Numerical Representation Research Articles

Critical evaluation of kinetic schemes for coagulation.

Two well-established numerical representations of the coagulation cascade either initiated by the intrinsic system (Chatterjee et al., PLOS Computational Biology 2010) or the extrinsic system (Butenas et al., Journal of Biological Chemistry, 2004) were compared with thrombin generation assays under realistic pathological conditions. Biochemical modifications such as the omission of reactions not relevant to the case studied, the modification of reactions related to factor XI activation and auto-activation, the adaptation of initial conditions to the thrombin assay system, and the adjustment of some of the model parameters were necessary to align in vitro and in silico data. The modified models are able to reproduce thrombin generation for a range of factor XII, XI, and VIII deficiencies, with the coagulation cascade initiated either extrinsically or intrinsically. The results emphasize that when existing models are extrapolated to experimental parameters for which they have not been calibrated, careful adjustments are required.

A realistic situation, proposed from data generated within the GeOrder Simulator to elicit the statistical reasoning

Construction and handling of important statistical concepts, such as statistical estimators computing, or organization of data in graphs and tables, are oftenly taught in a strict algorithmic manner. As consequence, students abilities regarding experimentation, data sampling, data collecting, data organization and results interpretation, are limited and their solutions lack of statistical sustain. In this work, in study class consisting in students of the Logistic and Transport Engineering program, it is developed and implemented a realistic problem situation: a sales e-commerce company has to decide, by analyzing received orders, whether to implement its own delivery system or hire a third-party delivery company. The software GeOrder Simulator is employed to generate random data for weight and location of simulated orders and the data are presented to students in tabular and iconic, onto a map geolocation, semiotic representation registers. The research aim of this work, is to analyze, from the perspective of Semiotic Representation Theory, the effect onto the statistical reasoning that is provoked in students when they solve the realistic problem situation with the data generated by the GeOrder Simulator. The results show that the addressing of the realistic problem situation together with the usage of the GeOrder Simulator, elicited the statistical reasoning in the students since it is observed the integration of statistical concepts within their argumentation, by means of the coordination of tabular, graphical and numerical semiotic representation registers.

Context-Dependent Multimodal Sentiment Analysis Based on a Complex Attention Mechanism

Multimodal sentiment analysis aims to understand people’s attitudes and opinions from different data forms. Traditional modality fusion methods for multimodal sentiment analysis con-catenate or multiply various modalities without fully utilizing context information and the correlation between modalities. To solve this problem, this article provides a new model based on a multimodal sentiment analysis framework based on a recurrent neural network with a complex attention mechanism. First, after the raw data is preprocessed, the numerical feature representation is obtained using feature extraction. Next, the numerical features are input into the recurrent neural network, and the output results are multimodally fused using a complex attention mechanism layer. The objective of the complex attention mechanism is to leverage enhanced non-linearity to more effectively capture the inter-modal correlations, thereby improving the performance of multimodal sentiment analysis. Finally, the processed results are fed into the classification layer and the sentiment output is obtained using the classification layer. This process can effectively capture the semantic information and contextual relationship of the input sequence and fuse different pieces of modal information. Our model was tested on the CMU-MOSEI datasets, achieving an accuracy of 82.04%.

An Authoritarian Spectacle: Visual Biopolitics and the Dramaturgy of the Poland-Belarus Border Migration Crisis

This article analyzes the 2021–22 migration crisis at the EU-Belarus border through the conceptual lens of visual biopolitics. Based on data available from the regime-run media in Belarus it demonstrates how the engineered crisis was a case of authoritarian dramaturgy relying on numerous visual representations of migrants. The carefully staged authoritarian spectacle exploits both the vulnerability and grievability of refugee life, and the regime’s role as a self-appointed sovereign exercising comprehensive biopolitical care and protection over the population of migrants stranded at the border between Belarus and Poland.

