Kinds Of Uncertainties Research Articles

Click-through Rate Prediction and Uncertainty Quantification Based on Bayesian Deep Learning.

Click-through rate (CTR) prediction is a research point for measuring recommendation systems and calculating AD traffic. Existing studies have proved that deep learning performs very well in prediction tasks, but most of the existing studies are based on deterministic models, and there is a big gap in capturing uncertainty. Modeling uncertainty is a major challenge when using machine learning solutions to solve real-world problems in various domains. In order to quantify the uncertainty of the model and achieve accurate and reliable prediction results. This paper designs a CTR prediction framework combining feature selection and feature interaction. In this framework, a CTR prediction model based on Bayesian deep learning is proposed to quantify the uncertainty in the prediction model. On the squeeze network and DNN parallel prediction model framework, the approximate posterior parameter distribution of the model is obtained using the Monte Carlo dropout, and obtains the integrated prediction results. Epistemic and aleatoric uncertainty are defined and adopt information entropy to calculate the sum of the two kinds of uncertainties. Epistemic uncertainty could be measured by mutual information. Experimental results show that the model proposed is superior to other models in terms of prediction performance and has the ability to quantify uncertainty.

Grey fuzzy prediction model of soil organic matter content using hyper-spectral data

PurposeIn order to overcome the uncertainty and improve the accuracy of spectral estimation, this paper aims to establish a grey fuzzy prediction model of soil organic matter content by using grey theory and fuzzy theory.Design/methodology/approachBased on the data of 121 soil samples from Zhangqiu district and Jiyang district of Jinan City, Shandong Province, firstly, the soil spectral data are transformed by spectral transformation methods, and the spectral estimation factors are selected according to the principle of maximum correlation. Then, the generalized greyness of interval grey number is used to modify the estimation factors of modeling samples and test samples to improve the correlation. Finally, the hyper-spectral prediction model of soil organic matter is established by using the fuzzy recognition theory, and the model is optimized by adjusting the fuzzy classification number, and the estimation accuracy of the model is evaluated using the mean relative error and the determination coefficient.FindingsThe results show that the generalized greyness of interval grey number can effectively improve the correlation between soil organic matter content and estimation factors, and the accuracy of the proposed model and test samples are significantly improved, where the determination coefficient R2 = 0.9213 and the mean relative error (MRE) = 6.3630% of 20 test samples. The research shows that the grey fuzzy prediction model proposed in this paper is feasible and effective, and provides a new way for hyper-spectral estimation of soil organic matter content.Practical implicationsThe research shows that the grey fuzzy prediction model proposed in this paper can not only effectively deal with the three types of uncertainties in spectral estimation, but also realize the correction of estimation factors, which is helpful to improve the accuracy of modeling estimation. The research result enriches the theory and method of soil spectral estimation, and it also provides a new idea to deal with the three kinds of uncertainty in the prediction problem by using the three kinds of uncertainty theory.Originality/valueThe paper succeeds in realizing both the grey fuzzy prediction model for hyper-spectral estimating soil organic matter content and effectively dealing with the randomness, fuzziness and grey uncertainty in spectral estimation.

Navigating uncertainties of death: Minimally Invasive Autopsy Technology in global health

ABSTRACT Global health practitioners and policymakers have become increasingly vocal about the complex challenges of identifying and quantifying the causes of death of the world’s poorest people. To address this cause-of-death uncertainty and to minimise longstanding sensitivities about full autopsies, the Bill and Melinda Gates Foundation have been one of the foremost advocates of minimally invasive autopsy technology (MIA). MIA involves using biopsy needles to collect samples from key organs and body fluids; as such, it is touted as potentially more acceptable and less invasive than a complete autopsy, which requires opening the cadaver. In addition, MIA is considered a good means of collecting accurate bodily samples and can provide the crucial information needed to address cause-of-death uncertainty. In this paper, we employ qualitative data to demonstrate that while MIA technology has been introduced as a solution to the enduring cause-of-death uncertainty, the development and deployment of technologies such as these always constitute interventions in complex social and moral worlds; in this respect, they are both the solutions to and the causes of new kinds of uncertainties. We deconstruct the ways in which those new dimensions of uncertainty operate at different levels in the global health context.

