System Disturbances Research Articles

Stochastic dynamics of coral reef system with stage-structure for crown-of-thorns starfish

The outbreak of crown-of-thorns starfish (CoTS) is one of the most critical biological disturbances in coral reef systems and contributes greatly to the degradation of coral. Although many studies have focused on coral-CoTS interactions, there is no effective method for suppressing CoTS outbreaks in the long term, in part because of incomplete knowledge of their life cycle. In this paper, a stochastic system is formulated to characterize the interactions among coral, immature CoTS, and mature CoTS, where environmental noise is introduced into the growth of coral. The existence and uniqueness of a global positive solution with any given initial value are investigated, sufficient conditions are established for the extinction of CoTS, and the existence and uniqueness of a stable stationary distribution are explored by applying the Markov semigroup theory. In particular, by solving the Fokker–Planck equation, the approximate expression of the probability density function of the distribution around its quasi positive equilibrium is expounded under certain parametric restrictions. By employing Milstein’s higher-order method, numerical simulations are presented to validate the theoretical results. This study reveals that coral are more vulnerable and are more likely to go extinct in the presence of strong noise, while weak noise is more conducive to the existence of a stable stationary distribution, which implies the long-term persistence of coral and CoTS.

The Design and Dynamic Control of a Unified Power Flow Controller with a Novel Algorithm for Obtaining the Least Harmonic Distortion

This study investigates the control and dynamic operation of the Unified Power Flow Controller made of shunt and series converters, a Static Synchronous Compensator, and a Static Synchronous Series Compensator, respectively, connected back-to-back through a common DC-link capacitor. The model of a 48-pulse Voltage Source Converter is constructed from a three-level Neutral Point Clamped converter, which allows the total harmonic distortion to be reduced. An optimal conduction angle tracking system of the three-level inverter is designed to minimize distortion by detecting proper harmonic component elimination. Starting from the six-step modulation strategy, the dq decoupled control schemes of both compensators in open and closed loops are presented. Finally, the MATLAB-Simulink model of the power flow controller is implemented and analyzed. The results show that the controller can track the power changes and apply a suitable voltage to the power system so that the power flow can be controlled. This way, the power flow controller dynamically improves the voltage and power quality across the power network while simultaneously improving the transient stability of the system. It can eliminate all system disturbances resulting from oscillations and harmonics in voltage and current within a very short time. The procedural approach used to model and simulate the Unified Power Flow Controller, as well as the new algorithm used to obtain the harmonic number that minimizes the total harmonic distortion, can be applied to any AC power system.

Connection between gut microbiota and Anorexia Nervosa. Literature review

Introduction: Anorexia Nervosa (AN) stands as a severe mental disorder characterized by the highest mortality rate within the realm of psychiatric conditions. Its etiology encompasses genetic, neurobiological, environmental, and developmental factors. Recent investigations have shed light on the potential impact of the gut microbiota on the genesis and progression of AN.
 Aim of the Study: The aim of this paper is to provide a comprehensive review of the existing findings on relationship between gut dysbiosis and AN. The intention is to present new perspectives that contribute to a more detailed understanding of the multifaceted nature of this serious disorder.
 Description of the State of Knowledge: The gut microbiota takes a central role in modulating various physiological processes. Dysbiosis, which refers to an imbalance in gut microbiota composition, has recently drawn attention because of its association with several mental disorders. In the context of AN, studies have shown reduced microbial diversity and notable changes in specific bacterial populations. The influence of the gut microbiota on AN includes disturbances in the digestive system, changes in eating behaviours, and associations with related conditions such as anxiety and depression. Mechanisms such as the gut-brain axis, hormonal regulation and molecular mimicry contribute to these associations.
 Conclusions: Recognition of the pivotal role played by the gut microbiota in AN opens up avenues for potential therapeutic interventions. Pro- and prebiotics, along with fecal transplantation, emerge as promising modalities in the treatment of AN. Ongoing research is essential to further elucidate this evolving field, ultimately facilitating the development of targeted interventions for individuals with AN.

Expression of Tissue Inhibitors of Metalloproteinases (TIMP-1, TIMP-2, TIMP-3, TIMP-4) in Blood Serum of Patients with Keratoconus.