Exploring the Effectiveness of Deep Learning in Analyzing Review Sentiment

This study aimed to analyze sentiment in office product reviews by using word embedding with three neural network modeling approaches: Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU). Office product review data is taken from Amazon's reviews of office products covering a wide range of sentiments. Word embedding converts text into a numerical vector representation for neural network processing. Experimental comparison of this model reveals that CNN achieves the highest accuracy, 77.99%. The CNN model effectively extracts significant features from review text, improving sentiment classification performance. Although the LSTM and GRU models show satisfactory results, they do not match CNN performance. These findings demonstrate the effectiveness of word embedding and neural networks for sentiment analysis in office product reviews. This provides valuable insights for companies to improve their products based on user feedback from online reviews. Additionally, this research serves as a foundation for further advances in sentiment analysis across a wide range of other products and services

Numerical evaluation of line, surface and toroidal integrals on level sets of toroidally symmetric functions

We investigate strategies to numerically integrate closed lines and surfaces that are implicitly defined by level sets (iso-contours) of continuously differentiable toroidally symmetric functions. The “grid-transform” approach transforms quantities given in non-surface-aligned coordinates onto a numerically constructed surface-aligned grid. Here, line and surface integrals, as well as so-called flux-surface averages, can be easily evaluated using high order integration formulas. We compare this method to ones that base on numerical representations of the delta-function.For the grid-transform method we observe high order convergence of line, surface and volume integration. Quantitatively, the errors for line and area integration are several orders of magnitude smaller than previously reported errors for delta-function methods. Furthermore, a delta-function method based on a Gaussian representation shows qualitatively wrong results of surface integrals near O- and X-points. Contrarily, the grid transform method suffers no deterioration near O-points. However, close to X-points we observe reduced first order convergence in volume integral and derivative tests due to the diverging volume element.Finally, we derive a toroidal integration based on toroidal summation and a smoothing kernel that assumes field-alignment of structures between the toroidal planes. The smoothing kernel can be interpreted as a partial flux-surface average. The resulting smoothed toroidal average eliminates unphysical poloidal oscillations that are otherwise present in the simple toroidal average.Our methods can be applied to toroidal and flux-surface averages in simulations of three-dimensional plasma dynamics on non-aligned grids. Further applications include closed line and surface integrals in level set methods. Efficient implementations can be found in the freely available Feltor library.

MHD 3D Mixed Upper Convective Flow of Maxwell Nanofluid Flow Past in the Presence of Diffusion Thermo and Thermophoresis Effect using Nonlinear Radiative Heat Flux

This article aims to investigate the impact of nanoparticles and magnetohydrodynamics (MHD) on the transfer of heat and mass using a three-dimensional upper-convected Maxwell (UCM) nanofluid flow across a stretched surface. A nonlinear radiative heat flow was included in formulating the equation that describes energy. The nonlinear partial differential equations of the issue are transformed into ordinary differential equations utilizing the similarity transformation. These equations are then solved using the well-known shooting approach in conjunction with the Runge-Kutta integration process of order four. To increase the dependability of our findings, make use of the MATLAB. On the velocities, temperatures, and concentrations of the particles, the graphical and numerical representations of the effects of the main parameters, such as the Dufour parameter, the Brownian motion parameter, the Prandtl number, the thermophoresis parameter, and the magnetic parameter, are presented. It has been shown that the flow velocity decreases as a function of both the linear and nonlinear thermal radiation parameters. In addition, increasing values of the Brownian motion parameter have the effect of reducing the nanoparticle concentration profile

Finite Deformation of Scleral Tissue under Electrical Stimulation: An Arbitrary Lagrangian-Eulerian Finite Element Method.

