Meaningful Fit Research Articles

Two step model for crosslinking polymerization with account of diffusion

The proposed model includes two parallel reactions, initiation and propagation that respectively follow the reaction order and autocatalytic kinetics. The effect of diffusion is accounted for in both steps by introducing a term that depends linearly on conversion. The term is introduced into the exponential part of the rate constants. Determining the magnitude of this term allows for estimating the diffusion effect on the overall activation energy in the final stages of crosslinking polymerization. The model is tested on five previously published datasets. It appears to fit all data very well and yield physically meaningful fit parameters. Algorithms for successful fitting of the model are suggested. It is also demonstrated that disregarding the effect of diffusion gives rise to markedly inferior results. The model is quite general and expected to be applicable to any polymerization complicated by diffusion or other processes that involve the interplay between the reaction and diffusion kinetics.

Polyurea-Graphene Nanocomposites-The Influence of Hard-Segment Content and Nanoparticle Loading on Mechanical Properties.

Polyurethane and polyurea-based adhesives are widely used in various applications, from automotive to electronics and medical applications. The adhesive performance depends strongly on its composition, and developing the formulation-structure-property relationship is crucial to making better products. Here, we investigate the dependence of the linear viscoelastic properties of polyurea nanocomposites, with an IPDI-based polyurea (PUa) matrix and exfoliated graphene nanoplatelet (xGnP) fillers, on the hard-segment weight fraction (HSWF) and the xGnP loading. We characterize the material using scanning electron microscopy (SEM) and dynamic mechanical analysis (DMA). It is found that changing the HSWF leads to a significant variation in the stiffness of the material, from about 10 MPa for 20% HSWF to about 100 MPa for 30% HSWF and about 250 MPa for the 40% HSWF polymer (as measured by the tensile storage modulus at room temperature). The effect of the xGNP loading was significantly more limited and was generally within experimental error, except for the 20% HSWF material, where the xGNP addition led to about an 80% increase in stiffness. To correctly interpret the DMA results, we developed a new physics-based rheological model for the description of the storage and loss moduli. The model is based on the fractional calculus approach and successfully describes the material rheology in a broad range of temperatures (-70 °C-+70 °C) and frequencies (0.1-100 s-1), using only six physically meaningful fitting parameters for each material. The results provide guidance for the development of nanocomposite PUa-based materials.

Artificial intelligence based analysis of nanoindentation load–displacement data using a genetic algorithm

We developed an automated tool, Nanoindentation Neo package for the analysis of nanoindentation load–displacement curves using a Genetic Algorithm (GA) applied to the Oliver-Pharr method (Oliver et al.,1992). For some materials, such as polycrystalline isotropic graphites, Least Squares Fitting (LSF) of the unload curve can produce unrealistic fit parameters. These graphites exhibit sharply peaked unloading curves not easily fit using the LSF, which tends to overestimate the indenter tip geometry parameter. To tackle this problem, we extended our general materials characterization tool Neo for EXAFS analysis (Terry et al., 2021) to fit nanoindentation data. Nanoindentation Neo automatically processes and analyzes nanoindentation data with minimal user input while producing meaningful fit parameters. GA, a robust metaheuristic method, begins with a population of temporary solutions using model parameters called chromosomes; from these we evaluate a fitness value for each solution, and select the best solutions to mix with random solutions producing the next generation. A mutation operator then modifies existing solutions by random perturbations, and the optimal solution is selected. We tested the GA method using Silica and Al reference standards. We fit samples of graphite and a high entropy alloy (HEA) consisting of BCC and FCC phases.

An innovative clustering technique to generate hybrid modeling of cooling coils for energy analysis: A case study for control performance in HVAC systems

Despite past studies, no comprehensive models or empirical correlations cover all aspects of performances of cooling coils under different flow regimes (laminar, transition, and turbulent). Moreover, the cooling coil is characterized by a highly nonlinear dynamic subject to multiple inputs, coupling between the latent and sensible heat transfer modes, uncertain disturbances, and strong dependence of the overall heat transfer coefficient on the flow type, all causing significant challenges when it comes to modeling. Therefore, a hybrid layer structure model was adopted in this study to overcome these challenges. The new approach used two different optimization methods, Neural Networks' Weights and Takagi-Sugeno (TS) fuzzy, and the hybrid layers tuned by the Gauss-Newton algorithm (GNA). The proposed model covered three types of fluid flow to represent the dynamic behavior of the water-side and air-side heat transfer coefficients, each of which was divided into seven clusters and had its unique TS consequence. This study also administered meaningful fitness tests in the responses of the eleven independent variables that serve as its inputs. Furthermore, its application shows the control performance saving more than 44% of HVAC system energy. Based on the results, it was concluded that the proposed model is suitable for estimating energy and cost savings for electric power and water flow rate efficiency. In addition, the response of all types of output flow can be evaluated when changing eleven independent variables that are manipulated by three different controllers.

