Existing mitigation strategies to reduce greenhouse gas (GHG) emissions are inadequate to reach the target emission reductions set in the Paris Agreement. Hence, the deployment of negative emission technologies (NETs) is imperative. Given that there are multiple available NETs that need to be evaluated based on multiple criteria, there is a need for a systematic method for ranking and prioritizing them. Furthermore, the uncertainty in estimating the techno-economic performance levels of NETs is a major challenge. In this work, an integrated model of fuzzy analytical hierarchy process (AHP) and interval-extended Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is proposed to address the multiple criteria, together with data uncertainties. The potential of NETs is assessed through the application of this hybrid decision model. Sensitivity analysis is also conducted to evaluate the robustness of the ranking generated. The result shows Bioenergy with Carbon Capture and Storage (BECCS) as the most optimal alternative for achieving negative emission goals since it performed robustly in the different criteria considered. Meanwhile, energy requirement emerged as the most preferred or critical criterion in the deployment of NETs based on the decision-maker. This paper renders a new research perspective for evaluating the viability of NETs and extends the domains of the fuzzy AHP and interval-extended TOPSIS hybrid model.
Signals consistent with the B_{c}^{+}(2S) and B_{c}^{*+}(2S) states are observed in proton-proton collisions at sqrt[s]=13 TeV, in an event sample corresponding to an integrated luminosity of 143 fb^{-1}, collected by the CMS experiment during the 2015-2018 LHC running periods. These excited b[over ¯]c states are observed in the B_{c}^{+}π^{+}π^{-} invariant mass spectrum, with the ground state B_{c}^{+} reconstructed through its decay to J/ψπ^{+}. The two states are reconstructed as two well-resolved peaks, separated in mass by 29.1±1.5(stat)±0.7(syst) MeV. The observation of two peaks, rather than one, is established with a significance exceeding five standard deviations. The mass of the B_{c}^{+}(2S) meson is measured to be 6871.0±1.2(stat)±0.8(syst)±0.8(B_{c}^{+}) MeV, where the last term corresponds to the uncertainty in the world-average B_{c}^{+} mass.
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.
Introduction : Economic evaluations can provide “value-for money” information to those making decisions about the allocation of limited health care resources. In particular, economic evaluations can be used to identify interventions that are worth providing and those that are not. Furthermore, evaluations can be used with other approaches to help set priorities, such as program-budgeting marginal-analysis.
Methodology : Compile and systematically describe from the publications, articles and reports on economic evaluation in healthcare decision making.
Result : A high quality economic evaluation should provide decision makers with information that is useful, relevant, and timely. In addition, evaluations should be based on rigorous analytical methods, be balanced and impartial (credible), and be transparent and accessible to the reader. There are many situations where economic evaluations can assist decision makers: decisions by various levels of government or administrative bodies (e.g., regional health authorities, hospitals, drug plans) to fund a program, service or technology, pricing decisions by government regulators and technology manufacturers, clinical practice guidelines, priorities for research funding by governments and researchbased firms, post-marketing surveillance and updates of economic information based on the use of the technology in the “real world” (which can then be used to inform one of the other types of decisions).
Conclusion: This requires that decision makers take a broad view of the impact of a technology, and decision that are more explicit and transparent. The ultimate test of an evaluation is whether it leads to better decision in the presence of uncertainty, and results in the more efficient and effective use of resources.
Distress has become a major issue in cancer population. Patients may suffer from either physical,psychological distress or both. Cancer patients who are undergoing chemotherapy are more likely to experience psychological distress. This could be due to the negative effects of chemotherapy agents, the uncertainty of post-treatment, and the occurrence of psychosocial problems. As a result, the patient may experience a normal reaction such as sadness or may develop common psychiatric disorders such as depression and anxiety.
Objectives: To determine the association of students’ perception of schooling with externalizing/internalizing scores; and to examine the different perceptions related to truancy. Methods:A total of 373 predominantly 16 year-old students attending three high risk schools in Pudu, Kuala Lumpur completed the questionnaires on schooling variables (four items) and externalizing/internalizing syndromes (Youth Self-Report, 112 items). Results: Certain negative perceptions (uncertainty of the schooling purpose, thinking schooling as time wasting) were significantly associated with higher internalizing (p
Two major types of services in sepak takraw are kuda and sila services. Even though both services are delivered at high speed, each is composed of different kinematic features. The purpose of the study was to determine the fundamental differences in perceptual strategies in
anticipating the kuda and sila services. The receiver of the game in sepak takraw makes decisions under severe time constraint in both spatial and temporal uncertainty. The study examined two groups of 12 players each; the experts and the novices. Perceptual displays in anticipation of the
kuda and sila services were prompted using video stimulations consisting of seven temporal occlusions t1 (240 milliseconds at pre-contact), t2 (160 milliseconds at pre-contact), t3 (80 milliseconds at pre-contact, t4 (0 millisecond at contact), t5 (80 milliseconds at post-contact), t6 (160
milliseconds at post-contact), and t7 (no occlusion). Significant differences amongst expert players in anticipating kuda and sila services were at t1 F (14, 180) = 2.37; p < 0.05], t2 F (14, 180) = 5.60; p < 0.05], t3 F (14, 180) = 3.81; p < 0.05] and t4 F (14, 180) = 2.00; p < 0.05]. Similar comparisons at t5, t6, and t7 did not yield any significant differences. In addition, there were significant differences amongst novice players in anticipating kuda and sila services at t2 F (14,
180) = 2.27; p < 0.05], t3 F (14, 180) = 1.94; p < 0.05], t4 F (14, 180) = 2.61; p < 0.05], and t5 F (14, 180) = 9.38; p < 0.05]. Overall findings revealed that expert players found it more difficult to anticipate kuda service compared to sila service at t1. Hence, the kuda service is more
difficult to anticipate than sila service. Participants of this study demonstrated a more effective visual perceptual strategy to counter attack a sila service than they would with a kuda service.