The etiology of keratoconus is unclear. Current evidence suggests that inflammatory and systemic mechanisms might play a role in its pathophysiology. The proper interaction of proteolytic enzymes-matrix metalloproteinases-and their specific tissue inhibitors (TIMPs) within the cornea is essential in maintaining its structure, transparency and healing processes. The aim of the study was to determine the concentration of the TIMPs TIMP-1, TIMP-2, TIMP-3, and TIMP-4 in the blood serum samples of patients with keratoconus compared to the control group. The study encompassed 132 patients, of which 83 people constituted the study group and 49 the control group. The concentration of selected TIMPs was determined using the Human Magnetic Luminex® Performance Assay method. In the study group, the concentrations of TIMP-1 and TIMP-3 were statistically significantly reduced, and TIMP-2 and TIMP-4 increased compared to the control group. The analysis of individual TIMPs in terms of their usefulness as potential predictors of keratoconus showed high results of diagnostic sensitivity and specificity for all TIMPs, in particular for TIMP-1 and TIMP-2. The above results may indicate systemic disturbances in the TIMPs regulation among keratoconus patients. High diagnostic sensitivity and specificity of all TIMPs, in particular TIMP-1 and TIMP-2, may confirm their participation in the etiopathogenesis of this disease.

Integrated multi-omics analysis reveals gut microbiota dysbiosis and systemic disturbance in major depressive disorder

Major depressive disorder (MDD) involves systemic changes in peripheral blood and gut microbiota, but the current understanding is incomplete. Herein, we conducted a multi-omics analysis of fecal and blood samples obtained from an observational cohort including MDD patients (n = 99) and healthy control (HC, n = 50). 16S rRNA sequencing of gut microbiota showed structural alterations in MDD, as characterized by increased Enterococcus. Metagenomics sequencing of gut microbiota showed substantial functional alterations including upregulation in the superpathway of the glyoxylate cycle and fatty acid degradation and downregulation in various metabolic pathways in MDD. Plasma metabolomics revealed decreased amino acids and bile acids, together with increased sphingolipids and cholesterol esters in MDD. Notably, metabolites involved in arginine and proline metabolism were decreased while sphingolipid metabolic pathway were increased. Mass cytometry analysis of blood immune cell subtypes showed rises in proinflammatory immune subsets and declines in anti-inflammatory immune subsets in MDD. Furthermore, our findings revealed disease severity-related factors of MDD. Interestingly, we classified MDD into two immune subtypes that were highly correlated with disease relapse. Moreover, we established discriminative signatures that differentiate MDD from HC. These findings contribute to a comprehensive understanding of the MDD pathogenesis and provide valuable resources for the discovery of biomarkers.

Event‐triggered disturbance rejection control for Markov jump systems: A GA‐optimized approach

AbstractThis paper investigates the event‐triggered disturbance rejection control problem for discrete‐time Markov jump systems by means of genetic algorithms. Firstly, an extended state observer is developed to estimate the system states and disturbances and construct the event‐triggered disturbance rejection controller. With resort to Lyapunov theory, the uniform ultimate boundedness of both estimated errors and states is guaranteed. The disturbance rejection performance is also analyzed. The genetic algorithm is adopted to avoid conservatism in solving the given nonlinear conditions and achieve a meaningful optimization arising from realizing better system performance. The designed approach not only realizes a trade‐off between the bounds of estimated errors and states but also rejects the disturbance. Finally, an example is given to illustrate the effectiveness of the proposed approach.

Research on Three-Closed-Loop ADRC Position Compensation Strategy Based on Winch-Type Heave Compensation System with a Secondary Component

To mitigate the interference of waves on an offshore operation ship, heave compensation systems find widespread application. The performance of heave compensation systems significantly influences the efficiency and safety of maritime operations. This study established a mathematical model for a winch-based active heave compensation system. It introduced a three-loop active disturbance rejection control (ADRC) strategy that encompasses piston position control, winch speed control, and load-displacement control to enable the real-time estimation and compensation of system disturbances, thereby enhancing the performance of the heave compensation system. To assess the effectiveness of this control strategy, this study employed Matlab/Simulink and AMESim to construct a co-simulation model and conducted a comparative analysis with traditional proportional integral derivative (PID) control systems. The research findings indicate that the three-loop ADRC position control strategy consistently delivered superior compensation performance across various operational scenarios.

