Terms Of Fitness Research Articles

Predicting the exclusive diffractive electron-ion cross section at small x with machine learning in Sartre

The event generator Sartre has been used extensively for simulations of electron-ion collisions in preparation for the Electron-Ion Collider (EIC). Sartre simulates exclusive diffraction in eA collisions, in principle for any nuclear species and exclusive final state, usually a vector meson. The coherent and incoherent cross sections for each process are calculated in the colour dipole model for small x from the first and second moments of the respective amplitude, averaged over initial state spatial configurations. Taking these averages is a very CPU demanding task. In order to function as an efficient event generator, these amplitude moments are saved into lookup tables which are used as input for the event generation, making the latter a very fast process. However, there are many recent and ongoing developments of the dipole models underlying the calculations, both in terms of fits of the model parameters to new data as well as new parametrisations of the dipole or proton geometries. Therefore, it is desirable to have a more flexible method for producing the lookup tables. Here, we propose a method using neural networks which can reduce the table production time by 90% while retaining the same precision in the resulting cross sections.

A drought year favored drier‐adapted origins over local origins in a reciprocal transplant experiment along a rainfall gradient

When plant populations show local adaptation along climatic gradients, climate change may displace them from their current fitness optima. Unclear is whether foreign genotypes originating from sites that approximate future climatic conditions can attain higher fitness and support existing populations, because foreign genotypes may lack important adaptations to other local conditions besides climate. We conducted a fully reciprocal transplant experiment with the winter annual Biscutella didyma across four sites along a steep natural rainfall gradient in Israel. It experienced an exceptional drought event that approximated future climatic conditions. This unique opportunity allowed to test whether genotypes transplanted from drier sites outperform local genotypes under a drier climate. The four population origins showed consistent clines in germination fraction, phenology, and vegetative biomass that suggest local adaptation along the rainfall gradient. In terms of fitness (survival, seed number), however, local origins were outperformed by genotypes originating from the next‐drier site along the gradient. Our results suggest that the experienced drought conditions favored origins from drier conditions over local origins despite local biotic interactions. Genotypes originating from drier sites may thus support existing populations and reduce their vulnerability to climate change. Moreover, we conceptualize under which conditions foreign genotypes may benefit local populations in general, which adds to clarify the importance of local adaptation to climatic and nonclimatic factors for populations under climate change.

Effect of a Kinect-Based Exercise on Improving Job-Related Physical Fitness Tests for Korean Firefighters

OBJECTIVES Firefighters play a critical role in high-temperature environments that demand substantial physical strength for efficient job performance. However, South Korean firefighters face challenges in maintaining optimal fitness levels due to limited opportunities for exercise. Factors that contribute to this issue include shift work, frequent dispatches, and the absence of exercise instructors.METHODS At total of 20 male Korean firefighters were divided into an exercise group (EG) and a control group (CG) with ten participants each. The EG group underwent an 8-week exercise intervention at a fire academy in Busan using a kinect-based exercise device called Virtual Mate (VM), while the CG group maintained their regular lifestyle without any additional exercise. Pre- and post-assessments were conducted, including the Candidate Physical Ability Test (CPAT), a physical fitness evaluation commonly used for firefighting duties in North America, the Korea Firefighter Performance Battery (KFPB), which is a modified version of CPAT, and basic physical fitness tests. The effect of exercise was analyzed using statistical methods, including repeated measures analysis of variance and paired t-tests.RESULTS Significant group-by-time interactions were observed for CPAT completion time (<i>p</i> = .004) and KFPB completion time (<i>p</i> = .012). After 8 weeks of exercise, the EG demonstrated a significant decrease in CPAT completion time (543.7 ± 93.0 sec vs. 500.8 ± 64.1 sec, <i>p</i> = .004) and KFPB completion time (584.1 ± 47.8 sec vs. 525.3 ± 16.2 sec, <i>p</i> = .001), while the CG did not show significant changes. In terms of basic physical fitness, only in EG, number of sit-ups performed in one minute significantly increased (<i>p</i> < .001)CONCLUSIONS The Kinect-based exercise device, VM, proves to be a valuable solution for addressing the practical challenges faced by Korean firefighters, ultimately enhancing their physical fitness.