Optimal Solar and Energy Storage System Sizing for Behind the Meter Applications

In this paper, we propose an optimal sizing model for a solar plus energy storage (PV-ESS) system for behind the meter applications. A dynamic optimization algorithm is proposed that maximizes the net worth of a project; the method can account for decreasing technology costs in the future and defer some of the investment costs. Two kinds of uncertainties are considered and mitigated according to their frequency of occurrence and forecast accuracy. The proposed optimization model is decomposed and structured in such a way that it can be efficiently solved using parallel computation. The simulation results provide evidence of the algorithm's ability to optimally size and time the investment in a PV-ESS system so that the total project cost is minimized.

Incremental Pedestrian Attribute Recognition via Dual Uncertainty-Aware Pseudo-Labeling

Incremental pedestrian attribute recognition (IncPAR) aims to learn novel person attributes continuously and avoid the catastrophic forgetting, which is an essential problem for image forensic and security applications, e.g., suspect search. Different from the conventional continual learning for visual classification, we formulate the IncPAR as a problem of multi-label continual learning with incomplete labels (MCL-IL), where the training samples in a novel task are annotated with only a few categories of interest but may implicitly contain other attributes of previous tasks. The incomplete label assignments is a challenging and frequently-encountered issue in real-world multi-label classification applications due to a number of reasons, e.g., incomplete data collection, moderate budget for annotations, etc. To tackle the MCL-IL problem, we propose a self-training based approach via dual uncertainty-aware pseudo-labeling (DUAPL) to transfer the knowledge learned in previous tasks to novel tasks. Specially, both kinds of uncertainties, i.e., aleatoric uncertainty and epistemic uncertainty, are modeled to mitigate the negative influences of noisy pseudo labels induced by low quality samples and immature models learned by inadequate training in early tasks. Based on the DUAPL, more reliable supervision signals can be estimated to prevent the model evolution from forgetting attributes seen in previous tasks. For standard evaluations of MCL-IL methods, two benchmarks on IncPAR, termed RAP-CL and PETA-CL, are constructed by re-organizing public human attribute datasets. Extensive experiments have been performed on these benchmarks to compare the proposed method with multiple baselines. The superior performance in terms of both recognition accuracies and forgetting ratios demonstrate the effectiveness of the proposed DUAPL for IncPAR.

Study for the optimization of the decommissioning project of nuclear facilities

Basically, decommissioning of nuclear facilities is a project that does not generate new profit because it is carried out with the reserve funds from operation, etc. Therefore, its cost should be minimized with optimization by shortening the process and minimizing the waste, etc. Meeting the requirements of exposure risk (safety) also affects the optimization. In this study, we decided to integrate these evaluation methods to develop a comprehensive optimization evaluation method. In this study, we established an average process for the current decommissioning plans of Japanese nuclear power plants and developed a cost evaluation method including sensitivity analysis. As a result of examining the feasibility of the deferred dismantling strategy using the above calculation method, it became clear that although there is a reduction in disposal and dismantling costs due to the natural decay of radioactive materials, the maintenance and management costs during the safe storage period account for a large proportion of the costs, and for this reason, immediate dismantling is unconditionally advantageous, at least in Japan. The components of optimization described above are naturally subject to various uncertainties and risks. For example, there are regulatory risks, and the location of waste disposal site is subject to social acceptance, so there is a great deal of uncertainty. In the future, these factors will be incorporated into the evaluation and studied, and the optimal strategy for decommissioning and what kind of uncertainty should be focused on will be clarified quantitatively.

