Uncertainty Analysis Research Articles

Variation Characteristics of Actual Evapotranspiration and Uncertainty Analysis of Its Response to Local Climate Change in Arid Inland Region of China

Exploring the variation characteristics of actual evapotranspiration (ETa) and its response to climate change in the arid inland region of China is of great significance for strengthening regional water resources management and maintaining ecological environment security and stability. Taking the Dulan River Basin as the research area, based on the meteorological data from the Wulan Station and hydrological data from the Shanggaba Station from 1981 to 2020, the variation characteristics of ETa at the annual scale were analyzed. The ETa estimation model and joint distribution model of P and potential evapotranspiration (ET0) was constructed based on climate factors, and the uncertainty of ETa response to climate change was explored with the water balance method, multiple linear regression, marginal distribution function, Copula function, and Monte Carlo algorithm. The results showed that the multi-year mean value of ETa in the study area was 261.6 mm, and the interannual process showed an insignificant upward trend, and had no abrupt change during the period. There were two obvious main cycles, which were 19-year periodic changes on the 30-year time scale and 6-year periodic changes on the 9-year time scale. The ETa estimation model based on precipitation (P) and ET0 had good simulation accuracy. The optimal marginal distributions of P and ET0 were Pearson-III (P-III) distribution and Generalized Extreme Value (GEV) distribution, respectively. The Copula joint distribution probability density of P and ET0 was a symmetric saddle-shaped distribution. ETa showed an inverted ‘S’ distribution with the change in joint guarantee rate of P and ET0, ranging from 116.9 mm to 498.6 mm. ETa was an interval range under a certain joint guarantee rate. The research results can provide support for the assessment of ETa, and help to further understand the driving mechanism of climate change on ETa in the arid inland region of China.

A Short-term PV Power Prediction and Uncertainty Analysis Model Based on CEEMDAN and AHA-BP

The stochastic and intermittent nature of photovoltaic (PV) generation brings a series of scheduling problems to the power system. An effective prediction of PV power is essential to minimize the impact of uncertainty. Therefore, this paper presents an integrated prediction model with complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), the artificial hummingbird algorithm (AHA), and the BP neural network (BPNN) for predicting power generation from PV power plants, and a methodology for uncertainty analysis by using the nonparametric kernel density estimation (NPKDE). First, one month of PV power is decomposed into an array of components using CEEMDAN. Then, the weights and thresholds of the BPNN are optimized by using AHA. These components are trained using the BPNN. Finally, the final prediction results are obtained by superimposing the components, and NPKDE is employed to compute the probability density and confidence interval of the prediction error. The proposed prediction method demonstrates superior predictive performance in comparison with other models. Also, the NPKDE approach better describes the accuracy of the probability density distribution.

Experimental and numerical study on cavitation pulsating pressure of water-jet propulsion axial-flow pump.

Cavitation may occur in water-jet pump during operation of water-jet propulsion vessel, and once cavitation occurs, the tip clearance pulsating pressure of the impeller may be intensified, resulting in increased vibration of the water-jet propulsion unit. In this paper, the cavitation pulsating pressure characteristics at different positions in the pump are studied by experiment and numerical simulation, and the pulsating pressure characteristics in tip clearance are mainly researched. Based on Star-CCM+ commercial software, unsteady Reynolds-averaged Navier-Stokes equations(RANS) numerical simulation is carried out, and the feasibility of the numerical simulation method is verified by uncertainty analysis. The results show that the cavitation pulsating pressure near the leading edge of the impeller in the tip clearance is the largest. The variation of the tip clearance pulsating pressure with the intensification of cavitation is studied by numerical simulation, and its mechanism is revealed. A dimensionless coefficient of net positive suction head (CNPSH) is proposed, and the study shows that the cavitation pulsation pressure coefficients of pumps of different scales are equal when the working conditions are similar and the CNPSH are equal, which indicates that the cavitation pulsating pressure performance of full scale pump can be predicted by model scale. It is of great significance to evaluate the vibration performance of the full scale water-jet propulsion.

