Use Of Graphical Representations Research Articles

Utilizing numerical techniques for modeling radiating Casson nanofluid flow with thermophoretic phenomenon on a heated stretching surface

This work aims to use mathematical modeling to investigate the mechanics of heat and mass transport in dissipative Casson nanofluid flows over a linear rough sheet. This study considers various elements such as thermal radiation, magnetic fields, heat generation, the varied properties of porous media, and the thermophoretic impact. Besides that, it looks into the variations in viscosity, diffusivity, and thermal conductivity, as well as the energy dissipation from viscous internal friction and fluid temperature modifications. The method involves coming up with and changing the boundary layer equations into a group of linked nonlinear ordinary differential equations that use variables that do not have any dimensions. The foundation of the solution strategy for these equations is the Hermite collocation method (HCM), which is renowned for its precision and adaptability. It offers an organized approach to solving the complex differential equations, allowing for accurate numerical solutions. The use of graphical representations ensures thorough data analysis and clarity, while also providing insightful information about the computed outcomes. The code validation method uses numerical comparisons with recent research to confirm the algorithm’s accuracy and dependability, as well as its resilience in comparison to the existing literature. Important conclusions from the study show that thermal boundary layers and nanofluid velocity decrease with increases in the porosity parameter, slip parameter, and magnetic field parameter.

The Impact of Thermal Radiation on Mixed Convective Unsteady Nanofluid Flow in a Revolving Vertical Cone

This study investigates the effects of an unsteady mixed convection nanofluid flow in a rotating vertical cone submerged in spinning nanofluid. Our analysis considered the impacts of heat flux, chemical reactions, and thermal radiation, with the thermal and concentration Biot numbers serving as constraints at the boundary. The governing unsteady and coupled partial differential equations were solved through appropriate similarity transformations, addressing the nonlinear terms inherent in these equations. The spectral quasi-linearisation method (SQLM) was employed to solve the higher-order nonlinear differential equations. This study elucidates and assesses the impact of diverse physical constraints and parameters through the use of graphical representations. Notably, the temperature distribution of the liquefied substance was intensified as the thermal and solutal Biot numbers increased.

Learning multi-satellite scheduling policy with heterogeneous graph neural network

In this paper, we investigate the multi-satellite scheduling (MSS) problem, a combinatorial optimization problem (COP) that aims to enhance satellite utilization. Numerous scholars have conducted in-depth studies on this matter. Handcrafted policies are commonly employed in various algorithms. Despite the use of graphical representations, designing policies manually becomes increasingly difficult, particularly as the number of satellites and tasks grows. Consequently, we propose a novel approach for automatically summarizing graph structures and learning policies for MSS problem. The proposed approach is developed based on graph neural network, Transformer, and sequence to sequence framework. First, MSS problem is modeled as a heterogeneous graph following various constraints. Next, the encoder, which is based on GNN and Transformer, is designed to summarize crucial information from the aforementioned graph. Finally, a two-stage decoder is proposed taking into account dynamic information in stepwise solution generation. Since obtaining optimal solutions in practical applications is challenging, we train the model using reinforcement learning. The results of the experiment demonstrate that our proposed method performs effectively in various scenarios. The quality of the solutions generated through this method transcends that of conventional construction methods. Additionally, the method generates solutions quickly, which are comparable in quality to those produced by meta-heuristic algorithms.

Local Non-Similar Solution for Non-Isothermal Electroconductive Radiative Stretching Boundary Layer Heat Transfer with Aligned Magnetic Field

Continuous two-dimensional boundary layer heat transfer in an electroconductive Newtonian fluid from a stretching surface that is biased by a magnetic field aligned with thermal radiation is the subject of this study. The effects of magnetic induction are induced because the Reynolds number is not small. The sheet is traveling with a temperature and velocity that are inversely related to how far away from the steady edge it is from the plane in which it is traveling. We also imposed external velocity u=uex=Dxp in the boundary. The necessary major equations are made dimensionless by the local non-similarity transformation and become a system of non-linear ordinary differential equations after being transformed from non-linear partial differential equations. The subsequent numerical solution of the arisen non-dimensional boundary value problem utilizes a sixth-order Runge–Kutta integration scheme and Nachtsheim–Swigert shooting iterative technique. A good correlation is seen when the solutions are compared to previously published results from the literature. Through the use of graphical representation, the physical impacts of the fluid parameters on speed, induced magnetic field, and temperature distribution are carried out. Furthermore, the distributions for skin friction coefficient and local Nusselt number are also studied for different scenarios. The skin friction coefficient and local Nusselt number are observed to increase with greater values of the temperature exponent parameter and velocity exponent parameter. However, as heat radiation increases, the local Nusselt number decreases even though temperatures are noticeably higher. The study finds applications in magnetic polymer fabrication systems.

