Displaying publications 21 - 40 of 137 in total

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  1. Fulltext Quantified Uncertainties in Comparative Life Cycle Assessment: What Can Be Concluded?
    Mendoza Beltran A, Prado V, Font Vivanco D, Henriksson PJG, Guinée JB, Heijungs R
    Environ Sci Technol, 2018 02 20;52(4):2152-2161.
    PMID: 29406730 DOI: 10.1021/acs.est.7b06365
    Interpretation of comparative Life Cycle Assessment (LCA) results can be challenging in the presence of uncertainty. To aid in interpreting such results under the goal of any comparative LCA, we aim to provide guidance to practitioners by gaining insights into uncertainty-statistics methods (USMs). We review five USMs-discernibility analysis, impact category relevance, overlap area of probability distributions, null hypothesis significance testing (NHST), and modified NHST-and provide a common notation, terminology, and calculation platform. We further cross-compare all USMs by applying them to a case study on electric cars. USMs belong to a confirmatory or an exploratory statistics' branch, each serving different purposes to practitioners. Results highlight that common uncertainties and the magnitude of differences per impact are key in offering reliable insights. Common uncertainties are particularly important as disregarding them can lead to incorrect recommendations. On the basis of these considerations, we recommend the modified NHST as a confirmatory USM. We also recommend discernibility analysis as an exploratory USM along with recommendations for its improvement, as it disregards the magnitude of the differences. While further research is necessary to support our conclusions, the results and supporting material provided can help LCA practitioners in delivering a more robust basis for decision-making.
    Matched MeSH terms: Uncertainty
  2. Robust stabilization of underactuated nonlinear systems: A fast terminal sliding mode approach
    Khan Q, Akmeliawati R, Bhatti AI, Khan MA
    ISA Trans, 2017 Jan;66:241-248.
    PMID: 27884392 DOI: 10.1016/j.isatra.2016.10.017
    This paper presents a fast terminal sliding mode based control design strategy for a class of uncertain underactuated nonlinear systems. Strategically, this development encompasses those electro-mechanical underactuated systems which can be transformed into the so-called regular form. The novelty of the proposed technique lies in the hierarchical development of a fast terminal sliding attractor design for the considered class. Having established sliding mode along the designed manifold, the close loop dynamics become finite time stable which, consequently, result in high precision. In addition, the adverse effects of the chattering phenomenon are reduced via strong reachability condition and the robustness of the system against uncertainties is confirmed theoretically. A simulation as well as experimental study of an inverted pendulum is presented to demonstrate the applicability of the proposed technique.
    Matched MeSH terms: Uncertainty
  3. Inclusive Search for a Highly Boosted Higgs Boson Decaying to a Bottom Quark-Antiquark Pair
    Sirunyan AM, Tumasyan A, Adam W, Ambrogi F, Asilar E, Bergauer T, et al.
    Phys Rev Lett, 2018 Feb 16;120(7):071802.
    PMID: 29542941 DOI: 10.1103/PhysRevLett.120.071802
    An inclusive search for the standard model Higgs boson (H) produced with large transverse momentum (p_{T}) and decaying to a bottom quark-antiquark pair (bb[over ¯]) is performed using a data set of pp collisions at sqrt[s]=13  TeV collected with the CMS experiment at the LHC. The data sample corresponds to an integrated luminosity of 35.9  fb^{-1}. A highly Lorentz-boosted Higgs boson decaying to bb[over ¯] is reconstructed as a single, large radius jet, and it is identified using jet substructure and dedicated b tagging techniques. The method is validated with Z→bb[over ¯] decays. The Z→bb[over ¯] process is observed for the first time in the single-jet topology with a local significance of 5.1 standard deviations (5.8 expected). For a Higgs boson mass of 125 GeV, an excess of events above the expected background is observed (expected) with a local significance of 1.5 (0.7) standard deviations. The measured cross section times branching fraction for production via gluon fusion of H→bb[over ¯] with reconstructed p_{T}>450  GeV and in the pseudorapidity range -2.5
    Matched MeSH terms: Uncertainty
  4. Fulltext A novel framework for addressing uncertainties in machine learning-based geospatial approaches for flood prediction
    Adnan MSG, Siam ZS, Kabir I, Kabir Z, Ahmed MR, Hassan QK, et al.
