Physical Representation Research Articles

Advanced VLSI-based digital image contrast enhancement: A novel approach with modified image pixel evaluation logic

This research uses an extremely memory-intensive and power-efficient very-large-scale-integration (VLSI) architecture to perform nonlinear scaling on pictures. Images can be scaled either up or down so that they can be adapted to the differing resolutions of a variety of devices, such as cameras and printers. Photography with a high resolution calls for significantly more storage space and processing power. The branch of image processing known as “image fusion” has recently had a meteoric rise in prominence. The rapidly growing usage of digital imagery in remote sensing and satellite applications has led to an increase in the need to both store and process massive amounts of picture data. This demand has resulted in an increase in the availability of storage space. Traditional fusion methods produce spatial distortions despite their generally high level of spatial performance. Because of this, the discrete wavelet transformation (DWT), currently the most efficient approach for processing images, focuses on finding a solution to this problem. Within the scope of this investigation, we propose the modified image pixel evaluation logic (MIPEL), a functional method for incorporating the lifting-based strategy utilized in DWT-based picture fusion. In order to avoid excessive delays, high-resolution photo processing typically makes use of hardware processing, which necessitates the utilization of more complex apparatus and more time. In this work, the hardware description language verilog is employed to evaluate the effectiveness of various fundamental approaches to the enhancement of pictures. VHDL is a relatively recent method that has replaced more traditional simulations in the process of producing results for signal processing. It does this by allowing rapid access to physical VLSI representations. Utilizing the proposed MIPEL as a starting point, this study aims to design, model, simulate, and develop several different strategies for enhancing photos using FPGA. Because they take place on the pixel level and in the immediate neighborhood of a pixel, the enhancements that may be made to an image can be thought of as point processing procedures. VLSI architecture is developed for contrast augmentation for low-contrast source photos using pixel-based image fusion, and in the simulation results that followed, we offer findings that clearly illustrate the efficiency of this technology.

BIM-LADM AMALGAMATION — A REVIEW

Abstract. This paper discussed two International Organization for Standardization (ISO) standards, namely, ISO 19152 – Land Administration Domain Model (LADM) and ISO 16739 – Industry Foundation Classes (IFC), to identify three-dimensional (3D) multi-floor building rights. These days, it is challenging to characterize all multifunctional structures due to the increase in complex buildings. High-rise building land and property information management is recommended through building information modeling (BIM). Countries like the Netherlands, Australia, and Turkey have considered adopting BIM for their land administration. A land administration standard, such as LADM, offers better legal and physical representation in identifying the rights, restrictions, and responsibilities (RRRs) of the spatial units and stakeholders. However, LADM falls short in representing all building elements, such as semantic information and invisible functional spaces. Hence, this paper reviews incorporating additional information from BIM to create a comprehensive three-dimensional building representation including legal information from LADM. Issues and challenges also being highlighted with some recommendations for possible future works.

A Physics-Informed Low-Shot Adversarial Learning for sEMG-Based Estimation of Muscle Force and Joint Kinematics.

Muscle force and joint kinematics estimation from surface electromyography (sEMG) are essential for real-time biomechanical analysis of the dynamic interplay among neural muscle stimulation, muscle dynamics, and kinetics. Recent advances in deep neural networks (DNNs) have shown the potential to improve biomechanical analysis in a fully automated and reproducible manner. However, the small sample nature and physical interpretability of biomechanical analysis limit the applications of DNNs. This paper presents a novel physics-informed low-shot adversarial learning method for sEMG-based estimation of muscle force and joint kinematics. This method seamlessly integrates Lagrange's equation of motion and inverse dynamic muscle model into the generative adversarial network (GAN) framework for structured feature decoding and extrapolated estimation from the small sample data. Specifically, Lagrange's equation of motion is introduced into the generative model to restrain the structured decoding of the high-level features following the laws of physics. A physics-informed policy gradient is designed to improve the adversarial learning efficiency by rewarding the consistent physical representation of the extrapolated estimations and the physical references. Experimental validations are conducted on two scenarios (i.e. the walking trials and wrist motion trials). Results indicate that the estimations of the muscle forces and joint kinematics are unbiased compared to the physics-based inverse dynamics, which outperforms the selected benchmark methods, including physics-informed convolution neural network (PI-CNN), vallina generative adversarial network (GAN), and multi-layer extreme learning machine (ML-ELM).

