Prescribed Performance Tracking Control of Time-Delay Nonlinear Systems With Output Constraints

The problem of prescribed performance tracking control for unknown time-delay nonlinear systems subject to output constraints is dealt with in this paper. In contrast with related works, only the most fundamental requirements, i.e., boundedness and the local Lipschitz condition, are assumed for the allowable time delays. Moreover, we focus on the case where the reference is unknown beforehand, which renders the standard prescribed performance control designs under output constraints infeasible. To conquer these challenges, a novel robust prescribed performance control approach is put forward in this paper.Herein, a reverse tuning function is skillfully constructed and automatically generates a performance envelop for the tracking error. In addition, a unified performance analysis framework based on proof by contradiction and the barrier function is established to reveal the inherent robustness of the control system against the time delays. It turns out that the system output tracks the reference with a preassigned settling time and good accuracy,without constraint violations. A comparative simulation on a two-stage chemical reactor is carried out to illustrate the above theoretical findings.

The Application of Information Systems to Improve Ambulance Response Times in the UK

Emergency ambulance services in the UK are tasked with providing pre-hospital patient care and clinical services with a target response time between call connect to on-scene attendance.In 2017,NHS England introduced four new response time categories based on patient needs.The most challenging is to be on-scene for a life-threatening situation within seven minutes of the call being connected when such calls are random in terms of time and place throughout a large territory.Recent evidence indicates emergency ambulance services regularly fall short of achieving the target ambulance response times set by the National Health Service(NHS).To achieve these targets,they need to undertake transformational change and apply statistical,operations research and artificial intelligence techniques in the form of five separate modules covering demand forecasting,plus locate,allocate,dispatch,monitoring and re-deployment of resources.These modules should be linked in real-time employing a data warehouse to minimise computational data and generate accurate,meaningful and timely decisions ensuring patients receive an appropriate and timely response.A simulation covering a limited geographical area,time and operational data concluded that this form of integration of the five modules provides accurate and timely data upon which to make decisions that effectively improve ambulance response times.

An Improved Text-Based and Image-Based CAPTCHA Based on Solving and Response Time

CAPTCHA is an acronym that stands for Completely Automated Public Turing Test to tell Computers and Humans Apart(CAPTCHA),it is a good example of an authentication system that can be used to determine the true identity of any user.It serves as a security measure to prevent an attack caused by web bots(automatic programs)during an online transaction.It can come as text-based or image-based depending on the project and the programmer.The usability and robustness,as well as level of security,provided each of the varies and call for the development of an improved system.Hence,this paper studied and improved two different CAPTCHA systems(the text-based CAPTCHA and image-based CAPTCHA).The textbased and image-based CAPTCHAwere designed using JavaScript.Response time and solving time are the two metrics used to determine the effectiveness and efficiency of the two CAPTCHA systems.The inclusion of response time and solving time improved the shortfall of the usability and robustness of the existing system.The developed system was tested using 200 students from the Federal College of Animal Health and Production Technology.The results of each of the participants,for the two CAPTCHAs,were extracted from the database and subjected to analysis using SPSS.The result shows that textbased CAPTCHAhas the lowest average solving time(21.3333 s)with a 47.8%success rate while image-based CAPTCHA has the highest average solving time was 23.5138 s with a 52.8%success rate.The average response time for the image-based CAPTCHA was 2.1855 s with a 37.9%success rate lower than the text-based CAPTCHA response time(3.5561 s)with a 62.1%success rate.This indicates that the text-based CAPTCHA is more effective in terms of usability tests while image-based CAPTCHA is more efficient in terms of system responsiveness and recommended for potential users.