The sclera is considered as the principal load-bearing tissue within the eye. The sclera is negatively charged; thus, it exhibits mechanical response to electrical stimulation. We recently demonstrated the electroactive behavior of sclera by performing experimental measurements that captured the deformation of the tip of scleral strips subjected to electric voltage. We also numerically analyzed the electromechanical response of the tissue using a chemo-electro-mechanical model. In the pre-sent study, we extended our previous work by experimentally characterizing the deformation profile of scleral strips along their length under electrical stimulation. In addition, we improved our previous mathematical model such that it could numerically capture the large deformation of samples. For this purpose, we considered the transient variability of the fixed charge density and the coupling between mechanical and chemo-electrical phenomena. These improvements in-creased the accuracy of the computational model, resulting in a better numerical representation of experimentally measured bending angles.

Thermal Diffusion and Diffusion Thermo Effects on MHD 3D Mixed Upper Convective Flow of Maxwell Nanofluid Flow Past using Nonlinear Radiative Heat Flux

This article aims to investigate the effect of nanoparticles and magnetohydrodynamics (MHD) on the transfer of heat and mass using a three-dimensional upper-convected Maxwell (UCM) nanofluid flow across a stretched surface in the presence of diffusion thermo and thermal diffusion. A nonlinear radiative heat flow was included in formulating the equation that describes energy. The nonlinear partial differential equations of the issue are transformed into ordinary differential equations utilizing the similarity transformation. These equations are then solved using the well-known shooting approach in conjunction with the Runge-Kutta integration process of order four. To increase the dependability of our findings, we additionally make use of the bvp4c built-in function that is available in MATLAB. On the velocities, temperatures, and concentrations of the particles, the graphical and numerical representations of the effects of the main parameters, such as the Dufour parameter, the Brownian motion parameter, the Prandtl number, the thermophoresis parameter, and the magnetic parameter, are presented. It has been shown that the flow velocity decreases as a function of both the linear and nonlinear thermal radiation parameters. In addition, increasing values of the Brownian motion parameter have the effect of reducing the nanoparticle concentration profile

A Graph-Based Methodology for the Sensorless Estimation of Road Traffic Profiles

Traffic forecasting models rely on data that needs to be sensed, processed, and stored. This requires the deployment and maintenance of traffic sensing infrastructure, often leading to unaffordable monetary costs. The lack of sensed locations can be complemented with synthetic data simulations that further lower the economical investment needed for traffic monitoring. One of the most common data generative approaches consists of producing real-like traffic patterns, according to data distributions from analogous roads. The process of detecting roads with similar traffic is the key point of these systems. However, without collecting data at the target location no flow metrics can be employed for this similarity-based search. We present a method to discover locations among those with available traffic data by inspecting topological features. These features are extracted from domain-specific knowledge as numerical representations (embeddings) to compare different locations and eventually find roads with analogous daily traffic profiles based on the similarity between embeddings. The performance of this novel selection system is examined and compared to simpler traffic estimation approaches. After finding a similar source of data, a generative method is used to synthesize traffic profiles. Depending on the resemblance of the traffic behavior at the sensed road, the generation method can be fed with data from one road only. Several generation approaches are analyzed in terms of the precision of the synthesized samples. Above all, this work intends to stimulate further research efforts towards enhancing the quality of synthetic traffic samples and thereby, reducing the need for sensing infrastructure.

COMPARISON OF SCALED HYBRID SIMULATIONS AND SHAKING TABLE TESTS ON A 2-SPAN REINFORCED CONCRETE BRIDGE

Hybrid simulation is a testing paradigm combining experimental and computational simulations through a hybrid model which comprises scaled physical and numerical representation of various parts of a structural system. The physical and numerical elements interact within a single model by satisfying the displacement compatibility and the force equilibrium at the common nodes. Hybrid simulation offers numerous advantages, including cost savings and a deeper understanding of complex structures. In this paper, an investigation has been carried out to study the effect of scaling on the accuracy of hybrid simulations of a 2-span reinforced concrete highway bridge. The experimental program included hybrid simulations on 1/10, 1/8 and 1/6 scaled models of a two-span reinforced concrete bridge loaded to failure under seven (7) consecutive seismic events. The seismic motions replicating the 1994 Northridge earthquake in California, were applied with progressively increasing amplitudes. The results of shaking table tests on a 1/4 scaled model of the bridge were used to study the accuracy and reliability of different scales of the hybrid simulations.