Kinetic studies of solid foam catalysts for the production of sugar alcohols: Xylitol from biomass resources

Structured catalysts, such as solid foams, represent a very promising technology for continuous and stable production of high-value compounds derived from biomass, traditionally produced with batch and semibatch technologies using suspended catalysts. However, the synthesis of structured catalysts presents additional challenges related to their structure and the generation of porous coatings with suitable properties for dispersing the catalytically active phase on the support.This work was focused on synthesizing a Ru/C solid foam catalyst and investigating its activity in the selective hydrogenation of xylose to xylitol under different operational conditions. The carbon coating, the key step of preparation, was based on the formation and pyrolysis of poly(furfuryl alcohol) in the presence of different amounts of poly(ethylene glycol) (PEG; M = 8 kDa) as a pore former, which enabled tuning the support porosity. Thus, the catalyst prepared with 5 wt% PEG presented a micro-to-mesopores volume ratio of 1, and a good dispersion of Ru nanoparticles, as well as a better stability compared to the catalyst prepared without PEG.The extensive kinetic data collected in this work were mathematically modelled using three different approaches to elucidate the reactant adsorption mode: a non-competitive adsorption model, a non-competitive adsorption model considering the effect of temperature, and a semi-competitive adsorption model. The non-competitive temperature-dependent model displayed better performance in terms of fitting and reliability of the estimated parameters and predicted the adsorption of xylose as an endothermic process. On the other hand, the semi-competitive model gave similar results in terms of fitting and a value for the competitiveness factor of 0.74, which matches the hypothesis that the larger molecules, sugars, can occupy most of the active sites, while some interstitial sites remain accessible for hydrogen adsorption. The modelling results revealed a complex mode of sugar adsorption on the catalyst surface. This modelling concept can be applied to any system in which the molecule sizes are very different.

On the integration of decision trees with mixture cure model.

The mixture cure model is widely used to analyze survival data in the presence of a cured subgroup. Standard logistic regression-based approaches to model the incidence may lead to poor predictive accuracy of cure, specifically when the covariate effect is non-linear. Supervised machine learning techniques can be used as a better classifier than the logistic regression due to their ability to capture non-linear patterns in the data. However, the problem of interpret-ability hangs in the balance due to the trade-off between interpret-ability and predictive accuracy. We propose a new mixture cure model where the incidence part is modeled using a decision tree-based classifier and the proportional hazards structure for the latency part is preserved. The proposed model is very easy to interpret, closely mimics the human decision-making process, and provides flexibility to gauge both linear and non-linear covariate effects. For the estimation of model parameters, we develop an expectation maximization algorithm. A detailed simulation study shows that the proposed model outperforms the logistic regression-based and spline regression-based mixture cure models, both in terms of model fitting and evaluating predictive accuracy. An illustrative example with data from a leukemia study is presented to further support our conclusion.

The assessment of the effectiveness of the unimodal and bimodal models to evaluate the water flow through nature-based solutions substrates.

Nature-based solutions are popular techniques for managing stormwater. Most of them allow porous media as their main layer. The description of the Soil Water Retention Curve (SWRC) as the Unsaturated Hydraulic Conductivity Curve (UHCC) is often required to run the hydrological simulations with the physically based models. Using the unimodal and bimodal models to assess the SWRC and UHCC of soils is a widespread technique but their evaluation is often present in literature only in terms of curve fitting. Based on these assumptions, this work presents the performance assessment of the van Genuchten unimodal and bimodal models by functional evaluation of them based on the runoff from several substrates. Four substrates were investigated to define the structure, the SWRC, and the UHCC. Results showed that all substrates had a bimodal behaviour with lowest values of RMSE (RMSE_Θ = 0.0023 to 0.0037, RMSE_K = 0.0636 to 0.1284). Finally, a numerical simulation using the HYDRUS-1D model was performed for a three-month data set to check the effectiveness of the unimodal model instead of the bimodal one. The findings have shown that the unimodal model must be preferred instead of the bimodal because it has fewer parameters and assured low discrepancies in runoff volume (ε=0.00% to 6.25%).

Cure rate models for heterogeneous competing causes.

