System Parameters Research Articles

Inferring system parameters from the bursts of the accretion-powered pulsar IGR J17498–2921

ABSTRACT Thermonuclear (type-I) bursts exhibit properties that depend both on the local surface conditions of the neutron stars on which they ignite, as well as the physical parameters of the host binary system. However, constraining the system parameters requires a comprehensive method to compare the observed bursts to simulations. We have further developed the beansp code for this purpose and analysed the bursts observed from IGR J17498$-$2921, a 401-Hz accretion-powered pulsar, discovered during it’s 2011 outburst. We find good agreement with a model having H-deficient fuel with $X=0.15\pm 0.4$, and CNO metallicity $Z=0.0014^{+0.0004}_{-0.0003}$, about a tenth of the solar value. The model has the system at a distance of $5.7^{+0.6}_{-0.5}$ kpc, with a massive (${\approx} 2\ \mathrm{ M}_\odot$) neutron star and a likely inclination of $60^\circ$. We also re-analysed the data from the 2002 outburst of the accretion-powered millisecond pulsar SAX J1808.4$-$3658. For that system we find a substantially closer distance than previously inferred, at $2.7\pm 0.3$ kpc, likely driven by a larger degree of burst emission anisotropy. The other system parameters are largely consistent with the previous analysis. We briefly discuss the implications for the evolution of these two systems.

Идиостиль автора: вопросы параметризации

The paper presents a systematization of the parameters identified for the study of the author’s individual style, which are found in scientific works of recent years, with the aim of creating a general research model. The work presents the results of analysing dissertations from 1996 to 2024, in which authors address the analysis of the author’s individual style in terms of material, object, or subject of study. As a basis for further systematization, features such as research approach, material, understanding of individual style, and, finally, the analyzed parameters were adopted. This work is the first one to present a systematization of parameters included in the system of the author’s individual style, based on dissertation research conducted within different fields, using different approaches and diverse material, with the aim of creating a general model, which accounts for the scientific novelty. The research findings are as follows: a system of parameters has been identified and subjected to authorial classification, consisting of six levels, which includes elements of a level model of language, a pragmatic component (functionality), and the author’s worldview.

A Real-Time System Status Evaluation Method for Passive UHF RFID Robots in Dynamic Scenarios

In dynamic scenarios, the status of a Radio Frequency Identification (RFID) system fluctuates with environmental changes. The key to improving system efficiency lies in the real-time monitoring and evaluation of the system status, along with adaptive adjustments to the system parameters and read algorithms. This paper focuses on the status changes in RFID systems in dynamic scenarios, aiming to enhance system robustness and reading performance, ensuring high link quality, reasonable resource scheduling, and real-time status evaluation under varying conditions. This paper comprehensively considers the system parameter settings in dynamic scenarios, integrating the interaction model between readers and tags. The system’s real-time status is evaluated from both the physical layer and the Medium Access Control (MAC) layer perspectives. For the physical layer, a link quality evaluation model based on Uniform Manifold Approximation and Projection (UMAP) and K-Means clustering is proposed from the link quality. For the MAC layer, a multi-criteria decision-making evaluation model based on combined weighting and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is proposed, which comprehensively considers both subjective and objective factors, utilizing the TOPSIS algorithm for an accurate evaluation of the MAC layer system status. For the RFID system, this paper proposes a real-time status evaluation model based on the Classification and Regression Tree (CART), which synthesizes the evaluation results of the physical layer and MAC layer. Finally, engineering tests and verification were conducted on the RFID robot system in mobile scenarios. The results showed that the clustering average silhouette coefficient of the physical layer link quality evaluation model based on K-Means was 0.70184, indicating a relatively good clustering effect. The system status evaluation model of the MAC layer, based on the combined weighting-TOPSIS method, demonstrated good flexibility and generalization. The real-time status evaluation model of the RFID system, based on CART, achieved a classification accuracy of 98.3%, with an algorithm runtime of 0.003 s. Compared with other algorithms, it had a higher classification accuracy and shorter runtime, making it well suited for the real-time evaluation of the RFID robot system’s status in dynamic scenarios.

Dual-channel bistability modulation in a bilayer graphene-based optomechanical system.