Graph Convolutional Network-Based Interpretable Machine Learning Scheme in Smart Grids

Smart grid is a typical application of industrial cyber-physical systems (ICPS) in the electric power industry. Due to the exposure to different kinds of uncertainties and unpredictable faults, how to reliably assess the short-term voltage stability (SVS) of smart grids to prevent the occurrence of large-scale blackouts is still of primary concern. To tackle this challenging problem, this article develops a novel machine learning scheme to achieve accurate and interpretable online SVS assessment in two steps. First, it utilizes time-series shapelet transform to extract key dynamics and convert the postfault time series into flat features. Second, it designs a graph convolutional network (GCN) to incorporate these features with topology information for SVS assessment. The GCN explores the spatial-temporal dynamics of power system via graph convolution and introduces a system layer to derive the final assessment result. Compared with conventional methods, this novel scheme makes full use of the spatial-temporal information in SVS dynamics, resulting in higher assessment accuracy and stronger adaptability. Besides, it is capable of discovering certain valuable underlying rules and patterns related to SVS. Test results on the IEEE 39-bus system and real-world Guangdong Power Grid in South China verify the effectiveness of the proposed scheme. Note to Practitioners—To achieve accurate and interpretable online short-term voltage stability (SVS) assessment in the challenging environment of smart grids, this article develops a novel machine learning scheme with full consideration of the spatial-temporal information in SVS dynamics. First, it utilizes the time series shapelet transform to convert the postfault time series into flat features. Second, it designs a graph convolutional network (GCN) to incorporate these features with topology information. The full consideration of spatial-temporal information in SVS dynamics can improve the assessment accuracy, and the integration of topology in the scheme can promote its adaptability to topology changes. Apart from the decent performances under changeable environments, the proposed scheme can provide certain valuable underlying rules and patterns related to SVS. Therefore, not only the proposed scheme for SVS assessment can work well in the practical challenging environment of smart grids, but also it helps the dispatchers in smart grids better understand and trust the proposed SVS assessment scheme.

Survival Function Estimation for Fuzzy Gompertz Distribution with neutrosophic data

A relatively recent area of research known as neutrosophic statistics deals with data that are ambiguous, indeterminate, and inconsistent. By embracing the idea of neutrosophy, which denotes the existence of three components in a statement: truth, falsity, and indeterminacy, it broadens the application of classical statistics. One of the significant offshoots of statistics is life-time data analysis. Traditional statistical methods only account for variation within the data and calculate lifetime observations as accurate numbers. Actually, there are two different kinds of uncertainty in data: fluctuation between observations and fuzziness. As a result, analysis techniques that solely employ precise lifetime data and ignore fuzziness use incomplete information and produce false results. This paper sought to generalize hazard rates, survival functions, and parameter estimates for fuzzy Gompertz Distribution. Simulation studies are implemented to examine the performance of the fuzzy Gompertz Distribution. The results show that the fuzzy Gompertz Distribution has better flexibility in handling over the standard Gompertz Distribution.

Neutrosophic Sets and Metaheuristic Optimization: A Survey

Smarandache presents neutrosophic sets and provides a domain area that is made up of three separate subsets to reflect the various kinds of uncertainty. Neutrosophic sets are defined as the sets where every other element of the universe possesses a degree of truthiness, indeterminacy, and falsity, which range from 0 to 1, and where these degrees are subsets of the neutrosophic sets that are independent of each other. Neutrosophic sets are also known as neutrosophical subsets. In the neutrosophic sets, impreciseness is represented as truth and falsity functions, but the indeterminacy function represents degrees of belongingness and non-belongingness and differentiates between absoluteness and relativeness. Neutrosophic sets can deal with the unpredictability of the system and cut down on the paralysis brought on by conflicting information thanks to this notation. As a result, one might argue that this capacity is the single most significant benefit offered by neutrosophic sets in comparison to the many other forms of fuzzy extensions. By making use of these three functions, neutrosophic sets are able to create a domain area. This area makes it possible for various kinds of mathematical operations to be carried out separately despite the presence of uncertainty. Due to the fact that the behavior of these methodologies is inspired by Nature and its capacity for adapting to issues, in addition to the potential for combining more than one method to reach the best alternatives, metaheuristic algorithms are employed to initiate the finest or the best possible alternatives to a lot of optimization techniques. This is possible because metaheuristic algorithms have the ability to adapt to problems. The fact that numerous academics have utilized these techniques with neutrosophic science to offer several systems in recent years was the impetus for writing this overview study in the first place, which was based on the above rationale.

