Scale Factor Research Articles

A scaling-based model to describe temporal variability in soil hydraulic properties

Accurate knowledge of temporal variability in soil hydraulic properties (SHPs) can improve the prediction capability of flow and transport models. Lab and field measurements for determining SHPs at multiple timescales are expensive and time-consuming. Further, the existing Fokker-Plank equation (FPE) based numerical and analytical models for describing temporal variation in SHPs require parameterization. In this study, a scaling model is proposed to describe temporal variation in SHPs of four different soils. As field studies related to temporal variation in SHPs are limited, first synthetic temporally varying SHPs, soil water characteristic curves and hydraulic conductivity curves are generated using the analytical solution of FPE. Functional normalization approach is then used to develop reference curves and to obtain scale factors at different time-steps. The time-varying scale factors and the initial SHPs are related by two-step regression equations. The reference curves and the regression equations together can be used to estimate SHPs at any time given the initial SHPs. The developed model is validated using a 5-fold cross validation method and with experimental data at two study sites. The average percentage error in predicted van Genuchten parameters (θs,n and Ksat) except for α are mostly less than 10 %. The performance of the proposed model is comparable to that of FPE analytical model for experimental data. The proposed method holds promise for describing temporal variation in SHPs using only the initial SHPs.

Transformer winding faults diagnosis using the transfer function of its high-frequency lumped equivalent model from the measured FRA data

The frequency response analysis (FRA) of the transformer is the data obtained in a wide frequency range that represents the transfer function (TF) that reflects the physical behavior of its coil. However, it can be considered one of the best tools for a better understanding of the electromagnetic characteristics of the winding. This article proposes the TF as a tool to locate and assess transformer winding failures. So, an autonomous algorithm has been developed to construct the TF in ‘s domain’ proposed in previous research. The TF has been determined from experimental measured FRA data from an isolated air-core winding, where the TF has been calculated for the axial defects (AD) realized on the winding discs. Besides, the diagnostic approach is based on the scaling factor (K) and the real zero (τ) of the TF that can be obtained from the measured equivalent capacitance, equivalent inductance, and the DC resistance of the winding in high and low-frequency ranges. However, the K, τ, and the slope of the equivalent inductance characteristics as a function of the winding defects degree have been used for locating and assessing the severity of the winding faults.

SCHOOL COUNSELORS’ SELF-EFFICACY IN ADDRESSING ADOLESCENTS’ MENTAL ISSUES IN THE AFTER-COVID-19 ERA

The unanticipated arrival of the COVID-19 pandemic has upset thousands of adolescents worldwide, whereas shifts to remote schooling have minimized their access to school-based support. This study focused on three research questions: How do School Counselors (a) evaluate the impact of COVID-19 restrictive measures on adolescents' mental health? (b) appraise their self-efficacy to help adolescents cope with the challenges? (c) Assess post-pandemic pupil behavior and their self-efficacy beliefs. It aimed to resource creative school-counseling approaches to improve youth resilience. Findings showed that counselors regarded generalized worry, concern about academic studies and job development, and increasing internet use as adolescents' most important psychological/behavioral issues. Most counselors felt efficient in their professional development, teamwork, cross-cultural sensitivity, leadership, assessment, personal and social growth, and development. Counselors who evaluated pupils as high/medium in generalized anxiety, depressive symptoms, academic/career development, and excessive internet time evaluated themselves significantly higher on all factors of the self-efficacy scale compared to those who assessed students as medium/low on the aforementioned factors, who scored significantly lower in self-efficacy.

