0.669, and 0.00001, respectively which signify a low to moderate risk. A sensitivity analysis revealed that intake of potato is the most sensitive parameter of the model, which is positively correlated (coeff. + 0.66) followed by concentration of Pb in the arable soil (+0.49), bioaccumulation in tubers (+0.37), consumption of salad vegetables (+0.20), and consumption of green vegetables (+0.13) (top 5). A back-calculated limit of Pb in the soil (51 mg kg-1) justifies the lower threshold limit of Pb (50-300 mg kg-1) in agricultural soil set by the European Union to mitigate potential bio-transfer into food products. The study concludes there is a low to moderate risk posed by Pb, within the system boundary of the probabilistic model, and highlights the significance of limiting Pb concentrations in the vegetable producing agricultural soil. ispartof: SCIENCE OF THE TOTAL ENVIRONMENT vol:810 ispartof: location:Netherlands status: published

The inclusion of pre-stressed PET profiles as reinforcing fibers in structural elements is proposed. The PET-profiles are placed in a unidirectional and bidirectional fiber matrix with soft pre-tensioned condition that they could have advantages in structural members made of concrete in some precise applications, for instance, road safety barriers. A universal machine (WDW-200E) is used to test PET-profiles with different width, knots and even other conditions. The desired PET profile is chosen and the concrete mixture is designed under the ACI standard guidance. Pre-stressed PET-profiles are included in wood molds shaped in beam-type and subsequently flexural strength is estimated. Furthermore, experimental tests results are presented for PET-profiles as structural reinforcement of beams made of concrete under pure flexural. Finally, a probabilistic model is used to estimate the PET contribution in terms of exceedance of the rupture modulus. Resumen Se propone la inclusión de perfiles de PET pretensados como fibras de refuerzo en elementos estructurales. Los perfiles de PET se colocan en una matriz de fibra unidireccional y bidireccional con una condición de pretensado suave que podría tener ventajas en elementos estructurales de concreto en algunas aplicaciones precisas, por ejemplo, barreras de contención para seguridad vial. Una máquina universal (WDW-200E) se usa para probar los perfiles de PET con diferentes anchos, nudos e incluso otras condiciones. Se elige el perfil de PET deseado y el concreto se diseña según la norma ACI. Los perfiles de PET pretensados se incluyen en moldes de madera con forma de vigas y, posteriormente, se estima la resistencia a la flexión. Los resultados de las pruebas experimentales de flexión son presentados. Finalmente, se usa un modelo probabilístico para estimar la contribución de PET en términos de excedencia del módulo de ruptura.

This study was performed to evaluate the probabilistic characteristics of the flexural strength of reinforced concrete (RC) flexural members adopted for underground box culverts. These probabilistic models were developed to be adopted for the development of limit state load combination formats for underground RC box culverts. The probabilistic models of uncertainties inherent in the basic design variables were developed to evaluate flexural strength using field material test data as well as field survey data collected from various domestic construction sites of underground box culverts in Korea. The basic design variables include concrete strength, steel rebar strength, and section dimensions, such as slab thickness and rebar locations. Some design variables are assumed to have inherent construction error characteristics, which may be different from those inherent in the RC members for buildings and bridges. The bias models on flexural strength were evaluated based on the experimental results of four-point flexural tests on one-way RC slabs, which were fabricated following the general practice adopted in the local underground box culvert construction process. Based on the probabilistic models of basic design variables, as well as the bias models of flexural strength, Monte Carlo simulations were performed to examine the probabilistic characteristics of both ultimate flexural strength and yield moment strength of RC slab members. Some sensitivity analyses were performed to confirm the soundness of various probability models and the assumptions adopted in the development procedure. The proposed procedure may be applied to develop probabilistic resistance models for structural members, in which the construction error characteristics are assumed to be different from other practices.