The use of machine learning tools for modeling the countermeasure environment of robotic means and mobile objects of monitoring

In this article, an analysis of research related to the problems and areas of application of machine learning was carried out and it becomes clear that reinforcement learning is an important tool for the development of highly effective and adaptive control systems. This approach opens perspectives for the development of intelligent systems that can effectively adapt to changes in the environment and provide a reliable and optimal level of control under conditions of uncertainty and variability. In the course of the research, a complex environment modelling technique was developed for the task of searching and chasing moving objects by robotic means as part of reinforcement learning. The methodology includes defining goals and objectives, modelling the environment, defining states, actions, and rewards, formulating the state transition function, defining the reward function, modelling completion criteria, using convolutional neural networks to analyse the environment, and integrating the model into reinforcement learning algorithms. A feature of the developed methodology is its ability to adapt to various environmental conditions and dynamic changes occurring in it. The use of graphical representation of the environment allows you to reproduce in detail the real conditions in which the robotic means operate, including obstacles, areas of visibility and different types of surfaces. The use of convolutional neural networks for environmental analysis is an innovative approach that allows you to automate the process of determining the impact of the environment on the parameters of robotic vehicles and moving objects. CNN training on the collected data about the environment provides accurate prediction of influence coefficients in different areas, which allows optimizing strategies of robot behaviour depending on changes in conditions. In general, the developed environment modelling technique allows not only to accurately reproduce the real conditions in which robotic means operate, but also provides flexibility and adaptability when environmental conditions change. This opens up vast opportunities for training and improving reinforcement learning algorithms, increasing the effectiveness of robots in performing their tasks in various real-world scenarios.

Dynamical behaviours of Chaplygin gas, cosmological and gravitational ‘constants’ with cosmic viscous fluid in Bianchi type V space-time geometry

This paper is devoted to study modified Chaplygin gas and cosmological ‘constant’ as candidates of dark energy in the presence of cosmic viscous fluid with reference to the Bianchi type V space-time geometry. To represent a more viable cosmological model, variation of gravitational ‘constant’ is also considered. Precise solutions of equations of field have been acquired, where scale factors expand as monomial functions of cosmic time. Further, by use of graphical representation, behaviours of various parameters are also examined.

The influence of narrative disclosure readability, information ordering and graphical representations on non-professional investors' judgment: evidence from an emerging market

PurposeThis study examines whether manipulation in attributes of corporate narrative disclosures and the use of graphical representations can bias non-professional investors' judgment towards firms' future performance, in an emerging market context.Design/methodology/approachThe authors conduct three different experiments with a 2 × 2 between-subjects design, using accounting and finance senior undergraduate students to proxy for the non-professional investors.FindingsResults show that simple (more readable) disclosures improve non-professional investors' judgment towards firms' future performance. In addition, it is found that non-professional investors are prone to a recency effect from the intentional ordering of narrative information, when using complex (less readable) narratives. However, no primacy effect is found, when using simple (more readable) disclosures. The results further provide evidence that the inclusion of graphical representations, along with the manipulated narrative disclosures, can moderate the recency effect of information order, when using less readable and complex narrative disclosures.Research limitations/implicationsThe results reveal that although the content of corporate disclosures can be objective, neutral and relevant, manipulation in textual features and the use of graphical presentations, can interact to impact how non-professional investors perceive and process the disclosed information. This study provides an Egyptian evidence regarding this issue, as the majority of prior studies concentrate on developed capital markets. In addition, it contributes to prior studies evaluating the appropriateness of the Belief Adjustment Model predictions about the effect of textual presentation order on decision-making, by providing evidence from an emerging market.Practical implicationsResults attempt to increase the awareness of investors and encourage them to use multiple sources of information to avoid the probable bias that can result from management's manipulation of narratives. In addition, the study could be of interest to regulators and standard-setters, where the results reveal the need for guidelines and regulations to guide the disclosure of narrative information and the use of graphical information in corporate reports.Originality/valueTo the best of the authors' knowledge, this is the first study to examine the effect of two impression management strategies in narrative disclosures (readability and information order), along with the use of graphical representations, on non-professional investors' judgment in an emerging market, like Egypt.

Dogs (Canis familiaris) recognise our faces in photographs: implications for existing and future research.

