Littérature scientifique sur le sujet « Adverse scenarios calibration »

Communication skills training is essential in nurse education. Miscommunication may lead to adverse events and unsafe healthcare. To date, valid and reliable instruments to serve both communication training and assessment purposes across different cultural contexts are scarce. The present study empirically tested a French-language version of the Health Communication Assessment Tool© (HCAT-f) across different levels of communication skills performance to establish its reliability and validity through a cognitive fluency framework. Ten experts in communication and 52 nurse educators rated three videos simulating conversations between a nurse and a patient scheduled for lumpectomy. Each video captured a different level of communication skills performed by the nurse: High, medium, and low. Three distinct constructs were identified, i.e., professional presentation, empathy, and trust building. At absolute single-measure, an ICC = .43 suggested adequate interrater reliability of the whole scale for the medium-performed scenario, which decreased in low-performed (ICC = .35) and high-performed (ICC = .18) scenarios. The HCAT-f fulfils the criteria of linguistic equivalence, contextual relevance, and demonstrates acceptable construct validity. It can be used as a summative assessment tool after prior training on scale calibration is in place because interrater agreement was difficult to be established in high and low performance scenarios.

About 44% of the Indian landmass experiences the adverse impact of land degradation. This loss of sediments caused by soil erosion reduces the water quality of local water bodies and decreases agricultural land productivity. Therefore, decision-makers must formulate policies and management practices for sustainable management of basins that are cost-effective and environment friendly. Application of the best management practices (BMPs) to properly manage river basins is difficult and time-consuming. Its implication under various climate change scenarios makes it more complicated but necessary to achieve sustainable development. In this study, the soil and water assessment tool (SWAT) model was employed to prioritize the Tons river basin’s critical areas in the central Indian states coupled with future climate scenario analysis (2030–2050) using Representative Concentration Pathway (RCP) 4.5 and RCP 8.5 scenarios. The SWAT model was calibrated and validated for simulation of streamflow and sediment yield for daily and monthly scales using the sequential uncertainty fitting (SUFI-2) technique. The values of coefficient of determination ( R2), Nash–Sutcliffe efficiency (NSE), percent bias (PBIAS), and root mean square error (RMSE)-observations standard deviation ratio (RSR) were .71, .70, −8.3, and .54, respectively during the calibration period, whereas for validation the values were .72, .71, −3.9, and .56, respectively. SWAT model underestimated the discharge during calibration and overestimated the discharge during validation. Model simulations for sediment load exhibited a similar trend as streamflow simulation, where higher values are reported during August and September. The average annual sediment yield of the basin for the baseline period was 6.85 Mg ha−1, which might increase to 8.66 Mg ha−1 and 8.79 Mg ha−1 in the future years 2031–2050 and 2081–2099, respectively. The BMPs such as recharge structure, contour farming, filter strip 3 and 6 m, porous gully plugs, zero tillage, and conservation tillage operations have been considered to evaluate the soil and water conservation measures. Recharge structure appeared to be the most effective measure with a maximum reduction of sediment by 38.98% during the baseline period, and a 37.15% reduction in the future scenario. Sub-watersheds, namely SW-8, SW-10, SW-12, SW-13, SW-14, SW-17, SW-19, SW-21, SW-22, and SW-23, fall under the high category and are thus considered a critical prone area for the implementation and evaluation of BMPs. Compared to the baseline period, the effectiveness of BMPs is slightly decreasing in the 2040s, increasing in the 2070s and decreasing in the 2090s. Recharge structure and filter strip 6 m have been found to nullify the high soil erosion class completely. Overall, SWAT model simulations under the RCP 8.5 scenarios were observed to be reliable and can be adopted to identify critical areas for river basins having similar climatic and geographical conditions.

