Physical Principles Research Articles

Detecting Ocean Eddies with a Lightweight and Efficient Convolutional Network

As a ubiquitous mesoscale phenomenon, ocean eddies significantly impact ocean energy and mass exchange. Detecting these eddies accurately and efficiently has become a research focus in ocean remote sensing. Many traditional detection methods, rooted in physical principles, often encounter challenges in practical applications due to their complex parameter settings, while effective, deep learning models can be limited by the high computational demands of their extensive parameters. Therefore, this paper proposes a new approach to eddy detection based on the altimeter data, the Ghost Attention Deeplab Network (GAD-Net), which is a lightweight and efficient semantic segmentation model designed to address these issues. The encoder of GAD-Net consists of a lightweight ECA+GhostNet and an Atrous Spatial Pyramid Pooling (ASPP) module. And the decoder integrates an Efficient Attention Network (EAN) module and an Efficient Ghost Feature Integration (EGFI) module. Experimental results show that GAD-Net outperforms other models in evaluation indices, with a lighter model size and lower computational complexity. It also outperforms other segmentation models in actual detection results in different sea areas. Furthermore, GAD-Net achieves detection results comparable to the Py-Eddy-Tracker (PET) method with a smaller eddy radius and a faster detection speed. The model and the constructed eddy dataset are publicly available.

Deep Thermalization in Gaussian Continuous-Variable Quantum Systems.

We uncover emergent universality arising in the equilibration dynamics of multimode continuous-variable systems. Specifically, we study the ensemble of pure states supported on a small subsystem of a few modes, generated by Gaussian measurements on the remaining modes of a globally pure bosonic Gaussian state. We find that beginning from highly entangled, complex global states, such as random Gaussian states and product squeezed states coupled via a deep array of linear optical elements, the induced ensemble attains a universal form, independent of the choice of measurement basis: it is composed of unsqueezed coherent states whose displacements are distributed normally and isotropically, with variance depending on only the particle-number density of the system. We further show that the emergence of such a universal form is consistent with a generalized maximum entropy principle, which endows the limiting ensemble, which we call the "Gaussian Scrooge distribution," with a special quantum information-theoretic property of having minimal accessible information. Our results represent a conceptual generalization of the recently introduced notion of "deep thermalization" in discrete-variable quantum many-body systems-a novel form of equilibration going beyond thermalization of local observables-to the realm of continuous-variable quantum systems. Moreover, it demonstrates how quantum information-theoretic perspectives can unveil new physical phenomena and principles in quantum dynamics and statistical mechanics.

METOD FOR INCREASING THE SPECTRAL EFFICIENCY OF TROPOSCATTER COMMUNICATION BASED ON THE USE OF COMPOSITE SIGNALS IN THE WALSH BASIS

Background. In modern telecommunications, troposcatter communication systems organize long-distance communication. These systems allow communication beyond the line of sight. An important factor that must be considered in these systems is multipath, due to the physical principle underlying the functioning of troposcatter systems. Diversity reception and broadband signals are used to overcome this factor's negative impact. However, broadband signals use an excessive frequency band and are characterized by a low spectral efficiency. Objective. The purpose of the paper is to develop a method for overcoming the negative impact of multipath by using composite signals on the Walsh basis. Methods. Parallel composite signals provide simultaneous packet transmission of a group of signals built based on a complete system of mutually orthogonal Walsh-Hadamard functions. Each signal transmits one bit of information, but the parallel transmission of a packet of mutually orthogonal signals avoids decreasing the system's information transmission rate. To counteract the effect of multipath, pilot signals are added to the composite signal, as individual Walsh-Hadamard functions with better auto- and cross-correlation properties are selected. The advantages of composite signals in the Walsh-Hadamard basis include their spectral efficiency, which significantly exceeds the spectral efficiency of broadband signals. The paper describes a method for forming a composite signal on a Walsh-Hadamard basis with pilot signals and a functional diagram of a receiving device that provides optimal pre-detector addition of signals from four independent diversity reception channels. Results. The method of forming the composite signal with pilot signals based on the Walsh-Hadamard basis is presented as a functional diagram of a reception device that will ensure optimal detection of composite signals from several independent channels of a separated receiver. The spectral efficiency of the composite signals is shown on the size of the Walsh-Hadamard vicor basis. The introduction of the pilot signals makes it possible to ensure the synchronous composition of signals received from several independent receiving channels. Conclusions. The proposed technical solutions using composite signals in the Walsh-Hadamar basis make it possible to create troposcatter communication systems that provide operation in multi-path conditions, and the spectral efficiency of which significantly exceeds this indicator for systems using M-sequence signals, which allows increasing in the information transmission rate at the same frequency bandwidth or increasing their noise immunity by using additional noise-resistant coding at a fixed information transmission rate.