Improving transparency: Development of reporting guidelines for burden of disease studies

Abstract The burden of disease (BOD) approach, originating with the Global Burden of Disease (GBD) study in the 1990s, has become a cornerstone for population health monitoring. Despite the widespread use of the Disability-Adjusted Life Year (DALY) metric, variations in methodological approaches and reporting inconsistencies hinder comparability across studies. To tackle this issue, we set out to develop guidelines for reporting DALY calculation studies to improve the transparency and comparability of BOD estimates. The development of the STROBOD statement began within the European Burden of Disease Network, evolving from initial concepts discussed in workshops and training sessions focused on critical analysis of BOD studies. In 2021, a working group was formed to refine the preliminary version into the final Standardised Reporting of Burden of Disease studies (STROBOD) statement, consisting of 28 items structured across six main sections. These sections cover the title, abstract, introduction, methods, results, discussion, and open science, aiming to ensure transparency and standardization in reporting BOD studies. Notably, the methods section of the STROBOD checklist encompasses aspects such as study setting, data inputs and adjustments, DALY calculation methods, uncertainty analyses, and recommendations for reproducibility and transparency. A pilot phase was conducted to test the efficacy of the STROBOD statement, highlighting the importance of providing clear explanations and examples for each reporting item. The inaugural STROBOD statement offers a crucial framework for standardizing reporting in BOD research, with plans for ongoing evaluation and potential revisions based on user feedback. While the current version focuses on general BOD methodology, future iterations may include specialized checklists for distinct applications such as injury or risk factor estimation, reflecting the dynamic nature of this field.

Raman Thermometry for Temperature Assessment of Inorganic Transformations During Microwave Heating

ABSTRACTMicrowave heating is an intriguing method for the synthesis of inorganic solids offering a variety of advantages over conventional furnace heating, such as fast heating and cooling rates as well as volumetric and selective heating of precursors. However, there are many open questions regarding this “black‐box” process, and insights into the effect of microwave radiation on different types of solids are generally missing. In situ Raman spectroscopy is a powerful technique to unravel chemical transformations and identify intermediate species during microwave solid‐state syntheses. A major challenge is the temperature measurement under microwave conditions because (metallic) thermocouples cannot be used and optical pyrometry has significant drawbacks. In contrast, Raman thermometry is a viable method that relies on the temperature‐induced shift of Raman signals. Here, we use this method to estimate the temperature during microwave heating of a model system (titania) that undergoes a phase transition at temperatures >800°C. The estimation is derived from a flexible double exponential calibration function applied to Raman spectroscopic peak shifts in the temperature‐resolved furnace heating data, which was found to describe two titania modes (one anatase and one rutile) extremely well. Based on a detailed error and uncertainty analysis, we suggest options to further optimize Raman thermometry for use in high‐temperature microwave heating conditions.

Investigating water sustainability towards indicators: An empirical illustration using country-level data

This paper contributes to the academic debate on the construction of synthetic indices with respect to water-based sustainability analysis. In this regard, the United Nations Sustainable Development Goal (SDG) 6.4 specifically considers the progress on water-use efficiency within the target of ensuring access to water and sanitation (SDG 6). Our contribution investigates the levels of water sustainability reached by countries around the world considering a suitable measure of the goal obtained by aggregating variables available at the national level, i.e. water efficiency (SDG 6.4.1) and stress (SDG 6.4.2).The construction of a composite index for SDG 6.4 is discussed through the usual steps of normalization, aggregation, and weighting, also highlighting the pros, cons and issues encountered in each step. Fundamental issues faced in the construction of synthetic indices are also discussed: different methods for performing the above steps are compared, and uncertainty analysis is carried out to understand which methods could be reasonably suitable to measure the synthetic indicator of the entire target. As a further theoretical contribution, we illustrate some arguments in favor of the application of categorical scales for normalization, even if the results of some countries seem to be particularly sensitive to the choice of weights in the aggregation phase.Results, obtained after examining univariate and multivariate descriptive statistics, show that the best performances can be observed in small and medium European countries and sub-Saharan African nations (such as Congo, Gabon, and Angola), while the lowest levels appear to be located in Central Asia, the Middle East, and North Africa.A natural implication of this research is its contribution to valorizing the social capital of a regional area, stimulating new approaches to environmental issues, informing public debate, and influencing policy-making.

