Scaling Method Research Articles

Large-Scalable CO-Tolerant Ultrathin PtTe2 Nanosheets for Formic Acid Oxidation.

Developing large-scale platinum (Pt) alloys that simultaneously exhibit high formic acid oxidation reaction (FAOR) activity and robust CO tolerance remains a significant challenge for practical fuel cell applications. Here, a facile and universal in situ synthesis approach is presented to create ultrathin platinum-tellurium nanosheets on carbon support (PtTe2 NSs/C), which enables high CO tolerance and FAOR activity while achieving the massive production of PtTe2 NSs/C. Specifically, the 10-gram-scale PtTe2 NSs/C achieves exceptional specific activity and mass activity of 14.3mA cm-2 and 3.6 A mgPt -1, respectively, which are 52.9 and 22.5 times greater than those of commercial Pt/C. Moreover, the 10-gram-scale PtTe2 NS/C exhibits significantly higher FAOR stability than pristine Pt NSs/C and commercial Pt/C. Detailed mechanism and computational investigations collectively reveal that the integration of Te into Pt lattices enhances the utilization of Pt while constructing high-density unsaturated "Pt-Te sites" on the surface of PtTe2 NSs/C, conferring high CO tolerance to PtTe2 NSs/C and thus substantially enhancing the FAOR activity. This work contributes to providing a universal method for scaling up next-generation high-performing FAOR catalysts.

Evaluation of the Impacts of Different Ground Motion Selection and Scaling Approaches on Seismic Performance of Bridges

ABSTRACT This paper investigates two ground motion selection and scaling (GMSS) methods for assessing the seismic performance of bridges in high seismic regions in California. It compares the traditional GMSS approach using elastic probabilistic seismic hazard analysis (PSHA) with a novel method based on inelastic PSHA, scaling ground motions to spectra across varying ductility levels. Nonlinear response history analyses of a 3-span bridge using 25 ground motions show that the inelastic PSHA-based method can reduce dispersion in responses, increasing confidence in seismic performance estimates. The traditional approach may produce conservative or unconservative results due to mismatches between scaled and target spectra.

Beyond the Classroom Walls: Metacognitive awareness in traditional and online setting

Objective – The aim of this study is to assess and compare the metacognitive awareness of university students in online and in-person learning environments. Secondly, the study aims to explore the students’ preferences for online and in-person learning to compare themetacognitive awareness of students based on their preferences. Methods – A total of 79 university students completed the Metacognitive Awareness Inventory, providing self-reported data on their metacognitive awareness. The study used a paired sample t-test to compare mean scores of metacognitive awareness between online and in-person classroom settings. Additionally, participants evaluated their experiences with online and in-person learning using a pairwise comparison as a scaling method. Results – The t-test results indicate statistically significant differences in metacognitive awareness between online and in-person classroom settings. Specifically, significant differences were found in declarative knowledge, procedural knowledge, conditional knowledge, planning, information management strategies, monitoring, debugging strategies, and evaluation of learning. Students showed higher levels of metacognitive awareness across all facets in traditional in-person learning setting. Descriptive analysis of scaling pairwise comparison revealed that online learning was strongly preferred for comfort, while in-person learning was preferred modality for motivation to study and active participation. In oppose to t-test results, ANOVA did not reveal significant differences in metacognitive awareness based on students’ preferences in online nor in traditional learning settings. Conclusions – The results provide valuable information on the metacognitive awareness and preferences of the students in two different learning environments.

Incorporating preference uncertainty in best worst scaling.