Development of a New Generalizable, Multivariate, and Physical-Body-Response-Based Extreme Heatwave Index

The primary goal of this study is to introduce the initial phase of developing an impact-based forecasting system for extreme heatwaves, utilizing a novel multivariate index which, at this early stage, already employs a combination of a statistical approach and physical principles related to human body water loss. This system also incorporates a mitigation plan with hydration-focused measures. Since 1990, heatwaves have become increasingly frequent and intense across many regions worldwide, particularly in Europe and Asia. The main health impacts of heatwaves include organ strain and damage, exacerbation of cardiovascular and kidney diseases, and adverse reproductive effects. These consequences are most pronounced in individuals aged 65 and older. Many national meteorological services have established metrics to assess the frequency and severity of heatwaves within their borders. These metrics typically rely on specific threshold values or ranges of near-surface (2 m) air temperature, often derived from historical extreme temperature records. However, to our knowledge, only a few of these metrics consider the persistence of heatwave events, and even fewer account for relative humidity. In response, this study aims to develop a globally applicable normalized index that can be used across various temporal scales and regions. This index incorporates the potential health risks associated with relative humidity, accounts for the duration of extreme heatwave events, and is exponentially sensitive to exposure to extreme heat conditions above critical thresholds of temperature. This novel index could be more suitable/adapted to guide national meteorological services when emitting warnings during extreme heatwave events about the health risks on the population. The index was computed under two scenarios: first, in forecasting heatwave episodes over a specific temporal horizon using the WRF model; second, in evaluating the relationship between the index, mortality data, and maximum temperature anomalies during the 2003 summer heatwave in Spain. Moreover, the study assessed the annual trend of increasing extreme heatwaves in Spain using ERA5 data on a climatic scale. The results show that this index has considerable potential as a decision-support and health risk assessment tool. It demonstrates greater sensitivity to extreme risk episodes compared to linear evaluations of extreme temperatures. Furthermore, its formulation aligns with the physical mechanisms of water loss in the human body, while also factoring in the effects of relative humidity.

Evolution of contactless conductometry methods

The development of chemical sensor devices operating in non-contact mode is of primary interest due to the demand from various industries for a fast, simple and inexpensive determination of chemical composition in different media in a non-invasive way. One of the promising directions for the development of analytical devices with such characteristics is the use of high-frequency electrical signals. The paper discusses the evolution of high-frequency contactless conductometry method, likewise other methods and devices operating on similar physical principles (dielectric spectroscopy, microwave sensors, C4D detectors).

From Elementary to Advanced Design of Functional Metal-Organic Frameworks: A User Guide to Deciphering the Reticular Chemistry Toolbox.

Here, the fundamental requirements are described for understanding and using topology tools in the design of porous materials, emphasizing the relationships between nets, metal-organic framework (MOF) structures, nodes, and building blocks. Common design approaches are discussed, highlighting prerequisites for the rational design of MOFs, such as those with simple pcu topology through the molecular building block approach, or axial-to-axial pillaring. The importance of highly connected nets and building units is emphasized for achieving structural predictability. The geometrical requirements are detailed for designing highly connected MOFs using more elaborate strategies: MOFs with rht topology through the supermolecular building block approach, tbo topology through the supermolecular building layer approach, and sph topology through a merged net approach The potential for innovation through deviations from default nets, such as introducing a geometry mismatch is addressed, which can lead to novel materials with unique zeolitic structures. Examples include MOFs with sodalite (sod) topology, developed through cantellation or mixed-ligand approaches inspired by ancestral architectural methods, utilizing centring structure-directing agents. Key insights for researchers are provided to facilitate the application and expansion of design strategies to new chemical systems. The only limit is imagination, along with some chemical, physical, and thermodynamical principles, of course.

