Scalar Value Research Articles

Critical solutions of nonminimally coupled scalar field theory and first-order thermodynamics of gravity

Analytical solutions of nonminimally coupled scalar field cosmology corresponding to critical scalar field values constitute a potential challenge to the recent first-order thermodynamics of scalar-tensor gravity (a formalism picturing general relativity as the zero-temperature equilibrium state for modified gravity). The critical solutions are unstable with respect to homogeneous perturbations, hence unphysical.

Early quantitative pupillary response parameters have high predictive value for 30-day mortality in patients resuscitated from out-of-hospital cardiac arrest

Abstract Background Despite adequate post-resuscitation treatment following out-of-hospital cardiac arrest (OHCA), early risk of death remains high due to irreversible anoxic brain injury. Objective Neuroprognostication is pivotal, and automated quantitative pupillometry has become an essential part of early multimodal approach. Several quantitative pupillary response parameters are now available, but little evidence exist on their value and timing in neuroprognostication. Purpose To evaluate prognostic value of standard parameters of quantitative pupillometry in OHCA patients. Methods In this observational study we included 138 comatose patients resuscitated from OHCA admitted to a tertiary cardiac intensive care unit. With quantitative pupillometry, change in pupil size (%CH, %), Neurological Pupil index (NPi, scalar value 0–5), constriction velocity (CV, mm/s), maximum CV (MCV, mm/s), dilation velocity (DV, mm/s) and latency of constriction (Lat, s) were measured at admission, at the 24 hours (24h), and 48-hours (48h) time points. Receiver operating characteristics (ROC) curves with calculation of area under the curve (AUC) were applied for assessment of association with 30-day mortality. Results The population were predominantly males, had a mean age of 61±12 years, and 120 (87%) had shockable primary rhythm, 122 (88%) had witnessed cardiac arrest and 51 (37%) died in the first 30 days. Median %CH, CV, MCV, DV and NPi values were significantly lower for diseased patients compared to survivors. ROC analyses (Figure 1) present all parameters, except NPi, at admission with numerically highest values of AUC at 0.82, 0.76, 0.73, and 0.81 for %CH, CV, MCV, and DV respectively, and for NPi at 48h with AUC at 0.81. The admission time point still presented the highest AUC values, >0.84, for CV, MCV, and DV in an adjusted model. Of all the parameters, %CH and NPi maintained the highest prognostic values through all time points, and %CH performed best at admission with AUC at 0.87 and NPi at 24h with AUC at 0.91. At admission the threshold value with 100% specificity for predicting 30-day mortality were 3.75, 0.09, 0.33, and 0.05, with negative predictive value (NPV [95% confidence interval]) at 71% [67–74], 66% [64–69], 67% [65–70], and 69% [66–72] for %CH, CV, MCV, and DV respectively. NPi had best performance at 24h with a cutoff value of 2.90 and NPV at 64% [63–64]. Conclusion %CH and NPi were the strongest prognostic tools for 30-day mortality. Values below 3.75% for %CH at admission and 2.90 for NPi at 24h after archived 100% specificity for 30-day mortality, with NPV at 71% [67–74] and 64% [63–64] respectively. Although performance was persistently high, %CH, CV, MCV, and DV presented numerically highest AUC at admission. Additional parameters of CV, MCV, and DV were also significantly associated with outcome and future research should focus on validation and possible combining these measures into useful indices. Funding Acknowledgement Type of funding sources: Private company. Main funding source(s): Novo Nordisk Foundation

Deep-learning based insitu ultrasound thermometry for thermal ablation monitoring

Noninvasive in situ temperature measurement (thermometry) is an attractive means of mapping the region of thermal damage during thermal ablation treatments. Existing methods of ultrasound thermometry are ineffective beyond 50 °C due to multiple physical limitations, including a non-monotonic relationship between temperature and sound speed that reaches a plateau around 60 °C, tissue phase transitions and deformation. An ultrasound-based technology that can monitor treatment over the entire therapeutic temperature range is desirable clinically. This paper describes a deep learning-based approach that uses thermometry data from the periphery of the heating zone (where temperatures are less than 50 °C) to infer temperature throughout the treatment zone. Spatiotemporal 2D temperature maps from 3–12 s HIFU heating exposures (in 0.5 s increments) were generated (using COMSOL) with a subset used for training the network and the rest for testing. Peripheral temperature values (excluding the first 5 mm closest to the axial focus), scalar time values, and a binary flag indicating heating/cooling were inputs to the network, and the temperature profile axially through the HIFU focus was predicted. The temperature prediction accuracy was better than 0.5 °C during heating and cooling. This paper will also address robustness to noise in the input temperature measurements and discuss future directions including experiments with ultrasound backscatter data and strategies to explicitly incorporate heat diffusion physics into the learning paradigm.