The χ_{b1}(3P) and χ_{b2}(3P) states are observed through their ϒ(3S)γ decays, using an event sample of proton-proton collisions collected by the CMS experiment at the CERN LHC. The data were collected at a center-of-mass energy of 13 TeV and correspond to an integrated luminosity of 80.0 fb^{-1}. The ϒ(3S) mesons are identified through their dimuon decay channel, while the low-energy photons are detected after converting to e^{+}e^{-} pairs in the silicon tracker, leading to a χ_{b}(3P) mass resolution of 2.2 MeV. This is the first time that the J=1 and 2 states are well resolved and their masses individually measured: 10513.42±0.41(stat)±0.18(syst) MeV and 10524.02±0.57(stat)±0.18(syst) MeV; they are determined with respect to the world-average value of the ϒ(3S) mass, which has an uncertainty of 0.5 MeV. The mass splitting is measured to be 10.60±0.64(stat)±0.17(syst) MeV.
The modeling of rainfall-runoff relationship in a watershed is very important in designing hydraulic structures, controlling flood and managing storm water. Artificial Neural Networks (ANNs) are known as having the ability to model nonlinear mechanisms. This study aimed at developing a Generalized Feed Forward (GFF) network model for predicting annual flood (depth) of Johor River in Peninsular Malaysia. In order to avoid over training, cross-validation technique was performed for optimizing the model. In addition, predictive uncertainty index was used to protect of over parameterization. The governing training algorithm was back propagation with momentum term and tangent hyperbolic types was used as transfer function for hidden and output layers. The results showed that the optimum architecture was derived by linear tangent hyperbolic transfer function for both hidden and output layers. The values of Nash and Sutcliffe (NS) and root mean square error (RMSE) obtained 0.98 and 5.92 for the test period. Cross validation evaluation showed 9 process elements is adequate in hidden layer for optimum generalization by considering the predictive uncertainty index obtained (0.14) for test period which is acceptable.
Soft computing is an alternative to hard and classic math models especially when it comes to uncertain and incomplete data. This includes regression and relationship modeling of highly interrelated variables with applications in curve fitting, interpolation, classification, supervised learning, generalization, unsupervised learning and forecast. Fuzzy cognitive map (FCM) is a recurrent neural structure that encompasses all possible connections including relationships among inputs, inputs to outputs and feedbacks. This article examines a new methods for nonlinear multivariate regression using fuzzy cognitive map. The main contribution is the application of nested FCM structure to define edge weights in form of meaningful functions rather than crisp values. There are example cases in this article which serve as a platform to modelling even more complex engineering systems. The obtained results, analysis and comparison with similar techniques are included to show the robustness and accuracy of the developed method in multivariate regression, along with future lines of research.
Carcinoma arising primarily from the jaw is a locally aggressive lesion with poor prognosis. Primary intraosseous carcinoma (PIOC) lesion develops either de novo remnants of odontogenic epithelium, odontogenic cyst/tumor, epithelium remnants, or/and salivary gland residues. We describe very interesting case of primary intraosseous carcinoma of mandible. This extensive lesion was sent for oncological opinion and further management. Due to the uncertainty of diagnostic criteria of PIOC, only few cases of this lesion with a typical presentation have been reported. This article presents a case of primary intraosseous carcinoma with a unique appearance and detailed review stating its clinicopathological correlation.
