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New technologies are facing the market to overcome high energy costs and efficiency regulations. Among these, the use of ozone in domestic appliances has been recently proposed for a cold-water sanitizing system for clothes or even a no-water cycle to refresh clothes. Ozone is a contaminant in indoor environments and its toxicity is mainly due to the strong oxidizing action on cellular components that can reduce lung function and increase respiratory symptoms. This study illustrates a risk assessment for ozone emissions released from new domestic clothes washer/dryer during normal operation and in the case of a failure. Indoor ozone concentrations were measured, and a mass-balance model was applied, considering ozone released from the appliance during the no-water cycle and the outdoor ozone contribution. Monte Carlo analysis was used to derive the probability of exceeding the air quality standards established by the main international organizations for the protection of human health. This study indicated the most suitable ozone generator and the best refresh cycle to minimize health risks. This method can be generally used to assess the potential health risk for the indoor environment, due to the release of harmful emissions from household appliances.

The efficiency of sampling is a critical concern in Monte Carlo analysis, which is frequently used to assess the effect of the uncertainty of the input variables on the uncertainty of the model outputs. The projection pursuit multivariate transform is proposed as an easily applicable tool for improving the efficiency and quality of a sampling design in Monte Carlo analysis. The superiority of the projection pursuit multivariate transform, as a sampling technique, is demonstrated in two synthetic case studies, where the random variables are considered to be uncorrelated and correlated in low (bivariate) and high (five-variate) dimensional sampling spaces. Five sampling techniques including Monte Carlo simulation, classic Latin hypercube sampling, maximin Latin hypercube sampling, Latin hypercube sampling with multidimensional uniformity, and projection pursuit multivariate transform are employed in the simulation studies, considering cases where the sample sizes (n) are small (i.e., 10≤n≤100), medium (i.e., 100

With technology scaling, stability, power dissipation, and device variability, the impact of process, voltage and temperature (PVT) variations has become dominant for static random access memory (SRAM) analysis for productivity and failure. In this paper, ten-transistors (10T) and low power eight-transistors SRAM cells are redesigned using floating-gate MOS transistors (FGMOS). Power centric parameters viz. read power, write power, hold power and delay are the performance analysis metrics. Further, the stochastic parameter variation to study the variability tolerance of the redesigned cell, PVT variations and Monte Carlo simulations have been carried out for 10T FGMOS SRAM cell. Stability has been illustrated with the conventional butterfly method giving read static noise margin (RSNM) and write static noise margin (WSNM) metrics for read stability and write ability, respectively. A comparative analysis with standard six-transistor SRAM cell is carried out. HSPICE simulative analysis has been carried out for 32 nm technology node. The redesigned FGMOS SRAM cells provide improved performance. Also, these are robust and reliability efficient with comparable stability. [ABSTRACT FROM AUTHOR]

Featured Application: The present manuscript shows an easy and achievable approach to improving flood hazard maps by delimiting the uncertainty derived from flood frequency analysis, and how this propagates through to the hydrodynamic modelling results on which these mappings are based. All flood hazard and risk assessment suffer from a certain degree of uncertainty due to multiple factors, such as flood frequency analysis, hydrodynamic model calibration, or flood damage (magnitude–damage functions) models. The uncertainty linked to the flood frequency analysis is one of the most important factors (previous and present estimation point to 40%). Flood frequency analysis uncertainty has been approached from different points of view, such as the application of complex statistical models, the regionalization processes of peak flows, or the inclusion of non-systematic data. Here, we present an achievable approach to defining the uncertainty linked to flood frequency analysis by using the Monte Carlo method. Using the city of Zamora as the study site, the uncertainty is delimited by confidence intervals of a peak flow quantile of a 500-year return period. Probabilistic maps are derived from hydrodynamic results, and further analysis include flood hazard maps for human loss of stability and vehicle damage. Although the effect of this uncertainty is conditioned by the shape of the terrain, the results obtained may allow managers to achieve more consistent land-use planning. All those Zamora city results point out the probable underestimation of flood hazard (the higher hazard areas increase around 20%) and risk when the uncertainty analysis is not considered, thus limiting the efficiency of flood risk management tasks. [ABSTRACT FROM AUTHOR]

Grid converters synchronized by phase-locked loops (PLLs) could suffer from stability problems, especially being connected to a weak grid or high penetration of converters. The existing literature assesses the stability of the paralleled PLL-synchronized converters at the same point of common coupling (PCC) using identical control and system parameters. However, in an actual grid, the parameters of grid converters are normally different due to different manufacturers. In this regard, this article aims to study the stability indices and margins of two PLL-synchronized converters with different parameters, particularly with different PLL bandwidths and power injections. The main purpose of this article is to provide a general design guidelines of PLL bandwidths of two converters (can be extended to two wind/solar farms) for stable operation in practical grids. The state-space model of the PLL-synchronized converter based on the component connection method is developed and eigenvalue-based analysis is used to investigate the interactions between the two converters. Moreover, the stability borders of two PLL-synchronized converters using different PLL bandwidths and power setpoints are studies. Monte Carlo simulations and experimental results are provided to validate the effectiveness of the developed model and theoretical analysis. [ABSTRACT FROM AUTHOR]