Dogs are an ideal species to investigate phylogenetic and ontogenetic factors contributing to face recognition. Previous research has found that dogs can recognise their owner using visual information about the person's face, presented live. However, a thorough investigation of face processing mechanisms requires the use of graphical representations and it currently remains unclear whether dogs are able to spontaneously recognise human faces in photographs. To test this, pet dogs (N = 60) were briefly separated from their owners and, to achieve reunion, they needed to select the location indicated by a photograph of their owner's face, rather than that of an unfamiliar person concurrently presented. Photographs were taken under optimal and suboptimal (non-frontally oriented and unevenly illuminated faces) conditions. Results revealed that dogs approached their owner significantly above chance level when presented with photos taken under optimal conditions. Further analysis revealed no difference in the probability of choosing the owner between the optimal and suboptimal conditions. Dogs were more likely to choose the owner if they directed a higher percentage of looking time towards the owner's photograph compared to the stranger's one. In addition, the longer the total viewing time of both photos, the higher the probability that dogs chose the stranger. A main effect of dogs' sex was also obtained, with a higher probability of male dogs choosing the owner's photograph. This study provides direct evidence that dogs are able to recognise their owner's face from photographs. The results imply that motion and three-dimensional information is not necessary for recognition. The findings also support the ecological valence of such stimuli and increase the validity of previous investigations into dog cognition that used two-dimensional representations of faces. The effects of attention may reflect differences at the individual level in attraction towards novel faces or in the recruitment of different face processing mechanisms.

Dependency on Verbal Versus Graphic Representations among Second Language Learners When Reading Illustrated Expository Materials

The use of graphic representations is ubiquitous and essential in science-related domains. Research into learners’ reading of materials that incorporate verbal and graphic representations (i.e. multi-representational materials) however suggests that graphics receive very little attention compared to text. Such a tendency is not ideal as comprehension of multi-representational materials requires learners to attend to the graphics and integrate information contained in these representations with those in the text. This field of research, however, has focused primarily on native language learners. Since the number of people learning in their non-native language is growing rapidly, it is crucial to understand how they engage and learn with these materials. The present paper is part of a larger study that focuses on the multi-representational reading of 42 Malay ESL undergraduates. Their eye fixations during the reading of a science passage and an accompanying diagram were collected and analyzed. To our knowledge, no such study has been undertaken in the Malaysian context. Hence, the findings will be significant contributions to this field. The results indicate that similar to the reading of multi-representational texts by native speakers in other contexts, the multi-representational reading by the Malay ESL undergraduates was also heavily text-based.

Students’ interpretation and use of graphical representations: insights afforded by modeling the varied population schema as a coordination class

Explanations of phenomena in chemistry are grounded in discussions of particulate-level behavior, but there are limitations to focusing on single particles, or as an extension, viewing a group of particles as displaying uniform behavior. More sophisticated models of physical processes evoke considerations related to the dynamic nature of bulk solutions, in which an ensemble of molecules exists with a distribution of values that vary with respect to different parameters (e.g., speed, kinetic energy, etc.). Viewing phenomena as a varied population instead of a homogenous solution has been identified as a foundational idea that is critical for reasoning in chemistry, but little work has investigated how students reason about these ideas and how instructors can support students in viewing phenomena as a distribution of states. In this qualitative study, during semi-structured interivews twelve undergraduate general chemistry students were provided with frequency distribution graphs (number of molecules vs. speed, number of molecules vs. kinetic energy) and were asked to provide explanations and make predictions. The design and analysis of this study was informed by coordination class theory, a model within the knowledge-in-pieces perspective of cognition that defines a concept as a combination of approaches for obtaining information (read-out strategies) and a cluster of knowledge elements used to draw conclusions (causal net). Framing the varied population schema as a coordination class, this work focuses on the interaction between features students attended to in distribution graphs and the ideas they discussed. Analysis indicates students have productive resources for reasoning about a varied population in general terms, but these ideas are not necessarily activated when interpreting graphs, as reflected in the students’ readout strategies. Moreover, we posit that one of the barriers toward interpreting distribution graphs was the inappropriate application of covariational reasoning. As a practical consideration, we encourage interested instructors to review the Appendix, which provides a short summary of the main findings and suggestions for practitioners.