Uncertainty factor in contemporary communities of the Russian Arctic: methodological approaches to research

The article introduces readers to work on a new research project “Overcoming Uncertainty in Interaction with the Physical and Social Environment in the Russian Arctic” by describing its conceptual and theoretical approach and premises as well as the steps of designing it. It opens with an analysis of the concept of risk, which is currently popular in the Arctic Social Studies and which is sometimes claimed to be the central concept in the field. It proceeds by describing the new approach, which is based on the concept of uncertainty, both natural and social, which constantly accompanies human life in the Far North. The inhabitants of the North know from experience that uncertainty cannot be ruled out, but one can be constantly prepared for it by planning one’s behavior with this factor in mind. Furthermore, one can suggest that the effects of ecological, economic and social changes are experienced by these inhabitants first and foremost as shifts in the level and kind of uncertainty. Therefore, the concept of uncertainty can replace the concept of risk in the studies of the effects of change on northern communities; in fact, “uncertainty” is preferable because its conceived nature is obvious, while in many risk studies, risk is still perceived as something objective. The main questions that a study of uncertainty can answer are: what is the role of natural and social uncertainty in the everyday life of the permanent population of the Arctic? What are the action strategies of the people in the face of uncertainty, including discursive strategies to explain the growing uncertainty? What is the role of state authorities, industrial companies, local organizations, urban and rural communities in overcoming the negative consequences of natural and social uncertainties? How are social connections and networks involved in dealing with situations of uncertainty? The paper demonstrates how these questions can be answered on the basis of anthropological fieldwork. It also uses examples from the Komi Republic (Vorkuta), Yakutia (Chersky settlement), and Magadan area to demonstrate the sort of problems the uncertainty-based approach can contribute to solving. A review of the existing literature in Russian and English is presented in order to give the reader useful insights into the field of uncertainty studies and outline the main directions of research that a study of uncertainty can take

An Innovative Hybrid Multi-Criteria Decision-Making Approach under Picture Fuzzy Information

These days, multi-criteria decision-making (MCDM) approaches play a vital role in making decisions considering multiple criteria. Among these approaches, the picture fuzzy soft set model is emerging as a powerful mathematical tool for handling various kinds of uncertainties in complex real-life MCDM situations because it is a combination of two efficient mathematical tools, namely, picture fuzzy sets and soft sets. However, the picture fuzzy soft set model is deficient; that is, it fails to tackle information symmetrically in a bipolar soft environment. To overcome this difficulty, in this paper, a model named picture fuzzy bipolar soft sets (PRFBSSs, for short) is proposed, which is a natural hybridization of two models, namely, picture fuzzy sets and bipolar soft sets. An example discussing the selection of students for a scholarship is added to illustrate the initiated model. Some novel properties of PRFBSSs such as sub-set, super-set, equality, complement, relative null and absolute PRFBSSs, extended intersection and union, and restricted intersection and union are investigated. Moreover, two fundamental operations of PRFBSSs, namely, the AND and OR operations, are studied. Thereafter, some new results (De Morgan’s law, commutativity, associativity, and distributivity) related to these proposed notions are investigated and explained through corresponding numerical examples. An algorithm is developed to deal with uncertain information in the PRFBSS environment. To show the efficacy and applicability of the initiated technique, a descriptive numerical example regarding the selection of the best graphic designer is explored under PRFBSSs. In the end, concerning both qualitative and quantitative perspectives, a detailed comparative analysis of the initiated model with certain existing models is provided.

