Number Of Experimental Data Research Articles

Empowering vocational students: Exploring mobile learning for sustainable high-level cognition in authentic contexts

Early studies show that learning with mobile devices, also known as mobile learning, improves students’ learning in authentic contextual learning–i.e., learning connected to the real world. However, no empirical evidence has yet to firmly prove the effects of mobile technology on specific student skillsets such as learning scalability which means learning can be applied in various scenarios and learning sustainability which means learning can be sustained in real-world environments. Therefore, this study aims to explore the effect of learning using a mobile app called mobile Smart-Physics on learning cognitive levels, learning scalability (e.g., number of learning locations and number of experimental data), and learning sustainability (e.g., number of completed assignments). Eleventh-grade vocational high school students volunteered for this quasi-experiment and were divided into an experimental group (EG), which used Smart-Physics, and a control group (CG), which used a mobile Ubiquitous-Physics (U-Physics) app. The findings show that the EG significantly outperformed the CG concerning learning cognitive levels, learning scalability and learning sustainability. Smart-Physics features enabled the students to tackle technical and pedagogical difficulties during physical investigations in real-world environments and, in some cases, improved their task accomplishment and sustained their motivation to learn. Location awareness promoted the students’ authentic experiential learning, which sharpened their ability to apply learning in real-world environments and upload more experimental data. Feedback helped the students consolidate their physics theories and practical experiences, thereby generating more learning records with meaningful multimedia content like experimental graphs, tables, and notes in various learning locations. Therefore, we encourage practitioners to use smart learning environment features in their learning tools and activity designs.

Renewal of the regression model for normalization of specific energy consumption

Purpose. To develop a method of updating the regression model for the normalization of specific energy consumption in the presence of frequent and significant changes in the energy efficiency of the production process. Methodology. Analysis of existing methods of updating regression models, comparison of their possibilities, and synthesis of the method of updating the model in conditions of frequent and significant changes in the energy efficiency of the production process. Findings. It was established that in the presence of a significant number of possible variants of structural and mode changes in the energy consumption of the control object, the introduction of associated variables into the regression model is problematic, as it requires an increase in the number of experimental data in conditions of their expected heterogeneity. The flaw of the well-known regression model for normalizing the power consumption of the object of control is revealed, which consists of the fact that the model does not take into account the last values in the sequence of their appearance of experimental data obtained in the process of energy efficiency control. This reduces the accuracy of predicted energy consumption values. It is proposed to update the regression model every time after performing the energy efficiency control and sample adjustment. Adjustments are implemented by checking the homogeneity of the obtained experimental data, followed by their addition to the elements of the existing sample and removal (if necessary) from the sample of outdated data. The defined sequence of adjustment of the initial data allows timely updating of the model and implementation of the forecast of specific energy consumption, entering data reflecting the latest changes that occurred in the facility's energy supply. The proposed method of updating the model implements the approximation in time of the moment of energy efficiency control to the moment of obtaining experimental data for building a regression dependence for normalizing energy consumption values. This helps to increase the accuracy of the forecast of normalized values. A significant change in the conditions of production of products with a violation of the homogeneity of data is accompanied by a transition to the transitional mode of adjustment, where it is proposed to reduce the number of elements of the existing sample, ensuring the sequential removal of the elements furthest from the next moment of control. Extraction continues until data homogeneity is achieved. During the daily control of the efficiency of electricity consumption, the change in the values of the regression model coefficients in the process of its renewal reflects the changes in the object's electricity consumption that occurred over the last day. This allows you to separate their impact from the impact of changes that occurred earlier and to assess the level of this impact. Originality. For the first time, the shortcomings of the existing methods of updating regression models in the conditions of frequent and significant changes in the energy efficiency of the production process were identified. A method of updating the model under these conditions has been developed, which involves adjusting the sample of experimental data by changing the number of its elements and checking the homogeneity of the data. Practical value is that the sequence of actions during the implementation of the developed method of updating the regression model is defined, which allows for an increase in the accuracy of calculating the normalized values of specific energy consumption.