Medical image segmentation using scalable functional variational Bayesian neural networks with Gaussian processes

Bayesian neural networks (BNNs) are widely used in medical image segmentation tasks because they provide a probabilistic view of deep learning models by placing a prior distribution over the model parameters, and allow the representation of uncertainties through posterior distribution over the model parameters. However, it is difficult to choose meaningful prior distributions and fit accurate posterior distributions in a high-dimensional weight space, which limit the practical effectiveness of BNNs. In this study, we propose a scalable functional variational BNN with Gaussian processes (GPs) for the purpose of medical image segmentation. Specifically, we regard the prior and variational posterior distributions as GPs, and perform variational inference in function space. We employ a variant of the functional evidence lower bound (fELBO) with a β-weight on the Kullback–Leibler divergence term as the loss objective, and design a content-aware UNet segmentation network, which utilizes content-aware reassembly of features (CARAFE) as an upsampling operator to extract semantic information from input feature maps. The proposed BNN framework enables efficient training and can perform predictive inference with one forward pass. Extensive experiments are conducted on four segmentation datasets; the experimental results demonstrate that the proposed approach improves the segmentation performance metrics and uncertainty estimates compared with several state-of-the-art methods.

Quantifying uncertainties in crystal electric field Hamiltonian fits to neutron data

We systematically examine uncertainties from fitting rare earth single-ion crystal electric field (CEF) Hamiltonians to inelastic neutron scattering data. Using pyrochlore and delafossite structures as test cases, we find that uncertainty in CEF parameters can be large despite visually excellent fits. These results show Yb^{3+}3+ compounds have particularly large gg-tensor uncertainty because of the few available peaks. In such cases, additional constraints are necessary for meaningful fits.

Development of the Hostility in Pandemic Scale (HPS): A Validity and Reliability Study

The aim of this research is to develop Hostility in Pandemic Scale (HPS) for Turkey Population to determine the hostility levels of individuals, which is a factor affecting the mental well-being of the society during the pandemic. The study group consists of 855 individuals between the ages of 18-65 from different genders, and have experienced the pandemic process. For the construct validity of the scale results, exploratory factor analysis was conducted and a one-dimensional structure consisting of 22 items was revealed. It was determined that the variance explained by the scale showing a one-dimensional structure was 41.5%. As a result of the confirmatory factor analysis performed through a separate study group, it was revealed that all items have significant t values, and the model established according to model fit indexes has meaningful and acceptable fit values. Buss-Perry Aggression Scale was applied with HPS for the criterion validity. As a result of the criterion validity analysis, a significant relationship was found between the scale scores. The Cronbach Alpha was calculated to analyses internal consistency of the scale and a reliability level of 0.93 was obtained. The test-retest reliability results were found as 0.89. In addition, item statistics revealed that all of the scale items can discriminate well among the respondents. Results of the analysis revealed that, the Hostility Scale in Pandemic Process provides valid and reliable results.

A content analysis of change management strategies used in technological transitions in higher education institutions from the lens of a strategic alignment framework

Technology innovations have the potential to significantly strengthen the ability of higher education institutions to deliver on their core educational mission with greater quality, efficiency, and effectiveness. Not surprisingly, managing technological changes is among the chief concerns for institutional leaders, and yet there is a dearth of research that provides concrete frameworks for managing this type of change in a higher education context. Using Guerra-López and Hicks’s Learning and Development Strategic Alignment (LDSA) framework, this qualitative study used a directed content analysis approach to develop a contextualized framework for planning and managing technology change in higher education institutions. The findings suggest that there is a meaningful fit between specific change management strategies found in the learning management systems (LMS) transition research literature and the LDSA framework. The various strategies were synthesized and grouped around LDSA dimensions and core functions, resulting in a technological change management framework contextualized for higher education.

