Changes In System Parameters Research Articles

A Comprehensive Approach to Load Frequency Control in Hybrid Power Systems Incorporating Renewable and Conventional Sources with Electric Vehicles and Superconducting Magnetic Energy Storage

This study addresses the load frequency control (LFC) within a multiarea power system characterized by diverse generation sources across three distinct power system areas. area 1 comprises thermal, geothermal, and electric vehicle (EV) generation with superconducting magnetic energy storage (SMES) support; area 2 encompasses thermal and EV generation; and area 3 includes hydro, gas, and EV generation. The objective is to minimize the area control error (ACE) under various scenarios, including parameter variations and random load changes, using different control strategies: proportional-integral-derivative (PID), two-degree-of-freedom PID (PID-2DF), fractional-order PID (FOPID), fractional-order integral (FOPID-FOI), and fractional-order integral and derivative (FOPID-FOID) controllers. The result analysis under various conditions (normal, random, and parameter variations) evidences the superior performance of the FOPID-FOID control scheme over the others in terms of time-domain specifications like oscillations and settling time. The FOPID-FOID control scheme provides advantages like adaptability/flexibility to system parameter changes and better response time for the current power system. This research is novel because it shows that the FOPID-FOID is an excellent control scheme that can integrate these diverse/renewable sources with modern systems.

Theoretical Analysis of Viscoelastic Friction System Characteristics of Robotic Arm Brake Based on Fractional Differential Theory

In engineering practice, the nonlinear vibration effect can easily lead to chaos in the system, which will not only reduce the performance of the system but also lead to premature fatigue of components, control failure, and increased safety risks. In view of the core position of the robotic arm in modern industry, this study relies on the robotic arm brake system to explore the theoretical basis of integrated viscoelastic materials as a vibration isolation layer. By analyzing the dynamic characteristics of the friction braking system with fractional differential terms, it aims to provide a new perspective for understanding and controlling the chaotic phenomena of a class of nonlinear friction systems. Firstly, we construct a model of a friction system and analyze its dynamic characteristics in detail. The self-excited vibration of the system under disturbance is studied. The relationship between amplitude and frequency is calculated by a nonlinear approximate analytical algorithm, and the accuracy of this relationship is verified by a numerical algorithm. Then, we compare the differences between non-fractional systems and fractional systems. It is found that with the increase in the fractional order term, the vibration amplitude of the system decreases significantly, which helps to reduce the nonlinear characteristics generated by the friction system and narrow the range of unstable solutions. Secondly, we also study the influence of parameter coefficients on the amplitude–frequency characteristics and analyze the local static bifurcation characteristics through singularity theory. Finally, we study the dynamic bifurcation behavior under different parameter perturbations and find that the change in system parameters will lead to the alternation of periodic motion and chaotic motion.

Experimental model of systemic inflammation during acetaminophen toxicity

Acetaminophen is one of the most toxic drugs that can cause liver damage. At the same time, acetaminophen-induced liver failure is closely associated with the development of systemic inflammatory response syndrome. However, there is no drug aimed at suppressing the systemic inflammatory response. That is, this issue needs to be studied experimentally, but a model of systemic inflammation during acetaminophen overdose has not yet been obtained. Therefore, it was decided to develop an experimental model of systemic inflammation during acetaminophen overdose. The purpose of this study was to experimentally substantiate the semi-lethal dose for acetaminophen overdose in C57Bl/6 mice and to evaluate the readings of blood tests after administration of the drug. To determine the semi-lethal dose, male C57Bl/6 mice were intraperitoneally injected with “Ifimol” (Unique Pharmaceutical Laboratories, India) or acetaminophen solution (Sigma-Aldrich, USA) with a concentration of 14 mg/ml in different doses. When introducing “Ifimol”, it was not possible to achieve a semi-lethal dose. When administering a solution of acetaminophen, 50% mortality was recorded at a dose of 600 mg/kg body weight. After establishing a semi-lethal dose, the experimental group was administered an acetaminophen solution (Sigma-Aldrich, USA) with a concentration of 14 mg/ml at a dose of 600 mg/kg. The control group was injected with saline in an equivalent volume. On the second day, liver and peripheral blood samples were taken. Subsequently, hematological and biochemical blood tests and histological analysis were performed. Histological examination revealed centrilobular necrosis and disorganization of the liver structure. According to the biochemical blood test, the activity of aspartate aminotransferase, alanine aminotransferase, creatinine concentration, and the de Ritis coefficient differed statistically significantly (p 0.05) in the experimental group compared to the control group. Among the hematological blood test parameters, there were statistically significant differences in the number of leukocytes, platelets, as well as the absolute and relative content of granulocytes and lymphocytes. Thus, 48 hours after administration of a semi-lethal dose of acetaminophen, there were signs of damage to internal organs (liver, kidneys) and changes in immune system parameters, which are similar to components of systemic inflammation in humans.