Experimental investigation and modeling of falling film heat transfer on partial dry-out condition

Falling film heat transfer has been applied for decay heat removal in more and more nuclear reactors, in which the coolant feed rate is set small to prolong the effective cooling time during accident conditions. Previous works have shown that the cooling surface is under partial dry-out condition for most of the time during the accident, while the existing falling film experiments are mainly focused on fully-wetted condition. In this paper, falling film experiments were conducted on a horizontal plain copper tube within 0.019–0.099 kg/m·s coolant flow rate, 0–164.1 kW/m2 heat flux, and dry-out condition is emphasized. In the experiments, it is found that the overall liquid coverage rate measured by infrared equipment was between 45.2 % and 100 %, and the dry-out process is recorded and analyzed as well. Moreover, HTC (Heat Transfer Coefficient) at wetting area is defined to characterize the heat transfer on the surface covered by liquid and is obtained based on a three-dimensional heat conduction model of test tube. Boiling HTCs on falling film are found 10 % approximately higher than that in pool boiling condition. The difference is attribute to the different mechanism of bubble departure and bubble growth environment, with which the falling film condition is found to result in a 1–3 times bubble departure diameter and a 0.85 times bubble growth rate compared with pool boiling condition. Finally, a general falling film heat transfer model is developed based on the conservation equations with consideration of liquid coverage, gas–liquid interface evaporation and liquid sub-cooling. The model shows effective for falling film heat transfer prediction on both fully-wetted and dry-out, convection and boiling conditions with different working fluids through the validation with a number of experimental data. The findings in this work could be helpful for the design and analysis of nuclear cooling system, as well as the falling film evaporation system.

Extraction of Malay Root Word that Starts with Letter P in Malay e-Khutbah using Rule Based

Stemming is an important process in text processing especially in Natural Language Processing (NLP). It could extract root word from the affix words in the text. In addition, it helps in extracting useful information that contributes to many area of research study such as Information Retrieval. Several stemming algorithms have been discussed in previous studies. However, there are limited studies on Malay stemming process and the number of experimental data used. In this study, we focus on stemming process of Malay stemming algorithm by using rule-based algorithm for a larger dataset of Malay language text. The syntactic linguistic rule-based method was used in the stemming process involves of removing prefixes, suffixes and, prefixes and suffixes. Training dataset was used in this study which consisted of 3233 sentences from e-khutbah text. The result of the experimental evaluation was done by measuring the precision, recall and f-measure. It was found that the algorithm used in this study showed a promising result based on total of dataset used for each test. The value of precision, recall and F-measure increase to 95%, 97% and 97% respectively. The enhancement of the stemming process has shown a significant impact on Malay text processing which in general improved the performance of NLP applications.

Применение СВЧ измерений для сортировки составных элементов археологических объектов

In this paper the design of a coaxial resonant measuring converter with a shortening capacitance used to determine the electrophysical parameters of the constituent elements of archaeological objects is proposed. Based on the numerical model, the optimal geometric dimensions of the resonant sensor are determined. The analysis of the main characteristics of the measuring transducer is carried out. A number of experimental data were obtained confirming the applicability of the proposed technique.

Experimental Investigation on Pedestrian Walking Load in Steel Footbridges

Accurate simulation of walking load is of great significance in conducting human-induced vibration analyses. However, accurate pedestrian walking load data obtained from long-span footbridges is scarce and data reliability depends on the sensor used for measurement. In the current work, Yanluo Footbridge with 102 m span was adopted as test site and Xnode high-precision acceleration wireless sensor was applied for measurements. An experimental investigation was performed on walking loads according to bipedal walking force model. In experimental studies, single-person and multi-person walking tests were performed at Yanluo Footbridge to measure corresponding stride frequency and dynamic load factor. The acceleration time-histories of walking pedestrians were accurately recorded using three-axis wireless acceleration sensor Xnode. Furthermore, the equation of dynamic load factor was derived by analyzing time-histories and power spectra and the design models of pedestrian walking load and crowd load were developed based on a great number of experimental data. Time histories of pedestrian walking loads showed regular periodic changes and dynamic load factor increased by increasing stride frequency. Using the walking load model developed in this work, the reliable structure response of human-induced vibration analysis can be obtained.