Robot Vitals and Robot Health: Towards Systematically Quantifying Runtime Performance Degradation in Robots Under Adverse Conditions

This letter addresses the problem of automatically detecting and quantifying performance degradation in remote mobile robots, in real-time, during task execution. A robot may encounter a variety of uncertainties and adversities during task execution, which can impair its ability to carry out tasks effectively and cause its performance to degrade. Such situations can be mitigated or averted by timely detection and intervention, e.g., by a remote human supervisor taking over control in teleoperation mode. Inspired by patient triaging systems in hospitals, we introduce the framework of “robot vitals” for estimating overall “robot health”. A robot's vitals are a set of lower-level metrics that estimate a variety of indicators of performance degradation faced by a robot at any given point in time. Robot health is a higher-level metric that combines robot vitals into a single scalar value estimate of performance degradation. Experiments, both in simulation and on a real mobile robot, demonstrate that the proposed robot vitals and robot health can be used effectively for online estimation of robot performance degradation during run-time.

CP-violation, asymmetries and interferences in toverline{t}phi

In this paper, we use the associated production of top-quark pairs ( toverline{t} ) with a generic scalar boson (ϕ) at the LHC (pp → toverline{t}phi ) to explore the sensitivity of a large set of observables to the sign of the CP mixing angle (α), present in the coupling between the scalar boson and the top quarks. The mass of the scalar boson is set to mϕ = 125 GeV (the Standard Model Higgs boson mass) and its coupling to top-quarks is varied such that α = 0°, 22.5°, 45.0°, 67.5°, 90.0°, 135.0° and 180.0°. Dileptonic final states of the toverline{t}phi system are used (pp → bℓ+νℓ overline{b} ℓ− overline{nu} ℓb overline{b} ), where the scalar boson is expected to decay according to ϕ → boverline{b} . A new method to reconstruct the scalar mass, originally designed for the low mass regime is used, improving the resolution of the Higgs mass by roughly a factor of two. A full phenomenological analysis is performed using Standard Model (SM) background and signal events generated with MadGraph5_aMC@NLO, in turn reconstructed using a kinematical fit. The most sensitive CP-observables are selected to compute Confidence Level (CL) limits as a function of the sign of the top quark Yukawa couplings to the ϕ boson. We also explore the sensitivity to interference terms using differential distributions and angular asymmetries. Given the significant difference between the pure scalar (σ0+) and pure pseudo-scalar (σ0−) production cross section values, it is unlikely the toverline{t}phi channel alone will be sensitive to the sign of the CP-mixing angle or interference terms, even at the end of the LHC. Using the {b}_2^{toverline{t}phi } and {b}_4^{toverline{t}phi } variables, exclusion limits at 95% CL for the CP-even and CP-odd components of the top quark Yukawa couplings are expected to be set to overset{sim }{kappa } ∈ [-0.698,+0.698] and |κ| ∈ [0.878,1.04], respectively, at the end of the High Luminosity phase of the LHC (HL-LHC) by using the dileptonic decay channel alone.

Opinion Polarization in Human Communities Can Emerge as a Natural Consequence of Beliefs Being Interrelated.

The emergence of opinion polarization within human communities—the phenomenon that individuals within a society tend to develop conflicting attitudes related to the greatest diversity of topics—has been a focus of interest for decades, both from theoretical and modelling points of view. Regarding modelling attempts, an entire scientific field—opinion dynamics—has emerged in order to study this and related phenomena. Within this framework, agents’ opinions are usually represented by a scalar value which undergoes modification due to interaction with other agents. Under certain conditions, these models are able to reproduce polarization—a state increasingly familiar to our everyday experience. In the present paper, an alternative explanation is suggested along with its corresponding model. More specifically, we demonstrate that by incorporating the following two well-known human characteristics into the representation of agents: (1) in the human brain beliefs are interconnected, and (2) people strive to maintain a coherent belief system; polarization immediately occurs under exposure to news and information. Furthermore, the model accounts for the proliferation of fake news, and shows how opinion polarization is related to various cognitive biases.

