Correct Representation Research Articles

GLSDC Based Parameter Estimation Algorithm for a PMSM Model

In this study, a GLSDC (Gaussian Least Squares Differential Correction) based parameter estimation algorithm is used to identify a PMSM (Permanent Magnet Synchronous Motor) model. In this method, a nonlinear model is assumed to be the correct representation of the underlying state dynamics and the output signals are assumed to be measured in a noisy environment. Using noisy input and output signals, parameters that constitute the coefficients of the nonlinear state and input signal terms are to be estimated using the state transition matrix which is computed by the numerical means that are detailed. Since a GLSDC algorithm requires correct initial state value, this term is also estimated in addition to the unknown coefficients whose bounds are assumed to be known, which is mostly the case in the industrial applications. The batch input and output signals are used to iteratively estimate the parameter set before and after the convergence, and to recover the filtered state trajectories. A couple of different scenarios are tested by means of numerical simulations and the results are addressed. Different methods are discussed to compute better initial estimate values, to shorten the convergence time.

Secondary ice production in summer clouds over the Antarctic coast: an underappreciated process in atmospheric models

Abstract. The correct representation of Antarctic clouds in atmospheric models is crucial for accurate projections of the future Antarctic climate. This is particularly true for summer clouds which play a critical role in the surface melting of the ice shelves in the vicinity of the Weddell Sea. The pristine atmosphere over the Antarctic coast is characterized by low concentrations of ice nucleating particles (INPs) which often result in the formation of supercooled liquid clouds. However, when ice formation occurs, the ice crystal number concentrations (ICNCs) are substantially higher than those predicted by existing primary ice nucleation parameterizations. The rime-splintering mechanism, thought to be the dominant secondary ice production (SIP) mechanism at temperatures between −8 and −3 ∘C, is also weak in the Weather and Research Forecasting model. Including a parameterization for SIP due to breakup (BR) from collisions between ice particles improves the ICNC representation in the modeled mixed-phase clouds, suggesting that BR could account for the enhanced ICNCs often found in Antarctic clouds. The model results indicate that a minimum concentration of about ∼ 0.1 L−1 of primary ice crystals is necessary and sufficient to initiate significant breakup to explain the observations, while our findings show little sensitivity to increasing INPs. The BR mechanism is currently not represented in most weather prediction and climate models; including this process can have a significant impact on the Antarctic radiation budget.

Economic growth and unemployment linkage in a developing economy: a gender and age classification perspective

This paper examined the growth and unemployment linkage from a gender-classification perspective using the Nigerian economic environment. The autoregressive distributed lag model in its baseline form, the bound test, and error correction representation were used as the estimation approach. Annualized time series spanning 1981 to 2017 were used for the variables of interest. Generally, it was found that female unemployment has a positive significant influence on GDP growth rate in Nigeria, while youth unemployment negatively and significantly influences GDP. It was also found that male unemployment does not significantly affect the GDP growth rate in Nigeria. In the long run, the main variables influencing GDP growth rate within the context of this study include unemployment rate, ratio of labor force size to the national population, female unemployment rate, and youth unemployment rate. The error correction representation and the bound test estimates confirm that growth adjusts to the dynamics of the studied unemployment variables. The study advocates for an increase in government capital expenditure, as this is theoretically and practically known to create new jobs. This spending should go into real and core productive sectors that would create upstream and downstream jobs opportunities.

Повышение качества автоматизированного проектирования в машиностроении на основе введения 3D чертежа

The article discusses the method of improving the quality of computer-aided design of machine-building products based on the introduction of the concept of 3D drawing. A comparative analysis of the application of a 3D drawing and the use of a standard flat drawing for the manufacture of machine-building profile products on modern CNC machines is given. The 3D drawings of the products are developed in the SolidWorks software environment. Based on the analysis, it is concluded that the use of 3D drawings in compliance with their correct representation in computer-aided design systems will reduce the time for the manufacture of relevant parts.

