System Parameters Research Articles

The evaluations of oxidative stress and neurotoxicity in threatened endemic fish Barbus meridionalis from Osor River (Spain)

Mediterranean barbel (Barbus meridionalis) an endemic species is currently facing habitat destruction and pollution in Osor River (Spain) due to mining runoff that has severely deteriorated the water quality by metals, primarily zinc (Zn). In order to assess the potential risk of metal contamination and hydrological changes in the Osor River by using oxidative stress and neurotoxicity biomarkers via IBR analyses in the barbel, five different stations were chosen: upstream (S1: reference site and S2: hydrologically changed), mine (S3), and downstream (S4 and S5). The highest tissue metal levels were measured particularly at S3 and following downstream sites. SOD activity and the GSH system parameters (GPX, GST, and GSH) were the most sensitive oxidative stress indicators among the antioxidant system parameters. The organs with the greatest changes in antioxidant biomarkers were the liver and gill. As a sign of neurotoxicity, AChE activities significantly raised in the brain and muscle but drastically lowered in the kidney, liver, and gill particularly in the area of mine and downstream compared to reference site. Integrated biomarker response index (IBR) method was applied to visualize the affect of metal and hydrological alterations with biomarker response according to sites in the Osor River. IBR analyzes together with correlations between metal levels and oxidative stress biomarkers, emphasized that S2 and S3 have the greatest impact on the biomarker levels due to mine activity and hydrological changes highlighting the vulnerability to extinction of native fish B. meridionalis. It is also critical to assess the current data based on the multi-biomarker approach for a range of detrimental effects on fish fitness at the individual level as well as population persistence from an ecological standpoint.

Hydrocarbon‐Based Ionomer/PTFE‐Reinforced Composite Membrane Through Multibar Coating Technique for Durable Fuel Cells

AbstractFor cost reduction and environmental‐friendly manufacturing, it is highly demanded to replace the current perfluorinated sulfonic acid‐based membrane in polymer electrolyte membrane fuel cells (PEMFCs) with inexpensive and readily available hydrocarbon‐based (HC) membranes. However, HC membranes suffer from profound dimensional changes caused by swelling and shrinking during operation, especially in automotive applications. These changes lead to severe mechanical degradation and shorten the service life of PEMFC. Herein, a multibar coating system is developed to manufacture HC/polytetrafluoroethylene (PTFE) composite membrane. This system facilitates capillary‐rise infiltration with the aid of an optimal amount of residual alcohol solvent on the PTFE. To address compatibility issues between PTFE and HC‐ionomer solutions, the effects of residual alcohol solvent on tuning the PTFE surface are investigated by controlling systemic parameters and performing diverse mechanical, optical, and electrochemical measurements. Based on its enhanced mechanical toughness (≈30.04%) and superior impregnation properties, the constructed HC/PTFE composite membrane exhibited more than seven‐fold improvement in mechanical durability under repeated accelerated wet–dry conditions compared with an unsupported pristine HC membrane while also mitigating performance loss (≈5.84%).

A close examination of BCRP's role in lactation and methods for predicting drug distribution into milk.

Breastfeeding is the most complete nutritional method of feeding infants, but several impediments affect the decision to breastfeed, including questions of drug safety for medications needed during lactation. Despite recent FDA guidance, few labels provide clear dosing advice during lactation. Physiologically based pharmacokinetic modeling (PBPK) is well suited to mechanistically explore pharmacokinetics and dosing paradigms to fill gaps in the absence of extensive clinical studies and complement existing real-world data. For lactation-focused PBPK (Lact-PBPK) models, information on system parameters (e.g., expression of drug transporters in mammary epithelial cells) is sparse. The breast cancer resistance protein (BCRP) is expressed on the apical side of mammary epithelial cells where it actively transports drugs/substrates into milk (reported milk: plasma ratios range from 2 to 20). A critical review of BCRP and its role in lactation was conducted. Longitudinal changes in BCRP mRNA expression have been identified in women with a maximum reached around 5 months postpartum. Limited data are available on the ontogeny of BCRP in infant intestine; however, data indicate lower BCRP abundance in infants compared to adults. Current status of incorporation of drug transporter information in Lact-PBPK models to predict active secretion of drugs into breast milk and consequential exposure of breast-fed infants is discussed. In addition, this review highlights novel clinical tools for evaluation of BCRP activity, namely a potential non-invasive BCRP biomarker (riboflavin) and liquid biopsy that could be used to quantitatively elucidate the role of this transporter without the need for administration of drugs and to inform Lact-PBPK models.