Derivation of Uncertainty Distributions for Channel Flow Rate and Fuel Critical Heat Flux Predictions for Best-Estimate Plus Uncertainty Analysis of Slow Loss-of–Reactor Power Regulation Accidents in CANDU Stations

Derivation of Uncertainty Distributions for Channel Flow Rate and Fuel Critical Heat Flux Predictions for Best-Estimate Plus Uncertainty Analysis of Slow Loss-of–Reactor Power Regulation Accidents in CANDU Stations

Incorporating crumb rubber in slag-based geopolymer: Experimental work and predictive modelling

Crumb rubber (CR), a prevalent hazardous waste material, poses significant environmental challenges that necessitate innovative management strategies. Recent studies have focused on incorporating CR and ground granulated blast furnace slag (GGBS) into geopolymer concrete (GC) to address this challenge. This research presents a novel approach that combines experimental analysis with machine learning (ML) techniques to investigate the chemical effects of these materials on GC. Initially, compressive strength (CS) tests are conducted on rubberized geopolymer (RG) concrete, and the resulting data are further analyzed through ML algorithms to enhance the understanding of material behavior. Despite numerous attempts by researchers to integrate CR into geopolymer-based materials, the challenge of mitigating strength degradation persists. Therefore, it becomes imperative to construct a predictive model that relies on new variables influencing the strength characteristics. This research develops a predictive model to assess concrete compressive strength (CS). Key variables such as CR grade (particle size), incorporation percentage, and oxide ratio were analyzed for their impact on geopolymer strength. Employing ensemble techniques, namely Bagging (BA) and Gradient boosting (GB) with five learners (M5P, random forest (RF), random tree (RT), reduced error pruning tree (REPT), and support vector machine (SVM), the study found that M5P-GB models offered the most accurate CS predictions with the highest accuracy and lowest errors. Moreover, the dataset was checked via the k-fold cross-validation technique and confirmed the developed models' robustness, reliability, and effectiveness. Furthermore, uncertainty analysis revealed that GB-M5P-unpruned models-maintained uncertainty percentages of 14.769 % at 7 days and 11.277 % at 28 days, which was under the 35 % threshold. Additionally, the sensitivity analysis identified the CR grade and replacement percentage by volume of crusher dust (CD) as the most critical factors affecting CS. These findings underscore the importance of considering predictive model development in decision-making processes related to RG concrete properties, providing valuable insights into optimizing the model's robustness and reliability for practical applications. However, the study's reliance on specific input parameters and controlled laboratory conditions may limit the generalizability of the predictive models, necessitating further validation under diverse real-world conditions and variations in CR source and environmental factors.

A Non-Stochastic Special Model of Risk Based on Radon Transform

The concept of risk is fundamental in various scientific fields, including physics, biology and engineering, and is crucial for the study of complex systems, especially financial markets. In our research, we introduce a novel risk model that has a natural transactional–financial interpretation. In our approach, the risk of holding a financial instrument is related to the measure of the possibility of its loss. In this context, a financial instrument is riskier the more opportunities there are to dispose of it, i.e., to sell it. We present a model of risk understood in this way, introducing, in particular, the concept of financial time and a financial frame of reference, which allows for associating risk with the subjective perception of the observer. The presented approach does not rely on statistical assumptions and is based on the transactional interpretation of models. To measure risk, we propose using the Radon transform. The financial concept of risk is closely related to the concepts of uncertainty, entropy, information, and error in physics. Therefore, the well-established algorithmic aspects of the computed tomography method can be effectively applied to the broader field of uncertainty analysis, which is one of the foundational elements of experimental physics.

