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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
Revisiting the environmental Kuznets curve: assessing the impact of climate policy uncertainty in the Belt and Road Initiative

Huang Y, Rahman SU, Meo MS, Ali MSE, Khan S

Environ Sci Pollut Res Int, 2024 Feb;31(7):10579-10593.
PMID: 38198084 DOI: 10.1007/s11356-023-31471-y

Climate change repercussions such as temperature shifts and more severe weather occurrences are felt globally. It contributes to larger-scale challenges, such as climate change and biodiversity loss in food production. As a result, the purpose of this research is to develop strategies to grow the economy without harming the environment. Therefore, we revisit the environmental Kuznets curve (EKC) hypothesis, considering the impact of climate policy uncertainty along with other control variables. We investigated yearly panel data from 47 Belt and Road Initiative (BRI) nations from 1998 to 2021. Pooled regression, fixed effect, and the generalized method of moment (GMM) findings all confirmed the presence of inverted U-shaped EKC in BRI counties. Findings from this paper provide policymakers with actionable ideas, outlining a framework for bringing trade and climate agendas into harmony in BRI countries. The best way to promote economic growth and reduce carbon dioxide emissions is to push for trade and climate policies to be coordinated. Moreover, improving institutional quality is essential for strong environmental governance, as it facilitates the adoption of environmentally friendly industrialization techniques and the efficient administration of climate policy uncertainties.

Matched MeSH terms: Uncertainty
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
Navigating the interconnected risks in currency valuation: unveiling the role of climate policy uncertainty

Afshan S, Razi U, Leong KY, Lelchumanan B, Cheong CWH

Environ Sci Pollut Res Int, 2023 Dec;30(58):122580-122600.
PMID: 37971587 DOI: 10.1007/s11356-023-30687-2

Given the significance of fostering sustainable climate conditions for long-term economic stability and financial resilience, this study probes the connection between climate-related policy ambiguity and its implications for currency valuation. In doing so, the current study investigates the interconnected effects of climate policy on economic policy uncertainty and geopolitical risk with the currency valuation in ASEAN countries. Employing wavelet coherence analysis and partial wavelet coherence analysis, the paper highlights the complex relationships among these factors and their implications for exchange rate fluctuations. Using data from 2000 to 2022, the findings reveal that climate policy uncertainty is an important driver of exchange rate movements, amplifying the impact of economic policy uncertainty and geopolitical risk. Furthermore, the study identifies a vicious cycle between climate policy uncertainty and exchange rates, potentially impacting the region's macroeconomic stability and long-term economic growth. The study presents several policy recommendations to address economic and climate policy uncertainties comprehensively based on the findings. These recommendations include establishing national frameworks for climate risk management, enhancing policy credibility and macroeconomic stability, and promoting regional integration to mitigate the influence of geopolitical risk on exchange rates.

Matched MeSH terms: Uncertainty
Fulltext Impact of Economic Policy Uncertainty and Pandemic Uncertainty on International Tourism: What do We Learn From COVID-19?

Zhao X, Meo MS, Ibrahim TO, Aziz N, Nathaniel SP

Eval Rev, 2023 Apr;47(2):320-349.
PMID: 36255210 DOI: 10.1177/0193841X221132125

Uncertainty is an overarching aspect of life that is particularly pertinent to the present COVID-19 pandemic crisis; as seen by the pandemic's rapid worldwide spread, the nature and level of uncertainty have possibly increased due to the possible disconnects across national borders. The entire economy, especially the tourism industry, has been dramatically impacted by COVID-19. In the current study, we explore the impact of economic policy uncertainty (EPU) and pandemic uncertainty (PU) on inbound international tourism by using data gathered from Italy, Spain, and the United States for the years 1995-2021. Using the Quantile on Quantile (QQ) approach, the study confirms that EPU and PU negatively affected inbound tourism in all states. Wavelet-based Granger causality further reveals bi-directional causality running from EPU to inbound tourism and unidirectional causality from PU to inbound tourism in the long run. The overall findings show that COVID-19 has had a strong negative effect on tourism. So resilient skills are required to restore a sustainable tourism industry.