We propose a flexible scheme for studying linear absorption response and optical bistability (OB) in a bilayer graphene-based optomechanical system. The results show that as the coupling between the G-mode phonon and excitons is turned on, the linear absorption spectrum will evolve from a single-peaked structure to a two-peaked one, and the spacing between two splitting peaks is equal to twice as large as the phonon-exciton coupling strength. Especially, we plot bistability phase diagrams within the system's parameter subspaces, demonstrating that the bistable switch can be controlled via no, single, or dual-channel by changing the intensity of the pump light in a weak phonon-exciton coupling regime. This holds promise for developing precision-measuring nanodevices and multi-channel optical bistable switches.

Intelligent robust controllers for tribotronic conical fluid film bearings

The article presents the results of the development of means for intelligent robust control of the parameters of a tribotronic rotor-support system with a tapered bearing with a removable bush. The proposed controller is implemented on the basis of deep Q-network reinforcement learning (DQN) synthesized on the basis of a numerical model of a rotor support system. The control strategy involved simultaneous control of the shaft position and friction in the lubrication layer. Methods for control synthesis are presented for both a deterministic system and a system with stochastic parameters. In the latter case, a controller synthesis technique is proposed that takes into account uncertainties in the system at the training stage. Testing of the resulting controllers shows the better ability of a controller trained to take into account uncertainties to cope with variable loads, as well as predict possible changes in the system and proactively transfer the system to more advantageous states.

Polarity of reticular faces and the thermodynamic state of the face system of a crystal

A new concept is introduced into crystallography – the polarity of the reticular faces of a crystal. Reticular faces are divided into basic and additional. The basal faces, systematic in a facet system, only increase as the crystal grows. The shape of the crystal is invariant, stable within the limits of its stability and does not affect the state of its equilibrium. Additional faces, on the contrary, shrink and disappear, and therefore are variable extensive parameters of the facet system. They do not form their own system and associate with the basic ones, determining the variation of the facet system. The polar system is nonequilibrium, spontaneously and irreversibly transforms into the final basic facet system and is in quasi-static equilibrium. The habit of a basic crystal of an invariant shape changes during its growth according to the mechanism of translational symmetry.

Discovering dynamics and parameters of nonlinear oscillatory and chaotic systems from partial observations

Despite rapid progress in data acquisition techniques, many complex physical, chemical, and biological systems remain only partially observable, thus posing the challenge to identify valid theoretical models and estimate their parameters from an incomplete set of experimentally accessible time series. Here, we combine sensitivity methods and ranked-choice model selection to construct an automated hidden dynamics inference framework that can discover predictive nonlinear dynamical models for both observable and latent variables from noise-corrupted incomplete data in oscillatory and chaotic systems. After validating the framework for prototypical FitzHugh-Nagumo oscillations, we demonstrate its applicability to experimental data from squid neuron activity measurements and Belousov-Zhabotinsky reactions, as well as to the Lorenz system in the chaotic regime. Published by the American Physical Society 2024

Selection of design parameters of a spacecraft electric propulsion engine ensuring the geostationary orbit raising mission

The problem of selecting suboptimal parameters of an electric propulsion system as part of a spacecraft, ensuring a geostationary orbit raising mission using a Soyuz-5 launch vehicle at the initial launch stage, is investigated. An expression is obtained for the main optimality criterion – the payload relative mass. An algorithm is obtained for solving the problem of parametric optimization of an electric propulsion system as part of a hybrid transportation system. The task of selecting the optimal electric propulsion engine for launching a spacecraft with an electric propulsion system to a geostationary orbit using the launch vehicle under consideration is solved. A design ballistic calculation and a comparative analysis of the obtained data are performed.

Incipient Fault Detection for the Spindle Bearing of a Cement Grinding Machine based on Vibrational Resonance

Abstract The spindle bearing of a circulation fan is an important component of a cement grinding machine. In addition to the faults on the spindle bearing, impeller wear and ash accumulation may cause dynamic unbalance and complex vibration interference as noise, which decreases the accuracy of fault detection based on vibrational signals and traditional signal processing-based methods at the early stage of a bearing fault. To address this issue, this paper presents a new fault detection method for the spindle bearing by utilizing extra injected noise and vibrational resonance. To enhance the fault signature and resonance performance, the nonlinear system of the traditional vibrational resonance is replaced by a new hybrid steady-state system, and the underdamped term is considered in the new system. The proposed system provides more possibilities to achieve resonance by adjusting the system parameters and overcomes the limitations of output saturation caused by the classical bistable system. The proposed method is validated by analysing the collected vibration signals from a spindle bearing of a circulation fan in practice and is compared with other noise-elimination fault detection methods. The results demonstrate the effectiveness and superior performance of the proposed method.