Electroelastic wave dispersion in the rotary piezoelectric NEMS sensors/actuators via nonlocal strain gradient theory

This article introduces a computational means for investigating the electroelastic nonlinear wave dispersion traits of the nano-dimension sandwich pipe, which is composed of a core formed of a bi-directional functionally graded (Bi-FG) material, together with a piezoelectric sensor/actuator. A combination of Hamilton’s principle, first-order shear deformation, along with Von-Karman nonlinearity, is used for modeling and obtaining the nonlinear governing equations of the nano-sized sandwich pipe connected to a piezoelectric part. The nonlinear governing equations to obtain the nonlinear phase velocity of the current system are determined using a combination of analytical and multiple scales approaches. Due to some computational cost for choosing the precise values of both the nonlocality factor and length scale of the nanopipe in the laboratory, for the first time in this research, with the aid of COMSOL multi-physics finite element simulation, the results are verified, and new outcomes for obtaining the exact functions for nonlocal and length scale factors is presented. In addition, an artificial neural network (ANN) is utilized in this study for the prediction of the results. The mathematical and finite element results were used to train the ANN. A newly presented optimization algorithm is exploited for the first time for optimizing the ANN parameters concluding higher accuracy of the ANN predictions. Consequently, to explore the influences of the location of the piezoelectric patch, nonlocality and length scale factors, and applied voltage parameter on the phase velocity characteristics of the nano-dimension sandwich pipe made of a Bi-FG core and electrically patch, an effort is performed. As an applicable result that can be useful in the related nano-industries, the current work presents exact nonlocal and length scale functions for different conditions.

Enhancing Dimensional Accuracy in Fused Filament Fabrication: A DOE Approach

3D printing is widely used in various fields and industries and is one of the most popular methods. Most popular advantage of this method is its flexibility in printing complex shapes and sizes. Quality is one of the important aspects when using this kind of technology which has a high advantage of printing complex shapes as this includes many factors like temperature, speed, material, infill types etc. In this paper one such analysis has been done by 3D printing parts under different factors. Analysis is done on how scaling factors affect the dimensions like height and diameter of a 3D printed part and check factors affecting these variations using Design of Experiments (DOE) analysis

Analyzing nitrogen dioxide to nitrogen oxide scaling factors for data-driven satellite-based emission estimation methods: A case study of Matimba/Medupi power stations in South Africa

In this paper, we propose improved nitrogen dioxide (NO2) to nitrogen oxide (NOx) scaling factors for several data-driven methods that are used for the estimation of NOx power plant emissions from satellite observations of NO2. The scaling factors are deduced from high-resolution simulations of power plant plumes with the MicroHH large-eddy simulation model with a simplified chemistry and then applied to Sentinel-5 Precursor (S5P) TROPOspheric Monitoring Instrument (TROPOMI) NO2 satellite observations over the Matimba/Medupi power stations in South Africa. We show that due to the non-linear chemistry the optimal NO2 to NOx scaling factors depend on both the method employed and the specific segments of the plume from which emission estimate is derived. The scaling factors derived from the MicroHH simulations in this study are substantially (more than 50%) higher than the typical values used in the literature with actual NO2 observations. The results highlight the challenge in appropriately accounting for the conversion from NO2 to NOx when estimating point source emissions from satellite NO2 observations.

Regional scaling of sea surface temperature with global warming levels in the CMIP6 ensemble

AbstractSea surface temperature (SST) and sea surface air temperature (SSAT) are commonly used as proxies for investigating the impact of climate change on oceans. These variables have been warming since pre-industrial times and are expected to continue to warm in the future under all Shared Socioeconomic Pathways (SSPs). However, they are warming in a spatially heterogeneous way, even with some cooling spots. In this work, we provide a general overview on the regional scaling of SST and SSAT with global warming, based on a 26-member CMIP6 ensemble. We utilize the global warming level (GWL) as a climate change dimension to analyze scaling patterns between sea temperature anomalies and the corresponding GWLs during the 21st century. This analysis is conducted globally, regionally, and on grid-point basis. The results show that SST and SSAT scale linearly with GWL at global scale, with scaling factors $$\beta $$ β = 0.71 ± 0.001 K/K and $$\beta $$ β = 0.86 ± 0.001 K/K, respectively. These results are robust, showing only minor differences between seasons, SSPs, and horizontal model resolutions. However, large differences emerge at regional scale, and the scaling of the two temperatures are strongly influenced by sea-ice. The lowest values are obtained for the Southern Ocean region, $$\beta $$ β = 0.54 ± 0.005 K/K, projecting that the mean SST will increase only half as fast as the global mean temperature. These results provide valuable insight for refining the ocean IPCC reference regions, considering spatial homogeneity in terms of the regional response to global warming. A refinement of six ocean reference regions has been proposed.