AbstractThe safety of the intended functionality (SOTIF) has become one of the hottest topics in the field of autonomous driving. However, no testing and evaluating system for SOTIF performance has been proposed yet. Therefore, this paper proposes a framework based on the advanced You Only Look Once (YOLO) algorithm and the mean Average Precision (mAP) method to evaluate the object detection performance of the camera under SOTIF-related scenarios. First, a dataset is established, which contains road images with extreme weather and adverse lighting conditions. Second, the Monte Carlo dropout (MCD) method is used to analyze the uncertainty of the algorithm and draw the uncertainty region of the predicted bounding box. Then, the confidence of the algorithm is calibrated based on uncertainty results so that the average confidence after calibration can better reflect the real accuracy. The uncertainty results and the calibrated confidence are proposed to be used for online risk identification. Finally, the confusion matrix is extended according to the several possible mistakes that the object detection algorithm may make, and then the mAP is calculated as an index for offline evaluation and comparison. This paper offers suggestions to apply the MCD method to complex object detection algorithms and to find the relationship between the uncertainty and the confidence of the algorithm. The experimental results verified by specific SOTIF scenarios proof the feasibility and effectiveness of the proposed uncertainty acquisition approach for object detection algorithm, which provides potential practical implementation chance to address perceptual related SOTIF risk for autonomous vehicles.

Potato is the most important non-cereal crop in the world and the most prominent winter season crop in India. Growth and yield of potato crop is very much sensitive to higher temperatures and the moisture stress. Hence, the anticipated increase of temperature due to global warming and climatic variability will have anadverse impact on potato production. Keeping this in view, a research work was carried out with the objectives to assess the impact of climate change on potato production and evaluating agronomic adaptation options through a crop growth simulation model (CGSM). Field experiments were carried out to prepare the minimum dataset for calibration and validation of one CGSM, namely InfoCrop. After validation, the model was used to predict the future tuber yield of ten selected stations situated under different agroclimatic regions of the State. In the future scenario 2050, the simulated yield for mid November planted crop likely to be about 11% less than the present level of mean yield. If the crop is planted in December, the percentage of yield reduction may be around 25%.The projected yield reduction, for the stations of higher latitude, is found to be negligible. Three possible agronomic adaptation options, viz., adjustment of date of planting, increase of seed rate and varying sprout length of seed tubers, have been tried as adaptation strategies to combat the adverse effects of climate change. It is concluded that the mid-November planting and longer sprout length will be the best adaptation option. However, the enhanced seed rate is not a viable adaptation option.

Abstract. China is undergoing rapid urbanization during the past decades. For example, the proportion of urban population in Beijing has increased from 57.6 % in 1980 to 86.3 % in 2013. Rapid urbanization has an adverse impact on the urban rainfall-runoff processes, which may result in the increase of urban flood risk. In the present study, the major purpose is to investigate the impact of land use/cover change on hydrological processes. The intensive human activities, such as the increase of impervious area, changes of river network morphology, construction of drainage system and water transfer, were considered in this study. Landsat TM images were adopted to monitor urbanization process based on Urban Land-use Index (ULI). The SWMM model considering different urbanized scenarios and anthropogenic disturbance was developed. The measured streamflow data was used for model calibration and validation. Precipitation with different return periods was taken as model input to analyse the changes of flood characteristics under different urbanized scenarios. The results indicated that SWMM provided a good estimation for storms under different urbanized scenarios. The volume of surface runoff after urbanization was 3.5 times greater than that before urbanization; the coefficient of runoff changed from 0.12 to 0.41, and the ratio of infiltration decreased from 88 to 60 %. After urbanization, the time of overland flow concentration increased while the time of river concentration decreased; the peak time did not show much difference in this study. It was found that the peak flow of 20-year return-period after urbanization is greater than that of 100-year return-period before urbanization. The amplification effect of urbanization on flood is significant, resulting in an increase of the flooding risk. These effects are especially noticeable for extreme precipitation. The results in this study will provide technical support for the planning and management of urban storm water and the evaluation on Low Impact Development (LID) measures.