Spaceborne SAR System Design Considerations: Minimizing Satellite Size and Mass, System Parameter Trade-Offs, and Optimization

The goal of this research is to assess and guide the development of next-generation synthetic aperture radar (SAR) satellites, optimize their performance, and present the requirements related to the design parameters. In the current era, characterized by the rapid advancement of SAR technologies, the challenge of designing compact and efficient satellites is more relevant than ever. The present research provides a comprehensive analysis of design parameters for microsatellite applications, including altitude, incidence angle, operating frequency, antenna sizing, and transmitting power. The complex relationships between these parameters and their combined impact on SAR system performance and satellite dimensions are demonstrated through various scenarios. Special attention is given to balancing the SAR antenna area and the transmitting power needs, which are primary constraints in SAR microsatellite design. A detailed comparative study is presented, highlighting how each design decision affects the overall functionality and performance. Modern SAR satellites with masses under 150 kg can operate with approximately 1 kW of transmitting power and a 10 m2 SAR antenna area. The present results analyze and validate the key parameters related to these satellites, coping with the challenging trade-offs through optimization. Furthermore, this study aims to guide future innovative spaceborne SAR system design, highlighting the potential of optimization techniques in advancing spaceborne SAR technology.

Transient behavior of pumped storage-wind hybrid power system under grid-connected operation condition

The large-scale grid connection of renewable energy represented by wind power threatens the safe and stable operation of electric system. The vigorous development pumped storage power station (PSPS) is a global consensus to support the grid-connected of renewable energy. This paper investigates the transient behavior of pumped storage-wind (PSW) hybrid power system under grid-connected operation condition (GCOC). Firstly, a model of PSW hybrid power system under GCOC is constructed. The stable domain is determined according to Hopf bifurcation theory. The influence of system parameters on the stability of PSW hybrid power system under GCOC is analyzed. A synergetic control strategy is designed to improve the dynamic performance of the system. Then, the mechanism of multi-time-scale dynamic response is revealed. Finally, the transient behavior of PSPS under different application scenarios and the interaction between PSPS and wind power station (WPS) are studied. The results show that the stability of PSW hybrid power system under GCOC can be judged by stable domain. The designed synergetic control strategy can improve the dynamic performance of the system. The dynamic response is composed of three oscillations with different frequencies, namely, mass oscillation at low frequency, water hammer oscillation at medium frequency, and electrical oscillation at high frequency. The principal oscillation is caused by water hammer. The interaction between PSPS and WPS has a significant effect on the transient behavior. By adjusting the interaction between PSPS and WPS, the system can absorb more wind power while ensuring safe and stable operation, which can increase profits and avoid unnecessary operation oscillations. The analytical methods and results presented in this paper are of great significance for improving the ability of power grid to accept renewable energy.

Prediction of unobserved bifurcation by unsupervised extraction of slowly time-varying system parameter dynamics from time series using reservoir computing

IntroductionNonlinear and non-stationary processes are prevalent in various natural and physical phenomena, where system dynamics can change qualitatively due to bifurcation phenomena. Machine learning methods have advanced our ability to learn and predict such systems from observed time series data. However, predicting the behavior of systems with temporal parameter variations without knowledge of true parameter values remains a significant challenge.MethodsThis study uses reservoir computing framework to address this problem by unsupervised extraction of slowly varying system parameters from time series data. We propose a model architecture consisting of a slow reservoir with long timescale internal dynamics and a fast reservoir with short timescale dynamics. The slow reservoir extracts the temporal variation of system parameters, which are then used to predict unknown bifurcations in the fast dynamics.ResultsThrough experiments on chaotic dynamical systems, our proposed model successfully extracted slowly varying system parameters and predicted bifurcations that were not included in the training data. The model demonstrated robust predictive performance, showing that the reservoir computing framework can handle nonlinear, non-stationary systems without prior knowledge of the system's true parameters.DiscussionOur approach shows potential for applications in fields such as neuroscience, material science, and weather prediction, where slow dynamics influencing qualitative changes are often unobservable.

