Low Uncertainty Research Articles

QCD predictions for simultaneously produced Higgs and vector bosons at s = 13, 14, 27 and 100 TeV with a comprehensive study of systematic uncertainty and charge asymmetry

A comprehensive study of high-order QCD cross-section for the Higgs boson that is simultaneously produced with a vector boson is presented at s=13,14,27 and 100 TeV in this manuscript. The simultaneous Higgs production with a vector boson facilitates the investigation of the Higgs signal. In the calculations, three different branching states of the Higgs (τ-τ+, bb¯, γγ) and vector bosons [W±(e±ν(ν¯)),Z(e-e+)] were taken into account. Here, leading order (LO), next-to-leading order (NLO), and next-to-NLO (NNLO) QCD cross-sections were calculated for these branching states. A simulation framework was built for the calculations at the TRUBA high-performance grid computing center. Then, the threshold values (selection cuts) close to the values used by the CMS and ATLAS Collaborations were selected and used on several parameters such as invariant mass, transverse momentum (pT), pseudorapidity (η), etc. In addition, NNPDF3.1 parton distribution functions (PDF) were used during the calculation. The results showed that the numerical value of the QCD prediction increases at NLO and NNLO as compared to the LO predictions. In addition to the higher-orders, the cross-section value increases as the center-of-mass energy increases. In addition to the QCD predictions, PDF, scale, and αS uncertainties of the QCD predictions were also calculated to test the reliability of the high-order QCDs. The results showed that total and scale uncertainties decrease as the QCD order increases. In addition, it was found that LO total and scale uncertainties increase significantly as the center-of-mass energy increases, while NLO and NNLO scale and total uncertainties remain almost constant or show a negligible increase as the center-of-mass energy increases. This indicates that high-order QCDs not only provide more accurate results by the addition of extra partonic diagrams but also provide lower uncertainties in the relevant production channels. Furthermore, the required data that provides the exact statistics for physics measurements of simultaneously produced Higgs and vector bosons as the data at 13 TeV were predicted at s = 14, 27, and 100 TeV. As a result, we found that the same statistics for accurate physics measurements can be obtained at s = 14, 27, and 100 TeV with approximately 1.1, 2.0, and 6.5 times less data than the amount of data at s = 13 TeV, respectively. In the last section of this study, W boson charge asymmetry was computed at NNLO QCD. The lowest charge asymmetry between W+ and W- was obtained at the WH(τ-τ+) decay channel and the highest charge asymmetry results were obtained at the WH(bb¯) decay channel.

Uncertainty Analysis of Remote Sensing Estimation of Chinese Fir (Cunninghamia lanceolata) Aboveground Biomass in Southern China

Forest aboveground biomass (AGB) is not only the basis for forest carbon stock research, but also an important parameter for assessing the forest carbon cycle and ecological functions of forests. However, there are various uncertainties in the estimation process, limiting the accuracy of AGB estimation. Therefore, we extracted the spectral features, vegetation indices and texture factors from remote sensing images based on the field data and Landsat 8 OLI remote sensing images in Southern China to quantify the uncertainties. Then, we established three AGB estimation models, including K Nearest Neighbor Regression (KNN), Gradient Boosted Regression Tree (GBRT) and Random Forest (RF). Uncertainties at the plot scale and models were measured by using error equations to analyze the influences of uncertainties at different scales on AGB estimation. Results were as follows: (1) The R2 of the per-tree biomass model for Cunninghamia lanceolata was 0.970, while the uncertainty of the residual and parameters for per-tree biomass model was 4.62% and 4.81%, respectively; and the uncertainty transferred to the plot scale was 3.23%. (2) The estimation methods had the most significant effects on the remote sensing models. RF was more accurate than other two methods, and had the highest accuracy (R2 = 0.867, RMSE = 19.325 t/ha) and lowest uncertainty (5.93%), which outperformed both the KNN and GBRT models (KNN: R2 = 0.368, RMSE = 42.314 t/ha, uncertainty = 14.88%; GBRT: R2 = 0.636, RMSE = 32.056 t/ha, uncertainty = 6.3%). Compared to KNN and GBRT, the R2 of RF was enhanced by 0.499 and 0.231, while the uncertainty was decreased by 8.95% and 0.37%, respectively. The uncertainty associated with the scale of remote sensing models remains the primary source of uncertainty when compared to the plot scale. On the remote sensing scale, RF is the model with the best estimation effect. This study examines the impact of both plot-scale and remote sensing model-scale methodologies on the estimation of AGB for Cunninghamia lanceolata. The findings aim to offer valuable insights and considerations for enhancing the accuracy of AGB estimations.

