Model In Scale Research Articles

INTERACTIVE PATHWAYS: EXPLORING STUDENTS’ ACCEPTANCE OF USING NEARPOD FOR ENGLISH GRAMMAR PROFICIENCY

Lately, technology-enhanced learning tools, such as Nearpod, demonstrate the efficacy of integrating learning, particularly in an English grammar class. This study aims to investigate students' reactions to technology used for teaching grammar, precisely the acceptance of using Nearpod for grammar class. This study employed a mixed-method design with questionnaire and in-depth interview. The questionnaire used in this study was based on Technology Acceptance Model (TAM) in a Likert scale. Thirty-eight undergraduate students were selected purposefully from Informatics Engineering Department who enrolled in Grammar class. The findings revealed a highly positive perception of Nearpod's utility for grammar instruction, confirming on students’ acceptance and highlighting its benefits for enhancing language skills, especially in grammar. Students agreed that Nearpod was beneficial to help understand and practice the language. The findings demonstrate that Nearpod is an effective tool for enhancing grammar instruction and suggest its implications for language educators and curriculum designers. This study contributes to our understanding of the role of technology in fostering integrative learning in grammar classes and highlights the need for further research in this area.

Statistical Analysis and Scenario Modeling of Non-Stationary Runoff Change in the Loess Plateau: A Novel Application of the Generalized Additive Model in Location, Scale and Shape

The hydrological series in the Loess Plateau region has exhibited shifts in trend, mean, and/or variance as the environmental conditions have changed, indicating a departure from the assumption of stationarity. As the variations accumulate, the compound effects caused by the driving variables on runoff variations grow complex and interactive, posing a substantial risk to water security and the promotion of high-quality development in regions or river basins. This study focuses on the Tuwei River Basin in the Loess Plateau, which experiences significant changes in vegetation coverage and minimal human disturbance, and examines the cross-driving relationship between the runoff change and its driving variables (including hydrometeorological and environmental variables). A quantitative statistical analysis method based on the GAMLSS is then developed to estimate the interacting effects of changes in the driving variables and their contribution to runoff changes. Finally, various anticipated scenarios are used to simulate the changes in driving variables and runoff disturbances. The findings indicate the following: (1) The developed GU, LO, and NO distribution-based GAMLSSs provide a notable advantage in effectively capturing the variations in groundwater storage variables, actual evapotranspiration, and underlying surface parameters, as well as accurately estimating the impacts of other relevant variables. (2) The precipitation and groundwater storage variables showed predominantly positive contributions to the runoff change, but actual evapotranspiration had an adverse effect. The changes in underlying surface parameters, particularly since 2000, increase actual evapotranspiration, while decreasing groundwater storage, resulting in a progressive decrease in runoff as their contribution grows. (3) The scenario simulation results reveal that alterations to the underlying surface have a substantial influence on the evolution of runoff in the Tuwei River Basin. Additionally, there are cross-effects between the impact of various driving variables on runoff, potentially compounding the complexity of inconsistent changes in runoff sequences.

ENTROPY-RANDOMIZED ESTIMATION OF NONLINEAR DYNAMICAL MODEL PARAMETERS ON OBSERVATION OF DEPENDENT PROCESS

The work is devoted to the development of the method of randomized machine learning in the direction of estimating dynamic models of related processes using real data, one of which is considered as the main one, and the other as a dependent one. The model of the main process in this concept is implemented by a dynamic model based on differential equations with parameters, which in turn are implemented by a static model in a different time scale. Randomized machine learning is a new theory at the intersection of data science, machine learning and data mining, based on the concept of entropy to estimate the probabilistic characteristics of model parameters. Such characteristics are the probability distributions of the corresponding objects, the estimates of which are the distributions implemented by the probability density functions or discrete distributions. Achieving this goal becomes possible thanks to the idea of moving from models with deterministic parameters to models with random parameters and, additionally, measured at the output with random noise, which achieves the consideration of the stochastic nature, which is obviously present in any natural phenomenon. As a demonstration of the proposed method, we consider the problem of predicting the total number of infected people based on the SIR dynamic epidemiological model, in which one of the parameters is considered as the state of the associated process implemented by the static model. Its evaluation is carried out according to the observations of the main process, and forecasting is carried out using the model of the associated one. Conducted experiment using real data on COVID-19 cases in Germany shows the efficiency of the proposed approach. The forecast obtained by the classical least squares method leads to an underestimation of the model output compared to the real observed data, while the proposed approach demonstrates more flexibility and potentially allows you to obtain forecasts that are more adequate to the real data, which confirms its effectiveness and adequacy under conditions a small amount of data with a high level of uncertainty.