Infrastructure Projects (IP) are characterised by their extreme complexity in construction and management. Lack of effective risk analysis in construction of IP can cause failure in projects delivery. This paper aims to produce specific guidelines for the efficient application of quantitative risk analysis process. The literature guided contractors into efficient quantitative risk analysis (QRA) of IP to improve contractors QRA practices in the presence of uncertainty during the construction stage. The paper reviewed the literature of QRA process in construction of IP, and then performed a full quantitative risk analysis on a case study of IP in Egypt. The study includes sewage networks, water networks, irrigation networks, and district cooling networks of Cairo festival city project. The paper used an infrastructure schedule to apply the quantitative risk analysis. The schedule included activities for the construction of sewage networks, irrigation network, water network, and district cooling network. Based on the findings, Monte Carlo analysis technique has proved to be an efficient quantitative technique in supporting project managers in allocating deviations. Whereas, sensitivity analysis technique helped in ranking the most driving activities to failure in delivery of IP. The paper added value by providing knowledge and practical guidance for contractors in improving their QRA practice and decision making during the construction of IP. [ABSTRACT FROM AUTHOR]

This statistical study refines and updates Sharpe's empirical paper (1975, Financial Analysts Journal) on switching between US common stocks and cash equivalents. According to the original conclusion, profitable market timing relies on a representative portfolio manager who can correctly forecast the next year at least 7 times out of 10. Four changes are made to the original setting. The new data set begins and ends with similar price-earnings ratios; a more accurate approximation of commissions is given; the rationality of assumptions is examined; a prospective and basic Monte Carlo analysis is carried out so as to consider the heterogeneous performance of a number of portfolio managers with the same forecasting accuracy. Although the first three changes improve retrospectively the odds of profitable market timing, the original conclusion is corroborated once more. [ABSTRACT FROM AUTHOR]

This paper introduces Bayesian statistical methods for studying the kinetics of biomass hydrothermal carbonization. Two simple, specially developed computer programs implement Markov-chain Monte Carlo methods to illustrate these techniques' potential, long since established in other areas of chemical reaction engineering. A range of experimental data, both from this study and the literature, test the soundness of a Bayesian approach to modeling biomass hydrothermal carbonization kinetics. The first program carries out parameter estimations and performs better or equal than the traditional deterministic methods (R2 as high as 0.9998). For three out of the 22 datasets, the program detected the global minima of the parameter space, while the deterministic least-square found local values. The second program uses Gillespie's algorithm for the statistical simulation of the reactions occurring in hydrothermal carbonization. Comparing six basic kinetic models with literature data tested the stochastic simulation as a tool for assessing biomass conversion reaction networks rapidly. Among the simple models discussed, reaction scheme 3 fitted better to the experimental data (R2 > 0.999). The proposed approach is worth extending to more complex, time-consuming computer models and could support other techniques for studying hydrothermal conversions.

Tropical deforestation has reduced the extent of natural forests, which conserve biodiversity, provide essential resources to people, and reduce climate change by storing carbon. Forest conservation projects need tree species data to effectively manage biodiversity while greenhouse gas reduction programs require robust methods to estimate forest carbon. Here, we use field measurements, remote sensing, and Monte Carlo analyses to quantify tree biodiversity and aboveground carbon changes and uncertainties in 5200km2 of Amazonian and Yungas rainforest and other land around the Parque Nacional Yanachaga-Chemillén and two other protected areas in the Selva Central, Peru. Field inventories of 17ha found 438 tree species in 156 families. Field measurements of 10,838 trees and Monte Carlo analyses of uncertainties in measurements, allometric equations, wood density, and the carbon fraction of biomass showed that aboveground live carbon densities were 93±39Mgha−1 (mean ±95% confidence interval [CI]) in old-growth forest and 40±10Mgha−1 in secondary forest. Carbon density was significantly correlated to tree species richness (P