This article presents the definition, building, calibration and application of a system dynamics simulation model to quantify the present and future comparative advantages and disadvantages of using forest and agricultural residual biomass for energetic purpose through different processes and technologies, on a regional basis. A dynamic structure of the agricultural and forestry biomass process activities (i.e. production, transport, conversion and consumption) based on regional time-series data was built, implemented into and calibrated within a dynamic simulation tool (Vensim software) within a Hungarian county. Besides the agriculture and forestry sectors, the model includes data about demography, economy, environment and land use, among others. For carrying out the assessment, realistic scenarios of future biomass processes and of biomass use were formulated, characterised by quantitative sustainable development indicators and simulated within the dynamic model. The results prove that the introduced model supports decision making of biomass utilisation in a regional scale, through comparing and analysing quantitative changes of economic, social and environment indicators, which characterise a sustainable regional development. The model therefore allows analyses and evaluation of not only partial prosperous or adverse characteristics of biomass processes and use, but also to relate regional components and their development over time. The developed dynamic tool is therefore a powerful method to model complex systems, such as a regional energy system, considering the framework of the regional preferences stated by the regional stakeholders. It can thus provide useful decision support, especially when used as a priority-based screening of potential regional development programmes in the energy field.

Abstract Changes in land cover, including deforestation, can have significant effect on watershed hydrology. We used hydrological model with distributed parameters to evaluate the effect of simulated deforestation on water balance components in the watershed Ulička (97 km2, 84.3% forest cover) located in the eastern Slovakia. Under the current land cover, average interception accounted for 21.1% of the total precipitation during the calibration period 2001-2013. Most of the precipitation (77%) infiltrated into the soil profile, and less than half of this amount percolated into the ground water aquifer. The surface runoff accounted for 1.2% of the total precipitation only, while the interflow accounted for ca. 12%. The largest proportion of the precipitation contributed to the base flow (23%). Watershed`s deforestation induced significant decrease in the interception and evapotranspiration (by 76% and 12%, respectively). At the same time, total runoff, surface runoff, interflow and base flow increased by 20.4, 38.8, 9.0 and 25.5%, respectively. Daily discharge increased by 20%. The deforestation significantly increased peak discharge induced by a simulated extreme precipitation event with the recurrence interval of 100 years. In the deforested watershed, the peak discharge was higher by 58% as compared with the current land cover. Peak discharge occurred in 432 minutes with the current land cover and in 378 minutes with deforestation, after the precipitation event had started. The presented assessment emphasized the risk of adverse effect of excessive deforestation on watershed hydrology. At the same time, the developed model allows testing the effect of other land cover scenarios, and thus supports management in the investigated watershed.

Climate changes are the main motivation for alteration of ecosystems; in fact the effects of these changes on biodiversity and ecosystems are considered as the most challenging cases in present century. Therefore, since the lakes are the most important services and functions of ecosystems, the effect of climate change on water level fluctuations of Aguelmam Sidi Ali Lake (Morocco) was analyzed as a natural ecosystem in this essay. The regular observations from the lake have found that a very sensitive withdrawal of their water level during the dry years. Therefore, a hydrodynamic model has been used to simulate the condition of Aguelmam Sidi Ali Lake, with observed field data (model has been set up to run annually for a total 35 year data, i.e., precipitation, evaporation, runoff discharges and water-level which are available for the last 35 years) being used for model calibration and validation. Additionally, the model validation process showed that the model results fit the observed data fairly well (R2 = 0.70−0.74, root mean square error [RMSE] = 1.63−1.71 m). On the other hand, different hydrological conditions regarding lake input and output data were tested and water depth was calculated using bathymetry to predict water-level fluctuations in the future. The results predict that the water-level will decrease continuously (In 2044, the water level will reach to 6.20 m). The water level decrease due to the climate change in both scenarios (dry and very dry) is dramatic and a profound adverse impact on the environmental balance is predicted in the region. Additionally, the lake will be dried up in about 20 years if very dry conditions continue in the region. This reveals the importance of this type of approach for obtaining a first-order estimate of water-level variations in Sidi Ali Lake, affected by climate change.