Two-time scale dynamic closed-loop scheduling for gas supply network with multiple air separation units

The gas supply network with multiple air separation units (ASUs) in steel enterprises provides the essential gas products for iron- and steel-making processes. Steel enterprises must make scheduling decisions for their gas supply networks to meet the fast-changing downstream demands. Therefore, the dynamic characteristics of the process must be considered during short-interval production scheduling to ensure more reasonable scheduling decisions. A two-scale dynamic scheduling method is first developed in this paper to consider the process dynamics. In this method, scheduling decisions are made in the slow time scale, and the nonlinear dynamic characteristics are described using a varied time constant dynamic model in the fast time scale. Then, an autoregressive moving average (ARMA) model is established to address the unavoidable model mismatch, which explicitly predicts and integrates future mismatches into the scheduling model. Moreover, a moving horizon closed-loop framework is designed to integrate the state feedback, mismatch modeling, and scheduling decision modification so that scheduling decisions can be modified in real time to avoid potential safety hazards. The results of the industrial application indicate that the proposed method can provide better scheduling decisions in case studies of model mismatch and unknown leakage compared with existing methods.

Presenting a mathematical model for reduction of delays in construction projects considering quality management criteria in uncertainty conditions

Nowadays, organizations and corporations are fully aware of the fact that they can't satisfy their customers' needs considering the rapid change of their needs and tastes. In addition, the attempts made by organizations to add variety to their products and improving their own status don’t seem to be sufficient to meet customers' needs. Orientation of organizations and corporations to benefit from each other's expertise and facilities for satisfaction of customers'' needs in the form of a coherent and efficient supply chain was a strategy which has been the mere result of increasing technology growth. Therefore, Chance-Constrained Programming approach has been used in this study to compensate for the uncertainties resulting from construction projects. Therefore, after defining uncertainty-related parameters, such parameters will be included in the optimization model. The mathematical model provided in this study was multi-objective and multi-modal. In other words, different modes and conditions have been assumed for any activity. Moreover, one of the objectives assumed for mathematical model was to minimize the maximum delay time in project implementation. Then, GAMS software has been used to solve the mathematical model in small and medium scale and Matlab and NSGA-II meta-heuristic algorithm have been used to solve the model in large scale.

Limit theorems on the mesoscopic scale for the Anderson model

In this paper, we study eigenvalue fluctuations of the finite volume Anderson model in the mesoscopic scale. We carry out this study in a regime of exponential localization and prove a central limit theorem for the eigenvalue counting function in a shrinking interval.

0779 Impact of 8-h Time Limited Eating on Sleep in Adolescents with Obesity

Abstract Introduction Time-based interventions, such as time-limited eating (TLE), have become a promising dietary strategy in adults with obesity. TLE involves shortening the daily eating window to a pre-specified number of hours and fasting for the remaining hours of the day without altering diet quality. No study to date has examined the impact of TLE on sleep quality in adolescents with obesity. Methods We conducted a secondary analysis of a 12-week pilot study that compared TLE (8-h eating window/16-h fasting window) to a prolonged window (12-h eating window/12-h fasting window) in adolescents with obesity (ages 14 to19, 72% female, 74% Hispanic). All youth completed the Pittsburgh Sleep Quality Index (PSQI) at baseline (n = 42), weeks 4 (n = 43), and 12 (n = 42). The seven component PSQI score, develops a global score that ranges from 0 to 21, where higher scores indicates worse sleep. Glycemic profiles were captured via continuous glucose monitors (CGM) worn for the duration of the study. Mixed effects generalized linear model in log scale and ordinal logistic regression models were utilized to (a) examine the change in PSQI total score and (b) assess the association between glycemic variability and PSQI total score overtime by intervention arm. Results Across all youth included in this analysis (n = 43), there was improvement in PSQI total score at week 12 compared to baseline that was marginally significant after adjusting for multiple testing (% change = -19.60, 95%CI = -31.83 to -5.18, p = 0.057). The median PSQI score for the TLE group (n = 29) was 6 at week 0 (5 to10) and 5 at week 12 (2.5 to 7), suggesting sleep quality improved. The changes in PSQI total score, sleep ratio, and sleep onset score over the course of study were not statistically different between groups (p > 0.05). There was no significant association between glycemic variability and PSQI score between the groups. Conclusion Findings from this secondary analysis indicate that TLE does not negatively alter sleep quality or duration in adolescents with obesity. The potential effects of TLE on sleep should be further investigated in larger randomized trials. Support (if any)