Validity and Reliability of the Solidarity in Pandemics Scale

This study developed a measure of solidarity levels during pandemics, the Solidarity in Pandemics Scale (SPS). This was achieved using a study group of 842 individuals aged between 18 and 65 from different segments of society who had experienced the COVID-19 pandemic and differed in age, gender, and socioeconomic status. Exploratory factor analysis was used to establish the construct validity of the scale, producing a 15-item scale with a one-dimensional structure that explained 34.36% of the variance. Confirmatory factor analysis using a different study group revealed that all items have significant t-values, and the model established according to model fit indexes has meaningful and acceptable fit values. The internal consistency of the scale results was calculated using the Cronbach Alpha coefficient, and a reliability of .85 in terms of internal consistency was obtained. Test-retest reliability results as another indicator of reliability were found to be .85. Scores on the Altruism Scale were compared with those on the SPS to evaluate the scale’s criterion validity, and a significant relationship was found between the two scale scores. This analysis indicates that the Solidarity in Pandemics Scale is a valid and reliable psychometric tool.

Clothing Fit Preferences and Dressing Behaviour of Undergraduate Students: The Case of Public Universities in Ghana

The study investigated clothing fit preferences and dressing behaviour of undergraduate students, focusing on public universities in Ghana. The study employed descriptive cross sectional survey design. The accessible population was all undergraduate students in four premium public universities in Ghana who were 93,457 as at 2019/2020 academic year. This comprised of 57,105 (61.1%) males and 36,352 (38.9%) females. A sample size of 400, made up of 245 males and 155 females, was obtained. In relation to sampling procedure, the multi-stage sampling procedures were used. A questionnaire, with a reliability coefficient of .827, was the instrument used. Both descriptive and inferential statistical tools were used to analyse the data. The study revealed that gender has no effect on clothing fit preferences of undergraduate students, however, in relation to dressing behaviour, it has an effect. Also, aesthetic, economic, and functional/comfort factors contributed 79.7 percent to dressing behaviour of the students. The study concludes that clothing fit preferences are able to influence positively students’ dressing behaviour. The study recommends that those who matter in the University or the powers that be must establish a form of a resocialisation programme to enlighten students on the desirable dressing behaviour on campus in order for them to adopt or adapt meaningful clothing fit preferences.

Physical models of the bidirectional reflectance of solar receiver coatings

We present measurements of the Bidirectional Reflectance Distribution Function (BRDF) of the state-of-the art solar receiver coating, Pyromark® 2500, and show how the data can be accurately described with physical models. In the conducted measurements of the receiver coating, a peak shift of the radiation away from the direction of ideal specular reflection was observed. Both the Cook–Torrance and the He–Torrance models reproduce this behaviour. For the application of the former model, an empirical angular dependence of the parameter describing the surface roughness has to be assumed in order to obtain an adequate fit to the data. The latter model is less exact in a statistical sense, but it is physically based, with six meaningful fit parameters. As we found that the He–Torrance model with its original form of the specular reflection yielded physically incorrect results for incidence angles larger than 80°, a modification is proposed to overcome this deficiency.Overall, the fitted models are able to provide a good description of the BRDF of the receiver coating over three orders of magnitude. Our results can play an important role in facilitating camera-based flux control or optical simulations of solar tower systems. Moreover, we think that our findings can serve as a sound starting point for the physical modelling of other absorber coatings currently being developed.

Analysis of Effects of Rock Physical Properties Changes from Freeze–Thaw Weathering in Ny-Ålesund Region: Part 2—Correlations and Prediction of Weathered Properties

From the examination of rock physical parameters’ changes of compressive strength, shore hardness, water absorption, P-wave velocity with increasing freeze–thaw cycles, correlations of these parameters were investigated. Rock samples were collected from Ny-Ålesund region in Norway. As compressive strength and shore hardness inherently have high uncertainties due to inhomogeneous rock composition and internal fissures and cracks, only the relationship between water absorption and P-wave velocity revealed high correlations, providing meaningful linear fitting equations. From the correlation analysis results and clear trends of increasing water absorption and decreasing P-wave velocity with increasing freeze–thaw cycle found in part one of the companion study, prediction equations of future changes of rock physical parameters are proposed using P-wave velocity or water absorption. In addition, future rock weathering grade changes with time can be predicted from estimation of water absorption or P-wave velocity change due to freeze–thaw cycles.