Matched MeSH terms: Uncertainty
Connectedness and spillover effects of US climate policy uncertainty on energy stock, alternative energy stock, and carbon future

Hoque ME, Soo-Wah L, Bilgili F, Ali MH

Environ Sci Pollut Res Int, 2023 Feb;30(7):18956-18972.
PMID: 36223011 DOI: 10.1007/s11356-022-23464-0

Global warming is pressuring policymakers to change climate policies in shifting the global economy onto a net-zero pathway. While financial assets are responsive to policy changes and development, climate change policies are becoming increasingly unpredictable, making policy decision less certain. This study investigates connectedness and spillover effects of US climate policy uncertainty on energy stocks, alternative energy stocks, and carbon emissions futures. We analyzed spillover and connectedness before and after the Paris Agreement. We employed monthly frequency data from August 2005 to March 2021 and applied DY (2012) method and MGARCH approach. We found that world energy stocks and carbon emissions futures are connected to US climate policy uncertainty. Uncertainty in climate policy and world energy stocks act as information transmitters in return spillover, while global alternative energy and carbon market are shock receivers. On volatility spillover, climate policy uncertainty, energy stocks, and carbon emissions future are shocks transmitters, while alternative energy stocks are receivers. We observe increase in connectedness following the Paris Agreement suggesting strengthened global efforts in tackling climate change. DCC and ADCC estimations revealed spillover effects of climate policy on futures returns and volatilities of world energy stocks and carbon emissions futures and the shocks could be transmitted through to the energy sector. During period of uncertainty in US climate policy, carbon allowances can potentially serve as a safe haven for energy stocks and provide downside protection for alternative energy stocks, hence hedging against climate transition risks.

Matched MeSH terms: Uncertainty
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
Does sectoral energy consumption depend on trade, monetary, and fiscal policy uncertainty? Policy recommendations using novel bootstrap ARDL approach

Bhowmik R, Durani F, Sarfraz M, Syed QR, Nasseif G

Environ Sci Pollut Res Int, 2023 Jan;30(5):12916-12928.
PMID: 36121630 DOI: 10.1007/s11356-022-22869-1

Since the inception of the twenty-first century, there has been a profound upsurge in economic policy uncertainty (EPU) with several economic and environmental impacts. Although there exists a growing body of literature that probes the economic effects of EPU, the EPU-energy nexus yet remains understudied. To fill this gap, the current study probes the impact of disaggregated EPU (i.e., monetary, fiscal, and trade policy uncertainty) on energy consumption (EC) in the USA covering the period 1990M1-2020M12. In particular, we use sectoral EC (i.e., energy consumed by the residential sector, the industrial sector, the transport sector, the electric power sector, and the commercial sector) in consort with total EC. The findings from the bootstrap ARDL approach document that monetary policy uncertainty (MP) plunges EC, whereas trade (TP) and fiscal policy uncertainty (FP) escalate EC in the long run. On the contrary, there is a heterogeneous impact of FP and MP across sectors in the short run, while TP does not affect EC. Keeping in view the findings, we propose policy recommendations to achieve numerous Sustainable Development Goals.

Matched MeSH terms: Uncertainty
Fulltext The impact of fiscal decentralization, green energy, and economic policy uncertainty on sustainable environment: a new perspective from ecological footprint in five OECD countries

Zahra S, Badeeb RA

Environ Sci Pollut Res Int, 2022 Aug;29(36):54698-54717.
PMID: 35305216 DOI: 10.1007/s11356-022-19669-y

The paper explores the short-run and long-run asymmetric impact of fiscal decentralization, green energy, and economic policy uncertainty on environmental sustainability proxied by ecological footprint. Using the Nonlinear Autoregressive Distributed lag (NARDL) approach in selected five OECD countries, we find that ecological footprint responds to positive and negative fiscal decentralization asymmetrically in the long run and short run. However, the nature of the response varies significantly across countries. The result also suggests that green energy is a major factor in reducing the ecological footprint in all countries except Canada. Finally, economic policy uncertainty plays a negative and significant role in the ecological footprint in the UK, USA, and Germany while insignificant in Australia and Canada. Implications for effective environmental policies are discussed.