Obstacles Avoidance for Mobile Robot Using Type-2 Fuzzy Logic Controller

Intelligent mobile robots need to deal with different kinds of uncertainties in order to perform their tasks, such as tracking predefined paths and avoiding static and dynamic obstacles until reaching their destination. In this research, a Robotino® from Festo Company was used to reach a predefined target in different scenarios, autonomously, in a static and dynamic environment. A Type-2 fuzzy logic controller was used to guide and help Robotino® reach its predefined destination safely. The Robotino® collects data from the environment. The rules of the Type-2 fuzzy logic controller were built from human experience. They controlled the Robotino® movement, guiding it toward its goal by controlling its linear and angular velocities, preventing it from colliding obstacles at the same time, as well. The Takagi–Sugeno–Kang (TSK) algorithm was implemented. Real-time and simulation experimental results showed the capability and effectiveness of the proposed controller, especially in dealing with uncertainty problems.

Self-paced uncertainty estimation for one-shot person re-identification

The one-shot person Re-ID scenario faces two kinds of uncertainties when constructing the prediction model from X to Y. The first is model uncertainty, which captures the noise of the parameters in DNNs due to a lack of training data. The second is data uncertainty, which can be divided into two subtypes: image noise, where severe occlusion and the complex background contain irrelevant information about the identity; and label noise, where mislabeling affects visual appearance learning. We find that the state-of-the-art one-shot person Re-ID addresses the first issue of model uncertainty via a dynamic sampling strategy, while the second issue of data uncertainty remains. In this paper, to simultaneously address both issues, we propose a novel SPUE-Net for one-shot person Re-ID. By introducing a self-paced sampling strategy, our method can estimate the pseudolabels of unlabeled samples iteratively to expand the labeled samples gradually and remove model uncertainty without extra supervision. We divide the pseudolabel samples into two subsets to make the use of training samples more reasonable and effective. In addition, we apply a co-operative learning method of local uncertainty estimation combined with determinacy estimation to achieve better-hidden space feature mining and to improve the precision of selected pseudolabeled samples, which reduces data uncertainty. Extensive comparative evaluation experiments on video-based and image-based datasets show that SPUE-Net has significant advantages over state-of-the-art methods.

Reliability assessment and improvement of distribution system with virtual energy storage under exogenous and endogenous uncertainty

Exploiting flexibility of low-cost but uncertain demand-side resources attracts great attention in the recent research of the distribution system with special respect to reliability assessment and improvement. While various kinds of uncertainties and risks exist, such as the stochastic power of renewable energy resources (RES), etc., the literature overlooked the exogenous and endogenous uncertainties along with the utilization of these resources denoted as virtual energy storage (VES). Instead, this manuscript proposes a novel approach for reliability assessment and improvement of the distribution system in special consideration of the endogenous uncertainties in VES operations, also denoted as decision-dependent uncertainties (DDUs). The modified and unified probabilistic model of VES is first presented to reveal the dynamic process and human behavior of VES. Additionally, we extend our recent work on probabilistic optimization under DDUs to the reliability assessment problem which addresses the optimal load curtailment with the support of VES. Moreover, reliability indices are newly introduced to (1) calculate the response losses of VES and the consequence of overlooking these uncertainties, and (2) explain the difference between theoretical and practical reliability. Case studies based on the ground truth data verify that the available capacity of VES will gradually reduce (maximum of 40 %–60 %) because of DDUs and it reveals the overlooked risks by using “greedy” strategies in the previous models, while the best practical reliability performance (increased 5 %) is witnessed via the proposed methods. We further investigate four key factors affecting the reliability performance, i.e., probability level of chance constraints, DDUs levels, dispatch time period, and locations of VES. Eventually, reasonable suggestions are provided for better flexibility utilization of VES, and the system's reliability is further improved by around 30 %.