Modeling global surface dust deposition using physics-informed neural networks

Paleoclimatic measurements serve to understand Earth System processes and evaluate climate model performances. However, their spatial coverage is generally sparse and unevenly distributed across the globe. Statistical interpolation methods are the prevalent techniques to grid such data, but these purely data-driven approaches sometimes produce results that are incoherent with our knowledge of the physical world. Physics-Informed Neural Networks follow an innovative approach to data analysis and physical modeling through machine learning, as they incorporate physical principles into the data-driven learning process. Here, we develop a machine-learning algorithm to reconstruct global maps of atmospheric dust surface deposition fluxes from paleoclimatic archives for the Holocene and Last Glacial Maximum periods. We design an advection-diffusion equation that prevents dust particles from flowing upwind. Our physics-informed neural network improves on kriging interpolation by allowing variable asymmetry around data points. The reconstructions display realistic dust plumes from continental sources towards ocean basins following prevailing winds.

Self-Oscillations Of Current In Silicon: Physical Principles, Mechanisms And Application

The article discusses self-oscillatory processes in silicon semiconductor devices, such as diodes and transistors, based on nonlinear effects of interaction of electric current and voltage. Particular attention is paid to the physical mechanisms of selfoscillations, including avalanche breakdown, thermal and electrical feedback, as well as their impact on the operation of silicon devices. Examples of the application of selfoscillatory modes in high-frequency signal generators and radio engineering devices are given. Methods for eliminating unwanted oscillations to ensure stable operation of circuits are also discussed

The interplay of energy and motion of coupled pendulums: a high school student exploration

Abstract The experiment of string-coupled pendulums offers high school students a hands-on opportunity to explore fundamental principles of physics while developing critical skills. In this experiment, students investigate how an oscillating pendulum transfers kinetic energy to a stationary one, resulting in a cyclical exchange marked by role reversals. The motion of the coupled pendulum is studied using a simple smartphone equipped with free software. Pendulums of equal length and point-mass are arranged in three different configurations: pendulum on a fixed rod, pendulum on a string, and string-coupled pendulum. These configurations not only exhibit varying time periods but also significantly different amplitude decay patterns. Despite inevitable energy dissipation due to frictional losses and air resistance, the oscillations exhibited by the coupled pendulums clearly demonstrate the interplay between synchronization and energy transfer. This initial hands-on investigation serves as a springboard for a more comprehensive exploration of the captivating energy transfer mechanisms and dynamical characteristics inherent to string-coupled pendulums.

Underwater Laguerre-Gaussian beam propagation and phase compensation method

Laguerre-Gaussian (LG) beams experience phase twist after long-distance transmission, making the orbital angular momentum (OAM) indistinguishable. This phenomenon becomes more severe in water due to the higher refractive index. Based on the physical principles of LG beams, this paper derives the mathematical expression for LG beam transmission in water to address this issue. It organizes and analyses the physical significance of each term. The exponential term responsible for phase twist is separated and phase compensation is applied to the initial LG beam. Simulation results show that after transmission through water, traditional LG beams exhibit a clockwise twisted distribution of isophase lines. By applying the phase compensation method proposed in this paper, the phase of the initial LG beam is modulated and when the LG beam reaches the observation plane, the isophase lines become straight, verifying the effectiveness of the method. This compensation method holds significant value for LG beams in advanced physics research.

Protein purification with light via a genetically encoded azobenzene side chain

Affinity chromatography is the method of choice for the rapid purification of proteins from cell extracts or culture supernatants. Here, we present the light-responsive Azo-tag, a short peptide comprising p-(phenylazo)-L-phenylalanine (Pap), whose side chain can be switched from its trans-ground state to the metastable cis-configuration by irradiation with mild UV light. Since only trans-Pap shows strong affinity to α-cyclodextrin (α-CD), a protein exhibiting the Azo-tag selectively binds to an α-CD chromatography matrix under daylight or in the dark but elutes quickly under physiological buffer flow when illuminating the column at 355 nm. We demonstrate the light-controlled single-step purification – termed Excitography – of diverse proteins, including enzymes and antibody fragments, without necessitating competing agents or harsh buffer conditions as normally applied. While affinity chromatography has so far been governed by chemical interactions, introducing control by electromagnetic radiation as a physical principle adds another dimension to this widely applied separation technique.