Evaluation of Heavy Metal Content in Plastic Bags Used as Improvised Food Cooking Covers: A Case Study from the Mozambican Community

The widespread use of plastic bags as improvised food cooking covers in Mozambican communities has raised public health concerns, increasing interest in studying these plastic bags, which contain heavy metals additives used to improve their physical and chemical properties. This study aims to evaluate the levels of heavy metals commonly used in plastic bags used as improvised food cooking covers, focused on Mozambican communities that have this habit. Using spectroscopic techniques, Fourier Transform Infrared Spectroscopy (FTIR) and Atomic Absorption Spectroscopy (AAS), we analyzed plastic bag samples to identify polymer types, chemical composition, and heavy metal concentrations. FTIR analysis confirmed low- and high-density polyethylene (LDPE and HDPE) as the primary materials, with spectra peaks between 2800 and 3000 cm−1, indicating stretching vibrations characteristic of LDPE and HDPE. The density measurements varied between 0.04 and 0.08 g/cm3 with very low uncertainty values (0.27% and 0.098%). The heavy metal analysis revealed concentrations higher than those stipulated in international standards. The results in terms of the percentage of LDPE samples in relation to the HDPE samples are as follows: Cd: 69.71% (LDPE < HDPE); Cu: 220.44% (LDPE > HDPE); Pb: 24% (LDPE < HDPE); and Zn: 51.53% (LDPE < HDPE). These findings highlight the potential public health risks associated with the use of plastic bags in cooking and underscore the need for regulatory intervention.

Evaluation of cumulative rainfall and rainfall event–duration threshold based on triggering and non-triggering rainfalls: Northern Thailand case

Abstract A recently introduced rainfall threshold for landslide early warning systems combined a cumulative rainfall threshold with a rainfall event–duration (ED) threshold. Cumulative rainfall with rainfall event–duration, known as the CED threshold, was reported to perform better than the conventional ED threshold. However, the establishment of the CED threshold was based on a frequentist approach which required an adequate number of landslide-triggering rainfall data. An alternative to the use of landslide-triggering rainfall data is the use of non-triggering rainfall data. These rainfall events supply much bigger amount of data to the susceptibility model. Although the establishment of a rainfall threshold based on non-triggering rainfall events is seldom considered, previous scholars reported that this approach has produced better results than the conventional approach based on landslide-triggering events. This study investigates the reliability and prediction performance of the CED threshold based on non-triggering rainfall data. The performance of this threshold, designated as the negative-CED (CEDN) threshold, was compared with the positive-CED (CEDP) threshold based on landslide-triggering rainfall data. North Thailand, a landslide hot spot, was chosen as the study area. The proposed threshold was assessed from three skill scores, including (1) the true positive fraction (TPF), (2) the false positive fraction (FPF), and (3) the positive predictive value (PPV), and their variations over the range of threshold uncertainties. Rather than possessing lower uncertainties of the threshold parameters, the negative threshold provided better compromise predictions of TPF and FPF scores than the positive thresholds. Integrating the cumulative rainfall threshold and an event-based rainfall threshold resulted in a significant improvement in FPF scores, and hence enhanced the compromise predictions of TPF and FPF scores. Keeping in mind that the negative thresholds were not established from landslide data, care must be taken when using these thresholds and it is recommended that they should be applied only to areas where landslide data are limited.

