System Parameters Research Articles

The application of time series deep learning model to the fast prediction of parameters in the MSLB accident

The Main Steam Line Break Accident (MSLB) threatens the safe operation of nuclear power plants. The transient safety parameters of the Passive Containment Cooling System (PCCS) in the MSLB accident are predicted by the time series deep learning model based on LSTM or RNN. In the dataset preprocessing, the linear normalization and two of feature label segmentation methods are used. The neural network models and the other models, namely the GBRT, RF and SVR, are used to construct the multi-parameter time series deep learning models to predict the transient safety parameters. The performance of the different models and the impact of two segmentation methods on models are compared, and it is obtained that the prediction accuracy of LSTM is higher than that of other models.

PhysioFit: a software to quantify cell growth parameters and extracellular fluxes.

Quantification of growth parameters and extracellular uptake and production fluxes is central in systems and synthetic biology. Fluxes can be estimated using various mathematical models by fitting time-course measurements of the concentration of cells and extracellular substrates and products. A single tool is available to non-computational biologists to calculate extracellular fluxes, but it is hardly interoperable and is limited to a single hard-coded growth model. We present our open-source flux calculation software, PhysioFit, which can be used with any growth model and is interoperable by design. PhysioFit includes some of the most common growth models, and advanced users can implement additional models to calculate extracellular fluxes and other growth parameters for metabolic systems or experimental setups that follow alternative kinetics. PhysioFit can be used as a Python library and offers a graphical user interface for intuitive use by end-users and a command-line interface to streamline integration into existing pipelines. PhysioFit v3 is implemented in Python 3 and was tested on Windows, Unix and MacOS platforms. The source code and the documentation are freely distributed under GPL3 license at https://github.com/MetaSys-LISBP/PhysioFit/ and https://physiofit.readthedocs.io/. Supplementary data are available at Bioinformatics online.

Radiation shielding competence of SeTeSnIn chalcogenide glassy system: resistance against gamma irradiation

This paper reports the study of the high-energy radiation shielding characteristics of the STS glassy system. Further, the work investigates the effect of indium concentration on the high energy radiation shielding characteristics of the STS glassy system. For this purpose, a glassy Se78-xTe20Sn2Inx (x = 0, 2, 4, and 6) system was synthesized using a renowned melt quench technique whose density was measured through the Archimedes method. We have utilized an online platform, i.e., Phy-X/PSD software, to measure the different radiation shielding parameters of the SeTeSnIn glassy system. Several shielding parameters such as linear and mass attenuation coefficient, half value layer (HVL), mean free path (MFP), effective atomic number (Z eff ), effective electron density (N eff ), effective conductivity (C eff ), equivalent atomic number (Zeq), energy absorption build-up factor (EABF), exposure build-up factor (EBF), and fast neutron removal cross-section (FNRCS) have been measured in the energy range of 15 keV to 15 MeV.

Time-Series Feature Extraction by Return Map Analysis and Its Application to Bearing-Fault Detection

Developing new features for time-series characterization is a current challenge in data science and machine learning. In this paper, we propose a new metric based on a simple and efficient algorithm, namely, the return map. The return map analysis is well established in the field of non-linear dynamics, in particular, for fitting the parameters of a chaotic system from a waveform, or to attack a chaotic communication channel. We show that our metrics work well for both non-linear dynamics and time-series feature extraction problems in the field of machine learning. In an experiment aiming to classify vibration signals of normal and damaged bearings, we compare our method with two other methods that reported to have excellent accuracy, based on entropy and statistical feature distribution, respectively. We show that our method achieves higher accuracy with almost the lowest time costs, which was confirmed in experiments with two different datasets containing three main classes of bearings: normal, with inner race faults, and with outer race faults, having different damage origins and recorded in various conditions. In particular, for the dataset supplied by Case Western Reserve University, our method reached an accuracy of 100% at signals of 5000 sample points length, with a total time of 0.4 s required for feature estimation, while the entropy-based method reached an accuracy of 95% with a time of 100 s, and a statistical feature distribution method reached an accuracy of 93% with a total time of 1.9 s. Results show that the developed method is better suited to real-time bearing condition monitoring applications than most of the methods reported to date.

Unveiling the value of C-reactive protein as a severity biomarker and the IL4/IL13 pathway as a therapeutic target in recessive dystrophic epidermolysis bullosa: A multiparametric cross-sectional study.

