Representation Of Processes Research Articles

Cascaded Thinning in Upscale and Downscale Representation for EEG Signal Processing.

Smoothing filters are widely used in EEG signal processing for noise removal while preserving signals' features. Inspired by our recent work on Upscale and Downscale Representation (UDR), this paper proposes a cascade arrangement of some effective image-processing techniques for signal filtering in the image domain. The UDR concept is to visualize EEG signals at an appropriate line width and convert it to a binary image. The smoothing process is then conducted by skeletonizing the signal object to a unit width and projecting it back to the time domain. Two successive UDRs could result in a better-smoothing performance, but their binary image conversion should be restricted. The process is computationally ineffective, especially at higher line width values. Cascaded Thinning UDR (CTUDR) is proposed, exploiting morphological operations to perform a two-stage upscale and downscale within one binary image representation. CTUDR is verified on a signal smoothing and classification task and compared with conventional techniques, such as the Moving Average, the Binomial, the Median, and the Savitzky Golay filters. Simulated EEG data with added white Gaussian noise is employed in the former, while cognitive conflict data obtained from a 3D object selection task is utilized in the latter. CTUDR outperforms its counterparts, scoring the best fitting error and correlation coefficient in signal smoothing while achieving the highest gain in Accuracy (0.7640%) and F-measure (0.7607%) when used as a smoothing filter for training data of EEGNet.

Effects of Riming on Ice‐Phase Precipitation Growth and Transport Over an Orographic Barrier

AbstractThe evolution of ice‐phase particles within precipitating clouds depends on the environmental properties of the cloud and on physical characteristics of the particles themselves, which can be modified by airflow over steep terrain. Through employing a unique Lagrangian particle‐based precipitation model, this study investigates the sensitivities in ice‐phase particle growth and transport due to variabilities in riming processes over an orographic barrier. This analysis is applied to two wintertime stratiform cyclones sampled by in situ aircraft over windward slopes during the Olympic Mountains Experiment. For both events, we simulate the ice‐phase particle evolution and trajectory within a two‐dimensional prescribed state representative of median observed cloud properties. Sensitivity simulations were constructed based on observed variabilities in supercooled liquid water (SLW) properties and its vertical extent above the melting level. Perturbations of SLW concentration equivalent to the 85th and 15th percentiles of observed values, which typically amounted to a change of less than 0.05 g m−3, resulted in respective increases or decreases in the ice‐phase contribution to surface precipitation mass by as much as 50% and horizontal particle trajectories differences exceeding 10 km. Similar sensitivities were found in response to varying the vertical extent of SLW above the melting level and to adjustments in mean SLW droplet size. The significant precipitation response to small variations in cloud properties principally arises from changing rates of rime mass accumulation and correspondingly, increases in particle fall speed. Considerations for the numerical representation of the riming process and its complex effects on precipitation are discussed.

The Significance of the Melt-Pond Scheme in a CMIP6 Global Climate Model

Abstract The impact of melt ponds on sea ice albedo has been observed and documented. In general circulation models, ponds are now accounted for through indirect diagnostic treatments (“implicit” schemes) or prognostic melt-pond parameterizations (“explicit” schemes). However, there has been a lack of studies showing the impacts of these schemes on simulated Arctic climate. We focus here on rectifying this using the general circulation model HadGEM3, one of the few models with a detailed explicit pond scheme. We identify the impact of melt ponds on the sea ice and climate, and associated ice–ocean–atmosphere interactions. We run a set of constant forcing simulations for three different periods and show, for the first time, that using mechanistically different pond schemes can lead to very significantly different sea ice and climate states. Under near-future conditions, an implicit scheme never yields an ice-free summer Arctic, while an explicit scheme yields an ice-free Arctic in 35% of years and raises autumn Arctic air temperatures by 5° to 8°C. We find that impacts on climate and sea ice depend on the ice state: under near-future and last-interglacial conditions, the thin sea ice is very sensitive to pond formation and parameterization, whereas during the preindustrial period the thicker sea ice is less sensitive to the pond scheme choice. Both of these two commonly used parameterizations of sea ice albedo yield similar results under preindustrial conditions but in warmer climates lead to very different Arctic sea ice and ocean and atmospheric temperatures. Thus, changes to physical parameterizations in the sea ice model can have large impacts on simulated sea ice, ocean, and atmosphere. Significance Statement This study investigates the impacts of melt ponds on Arctic sea ice under different climate conditions, using the HadGEM3-GC3.1-LL general circulation model (GCM). Additionally, we study the impact of changing the type of pond scheme used. We find that changing the pond scheme causes large differences to how a GCM simulates Arctic sea ice, the ocean, and the atmosphere, for both near-future and warmer paleoclimate conditions. These large differences have not been found previously, because this is one of the first GCM studies of this type. Our results demonstrate the importance of melt ponds, and their wider impacts on ocean and atmosphere. Furthermore, they suggest that better evaluation of the representation of sea ice processes is vital for the robust projection of future climate change.