Topological Data Analysis of m-Polar Spherical Fuzzy Information with LAM and SIR Models

The concept of m-polar spherical fuzzy sets (mPSFS) is a combination of m-polar fuzzy sets (mPFS) and spherical fuzzy sets (SFS). An mPSFS is an optimal strategy for addressing multipolarity and fuzziness in terms of ordered triples of positive membership grades (PMGs), negative membership grades (NMGs), and neutral grades (NGs). In this study, the innovative concept of m-polar spherical fuzzy topology (mPSF-topology) is proposed for data analysis and information aggregation. We look into the characteristics and results of mPSF-topology with the help of several examples. Topological structures on mPSFSs help with both the development of new artificial intelligence (AI) tools for different domain strategies and the study of different kinds of uncertainty in everyday life problems. These strategies make it possible to recognise and look into a situation early on, which helps professionals to reduce certain risks. In order to address various group decision-making issues in the m-polar spherical fuzzy domain, one suggestion has been to apply an extended linear assignment model (LAM) along with the SIR method known as superiority and inferiority ranking methodology in order to analyze road accident issues and dispute resolution. In addition, we examine the symmetry of optimal decision and perform a comparative study between the research carried out using the suggested methodology and several existing methods.

New adaptive robust [formula omitted] control of smart structures using synchrosqueezed wavelet transform and recursive least-squares algorithm

Two kinds of uncertainties, one due to the dynamic earthquake loads with a wide frequency band and the other due to structural parameters exist in large and complex real-life structures. Most existing control algorithms consider only one of them, resulting in difficulty to guarantee necessary control performance for large complex structures, such as better vibration suppression on structural peak response and robustness performance. Considering the two uncertainties simultaneously, in this paper, a new adaptive robust H∞ control methodology is presented for vibration control of structures through adroit integration of synchrosqueezed wavelet transform (SWT) and recursive least-squares (RLS) algorithm. The robust H∞ control is more effective than the traditional LQR/LQG control in terms of the stability and robustness of the control system. The external excitation signal from ground sensors is filtered by a low-pass filter based on SWT and then inputted into the filtered-x RLS adaptive controller. The effectiveness, accuracy, and computational efficiency of the new adaptive control method is demonstrated using a 76-story wind-excited benchmark super high-rise building structure and a 24-story shear-wall building with an active tuned mass damper (ATMD) system on the top floor. Compared with the existing linear quadratic Gaussian control algorithm, the wavelet-hybrid feedback-least mean square algorithm and the robust H∞ control algorithm, the simulation results show that the control effect and robust performance indexes of the structure are increased by 5%–35% and 5%–25%, respectively, using the new control methodology.

Oxygen Isotope Phosphoric Acid Fractionation Factor and Uncertainty on the δ18O Measurements of Calcite

At present, the isotope ratio 18O/16O (in the text expressed as δ18O) of carbonate (CO32−) is usually determined by isotope ratio mass spectrometry measuring the CO2 gas produced dissolving the CO32−-bearing substance in concentrated H3PO4. As with any analytical data, the δ18O values of carbonate are also affected by uncertainty which must be considered mainly when data from different laboratories are compared. Usually, scientific papers report only repeatability and/or reproducibility of analytical results, which, have scarce significance for data comparison. With the aim of evaluating the overall uncertainty for new analytical data for low-Mg calcites, in this paper we reconsidered the δ18O data, which are reported in the literature. Two kinds of uncertainty must be taken into account: (1) that due to the calibration of the delta values using international standards (prediction uncertainty), (2) that due to small differences in the chemistry of low-Mg calcites. The two uncertainties must be added when comparing data produced in the same or different laboratories. We found that this overall uncertainty cannot be lower than about 0.2‰. Thus, uncertainty lower than 0.2‰, sometimes reported in the literature, is misleading.