Crowdsourcing has been established as an essential means to scale human computation in diverse Web applications, reaching from data integration to information retrieval. Yet, crowd workers have wide-ranging levels of expertise. Large worker populations are heterogeneous and comprise a significant amount of faulty workers. As a consequence, quality insurance for crowd answers is commonly seen as the Achilles heel of crowdsourcing. Although various techniques for quality control have been proposed in recent years, a post-processing phase in which crowd answers are validated is still required. Such validation, however, is typically conducted by experts, whose availability is limited and whose work incurs comparatively high costs. This work aims at guiding an expert in the validation of crowd answers. We present a probabilistic model that helps to identify the most beneficial validation questions in terms of both improvement in result correctness and detection of faulty workers. By seeking expert feedback on the most problematic cases, we are able to obtain a set of high-quality answers, even if the expert does not validate the complete answer set. Our approach is applicable for a broad range of crowdsourcing tasks, including classification and counting. Our comprehensive evaluation using both real-world and synthetic datasets demonstrates that our techniques save up to 60% of expert efforts compared to baseline methods when striving for perfect result correctness. In absolute terms, for most cases, we achieve close to perfect correctness after expert input has been sought for only 15% of the crowdsourcing tasks.

This article investigates how the-organization of the shipbuilding process influences production efficiency-by creating a model of the shipbuilding process utilizing a Bayesian network. In this model, the variables are determined by factors having an impact on production efficiency. Causal dependencies between these variables create the network. Three different configurations of organization are included, namely, line organization, project organization, and a hybrid organization. All model probabilities for reaching the targeted production efficiency are elicited from experts. Based on the results, project organization has the highest probability of achieving the desired efficiency. The higher probability is mainly because, in project organization, clear responsibilities lead to target-oriented problem solving and decision making. The model demonstrates that organization has impact on production efficiency. The model can also be used for further developing the cruise shipbuilding process.

We introduce a probabilistic extension of our previous work SPLA: a formal framework to specify and analyze software product lines. We use probabilistic information to identify those features that are more frequently used. This is done by computing the probability of having a feature in a specific software product line, from now on SPLA(P). We redefine the syntax of SPLA to include probabilistic operators and define new operational and denotational semantics. We prove that the expected equivalence between these two semantic frameworks holds. Our probabilistic framework is supported by a set of scripts to show the model behavior. We briefly comment on the characteristics of the scripts and discuss the advantages of using probabilities to quantify the likelihood of having features in potential software product lines. (C) 2019 Elsevier Inc. All rights reserved.

An important issue in cloud computing is the balanced flow of big data centers, which usually transfer huge amounts of data. Thus, it is crucial to achieve dynamic, load-balanced data flow distributions that can take into account the possible change of states in the network. A number of scheduling techniques for achieving load balancing have therefore been proposed. To the best of my knowledge, there is no tool that can be used independently for different algorithms, in order to model the proposed system (network topology, linking and scheduling algorithm) and use its own probability-based parameters to test it for good balancing and scheduling performance. In this paper, a new, Probabilistic Model (ProMo) for data flows is proposed, which can be used independently with a number of techniques to test the most important parameters that determine good load balancing and scheduling performance in the network. In this work, ProMo is only used for testing with two well-known dynamic data flow scheduling schemes, and the experimental results verify the fact that it is indeed suitable for testing the performance of load-balanced scheduling algorithms.

The large penetration of wind generators in existing electrical grids induces critical issues that are pushing the system operators to improve several critical operation functions, such as the security analysis and the spinning reserve assessment, with the purpose of mitigating the effects induced by the injected power profiles, which are ruled by the intermittent and not-programmable wind dynamics. Although numerous forecasting tools have been proposed in the literature to predict the generated power profiles in function of the estimated wind speed, further and more complex phenomena need to be investigated in order to take into account the effects of the forecasting uncertainty on power system operation. In order to deal with this issue, this paper proposes a probabilistic model based on Markov chains, which predicts the wind power profiles injected into the grid, considering the real generator model and the effects of the power curtailments imposed by the grid operator. Experimental results obtained on a real case study are presented and discussed in order to prove the effectiveness of the proposed method.

