Standard Input Parameters Research Articles

Comparative assessment of airborne infection risk tools in enclosed spaces: Implications for disease control

The COVID-19 pandemic, caused by SARS-CoV-2, highlighted the importance of understanding transmission modes and implementing effective mitigation strategies. Recognizing airborne transmission as a primary route has reshaped public health measures, emphasizing the need to optimize indoor environments to reduce risks. Numerous tools have emerged to assess airborne infection risks in enclosed spaces, providing valuable resources for public health authorities, researchers, and the general public.However, comparing the outputs of these tools is challenging because of variations in assumptions, mathematical models, and data sources. We conducted a comprehensive review, comparing digital airborne infection risk calculators using standardized building-specific input parameters. These tools generally produce similar and consistent outputs with identical inputs. Variations mainly stem from model selection and the handling of unsteady viral load conditions. Differences in source term calculations, including particle emission concentrations and respiratory activity, also contribute to disparities. These differences are minor compared to the inherent uncertainties in risk assessment. Consistency in results increases with higher ventilation rates, showing a robust trend across models. However, inconsistencies arose in the inclusion of face masks, often due to the lack of detailed efficiency values. Despite some differences, the overall consistency underscores the value of these tools in public health strategy and infectious disease control.We also compared some of the model's efforts to conduct retrospective assessments against reported transmission events by assuming input parameters to the models so that the calculated risk would closely fit the original outbreak infection rate. Thus, validating these models against past outbreaks remains challenging because of the lack of essential input information from observed events. This comparative analysis demonstrates the importance of transparent data sources and justifiable model assumptions to enhance the reliability and precision of risk assessments.

How Hard Is Weak-Memory Testing?

Weak-memory models are standard formal specifications of concurrency across hardware, programming languages, and distributed systems. A fundamental computational problem is consistency testing : is the observed execution of a concurrent program in alignment with the specification of the underlying system? The problem has been studied extensively across Sequential Consistency (SC) and weak memory, and proven to be NP-complete when some aspect of the input (e.g., number of threads/memory locations) is unbounded. This unboundedness has left a natural question open: are there efficient parameterized algorithms for testing? The main contribution of this paper is a deep hardness result for consistency testing under many popular weak-memory models: the problem remains NP-complete even in its bounded setting, where candidate executions contain a bounded number of threads, memory locations, and values. This hardness spreads across several Release-Acquire variants of C11, a popular variant of its Relaxed fragment, popular Causal Consistency models, and the POWER architecture. To our knowledge, this is the first result that fully exposes the hardness of weak-memory testing and proves that the problem admits no parameterization under standard input parameters. It also yields a computational separation of these models from SC, x86-TSO, PSO, and Relaxed, for which bounded consistency testing is either known (for SC), or shown here (for the rest), to be in polynomial time.

Simulation of the E-Defense 2015 test on a 10-storey building using macro-models

The capabilities of certain standard macro numerical models were evaluated by simulating a shaking table experiment that was performed on a full-scale ten-storey fixed-base building with a frame and dual structural system in two perpendicular directions (denoted as the frame and wall directions) at the largest shaking table in the E-Defense centre in Japan. The lumped plasticity model for columns and beams, the multiple-vertical-line-element model for walls and the scissors model for beam-column joints were evaluated. The results indicated that the experiment was simulated reasonably well. The most significant discrepancy was observed between the maximum drifts along the wall direction in the strongest cycle of the strongest test (calculated drift of 1.9% versus measured drift of 1.5%). In other cycles and tests, these differences were smaller. The calculated and measured maximum accelerations along the wall direction in the strongest test were 13.8 m/s2 and 13.5 m/s2, respectively. The discrepancy between the analysis and experiment results was smaller along the frame direction. The maximum calculated and measured drifts were 2.9% and 3.1%, respectively. The maximum calculated and measured accelerations were 15.8 m/s2 and 19.0 m/s2, respectively. In general, the standard input parameters were used in the evaluated models. However, some parameters required modifications, particularly when modelling weakly reinforced beam-column joints with substandard reinforcement that were considerably damaged. Their yielding rotation and near-collapse strength were, on average, reduced to 55% and 30% of the standard value, respectively. One of the most important parameters influencing the response was the effective width of the slabs, which was increased to the total span length for the highly loaded beams. The ratios of the strength, stiffness and amount of dissipated energy in the joints, beams and columns also significantly influenced the response. The adequate ratio of the dissipated energy was obtained by reducing the standard unloading stiffness in the beams and columns. The initial stiffness considerably influenced the response, particularly under weaker excitations. This stiffness was reduced threefold to account for various factors that typically reduce its value, which, among others, includes the influence of preceding tests on the same building with sliding foundations, as well as the assembly, transportation and handling of the specimen.

