Implicit Approach Research Articles

The role of deliberation in intuitive decision-making in sports

ABSTRACT In sports, athletes are frequently challenged to make decisions under time constraints. The ability of skilled athletes to make successful and seemingly effortless decisions in these situations is often referred to as intuition. However, how athletes develop intuition remains to be elucidated. A key issue highly debated between proponents of opposing theoretical views is whether the development of intuitive decision-making benefits from the involvement of deliberate cognitive processes (explicit vs. implicit). In this perspective paper, we critically assess theoretical arguments and empirical evidence for these contradictory views. Moreover, by delving into cognitive psychological assumptions rooted in dual-process theory, we elucidate the proposed processes underlying the development of intuitive decision-making. While the use of implicit approaches seems to be increasingly questioned in this domain, a promising recent theoretical proposal argues that intuition is formed through repeated practice of deliberative processes. We argue in favor of this approach and present suggestions for empirical research to test the proposal for the development of intuitive decision-making in sport. Finally, we illustrate suggestions for future research and critically discuss the potential and limitations of such an approach.

Explicit Group Iterative Method with Semi-Approximate Implicit Approach for Solving One-Dimensional Burgers’ Equation

This paper is considering a semi-approximate approach in investigating the Burgers’ problem. The process of the discretization of Burgers’ problem has taken place which it starts with the second-order finite difference in the discretize process together with the linearization part by using the semi-approximate implicit scheme in a way to achieve the approximation equation, thus generating the corresponding linear system equations. Besides, the Gauss-Seidel (GS), Successive Over-Relaxation (SOR) and explicit group (EG) iterative method has combined together with the SOR iterative method, namely as 4-point EGSOR (4EGSOR) has been introduced in this study for resolving the linear system. To assess the proficiency of the suggested methods on the approximation equation, the numerical test has been conducted by considering three parameters, which are computational time, iteration number and maximum absolute error. The findings indicate that the 4EGSOR method outperforms both SOR and GS iterative methods.

Transient temperature and pressure coupling model of cementing injection stage in ultra deep wells considering segmental rheological model, casing eccentricity and U-tube effect

The geological environments of ultra deep wells such as high temperature and high pressure (HTHP), narrow safety density window pose significant challenges to cementing operation. Considering segmental rheological model and density model, casing eccentricity, U-tube effect and viscous dissipation, this paper established a transient temperature and pressure coupling model during cementing injection stage of ultra deep wells. The segmental rheological model can describe the rheological properties of cementing fluids under HTHP much better. Considering casing eccentricity and viscous dissipation can accurately model the variation of temperature and pressure profiles. Taking U-tube effect into the model, the formation process of vacuum section at top of the casing can be simulated. The fully implicit finite-difference approach was used to solve the coupling model, and the calculation results were compared with field measurement data and simulation data to verify its accuracy. The calculation errors of the developed model are respectively 10% and 5% compared to field measurement data and Wellplan’s simulation data. Numerical simulations were conducted to reveal the cementing fluid distribution, transient temperature distribution and pressure distribution during the cementing injection stage. The simulation results show that the variety of cementing fluids with different density, rheological properties and thermophysical parameters complicates the change mechanism of temperature and pressure. The temperature and pressure distributions both present unsteady characteristics during cementing injection stage, which puts forward higher requirements of pressure control for narrow safety density window formation. The influence of temperature and pressure on rheology of cementing fluids cannot be ignored, and segmental rheological model should be considered for accurate pressure prediction. Also the casing eccentricity cannot be ignored for cementing pressure calculation in deviated or horizontal wells. Managed pressure cementing (MPC) has a greater advantage in dealing with narrow density window formation. The synergistic control of cementing fluid density and back pressure can keep the equivalent circulating density (ECD) within the safe density window. Through the application of the transient temperature and pressure coupling model, we can simulate the variations of key dynamic parameters during cementing injection stage and optimize the cementing parameters.

Partially and fully implicit multi-step SAV approaches with original dissipation law for gradient flows

The scalar auxiliary variable (SAV) approach was considered by Shen et al. in Shen et al. (2019) and has been widely used to simulate a series of gradient flows. However, the SAV-based schemes are known for the stability of a ‘modified’ energy. In this paper, we construct a series of modified SAV approaches with unconditional energy dissipation law based on several improvements to the classic SAV approach. Firstly, by introducing the three-step technique, we can reduce the number of constant coefficient linear equations that need to be solved at each time step, while retaining all of its other advantages. Secondly, the addition of energy-optimal technique and Lagrange multiplier technique can make the numerical schemes have the advantage of preserving the original energy dissipation. Thirdly, we use the first-order approximation of the energy balance equation in the GSAV approach, instead of discretizing the dynamic equation of the auxiliary variable, so that we can construct the high-order unconditional original energy stable numerical schemes. Finally, representative numerical examples show that the efficiency and accuracy of the proposed schemes are improved.