Uncovering the invisible: A study of Gaia18ajz, a candidate black hole revealed by microlensing

Identifying black holes is essential for our understanding of the development of stars and can reveal novel principles of physics. Gravitational microlensing provides an exceptional opportunity to examine an undetectable population of black holes in the Milky Way. In particular, long-lasting events are likely to be associated with massive lenses, including black holes. We present an analysis of the Gaia18ajz microlensing event reported by the Gaia Science Alerts system. Gaia18ajz is a long-timescale event exhibiting features indicative of the annual microlensing parallax effect. Our objective is to estimate its lens parameters based on the best-fitting model. We used photometric data obtained from the Gaia satellite and terrestrial observatories to investigate a variety of microlensing models and calculate the most probable mass and distance to the lens, taking into consideration a Galactic model as a prior. Subsequently, we applied a mass--brightness relation to evaluate the likelihood that the lens is a main sequence star. We also describe the (DLC), an open-source routine that computes the distribution of probable lens mass, distance, and luminosity employing the Galaxy priors on stellar density and velocity for microlensing events with detected microlensing parallax. We modelled the Gaia18ajz event and found its two possible models, the most probable Einstein timescales for which are $316^ $ days and $299^ $ days. Applying Galaxy priors for stellar density and motion, we calculated a most probable lens mass of $4.9^ M_ located at kpc $ and a less probably mass of $11.1^ M_ located at kpc $. Our analysis of the blended light suggests that the lens is likely a dark remnant of stellar evolution rather than a main sequence star.

Synergic conditions for learning the organic laws of society

In the modern world, where social processes are becoming more and more complex and unpredictable, the search for new methods and their research becomes especially relevant. One of these approaches is synergetics - the science of self-organization of complex systems. Synergetics makes it possible to reveal regularities in social processes, which at first glance seem chaotic, disorderly. Of particular importance is the study of the organic laws of society, which regulate its development and stability. The relationship between chaotic and ordered elements of social systems can be understood only through synergistic principles, which makes their knowledge especially important in the context of modern global challenges. Modern researchers continue to develop synergistic approaches in the social sciences, applying them to the analysis of global social transformations, economic crises, the evolution of technology, as well as to study the impact of globalization on local social processes. Synergetics helps explain how small changes in one part of a social system can have profound consequences for the entire society. It should be emphasized that the synergistic laws of society are the fundamental principles by which the social system functions as a whole organism. These laws determine the rules and mechanisms of interaction between various elements of society, ensuring its stability and development. Like biological organisms, society is subject to certain laws that determine its ability to adapt in response to external and internal challenges. The ideas of synergetics began to permeate the social sciences in the 1970s through the work of scientists such as Ilya Prigozhin and Herman Hacken, who applied physical principles to social systems. Prigozhin studied thermodynamic systems far from equilibrium and discovered that such systems can self-organize into stable structures. This discovery became the basis for understanding social systems as dynamic and unstable formations that can independently form new mechanisms of order. Traditional approaches are often based on the assumption of linear development of society, where all changes occur gradually and predictably. However, reality shows that social changes are chaotic and can be abrupt and unpredictable. It is the synergistic approach that allows us to analyze these chaotic processes, understanding them as part of a complex mechanism of self­organization that leads to the emergence of new social structures.

