Multidimensional Test Research Articles

Aesthetic Cognitive Computing Clues of Materials Based on Multidimensional Perception

Abstract Based on the multidimensional visual perception of materials, the Kansei engineering method was employed to investigate the multidimensional perceptual strategy and the basis of aesthetic perception of materials. Solid wood and metal, common materials in interior environments that are closely related to health care, were used as material samples. The study was conducted on an online, self-developed collection, selecting more than 300 participants among designers and consumers with a mixed ratio of males to females to participate in the experiments. The first study screened out eight dimensions of material perception by visual semantic differences, selecting 80 metal materials and 14 solid wood materials for multidimensional perception tests. According to the test results, 12 metal materials and six solid wood materials were assigned systematically to 72 dual-material products to calculate the perceptual distance of each dimension among these materials. The results demonstrate that people have similar perception clue strategies in most perception dimensions between different materials. Regarding products with multiple materials, the perception of the materials on the main display surface has a more powerful influence on the overall product perception. The harmonious combination of materials will enhance health care in daily life. This article provides a possible cognitive clue on materials. The experimental results provide reference for medical product design.

Implementation of advanced Riemann solvers in a neutrino-radiation magnetohydrodynamics code in numerical relativity and its application to a binary neutron star merger

We implement advanced Riemann solvers HLLC and HLLD \cite{Mignone:2005ft,MUB:2009} together with an advanced constrained transport scheme \cite{Gardiner:2007nc} in a numerical-relativity neutrino-radiation magnetohydrodynamics code. We validate our implementation by performing a series of one- and multi-dimensional test problems for relativistic hydrodynamics and magnetohydrodynamics in both Minkowski spacetime and a static black hole spacetime. We find that the numerical solutions with the advanced Riemann solvers are more accurate than those with the HLLE solver \cite{DelZanna:2002rv}, which was originally implemented in our code. As an application to numerical relativity, we simulate an asymmetric binary neutron star merger leading to a short-lived massive neutron star both with and without magnetic fields. We find that the lifetime of the rotating massive neutron star formed after the merger and also the amount of the tidally-driven dynamical ejecta are overestimated when we employ the diffusive HLLE solver. We also find that the magnetorotational instability is less resolved when we employ the HLLE solver because of the solver's large numerical diffusivity. This causes a spurious enhancement both of magnetic winding resulting from large scale poloidal magnetic fields, and also of the energy of the outflow induced by magnetic pressure.

Concept of Vector Multicomponent Physical Quantities, Models and Measurement Method

The paper presents a new view of vector physical quantities as multicomponent quantities. Each of the components of the mentioned multicomponent quantities can carry important and even unique information about the sources and causes of their occurrence. Looking at the vector quantity as the multicomponent quantity led to the need to form the corresponding conception. There are three positions of this conception in this paper, which are formulated as follows: vector multicomponent physical quantities are considered as functions of the set of their constituent information components; the communication functions of the specified information components in the models of multicomponent physical quantities are determined by the laws of vector algebra; information models of vector multicomponent physical quantities allow an alternative representation of information components depending on the selected coordinate system.The mathematical model of the vector multicomponent physical quantity is presented. This model is fundamental and directly follows from the positions of the conception formulated above. This model can be applied to describe multicomponent displacements and deformations that both simple and complex objects undergo. An example of the complex object can be the manipulator of the universal industrial robot. The space for modeling multicomponent displacements of simple objects was shown in the paper. Information models of vector multicomponent physical quantities allow one to alternatively represent informative components. And the task of constructing such models is complex and ambiguous. Therefore, the formal apparatus for the synthesis of such models, which is based on certain rules and conventions, was proposed in the paper. The theoretical foundations of the method of optical measurements of informative components of multicomponent displacements and deformations of simple objects, which involves the use of multidimensional test objects, are presented.

On the application of Jacobian-free Riemann solvers for relativistic radiation magnetohydrodynamics under M1 closure

Radiative transfer plays a major role in high-energy astrophysics. In multiple scenarios and in a broad range of energy scales, the coupling between matter and radiation is essential to understand the interplay between theory, observations and numerical simulations. In this paper, we present a novel scheme for solving the equations of radiation relativistic magnetohydrodynamics within the parallel code Lóstrego. These equations, which are formulated taking successive moments of the Boltzmann radiative transfer equation, are solved under the grey-body approximation and the M1 closure using an IMEX time integration scheme. The main novelty of our scheme is that we introduce for the first time in the context of radiation magnetohydrodynamics a family of Jacobian-free Riemann solvers based on internal approximations to the Polynomial Viscosity Matrix, which were demonstrated to be robust and accurate for non-radiative applications. The robustness and the limitations of the new algorithms are tested by solving a collection of one-dimensional and multi-dimensional test problems, both in the free-streaming and in the diffusion radiation transport limits. Due to its stable performance, the applicability of the scheme presented in this paper to real astrophysical scenarios in high-energy astrophysics is promising. In future simulations, we expect to be able to explore the dynamical relevance of photon-matter interactions in the context of relativistic jets and accretion discs, from microquasars and AGN to gamma-ray bursts.