Fast machine-learning-enabled size reduction of microwave components using response features

Achieving compact size has emerged as a key consideration in modern microwave design. While structural miniaturization can be accomplished through judicious circuit architecture selection, precise parameter tuning is equally vital to minimize physical dimensions while meeting stringent performance requirements for electrical characteristics. Due to the intricate nature of compact structures, global optimization is recommended, yet hindered by the excessive expenses associated with system evaluation, typically conducted through electromagnetic (EM) simulation. This challenge is further compounded by the fact that size reduction is a constrained problem entailing expensive constraints. This paper introduces an innovative method for cost-effective explicit miniaturization of microwave components on a global scale. Our approach leverages response feature technology, formulating the optimization problem based on a set of characteristic points derived from EM-analyzed responses, combined with an implicit constraint handling approach. Both elements facilitate handling size reduction by transforming it into an unconstrained problem and regularizing the objective function. The core search engine employs a machine-learning framework with kriging-based surrogates refined using the predicted improvement in the objective function as the infill criterion. Our algorithm is demonstrated using two miniaturized couplers and is shown superior over several benchmark routines, encompassing both conventional (gradient-based) and population-based procedures, alongside a machine learning technique. The primary strengths of the proposed framework lie in its reliability, computational efficiency (with a typical optimization cost ranging from 100 to 150 EM circuit analyses), and straightforward setup.

The Neural Correlates of Alcohol Approach Bias - New Insights from a Whole-Brain Network Analysis Perspective.

Alcohol approach bias, a tendency to approach rather than to avoid alcohol and alcohol-related cues regardless of associated negative consequences, is an emerging key characteristic of alcohol use disorder (AUD). Reaction times from the Approach-Avoidance Task (AAT) can be used to quantify alcohol approach bias. However, only a handful of studies have investigated the neural correlates of implicit alcohol approach behavior. Graph Theory Analysis (GTA) metrics, specifically, weighted global efficiency (wGE), community detection, and inter-community information integration were used to analyze functional magnetic resonance imaging (fMRI) data of an in-scanner version of the AAT from 31 heavy drinking Veterans with AUD (HDV) engaged in out-patient treatment and 19 healthy Veterans as controls (HC). We found a functional imprint of alcohol approach bias in HDVs. HDVs showed significantly higher wGE values for approaching than for avoiding alcohol, indicating that their brain was more efficiently organized or functionally set to approach alcohol in the presence to alcohol-related external cues. In contrast, Brains of HCs did not show such a processing advantage for either the approach or avoid condition. Further post-hoc analyses revealed that HDVs and HCs differed in how they implemented top-down control when approaching/avoiding alcohol and in how the fronto-parietal control network interacted with subsystems of the default mode network. These findings contribute to understanding the complex neural underpinnings of alcohol approach bias and lay the foundation for developing more potent and targeted interventions to modify these neural patterns in AUD patients.

Economic and Management Sciences Teachers’ Experiences of Teaching Financial Literacy: Implications for Teacher Training

This paper reports on economic and management sciences (EMS) teachers’ experiences of teaching the financial literacy component in this learning area. It also provides a holistic understanding of their challenges and professional development needs in this integrated field. A qualitative case study approach was used to allow for an in-depth understanding of EMS teachers’ views and experiences. Data were collected through open-ended questionnaires and semi-structured interviews. Purposive sampling was used to select information-rich participants from selected government secondary schools in Gauteng province, South Africa. Participants in the study included grades 8 and 9 EMS teachers. A thematic analysis approach was used to analyze data collected from the open-ended questionnaires and interview responses. The findings show that, due to poor training, EMS teachers continue to experience challenges in integrating the financial literacy component. Consequently, teachers default to fragmented approaches to teaching EMS, which undermines government initiatives aimed at realizing social transformation objectives through the EMS curriculum. The study recommends an implicit approach to teacher development programs by incorporating EMS teachers’ views, experiences, and specific needs as well as contexts. The research contributes to the literature on commerce education in South Africa by offering ways in which education can be transformative and how the quality of teaching financial literacy in EMS can improve.