Background: Children and young adults diagnosed with HR-HL have a 5-year survival rate of >95%. Yet, as compared to the general population, survivors remain at elevated risk for therapy-related early mortality and excess long-term morbidity. Second malignancies (SMNs), cardiac disease, and pulmonary complications are major contributors to excess mortality/morbidity experienced by survivors of HL. Since several treatment regimens used for younger HR-HL all offer excellent overall survival and tolerable short-term morbidity profiles, it is not clear which is best. The objective of this study was to compare the life-long impact of treatment-related toxicity and early mortality associated with 4 common treatment scenarios used to treat HR-HL. Outcomes measured included overall life expectancy (LE), 10-year post-diagnosis QALE and cause of death. Methods: A microsimulation model was developed using the lifetime horizon for a cohort of 10-year survivors of HR-HL treated with 4 common treatment scenarios utilized by pediatric and adult oncology groups. The data from 5522 adult 10-year survivors of childhood cancers in the SJLIFE cohort (56% males; mean age at last follow-up=35.3 years [y], range 18.9-67.9; ≥18y at study enrollment) was used to estimate the mortality risk and incidence of cardiac and pulmonary complications and SMNs as functions of age at primary cancer diagnosis, attained age, sex, race, and treatment exposures including cumulative doses of anthracyclines, alkylating agents, and bleomycin plus radiation dose to the heart and chest. Prospective and retrospective clinical assessments were used to obtain and grade late-effect outcomes using a modified Common Terminology Criteria for Adverse Events. Rates from population-based registries and published systematic analyses were used for background mortality, mortality risk from the complications and for extrapolation. Calibration was performed against SJLIFE data to ensure model consistency. Quality of life utility weights (ranging from 1 for perfect health to 0 for dead) associated with complications were estimated from the SJLIFE cohort. The mortality and incidence functions and utility weights were applied to the lifetime of 10,000 simulated HL survivors (same demographic distribution as in the 667 SJLIFE HL participants: 58% males; mean age at HL diagnosis=13.9y, range 3-25) starting at 10y after HL diagnosis to death to calculate the average LE and QALE associated with the 4 treatment scenarios. Results: The figure shows the results for 4 scenarios based on OEPA [vincristine, etoposide, prednisone, doxorubicin] (2 cycles) plus COPDac [cyclophosphamide, vincristine, prednisone, dacarbazine] (4 cycles) and ABVD [doxorubicin, bleomycin, vincristine, dacarbazine] (6 cycles), both with and without heart and chest radiation. The average LE for the cohort assuming general population mortality risk is 80.8y (dashed line). Average LEs associated with the 4 scenarios are represented by the asterix above each bar. The average 10-year post-diagnosis QALE and the proportion of time spent living with cardiac, pulmonary and/or SMN are depicted in the colored stacked bar-chart. The decrement in average LE due to treatment for HR-HL ranged from 12.8y for OEPA/COPDac alone to 25.8y for ABVD with radiation. OEPA/COPDac based regimens without and with radiation had higher average LE (68.1y and 64.1y, respectively) and QALE (29.9y and 27.4y, respectively) compared to the ABVD regimens (LE: 60.9y and 55.0y; QALE: 25.0y and 22.7y). The addition of radiation reduced average LE and QALE in both the OEPA/COPDac (4.0y and 2.5y) and ABVD (5.8y and 2.3y) regimens and increased the proportion of the cohort who developed pulmonary disease and SMNs for all scenarios. The cohort receiving OEPA/COPDac with radiation had similar average post-10 year QALE compared to those treated with ABVD without radiation. Increased proportional mortality due to SMN was observed among the cohort exposed to ABVD. Conclusion: Using a lifetime horizon simulation approach, we demonstrate that the OEPA/COPDac regimen results in greater average LE and QALE when compared against ABVD for HR-HL. This approach highlights a yet untapped opportunity to leverage data from survivorship cohorts by evaluating late-effect trade-offs to inform clinical trial design and aid patient decision-making when choosing between different treatment options that have similar efficacy. Disclosures No relevant conflicts of interest to declare.