Connecting Cell Structure and Current‐Dependent Environment Changes in CO2 Electrolysis to GDE Operation Regimes and Multi‐Cell Interaction

ABSTRACTConsecutive development of materials, components, and ultimately, devices does not appear to be a promising strategy in CO2 electroreduction because maintaining comparability and transferring results between idealized and application‐oriented systems proves challenging. A modular cell design and tracking cell conditions via sensors may be a solution. We displayed a strategy to characterize gas diffusion electrode operating regimes in a flow cell with regard to different current density ranges, as well as the impact of the flow gap design. We revealed strong interdependencies between cell components, their functions as well as individual cells when integrated into a stack. Expanding the scope and resolution of experimental data made new information on the change of system parameters in flow cells accessible.

Numerical investigations on the performance of coupled NCL based passive heat removal system of an IPHWR

Passive heat removal systems (PHRS) are indispensable additional safety features in nuclear reactors to deal with station blackout (SBO) conditions. These systems are based on coupled natural circulation loops (C-NCL) connected in series configuration. Further, these system's design, operating conditions, working fluid, heat removal capacity, etc., are specific to the reactor type. In the present work, the operational performance of such first of a kind PHRS of 700 MWe Indian pressurized heavy water reactor (IPHWR) is investigated. For this purpose, a coupled two-phase NCL code is developed, validated and employed. This PHRS consists of two NCLs connected in HHVC-VHVC (horizontal heater and vertical cooler - vertical heater and vertical cooler) configuration. In this study, initially, the full power reactor thermal-hydraulic conditions are established, subsequently, the performance of the PHRS under SBO is investigated. The analyses demonstrated that the system is adequate to effectively eradicate the decay heat by establishing single and two-phase natural circulation (NC) flows of ∼4.5 % and ∼5.6 % of rated conditions in primary and secondary transport loops, respectively. The maximum PHRS capability is predicted to be ∼72 MWth which is more than 3 % of full power. The transients of flow, pressure, energy transfer and heat sink thermal conditions are analyzed when the system changes from forced to NC. Further, the influence of system conditions such as loop diameter, loop height, sink volume, sink temperature and heat exchanger area on PHRS performance was predicted and the changes in system parameters were analyzed. The vital parameters influencing the PHRS performance are identified based on sensitivity analyses. This investigation provides significant insight into the flow and heat transfer characteristics of two-phase and coupled NCLs in the PHRS.

Pricing Decisions in Dual-Channel Supply Chains Considering the Offline Channel Preference and Service Level