Cure rate models have been widely studied to analyze time-to-event data with a cured fraction of patients. In this type of model, the number of concurrent causes is assumed to be a random variable. However, in practice, it is natural to admit that the distribution of the number of competing causes is different from individual to individual. Our proposal is to assume that the number of competing causes belongs to a class of a finite mixture of competing causes distributions. We assume the number of malignant cells follow a mixture of two power series distributions and suppose that the time to the event of interest follows a Weibull distribution. We consider the proportion of the cured number of competing causes depending on covariates, allowing direct modeling of the cure rate. The proposed model includes several well-known models as special cases and defines many new special models. An expectation-maximization algorithm is proposed for parameter estimation, where the expectation step involves the computation of the expected number of concurrent causes for each individual. A Monte Carlo simulation is performed to assess the behavior of the estimation method. In order to show the potential for the practice of our model, we apply it to the real medical data set from a population-based study of incident cases of cutaneous melanoma diagnosed in the state of São Paulo, Brazil, illustrating that the model proposed can outperform traditional models in terms of model fitting.

Extending the Simple View of Reading in Second and Foreign Language Learning: A Meta-Analytic Structural Equation Modeling Approach

This study used a meta-analytic structural equation modeling approach to build extended versions of the simple view of reading (SVR) model in second and foreign language (SFL) learning contexts (i.e., SVR-SFL). Based on the correlation coefficients derived from primary studies, we replicated and integrated two previous extended meta-analytic SVR models, that is, Quinn and Wagner’s (2018) model with an English-speaking population with a cognitive factor and Peng et al.’s (2021) model with Chinese-speaking readers with metalinguistic skills. A total of 180 independent samples (N = 36,235) obtained from 152 empirical studies in SFL contexts were included in our meta-analytic structural equation model. The results revealed that the collected data successfully replicated H. Lee et al.’s (2022) SVR model in SL contexts (SV2R) in terms of overall model fit and moderation effects; in addition, the results confirmed that the data fit well with the extended SVR-SFL models.

Insights from a comparison of two hydrological modelling approaches in the Kwando (Cuando) River and the western tributaries of the Zambezi River basin

Study Region: The Kwando (Cuando) River and the western headwaters of the Zambezi River, which are data-scarce basins of southern Africa. Study Focus: A comparative analysis of the performance of two fundamentally different hydrological modelling approaches (a conceptual model and a theory guided machine learning model) in a data-sparse region. New Hydrological Insights for the Region: The machine learning model (HydroForecast) generally performs better – in terms of statistical fit between simulated and observed flows – than the conceptual model (Pitman). For the Kwando River, the conceptual model explicitly simulates the expected attenuation effects of a large floodplain, while the machine learning model represents this and other processes implicitly. The two models quantify the Kwando sub-basin flow contributions differently, with the conceptual model calibrated manually to align with the available qualitative information that suggests that the majority of the runoff is generated in the upstream sub-basin and then attenuated in the downstream floodplain. Generally, this work offers insight into how the two very different models can simulate historical flows in a large basin when streamflow observations and the forcing rainfall data are limited and of unknown quality, and suggests that a machine learning model better leverages information from multiple training parameters to reproduce the measured streamflows.

Marginal gap and internal fit of 3D printed versus milled monolithic zirconia crowns

BackgroundThis study aimed to evaluate and compare the marginal gap using two different methods and the internal fit of 3D printed and zirconia crowns.Methods3Y-TZP zirconia crowns (n = 20) were manufactured using subtractive milling (group M) and 3D printed (group P). The marginal gap was measured at 60 points using vertical marginal gap technique (VMGT). On the other hand, the silicone replica technique (SRT) was used to evaluate the internal fit and was divided into 4 groups: marginal gap, cervical gap, axial gap, and occlusal gap where the thickness of light impression was measured at 16 references. The numerical data was tested for normality using Shapiro–Wilk's test. They were found to be normally distributed and were analyzed using an independent t-test.ResultsUsing VMGT, group P had significantly higher mean marginal gap values of 80 ± 30 µm compared to group M = 60 ± 20 µm (p < 0.001). Also, with the SRT, the marginal gap of group P (100 ± 10 µm) had significantly higher values compared to group M (60 ± 10 µm). The internal fit showed significant difference between the tested groups except for Axial Gap.ConclusionsAlthough milled crowns showed better results. The 3D printed zirconia crowns offer clinically acceptable results in terms of marginal adaptation and internal fit. Both VMGT and SRT are reliable methods for the assessment of the marginal gap.