Melt pond fractions on Arctic summer sea ice retrieved from Sentinel-3 satellite data with a constrained physical forward model

Abstract. The presence of melt ponds on Arctic summer sea ice significantly alters its albedo and thereby the surface energy budget and mass balance. Large-scale observations of melt pond coverage and sea ice albedo are crucial to investigate the role of sea ice for Arctic amplification and its representation in global climate models. We present the new Melt Pond Detection 2 (MPD2) algorithm, which retrieves melt pond, sea ice, and open-ocean fractions as well as surface albedo from Sentinel-3 visible and near-infrared reflectances. In contrast to most other algorithms, our method uses neither fixed values for the spectral albedo of the surface constituents nor an artificial neural network. Instead, it aims for a fully physical representation of the reflective properties of the surface constituents based on their optical characteristics. The state vector X, containing the optical properties of melt ponds and sea ice along with the area fractions of melt ponds and open ocean, is optimized in an iterative procedure to match the measured reflectances and describe the surface state. A major problem in unmixing a compound pixel is that a mixture of half open water and half bright ice cannot be distinguished from a homogeneous pixel of darker ice. In order to overcome this, we suggest constraining the retrieval with a priori information. Initial values and constraint of the surface fractions are derived with an empirical retrieval which uses the same spectral reflectances as implemented in the physical retrieval. The snow grain size and optical thickness change with time, and thus the ice surface albedo changes throughout the season. Therefore, field observations of spectral albedo are used to develop a parameterization of the sea ice optical properties as a function of the temperature history of the sea ice. With these a priori data, the iterative optimization is initialized and constrained, resulting in a retrieval uncertainty of below 8 % for melt pond and 9 % for open-ocean fractions compared to the reference dataset. As reference data for evaluation, a 10 m resolution product of melt pond and open-ocean fraction from Sentinel-2 optical imagery is used.

НАУЧНЫЕ ОСНОВЫ ПРЕДУПРЕЖДЕНИЯ ВОЗГОРАНИЯ АВТОМОБИЛЬНЫХ НЕЙТРАЛИЗАТОРОВ В ЭКСПЛУАТАЦИИ

The issue of physical and mathematical representation of the thermal dynamics (kinetics) of the process of conversion of exhaust gases in a catalytic converter in dangerous, from the point of view of fire, uncontrolled emergency operating modes of its operation
 is considered. Modern automobile engines are required to ensure fire safety. On engines with converters, this conflicts with stringent international environmental standards, the satisfaction of which requires the dangerously heated converter to be placed in the engine compartment next to flammable combustible materials. It is proposed to solve the problematic issue by monitoring and diagnosing fire and emergency operating conditions of neutralizers in an in-place method, scientifically substantiated on an interdisciplinary basis – by analyzing the composition of the exhaust gases. The method has been positively tested under real operating conditions.

Research on an Enhanced Multimodal Network for Specific Emitter Identification

Specific emitter identification (SEI) refers to the task of distinguishing similar emitters, especially those of the same type and transmission parameters, which is one of the most critical tasks of electronic warfare. However, SEI is still a challenging task when a feature has low physical representation. Feature representation largely determines the recognition results. Therefore, this article expects to move toward robust feature representation for SEI. Efficient multimodal strategies have great potential for applications using multimodal data and can further improve the performance of SEI. In this research, we introduce a multimodal emitter identification method that explores the application of multimodal data, time-series radar signals, and feature vector data to an enhanced transformer, which employs a conformer block to embed the raw data and integrates an efficient multimodal feature representation module. Moreover, we employ self-knowledge distillation to mitigate overconfident predictions and reduce intra-class variations. Our study reveals that multimodal data provide sufficient information for specific emitter identification. Simultaneously, we propose the CV-CutMixOut method to augment the time-domain signal. Extensive experiments on real radar datasets indicate that the proposed method achieves more accurate identification results and higher feature discriminability.