Background Although immunotherapy (IMT) provides significant survival benefits in selected patients, approximately 10% of patients experience (serious) immune-related adverse events (irAEs). The early detection of adverse events will prevent irAEs from progressing to severe stages, and routine testing for irAEs has become common practice. Because a positive test outcome might indicate a clinically manifesting irAE that requires treatment to (temporarily) discontinue, the occurrence of false-positive test outcomes is expected to negatively affect treatment outcomes. This study explores how the UPPAAL modeling environment can be used to assess the impact of test accuracy (i.e., test sensitivity and specificity), on the probability of patients entering palliative care within 11 IMT cycles. Methods A timed automata-based model was constructed using real-world data and expert consultation. Model calibration was performed using data from 248 non–small-cell lung cancer patients treated with nivolumab. A scenario analysis was performed to evaluate the effect of changes in test accuracy on the probability of patients transitioning to palliative care. Results The constructed model was used to estimate the cumulative probabilities for the patients’ transition to palliative care, which were found to match real-world clinical observations after model calibration. The scenario analysis showed that the specificity of laboratory tests for routine monitoring has a strong effect on the probability of patients transitioning to palliative care, whereas the effect of test sensitivity was limited. Conclusion We have obtained interesting insights by simulating a care pathway and disease progression using UPPAAL. The scenario analysis indicates that an increase in test specificity results in decreased discontinuation of treatment due to suspicion of irAEs, through a reduction of false-positive test outcomes.

This thesis consists in three essays that study the linkages between real and financial factors from different perspectives. Chapter 1, co-authored with Ester Faia and Valeria Patella, introduces a full set of ambiguity attitudes, which endogenously induces agents' optimism in booms and pessimism in recessions, in a model where borrowers face occasionally binding collateral constraints. We use GMM techniques with latent value functions to estimate the ambiguity attitudes process, showing that agents update their belief over the credit cycle in a way coherent with our preferences specification. By simulating a crisis scenario, we show that optimism in booms is responsible for strong leverage build-up before the crises while pessimism in recessions implies sharper de-leveraging and asset price bursts. Analytically and numerically, using global non-linear methods, we show that our ambiguity attitudes coupled with the collateral constraints help to explain relevant asset price and leverage cycle facts around the unfolding of financial crises. Chapter 2, co-authored with Carmelo Salleo, studies the strategic interactions between monetary and macroprudential authorities through the lens of an open-economy monetary model featuring trade and financial ows between two symmetric countries. Characterizing a set of Within-Country Cooperative and Nash Equilibria for different degrees of trade and financial integration, the analysis identifies large costs associated to the strategic interaction between the domestic authorities. Moreover, the gains from cooperation are strongly affected by the degree of cross-country integration and by the channel through which the integration is realized: larger trade ows reduce the gains, while higher financial globalization makes cooperation more valuable. Then, moving to a Between-Countries Cooperative and Nash Equilibria analysis, we confirm that cooperation is beneficial from both the country-specific and the global perspective. Chapter 3, co-authored with Javier Ojea Ferreiro and Elena Rancoita proposes an innovative methodology for the design of adverse scenarios for macroprudential policies calibration and impact assessment. Our methodology allows building tailored scenarios characterized by two main features. First, there is a stable and transparent mapping of the cyclical systemic risk level into the path of the scenario's target variables, which are those variables that determine the overall scenario's severity. Second, the path of the other complementary variables is calibrates with a multivariate copula model estimated with macro and financial data (MacroFin Copula). Simulating the model for Euro Area countries, we show that our methodology is able to calibrate adverse scenarios that properly replicate the global financial crises dynamics in terms of severity and co-movement between the key macroeconomic and financial variables.