Matched MeSH terms: Uncertainty
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
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
Fulltext Association of Environmental Uncertainty With Altered Decision-making and Learning Mechanisms in Youths With Obsessive-Compulsive Disorder

Marzuki AA, Tomic I, Ip SHY, Gottwald J, Kanen JW, Kaser M, et al.

JAMA Netw Open, 2021 Nov 01;4(11):e2136195.
PMID: 34842925 DOI: 10.1001/jamanetworkopen.2021.36195

IMPORTANCE: Adults with obsessive-compulsive disorder (OCD) display perseverative behavior in stable environments but exhibit vacillating choice when payoffs are uncertain. These findings may be associated with intolerance of uncertainty and compulsive behaviors; however, little is known about the mechanisms underlying learning and decision-making in youths with OCD because research into this population has been limited.
OBJECTIVE: To investigate cognitive mechanisms associated with decision-making in youths with OCD by using executive functioning tasks and computational modeling.
DESIGN, SETTING, AND PARTICIPANTS: In this cross-sectional study, 50 youths with OCD (patients) and 53 healthy participants (controls) completed a probabilistic reversal learning (PRL) task between January 2014 and March 2020. A separate sample of 27 patients and 46 controls completed the Wisconsin Card Sorting Task (WCST) between January 2018 and November 2020. The study took place at the University of Cambridge in the UK.
MAIN OUTCOMES AND MEASURES: Decision-making mechanisms were studied by fitting hierarchical bayesian reinforcement learning models to the 2 data sets and comparing model parameters between participant groups. Model parameters included reward and punishment learning rates (feedback sensitivity), reinforcement sensitivity and decision consistency (exploitation), and stickiness (perseveration). Associations of receipt of serotonergic medication with performance were assessed.
RESULTS: In total, 50 patients (29 female patients [58%]; median age, 16.6 years [IQR, 15.3-18.0 years]) and 53 controls (30 female participants [57%]; median age, 16.4 years [IQR, 14.8-18.0 years]) completed the PRL task. A total of 27 patients (18 female patients [67%]; median age, 16.1 years [IQR, 15.2-17.2 years]) and 46 controls (28 female participants [61%]; median age, 17.2 [IQR, 16.3-17.6 years]) completed the WCST. During the reversal phase of the PRL task, patients made fewer correct responses (mean [SD] proportion: 0.83 [0.16] for controls and 0.61 [0.31] for patients; 95% CI, -1.31 to -0.64) and switched choices more often following false-negative feedback (mean [SD] proportion: 0.09 [0.16] for controls vs 0.27 [0.34] for patients; 95% CI, 0.60-1.26) and true-positive feedback (mean [SD] proportion: 0.93 [0.17] for controls vs 0.73 [0.34] for patients; 95% CI, -2.17 to -1.31). Computational modeling revealed that patients displayed enhanced reward learning rates (mean difference [MD], 0.21; 95% highest density interval [HDI], 0.04-0.38) but decreased punishment learning rates (MD, -0.29; 95% HDI, -0.39 to -0.18), reinforcement sensitivity (MD, -4.91; 95% HDI, -9.38 to -1.12), and stickiness (MD, -0.35; 95% HDI, -0.57 to -0.11) compared with controls. There were no group differences on standard WCST measures and computational model parameters. However, patients who received serotonergic medication showed slower response times (mean [SD], 1420.49 [279.71] milliseconds for controls, 1471.42 [212.81] milliseconds for patients who were unmedicated, and 1738.25 [349.23] milliseconds for patients who were medicated) (control vs medicated MD, -320.26 [95% CI, -547.00 to -88.68]) and increased unique errors (mean [SD] proportion: 0.001 [0.004] for controls, 0.002 [0.004] for patients who were unmedicated, and 0.008 [0.01] for patients who were medicated) (control vs medicated MD, -0.007 [95% CI, -3.14 to -0.36]) on the WCST.
CONCLUSIONS AND RELEVANCE: The results of this cross-sectional study indicated that youths with OCD showed atypical probabilistic reversal learning but were generally unimpaired on the deterministic WCST, although unexpected results were observed for patients receiving serotonergic medication. These findings have implications for reframing the understanding of early-onset OCD as a disorder in which decision-making is associated with uncertainty in the environment, a potential target for therapeutic treatment. These results provide continuity with findings in adults with OCD.