Study of Factors for Heart Disease Prediction between Men and Women based on Multiple Regression Models

Although previous studies have shown that there are differences in heart disease between men and women, the importance of some specific physical and chemical factors in the prediction of heart disease in different genders has not been clearly clarified. In this research, K-means clustering, multiple linear regression, logistic regression and random forest are adopted to analyze the UCI Heart Disease Data Set, which contains various physical and chemical indicators worth studying. The results demonstrate that exercise induced angina is more significant to the judgement of heart disease in women, while number of major vessels colored by fluoroscopy is more significant to the judgement of heart disease in men and type of chest pain is a statistically significant variable for both men and women. Thalassemia, ST depression induced by exercise relative to rest, greatest number of heartbeats per minute, age, resting blood pressure also have reference value for the judgment of heart disease. In terms of each model's fit to heart disease prediction, for women, the accuracy of random forest is the first, logistic regression is the second, and multiple linear regression is the third, while for men, the accuracy of random forest is the first, multiple linear regression is the second, and logistic regression is the third. These conclusions are an optimization of previous studies, and to a certain extent reflect that this study is of great significance to the prevention of heart disease in different groups of people.

문항반응이론을 적용한 범죄두려움 척도 타당화

Research on the fear of crime is constantly being conducted both at home and abroad. According to previous studies, the question of whether general fear and specific fear can be viewed on the same level or whether fear of crime can be explained by integrating them despite the differences in the characteristics of general fear and specific fear while explaining fear of crime has been raised.&#x0D; Therefore, in this study, we tried to verify the appropriateness of item fit, item difficulty, and item category by using the Rasch model based on Item Response Theory to measure fear of crime. The data of this study was conducted using the data of the National Crime Victim Survey, which is most widely used in research on fear of crime. As a result of the analysis, it was confirmed that the item corresponding to generalized fear had a strong heterogeneity with the item of specific ear in terms of item fit. In terms of item difficulty, it was found that fear of crime items were not appropriate for respondents with a low trait of fear of crime. Finally, as a result of confirming the appropriateness of the response category, the higher the respondent's characteristic, the higher the fear of crime, so the 5-point scale was investigated as appropriate. As a result of this study, it is expected that it will be used as basic data to provide public security services related to the fear of crime of the people in the academic context and the police in the study of fear of crime.

Kth-order equilibrium Weibull distribution: properties, simulation, and its applications

A generalization of some well-known probability distributions (viz., Weibull, exponential, gamma, Maxwell, and Chi-square) is introduced using the concept of weighted probability distributions. The introduced model is named Kth-order equilibrium Weibull distribution (KEWD). Various properties of the new distribution are studied in detail. One of the important properties of KEWD is that its hazard rate shows increasing, decreasing, and constant behavior. It is also shown that the new model belongs to the log exponential family. Various ordering relations of the KEWD are studied in comparison with the baseline model, i.e. Weibull distribution. Parameters are estimated using the concept of the maximum likelihood estimation technique. Using the Anderson–Darling test statistic, a simulation study is carried out to analyze the asymptotic normality behavior of maximum likelihood estimators. The behaviors of bias and mean square error are observed with the increase in sample size. The applications of new distribution are illustrated through its fitting to some incorporated real-life data sets. Finally, a comparison is made between KEWD, its sub-models, and some other introduced extensions of Weibull distribution in terms of fitting using the Akaike information criterion (AIC).

Validation of a Sejahtera Living Index Using the Rasch Model

The Maqāṣid al-Sharī’ah is a framework of divine values and beliefs that govern the life of a Muslim person. It contains clear guidelines on how to live the good life the way the religion sees it and on what constitutes quality of life, happiness, and wellbeing for the Muslim. In more precise terms, the maqāṣid al-sharī’ah guides Muslim in regard to how they can achieve a sejahtera life. Although sejahtera living is a fundamental and a highly important concept, it is not widely researched or addressed in the current body of empirical literature. One can see that there is a paucity of empirical data on sejahtera living. To address this research gap, this study first developed a scale to measure sejahtera living and subsequently examined its validity. It also estimated the index scores of sejahtera living among students and employees of a public university in Malaysia. This paper reports on the findings of this validation exercise which involved a sample 1,057 lecturers, nonacademic staff, and postgraduate and undergraduate students. Data were collected using a content validated 34-item questionnaire created based on the dimensions of the maqāṣid al-sharī’ah. The results of the Rasch measurement analysis provided evidence for the validity of the sejahtera living construct in terms of the good fit of the items, reliability and separation indexes, response category functioning, dimensionality, and construct validity. The extracted logit interval scores were used to construct linear measures of the baseline sejahtera living index. The findings support the need for instructional interventions and training to sustain and improve the quality of sejahtera living among Muslims.