A Generator of Multiextremal Test Classes With Known Solutions for Black-Box-Constrained Global Optimization

A generator of classes of multidimensional test problems for benchmarking continuous constrained global optimization methods is proposed. It is based on the generator of test classes for global optimization proposed in 2003 by Gaviano, Kvasov, Lera, and Sergeyev and extends the previous generation procedure from the box-constrained case to the case of nonlinear constraints. The user has the possibility to fix the difficulty of tests in an intuitive way by choosing several types of constraints. A detailed information (including the global solution) for each of 100 problems in each generated class is provided to the user. The generator is particularly suited for testing black-box optimization algorithms that normally address low or medium dimensional problems with hard to evaluate objective functions.

Self-directed learning quotient and common learning types of pre-medical students in Korea by the Multi-Dimensional Learning Strategy Test 2nd edition: a descriptive study.

This study aimed to find the self-directed learning quotient and common learning types of pre-medical students through the confirmation of 4 characteristics of learning strategies, including personality, motivation, emotion, and behavior. The response data were collected from 277 out of 294 target first-year pre-medical students from 2019 to 2021, using the Multi-Dimensional Learning Strategy Test 2nd edition. The most common learning type was a self-directed type (44.0%), stagnant type (33.9%), latent type (14.4%), and conscientiousness type (7.6%). The self-directed learning index was high (29.2%), moderate (24.6%), somewhat high (21.7%), somewhat low (14.4%), and low (10.1%). This study confirmed that many students lacked self-directed learning capabilities for learning strategies. In addition, it was found that the difficulties experienced by each student were different, and the variables resulting in difficulties were also diverse. It may provide insights into how to develop programs that can help students increase their self-directed learning capability.

Age, Sex, Education, Emotional Intelligence, Cognitive Intelligence, and Management Level: A Study from Great Britain

Do non-, first-line, middle and senior managers differ in their cognitive ability and emotional intelligence? In this study we interested in the demographic and ability differences of people at different management levels. Over 6000 adults completed a multidimensional intelligence test (IQ) with five subscales and a measure of Trait Emotional Intelligence (EQ) with 15 subscales. First, we examined sex, age, educational and management level correlates of both EQ and IQ. Whilst there were many significant results, effect sizes were small. The focus of the paper was the regression using management level as the criterion variable and demography, EQ and, IQ as the predictor variables at facet and domain levels. Age and sex, particularly the former, accounted for nearly 30% of the variance, but both EQ and IQ added incremental variance. Facet level variance showed that specifically IQ number speed, and EQ sociability and emotionality (negatively) related to managerial level. Implications for general management and limitations are acknowledged.

Extreme Months: Multidimensional Studies in the Carpathian Basin

In addition to the one-dimensional mathematical statistical methods used to study the climate and its possible variations, the study of several elements together is also worthwhile. Here, a combined analysis of precipitation and temperature time series was performed using the norm method based on the probability distribution of the elements. This means, schematically speaking, that each component was transformed into a standard normal distribution so that no element was dominant. The transformed components were sorted into a vector, the inverse of the correlation matrix was determined and the resulting norm was calculated. Where this norm was at the maximum, the extreme vector, in this case the extreme month, was found. In this paper, we presented the results obtained from a joint analysis of the monthly precipitation and temperature time series for the whole territory of Hungary over the period 1871–2020. To do this, multidimensional statistical tests that allowed the detection of climate change were defined. In the present analysis, we restricted ourselves to two-dimensional analyses. The results showed that none of the tests could detect two-dimensional climate change on a spatial average for the months of January, April, July and December, while all the statistical tests used indicated a clear change in the months of March and August. As for the other months, one or two, but not necessarily all tests, showed climate change in two dimensions.

