Uncertain Response Research Articles

Improving Autonomous Vehicle Controls and Quality Using Natural Language Processing-Based Input Recognition Model

In contemporary development, autonomous vehicles (AVs) have emerged as a potential solution for sustainable and smart transportation to fulfill the increasing mobility demands whilst alleviating the negative impacts on society, the economy, and the environment. AVs completely depend on a machine to perform driving tasks. Therefore, their quality and safety are critical concerns for driving users. AVs use advanced driver assistance systems (ADASs) that heavily rely on sensors and camera data. These data are processed to execute vehicle control functions for autonomous driving. Furthermore, AVs have a voice communication system (VCS) to interact with driving users to accomplish different hand-free functions. Some functions such as navigation, climate control, media and entertainment, communication, vehicle settings, vehicle status, and emergency assistance have been successfully incorporated into AVs using VCSs. Several researchers have also implemented vehicle control functions using voice commands through VCSs. If a situation occurs when AV has lost control due to malfunctioning or fault in the installed computer, sensors and other associated modules, driving users can control the AV using voice notes to perform some driving tasks such as changing speeds, lanes, breaking, and directing the car to reach a safe condition. Furthermore, driving users need manual control over AV to perform these tasks in some situations, like lane changing or taking an exit due to divergence. These tasks can also be performed with the help of voice commands using VCSs. Therefore, finding the exact voice note used to instruct different actuators in risk situations is crucial. As a result, VCSs can greatly improve safety in critical situations where manual intervention is necessary. AVs’ functions and quality can be significantly increased by integrating a VCS with an ADAS and developing an interactive ADAS. Now, the driver functions are controlled by voice features. Therefore, natural language processing is utilized to extract the features to determine the user’s requirements. The extracted features control the vehicle functions and support driving activities. The existing techniques consume high computation while predicting the user command and causing a reduction in the AVs’ functions. This research issue is overcome by applying the variation continuous input recognition model. The proposed approach utilizes the linear training process that resolves the listening and time-constrained problems and uncertain response issues. The proposed model categorizes the inputs into non-trainable and trainable data, according to the data readiness and listening span. Then, the non-distinguishable data were validated by dividing it into the linear inputs used to improve the response in the AVs. Thus, effectively utilizing training parameters and the data decomposition process minimizes the uncertainty and increases the response rate. The proposed model has significantly improved the exact prediction of users’ voice notes and computation efficiency. This improvement enhances the VCS quality and reliability used to perform hand-free and vehicle control functions. The reliability of these functions ultimately improves the safety of AVs’ driving users and other road users.

Avances importantes en medicina regenerativa. Una visión al futuro de la oftalmología

The treatment of corneal lesions continues to be a topic of great interest. However, despite the development of new technologies and due to the slow evolution and uncertain healing of corneal lesions, they still represent a public health problem since patients can be affected for life, affecting the family and the health system due to the costly and uncertain response to treatment. For this reason, the need arises to seek therapeutic alternatives to treat these patients.

Optimizing the configuration of plasma radiation detectors in the presence of uncertain instrument response and inadequate physics

We present a general method for optimizing the configuration of an experimental diagnostic to minimize uncertainty and bias in inferred quantities from experimental data. The method relies on Bayesian inference to sample the posterior using a physical model of the experiment and instrument. The mean squared error (MSE) of posterior samples relative to true values obtained from a high fidelity model (HFM) across multiple configurations is used as the optimization metric. The method is demonstrated on a common problem in dense plasma research, the use of radiation detectors to estimate physical properties of the plasma. We optimize a set of filtered photoconducting diamond detectors to minimize the MSE in the inferred X-ray spectrum, from which we can derive quantities like the electron temperature. In the optimization we self-consistently account for uncertainties in the instrument response with appropriate prior probabilities. We also develop a penalty term, acting as a soft constraint on the optimization, to produce results that avoid negative instrumental effects. We show results of the optimization and compare with two other reference instrument configurations to demonstrate the improvement. The MSE with respect to the total inferred X-ray spectrum is reduced by more than an order of magnitude using our optimized configuration compared with the two reference cases. We also extract multiple other quantities from the inference and compare with the HFM, showing an overall improvement in multiple inferred quantities like the electron temperature, the peak in the X-ray spectrum and the total radiated energy.

