Measure Of Likelihood Research Articles

Correlation between HbA1c level and LDL cholesterol level in ischemic stroke patients

Background: Defects in insulin action and hyperglycemia could lead to Dyslipidemia in patients with diabetes.Methods: Cross sectional study carried out in patients presenting with ischemic stroke to medicine emergency unit of the LLRH Hospital from January 2018 to October 2019.Patient of age group 40-80 years The subjects in the present study were 450 patient comprised of 235 cases with ischemic stroke of age group of 40-80 years and 215 health y controls of age group of 40-80 years.Results: The correlation between these two variable as elicited from data obtained from study and, is significant (p<0.0001). The sensitivity of this correlation in the context of ischemic stroke is 75%, while the specificity is 60%. However, an impressive negative predictive value of 91% suggests a beneficial effect of normal or well controlled diabetes and Dyslipidemia. Conversely, a modest positive predictive value of 29% does not appear to help in utilizing this correlation as a measure of future likelihood of ischemic stroke. p value 0.00005 inference-highly significant.Conclusions: Study show statistically significant association (p < 0.001) of increased levels of HbA1c and LDL-C in patients of ischemic stroke.

The Relationship of Loneliness to End-of-Life Experience in Older Americans: A Cohort Study.

Little is known about the relationship between loneliness and end-of-life (EOL) experience including symptom burden, intensity of care, and advance care planning among older adults. Secondary analysis of the Health and Retirement Study (HRS). Population based. Decedents older than 50 years who died between 2004 and 2014 (n = 8700). Exclusions included those who were ineligible for surveys assessing loneliness (n = 2932) or had missing or incomplete loneliness or symptom data (n = 2872). Individuals were characterized as lonely based on responses to the three-item Revised University of California, Los Angeles Loneliness Scale in the most recent HRS survey before death. Outcomes were proxy reports of total EOL symptom burden, intensity of EOL care (eg, late hospice enrollment, place of death, hospitalizations, use of life support), and advance care planning. Results were expressed as adjusted odds ratios (aORs) with 95% confidence intervals (CIs). One-third of 2896 decedents (n = 942) were lonely. After adjusting for demographics, socioeconomic status, multimorbidity, depressive symptoms, family and friends, and social support, loneliness was independently associated with increased total symptom burden at EOL (ß = .13; P = .004). Compared with nonlonely individuals, lonely decedents were more likely to use life support in the last 2 years of life (35.5% vs 29.4%; aOR = 1.36; 95% CI = 1.08-1.71) and more likely to die in a nursing home (18.4% vs 14.2%; aOR = 1.78; 95% CI = 1.30-2.42). No significant differences in other measures of intense care (late hospice enrollment, number of hospitalizations, or dialysis use) or likelihood of advance care planning were observed. Lonely older people may be burdened by more symptoms and may be exposed to more intense EOL care compared with nonlonely people. Interventions aiming to screen for, prevent, and mitigate loneliness during the vulnerable EOL period are necessary. J Am Geriatr Soc 68:1064-1071, 2020.

Young Adult Correlates of IQOS Curiosity, Interest, and Likelihood of Use.

Philip Morris International's heated tobacco product, Marlboro IQOS, is available internationally and will soon be marketed in the U.S. We examined correlates of curiosity, interest, and likelihood to use IQOS among U.S. young adults. Young adults ages 18 - 30 years (N = 346) were recruited online, viewed a description of IQOS, and completed measures of sociodemographic characteristics, perceived risks, curiosity, interest, and likelihood to use IQOS. Males had greater curiosity, interest, and likelihood to use IQOS than females. Individuals with household income of $50,000 - $75,000 were more curious than those with household income of greater than $75,000. Cigarette smokers, e-cigarette and dual users had greater curiosity, interest, and likelihood of use than non-tobacco users. Non-white individuals and older young adults had greater likelihood of use. Greater perceived risks of IQOS were negatively associated with curiosity, interest, and likelihood of use. Among young adults, IQOS has greater appeal among males, non-white individuals, those who are older, those with household income of $50,000-$75,000, cigarette smokers, e-cigarette and dual users, and those with less perceived risks of IQOS. Research on public education communicating the potential risks of IQOS to vulnerable young adults is warranted.