Tapping into User Effort? Improving Referral Outcomes in Crowdfunding Markets

Referral programs have received increasing attention as a way to enhance project fundraising in crowdfunding. To improve operational efficiency and performance, many crowdfunding platforms allow users to exert more effort (referral effort) when making referrals. In this paper, we study the impact of users’ referral efforts on referral outcomes, using a unique data set from a reward-based crowdfunding platform. We analyze three types of referral effort beyond merely sharing the referral link, namely, writing the referral message, matching the referral recipients’ pledges, and supporting their own referrals. Our results suggest that users’ referral efforts can help improve the outcome of referrals, as these efforts may be perceived as positive signals of quality, thus reducing referral recipients’ uncertainty about the crowdfunding project. We further explore the heterogeneity in this observed impact, and find that matching recipients’ pledges have a greater impact than the other two types of referral effort. Furthermore, the impact of referral effort is attenuated under conditions of low uncertainty, wherein project quality can be inferred from observable features, such as projects launched by large creators, selected by editors, or referred by experienced backers. Our analyses also indicate that referring users’ efforts can collectively improve the fundraising performance of projects that receive referrals. We discuss the implications of our findings for platform managers, policy makers, and users in crowdfunding marketplaces.

Spectrum Sensing in Very Low SNR Environment Using Multi-Scale Temporal Correlation Perception with Residual Attention.

Spectrum sensing is recognized as a viable strategy to alleviate the scarcity of spectrum resources and to optimize their usage. In this paper, considering the time-varying characteristics and the dependence on various timescales within a time series of samples composed of in-phase (I) and quadrature (Q) component signals, we propose a multi-scale time-correlated perceptual attention model named MSTC-PANet. The model consists of multiple parallel temporal correlation perceptual attention (TCPA) modules, enabling us to extract features at different timescales and identify dependencies among features across various timescales. Our simulations show that MSTC-PANet significantly improves the detection of channel occupancy at low signal-to-noise ratios (SNR), particularly in untrained scenarios with lower SNR conditions and modulation uncertainties. The analysis of the ROC curve indicates that at an SNR of -20 dB, the proposed MSTC-PANet achieves a detection rate of 98% with a false alarm rate of 10%. Furthermore, MSTC-PANet, which has been trained using digital modulation techniques, also demonstrates applicability to analog modulation.

An ML-Enhanced Laser-Based Methane Slip Sensor Using Wavelength Modulation Spectroscopy.

Natural gas (NG) is a promising alternative to diesel for sustainable transport, potentially reducing GHG and air quality emissions significantly. However, the GHG benefits hinge on managing methane slip, the unburned methane in the exhaust of NG engines, which carries a significant global warming potential. The CH4 slip from NG engines is highly dependent on engine type and operation, and effective greenhouse gas emission mitigation requires that the actual operation of real-world engines is monitored. This requires suitable instrumentation for online robust CH4 measurement in engine exhaust. Traditional methane slip measurement methods need frequent calibration, may not be suited to dynamic operational conditions, carry significant costs, or require expert users. Furthermore, the significant computational demands associated with calibration-free spectroscopic methods and the prevalent noise uncertainty underscore the urgent requirement for innovative sensors. These sensors must not only respond rapidly but also have low uncertainty in their readings. This paper presents a machine learning (ML)-enhanced, laser-based methane slip sensor using wavelength modulation spectroscopy (WMS) for rapid, accurate, and calibration-free CH4 measurements for application in the exhaust of NG engines. The sensor utilizes a distributed feedback (DFB) laser diode emitting around 1.65 μm propagated through a multipass optical cell. An ML-based approach is used to invert the recorded WMS signal, which reduces computational cost and uncertainty due to noise vulnerabilities inherent in traditional measurement inversion approaches. A Gaussian process regression (GPR) model, trained on measured and simulated WMS signals, was selected for its high predictive accuracy, where it achieved a mean absolute percent error (MAPE) of 0.24%. For exhaust measurement on an in-use natural gas marine vessel, a mean absolute difference of 3.95% was observed, relative to simultaneous reference Fourier transform infrared spectroscopy measurements. The ML-based WMS inversion system marks a significant advancement in methane slip measurement, offering real-time monitoring capabilities with reduced computational demands. Its development supports the realization of NG environmental benefits for transport by providing accurate CH4 slip data, which are essential for engine performance optimization, regulatory adherence, and sustainable policy decisions.

Value-Based Pricing for Drugs With Uncertain Clinical Benefits.