Assessment of metabolic indicators of the cardiovascular system in children with nephrotic syndrome

Objective: to evaluate the metabolic parameters of the cardiovascular system in nephrotic syndrome in children Material and methods: 110 children hospitalized with nephrotic syndrome in the nephrology department of the Samarkand Regional Children’s Multidisciplinary Medical Center were examined. Results: Renocardial syndrome with nephrotic syndrome develops in 64.8% of cases, the majority of patients are children 3-6 years of age (44.1%), with a predominance of boys (58.8%). In a cardiometric study in patients with nephrotic syndrome, renocardial syndrome is manifested by significant changes in metabolic and adaptation parameters: oxygen and lactate content - by 1.5 times, creatine phosphate - by 20%, tension index - by 4.8 times and vascular stiffness index - by 5.2 times. Conclusion: Deviation of cardiometry indicators in different age groups indicates a decrease in the intensity of blood circulation in organs and systems, indicating a significant load on the cardiovascular system in nephrotic syndrome.

The 4-Octahedra model

This work comprises a generalization of a simple geometric model originally developed to describe coupled rotations of corner-sharing octahedra in the surface layer of Sr2RuO4 under uniaxial compression. The main objective of the model is to establish a link between the experimental global strain configuration and the possible microscopic mechanisms and compatible geometries by which the octahedra accommodate the applied strain. In achieving this, a useful and intuitive parametrization of four-site two-dimensional systems of corner-sharing octahedra has been established, which can be readily extended to three dimensions and N>4 inequivalent sites or directly employed to analyze the octahedral configuration of many perovskites, transition metal oxides (TMOs), and layered compounds.

Effect of graphene on absorption spectrum of Si/SiO2 multilayer

In recent decades, photonic crystals have emerged as a key technology in several fields, including light management, photovoltaics, detection, and lasing. This success can be attributed to the ability to regulate electromagnetic waves, which can be achieved by, through a variety of techniques, disruption the periodicity of crystals. In this study, the introduction of graphene as a material defect in a one-dimensional photonic crystal consisting of six Si/SiO2 patterns is subjected to investigation. The calculations are performed using the transfer matrix method, with the experimental data serving as the input, following a protocol that involves exploring the variation of the absorption spectra as a function of the parameters of the material system and the incident electromagnetic wave. The findings illustrate that the absorption peak exhibits a pronounced dependency on the positioning of the material defect, the graphene layer thickness, and the incident angle. This paves the way for a more detailed inquiry into its potential utility in the domain of detection.

Noise reduction method for mine wind speed sensor data based on CEEMDAN-wavelet threshold

The mine wind speed sensor is an important intelligent sensing equipment in the mine intelligent ventilation system that can provide accurate and key wind speed parameters for the intelligent ventilation system. The turbulent pulsation characteristics of the airflow in the underground tunnel are a major factor for the inaccurate measurement of mine wind speed. Therefore, according to the random non-stationary characteristics of a turbulent pulsation signal, a denoising method based on adaptive complete ensemble empirical mode decomposition (CEEMDAN) combined with the wavelet threshold is proposed for suppressing the turbulent pulsation noise in the wind speed signal. First, the CEEMDAN algorithm is used for decomposing the wind speed signal into a series of IMF components. Second, the continuous mean square error criterion is used for determining the high-frequency IMF components with more noise. The wavelet threshold denoising method is used for denoising the high-frequency IMF components with more noise. Finally, the denoised IMF components and remaining low-frequency IMF components are reconstructed for obtaining the denoised signal. The results of the denoising analysis of measured turbulent pulsation signals, comparative analysis of denoising of simulated turbulent pulsation signals by different joint denoising methods, and denoising analysis of actual mine wind speed sensor data indicate that the joint denoising method proposed in this study has a higher signal-to-noise ratio and lower root mean square error of the wind speed signal after denoising. Compared with the EMD-wavelet threshold and EEMD-wavelet threshold denoising methods, the denoising method proposed in this study is better and has higher denoising accuracy, which provides a new method for processing actual mine wind speed sensor data.

Clinical and Financial Validation of the International Study Group for Pancreatic Surgery (ISGPS) Definition of Post-Pancreatectomy Acute Pancreatitis (PPAP): International Multicenter Prospective Study.