With the rapid development of e-commerce, the online channels encroaching on the offline sales market are becoming more serious, which will definitely harm the offline market. Moreover, there exists a certain percentage of consumers (mostly elderly people) who are not able to purchase online because they lack digital skills. Therefore, understanding the impact of the purchase channel preference and service level on pricing decisions is vital for the dual-channel supply chain management. Focusing on the channel preference and service level, we first develop an optimal pricing model containing centralized and decentralized decision-making for an online and offline retailer by deploying the Stackelberg game. We first develop a Stackleberg game to capture such a dual-channel supply chain with the offline channel preference and service level. Secondly, under centralized decision-making, we derive the optimal retail prices and obtain the optimal total profit. Thirdly, under decentralized decision-making, we obtain the optimal retail prices and optimal total profit as well. Moreover, extensive monotonicity properties when system parameters change are obtained. Relying on the theoretical results, firstly, we show that the improvement of the offline service level would lead to higher pricing of the commodities for both online and offline channels. From our numerical results, when the service level is improved, the offline and online optimal pricing increases by 47.5% and 31.1%, respectively, which may contradict the conventional belief that the improvement of one channel would harm another one. Secondly, we demonstrate that the benefit of improving the offline service level has a diminishing marginal effect. The numerical results show that when the current service level is low, the effectives of improving the service level is roughly five times that when the service level is high. This indicates that the investment in improving the offline service level should not be unlimited. Thirdly, we show that the pricing decision under centralized decision-making should be adopted with the existence of both the offline channel preference and offline service.

A fuzzy-PID controller for load frequency control of a two-area power system using a hybrid algorithm

This paper presents the use of a new hybrid optimization approach known as particle swarm optimization and grey wolf optimizer (PSO-GWO) for improving frequency stability load frequency control (LFC) in tow-area power systems. The approach consists in optimizing the fuzzy proportional-integral-derivative (fuzzy-PID) controller parameters with meta-heuristic hybrid algorithm: PSO-GWO. This technique allows to have dynamic responses with the least possible frequency deviation in very short response times. The approach proposes to controls the tie-line power and the frequency deviation in the considered two-area power systems under variable perturbation in load and changing of system parameters in order to evaluate its effectiveness. The suggested hybrid algorithm-based fuzzy-PID controller is compared with various widely used control methods in the literature such as PID controller and algorithms such as PSO and GWO in order to evaluate its effectiveness and its robustness. Through the simulations carried out on MATLAB/Simulink, the proposed PSO-GWO fuzzy-PID and the objective function exhibit improved performance, achieving minimal objective values. The proposed technique proved to be quite powerful tool in the resolution of problems related to electrical power systems, particularly in load frequency control.

Inventory rationing, admission control, and production capacity allocation in a make‐to‐stock/make‐to‐order manufacturing system

AbstractThis paper considers a manufacturing system in which products are produced in both make‐to‐stock (MTS) and make‐to‐order (MTO) modes. Production of MTS and MTO products is done in batches, incurs a setup cost, and is non‐preemptive. The inventory of MTS products fulfills the demand of multiple classes, and each class demand can be satisfied or rejected. Customer orders for MTO production can be accepted or rejected, and their size is the same as the production batch. The primary goal of this paper is to study a policy that coordinates inventory rationing, admission control, and production capacity allocation to maximize the system's profit. We formulate the problem as a Markov decision process model and identify the structure of optimal control policies. We investigate the effect of inventory rationing on the profit by comparing its performance to that of the system with a first‐come‐ first‐serve policy to allocate inventory to multiple demand classes and study the extent to which the benefit of inventory rationing can be affected by system parameter changes. We also propose a heuristic that manages control decisions from linear threshold functions. Our test results from numerical examples show that the average percentage difference between the optimal and heuristic policies is within 1.2%.

Nonparametric statistical analysis of system resilience migration and application for electric distribution structures

This paper proposes a set of nonparametric statistical tools for analyzing the system resilience of civil structures and infrastructure and its migration upon changes in critical system parameters. The work is founded on the classic theoretic framework that system resilience is defined in multiple dimensions for a constructed system. Consequentially, system resilience can lose its parametric form as a random variable, falling into the realm of nonparametric statistics. With this nonparametric shift, traditional distribution-based statistics are ineffective in characterizing the migration of system resilience due to the variation of system parameters. Three statistical tools are proposed under the nonparametric statistical resilience analysis (npSRA) framework, including nonparametric copula-based sensitivity analysis, two-sample resilience test analysis, and a novel tool for resilience attenuation analysis. To demonstrate the use of this framework, we focus on electric distribution systems, commonly found in many urban, suburban, and rural areas and vulnerable to tropical storms. A novel procedure for considering resourcefulness parameters in the socioeconomic space is proposed. Numerical results reveal the complex statistical relations between the distributions of system resilience, physical aging, and socioeconomic parameters for the power distribution system. The proposed resilience distance computing and resilience attenuation analysis further suggests two proper nonparametric distance metrics, the Earth Moving Distance (EMD) metric and the Cramévon Mises (CVM) metric, for characterizing the migration of system resilience for electric distribution systems.