Predicting the position-dependent dynamics of machine tools using progressive network

The structural dynamics of a machine tool exhibit position-dependent behavior under operation due to the kinematic reconfiguration of its feed drives. Experimental identification of position-dependent dynamics can be time-consuming, and simulation models may lack accuracy due to modeling uncertainties. In this paper, the prediction of a machine tool's position-dependency is approached under the progressive network, a transfer learning technique to bridge the gap between the simulation and real-world domains. The dynamics simulation model may be somewhat inaccurate, but still describes a machine tool's overall position-dependency. The real-world domain is then characterized by sparse dynamics measurements made on the real machine tool. Under the progressive network architecture, a neural network is first trained in the simulation domain to extract valuable prior knowledge about the machine's position-dependency. A second network posed in the real-world domain then accesses such prior knowledge from the simulation domain via lateral connections to improve its dynamics predictions. Such transfer learning methodology may be most advantageous when the number of experimental data is limited, and the machine is unavailable for further testing due to production purposes. The proposed methodology was validated under numerical and experimental studies.

IMPROVEMENT OF THE ROUGHNESS FORECASTING MODEL OF COATING OF NON-RIGID ROAD PAVEMENT COATING

To ensure safe, comfortable driving at high speeds, a strong, even surface is required for the entire life of the pavement. In this regard, the issue of predicting changes in the equality of coverage over time is very important. The article considers the peculiarities of changing the longitudinal equality of the road surface. Purpose is to improve the model of forecasting the equality of non-rigid pavement. Methods – analytical and experimental. The analysis of existing decisions on the issue of forecasting the equality of coverage is performed. The most important factors influencing the change in the equality of road coverage have been identified. Based on the analysis of theoretical models and a number of experimental data, an improved model for predicting the equality of coverage of non-rigid pavement is proposed. MathCAD and MS Excel were involved in the development of an improved model that takes into account the most important factors. The model of change of coverage roughness, where increase in the roughness index over time is considered as a function of such parameters, is improved: the modulus of pavement elasticity (actual or required); the number of load cycles for t years of pavement operation; the share of trucks in the traffic flow; the factor of safety margin of the pavement structure. The adequacy of the developed model of changing the roughness of coverage is confirmed by statistical processing of experimental data obtained by the thesis author and other researchers on public roads with different service life, and calculated data under the theoretical model. The Pearson correlation coefficient between experimental and calculated data is more than 0.95, which indicates the adequacy of the developed model. Compared to the well-known models of forecasting coverage roughness, the improved model allows to apply a wider range of values of the general equivalent modulus of elasticity of pavement design (from 100 MPa to 600 MPa) and to receive forecast values of roughness for service life of non-rigid pavement over 5 years.

Minimum Number of Experimental Data for the Thermal Characterization of a Hot Water Storage Tank

This paper demonstrates that it is possible to characterize the water temperature profile and its temporal trend in a hot water storage tank during the thermal charge process, using a minimum number of thermocouples (TC), with minor differences compared to experimental data. Four experimental tests (two types of inlet and two water flow rates) were conducted in a 950 L capacity tank. For each experimental test (with 12 TC), four models were developed using a decreasing number of TC (7, 4, 3 and 2, respectively). The results of the estimation of water temperature obtained with each of the four models were compared with those of a fifth model performed with 12 TC. All models were tested for constant inlet temperature. Very acceptable results were achieved (RMSE between 0.2065 °C and 0.8706 °C in models with 3 TC). The models were also useful to estimate the water temperature profile and the evolution of thermocline thickness even with only 3 TC (RMSE between 0.00247 °C and 0.00292 °C). A comparison with a CFD model was carried out to complete the study with very small differences between both approaches when applied to the estimation of the instantaneous temperature profile. The proposed methodology has proven to be very effective in estimating several of the temperature-based indices commonly employed to evaluate thermal stratification in water storage tanks, with only two or three experimental temperature data measurements. It can also be used as a complementary tool to other techniques such as the validation of numerical simulations or in cases where only a few experimental temperature values are available.