To validate the ISGPS definition and grading system of PPAP after pancreatoduodenectomy (PD). In 2022, the International Study Group for Pancreatic Surgery (ISGPS) defined post-pancreatectomy acute pancreatitis (PPAP) and recommended a prospective validation of its diagnostic criteria and grading system. This was a prospective, international, multicenter study including patients undergoing PD at 17 referral pancreatic centers across Europe, Asia, Oceania, and the United States. PPAP diagnosis required the following three parameters: (1) postoperative serum hyperamylasemia /hyperlipasemia (POH) persisting on postoperative days 1 and 2, (2) radiologic alterations consistent with PPAP, and (3) a clinically relevant deterioration in the patient's condition. To validate the grading system, clinical and economic parameters were analyzed across all grades. Among 2902 patients undergoing PD, 7.5% (n=218) developed PPAP (6.3% grade B and 1.2% grade C). POH occurred in 24.1% of patients. Hospital stay was associated with PPAP grades (No POH/PPAP 10 days (IQR 7-17) days, grade B 22 days (IQR 15-34) days, and grade C 43 days (IQR 27-54) days; P<0.001), as well as intensive care unit admission (No POH/PPAP 5.4%, grade B 12.6%, grade C 82.9%; P<0.010), and hospital readmission rates (No POH/PPAP 7.3%, grade B 16.1%, grade C 18.5%; P<0.05). Costs of grade B and C PPAP were 2 and 11 times greater than uncomplicated clinical course, resp. (P<0.001). This first prospective, international validation study of the ISGPS definition and grading system for PPAP highlighted the relevant clinical and financial implications of this condition. These results stress the importance of routine screening for PPAP in patients undergoing PD.

Repetitive transcranial magnetic stimulation elicits weight loss and improved insulin sensitivity in type 2 diabetic rats.

Type 2 diabetes (T2D) accounts for the majority of diabetes incidences and remains a widespread global chronic disorder. Apart from early lifestyle changes, intervention options for T2D are mainly pharmaceutical. Repetitive transcranial magnetic stimulation (rTMS) has been approved by the FDA as a therapeutic intervention option for major depressive disorders, with further studies also indicating its role in energy metabolism and appetite. Considering its safe and non-invasive properties, we evaluated the effects of rTMS on systemic metabolism using T2D rats. We observed that rTMS improved glucose tolerance and insulin sensitivity in T2D rats after a 10-day exposure. Improved systemic insulin sensitivity was maintained after a 21-day treatment period, accompanied by modest yet significant weight loss. Circulating serum lipid levels, including those of cholesteryl ester, tryglyceride and ceramides, were also reduced following rTMS application. RNA-seq analyses further revealed a changed expression profile of hepatic genes that are related to sterol production and fatty acid metabolism. Altered expression of hypothalamic genes that are related to appetite regulation, neural activity and ether lipid metabolism were also implicated. In summary, our data report a positive impact of rTMS on systemic insulin sensitivity and weight management of T2D rats. The underlying mechanisms via which rTMS regulates systemic metabolic parameters partially involve lipid utilization in the periphery as well as central regulation of energy intake and lipid metabolism.

Biometric and Geographic Information-based Identity Authentication Method

With the ongoing advancements in computer technology and mobile internet, mobile payment has become an integral component of daily life. Identity authentication technology serves as a critical measure to ensure the security of mobile payments. This paper addresses the issue of low security associated with single-factor identity authentication while highlighting the substantial overhead involved in multi-factor authentication methods. We propose a novel identity authentication method that integrates biometric data and geographic information. In this framework, the identity authentication server first performs system initialization, configures system parameters, and stores them securely. Subsequently, the identity authentication client submits an authentication request to the server and completes its registration process. Finally, the server processes this request from the client, conducts user identity verification, and returns an authentication result. This approach effectively mitigates risks posed by attackers attempting to falsify biometric data or geographic information during the identity verification process and offers robust services for biometric- and geography-based identity authentication.

Nonlinear Frequency Response Analysis of Distributed Parameter Systems with One Spatial Coordinate

AbstractThe objective of this paper is to investigate possible strategies for applying nonlinear frequency response (NFR) analysis based on the concept of higher‐order frequency response functions to distributed parameter systems. Three approaches are presented and compared: one based on applying the existing procedure directly to the partial differential model, and two based on approximation of the distributed parameter system with a series of lumped parameter segments. One of them treats the complete series of segments integrally, while the other treats it segment by segment, so it uses only the model of a single segment. A simple example, an isothermal plug‐flow reactor with a simple reaction mechanism, is used as a case study. Pros and cons for all three approaches are given.