Gig-economy as a safety gradient for sustainable development of the mining industry

Purpose. To evaluate causality of expanding the gig-economic space and the socioeconomic and ecological security of Ukraines mining industry in the context of the changing technological structures caused by the emergence of a new security structural and functional post Covid-19 component. To propose typical scenarios for sustainable development of the gig-economy in the context of improving security policy of Ukraines mining industry. Methodology. To achieve this goal, a scenario planning, Delphi, SMART, SWOT analysis and mathematical research methods econometric and gradient analysis, were used to determine the scalar values and directions of extreme innovative changes in the gig economy in the context of sustainable development of mining industry in Ukraine. The calculations were carried out using the SPSS Data Analysis Software. Findings. The work systematically analyzes key safety indicators of scientific-technological progress for sustainable development of the mining industry and establishes a one-to-one relationship between existing socio-economic phenomena and gig-economic processes, which in recent years have been asymmetrically activated under the influence of the post Covid-2019. The security specifics of tactical and strategic management of the gig-economy are outlined. Originality. A methodological approach to the quantitative and qualitative determination of the safety gradients of the development of the mining industry enables the consideration of transformational Post-Covid consequences, as well as diversifies tactical and strategic management. In particular, it is proposed to use an innovative digital platform CENSIE Central European Network for Sustainable and Innovative Economy. Practical value. A proposed approach would facilitate to improve the efficiency of strategic and tactical management of personnel policy of the mining industry in the context of the formation of a gig-economy, as well as synchronize time and space parameters, increasing the targeting of management decisions. It was proposed to introduce a special remote platform MiningFreelance to provide freelance services to households, family farms and other manufacturers which will be protected by the Blockchain registry based on existing ProZorro programs. At the same time, Blockchain technologies will increase the role of trade unions, which will be able to unite gig workers into a single system and control working conditions, taking on certain social obligations.

Interpreting electroweak precision data including the W-mass CDF anomaly

We perform a global fit of electroweak data, finding that the anomaly in the W mass claimed by the CDF collaboration can be reproduced as a universal new-physics correction to the T parameter or |H†DμH|2 operator. Contributions at tree-level from multi-TeV new physics can fit the anomaly compatibly with collider bounds: we explore which scalar vacuum expectation values (such as a triplet with zero hypercharge), Z′ vectors (such as a Z′ coupled to the Higgs only), little-Higgs models or higher-dimensional geometries provide good global fits. On the other hand, new physics that contributes at loop-level must be around the weak scale to fit the anomaly. Thereby it generically conflicts with collider bounds, that can be bypassed assuming special kinematics like quasi-degenerate particles that decay into Dark Matter (such as an inert Higgs doublet or appropriate supersymmetric particles).

On the Conditional Value at Risk Based on the Laplace Distribution with Application in GARCH Model

In this article, the Laplace distribution is employed in lieu of the well-known normal distribution for finding better scalar values of risk. Explicit formulas for value-at-risk (VaR) and conditional value-at-risk (CVaR) are studied and used to manage the risk involved in a stock movement by using the GARCH model. Numerical simulations are given for a variety of stocks in equity markets to uphold the findings.

Effect of number of diffusion-encoding directions in diffusion metrics of 5-year-olds using tract-based spatial statistical analysis.

Methodological aspects and effects of different imaging parameters on DTI (diffusion tensor imaging) results and their reproducibility have been recently studied comprehensively in adult populations. Although MR imaging of children's brains has become common, less interest has been focussed on researching whether adult‐based optimised parameters and pre‐processing protocols can be reliably applied to paediatric populations. Furthermore, DTI scalar values of preschool aged children are rarely reported. We gathered a DTI dataset from 5‐year‐old children (N = 49) to study the effect of the number of diffusion‐encoding directions on the reliability of resultant scalar values with TBSS (tract‐based spatial statistics) method. Additionally, the potential effect of within‐scan head motion on DTI scalars was evaluated. Reducing the number of diffusion‐encoding directions deteriorated both the accuracy and the precision of all DTI scalar values. To obtain reliable scalar values, a minimum of 18 directions for TBSS was required. For TBSS fractional anisotropy values, the intraclass correlation coefficient with two‐way random‐effects model (ICC[2,1]) for the subsets of 6 to 66 directions ranged between 0.136 [95%CI 0.0767;0.227] and 0.639 [0.542;0.740], whereas the corresponding values for subsets of 18 to 66 directions were 0.868 [0.815;0.913] and 0.995 [0.993;0.997]. Following the exclusion of motion‐corrupted volumes, minor residual motion did not associate with the scalar values. A minimum of 18 diffusion directions is recommended to result in reliable DTI scalar results with TBSS. We suggest gathering extra directions in paediatric DTI to enable exclusion of volumes with motion artefacts and simultaneously preserve the overall data quality.