Patients with recessive dystrophic epidermolysis bullosa (RDEB) experience numerous complications, which are exacerbated by inflammatory dysregulation and infection. Understanding the immunological mechanisms is crucial for selecting medications that balance inflammation control and immunocompetence. In this cross-sectional study, aiming to identify potential immunotherapeutic targets and inflammatory biomarkers, we delved into the interrelationship between clinical severity and systemic inflammatory parameters in a representative RDEB cohort. Encompassing 84 patients aged 1-67 and spanning all three Epidermolysis Bullosa Disease Activity and Scarring Index (EBDASI) severity categories, we analysed the interrelationship of infection history, standard inflammatory markers, systemic cytokines and Ig levels to elucidate their roles in RDEB pathophysiology. Our findings identify C-reactive protein as an excellent biomarker for disease severity in RDEB. A type 2 inflammatory profile prevails among moderate and severe RDEB patients, correlating with dysregulated circulating IgA and IgG. These results underscore the IL4/IL13 pathways as potential evidence-based therapeutic targets. Moreover, the complete inflammatory scenario aligns with Staphylococcus aureus virulence mechanisms. Concurrently, abnormalities in IgG, IgE and IgM levels suggest an immunodeficiency state in a substantial number of the cohort's patients. Our results provide new insights into the interplay of infection and immunological factors in the pathogenesis of RDEB.

Disparate Effects of Stressors on Met-Enkephalin System Parameters and on Plasma Concentrations of Corticosterone in Young Female Chickens

Disparate Effects of Stressors on Met-Enkephalin System Parameters and on Plasma Concentrations of Corticosterone in Young Female Chickens

Self-oscillation of cantilevered silicone oil paper sheet system driven by steam

Self-oscillation is to draw energy from a stable external environment to maintain its own continuous motion, making it a highly relevant concept for intelligent materials, soft robots and medical devices. Exploring a simple and durable system driven by steam for self-sustained motion is a tough challenge. In this article, a novel steam-driven self-oscillation cantilevered silicon oil paper is proposed, incorporating a rectangular silicon oil paper sheet, a fixed end and a set of vertically rising steam. Experiments show that the silicone oil paper sheet can engage in continuous periodic self-oscillation in a steady steam. According to the above phenomenon, the dynamic model of the self-oscillation system is established, and the control equation is derived. The semi-analytical form of solution is obtained using the modal superposition method. It shows that the paper sheet has two motion patterns, namely, the static pattern and the self-oscillation pattern. The theoretical predictions are in general agreement with the experimental results. The silicone oil paper sheet maintains the self-oscillation by absorbing thermal energy to offset the damping dissipation. In addition, the critical conditions for self-oscillation and the key system parameters affecting its frequency and amplitude are also examined thoroughly. The oscillation amplitude can be controlled by adjusting the inclined angle, steam temperature factor, damping coefficient and thermal characteristic time, while the frequency mainly depends on the inclined angle and steam temperature factor. The cantilevered silicone oil paper sheet has advantages of simple structure, flexible adjustment, customizable size, and easy assembly. It is expected to have a certain guiding significance in the fields of remote drives, sensors, and micro-robot design.

Influence of Diluent on Extraction Parameters of Systems for Separation Am(III) and Ln(III) Based on 1,10-Phenanthroline-2,9-Diamide

Influence of Diluent on Extraction Parameters of Systems for Separation Am(III) and Ln(III) Based on 1,10-Phenanthroline-2,9-Diamide

Improved control strategy and designed control parameters of pitch system for wind turbine considering blade load reduction

As wind turbines increase in the power, the longer the blade, the more severe the blade load. Traditional control strategies and control parameters do not consider to reduce blade load, which seriously threatens the safe operation of wind turbines. Therefore, this study proposes a pitch control strategy based on blade active damping control and a multistage control parameter design method. Firstly, this study proposes a pitch control strategy based on blade active damping control to reduce the blade load based on the establishment of a mathematical model of the pitch system considering the blade load and a dynamic model of blade active damping control. Secondly, in the control parameter initial values design of Stage Ⅰ, this study proposes a variable forgetting factor recursive least squares to identify a simplified mathematical model of the pitch system and a Low–overshoot Chien–Hrones–Reswisk to solve the proportional integral control parameter initial values. Thirdly, in the control parameter optimal values design of Stage Ⅱ, this study proposes an adaptive genetic algorithm with improved hyperbolic tangent function to optimize control parameter at the equilibrium points and proposes an adaptive control to calculate control parameter at the non–equilibrium points. Finally, based on the Bladed software for 2 MW and 5 MW wind turbines, the superiority of the proposed methods, effectiveness and applicability in improving the control accuracy of the output power and generator speed, and reducing the load situation at the blade root position are verified.