In the medical field, experts usually annotate the bio-signals manually, and this is regarded as the gold standard. The manual annotating mode is time-consuming so widely replaced by an automated annotating algorithm. To address the low precision and low robustness of algorithm, we used a probabilistic model to synthesize the heart rate (HR) annotations from multiple annotators for electrocardiograph (ECG) signals and inferred the underlying true annotations and the precision of each annotator when the ground truth was not available. We further introduced signal quality indices in the model to improve our estimation. The 100 noisy ECG recordings in 2014 PhysioNet/computing in cardiology challenge database were divided into two parts, and various annotations for HR were generated by six available annotators. By employing the expectation maximization algorithm, we obtained the estimated true annotations for 80 recordings, and this result had an improvement not only over the best single annotator (17.46%) used in this paper but also to the mean and median strategies (the highest of 23.12% and 42.23%). Furthermore, the estimated precision of the single annotator from the proposed model served as the weight of the test data. In independent test, the weighted average of multiple annotations was superior to the single annotator and the mean strategy on 20 recordings, and its root mean square error (14.22 bpm) was close to that (13.96 bpm) of the proposed model on 80 recordings, demonstrating the robustness of the proposed continuous-valued annotation aggregation model.

The selection of a suitable customer lifetime value (CLV) model is a key issue for companies that are introducing a CLV managerial approach in their online B2C relationship stores. The online retail environment places CLV models on several specific assumptions, e.g. non-contractual relationship, continuous purchase anytime, variable-spending environment. The article focuses on empirical statistical analysis and predictive abilities of selected probabilistic CLV models that show very good results in an online retail environment compared to different model families. For comparison, eleven CLV models were selected. The comparison has been made to the online stores’ datasets from Central and Eastern Europe with annual revenues of hundreds of millions of euros and with almost 2.3 million customers. Probabilistic models have achieved overall good and consistent results on the majority of the studied transactional datasets, with BG/NBD and Pareto/NBD models that can be considered stable with significant lifts from the baseline Status quo model. Abe's variant of Pareto/NBD have underperformed multiple criterions and would not be fully useful for the studied datasets without further improvements. In the end, the authors discuss the deployment implications of selected CLV models and propose further issues for future research to address.

International audience; Dietary iron deficiency (ID) is the first nutritional deficiency in the world, in terms of disability adjusted life years (DALY). This nutritional deficiency may lead to anemia, especially among children, adolescents, and adult women. The aim of this study was to build an original probabilistic model to quantitatively assess the ID, the iron deficiency anemia (IDA) and the subsequent health burden in France expressed in DALY, per age class and gender. The model considered the distribution of absorbed iron intake, the iron requirement distribution established by the European Food Safety Authority and the iron status in France. Uncertainty due to lack of data and variability due to biological diversity were taken into account and separated using a second-order Monte Carlo procedure. A total of 1290 (95% CI = 1230–1350) IDA cases corresponding to 16 (95% CI = 11–20) DALY were estimated per 100,000 individuals per year. The major contributors to IDA burden were menstruating females aged from 25 to 44 years old. Then, a consumption scenario was built with ground beef as intake, an increase in red meat consumption to 100 g/d would not eliminate entirely the IDA burden. The quantitative methodology applied here for France could be reused for other populations. View Full-Text

Dams provide water supply, flood protection, and hydropower generation benefits, but also harm native species by altering the natural flow regime, and degrading the aquatic and riparian habitats. In the present study, which comprised the Douro River basin located in the North of Portugal, the cost-benefit assessment of dams was based upon a balance between the touristic benefits of a dammed Douro, and the ecological benefits of less fragmented Douro sub-catchments. Focused on four sub-catchments (Sabor, T&acirc, mega, C&ocirc, a and Corgo), a probabilistic stream connectivity model was developed and implemented to recommend priorities for dam removal, where this action could significantly improve the movement of potadromous fish species along the local streams. The proposed model accounts for fish movement across the dam or weir (permeability), which is a novel issue in connectivity models. However, before any final recommendation on the fate of a dam or weir, the connectivity results will be balanced with other important socio-economic interests. While implementing the connectivity model, an inventory of barriers (dams and weirs) was accomplished through an observation of satellite images. Besides identification and location of any obstacles, the inventory comprised the compilation of data on surrounding land use, reservoir water use, characteristics of the riparian gallery, and permeability conditions for fish, among others. All this information was stored in a geospatial dataset that also included geographical information on the sub-catchment drainage network. The linear (drainage network) and point (barriers) source data were processed in a computer program that provided or returned numbers for inter-barrier stream lengths (habitat), and the barrier permeability. These numbers were finally used in the same computer program to calculate a habitat connector index, and a link improvement index, used to prioritize dam removal based upon structural connectivity criteria. The results showed that habitat patch connectivity in the Sabor, T&acirc, mega and C&ocirc, a sub-catchments is not dramatically affected by the installed obstacles, because most link improvement values were generally low. For the opposite reason, in the Corgo sub-catchment, obstacles may constitute a relatively higher limitation to connectivity, and in this case the removal of eight obstacles could significantly improve this connectivity. Using the probabilistic model of structural connectivity, it was possible to elaborate a preliminary selection of dams/weirs that critically limit stream connectivity, and that will be the focus of field hydraulic characterization to precisely determine fish movement along the associated river stretches. Future work will also include the implementation of a multi-criteria decision support system for dam removal or mitigation of the critical structures, as well to define exclusion areas for additional obstacles.