Path Learning in Human–Robot Collaboration Tasks Using Iterative Learning Methods

In a repetitive human–robot collaboration (HRC) task, robots typically are required to learn the intended motion of the human user to improve the collaboration efficiency. However, the human user’s trajectory is of uncertainty when repeating the same task (e.g., human hand tremor and uncertain movement speed), which may directly deteriorate the learning performance. To address this issue, a path characterized by spatial correlation parameters, is of necessity to be learned by robots so that the aforementioned time-related uncertainty will be avoided. In this article, based on the path parameterization, a gradient-based iterative path learning (IPL) strategy is designed for the robot to learn the desired path of human. The proposed IPL strategy draws on the iterative learning methods with a properly designed performance index. Since the gradient of the performance index is hard to directly obtain in HRC, two learning methods with gradient search (GS) and gradient estimation (GE) are developed. The GS estimates the gradient online and has an advantage of easy implementation. By contrast, the advantage of GS is more obvious when the number of learned parameters is considerable due to its high learning efficiency. With these two methods, the unknown path parameters can be iteratively updated toward the desired values. To verify the effectiveness of the proposed IPL algorithm, experiments are carried out. In the experiment, a comparison between GS and GE methods is made to display their respective advantages. Besides, the proposed IPL is compared with an existing trajectory learning method subject to two different kinds of uncertainties and its better learning performance verifies its stability and capability in dealing with uncertainty.

A parametric neutrosophic model for the solid transportation problem

PurposeThis research attempts to present a solid transportation problem (STP) mechanism in uncertain and indeterminate contexts, allowing decision makers to select their acceptance, indeterminacy and untruth levels.Design/methodology/approachDue to the lack of reliable information, changeable economic circumstances, uncontrolled factors and especially variable conditions of available resources to adapt to the real situations, the authors are faced with a kind of uncertainty and indeterminacy in constraints and the nature of the parameters of STP. Therefore, an approach based on neutrosophic logic is offered to make it more applicable to real-world circumstances. In this study, the triangular neutrosophic numbers (TNNs) have been utilized to represent demand, transportation capacity, accessibility and cost. Then, the neutrosophic STP was converted into an interval programming problem with the help of the variation degree concept. Then, two simple linear programming models were extracted to obtain the lower and upper bounds of the optimal solution.FindingsThe results reveal that the new model is not complicated but more flexible and more relevant to real-world issues. In addition, it is evident that the suggested algorithm is effective and allows decision makers to specify their acceptance, indeterminacy and falsehood thresholds.Originality/valueUnder the transportation literature, there are several solutions for TP and STP in crisp, fuzzy set (FS) and intuitionistic fuzzy set (IFS) conditions. However, the STP has never been explored in connection with neutrosophic sets to the best of the authors’ knowledge. So, this work tries to fill this gap by coming up with a new way to solve this model using NSs.

Enhanced simulated early 21st century Arctic sea ice loss due to CMIP6 biomass burning emissions

The mechanisms underlying decadal variability in Arctic sea ice remain actively debated. Here, we show that variability in boreal biomass burning (BB) emissions strongly influences simulated Arctic sea ice on multidecadal time scales. In particular, we find that a strong acceleration in sea ice decline in the early 21st century in the Community Earth System Model version 2 (CESM2) is related to increased variability in prescribed BB emissions in the sixth phase of the Coupled Model Intercomparison Project (CMIP6) through summertime aerosol-cloud interactions. Furthermore, we find that more than half of the reported improvement in sea ice sensitivity to CO2 emissions and global warming from CMIP5 to CMIP6 can be attributed to the increased BB variability, at least in the CESM. These results highlight a new kind of uncertainty that needs to be considered when incorporating new observational data into model forcing while also raising questions about the role of BB emissions on the observed Arctic sea ice loss.