Validity of donor-reported iron supplementation practices obtained at the time of donation.

Iron supplementation (IS) improves blood donors' iron stores and allows more frequent blood donation. Understanding the accuracy of self-reported IS is helpful for potential application of IS practices to donor eligibility or donation intervals. Successful whole blood and red cell apheresis donors completed a survey at donation on the use of select dietary supplements. Respondents reporting use of either iron pills (IP) or multivitamins (MV) were invited by email to complete a similar follow-up survey 6-8 weeks later and to provide the quantitative iron content of IS by referring the donor to the pill bottle label. Consistency between baseline and follow-up responses was assessed overall and by pill type and demographic variables. Of 2444 donors answering the baseline survey, 40% (978) reported MV or IP at donation, 354 of whom completed the follow-up survey. A majority of survey respondents (56%-61%) reported taking iron across the two surveys, and 21%-24% took MV but were uncertain if their pills contained iron. Of 215 reporting IS at baseline, overall concordance at follow-up was 68% and was higher for donors who were female, ≥50-years old, and taking iron as an iron pill rather than in a multivitamin. Consistency of donor responses may be insufficient for use in guiding donor eligibility. Referring donors to their pill bottles was unsuccessful in improving the high frequency of uncertain responses. Incorporating IS into donor eligibility determinations is a complex endeavor that will benefit from careful planning and from post-implementation monitoring.

Coverage and Determinants of Birth Dosage of Newborn Immunization in Rural Areas of Dehradun District, Uttarakhand, India: A Community-Based Cross-Sectional Study.

The Universal Immunization Program of India is one of the most cost-effective interventions in public health. Missing or delaying vaccination may elicit an uncertain immune response in the body, making the population susceptible to vaccine-preventable diseases. The objectives of this study were to determine the coverage of newborn immunization for birth doses under the National Immunization Schedule and its epidemiological determinants in the rural areas of the Dehradun district, Uttarakhand. A community-based cross-sectional study was conducted for a period of one year with a sample size of 570. World Health Organization (WHO) 30 Cluster sampling technique was used with 19 children in each cluster. The data on determinants were collected using a semi-structured, pre-designed schedule through a house-to-house survey. Multivariate analysis was conducted to identify factors associated with immunization coverage, taking the significance level as P < 0.05. The coverage of the Bacillus Calmette-Guérin vaccine, oral polio vaccine zero dose and hepatitis B birth dose were 100%, 91.9%, and 58.8%, respectively in the study area with an overall prevalence of incomplete coverage of newborn immunization as 42.5%. The most cited reason for children being unimmunized with birth doses was the unavailability of vaccines at the birthplace center (29.6%). The prevalence of incomplete coverage of newborn immunization was quite high, which was undermining the holistic approach of the National Immunization Program. Vaccine availability and accessibility at the birthplace with capacity building and training of the healthcare workers may be considered to ensure coverage of birth doses.

Saphenous sparing evolution

While Saphenous stripping became the gold standard technique for varicose veins treatment in the second part of last century, soon, leaving the Saphenous stem in place for surgical simplification and, successively, for arterial bypass use became a subject of research, showing uncertain responses. Ultrasound facilities allowed better hemodynamic knowledge; a new hemodynamic approach (CHIVA) appeared that limited venous ablation and redirected the blood to the deep system normalizing the pressures, allowing Great Saphenous Vein preservation. Even new technologic evolutions (thermal, chemical), created for venous ablation, could be converged toward this conservatory attitude simply changing the mental scheme.

RNA Interference Approach Is a Good Strategy against SARS-CoV-2

COVID-19, caused by SARS-CoV-2, created a devastating outbreak worldwide and consequently became a global health concern. However, no verifiable, specifically targeted treatment has been devised for COVID-19. Several emerging vaccines have been used, but protection has not been satisfactory. The complex genetic composition and high mutation frequency of SARS-CoV-2 have caused an uncertain vaccine response. Small interfering RNA (siRNA)-based therapy is an efficient strategy to control various infectious diseases employing post-transcriptional gene silencing through the silencing of target complementary mRNA. Here, we designed two highly effective shRNAs targeting the conserved region of RNA-dependent RNA polymerase (RdRP) and spike proteins capable of significant SARS-CoV-2 replication suppression. The efficacy of this approach suggested that the rapid development of an shRNA-based therapeutic strategy might prove to be highly effective in treating COVID-19. However, it needs further clinical trials.