Clinical outcomes of implant-supported and tooth-supported fixed prostheses fabricated from digital versus analogue impression: a systematic review and meta-analysis.

To analyze the clinical outcomes of implant-supported prostheses and tooth-supported fixed prostheses, fabricated from digital and conventional impression. The literature search was carried out on two electronic databases (PubMed and Cochrane Library). Randomized controlled trials (RCT) published from January 2011 to September 2022 were included. The bias risk was evaluated using Cochrane Risk of Bias Tool 2.0. Further screening was done for meta-analysis according to modified Newcastle-Ottawa scoring criteria. Forest plot was generated using a statistical method of inverse variance of random effect with 95% confidence interval. A total of 8 randomized controlled trials were included for systematic review out of which four studies were based on tooth-supported fixed prosthesis and remaining four were based on implant-supported prosthesis. Further screening was conducted and three studies were eligible for meta-analysis. Tooth-supported fixed prosthesis fabricated from digital impression showed no significant difference in the marginal fit in any region measured, except for occlusal region where conventional impression showed more favorable marginal fit. Implant-supported prosthesis fabricated from digital impression showed survival rates ranging from 97.3 to 100% and there was no statistically significant difference in marginal bone loss (p = 0.14). Implant-supported prostheses fabricated from digital and conventional impressions show no significant differences in their clinical outcomes. Tooth-supported fixed prostheses fabricated from digital impression have shown favorable findings in terms of marginal fit. Despite that, there is still lack of clinical trials with larger sample size and longer follow-up periods. Future studies that fulfill these two criteria are deemed necessary.

Do professional year placements matter for job quality? The case of economics graduates

ABSTRACT We study whether the completion of an optional professional year placement during undergraduate studies enhances job quality, in terms of earnings, job security and career fit, for economics graduates from a UK university. Using linear and discrete choice models, we estimate the effect of doing a professional year placement on four graduate outcomes that capture job quality and use a rich data set to control for demographics, educational background, academic achievement, degree, and graduate job characteristics. To account for possible self-selection bias, we use propensity score matching. We find a positive but weak effect of the professional year placement on earnings, but the salary gap becomes statistically insignificant once we control for self-selection. Similarly, we find no conclusive evidence supporting a positive effect of professional year placement on job security. However, we find a positive and robust effect of professional year placement on career fit: placement graduates are more likely to find jobs that fit their career plans than non-placement graduates, which holds even after controlling for self-selection. The empirical findings also show that job characteristics, like location and type of industry, and school background are additional factors contributing to graduates' employment quality. Finally, we find no differences in job quality due to gender.

Special physical training of qualified wrestlers of individual styles of wrestling