International audience; Human activity recognition plays an important role especially in medical applications. This paper proposes a formal approach to model such activities, taking into account possible variations in human behavior. Starting from an activity description enriched with event occurrence probabilities, we translate it into a corresponding formal model based on discrete-time Markov chains (DTMCs). We use the PRISM framework and its model checking facilities to express and check interesting temporal logic properties concerning the dynamic evolution of activities. We illustrate our approach with the models of several serious games used by clinicians to monitor Alzheimer patients. We expect that such a modeling approach could provide new indications for interpreting patient performances. This paper addresses the definition of patient's models for three serious games and the suitability of this approach to check behavioral properties of medical interest. Indeed, this is a mandatory first step before clinical studies with patients playing these games. Our goal is to provide a new tool for doctors to evaluate patients.; La reconnaissance d'activités humaines joue un rôle important en particulier dans les applications médicales. Cet article propose une approche formelle pour modéliser ces activités en prenant en compte les variations possibles du comportement humain. À partir d'une description de l'activité dans laquelle les probabilité associées aux évènements sont données, nous conceptualisons un modèle formel fondé sur les chaînes de Markov à temps discret. Nous utilisons l'environnement PRISM avec son système de vérification de modèles pour exprimer en logique temporelle et vérifier des propriétés intéressantes concernant l'évolution dynamique des activités. Nous illustrons notre approche avec plusieurs jeux sérieux utilisés par les cliniciens pour évaluer les patients Alzheimer. Nous espérons que cette approche de modélisation apporte de nouvelles indications dans l'interprétation des performances des patients. Cet article se concentre sur la définition des modèles de patients pour trois jeux sérieux ainsi que sur la pertinence d'une telle approche formelle pour vérifier des propriétés sur les comportements de patients ayant un intérêt médical. Il s'agit d'une étape préliminaire obligatoire avant de passer à une étude clinique où de vrais patients joueront à ces jeux. Notre but est de proposer un nouvel outil aux médecins pour évaluer leurs patients.

The analysis and detection of skin cancer diseases from skin lesion have always been tedious when done manually. The complex nature of skin lesion images is one of the key reasons for this. The skin lesion images contain noise and artifacts such as hairs, oil and bubbles, blood vessels, and skin lines. They also have variegated colors, low contrast, and irregular borders. Various computational approaches have been designed in the past for aiding in the detection and diagnosis of skin cancer diseases using skin lesion images. The existing techniques have been limited due to the interference of the aforementioned features of skin lesion. Recently, machine learning techniques, in particular the deep learning techniques have been used for the detection of skin cancer. However, they are still limited to the fuzzy and irregular borders of skin lesion images coupled with the low contrast that exists between the diseased lesion and healthy tissues. In this paper, we utilized a probabilistic model for the enhancement of a fully convolutional network-based deep learning system to analyze and segment skin lesion images. The probabilistic model employs an efficient mean-field approximate probabilistic inference approach with a fully connected conditional random field that utilizes a Gaussian kernel. The probabilistic model further performs a refinement of skin lesion borders. The whole framework is tested and evaluated on publicly available skin lesion image datasets of ISBI 2017 and PH2. The system achieved a better performance, having an accuracy of 98%.