    J Environ Manage, 2023 Jan 15;326(Pt B):116813.
    PMID: 36435143 DOI: 10.1016/j.jenvman.2022.116813
    Globally, many studies on machine learning (ML)-based flood susceptibility modeling have been carried out in recent years. While majority of those models produce reasonably accurate flood predictions, the outcomes are subject to uncertainty since flood susceptibility models (FSMs) may produce varying spatial predictions. However, there have not been many attempts to address these uncertainties because identifying spatial agreement in flood projections is a complex process. This study presents a framework for reducing spatial disagreement among four standalone and hybridized ML-based FSMs: random forest (RF), k-nearest neighbor (KNN), multilayer perceptron (MLP), and hybridized genetic algorithm-gaussian radial basis function-support vector regression (GA-RBF-SVR). Besides, an optimized model was developed combining the outcomes of those four models. The southwest coastal region of Bangladesh was selected as the case area. A comparable percentage of flood potential area (approximately 60% of the total land areas) was produced by all ML-based models. Despite achieving high prediction accuracy, spatial discrepancy in the model outcomes was observed, with pixel-wise correlation coefficients across different models ranging from 0.62 to 0.91. The optimized model exhibited high prediction accuracy and improved spatial agreement by reducing the number of classification errors. The framework presented in this study might aid in the formulation of risk-based development plans and enhancement of current early warning systems.
    Matched MeSH terms: Uncertainty
  5. Suspended sediment load prediction using artificial neural network and ant lion optimization algorithm
    Banadkooki FB, Ehteram M, Ahmed AN, Teo FY, Ebrahimi M, Fai CM, et al.
    Environ Sci Pollut Res Int, 2020 Oct;27(30):38094-38116.
    PMID: 32621196 DOI: 10.1007/s11356-020-09876-w
    Suspended sediment load (SSL) estimation is a required exercise in water resource management. This article proposes the use of hybrid artificial neural network (ANN) models, for the prediction of SSL, based on previous SSL values. Different input scenarios of daily SSL were used to evaluate the capacity of the ANN-ant lion optimization (ALO), ANN-bat algorithm (BA) and ANN-particle swarm optimization (PSO). The Goorganrood basin in Iran was selected for this study. First, the lagged SSL data were used as the inputs to the models. Next, the rainfall and temperature data were used. Optimization algorithms were used to fine-tune the parameters of the ANN model. Three statistical indexes were used to evaluate the accuracy of the models: the root-mean-square error (RMSE), mean absolute error (MAE) and Nash-Sutcliffe efficiency (NSE). An uncertainty analysis of the predicting models was performed to evaluate the capability of the hybrid ANN models. A comparison of models indicated that the ANN-ALO improved the RMSE accuracy of the ANN-BA and ANN-PSO models by 18% and 26%, respectively. Based on the uncertainty analysis, it can be surmised that the ANN-ALO has an acceptable degree of uncertainty in predicting daily SSL. Generally, the results indicate that the ANN-ALO is applicable for a variety of water resource management operations.
    Matched MeSH terms: Uncertainty
  6. Predicting evaporation with optimized artificial neural network using multi-objective salp swarm algorithm
    Ehteram M, Panahi F, Ahmed AN, Huang YF, Kumar P, Elshafie A
    Environ Sci Pollut Res Int, 2022 Feb;29(7):10675-10701.