大規模マイクロマグネティックシミュレーションを用いた永久磁石の保磁力解析の最近の進歩と展望

The coercivity of permanent magnets is an important factor for the various applications including the electric vehicle motors. Improving the coercivity is highly demanded, however, it is quite sensitive to the microstructure of the magnet. For understanding the relation between the coercivity and the microstructure, the micromagnetic simulations of the permanent magnets have been studied extensively. Recently, as improving the computer performance and applying numerical techniques, such as the energy minimization, we can treat a multi-grain permanent magnet model in sub-micrometer scale. Hence, the effects of the magnetic grain size, the grain orientation and the magnetic properties of the grain boundary have been analyzed by the micromagnetic simulation. In addition, combining the micromagnetic simulation and the path analysis method make the thermal effect analysis enable. In this paper we summarize the foundation of the micromagnetic simulation for the coercivity analysis and review the studies on the coercivity analyses of the permanent magnet by using micromagnetic simulations.

Application of physical models to improve the mitigation structures of debris flow in a case study

Debris flows are a phenomenon in mountains areas, where a large amount of mobile rock material, large amounts of water and steep slopes meet together. They are characterized by high capacity of sediment transport, high concentration of sediment with wide range of grain sizes and high front-velocity. To improve the protection measures against debris flow, it is necessary to know the processes of debris flows and its interaction with structures of the measures. By investigation of the mitigation structures in experimental scaled physical models it is possible to understand the functionality of the structures under different scenarios as well as optimization of the structures. For the protection of the counties Agarn und Leuk in Valais in Switzerland a multi-level debris flow basin system has been designed. By means the physical model in scale of 1:55 the functionality and the dimensions as well as the details of the structures could be optimized.

Forest Structure Simulation of Eucalyptus Plantation Using Remote-Sensing-Based Forest Age Data and 3-PG Model

Eucalyptus plantations play an important role in the timber supply and global warming mitigation around the world. Forest age is a critical factor for evaluating and modeling forest structure (e.g., diameter at breast height (DBH), height (H), aboveground carbon stocks (ACS)) and their dynamics. Recently, the spatial distribution of forest age at different scales based on time series remote sensing data has been widely investigated. However, it is unclear whether such data can effectively support the simulation and assessment of forest structure, especially in fast-growing plantation forests. In this study, the physiological principles in predicting growth (3-PG) model was firstly optimized and calibrated using survey and UAV lidar data at the sample plot (SP) scale, and was then applied at the forest sub-compartment (FSC) scale by designing different simulation scenarios driven by different forest age data sources and adjustments. The sensitivity of the simulated forest structure parameters to forest age was assessed at the SP and FSC levels. The results show that both the survey forest age data and the remote-sensing-derived forest age data could accurately estimate the DBH, H, and ACS of eucalyptus plantations with the coefficients of determination (R2) ranging from 0.87 to 0.94, and the relative root mean square error (RRMSE) below 20% at SP level. At the FSC level, the simulation results based on remotely sensed forest age data are significantly better than FSC forest age data from surveys by forestry bureaus, with R2 of ACS 0.7, RMSE 9.12 Mg/ha, and RRMSE 28.24%. The results of the sensitivity analysis show that the DBH, H, and ACS show different degrees of variation under different adjusted forest ages at SP and FSC level. The maximum difference in ACS is 82.91% at the SP scale if the forest age decreases 12 months and 41.23% at the FSC scale if the forest age increases 12 months. This study provides an important reference for future studies using forest age data obtained by remote sensing to drive the forest carbon model in a large spatial scale.