Magnetic biochar enhanced microbial electrolysis cell with anaerobic digestion for complex organic matter degradation in landfill leachate

Combining microbial electrolytic cells with anaerobic digestion (MEC-AD) was considered as an important method for enhancing complex organic matter degradation. However, the magnetic biochar (MBC) addition would be an effective approach for enhancing biodegradation in MEC-AD. By designing orthogonal experiments, the optimal parameters of MBC-enhanced MEC-AD system for landfill leachate treatment were determined. The results indicated that the optimal conditions were identified as HRT of 72 h, electrode spacing of 2.5 cm, and applied voltage of 0.8 V. Under these conditions, the COD removal efficiency reached a maximum of 54.7 %. Additionally, the UV–vis, 3D-EEM, and GC–MS indicated the macromolecules 13-Docosenamide (Z), Bis(2-ethylhexyl) benzene-1,4-dicarboxylate and bis(2-ethylhexyl) phthalate were degraded. 13-Docosenamide (Z) was almost completely removed under the conditions of 0.8 V applied voltage, 2.5 cm electrode spacing and 24 h HRT, with a removal efficiency of 99.91 %. Significant differences were observed in the microbial core genera among the MEC-AD systems. The core genera in the anodic and cathodic biofilms were primarily fermentative and electroactive bacteria, including Soehngenia (2.2 % - 32.1 %, 3.2 % - 26.4 %) and Desulfomicrobium (1.1 % - 10.2 %, 2.0 % - 29.3 %). Fermentative bacteria, norank_f__Bacteroidetes_vadinHA17, established cooperative relationships with electroactive bacteria Acinetobacter. The enrichment of electrochemically active bacteria optimized microbial interactions, thereby synergistically enhancing the biotransformation of complex organic matter in landfill leachate.

A fast multi-objective optimization method for control parameters of high-speed maglev vehicle-bridge system

A fast multi-objective optimization method for control parameters of high-speed maglev vehicle-bridge system

Optimizing DSFH communication system performance via multi-feedback unsaturated tri-stable stochastic resonance for enhancement of periodic signal

To address the challenges in dual-sequence frequency-hopping (DSFH) communication caused by the difficulty in detecting intermediate-frequency (IF) signals under low signal-to-noise ratio conditions, this study proposes a method based on detecting enhanced periodic signals using a multi-feedback unsaturated tri-stable stochastic resonance (MFUTSR) system. Initially, we construct an unsaturated tri-stable stochastic resonance (UTSR) system by leveraging the anti-saturation characteristics of the segmented potential function. This UTSR system is then serially connected with a monostable system and incorporates feedback control to establish the MFUTSR system. Applying the adiabatic approximation theory, we derive the steady-state probability density (SPD) and signal-to-noise ratio (ISNR) of the MFUTSR system, investigating the influence of system parameters on these factors. Finally, to enhance the reliability of the DSFH communication system, we introduce a new evaluation metric, AC. Through numerical simulations and practical application in DSFH communication systems, it can be concluded that the MFUTSR system has significant advantages in periodic feature enhancement and noise suppression. Additionally, using AC as the evaluation metric for symbol decision can greatly reduce the system's bit error rate (BER). The innovative MFUTSR system introduced in this study enhances periodic signal detection in DSFH communication, demonstrating superior robustness against noise and significantly improving system reliability.

Finite-time synchronization of fractional-order uncertain quaternion-valued neural networks via slide mode control

This paper investigates finite-time synchronization of fractional-order quaternion-valued neural networks (F-QV-NNs), involving uncertainties in the system parameters. We adopt a combined approach of sliding mode control (SMC) and a non-separation strategy to achieve finite-time synchronization. Based on SMC theory, we construct a specific sliding surface and design a controller to ensure the occurrence of sliding motion. To achieve the desired sliding motion, we apply the fractional Lyapunov direct method to guide the system's states to the designed sliding surface. Moreover, the derived sufficient conditions ensure finite-time synchronization within the models. Lastly, we give a numerical example to validate the effectiveness of the acquired results.