Minimal neural network topology optimization for aesthetic classification

In this article, a minimum neural network topology in terms of units and connections (neurons and weights), making visual aesthetically categorized images, will be identified. For this purpose, an analysis to compact the initial neural network will be conducted by reducing the amount of connections and neurons required for an a esthetic classification function in hidden layers. A collection of photographs and aesthetic evaluations from an online voting site for photographs such as Photo.net was used. Such pictures are defined by ad-hoc metrics used in previous studies. In this way, the input data, the images, are interpreted within a minimum complexity scheme by complexity estimators (internal representations). The results obtained show that the optimized topology found retains both its performance and its ability to generalize. As part of this study, it has been found a relationship concerning the input data and minimum topology necessary for its correct representation, demonstrating statistically a performance comparable to other not minimized topologies.

Adventures of a Light Blockchain Protocol in a Forest of Transactions: A Subset of a Story

The Ethereum blockchain is one of the most popular permissionless blockchains. A consequence of its popularity has been the growth of processing and data storage requirements for any node participating in the Ethereum blockchain network. For constrained devices such requirements are often infeasible to meet. To cater for such nodes, a so-called light protocol has been proposed for Ethereum where the responsibility of maintaining a correct state representation is delegated to light protocol servers. Previous research has identified dependence on external state management as a potential security vulnerability that exclusively impacts light nodes. Although a simple mitigation strategy is available, it comes at the expense of increased latency. In this work, we propose a new Ethereum node type, which we call a subset node, as an extension of the light protocol. Our proposal allows subset nodes to gain a lower latency than a pure light node with comparable or even higher security assurances by tracking and evaluating only a subset of all of the transactions issued on the blockchain. We provide a formal proof on the correctness of the blockchain state used by the subset node under the proposed model. To evaluate the practical feasibility of the subset node model, we analyze one year of historical transaction data from Ethereum, and demonstrate that a subset node tracking the state of a single account can achieve a significant reduction in storage and computational requirements when compared to a full node.

Investigating the impact of three-dimensional learning interventions on student understanding of structure–property relationships

The ability to predict macroscopic properties using a compound's chemical structure is an essential idea for chemistry as well as other disciplines such as biology. In this study we investigate how different levels of interventions impact the components of students’ explanations (claims, evidence, and reasoning) of structure–property relationships, particularly related to boiling point trends. These interventions, aligned with Three-Dimensional Learning (3DL), were investigated with four different cohorts of students: Cohort 1 – a control group of students enrolled in an active learning general chemistry course; Cohort 2 – students enrolled in the same active learning general chemistry course but given Intervention 1 (a 3DL worksheet administered during class time); Cohort 3 – students enrolled in the same active learning general chemistry course but given Intervention 1 and Intervention 2 (a 3DL course exam question administered after instruction); and Cohort 4 – a reference group of students enrolled in a transformed active learning general chemistry curriculum in which 3DL is an essential feature and includes Intervention 1 and Intervention 2 as part of the curriculum. We found that Cohort 2 students (with the 3DL worksheet intervention) were more likely than the control group (Cohort 1) to correctly predict the compound with a higher boiling point as well as incorporate ideas of strength of intermolecular forces into their explanations of boiling point differences. When a 3DL exam question was given as a follow up to the 3DL worksheet, students in Cohort 3 were more likely than Cohorts 1 and 2 to correctly identify the claim. Further comparison showed that Cohort 4 (transformed general chemistry curriculum) were more likely than Cohorts 1–3 to also include the ideas of energy needed to overcome stronger forces for a more sophisticated explanation (50% of Cohort 4 students compared to 17–33% for Cohorts 1–3). In addition, 80% of Cohort 4 students were able to construct a correct representation of hydrogen bonding as a non-covalent interaction compared to 13–57% for the other three cohorts.

Representation in Cognitive Science

<i>Representation in Cognitive Science</i>

Time knowledge impairments in children with ADHD

BackgroundA large number of studies have shown time perception impairment and reaction time (RT) variability in children with attention deficit hyperactivity disorder (ADHD), and have discussed the causes of such difficulties. However, very few studies have investigated time knowledge (i.e., the correct representation and use of time units) in children with ADHD. MethodsWe evaluated time knowledge in 33 children with ADHD, aged 8–12 years, who had consulted a reference center for learning disabilities in Paris, matched for age and gender with 33 typically developing (TD) children. We used a simple questionnaire-based survey and neuropsychological tests for cognitive and attentional skills. ResultsThe acquisition of time knowledge was delayed in children with ADHD compared with TD children (P<0.01). At the end of primary school, children with ADHD obtained time knowledge scores that were close to those of TD children at the beginning of primary school. In children with ADHD, time knowledge was significantly related to the working memory index (P<0.05), but not to ADHD presentation (with or without hyperactivity). ConclusionThis study shows time knowledge impairment in children with ADHD, and paves the way for new screening tests and rehabilitation focused on time knowledge and time-related skills, in order to improve patient care and autonomy.