Purpose: It has been recognised by life cycle assessment (LCA) practitioners that uncertainty analysis needs to be incorporated into LCA studies to improve the reliability of the results; however, case studies still report results without uncertainty. Reasons for ignoring uncertainty include resource constraints or a lack of knowledge or expertise. This paper presents a structured uncertainty management method that aims to improve uncertainty reporting in LCA. Methods: The most common uncertainty classification for LCA is parameter, model and scenario; however, multiple classifications exist in literature. The latest classification published by Igos et al. (2019) divides uncertainty into three dimensions: location, level and nature, based on previous research (Walker et al. 2003; Warmink et al. 2010). In this paper, the three-dimensional uncertainty classification is further developed for practical implementation in LCA. The classification is incorporated into an uncertainty management methodology that is divided into five steps: identification, classification, quantification or qualification, reduction and reporting, and is integrated into the iterative steps of an LCA in accordance with ISO 14044 (2006). The method is demonstrated in a tiered-hybrid case study of an Irish apartment development from cradle-to-gate that focuses on climate change. The data sources include the bill of quantities, Ecoinvent datasets, Irish input-output tables and Irish environmental accounts data. Results and discussion: The initial uncertainty assessment of the case study found that the deterministic value likely underestimates the total tonnes of carbon dioxide equivalents (t CO2-eq.) for the apartment development. The probability that the impact is greater than the deterministic value is approximately 93%, prior to uncertainty reduction. The main contributors to the total uncertainty were identified as the choice of Ecoinvent dataset, the sectoral emission intensities and the Intergovernmental Panel for Climate Change Global Warming Potentials. Therefore, work to reduce the total uncertainty should focus on identifying the most suitable dataset for the building material to reduce the input distribution for that material and on acquiring more product-specific data. Conclusions and recommendations: The developed uncertainty management method improves the way uncertainty is managed in practice in LCA case studies by providing a detailed and structured way for uncertainty to be identified, classified, measured and reported. It further identifies where resources can be focused to iteratively reduce the overall uncertainty of the results and thus improve their reliability. It is recommended that the developed method is tested across other case studies, life cycle stages and impact categories in further work. [ABSTRACT FROM AUTHOR]

The use of standardized anthropomorphic test devices and test conditions prevent current vehicle development and safety assessments from capturing the breadth of variability inherent in real-world occupant responses. This study introduces a methodology that overcomes these limitations by enabling the assessment of occupant response while accounting for sources of human- and non-human-related variability. Although the methodology is generic in nature, this study explores the methodology in its application to human response in far-side motor vehicle crashes as an example. A total of 405 human body model simulations were conducted in a mid-sized sedan vehicle environment to iteratively train two neural networks to predict occupant head excursion and thoracic injury as a function of occupant anthropometry, impact direction and restraint configuration. The neural networks were utilized in Monte Carlo simulations to calculate the probability of head-to-intruding-door impacts and thoracic AIS 3+ as a function of the restraint configuration. This analysis indicated that the vehicle used in this study would lead to a range of 667 to 2,448 head-to-intruding-door impacts and a range of 3,041 to 3,857 cases of thoracic AIS 3+ in the real world, depending on the seatbelt load limiter. These real-world results were later successfully validated using United States field data. This far-side assessment illustrates how the methodology incorporates the human and non-human variability, generates response surfaces that characterize the effects of the variability, and ultimately permits vehicle design considerations and injury predictions appropriate for real-world field conditions. [ABSTRACT FROM AUTHOR]

Bioaccessibility of eight potentially toxic elements (PTEs), their human exposure and health risk assessments were determined in the indoor dust of residence and schools from the Asansol Industrial area, India. The PTEs concentrations were maximum during the winter both at houses and schools. The average PTEs concentrations throughout the year in Asansol were 3.16, 120, 156, 41708, 2354, 61.3, 115 and 345 mg.kg-1 for Cd, Cr, Cu, Fe, Mn, Ni, Pb and Zn respectively. X-ray powder diffraction indicated an abundance of quartz in the indoor dust. Principal component analysis (PCA) indicated multiple sources such as traffic, industries, and lithogenic sources for PTEs in indoor dust. Percentage of bioaccessibility was maximum for Cd (55.3% throughout the year). Total PTEs concentration and a bioaccessible fraction of PTEs both were used for health risk assessment, and non-carcinogenic health risk was <1 for total PTEs and the bioaccessible fraction of PTEs. Health risk of total PTEs’ (HItotal) for Mn was high for both children and adult (6.76E-01 and 1.3E-01, respectively). Monte Carlo simulation model indicated that all the cumulative probability of Hazard Quotient (HQ) for collectively eight metals was below 1. [ABSTRACT FROM AUTHOR]

To speed up evaluation, development and upscaling of new processes, the use of engineering methodology can have a great impact. Here we show the application of an engineering approach to find the reaction conditions allowing the best process metrics. An experimentally validated mathematical model for the MenD-catalyzed synthesis of a commercially unavailable product, 6-cyano-4-oxohexanoic acid, with a potential industrial use as a building block, was used for process optimization. Using the optimized conditions, 62.4 g dm–3 of product, volume productivity of 87.1 g dm–3 d–1, product yield of 96 %, and biocatalyst productivity of 25.8 kgP kg–1 MenD can be achieved. Based on the optimized production procedure, economic analysis was performed to determine minimal product price required for project to be profitable in 8 years economic lifetime. In addition, Monte Carlo analysis (MCA) was used to assess the influence of uncertainties in estimation of input variables on overall economic performance.