Classical and Bayesian Uncertainty Intervals for the Reliability of Multidimensional Scales

The reliability of a multidimensional test instrument is commonly estimated using coefficients ωt (total) and ωh (hierarchical). However, point estimates for the coefficients are rarely accompanied by uncertainty estimates. In this study, we compare bootstrap and normal-theory confidence intervals. In addition, we develop Bayesian versions of coefficients ωt and ωh by sampling from a second-order factor model. Results from a comprehensive simulation study show that the studied confidence intervals performed well when the sample size was sufficiently large (). The Bayesian estimates performed well across most studied conditions. When the sample size was small and the reliability low, only the bias-corrected and accelerated bootstrap confidence interval approached a satisfactory coverage among all intervals. This study guides on ωt and ωh confidence intervals and introduces ωt and ωh credible intervals that are easy to use and come with the benefits of Bayesian parameter estimation.

Characterization of hydrogen assisted corrosion cracking of a high strength aluminum alloy

Abstract Environmentally and hydrogen assisted cracking can occur during application of high-strength aluminum alloys. However, there are only few suitable laboratory procedures to characterize and evaluate the environmentally and hydrogen assisted cracking behavior of materials. By optimizing the hydrogen charging parameters and slow strain rate, a multidimensional test procedure was established, which could simulate the actual working environment and could realize the test and evaluation of hydrogen assisted cracking susceptibility in the laboratory. Moreover, it provides a new environmental adaptability evaluation method for the high-strength aluminum alloy materials.

A miniaturized electrothermal array for rapid analysis of temperature preference behaviors in ecology and ecotoxicology

Due to technical limitations, there have been minimal studies performed on thermal preferences and thermotactic behaviors of aquatic ectotherm species commonly used in ecotoxicity testing. In this work, we demonstrate an innovative, purpose-built and miniaturized electrothermal array for rapid thermal preference behavioral tests. We applied the novel platform to define thermal preferences in multiple invertebrate and vertebrate species. Specifically, Dugesia notogaea (freshwater planarians), Chironomus tepperi (nonbiting midge larvae), Ostracoda (seed shrimp), Artemia franciscana (brine shrimp), Daphnia carinata (water flea), Austrochiltonia subtenuis (freshwater amphipod), Physa acuta (freshwater snail), Potamopyrgus antipodarum (New Zealand mud snail) and larval stage of Danio rerio (zebrafish) were tested. The Australian freshwater water fleas, amphipods, snail Physa acuta as well as zebrafish exhibited the most consistent preference to cool zones and clear avoidance of zones >27 °C out of nine species tested. Our results indicate the larval stage of zebrafish as the most responsive species highly suitable for prospective development of multidimensional behavioral test batteries. We also showcase preliminary data that environmentally relevant concentrations of pharmaceutical pollutants such as non-steroidal anti-inflammatory drug (NSAID) ibuprofen (9800 ng/L) and insecticide imidacloprid (4600 ng/L) but not anti-depressant venlafaxine (2200 ng/L) and (iv) anticonvulsant medications gabapentin (400 ng/L) can perturb thermal preference behavior of larval zebrafish. Collectively our results demonstrate the utility of simple and inexpensive thermoelectric technology in rapid exploration of thermal preference in diverse species of aquatic animals. We postulate that more broadly such technologies can also have added value in ecotoxicity testing of emerging contaminants.

Consistency and accuracy in the simulation of two-phase flows with phase change using sharp interface capturing methods

In this paper, the modeling of phase change in an incompressible two-phase flow solver is detailed without restricting the numerical methods to a specific interface capturing method. Starting from existing methodologies of the literature, the present work gives a step-by-step investigation of each numerical aspect of phase change to obtain an accurate and consistent solver.The main challenge when including phase-change is the handling of flux discontinuities at the interface when advancing temperature and species mass fraction. An accurate and second order discretization is proposed for any Eulerian representation of the interface either by adding a sharp source term or by imposing a boundary condition at the interface. As the accuracy and convergence rate of such solver are driven by the reconstruction of the evaporation rate m˙, particular attention is devoted to the reconstruction of gradient normal to the interface. Several methodologies are proposed to compute second-order gradients at the interface location adapted to any interface representation. Applying such techniques to a second-order accurate field leads to an expected first order accuracy of m˙ but with remarkable accuracy improvements using ghost cell methods with quadratic extrapolation. Then, several phase-change procedures are built by combining a selection of numerical methods to handle flux discontinuities and evaluate gradients. The procedures are investigated on planar phase-change simulations to bring out inconsistent combination choices. Finally, a multidimensional evaporation test case is presented to show the final accuracy and limitations of phase-change modeling in today two-phase flow solvers.