A single-atom mechano-optical transducer for sensing sub-attonewton vector DC force

Mechano-optical transducers are devices that convert a force or displacement signal to an optical one, enabling ultrasensitive mechanical detection. Currently, ultraweak DC force sensors with high spatial resolution are in high demand for the search of possible exotic spin-dependent interactions beyond the standard model in sub-millimeter scale. Here, we demonstrate a mechano-optical transducer of a single trapped ion with the force sensitivity about 600 zN/Hz for the DC force. This method utilizes the Doppler shift of the time-resolved fluorescence to detect the ion's micromotion that is coupled to a vector force. By alternating the directions of the detection laser beams, the vector DC forces can be precisely measured. Such a mechano-optical transducer provides sub-attonewton sensitivity with the spatial resolution in single-atom level, enabling various uses for both scientific and industrial purposes.

PostWRF: Interactive tools for the visualization of the WRF and ERA5 model outputs

PostWRF is an open-source software toolkit to facilitate the main visualization tasks and data handling for the WRF model outputs. The toolkit is mostly written in NCL and Shell, with a namelist that resembles the WRF or WPS namelists. Besides the visualizations, PostWRF provides WRF-NetCDF to GeoTIFF conversion for GIS applications, ERA5-NetCDF reanalysis data plotting and extraction, and preparation and plotting of the RTTOV model inputs (from WRF) and outputs. The primary purpose of PostWRF is to benefit the environmental researchers (both experienced and inexperienced) to make use of the WRF model simulations, in a straightforward and efficient way, without dealing with coding and syntax errors. Since the WRF model simulates most aspects of a full atmospheric model in the regional scale, the toolkit can also be used as an educational aid in meteorological and environmental science. PostWRF is freely available on GitHub (https://github.com/anikfal/PostWRF), provided with HTML documentations (https://postwrf.readthedocs.io/en/master) and guided examples.

Bifurcation analysis based on new macro two-velocity difference model

Based on the new macroscopic two-velocity difference model, this paper analyzes the linear stability of the new model and studies the nonlinear bifurcation theory. First, the linear stability analysis method is used to study the stability conditions of the shock wave in the model. Then, considering the long wave model in the coarse-grained scale, the reduced perturbation method is used to analyze the characteristics of the traffic flow in the metastable region, and the solitary wave solution of the Korteweg-de Vries (KdV) equation in the metastable region is derived. In addition, by using the bifurcation analysis method, the type, and stability of the equilibrium solution are discussed and the existing conditions of the saddle-node bifurcation are proven. Then, taking the saddle-node bifurcation as the starting point, we draw the density space-time diagram and phase plane diagram of the system. It is proven that the newly proposed model can describe complex traffic phenomena such as stop-and-go and sudden changes in stability, which is of great help to solve traffic congestion.

Face to face or blended learning? A case study: Teacher training in the pedagogical use of ICT.

We are experiencing a transitional period in education: from the traditional, face to face teaching model to new teaching and learning models that apply modern pedagogical approaches, utilize technological achievements and respond to current social needs. For a number of reasons including the recent pandemic covid-19 situation, technology enhanced distance learning, seems to gain ground against traditional face to face teaching and in fact, in a sharp way. Acknowledging that changes in education need time, research and careful steps in order to be successfully applied and established at large scale, in this paper we attempt to compare face to face (“traditional”) teacher training with teacher training through a blended learning approach/ model. The latest combines characteristics of both face to face and distance learning models. The case study is based on a large-scale in-service teacher training initiative which has been taking place in Greece for over a decade to train teachers in the utilization and application of digital technologies in the teaching practice (i.e. B-Level ICT Teacher Training). The B-Level ICT Teacher Training was initially based exclusively on face to face teaching but it was later adapted to a specially designed blended learning model which combined both face to face and synchronous distance sessions, accompanied by asynchronous activities and supported by specific e-learning platforms and tools. The comparison refers to the effectiveness of the two models/ approaches, as it derives from teacher trainees’ performance, especially in the framework of the certification procedure that takes place through nationwide, independent exams that follow the training and assesses the relevant knowledge and skills acquired. Research findings point out better performances of a small or marginal scale for the teachers of various specialties who participated in blended learning teacher training programs compared to those who participated in traditional teacher training programs. Actually, it is shown that blended learning model trainees i) feel more comfortable to participate in the exams for the certification of knowledge and skills acquired, ii) have some better success rate and iii) get a bit higher grades in these exams. Thus, it can be argued that learning outcomes of the blended learning application in this teacher training initiative, overstep those of the “traditional” model in a small scale and with some slight differentiations among teacher specialties.