Development of a Static Equivalent Model for Korean Power Systems Using Power Transfer Distribution Factor-Based k-Means++ Algorithm

This paper presents a static network equivalent model for Korean power systems. The proposed equivalent model preserves the overall transmission network characteristics focusing on power flows among areas in Korean power systems. For developing the model, a power transfer distribution factor (PTDF)-based k-means++ algorithm was used to cluster the bus groups in which similar PTDF characteristics were identified. For the reduction process, the bus groups were replaced by a single bus with a generator or load, and an equivalent transmission line was determined to maintain power flows in the original system model. Appropriate voltage levels were selected, and compensation for real power line losses was made for the correct representation. A Korean power system with more than 1600 buses was reduced to a 38-bus system with 13 generators, 25 loads, and 74 transmission lines. The effectiveness of the developed equivalent model was evaluated by performing power flow simulations and comparisons of various characteristics of the original and reduced systems. The simulation comparisons show that the developed equivalent model maintains inter-area power flows as close as possible to the original Korean power systems.

Roadmap for Monomer Conversion and Chain Length-Dependent Termination Reactivity Algorithms in Kinetic Monte Carlo Modeling of Bulk Radical Polymerization

Radical polymerization is prone to diffusional limitations on termination, leading to a rate acceleration by the gel-effect and molecular changes, with strong increases in average chain length in free radical polymerization (FRP) and improvement of the end-group functionality in reversible deactivation radical polymerization. What complicates the correct implementation of the termination rates in kinetic modeling studies is (i) the chain length and monomer conversion dependence of the individual (apparent) termination rate coefficients (k(t,app,ij) values; i,j: chain length) and (ii) the wide variations in macroradical chain length distribution (CLD) types and shapes along the polymerization and upon altering the reaction conditions, including variations in CLD modality and broadness. In this work, we compare four major kinetic Monte Carlo modeling algorithms to sample termination reaction events in bulk radical polymerization, using literature k(t,app,ij) values. We present a roadmap allowing one to select the most suited algorithm depending on the radical polymerization technique and reaction conditions, therefore opening the pathway to more correct representations of chain length and monomer conversion dependencies in radical polymerization. Case studies are related to bulk nitroxide-mediated polymerization, FRP, and pulsed laser polymerization, following an increasing complexity in the active macroradical CLD type and shape.

Improved friction force calculation with an augmented open-close iteration formulation in discontinuous deformation analysis

In applying discrete numerical methods to model large-displacement problems, such as rockslides, accurate contact force calculation requires correct representation of the evolving contact status. In the discontinuous deformation analysis (DDA), open-close iteration (OCI) serves the crucial role of ascertaining that contact status is properly updated throughout an assembly of discrete blocky elements. OCI is a simple, straightforward and intuitive procedure but has shortcomings that affect analysis accuracy. Alternatives are generally computationally involved and have other issues, and OCI with its large installed base can still be one of the most desirable ways to track contact state with proper improvements. This is exactly this study aims to achieve. This study proposes an augmented open-close iteration (AOCI) algorithm that amends the shortcomings of OCI through: (1) it enhances the modeling of transition between contact states; (2) it provides the control mechanism for improving shear forces and shear movement direction also during OCI iteration. The AOCI is easy to add to the existing structure of OCI. The improved performance of AOCI over the traditional approach is demonstrated using both static and dynamic test cases.

Regional Economic Integration and Productivity Convergence: Empirical Evidence from East Asia

The study attempts to seek evidence on regional economic integrationin driving labor productivity convergence in low-and middle-income East Asian states towards Japan, the country assumed to be the regional technology leader. The labor productivity convergence of low-and middle-income East Asian countries towards their rich neighbor is modelled against their national levels of innovation, technology spill-oversfrom the regional economic leader and their productivity differential with the frontier country. The hypothesized relationship is empirically verified for seven East Asian states, using a robust econometric approach. The time-series test estimates under Error Correction Representation yield absolute support in favor of valid productivity convergence occurring between Japan and its low-and middle income neighbors. However, panel data estimates generated with better statistical power outperform the time-series test findingsand these results reject the significance of Japan as the regional productivity growth driver for its regionaldevelopingstates.