Due to excellent characteristics of specific stiffness and thermal stability, silicon carbide-based (SiC) material is commonly selected to construct large-scale lightweight mirror. In general, the fabrication process of SiC mirror is similar to the casting process. The blank error of SiC mirror is 0~1 mm. Due to the high hardness of SiC, only the mirror surface and some positioning surface will be milled. The mirror surface accuracy will be degraded due to the fact that the blank error can cause significant changes in weight distribution. In this paper, Monte Carlo analysis is firstly performed to examine the blank error on gravity center, stiffness and mirror accuracy of a SiC mirror. It is found that according to the designed mount location, the amount of degradation is more than 2.5 nm of which the probability is 40.3%. It is known that the error of gravity center can be compensated by optimizing the axial mount location. Then inverse modeling and testing of gravity center for the SiC mirror is carried out in order to determine the optimal axial mount location. Based on the proposed method, the mirror degradation introduced by the blank error has been eliminated to the greatest extend.

Greenhouse gas inventories and emissions reduction programs require robust methods to quantify carbon sequestration in forests. We compare forest carbon estimates from Light Detection and Ranging (Lidar) data and QuickBird high-resolution satellite images, calibrated and validated by field measurements of individual trees. We conducted the tests at two sites in California: (1) 59 km2 of secondary and old-growth coast redwood (Sequoia sempervirens) forest (Garcia-Mailliard area) and (2) 58 km2 of old-growth Sierra Nevada forest (North Yuba area). Regression of aboveground live tree carbon density, calculated from field measurements, against Lidar height metrics and against QuickBird-derived tree crown diameter generated equations of carbon density as a function of the remote sensing parameters. Employing Monte Carlo methods, we quantified uncertainties of forest carbon estimates from uncertainties in field measurements, remote sensing accuracy, biomass regression equations, and spatial autocorrelation. Validation of QuickBird crown diameters against field measurements of the same trees showed significant correlation (r = 0.82, P < 0.05). Comparison of stand-level Lidar height metrics with field-derived Lorey's mean height showed significant correlation (Garcia-Mailliard r = 0.94, P < 0.0001; North Yuba R = 0.89, P < 0.0001). Field measurements of five aboveground carbon pools (live trees, dead trees, shrubs, coarse woody debris, and litter) yielded aboveground carbon densities (mean ± standard error without Monte Carlo) as high as 320 ± 35 Mg ha- 1 (old-growth coast redwood) and 510 ± 120 Mg ha- 1 (red fir [Abies magnifica] forest), as great or greater than tropical rainforest. Lidar and QuickBird detected aboveground carbon in live trees, 70-97% of the total. Large sample sizes in the Monte Carlo analyses of remote sensing data generated low estimates of uncertainty. Lidar showed lower uncertainty and higher accuracy than QuickBird, due to high correlation of biomass to height and undercounting of trees by the crown detection algorithm. Lidar achieved uncertainties of < 1%, providing estimates of aboveground live tree carbon density (mean ± 95% confidence interval with Monte Carlo) of 82 ± 0.7 Mg ha- 1 in Garcia-Mailliard and 140 ± 0.9 Mg ha- 1 in North Yuba. The method that we tested, combining field measurements, Lidar, and Monte Carlo, can produce robust wall-to-wall spatial data on forest carbon. © 2010 Elsevier Inc. All rights reserved.

In this paper a multifunction transadmittance-mode fractional-order filter is proposed for the very first time. The proposed structure provides fractional-order low-pass filter, fractional-order high-pass filter and fractional-order all-pass filter. It uses two operational transconductance amplifier and one fractional capacitor (FC). The FC is realized using the twelfth-order continued fraction expansion-based RC ladder. Functional verification of proposed fractional-order filter is verified through PSPICE using 180-nm CMOS technology model parameters. The transfer functions sensitivities' with respect to various circuit parameters are examined through simulations. The effect of parameter variation on filter responses is studied using Monte Carlo analysis. Furthermore, the proposed filter possess electronic tunability through bias currents. Moreover, the circuit is tested experimentally using discrete components to realize the fractional-order capacitor showing great matching with the theoretical data. [ABSTRACT FROM AUTHOR]

Mega projects (MP) require efficient management of risks during their construction. Therefore, it is crucial to identify any possible deviations towards meeting their objectives. Such deviation forced MP to be delivered behind schedule and over budget. According references, MP does not require only qualitative analysis but requires an accurate quantitative analysis based on knowledge and practice. Thus, this paper aims to undergo a systematic review of quantitative analysis literature in construction of worldwide MP, with the utmost aim to improve contractors quantitative risk analysis practices in the presence of uncertainty. A time line was produced which shows the process of quantitative risk analysis in this literature including the past six years from 2013 to 2018. This was followed by a critical analysis in order to account for quantitative risk analysis techniques highlighted throughout literature sources. Furthermore, the paper reviews the literature of worldwide mega projects by which quantitative risk analysis process was practiced. It was observed that Monte Carlo analysis technique has succeeded in supporting project managers in allocating deviations in the objectives of MP. The paper adds value to practitioners using the process of quantitative risk analysis as well as contractors working on construction of MP. [ABSTRACT FROM AUTHOR]