Learning facial structural dependency in 3D aligned space for face alignment

Facial structure's statistical characteristics offer pivotal prior information in facial landmark prediction, forming inter-dependencies among different landmarks. Such inter-dependencies ensure that predictions adhere to the shape distribution typical of natural faces. In challenging scenarios like occlusions or extreme facial poses, this structure becomes indispensable, which can help to predict elusive landmarks based on more discernible ones. While current deep learning methods do capture these landmark dependencies, it's often an implicit process heavily reliant on vast training datasets. We contest that such implicit modeling approaches fail to manage more challenging situations. In this paper, we propose a new method that harnesses the facial structure and explicitly explores inter-dependencies among facial landmarks in an end-to-end fashion. We propose a Structural Dependency Learning Module (SDLM). It uses 3D face information to map facial features into a canonical UV space, in which the facial structure is explicitly 3D semantically aligned. Besides, to explore the global relationships between facial landmarks, we take advantage of the self-attention mechanism in the image and UV spaces. We name the proposed method Facial Structure-based Face Alignment (FSFA). FSFA reinforces the landmark structure, especially under challenging conditions. Extensive experiments demonstrate that FSFA achieves state-of-the-art performance on the WFLW, 300W, AFLW, and COFW68 datasets.

Analyzing Bifurcations and Optimal Control Strategies in SIRS Epidemic Models: Insights from Theory and COVID-19 Data

This study investigates the dynamic behavior of an SIRS epidemic model in discrete time, focusing primarily on mathematical analysis. We identify two equilibrium points, disease-free and endemic, with our main focus on the stability of the endemic state. Using data from the US Department of Health and optimizing the SIRS model, we estimate model parameters and analyze two types of bifurcations: Flip and Transcritical. Bifurcation diagrams and curves are presented, employing the Carcasses method. for the Flip bifurcation and an implicit function approach for the Transcritical bifurcation. Finally, we apply constrained optimal control to the infection and recruitment rates in the discrete SIRS model. Pontryagin’s maximum principle is employed to determine the optimal controls. Utilizing COVID-19 data from the USA, we showcase the effectiveness of the proposed control strategy in mitigating the pandemic’s spread.

RobotSDF: Implicit Morphology Modeling for the Robotic Arm.

The expression of robot arm morphology is a critical foundation for achieving effective motion planning and collision avoidance in robotic systems. Traditional geometry-based approaches usually suffer from the contradiction between the high demand for computing resources for fine expression and the insufficient detail expression caused by the pursuit of efficiency. The signed distance function addresses these drawbacks due to its ability to handle complex and arbitrary shapes and lower computational requirements. However, conventional robotic morphology methods based on the signed distance function often face challenges when the robot moves dynamically, since robots with different postures are modeled as independent individuals but the postures of robots are infinite. In this paper, we introduce RobotSDF, an implicit morphology modeling approach that can express the robot shape of arbitrary posture precisely. Instead of depicting a whole model of the robot arm, RobotSDF models the robot morphology as integrated implicit joint models driven by joint configurations. In this approach, the dynamic shape change process of the robot is converted into the coordinate transformations of query points within each joint's coordinate system. Experimental results with the Elfin robot demonstrate that RobotSDF can accurately depict robot shapes across different postures up to the millimeter level, which exhibits 38.65% and 66.24% improvement over the Neural-JSDF and configuration space distance field algorithms, respectively, in representing robot morphology. We further verified the efficiency of RobotSDF through collision avoidance in both simulation and actual human-robot collaboration experiments.

Geometric-aware RGB-D representation learning for hand–object reconstruction

Reconstructing hand–object interaction from single images, crucial for interactive applications, is challenging due to the diversity of hand–object poses and shapes. Depth maps effectively complement RGB data in understanding these interactions geometrically within challenging scenes. However, most existing methods do not fully utilize the potential benefits of RGB-D information fused at different feature levels for reconstruction, limiting their capability to capture the geometric details of hand–object interaction. To address this, we propose an implicit geometric-aware RGB-D representation learning approach using adaptive bidirectional RGB-D feature fusion (ABF) and geometric Fourier feature encoding (GFFE). This method innovatively leverages the hierarchical and complementary high-level features of RGB-D information to enhance the neural implicit representations of hand–object interaction during the feature extraction and encoding stages of RGB-D data. Initially, a two-stream RGB-point cloud encoder, combining CNN and Transformer architectures, extracts appearance and geometric information from RGB and point cloud data. Subsequently, ABF fuses this information, generating dense RGB-D features by leveraging the complementary of appearance and geometric features along with the varying sensitivities of different layers. Finally, GFFE introduces frequency domain information to capture both high-frequency and low-frequency features of objects equally to precisely capture the geometric details of hand–object interaction. Experiments conducted on the real-world DexYCB and the synthetic ObMan benchmarks demonstrate that our approach significantly outperforms existing approaches.