On Zero-Temperature Current through Atomic Chain Subjected to a Uniformly Varying Field: Green's Function Formalism

On the basis of the tight-binding formalism and Green’s function technique, we obtain all matrix elements of Green’s functions for a biased chain with linear variations of the electron on-site energy. Their dependence on system parameters is analyzed in the context of through-molecule electron transport.

Concurrent learning for adaptive pontryagin's maximum principle of nonlinear systems with inequality constraints

AbstractIn this article, a finite‐horizon adaptive Pontryagin's maximum principle is presented for nonlinear systems with state inequality constraints. Concurrent learning (CL) technique is adopted to identify the unknown parameters of the dynamic systems. Based on the identification model, a novel adaptive iterative algorithm under the Pontryagin's framework is introduced to learn the finite‐horizon optimal control solution. Convergence analysis of the algorithm is provided by showing that the cost function sequence is monotonically decreasing. Furthermore, we extend the adaptive iterative algorithm to time‐varying nonlinear systems. The new algorithm overcomes the technical obstacles of the existing adaptive/approximate dynamic programming (ADP) approaches to deal with the time‐varying characteristic of Hamilton–Jacobi–Bellman (HJB) partial differential equation (PDE), especially when state constraints exist. Simulation examples are carried out to validate the effectiveness of the theoretical results.

Discovery of SRGA J144459.2-604207 with the SRG/RT-XC telescope : A well-tempered bursting accreting millisecond X-ray pulsar

We report the discovery of the new accreting millisecond X-ray pulsar SRGA\,J144459.2$-$604207 using data of the SRG The source was observed twice in February 2024 during the declining phase of the outburst. The timing analysis revealed a coherent signal near 447.9 Hz modulated by the Doppler effect due to the orbital motion. The derived parameters for the binary system are consistent with a circular orbit with a period of sim 5.2 h. The pulse profiles of the persistent emission, showing a sine-like part during half a period with a plateau in between, can be well modeled by emission from two circular spots that are partially eclipsed by the accretion disk. Additionally, during our observations with an exposure of 133 ks, we detected 19 thermonuclear X-ray bursts. All bursts have similar shapes and energetics, and none show any signs of an expanding photospheric radius. The burst recurrence times decreases linearly from sim 1.6 h at the beginning of observations to sim 2.2 h at the end and anticorrelate with the persistent flux. The spectral evolution during the bursts is consistent with the models of the neutron star atmospheres that are heated by accretion and implies a neutron star radius of 11--12 km and a distance to the source of 8--9 kpc. We also detected coherent pulsations during the bursts and showed that the pulse profiles differ substantially from those observed in the persistent emission. However, we could not find a simple physical model explaining the pulse profiles detected during the bursts.

Analysis and comparison of retinal vascular parameters under different glucose metabolic status based on deep learning.

To develop a deep learning-based model for automatic retinal vascular segmentation, analyzing and comparing parameters under diverse glucose metabolic status (normal, prediabetes, diabetes) and to assess the potential of artificial intelligence (AI) in image segmentation and retinal vascular parameters for predicting prediabetes and diabetes. Retinal fundus photos from 200 normal individuals, 200 prediabetic patients, and 200 diabetic patients (600 eyes in total) were used. The U-Net network served as the foundational architecture for retinal artery-vein segmentation. An automatic segmentation and evaluation system for retinal vascular parameters was trained, encompassing 26 parameters. Significant differences were found in retinal vascular parameters across normal, prediabetes, and diabetes groups, including artery diameter (P=0.008), fractal dimension (P=0.000), vein curvature (P=0.003), C-zone artery branching vessel count (P=0.049), C-zone vein branching vessel count (P=0.041), artery branching angle (P=0.005), vein branching angle (P=0.001), artery angle asymmetry degree (P=0.003), vessel length density (P=0.000), and vessel area density (P=0.000), totaling 10 parameters. The deep learning-based model facilitates retinal vascular parameter identification and quantification, revealing significant differences. These parameters exhibit potential as biomarkers for prediabetes and diabetes.