Modeling the impact of digital economy on urban environmental pollution: Empirical evidence from 277 prefecture-level cities in China

With the recent technological advancements, such as the internet, big data, and cloud computing, China’s digital economy plays a significant role in economic development. However, the digital economy may also affect the environmental quality, but the prevailing literature is scant on how the digital economy affects urban environmental pollution. To fill this gap, this study established an urban digital economy index system based on 277 prefecture-level cities in China and calculated their digital economy indicators from 2011 to 2019. Using a two-way fixed effects model, a mediation effect model and a moderation effect model, a multi-dimensional empirical test is conducted to determine the impact of the digital economy on urban pollution effects and internal mechanisms. Empirical results indicate that the digital economy significantly reduces urban pollutant emissions and shows spatial heterogeneity. After a series of robustness tests and endogenous analysis, our findings are consistent. The mechanism test results reveal that the digital economy can help mitigate pollution emissions through improved industrial structure, promotion of green innovation, and financial development. Further, the empirical results also demonstrate that government intervention can significantly enhance the negative environmental impact of the digital economy. Moreover, the findings from the heterogeneity test (i.e., city size, time, and space heterogeneity) show that the development of the digital economy is more significant in reducing urban pollution in large and eastern coastal cities. While the digital economy development policy impact of the digital economy on reducing pollution has been found after being adjusted by the national strategy. Our study enriches the research regarding the causes and mechanisms of environmental pollution, provides empirical evidence that the digital economy contributes to pollution control and provides decision-making references for enabling the growth of the digital economy and maximizing its pollution reduction power.

The Relationship between the Level of Self-Esteem, Cooperative Learning and Anxiety in Group Tasks in the Pandemic Context

Pandemic Covid-19 changed the course of education. During the pandemic, many tough measures were being taken to stop the spread of the virus, one of which is schools closure. The purpose of this study is to determine the relation between the levels of self-esteem, cooperative learning and anxiety of primary school pupils in group tasks in the pandemic context. The participants selected for this study were 110 children (45 females, 65 males) with an average age of 8 years (range 7-9 years), attending the second grade of primary school. One of the issue that emerges from this study is the need to implement preventions activities to reduce the level of the anxiety during the pandemic context. The methods used where three test developed to analyze the main concepts of this study: The Multidimensional Test of Self-Esteem, The Raven’s Progressive Matrices and SAFA. Analyzing the results, the first hypothesis was confirmed by r = -.609 which indicates a negative correlation between self-esteem and anxiety. The internal consistency index of Cronbach's alpha indicates that self-esteem and cooperative learning are correlated (α SE= 0.72, α CL= 0.79) which indicate a good internal consistency.

High Intensity Exercise Targets Can Only Be Prescribed Accurately With Multidimensional Assessment Tests

High Intensity Exercise Targets Can Only Be Prescribed Accurately With Multidimensional Assessment Tests

Developing College students’ computational thinking multidimensional test based on Life Story situations

At present, research on computational thinking in universities is gaining interest, and more attention is being paid to the cultivation and teaching of computational thinking. However, there is a lack of computational thinking assessment tools for college students, which makes it difficult to understand the current status and development of their computational thinking. In this study, computational thinking is regarded as the ability to solve practical problems. By analyzing the relevant literature, we identified five dimensions of computational thinking – decomposition, generalization, abstraction, algorithm and evaluation – and described their operational definitions. Referring to the Bebras and the problem situations in Google computational thinking education, we set up a life-based situation that college students are familiar with. Based on the life story situation, we developed a multidimensional assessment for college students’ computational thinking. This assessment tool contains 14 items, all of which are multiple-choice questions, and the structure and quality of the tool are verified by multidimensional item response theory. The results show that the assessment tool has good internal validity and can discriminate different disciplines of college students. The college students’ computational thinking test developed in this study can be used as an effective tool to assess college students’ computational thinking.

Unravelling the regulation mechanism of nanoflower shaped Na3V2(PO4)3 in methanol–water system for high performance sodium ion batteries

Researchers are accustomed to utilizing traditional solid-state and sol–gel routes to synthesize Na3V2(PO4)3 (NVP). No regulation mechanism is proposed to illustrate the growth process of NVP in solvothermal environment. In current work, uniform spherical particles with positive charge are synthesized by molecular self-assembly of nanosphere micelles using structural guiding agent polyethylene glycol (PEG) and surfactant Cetyltrimethylammonium bromide (CTAB). Nanoflower-shaped NVP sample is successfully prepared in the 100 % methyl alcohol medium, derived from the plate V2O5 (negative charge) combining with the nuclear nanospheres (positive charge) with the induction of electrostatic attraction. Based on the multi-dimensional tests and molecular dynamic calculation, the protective mechanism of polar solution for NVP system is proposed for the first time, that is, methanol solvent system can better protect the morphological features of NVP than water environment to maintain the original special nanoflower morphology. Due to the distinctive and stabilized nanoflower construction, the optimized NVP60 sample can provide more extra active sites for the efficient de-intercalation of Na+ and possess a large contacting area with electrolyte, resulting in the facilitated kinetic characteristics and superior electrochemical performance. The optimized NVP60 cathode possesses superior sodium storage property in both half cell and full cell. This work proposes a synergetic strategy combining theory and experiment to design and synthesize the modified NVP-based cathode materials from the molecular bonding aspect, further promoting the development of high performance cathode for SIBs.