Deep learning-enhanced ghost imaging through dynamic and complex scattering media with supervised corrections of dynamic scaling factors

Ghost imaging (GI) through dynamic and complex scattering media remains challenging. The existence of dynamic scattering gives rise to a failure of GI schemes. Here, we report a deep learning-enhanced GI scheme with supervised corrections (SCGI) of dynamic scaling factors to realize high-resolution ghost reconstruction through dynamic and complex scattering media. The SCGI scheme is developed to approximate the variation of dynamic scaling factors in an optical channel and correct the recorded light intensities with a Gaussian prior. An untrained neural network powered by regularization by denoising for the SCGI scheme (SCGI-URED) is developed to further recover high-visibility ghost images. Experimental results demonstrate that high-resolution and high-visibility GI can be realized in dynamic and complex scattering media. The proposed method provides a reliable tool for implementing high-resolution and high-visibility GI through dynamic and complex scattering media and could give an impetus to developing dynamic scattering imaging in real-world scenarios.

Analysis of reconstructed modified [formula omitted] gravity

This paper studies the reconstruction criterion in the framework of f(Q,T) gravity using a ghost dark energy model, where Q represents the non-metricity and T is the trace of energy–momentum tensor. In this regard, the correspondence scenario for a non-interacting scheme is used to construct the ghost dark energy f(Q,T) model. We consider the flat Friedmann–Robertson–Walker universe with a power-law scale factor and pressure-less matter. We investigate the behavior of the equation of state parameter and examine the stability of the ghost dark energy model via the squared speed of sound parameter. The equation of state parameter depicts the phantom era, while the squared speed of sound reveals a stable ghost dark energy model for the entire cosmic evolutionary paradigm. Finally, we study the cosmography of the ωGDE−ωGDE′ and r−s planes that correspond to a freezing region and Chaplygin gas model, respectively. It is concluded that the reconstructed ghost dark energy model represents the evolution of the cosmos for suitable choices of the parametric values.

Dynamics of Cosmological Scalar Fields Revisited

This paper reviews the dynamics of a single isotropic and homogeneous scalar field φ(t) in the context of cosmological models. A non-standard approach to the solution of the Einstein–Klein–Gordon equations is described which uses the scalar field as the evolution parameter for cosmic dynamics. General conclusions about the qualitative behaviour of the solutions can be drawn, and examples of how to obtain explicit solutions for some cosmological models of interest are given. For arbitrary potentials, analytical results can be obtained from the slow-roll approximation by using a series expansion for the Hubble parameter H[φ], from which a quantitative estimate for the number of e-folds of expansion is obtained. This approach is illustrated with the examples of quadratic potentials and hilltop models, with special consideration of Higgs-type potentials. The GUT-scale is shown to come out of such a model quite naturally. Finally, it is discussed how to find scalar potentials giving rise to a predetermined scalar-field behaviour and the associated evolution of the scale factor.

Seasonal bee communities vary in their responses to resources at local and landscape scales: implication for land managers

ContextThere is great interest in land management practices for pollinators; however, a quantitative comparison of landscape and local effects on bee communities is necessary to determine if adding small habitat patches can increase bee abundance or species richness. The value of increasing floral abundance at a site is undoubtedly influenced by the phenology and magnitude of floral resources in the landscape, but due to the complexity of measuring landscape-scale resources, these factors have been understudied.ObjectivesTo address this knowledge gap, we quantified the relative importance of local versus landscape scale resources for bee communities, identified the most important metrics of local and landscape quality, and evaluated how these relationships vary with season.MethodsWe studied season-specific relationships between local and landscape quality and wild-bee communities at 33 sites in the Finger Lakes region of New York, USA. We paired site surveys of wild bees, plants, and soil characteristics with a multi-dimensional assessment of landscape composition, configuration, insecticide toxic load, and a spatio-temporal evaluation of floral resources at local and landscape scales.ResultsWe found that the most relevant spatial scale and landscape factor varied by season. Early-season bee communities responded primarily to landscape resources, including the presence of flowering trees and wetland habitats. In contrast, mid to late-season bee communities were more influenced by local conditions, though bee diversity was negatively impacted when sites were embedded in highly agricultural landscapes. Soil composition had complex impacts on bee communities, and likely reflects effects on plant community flowering.ConclusionsEarly-season bees can be supported by adding flowering trees and wetlands, while mid to late-season bees can be supported by local addition of summer and fall flowering plants. Sites embedded in landscapes with a greater proportion of natural areas will host a greater bee species diversity.

