System State Changes Research Articles

Optimizing Resource Scheduling for Multi-Scenario Mixed Service Groups under Edge Cloud-Native Environments Using Simulation Learning

The evolution of cloud and edge computing technologies has brought about resource management challenges. Traditional resource scheduling strategies fall short in dynamic cloud-edge environments, one of the challenges is identifying system state changes in multi-scenario edge cloud-native environments. The dynamic orchestration and deployment of container resources are crucial. To address this issue, we introduce a virtual environment, which generates interactions of multi-scenario mixed service groups. Furthermore, we proposed a multi-agent adversarial imitation learning approach, which is trained in the virtual environment. Experiments reveal that our approach, which is fully trained in the virtual mixed-service environment, results in no physical sampling costs and significantly outperforms traditional supervised approaches.

Dynamic Survivability Centrality in Nonlinear Oscillator Systems

In light of the fact that existing centrality indexes disregard the influence of dynamic characteristics and lack generalizability due to standard diversification, this study investigates dynamic survivability centrality, which enables quantification of oscillators’ capacity to impact the dynamic survivability of nonlinear oscillator systems. Taking an Erdős–Rényi random graph system consisting of Stuart–Landau oscillators as an illustrative example, the typical symmetry synchronization is considered as the key mission to be accomplished in light of the study and the dynamic survivability centrality value is found to be dependent on both the system size and connection density. Starting with a small scale system, the correctness of the theoretical results and the superiority in comparison to traditional indexes are verified. Further, we present the quantitative results by means of error analysis, distribution comparison of various indexes and relationship with system structure exploration, and give the position of the key oscillator. The results demonstrate a negligible error between the theoretical and numerical outcomes, and highlighting that the distribution of dynamic survivability centrality closely resembles the distribution of system state changes. The conclusions serve as evidence for the accuracy and validity of the proposed index. The findings provide an effective approach to protect systems to improve dynamic survivability.

A study on the Practical Group work to Enhance the Resilience of "Quasi-Family" Children's Welfare Institutions

Orphans and disabled children are a special group of children, and the welfare of orphans and disabled children is an important part of the social security system. At present, there are mainly institutional care, foster family care, "quasi-family" care, and adoption. Among them, "quasi-family" is an innovative model that recruits loving and responsible parents from the society to form a simulated family to raise orphans and disabled children, which supplements the family support that children lack and allows them to grow up in a warm and loving life and interaction. However, in practice, it is also found that the "quasi-family" parenting model still has problems in family organization model, family belief system, family communication ability, etc. Therefore, using group work methods, guided by family resilience theory, and starting from the perspective of strengths, professional social work services are provided to quasi-family, promoting changes in quasi-family belief system, organization model and communication status, so as to improve the quality of raising orphans and disabled children.

Sliding-mode control for target tracking of omnidirectional mobile robots subject to impulsive deception attacks

This article focuses on the target tracking of omnidirectional mobile robots (OMRs) subject to impulsive deception attacks. The impulsive deception attacks describe the cyber attacks causing instantaneous changes of system states at uncertain moments. To resist such kind of attacks, sliding-mode control (SMC) strategies are designed for the global asymptotic stability of the tracking errors of the OMRs. With the designed sliding-mode controller, the OMRs are capable of tracking the fixed and time-varying target in an experiment platform consisting of OMRs, wireless router and control center, which shows the robustness of the system with respect to impulsive deception attacks and the effectiveness of theoretical results.

Intratumoral microbiota associates with systemic immune inflammation state in nasopharyngeal carcinoma

BackgroundThe nasopharynx serves as a crucial niche for the microbiome of the upper respiratory tract. However, the association between the intratumoral microbiota and host systemic inflammation and immune status in nasopharyngeal carcinoma (NPC) remain uncertain. MethodsWe performed 5R 16S rDNA sequencing on NPC tissue samples, followed by diversity analysis, LEfSe differential analysis, and KEGG functional prediction. The analyses were based on indices such as AISI, SIRI, PAR, PLR, and NAR. Correlation analyses between microbes and these indices were performed to identify microbes associated with inflammation and immune status. Additionally, regression analysis based on tumor TNM stage was performed to identify key microbes linked to tumor progression. The head and neck squamous cell carcinoma (HNSC) transcriptome and the paired HNSC microbiome data from TCGA were utilized to validate the analyses. ResultsThe Proteobacteria, Actinobacteria, Firmicutes, and Bacteroidetes were the most enriched phyla in NPC tissues. Microbes within these phyla demonstrated high sensitivity to changes in host systemic inflammation and immune status. Proteobacteria and Firmicutes showed significant differences between inflammation groups. Actinobacteria varied specifically with platelet-related inflammatory indices, and Bacteroidetes genera exhibited significant differences between NAR groups. Corynebacterium and Brevundimonas significantly impacted the T stage of tumors, with a high load of Corynebacterium within tumors associated with a better prognosis ConclusionOur analysis indicates that Proteobacteria play a crucial role in the inflammatory state of NPC, while Bacteroidetes are more sensitive to the tumor immune status.