Optimization of process parameters in 3D-nanomaterials printing for enhanced uniformity, quality, and dimensional precision using physics-guided artificial neural network

Pneumatic 3D-nanomaterial printing, a prominent additive manufacturing technique, excels in processing advanced materials like MXene, crucial for applications in nano-energy, flexible electronics, and sensors. A key challenge in this domain is optimizing process parameters—applied pressure, ink concentration, nozzle diameter, and printing velocity—to achieve uniform, high-quality prints with the desired filament diameter. Traditional trial-and-error methods often result in significant material waste and time consumption. To address this, our study introduces a comprehensive pipeline that initially assesses whether the selected process parameters yield uniform, high-quality MXene prints. Subsequently, it employs a Physics-Guided Artificial Neural Network (PGANN) to predict the filament diameter based on these parameters, integrating fundamental physical principles of the printing process with experimental data. Our findings demonstrate that using an XGBoost classifier, we can classify printed filament quality with an accuracy of 90.44%. Furthermore, the PGANN model shows exceptional performance in predicting the filament diameter, achieving a Pearson Correlation Coefficient (PCC) of 0.9488, a Mean Squared Error (MSE) of 0.000092 mm2, and a Mean Absolute Error (MAE) of 0.00711 mm. This pipeline significantly streamlines the process for researchers, facilitating the selection of optimal printing parameters to consistently achieve high-quality prints and accurately produce the desired filament diameter tailored to specific applications.

Entropy as a Tool for the Analysis of Stock Market Efficiency During Periods of Crisis.

In the article, we analyse the problem of the efficiency market hypothesis using entropy in moments of transition from a normal economic situation to crises or slowdowns in European, Asian and US stock markets and the economy in the years 2007-2023 (2008-2009, U.S. financial sector crises; 2020-2021, Pandemic period; and the 2022-2023 period of Russia's attack on Ukraine). The following hypothesis is put forward in the article: In periods of economic slowdown and economic crises, the entropy of prices and return rates decreases. According to the principles of physics, in an isolated system, entropy increases and decreases at the moment of external intervention, similar to finance, where during crises and economic slowdowns, there is interference from governments introducing new regulations and intervening in financial markets. The article uses the Shannon entropy method. This measure, as a statistical measure, does not require the assumption of stationarity of time series or a known probability distribution, unlike classical statistical methods. Our results confirm decreased entropy in stock markets during crisis.

Hybrid mechanism‐data‐driven iron loss modelling for permanent magnet synchronous motors considering multiphysics coupling effects

AbstractThe precise calculation of iron losses in permanent magnet synchronous motors (PMSMs) remains challenging due to the interplay between various disciplines such as electromagnetism, magnetism, and thermal/mechanical dynamics. Purely mechanistic models require detailed theoretical knowledge and exact parameters, often struggling to accurately describe complex systems, while purely data‐driven methods lack interpretability, which are susceptible to data noise and outliers in feature extraction and complicated pattern recognition. Consequently, this paper aims to present a hybrid mechanism‐data‐driven model for accurately estimating the iron loss for PMSMs, considering the multiphysics coupling effects. Specifically, based on the well‐defined physical principles, an advanced iron loss analytical model that simultaneously considers mechanical stress, temperature rise, harmonics, load currents, and changing frequency is developed and then utilised to calculate numerous loss data under different operating conditions, providing a certain level of stability and reliability for prediction accuracy. Subsequently, a convolutional neural network (CNN) algorithm is employed to perform deep learning to extract features and patterns from the data. By defining a suitable loss function, the pre‐trained model was fine‐tuned and optimised using a small amount of actual data. To validate its superiority, extensive numerical and experimental analyses are conducted on the prototype. The results demonstrate that the iron losses computed using this hybrid model overcome the limitations of singular methods by effectively leveraging both theoretical knowledge and real‐world data, thus accurately accommodating various application scenarios. This integrated approach enhances the accuracy, stability, and interpretability of the model, laying a solid foundation for more specialised applications in the future.

A Review of Cascaded Metasurfaces for Advanced Integrated Devices

This paper reviews the field of cascaded metasurfaces, which are advanced optical devices formed by stacking or serially arranging multiple metasurface layers. These structures leverage near-field and far-field electromagnetic (EM) coupling mechanisms to enhance functionalities beyond single-layer metasurfaces. This review comprehensively discusses the physical principles, design methodologies, and applications of cascaded metasurfaces, focusing on both static and dynamic configurations. Near-field-coupled structures create new resonant modes through strong EM interactions, allowing for efficient control of light properties like phase, polarization, and wave propagation. Far-field coupling, achieved through greater interlayer spacing, enables traditional optical methods for design, expanding applications to aberration correction, spectrometers, and retroreflectors. Dynamic configurations include tunable devices that adjust their optical characteristics through mechanical motion, making them valuable for applications in beam steering, varifocal lenses, and holography. This paper concludes with insights into the potential of cascaded metasurfaces to create multifunctional, compact optical systems, setting the stage for future innovations in miniaturized and integrated optical devices.