Epistemic outsiders: Unpacking and utilising the epistemic dimension of disruptive agency in sustainability transformations

Disruptions (systemic disturbances) are crucial to initiate and accelerate sustainability transformations of large-scale social systems (be they socio-ecological, socio-technical, or socio-institutional). Their emergence, characteristics and effects strongly relate to the role of agents who aim to disrupt and transform the status quo, and which thus possess what we call disruptive agency. In this paper, we highlight the epistemic dimension of disruptive agency in social transformations, first by conceptualizing disruptive agents as epistemic outsiders with respect to the social system that they intend to disrupt and transform, and second by connecting this conceptualization to notions of belief, social practices, social networks, discourses, or institutions. We identify five advantages of this approach. Firstly, it informs and conceptually enables various promising interdisciplinary avenues to explore and potentially influence transformative change towards sustainability. Secondly, an epistemic conception of disruptive agency offers a key for an integrated analysis of the individual and collective levels of agency involved in sustainability transformations. Thirdly, the notion of epistemic outsiders conceptually connects agent positions across system boundaries that are understood to be of crucial importance for sustainability transformations respectively (e.g., “niche innovators” or “regime intermediaries”) but which lack an integrated understanding. Fourthly, an epistemic perspective additionally highlights the changing requirements and challenges resulting in two principal stages of transformations unfolding over time, namely before/after a new epistemic layout is shared by a majority of agents. Finally, the above features allow to derive and conceive of new intervention formats and strategies.

Finite time fault estimation and fault‐tolerant control for a class of uncertain nonlinear systems based on cascaded observers

AbstractIn this paper, a finite time fault estimation and fault‐tolerant control method is proposed for a class of uncertain nonlinear systems that have both actuator faults and sensor faults. First, the outputs of the original system pass through the low‐pass filters to obtain the augmented states, and then an augmented system is composed of the states of the low‐pass filters and the original system, so as to realize the conversion of the sensor faults of the original system into partial actuator faults of the augmented system. Next, design state observers to reconstruct the unknown states of the augmented system, and develop extended state observers to estimate the actuator faults and disturbance of the augmented system. Again, the fault‐tolerant controllers are completed based on the backstepping control and the finite time control, and the finite time convergences of the designed observers and controllers are proved by theoretical analyses. Finally, based on a permanent magnet synchronous motor (PMSM) speed system, the simulation research and dSPACE simulated experiment research are conducted to verify the effectiveness of the proposed control method and its feasibility in the actual application.

Methodology for the Detection and Classification of Power Quality Disturbances Using CWT and CNN

Energy generation through renewable processes has represented a suitable option for power supply; nevertheless, wind generators and photovoltaic systems can suddenly operate under undesired conditions, leading to power quality problems. In this regard, the development of condition-monitoring strategies applied to the detection of power quality disturbances becomes mandatory to ensure proper working conditions of electrical machinery. Therefore, in this work we propose a diagnosis methodology for detecting power quality disturbances by means of the continuous wavelet transform (CWT) and convolutional neural network (CNN). The novelty of this work lies in the image processing that allows us to precisely highlight the discriminant patterns through spectrograms into 2D images; the images are cropped and reduced to a standard size of 128x128 pixels to retain the most relevant information. Subsequently, the identification of six power quality disturbances is automatically performed by a convolutional neural network. The effectiveness of the proposed method is validated under a set of synthetic data as well as a real data set; the obtained results make the proposal suitable for being incorporated into the monitoring of power quality disturbances in renewable energy systems.