Global Positioning System Signal Verification through Correlation Function Distortion and Received Power Tracking

Purpose: This study proposes a significantly improved method for detecting, classifying, and isolating Global Navigation Satellite System (GNSS) signals using the relationship between the measured power and the distortion of the correlation function to achieve the signal verification required for Global Positioning System (GPS) civil applications such as safe civil aircraft navigation. Methodology: The suggested approach uses power and distortion measurements in the received signal to identify it as jammed, multipath, spoofing, or no-interference. By adding an isolator scaling factor to the detector, the signal patterns will be induced with a unique temporary factor that will set it apart from the rest and make it possible to easily position each signal in its own zone. The detector divides the four signal types into distinct zones for verification. Findings: The sufficient signal data is analyzed and the extensive simulation conducted indicates that about 94% detection accuracy is achieved which is relatively high. Unique contribution to theory, practice and policy: This study is implemented through the development of relevant detection software tools with a user-friendly interface for GNSS signal detection, validation and analysis.

AST: An OpenSim-based tool for the automatic scaling of generic musculoskeletal models

Background and objectivesThe paper introduces a tool called Automatic Scaling Tool (AST) designed for improving and expediting musculoskeletal (MSK) simulations based on generic models in OpenSim. Scaling is a crucial initial step in MSK analyses, involving the correction of virtual marker locations on a model to align with actual experimental markers. MethodsThe AST automates this process by iteratively adjusting virtual markers using scaling and inverse kinematics on a static trial. It evaluates the root mean square error (RMSE) and maximum marker error, implementing corrective actions until achieving the desired accuracy level. The tool determines whether to scale a segment with a marker-based or constant scaling factor based on checks on RMSE and segment scaling factors. ResultsTesting on three generic MSK models demonstrated that the AST significantly outperformed manual scaling by an expert operator. The RMSE for static trials was one order of magnitude lower, and for gait tasks, it was five times lower (8.5 ± 0.76 mm vs. 44.5 ± 7.5 mm). The AST consistently achieved the desired level of accuracy in less than 100 iterations, providing reliable scaled MSK models within a relatively brief timeframe, ranging from minutes to hours depending on model complexity. ConclusionsThe paper concludes that AST can greatly benefit the biomechanical community by quickly and accurately scaling generic models, a critical first step in MSK analyses. Further validation through additional experimental datasets and generic models is proposed for future tests.

A Deterministic Calibration Method for the Thermodynamic Model of Gas Turbines

Performance adaptation is an effective way to improve the accuracy of gas turbine performance models. Although current performance adaptation methods, such as those using genetic algorithms or evolutionary computation to modify component characteristic maps, are useful for finding good solutions, they are essentially searching methods and suffer from long computation time. This paper presents a novel approach that can achieve good performance adaptation with low time complexity and without using any searching method. In this method, the actual component performance parameters are first estimated using engine measurements at different operating conditions. For each operating condition, some scaling factors are introduced and calculated to indicate the difference between the actual and predicted component performance parameters. Afterward, an interpolating algorithm is adopted to synthesize the scaling factors for modifying all major component maps. The adapted component maps are then able to make the engine model match all the gas path measurements and achieve the required accuracy of the engine performance model. The proposed approach has been tested with a model high-bypass turbofan engine using simulated data. The results show that the proposed performance adaptation approach can effectively improve the model’s accuracy. Specifically, the prediction errors can be reduced from about 9% to about 0.6%. In addition, this approach has much less computational complexity compared to other optimization-based counterparts.