A novel fuzzy clustering algorithm by minimizing global and spatially constrained likelihood-based local entropies for noisy 3D brain MR image segmentation

In this paper, we propose a novel fuzzy clustering algorithm by minimizing global and spatially constrained likelihood-based local entropies (FCMGsLE) for segmenting noisy 3D brain magnetic resonance (MR) image volumes. For each voxel, in order to measure uncertainties that arise while identifying its class, two different entropies are defined. In particular, they measure the amount of uncertainties in terms of global entropy using fuzzifier weighted global membership function and spatially constrained likelihood-based local entropy using fuzzifier weighted local membership function. To mitigate the effect of noise and intensity inhomogeneity (IIH) or radio frequency (RF) inhomogeneity, the local membership function is induced by spatially constrained likelihood measure. These entropies are minimized through a fuzzy objective function to obtain the cluster prototypes and membership functions. The final membership function is obtained by integrating these global and local membership functions using weighted parameters. The algorithm is assessed both qualitatively and quantitatively on ten 3D volumes of simulated and clinical brain MR image data having high levels of noise and intensity inhomogeneity and a synthetic 3D image volume with Rician noise. The simulation results reveal that the proposed algorithm outperforms several state-of-the-art algorithms devised in recent past when evaluated in terms of segmentation accuracy, Dice similarity coefficient, partition coefficient, and partition entropy

Mitigation of Classes of Attacks using a Probabilistic Discrete Event System Framework

Abstract Cyber-attack models and their respective mitigation strategies have been recently studied in the discrete event systems setting. Previous work focuses on whether unsafe behaviour can be prevented using supervisory control theory. When unsafe behaviour cannot be prevented with certainty, mitigation strategies are limited. This paper proposes the use of a probabilistic discrete event system (PDES) framework to incorporate a likelihood measure for unsafe behaviour in the attack models presented in Carvalho et al. (2018). The least-unsafe (LU) supervisor problem is introduced to minimize this unsafe likelihood measure and improve existing attack mitigation techniques. The LU supervisor problem under full observability is solved by reformulating it into an MDP problem, and a computable algorithm is developed. Lastly, the implementation of LU supervisors is discussed and illustrated with an example.

Corrigendum to: Prioritising fuels reduction for water supply protection

Concerns over wildfire impacts to water supplies have motivated efforts to mitigate risk by reducing forest fuels. Methods to assess fuel treatment effects and prioritise their placement are needed to guide risk mitigation efforts. We present a fuel treatment optimisation model to minimise risk to multiple water supplies based on constraints for treatment feasibility and cost. Risk is quantified as the expected sediment impact costs to water supplies by combining measures of fire likelihood and behaviour, erosion, sediment transport and water supply vulnerability. We demonstrate the model's utility for prioritising fuel treatments in two large watersheds in Colorado, USA, that are critical for municipal water supply. Our results indicate that wildfire risk to water supplies can be substantially reduced by treating a small portion of the watersheds that have dense, fire-prone forests on steep slopes that drain to water supply infrastructure. Our results also show that the cost of fuel treatments outweighs the expected cost savings from reduced sediment inputs owing to the low probability of fuel treatments encountering wildfire and the high cost of thinning forests. This highlights the need to expand use of more cost-effective treatments, like prescribed fire, and to identify fuel treatment projects that benefit multiple resources.