Research on the Control Strategy for Handling Stability of Electric Power Steering System with Active Front Wheel Steering Function

<div>Due to the presence of uncertain disturbances in the actual steering system, disturbances in the system may affect the handling stability of the vehicle. Therefore, this article proposes an integrated steering system control strategy with stronger anti-disturbance performance. When disturbances exist in the system, the proposed control strategy effectively reduces the attitude changes during the vehicle steering process. In the upper-level control strategy, a variable transmission ratio curve is designed to coordinate the high-speed handling stability and low-speed steering sensitivity of the vehicle. On this basis, a sideslip angle observer is proposed based on the extended state observation theory, which does not depend on an accurate system model, thus determining the intervention timing of the active front wheel steering system. In the lower-level control strategy, DR-PI/DR-PID controllers are designed for the integrated steering system. Finally, experiments are conducted in the CarSim/Simulink joint simulation environment. The results indicate that compared to traditional PI/PID controllers and advanced ADRC controllers, DR-PI/DR-PID controllers can effectively suppress step disturbances, sinusoidal disturbances, and white noise disturbances while ensuring the dynamic response characteristics of the system. Under double-lane conditions, the root mean square values of the yaw rate and sideslip angle decrease by 4.35% to 12.3% and 3.21% to 7.72%, respectively. Therefore, the designed control strategy can improve handling stability while ensuring the robustness of the system.</div>

Bulk Power Systems Emergency Control Based on Machine Learning Algorithms and Phasor Measurement Units Data: A State-of-the-Art Review

Modern electrical power systems are characterized by a high rate of transient processes, the use of digital monitoring and control systems, and the accumulation of a large amount of technological information. The active integration of renewable energy sources contributes to reducing the inertia of power systems and changing the nature of transient processes. As a result, the effectiveness of emergency control systems decreases. Traditional emergency control systems operate based on the numerical analysis of power system dynamic models. This allows for finding the optimal set of preventive commands (solutions) in the form of disconnections of generating units, consumers, transmission lines, and other primary grid equipment. Thus, the steady-state or transient stability of a power system is provided. After the active integration of renewable sources into power systems, traditional emergency control algorithms became ineffective due to the time delay in finding the optimal set of control actions. Currently, machine learning algorithms are being developed that provide high performance and adaptability. This paper contains a meta-analysis of modern emergency control algorithms for power systems based on machine learning and synchronized phasor measurement data. It describes algorithms for determining disturbances in the power system, selecting control actions to maintain transient and steady-state stability, stability in voltage level, and limiting frequency. This study examines 53 studies piled on the development of a methodology for analyzing the stability of power systems based on ML algorithms. The analysis of the research is carried out in terms of accuracy, computational latency, and data used in training and testing. The most frequently used textual mathematical models of power systems are determined, and the most suitable ML algorithms for use in the operational control circuit of power systems in real time are determined. This paper also provides an analysis of the advantages and disadvantages of existing algorithms, as well as identifies areas for further research.

An Insidious Comorbidity: Depression and Chronic Heart Failure

Depression and heart failure (HF) are in a close bilateral relationship: the appearance of one is potentiated by the other entity and the effects on the outcome are amplified within this ”dangerous liaison”. Depression is present in about one in five HF patients and is severe in almost half of them. The risk factors for depression in HF are female gender, elderly, the severity of HF symptoms, a history of isolated systolic hypertension or coronary heart disease (CHD), previous hospitalization and previous depressive episodes. Depression in HF patients increases medication non-adherence, is associated with poor outcomes after heart transplantation and is predictive of events and mortality in cardiac resynchronization therapy (CRT). The current assumptions related to the physiopathological mechanisms include a common genetic polymorphisms, inflammation, generalized immune system disturbance, disturbances of platelet function, increased catecholamine levels, deamination of monoamines linked with monoamine oxidase activity, a dysregulation of the autonomic nervous system, increased serotonin levels, ischemia, life-style and medication non-compliance, a poor familial support and social isolation. Assessment of cognitive functioning should be part of routine clinical examination in HF, the diagnosis of depression is based on questionnaires (e.g. Patient Health Questionnaire-2 - PHQ-2) and on clinical interview. There is still no consensus on the best therapy for HF patients with depression (non-pharmacological and pharmacological therapies) but, for sure, depression in HF is harder to treat.