    PMID: 34528189 DOI: 10.1007/s11356-021-16301-3
    Evaporation is a crucial component to be established in agriculture management and water engineering. Evaporation prediction is thus an essential issue for modeling researchers. In this study, the multilayer perceptron (MLP) was used for predicting daily evaporation. MLP model is as one of the famous ANN models with multilayers for predicting different target variables. A new strategy was used to enhance the accuracy of the MLP model. Three multi-objective algorithms, namely, the multi-objective salp swarm algorithm (MOSSA), the multi-objective crow algorithm (MOCA), and the multi-objective particle swarm optimization (MOPSO), were respectively and separately coupled to the MLP model for determining the model parameters, the best input combination, and the best activation function. In this study, three stations in Malaysia, namely, the Muadzam Shah (MS), the Kuala Terengganu (KT), and the Kuantan (KU), were selected for the prediction of the respective daily evaporation. The spacing (SP) and maximum spread (MS) indices were used to evaluate the quality of generated Pareto front (PF) by the algorithms. The lower SP and higher MS showed better PF for the models. It was observed that the MOSSA had higher MS and lower SP than the other algorithms, at all stations. The root means square error (RMSE), mean absolute error (MAE), percent bias (PBIAS), and Nash Sutcliffe efficiency (NSE) quantifiers were used to compare the ability of the models with each other. The MLP-MOSSA had reduced RMSE compared to the MLP-MOCA, MLP-MOPSO, and MLP models by 18%, 25%, and 35%, respectively, at the MS station. The MAE of the MLP-MOSSA was 2.7%, 4.1%, and 26%, respectively lower than those of the MLP-MOCA, MLP-MOPSO, and MLP models at the KU station. The MLP-MOSSA showed lower MAE than the MLP-MOCA, MLP-MOPSO, and MLP models by 16%, 18%, and 19%, respectively, at the KT station. An uncertainty analysis was performed based on the input and parameter uncertainty. The results indicated that the MLP-MOSSA had the lowest uncertainty among the models. Also, the input uncertainty was lower than the parameter uncertainty. The general results indicated that the MLP-MOSSA had the high efficiency for predicting evaporation.
    Matched MeSH terms: Uncertainty
  7. Fulltext Assessing Financial Risk Spillover and Panic Impact of Covid-19 on European and Vietnam Stock market
    Moslehpour M, Al-Fadly A, Ehsanullah S, Chong KW, Xuyen NTM, Tan LP
    Environ Sci Pollut Res Int, 2022 Apr;29(19):28226-28240.
    PMID: 34993822 DOI: 10.1007/s11356-021-18170-2
    This study examined the influence of tail risks on global financial markets, which aids in better understanding of the emergence of COVID-19. This study looks at the global and Vietnamese stock markets impacted by the COVID-19 pandemic to identify systemic emergencies. Risk dependent value (CoVaR) and Delta link VaR are two important tail-related risk indicators used in Conditional Bivariate Dynamic Correlation (DCC) (CoVaR). The empirical findings demonstrate that when COVID-19's worldwide spread widens, the volatility transmission of systemic risks across the global stock market and multiple exchanges shifts and becomes more relevant over time. At the time of COVID-19, the world industrial market was larger than the Vietnamese stock market, and the Vietnamese stock market posed a lesser danger to the global market. A closer examination of the link between the Vietnam value-at-risk (VaR) range index sample and the world stock index indicates a significant degree of downside risk integration in key monetary systems, particularly during the COVID-19 era. Our study findings may help regulators, politicians, and portfolio risk managers in Vietnam and worldwide during the unique moment of uncertainty created by the COVID-19 epidemic.
    Matched MeSH terms: Uncertainty
  8. System of systems engineering and risk management of extreme events: concepts and case study
    Bristow M, Fang L, Hipel KW
    Risk Anal, 2012 Nov;32(11):1935-55.
    PMID: 22804565 DOI: 10.1111/j.1539-6924.2012.01867.x
    The domain of risk analysis is expanded to consider strategic interactions among multiple participants in the management of extreme risk in a system of systems. These risks are fraught with complexity, ambiguity, and uncertainty, which pose challenges in how participants perceive, understand, and manage risk of extreme events. In the case of extreme events affecting a system of systems, cause-and-effect relationships among initiating events and losses may be difficult to ascertain due to interactions of multiple systems and participants (complexity). Moreover, selection of threats, hazards, and consequences on which to focus may be unclear or contentious to participants within multiple interacting systems (ambiguity). Finally, all types of risk, by definition, involve potential losses due to uncertain events (uncertainty). Therefore, risk analysis of extreme events affecting a system of systems should address complex, ambiguous, and uncertain aspects of extreme risk. To accomplish this, a system of systems engineering methodology for risk analysis is proposed as a general approach to address extreme risk in a system of systems. Our contribution is an integrative and adaptive systems methodology to analyze risk such that strategic interactions among multiple participants are considered. A practical application of the system of systems engineering methodology is demonstrated in part by a case study of a maritime infrastructure system of systems interface, namely, the Straits of Malacca and Singapore.