Modeling the dynamic interaction between machine tools and their foundations

The performance of a machine tool is directly influenced by the characteristics of the floor, subsoil, and their interaction with the installed machine. Installing a machine tool in its operational environment poses a distinct challenge that bridges mechanical and civil engineering disciplines. This interdisciplinary issue is often overlooked within the individual separate disciplines. However, effectively addressing this challenge requires a comprehensive understanding of mechanical and civil engineering principles. To address this problem, the present study proposes a method for improved modeling of the dynamic properties of the machine tool by considering the foundation and the subsoil on which it is installed. The method is based on finite element modeling. Linear models of the system components and the connections between them were used. These, supplemented with damping expressed by complex stiffness, made it possible to determine the natural frequencies, mode shapes, and frequency response functions (based on which the transmissibilities were obtained). Based on the experimentally verified models of vertical and horizontal lathes, the sensitivity analysis aimed at estimating the impact of changes in system parameters on vibration transmissibility for a floor-type and a block-type foundation was conducted. Thus, it was possible to identify those machine tool-support-foundation-subsoil system parameters that had the most significant impacts on the vibration's transmissibility. After analyzing the cases discussed, it became evident that the transmissibility of vibrations is primarily influenced by two key factors. First and foremost, the properties of the structural loop of the machine tool played a significant role. Additionally, the characteristics of the subsoil on which the foundation was situated emerged as a crucial determinant in the observed vibration transmissibility.

Load frequency control in power systems with high renewable energy penetration: A strategy employing P[formula omitted](1+PDF) controller, hybrid energy storage, and IPFC-FACTS

The high penetration of Renewable Energy Sources (RESs) in the modern power system poses a challenge to power system stability. This stability is affected by the stochastic, fluctuating output of RESs, which is influenced by weather conditions, and a lack of inertia resulting from reduced rotating mass. To address this issue, a new controller, referred to as Proportional-Fractional Integrator Plus Proportional-Derivative with Filter, PIλ(1+PDF), is designed for Load Frequency Control (LFC) with the support of a Hybrid Energy Storage System (HESS) for power systems with high-RES penetration. The HESS comprises a Superconducting Magnetic Energy Storage System (SMES) and a Vanadium Redox Flow Battery (VRFB) coupled with an Interline Power Flow Controller Flexible AC Transmission Systems (IPFC-FACTs) controller. The HESS, working in conjunction with the proposed LFC, injects virtual inertia and maintains power flow to expedite the frequency stability process. These systems are also integrated with Alternating Current (AC) and High Voltage Direct Current (HVDC) transmission lines to collectively enhance both the system's stability and the capacity of its transmission lines. To optimize the PIλ(1+PDF) controller parameters, Zebra Optimization Algorithm (ZOA) is employed utilizing an Integral Time Absolute Error (ITAE) objective function. The proposed controller is tested on a four-area power system integrated with a wind turbine, photovoltaic (PV) panels, a biodiesel generator, and a hydrogen aqua electrolyzer fuel cell, representing a high penetration of RESs in modern power systems. The results are compared with those obtained using Proportional-Integral-Derivative (PID) and Fractional Order Proportional Integral Derivative (FOPID) controllers. Sensitivity analysis and robustness tests are also performed to verify the stability of the power network by changing system parameters and under randomly chosen loading conditions. The proposed PIλ(1+PDF) controller tuned with ZOA outperforms PID and FOPID controllers by minimizing settling time for frequency changes by 62 %, eliminating overshoot, and reducing undershoots for frequency and tie-line power changes by 73 % and 55 %, respectively. Simulation results demonstrate that the proposed controller outperforms PID and FOPID controllers by effectively damping frequency and tie-line deviations, resulting in reduced frequency overshoots, undershoots, and shorter settling times.