Generation of strong mechanical squeezing through the joint effect of two-tone driving and parametric pumping

We propose an innovative scheme to efficiently prepare strong mechanical squeezing by utilizing the synergistic mechanism of two-tone driving and parametric pumping in an optomechanical system. By reasonably choosing the system parameters, the proposal highlights the following prominent advantages: the squeezing effect of the cavity field induced by the optical parametric amplifier can be transferred to the mechanical oscillator, which has been squeezed by the two-tone driving, and the degree of squeezing of the mechanical oscillator will surpass that obtained by any single mechanism; the joint mechanism can enhance the degree of squeezing significantly and break the 3 dB mechanical squeezing limit, which is particularly evident in range where the red/blue-detuned ratio is sub-optimal; the mechanical squeezing achieved through this distinctive joint mechanism exhibits notable robustness against both thermal noise and decay of mechanical oscillator. Our project offers a versatile and efficient approach for generating strong mechanical squeezing across a wide range of conditions.

Bio-inspired MPPT and advanced control for grid-connected solar photovoltaic systems with flyback converter

ABSTRACT This paper presents an enhanced approach for grid-connected photovoltaic (PV) systems using a flyback converter and Sovereign Butterfly Optimization for advanced Maximum Power Point Tracking (MPPT). The focus is on improving energy extraction efficiency from PV panels by dynamically adjusting system parameters. A flyback converter ensures reliable grid integration, while the Butterfly Optimization Algorithm, inspired by butterfly foraging, provides real-time MPPT to rapidly and accurately track the maximum power point, even under varying environmental conditions. A novel voltage harmonic mitigation technique processes three-phase currents through dq0 frames for closed-loop control, reducing harmonics and optimising voltage precision. The DC/DC flyback converter stabilizes AC conversion, minimizing high-frequency harmonics. Additionally, a Subsystem Synchronization Control Strategy synchronizes active and non-active grid currents, improving system stability. Simulations show a 99.3% tracking accuracy, 2% power oscillation around the maximum power point, and a Total Harmonic Distortion (THD) of 1.78%, meeting IEC 61,727 standards.

Nonlinear parametric generation and optical vortex transfer in graphene ensemble under Landau quantization

We explore the dynamics of nonlinear parametric generation and light beam propagation in a Landau-quantized graphene structure with three energy levels interacting with two laser pulses, utilizing the Maxwell-Bloch equations. By applying a laser field to one transition of the graphene sample while keeping the second beam initially absent, the distinctive preparation of the graphene sample, coupled with its weak interaction with laser radiation, results in the parametric generation of a new laser beam in a different transition. We investigate the influence of diverse system parameters on both the efficiency of the generated beam and the propagation dynamics of both beams. Our findings reveal that manipulating these parameters can induce oscillations in the intensity of propagated beams, mitigate absorption losses during propagation allowing for earlier relaxation, and enhance the efficiency of energy transfer from the initial to the generated beam. Additionally, we demonstrate the transfer of optical vortices within the graphene ensemble by introducing an optical vortex to the initial beam. This scheme holds promise for applications in high-dimensional quantum information processing.

Analytical prediction of temperature effects on Lamb wave response in smart plates and its experimental verification

Abstract This article examines the effectiveness of a recently developed theoretical model for the generation and sensing of Lamb waves in thin plates with surface-bonded piezoelectric transducers in predicting temperature effects on Lamb waves and their time reversibility. In particular, the analytical model provides a closed-form solution, which incorporates both the shear-lag effect of the bonding layer and the system inertia in transducer-plate interaction modeling. Temperature-dependent material properties and thermal expansion of the system constituents are considered to predict the Lamb wave signal under a thermal environment. The accuracy of the theoretical prediction is assessed in comparison with experimental results obtained using an aluminum plate with adhesively bonded lead zirconate titanate transducers to its surface at different system temperatures ranging from 20°C to 75°C. Comparison is also made with experimental data and analytical solutions presented earlier without considering the inertia effect. The study reveals that the current solution accurately predicts the change in Lamb wave signal due to temperature variation, including the frequency dependency of the peak amplitude change with temperature rise. However, the theoretical model fails to predict the experimental trends when the inertia terms are neglected. The current model is also used to study the contributions of individual system parameters to the overall temperature effect on the time reversibility of Lamb waves and its dependence on the excitation frequency.