A Practical Guide to the Representation of Protein Regulation in the Web Application ChloroKB.

ChloroKB ( http://chlorokb.fr ) is a knowledge base providing synoptic representations of the metabolism of the model plant Arabidopsis thaliana and its regulation. Initially focused on plastid metabolism, ChloroKB now accounts for the metabolism throughout the cell. ChloroKB is based on the CellDesigner formalism. CellDesigner supports graphical notation and listing of the corresponding symbols based on the Systems Biology Graphical Notation. Thus, this formalism allows biologists to represent detailed biochemical processes in a way that can be easily understood and shared, facilitating communication between researchers. In this chapter, we will focus on a specificity of ChloroKB, the representation of multilayered regulation of protein activity. Information on regulation of protein activity is indeed central to understanding the plant response to fluctuating environmental conditions. However, the intrinsic diversity of the regulatory modes and the abundance of detail may hamper comprehension of the regulatory processes described in ChloroKB. With this chapter, ChloroKB users will be guided through the representation of these sophisticated biological processes of prime importance to understanding metabolism or for applied purposes. The descriptions provided, which summarize years of work and a broad bibliography in a few pages, can help speed up the integration of regulatory processes in kinetic models of plant metabolism.

An improved Approach for Sustainable Risk Management Practices in Modern Businesses

The complexity of the business environment, which is shaped by dynamic economic, environmental, and social factors, makes it more important for companies to develop sustainable risk management practices. This research proposes a novel approach that combines traditional methods with modern machine learning techniques in response to the complex challenges faced by contemporary businesses in balancing risk and sustainability. Our study uses a public bank loan default dataset as a case study to address missing data systematically through robust imputation mechanisms and transform categorical variables using feature encoding. Spearman correlation analysis helps us understand complex variable relationships and guides subsequent feature selection. The decision tree classifier, a powerful machine learning algorithm known for its interpretability, is applied to identify key factors contributing to risk assessment. The hierarchical structure of the decision tree not only reveals important variables but also provides an explicit representation of the decision-making process. ROC curve analysis shows how well our predictive model can differentiate potential loan defaults.

Single-image super-resolution reconstruction based on phase-aware visual multi-layer perceptron (MLP).

Many advanced super-resolution reconstruction methods have been proposed recently, but they often require high computational and memory resources, making them incompatible with low-power devices in reality. To address this problem, we propose a simple yet efficient super-resolution reconstruction method using waveform representation and multi-layer perceptron (MLP) for image processing. Firstly, we partition the original image and its down-sampled version into multiple patches and introduce WaveBlock to process these patches. WaveBlock represents patches as waveform functions with amplitude and phase and extracts representative feature representations by dynamically adjusting phase terms between tokens and fixed weights. Next, we fuse the extracted features through a feature fusion block and finally reconstruct the image using sub-pixel convolution. Extensive experimental results demonstrate that SRWave-MLP performs excellently in both quantitative evaluation metrics and visual quality while having significantly fewer parameters than state-of-the-art efficient super-resolution methods.