Evaluation of Managerial Economics Applications Strategy using the SPSS Method

Managerial Economics is an economy branch is, this economic theory and size using methods solve real-world managerial problems. It helps businesses and organizations make informed decisions by using economic analysis. Here are some common applications of managerial economics: Demand Analysis and Forecasting: Businesses use managerial economics to understand consumer behavior and forecast future demand for their products or services. This involves studying factors that affect demand, such as price, income levels, and consumer preferences. Production and Cost Analysis: Managerial economics helps businesses optimize production processes and minimize costs. It involves analyzing production functions, cost structures, and economies of scale to determine the most efficient way to produce goods or services. Pricing Strategies: A product or the right price for the service It is up to a company to decide Crucial to profitability. Managerial economics helps businesses set prices by considering factors like production costs, competition, and consumer willingness to pay. Market Structure Analysis: Understanding the type of market, a company operates in (e.g., perfect competition, monopoly, oligopoly) is essential for making strategic decisions. Managerial economics helps analyses market structures and determine appropriate strategies for each. Risk and Uncertainty Analysis: Businesses often face uncertainty in various aspects, such as demand fluctuations, production costs, and market conditions. Managerial economics provides tools to assess and manage risks, including techniques like decision tree analysis and expected value calculations. Capital Budgeting and Investment Analysis: Managerial economics assists in evaluating investment opportunities and capital projects. It involves methods Payback period analysis of investments. Regulatory and Government Policy Analysis: Managerial economics helps businesses understand and navigate government regulations and policies that affect their operations. This can include areas like taxation, environmental regulations, and trade policies. Strategic Planning: Using economic analysis, businesses can develop long-term strategies for growth and sustainability. This may involve assessing market trends, competitive dynamics, and potential expansion opportunities. Game Theory and Competitive Strategy: Managerial economics incorporates game theory to analyze strategic interactions between competitors. It helps businesses formulate optimal strategies in competitive environments. Resource Allocation: Efficient allocation of resources, including labor, capital, and technology, is crucial for a company's success. Managerial economics provides tools to distribute funds in a manner that optimisesoverall productivity and profitability. Demand Analysis and Forecasting, Production and Cost Analysis, Pricing Strategies, Market Structure Analysis, Risk and Uncertainty Analysis, Capital Budgeting and Investment Analysis and Regulatory and Government Policy Analysis. The Cranach’s Alpha Reliability result. The overall Cranach’s Alpha value for the model is .744 which indicates 74% reliability. From the literature review, the above 50% Cranach’s Alpha value model can be considered for analysis.

Stochastic error propagation with independent probability distributions in LCA does not preserve mass balances and leads to unusable product compositions—a first quantification

Abstract Purpose To mitigate the effects of the triple planetary crisis of climate change, pollution and biodiversity loss, a system-based approach to estimating environmental impacts—such as life cycle assessment (LCA)—is critical. International standards recommend using uncertainty analysis to improve the reliability of LCA, but there has been debate about how to do this for many years. In particular, in order to characterise uncertainty in the inputs and outputs of each unit process in an LCA, a prevalent approach is to represent each one by an independent probability distribution. Thus, any physical relationships between inputs and outputs are ignored, which causes two potential errors during Monte Carlo simulation (a popular method for propagating uncertainty through an LCA model). First, the sum of the inputs to a unit process may not equal the sum of the outputs (i.e. there may be a mass imbalance), and second, the proportions of each input and output may be unrealistic (e.g. too much cement in a concrete production unit process). However, while some literature has discussed the problem, it has not yet been quantified. Methods Therefore, this paper investigates the extent to which existing uncertainty characterisation approaches, where there is a lack of parameterisation or correlations in databases, lead to mass imbalances and unrealistic variations in unit process compositions when performing uncertainty analysis. The matrix-based structure of LCA and the standard uncertainty analysis procedure using Monte Carlo (MC) simulation to propagate uncertainty are described. We apply the procedure to a concrete production process. Two uncertainty characterisation approaches are also explored to assess the effect of data quality scoring on mass imbalances and the mass contribution of each exchange (i.e. production compositions). Results and discussion For median data quality scores and using a typical (basic + additional uncertainty) uncertainty characterisation approach, the 1000-iteration MC simulation leads to mass imbalances ranging from − 49 to + 30% of the original mass and found that the mass imbalance exceeded existing prescribed plausibility limits on 62.7% of MC runs. On average across all exchanges, the exchange mass exceeded the 5% plausible variation limit on 77.7% of MC runs. This means that the final concrete product compositions are unlikely to be realistic or functionally equivalent to one another. We discuss the appropriateness of using universal variances for the underlying normal distribution for data quality scores (“additional uncertainty”) when input exchange quantities are of different scales. Additionally, we discuss potential solutions to the mass imbalance problem and their suitability for implementation at a database scale. Conclusions We have quantified, for the first time, the significant impact that uncertainty characterisation via independent probability distributions has on maintaining mass balances and plausible product compositions in unit processes. To overcome these challenges, databases would need to be parameterised and have the ability to sum quantities to perform mass balance checks during uncertainty analysis.