A physically consistent AI-based SPH emulator for computational fluid dynamics

Abstract The integration of artificial intelligence (AI) into computational fluid dynamics (CFD) has significantly expanded the scope of fluid modeling, allowing enhanced analysis capabilities and improved simulation performance. While Eulerian methods already benefit extensively from AI, notably in reliable weather prediction, the application of AI to Lagrangian methods remains less consolidated. Smoothed particle hydrodynamics (SPH) is a Lagrangian mesh-less numerical method for CFD with well-established advantages for the simulation of highly dynamic free-surface flows. Here, we explore an application of AI to SPH simulations, utilizing an artificial neural network (ANN) to estimate hydrodynamic forces between particle pairs, learning from SPH-simulated results. A model of this nature, which emulates the mathematical representation of physics, is termed an emulator. We examine the physical significance of the emulator, presenting its applications in benchmark tests, assessing its faithfulness to traditional SPH simulations, and highlighting its ability to generalize and simulate test cases with varying levels of complexity beyond its training data.

An intercomparison of EarthCARE cloud, aerosol, and precipitation retrieval products

Abstract. The objective of the Earth Cloud, Aerosol, and Radiation Explorer (EarthCARE) mission is to infer attributes of cloud, aerosol, precipitation, and radiation from observations made by four complementary instruments. This requires the development of single-instrument and multiple-instrument (i.e. synergistic) retrieval algorithms that employ measurements made by one, or more, of EarthCARE's cloud-profiling radar (CPR), atmospheric lidar (ATLID), and multi-spectral imager (MSI); its broadband radiometer (BBR) places the retrieved quantities in the context of the surface–atmosphere radiation budget. To facilitate the development and evaluation of ESA's EarthCARE production model prior to launch, sophisticated instrument simulators were developed to produce realistic synthetic EarthCARE measurements for simulated conditions provided by cloud-resolving models. While acknowledging that the physical and radiative representations of cloud, aerosol, and precipitation in the test scenes are based on numerical models, the opportunity to perform detailed evaluations wherein the “truth” is known provides insights into the performance of EarthCARE's instruments and retrieval algorithms. This level of omniscience will not be available for the evaluation of in-flight EarthCARE retrieval products, even during validation activities coordinated with ground-based and airborne measurements. In this study, we compare EarthCARE retrieval products both statistically across all simulated scenes and from a specific time series from a single scene. For ice clouds, it is shown that retrieved profiles of ice water content and effective particle size made by the ATLID-CPR-MSI cloud, aerosols, and precipitation (ACM-CAP) synergistic algorithm are consistently more accurate than those from its single-instrument counterparts. While liquid clouds are often difficult to detect from satellite-borne sensors, especially for multi-layered clouds, ACM-CAP benefits from combined constraints from lidar backscatter, solar radiances, and radar-path-integrated attenuation but still exhibits non-trivial random error. For precipitation retrievals, the CPR cloud and precipitation product (C-CLD) and ACM-CAP have a similar performance when well-constrained by CPR measurements. The greatest differences are in coverage, with ACM-CAP reporting retrievals in the melting layer, and in heavy precipitation, where CPR signals are dominated by multiple scattering and attenuation. Aerosol retrievals from ATLID compensate for a high degree of measurement noise in a number of ways, with the ATLID extinction, backscatter, and depolarisation (A-EBD) product and ACM-CAP demonstrating similar performance. The multi-spectral imager (MSI) cloud optical properties (M-COP) product performs very well for unambiguous cloud layers. Similarly, the MSI aerosol optical thickness (M-AOT) product performs well when radiances are unaffected by cloud, but both products provide little information about vertical profiles of properties. Finally, a summary of the performance of all retrieval products and their random errors is provided.

New Features and Enhancements in Community Land Model (CLM5) Snow Albedo Modeling: Description, Sensitivity, and Evaluation

AbstractWe enhance the Community Land Model (CLM) snow albedo modeling by implementing several new features with more realistic and physical representations of snow‐aerosol‐radiation interactions. Specifically, we incorporate the following model enhancements: (a) updating ice and aerosol optical properties with more realistic and accurate data sets, (b) adding multiple dust types, (c) adding multiple surface downward solar spectra to account for different atmospheric conditions, (d) incorporating a more accurate adding‐doubling radiative transfer solver, (e) adding nonspherical snow grain representations, (f) adding black carbon‐snow and dust‐snow internal mixing representations, and (g) adding a hyperspectral (480‐band vs. the default 5‐band) modeling capability. These model features/enhancements are included as new CLM physics/namelist options, which allows for quantification of model sensitivity to snow albedo processes and for multi‐physics model ensemble analyses for uncertainty assessment. The model updates have been included in the latest released CLM version. Sensitivity analyses reveal strong impacts of using the new adding‐doubling solver, nonspherical snow grains, aerosol‐snow internal mixing, updated aerosol optics, and different dust types. These enhanced snow albedo representations improve the CLM simulated global snowpack evolution and land surface conditions, with reduced biases in simulated snow surface albedo, snow cover, snow water equivalent, snow depth, and surface (2‐m) air temperature over many mid‐latitude mountainous regions and seasonal snowpacks but degraded performance in some northern high‐latitude regions.