Green or gas in OPEC member countries: a linear and asymmetric investigation of energy–growth nexus

AbstractThis study employed a combination of linear and nonlinear models to investigate the energy–growth nexus of OPEC member countries from 1960 to 2018 in a country‐specific form. It questioned the environmental friendliness of the energy initiatives of OPEC member countries as major producers and exporters of crude oil. Primary evidence of the study shows energy dependence among OPEC member countries supporting the growth hypothesis. We also found complementarity in fossil fuel and renewables as inducers of growth in 70% of the studied countries and found greater growth influence of fossil fuel in the case of 16% of the studied countries. In a country like Congo, we found greater influence from renewable energy, while Gabon's status was indeterminate. The dynamic profile of the energy–growth nexus evidenced by the error correction representations in their linear and asymmetric forms shows that growth adjusts nonlinearly to energy use in about 39% of the investigated OPEC member countries, and linearly in 61% of the countries. It can be said from our findings that global initiatives on green energy are gaining grounds in a preponderance of OPEC member countries.

Towards a more consistent eco-hydrological modelling through multi-objective calibration: a case study in the Andean Vilcanota River basin, Peru

ABSTRACT Most hydrological studies rely on a model calibrated using discharge alone. However, judging the model reliability based on such calibration is problematic, as it does not guarantee the correct representation of internal hydrological processes. This study aims (a) to develop a comprehensive multi-objective calibration framework using remote sensing vegetation data and hydrological signatures (flow duration curve – FDC, and baseflow index) in addition to discharge, and (b) to apply this framework for calibration of the Soil and Water Assessment Tool (SWAT) in a typical Andean catchment. Overall, our calibration approach outperformed traditional discharge-based and FDC signature-based calibration strategies in terms of vegetation, streamflow, and flow partitioning simulation. New hydrological insights for the region are the following: baseflow is the main component of the streamflow sustaining the long dry-season flow, and pasture areas offer higher water yield and baseflow than other land-cover types. The proposed approach could be used in other data-scarce regions with complex topography.

Municipalities in the Czech Republic—Compilation of “a Universal” Dataset

There have been many changes in the spatial composition and formal delimitation of administrative boundaries of Czech municipalities over the past 30 years. Many municipalities have changed their official status; they separated into ones that were more independent or were merged with existing ones, or formally redrew their boundaries due to advances in mapping technology. Such changes have made it almost impossible to analyze and visualize the temporal development of selected socioeconomic indicators, in order to deliver spatially coherent and time-comparable results. In this data description, we present an evolution of a unique (geo) dataset comprising of the administrative borders of the Czech municipalities. The uniqueness lies in time and topologically justified spatial data resulting in a common division of the administrative units at the LAU2 level, valid from 1995 to 2019. Besides the topologically correct spatial representations of municipalities in Czechia, we also provide correspondence tables for each year in the mentioned period, which allows joining tabular statistics to spatial data. The dataset is available as a base layer for further temporal and spatial analyses and visualization of various socioeconomic statistical data.

Models transport Saharan dust too low in the atmosphere: a comparison of the MetUM and CAMS forecasts with observations

Abstract. We investigate the dust forecasts from two operational global atmospheric models in comparison with in situ and remote sensing measurements obtained during the AERosol properties – Dust (AER-D) field campaign. Airborne elastic backscatter lidar measurements were performed on board the Facility for Airborne Atmospheric Measurements during August 2015 over the eastern Atlantic, and they permitted us to characterise the dust vertical distribution in detail, offering insights on transport from the Sahara. They were complemented with airborne in situ measurements of dust size distribution and optical properties, as well as datasets from the Cloud–Aerosol Transport System (CATS) spaceborne lidar and the Moderate Resolution Imaging Spectroradiometer (MODIS). We compare the airborne and spaceborne datasets to operational predictions obtained from the Met Office Unified Model (MetUM) and the Copernicus Atmosphere Monitoring Service (CAMS). The dust aerosol optical depth predictions from the models are generally in agreement with the observations but display a low bias. However, the predicted vertical distribution places the dust lower in the atmosphere than highlighted in our observations. This is particularly noticeable for the MetUM, which does not transport coarse dust high enough in the atmosphere or far enough away from the source. We also found that both model forecasts underpredict coarse-mode dust and at times overpredict fine-mode dust, but as they are fine-tuned to represent the observed optical depth, the fine mode is set to compensate for the underestimation of the coarse mode. As aerosol–cloud interactions are dependent on particle numbers rather than on the optical properties, this behaviour is likely to affect their correct representation. This leads us to propose an augmentation of the set of aerosol observations available on a global scale for constraining models, with a better focus on the vertical distribution and on the particle size distribution. Mineral dust is a major component of the climate system; therefore, it is important to work towards improving how models reproduce its properties and transport mechanisms.