Investigation of associative rule search method for detection of cyber incidents in information management systems and security events using CICIDS2018 test data set

Automated rule generation for cyber incident identification in information management and security event systems (SIEM, SYSTEM, etc.) plays a crucial role in modern cyberspace defense, where data volumes are exponentially increasing, and the complexity and speed of cyber-attacks are constantly rising. This article explores approaches and methods for automating the process of cyber incident identification rule generation to reduce the need for manual work and ensure flexibility in adapting to changes in threat models. The research highlights the need for utilizing modern techniques of Intelligent Data Analysis (IDA) to process large volumes of data and formulate behavior rules for systems and activities in information systems. The conclusion emphasizes the necessity of integrating multiple research directions, including analyzing existing methods and applying IDA algorithms to search for associative rules from large datasets. Key challenges addressed include the complexity of data modeling, the need to adapt to changes in data from dynamic cyber attack landscapes, and the speed of rule generation algorithms for their identification. The issue of the "dimensionality curse" and the identification of cybersecurity event sequences over time, particularly relevant to SIEM, are discussed. The research objective is defined as the analysis and evaluation of various mathematical methods for automated associative rule generation to identify cyber incidents in SIEM. The most effective strategies for enhancing the efficiency of associative rule generation and their adaptation to the dynamic change of the cybersecurity system state are identified to strengthen the protection of information infrastructure.

Autonomic biosignals, seizure detection, and forecasting.

Wearable devices have attracted significant attention in epilepsy research in recent years for their potential to enhance patient care through improved seizure monitoring and forecasting. This narrative review presents a detailed overview of the current clinical state of the art while addressing how devices that assess autonomic nervous system (ANS) function reflect seizures and central nervous system (CNS) state changes. This includes a description of the interactions between the CNS and the ANS, including physiological and epilepsy-related changes affecting their dynamics. We first discuss technical aspects of measuring autonomic biosignals and considerations for using ANS sensors in clinical practice. We then review recent seizure detection and seizure forecasting studies, highlighting their performance and capability for seizure detection and forecasting using devices measuring ANS biomarkers. Finally, we address the field's challenges and provide an outlook for future developments.

Security concern and fuzzy output sliding mode load frequency control of power systems

This research investigates the concern of security in multi-area load frequency control (LFC) power systems using fuzzy logic output sliding mode control approach. A motion-trends-based deception attack model is proposed to represent malicious threats from communication networks. The motion-trends-based deception attack model enhances the destructive effect of deception attacks by monitoring changes in system state. In terms of the security concerns, a fuzzy-based output sliding mode control (OSMC) is applied to protect the power system from potential cyber-attacks. We provide sufficient conditions for the power systems stability under motion-trends-based deception attack. An initial controller gain that will bring the power system to stability is calculated by linear matrix inequalities (LMIs). The obtained controller gain provides a design method for fuzzy logic member functions. Fuzzy logic is then used to adjust the resulting controller gain to improve power system performance. The simulation results validate the usefulness of the proposed strategy, and indicate the viability of fuzzy logic in fine-tuning controllers.

Optimizing power sharing and voltage control in DC microgrids using a novel adaptive droop control strategy based on current consensus algorithm

In DC microgrids, a contradiction between power equalization and bus voltage control exists under conventional droop control. To address this issue, this study proposes a current consensus algorithm-based adaptive droop control for hierarchical controlled DC microgrids. The strategy includes primary, secondary, and current-consensus algorithms. In the primary and secondary control layers, an improved droop control strategy is realized, where the secondary layer provides adaptive parameters for primary control. Also, a current consensus algorithm is suggested to ensure equal current sharing, where the iterative calculation results are sent as the input for two control layers. Based on the novel strategy, a small signal analysis is carried out to investigate the relationship between system stability and controller parameters. Finally, simulation and experiments are conducted to assess the effectiveness of the proposed control strategy in complex operating conditions such as resistive load step-change and plug-and-play situation. Experiment and simulation results indicate that the novel control strategy guarantees accurate bus voltage control with a maximum steady-state error of 1.58 % and simultaneously achieves power equalization regardless of line impedance differences and the abrupt change of system status. Moreover, the comparative experiment between the traditional droop control and the proposed strategy verifies the superiority of this novel method.