Ninth‐Grade Students’ Use of Graphical Representations in Social Studies Writing

AbstractGraphical representations are frequently used but normally not explicitly taught in social studies. The authors explore how to utilize the often unused potential of disciplinary graphical representations in student writing by offering insight into a Norwegian ninth‐grade classroom where students were scaffolded to use such representations. The authors scrutinized how these students invented connections between modes as they combined and transformed different representations in their texts. The authors found high density but not necessarily critical use of visuals. Texts designed with spatial and linear structures integrated visuals and verbal differently. The students’ main challenges concerned cohesion, redundancy, and utilization of modal affordances to communicate their message to a particular audience. To build students’ graphical literacy in social studies over time, the authors recommend overt instruction with model texts, critically framed with an emphasis on affordances and functions, and giving students opportunities to use this knowledge in a social studies practice.

Multi-representation ability of students on the problem solving physics

Accuracy in representing knowledge possessed by students will show how the level of student understanding. The multi-representation ability of students on the problem solving of physics has been done through qualitative method of grounded theory model and implemented on physics education student of Unnes academic year 2016/2017. Multiforms of representation used are verbal (V), images/diagrams (D), graph (G), and mathematically (M). High and low category students have an accurate use of graphical representation (G) of 83% and 77.78%, and medium category has accurate use of image representation (D) equal to 66%.

Risk literacy: Concepts and pedagogical implications for early childhood education

Taking risks and enjoying challenges are fundamental to the lives of young children from a developmental and evolutionary point of view. However, in modern societies, increasing concern about dangers and injuries has led to the escalation in regulation and provisions for the safety of young children. This intent to establish secure and risk-free environments for young children reaches, in some cases, the other end of the spectrum – that of overprotection, constraining children’s drive to explore, dare and experiment. This article explores the relationship between children and risk by focusing on the processes of thinking and acting, drawing on positive and negative discourses around risk. The article proposes that more interest should be directed towards enabling children’s own knowledge and understanding of risk, through early childhood education and risk literacy. The use of graphical representations, children’s probabilistic and possibility thinking, the risk culture of the classroom and a cross-curricular approach are pedagogical implications that could inform policy and practice in early childhood education aiming at present and future agents who are risk literate.

Specific emitter identification based on graphical representation of the distribution of radar signal parameters

Abstract The article presents some possibilities of same type radar copies identification with the use of graphical representation. The procedure described by the authors is based on transformation and analysis of basic parameters distribution which are measured by the radar signal especially Pulse Repetition Interval. A radar intercept receiver passively collects incoming pulse samples from a number of unknown emitters. Information such as Pulse Repetition Interval, Angle of Arrival, Pulse Width, Radio Frequency and Doppler shifts are not usable. The most important objectives are to determine the number of emitters present and classify incoming pulses according to emitters. To classify radar emitters and precisely identification the copy of the same type of an emitter source in surrounding environment, we need to explore the detailed structure i.e. intra-pulse information, unintentional radiated electromagnetic emission and fractal features of a radar signal. An emitter has its own signal structure. This part of radar signal analysis is called Specific Emitter Identification. Utilization of some specific properties of electronic devices can cause heightening probability of a correct identification

WaySafe: improving decisionmaking around health risk behaviors for prisoners transitioning back to the community

Many individuals enter the U.S. prison system with a history of high-risk drug and sexual practices, which are two primary contributors to the high rates of HIV and hepatitis cases among this population. As these individuals return to the community, they are likely to continue high-risk behaviors, making it critical that decisionmaking programs designed to reduce risky behaviors be instituted during incarceration close to their release. Prior research on Texas Christian University (TCU) Mapping-Enhanced Counseling (included in the National Registry of Evidence-based Programs and Practices as an evidence-based practice) has identified its utility for improving decisionmaking, communication, problem exploration, and personal planning through the use of graphical representations designed to help clients “see” the links and relationships among thoughts, ideas, feelings, and behaviors. It serves as an important tool for helping move traditional, public health-focused educational efforts toward a more comprehensive approach that addresses the challenges of personal decisionmaking around sex and drug use behaviors. The TCU Disease Risk Reduction (DRR-1) project developed and tested an intervention, WaySafe, designed to increase positive decisionmaking skills among offenders for healthy living, including skills for reducing disease risk behaviors. WaySafe uses TCU Mapping-Enhanced Counseling to focus on the cognitive aspects of risky sexual and drug use behaviors during re-entry to improve problem recognition, commitment to change, and strategies for avoiding behavioral risks of infections. WaySafe includes six 1-hour, highly interactive sessions to increase motivation and planning. A total of 1398 incarcerated offenders from eight different correctional facilities in two states participated in the study during the last phase of their in-prison substance abuse treatment prior to transition back to the community. All participating offenders completed baseline surveys and then were randomly assigned to the WaySafe intervention or treatment as usual (TAU). At the completion of the 6-week WaySafe curriculum, participating offenders completed follow-up surveys. Baseline and follow-up surveys included measures of knowledge, confidence, and motivation regarding general HIV information, risky sex and drug use, what to do if exposed, and general life skills. Results supported hypotheses that offenders in WaySafe improved significantly more than TAU clients on knowledge, attitudes, and behavioral intentions to avoid risks. Finding new methods to reinforce the lessons learned in the community is a critical next step, because these changes in attitudes and intentions will degrade over time with the stress and pressures of transitioning from prison to the community. Reinforcing lessons learned while offenders are currently facing risky situations may be helpful.