Mental Health, Work Engagement and Meaningful Work-Role Fit of Industrial Psychologists: A Latent Profile Analysis

The purpose of this study was to identify distinctive mental health profiles for industrial psychologists based on the Mental Health Continuum. Further, it aimed to determine how these profiles differ with respect to work-role fit, meaningfulness and work engagement. It also aimed to investigate whether industrial psychologists within managerial or specialist differ in respect of different types of mental health. An online cross-sectional survey design was employed to draw a census sample (n = 274) from all South African industrial psychologists. A biographical questionnaire, the Work-Role Fit Scale, the Psychological Meaningfulness Scale, the Work Engagement Scale, and the Mental Health Continuum–Short Form were administered. Descriptive statistics, correlations, latent profile analysis, MANOVAs and ANOVAs were computed. Three mental health profiles for industrial psychologists were identified: languishing, moderately mentally healthy and flourishing. Significant differences between the three mental health profiles and experiences of meaningful work-role fit and work engagement were found, but not between experiences of managerial roles. The results show that individuals with different mental health profiles, experience work and its related outcomes, differently. Therefore, in order to enhance meaningful work-role fit and work engagement of industrial psychologists, a one-size-fits-all model may not be appropriate.

A semi-empirical, electrochemistry-based model for Li-ion battery performance prediction over lifetime

• The semi-empirical model suggested has physically meaningful fitting parameters. • The model displays comparable accuracy and computational efficiency to state-of-the-art mathematical equations. • The model successfully predicts terminal voltage for both cycle testing and load testing protocols. Predicting the performance of Li-ion batteries over lifetime is necessary for design and optimal operation of integrated energy systems, as electric vehicles and energy grids. For prediction purposes, several models have been suggested in the literature, with different levels of complexity and predictability. In particular, electrochemical models suffer of high computational costs, while empirical models are deprived of physical meaning. In the present work, a semi-empirical model is suggested, holding the computational efficiency of empirical approaches (low number of fitting parameters, low-order algebraic equations), while providing insights on the processes occurring in the battery during operation. The proposed model is successfully validated on experimental battery cycles: specifically, in conditions of capacity fade >20%, and dynamic cycling at different temperatures. A comparable performance to up-to-date empirical models is achieved both in terms of computational time, and correlation coefficient R 2 . In addition, analyzing the evolution of fitting parameters as a function of cycle number allows to identify the limiting processes in the overall battery degradation for all the protocols considered. The model suggested is thus suitable for implementation in system modelling, and it can be employed as an informative tool for improved design and operational strategies.

Linking cognition with fitness in a wild primate: fitness correlates of problem-solving performance and spatial learning ability.

Linking the cognitive performance of wild animals with fitness consequences is crucial for understanding evolutionary processes that shape individual variation in cognition. However, the few studies that have examined these links revealed differing relationships between various cognitive performance measures and fitness proxies. To contribute additional comparative data to this body of research, we linked individual performance during repeated problem-solving and spatial learning ability in a maze with body condition and survival in wild grey mouse lemurs (Microcebus murinus). All four variables exhibited substantial inter-individual variation. Solving efficiency in the problem-solving task, but not spatial learning performance, predicted the magnitude of change in body condition after the harsh dry season, indicating that the ability to quickly apply a newly discovered motor technique might also facilitate exploitation of new, natural food resources. Survival was not linked with performance in both tasks, however, suggesting that mouse lemurs' survival might not depend on the cognitive performances addressed here. Our study is the first linking cognition with fitness proxies in a wild primate species, and our discussion highlights the importance and challenges of accounting for a species' life history and ecology in choosing meaningful cognitive and fitness variables for a study in the wild.This article is part of the theme issue 'Causes and consequences of individual differences in cognitive abilities'.

A Measurement Model of Gestures in an Embodied Learning Environment: Accounting for Temporal Dependencies

Interactive learning environments with body-centric technologies lie at the intersection of the design of embodied learning activities and multimodal learning analytics. Sensing technologies can generate large amounts of fine-grained data automatically captured from student movements. Researchers can use these fine-grained data to create a high-resolution picture of the activity that takes place during these student–computer interactions and explore whether the sequence of movements has an effect on learning. We present a use-case modelling of temporal data in an interactive learning environment with hand gestures, and discuss some validity threats if temporal dependencies are not accounted for. In particular, we assess how, if ignored, the temporal dependencies in the measurement of hand gestures might affect the goodness of fit of the statistical model and would affect the measurement of the similarity between elicited and enacted movement. Our findings show that accounting for temporality is crucial for finding a meaningful fit to the data. In using temporal analytics, we are able to create a high-resolution picture of how sensorimotor coordination correlates with learning gains in our learning system.