Application of early warning signs to physiological contexts: a comparison of multivariate indices in patients on long-term hemodialysis.

Early warnings signs (EWSs) can anticipate abrupt changes in system state, known as "critical transitions," by detecting dynamic variations, including increases in variance, autocorrelation (AC), and cross-correlation. Numerous EWSs have been proposed; yet no consensus on which perform best exists. Here, we compared 15 multivariate EWSs in time series of 763 hemodialyzed patients, previously shown to present relevant critical transition dynamics. We calculated five EWSs based on AC, six on variance, one on cross-correlation, and three on AC and variance. We assessed their pairwise correlations, trends before death, and mortality predictive power, alone and in combination. Variance-based EWSs showed stronger correlations (r = 0.663 ± 0.222 vs. 0.170 ± 0.205 for AC-based indices) and a steeper increase before death. Two variance-based EWSs yielded HR95 > 9 (HR95 standing for a scale-invariant metric of hazard ratio), but combining them did not improve the area under the receiver-operating curve (AUC) much compared to using them alone (AUC = 0.798 vs. 0.796 and 0.791). Nevertheless, the AUC reached 0.825 when combining 13 indices. While some indicators did not perform overly well alone, their addition to the best performing EWSs increased the predictive power, suggesting that indices combination captures a broader range of dynamic changes occurring within the system. It is unclear whether this added benefit reflects measurement error of a unified phenomenon or heterogeneity in the nature of signals preceding critical transitions. Finally, the modest predictive performance and weak correlations among some indices call into question their validity, at least in this context.

Multistate Reliability Analysis of Solid Oxide Fuel Cells Using Automatic Spectral Clustering and Neighborhood Rough Sets

To optimize the reliability of solid oxide fuel cells (SOFCs), an analysis method based on automatic spectral clustering and neighborhood rough sets is proposed in this paper. This is the first application of multi-state reliability theory to SOFC systems. Firstly, a state partitioning method based on automatic spectral clustering is proposed to partition the operational data into different classes. Then, the feature extraction method based on the neighborhood rough set is used to find the most sensitive variables to system state changes. Finally, the accuracy of state segmentation and feature extraction is verified by training state classifiers. In addition, the generality of the method is verified by migrating it to another pure hydrogen-fueled SOFC system. The results show that the state segmentation method successfully partitions the electrical characteristics into 10 states. Even the data in the early stage of failure can be well segmented. Moreover, the feature extraction method extracts 6 variables that are most sensitive to state changes, which reduces the number of the variables by 85.7%. The state classifier can achieve over 94% correct state recognition rate within the 30s. Meanwhile, the method has good generality and transferability.

Triggering Cockpit Alerts Using an Eye-Tracking-Based Measure of Monitoring Performance

Abstract: This study explored the potential for enhancing pilot performance via an alerting system that adapts according to an eye-tracking-based measure of monitoring. The novel measure combines gaze analysis with system state assessment to estimate the pilot’s understanding of the current system state. On this basis, an alerting system was developed to direct pilot attention to unnoticed system state changes, thereby improving system state monitoring. In a flight simulator study involving 10 participants in a generic jet cockpit, we compared the adaptive alerting system with a no-assistance condition. Although alerting improved the participants’ performance in two tracking tasks, it adversely impacted performance in a third task. Nonetheless, alerting resulted in a decrease in both variance and mean detection time of critical changes. Participants’ subjective ratings were generally positive, yet they criticized the lack of transparency of the alerting mechanism. Alerts triggered based on eye-tracking and system state show potential for improving operator task performance. Nonetheless, for the system to reach its full performance potential, it is critical that the operator understand the principles underlying the alert triggers. False positives and alert design were identified as key areas for improvement to maintain user trust and task flow.

Detecting shifts in nonlinear dynamics using Empirical Dynamic Modeling with Nested-Library Analysis.

Abrupt changes in system states and dynamical behaviors are often observed in natural systems; such phenomena, named regime shifts, are explained as transitions between alternative steady states (more generally, attractors). Various methods have been proposed to detect regime shifts from time series data, but a generic detection method with theoretical linkage to underlying dynamics is lacking. Here, we provide a novel method named Nested-Library Analysis (NLA) to retrospectively detect regime shifts using empirical dynamic modeling (EDM) rooted in theory of attractor reconstruction. Specifically, NLA determines the time of regime shift as the cutting point at which sequential reduction of the library set (i.e., the time series data used to reconstruct the attractor for forecasting) optimizes the forecast skill of EDM. We illustrate this method on a chaotic model of which changing parameters present a critical transition. Our analysis shows that NLA detects the change point in the model system and outperforms existing approaches based on statistical characteristics. In addition, NLA empirically detected a real-world regime shift event revealing an abrupt change of Pacific Decadal Oscillation index around the mid-1970s. Importantly, our method can be easily generalized to various systems because NLA is equation-free and requires only a single time series.