Policymakers can use cost-effectiveness analysis to set value-based prices (VBP) for new pharmaceuticals. However, the uncertainty of investigational drug benefits complicates this pricing strategy. Such complexity stems from decision-makers' risk aversion and the potential change in the estimated value with emerging evidence. The recent surge in drugs approved via the Accelerated Approval (AA) pathway in the U.S. has made incorporating uncertainty into VBP crucial. We propose to estimate risk-adjusted VBP (rVBP) for drugs with uncertain benefits via integrating value of information and expected utility theory. Our approach involves two assessment points: an initial assessment with existing evidence; and a reassessment with new evidence that reduces uncertainty. This approach enables decision-makers to set rVBP in the initial assessment such that the expected utility, from the exisiting evidence, aligns with the benchmark uncertainty. We evaluate two benchmarks: one with no uncertainty, and one with a decision-maker's acceptable uncertainty level. We show in a case study of a hypothetical AA drug that rVBP may be lower than traditional VBP, especially under high risk aversion or low acceptable uncertainty. Our methodology adjusts VBP to account for uncertainty, supporting decision-makers in balancing timely market access with the risks associated with uncertainty in the benefits of new pharmaceuticals.

Once concessioned, twice shy? Asymmetries of “reconcession” in national PPP programs

In Public-Private Partnerships, theoretically, once the concession period finishes, the infrastructure should be transferred back to the public sector. However, governments are prompt to promote reconcessions by rebidding PPPs. The implications of reconcessioning a PPP that has undergone a previous concession have not been thoroughly considered in the literature. This study develops a System Dynamics (SD) model to examine the implications of reconcession in a national user-pay PPP program over the last 30 years aiming to provide a tool for decision-makers to understand the interrelations between reconcessions and the main PPP outcomes. This model explores the implications of periodical reconcessions by unravelling five asymmetries derived from reconcessions and assesses their impacts on longer average concession periods, higher project expenditures, higher user fees, lower uncertainty and reduced demand volatility. Findings have important implications for PPP policymakers who should thoroughly consider reconcessions implications on PPP program expenses and reliance on user revenues that may trigger erosion of social legitimacy and potential program closure due to user fee increases, demand elasticity, and demand volatility when making decisions about reconcessions in PPP programs. The model’s reliability is tested based on a national toll road PPP program including more than 60 projects.

The Climatic Resilience of the Sasanian Empire

Abstract The Sasanian Empire (224–651 CE) has been given relatively little attention in research on climate-society interactions when compared to the neighboring Byzantine Empire, despite evidence of changing conditions and an agricultural economy that is theoretically vulnerable to droughts due to low annual precipitation. We review the available historical, archaeological, paleo-environmental, and paleo-climatic evidence to assess whether climatic conditions factored into periods of Sasanian growth and decline. We find evidence for drier conditions across Sasanian territories at the turn of the sixth century, a pattern that extends to the Aegean, Anatolia, and Central Asia. These same conditions contributed to a significant decline for the nearby Kingdom of Himyar but occurred alongside a period of expansion and intensification for the Sasanian Empire. We suggest that a combination of careful management of water infrastructure, including qanats, which can conserve water resources during dry periods, and land-use strategies that are both diverse and flexible, may have mitigated the worst impacts of this dry period. However, we note several weaknesses in the available data that still hinder confident interpretations of the potential impacts of climate change in the Sasanian Empire. Notably, there are gaps in the coverage of paleo-hydrological records and a complete lack of terrestrial paleo-temperature records in the region, as well as low resolution and high chronological uncertainties in the archaeological and paleo-environmental evidence.

A multistage algorithm to generate a predictive porphyry intrusion evidential map with low uncertainty for mineral prospectivity mapping, case study in Pariz Area, Iran

A multistage algorithm to generate a predictive porphyry intrusion evidential map with low uncertainty for mineral prospectivity mapping, case study in Pariz Area, Iran

Exploration of subgroups and associated factors of the uncertainty in illness among older adults with chronic heart failure: A latent profile analysis.