Matched MeSH terms: Uncertainty*
Foul sewer model development using geotagged information and smart water meter data

Jia Y, Zheng F, Zhang Q, Duan HF, Savic D, Kapelan Z

Water Res, 2021 Oct 01;204:117594.
PMID: 34474249 DOI: 10.1016/j.watres.2021.117594

Hydraulic modeling of a foul sewer system (FSS) enables a better understanding of the behavior of the system and its effective management. However, there is generally a lack of sufficient field measurement data for FSS model development due to the low number of in-situ sensors for data collection. To this end, this study proposes a new method to develop FSS models based on geotagged information and water consumption data from smart water meters that are readily available. Within the proposed method, each sewer manhole is firstly associated with a particular population whose size is estimated from geotagged data. Subsequently, a two-stage optimization framework is developed to identify daily time-series inflows for each manhole based on physical connections between manholes and population as well as sewer sensor observations. Finally, a new uncertainty analysis method is developed by mapping the probability distributions of water consumption captured by smart meters to the stochastic variations of wastewater discharges. Two real-world FSSs are used to demonstrate the effectiveness of the proposed method. Results show that the proposed method can significantly outperform the traditional FSS model development approach in accurately simulating the values and uncertainty ranges of FSS hydraulic variables (manhole water depths and sewer flows). The proposed method is promising due to the easy availability of geotagged information as well as water consumption data from smart water meters in near future.

Matched MeSH terms: Uncertainty
Fulltext Dynamics of fractional order nonlinear system: A realistic perception with neutrosophic fuzzy number and Allee effect

Alam Khan N, Abdul Razzaq O, Riaz F, Ahmadian A, Senu N

J Adv Res, 2021 09;32:109-118.
PMID: 34484830 DOI: 10.1016/j.jare.2020.11.015

Introduction: The fusion of fractional order differential equations and fuzzy numbers has been widely used in modelling different engineering and applied sciences problems. In addition to these, the Allee effect, which is of high importance in field of biology and ecology, has also shown great contribution among other fields of sciences to study the correlation between density and the mean fitness of the subject.
Objectives: The present paper is intended to measure uncertain dynamics of an economy by restructuring the Cobb-Douglas paradigm of the renowned Solow-Swan model. The purpose of study is further boosted innovatively by subsuming the perception of logistic growth with Allee effect in the dynamics of physical capital and labor force.
Methods: Fractional order derivative and neutrosophic fuzzy (NF) theory are applied on the parameters of the Cobb-Douglas equation. Distinctively, cogitating fractional order derivative to study the change at each fractional stage; single-valued triangular neutrosophic fuzzy numbers (SVTNFN) to cope the uncertain situations; logistic growth function with Allee effect to analyze the factors in natural way, are the significant and novel features of this endeavor.
Results: The incorporation of the aforementioned theories and effects in the Cobb-Douglas equation, resulted in producing maximum sustainable capital investment and maximum capacity of labor force. The solutions in intervals located different possible solutions for different membership degrees, which accumulated the uncertain circumstances of a country.
Conclusion: Explicitly, these notions add new facts and figures not only in the dynamical study of capital and labor, which has been overlooked in classical models, but also left the door open for discussion and implementation on classical models of different fields.

Matched MeSH terms: Uncertainty
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
Fulltext Urinary Sodium and Potassium, and Risk of Ischemic and Hemorrhagic Stroke (INTERSTROKE): A Case-Control Study

Judge C, O'Donnell MJ, Hankey GJ, Rangarajan S, Chin SL, Rao-Melacini P, et al.