In this study, we present a novel methodology that may be used to analyze tsunami risk along coastal regions. The application of the proposed methodology is demonstrated for the Eastern Mediterranean coast. Economic, social and environmental dimensions of risk are calculated and aggregated to obtain the overall risk maps for a number of elements at risk (EaR) that are identified from seven countries located along the Eastern Mediterranean coastline. Historical earthquakes that are recorded in the region during the period 1900–2013 are used as data, and probabilistic tsunami modeling is carried out using Monte Carlo analysis. Based on historical data, randomly generated earthquakes are simulated, and wave propagation analysis is performed using NAMI-DANCE software. Inundation depth–damage analysis and exceedance probabilities of tsunami inundations are used to evaluate social and economic risks while the environmental risk is calculated using a binary approach. The overall risk map is constructed in the geographic information system environment. Cairo Agricultural Area in Egypt and Fethiye City in Turkey are identified as high-risk EaR in the Eastern Mediterranean coastline. The new risk assessment methodology may be utilized in coastal regions of the world, and the results obtained for the Eastern Mediterranean may be effectively used in developing preventive measures and disaster management strategies for tsunamis that may develop the region. [ABSTRACT FROM AUTHOR]

In this reply, we would like to comment on the discussion paper of AlQasimi and Mahdi (Nat Hazards 97:1–4, 2020. 10.1007/s11069-020-03904-1). We discuss their view and possible improvements of the paper of Bomers et al. (Nat Hazards 97(1):309–334, 2019b. 10.1007/s11069-019-03643-y) in more detail and explain how large-flood modelling methods can benefit from the suggestions given by AlQasimi and Mahdi (2020). [ABSTRACT FROM AUTHOR]

To increase the density of magnetoresistive random access memory (MRAM) beyond the 1T1MTJ MRAM cell in use today, the design space for 1S1MTJ MRAM array is analyzed by cooptimizing both selectors and MTJs. Current low-resistance MTJs for 1T1MTJ MRAM are not suitable for 1S1MTJ MRAM. Threshold-type selectors would induce a strong read disturb on the MTJ due to the snapback voltage (VTH – VHOLD) when the selector is turned on. Also, exponential-type selectors would degrade the read margin (RM) due to its large ON-state resistance. When using existing selectors to achieve a 1-M-bit 1S1MTJ array, it is necessary to adjust the product of resistance and area (RA) and the diameter of the MTJ. An MTJ with the RA = 15 Ω ⋅ μm2 and the diameter = 50 nm can meet the criterion of RM > 10% for both exponential-type selectors (exponential slope = 300–500 mV/decade with the current density ~ 1 MA/cm2) and threshold-type selectors (VTH – VHOLD ~250 mV). A design space accommodating a selector variation of around 1% can be found for MTJs with tunnel magnetoresistance ratio (TMR) < 250%. With an increased TMR of 250%–350% of the MTJ, the tolerance of variations for exponential-type selectors and threshold-type selectors can be improved to 2% and 4%, respectively. This provides a chance for the 1S1MTJ MRAM with existing selectors. [ABSTRACT FROM AUTHOR]

Summary: The dominance of leakage currents in circuit design has been impelled by steady downscaling of MOSFET into nanometer regime, and has become a significant component of total IC power dissipation. The issue is further aggravated with the inability to gauge the tolerance of process parameters around their nominal value. Consequently, the drive to improve the static power prediction has enticed accurate and reliable modeling of leakage current, specifically for ultralow power applications. In contrast to gate‐ and band‐to‐band‐tunneling leakages, subthreshold leakage exhibits high susceptibility to process variations and hence has been considered for variability modeling. Fluctuations in the device electrical and geometry parameters result in a wider distribution of subthreshold leakage current. Hence, taking into account stacking effect, an analytical variability model to estimate subthreshold leakage power in subthreshold circuits, in the presence of threshold voltage variations is proposed. Further, the impact of threshold voltage variability on subthreshold leakage power is modeled in conjunction with simultaneous variations in gate length and width. The leakage power variability is characterized by model‐generated distributions obtained using Monte Carlo analysis and validated against SPICE simulations. The proposed model is about 700 × computationally faster than SPICE simulations with mean error being less than 0.19%. [ABSTRACT FROM AUTHOR]

Intraoperative fluoroscopic images, as one of the most important input data for computer-assisted orthopedic surgery (CAOS) systems, have a significant influence on the positioning accuracy of CAOS system. In this study, we proposed to use multi-angle intraoperative fluoroscopy images as input based on real clinical scenario, and the aim was to analyze the positioning accuracy and the error propagation rules with multi-angle input images compared with traditional two input images. In the experiment, the positioning accuracy of the C-arm calibration-based algorithm was studied, respectively, using two, three, four, five, and six intraoperative fluoroscopic images as input data. Moreover, the error propagation rules of the positioning error were analyzed by the Monte Carlo method. The experiment result showed that increasing the number of multi-angle input fluoroscopic images could reduce the positioning error of CAOS system, which has dropped from 1.01 to 0.61 mm. The Monte Carlo simulation analysis showed that for random input errors subject to normal distribution (μ = 0, σ = 1), the image positioning error dropped from 0.29 to 0.23 mm, and the staff gauge positioning error dropped from 1.36 to 1.19 mm, while the tracking device positioning error dropped from 3.41 to 2.13 mm. In addition, the results showed that image positioning error and staff gauge positioning error were all nonlinear error for the whole system, but tracker device positioning error was a strictly linear error. In conclusion, using multi-angle fluoroscopy images was helpful for clinic, which could improve the positioning accuracy of the CAOS system by nearly 30%. Graphical abstract The experiment process and Monte Carlo analysis of spatial positioning accuracy (A: Setup for the experiment; B: The process of Monte Carlo analysis; C: Results). [ABSTRACT FROM AUTHOR]