Artificial intelligence algorithms for prediction of cyclic stress ratio of soil for environment conservation

Civil Engineering structures are constantly subjected to environmental forces and changes around the surroundings hence a strong need for assessment of the interaction and predictive measures that lead to pro-active actions towards the structural safety and health monitoring. Significant damage is caused to buildings and port infrastructure when soil is subjected to dynamic loadings and high-frequency vibrations as in earthquakes and events that involve the interaction of soil structures. Dynamic loadings, cause significant displacements and tilting in soil-supported structures. Sands, gravels, and other granular materials that support buildings, bridges, port infrastructure, and retaining structures have to withstand the impact of dynamic loadings, with the requirement of serious considerations, to account for liquefaction risk in the soil. Hence there is a need for careful assessments of soil under dynamic loadings. Data from the laboratory for cyclic triaxial experiments and cyclic direct shear experiments from LEAP-2020 (Liquefaction Experiment and Analysis Project) have been used to assess soil fatigue by comparing cyclic triaxial and direct shear strength data. CSR (Cyclic Stress ratio) had correlations with cycle count, axial strain, axial stress, load in KPa, void ratio. In this paper, machine learning (decision tree, extra tree, knn, random forest) and deep learning (artificial neural networks) techniques are applied to the laboratory dataset and results include prediction of the cyclic stress ratio of soil corresponding to the standard input parameters as per ASTM D 3999–91 and ASTM D 6528–07. This study predicts the CSR of soil that can be used for the predictive assessment of the soil. Additionally, CSR (cyclic stress ratio) is a crucial element in determining soil liquefaction

ASSESSING LAND SURFACE TEMPERATURE IN URBAN AREAS USING OPEN-SOURCE GEOSPATIAL TOOLS

Abstract. Land surface temperature (LST) in urban areas can be traditionally observed by thermal remote sensors. The increasing availability of 3D city models provides an alternative approach based on geospatial modeling. Using solar radiation tools and scripting in GRASS GIS, we have developed a physically-based LST model that can be used to estimate LST in urban areas represented by vector 3D city models. It uses standard input parameters such as solar irradiance, albedo or convection heat transfer coefficient for urban surfaces. The solar irradiance is estimated using the v.sun solar radiation add-on module in GRASS GIS. The LST values are calculated using map algebra operations using a Python script. The suggested methodology was applied to the study area in the city of Košice, Slovakia. Results indicate that urban morphology has a strong impact on spatial distribution of LST during the daylight hours. The accurate parameterization of urban surfaces can increase the accuracy of the model that can be used for urban planning, optimization of energy use in buildings or mitigation of urban heat island effects.

Development of high‐fidelity design‐driven wind blade manufacturing process models to investigate labor predictions in wind blade manufacture

AbstractWind turbines have shown significant advancements in efficiency and power output in the last 20 years. However, during the same time period, parallel advances in the manufacturing of wind blades have not happened due to the reluctance of wind blade manufacturers to make significant capital investments into unproven automation technologies. This reluctance is due in part to the lack of access to a robust techno‐economic model that can give feedback on if and how these investments will impact the overall cost to make a blade. In the current research, existing costing methods are reviewed and a new techno‐economic model for estimating the number of man‐hours required to manufacture a wind turbine blade is proposed. A set of standardized input parameters for the proposed model is developed from time studies completed at three wind blade manufacturing facilities. The proposed model uses 77 discrete processing steps. The credibility of the proposed model is validated by its ability the predict the required man‐hours for the manufacture of previously produced blades by the blade manufacturers. Additionally, validation is done against models of publicly available designs. The proposed model delivers more accurate and realistic estimates over prior models reflecting the application of current production techniques and detailed design information.