A novel hybrid forecasting system for crude oil futures prices: A dual perspective of deterministic forecasting and uncertainty analysis

The crude oil market occupies a crucial place in the composition of the global economy, however, the high level of uncertainty in crude oil prices makes it a challenging task to construct reliable forecasting techniques to obtain reliable expected results. Therefore, to effectively cope with the high uncertainty and the technical limitations of existing forecasting studies, this study constructs a hybrid forecasting system from the dual perspectives of deterministic forecasting and uncertainty analysis. With the concept of “denoising-integration” as the core, the system integrates several denoising techniques, econometric methods, and artificial intelligence methods, and establishes a hybrid forecasting model with high accuracy and robustness with the assistance of multi-objective optimization theory. Furthermore, by introducing fuzzy theory onto the basis of deterministic prediction results, an interval forecasting model with a narrower interval width and better comprehensive performance is constructed under the premise of ensuring that the coverage of prediction intervals meets the requirements of the confidence level. In this study, the forecasting system is applied to four global core crude oil futures markets, and the empirical study finds that the performance is improved by more than 50 % compared with the benchmark model, which has an impressive performance advantage.

A distributed simulation-optimization framework for many-objective water resources allocation in canal-well combined irrigation district under diverse supply and demand scenarios

To address the issues of both water resources allocation and sustainable management in agriculture areas with rising food demand, a simulation-optimization framework based on Flopy and Pymoo was proposed and developed for canal-well combined irrigation districts. The proposed framework first solved the many-objective water resources allocation problem which integrates groundwater simulation, crop production, and farmer income modules to quantitatively reveal the various trade-offs and synergies by using NSGA-III algorithm. The Entropy-TOPSIS method was then applied to recommend proper water allocation schemes. The proposed framework was further tested in Baojixia irrigation district considering various water supply and crop demand scenarios based on Copula-based uncertainty analysis. The Key findings are as follows: (1) the proposed framework could effectively optimize conjunctive water resources allocation problems of both surface water and groundwater; (2) low supply combined with high demand (p=0.17) is more likely to occur than high supply with high demand (p=0.02); (3) increased crop demand and restricted surface water negatively impact both water productivity and groundwater sustainability; and (4) the cumulative groundwater drawdown of recommend schemes is 36.9 % and 6.5 % higher under low to medium supply scenarios, while water productivity of recommend schemes decreases 28.2 % and 9.7 % with high and medium demand. This framework could provide useful insights for sustainable agricultural water management in canal-well combined irrigation district with various uncertainties in supply and demand scenarios.

Development of a novel process towards an l-malate biorefinery using methanol as feedstock

Malate is primarily obtained from fossil resources. However, growing environmental concerns are rerouting chemical manufacturing to biomanufacturing. Despite significant ‘discovery research’ efforts for sustainable malate production, a successful industrial implementation remains elusive. A novel methanol-based l-malate biomanufacturing process using the methylotrophic bacterium Bacillus methanolicus is designed and the relationship between R&D and techno-economic feasibility is assessed. Two scenarios are modeled and compared using SuperPro Designer®. First, with limited R&D success a 65 g/L titer is achievable. Then with further strain improvement and process development titer is increased to 100 g/L. Four independent design parameters; biomass formation, CO2 evolution, methanol loss and selling price are selected to analyze techno-economic metrics of interest (yield per batch, yield on methanol, unit production cost, and net present value). Sensitivity analysis reveals the dependence of techno-economic feasibility to R&D success, while uncertainty analysis quantifies how uncertainty in process development propagates into uncertainty in process performance.