Detection of Demagnetization Faults in Electric Motors by Analyzing Inverter Based Current Data Using Machine Learning Techniques

Demagnetization of the rotor magnets is a significant failure mode that can occur in permanent magnet synchronous machines (PMSMs). Early detection of demagnetization faults can help change system parameters to reduce power output or ensure safety. In this paper, the effects of demagnetization faults were analyzed both in simulation and experiments using the example of drone motors. An approach was investigated to detect even minor demagnetization faults that does not require any additional sensing effort. Machine learning (ML) techniques are used to analyze the phase current data directly received from the inverter to enable anomaly detection. For this purpose, the phase current is transformed by the Fast Fourier Transform (FFT), the spectral data is then reduced in dimensionality, followed by an anomaly detection algorithm using a one-class support vector machine (OC-SVM). To ensure simplified initialization of the ML model without the need for training sets of damaged drives, only data from magnetically undamaged motors was used to train the models for anomaly detection. Different selections of considered harmonics and different metrics were investigated using the experimental data, achieving a precision of up to 99%, a specificity of up to 98%, and an accuracy of up to 90%.

Performance evaluation of the SMART passive safety injection system against the SBLOCA scenario with the SMART-ITL facility

A set of system performance tests has been performed and the thermal-hydraulic behaviors were investigated for the passive safety injection system (PSIS) of the SMART design. Major thermal-hydraulic characteristics of core cooling in reactor coolant system and safety injection (SI) in PSIS were investigated using the SMART-ITL. The parametric effects of break size, break location and train number on the PSIS performance were discussed with the relevant SMART-ITL data. Compared with the 2.0 inch SI line break test, the 0.4 inch SI line break test has a milder change of system parameters. The pressure is reduced more quickly, the decrease of water level is slower and the break mass flow rate is higher during the pressurizer safety valve line break than during the SI line break. Major thermal-hydraulic parameters were compared among single, two and full train operation of the PSIS focusing on the effect of train number. It was shown that multiple trains of CMT and SIT were operated independently and the reactor vessel (RV) inventory increased proportionally with the addition of each train. Sufficient SI water was injected into the RV in a passive manner to cool down the reactor core efficiently during the full train test.

Evolutionary Implications of Self-Assembling Cybernetic Materials with Collective Problem-Solving Intelligence at Multiple Scales.

In recent years, the scientific community has increasingly recognized the complex multi-scale competency architecture (MCA) of biology, comprising nested layers of active homeostatic agents, each forming the self-orchestrated substrate for the layer above, and, in turn, relying on the structural and functional plasticity of the layer(s) below. The question of how natural selection could give rise to this MCA has been the focus of intense research. Here, we instead investigate the effects of such decision-making competencies of MCA agential components on the process of evolution itself, using in silico neuroevolution experiments of simulated, minimal developmental biology. We specifically model the process of morphogenesis with neural cellular automata (NCAs) and utilize an evolutionary algorithm to optimize the corresponding model parameters with the objective of collectively self-assembling a two-dimensional spatial target pattern (reliable morphogenesis). Furthermore, we systematically vary the accuracy with which the uni-cellular agents of an NCA can regulate their cell states (simulating stochastic processes and noise during development). This allows us to continuously scale the agents' competency levels from a direct encoding scheme (no competency) to an MCA (with perfect reliability in cell decision executions). We demonstrate that an evolutionary process proceeds much more rapidly when evolving the functional parameters of an MCA compared to evolving the target pattern directly. Moreover, the evolved MCAs generalize well toward system parameter changes and even modified objective functions of the evolutionary process. Thus, the adaptive problem-solving competencies of the agential parts in our NCA-based in silico morphogenesis model strongly affect the evolutionary process, suggesting significant functional implications of the near-ubiquitous competency seen in living matter.

Spectral Efficiency Maximization for Mixed-Structure Cognitive Radio Hybrid Wideband Millimeter-Wave Transceivers in Relay-Assisted Multi-User Multiple-Input Multiple-Output Systems.