Effect of shelling speed, moisture content and number of beaters on the cleaning and recovery efficiency of a mechanized centrifugal Melon shelling machine

A response surface methodology (RSM) has been utilized for investigating the effects of the speed of shelling, melon seed moisture content and the number of beaters of a developed mechanized centrifugal melon shelling and cleaning machine. The machine shells the melon and then cleans the shelled seed from the shells and other impurities. The experiment was based on a central composite rotatable design (CCRD). The results of the experiments revealed that the highest shelling efficiency of 88.5% was obtained from a combination of a speed of 2300 rpm, moisture content of 15% (w.b) and 20 beaters, while the least efficiency of 25.11% was obtained from an interaction between a speed of 959 rpm, moisture content of 20% (w.b) and 18 beaters. Numerical optimization carried out with the goal of maximizing the shelling efficiency revealed optimum values of speed of 2200 rpm, moisture content of 12% (w.b) and 19 beaters for shelling efficiency of 88.80%. The result of this study provided standard input parameters capable of yielding high cleaning and recovery efficiency.

Assessing parameters for ring polymer molecular dynamics simulations at low temperatures: DH + H chemical reaction

Ring polymer molecular dynamics (RPMD) is an accurate method for calculating thermal chemical reaction rates. It has recently been discovered that low-temperature calculations are strongly affected by the simulation parameters. Here, for the thermally activated reaction DH + H → D + H2, we calculate the RPMD rate constants at T=50, 100, and 300 K and demonstrate that for T≥100 K the standard input parameters yield accurate results, but at low temperatures (e.g., 50 K) one must increase the asymptotic distance and force constant, and decrease the umbrella integration step.

Fuzzy Logic Based Prospects Identification System for Foreign Language Learning Through Serious Games

Interest in serious games for education has grown during the last years. The credit goes to the potential for engaging students with new ways to capture and maintain attention. The goal of this paper is to introduce a new multidisciplinary approach that incorporates psychological analysis theory with fuzzy inference and neural networks in the Foreign Language Learning prospects identification through serious games. Our research first used a Delphi method to accumulate Information Systems students’ opinions resulting from a SWOT analysis of the use of serious games to learn English language. Then, we designed a Fuzzy Logic based Foreign Language Learning Prospects Identification System through serious games that takes four standard input parameters (Strengths, Weaknesses, Opportunities, Threats) already identified during our SWOT analysis and predicts the output (Learning Prospects of Foreign Language) by considering impact of certain variations in input parameters. Implementation results have been obtained through (MATLAB R2020a) and have shown reliable incites and findings.

Optimasation of Mechanical Cassava Peeling System Parameters

This study focused on investigations of effects of mechanical parameters (peeling speed, cutter length) and handling parameter (cassava tuber length) of a cassava peeling machines on the machine output (peeling and recovery efficiencies) with the view of optimizing the parameters. The serious issues of existing techniques of peeling cassava are moderately low peeling and recovery efficiencies because of the irregular shape and size of cassava tubers. The results of the trial of the machine utilizing the cassava tubers revealed that all the parameters have significant effects on the peeling and recovery efficiencies of the machine. The cutter length had a more significant impact on the peeling and recovery efficiencies. Maximum values of 83.5 % and 97.2% for peeling and recovery efficiencies respectively, with an attractive quality of 0.864 were achieved from peeling cassava tuber of 50 mm length with cutter length of 2 mm at a speed of 328 rpm. The investigation’s discoveries give the standard machine input parameters which are equipped for improving quality, peeling and recovery efficiencies of a mechanical cassava peeling system.

Sensitivity analysis of atmospheric spectral irradiance model

Many Radiative Transfer Models (RTM) have been developed to simulate and estimate solar irradiance. Theirs accuracy is well documents in literature nonetheless the effect of the parameters uncertainties on the established models has not been well studied yet. This work focuses on implementing a RTM based on the models found in the literature along with some updates, with the aim to study the sensitivity of the model towards the variations of the input parameters. The parameters studied in this paper are: the day of the year, the solar zenith angle, the local atmospheric pressure, the local temperature, the relative humidity, the height of ozone layer concentration, the ozone concentration, the single scattering albedo, the ground albedo, the Ångström’s exponent and the aerosol optical depth. The sensibility analysis is achieved using the Normalized Root Mean Square Error (NRMSE) as an independent function, calculated with a set of simulated measurements of spectral global solar irradiance and a reference spectrum generated with a group of standard input parameters.