Novel analysis of alignment error on spherical Fizeau interferometer and uncertainty evaluation of sphericity calibration system based on random ball test

The National Metrology Institute of Japan (NMIJ) has developed a high-precision sphericity calibration system utilizing a Fizeau interferometer and conducted a thorough evaluation of the system's measurement uncertainty. Generally, there are two principal sources of uncertainty in the measurement outcomes when utilizing a spherical Fizeau interferometer. First, the accuracy of the system is significantly influenced by the prior knowledge of the reference sphere surface's absolute profile. To address this, the study established a straightforward yet effective calibration system for the reference sphere surface using a random ball test method. Second, the system's precision is affected by misalignment aberration, which occurs when there is any lateral or longitudinal displacement of the test sphere from the confocal position relative to the reference sphere. This misalignment can introduce both high-order shape errors (misalignment aberrations) and low-order shape errors (alignment errors). Through analytical consideration of an observation coordinate system on the camera plane, this study delves into misalignment aberrations, suggesting that the impact of misalignment should be determined experimentally for each reference sphere unit due to potential imperfections that may be revealed by misalignment. Furthermore, the study proposes that uncertainties related to misalignment aberrations are theoretically confirmed to be smaller than previous studies by conducting an in-depth uncertainty analysis of the misalignment, with a focus on the observation coordinate system on the camera plane. Notably, the research demonstrates that a measurement uncertainty level of λ/100 is achievable, maintaining a broader tolerance for misalignment than previously reported studies. The uncertainty of calibration of the reference sphere unit's absolute profile was 4.2 nm and the uncertainty of sample measurement was determined to be 4.6 nm with misalignment tolerance of ±λ/10. This advancement marks a stride toward improving the accuracy and reliability of sphericity measurements, offering potential for widespread application in precision engineering and metrology.

Environmental sustainability, energy efficiency and uncertainty analysis of agricultural residue-based bioethanol production: A comprehensive life cycle assessment study

Bioethanol produced from agro-residue provides a sustainable alternative to fossil fuels, mitigating pollution, reducing waste, and promoting a circular economy. However, challenges arise in sustainable sourcing, resource conflicts, water quality concerns, and managing environmental footprints to maximize agro-residue-based bioethanol benefits. The environmental impacts, net energy ratio (NER), net energy value, water footprint, and uncertainties associated with bioethanol production from agro-residues are analyzed in this study. The analysis followed a cradle-to-gate approach using 1 L of bioethanol produced as a functional unit (FU), allowing for a direct comparison of the environmental performance of the different feedstocks. The result shows that rice straw-based bioethanol exhibited higher environmental impacts, particularly in terms of ecotoxicity, ionizing radiation, human toxicity, and ozone layer depletion, except for greenhouse gas emission, which is lowest for corn stover (0.37 kg CO2 eq. per FU) and highest for cassava straw (1.1 kg CO2 eq. per FU). Furthermore, the Cumulative Energy Demand analysis highlighted a significant reliance on fossil fuels from agricultural biomass throughout the bioethanol production process. The energy balance analysis shows that the agricultural residues are feasible for bioethanol production with NER of 1.20, 3.10, and 3.28 for rice straw, corn stover, and cassava straw, respectively. To enhance the accuracy and reliability of the LCA results, Monte Carlo simulation was employed to account for uncertainty and variability in the data. This methodology provides a better understanding of the impacts of bioethanol production, with a focus on sustainability and advancements in reducing our reliance on fossil fuels.