    Matched MeSH terms: Uncertainty
  9. Nexus of the CSR disclosures and corporate financial performance: evidence from Asian countries climate policy uncertainty
    Waris M, Din BH
    Environ Sci Pollut Res Int, 2024 Jan;31(2):1995-2008.
    PMID: 38049691 DOI: 10.1007/s11356-023-31307-9
    Financial performance is a critical aspect of a company's overall health and sustainability. It directly influences investor decisions, stock market performance, credit ratings, and the company's ability to access capital. Corporate financial performance is influenced by multitude of facts, both internal and external such as disclosure of the information, and social and environmental factors. On the ground of the facts, we aimed to investigate non-financial firms that belong to Asian economies affected by climate policy uncertainty and corporate social responsibility disclosures in terms of their financial performance. To conduct quantitative study analysis, we used the two effective statistical tools such as two-stage regression method and generalized method of movement (GMM). Our results show that corporate high value of social responsibility disclosure and climate policy uncertainty has significant negative impact on return on asset (ROA) of the listed organizations of China, Pakistan, and India. Moreover, CSR disclosure attributes higher values such as social (SC) and governance score (GOV), and climate policy uncertainty (CPU) has significant negative relationship with return on equity (ROE) and earning per share (EPS) respectively, while a higher value of ESG total score and the environmental (ENV) score has a significant positive impact on ROE and EPS. Additionally, the research concludes that climate policy uncertainty is a key factor that motivates CSR disclosure practices, which ultimately improves corporate financial performance. Moreover, we concluded from our finding that the climate policy uncertainty creates ambiguity surrounding government regulations, international agreements, or market mechanisms that affect financial performance. Moreover, environmental disclosure information that has the large part in total ESG scores attract the investors around the globe which leads to rise in the financial performance, while the other attributes of the CSR disclosure decrease performance. This study advocated the great implications for researchers, investors, the government, and regulatory authorities. Policy makers can make the policy about the CSR disclosure for creating the good image of the organization to attract investors around the globe.
    Matched MeSH terms: Uncertainty
  10. Baby cry recognition based on WOA-VMD and an improved Dempster-Shafer evidence theory
    Zhang K, Ting HN, Choo YM
    Comput Methods Programs Biomed, 2024 Mar;245:108043.
    PMID: 38306944 DOI: 10.1016/j.cmpb.2024.108043
    BACKGROUND AND OBJECTIVE: Conflict may happen when more than one classifier is used to perform prediction or classification. The recognition model error leads to conflicting evidence. These conflicts can cause decision errors in a baby cry recognition and further decrease its recognition accuracy. Thus, the objective of this study is to propose a method that can effectively minimize the conflict among deep learning models and improve the accuracy of baby cry recognition.

    METHODS: An improved Dempster-Shafer evidence theory (DST) based on Wasserstein distance and Deng entropy was proposed to reduce the conflicts among the results by combining the credibility degree between evidence and the uncertainty degree of evidence. To validate the effectiveness of the proposed method, examples were analyzed, and applied in a baby cry recognition. The Whale optimization algorithm-Variational mode decomposition (WOA-VMD) was used to optimally decompose the baby cry signals. The deep features of decomposed components were extracted using the VGG16 model. Long Short-Term Memory (LSTM) models were used to classify baby cry signals. An improved DST decision method was used to obtain the decision fusion.

    RESULTS: The proposed fusion method achieves an accuracy of 90.15% in classifying three types of baby cry. Improvement between 2.90% and 4.98% was obtained over the existing DST fusion methods. Recognition accuracy was improved by between 5.79% and 11.53% when compared to the latest methods used in baby cry recognition.

    CONCLUSION: The proposed method optimally decomposes baby cry signal, effectively reduces the conflict among the results of deep learning models and improves the accuracy of baby cry recognition.

    Matched MeSH terms: Uncertainty
  11. Demystifying uncertainty in PM10 susceptibility mapping using variable drop-off in extreme-gradient boosting (XGB) and random forest (RF) algorithms
    AlThuwaynee OF, Kim SW, Najemaden MA, Aydda A, Balogun AL, Fayyadh MM, et al.
    Environ Sci Pollut Res Int, 2021 Aug;28(32):43544-43566.