Combine thermal performance based on dust particles and Hall and ion slip currents including tetra-hybrid nanoparticles on Howarth's wavy pipe

The flow of a three-dimensional incompressible Carreau model by inserting iron oxide, titanium, copper and aluminum in a stretched wavy cylinder is inspected in this research by mixing the nanoparticles in two phases i.e. dust and fluid phase. The rheology of the Carreau model is considered to present the behavior of ethylene glycol liquid. The contribution of Hall current with the ionization of ion slips under convective boundary conditions by utilizing the comportment of radiation and external heat source in thermal transport expression is considered. Numerically, an implicit finite difference-based approach (FDBA) namely the finite element method (FEM) is used to handle nonlinear ODEs and several graphs are plotted against numerous emerging parameters. Moreover, shear stresses and heat transfer coefficient are calculated and their behavior is discussed. The velocity field's inclination is depicted graphically by augmenting the values of the Biot number, which further describes the comparative study for dust and fluid phases to monitor motion.

An implicit large-eddy simulation perspective on the flow over periodic hills

The periodic hills simulation case is a well-established benchmark for computational fluid dynamics solvers due to its complex features derived from the separation of a turbulent flow from a curved surface. We study the case with the open-source implicit large-eddy simulation (ILES) software Lethe. Lethe solves the incompressible Navier–Stokes equations by applying a stabilized continuous finite element discretization. The results are validated by comparison to experimental and computational data available in the literature for Re = 5600. We study the effect of the time step, averaging time, and global mesh refinement. The ILES approach shows good accuracy for average velocities and Reynolds stresses using less degrees of freedom than the reference numerical solution. The time step has a greater effect on the accuracy when using coarser meshes, while for fine meshes the results are rapidly time-step independent when using an implicit time-stepping approach. A good prediction of the reattachment point is obtained with several meshes and this value approaches the experimental benchmark value as the mesh is refined. We also run simulations at Reynolds equal to 10600 and 37000 and observe promising results for the ILES approach.

A monolithically implicit time-integration approach for a dislocation-density-based b.c.c. single crystal plasticity model

A monolithically implicit time-integration approach for a dislocation-density-based b.c.c. single crystal plasticity model

Educational practices for the language development of students with intellectual developmental disorder in the school setting: a systematic review

BackgroundIntellectual development disorder (IDD) is a challenging disorder that professionals, most notably teachers, are confronted with daily. Supporting oral language in the classroom is a complex challenge as teachers are expected to implement effective methods to support students’ language. However, they face a persistent scarcity of scientific evidence to draw upon in their practice.AimsThis research provides a first overview of instructional methods and their effectiveness in this population.MethodOur search was conducted through various databases, generating a total of 931 articles. Data from the 14 selected studies were analyzed.Outcomes and resultsThe results reveal limited, heterogenous, and hardly comparable studies in terms of their characteristics (samples, language area, strategies, and effectiveness measures) suggesting a low level of evidence regarding the effectiveness of these practices.Conclusion and implicationsWe discuss the implications of these findings in light of the different factors contributing to the interventions’ effectiveness, as well as considering the issue of transfer and generalization of acquired skills in IDD. We also reveal the urgent need for developing methods addressing cognitive load limitations such as implicit learning approaches, to enhances effectiveness and compensate the gap of available resources tailored to improve oral language outcomes in the IDD population.

Effect of installing branch-shaped fin on cold energy saving during freezing considering nanomaterial

This research examines the solidification through a finned tank containing alumina nano-powders and water as PCM (phase change material). Mathematical models were developed, assuming a uniform concentration of nano-powders and neglecting convective term effects. The model includes three time-dependent terms, discretized using an implicit approach, with solutions obtained via the Galerkin method and convergence achieved using an adaptive mesh. The thermal conductivity of NEPCM improves with increased concentration (ϕ), resulting in a faster transition from liquid NEPCM to ice. For pure water, the complete freezing time is 6795.38 s; however, with additives, this time is reduced by 26.78 %. Additionally, using powders with a higher "m" value further accelerates the freezing process, decreasing the completion time by about 6.98 %. The effect of powder configuration becomes more pronounced with increasing concentration. These findings are crucial for enhancing the sustainability of natural resources by improving cold storage and solidification processes.