A new heat transfer correlation for supercritical water flowing in a vertical tube - an hitherto approach

This paper provides an hitherto new approach of a heat transfer correlation based on heat flux to mass flux ratio for predicting wall temperature of supercritical water flowing in a bare vertical tube. Totally 10,174 numbers of experimental data were collected, Out of which 4236 data are heat flux/mass flux ≤0.4 and 5938 data are heat flux/mass flux>0.4. These data were obtained from various experimental resources and Lookup table and it covers q/G ranges from 0.085 to 1.295, mass flux ranges from 201 to 3500 kgm−2 s−1, heat flux ranges from 129 to 1600 kWm−2, pressure ranges from 225 to 320 bar, diameter of the tube 4.4 to 38 mm and bulk fluid enthalpy ranges of 250 to 3200 kJkg−1 of vertical tube working at various supercritical working conditions based on normal, enhancement and deteriorated heat transfer regions. By using these data, a new heat transfer correlation is derived for finding the wall temperature by Levenberg-Marquardt (LM) algorithm of multi dimensionless analysis method with Nonlinear curve fitting method. A general form of forced convection heat transfer correlation was obtained by Buckingham Π theorem. The Wall temperatures predicted by various 7 existing correlations along with this new correlation are found by Visual basic program and compared with experimental data for evaluating the deviations. The rank was provided based on error band from ±0.5% to ±50% etc., and error analysis were conducted for proving the best fitting correlation.

Statistical data expansion using Kriging for probabilistic capacity factor calibration

This study proposes a new reliability-based capacity factor calibration method based on the EN 1990 Annex D method in combination with Kriging for a statistical data expansion. The original EN 1990 Annex D method has been widely used in partial safety factor or capacity factor calibrations in international structural design standards by rigorously considering the modelling error of the design equations in comparison with real experimental data. However, as the number of experimental data is often limited in practical situations, large statistical uncertainty needs to be incorporated in the safety factor calibration process, and the calibrated partial safety factors or capacity factors have large variations. In the proposed method, Kriging, a data-driven nonlinear interpolation method, is utilised to statistically expand the experimental database used for modelling error estimation when experimental data are limited. To make the calculation rigorous, the Kriging error of the statistically expanded data is also estimated and incorporated into the proposed framework. The proposed method is demonstrated through two numerical examples and three real world structural design examples including the shear strength of reinforced concrete beams and the axial resistance of concrete-filled steel tubular stub columns with circular and rectangular sections, each of which has more than 300 experimental data for verification. The results show that the proposed method always improves the calibration results by statistical data expansion, which does not need any additional physical costs for experiments.

Divergently Transcribed ncRNAs in Escherichia coli: Refinement of the Transcription Starts Assumes Functional Diversification.