Network Traffic Anomaly Detection: A Revisiting to Gaussian Process and Sparse Representation

Network Traffic Anomaly Detection: A Revisiting to Gaussian Process and Sparse Representation

The Phonological Process in the Plurality of Pashto Nouns

This research aims to provide a comprehensive representation of the intricate phonological processes involved in Pashto noun pluralization. The study focuses on identifying these phonological processes, examining the patterns of plural formation, and understanding their implications for Pashto's phonological structure. To obtain the objectives of the study, the researchers collected the data from the native speakers of Pashto especially joined the elders. Along with this, the data were collected from Pashto books, magazines and dictionaries followed by the discussion of experts. The finding of the study showed that Pashto carries complicated pluralization of noun phonologically. It revealed that there are nouns having single pattern, two patterns, three patterns, four patterns and some nouns even don’t carry plural form. This complexity underscores Pashto's versatility conveying precise meanings and contextual differences through diverse pluralization forms, distinguishing it from other languages.

РИМСКОЕ ПРАВО И ЮРИСПРУДЕНЦИЯ У ИСТОКОВ ПРАВОВЕДЕНИЯ ЦИФРОВОЙ ЭПОХИ (ПОНЯТИЙНО-ТЕРМИНОЛОГИЧЕСКИЙ АСПЕКТ)

The article considers methodological features of Roman law and jurisprudence within the context of their concepto-terminological foundations. The study argues the naturalized character of Roman legal imago mundi, which became the ontological base of legal dogmatics. Taking from this Roman legal constructs were thought as functio-nominative entities derived from the natural language (terms for the legal events and processes), and not as abstract theoretical structures (concepts). The research proves the way of Roman legal cognition to be organized in the logic of “an object — a nomination — a construct”. Affecting objectively the organization of continental legal thought, this method remains in demand in jurisprudence of digital age. The author shows that basic constructs of legal dogma predetermined the impossibility of representation of many phenomena and processes of modern high-tech world, especially digital property. The study concludes that methodological issues are most significant for the legal science in digital age.

Phase-field modeling of anisotropic crack propagation based on higher-order nonlocal operator theory

This paper presents a novel higher-order nonlocal operator theory for the phase-field modeling of brittle fracture in anisotropic materials. Incorporating higher order nonlocal operators can enhance the accuracy of the phase-field model by effectively capturing long-range interactions that hold significance in numerous materials. The reproducing kernel particle method is employed to derive a nonlocal differential operator to enhance computational stability and accuracy. Moreover, the proposed method eliminates the need for direct computation of derivatives of the modified kernel function, which avoids the calculation of moment matrix derivatives and improves computational efficiency. The phase-field modeling of polycrystalline materials, considering the anisotropic fracture resistance of each grain, is implemented using this numerical framework. The present method is able to capture different scenarios intergranular and transgranular crack propagation patterns in polycrystalline materials. The proposed method involves a detailed representation of the complex process of crack initiation and propagation in 2D and 3D models of polycrystalline materials.

Understanding contributing factors to exoskeleton use-intention in construction: a decision tree approach using results from an online survey

Work-related musculoskeletal disorders (WMSDs) are a major health concern in the construction industry. Occupational exoskeletons (EXOs) are a promising ergonomic intervention to help reduce WMSD risk. Their adoption, however, has been low in construction. To understand the contributing factors to EXO use-intention and assist in future decision-making, we built decision trees to predict responses to each of three EXO use-intention questions (Try, Voluntary Use, and Behavioural Intention), using online survey responses. Variable selection and hyperparameter tuning were used respectively to reduce the number of potential predictors and improve prediction performance. The importance of variables in each final tree was calculated to understand which variables had a greater influence. The final trees had moderate prediction performance. The root node of each tree included EXOs becoming standard equipment, fatigue reduction, or performance increase. Important variables were found to be quite specific to different decision trees. Practical implications of the findings are discussed. Practitioner summary: This study used decision trees to identify key factors influencing the use-intention of occupational exoskeletons (EXOs) in construction, using online survey data. Key factors identified included EXOs becoming standard equipment, fatigue reduction, and performance improvement. Final trees provide intuitive visual representations of the decision-making process for workers to use EXOs.