    PMID: 33834339 DOI: 10.1007/s11356-021-13255-4
    This study investigates uncertainty in machine learning that can occur when there is significant variance in the prediction importance level of the independent variables, especially when the ROC fails to reflect the unbalanced effect of prediction variables. A variable drop-off loop function, based on the concept of early termination for reduction of model capacity, regularization, and generalization control, was tested. A susceptibility index for airborne particulate matter of less than 10 μm diameter (PM10) was modeled using monthly maximum values and spectral bands and indices from Landsat 8 imagery, and Open Street Maps were used to prepare a range of independent variables. Probability and classification index maps were prepared using extreme-gradient boosting (XGBOOST) and random forest (RF) algorithms. These were assessed against utility criteria such as a confusion matrix of overall accuracy, quantity of variables, processing delay, degree of overfitting, importance distribution, and area under the receiver operating characteristic curve (ROC).
    Matched MeSH terms: Uncertainty
  12. Fulltext Sustainable agility of product development process based on a rough cloud technique: A case study on China's small and medium enterprises
    Zhao Z, Alli H, Ahmadipour M, Che Me R
    PLoS One, 2024;19(8):e0300266.
    PMID: 39173012 DOI: 10.1371/journal.pone.0300266
    The importance of incorporating an agile approach into creating sustainable products has been widely discussed. This approach can enhance innovation integration, improve adaptability to changing development circumstances, and increase the efficiency and quality of the product development process. While many agile methods have originated in the software development context and have been formulated based on successful software projects, they often fail due to incorrect procedures and a lack of acceptance, preventing deep integration into the process. Additionally, decision-making for market evaluation is often hindered by unclear and subjective information. Therefore, this study introduces an extended TOPSIS (Technique for Order Performance by Similarity to Ideal Solution) method for sustainable product development. This method leverages the benefits of cloud model theory to address randomness and uncertainty (intrapersonal uncertainty) and the advantages of rough set theory to flexibly handle market demand uncertainty without requiring extra information. The study proposes an integrated weighting method that considers both subjective and objective weights to determine comprehensive criteria weights. It also presents a new framework, named Sustainable Agility of Product Development (SAPD), which aims to evaluate criteria for assessing sustainable product development. To validate the effectiveness of this proposed method, a case study is conducted on small and medium enterprises in China. The obtained results show that the company needs to conduct product structure research and development to realize new product functions.
    Matched MeSH terms: Uncertainty
  13. Parceling Cannot Reduce Factor Indeterminacy in Factor Analysis: A Research Note
    Rigdon EE, Becker JM, Sarstedt M
    Psychometrika, 2019 09;84(3):772-780.
    PMID: 31292860 DOI: 10.1007/s11336-019-09677-2
    Parceling-using composites of observed variables as indicators for a common factor-strengthens loadings, but reduces the number of indicators. Factor indeterminacy is reduced when there are many observed variables per factor, and when loadings and factor correlations are strong. It is proven that parceling cannot reduce factor indeterminacy. In special cases where the ratio of loading to residual variance is the same for all items included in each parcel, factor indeterminacy is unaffected by parceling. Otherwise, parceling worsens factor indeterminacy. While factor indeterminacy does not affect the parameter estimates, standard errors, or fit indices associated with a factor model, it does create uncertainty, which endangers valid inference.
    Matched MeSH terms: Uncertainty*
  14. Comprehensive multi-locus phylogeny of Old World tree frogs (Anura: Rhacophoridae) reveals taxonomic uncertainties and potential cases of over- and underestimation of species diversity
    Chan KO, Grismer LL, Brown RM
    Mol Phylogenet Evol, 2018 10;127:1010-1019.