Statistical perspective on embrittling potency for intergranular fracture

Embrittling potency is a thermodynamic metric that assesses the influence of solute segregation to a grain boundary (GB) on intergranular fracture. Historically, authors of studies have reported embrittling potency as a single scalar value, assuming a single segregation site of importance at a GB and a particular cleavage plane. However, the topography of intergranular fracture surfaces is not generally known a priori. Accordingly, in this paper, we present a statistical ensemble approach to compute embrittling potency, where many free surface (FS) permutations are systematically considered to model fracture of a GB. The result is a statistical description of the thermodynamics of GB embrittlement. As a specific example, embrittling potency distributions are presented for Cr segregation to sites at two Ni $\ensuremath{\langle}111\ensuremath{\rangle}$ symmetric tilt GBs using atomistic simulations. We show that the average embrittling potency for a particular GB site, considering an ensemble of FS permutations, is not equal to the embrittling potency computed using the lowest energy pair of FSs. A mean GB embrittlement is proposed, considering both the likelihood of formation of a particular FS and the probability of solute occupancy at each GB site, to compare the relative embrittling behavior of two distinct GBs.

Improved Estimation of Population Variance Utilizing Known Auxiliary Parameters

Even similar things, whether created artificially or naturally, can vary. We should therefore try to estimate this variation. For improved population variance estimate, we propose a Searls ratio type estimator in the current research employing data on the tri-mean and the third quartile of the auxiliary variable. Up to the first-degree approximation, the suggested estimator's bias and mean squared error (MSE) are determined. The characterising scalar's ideal value is discovered, and given this ideal value, the least MSE is discovered. The mean squared errors of the suggested estimator and the competing estimators are contrasted conceptually and experimentally. Given that it has the lowest MSE of the above competing estimators, the recommended estimator has been shown to be the most effective.

ANTROPIC PRINCIPLE OF COGNITION OF THE UNIVERSE

The article proposes an alternative view of the theory of the Big Bang, which served as the initial point for the formation of the Universe. The anthropic hypothesis sees our universe as a single electromagnetic entity, consistently realizing itself at seven stages of evolution.
 1. The germ (Genome) of the universe, which is represented by a scalar value (field) in the form of a noumenal (hypothetical) electromagnetic dipole, that is in a state of cosmological singularity.
 2. The spatial environment of “dark energy” in the form of a chaos of a volumetric web of point sources of electric charges and magnetic lines of force, which should not be associated with electrons and positrons of electricity familiar to our ear and the north and south poles of a magnet.
 3. The spatial environment of “dark matter” (ether) in the form of a three-dimensional web of negatively and positively charged magnetic monopoles.
 4. The physical Universe in the form of photon (electromagnetic) gratings - fluctuations, black holes and stars of galaxies that we observe, as well as objects of knowledge in our solar system and directly on Earth.
 5. Psychic (Intellectual) Universe in the form of a single consciousness / mind continuum, which physically manifests itself
 as Ego – I.
 6–7. Currently with our imperfect mind Ego – I we can hardly realize the subsequent stages of the evolution of the consciousness / mind continuum, but the anthropic hypothesis suggests that humanity, as well as every human being that is
 aware of himself as a creative individual, is able to foresee possible options for the development of our civilization up to the
 intuitive (holistic) perception of the single essence of the universe and the subsequent formation of the germ (Genome), which
 ensures its subsequent life cycle.

Color contour texture based peanut classification using deep spread spectral features classification model for assortment identification