Pendidikan Kesehatan Batuk, Pilek Dan Pijat Common Cold Dalam Upaya Mengatasi Ketidakefektifan Bersihan Jalan Napas Pada Balita

Cough and cold symptoms are indicative of respiratory system disturbances that commonly affect toddlers (1-5 years old). Respiratory disorders, or Acute Respiratory Infections (ARIs), are a leading cause of mortality in children. The disease can quickly spread to others through droplets, and its transmission is particularly rapid during the current rainy season due to the weakened immune systems of toddlers. However, parents often perceive coughs and colds as common occurrences, leading them to neglect providing treatment to their children. This becomes problematic when children experience respiratory distress, prompting confusion among parents who then rush their children to the hospital. The community engagement activities involve interactive lectures and training sessions, including role-playing with phantom children, for 15 mothers with toddlers. The activities are conducted over a period of two weeks in Alalak Island RT 3. The mothers' knowledge is assessed before and after the sessions through questionnaires. To evaluate the effectiveness of the common cold massage technique, observations are made following established Standard Operating Procedures (SOPs). The results of the community engagement show an improvement in knowledge levels before and after health education. Regarding the common cold massage technique, a small percentage can perform the procedure directly, while the majority can follow the SOPs using phantom children. It is hoped that the increased knowledge and skills of mothers will enable them to promptly address cough and cold symptoms in their children.

Nidra: An Ayurvedic Perspective

Introduction: Sleep is a natural, essential activity for the restoration of fatigued tissues, with its mechanism not fully elucidated. Ayurveda considers sleep, along with diet and celibacy, as fundamental pillars of life. Materials & Methods: Various literatures like Charaka Samhita, Sushruta Samhita, Astanga Hridaya, modern medical textbooks, journals and online databases are reviewed Results: Proper sleep promotes happiness, nourishment, strength, knowledge, and longevity. Day sleep (Divaswapna) is recommended under specific conditions, aligning with seasonal rhythms. Insomnia (Nidranasa) is linked to Dosha imbalances, mental exhaustion, and systemic disturbances. Discussion: Ayurvedic recommendations for daytime sleep vary based on individual constitutions and health conditions. The approach underscores the significance of balancing these factors for sound and restorative sleep. Conclusion: The Ayurvedic perspective emphasizes obtaining the right amount of sleep at the right time for overall well-being. Sleep is deemed a rejuvenating force, contributing to strength, nourishment, and satisfaction. Individualized regimens are crucial to ensure proper sleep and address potential imbalances, aligning with Ayurvedic principles for holistic health.

Research on primary frequency regulation hybrid control strategy of battery energy storage system in new type power system

The integration of a significant amount of renewable energy into the power system brings uncertainties in terms of source-side output and the balance between source and load supply and demand. This increase in uncertainty, following system disturbances, poses challenges for frequency regulation and stable operation. This paper presents a primary frequency control strategy with energy storage assistance. It employs a combination of droop control and virtual inertia control to effectively modulate the frequency. The strategy utilizes energy storage system monitoring of the power grid’s operating state. In cases of significant disturbances, it employs a combination of droop control and virtual inertia control to mitigate frequency decline and assist in the recovery of system frequency. For minor disturbances, it directly utilizes droop control to maintain system frequency stability. Simulation experiments were conducted on the enhanced IEEE 9-bus system model using Matlab/Simulink. The results demonstrate that the proposed algorithm maintains system frequency stability under different circumstances.

Scale based entropy measures and deep learning methods for analyzing the dynamical characteristics of cardiorespiratory control system in COVID-19 subjects during and after recovery