Proximal Gauss-Newton Method for Box-constrained Parameter Identification of a Nonlinear Railway Suspension System

The identification of railway vehicle components’ characteristics from measured data is a challenging task with compelling applications in health monitoring, fault detection, and system prognosis. Usually, though, such systems are highly nonlinear, and naive identification techniques may lead to unstable methods and inaccurate results. In this paper, we show that these issues can be easily tackled with the recently introduced proximal Gauss–Newton method, which we employ to identify the parameters of a railway nonlinear suspension system. In the proposed model, the parameters are subject to safety bounds in form of box constraints, which allows preventing nonphysical solutions. The suspension system we consider is highly nonlinear due to the presence of an airspring in the secondary suspension, which we introduce in a simplified Berg model. Numerical examples, featuring data corrupted by various noise levels, demonstrate the accuracy and efficiency of our proposed method. Comparisons with state-of-the-art approaches are also provided.

Phenomenological Study of the Atomization Process in Pre‐Filming Nozzles Typically Used for Steel Atomization

Liquid steel atomization using close‐coupled nozzles is highly dependent on the relationship between momentum flux ratio, gas–liquid ratio, aspiration pressure, and operating pressure of inert gases. A strong correlation between these parameters and the final powder must be assumed. Understanding these parameters, their influence on the process, and their interactions with each other is indispensable for efficient powder production. In particular, the industrial application of close‐coupled atomization nozzles, which develop a thin pre‐film on the nozzle tip, is not yet fully understood. A model experiment is presented to investigate interactions between the flow conditions of the gaseous and liquid phases, focusing on recirculation phenomena inside the atomization zone. The experiments show the influence of liquid and gas flow conditions on the spray geometry and the liquid core length, as well as the strong dependence of the liquid mass flux on the gas flow. The aspiration pressure below the liquid nozzle is sensitive to gas pressure and has a major influence on the development of the liquid core and the pre‐film, as it increases the liquid mass flux. For practical application, the results confirm an optimal operating point of interacting system parameters, which leads to high‐quality atomization with minimal use of resources.

Dynamics of hemostasis system parameters in assessing the risk of complications in the patients with acute myocardial infarction receiving antiplatelet therapy

Background. Current treatment of patients with myocardial infarction is based on the strategy of early invasive coronary intervention in combination with dual antiplatelet therapy - with acetylsalicylic acid and a P2Y12 blocker of platelet adenosine diphosphate receptors. In patients with thrombosis of the infarct-related artery, the phenomenon of “slow/ no reflow” (slowing of blood flow due to distal embolization of the artery), inhibitors of glycoprotein IIb/IIIa platelet receptors are administered as additional disaggregant therapy. In patients undergoing standard antiplatelet therapy in combination with glycoprotein IIb/IIIa inhibitors, there is a risk of hemorrhagic complications, therefore, monitoring of hemostasis parameters is necessary. Currently, there are no standard approaches to monitor the antiplatelet therapy.Objective. To study the dynamics of hemostatic system parameters in patients with acute myocardial infarction during antiplatelet therapy.Material and methods. We assessed platelet aggregation with 10 µmol of adenosine phosphate as an inducer in patients with ST-segment elevation myocardial infarction with different options of standard antiplatelet therapy in combination with GPIIb/IIIa inhibitors. Group 1 included 20 patients on dual antiplatelet therapy (clopidogrel 75 mg + acetylsalicylic acid 100 mg) + GPIIb/IIIa inhibitor (tirofiban). Group 2 included 15 patients on dual antiplatelet therapy (ticagrelor 180 mg + acetylsalicylic acid 100 mg) + GPIIb/IIIa inhibitor.Results. While on antiplatelet therapy the patients in both groups 1 and 2 demonstrated a decrease in platelet aggregation ability under the impact of adenosine phosphate, relative to the norm: 38 (21;43) % and 14 (11;15) %, respectively, the norm being 79 (73;84) % (p<0.05). Meantime, no thrombotic events in the form of stent thrombosis were noted, which indicated antiplatelet therapy efficacy. In an intragroup comparison, the decrease in the platelet aggregation ability was most pronounced in group 2 (p<0.05). By the 7th day of myocardial infarction, the platelet aggregation had continued to decrease to 26 (17;43) % in group 1, to 10 (7;11) % in group 2. The most pronounced effect of antiplatelet therapy was observed in group 2 (p<0.05), which was manifested by a statistically significant decrease in platelet aggregation ability under the impact of 10 µmol of adenosine phosphate.Conclusions. While studying the hemostasis system changes over time in patients with myocardial infarction receiving antiplatelet therapy, we have found that making the platelet aggregation ability assessment with 10 µmol of adenosine phosphate as an inducer is possible to identify the effect of medications. However, further studies including larger patient groups are needed to determine the target values of platelet aggregation with 10 µmol of adenosine phosphate and assess the therapy efficacy.