Am J Hypertens, 2021 04 20;34(4):414-425.
PMID: 33197265 DOI: 10.1093/ajh/hpaa176

BACKGROUND: Although low sodium intake (<2 g/day) and high potassium intake (>3.5 g/day) are proposed as public health interventions to reduce stroke risk, there is uncertainty about the benefit and feasibility of this combined recommendation on prevention of stroke.
METHODS: We obtained random urine samples from 9,275 cases of acute first stroke and 9,726 matched controls from 27 countries and estimated the 24-hour sodium and potassium excretion, a surrogate for intake, using the Tanaka formula. Using multivariable conditional logistic regression, we determined the associations of estimated 24-hour urinary sodium and potassium excretion with stroke and its subtypes.
RESULTS: Compared with an estimated urinary sodium excretion of 2.8-3.5 g/day (reference), higher (>4.26 g/day) (odds ratio [OR] 1.81; 95% confidence interval [CI], 1.65-2.00) and lower (<2.8 g/day) sodium excretion (OR 1.39; 95% CI, 1.26-1.53) were significantly associated with increased risk of stroke. The stroke risk associated with the highest quartile of sodium intake (sodium excretion >4.26 g/day) was significantly greater (P < 0.001) for intracerebral hemorrhage (ICH) (OR 2.38; 95% CI, 1.93-2.92) than for ischemic stroke (OR 1.67; 95% CI, 1.50-1.87). Urinary potassium was inversely and linearly associated with risk of stroke, and stronger for ischemic stroke than ICH (P = 0.026). In an analysis of combined sodium and potassium excretion, the combination of high potassium intake (>1.58 g/day) and moderate sodium intake (2.8-3.5 g/day) was associated with the lowest risk of stroke.
CONCLUSIONS: The association of sodium intake and stroke is J-shaped, with high sodium intake a stronger risk factor for ICH than ischemic stroke. Our data suggest that moderate sodium intake-rather than low sodium intake-combined with high potassium intake may be associated with the lowest risk of stroke and expected to be a more feasible combined dietary target.

Matched MeSH terms: Uncertainty
Fulltext Online Learning Approach for Predictive Real-Time Energy Trading in Cloud-RANs

Wan Ariffin WNSF, Zhang X, Nakhai MR, Rahim HA, Ahmad RB

Sensors (Basel), 2021 Mar 25;21(7).
PMID: 33806215 DOI: 10.3390/s21072308

Constantly changing electricity demand has made variability and uncertainty inherent characteristics of both electric generation and cellular communication systems. This paper develops an online learning algorithm as a prescheduling mechanism to manage the variability and uncertainty to maintain cost-aware and reliable operation in cloud radio access networks (Cloud-RANs). The proposed algorithm employs a combinatorial multi-armed bandit model and minimizes the long-term energy cost at remote radio heads. The algorithm preschedules a set of cost-efficient energy packages to be purchased from an ancillary energy market for the future time slots by learning both from cooperative energy trading at previous time slots and by exploring new energy scheduling strategies at the current time slot. The simulation results confirm a significant performance gain of the proposed scheme in controlling the available power budgets and minimizing the overall energy cost compared with recently proposed approaches for real-time energy resources and energy trading in Cloud-RANs.

Matched MeSH terms: Uncertainty
Application of life cycle assessment for desalination: Progress, challenges and future directions

Aziz NIHA, Hanafiah MM

Environ Pollut, 2021 Jan 01;268(Pt B):115948.
PMID: 33187839 DOI: 10.1016/j.envpol.2020.115948

The sustainability performance of the desalination processes has received increasing attention in recent years. In this study, the current progress and future perspective of a life cycle assessment (LCA) of desalination technology in 62 previous studies have been reviewed for the period 2004-2019. It was found that the number of LCA studies related to seawater reverse osmosis has gained popularity compared to other types of desalination technologies. The review emphasized the application of LCA to desalination by means of research objective, scope of study, life stages, and impact assessment. Although previous LCA studies were conducted to assess the environmental performance of the desalination technology, little attention was given to evaluating the impact of other sustainability aspects (i.e., economic and social). The latter part of this study discusses the challenges, feasibility, and recommendations for future LCA studies on desalination technology. The integration of the LCA approach with other approaches allows a comprehensive assessment of the sustainability performance of desalination technology. Thus, the combined approaches should be explored in future studies to gain insight into the sensitivity and uncertainty of the data to make an assessment that can be useful in policy-making.