A non-intrusive frequency normalisation approach for uncertain response analysis of nonlinear dynamic systems

This paper proposes a non-intrusive technique based on the frequency normalisation for non-intrusive propagations of parameter uncertainties in nonlinear mechanical systems. The multi-solution dilemma found in resonance areas, which prohibits successful applications of uncertainty quantification methods, is alleviated by the additional normalised frequency measure. The spurious peaks around the nonlinear peaks and discontinuous points, known as the Gibbs phenomenon, due to parameter variabilities are resolved as well. The proposed technique coupled with the non-intrusive surrogate modelling is applied to two deliberately constructed nonlinear dynamic systems, i.e., a mass-spring system with interactive cubic nonlinearity and a piece-wise rotor/stator contact problem in rotating machines. The two systems exhibit complex amplitude-frequency response characteristics featured by the softening and hardening effects. Numerous case investigations show the effectiveness of the proposed normalisation method for uncertainty analysis of complex nonlinear vibration systems and its working principle is demonstrated in detail via examples. Accuracy tests against the traditional sampling methods are carried out in both systems. The proposed technique can be easily generalised to other nonlinear systems for random and non-random uncertainty propagations because it works non-intrusively and permits users to choose arbitrary nonlinear tools.

Uncertain frequency responses of CNT – reinforced polymeric graded structure using fuzzified elastic properties – fuzzy finite element approach

A novel fuzzy finite element computational model is prepared to predict the frequency responses of the functionally graded carbon nanotube (FG-CNT) reinforced composite panel structure. The nanotube distributions in the matrix have an affinity towards the agglomeration and affect final properties. The model incurred the fuzzification of elastic properties (E 11, E 22, G 12, and υ 12) considering a ±20% as the degree of uncertainty via Mamdani’s fuzzy inference system (FIS). The extended rule of the mixture has been adopted to count the CNT properties for different grading patterns to achieve the realistic one in the framework of the higher-order polynomial model. Further, a set of fuzzy rules have been adopted to count the uncertainty in elastic properties with the help of the design of the experiment (Taguchi-L25 orthogonal array). The equation of motion is derived for the CNT-reinforced composite in association with Hamilton’s principle for the computation of natural frequency. An in-house computer code is derived in MATLAB with the help of a higher-order fuzzified finite element model (FFEM) for the computation. The suitability of the proposed model has been tested by solving a series of numerical examples and the output is discussed in detail.

Uncertainty in individual risk judgments associates with vulnerability and curtailed climate adaptation

Risk assessments are key for the effective management of potential environmental threats. Across probabilistic phenomena, climate change is an exemplar of paramount uncertainties. These uncertainties have been embraced in supporting governments’ decisions; yet receive scarce attention when studying individual behavior. Analyzing a survey conducted in the USA, China, Indonesia, and the Netherlands (N=6242), we explore socio-economic, psychological, and behavioral differences between individuals who can subjectively assess risks, and those who are risk-uncertain. We find that risk-uncertain individuals are more likely to belong to societal subgroups classically considered as vulnerable, and have reduced capacities and intentions to adapt to hazards—specifically floods. The distinctions between risk-aware and risk-uncertain individuals indicate that ignoring differences in individuals’ capacity to assess risks could amount to persistent vulnerability and undermine climate-resilience efforts. While we use floods emblematically, these finding have consequences for the standard practice of dropping or bootstrapping uncertain responses, irrespective of the hazard, with implications for environmental management.