The risk of a large-scale oil spill remains significant in marine environments as international maritime transport continues to grow. The environmental as well as the socio-economic impacts of a large-scale oil spill could be substantial. Oil spill models and modeling tools for Pollution Preparedness and Response (PPR) can support effective risk management. However, there is a lack of integrated approaches that consider oil spill risks comprehensively, learn from all information sources, and treat the system uncertainties in an explicit manner. Recently, the use of the international ISO 31000:2018 risk management framework has been suggested as a suitable basis for supporting oil spill PPR risk management. Bayesian networks (BNs) are graphical models that express uncertainty in a probabilistic form and can thus support decision-making processes when risks are complex and data are scarce. While BNs have increasingly been used for oil spill risk assessment (OSRA) for PPR, no link between the BNs literature and the ISO 31000:2018 framework has previously been made. This study explores how Bayesian risk models can be aligned with the ISO 31000:2018 framework by offering a flexible approach to integrate various sources of probabilistic knowledge. In order to gain insight in the current utilization of BNs for oil spill risk assessment and management (OSRA-BNs) for maritime oil spill preparedness and response, a literature review was performed. The review focused on articles presenting BN models that analyze the occurrence of oil spills, consequence mitigation in terms of offshore and shoreline oil spill response, and impacts of spills on the variables of interest. Based on the results, the study discusses the benefits of applying BNs to the ISO 31000:2018 framework as well as the challenges and further research needs.

Technologies are increasingly taking advantage of the explosion of social media (e.g., web searches, ad targeting, personalized geo-social recommendations, urban computing). Estimating the characteristics of users, or user profiling, is one of the key challenges for such technologies. This paper focuses on the important problem of automatically estimating social networking service (SNS) user authority with a given city, which can significantly improve location-based services and systems. The “authority” in our work measures a user’s familiarity with a particular city. By analyzing users’ social, temporal, and spatial behavior, we respectively propose and compare three models for user authority: a social-network-driven model, time-driven model, and location-driven model. Furthermore, we discuss the integration of these three models. Finally, by using these user-profiling models, we propose a new application for geo-social recommendations. In contrast to related studies, which focus on popular and famous points of interests (POIs), our models help discover obscure POIs that are not well known. Experimental evaluations and analysis on a real dataset collected from three cities demonstrate the performance of the proposed user-profiling models. To verify the effect of discovering obscure POIs, the proposed application was implemented to discover and explore obscure POIs in Kyoto, Japan.

The objective of the study is to build an automated system to monitor and control the turbine generator (TG) processes, based on acoustic analysis of the stator failures, for the purpose of improving the operating efficiency of the nuclear power plant (NPP) as a whole. This requires the development of an information and measurement system (IMS) which receives information on the status of the TG stator windings from the noise detectors installed on the TG surface. Acoustic analysis of the TG “hot” spots is used to identify the TG status. Petri nets were used to build a stochastic model of the automated TG monitoring and control system. The model in question allows local changes in the status of the TG stator windings to be detected and makes it possible to reduce the problem dimensionality through the selection of a subset of conditions leading to a risk of the stator failure. The model is simple and features a high speed of response. The use of the IMS structure under consideration enables the TG condition to be detected at the initial failure stage and the measurement process rate to be increased tenfold.

Summary This paper aims to solve the model and parameters with the discussion from the algorithm characteristics of model, the analysis of each algorithm, solving the difficulties, problems and development direction. The paper tries to analyze and summarize the evolution law of algorithm and solution thinking. Some improvements are given on the existing community detection algorithm based on statistical inference.

Gossip based communication is an efficient tool to manage unstructured decentralized Peer-to-Peer (P2P) overlay connectivity. Such unstructured overlay connectivity is prone to security risks. Few malicious nodes are able to isolate all peer of the entire overlay as in Hub attack. Several existing security protocols such as SPSS, TooLate, and S-Gossip have dealt with this Hub attack. A probabilistic gossip-based secure protocol is introduced to handle this attack efficiently. It senses faulty neighbours before gossip with high probability. It uses a single control table to seize the malicious nodes. The comparison shows that the effects of the proposed security protocol are better than others.