Matched MeSH terms: Uncertainty
Fulltext Burden of disease and productivity impact of Streptococcus suis infection in Thailand

Rayanakorn A, Ademi Z, Liew D, Lee LH

PLoS Negl Trop Dis, 2021 01;15(1):e0008985.
PMID: 33481785 DOI: 10.1371/journal.pntd.0008985

BACKGROUND: Streptoccocus suis (S.suis) infection is a neglected zoonosis disease in humans mainly affects men of working age. We estimated the health and economic burden of S.suis infection in Thailand in terms of years of life lost, quality-adjusted life years (QALYs) lost, and productivity-adjusted life years (PALYs) lost which is a novel measure that adjusts years of life lived for productivity loss attributable to disease.
METHODS: A decision-analytic Markov model was developed to simulate the impact of S. suis infection and its major complications: death, meningitis and infective endocarditis among Thai people in 2019 with starting age of 51 years. Transition probabilities, and inputs pertaining to costs, utilities and productivity impairment associated with long-term complications were derived from published sources. A lifetime time horizon with follow-up until death or age 100 years was adopted. The simulation was repeated assuming that the cohort had not been infected with S.suis. The differences between the two set of model outputs in years of life, QALYs, and PALYs lived reflected the impact of S.suis infection. An annual discount rate of 3% was applied to both costs and outcomes. One-way sensitivity analyses and Monte Carlo simulation modeling technique using 10,000 iterations were performed to assess the impact of uncertainty in the model.
KEY RESULTS: This cohort incurred 769 (95% uncertainty interval [UI]: 695 to 841) years of life lost (14% of predicted years of life lived if infection had not occurred), 826 (95% UI: 588 to 1,098) QALYs lost (21%) and 793 (95%UI: 717 to 867) PALYs (15%) lost. These equated to an average of 2.46 years of life, 2.64 QALYs and 2.54 PALYs lost per person. The loss in PALYs was associated with a loss of 346 (95% UI: 240 to 461) million Thai baht (US$11.3 million) in GDP, which equated to 1.1 million Thai baht (US$ 36,033) lost per person.
CONCLUSIONS: S.suis infection imposes a significant economic burden both in terms of health and productivity. Further research to investigate the effectiveness of public health awareness programs and disease control interventions should be mandated to provide a clearer picture for decision making in public health strategies and resource allocations.

Matched MeSH terms: Uncertainty
Security and privacy issues related to the workplace-based security robot system

Guangnan Z, Tao H, Rahman MA, Yao L, Al-Saffar A, Meng Q, et al.

Work, 2021;68(3):871-879.
PMID: 33612530 DOI: 10.3233/WOR-203421

BACKGROUND: An isolated robot must take account of uncertainty in its world model and adapt its activities to take into account such as uncertainty. In the same way, a robot interaction with security and privacy issues (RISAPI) with people has to account for its confusion about the human internal state, as well as how this state will shift as humans respond to the robot.
OBJECTIVES: This paper discusses RISAPI of our original work in the field, which shows how probabilistic planning and system theory algorithms in workplace robotic systems that work with people can allow for that reasoning using a security robot system. The problem is a general way as an incomplete knowledge 2-player game.
RESULTS: In this general framework, the various hypotheses and these contribute to thrilling and complex robot behavior through real-time interaction, which transforms actual human subjects into a spectrum of production systems, robots, and care facilities.
CONCLUSION: The models of the internal human situation, in which robots can be designed efficiently, are limited, and achieve optimal computational intractability in large, high-dimensional spaces. To achieve this, versatile, lightweight portrayals of the human inner state and modern algorithms offer great hope for reasoning.

Matched MeSH terms: Uncertainty

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