A Postcolonial Criticism in Jane Eyre

Jane Eyre was first published in 1847. It is a novel that in the 19th century contributed hugely to the colonial discourse and the depiction of the colonized other in terms of physical representation, stereotypes, and female agency. It was created during the age of the British colonial expansion at the time when Britain shifted its interest from the West Indies to the East. This paper aims at analyzing colonial issues included by Bronte in the novel. The paper will focus on analysis of the colonialist ideology, sexism, freedom, ethnicity, and injustice of Jane Eyre in the context of colonial discourse. And finally, a study of the colonial wealth and inheritance will be conducted. To do this, a historical and literary analysis of the text will be needed

A new quantum machine learning algorithm: split hidden quantum Markov model inspired by quantum conditional master equation

The Hidden Quantum Markov Model (HQMM) has significant potential for analyzing time-series data and studying stochastic processes in the quantum domain as an upgrading option with potential advantages over classical Markov models. In this paper, we introduced the split HQMM (SHQMM) for implementing the hidden quantum Markov process, utilizing the conditional master equation with a fine balance condition to demonstrate the interconnections among the internal states of the quantum system. The experimental results suggest that our model outperforms previous models in terms of scope of applications and robustness. Additionally, we establish a new learning algorithm to solve parameters in HQMM by relating the quantum conditional master equation to the HQMM. Finally, our study provides clear evidence that the quantum transport system can be considered a physical representation of HQMM. The SHQMM with accompanying algorithms present a novel method to analyze quantum systems and time series grounded in physical implementation.

Coastal Compound Flood Simulation through Coupled Multidimensional Modeling Framework

Coastal watersheds are prone to compound flooding caused by heavy rainfall, storm surge, and high tide. Accurate prediction and modeling of coastal compound floods (CCF) require precise physical representation in numerical models with appropriate numerical approximation associated with multi-driver physical processes for simulating the flood dynamics. To address this, an integrated and tightly coupled modeling approach is implemented to capture the nonlinear and complex processes involved in CCF and interconnectivity among Multidimensional Hydraulics (pipe and channel), Hydrodynamics (2D overland flow), and distributed Hydrologic (MH3) models are developed. The coupled modeling framework focuses on the development of interconnected meshes of the node-link-basin using the Interconnected Channel and Pond Routing (ICPR) model. This framework effectively incorporates the complex drainage network system of tidal creeks, tidal channels, underground sewer networks, and detention ponds of the Charleston Peninsula of South Carolina (SC). The dynamics of the urbanized floodplain in the peninsula are represented using high-resolution LiDAR-derived Digital Elevation Model (DEM) and Digital Surface Model (DSM). The overland flow is simulated using energy balance, momentum balance, and diffusive wave methods. The performance of the CCF model is tested for the 2015 SC historical flood and a high tide flood event. The momentum balance-based CCF model shows 98.35% accuracy in identifying street-level flooding locations. Furthermore, the model shows that incorporating DSM processing in the current study improves the accuracy of urban flood simulation by approximately 15% to 33% compared to LiDAR DEM. It is revealed that the momentum balance method outperforms to simulate the flood dynamics. Consequently, this study contributes to the modeling of CCF by developing an integrated MH3 system that enhances the physical representation of coastal hydrology at a fine scale.