Statistical power in two-level models: A tutorial based on Monte Carlo simulation.

The estimation of power in two-level models used to analyze data that are hierarchically structured is particularly complex because the outcome contains variance at two levels that is regressed on predictors at two levels. Methods for the estimation of power in two-level models have been based on formulas and Monte Carlo simulation. We provide a hands-on tutorial illustrating how a priori and post hoc power analyses for the most frequently used two-level models are conducted. We describe how a population model for the power analysis can be specified by using standardized input parameters and how the power analysis is implemented in SIMR, a very flexible power estimation method based on Monte Carlo simulation. Finally, we provide case-sensitive rules of thumb for deriving sufficient sample sizes as well as minimum detectable effect sizes that yield a power ≥ .80 for the effects and input parameters most frequently analyzed by psychologists. For medium variance components, the results indicate that with lower level (L1) sample sizes up to 30 and higher level (L2) sample sizes up to 200, medium and large fixed effects can be detected. However, small L2 direct- or cross-level interaction effects cannot be detected with up to 200 clusters. The tutorial and guidelines should be of help to researchers dealing with multilevel study designs such as individuals clustered within groups or repeated measurements clustered within individuals. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

PMS31 - Quantifying the Importance of Model Parameters and Structural Assumptions on the Value of Treatments for Rheumatoid Arthritis Using Metamodeling

Our aim was to quantify the relative importance of model parameters and model structures on the cost-effectiveness estimates of biologic disease modifying anti-rheumatic drugs (bDMARDs) vs conventional DMARDs (cDMARDs) for treatment of patients with moderate to severe rheumatoid arthritis with inadequate response to cDMARDs. We used the Innovation and Value Initiative’s open-source rheumatoid arthritis individual patient simulation to simulate outcomes. 1,000 sets of parameter values were sampled in a probabilistic sensitivity analysis (PSA) and 32 model structures were simulated. For each parameter set and model structure, 1,000 patients were simulated to calculate the mean incremental net monetary benefit (iNMB). The simulated mean iNMB was regressed on values of the model parameters and characteristics of the model structures using linear regression models. The absolute values of the coefficients of standardized variables were used to rank the model inputs by their relative importance. Structural assumptions related to treatment switching, long-term progression of the Health Assessment Questionnaire (HAQ) score, utility, and the effect of treatment on HAQ had large impacts on the iNMB. For example, models that use a latent class growth model to simulate HAQ progression for patients using cDMARDs were predicted to increase the iNMB by around $45,000 relative to models that assumed a linear rate of progression. Standardized input parameters with the largest coefficients in absolute value included the extent to which the HAQ score rebounded after treatment failure and the impact of changes in the HAQ score on mortality. Cost-effectiveness estimates for rheumatoid arthritis vary due to both parameter and structural uncertainty. New studies are needed to improve the quality of parameter estimates; consensus-driven approaches such as Delphi panels can help determine appropriate model structures. Research should prioritize the most sensitive model parameters and debates should focus on the structural assumptions most likely to influence results.

Improving Empirical Magnetic Field Models by Fitting to In Situ Data Using an Optimized Parameter Approach

AbstractA method for comparing and optimizing the accuracy of empirical magnetic field models using in situ magnetic field measurements is presented. The optimization method minimizes a cost function—τ—that explicitly includes both a magnitude and an angular term. A time span of 21 days, including periods of mild and intense geomagnetic activity, was used for this analysis. A comparison between five magnetic field models (T96, T01S, T02, TS04, and TS07) widely used by the community demonstrated that the T02 model was, on average, the most accurate when driven by the standard model input parameters. The optimization procedure, performed in all models except TS07, generally improved the results when compared to unoptimized versions of the models. Additionally, using more satellites in the optimization procedure produces more accurate results. This procedure reduces the number of large errors in the model, that is, it reduces the number of outliers in the error distribution. The TS04 model shows the most accurate results after the optimization in terms of both the magnitude and direction, when using at least six satellites in the fitting. It gave a smaller error than its unoptimized counterpart 57.3% of the time and outperformed the best unoptimized model (T02) 56.2% of the time. Its median percentage error in |B| was reduced from 4.54% to 3.84%. The difference among the models analyzed, when compared in terms of the median of the error distributions, is not very large. However, the unoptimized models can have very large errors, which are much reduced after the optimization.