In this paper, we enhance the Best-Worst Scaling (BWS) method by incorporating participants' preference uncertainty into the conventional BWS, known as case 1. In this context, respondents are tasked with making trade-offs among a set of items of interest. Applying this novel extended BWS method to a sample of Argentinian wine consumers (n = 342), we aim to a) provide a more informative elicitation of consumers' relative preferences for 16 wine attributes; b) identify the level of uncertainty with each of the attributes, exploring differences between the most and least important wine attributes influencing purchasing wine; and c) compare the results of the extended BWS with the standard BWS. Our findings indicate variability in uncertainty levels on the importance of wine attributes when purchasing wine within and across attributes. Moreover, accounting for participants' preference uncertainty can alter the ranking of preferences obtained through the standard approach. This alteration is due to both accounting for preference uncertainty itself as well as the uncertainty indicator used. Although this approach is a way to mitigate biases associated with respondents' preference certainty, it is recommended that preference uncertainty heterogeneity is investigated using different indicators.

Post-processing methods for delay embedding and feature scaling of reservoir computers

Reservoir computing is a machine learning method that is well-suited for complex time series prediction tasks. Both delay embedding and the projection of input data into a higher-dimensional space play important roles in enabling accurate predictions. We establish simple post-processing methods that train on past node states at uniformly or randomly-delayed timeshifts. These methods improve reservoir computer prediction performance through increased feature dimension and/or better delay embedding. Here we introduce the multi-random-timeshifting method that randomly recalls previous states of reservoir nodes. The use of multi-random-timeshifting allows for smaller reservoirs while maintaining large feature dimensions, is computationally cheap to optimise, and is our preferred post-processing method. For experimentalists, all our post-processing methods can be translated to readout data sampled from physical reservoirs, which we demonstrate using readout data from an experimentally-realised laser reservoir system.

Amplitude-Scaling Bias Analysis of Ground Motion Record Set in Strip Method for Structural Seismic Fragility Assessment

The multi-strip method is often used to establish a demand model for fragility analysis. Using the multi-strip method to scale the ground motion record may cause uncertainty and bias in structural response calculation and fragility assessment. It is necessary to analyze the effect of differences in the amplitude scaling range in different strips on structural seismic response calculation and seismic fragility assessment. In this paper, the multi-strip method was used to analyze the seismic demand bias based on four multi-story reinforced concrete frame structures subjected to eight ground motion record sets. The bias, variance, and coefficient of variation in different strips in each group of ground motion records were obtained. The effect of different strips on the demand bias was investigated by analysis of variance (ANOVA). Uncertainty quantification of structural demand and fragility curves was carried out using the bootstrap sampling method. The results for structures in different ground motion record sets verify that the differences between the demand bias for different strips by amplitude scaling are statistically insignificant for a 95% confidence level. These findings will contribute to the use of scaling methods for ground motion record sets in a probabilistic seismic demand assessment, allowing for a more reliable prediction of structural seismic fragility.

Testing hypotheses of skull function with comparative finite element analysis: three methods reveal contrasting results.

Comparative finite element analysis involves standardising aspects of models to test equivalent loading scenarios across species. However, regarding feeding biomechanics of the vertebrate skull, what is considered "equivalent" can depend on the hypothesis. Using 13 diversely-shaped skulls of marsupial bettongs and potoroos (Potoroidae), we demonstrate that scaling muscle forces to standardise specific aspects of biting mechanics can produce clearly opposing comparisons of stress or strain that are differentially suited to address specific kinds of hypotheses. We therefore propose three categories of hypotheses for skull biting mechanics, each involving a unique method of muscle scaling to produce meaningful results: those comparing (1) the skull's efficiency in distributing muscle forces to the biting teeth, via standardising input muscle force to skull size, (2) structural biting adaptation through standardising mechanical advantage to simulate size-independent, equivalent bites, and (3) feeding ecology affected by size, such as niche partitioning, via standardising bite reaction force.

Machine learning prediction model for oral mucositis risk in head and neck radiotherapy: a preliminary study.