An alternate method to extract performance characteristics in dye sensitized solar cells

Modeling the electrical properties of dye-sensitized solar cells (DSSCs) can fill the gap between the experimental and ideal performance observations for a reliable device diagnosis, design and optimization. The complex physical and chemical reactions between nanocrystalline semiconductor, electrolyte ions and dye molecules make their simulation an open issue to the researchers. Compared to the research works presented in literature, here, we provide a simpler, but more meaningful fit of current −voltage curves by developing a simulation model. The present work provides a reliable framework to extract electrical transport properties of the device, namely, diffusion coefficient, transport time, diffusion length, series resistance and performance parameters from steady state current–voltage curves without using interpretable frequency dependent methods, as well as transient characteristics. The model versatility makes it also capable of predicting the dye regeneration efficiency under short circuit, mid-voltages and high voltage ranges. The simulation method can be also implemented to compare the effect of different electrolytes as well as their species concentrations on regeneration efficiency and overall DSSCs performance. The whole model is designed in a flexible framework to be adapted to various kind of solar cells such as quantum dot and perovskite solar cells.

Diurnal Air Temperature Modeling Based on the Land Surface Temperature

The air temperature is an essential variable in many applications related to Earth science. Sporadic spatial distribution of weather stations causes a low spatial resolution of measured air temperatures. This study focused on modeling the air diurnal temperature cycle (DTC) based on the land surface temperature (LST) DTC. The air DTC model parameters were estimated from LST DTC model parameters by a regression analysis. Here, the LST obtained from the INSAT-3D geostationary satellite and the air temperature extracted from weather stations were used within the time frame of 4 March 2015 to 22 May 2017 across Iran. Constant parameters of the air DTC model for each weather station were estimated based on an experimental approach over the time period. Results showed these parameters decrease as elevation increases. The mean absolute error (MAE) and the root mean square error (RMSE) for three hours sampling were calculated. The MAE and RMSE ranges were between [0.1, 4] °C and [0.1, 3.3] °C, respectively. Additionally, 95% of MAEs and RMSEs were less than 2.9 °C and 2.4 °C values, correspondingly. The range of the mean values of MAEs and RMSEs for a three-hour sampling time were [−0.29, 0.6] °C and [2, 2.11] °C. The DTC model results showed a meaningful statistical fitting in both air DTCs modeled from LST and weather station-based DTCs. The variability of mean error and RMSE in different land covers and elevation classes were also investigated. In spite of the complex behavior of the environmental variables in the study area, the model error bar did not show significantly biased estimations for various classes. Therefore, the developed model was less sensitive to variations of land covers and elevation changes. It can be conclude that the coefficients of regression between LST and air DTC could model properly the environmental factors.

Service use of older people who participate in primary care health promotion: a latent class analysis

BackgroundRecruiting patients to health promotion programmes who will benefit is crucial to success. A key policy driver for health promotion in older people is to reduce health and social care use. Our aim was to describe service use among older people taking part in the Multi-dimensional Risk Appraisal for Older people primary care health promotion programme.MethodsA random sample of 1 in 3 older people (≥65 years old) was invited to participate in the Multi-dimensional Risk Appraisal for Older people project across five general practices in London and Hertfordshire. Data collected included socio-demographic characteristics, well-being and functional ability, lifestyle factors and service use.Latent class analysis (LCA) was used to identify groups based on use of the following: secondary health care, primary health care, community health care, paid care, unpaid care, leisure and local authority resources. Differences in group characteristics were assessed using univariate logistic regression, weighted by probability of class assignation and clustered by GP practice.ResultsResponse rate was 34% (526/1550) with 447 participants presenting sufficient data for analysis. LCA using three groups gave the most meaningful interpretation and best model fit. About a third (active well) were fit and active with low service use. Just under a third (high NHS users) had high impairments with high primary, secondary and community health care contact, but low non-health services use. Just over a third (community service users) with high impairments used community health and other services without much hospital use.ConclusionOlder people taking part in the Multi-dimensional Risk Appraisal for Older people primary care health promotion can be described as three groups: active well, high NHS users, and community service users.

SiC BJT Compact DC Model With Continuous- Temperature Scalability From 300 to 773 K

The first vertical bipolar intercompany (VBIC)-based compact dc model has been developed and verified for a low-voltage 4H-SiC bipolar junction transistor to continuously map a wide temperature range from 300 to 773 K. Temperature and doping dependent physical models for bandgap, incomplete ionization, carrier mobility, and lifetime have been taken into account to give physically meaningful fitting parameters for the compact model. Isothermal simulations using the default VBIC model are performed to extract key parameter sets from measured data at seven different temperature points. Then new temperature dependent equations for the key parameters are proposed and embedded in the default VBIC model. Consequently, a single set of model parameters at 300 K is used to achieve fitting over a wide temperature range from 300 to 773 K. This new model can be used for simulating circuits that require continuous description of device dc performance over a wide temperature range.