Uncertainty in illness is regarded as a source of stress, and tends to have adverse consequences on quality of life among older adults with chronic heart failure (CHF). The purpose of this study was to identify distinct subgroups in uncertainty in illness, and to explore associated factors within the population of older adults with CHF. We conducted a cross-sectional study using convenience sampling to survey 311 hospitalized older adults with CHF. The Mishel Uncertainty in Illness Scale, the 14-item Fatigue Scale, and the Perceived Social Support Scale were administered through self-reported questionnaires. Statistical analyses were conducted using latent profile analysis to precisely categorize participants based on the variable of uncertainty in illness, and multinomial logistic regression was applied to identify factors associated with subgroup heterogeneity. Participants were classified as four subgroups: overall low uncertainty group (27.3%), moderate uncertainty-inconsistency fluctuations group (43.7%), moderate uncertainty-strong inconsistency group (21.2%) and overall high uncertainty group (7.8%). In comparison to the first subgroup (the overall low uncertainty group), the marital status, educational background, monthly household income, number of comorbidities, fatigue level, and social support were associated with heterogeneity of uncertainty in illness among participants in other three subgroups. The findings identified four distinct subgroups characterized by uncertainty in illness among older adults with CHF and revealed the demographic and clinical factors associated with each subgroup. Healthcare professionals should prioritize precise assessments and consider developing tailored interventions to reduce chronic uncertainty in illness among older patients.

A calibration approach for compensating hysteresis and nonlinearity error in an MEMS based instrument for porewater pressure monitoring

AbstractMicro‐electro‐mechanical system (MEMS) technology is becoming increasingly popular in geotechnical and hydrological monitoring due to their distinct features, including its small size, low cost, low energy consumption, and resistance to vibration shock. Due to the nature of design, they exhibit a series of errors, and most importantly, hysteresis and nonlinearity, which should be compensated before any practical application in order to achieve the highest degree of accuracy and precision. This article proposes a practical approach to enhance the accuracy of a newly developed MEMS instrument for groundwater monitoring. The hysteresis and nonlinearity errors were compensated using an integrated two‐step approach with an optimized Preisach model and an optimized spline approximation, respectively. A hybrid differential evolution‐hill climbing algorithm was utilized to achieve optimum models. This optimization algorithm integrates global and local search, offering higher accuracy and computational stability with a lower calibration point requirement. Analysing the results indicates that the nonlinearity error shows an improvement of more than 94% and hysteresis decreased up to 67%. The results illustrate that the compensation can be performed very well using the proposed methodology with lower uncertainty, mean square error, and standard deviation compared to non‐optimized models.

Quantifying Uncertainty in Laser-Induced Damage Threshold for Cylindrical Gratings.

The laser-induced damage threshold (LIDT) is a key measure of an optical component's resistance to laser damage, making its accurate determination crucial. Following the ISO 21254 standards, we studied the measurement strategy and uncertainty fitting method for laser damage, establishing a calculation model for uncertainty. Research indicates that precise LIDT measurement can be achieved by using a small energy level difference and conducting multiple measurements. The LIDT values for the cylindrical grating are 15.34 ± 0.00052 J/cm2 (95% confidence) and 15.34 ± 0.00078 J/cm2 (99% confidence), demonstrating low uncertainty and reliable results. This strategy effectively measures the LIDT and uncertainty of various grating surface shapes, offering reliable data for assessing their anti-laser-damage performance.

A Probabilistic Model-Based Framework for Damage Detection in Beam-Like Structures Considering Temperature Effects

To improve the assessment outcomes of structural health monitoring systems, it has been acknowledged that the damage diagnostics process is associated with a considerable amount of uncertainties from operational and environmental sources such as temperature. This study presents a probabilistic framework for structural health monitoring aimed at identifying common defects such as cracks and corrosion in beam-like structures under varying temperature conditions. The framework utilizes optimization methods integrated with a probabilistic approach, comprising two levels of damage identification: first, determining the optimal probability mass function for the damage location ratio, followed by the optimal conditional for the damage depth ratio. A simply supported beam divided into four segments was modeled to evaluate the effectiveness of the proposed algorithm across various damage scenarios using frequency response signals. Structural damage was simulated by manipulating the length and depth ratios of specific beam segments. The modeling procedure for defects in the simply supported beam was validated through comparison with experimental measurements, achieving a maximum difference of less than 1%. The proposed algorithm consistently yielded improved damage detail outcomes in the second level, characterized by higher probabilities and lower uncertainties for both damage location and depth ratios. Identifying cracks and corrosion with a 0.1 depth ratio at low temperatures proved challenging, but the algorithm effectively addressed such scenarios, achieving identification probabilities exceeding 90%. The analysis outcomes demonstrate the advantages of the proposed framework for structural health monitoring that accounts for temperature effects, thereby improving the fidelity of damage assessment in real-world conditions.