ESTIMATION OF PROBABILISTIC AND STATISTICAL CHARACTERISTICS OF THE INFORMATIONAL STATE OF A COMMUNICATION CHANNEL

The methods for determining the informational state of a communication channel based on the assessment of probabilistic and statistical characteristics of errors at the channel output are presented. Adaptive information protection methods in digital communication systems, where encoder parameters change depending on the channel state, require the use of network quality control methods based on determining the statistical characteristics of the channel. It is shown that the bit error probability in the channel is constant for the presented error generation process and is independent of the method of dividing information into code blocks. The methods for assessing the statistical characteristics of the channel are based on the topological representation of error generation processes over the length of code blocks. It is demonstrated that, with a fixed code sequence length and a constant bit error probability at the output of the information channel, different error distributions correspond to sets of n-sequences. With a known number of states in the likelihood transition matrix, calculation of observation intervals for counting erroneous blocks is proposed. The presented method of researching statistical characteristics, based on the assumption of error independence, allows for the development of algorithms and procedures for assessing the state of discrete communication channels. The evaluation of potential bounds and statistical characteristics for the decoding error probability enables establishing a connection between the topological characteristics of error sequence partitioning and code parameters. The work confirms that the proposed method allows the synthesis of algorithms for determining code parameters, whose numerical values are determined based on minimizing potential decoding error probabilities.

COUNTERFEIT CURRENCY DETECTION USING MACHINE LEARNING

This research addresses the pervasive issue of counterfeit currency through a comprehensive approach integrating advanced image processing techniques and machine learning algorithms. The methodology encompasses crucial stages, including image comparison, segmentation, edge detection, feature extraction, and grayscale conversion, coupled with the implementation of machine learning models such as K-Nearest Neighbors (KNN),and theefficient MobileNetV2. In tackling the challenge of counterfeit currency, image processing techniques play a pivotal role by enabling the extraction and analysis of distinct features. From isolating patterns through segmentation to refining with edge detection and feature extraction, these techniques enhance the identification of intricate characteristics inherent in legitimate banknotes. Grayscale conversion further standardizes the representation for effective processing. Keywords: KNN, MobileNetV2, Image Processing, Machine learning algorithms, Feature extraction.

Abortion in French cinema during the long 1960s and beyond

ABSTRACT This article examines the representation of abortion in French cinema during one of the most pivotal periods for women’s rights and lives—the ‘long’ 1960s (1959–1973), one caught between post-war conservatism and the more tangible feminism of the mid-1970s. Using a feminist and sociocultural approach alongside statistical analysis of all 1,455 French-produced feature films released during this period and textual analysis of case-study films, we examine various aspects of abortion, including representation of the decision process, the procedure itself and the aftermath, alongside physical and emotional aspects. Before the long 1960s, films primarily represented abortions as a taboo: dangerous, violent, and often resulting in death. This reinforced stigma, fear and misunderstanding surrounding the procedure, although films like Claude Autant-Lara’s Une femme en blanc series (1965 and 1966), Mon amour, mon amour (Nadine Trintignant, 1967) and Avortement clandestin! (Pierre Chevalier, 1973) worked to challenge these stereotypes. We look beyond the long 1960s to filmmaking in the direct aftermath of Loi Veil before concluding with a discussion of recent French television series. This paper mitigates a gap in current scholarship by unpacking the representation of abortion in French popular media, a woefully under-studied but critical subject—essential for understanding the status of women’s reproductive rights in France.

Mapowanie przeszłości. Systemy Informacji Geograficznej (GIS) a badanie przeszłości

Interactive online maps enable new perspectives and allow for multi-dimensional interaction with source data, becoming – thanks to that – an intuitive analytical resource for the purpose of exploring relations between the information, which were obscured up to this moment. Visualization of data in the form of interactive maps is becoming not only an attractive means for presenting the knowledge – e.g., in the form of diagrams, tables, and images – but, first and foremost, provides the user with a possibility to perform a more profound analysis of historical phenomena. Visualizations of this kind, revealing unnoticed relations and patterns, provoke new questions and prompt historians to search for the answers. Newly created interactive historical maps can become a starting point to formulate new representations of past phenomena and processes. They can also serve to organize the knowledge and to integrate source materials of different sorts. As they link various forms of data (linguistic, visual, geographical), they form an analytical tool used to interpret historical processes and phenomena that have been of interest, but also to explore new ones.