Density of pure and mixed NaCl and CaCl2 aqueous solutions at 293 K to 353 K and 0.1 MPa: an integrated comparison of analytical and numerical data

This study reports on newly acquired density data of synthetically prepared pure and mixed NaCl and CaCl2 aqueous solutions that span a wide range of geothermally encountered concentrations and mixing ratios. The analytical data are provided for the temperature range of 293–353 K at ambient pressure. For the reproduction of that data, PHREESCALE was used. The predictive potential of this numerical tool regarding the density of geothermal fluids of known composition was the major target herein. As a result, the measured data are in good agreement with previous analytical studies found in the literature. Possible sources of errors are discussed in this paper. Density data of the mixed solutions at temperatures other than ambient are unique and close existing data gaps. The numerical model reproduces the newly measured and already existing density data within an error band of approximately 1%. For further use in geothermal applications, this can be considered an excellent agreement. Moreover, the model yields a direct calculation of density without the need to establish complex empirical equations of state and mixing rules. Finally, sensitivity calculations performed with a thermal–hydraulic (TH) numerical reservoir model demonstrate the required accuracy of fluid density for reliably predicting the long-term performance of deep geothermal energy systems. In terms of the productivity index and the timing of thermal breakthrough it shows that the present analytical and numerical uncertainty in density is small enough to reliably state both reservoir parameters.

Development of 1 ×1 km gridded emission inventory for air quality assessment and mitigation strategies from open biomass burning in Karnataka, India

In this work, we have developed a high-resolution (1 ×1 km grid) emission inventory of greenhouse gases and air pollutants from open biomass burning (OBB) in Karnataka for 2000, 2005, 2010, 2015, 2020, and 2022. Secondary and field datasets were used to validate the emission inventories. The GIS-based statistical model and activity data were used to estimate greenhouse and air pollutant gas emissions from OBB (Forest, grassland, and crop fires). The uncertainty analysis of the emissions were done using the Monte Carlo Simulation (MCS) model and Low Emission Analysis Platform (LEAP) tool was used for projecting the emissions to 2050 based on the annual average growth rate. According to our results, the total OBB burned areas (%) in Karnataka during the aforementioned years were 24.43, 15.69, 15.95, 11.41, 26.73, and 5.78. The results showed that total annual average OBB emissions in Karnataka for 2000–2022 in Gg were SO2 (6.67), NOx (9.48), NH3(9.80), CO (670.12), OC (59.78), BC (5.09), CO2 (11071 .11), CH4(33.68), PM10(84.29), PM2.5(81.08), NMVOC (74.13), and NO2 (4.80) respectively. The districts of Kalburgi, Raichur, Bagalkot, Uttara Kannada, and Shivamogga have the highest emissions from OBB in Karnataka from 2000 to 2022. The emission uncertainty results showed ±26.92, ±28.17, and ±30.27 % for the crop, grassland, and forest fires, respectively. Under the business-as-usual (BAU) scenarios, the CH4 emissions from forest, grassland, and crop fires are projected at 8823, 2.64, and 5262 metric tonnes by 2030 and 58046, 10, and 30700 metric tonnes by 2050, respectively. The developed high-resolution inventories are the most up-to-date surface emission datasets with hotspots in Karnataka from OBB emission. National Clean Air Programme (NCAP) and India’s Net-Zero Emission target by 2070, our high-resolution emission inventories and mitigation strategies may be helpful for air quality monitoring, health benefits analysis, and policy-making studies in Karnataka, India.

A computational framework to systematize uncertainty analysis in the sediment fingerprinting approach using least square methods

A computational framework to systematize uncertainty analysis in the sediment fingerprinting approach using least square methods

Effective field theory descriptions of Higgs boson pair production

Higgs boson pair production is traditionally considered to be of particular interest for a measurement of the trilinear Higgs self-coupling. Yet it can offer insights into other couplings as well, since – in an effective field theory (EFT) parameterisation of potential new physics – both the production cross section and kinematical properties of the Higgs boson pair depend on various other Wilson coefficients of EFT operators. This note summarises the ongoing efforts related to the development of EFT tools for Higgs boson pair production in gluon fusion, and provides recommendations for the use of distinct EFT parameterisations in the Higgs boson pair production process. This document also outlines where further efforts are needed and provides a detailed analysis of theoretical uncertainties. Additionally, benchmark scenarios are updated. We also re-derive a parameterisation of the next-to-leading order (NLO) QCD corrections in terms of the EFT Wilson coefficients both for the total cross section and the distribution in the invariant mass of the Higgs boson pair, providing for the first time also the covariance matrix. A reweighting procedure making use of the newly derived coefficients is validated, which can be used to significantly speed up experimental analyses.