Article accessible en ligne (jusqu'au 30 décembre 2015) à partir du lien suivant : http://authors.elsevier.com/a/1S0dA_61OxokRr; International audience; In most developed countries, the acceleration of coastal urbanization during the second half of the twentieth century has gradually resulted in a concentration of residential housing and associated infrastructure and facilities along a narrow coastal strip, along with various environmental, functional and social impacts. This has led certain countries, such as France, to adopt protection legislation with respect to their coastlines. However, while numerous studies describe and analyse the consequences of urbanization on the coastal environment, few of them examine the influence of the coast on urbanization. This paper focuses on the residential development process, considering the coastline as both a pull factor on account of its amenities, and as a constraint due to the legislation put in place to protect it. Our study aims to build a database describing the factors that influence the probability of housing development on vacant land and to analyse the spatiotemporal evolution through a logistic regression modelling approach. While controlling the factors usually mentioned in the scientific literature on urban sprawl and suburbanization, this method is also able to isolate the effects of coastal attractiveness, taking laws and bylaws regulating urbanization in such areas into consideration. It shows in particular that, since the early 2000s, the gradual implementation of a land planning framework specific to the coastal zone has led to improved regulation of housing development in the study area. Throughout the country, the French Coastal Law limited available land for housing development, particularly in coastal areas, by enforcing building restrictions in accordance with conservation principles. The paper concludes with potential improvements to our models, e.g. the integration of local economic factors, such as land costs or changes in taxation, all of which influence housing choices and could potentially regulate suburbanization.

Placement of steel plate shear walls (SPSW) in the building cores around the elevators and stairs necessitates door-type openings in these systems. Because of large dimensions of door openings, the energy dissipation capacity drops significantly and thus, the probability of out-of-plane buckling under lateral load increases. Accordingly, introducing stiffeners around the opening increases the amount of dissipated energy and improves the performance of the SPSW system. This paper evaluates the seismic risk of SPSW systems with different arrangements of stiffeners around the door opening. Risk, in this context, denotes the probability of failure times the cost of failure of a given SPSW. The probability of failure is computed through a finite element reliability analysis in which material properties, element geometries, and the lateral force are random variables. The failure event is described by a limit-state function as the exceedance of the drift ratio of the SPSW from a prescribed threshold. The drift ratio is computed by subjecting the finite element model to non-linear static analysis in ABAQUS. The reliability analysis is conducted for a variety of single-story SPSW models having door opening with different arrangements of stiffeners and also for a typical SPSW model without opening as a base model. Next, decision analysis is employed to identify the optimal arrangement, i.e., the one that is associated with the minimum risk. Finally, the effect of risk aversion on the optimal decision is studied by introducing risk-averse utility functions with different degrees of risk aversion.

The use of ontological knowledge to improve classification results is a promising line of research. The availability of a probabilistic ontology raises the possibility of combining the probabilities coming from the ontology with the ones produced by a multi-class classifier that detects particular objects in an image. This combination not only provides the relations existing between the different segments, but can also improve the classification accuracy. In fact, it is known that the contextual information can often give information that suggests the correct class. This paper proposes a possible model that implements this integration, and the experimental assessment shows the effectiveness of the integration, especially when the classifier&rsquo, s accuracy is relatively low. To assess the performance of the proposed model, we designed and implemented a simulated classifier that allows a priori decisions of its performance with sufficient precision.

This study aims to propose a statistical model to automatically assess the correctness of rehabilitation movements performed by patients. Ten Hidden Markov Models are developed and trained, in order to discriminate in real time the main faults in the execution of therapeutic exercises for reeducation after hip replacement surgery. An experiment on real patients shows that the algorithm is as accurate as the physiotherapists to discriminate and identify the error in the movement. The results are discussed in terms of the required setup for a successful implementation of this method in a tele-rehabilitation platform.

In this paper, tests of plug-pin joints are conducted in order to obtain their mechanical parameters, including semi-rigid property. To solve the difficulties of multi-point displacement measurements for small joints, this investigation proposes a vision-based measurement system based on the principle of binocular stereo vision to improve measurement accuracy. Accurate sub-pixel location is achieved according to a template-matching algorithm based on grayscale. Joint performance, including horizontal bar joint tension and compression, semi-rigidity between horizontal bars and upright rods and bracing tension and compression, is investigated in order to acquire joint failure modes as well as load and displacement (or moment and rotation angle) curves. Through data fitting, multi-linear simplified models are proposed to illustrate the joints’ mechanical performance. This paper also investigates geometric imperfection of temporary member structure with plug-pin joints based on several substructure models and temporary grandstand units using a total station theodolite. The probabilistic models of initial member out-of-straightness and story frame out-of-plumb have been acquired, which can be used into Monte Carlo simulation to create stochastic model of the temporary member structure.