Finite-Time Disturbance Observer-Based Adaptive Course Control for Surface Ships

In this paper, a finite-time disturbance observer-based adaptive control strategy is proposed for the ship course control system subject to input saturation and external disturbances. Based on the Gaussian error function, a smooth saturation model is designed to avoid the input saturation of the system and reduce steering engine vibrations, and an auxiliary dynamic system is introduced to compensate for the effect of the rudder angle input inconsistency on the system. By constructing an auxiliary dynamic, a finite-time disturbance observer is designed to approximate the external disturbance of the system; an adaptive updating law is also constructed to estimate the upper bound of the derivative of the external disturbance. Combining the finite-time disturbance observer with the auxiliary dynamic system, a novel adaptive ship course control law is proposed by using the hyperbolic tangent function. Moreover, according to LaSalle’s Invariance Principle, a system stability analysis method with loose stability conditions and easy realizations is designed, while the stability of the closed-loop system and the ultimately uniformly boundedness of all its signals are proven. Finally, the course control simulation analysis of a surface ship is carried out. The results show that the proposed control law has a strong resistance to external disturbances and a strong non-fragility to system parameter perturbations, which ensure that the course control system has great control performance.

Centromedian region thalamic responsive neurostimulation mitigatesidiopathic generalized and multifocal epilepsy with focal to bilateral tonic-clonic seizures.

Although >30% of epilepsy patients have drug-resistant epilepsy (DRE), typically those with generalized or multifocal disease have not traditionally been considered surgical candidates. Responsive neurostimulation (RNS) of the centromedian (CM) region of the thalamus now appears to be a promising therapeutic option for this patient population. We present outcomes following CM RNS for 13 patients with idiopathic generalized epilepsy (IGE) and eight with multifocal onsets that rapidly generalize to bilateral tonic-clonic (focal to bilateral tonic-clonic [FBTC]) seizures. A retrospective review of all patients undergoing bilateral CM RNS by the senior author through July 2022 were reviewed. Electrodes were localized and volumes of tissue activation were modeled in Lead-DBS. Changes in patient seizure frequency were extracted from electronic medical records. Twenty-one patients with DRE underwent bilateral CM RNS implantation. For 17 patients with at least 1 year of postimplantation follow-up, average seizure reduction from preoperative baseline was 82.6% (SD = 19.0%, median = 91.7%), with 18% of patients Engel class 1, 29% Engel class 2, 53% Engel class 3, and 0% Engel class 4. There was a trend for average seizure reduction to be greater for patients with nonlesional FBTC seizures than for other patients. For patients achieving at least Engel class 3 outcome, median time to worthwhile seizure reduction was 203.5 days (interquartile range = 110.5-343.75 days). Patients with IGE with myoclonic seizures had a significantly shorter time to worthwhile seizure reduction than other patients. The surgical targeting strategy evolved after the first four subjects to achieve greater anatomic accuracy. Patients with both primary and rapidly generalized epilepsy who underwent CM RNS experienced substantial seizure relief. Subsets of these patient populations may particularly benefit from CM RNS. The refinement of lead targeting, tuning of RNS system parameters, and patient selection are ongoing areas of investigation.

Spatiotemporal Transformation-Based Neural Network With Interpretable Structure for Modeling Distributed Parameter Systems.

Many industrial processes can be described by distributed parameter systems (DPSs) governed by partial differential equations (PDEs). In this research, a spatiotemporal network is proposed for DPS modeling without any process knowledge. Since traditional linear modeling methods may not work well for nonlinear DPSs, the proposed method considers the nonlinear space-time separation, which is transformed into a Lagrange dual optimization problem under the orthogonal constraint. The optimization problem can be solved by the proposed neural network with good structural interpretability. The spatial construction method is employed to derive the continuous spatial basis functions (SBFs) based on the discrete spatial features. The nonlinear temporal model is derived by the Gaussian process regression (GPR). Benefiting from spatial construction and GPR, the proposed method enables spatially continuous modeling and provides a reliable output range under the given confidence level. Experiments on a catalytic reaction process and a battery thermal process demonstrate the effectiveness and superiority of the proposed method.

Effects of Endurance Training on Antioxidant and Hormonal Status in Peripheral Blood of Young Healthy Men

(1) Background: Physical activity may cause an imbalance in the major functions of the human body. This study aimed to investigate the effects of endurance running training on the parameters of the antioxidant defense system (SOD, CAT, GPx, GR, GSH), LPO (malondialdehyde, MDA), and stress hormones (A, NA) in young healthy, previously untrained men. (2) Methods: The training program was as follows: 8 weeks of running, three times per week; the duration of a single session was 30–70 min, the intensity was twice a week in the so-called extensive endurance zone, and once a week in the anaerobic threshold zone. Blood samples were collected from the subjects, before and after the running program. (3) Results: The training program resulted in a significant increase in maximal oxygen consumption (p < 0.001). The activities of SOD, GPx, and GR also increased significantly (p < 0.05, p < 0.01, and p < 0.05, respectively), while CAT activity and GSH and MDA concentrations remained unchanged. The concentration of A decreased (p < 0.05), while the NA concentration increased significantly (p < 0.05). SOD, GPx, GR, and NA positively correlated with VO2max (p < 0.05, p < 0.001, p < 0.01, p < 0.05, respectively), while a negative correlation was detected between A and VO2max (p < 0.05). (4) Conclusions: These results indicate that there is no persistent oxidative stress in response to the applied 8-week running program, probably due to exercise-induced protective alterations in the antioxidant defense system. Furthermore, adaptations occurred at the hormonal level, making the organism more ready for a new challenge.