Peirce’s Pragmatism, Semiotics, and Physical Representation

Charles Peirce is widely credited as the originator of semiotics, or the general theory of signs, and recognized as the founder of American pragmatism, albeit in that peculiar kind Peirce himself went on to distinguish as “pragmaticism.” The semiotic and pragmatist strands in Peirce’s thought come together in an appraisal of scientific diagrammatic representations. Peirce’s pragmatic maxim entails that the consequences of a sign inform our entire conception of the sign, while his most considered semiotic system entails that a complex sign partakes of iconic, indexical, and symbolic aspects. We illustrate Peirce’s insights by means of the celebrated Feynman Diagrams in Quantum Electrodynamics. We argue that Peirce’s late, post-1903, semiotics provides an adequate characterization of such “hypoicons,” and his pragmatic maxim explains how they can motivate and drive inquiry. We conclude that Peirce’s pragmatic approach to scientific diagrams remains a viable position that skillfully avoids dogmatic scientism and nihilistic skepticism alike.

Digital Twin in Fluid Power: Review- Technology Trends

The Digital Twin (DT) is the most modern technology in Industry 4.0, which combines digital and physical components, accurate visualization, and multifaceted perception to represent the digital-physical world in real-time. The use of simulation, monitoring, optimized performance, and projection are crucial and increasingly popular, particularly in the field of industrial automation. Fluid Power Application (FPA) is an essential industrial automation technology that offers accurate monitoring, immense power, flexibility, reliability, cost-effectiveness, and seamless compatibility with pre-existing automated systems through the utilization of hydraulic and pneumatic power. Nevertheless, certain technological elements and emergent DT technology in fluid power systems have not been extensively explored. Based on the conception and constituents addressed in a companion review paper (1), this paper looks into various technologies that allow the seamless integration of both physical and digital representations in diversified FPAs. This article intends to thoroughly examine the crucial role of technological enablers in the successful implementation of digital twin. The paper provides an in-depth review of the technological enablers that support DT, specifically focusing on software used for the simulation of FPA and programmable logic controllers (PLCs) with the accompanying software. This review paper enhances the academic discourse by emphasizing different emerging technologies and acts as a catalyst for DT in current investigation efforts and breakthroughs in FPAs.

Empirical models of electron precipitations at the Earth’s ionosphere high latitudes: a review

This paper presents a review of the Earth’s ionosphere empirical models of high-latitude energetic electron precipitations. In total, the review includes twelve models. Those models represent the full spectra from the earliest to the most recently developed ones. The majority of models were derived from the DMSP mission data. Models by McDiarmid et al., Spiro et al., Zhang & Paxton, Kamp et al., and Wu et al. are the exceptions. The first two models were developed before the DMSP mission era. Most of considered models are based on a physical representation of precipitating electron fluxes. However, some models are utilizing the purely “technician” approach. Those models are constructed for each channel of the DMSP/SSJ4 detector separately. Nowadays, AFGL, Ovation Prime (OP), and OP-2013 are the most popular models. The AFGL model is the first one with reasonable accuracy being available for public use. However, OP/OP-2013 models are becoming more popular now. They provide the finer specification of the electron precipitations over the AFGL model but require the solar wind in situ observations as the input parameter.

Exploring protein-ligand binding affinity prediction with electron density-based geometric deep learning.

Rational structure-based drug design relies on accurate predictions of protein-ligand binding affinity from structural molecular information. Although deep learning-based methods for predicting binding affinity have shown promise in computational drug design, certain approaches have faced criticism for their potential to inadequately capture the fundamental physical interactions between ligands and their macromolecular targets or for being susceptible to dataset biases. Herein, we propose to include bond-critical points based on the electron density of a protein-ligand complex as a fundamental physical representation of protein-ligand interactions. Employing a geometric deep learning model, we explore the usefulness of these bond-critical points to predict absolute binding affinities of protein-ligand complexes, benchmark model performance against existing methods, and provide a critical analysis of this new approach. The models achieved root-mean-squared errors of 1.4-1.8 log units on the PDBbind dataset, and 1.0-1.7 log units on the PDE10A dataset, not indicating significant advantages over benchmark methods, and thus rendering the utility of electron density for deep learning models context-dependent. The relationship between intermolecular electron density and corresponding binding affinity was analyzed, and Pearson correlation coefficients r > 0.7 were obtained for several macromolecular targets.