This chapter examines financial stress-testing, most notably microprudential bank stress-testing in the EU. It begins by differentiating between micro and macroprudential stress tests. Microprudential stress-testing takes place at the level of individual financial institutions (micro perspective) serving as a risk management tool used both by individual banks (to gauge risk exposures for internal purposes) and supervisors (to assess the resilience of banking institutions to adverse market developments). Meanwhile, macroprudential stress tests are a tool designed to assess the system-wide resilience to shocks to support the design and calibration of macroprudential policy with the objective to identify and reduce systemic risk. Therefore, the focus is on the system-wide resilience and not on individual resilience, with the possible application for defining the size of counter-cyclical buffers. The chapter then addresses the topic of EU-wide stress tests from three perspectives, namely (i) its implementation and the methodologies used from 2009 onwards; (ii) the outcomes of such stress-testing exercises; and (iii) a comparative analysis of EU-wide stress tests with its US and UK counterparts, also including the sensitivity scenarios developed to assess the possible effects of the COVID-19 pandemic.

Abstract One of the main challenges for the safety validation of autonomous driving vehicles lies in the influence of weather phenomena. As each of the main sensors, namely LIDAR, radar, and cameras increases its sensitivity to detect smaller objects faster and hence be able to drive autonomously at higher speeds, the possible influence of environmental perturbations on their perception increases. Those perturbations could cause false positives, confuse self-calibration algorithms and reduce the sensor range. On the other hand, they could constitute a source of valuable information if the dependencies are known and properly characterized to better evaluate and predict road conditions or adapt its operation mode. The main purpose of this work is to evaluate the impact of adverse weather conditions mainly heavy snow on LIDAR sensor performance in dynamic conditions, i.e. moving sensors and moving-target scenarios.

Abstract Hydraulic fracturing operations are increasingly becoming larger through a reduction in cluster spacing, wellbore proximity, and fast-paced "zipper" operations to maximize efficiency. Low-cost operation is the primary motive behind this. Some issues hindering optimal performance include fracturing plug failure, under/over stimulation and adverse well performance as stated in published literature. This paper addresses plug effectiveness and impacts on production, fracture geometry and well spacing using a historical review of fracturing plugs and a simulation-based approach. Using numerical simulation, the study also shows that if a fracture plug failure happens, it results in under or overstimulation which leads to inefficient reservoir drainage. This paper will elaborate on the technological growth for fracturing plugs (material choices and plug architecture). A simulation-based approach will then be used to understand the effect of fracturing plug failures on well interference and the production impact on a three-well pad containing one parent and two child wells in an unconventional reservoir. A parent well will be produced for 1.5 years before two child wells are drilled and completed. Knowing the performance from the three-well case, a comparative simulation study is performed for frac plug failure at 25%, 50%, and 75% of the job pumped on the child wells. Quantifying the potential losses from plug failure can help to understand potential effects on well recovery. Such an event can be recognized through a surface pressure signature or using DAS/DTS or microseismic measurements or even using sealed wellbore pressure monitoring (SWPM) on parent wells. A comparative analysis using numerical simulation of hydraulic fractures and their calibration and production modeling is presented in this paper. Three cases for 25%, 50% and 75% understimulation is considered in failed stages. One-third of the well portion of the well is considered understimulated, one third is overstimulated and one third is treated as per design. Impact of well spacing (330 ft and 660 ft) as well as parent well depletion is considered to create a realistic scenario of plug failures to quantify the impact. Applying advanced materials and using the bottom set, mandrel-less concept has resulted in the shortest, strongest, best milling and most reliable plugs currently available. At lesser well spacing, this is even more drastic when parent wells get fracture hits due to prior depletion and also because stages larger than planned are pumped at the neighboring child wells due to fracture plug failure. This paper will enable the reader to make better decisions about the best plug technology depending on the application. It also provides a quantifiable understanding on the adverse effects of frac plug failure on production. So far, this understanding has been limited to field observations alone. This paper combines the understanding from field evidence, literature and modeling-based approach.