Susceptibility modeling of hydro-morphological processes considered river topology

ABSTRACT Hydro-Morphological Processes (HMP, any natural phenomenon contained within the spectrum defined between debris flows and flash floods) are most likely to occur in small catchments, especially buffer zones along or near rivers. Rivers transfer matter and energy between hydrographic units, thus potentially affecting the occurrence of HMPs in nearby catchments. To date, previous HMP susceptibility studies based on data-driven modeling lacked taking into account these interactions between catchments. In this work, we fully considered the role played by river topology and developed a Topology-based HMP susceptibility model (Topo-HMPSM) to emulate the interactions between catchments and predict the susceptibility of HMPs for the Yangtze River Basin during 1985–2015. Results confirmed that our proposed model outperforms four selected baseline models with the best F1-score (mean = 0.744, best = 0.756) and relatively lower uncertainties. A graph-based deep neural network improves the predictive and interpretability of HMP susceptibility modeling using embedding learning techniques. This work attempts to set a standard for incorporating river topology into deep learning models. Our findings highlight the importance of river topology in predicting HMP and support better informed hazard mitigation strategies.

Development of three biofuel CRMs for the quality parameters in biodiesel and wood pellet via a joint research project.

Biomass is a key element in biofuels which can be defined as a fuel produced through contemporary biological processes, and its increased use can support the EU's aims of reducing greenhouse gas emissions. Information on the nature and the quality of the biomass or biofuel is important in order to support the optimization of their combustion with respect to realizing higher efficiencies and lower emissions during energy production. Three reference materials were produced by a collaborative approach among national metrology institutes and designated institutes within the scope of the EMPIR project: BIOFMET.The project was aimed to establish advanced traceable measurement standards for the determination of the calorific value, impurities, and other parameters such as density, kinematic viscosity, moisture, and ash. This paper presents the sampling and processing methodology, homogeneity, stability, characterization campaign, the assignment of property values, and their associated uncertainties in compliance with ISO 17034 for biofuel reference materials: biodiesel, wood pellet powder, and wood pellet. Parameters of interest in biodiesel reference material-UME BIOFMET CRM 01 are gross calorific value (GCV), density, viscosity, and mass fractions of Ca, K, Mg, Na, P, and S elements. Parameters to be certified in wood pellet powder reference material-UME BIOFMET CRM 02 are GCV, moisture, ash, and mass fractions of Al, Cr, K, Mg, Mn, Ni, S, and Zn elements. Parameters to be certified in the wood pellet reference material-UME BIOFMET CRM 03 are GCV and moisture. The homogeneity and stability of the materials were assessed in accordance with ISO 33405. The materials were characterized by interlaboratory comparison studies among competent metrology institute and designated institute laboratories. Assigned values and uncertainties of the certified values were calculated in accordance with ISO 33405, and uncertainties include characterization, homogeneity, and stability components. The developed CRMs are intended to be used for the development and validation of measurement procedures for the determination and quality control/assurance purposes of the quality parameters for biofuels. It should be emphasized that the UME BIOFMET CRM 01-Biodiesel CRM is the first biodiesel reference material certified for calorific value. Among the developed wood CRMs, thepellet form, UME BIOFMET CRM 03, was found to be more stable than the powder one, UME BIOFMET CRM 02, for the moisture parameter. Sixfold lower relative uncertainty value for short-term stability at 45°C and twofold lower relative uncertainty value for long-term stability at 22°C were obtained for the moisture parameter of the CRM in pellet form compared to the CRM in powder form.