    PMID: 30030179 DOI: 10.1016/j.ympev.2018.07.005
    The family Rhacophoridae is one of the most diverse amphibian families in Asia, for which taxonomic understanding is rapidly-expanding, with new species being described steadily, and at increasingly finer genetic resolution. Distance-based methods frequently have been used to justify or at least to bolster the recognition of new species, particularly in complexes of "cryptic" species where obvious morphological differentiation does not accompany speciation. However, there is no universally-accepted threshold to distinguish intra- from interspecific genetic divergence. Moreover, indiscriminant use of divergence thresholds to delimit species can result in over- or underestimation of species diversity. To explore the range of variation in application of divergence scales, and to provide a family-wide assessment of species-level diversity in Old-World treefrogs (family Rhacophoridae), we assembled the most comprehensive multi-locus phylogeny to date, including all 18 genera and approximately 247 described species (∼60% coverage). We then used the Automatic Barcode Gap Discovery (ABGD) method to obtain different species-delimitation schemes over a range of prior intraspecific divergence limits to assess the consistency of divergence thresholds used to demarcate current species boundaries. The species-rich phylogeny was able to identify a number of taxonomic errors, namely the incorrect generic placement of Chiromantis inexpectatus, which we now move to the genus Feihyla, and the specific identity of Rhacophorus bipunctatus from Peninsular Malaysia, which we tentatively reassign to R. rhodopus. The ABGD analysis demonstrated overlap between intra- and interspecific divergence limits: genetic thresholds used in some studies to synonymize taxa have frequently been used in other studies to justify the recognition of new species. This analysis also highlighted numerous groups that could potentially be split or lumped, which we earmark for future examination. Our large-scale and en bloc approach to species-level phylogenetic systematics contributes to the resolution of taxonomic uncertainties, reveals possible new species, and identifies numerous groups that require critical examination. Overall, we demonstrate that the taxonomy and evolutionary history of Old-World tree frogs are far from resolved, stable or adequately characterized at the level of genus, species, and/or population.
    Matched MeSH terms: Uncertainty*
  15. The systematic status of Gonocephalus robinsonii Boulenger, 1908 (Squamata: Agamidae: Draconinae)
    Denzer W, Manthey U, Mahlow K, Böhme W
    Zootaxa, 2015;4039(1):129-44.
    PMID: 26624470 DOI: 10.11646/zootaxa.4039.1.5
    The generic assignment of the draconine lizard Gonocephalus robinsonii from the highlands of West-Malaysia has been uncertain since the original description. Here we present a study based on morphology, previously published karyotype data and molecular phylogenetics using 16S rRNA sequences to evaluate the systematic status of G. robinsonii. As a result we describe Malayodracon gen. nov. to accommodate the species.
    Matched MeSH terms: Uncertainty
  16. Direct period measurement for fiber Bragg grating using an optical imaging technique
    Ali MM, Lim KS, Yang HZ, Chong WY, Lim WS, Ahmad H
    Appl Opt, 2013 Aug 1;52(22):5393-7.
    PMID: 23913056 DOI: 10.1364/AO.52.005393
    This paper proposes an approach based on an optical imaging technique for the period measurement of fiber Bragg gratings (FBG). The simple, direct technique involves a differential interface contrast (DIC) microscope and a high-resolution CCD camera. Image processing is performed on the microscope images to obtain low-noise grating profiles and then the grating periods. Adopting a large image sample size in the image processing can reduce uncertainty. During the investigation, FBGs of different grating periods are fabricated by prestraining the photosensitive fibers during the UV-writing process. A good linearity between the measured Bragg wavelengths and grating periods is observed and the measured strain-optics coefficient was found to be in agreement with reported literature.
    Matched MeSH terms: Uncertainty
  17. Fulltext An Integrated Approach of Fuzzy Linguistic Preference Based AHP and Fuzzy COPRAS for Machine Tool Evaluation
    Nguyen HT, Md Dawal SZ, Nukman Y, Aoyama H, Case K
    PLoS One, 2015;10(9):e0133599.
    PMID: 26368541 DOI: 10.1371/journal.pone.0133599
    Globalization of business and competitiveness in manufacturing has forced companies to improve their manufacturing facilities to respond to market requirements. Machine tool evaluation involves an essential decision using imprecise and vague information, and plays a major role to improve the productivity and flexibility in manufacturing. The aim of this study is to present an integrated approach for decision-making in machine tool selection. This paper is focused on the integration of a consistent fuzzy AHP (Analytic Hierarchy Process) and a fuzzy COmplex PRoportional ASsessment (COPRAS) for multi-attribute decision-making in selecting the most suitable machine tool. In this method, the fuzzy linguistic reference relation is integrated into AHP to handle the imprecise and vague information, and to simplify the data collection for the pair-wise comparison matrix of the AHP which determines the weights of attributes. The output of the fuzzy AHP is imported into the fuzzy COPRAS method for ranking alternatives through the closeness coefficient. Presentation of the proposed model application is provided by a numerical example based on the collection of data by questionnaire and from the literature. The results highlight the integration of the improved fuzzy AHP and the fuzzy COPRAS as a precise tool and provide effective multi-attribute decision-making for evaluating the machine tool in the uncertain environment.