Electricity retail market and accountability-based strategic bidding model with short-term energy storage considering the uncertainty of consumer demand response

Electricity retailers participate in electricity markets as intermediaries between wholesale and retail markets. They acquire energy on the wholesale side by participating in next-day markets and the pool of power. On the retail side, they make contracts with consumers in order to meet their energy demand at a fixed price for a set period of time –generally a year. To maximize profit in planning, a retailer must choose the best strategy, which should be able to reduce the cost of purchasing energy in the wholesale market while simultaneously determining the best selling price for consumers. Customers may choose a different retailer if the selling price is too high, and the retailer may take a loss if the price is too low. One of the issues that complicate retailers’ decision is the uncertain demand response parameters that affect profit. This paper contributes with a strategic bidding model for planning with short-term energy storage while considering the uncertainty of consumer demand response and load response programs simultaneously. GAMS and MATLAB are implemented in this research to analyze the data and review the results, which indicate that an increase in profit is expected to be greater than when the retailer uses only a load response program or a short-term energy storage system. As uncertainty grows, so does local price sensitivity, and, as a result, so does the predicted rate of profit. Profits from participatory reservation, energy, and regulation markets increase in the robust model, while profits from the common participatory market decrease, i.e., according to this study, which looked at both probabilistic and robust models of retail market participation. When a robust model is used, the overall profit is higher than that obtained from a probabilistic model.

A hybrid probabilistic information gap decision theory based energy management of an active distribution network

The optimal operation of an active distribution network requires proper coordination between multiple entities like renewable energy sources, storage units, power electronic devices, demand response implementation, and network reconfiguration. In this context, this paper proposes optimal energy coordination and management scheme among various entities with different ownership in a reconfigurable active distribution network. The distribution system operator manages the coordinated energy procurement scheme from multiple sources to maximize the expected hourly profit of operation. Uncertainties of load, renewable generation, grid energy price, and the charging power of hybrid electric vehicles are modelled using a probabilistic “Hong’s 2m point estimate” approach. On the other hand, the uncertainty of the demand response is modelled using information gap decision theory. The optimization problem is solved in a hybrid framework encompassing probability and information gap decision theory. Simulation studies on a thirty-three-node distribution network validate the efficacy of the proposed method. With the proposed energy management and coordination scheme, the daily expected operating cost reduces by ∼6.66% while the daily expected energy loss reduces by ∼26.05%. Implementing a risk-averse strategy to hedge against the risk of uncertain demand response participation reduces the profit by ∼3.21% for a tolerance limit of 5%.

A hybrid algorithm for studying the effects of various uncertainties on the nonlinear dynamic responses of steel storage tanks subjected to seismic base excitation

Uncertainty in the basic load and strength variables of a steel storage tank can significantly affect its structural performance and safety. Additionally, due to the inherent uncertainties affecting the various sources in practical situations, their handling in the nonlinear modeling methodology has nowadays become essential. However, it is still an issue to be investigated. Thus, this paper presents a numerical modeling methodology intended to determine the effects of various uncertainties on the dynamic responses of the liquid-shell systems of aboveground steel storage tanks under seismic base excitations. The uncertainties include mechanical properties of materials, geometry properties, liquid properties, and excitation parameters. The uncertain dynamic response of large steel aboveground storage tanks is investigated by using a hybrid algorithm. Eight tall liquid storage tanks with aspect ratios greater than unity are considered. The considered tanks have a similar radius but different heights. The effects of aspect ratio and liquid level on nonlinear dynamic responses are discussed. The point estimation method and the artificial neural network-based algorithm were used to simulate the sources of uncertainty and extract the statistical properties, including the probability density functions (PDF), and the cumulative distribution functions (CDF). An efficient approach with low computational cost compared to the conventional finite element method is proposed based on a combination of the boundary element method and the semi-analytical finite strip to analyze the flexible steel storage tanks subjected to seismic base excitation. Concentric annular rings are used to model the free surface of the liquid. The nonlinearities corresponding to both material properties and geometry deformations are included. According to uncertainty sources, a sensitivity analysis is also carried out. The results show that lognormal probability distribution is more suitable for predicting nonlinear dynamic responses of steel storage tanks under seismic base excitations.