Robustness of aerial-ground multi-source image matching is closely related to the quality of the ground reference image. To explore the influence of reference images on the performance of air-ground multi-source image matching, we focused on the impact of the control point projection accuracy and tie point accuracy on bundle adjustment results for generating digital orthophoto images by using the Structure from Motion algorithm and Monte Carlo analysis. Additionally, we developed a method to learn local deep features in natural environments based on fine-tuning the pre-trained ResNet50 model and used the method to match multi-scale, multi-seasonal, and multi-viewpoint air-ground multi-source images. The results show that the proposed method could yield a relatively even distribution of feature corresponding points under different conditions, seasons, viewpoints, illuminations. Compared with state-of-the-art hand-crafted computer vision and deep learning matching methods, the proposed method demonstrated more efficient and robust matching performance that could be applied to a variety of unmanned aerial vehicle self- and target-positioning applications in GPS-denied areas.

Uncertainty and sensitivity of ozone and PM2.5 aerosol to variations in selected input parameters are investigated with a Monte Carlo methodology using a three-dimensional air quality model. The selection of input parameters is based on their potential to affect concentration levels of ozone and PM2.5 predicted by the model and to reflect changes in emissions due to the implementation of distributed generation (DG) in the South Coast Air Basin (SoCAB) of California. Numerical simulations are performed with the CIT air quality model. Response of the CIT predictions to the variation of selected input parameters is investigated to separate the potential air quality impacts of DG from model uncertainty. This study provides a measure of the model errors for selected species concentrations. A spatial sensitivity analysis is used to investigate the effect of placing DG in specific regions of the SoCAB. In general, results show that confidence in the model results is greatest in locations where ozone and PM2.5 concentrations are the highest. Changes no greater than 80% in the nominal values of selected input variables, cause changes of 18% in ozone mixing ratios and 25% for PM2.5 aerosol concentrations. Sensitivity analysis reveals that nitrogen oxides (NOx) emissions and side boundary conditions of volatile organic compounds (VOC) are the major contributors to uncertainty and sensitivity of ozone predictions. An increase in NOx emissions leads to reductions in ozone mixing ratios at peak times and sites where the maximum values are located. PM2.5 aerosol is most sensitive to changes in NH3 and NOx emissions. Increasing these emissions leads to higher aerosol concentrations. Sensitivity analyses show that the impacts of DG implementation are highly dependent on both space and time. In particular, ozone concentrations are reduced during the nighttime nearby locations where DGs are installed. However, during the daytime ozone concentrations increase downwind from the sources. A major finding of this study is that the emissions of DG installed in coastal areas produce a significant impact on the production of ozone and PM2.5 aerosol in the eastern regions of the SoCAB. © 2007 Elsevier Ltd. All rights reserved.

This paper proposes a straightforward, easy to implement approximate F-test which is useful for testing restrictions in multivariate regression models. We derive the asymptotics for our test statistic and investigate its finite sample properties through a series of Monte Carlo experiments. Both theory suggests and simulations confirm that our approach will result in strictly better inference than the leading alternative

This paper employs the one-sector Real Business Cycle model as a testing ground for four different procedures to estimate Dynamic Stochastic General Equilibrium (DSGE) models. The procedures are: 1) Maximum Likelihood (with and without measurement errors and incorporating priors), 2) Generalized Method of Moments, 3) Simulated Method of Moments, and 4) the Extended Method of Simulated Moments proposed by Smith (1993). Monte Carlo analysis shows that although all procedures deliver reasonably good estimates, there are substantial differences in statistical and computational efficiency in the small samples currently available to estimate DSGE models. The implications of the singularity of DSGE models for each estimation procedure are fully discussed.