Background and Study Aim: Special physical preparedness of wrestlers is one of the main factors for achieving the highest possible results in competitive activity. Physical qualities can differ significantly among wrestlers of the same skill level, but different individual styles. In this regard, this study is aimed at improving the methods of special physical training of qualified wrestlers, taking into account individual styles of wrestling. Material and Methods: The study involved 30 wrestlers of middle weight categories (72 kg, 77 kg, 82 kg). The participants were divided into two groups: control group - 15 athletes, average age (18.96±1.09) years, average weight (76.27±5.58) kg; experimental group - 15 people, average age (19.07±0.95) years, average weight (75.87±5.68) kg. All participants belonged to athletes of a high level of skill. Training tasks were developed to improve the methodology of special physical training of wrestlers, taking into account the styles of wrestling. The proposed program was tested in a pedagogical experiment that lasted one year. Significance of differences between groups was assessed using nonparametric signs (z) and Rosenbaum (Q) tests. Results: during the experiment, the increase in the results of special physical preparedness among the wrestlers of the experimental group is much higher in all tests and it is in the range from 6% to 14%, and in the control group it is lower - from 2% to 3%. Also, these results were confirmed by a non-parametric criterion of signs. So, the wrestlers of the experimental group at the end of the experiment showed an improvement in performance. A significant increase in the results in the «Pull up» test was shown (z=1; p&lt;0.01). The time to complete the tests significantly decreased: «10 back arch throws» (z=0; p&lt;0.01), «10 suplexes» (z=0; p&lt;0.01), «Flips on the gymnastic “bridge”» (z=0; p &lt;0.01), «Running on the wrestling “Bridge”» (z=0; p&lt;0.01), «10 forward somersaults» (z=0; p&lt;0.01), «Three series of 15 back arch throws» (z=3; p&lt;0.05). The number of repetitions in the tests increased: «Pull-up in 20 s» (z=4; p&gt;0.05), «Push-up in 20 s» (z=4; р&gt;0.05). Conclusions: The fulfillment of the developed training tasks by the wrestlers of the experimental group made it possible at the end of the experiment to show significantly better results in terms of special physical fitness than the athletes of the control group (p&lt;0.05-0.01). Such results testify to the effectiveness of the proposed methodology and the possibility of its implementation in the training process.

Dioxin emission modeling using feature selection and simplified DFR with residual error fitting for the grate-based MSWI process

Municipal solid waste incineration (MSWI) with grate technology is a widely applied waste-to-energy process in various cities in China. Meanwhile, dioxins (DXN) are emitted at the stack and are the critical environmental indicator for operation optimization control in the MSWI process. However, constructing a high-precision and fast emission model for DXN emission operation optimization control becomes an immediate difficulty. To address the above problem, this research utilizes a novel DXN emission measurement method using simplified deep forest regression (DFR) with residual error fitting (SDFR-ref). First, the high-dimensional process variables are optimally reduced following the mutual information and significance test. Then, a simplified DFR algorithm is established to infer or predict the nonlinearity between the selected process variables and the DXN emission concentration. Moreover, a gradient enhancement strategy in terms of residual error fitting with a step factor is designed to improve the measurement performance in the layer-by-layer learning process. Finally, an actual DXN dataset from 2009 to 2020 of the MSWI plant in Beijing is utilized to verify the SDFR-ref method. Comparison experiments demonstrate the superiority of the proposed method over other methods in terms of measurement accuracy and time consumption.

Person Specific Parameter Heterogeneity in the 2PL IRT Model

Following Kelderman and Molenaar’s demonstration that a factor model with person specific factor loadings is almost indistinguishable from the standard factor model in terms of overall fit, we examined person specific measurement models in Item Response Theory, person specific discrimination and difficulty parameters were created by adding random variation at the item by person level. Using standard fitting algorithms for the 2PL IRT there was modest evidence of person- or item-level misfit using common diagnostic tools. The item difficulties were well-estimated, but the item discriminations were noticeably underestimated. As found by Kelderman and Molenaar, factor scores were estimated with less than expected reliability due to the underlying heterogeneity. The person specific models considered here are basically limiting cases of IRT models with multilevel, mixture, or differential item functioning structure. We conclude with some thoughts regarding real-world sources of heterogeneity that might go unacknowledged in common testing applications.

Model Fitness and Predictive Accuracy in Linear Mixed-Effects Models with Latent Clusters

In clustered data, observations within a cluster show similarity between themselves because they share common features different from observations in the other clusters. In a given population, different clustering may surface because correlation may occur across more than one dimension. The existing multilevel analysis techniques of the primal linear mixed-effect models are limited to natural clusters which are often not realistic to capture in real-life situations. Therefore, this paper proposes dual linear mixed models (DLMMs) for modeling unobserved latent clusters when such are present in data sets to yield appreciable gains in model fitness and predictive accuracy. The methodology explored the development and analysis of the dual linear mixed models (DLMMs) based on the derived latent clusters from the natural clusters using multivariate cluster analysis. A published data set on political analysis was used to demonstrate the efficiency of the proposed models. The proposed DLMMs have yielded minimum values of the models' assessment criteria (Akaike information criterion, Bayesian information criterion, and root mean squared error), and hence, outperformed the classical PLMMs in terms of model fitness and predictive accuracy.