GaiaFGK benchmark stars: opening the black box of stellar element abundance determination

Gaia and its complementary spectroscopic surveys combined will yield the most comprehensive database of kinematic and chemical information of stars in the Milky Way. The Gaia FGK benchmark stars play a central role in this matter as they are calibration pillars for the atmospheric parameters and chemical abundances for various surveys. The spectroscopic analyses of the benchmark stars are done by combining different methods, and the results will be affected by the systematic uncertainties inherent in each method. In this paper we explore some of these systematic uncertainties. We determined line abundances of Ca, Cr, Mn and Co for four benchmark stars using six different methods. We changed the default input parameters of the different codes in a systematic way and found in some cases significant differences between the results. Since there is no consensus on the correct values for many of these default parameters, we urge the community to raise discussions towards standard input parameters that could alleviate the difference in abundances obtained by different methods. In this work we provide quantitative estimates of uncertainties in elemental abundances due to the effect of differing technical assumptions in spectrum modelling.

Long-range forecast of all India summer monsoon rainfall using adaptive neuro-fuzzy inference system: skill comparison with CFSv2 model simulation and real-time forecast for the year 2015

All India summer monsoon rainfall (AISMR) characteristics play a vital role for the policy planning and national economy of the country. In view of the significant impact of monsoon system on regional as well as global climate systems, accurate prediction of summer monsoon rainfall has become a challenge. The objective of this study is to develop an adaptive neuro-fuzzy inference system (ANFIS) for long range forecast of AISMR. The NCEP/NCAR reanalysis data of temperature, zonal and meridional wind at different pressure levels have been taken to construct the input matrix of ANFIS. The membership of the input parameters for AISMR as high, medium or low is estimated with trapezoidal membership function. The fuzzified standardized input parameters and the de-fuzzified target output are trained with artificial neural network models. The forecast of AISMR with ANFIS is compared with non-hybrid multi-layer perceptron model (MLP), radial basis functions network (RBFN) and multiple linear regression (MLR) models. The forecast error analyses of the models reveal that ANFIS provides the best forecast of AISMR with minimum prediction error of 0.076, whereas the errors with MLP, RBFN and MLR models are 0.22, 0.18 and 0.73 respectively. During validation with observations, ANFIS shows its potency over the said comparative models. Performance of the ANFIS model is verified through different statistical skill scores, which also confirms the aptitude of ANFIS in forecasting AISMR. The forecast skill of ANFIS is also observed to be better than Climate Forecast System version 2. The real-time forecast with ANFIS shows possibility of deficit (65–75 cm) AISMR in the year 2015.

Digital contrast enhancement of (18)Fluorine-fluorodeoxyglucose positron emission tomography images in hepatocellular carcinoma.

Purpose:The role of 18fluorodeoxyglucose positron emission tomography (PET) is limited for detection of primary hepatocellular carcinoma (HCC) due to low contrast to the tumor, and normal hepatocytes (background). The aim of the present study was to improve the contrast between the tumor and background by standardizing the input parameters of a digital contrast enhancement technique.Materials and Methods:A transverse slice of PET image was adjusted for the best possible contrast, and saved in JPEG 2000 format. We processed this image with a contrast enhancement technique using 847 possible combinations of input parameters (threshold “m” and slope “e”). The input parameters which resulted in an image having a high value of 2nd order entropy, and edge content, and low value of absolute mean brightness error, and saturation evaluation metrics, were considered as standardized input parameters. The same process was repeated for total nine PET-computed tomography studies, thus analyzing 7623 images.Results:The selected digital contrast enhancement technique increased the contrast between the HCC tumor and background. In seven out of nine images, the standardized input parameters “m” had values between 150 and 160, and for other two images values were 138 and 175, respectively. The value of slope “e” was 4 in 4 images, 3 in 3 images and 1 in 2 images. It was found that it is important to optimize the input parameters for the best possible contrast for each image; a particular value was not sufficient for all the HCC images.Conclusion:The use of above digital contrast enhancement technique improves the tumor to background ratio in PET images of HCC and appears to be useful. Further clinical validation of this finding is warranted.