Although estimating the uncertainty of models used for modelling nitrate contamination of groundwater is essential in groundwater management, it has been generally ignored. This issue motivates this research to explore the predictive uncertainty of machine-learning (ML) models in this field of study using two different residuals uncertainty methods: quantile regression (QR) and uncertainty estimation based on local errors and clustering (UNEEC). Prediction-interval coverage probability (PICP), the most important of the statistical measures of uncertainty, was used to evaluate uncertainty. Additionally, three state-of-the-art ML models including support vector machine (SVM), random forest (RF), and k-nearest neighbor (kNN) were selected to spatially model groundwater nitrate concentrations. The models were calibrated with nitrate concentrations from 80 wells (70% of the data) and then validated with nitrate concentrations from 34 wells (30% of the data). Both uncertainty and predictive performance criteria should be considered when comparing and selecting the best model. Results highlight that the kNN model is the best model because not only did it have the lowest uncertainty based on the PICP statistic in both the QR (0.94) and the UNEEC (in all clusters, 0.85-0.91) methods, but it also had predictive performance statistics (RMSE = 10.63, R2 = 0.71) that were relatively similar to RF (RMSE = 10.41, R2 = 0.72) and higher than SVM (RMSE = 13.28, R2 = 0.58). Determining the uncertainty of ML models used for spatially modelling groundwater-nitrate pollution enables managers to achieve better risk-based decision making and consequently increases the reliability and credibility of groundwater-nitrate predictions.
The presentation of this data focuses on analysing the dynamic role of economic growth, foreign direct investment and financial globalization uncertainty on financial development of selected leading African economies, spanning the year 1970 to 2018, and the data were obtained from world development indicators and global financial development databases. Second generation econometrics techniques were deployed for the analysis. We began with the descriptive and correlation statistics in order to ascertain the normality of the data. Also, homogeneity and cross-sectional dependency tests were carried out to validate the whether or not the data is heterogeneous and depend upon each other across the series. As well, the [3] co-integration and dynamic common correlated effect [1] and pool mean group [2] estimates were applied to confirm the presence of long-run relationship and their effects on the financial development among the sampled countries.
186 Re (T1/2= 89.24 h, [Formula: see text] 346.7 keV, [Formula: see text] ), an intense beta-emitter shows great potential to be used as an active material in therapeutic radiopharmaceuticals due to its suitable physico-chemical properties.186Re can be produced in several ways, however charged-particle induced reactions show to be promising towards no carrier added production. In this work, production cross-sections of186Re were evaluated following the light-charged particle induced reactions on tungsten. An effective evaluation technique such as Simultaneous Evaluation on KALMAN code combined with least squares concept was used to obtain the evaluated data together with covariances. Knowledge of the underlying uncertainties in evaluated nuclear data, i.e., covariances are useful to improve the accuracy of nuclear data.
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.
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.
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.
Magnetoencephalography (MEG) has been extensively used to measure small-scale neuronal brain activity. Although it is widely acknowledged as a sensitive tool for deciphering brain activity and source localisation, the accuracy of the MEG system must be critically evaluated. Typically, on-site calibration with the provided phantom (Local phantom) is used. However, this method is still questionable due to the uncertainty that may originate from the phantom itself. Ideally, the validation of MEG data measurements would require cross-site comparability.
Although recent molecular studies have indicated the presence of a number of distinct species within the Caulerpa racemosa-peltata complex, due to the difficulties presented by high levels of phenotypic plasticity and the large number of synonyms, infra-specific taxa, and names of uncertain affinity, taxonomic proposals are yet to be made. In this study, we aimed to resolve the taxonomy of the complex and provide an example of how historical nomenclature can best be integrated into molecular based taxonomies. We accomplished this by first determining the number of genetic species within our globally sampled data set through a combination of phylogenetic and species-delimitation approaches of partial elongation factor TU and RUBISCO large subunit gene sequences. Guided by these results, comparative morphological examinations were then undertaken to gauge the extent of phenotypic plasticity within each species, as well as any morphological overlap between them. Our results revealed the presence of 11 distinct species within the complex, five of which showed high levels of phenotypic plasticity and partial overlap with other species. On the basis of observations of a large number of specimens, including type specimens/descriptions, and geographic inferences, we were able to confidently designate names for the lineages. Caulerpa peltata, C. imbricata and C. racemosa vars. laetevirens, occidentalis and turbinata were found to represent environmentally induced forms of a single species, for which the earlier-described C. chemnitzia, previously regarded as a synonym of C. racemosa var. turbinata, is reinstated. C. cylindracea, C. lamourouxii, C. macrodisca, C. nummularia and C. oligophylla are also reinstated and two new species, C. macra stat. nov. and C. megadisca sp. nov., are proposed.
Risk assessment relies on the accuracy of the information provided by the traveller. A questionnaire was administered to 83 consecutive travellers attending a travel medicine clinic. The majority of travellers was uncertain about destinations within countries, transportation or type of accommodation. Most travellers were uncertain if they would be visiting malaria regions. The degree of uncertainty about itinerary potentially impacts on the ability of the travel medicine specialist to perform an adequate risk assessment, select appropriate vaccinations and prescribe malaria prophylaxis. This study reveals high levels of traveller uncertainty about their itinerary which may potentially reduce the effectiveness of their pre-travel consultation.