Among the factors that mostly influence the damage of ancient masonry structures, viscous sliding and creep-fatigue interaction are among those that are worth mentioning; in particular, persistent heavy loads have proved to be the cause of a continuous and slow damage. The problem of obtaining a reliable estimate of the service life of historical masonry buildings toward the effects of persistent load involves many uncertainties; therefore it can conveniently be treated with a probabilistic method. The proposed stochastic approach is rather simple and finds its basis in that proposed in the previous chapter for the evaluation of the durability of stone material over time. In this chapter, the results of laboratory pseudo-creep tests on ancient masonry of different ages will be interpreted through the application of a probabilistic model; the problem of progressive damage for high persistent loads is therefore treated as a classic problem of reliability by arriving at a probabilistic forecast of the times of collapse.

The Kingdom of Saudi Arabia (KSA) is situated in an arid region and faces a chronic challenge to meet its increasing water demand. Riyadh is the capital of KSA and home to about six million people. The water demand is mostly met by groundwater resources (up to 48%), while the desalination plants cover the rest of the water supply requirements. There is a potential risk of a significant gap in water demand–supply due to the retirement of old desalination plants. This study, therefore, developed a probabilistic model to forecast desalinated water demand in Riyadh for domestic purposes up to the year 2040 based on three scenarios: low growth, the most likely (mean), and high growth scenario. The results showed that an investment of about US$6.24, 11.59, and 16.04 billion is required to meet the future domestic water demand of the city for the next 25 years based on low, mean, and high growth scenarios, respectively. Moreover, a strong commitment to public–private partnership is required to remove the fiscal budget burden related to the desalination along with public awareness campaigns to reduce per capita water consumption, upgrading the water tariff system and using renewable energy to run desalination plants.

Despite the efforts in recent years to improve safety on construction sites, construction still has one of the highest fatalities rates among Canadian industries. The objective of this study is to improve the safety on construction sites by developing an Accident Warning System that is capable of predicting potential accidents, and prevent them by alerting the involved parties. Recent studies have shown that close monitoring the real-time status of moving objects and providing immediate feedback to the workers can improve the effectiveness of any safety strategies. The proposed system consists of an Ultra Wideband Real-Time Locating System and a probabilistic Accident Prediction Model. Using the information provided by the UWB RTLS, the model predicts the future locations of the monitored objects. These are analyzed, and a warning is issued when the potential occurrence of an accident is identified. As such, rather than detecting accidents shortly before they occur, the model is able to predict them and alert the involved parties in advance, providing ample time for reaction. The decision of issuing a warning is taken based on the expected consequences of the probable accident. The proposed system can improve the current status of safety on construction sites through predicting and preventing accidents. It can be used to monitor the operations on construction sites in order to reduce the number of injuries and fatalities.

International audience; Wireless communication links tend to be employed more and more in safety-critical railway applications. Their safe use in an advanced train control system (TCS) is an issue that is addressed in this paper by characterizing the TCS service inter- ruption due to communication errors. More precisely, occurrence probabilities of single errors are first discussed. Then, we obtain probabilistic analytical expressions of several temporal conditions that lead to a TCS service interruption, here a train emergency braking (the critical event). The accuracy of this analytical ap- proach is proved when the results are compared with those given by a simulation approach with a Petri net model. Additionally, as the use case related to the “trains’ separation” is considered in this paper, an analytical evaluation process is proposed to discuss the tolerated time margins that can be fixed to limit the critical-event occurrence probability due to the wireless communication errors.; Wireless communication links tend to be employed more and more in safety-critical railway applications. Their safe use in an advanced train control system (TCS) is an issue that is addressed in this paper by characterizing the TCS service interruption due to communication errors. More precisely, occurrence probabilities of single errors are first discussed. Then, we obtain probabilistic analytical expressions of several temporal conditions that lead to a TCS service interruption, here a train emergency braking (the critical event). The accuracy of this analytical approach is proved when the results are compared with those given by a simulation approach with a Petri net model. Additionally, as the use case related to the 'trains' separation' is considered in this paper, an analytical evaluation process is proposed to discuss the tolerated time margins that can be fixed to limit the critical-event occurrence probability due to the wireless communication errors.