Strength in numbers: Unleashing the potential of trans-scale scope AMATERAS for massive cell quantification.

Singularity biology is a scientific field that targets drastic state changes in multicellular systems, aiming to discover the key cells that induce the state change and investigate the mechanisms behind them. To achieve this goal, we developed a trans-scale optical imaging system (trans-scale scope), that is capable of capturing both macroscale changes across the entire system and the micro-scale behavior of individual cells, surpassing the cell observation capabilities of traditional microscopes. We developed two units of the trans-scale scope, named AMATERAS-1 and -2, which demonstrated the ability to observe multicellular systems consisting of over one million cells in a single field of view with sub-cellular resolution. This flagship instrument has been used to observe the dynamics of various cell species, with the advantage of being able to observe a large number of cells, allowing the detection and analysis of rare events and cells such as leader cells in multicellular pattern formation and cells that spontaneously initiate calcium waves. In this paper, we present the design concept of AMATERAS, the optical configuration, and several examples of observations, and demonstrate how the strength-in-numbers works in life sciences.

Evaluating goal threat in football using player and ball locations

Goal scoring in football is relatively low but vitally important, hence research has considered how goals are created and scored with measures such as expected goals prevalent. The dynamical systems theoretical perspective, considers a collective system, such as football, as existing in two states, stable (no substantive advantage for either team) or unstable (advantage present). Hence, goal scoring events occur when the system has become unstable, with a “perturbation” the event causing the system state change. Here, a “goal threat” value was calculated every second (scaled from 0 to 100) using the XY coordinates of players and the ball, weighted in relation to proximity to the goal (a potential proxy for the degree of system instability). Video recordings and synchronised Amisco 2D representations of goals (n=64) scored in Swansea City AFC English Premier League 2012/2013 matches (n=20) were analysed using Dartfish v10 Pro software. Each goal was analysed from when the play was judged to be stable (no obvious goal scoring opportunity), or the start of possession, until the goal had been scored. Goals were not always preceded by high goal threat values (maximum goal threat values ranged from 13.4 to 99.0). The authors independently subjectively determined that perturbations occurred up to 7 seconds from when the goal threat value increased by at least 40%. Thus, perturbations were not directly related to goal scoring opportunities. This novel method provides a useful, quantifiable, and simple measure of goal threat that may also aid audience engagement and measure defensive effectiveness

Asynchronous event‐triggered adaptive robust switching filtering for nonlinear industrial cyber physical systems under data injection attacks

AbstractBy fully taking the effects of data injection attacks and nonlinear disturbances into consideration, based on dynamic asynchronous event‐triggered approach, an adaptive robust H‐Infinity switching filtering (ARSF) method is proposed for the stability of industrial cyber physical systems. An adaptive feedback controller utilizing anticipated states and error‐dependent parameters can proficiently counteract the deleterious impacts of data injection attacks originating from the measurement channel. Furthermore, a filter, which includes the value of the measurement signal, is designed to monitor the system's state changes in real‐time and ensure the stable operation of the system under attacks. Based on the design of an adaptive feedback controller and a filter eliminating the effects of data injection attacks originating from the measurement channel, a switching scheme is further proposed for compensating the effect of data injection attacks in the control channel. Finally, simulation tests are conducted by using MATLAB to control the robustness of a practical quadrotor UAV system's angles by adopting ARSF, whose results verify that the correctness and effectiveness of the proposed ARSF.

Adaptive fault-tolerant control based on linear matrix inequalities for actuator failures in steer-by-wire system

Aiming at the vehicle stability and safety problems caused by the actuator failure of Steer-By-Wire (SBW) system, a two-layer control strategy combining the Performance-Preserving Robust Control (PPRC) and Adaptive Fault-Tolerant Control (AFTC) using the Linear Matrix Inequality (LMI) processing is proposed. The PPRC of the SBW system by tracking the ideal reference model is the first layer of control, the vehicle is robust to speed and low failure rate defects of the SBW system actuator within the performance range of the SBW system, and the side slip angle ([Formula: see text]) and yaw rate ([Formula: see text]) of the vehicle can approach optimal values; The second layer of control: AFTC is added to the above PPRC based on the online estimation of the final failure, when a high failure rate failure in the actuator of the SBW system occurs and the performance limit of the PPRC is exceeded, this allows the vehicle state in the high failure rate failure mode of the actuator to still track the specified ideal reference state. Compared with the traditional Fault-Tolerant Control (FTC) that requires Fault Detection and Isolation (FDI), the control method used in this paper does not require accurate fault information, but only uses the system state change generated by the fault as the information to automatically adjust the control input, which can still achieve the purpose of stabilizing the whole SBW system when the actuator fails.