ON A CLASS OF NONSTATIONARY CURVES IN HILBERT SPACE

Stationary random processes have been studied quite well over recent years starting with the works of A. N. Kolmogorov. The possibility of building nonstationary random process correlation theory was considered in the monographs by M. S. Livshits, A. A. Yantsevich, V. A. Zolotarev and others. Some classes of nonstationary curves were investigated by V. E. Katsnelson. In this paper nonstationary random processes are represented as curves in Hilbert space which "slightly deviate" from random processes with the correlation function of special kind. The infinitesimal correlation function has been introduced; in essence, this function characterizes the deviation from the correlation process with the given correlation function. The paper discusses the cases of nonstationary random processes, the operator of which has one‑dimensional imaginary component. Cases of a dissipative operator with descrete spectrum are also considered in this work. It is shown that the nonstationarity of the random process is closely related to the deviation of the operator from its conjugated operator. Using the triangle and universal models of non‑self‑ajoint operators it is possible to obtain the representation for the correlation function in the case of nonstationary process which replaces the Bochner – Khinchin representation for stationary random processes. The expresson for the infinitesimal correlation function was obtained for different cases of operator spectrum: for the descrete spectrum placed in the upper half‑plane and for the contrast‑free spectrum at zero. In the case of dissipative operator with descrete spectrum the infinitesimal function can be found in terms of special lambda function. For Lebesque spaces of compex‑valued squared integratable functions the expresson of infinitesimal function was found in terms of special zero order modified Bessel function. It was shown that a similar approach can be applied for the evolutionarily represented sequences in Hilbert spaces.

Seismic Velocity Inversion via Physical Embedding Recurrent Neural Networks (RNN)

Seismic velocity inversion is one of the most critical issues in the field of seismic exploration and has long been the focus of numerous experts and scholars. In recent years, the advancement of machine learning technologies has infused new vitality into the research of seismic velocity inversion and yielded a wealth of research outcomes. Typically, seismic velocity inversion based on machine learning lacks control over physical processes and interpretability. Starting from wave theory and the physical processes of seismic data acquisition, this paper proposes a method for seismic velocity model inversion based on Physical Embedding Recurrent Neural Networks. Firstly, the wave equation is a mathematical representation of the physical process of acoustic waves propagating through a medium, and the finite difference method is an effective approach to solving the wave equation. With this in mind, we introduce the architecture of recurrent neural networks to describe the finite difference solution of the wave equation, realizing the embedding of physical processes into machine learning. Secondly, in seismic data acquisition, the propagation of acoustic waves from multiple sources through the medium represents a high-dimensional causal time series (wavefield snapshots), where the influential variable is the velocity model, and the received signals are the observations of the wavefield. This forms a forward modeling process as the forward simulation of the wavefield equation, and the use of error back-propagation between observations and calculations as the velocity inversion process. Through time-lapse inversion and by incorporating the causal information of wavefield propagation, the non-uniqueness issue in velocity inversion is mitigated. Through mathematical derivations and theoretical model analyses, the effectiveness and rationality of the method are demonstrated. In conjunction with simulation results for complex models, the method proposed in this paper can achieve velocity inversion in complex geological structures.

Simulation-Based Medical Education: A Boon for Medical Students? - An Integrative Review

Simulation is a synthetic representation of a real-world process with sufficient reliability to facilitate learning through contemplation and practice without the hazard, innate in a real-life experience. Nowadays, simulation is a useful accompaniment to medical education as pre-exposure to necessary clinical skills as exposure in the real clinical setting may be insufficient. Clinical skills and performance are considered core proficiency and are crucial to the professionals. This can enable the students to familiarize themselves with patient examination and hands-on- training by using models before coming across patients directly. Simulators are broadly classified into two broad categories: 1. High-fidelity Simulators and 2. Low-fidelity simulators. The fidelity of a simulator is decided by the extent to which it provides realism through characteristics. Simulation Based Medical Education (SBME) provides a safe environment for the students to acquire their psychomotor skill but is not necessarily better than other types of instruction as there is a high degree of variability between studies. SBME has been introduced in the health care field and now it is becoming one of the most popular teaching techniques for improving patient safety and care. It would be advantageous if it is included in medical curricula as it may proof boon for the young medicos. Keywords: Medical simulation; SBME; Simulators