Oral mucositis (OM) reflects a complex interplay of several risk factors. Machine learning (ML) is a promising frontier in science, capable of processing dense information. This study aims to assessthe performance of ML in predicting OM risk in patients undergoing head and neck radiotherapy. Clinical data were collected from 157 patients with oral and oropharyngeal squamous cell carcinoma submitted to radiotherapy. Grade 2 OM or higher was considered (NCI). Two dataset versions were used; in the first version, all data were considered, and in the second version, a feature selection was added. Age, smoking status, surgery, radiotherapy prescription dose, treatment modality, histopathological differentiation, tumor stage, presence of oral cancer lesion, and tumor location were selected as key features. The training process used a fivefold cross-validation strategy with 10 repetitions. A total of 4 algorithms and 3 scaling methods were trained (12 models), without using data augmentation. A comparative assessment was performed. Accuracy greater than 55% was considered. No relevant results were achieved with the first version, closest performance was Decision Trees with 52% of accuracy, 42% of sensitivity, and 60% of specificity. For the second version, relevant results were achieved, K-Nearest Neighbors outperformed with 64% accuracy, 58% sensitivity, and 68% specificity. ML demonstrated promising results in OM risk prediction. Model improvement was observed after feature selection. Best result was achieved with the KNN model. This is the first study to test ML for OM risk prediction using clinical data.

Design Process for Scaled Model Wind Turbines Using Field Measurements

Abstract This paper presents a method for designing wind turbine-scaled models based on field data. The scaling process requires careful consideration of the system's physical laws and similarity criteria. Scaling methods depend on the prototype's operating conditions (full-size wind turbine) and the experimental conditions (scaled model dimensions and wind tunnel capabilities). The data from a field turbine are the input for creating a scaled model and testing it in a wind tunnel. There are few field data available. The task is not straightforward since most operating conditions must be satisfied, and the similarity criteria could result in different scaled models and wind tunnel conditions. The drawback of the scaling models is their inability to reproduce all the operating conditions. The method presented here considers most of the recommended similarities, including the dynamic modeling of the system and two additional similarities, one related to the blade's natural frequencies and the other to the rotor's moment of inertia. The additional similarities allow the designers to define the blade's mass and stiffness. The final scaled-model design was obtained by modifying the blade profile to reproduce similar power, pitch momentum, and trust coefficients, keeping the same twist angle as the prototype. Since the possible profiles are too large, the best approximation was obtained by comparing the coefficient curves of different blade profiles with the prototype's curves. The curves were calculated using the blade element momentum (BEM) method. It was found that the scaled model has an entirely different design.

Robust scaling strategies for outlier handling in orthogonal projection to latent structures discriminant analysis (OPLS-DA)

Orthogonal projection to latent structures discriminant analysis (OPLS-DA) is a widely used statistical method in multivariate data analysis, particularly in class pattern recognition. In addition, OPLS-DA is an extension of partial least square (PLS) that separately considers the variance of correlated and orthogonal predictors in the projection process to the response. PLS is a linear method that is very sensitive to outliers, making OPLS-DA susceptible to the influence of outliers. Investigations have shown that outliers commonly exist in datasets and can significantly affect statistical analysis. Therefore, this research aimed to propose a robust scaling method in the pre-processing step of OPLS-DA to address outliers. The proposed method was tested using both simulated and actual datasets, and the result showed effectiveness in handling outliers and improving classification accuracy. Consequently, Robust OPLS-DA could be designed to achieve optimal classification results by effectively separating classes and increasing robustness against outliers.

Efficient Scaling and Squaring Method for the Matrix Exponential

Efficient Scaling and Squaring Method for the Matrix Exponential

LiDAR-based scaling of OpenSim musculoskeletal human models is a viable alternative to marker-based approaches - A preliminary study.