Inter-virus relationships in mixed infections and virus-drought relationships in plants: a quantitative review.

Inter-virus relationships in mixed infections and virus-drought relationships are important in agriculture and natural vegetation. In this quantitative review, we sampled published factorial experiments to probe for relationships against the null hypothesis of additivity. Our sample captured antagonistic, additive and synergistic inter-virus relationships in double infections. Virus-drought relationships in our sample were additive or antagonistic, reinforcing the notion that viruses have neutral or positive effects on droughted plants, or that drought enhances plant tolerance to viruses. Both inter-virus and virus-drought relationships vary with virus species, host plant to the level of cultivar or accession, timing of infection, plant age and trait and growing conditions. The trait-dependence of these relationships has implications for resource allocation in plants. Owing to lagging theories, more experimental research in these fields is bound to return phenomenological outcomes. Theoretical work can advance in two complementary directions. First, the effective theory models the behaviour of the system without specifying all the underlying causes that lead to system state change. Second, mechanistic theory based on a nuanced view of the plant phenotype that explicitly considers downward causation; the influence of the plant phenotype on inter-virus relations and vice versa; the impact of timing, intensity and duration of drought interacting with viruses to modulate the plant phenotype; both the soil (moisture) and atmospheric (vapour pressure deficit) aspects of drought. Theories should scale in time, from short term to full growing season, and in levels of organisation up to the relevant traits: crop yield in agriculture and fitness in nature.

Assessment of antioxidant system status before and after operation in neonates with congenital heart disease.

Oxidative stress is considered to have a significant role in the development of cardiovascular diseases (CVDs) as well as many other diseases. Therefore, the purpose of the study is to evaluate the antioxidant system status at pre- and post-operative period in newborns with congenital heart disease (CHD) requiring operation. Fifty CHD patients participated in this research. Superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GPx), and thiobarbituric acid reactive substances (TBARS) levels were studied in blood samples. RACHS-1 score, blood lactate levels, and hypoxic events were also recorded. Comparisons of antioxidant system parameters were conducted at pre- and post-operative periods and also between exitus and discharged groups. GPx activity and TBARS levels were significantly higher in the pre-operative period than post-operative period though the other antioxidant enzymes were not altered. In pre-operative period, GPx activity was low in addition to rarer hypoxic events in the discharged group. Also, a negative correlation was found between SOD and GPx activities in pre-operative period. The results provide fundamental data showing the lowered GPx activity and TBARS levels considered as sensitive oxidative biomarkers after the operation. It was assumed that antioxidant system parameters might show changes after the operation, and GPx is prominent for resistance to hypoxic conditions. Post-operative reduction of GPx and TBARS levels is significant for evaluating the antioxidant system alterations after the operation. However, further investigations follow long-term duration for post-operative monitoring to estimate how antioxidant system status changes to improve the treatment of the health condition.

Distributed secondary optimal control for self‐maintaining microgrids on pelagic islands

AbstractConsidering the geographic distance, self‐maintaining microgrids are widely built to supply power on pelagic islands, where developing distributed autonomous control strategies while guaranteeing microgrids’ resilience and energy security have gained increasing attention. However, most existing distributed control strategies restore frequencies in an asymptotic manner, which may not meet the requirement for safe and rapid recovery after disasters. Meanwhile, self‐maintaining microgrids have relatively low percentages of fossil fuels. Power sharing among distributed energy sources (DERs) under most existing control relies on the droop coefficients, which may lead to inefficient operations. To overcome these challenges, this paper proposed a distributed control strategy for self‐maintaining microgrids. The frequency restoration time can be explicitly optimized and is robust to changes of system states, which provides faster recovery performance under disasters. The steady‐state powers of different DERs are regulated for different kinds of DERs separately and are independent of the droop coefficients, where the proper use of renewable energy under energy security can be achieved. The proposed control enhances the resilience of microgrids against disasters and meets the requirement of energy security on islands. The algorithms in the paper are verified by both simulation and experiment studies under various conditions.