COVID-19, known as Coronavirus Disease 2019 primarily targets the respiratory system and can impact the cardiovascular system, leading to a range of cardiorespiratory complications. The current forefront in analyzing the dynamical characteristics of physiological systems and aiding clinical decision-making involves the integration of entropy-based complexity techniques with artificial intelligence. Entropy-based measures offer promising prospects for identifying disturbances in cardiorespiratory control system (CRCS) among COVID-19 patients by assessing the oxygen saturation variability (OSV) signals. In this investigation, we employ scale-based entropy (SBE) methods, including multiscale entropy (MSE), multiscale permutation entropy (MPE), and multiscale fuzzy entropy (MFE), to characterize the dynamical characteristics of OSV signals. These measurements serve as features for the application of traditional machine learning (ML) and deep learning (DL) approaches in the context of classifying OSV signals from COVID-19 patients during their illness and subsequent recovery. We use the Beurer PO-80 pulse oximeter which non-invasively acquired OSV and pulse rate data from COVID-19 infected patients during the active infection phase and after a two-month recovery period. The dataset comprises of 88 recordings collected from 44 subjects(26 men and 18 women), both during their COVID-19 illness and two months post-recovery. Prior to analysis, data preprocessing is performed to remove artifacts and outliers. The application of SBE measures to OSV signals unveils a reduction in signal complexity during the course of COVID-19. Leveraging these SBE measures as feature sets, we employ two DL techniques, namely the radial basis function network (RBFN) and RBFN with dynamic delay algorithm (RBFNDDA), for the classification of OSV data collected during and after COVID-19 recovery. To evaluate the classification performance, we employ standard metrics such as sensitivity, specificity, false positive rate (FPR), and the area under the receiver operator characteristic curve (AUC). Among the three scale-based entropy measures, MFE outperformed MSE and MPE by achieving the highest classification performance using RBFN with 13 best features having sensitivity (0.84), FPR (0.30), specificity (0.70) and AUC (0.77). The outcomes of our study demonstrate that SBE measures combined with DL methods offer a valuable approach for categorizing OSV signals obtained during and after COVID-19, ultimately aiding in the detection of CRCS dysfunction.

Development of intelligent protection and automation control systems using fuzzy logic elements

In this article, the causes of technological disturbances in electrical systems are considered, and several characteristic disadvantages of the protection and automation of elements of electrical systems are highlighted. The tendency to decrease the reliability of relay protection associated with the transition from analog to digital types of protection is substantiated. Based on the studied examples, the use of fuzzy logic in protections, the expediency of using fuzzy logic elements in protection devices, and the automation of electrical systems to identify types of short circuits are justified. This article analyzes the most common damages and presents the results of modeling an electrical system with transformer coupling, where all types of asymmetric short circuits were initiated. The dynamics of changes in the symmetrical components of short-circuit currents of the forward, reverse, and zero sequences are determined. Rules have been created for the identification of asymmetric types of short circuits. An algorithm of protection and automation operation using fuzzy logic elements has been developed. The proposed algorithm of protection and automation will reduce the time to determine the type of damage and trigger protections.

Data-Driven Decentralized Online System Identification-Based Integral Model-Predictive Voltage and Frequency Control in Microgrids

This article proposes a decentralized online system identification-based integral model-predictive control strategy for the voltage and frequency regulation in the microgrids (MGs). The main advantage of the proposed architecture is that the approach is inherently adaptive to the changing system conditions and does not require the knowledge of system parameters. In addition, as the decentralized system parameters are identified online utilizing local/global measurements, the controller is robust against unknown system disturbances. The decentralized nature of the proposed architecture is evolved from an extended Kalman filter that is utilized to synchronize the states of the identified model, thereby enabling a plug-and-play capability. The proposed controller is constructed utilizing the identified augmented incremental model that incorporates optimal integral action required to mitigate the steady-state errors in the MG voltage and frequency. Furthermore, controller formulation as a quadratic optimization problem including the constraints limits the control inputs within the bounds during electrical faults, thus assisting the downstream primary controller in accomplishing ride-through capability. The proposed framework is validated utilizing a section of the IEEE 123-bus distribution network for the different grid events including the effect of communication latency. Model-in-the-loop real-time simulation results demonstrate that the proposed framework offers significant transient performance improvement compared to the optimal proportional–integral strategy, especially around 50% faster regulation, when the communication latency is considered.

Rotor-Flux Vector Based Observer of Interior Permanent Synchronous Machine

The sensorless control system of the interior permanent magnet machine (IPMSM) is considered in this paper. The control system is based on classical linear controllers. In IPMSM, there occurs non-sinusoidal distribution of rotor flux together with the slot harmonics, these are treated as control system disturbances. In this case, the classical observer structure in the (d-q) is unstable for the low range of rotor speed resulting in disturbances. This negative effect can be minimized by using the observer structure which is based on the rotor flux vector in (α-β) stationary frame together with the classical rotor speed and position law of estimation. The performance of the observer structure is validated by simulation and experimental results in the sensorless control system with field-oriented control.