Development and verification of real-time hybrid model test delay compensation method for monopile-type offshore wind turbines

The real-time hybrid model (RTHM) test is adept at addressing the scale contradiction, the lack of fidelity in wind modelling in hydrodynamic testing facilities and spatial constraints inherent in conventional monopile-type offshore wind turbine (OWT) model testing methods, thus emerging as an effective avenue for conducting physical model tests of Monopile-type OWTs. This method entails the reproduction of aerodynamic loads or platform motions using loading device or vibration tables. Time delays in the physical attributes of the loading device and signal transmission processes within the system can result in error accumulation, with the potential to impact overall system stability. Moreover, time delay compensation algorithms for hybrid model test systems with force control loading can easily generate excessive noise, leading to system divergence. As a result, time delay has emerged as a technical challenge in the RTHM test. To address this issue, this paper has developed second-order and third-order polynomial extrapolation algorithms, alongside an adaptive compensation algorithm. The adaptive compensation algorithm employs the least squares method to identify parameters of the loading system, enabling it to address variations in the time delay of the experimental system caused by the nonlinearity of the loading system and changes in the physical properties of the model. The feasibility and effects of time delay compensation for various algorithms are validated through numerical simulation. Results indicate that the adaptive compensation algorithm surpasses second and third-order polynomial extrapolation compensation algorithms in terms of accuracy and compensation effectiveness. To validate the applicability of the adaptive compensation algorithm, a RTHM test was conducted. Across rotor thrust force (RotThrust) and tower top displacement, there was an average reduction of approximately 5 % and 9 % in the maximum and minimum synchronization errors, respectively. This highlights the efficacy of the delay compensation algorithm in practical applications, notably diminishing time delay errors within the experimental system. The adaptive compensation algorithm continuously adjusts and updates parameters, enhancing the adaptability of the compensation process to time-varying systems.

Coordination of Renewable Energy Integration and Peak Shaving through Evolutionary Game Theory

This paper presents a novel approach to optimizing the coordination between renewable energy generation enterprises and power grid companies using evolutionary game theory. The research focuses on resolving conflicts and distributing benefits between these key stakeholders in the context of large-scale renewable energy integration. A theoretical model based on replicator dynamics is developed to simulate and analyze the evolutionary stable strategies of power generation enterprises and grid companies with particular emphasis on peak shaving services and electricity bidding. These simulations are based on theoretical models and do not incorporate real-world data directly, but they aim to replicate scenarios that reflect realistic behaviors within the electricity market. The model is validated through dynamic simulation under various scenarios, demonstrating that the final strategic choices of both thermal power and renewable energy enterprises tend to evolve towards either high-price or low-price bidding strategies, significantly influenced by initial system parameters. Additionally, this study explores how the introduction of peak shaving compensation affects the coordination process and stability of renewable energy integration, providing insights into improving grid efficiency and enhancing renewable energy adoption. Although the results are simulation-based, they are designed to offer practical recommendations for grid management and policy development, particularly for the integration of renewable energies such as wind power in competitive electricity markets. The findings suggest that effective government regulation, alongside well-designed compensation mechanisms, can help establish a balanced interest distribution between stakeholders. By offering a clear framework for analyzing the dynamics of renewable energy integration, this work provides valuable policy recommendations to promote cooperation and stability in electricity markets. This study contributes to the understanding of the complex interactions in the electricity market and offers practical solutions for enhancing the integration of renewable energy into the grid.