On the Complexiton Solutions to the Conformable Fractional Hirota–Satsuma–Ito Equation

This study analyzes the Hirota–Satsuma–Ito equation, which discusses the propagation of unidirectional shallow‐water waves and the interactions between two long waves with different dispersion forms. For the proposed equation, the sine‐Gordon expansion method has been considered. This method is derived from the sine‐Gordon equation. Different types of solutions, namely, bright, periodic, and dark‐bright soliton solutions, are derived. When these solutions are compared to other previously published research, to our knowledge, the study concludes that they are innovative, and this method was not applied to this equation. The validation of the obtained solutions is verified and plotted as three‐dimensional figures to comprehend physical phenomena. With the proper parameter values, distinct graphs are created to convey the physical representation of specific solutions. The results of this paper show that the method effectively improves a system’s nonlinear dynamical behavior. This study will be useful to a wide range of engineers who specialize in engineering models. The findings show that the computational approach is successful, simple, and even applicable to complex systems.

On the Pathways of Wind-Driven Coastal Upwelling: Nonlinear Momentum Flux and Baroclinic Instability

Abstract Wind-driven upwelling of cold, nutrient-rich water is a key feature near the eastern boundaries of major ocean basins, with significant implications for the local physical environment and marine ecosystems. Despite the traditional two-dimensional description of upwelling as a passive response to surface offshore Ekman transport, recent observations have revealed spatial variability in the circulation structures across different upwelling locations. Yet, a systematic understanding of the factors governing the spatial patterns of coastal upwelling remains elusive. Here, we demonstrate that coastal upwelling pathways are influenced by two pairs of competing factors. The first competition occurs between wind forcing and eddy momentum flux, which shapes the Eulerian-mean circulation; the second competition arises between the Eulerian-mean and eddy-induced circulation. The importance of nonlinear eddy momentum flux over sloping topography can be described by the local slope Burger number, S = αN/f, where α is the topographic slope angle and N and f are the buoyancy and Coriolis frequencies. When S is small, the classic coastal upwelling structure emerges in the residual circulation, where water upwells along the sloping bottom. However, this comes with the added complexity that mesoscale eddies may drive a subduction route back into the ocean interior. As S increases, the upwelling branch is increasingly suppressed, unable to reach the surface and instead directed offshore by the eddy-induced circulation. The sensitivity of upwelling structures to variable wind stress and surface buoyancy forcing is further explored. The diagnostics may help to improve our understanding of coastal upwelling systems and yield a more physical representation of coastal upwelling in coarse-resolution numerical models.

Indian Editors in Physics and Astronomy

Science stands on the shoulders of its community to ensure the quality of its scholarly communications. Hence, editorship and editorial positions in scholarly journals is a prestigious and rewarding activity for scientists. Physics as a branch of science is no exception to this. The present work endeavoured to look at the situation of Indian physicists’ (with Indian affiliations) representation in the top-ranking physics and astronomy journals as per the Scimago Journal and Country Ranking. Spatio-temporal analysis was undertaken to understand India’s position concerning the other established science research countries like the USA, China, Germany and the UK. It shows that the presence of Indian physicists in these quality journals’ editorship or editorial board membership positions is minimal. Some areas of physics are not represented at all. It may be added that more such positions for Indian physicists in these journals may bring more prestige for them and the country as a whole.

Cross-Wired Memristive Crossbar Array for Effective Graph Data Analysis.

Graphs adequately represent the enormous interconnections among numerous entities in big data, incurring high computational costs in analyzing them with conventional hardware. Physical graph representation (PGR) is an approach that replicates the graph within a physical system, allowing for efficient analysis. This study introduces a cross-wired crossbar array (cwCBA), uniquely connecting diagonal and non-diagonal components in a CBA by a cross-wiring process. The cross-wired diagonal cells enable cwCBA to achieve precise PGR and dynamic node state control. For this purpose, a cwCBA is fabricated using Pt/Ta2O5/HfO2/TiN (PTHT) memristor with high on/off and self-rectifying characteristics. The structural and device benefits of PTHT cwCBA for enhanced PGR precision are highlighted, and the practical efficacy is demonstrated for two applications. First, it executes a dynamic path-finding algorithm, identifying the shortest paths in a dynamic graph. PTHT cwCBA shows a more accurate inferred distance and ≈1/3800 lower processing complexity than the conventional method. Second, it analyzes the protein-protein interaction (PPI) networks containing self-interacting proteins, which possess intricate characteristics compared to typical graphs. The PPI prediction results exhibit an average of 30.5% and 21.3% improvement in area under the curve and F1-score, respectively, compared to existing algorithms.