This paper proposes a cognitive radio network (CRN)-based hybrid wideband precoding for maximizing spectral efficiency in millimeter-wave relay-assisted multi-user (MU) multiple-input multiple-output (MIMO) systems. The underlying problem is NP-hard and non-convex due to the joint optimization of hybrid processing components and the constant amplitude constraint imposed by the analog beamformer in the radio frequency (RF) domain. Furthermore, the analog beamforming solution common to all sub-carriers adds another layer of design complexity. Two hybrid beamforming architectures, i.e., mixed and fully connected ones, are taken into account to tackle this problem, considering the decode-and-forward (DF) relay node. To reduce the complexity of the original optimization problem, an attempt is made to decompose it into sub-problems. Leveraging this, each sub-problem is addressed by following a decoupled design methodology. The phase-only beamforming solution is derived to maximize the sum of spectral efficiency, while digital baseband processing components are designed to keep interference within a predefined limit. Computer simulations are conducted by changing system parameters under different accuracy levels of channel-state information (CSI), and the obtained results demonstrate the effectiveness of the proposed technique. Additionally, the mixed structure shows better energy efficiency performance compared to its counterparts and outperforms benchmarks.

Dynamic model of the functional parameter degradation changes in radio-electronic systems with two non-stationary random components corresponding to two types of noise

Problem statement. The processes of aging and degradation caused by the influence of environmental conditions, leading to changes in the functional parameters of radio-electronic systems, negatively affect the output characteristics and reduce the period of operation. The currently existing model of changes in radio-electronic system parameters, used for individual forecasting by extrapolation, includes only one random component representing various types of noise. At the same time, practical studies shows that the maximum amplitudes of different types of noise change at different rates. Therefore, for a more complete representation of the process of electromagnetic parameter changes over time, it is necessary to develop a model that takes into account this effect. The article discusses this problem using the example of the linear stabilizer output voltage. Goal. The main goal of the research presented in this article is to develop a model of functional parameter degradation that takes into account changes in maximum amplitudes of various types of noise in order to increase the accuracy of individual forecasting and the potential period of the device operation. Results. A generalized model of the electromagnetic parameter changes of the radio-electronic systems with two nonstationary random components characterizing various types of noise is presented. A software implementation of the presented model successfully verified on experimental data. Practical significance. The extended model of the functional parameter changes in radio-electronic systems can be used both in individual forecasting by extrapolation, and in the development of systems with a long period of operation, as well as devices intended for autonomous use. The prospects of using the presented model to simulate changes in functional parameters and determine the possibilities of compensating these changes are shown on the example of a frequency synthesizer with an automatic output power control system.

Nonlinear classifiers for wet-neuromorphic computing using gene regulatory neural network

The gene regulatory network (GRN) of biological cells governs a number of key functionalities that enable them to adapt and survive through different environmental conditions. Close observation of the GRN shows that the structure and operational principles resemble an artificial neural network (ANN), which can pave the way for the development of wet-neuromorphic computing systems. Genes are integrated into gene-perceptrons with transcription factors (TFs) as input, where the TF concentration relative to half-maximal RNA concentration and gene product copy number influences transcription and translation via weighted multiplication before undergoing a nonlinear activation function. This process yields protein concentration as the output, effectively turning the entire GRN into a gene regulatory neural network (GRNN). In this paper, we establish nonlinear classifiers for molecular machine learning using the inherent sigmoidal nonlinear behavior of gene expression. The eigenvalue-based stability analysis, tailored to system parameters, confirms maximum-stable concentration levels, minimizing concentration fluctuations and computational errors. Given the significance of the stabilization phase in GRNN computing and the dynamic nature of the GRN, alongside potential changes in system parameters, we utilize the Lyapunov stability theorem for temporal stability analysis. Based on this GRN-to-GRNN mapping and stability analysis, three classifiers are developed utilizing two generic multilayer sub-GRNNs and a sub-GRNN extracted from the Escherichia coli GRN. Our findings also reveal the adaptability of different sub-GRNNs to suit different application requirements.