Advective gas flow in bentonite: Development and comparison of enhanced multi-phase numerical approaches

Understanding the impact of repository gas, generated from degradation of waste and its interaction with the host rock, is essential when assessing the performance and safety function of long-term disposal systems for radioactive waste. Numerical models based on conventional multi-phase flow theory have historically been applied to predict the outcome and impact of gas flow on different repository components. However, they remain unable to describe the full complexity of the physical processes observed in water-saturated experiments (e.g., creation of dilatant pathways) and thus, the development of novel representations for their description is required when assessing fully saturated clay-based systems. This was the primary focus of Task A within the international cooperative project DECOVALEX-2019 (D-2019) and refinement of these approaches is the primary focus of this study (Task B in the current phase of DECOVALEX-2023).This paper summarises development of enhanced numerical representations of key processes and compares the performance of each model against high-quality laboratory test data. Experimental data reveals that gas percolation in water-saturated compacted bentonite is characterised by four key features: (i) a quiescence phase, followed by (ii) the gas breakthrough, which leads to a (iii) peak value, which is then followed by (iv) a negative decay. Three models based on the multiphase flow theory have been developed. These models can provide good initial values and reasonable responses for gas breakthrough (although some of them still predict a too-smooth response). Peak gas pressure values are in general reasonably well captured, although maximum radial stress differences are observed at 48 mm from the base of the sample. Here, numerical peak values of 12.8 MPa are predicted, whereas experimental values are about 11 MPa. These models are also capable of providing a reasonable representation of the negative pressure decay following peak pressure. However, other key specific features (such as the timing of gas breakthrough) still require a better representation. The model simulations and their comparison with experimental data show that these models need to be further improved with respect to model parameter calibration, the numerical representation of spatial heterogeneities in material properties and flow localisation, and the upscaling of the related physical processes and parameters. To further understand gas flow localisation, a new conceptual model has been developed, which shows that discrete channels can possibly be induced through the instability of gas-bentonite interface during gas injection, thus providing a new perspective for modeling gas percolation in low-permeability deformable media.

VERIFICATION OF THE MATHEMATICAL FLIGHT MODEL THE PROJECTILE AS A MODIFIED MODEL OF THE MATERIAL POINT

The analysis of mathematical models of the flight of projectiles has been carried out, it is shown that the nature of their provision varies depending on the degree of reliability of the representation of the real physical process of its flight by a mathematical model, the adequate consideration of certain forces (moments) acting on the projectile, as well as the level of information about external flight conditions. In the simplest mathematical model with three degrees of freedom, the projectile is considered as a material point that moves in the atmosphere under the influence of gravity and full aerodynamic force. The dynamics of projectile flight is most completely described by the mathematical model of the movement of a solid body with six degrees of freedom. In the intermediate model with four degrees of freedom, all aerodynamic forces are taken into account, the orientation of the projectile is characterized by taking into account the angle of nutation, and the kinetic energy of the rotational movement is taken into account through the angular velocity of the projectile around its axis of symmetry. The process of restori ng the coefficients of aerodynamic forces using the material point model does not provide the necessary accuracy and adequacy of the description of the projectile flight process; of the modified material point model is much simpler (the least complex) compared to the model of the motion of a rigid body with six degrees of freedom due to the smaller number of coefficients and differential equations included in its composition. The differential equations of the modified material point model are provided - the equation of motion of the center of mass and the equation of nutational oscillations of the projectile in scalar form; an assessment of its accuracy (adequacy) was carried out on the example of the 155-mm PF projectile M2000, one of the family of projectiles of the company Denel Naschem – Assegai M200X Series 155-mm Projectiles. A comparison of the characteristics of the 155-mm PF of the M2000 projectile calculated according to the 6DoF model and the modified material point model showed their high convergence; the error does not exceed 1%. Keywords: aerodynamic forces (moments), projectile, mathematical models, modified model, aerodynamic coefficients, simulation, accuracy, flight parameters.