On the Robustness of the Sign of Nonadditivity Index in a Choquet Integral Model

In the context of Multiple Criteria Decision Making, this paper studies the robustness of the sign of nonadditivity index for subset of criteria in a Choquet integral model. In the case where the set of alternatives is discrete, the use of the nonadditivity index proposed in the literature often leads to interpretations which are not always robust. Indeed, the sign of this nonadditivity index can depend on the arbitrary choice of a numerical representation in the set of all numerical representations compatible with the ordinal preferential information given by the Decision Maker. We characterize the ordinal preferential information for which the problem appears. We also propose a linear program allowing to test the non robustness of the sign of nonadditivity index for subset of criteria.

Determining the instantaneous bruising pattern in a sample potato tuber subjected to pendulum bob impact through finite element analysis

AbstractPotato bruising resulting from mechanical impact during production operations including harvest and postharvest is a significant concern within the potato production sector, leading to consumer complaints and economic losses. The detection of instantaneous internal bruising poses a particular challenge as it can progress over time during storage or transportation, making it difficult to identify immediately after external impact. This study aims to investigate the progression of bruising and accurately represent the instantaneous dynamic deformation behavior of potato tubers under four pendulum bob impact cases (pendulum arm angles of 30°, 45°, 60°, and 90°). To analyze the dynamic impact deformation characteristics of the tubers, solid modeling based on a reverse engineering approach and explicit dynamic engineering simulations were employed. The simulation results yielded valuable numerical data and visual representation of the deformation progression. The loading conditions considered in this study indicated that the maximum stress values, reaching 0.818 MPa at a pendulum arm angle of 90°, remained below the bio‐yield stress point of the tuber flesh (1.05 MPa) determined through experimental compression tests. Therefore, it was concluded that the impact did not cause permanent deformation (i.e., permanent bruising) in the tuber. However, the numerical analysis clearly demonstrated the sequence of stress occurrences, which is a key contributing factor to potential permanent bruising. In this regard, the bruising energy threshold of 318.314 mJ (R2: 0.96) was extrapolated. The numerical findings presented in this study can aid in evaluating the susceptibility of tuber samples to bruising. By employing nonlinear explicit dynamics simulations, this research contributes to the advancement of understanding complex deformation and bruising in solid agricultural products. Moreover, the application of these techniques holds significant industrial implications for enhancing the handling and transportation of agricultural produce.Practical applicationsThis research aims to tackle the challenge of accurately representing the immediate internal bruising pattern in potato tubers resulting from mechanical impact. Conventional methods, such as physical or analytical expressions, may not fully capture the distribution of bruising experienced by the tubers. To overcome this limitation, an engineering simulation approach is proposed to provide a more precise depiction of the instantaneous bruising pattern. By advancing the understanding of complex deformation and bruising in solid agricultural products, this research contributes to improving the efficiency and quality of agricultural production in the industry. Additionally, this study offers a step‐by‐step guide on how to conduct these simulations effectively.

Numerical Solution of Stagnation Point Flow of Non-Newtonian Fluid and Heat Transfer over a Porous Medium under Effect of Thermal Radiation and Magnetic Field

We study three key topics in this article, including Casson fluid flow at its stagnation point, heat transfer in the current magnetic field, and thermal radiation. Several equations related to coupled mass, momentum, and energy are controlled by the mathematical model. This is accomplished by using appropriate transformations to study dimensionless partial differential equations of motion, energy, and continuity. The Adam-Bashforth method is used to solve several nonlinear dimensionless partial differential equations. The analysis is based both on analytic and numerical representations of the impact of parameters which are relevant to the analysis, such as the Casson parameter β, porosity parameter K, magnetic field M, mixed convection parameter γ, and thermal radiation R. Finally, using the Adam-Bashforth approach, we are able to determine velocity profiles as well as temperature profiles.