Symmetry reduction and recovery of trajectories of optimal control problems via measure relaxations

We address the problem of symmetry reduction of optimal control problems under the action of a finite group from a measure relaxation viewpoint. We propose a method based on the moment-SOS (Sum of Squares) aka Lasserre hierarchy which allows one to significantly reduce the computation time and memory requirements compared to the case without symmetry reduction. We show that the recovery of optimal trajectories boils down to solving a symmetric parametric polynomial system. Then we illustrate our method on the symmetric integrator and the time-optimal inversion of qubits.

Association between immune system parameter and clinical characteristics among patients with solid cancer

BACKGROUND Lymphopenia has been reported to be a major predictor of chemotherapy-related toxicity. This study aimed to investigate the correlation between neutrophils, lymphocytes, CD4, and CD8 in solid cancer patients and cancer clinical characteristics. METHODS This was a cross-sectional study of patients who will undergo chemotherapy at the Hematology and Medical Oncology Clinic, Cipto Mangunkusumo Hospital, from June to September 2023. Clinical characteristics, CD4 and CD8 levels, and neutrophil and lymphocyte counts were assessed at the first visit. A comparative test was carried out on the patients’ average CD4, CD8, neutrophil, and lymphocyte counts. RESULTS Types of cancer were associated with CD4 levels. Patients with head and neck cancer had lower CD4 levels (411.3 [119.3–1,427.5] cells/mm³) compared with colorectal (514.7 [129.2–861.3] cells/mm³), breast and gynecological (567.5 [180.1–939 cells/mm³), and other cancers (681.4 [175.1–2,056.9] cells/mm³), with p = 0.009. Patients aged ≥40 years had higher CD8 levels than those aged <40 years (376.4 [142.8–1,293.1] cells/mm³ versus 565.3 [185.9–1,944] cells/mm³, p = 0.01). Additionally, lymphocyte count was associated with cancer type, with the lowest number in head and neck cancer (1,380 [280–2,660] μl, p = 0.044). CONCLUSIONS CD4 levels and lymphocyte counts were associated with the cancer type, whereas CD8 levels were influenced by age.

Exploration of a brain-inspired photon reservoir computing network based on quantum-dot spin-VCSELs

Based on small-world network theory, we have developed a brain-inspired photonic reservoir computing (RC) network system utilizing quantum dot spin-vertical-cavity surface-emitting lasers (QD spin-VCSELs) and formulated a comprehensive theoretical model for it. This innovative network system comprises input layers, a reservoir network layer, and output layers. The reservoir network layer features four distinct reservoir modules that are asymmetrically coupled. Each module is represented by a QD spin-VCSEL, characterized by optical feedback and optical injection. Within these modules, four chaotic polarization components, emitted from both the ground and excited states of the QD Spin-VCSEL, form four distinct reservoirs through a process of asymmetric coupling. Moreover, these components, when emitted by the ground and excited states of a driving QD spin-VCSEL within a specific parameter space, act as targets for prediction. Delving further, we investigated the correlation between various system parameters, such as the sampling period, the interval between virtual nodes, the strengths of optical injection and feedback, frequency detuning, and the predictive accuracy of each module’s four photonic RCs concerning the four designated predictive targets. We also examined how these parameters influence the memory storage capabilities of the four photonics RCs within each module. Our findings indicate that when a module receives coupling injections from more than two other modules, and an RC within this module is also subject to coupling injections from over two other RCs, the system displays reduced predictive errors and enhanced memory storage capacities when the system parameters are fixed. Namely, the superior performance of the reservoir module in predictive accuracy and memory capacities follows from its complex interaction with multiple light injections and coupling injections, with its three various PCs benefiting from three, two, and one coupling injections respectively. Conversely, variations in optical injection and feedback strength, as well as frequency detuning, introduce only marginal fluctuations in the predictive errors across the four photonics RCs within each module and exert minimal impact on the memory storage capacity of individual photonics RCs within the modules. Our investigated results contribute to the development of photonic reservoir computing towards fast response biological neural networks.