Stochastic Demand Response Management Using Mixed-Strategy Stackelberg Game

Stochastic solutions to the demand response (DR) problem enable utility companies to address the uncertainties in customer demand. In another words, stochastic DR solutions are recently developed considering storage systems, renewable resources, and the electricity market. However, a DR approach for uncertain response of consumers to an incentive-based DR problem has not been studied before. This article presents a new prospective stochastic DR approach by using a mixed-strategy Stackelberg game (MSG) between an aggregator and residential customers during peak load periods. We use real dataset collected by a local utility. We cluster customers based on customer baseline load (CBL), select one reference customer for each cluster, and model the MSG between that customer and the aggregator. We map the MSG to subgames and prove the equilibrium using mixed-strategy Nash Equilibrium. Our results show that the peak load can be shifted to off-peak hours for almost 40 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> of load, and players gain monetary rewards in return.

Recent climate change has driven divergent hydrological shifts in high-latitude peatlands

High-latitude peatlands are changing rapidly in response to climate change, including permafrost thaw. Here, we reconstruct hydrological conditions since the seventeenth century using testate amoeba data from 103 high-latitude peat archives. We show that 54% of the peatlands have been drying and 32% have been wetting over this period, illustrating the complex ecohydrological dynamics of high latitude peatlands and their highly uncertain responses to a warming climate.

Attended home delivery under uncertain travel and response time: a case of Indian public distribution system

PurposeBeing the final end of the logistic distribution, attended home delivery (AHD) plays an important role in the distribution network. AHD typically refers to the service provided by the distribution service provider to the recipient's doorstep. Researchers have always identified AHD as a bottleneck for last-mile delivery. This paper addresses a real-life stochastic multi-objective AHD problem in the context of the Indian public distribution system (PDS).Design/methodology/approachTwo multi-objective models are proposed. Initially, the problem is formulated in a deterministic environment, and later on, it is extended to a multi-objective AHD model with stochastic travel and response time. This stochastic AHD model is used to extensively analyze the impact of stochastic travel time and customer response time on the total expected cost and time-window violation. Due to the NP-hard nature of the problem, an ant colony optimization (ACO) algorithm, tuned via response surface methodology (RSM), is proposed to solve the problem.FindingsExperimental results show that a change in travel time and response time does not significantly alter the service level of an AHD problem. However, it is strongly correlated with the planning horizon and an increase in the planning horizon reduces the time-window violation drastically. It is also observed that a relatively longer planning horizon has a lower expected cost per delivery associated.Research limitations/implicationsThe paper does not consider the uncertainty of supply from the warehouse. Also, stochastic delivery failure probabilities and randomness in customer behavior have not been taken into consideration in this study.Practical implicationsIn this paper, the role of uncertainty in an AHD problem is extensively studied through a case of the Indian PDS. The paper analyzes the role of uncertain travel time and response time over different planning horizons in an AHD system. Further, the impact of the delivery planning horizon, travel time and response time on the overall cost and service level of an AHD system is also investigated.Social implicationsThis paper investigates a unique and practical AHD problem in the context of Indian PDS. In the present context of AHD, this study is highly relevant for real-world applications and can help build a more efficient delivery system. The findings of this study will be of particular interest to the policy-makers to build a more robust PDS in India.Originality/valueThe most challenging part of an AHD problem is the requirement of the presence of customers during the time of delivery, due to which the probability of failed delivery drastically increases if the delivery deviates from the customer's preferred time slot. The paper modelled an AHD system to incorporate uncertainties to attain higher overall performance and explore the role of uncertainty in travel and response time with respect to the planning horizon in an AHD, which has not been considered by any other literature.