WHGDroid: Effective android malware detection based on weighted heterogeneous graph

The growing Android malware is seriously threatening the privacy and property security of Android users. However, the existing detection methods are often unable to maintain sustainability as Android malwares evolve. To address this issue, instead of directly using the intra-App feature, we exploit diverse inter-App relations to build a higher-level semantic association, making it more difficult for malware to evade detection. In this paper, we propose WHGDroid, a new malware detection framework based on weighted heterogeneous graph, which helps detect malware by implicit higher-level semantic connectivity across Apps. To comprehensively analyze Apps, we first extract five different Android entities and five relations, and then model the entities and relations among them into a weighted heterogeneous graph (WHG), in which weights are used to represent the importance of entities. Rich-semantic metapaths are proposed to establish the implicit associations between App nodes and derive homogeneous graphs containing only App nodes. Finally, graph neural network is used to learn the numerical embedding representations of Apps. We make a comprehensive comparison with five baseline methods on large datasets in different read scenarios. The experimental results show that WHGDroid is superior to two state-of-the-art methods in all cases.

Improving Graphical and Numerical Representational Competence Coherence with Laboratory Investigation and Computer to Solve Problem-Solving of Geometry Optical Materials

Learning physics through the investigation of phenomena using practicum equipment in the laboratory is the real core of learning physics. The basic competence of prospective science teachers towards understanding concepts through investigating phenomena is well developed. The achievement of other, more abstract concept representation competencies such as numerical, graphical, and mathematical representations requires new methods or ways in the learning process. The development of computer technology as part of further investigation of learning in the laboratory needs to be developed to facilitate learning. The purpose of this research is to investigate how to improve competence in understanding concepts based on verbal representations (Verb Rep) in the form of investigations of phenomena, to achieve multiple representations (MR) (Formal Representation, Numerical Representation, Graphical Representation, Pictorial Representation) with computer-based learning. The results showed that the conventional group (N=30; M=72.87; SD = 7.03; SE = 1.28) had a lower average score on the test results than the experimental group (N = 30; M = 83.27; SD = 4.93; SE = 0.89). Independent mean statistical tests showed significant differences (t (30) = -6.63; p <0.001; r = 0.78) between groups learning to use one representation (conventional) and those using MR to solve problem-solving cases of lens content and curved mirrors, with the category of effect size (r)0.78 exceeding 0.50 (large effect).

Developments to the Bonded Block Modeling technique for Discrete Element simulation of transversely isotropic rocks

In this research, a novel three-dimensional Bonded Block Model (BBM) for the Discrete Element Method is proposed for the simulation of transversely isotropic rocks. The model stands on three new developments proposed to the conventional BBM: the adoption of Rigid Body Spring Network (RBSN) bonds combined with a fictitious stress method for the realization of transversely isotropic elastic behavior; a replaceable particle scheme for the explicit representation of cleavage fracture; and a dual Cohesive Zone Model for the simulation of fracture propagation on cleavage and rock matrix according to non-linear fracture mechanics theory. The model is verified by comparing numerical unconfined compression, direct tension and three point bending tests’ results with analytical and finite element solutions, and it is validated against laboratory data reported in literature for three transversely isotropic rock types. The results show that the improved BBM provides for mesh objective numerical representation of the anisotropy in deformability, peak strength and mode-I non-linear fracture mechanics properties of transversely isotropic rocks. In addition, the new model manifests realistic failure modes and the macroscale properties can be defined directly, without trial-and-error calibrations.