Algılanan kolektif süreklilik ölçeğinin (kültürel ve tarihsel süreklilik) Türkçeye uyarlanması: Geçerlik ve güvenirlik çalışması

Cultural and historical continuity is a subject that is discussed in many branches of science from the last periods of the Ottoman Empire to the present. However, to what extent individuals perceive cultural and historical continuity has been ignored in the domestic literature. In order to fill this gap in the literature, the research aims to adapt the perceived collective continuity scale (PCC) developed by Sani et al. (2007) to Turkish. The original form of the scale, which included twelve items, consists of two dimensions: perceived cultural continuity and perceived historical continuity. Exploratory (n=249) and confirmatory factor analyzes (n=295) were conducted on the Turkish version in order to test the validity and reliability. According to the results of the exploratory factor analysis, the total variance explained by the two-factor, 10-items Turkish scale form was found to be 72.48%. The fit indices of the first-level and second-level models related to the two-factor model in the original scale showed that the model was confirmed. In addition, the reliability values of the scale were found at a high level. These results revealed that the Turkish version of the scale shows the two-factor structure in its original form. In conclusion, the current research indicated that the Turkish form of the scale (PCC) is a valid and reliable measurement tool that measures individuals' collective continuity perceptions. It is believed that the scale (PCC) will contribute to future studies in many fields such as history education research, identity research, migration research, and social psychology by filling the gap in the domestic literature.

The Edinburgh feeding evaluation in dementia (EdFED) scale: ATurkish validity and reliability study.

The present study aimed to adapt the Edinburgh Feeding Evaluation in Dementia (EdFED) Scale to Turkish. This methodological study was reported using STARD (The STAndards for Reporting of Diagnostic Accuracy). The sample included 200 people with dementia. The data were obtained using the Personal Information Form, the Edinburgh Feeding Evaluation in Dementia Scale, and the Mini Nutritional Assessment Test. For the Turkish adaptation of the scale, construct validity (confirmatory factor analysis), criterion validity (concurrent scale validity) and reliability analysis (Cronbach's α coefficient, item-total score correlation) were performed, respectively. The three-factor model in the original scale was verified. The content validity index was 0.95. The Cronbach's α coefficient factors were as follows: 'Indicators of Patient Difficulty' α=0.81, 'Patient's Need for Assistance' α=0.79 and 'Indicators of Feeding Difficulty' α=0.64, respectively. When the CFA fit indexes were examined, the model fit values were good. The three-factor structure was verified, compared with the original model and was compatible. No modification was needed in the model. The Edinburgh Feeding Evaluation in Dementia Scale Turkish version provides reliable and valid measures of feeding difficulties in people with dementia. It has satisfactory psychometric properties and is suitable to use in clinical practice. Feeding is one of the most neglected subjects in caring for people with dementia. Screening is recommended to evaluate feeding and malnutrition, but, to our knowledge, there is no tool/scale to evaluate the feeding of the dementia patient in Turkish. The EdFED scale can serve healthcare professionals and caregivers as a practical tool for feeding difficulties in people with dementia.

A Non-stationary and Probabilistic Approach for Drought Characterization Using Trivariate and Pairwise Copula Construction (PCC) Model

Under variable climatic conditions, the conventional Standardized Precipitation Index (SPI) and Reconnaissance Drought Index (RDI) are inadequate for predicting extreme drought characteristics. Non-stationary Standardized Precipitation Index (NSPI) and Non-stationary Reconnaissance Drought Index (NRDI) are, therefore, developed by fitting non-stationary distributions. The Generalized Additive Model in Location, Scale and Shape (GAMLSS) framework, with time varying location parameters considering the external covariates, is used to fit the non-stationary distributions. Multivariate ENSO Index (MEI), Southern Oscillation Index (SOI), Sea Surface Temperature (SST), and Indian Ocean Dipole (IOD) are considered as external covariates for the non-stationary drought assessment. The performances of stationary and non-stationary models are compared. The study also concentrated on the trivariate and the Pairwise Copula Construction (PCC) models to estimate the drought return periods. The comparison of two copula models revealed that the PCC model performed better than the trivariate Student’s t copula model. The recurrence intervals arrived at for the drought events are different for trivariate copula model and PCC model. This study showed that non-stationary drought indices will be helpful in the accurate estimate of the drought characteristics under the changing climatic scenario.