Abstract Background The objective of this study is to investigate pharmacokinetics and dose regimens of cefquinome in black swans following intravenous (IV) and intramuscular (IM) administration at a single dose of 2 mg/kg. The MICs of cefquinome against 49 Escherichia coli isolates from black swans were determined. Monte Carlo simulation was applied to conduct the dose regimen assessment and optimization of cefquinome against E. coli in black swans, and a pharmacokinetic/pharmacodynamic (PK/PD) cutoff was established for E. coli isolates obtained in this study. Results The PK parameters of T1/2α (0.31 h), T1/2β (1.69 h) and ClB (0.13 L/kg·h) indicated a rapid distribution and elimination of cefquinome in black swans after IV administration. After IM injection, the corresponding PK parameters of T1/2Ka, T1/2Ke, Tmax, Cmax, and F were 0.12 h, 1.62 h, 0.39 h, 5.71 μg/mL and 74.2%, respectively. The MICs of cefquinome against black swans E. coli ranged from 0.03 to 8 μg/mL, with MIC50 and MIC90 of 0.06 and 0.5 μg/mL, respectively. The PK/PD cutoff of cefquinome against E. coli was determined to be 0.2 μg/mL. Monte Carlo simulation showed that the nominal dose regimen (2 mg/kg/24 h) could not achieve a satisfactory probability of target attainment (PTA) for %TMIC ≥ 50%, indicating a risk of treatment failure and the development of potential drug resistance. Conclusions The current daily dosage of cefquinome when divided into 12-h interval (1 mg/kg/12 h) may be effective for the treatment of E. coli infections with an MIC ≤0.5 μg/mL.

Uncontrolled cattle slaughtering may result in reduce water quantity and quality, number of cattle, and product of animal origin that does not fit the criteria of safety-healthy-whole-halal. The research objective was to analyze and measure important variables which might be required in developing a modelas well as to formulate strategic plans for managing sustainable abattoir services. Analysis methods used in this study were: Multidimensional Scaling (MDS) or rap-slaughterhouse, Monte Carlo Analysis, and Analytical Hierarchy Process (AHP). Results of the analysis showed that the important variables were, i.e.: water availability and animal health status (ecological dimension); willingness to conduct hygienic and sanitation procedures at business scale level, service business orientation, availability of animal and slaughtering schedule management (economical dimension); workers safety and security (sociological dimension), noncompliance of the use of infrastructure by managers, workers and service users; lack off ulfillment of the requirements of the building frame (dimension regulations); and processing technology (technological dimension). The priority strategic analysis concluded that supply services of product of animal origin is eligible to be considered as components required in the food security program through the development and renovation program of effective slaughter houses. ABSTRAK Pemotongan hewan yang tidak terkendali mengakibatkan penurunan ketersediaan jumlah dan mutu air, hewan, dan pangan asal ternak yang tidak sesuai kriteria aman-sehat-utuh-halal (ASUH). Tujuan penelitian adalah menganalisis dan mengukur peubah-peubah penting yang diperlukan untuk menyusun model dan merancang rencana strategik pengelolaan usaha jasa rumah pemotongan hewan ruminansia secara berkelanjutan. Metode analisis yang digunakan adalah Multi Dimensional Scaling (MDS) atau rap-slaughterhouse, Monte Carlo, dan Analytical Hierarchy Process. Hasil analisis mendapatkan beberapa faktor-faktor penting, yaitu ketersediaan air bersih dan status kesehatan hewan untuk dimensi ekologi; kesediaan melaksanakan perlakuan yang higienis dan sanitasi pada tingkatan skala usaha; orientasi usaha jasa; ketersediaan hewan; pengaturan jadwal waktu pemotongan untuk dimensi ekonomi; keselamatan dan keamanan pekerja (dimensi sosial); ketidaktaatan penggunaan sarana dan prasarana oleh pengelola, pekerja dan pengguna jasa; kurang dipenuhinya persyaratan rangka bangunan (dimensi peraturan); dan teknologi pengolahan (dimensi teknologi). Strategi prioritas yang dihasilkan adalah pelayanan penyediaan pangan asal ternak layak untuk dijadikan komponen yang wajib dalam program ketahanan pangan melalui program pembangunan dan renovasi rumah pemotongan hewan ruminansia yang efektif.

Random-wound electrical machines often suffer from high circulating current losses. These losses vary from machine to machine in a stochastic fashion. This study proposes an improved sampling algorithm for quantifying the uncertainty inherent in random windings. The algorithm is then combined with a circuit model to perform Monte Carlo analysis on the losses. The results are compared to measurements, and a good agreement is observed.

All flood hazard and risk assessment suffer from a certain degree of uncertainty due to multiple factors, such as flood frequency analysis, hydrodynamic model calibration, or flood damage (magnitude–damage functions) models. The uncertainty linked to the flood frequency analysis is one of the most important factors (previous and present estimation point to 40%). Flood frequency analysis uncertainty has been approached from different points of view, such as the application of complex statistical models, the regionalization processes of peak flows, or the inclusion of non-systematic data. Here, we present an achievable approach to defining the uncertainty linked to flood frequency analysis by using the Monte Carlo method. Using the city of Zamora as the study site, the uncertainty is delimited by confidence intervals of a peak flow quantile of a 500-year return period. Probabilistic maps are derived from hydrodynamic results, and further analysis include flood hazard maps for human loss of stability and vehicle damage. Although the effect of this uncertainty is conditioned by the shape of the terrain, the results obtained may allow managers to achieve more consistent land-use planning. All those Zamora city results point out the probable underestimation of flood hazard (the higher hazard areas increase around 20%) and risk when the uncertainty analysis is not considered, thus limiting the efficiency of flood risk management tasks.