The Model and Simulation of Memristor

Memristor is an emerging electronic component, tremendous efforts have been made in the fields of memristor, motivated by its unique resistor switching feature, as well as the good compatibility with the current integrated circuit (IC) technologies. It has been widely accepted that memristor would be a powerful candidate for constructing the brain-like computing hardware system in the next generation of electronics. Even so, there are still challenges in the development of the memristor IC and the calculation system based on it. For an example, the lack of memristor simulation module hinders its automatic design. Herein, we would like to propose a method for simulating the feature of the memristor. Firstly, based on the conductive filament based physical principle of memristor, calculations are conducted to simulate the basic electronic features of memristor. The good fitness confirms the feasibility of the proposed method. Second, the memristor’s resistance switching feature is modelled by Spice, a popular design platform. With the use of Voltage-Controlled Current Source (VCCS) in Spice, a modulus of memristor is established and executed to simulate the switched resistances under different bias voltages. In summary, this work demonstrates the feasibility of the proposed simulating method, it may provide an easy-to-operate way to improve design efficiency of active memristor arrays

State primary standard of units of absorbed dose, absorbed dose rate, ambient and individual dose equivalents, rates of ambient and individual dose equivalents of neutron radiation GET 117-2023

The issues related to ensuring the uniformity of measurements and traceability of measurement results obtained using clinical dosimeters in neutron radiation therapy are considered in relation to the State Primary Standard of units of absorbed dose, absorbed dose rate, ambient and individual dose equivalents, ambient and individual dose equivalent rates of neutron radiation GET 117-2023. The methods and means of reproduction and transmission of units of ambient and individual equivalents of neutron radiation dose and their powers are described. Reproduction of these units of quantities is carried out on the basis of the reconstructed energy distribution of neutrons in the energy range of 0.001–20 MeV. Requirements for the measurement error of these units of quantities are presented, in particular, the relative expanded uncertainty of measuring the absorbed dose and the absorbed dose rate of neutron radiation should not exceed 3 % with a coverage factor of 2. The work describes the composition and physical principles of operation of the State Primary Standard. The standard includes new technical means: hardware and methodological complexes for reproduction, methods and means of measurement, a neutron radiation verification unit with an automated positioning system for neutron radiation sources UKPN-1MDA, a system for filling chambers with gases. The authors present a description of the methods for reproducing units of quantities and their functional dependence. The results of the study of the metrological characteristics of GET 117-2023 are presented. The metrological characteristics of the updated standard correspond to modern domestic and international standards for accuracy and reproduction ranges. The uniformity of measurements of clinical dosimeters required for neutron radiation therapy has been ensured. GET 117-2023 plays a key role in the system of metrological support for dosimetric measurements of neutron radiation in such areas as healthcare, ecology, nuclear energy, industry, defense and security, science, as well as in a number of other areas.

Fundamentals of dual-energy computed tomography and its emerging applications in bladder cancer

Nowadays, computed tomography urography and multiparametric magnetic resonance are the most often used imaging techniques in patients with bladder cancer; however, dual-energy computed tomography is making its way into the oncological field. This narrative review article aimed to show the fundamentals of dual-energy computed tomography by outlining its physical principles, techniques, protocols, and postprocessing images to help those who used this cutting-edge technology as the first approach to fully understand its emerging applications in the evaluation of bladder cancer, a field yet to be explored. In particular, we discuss the usefulness of dual-energy computed tomography by focusing on the main images obtained, such as the iodine map, comparing them to the images obtained with conventional computed tomography scans. Dual-energy computed tomography applications may be beneficial for bladder cancer diagnosis, staging, and treatment planning. Nevertheless, its application is limited to its availability in healthcare structures and the training of healthcare personnel who can perform and interpret the scans correctly.