    Matched MeSH terms: Uncertainty
  18. Be Careful Where You Smile: Culture Shapes Judgments of Intelligence and Honesty of Smiling Individuals
    Krys K, -Melanie Vauclair C, Capaldi CA, Lun VM, Bond MH, Domínguez-Espinosa A, et al.
    Journal of nonverbal behavior, 2015 12 30;40:101-116.
    PMID: 27194817
    Smiling individuals are usually perceived more favorably than non-smiling ones-they are judged as happier, more attractive, competent, and friendly. These seemingly clear and obvious consequences of smiling are assumed to be culturally universal, however most of the psychological research is carried out in WEIRD societies (Western, Educated, Industrialized, Rich, and Democratic) and the influence of culture on social perception of nonverbal behavior is still understudied. Here we show that a smiling individual may be judged as less intelligent than the same non-smiling individual in cultures low on the GLOBE's uncertainty avoidance dimension. Furthermore, we show that corruption at the societal level may undermine the prosocial perception of smiling-in societies with high corruption indicators, trust toward smiling individuals is reduced. This research fosters understanding of the cultural framework surrounding nonverbal communication processes and reveals that in some cultures smiling may lead to negative attributions.
    Matched MeSH terms: Uncertainty
  19. Secondary triage classification using an ensemble random forest technique
    Azeez D, Gan KB, Mohd Ali MA, Ismail MS
    Technol Health Care, 2015;23(4):419-28.
    PMID: 25791174 DOI: 10.3233/THC-150907
    BACKGROUND: Triage of patients in the emergency department is a complex task based on several uncertainties and ambiguous information. Triage must be implemented within two to five minutes to avoid potential fatality and increased waiting time.
    OBJECTIVE: An intelligent triage system has been proposed for use in a triage environment to reduce human error.
    METHODS: This system was developed based on the objective primary triage scale (OPTS) that is currently used in the Universiti Kebangsaan Malaysia Medical Center. Both primary and secondary triage models are required to develop this system. The primary triage model has been reported previously; this work focused on secondary triage modelling using an ensemble random forest technique. The randomized resampling method was proposed to balance the data unbalance prior to model development.
    RESULTS: The results showed that the 300% resampling gave a low out-of-bag error of 0.02 compared to 0.37 without pre-processing. This model has a sensitivity and specificity of 0.98 and 0.89, respectively, for the unseen data.
    CONCLUSION: With this combination, the random forest reduces the variance, and the randomized resembling reduces the bias, leading to the reduced out-of-bag error.
    KEYWORDS: Decision support system; emergency department; random forest; randomized resampling
    Matched MeSH terms: Uncertainty
  20. Fulltext Mathematical Model for Steady State Subsea Cable Laying Problem
    Ahmad Razin Zainal Abidin, Shaymaa Mustafa, Zainal Abdul Aziz and, Kamarudin Ismail
    MATEMATIKA, 2018;34(2):173-186.
    MyJurnal
    Subsea cable laying process is a difficult task for an engineer due to many
    uncertain situations which occur during the operation. It is very often that the cable being
    laid out is not perfectly fit on the route being planned, which results in the formation of
    slack. In order to control wastages during installation, the slack needs to be minimized
    and the movement of a ship/vessel needs to be synchronized with the cable being laid out.
    The current problem was addressed using a mathematical model by considering a number
    of defining parameters such as the external forces, the cable properties and geometry. Due
    to the complexity, the model is developed for a steady-state problem assuming velocity
    of the vessel is constant, seabed is flat and the effect of wind and wave is insignificant.
    Non-dimensional system is used to scale the engineering parameters and grouped them
    into only two main parameters which are the hydrodynamic drag of the fluid and the
    bending stiffness of the cable. There are two solutions generated in this article; numerical
    and asymptotic solutions. The result of these solutions suggests that the percentage of
    slack can be reduced by the increase of the prescribed cable tension, and also the increase
    in either the drag coefficient of the sea water or the bending stiffness of the cable, similarly
    will result in lower slack percentage
    Matched MeSH terms: Uncertainty
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