The Application of Epidemiology for Categorising DNS Cyber Risk Factors

This Epidemiology can be applied to cybersecurity as a novel approach for analysing and detecting cyber threats and their risks. It provides a systematic model for the analysis of likelihood, consequence, management, and prevention measures to examine malicious behaviours like disease. There are a few research studies in discrete cybersecurity risk factors; however, there is a significant research gap on the analysis of collective cyber risk factors and measuring their cyber risk impacts. Effective cybersecurity risk management requires the identification and estimation of the probability of infection, based on a comprehensive range of historical and environmental factors, including human behaviour and technology characteristics. This paper explores how an epidemiological principle can be applied to identify cybersecurity risk factors. These risk factors comprise both human and machine behaviours profiled as risk factors. This paper conducts a preliminary analysis of the relationships between these risk factors utilising Domain Name System (DNS) data sources. The experimental results indicated that the epidemiological principle can effectively examine and estimate cyber risk factors. The proposed principle has a great potential in enhancing new machine learning-enabled intrusion detection solutions by utilising this principle as a risk assessment module of the solutions.

Assessing the Level of Enterprise Reengineering in the Context of Global Digitalization

A simulation of the level of re-engineering organization is presented on the example of a selected group of four enterprises. It is assumed that in the conditions of the experiment there is an occurrence of an event of effective re-engineering in the field of financial relations of enterprises forming a complete group of independent events - incompatible hypotheses of quantitative and qualitative measurement of the statistical likelihood of relevant indicators of experimental events in the organization of enterprise re-engineering. The probabilities of full group events were determined using an expert evaluation matrix by the principle of pairwise comparison of the importance of each event using a 5-point scale of intensity of significance of the items in the hypothesis group. The conditional probabilities of quantitative and qualitative measurement of the statistical probability of relevant indicators in the plane of action of the factors of influence on the organization of re-engineering of the enterprise are established. The formula of full probability presents the visualization of the dynamics of the level of organization of reengineering in financial relations of enterprises in the conditions of global digitization in four time intervals since 2012. by 2019. It has been proven that the reengineering process remains an effective tool for companies seeking to operate in a competitive world; companies are required to re-engineer their business processes to deliver breakthrough results and a long-term strategy for company development.

Is the LITE version of the usability metric for user experience (UMUX-LITE) a reliable tool to support rapid assessment of new healthcare technology?

ObjectiveTo ascertain the reliability of a standardised, short-scale measure of satisfaction in the use of new healthcare technology i.e., the LITE version of the usability metric for user experience (UMUX-LITE). Whilst previous studies have demonstrated the reliability of UMUX-LITE, and its relationship with measures of likelihood to recommend a product, such as the Net Promoter Score (NPS) in other sectors no such testing has been undertaken with healthcare technology. Materials and methodsSix point-of-care products at different stages of development were assessed by 120 healthcare professionals. UMUX-LITE was used to gather their satisfaction in use, and NPS to declare their intention to promote the product. Inferential statistics were used to: i) ascertain the reliability of UMUX-LITE, and ii) assess the relationship between UMUX-LITE and NPS at different stages of products development. ResultsUMUX-LITE showed an acceptable reliability (α = 0.7) and a strong positive correlation with NPS (r = 0.455, p < .001). This is similar to findings in other fields of application. The level of product development did not affect the UMUX-LITE scores, while the stage of development was a significant predictor (R2 = 0.49) of the intention to promote. Discussion and conclusionPractitioners may apply UMUX-LITE alone, or in combination with the NPS, to complement interview and ‘homemade’ scales to investigate the quality of new products at different stages of development. This shortened scale is appropriate for use in the context of healthcare in which busy professionals have a minimal amount of time to support innovation.

Bayesian calibration of HEC-HMS model using two different algorithms

The assessment of hydrological model parameters is impacted by uncertainties that may be attributed to several sources of errors. Many methods have been developed and applied to various hydrological models in order to address these uncertainties. The present study aims to apply two uncertainty analysis methods, i.e., generalized uncertainty estimation (GLUE) and differential evolution adaptive metropolis (DREAMzs), for the purpose of evaluating the parametric and predictive uncertainty of the HEC-HMS hydrological model. The HEC-HMS model was coupled to these two approaches in order to simulate flash floods in the Mekerra watershed (Algeria) and to estimate the uncertainty of 14 model parameters which are due to be calibrated. For this, six flood events were selected for the purpose of calibrating and validating the model, two informal likelihood measures L1 and L2 were applied in GLUE and one formal likelihood measure was applied in DREAMzs. Considering the difference between the results, and not the principles of the two methods, the analysis indicated that these parameters can be better identified by DREAMzs algorithm. Indeed, the coefficients of variation of their posterior distributions are generally lower. Compared to GLUE, DREAMzs algorithm is more efficient in exploring the parametric space. With regard to the predictive uncertainty, 95% of the uncertainty intervals were estimated by both methods and then analyzed. The DREAMzs algorithm results suggested a good dynamics of the simulated floods, associated with a significant uncertainty that is due not to the parametric uncertainty but to other sources of errors. The GLUE algorithm generates similar results but does not distinguish between the total uncertainty and parametric uncertainty. Further studies would be desirable to evaluate the impact of other likelihood measures on the simulation results with both algorithms