Biomechanical analysis is increasingly being undertaken in field-based settings, often using inertial sensors or video-based pose estimation. These advancements necessitate more practical and accessible scaling methods as alternatives to traditional laboratory-based techniques like optical marker-based scaling. LiDAR scanning is a technique that could provide a reliable and efficient means of scaling biomechanical models. This study tested a scaling method for OpenSim models and comparing outcomes with those of traditional marker-based scaling in healthy adult participants. An anatomical skeleton was inferred from a LiDAR scan taken with an iPad. Key skeletal landmarks were then used to generate scaling factors using statistical shape models. The scaling factors of the pelvis, femur and tibia body segments derived from the LiDAR-based method demonstrated excellent reliability, with repeated scans of seven subjects producing an ICC value of 0.961. When comparing the scaling factors of eight additional subjects with the current gold standard technique of marker-based optical motion capture, a Bland-Altman analysis revealed differences of -0.5% ± 5.3 (95CI = [-10.8, 10]). Joint kinematics calculated using LiDAR scaled models had an average RMSD of 3.7° ± 0.1°when compared with those calculated with a marker-scaled model. These results indicate that a LiDAR-based scaling method can address the challenge of accurate and reliable scaling methods that are practical for use in the field. Future work with larger cohorts and diverse populations, and scaling of other body segments will provide further validation and enhance the generalizability and robustness of this approach.

Sub-scale helium tests and numerical simulations for studying the effect of location and magnitude of BIPV double skin façade fires on the smoke spread

Nowadays climate change all over the world, imposed risk to human health, security, and economies. Thus, renewable clean energy sources have been recently promoted as the means of sustainability of world’s energy system and mitigation for the climate change impacts. It is still questionable if the new building technologies such as photovoltaics are well observed in terms of finding the associated risks. According to the wide application of PV systems and lack of investigation on the BIPV (specifically on the façade), there is high demand of studying scenarios through which the fire behavior and smoke propagation are extensively noticed. Hence in the current research, the smoke spread from BIPV DSF (Double Skin Façade) fire is investigated employing the helium tests and a newly proposed helium similarity. The experiments are conducted for the parametric study of the case study building for different location of the fire on the façade and different heat release rates (HRR). Moreover, to link the dimensionless full-scale real fire simulated temperatures with the downscaled dimensionless helium test measurements, a new equation of similarity is proposed. Subsequently, via proposed theory and Froude modeling, the helium test is designed and carried out. The validated numerical simulation in Fire Dynamics Simulator (FDS) can verify the governing theory and scaling method between small-scale helium test and full-scale real fire smoke test.

Critical Behavior of Cobalt-doped van der Waals Ferromagnet (Fe0.74Co0.26)3GeTe2

Critical Behavior of Cobalt-doped van der Waals Ferromagnet (Fe0.74Co0.26)3GeTe2

Innovative use of Biomagic-Enzyme complex for pig farm odor reduction, harmful bacteria inhibition, and immune enhancement

The global increase in livestock production has correspondingly intensified farm odors due to harmful bacteria, reduced immunity, and disease progression. In this study, we treated feces with Biomagic-Enzyme complex for 4 months to understand the relationship between farm odor, immunity against common viral diseases, immune cytokines, and changes in the microbiota. A gas meter (MultiRAE) was used to measure ammonia (NH3) and hydrogen sulfide (H2S) while odor intensity and offensiveness were characterized by the non-objective scaling method. A complete blood count was performed and plasma was obtained after blood centrifugation at 3,000 rpm for 20 minutes. The cytokine profile was evaluated using commercial kits. Microbial DNA was extracted and purified from fecal samples to analyze the microbiota. Microbial DNA and viral RNA/DNA were obtained from fecal samples and amplified to determine the expression of transmissible gastroenteritis virus (TGEV), porcine reproductive and respiratory syndrome (PRRS), and porcine circovirus type 2 (PCV2). Our results indicated that Biomagic reduced odor nuisance by decreasing ammonia levels, resulting in faint and fairly offensive odor intensity. After the enzyme treatment, Escherichia coli populations significantly reduced across all 3 farms. In contrast, beneficial Lactobacillus spp. levels remained stable, indicating the enzyme selectively targeted harmful bacteria while preserving beneficial ones. The beneficial Lachnospiraceae, Spirochaetaceae, and Bacteroidaceae were found to be higher in the third month of treatment. TGEV was not detected, while PRRS and non-pathogenic PCV2 showed a positive infection rate. In conclusion, Biomagic reduced ammonia, prevented viral infection from pig farms, and improved gut-beneficial bacteria and microbiota.