Assessing the Psychometric Properties of the Functional Analytic Psychotherapy Intimacy Scale (FAPIS) in a Spanish-Speaking Population

AbstractIntimacy is an interpersonal repertoire related to physical and behavioral health indicators. Functional analytic psychotherapy (FAP) is a behavioral intervention that has demonstrated utility in improving intimacy repertoires, particularly in experimental single-case designs. The FAP Intimacy Scale (FAPIS) was developed with the aim of using a theoretically sound measure to evaluate FAP effects on a large scale. This study sought to culturally adapt and validate the FAPIS for Spanish-speaking populations. A total sample of 509 Spanish-speaking participants completed the survey. Exploratory and confirmatory factor analyses were conducted to examine FAPIS psychometric properties. A network analysis was also performed to assess the relation among scale factors. As expected, the FAPIS Spanish version demonstrated a good fit, meeting the same three-factor structure as the original version. Network loading along three factors was also found, and item 3 was identified as the most central item in the scale. Low and negative correlations among FAPIS, experiential avoidance, emotional sensitivity, and social impact were found. Findings showed that FAPIS is a valid and reliable measure for assessing intimacy in Spanish-speaking populations.

The Influence of COVID on Emergency Medicine Career Choice: A Survey of Medical Students.

Background The COVID-19 pandemic has led to substantial changes in the delivery of healthcare and medical education. Little is known about how the pandemic has altered medical students' perceptions in regard to career choice. Methods The authors developed and implemented a multi-center survey that evaluated medical students' preferred career choice before and during the coronavirus pandemic, as well as the influence of pandemic-related factors on that choice. The survey was distributed to all levels of medical students (MS) at nine medical schools across the country from November 2020 to January 2021 and represented a convenience sample. Preferred career choice was assessed through the use of a Likert scale and additional factors affecting career choice were solicited.The degree of interest before and during the pandemic, as well as factors influencing the shift, were treated as ordinal variables and compared using chi-squared testing. Cohen's Kappa statistic was calculated to assess the degree of shifts of interest in Emergency Medicine among students. The study was deemed exempt by the Institutional Review Board at the host institution, Sidney Kimmel Medical College at Thomas Jefferson University, and all participating sites. Results A total of 1431 of 6710 (21.3%) eligible students completed the survey. The COVID pandemic was cited as a reason for a changed interest in specialty by 193 (13.5%) students.The most common reason for specialty change was the students' clinical experience, followed by a desire to be on the front lines, and personal/family health concerns. There was a significant association between career change and degree of interest among students interested in emergency medicine (EM) as their future specialty before the COVID pandemic as well as during the COVID pandemic. Living with an immunocompromised individual had a significant association with a reducedinterest in EM. There was a significant association between EM rotation completion and how interested students were in EM as their future specialty before the COVID pandemic and during the COVID pandemic. Among EM-interested students whose specialty interest was changed by the COVID pandemic, 34 (41.5%) became less favorable to EM, 28 (34.2%) stayed the same, and 20 (24.4%) students became more favorable to EM. Conclusions The impact of COVID-19 on medical students' career choice is a complicated matter that involves both personal and professional factors. It appears that there is a trend towards less interest in the field of EM with multifactorial influences, some of which are related to the COVID-19 pandemic.

Evaluation of COSMO-CLM Model Parameter Sensitivity in the Study of Extreme Events across the Eastern Region of India