Non-coding regulatory RNAs (ncRNAs) comprise specialized group of essential genetically encoded biological molecules involved in the wide variety of cellular metabolic processes. The progressive increase in the number of newly identified ncRNAs and the defining of their genome location indicate their predominant nesting in intergenic regions and expression under the control of their own regulatory elements. At the same time, the regulation of ncRNA’s transcription cannot be considered in isolation from the processes occurring in the immediate genetic environment. A number of experimental data indicate the notable impact of positional regulation of gene expression mediated by dynamic temporal DNA rearrangements accompanying transcription events in the vicinity of neighboring genes. This issue can be perceived as particularly significant for divergently transcribed ncRNAs being actually subjected to double regulatory pressure. Based on available results of RNAseq experiments for Escherichia coli, we screened out divergent ncRNAs and the adjacent genes for the exact positions of transcription start sites (TSSs) and relative efficiency of RNA production. This analysis revealed extension or shortening of some previously annotated ncRNAs resulting in modified secondary structure, confirmed stable expression of four ncRNAs annotated earlier as putative, and approved the possibility of expression of divergently transcribed ncRNAs containing repetitive extragenic palindromic (REP) elements. The biogenesis of secreted ncRNAs from divergently transcribed ffs, chiX, ralA, and ryhB is discussed taking into account positions of TSSs. Refinement of TSSs for the neighboring genes renders some ncRNAs as true antisense overlapping with 5′UTR of divergently transcribed mRNAs.

Influence of rotational speed and torque on meshing efficiency of double helical gear transmission system

The accuracy of gear meshing efficiency model is the key to study the influence factors of gear meshing efficiency. Experiment is an effective method to verify the theory model. Therefore, the acquisition of experimental value of gear meshing efficiency is particularly important. Taken double helical gears as the research object, on the analysis of a large number of experimental data, the experimental value of gear meshing efficiency for double helical gears are calculated and then the influence of rotational speed and torque on meshing efficiency of double helical gears is studied. Firstly, the calculation method of transmission efficiency for experimental value in different layout of gear test-rigs with closed power flow is summarized. Secondly, the calculation method of meshing efficiency for experimental value in gear test-rig with closed power flow is introduced. Thirdly, the calculation method of load-dependent bearing loss is given. Finally, the experimental value of meshing efficiency for double helical gears is calculated and the influence of rotational speed and torque on meshing efficiency of double helical gears is obtained, which lays a theoretical foundation for the further improvement of the transmission efficiency of double helical gears.

Light hydrocarbons solvents solubility modeling in bitumen using learning approaches

In this paper, two types of machine learning, namely neuro-fuzzy inference system (ANFIS) and multilayer perceptron (MLP), have been studied to model light hydrocarbons’ solubility solvent in bitumen. The 268 number of experimental data is used in this work from different articles. The input parameters are Temperature (T), pressure (P), and molecular weight (MW) of hydrocarbons. The result shows the high performance of the MLP model with a two layers to predict the experimental values. The estimated values were investigated by statistical parameters such as R 2, MSE, and MARD%. According to, statistical parameters, the values of 0.99, 0.00081, and 0.68 for MLP, and 0.96, 0.0029, and 0.78 for ANFIS indicate the high performance of the MLP model. Comparison between established models and previous work indicates that the developed model can be a suitable technique for solubility modeling

Niedobór snu a neuroplastyczność

Niedobór snu wywiera negatywny wpływ na funkcje poznawcze mózgu, w tym zdolności do uczenia się i zapamiętywania. Szereg danych doświadczalnych wskazuje, iż sen ma zasadnicze znaczenie dla utrzymania homeostazy mózgu oraz konsolidacji śladów pamięciowych. Obydwa zjawiska opierają się w dużej mierze na procesach plastyczności synaptycznej, uzależnionej od związanych ze snem wzorców aktywności bioelektrycznej mózgu. Niniejszy wykład koncentruje się na dwóch hipotezach, które zdominowały badania nad funkcją i znaczeniem snu w ostatnich latach ? hipotezą homeostazy synaptycznej i hipotezą aktywnej konsolidacji systemowej i przytacza dane uzyskane z badań na modelach zwierzęcych, które wykazały, jak deficyt snu wpływa na leżące u podstaw tych zjawisk, mechanizmy plastyczności synaptycznej.