Influence of Selected Geopolitical Factors on Municipal Waste Management

The collection and transportation of municipal solid waste create a significant energy and carbon footprint, resulting in a significant environmental impact. Proper waste management organization is necessary to minimize this impact. This research aims to identify differences and similarities in waste collection sectors, distinguish affiliation clusters for different waste types, and determine the impact of geopolitical factors on waste production in the analyzed region. Therefore, the similarities of waste production in the separated sectors for different waste types were analyzed. Instead of using the Kolmogorov–Smirnov distance between distributions of waste production, the statistics have been calculated based on L1 and L2 norm because they give the scale of differences. The multidimensional scaling method (MDS) and cluster analysis with a Gaussian mixed model (GMM) were used to identify changes in waste production. This technique makes it possible to detect changes between sectors in the analyzed region. Significant differences in cluster membership of sectors by waste type were observed. Geopolitical factors such as the COVID-19 pandemic and the war in Ukraine have caused changes in the sector affiliations of the waste clusters under analysis. The pandemic caused changes in the affiliation of non-segregated waste, plastics, and glass, while no change in waste generation preferences was observed for paper and cardboard waste. The war in Ukraine caused changes in the generation preferences of all waste types in the analyzed region.

Psychometric properties of the Social Anxiety Scale (SAS) using psychophysical and categorical scaling

This study evaluated the psychometric properties of a newly developed instrument, the Social Anxiety Scale (SAS) within a non-clinical Swedish adult population (n: 384), in which psychophysical (SAS-cMax) and categorical (SAS-C) scaling methods were compared. Unlike many common rating scales, the SAS items were designed to align with the perceived intensity of symptom severity. With a reduced number of items, both scaling methods (SAS11-cMax and SAS13-C) demonstrated robust conceptual validity, with a three-factor model encompassing “fear of interaction in groups and with strangers”, “fear of speaking in front of others and being in the center of attention”, and “fear of performing tasks while being observed”. In addition, the SAS showed an excellent internal consistency (SAS11-cMax, α = 0.93 and SAS13-C, α = 0.93), as well as good to satisfactory convergent and discriminant validity. ROC-analyses revealed an excellent accurcay of both scaling methods to distinguish between high and low social anxiety levels. A score of 25.3 cM (“moderate social anxiety”) yielded the most optimal sensitivity (0.80) and specificity (0.80) of the SAS11-cMax. A score of 18.5 points yielded the most optimal sensitivity (0.85) and specificity (0.87) of the SAS13-C. Overall, its concluded that the SAS exhibits sound psychometric properties regardless of scaling method. Further validation with other social anxiety scales and within a clinical population, is needed to establish its effectiveness as a screening tool for social anxiety.

Study on the scale effect and mechanical properties of gravel materials in riverbed cover layers