Improved aboveground biomass estimation and regional assessment with aerial lidar in California’s subalpine forests

BackgroundUnderstanding the impacts of climate change on forest aboveground biomass is a high priority for land managers. High elevation subalpine forests provide many important ecosystem services, including carbon sequestration, and are vulnerable to climate change, which has altered forest structure and disturbance regimes. Although large, regional studies have advanced aboveground biomass mapping with satellite data, typically using a general approach broadly calibrated or trained with available field data, it is unclear how well these models work in less prevalent and highly heterogeneous forest types such as the subalpine. Monitoring biomass using methods that model uncertainty at multiple scales is critical to ensure that local relationships between biomass and input variables are retained. Forest structure metrics from lidar are particularly valuable alongside field data for mapping aboveground biomass, due to their high correlation with biomass.ResultsWe estimated aboveground woody biomass of live and dead trees and uncertainty at 30 m resolution in subalpine forests of the Sierra Nevada, California, from aerial lidar data in combination with a collection of field inventory data, using a Bayesian geostatistical model. The ten-fold cross-validation resulted in excellent model calibration of our subalpine-specific model (94.7% of measured plot biomass within the predicted 95% credible interval). When evaluated against two commonly referenced regional estimates based on Landsat optical imagery, root mean square error, relative standard error, and bias of our estimations were substantially lower, demonstrating the benefits of local modeling for subalpine forests. We mapped AGB over four management units in the Sierra Nevada and found variable biomass density ranging from 92.4 to 199.2 Mg/ha across these management units, highlighting the importance of high quality, local field and remote sensing data.ConclusionsBy applying a relatively new Bayesian geostatistical modeling method to a novel forest type, our study produced the most accurate and precise aboveground biomass estimates to date for Sierra Nevada subalpine forests at 30 m pixel and management unit scales. Our estimates of total aboveground biomass within the management units had low uncertainty and can be used effectively in carbon accounting and carbon trading markets.

Study on the Uncertainty of Input Variables in Seismic Fragility Curves Based on the Number of Ground Motions

Seismic fragility curves, derived from ground motion data, are essential tools for predicting and assessing potential damage to structures during earthquakes. Seismic fragility curves are vital for assessing the structural behavior of buildings and establishing disaster response criteria when an earthquake occurs. We performed an incremental dynamic analysis based on 400 ground motion data. We sampled various sets of ground motions (10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 150, 200, 250, 300, and 350) and derived seismic fragility curves for three performance criteria, based on inter-story drift, by conducting 100,000 simulations for two steel frame structures each (6-story and 13-story). Fewer ground motions increase the uncertainty of the seismic fragility curve, distorting the results. Conversely, increasing the number of ground motions improves the reliability of the input variables and enhances the consistency of the results. The median and the logarithmic standard deviation for both structures converged toward the reference values when 30 or more ground motions were used. Similar results were observed when ≥50 ground motions were used. Specifically, more ground motions corresponded with a lower uncertainty in deriving the input variables for the seismic fragility curve, improving the reliability of the results. In conclusion, the number of ground motions used is directly related to the computational time for numerical analysis when deriving seismic fragility curves. Therefore, considering an appropriate number of ground motions is crucial to enhancing the reliability of the input variables used in evaluating the structural performance.

Price markdowns to induce customers to opt out of free returns

With consumers increasingly relying on online shopping, returns have become a considerable issue in the retail e-commerce industry. Major e-retailers typically impose either “free returns” or a “partial refund” policy. This article studies a flexible return policy in which customers get to self-select return options. We consider a setting where, alongside the conventional free return (FR) option, consumers are offered an additional return alternative called opt-out of free return (FO). Choosing FR allows consumers to return items within a grace period for a full refund. Alternatively, selecting FO gives them a price markdown, but involves paying a fixed and variable penalty if they choose to return the product. We aim to derive managerial insights on when offering FO would be beneficial to firms and, if so, how to set the optimal markdown level. We find that the uncertainty level in the consumers’ valuation distribution has a subtle impact on the profitability of providing the FO option alongside the FR. While conventional wisdom suggests that offering the FO option is more likely to be beneficial as customers’ uncertainty about the product’s valuation pre-purchase increases, we show that the opposite could be true. Specifically, we show that if the product’s expected valuation is high (low), higher (lower) uncertainty in the valuation distribution makes it more likely that offering FO alongside FR is advantageous to the firm.