Analysis of three leakage-correction methods for DSC-based measurement of relative cerebral blood volume with respect to heterogeneity in human gliomas

PurposeAim of this study was to investigate the influence of contrast agent leakage on relative cerebral blood volume (rCBV), using clinical dynamic susceptibility contrast (DSC) protocols. Different correction methods were compared, in order to identify a clinically reliable method. Materials and methodsDSC perfusion data from patients with glioma were acquired with a single-shot EPI technique at 3.0T using a pre-dose. Three different post-processing methods for leakage correction were compared, concerning rCBV, the permeability related parameter K2 and the predominant leakage effect in tumor regions (T1 effect: K2>0; T2* effect: K2<0). Additionally, simulations were performed, to investigate the influence of noise and input curve modifications on correction results. ResultsOur results indicate several differences between post-processing methods with regard to rCBV values, reflected by the fact that the distribution of detected leakage effects and the correction strength differed between methods. Leakage was heterogeneous within tumorous tissue and between patients, with a general predominance of T2* effects but an increased amount of T1 effects in low grade glioma. Simulations confirmed differential dependencies on signal-to-noise ratios, mean transit times and input curves as possible reasons. ConclusionThe impact of leakage is complex, thus adequate correction necessitates care. Standardized input parameters are one important factor for comparability of rCBV and K2 values among patients. The extension of DSC analysis with K2 maps could potentially allow improved differentiation between tumor grades. Further methods need to integrate special advantages of existing approaches to achieve more reliable rCBV estimates within clinically reasonable calculation times.

Comparison of U.S. Environmental Protection Agency’s CAP88 PC Versions 3.0 and 4.0

The Savannah River National Laboratory (SRNL) with the assistance of Georgia Regents University, completed a comparison of the U.S. Environmental Protection Agency's (U.S. EPA) environmental dosimetry code CAP88 PC V3.0 with the recently developed V4.0. CAP88 is a set of computer programs and databases used for estimation of dose and risk from radionuclide emissions to air. At the U.S. Department of Energy's Savannah River Site, CAP88 is used by SRNL for determining compliance with U.S. EPA's National Emission Standards for Hazardous Air Pollutants (40 CFR 61, Subpart H) regulations. Using standardized input parameters, individual runs were conducted for each radionuclide within its corresponding database. Some radioactive decay constants, human usage parameters, and dose coefficients changed between the two versions, directly causing a proportional change in the total effective dose. A detailed summary for select radionuclides of concern at the Savannah River Site (60Co, 137Cs, 3H, 129I, 239Pu, and 90Sr) is provided. In general, the total effective doses will decrease for alpha/beta emitters because of reduced inhalation and ingestion rates in V4.0. However, for gamma emitters, such as 60Co and 137Cs, the total effective doses will increase because of changes U.S. EPA made in the external ground shine calculations.

Effect of thermostat and window opening occupant behavior models on energy use in homes

Existing dynamic energy simulation tools exceed the static dimension of the simplified methods through a better and more accurate prediction of energy use; however, their ability to predict real energy consumption is undermined by a weak representation of human interactions with the control of the indoor environment. The traditional approach to building dynamic simulation considers energy consumption as fully deterministic, taking into account standardized input parameters and using fixed and unrealistic schedules (lighting level, occupancy, ventilation rate, thermostat set-point). In contrast, in everyday practice occupants interact with the building plant system and building envelope in order to achieve desired indoor environmental conditions. In this study, occupant behavior in residential building was modelled accordingly to a probabilistic approach. A new methodology was developed to combine probabilistic user profiles for both window opening and thermostat set-point adjustments into one building energy model implemented in the dynamic simulation tool IDA Ice. The aim of the study was to compare mean values of the probabilistic distribution of the obtained results with a singular heating energy consumption value obtained by means of standard deterministic simulations. Major findings of this research demonstrated the weakness of standardized occupant behavior profile in energy simulation tools and the strengths of energy models based on measurements in fields and probabilistic modelling providing scenarios of occupant behavior in buildings.