Agriculture is one of great economic growth in India by producing variety of peanuts for various co products development. Peanut Crop and weed identification is an important step in developing a highly efficient automotive peanut production system. Most of the exiting classification fix the threshold margins to select the inappropriate features cause classification inaccuracy. Deep learning, as a powerful image processing technology, can extract conventional and characteristic features in layers. Spectral information is used as the input of the network, and the output spectral characteristics of the network are directly passed to the subsequent classifiers to achieve pixel-level classification. To resolve the classification categorization problem, we propose a Color contour texture based peanut classification using deep spread spectral features classification model for variety identification. Initial the Cascading subset Feature Filtering (CSFF) is applied to select the shape textures, color gradient rate form histogram equivalence and returns the feature scalar values. Based in CSFF the feature weight is attained to Social Spider Optimization to select the Impact Features Weight (SSO-IFW). The optimal object segmentation war applied based on cross fold object verification to select the shape projection features. Then Ada Boosting classifier generate the type of object structural for identifying type definition to generate pattern to produce peanut characteristic clusters. Based on Image Features recognition clusters, deep spread spectral neural classification is applied to train the features with soft-max logical activation function with Adaptive Convolution Neural Network (ACNN) to categorize the peanuts based on feature characteristics using image recognition, 20 varieties of peanut are collected through scanner type dataset to train the images into deep neural network. This produce high classification accuracy up to 89 %, high precision, recall rate under F-measure with low false rate. This achieves high classification than other existing methods.

Vector centrality in hypergraphs

Identifying the most influential nodes in networked systems is of vital importance to optimize their function and control. Several scalar metrics have been proposed to that effect, but the recent shift in focus towards network structures which go beyond a simple collection of dyadic interactions has rendered them void of performance guarantees. We here introduce a new measure of node's centrality, which is no longer a scalar value, but a vector with dimension one lower than the highest order of interaction in a hypergraph. Such a vectorial measure is linked to the eigenvector centrality for networks containing only dyadic interactions, but it has a significant added value in all other situations where interactions occur at higher-orders. In particular, it is able to unveil different roles which may be played by the same node at different orders of interactions – information that is otherwise impossible to retrieve by single scalar measures. We demonstrate the efficacy of our measure with applications to synthetic networks and to three real world hypergraphs, and compare our results with those obtained by applying other scalar measures of centrality proposed in the literature.

MRI diffusion tensor imaging scalar values in dogs with intervertebral disc herniation: A comparison between manual and semiautomated region of interest methods.

Magnetic resonance imaging (MRI) diffusion tensor imaging (DTI) measures have been described as methods for quantifying spinal cord injury and predicting outcome in dogs with intervertebral disc herniation (IVDH); however, studies comparing methods for selecting regions of interest (ROIs) are currently lacking. The aims of this retrospective, methods comparison, observational study were to compare DTI measurements acquired using manual (mROI) versus semiautomated ROI (sROI) methods and to compare DTI measurements with patient outcomes. Magnetic resonance imaging scans that included DTI pulse sequences were retrieved for 65 dogs with confirmed IVDH. Regions of interest were placed at one vertebral length cranial and caudal to the region of spinal cord compression (RSCC) using the mROI and sROI methods. Scalar values based on the mROI and sROI methods were compared. There was a significant difference for all DTI measures (P<0.0001), where fractional anisotropy was higher (95% confidence interval [CI]: 0.15, 0.19) and mean diffusivity (MD; CI: -0.41, -0.35), axial diffusivity (AD; CI: -0.47, -0.36) and radial diffusivity (RD; CI: -0.36, -0.27) were lower for the mROI than for the sROI. For both the mROI and sROI, MD, AD, and RD were significantly lower (p<0.05) at the RSCC in paraplegic dogs that did not regain motor function. The findings indicated that DTI methods for quantifying SCI using open source software and ROI were feasible for use in dogs with IVDH; however, values based on sROI methods differed from values based on mROI methods. Some DTI measures based on both the mROI and sROI methods were predictive of poor patient outcome.

Dopamine errors drive excitatory and inhibitory components of backward conditioning in an outcome-specific manner

For over two decades, phasic activity in midbrain dopamine neurons was considered synonymous with the prediction error in temporal-difference reinforcement learning.1-4 Central to this proposal is the notion that reward-predictive stimuli become endowed with the scalar value of predicted rewards. When these cues are subsequently encountered, their predictive value is compared to the value of the actual reward received, allowing for the calculation of prediction errors.5,6 Phasic firing of dopamine neurons was proposed to reflect this computation,1,2 facilitating the backpropagation of value from the predicted reward to the reward-predictive stimulus, thus reducing future prediction errors. There are two critical assumptions of this proposal: (1) that dopamine errors can only facilitate learning about scalar value and not more complex features of predicted rewards, and (2) that the dopamine signal can only be involved in anticipatory cue-reward learning in which cues or actions precede rewards. Recent work7-15 has challenged the first assumption, demonstrating that phasic dopamine signals across species are involved in learning about more complex features of the predicted outcomes, in a manner that transcends this value computation. Here, we tested the validity of the second assumption. Specifically, we examined whether phasic midbrain dopamine activity would be necessary for backward conditioning-when a neutral cue reliably follows a rewarding outcome.16-20 Using a specific Pavlovian-to-instrumental transfer (PIT) procedure,21-23 we show rats learn both excitatory and inhibitory components of a backward association, and that this association entails knowledge of the specific identity of the reward and cue. We demonstrate that brief optogenetic inhibition of VTADA neurons timed to the transition between the reward and cue reduces both of these components of backward conditioning. These findings suggest VTADA neurons are capable of facilitating associations between contiguously occurring events, regardless of the content of those events. We conclude that these data may be in line with suggestions that the VTADA error acts as a universal teaching signal. This may provide insight into why dopamine function has been implicated in myriad psychological disorders that are characterized by very distinct reinforcement-learning deficits.