Willing to do what others won't: Narcissism, risk-taking, and social approval

Prior research suggests that narcissism is positively associated with risk-taking behavior (e.g., gambling). The present study sought to test a contextual factor that may moderate this association. Specifically, we hypothesized that, because narcissists are generally less concerned with whether others approve of their behavior, narcissism would be more strongly associated with risk-taking when the risk-taking behaviors are socially disapproved of (e.g., illegal activity). We tested this by reanalyzing a previously published dataset (N == 605; Foster, Shenesey, & Goff, 2009) that contains measures of narcisissm and likelihood of engaging in various risk-taking behaviors. We added to these data an independent set of ratings that assessed to what extent each risk-taking behavior was socially approved. Consistent with out hypothesis, narcissism was more strongly linked to risk-taking behaviors that were socially disapproved of than behaviors that were socially approved, although the moderation effect was weak. A test of a moderated mediation suggest that narcissists discount social disapproval from perceptions of benefits derived from risk-taking less than others, which might account for narcissists higher willingness to engage in socially diapproved of risk-taking behaviors.

Subjective modeling of image shape

The paper presents two approaches to subjective modelling incomplete and uncertain information about possible shape of images. The shape is understood as a set of images of the scene, recorded under all possible conditions (lighting, exposure, etc.). To simulate the likelihood, the measure of likelihood introduced by Yu.P. Pytyev is used. This measure is a function defined on a set of statements and ordering them according to plausibility. The obtained subjective models of shapes allow using the theory of optimal strategies for solving morphological problems.

Assessment of the uncertainty in the SWAT parameters based on formal and informal likelihood measure

Assessment of the uncertainty in the SWAT parameters based on formal and informal likelihood measure

A Continuous-Time Model-Based Approach for Activity Recognition in Pervasive Environments

We present a model-based approach to Activity Recognition (AR) in Ambient Assisted Living (AAL). The approach leverages an a priori stochastic model termed Continuous-Time Hidden Semi-Markov Model (CT-HSMM), capturing the continuous-time durations of activities and inter-event times. The model is enhanced according to the observed statistics, associating the events with an occurrence probability, and the sojourn time and the inter-event time in each activity with a continuous-time probability density function, allowing effective fitting of observed durations through non-Markovian distributions. The model is updated at run time according to a sequence of time-stamped observations, exploiting the method of stochastic state classes to perform transient analysis and derive a measure of likelihood that an activity is currently performed. The approach supports both online AR, predicting the activity performed at time $t$ using only the events observed until that time, and offline AR, applying a forward–backward procedure that exploits all the events observed before and after time $t$ . The approach is experimented on a real dataset of the literature, providing performance measures that can be compared with those of offline Hidden Markov Models (HMMs) and offline Hidden Semi-Markov Models (HSMMs).