What you read versus what you know: Neural correlates of accessing context information and background knowledge in constructing a mental representation during reading.

A core issue in psycholinguistic research is what the online processes are by which we combine language input and our background knowledge to construct the meaning of a message. We investigate this issue in the context of reading. To build a coherent and correct mental representation of a text readers monitor incoming information for consistency with the preceding text and with their background knowledge. Prior studies have not distinguished between text-based and knowledge-based monitoring, therefore it is unclear to what extent these two aspects of text comprehension proceed independently or interactively. We addressed this issue in a contradiction paradigm with coherent and incoherent versions of texts. We combined behavioral data with neuroimaging data to investigate shared and unique brain networks involved in text-based and knowledge-based monitoring, focusing on monitoring processes that affected long-term memory representations. Consistent with prior findings, behavioral results indicate that text and background knowledge each have a unique influence on processing. However, neuroimaging data suggests a more nuanced interpretation: Text-based and knowledge-based monitoring involve shared and unique brain regions, as well as regions that are sensitive to interactions between the two sources. It appears that the (d)mPFC and hippocampus-which are important for the influence of existing knowledge on encoding processes in nonreading contexts-are particularly involved in knowledge-based monitoring. In contrast, the right IFG is primarily involved in text-based monitoring, whereas left IFG and precuneus are implicated in integration processes. Furthermore, processes during reading affect recall of information (in)consistent with prior text or background knowledge. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Testing for general and inorganic chemistry in the MOODLE system

The current epidemiological situation in the world forces the pedagogical community to switch to the use of various forms of education using distance educational technologies. One of the most difficult problems of organizing distance learning is the problem of various forms of monitoring students knowledge. The most promising area for assessing the quality of knowledge is computer testing. Based on many years of experience in the development of computer test tasks, the basic requirements for computer test tasks and for computer tests are formulated, which make it possible to increase the efficiency of their use to control students knowledge. Examples of the correct representation of answer options in tasks with a choice of answer options (in closed test tasks) are given and ways to reduce the probability of guessing the correct answer are considered. With regard to the course "General and Inorganic Chemistry", the features of the use of various forms of test tasks are analyzed using specific examples. For the organization of computer testing of first-year bachelor degree students in the course "General and Inorganic Chemistry" the Moodle system was chosen. This system has various functions for creating tests, as well as for conducting training and control testing. For the convenience of entering test tasks into the Moodle system, these tasks must be converted into GIFT format. After that, GIFT tasks are easily imported into Moodle. Examples of different forms of test items in WORD and GIFT formats are given. The Moodle distance learning system (platform) has introduced a bank of tests for the course "General and Inorganic Chemistry", which is taught to first-year bachelor degree students of the Mendeleev University of Chemical Technology of Russia. The features of using this platform for organizing computer testing when using distance and blended forms of education are shown.

Transmission Effect of Uncertainties in USA &amp; China Trade Spat on West Africa Trade Relationships

We investigated the transmission effects of the trade spat between the United States and China on the relative trade openness of the West African sub-region. We found that economic policy uncertainty in China positively and significantly influence relative openness and the trade war is a positively significant driver of relative openness of the West African economy to China unlike the United States, with no significant influence in the short run, but a relative elasticity of 32% in the long run of openness to the trade war. The speed of adjustment as shown by the error correction representation is faster in openness to China than it is for the United States. As the economies of the West African sub-region appear vulnerable to the trade war and uncertainties in the United States and China, it is expected that deliberate policy mitigants be put in place to cushion the transmission effects of the trade war.