The complex characteristics of explosion load as well as its increasingly high frequency in the civil environment highlight the need to develop models representing the behavior of structures under blast load. This work presents a probabilistic study of the performance of framed reinforced concrete buildings designed according to the current Italian NTC18 and European EC8 technical standards. First, a simplified single degree of freedom model representing the structural system under blast load has been developed. Then, a probabilistic approach based on Monte Carlo simulation analysis highlighted the influence of seismic demand on the behavior of Reinforced Concrete RC buildings subjected to blast load. [ABSTRACT FROM AUTHOR]

A low‐voltage ultra‐low‐power two‐quadrant current squarer based‐true RMS to DC converter circuit is presented in this study. The proposed current squarer is based on two translinear loops with matched NMOS transistors operating in the weak inversion region. Ultra‐low‐power dissipation of 1.4 nW, multi‐decade frequency range of 0.5 Hz–55 kHz and large input dynamic range of 72 pA–1 nA (with error <3%) are the main achievements of the proposed design while constructed of only ten transistors. Post‐layout simulations with 0.8‐V supply voltage using HSPICE software in 0.18 μm TSMC CMOS process (level‐49 parameters) verified the good functionality of the proposed circuit. Moreover, Monte Carlo analysis is performed to validate the satisfactory PVT robustness and reliability of the design's performance. [ABSTRACT FROM AUTHOR]

The aim of this study is to investigate the sensitivity of hydrologic parameters on peak discharge. The basin model of Akçay Basin which is ungauged and located in the Western Black Sea Basin was created by using ArcGIS and HECGeo-HMS softwares. Using the soil type and land use maps of the basin, the curve number of the basin was estimated. Time of concentration and storage coefficient of the basin were estimated taking into consideration the characteristics of the basin and the direct runoff was calculated using the HEC-HMS, semi-distributed hydrological model. Since the predicted hydrological parameters contain uncertainty, 10000 numbers of parameters were produced by using Monte Carlo approach by taking uniform, normal and triangular statistical distributions into consideration and using equal coefficient of variation. Peak flow rates were calculated with these parameters, confidence intervals were determined and local sensitivity analyses were performed. Regardless of the statistical distribution, the curve number has the most sensitivity, while the time of concentration has the least sensitivity on the peak discharge. It was also noted that the storage coefficient of the basin which was efficient in transformation of effective precipitation to direct runoff had more sensitivity on the peak flow than the time of concentration. [ABSTRACT FROM AUTHOR]

Parameterization of transformation and transport processes of dissolved organic carbon (DOC) in soils is challenging especially under variable hydrological conditions. In this study, DOC concentrations in stormflow were analysed with a physically based modelling approach. A one‐dimensional dual‐continuum vertical flow and transport model was applied to simulate subsurface processes in a macroporous forest hillslope soil over a period of 4.5 years. Microbially mediated transformations of DOC were assumed to depend primarily on soil moisture and soil temperature. Two conceptually different descriptions of the sorption of DOC to soil were examined with equilibrium and kinetic approaches. In order to quantify the uncertainties associated with the model parameterization, Monte Carlo analyses in conjunction with Latin hypercube sampling was performed. Despite the complexity of microbial transformations, the simulated temporal patterns of DOC concentrations in stormflow showed similar behaviour to those reflected in the observed DOC fluxes. Due to preferential flow, the hillslope DOC export (5.0 ± 0.5 g C · m−2 · year−1) was higher than the amounts usually reported in the literature. Overall DOC transport in hillslope scenarios could be described appropriately using the equilibrium sorption assumption. The performed analyses showed that the inclusion of the kinetic description of DOC sorption only slightly improved the predictions of the DOC hillslope export. Moreover, influences of seasonal hydro‐climatological conditions on hillslope export of DOC could be observed. Reduced DOC transport during an extreme warm and dry summer was described with lower accuracy, thus indicating the difficulties in the representation of DOC transformations under dry conditions. [ABSTRACT FROM AUTHOR]

There is always uncertainty in industrial manufacturing. These uncertainties have an undesirable impact on the products if deterministic optimization approaches are employed. In order to have products as desired, uncertainties must be quantified in the design process. This paper presents a robust design optimization of an outer rotor surface mounted permanent magnet motor with particular application in the hybrid vehicle using the design for six-sigma methodology. Due to very long computational time of robust optimization, a ten high-dimensional surrogate model of the system using the Box–Behnken response surface methodology (RSM) is integrated with the particle swarm optimization (PSO). This causes a significant improvement in the effectiveness and efficiency of optimization. The results acquired from RSM are verified by their simulation using the finite-element method, and the accuracy of RSM is proved. Finally, the deterministic and robust optimized motors are simulated in mass production using Monte Carlo analysis, and six-sigma quality achievements are demonstrated. [ABSTRACT FROM AUTHOR]