An evaluation of two statistical downscaling models for downscaling monthly precipitation in the Heihe River basin of China

The stepwise regression model (SRM) is a widely used statistical downscaling method that could be used to establish a statistical relationship between observed precipitation and predictors. However, the SRM cannot reflect the contributions of predictors to precipitation reasonably, which may not be the best model based on several possible competing predictors. Bayesian model averaging (BMA) is a standard inferencing approach that considers multiple competing statistical models. The BMA infers precipitation predictions by weighing individual predictors based on their probabilistic likelihood measures over the training period, with the better-performing predictions receiving higher weights than the worse-performing ones. Furthermore, the BMA provides a more reliable description of all the predictors than the SRM, leading to a sharper and better calibrated probability density function (PDF) for the probabilistic predictions. In this study, monthly precipitation at fifteen meteorological stations over the period of 1971–2012 in the Heihe River basin (HRB), which is located in an arid area of Northwest China, was simulated using the Bayesian model averaging (BMA) and the stepwise regression model (SRM), which was then compared with the observed datasets (OBS). The results showed that the BMA produced more accurate results than the SRM when used to statistically downscale large-scale variables. The multiyear mean precipitation results for twelve of the fifteen meteorological stations that were simulated by the BMA were better than those simulated by the SRM. The RMSE and MAE of the BMA for each station were lower than those of the SRM. The BMA had a lower mean RMSE (− 13.93%) and mean MAE (− 14.37%) compared with the SRM. The BMA could reduce the RMSEs and MAEs of precipitation and improve the correlation coefficient effectively. This indicates that the monthly precipitation simulated by the BMA has better consistency with the observed values.

A Model-Based Approach to Detection Limits in Studying Environmental Exposures and Human Fecundity.

Human exposure to persistent environmental pollutants often results in concentrations with a range of values below the laboratory detection limits. Growing evidence suggests that inadequate handling of concentrations below the limit of detection (LOD) may bias assessment of health effects in relation to environmental exposures. We seek to quantify such bias in models focusing on the day-specific probability of pregnancy during the fertile window and propose a model-based approach to reduce such bias. A multivariate skewed generalized t-distribution constrained by the LOD is assumed for the chemical concentrations, which realistically represents the underlying distribution. A latent variable-based framework is used to model fecundibility, which nonlinearly relates conception probability to chemical concentrations, daily intercourses, and other important covariates. The advantages of the proposed approach include the use of multiple chemical concentrations to aid the estimation of left censored chemical exposures, as well as the model-based feedback mechanism for fecundibility outcome to inform the estimations, and an adequate handling of model uncertainty through a joint modeling framework. A Markov chain Monte Carlo sampling algorithm is developed for implementing the Bayesian computations and the logarithm of pseudo-marginal likelihood measure is used for model choices. We conduct simulation studies to demonstrate the performance of the proposed approach and apply the framework to the Longitudinal Investigation of Fertility and the Environment study which evaluates the effects of exposures to environmental pollutants on the probability of pregnancy. We found that p,p'-DDT is negatively associated with the day-specific probability of pregnancy.

An uncertain Z-number multicriteria group decision-making method with cloud models

This study focuses on developing an innovative multicriteria group decision-making (MCGDM) method to handle practical problems based on information description, information fusion and information measure. Given that various uncertain data exist in the process of managing problems, uncertain Z-numbers are introduced to represent evaluation information and measure information reliability simultaneously. Processing uncertain information requires the effective support of reliable tools; then, cloud models can be used to address uncertain Z-number information. First, uncertain Z-numbers are converted, and a novel concept of Z-trapezium-normal clouds (ZTNCs) is introduced. Next, the operations, power aggregation operators, and distance and likelihood measures of ZTNCs are defined. Subsequently, the likelihood power aggregation operator and the priority vector of the likelihood matrix are proposed. Moreover, a new MCGDM method is developed by combining the two kinds of proposed power aggregation operators with the likelihood measure. Finally, an illustrative example of new energy evaluation is provided to demonstrate the developed method, and its feasibility and validity are further verified by a comparative analysis with other existing methods.