Diagrammatic Elicitation: Defining the Use of Diagrams in Data Collection

The use of graphic representations of experience and the social environment in the data collection process is an emerging approach. The terms diagramming, mapping and drawing are often used interchangeably, with no common interdisciplinary understanding of what they mean. The lack of a unifying terminology has resulted in simultaneous but separate developments undermining a more coherent approach to this emergent method. By defining what a diagram is and examining where diagramming fits amongst other data collection approaches, this paper proposes the term diagrammatic elicitation to refer to the use of diagrams in the data collection process. Two subcategories of this approach include: (a ) participant - led diagrammatic elicitation, where participants create original diagrams and (b ) researcher - led diagrammatic elicitation, where the researcher draws the diagram during the data collection process for discussion or participants edit a researcher - prepared diagram. Establishing these terms will allow researchers to share best practice and developments across disciplines.

Generating Symbolic Traces in the Insertion Modeling System

This paper describes a new generator of symbolic traces which is designed for the latest version of the insertion modeling system. The basic characteristics of the generator are the use of graphic representations of descriptions of multilevel models, division of local descriptions and consequence relation, possibility of adjustment to different search strategies, and application of a new predicate transformer that admits generality quantifiers with constraints relaxed relative to the previous versions.

The teaching of the function at high school: The graphic representation of a function at first year, section experimental sciences

The study of the concept of the graphic representation in Moroccan first year Baccalaureate, section experimental sciences, finds its origin in the mathematics curriculum that gives an important place to the study of functions. This research aims at exploring how this concept is presented in the Moroccan mathematics curriculum using the theoretical framework of didactic transposition, as it was developed by Y.Chevallard (1985) and that of R. Duval (1993) on the semiotic representations, specifically on the graphic register, and on the conversion of the algebraic register to the graphic register. Our article relies more precisely on the ability of students to solve various tasks involving the use of graphic representation of a function.

A systematic review of the usage of flow diagram in cluster randomized trials

Flow diagram represent an integral part of consolidated standards of reporting trials (CONSORT). Its use in reporting cluster randomization trials is highly recommended. The aim of this article is to present frequency of the use of flow diagram in cluster randomized trials in accordance with standards of reporting. The team has researched Medline database and singled-out 474 studies with cluster randomization for analysis. The studies were reviewed to identify the use of graphic representation, compliance with standards of reporting and the date when study was published. Depending from its duration, studies were divided on completed, and those still ongoing. Usage of CONSORT is recorded in 145 (31%) literature units. Frequency of flow diagram was statistically much higher in studies which were in compliance with standards (86,2%), in comparison to those which did not use CONSORT guidelines (71,4%), as well as in completed studies (81,2%) in comparison to pilot project studies (54,3%). Number of cluster randomized trials gathered through MEDLINE's search of key words 'cluster randomized trial [ti]' and 'cluster randomised trial [ti]', as well as the use of CONSORT in the reports of cluster randomized trials, are showing linear growth over time (p<0,001). Frequency of flow diagram is higher in the reports of cluster randomized trials that were done in accordance with the standards of reporting.

Computer-Based Learning: Graphical Integration of Whole and Sectional Neuroanatomy Improves Long-Term Retention

A study was conducted to test the hypothesis that instruction with graphically integrated representations of whole and sectional neuroanatomy is especially effective for learning to recognize neural structures in sectional imagery (such as magnetic resonance imaging [MRI]). Neuroanatomy was taught to two groups of participants using computer graphical models of the human brain. Both groups learned whole anatomy first with a three-dimensional model of the brain. One group then learned sectional anatomy using two-dimensional sectional representations, with the expectation that there would be transfer of learning from whole to sectional anatomy. The second group learned sectional anatomy by moving a virtual cutting plane through the three-dimensional model. In tests of long-term retention of sectional neuroanatomy, the group with graphically integrated representation recognized more neural structures that were known to be challenging to learn. This study demonstrates the use of graphical representation to facilitate a more elaborated (deeper) understanding of complex spatial relations.