Surgical - orthodontic planning of complex cases through the use of 3D models: A case report

Stereolithographic models are three-dimensional reproductions of digital data, created using CAD-CAM software. In recent years, the use of these three-dimensional models has increased as an aid in orthodontic diagnostics and in the planning of oral and maxillofacial surgery [1]. Unlike conventional diagnostic imaging techniques, such as Orthopanoramic radiographs and Cone-Beam CT (CBCT), which provide valuable digital information on bone size and geometry, stereolithographic models exhibit a physical three-dimensional reproduction of the anatomical area of interest with maximum accuracy. These truly guide both the diagnosis process and the surgical operation itself, in order to improve preoperative planning and minimize operative and postoperative morbidity. For the reproduction of these models, a CBCT scan is necessary. Through a 3D printing software, the data is converted into a stereolithographic model in scale. It is produced with transparent resin for better visualization of the bone structures and internal anatomical structures involved, such as the path of the inferior alveolar nerve or, as in this case report, the presence of dental elements totally impacted in the bone [2,3]. The use of these three-dimensional models has demonstrated a series of advantages such as a more accurate planning both orthodontic and surgical treatment, a significant reduction of operating times, a reduction in the time of exposure of the wound, better communication with colleagues and patients and more predictable surgical results [1]. Toothimpaction is defined as the failed eruption of a tooth, which remains embedded in the maxillary or mandibular bone after its period of physiological eruption. It can be partial or total and it most commonly happensto third molars, especially mandibular wisdom teeth, followed by the maxillary canines. Unlike the third molars, the maxillary canines constitute dental elements of great functional importance. They play agreat role of control in the movements of mandibular excursion. According to the classification of included maxillary canines by Yamamoto et al. there are 7 types (Figure 1), with the first three types showing more predictable results in the surgical orthodontic treatment and the following four types considered untreatable with orthodontic anchorage [4].

Experimental Study on Flow Mixing Characteristics of Lower Plenum of Small PWR

In order to study the flow mixing characteristics of lower plenum of small pressurized-water reactor (PWR), this study conducts a hydraulic experiment on the reactor model in scale of 1∶3 based on the scaling method, and obtains the flow mixing factor matrices for the core inlet under the balanced and unbalanced flow conditions of the cold leg by measuring the solution concentration change. The results show that under the balanced flow condition, the changes of cold leg flow have slight effect on the core inlet flow mixing factor matrix, while under the unbalanced flow condition, the fuel assembly flow mixing factor near the outlet pipe is significantly affected by the flow unbalance, and that small change occurs to the flow mixing factor in the central region. Therefore, the flow mixing characteristics of lower plenum of small PWR are stable at the balanced flow, and focus should be put on the change in flow mixing characteristics of fuel assemblies near the outlet under the unbalanced flow condition.

Reassessing the frequency and severity of meteorological drought considering non-stationarity and copula-based bivariate probability

Drought is a multifaceted natural phenomenon with diverse spatial and temporal characteristics and significant environmental, social, and economic effects. The utility of conventional drought indices based on the assumption of stationarity is questionable due to the advent of rapid environmental change. We proposed a non-stationary standardized precipitation index (SPINS) to reassess the nature of meteorological drought. Precipitation observations from eight rain gauge stations for 1961–2017 in South Korea were fitted to a non-stationary gamma distribution using a generalized additive model in location, scale, and shape algorithm. Drought duration and severity were identified by SPINS and joint and conditional return periods were estimated using copula-based bivariate probabilities. The results show that average drought duration, severity, and frequency obtained from SPINS were more severe than those produced by a conventional standardized precipitation index (SPIC). The average inter-arrival time was 41.46 months and 39.86 months for SPIC and SPINS, respectively, at the Seoul station. The joint return periods of observed drought event from March 2008 to June 2008 with a duration of 4 months and a severity of 4.8 were 10.4 and 6.4 years for the SPIC and SPINS, respectively, at the Seoul station. We conclude that return periods calculated using the SPINS are more reliable than those calculated by the SPIC. These findings should prove helpful in drought management and risk assessment under a changing environment as effective management requires prior knowledge of events.