Application of Z-Numbers Based Approach to Project Risks Assessment

At every phase of the project management process various risks originate owing to occurrence of uncertain events. In this research, we are analyzing potentialities of the Z-numbers in improving the quality of risk assessment. Risk assessment uses probability theory, theory of possibilities, fuzzy approach, Z-number based approach etc. Combined risk measure based on probability and consequence is calculated by applying the disjoint events probability formula or as a product of events. Reliability of relevant information unaccounted in this approach and this circumstance limits the descriptive power of the approach. Suggested by L. Zade a bi-component Z-number Z = (A, B) represents in a unified way a restriction on the values of the uncertain variable (A) and its certainty (B) and allows to take into account the reliability of information. Prediction identical to (High, Very Sure) can be formalized as a Z- evaluation “X is Z (A,B)”, where X is random variable of Risk Likelihood, A and B are fuzzy sets, describing soft constraints on a risk likelihood and a partial reliability, respectively. Usually, A and B are sense-based and in effect are imprecise.Z-number describes a probability of threat as: Likelihood =Z1(High,Very Sure),where A is expressed by linguistic terms High, Medium, Low, and B is expressed by terms Very Sure, Sure and so on. Similarly, Сonsequence measure is described as Сonsequence measure = Z2 (Low, Sure).Risk levels (Z12) is calculated as the product of the likelihood (Z1) and consequence measures (Z2).Effectiveness of the approach illustrated by examples. A general and computationally effective approach suggested to computation with Z-numbers allows using Z-information for the solving decision-making problem which can be utilized for risk factors estimation.. Application of the Z-number based approach for a project risks assessment increases adequacy of the risks representation due to better approximation of the combined effects

Application of Z-Numbers Based Approach to Project Risks Assessment

At every phase of the project management process various risks originate owing to occurrence of uncertain events. In this research, we are analyzing potentialities of the Z-numbers in improving the quality of risk assessment. Risk assessment uses probability theory, theory of possibilities, fuzzy approach, Z-number based approach etc. Combined risk measure based on probability and consequence is calculated by applying the disjoint events probability formula or as a product of events. Reliability of relevant information unaccounted in this approach and this circumstance limits the descriptive power of the approach. Suggested by L. Zade a bi-component Z-number Z = (A, B) represents in a unified way a restriction on the values of the uncertain variable (A) and its certainty (B) and allows to take into account the reliability of information. Prediction identical to (High, Very Sure) can be formalized as a Z- evaluation “X is Z (A,B)”, where X is random variable of Risk Likelihood, A and B are fuzzy sets, describing soft constraints on a risk likelihood and a partial reliability, respectively. Usually, A and B are sense-based and in effect are imprecise.Z-number describes a probability of threat as: Likelihood =Z1(High,Very Sure),where A is expressed by linguistic terms High, Medium, Low, and B is expressed by terms Very Sure, Sure and so on. Similarly, Сonsequence measure is described as Сonsequence measure = Z2 (Low, Sure).Risk levels (Z12) is calculated as the product of the likelihood (Z1) and consequence measures (Z2).Effectiveness of the approach illustrated by examples. A general and computationally effective approach suggested to computation with Z-numbers allows using Z-information for the solving decision-making problem which can be utilized for risk factors estimation.. Application of the Z-number based approach for a project risks assessment increases adequacy of the risks representation due to better approximation of the combined effects

A Multi-featured Hybrid Model for Speaker Recognition on Multi-person Speech

The identification of the speakers from the continuous speech requires manifold algorithms and mathematical formulation at each integrated work stage. In this paper, a multi-feature adaptive hybrid model is provided to process the real time uninterrupted speech which provide 98.8% accuracy rate. To remove the speech impurities, wavelet decomposition and GMM are applied in an integrated form. The filtered speech is breakdown to smaller speech segments by applying a multi-phase clipper. Then frequency and time gap analysis are implied to recognize the silence and speech signal sections. The speech localization without content loss is obtained by combining the mutual tracing, dictionary tracing and the channel compensation method. The extracted independent speech segments are processed to generate the feature vector. The acoustic, statistical, reference and block adaptive features are combined to generate the vast adaptive feature set. I-vector scoring, acoustic scoring, likelihood measure and MFCC are applied sequentially to generate the feature pool. The extracted feature pool is processed under probabilistic deep neural network to classify and validate the speakers. The analysis is applied to CSTR VCTK Corpus speech datasets. The comparative evaluation using various methods is made. The evaluation results signify that the proposed model has improved the accuracy of speaker classification significantly.