Comparative structural analysis of anhydrous and monohydrated polymorphs of diclofenac diethylammonium: Implications for stability, dissolution, and bioavailability

Diclofenac diethylammonium belongs to the class of non-steroidal anti-inflammatory drugs, and it is widely used to relieve pain and reduce systemic inflammation. This drug exists in polymorphic forms, which can directly affect the ability to process the drug, as well as drug product dissolution, stability, and bioavailability. Considering the worldwide regulatory guidance for polymorphs in drug substances, a detailed structural analysis of two polymorphs exhibiting the anhydrous and monohydrated structures of diclofenac diethylammonium is presented here, using the supramolecular arrangement and its relationship with structural-physicochemical properties. The polymorph’s structures show conformational differences in the carboxylic group caused by coordinated water molecules presented in the monohydrated polymorphic structure. Both polymorphs have the supramolecular arrangement stabilized by NH···O and CH···Cl intermolecular interactions, but the monohydrated molecule presents a strong OH···O intermolecular interaction due to the presence of a water molecule. Hirshfeld surface analysis showed CH···π and π···π contacts in the anhydrous and monohydrated polymorphs, respectively. Theoretical results, including solvent effect with explicit and implicit approaches for water, indicated the kinetic stability of the diclofenac diethylammonium drug and showed susceptible electrophilic attacks occurring in the region close to the carbonyl oxygen atom.

Fairer AI in ophthalmology via implicit fairness learning for mitigating sexism and ageism

The transformative role of artificial intelligence (AI) in various fields highlights the need for it to be both accurate and fair. Biased medical AI systems pose significant potential risks to achieving fair and equitable healthcare. Here, we show an implicit fairness learning approach to build a fairer ophthalmology AI (called FairerOPTH) that mitigates sex (biological attribute) and age biases in AI diagnosis of eye diseases. Specifically, FairerOPTH incorporates the causal relationship between fundus features and eye diseases, which is relatively independent of sensitive attributes such as race, sex, and age. We demonstrate on a large and diverse collected dataset that FairerOPTH significantly outperforms several state-of-the-art approaches in terms of diagnostic accuracy and fairness for 38 eye diseases in ultra-widefield imaging and 16 eye diseases in narrow-angle imaging. This work demonstrates the significant potential of implicit fairness learning in promoting equitable treatment for patients regardless of their sex or age.

Optimization of Welded Joints under Fatigue Loadings

In this paper, the notch effect in weldments has been investigated, and the optimal configuration of different types of welded joints has been analysed using the implicit gradient approach. By considering this implicit gradient method, it is possible to calculate the effective stress related to fatigue damage, with the effective stress being a continuous scalar function of the real stress tensor components, even in the presence of sharp edges. Hence, the search for the optimal configuration that maximises fatigue life can be tackled as the condition of minimum effective stress obtained by changing the weld shape and geometrical parameters. Both load-carrying cruciform joints and spot welds made of steel have been considered. The structural details have been studied by modelling actual shapes without any geometric simplification. Moreover, the same numerical procedure has been considered independently of the size, shape or load condition without imposing restrictive rules on the FE mesh.

Thermal radiation and soret/dufour effects on amplitude and oscillating frequency of darcian mixed convective heat and mass rate of nanofluid along porous plate

The influence of thermal radiations and Soret/Dufour significantly increases the mass and heat transport in material science, geothermal process, chemical rectors and heat exchangers due to temperature and concentration variation of nanoparticles. Main objective is to enhance the amplitude, oscillation and frequency of heat transfer using the Soret and Dufour characteristics in Darcian radiating flow of thermophoretic nanoparticles along vertical porous surface. The dimensionless analysis is performed to develop the convenient form of governing thermodynamic Darcian nanofluid formulation. To develop the programming algorithm in FORTRAN language, the primitive variable formulation is used for both time-dependent and steady models. The numerical and graphical outcomes of primitive type equations under boundary conditions are explored by using implicit finite difference approach with Gaussian elimination scheme. Various pertinent parameters are used to elaborate the steady fluid velocity, surface temperature and steady concentrated nanoparticle volume fraction. Valuable novelty of current research is to use steady results for fluctuating shear stress, fluctuation heating rate and fluctuating mass rate. It is found that the influence of Soret and Dufour parameter enhances the fluid velocity and temperature distribution with maximum variations. It is noticed that the temperature distribution enhances as thermal radiation parameter increases. The oscillation and transient stability of heating rate enhances with prominent variations as thermal radiation and Soret/Dufour parameter increases. It is depicted that amplitude in shear stress and mass rate enhances as Soret and Dufour parameter enhances.