Elevation change of the Antarctic Ice Sheet: 1985 to 2020

Abstract. The largest uncertainty in future projections of sea level change comes from the uncertain response of the Antarctic Ice Sheet to the warming oceans and atmosphere. The ice sheet gains roughly 2000 km3 of ice from precipitation each year and loses a similar amount through solid ice discharge into the surrounding oceans. Numerous studies have shown that the ice sheet is currently out of long-term equilibrium, losing mass at an accelerated rate and increasing sea level rise. Projections of sea level change rely on accurate estimates of the contribution of land ice to the contemporary sea level budget. The longest observational record available to study the mass balance of the Earth's ice sheets comes from satellite altimeters. This record, however, consists of multiple satellite missions with different life spans and inconsistent measurement types (radar and laser) of varying quality. To fully utilize these data, measurements from different missions must be cross-calibrated and integrated into a consistent record of change. Here, we present a novel approach for generating such a record that implies improved topography removal, cross-calibration, and normalization of seasonal amplitudes from different mission. We describe in detail the advanced geophysical corrections applied and the processes needed to derive elevation change estimates. We processed the full archive record of satellite altimetry data, providing a seamless record of elevation change for the Antarctic Ice Sheet that spans the period 1985 to 2020. The data are produced and distributed as part of the NASA MEaSUREs ITS_LIVE (Making Earth System Data Records for Use in Research Environments Inter-mission Time Series of Land Ice Velocity and Elevation) project (Nilsson et al., 2021, DOI: https://doi.org/10.5067/L3LSVDZS15ZV​​​​​​​).

Impact of scalar mixing uncertainty on the predictions of reactor-based closures: Application to a lifted methane/air jet flame

This work is devoted to quantify the predictive uncertainty in RANS simulation of a non-premixed lifted flame due to uncertainty in the model parameters of the scalar dissipation rate transport equation. The uncertainty propagation and the global sensitivity analysis of the effect of such parameters on the quantities of interest (QoIs) is performed employing Polynomial Chaos Expansions as surrogate models of the uncertain response. This approach is applied on a lifted methane-air jet flame in vitiated coflow, already experimentally investigated by Cabra et al [1]. The results show the effectiveness of the approach to provide predictions with estimates of uncertainty. It is shown that the the uncertainty in the mixture fraction and temperature is negligible as long as only pure mixing happens, then it becomes significant in the regions where ignition begins, starting from z/D=30. Of the four parameters considered, i.e., CD1, CD2, CP1 and CP2, main and total effect sensitivity indices show that the largest contribution to the uncertainty in the flame temperature is given by the two dissipation parameters CD1 and CD2, while the production parameter CP2 has almost negligible impact on the predictions. Lastly, the surrogate models are used to determine an optimum set of parameters that minimizes the distance with the experimental measures, leading to improved predictions of the QoIs.

Topology optimization of multi-story buildings under fully non-stationary stochastic seismic ground motion

Topology optimization has been mainly addressed for structures under static loads using a deterministic setting. Nonetheless, many structural systems are subjected to uncertain dynamic loads, and thus efficient approaches are required to evaluate the optimal topology in such kind of applications. Within this framework, the present paper deals with the topology optimization of multi-story buildings subjected to seismic ground motion. Because of the inherent randomness of the earthquakes, the uncertain system response is determined through a random vibration-based approach in which the seismic ground motion is described as filtered white Gaussian noise with time-varying amplitude and frequency content (i.e., fully non-stationary seismic ground motion). The paper is especially concerned with the assessment of the dynamic response sensitivity for the gradient-based numerical solution of the optimization problem. To this end, an approximated construction of the gradient is proposed in which explicit, exact derivatives with respect to the design variables are computed analytically through direct differentiation for a sub-assembly of elements (up to a single element) resulting from the discretization of the optimizable domain. The proposed strategy is first validated for the simpler case of stationary base excitation by comparing the results with those obtained using an exact approach based on the adjoint method, and its correctness is ultimately verified for the more general case of non-stationary seismic ground motion. Overall, this validation demonstrates that the proposed approach leads to accurate results at low computational effort. Further numerical investigations are finally presented to highlight to what extent the features of the non-stationary seismic ground motion influence the optimal topology.

Uncertainty propagation of correlated lubricant properties in gear tribodynamic system

An uncertain gear tribodynamic model considering correlation is proposed. Based on this model, uncertainty propagation of correlated lubricant properties in the gear tribodynamic system is investigated for the first time. Compared with previous tribodynamic models ignoring uncertainty correlation, this model contributes to a much more accurate prediction towards the uncertain domain of responses. The effects of uncertainty level, correlation coefficient, and operating conditions on the uncertain responses including vibration, flash temperature and fatigue life are discussed. The results show that large uncertain level of lubricant properties and positive correlation between them deteriorate the tribodynamic performance. Moreover, high speed and large torque magnify the response uncertainties and result in less correlated responses generally.