Sensory-Motor Perturbations in Larval Zebrafish (Danio rerio) Induced by Exposure to Low Levels of Neuroactive Micropollutants during Development.

Due to increasing numbers of anthropogenic chemicals with unknown neurotoxic properties, there is an increasing need for a paradigm shift toward rapid and higher throughput behavioral bioassays. In this work, we demonstrate application of a purpose-built high throughput multidimensional behavioral test battery on larval stages of Danio rerio (zebrafish) at 5 days post fertilization (dpf). The automated battery comprised of the established spontaneous swimming (SS), simulated predator response (SPR), larval photomotor response (LPR) assays as well as a new thermotaxis (TX) assay. We applied the novel system to characterize environmentally relevant concentrations of emerging pharmaceutical micropollutants including anticonvulsants (gabapentin: 400 ng/L; carbamazepine: 3000 ng/L), inflammatory drugs (ibuprofen: 9800 ng/L), and antidepressants (fluoxetine: 300 ng/L; venlafaxine: 2200 ng/L). The successful integration of the thermal preference assay into a multidimensional behavioral test battery provided means to reveal ibuprofen-induced perturbations of thermal preference behaviors upon exposure during embryogenesis. Moreover, we discovered that photomotor responses in larval stages of fish are also altered by the as yet understudied anticonvulsant gabapentin. Collectively our results demonstrate the utility of high-throughput multidimensional behavioral ecotoxicity test batteries in prioritizing emerging risks associated with neuroactive drugs that can perturb neurodevelopment. Moreover, we showcase the added value of thermotaxis bioassays for preliminary screening of emerging contaminants.

Relational Competence, School Adjustment and Emotional Skills: A Cross-Sectional Study in a Group of Junior High School Students of the Sicilian Hinterland

Literature has demonstrated the influence of emotional adjustment toward manifestation of problem behaviors in adolescence above all during the Covid-19 pandemic. The research goal is to investigate the relational competence, the school adjustment and emotional skills in a group of junior high school students of the Sicilian hinterland. The research involved 213 students, among which 82 boys (38.1%) and 131 girls (61.9%), aged between 11 and 14 (M=12.98; S.D.=1.05), of which 6.7% have repeated the school year once. Participants completed following instruments: the Test of Interpersonal Relationships (TRI) developed by Bracken (1993) in order to value the perception of adolescents concerning the quality of their relationships in social, familial and school contexts; the Multidimensional Test of Self-Esteem (TMA) created by Bracken (2003), structured in six scales, which coincide with the dimensions considered constitutive of self-esteem, namely: Interpersonal Relationships, Emotional Competence, Control over the Environment, School Success, Family Life and Body Image Perception. Data show that a good emotional competence of adolescents has a positive influence on learning and scholastic success, but above all on their relational ability towards peer-group.

Peripheral-Free Calibration Method for Redundant IMUs Based on Array-Based Consumer-Grade MEMS Information Fusion.

The MEMS array-based inertial navigation module (M-IMU) reduces the measurement singularities of MEMS sensors by fusing multiple data processing to improve its navigation performance. However, there are still existing random and fixed errors in M-IMU navigation. The calibration method calibrates the fixed error parameters of M-IMU to further improve navigation accuracy. In this paper, we propose a low-cost and efficient calibration method to effectively estimate the fixed error parameters of M-IMU. Firstly, we manually rotate the M-IMU in multiple sets of different attitudes (stationary), then use the LM-calibration algorithm to optimize the cost function of the corresponding sensors in different intervals of the stationary-dynamic filter separation to obtain the fixed error parameters of MEMS, and finally, the global fixed error parameters of the M-IMU are calibrated by adaptive support fusion of the individual MEMS fixed error parameters based on the benchmark conversion. A comparison of the MEMS calibrated separately by the fusion-calibration algorithm and the LM-calibration algorithm verified that the calibrated MEMS array improved the measurement accuracy by about 10 db and reduced the dispersion of the output data by about 8 db compared to the individual MEMS in a multi-dimensional test environment, indicating the robustness and feasibility of the fusion calibration algorithm.