An Improved Predictive Controller for PMSM based on Dual-Channel Feedback

Abstract High-performance and high-power-density DC servo drives of permanent magnet synchronous motors (PMSM) are applied to the military, especially in airborne optoelectronic pods, light mines, and launch turntables in the aerospace field. The above application scenarios require the volume of drivers to be smaller and have large power outputs. In the field of pods and light mines, high bandwidth and good tracking performance are required, especially in terms of steady-state accuracy. Since light mines are used for target tracking scanning, the errors at the end of the optical rotating mechanism are amplified tens or even hundreds of times after transmission through the line of sight to the target end. Therefore, the requirements for steady-state accuracy are extremely high. In this article, a predictive control method is proposed, using a sliding mode variable structure controller to make up for the error arising from system parameter changes in predictive control. Finally, a dual-channel rotary transformer feedback position coarse-fine combination algorithm method is studied to reduce the angular measurement error and further improve steady-state accuracy.

Effects of high temperature and LPS injections on the hemocytes of the crab Neohelice granulata

Temperature fluctuations, particularly elevated temperatures, can significantly affect immune responses. These fluctuations can influence the immune system and alter its response to infection signals, such as lipopolysaccharide (LPS). Therefore, this study was designed to investigate how high temperatures and LPS injections collectively influence the immune system of the crab Neohelice granulata. Two groups were exposed to 20 °C (control) or 33 °C for four days. Subsequently, half were injected with 10 μL of physiological crustacean (PS), while the rest received 10 μL of LPS [0.1 mg.kg−1]. After 30 min, the hemolymph samples were collected. Hemocytes were then isolated and assessed for various parameters using flow cytometry, including cell integrity, DNA fragmentation, total hemocyte count (THC), differential hemocyte count (DHC), reactive oxygen species (ROS) level, lipid peroxidation (LPO), and phagocytosis. Results showed lower cell viability at 20 °C, with more DNA damage in the same LPS-injected animals. There was no significant difference in THC, but DHC indicated a decrease in hyaline cells (HC) at 20 °C following LPS administration. In granular cells (GC), an increase was observed after both PS and LPS were injected at the same temperature. In semi-granular cells (SGC), there was a decrease at 20 °C with the injection of LPS, while at a temperature of 33 °C, the SGC there was a decrease only in SGC injected with LPS. Crabs injected with PS and LPS at 20 °C exhibited higher levels of ROS in GC and SGC, while at 33 °C, the increase was observed only in GC and SGC cells injected with LPS. A significant increase in LPO was observed only in SGC cells injected with PS and LPS at 20 °C and 33 °C. Phagocytosis decreased in animals at 20 °C with both injections and exposed to 33 °C only in those injected with LPS. These results suggest that elevated temperatures induce changes in immune system parameters and attenuate the immune responses triggered by LPS.

Combination of fuzzy control and reinforcement learning for wind turbine pitch control

Abstract The generation of the pitch control signal in a wind turbine (WT) is not straightforward due to the nonlinear dynamics of the system and the coupling of its internal variables; in addition, they are subjected to the uncertainty that comes from the random nature of the wind. Fuzzy logic has proved useful in applications with changing system parameters or where uncertainty is relevant as in this one, but the tuning of the fuzzy logic controller (FLC) parameters is neither straightforward nor an easy task. On the other hand, reinforcement learning (RL) allows systems to automatically learn, and this capability can be exploited to tune the FLC. In this work, a WT pitch control architecture that uses RL to tune the membership functions and scale the output of a fuzzy controller is proposed. The RL strategy calculates the fuzzy controller gains in order to reduce the output power error of the WT according to the wind speed. Different reward mechanisms based on the output power error have been considered. Simulation results with different wind profiles show that this architecture performs better (123.7 W) in terms of power errors than an FLC without RL (133.2 W) or a simpler PID (208.8 W). Even more, it provides a smooth response and outperforms other hybrid controllers such as RL-PID and radial basis function neural network control.