Modeling the Optimal Distance Between Pipes in a Solar Water Heating Collector

This study proposes a mathematical model based on normal distribution to determine the optimal distance between pipes in a solar water heating collector. The model takes into account key system parameters such as material thermal conductivity, heat losses, and solar radiation intensity. Experimental data collected for model validation confirm its accuracy and adequacy. The main results show that the maximum efficiency (η) of the solar water heating collector is achieved at a pipe spacing of approximately 0.0469 m. The analytical method demonstrates a smooth decrease in efficiency when deviating from the optimal distance, whereas the experimental data indicate a high sensitivity of the system to changes in this parameter. Thus, the proposed model is a useful tool for designers, allowing for precise prediction of optimal system parameters and increased efficiency. Further research and experiments are planned to improve model accuracy and expand its applicability.

EXPERIMENTAL ASSESSMENT OF CONSUMER UNBALANCE AND NONLINEARITY EFFECTS IN THREE-PHASE NETWORKS WITHOUT NEUTRAL CONDUCTOR

The present study proposes a feedforward angle droop strategy based on a discrete-time mathematical model for operation dispatchable distributed generation (DG) units connected directly or through voltage source converters (VSC) to the microgrid. These units should supply their local and common loads. Droop coefficients should be increased to improve power division between different DG units. Also, a time delay in systems’ state variables and controller input is a disruptive parameter for the control system's performance. These two parameters have negative effects on network stability. To ensure the stability of the closed-loop system, a predictive sliding mode controller can be used based on discrete-time systems. However, this strategy cannot reduce the chattering phenomenon. This paper discusses the effect of time delay and increases in droop angle coefficients on the whole system and how these impacts can be eliminated. So, a combination of integral sliding mode controller (ISMC), composite nonlinear feedback (CNF), and sliding mode learning control (SMC), which is called hybrid SMC, is used with an angle droop controller.

THE EFFECTS OF HYBRID SLIDING MODE LEARNING CONTROL AND FEEDFORWARD ANGLE DROOP CONTROLLER WITH HIGH DROOP GAIN IN HYBRID MICROGRID WITH LOAD UNCERTAINTY AND NONLINEARITY

The present study proposes a feedforward angle droop strategy based on a discrete-time mathematical model for operation dispatchable distributed generation (DG) units connected directly or through voltage source converters (VSC) to the microgrid. These units should supply their local and common loads. Droop coefficients should be increased to improve power division between different DG units. Also, a time delay in systems’ state variables and controller input is a disruptive parameter for the control system's performance. These two parameters have negative effects on network stability. To ensure the stability of the closed-loop system, a predictive sliding mode controller can be used based on discrete-time systems. However, this strategy cannot reduce the chattering phenomenon. This paper discusses the effect of time delay and increases in droop angle coefficients on the whole system and how these impacts can be eliminated. So, a combination of integral sliding mode controller (ISMC), composite nonlinear feedback (CNF), and sliding mode learning control (SMC), which is called hybrid SMC, is used with an angle droop controller.

A CNN-based approach for anomaly detection in smart grid systems

Power grids are always a major center of attraction for attackers due to their large coverage area, complex technological infrastructure, remote access facilities, connectivity, and many more. The power grids that have bidirectional power flow, information interchange, and advanced communication technologies to improve the quality of power transmission and distribution are called smart grids. DoS attacks and false data injection attacks are the two major attacks on smart grids. This paper considers a false data injection attack considering noise reduction, state estimation, system parameters, and control system generation. Firstly, we measure the noise and mitigate it using the data matrix of the devices (sensors, generators, substations, and breaker systems). Secondly, state estimation uses the CNN to track the injected attack types. Finally, a control system is designed to provide anomaly patterns to the devices as input to prevent attacks in the future. The experimental results show that the proposed method achieves a higher anomaly detection rate with a lower error rate (approximately . 1.43%). The network performance parameters are providing significant results; throughput (12 Mbps), PDR (0.005%), execution time (1.43 s), and energy consumption are reduced by 12%–15% as compared to the existing research findings.