Research on Treatment of Retaining Wall Foundation with Geosynthetics Based on BP Neural Network

Through the long-term load creep test of CE131 geonet and SD L25 retaining wall foundation, which are widely used in reinforced earth engineering, a large number of experimental data are obtained. On this basis, the least-squares and BP neural network are used to predict its creep variables. The principle of least squares is to find a curve in the curve family to fit the experimental data. From the sum of the squared errors σ = 0. 001 16, the fitting accuracy is higher. The BP neural network has adaptive learning and memory capabilities, especially the three-layer BP neural network model. The maximum error between the predicted value and the actual value is 0.91%, which is a lot better than the error of the least square 3.4%. This method Found a new way for creep prediction.

The Optimization Criteria of EAST High Quality Discharge Based on Key Physical Parameters

As the first full superconducting tokamak in the world, the equipment and the experimental level on EAST have been constantly improved. A large number of experimental data are produced, and then the high-quality discharge has great significance to EAST project. In order to improve the experimental level of EAST and guide the high-quality discharge, this paper selected the high-quality discharge shots from the historical experimental data of EAST, and statistically analyzed the key physical parameters of this shots. Finally, the standard of high-quality discharge shots has been conclude, so as to guide the experimenter to obtain the optimal parameter configuration and improve the experimental level of EAST.

Hyperfine Anomaly in Eu Isotopes and the Universiability of the Moskowitz–Lombardi Formula

A method for determining the hyperfine anomaly, without using the nuclear magnetic moments, is used on a series of unstable isotopes of Eu. The large number of experimental data in Eu makes it possible to extract the hyperfine anomaly for a number of unstable isotopes. Calculations of the Bohr–Weisskopf effect and hence the hyperfine anomaly are performed using the particle-rotor formalism. The result from the calculations and experiments is compared with other theoretical calculations and the empirical Moskowitz–Lombardi formula. The results show that the Moskowitz–Lombardi formula is not universal.

A Response-Adaptive Method for Design of Validation Experiments in Computational Mechanics

For model verification and validation (V & V) in computational mechanics, a hypothesis test for the validity check (HTVC) is useful, in particular, with a limited number of experimental data. However, HTVC does not address how type I and II errors can be reduced when additional resources for sampling become available. For the validation of computational models of safety-related and mission-critical systems, it is challenging to design experiments so that type II error is reduced while maintaining type I error at an acceptable level. To address the challenge, this paper proposes a new method to design validation experiments, response-adaptive experiment design (RAED). The RAED method adaptively selects the next experimental condition from among candidates of various operating conditions (experimental settings). RAED consists of six key steps: (1) define experimental conditions, (2) obtain experimental data, (3) calculate u-values, (4) compute the area metric, (5) select the next experimental condition, and (6) obtain additional experimental datum. To demonstrate the effectiveness of the RAED method, a case study of a numerical example is shown. It is demonstrated that additional experimental data obtained through the RAED method can reduce type II error in hypothesis testing and increase the probability of rejecting an invalid computational model.

In-silico study of age-related ionic remodeling in human ventricular cardiomyocytes

Ageing is one of the dominant risk factors for cardiovascular diseases. A large number of experimental data is collected on the cellular remodeling in the ageing myocardium from mammals, but very little is known about the human cardiomyocytes. We used a combined electro-mechanical model of human ventricular cardiomyocytes and a population of models approach to investigate the variability in the response of cardiomyocytes to age-related changes in model parameters of the ionic currents. To generate a control model population, we varied 9 ionic parameters and excluded model samples with biomarkers of cellular action potential (AP) and Ca2+ transient (CT) falling outside the physiological ranges. Using the control population of models, we evaluated the response to age-related reduction in the K+ transient outward current, SERCA pump, and an increase in the Na+Ca2+ exchange current and L-type Ca2+ current. Then, we randomly generated 60 age-related sets of the 4 parameters and applied each set to every model in the control population. We showed an increase in the frequency of repolarization anomalies (RA) and critical AP prolongation in the ageing model populations suggesting arrhythmogenic effects of the ionic remodeling. The population based approach allowed us to assess the pro-arrhythmic contribution of the ionic parameters in ageing cardiomyocytes.