To address the challenges of performing in-situ tests on riverbed overburden gravel, this study employs three scaling methods—equal mass substitution, similar gradation, and the mixed method—to investigate the original gradation of the gravel. Large-scale triaxial consolidated drained shear tests were conducted to evaluate the effects of the maximum particle size reduction ratio (M) and confining pressure on the stress–strain behavior, fractal dimension, particle breakage, and the parameters of the Duncan-Chang model (an elastic model describing nonlinear stress-strain relationships). The study explores how scaling, based on fractal dimension and particle breakage rate, impacts the strength and deformation characteristics of gravel materials. The results show that increasing confining pressure typically leads to higher material strength. As confining pressure increases, particle breakage becomes more pronounced across the various scaling methods. Gravel processed using the similar gradation method exhibited the highest strength, the smallest change in fractal dimension, and the lowest breakage rate, followed by the mixed method. Furthermore, as the M value increases, peak stress also rises, with the modulus coefficient (k) and bulk modulus coefficient (Kb) in the Duncan-Chang model increasing by 4.5 times and 3.3 times, respectively, for M = 15 compared to M = 0. A clear relationship was observed between the change in fractal dimension before and after testing and the breakage index (Bg). As the M value increases, both the difference in fractal dimension and the breakage rate decrease, suggesting that larger M values lead to reduced particle breakage and improved mechanical stability.

Study results of the stress-strain states of the crown parts of molars restored with composite, ceramic and zirconium onlays

The relevance of the research is determined by the significant need for effective restorative treatment of caries decayed teeth. The purpose of the study is comparison of the stress-strain states in simulated models of non-homogeneous composite structural elements for typical geometric characteristics of onlays made of composite, ceramic, and zirconium dioxide in the restoration of the decayed mandibular molar. Computer simulation models of biomechanical systems were obtained by digital scanning of the mandibular molar in STEP format. Control was carried out in the form of numerical characteristics and graphical visualization in the EXOCAD program. The numerical method of finite elements, information technologies and program codes of the ANSYS Workbench 12.1 system were used to solve applied problems of biomechanics. After testing the developed discrete models, they were checked for adequacy and coincidence of numerical results in areas of high voltage gradients using the tools and methods of the ANSYS 12.1 code system. The maximum displacements, gradients, and amplitudes of the Mises-equivalent stress in the structural elements of each biomechanical system were evaluated. The stress values were calculated using the method of linear scaling of the results of the numerical solution of the boundary value problems of the theory of elasticity for small deformations to correspond to the functional force load of the mandibular molar of 100 N. The estimated values of the coefficients of the strength reserve of the structural elements were calculated as the ratio of the strength limit values to the maximum calculated values of the Mises-equivalent stress, scaled by a coefficient of 10 for a force load of 100 N. It was established that the largest voltage gradients are registered at the border of the cement layer. However, the nature of force transmission, as well as stress distribution, was different for different structural materials. The maximum stress was observed in the model with a composite onlay in the local area of the surface of the force load in the cement layer. For the ceramic onlay, the maximum stress was found on the lower support surface of contact with the onlay, where its values were 1.4 times higher than on other surfaces. The analysis of the zones of maximum stress for the zirconium onlay revealed their predominant localization in the center of its occlusal surface and in the zone of change in its spatial configuration at the border with cement. The highest stress values, along with the lowest coefficients of safety margin, were found for the molar model restored with a composite onlay, which indicated the lowest endurance of this material to functional load. While the zirconium onlay provided optimal stress distribution and it was characterized by the largest safety factor, which makes this method the most acceptable for molar restoration. The obtained results should be taken into account when choosing a material for the prosthetics of lateral teeth with onlays.

Computationally efficient upscaling of Markovian occupancy models for urban-level building simulations

Despite recent progress in the field of urban building energy modelling (UBEM), integration of occupant behaviour models is still an area in need of development. Although abundant for building energy modelling (BEM), stochastic occupancy models tailored for urban-level simulations are lacking, as well as systematic analyses of accuracy versus complexity. Occupancy models aimed for BEM typically introduce a heavy computational load, but a small reduction in accuracy might allow a large increase in speed. This paper proposes methods for scaling up stochastic Markov-chain (MC) occupancy models previously developed for BEM to fit UBEM, and compares them in terms of accuracy and computational efficiency. For the scales normally involved in UBEM (tens to hundreds of occupants per zone and hourly time resolution), a model based on multinomial sampling is proposed that is several hundred times faster than the original MC model and only slightly overestimates the variability in the occupancy patterns.