The present study aims to identify the parameters from the Consortium for Small-scale Modelling in CLimate Mode (COSMO-CLM) regional climate model that strongly controls the prediction of extreme events over West Bengal and the adjoining areas observed between 2013 to 2018. Metrics, namely Performance Score (PS) screen out the most persuasive parameter on model output. Additionally, the Performance Index (PI) measure the reliability of the model and Skill Score (SS) establishes the model performance against the reference simulation leading to the optimization of the model for a given variable. In this study, parameter screening for four output variables such as 2m-temperature, surface latent heat flux, precipitation and cloud cover of COSMO-CLM is accomplished. For heat wave simulations, 2m-temperature and surface latent heat flux are explored whereas cloud cover and precipitation are examined for extreme rainfall events. A total of 25 adjustable parameters representing the following parameterization schemes: turbulence, land surface process, microphysics, convection, radiation and soil. Out of the six parameterization schemes, the scaling factor of the laminar boundary layer for heat (rlam_heat) and the ratio of laminar scaling factors for heat over sea and land (rat_sea) from the land surface process is sensitive to SLH, TP. The exponent to get the effective surface area (e_surf) from the land surface has a large impact on 2m-temperature. A few parameters from microphysics (cloud ice threshold for auto conversion), convection (mean entrainment rate for shallow convection) and radiation (parameter for computing the amount of cloud cover in saturated conditions) play a significant role in producing TP, and TCC fields. It is evident from the results that the parameter sensitivities on model performance depend on the choice of the meteorological field. Furthermore, in almost all input model parameters, the model performance reveals the opposite character in different domains for a given meteorological field.

Tissue equivalent conversion of silicon-based microdosemeters in hadron therapy.

Silicon has been developed as a microdosemeter, as it can provide sensitive volumes at submicrometric levels, does not need a gas supply, has a fast response, and has low power consumption. However, since the energy response in silicon is not the same as that in tissue, a spectral conversion from silicon to tissue is necessary to obtain the probability distribution of energy deposition in tissue. In this work, we present a method for microdosimetric spectra conversion from silicon to tissue based on the scaled Fourier transformation and the geometric scaling factor, which shows relatively good results in the spectral conversion from diamond to tissue. The results illustrate that the method can convert the energy deposition spectra from silicon to tissue with proper accuracy. Meanwhile, the inconsistency between the converted and actual spectra due to the inherent difference was also observed. Whereas, the reasons for the disagreement are different. For the plateau part of the Bragg curve, the discrepancy between the converted and actual spectra is due to the poor tissue equivalent of silicon. For the proximal part of the Bragg curve, the spectral difference is attributed to the different shapes of the energy deposition spectra obtained in silicon and water, which is the same as that in the diamond. In summary, this method can be employed in the tissue equivalent conversion of silicon microdosemeter, but the poor tissue equivalent of silicon limited the accuracy of this method. In addition, the correction for the deviation between the converted and calculated spectra due to the difference in spectral shapes is required to improve the practicality of this mod.

Non-Commutative Classical and Quantum Fractionary Cosmology: FRW Case

In this work, we will explore the effects of non-commutativity in fractional classical and quantum schemes using the flat Friedmann–Robertson–Walker (FRW) cosmological model coupled to a scalar field in the K-essence formalism. In previous work, we have obtained the commutative solutions in both regimes in the fractional framework. Here, we introduce non-commutative variables, considering that all minisuperspace variables qnci do not commute, so the symplectic structure was modified. In the quantum regime, the probability density presents a new structure in the scalar field corresponding to the value of the non-commutative parameter, in the sense that this probability density undergoes a shift back to the direction of the scale factor, causing classical evolution to arise earlier than in the commutative world.

Research on the Autonomous Control Technology Used in the Slurry Mixing System of Cementing Units

Cementing is a critical link in oil and gas exploitation, in which slurry density control is particularly important. In this study, we examined a slurry mixing control system in order to solve the problem of time delays in the mixing system. The model of a slurry mixing system was built in accordance with the system’s structure. A Smith fuzzy PID (proportion integration differentiation) composite control solution is proposed herein, and the simulation results show that the adjustment time and overshoot are lower than those of the conventional PID control and Smith predictive compensation control. A genetic algorithm is utilized to optimize the quantization factor and scale factor of the Smith fuzzy PID controller. Following optimization, the rise time of the controller was found to be 0.45 s, which represents a decrease of 35.9%, the overshoot was reduced by 0.4%, and the stabilization time was reduced by 36.6%. Afterward, we built a cementing slurry mixing simulation experimental platform, and experiments were used to verify the feasibility and superiority of the Smith fuzzy PID controller optimized by the genetic algorithm in comparison with the conventional controllers. The study results thus provide a scientific basis for the engineering application of the autonomous control technology of the slurry mixing system in cementing units.