SABER: A Model-Agnostic Postprocessor for Bias Correcting Discharge from Large Hydrologic Models

Hydrologic modeling is trending toward larger spatial and temporal domains, higher resolutions, and less extensive local calibration and validation. Thorough calibration and validation are difficult because the quantity of observations needed for such scales do not exist or is inaccessible to modelers. We present the Stream Analysis for Bias Estimation and Reduction (SABER) method for bias correction targeting large models. SABER is intended for model consumers to apply to a subset of a larger domain at gauged and ungauged locations and address issues with data size and availability. SABER extends frequency-matching postprocessing techniques using flow duration curves (FDC) at gauged subbasins to be applied at ungauged subbasins using clustering and spatial analysis. SABER uses a “scalar” FDC (SFDC), a ratio of simulated to observed FDC, to characterize biases spatially, temporally, and for varying exceedance probabilities to make corrections at ungauged subbasins. Biased flows at ungauged locations are corrected with the scalar values from the SFDC. Corrected flows are refined to fit a Gumbel Type 1 distribution. We present the theory, procedure, and validation study in Colombia. SABER reduces biases and improves composite metrics, including Nash Sutcliffe and Kling Gupta Efficiency. Recommendations for future work and a discussion of limitations are provided.

Competitive Algorithms for Online Multidimensional Knapsack Problems

In this work, we study the online multidimensional knapsack problem (called OMdKP) in which there is a knapsack whose capacity is represented in m dimensions, each dimension could have a different capacity. Then, n items with different scalar profit values and m-dimensional weights arrive in an online manner and the goal is to admit or decline items upon their arrival such that the total profit obtained by admitted items is maximized and the capacity of knapsack across all dimensions is respected. This is a natural generalization of the classic single-dimension knapsack problem with several relevant applications such as in virtual machine allocation, job scheduling, and all-or-nothing flow maximization over a graph. We develop an online algorithm for OMdKP that uses an exponential reservation function to make online admission decisions. Our competitive analysis shows that the proposed online algorithm achieves the competitive ratio of O(log (Θ α)), where α is the ratio between the aggregate knapsack capacity and the minimum capacity over a single dimension and θ is the ratio between the maximum and minimum item unit values. We also show that the competitive ratio of our algorithm with exponential reservation function matches the lower bound up to a constant factor.

Complex probabilistic fuzzy set and their aggregation operators in group decision making extended to TOPSIS

Randomness and fuzziness create uncertainty in the system at the same time. To combine randomness and cognitive uncertainty into a single paradigm and incorporate more data, this article focuses on developing complex probabilistic fuzzy set. The main intention of this article is to develop a method that can solve both statistical and non-statistical uncertainties. Statistical uncertainty depicts the type of uncertainty concerning the possibility of a future event occurring and non-statistical uncertainties denote the concept of partial truth and imprecise reason. Here, statistical uncertainty is given through probability and non-statistical uncertainty is denoted via the complex fuzzy sets and are incorporated together to form the complex probabilistic fuzzy set. This combination summarizes the importance of this article as they could depict the real life situations more precisely. Proposing the complex probabilistic fuzzy set and studying its basic operations are the article’s main contributions. Also, various aggregation operators are developed for the same and their essential properties are duely explained. Further on, these operators are extended to TOPSIS which is utilized for a group decision making problem with data obtained in the form of complex probabilistic fuzzy number to find the best site for construction of ethanol plant to produce biofuel. The main reason for considering TOPSIS here is because it is a concept that formulates a scalar value taking into consideration both the best and worst alternatives, it represents the rationale of human choice in a more simplistic and straightforward logic and also, it takes less computation time.