Detection of Sarcasm Using Bi-Directional RNN Based Deep Learning Model in Sentiment Analysis

Detecting sarcasm in text can be challenging, but it's an opportunity to exercise the ability to consider various perspectives and aspects. Considering multiple viewpoints while analysing sentences can lead to comprehensive results and constructive discussions. Sentiment analysis algorithms are constantly improving and becoming more accurate in identifying sarcasm, even in implicit expressions where the sarcasm is not explicitly stated. It's exciting to have the opportunity to detect sarcasm conveyed through subtle cues, as it adds another layer of complexity to the task. The field of sarcasm detection has made great strides with the use of supervised classification models that can accurately distinguish between sarcastic and non-sarcastic sentences through labelled data. This approach shows promise for identifying explicitly incongruous statements, and there is an increasing need for techniques that can successfully detect sarcasm in sentences with implicit sentiment incongruity. This study employs a deep learning model to tackle the challenge of distinguishing implicit sarcasm. The model being used is a recurrent neural network (RNN), which has the ability to retain numerical representations of previously processed information. The proposed model is designed to effectively identify sarcasm by capturing context-based incongruity within the presented text, which is a crucial factor. The proposed model, Proposed LSTM (P-LSTM), effectively captures the dependencies among words in both suffixes and prefixes by scanning the given sentences bi-directionally. The classification is organised into two levels: emotional and semantic. The suggested approach offers a way to accurately evaluate and categories tweets based on their sarcastic state. Excitingly, the proposed method is being evaluated through experiments on two automatically annotated datasets and two manually annotated datasets to determine its effectiveness. The suggested model is compared to numerous state-of-the-art methods, highlighting its efficacy. The findings demonstrate that the suggested model is highly effective for sarcasm detection compared to other methods currently in use. The suggested model has great potential in recognising implicit sarcasm by utilising deep learning and context-based incongruity analysis, which could lead to significant improvements over existing methods.

Enhancing CSI-Based Human Activity Recognition by Edge Detection Techniques

Human Activity Recognition (HAR) has been a popular area of research in the Internet of Things (IoT) and Human–Computer Interaction (HCI) over the past decade. The objective of this field is to detect human activities through numeric or visual representations, and its applications include smart homes and buildings, action prediction, crowd counting, patient rehabilitation, and elderly monitoring. Traditionally, HAR has been performed through vision-based, sensor-based, or radar-based approaches. However, vision-based and sensor-based methods can be intrusive and raise privacy concerns, while radar-based methods require special hardware, making them more expensive. WiFi-based HAR is a cost-effective alternative, where WiFi access points serve as transmitters and users’ smartphones serve as receivers. The HAR in this method is mainly performed using two wireless-channel metrics: Received Signal Strength Indicator (RSSI) and Channel State Information (CSI). CSI provides more stable and comprehensive information about the channel compared to RSSI. In this research, we used a convolutional neural network (CNN) as a classifier and applied edge-detection techniques as a preprocessing phase to improve the quality of activity detection. We used CSI data converted into RGB images and tested our methodology on three available CSI datasets. The results showed that the proposed method achieved better accuracy and faster training times than the simple RGB-represented data. In order to justify the effectiveness of our approach, we repeated the experiment by applying raw CSI data to long short-term memory (LSTM) and Bidirectional LSTM classifiers.

Compressed Gaussian Estimation under Low Precision Numerical Representation.

This paper introduces a novel method for computationally efficient Gaussian estimation of high-dimensional problems such as Simultaneous Localization and Mapping (SLAM) processes and for treating certain Stochastic Partial Differential Equations (SPDEs). The authors have presented the Generalized Compressed Kalman Filter (GCKF) framework to reduce the computational complexity of the filters by partitioning the state vector into local and global and compressing the global state updates. The compressed state update, however, still suffers from high computational costs, making it challenging to implement on embedded processors. We propose a low-precision numerical representation for the global filter, such as 16-bit integer or 32-bit single-precision formats for the global covariance matrix, instead of the expensive double-precision, floating-point representation (64 bits). This truncation can inevitably cause filter instability since the truncated covariance matrix becomes overoptimistic or even turns to be an invalid covariance matrix. We introduce a Minimal Covariance Inflation (MCI) method to make the filter consistent while minimizing the truncation errors. Simulation-based experiments results show significant improvement of the proposed method with a reduction in the processing time with minimal loss of accuracy.