Semi-analytical steady-state response prediction for multi-dimensional quasi-Hamiltonian systems

The majority of nonlinear stochastic systems can be expressed as the quasi-Hamiltonian systems in science and engineering. Moreover, the corresponding Hamiltonian system offers two concepts of integrability and resonance that can fully describe the global relationship among the degrees-of-freedom(DOFs) of the system. In this work, an effective and promising approximate semi-analytical method is proposed for the steady-state response of multi-dimensional quasi-Hamiltonian systems. To be specific, the trial solution of the reduced Fokker–Plank–Kolmogorov(FPK) equation is obtained by using radial basis function(RBF) neural networks. Then, the residual generated by substituting the trial solution into the reduced FPK equation is considered, and a loss function is constructed by combining random sampling technique. The unknown weight coefficients are optimized by minimizing the loss function through the Lagrange multiplier method. Moreover, an efficient sampling strategy is employed to promote the implementation of algorithms. Finally, two numerical examples are studied in detail, and all the semi-analytical solutions are compared with Monte Carlo simulations(MCS) results. The results indicate that the complex nonlinear dynamic features of the system response can be captured through the proposed scheme accurately.

Influence of the time delay of signal transmission on synchronization conditions in drive-response systems

Conditions for complete and lag synchronizations in drive-response systems are considered under the unified framework of generalized synchronization. The question is addressed that whether the synchronization conditions achieving complete synchronization is still valid for lag synchronization when the time delay of signal transmission between the drive and response systems increases from 0. Theoretical and numerical results show that whether the synchronization conditions is stable for the influence of the time delay of signal transmission depends on a particular form of equilibria of the drive and response systems. Furthermore, it seems that the less the number of the equilibria of the drive system, the more likely the synchronization conditions are stable for the time delay of signal trans- mission.

Designing Proportional-Integral Consensus Protocols for Second-Order Multi-Agent Systems Using Delayed and Memorized State Information

This paper is concerned with consensus of a secondorder linear time-invariant multi-agent system in the situation that there exists a communication delay among the agents in the network.A proportional-integral consensus protocol is designed by using delayed and memorized state information.Under the proportional-integral consensus protocol,the consensus problem of the multi-agent system is transformed into the problem of asymptotic stability of the corresponding linear time-invariant time-delay system.Note that the location of the eigenvalues of the corresponding characteristic function of the linear time-invariant time-delay system not only determines the stability of the system,but also plays a critical role in the dynamic performance of the system.In this paper,based on recent results on the distribution of roots of quasi-polynomials,several necessary conditions for Hurwitz stability for a class of quasi-polynomials are first derived.Then allowable regions of consensus protocol parameters are estimated.Some necessary and sufficient conditions for determining effective protocol parameters are provided.The designed protocol can achieve consensus and improve the dynamic performance of the second-order multi-agent system.Moreover,the effects of delays on consensus of systems of harmonic oscillators/double integrators under proportional-integral consensus protocols are investigated.Furthermore,some results on proportional-integral consensus are derived for a class of high-order linear time-invariant multi-agent systems.

A Distributionally Robust Optimization Scheduling Model for Regional Integrated Energy Systems Considering Hot Dry Rock Co-Generation

Hot dry rock(HDR)is rich in reserve,widely distributed,green,low-carbon,and has broad development potential and prospects.In this paper,a distributionally robust optimization(DRO)scheduling model for a regionally integrated energy system(RIES)considering HDR co-generation is proposed.First,the HDR-enhanced geothermal system(HDR-EGS)is introduced into the RIES.HDR-EGS realizes the thermoelectric decoupling of combined heat and power(CHP)through coordinated operation with the regional power grid and the regional heat grid,which enhances the system wind power(WP)feed-in space.Secondly,peak-hour loads are shifted using price demand response guidance in the context of time-of-day pricing.Finally,the optimization objective is established to minimize the total cost in the RIES scheduling cycle and construct a DRO scheduling model for RIES with HDR-EGS.By simulating a real small-scale RIES,the results show that HDR-EGS can effectively promote WP consumption and reduce the operating cost of the system.

Practical Prescribed Time Tracking Control With Bounded Time-Varying Gain Under Non-Vanishing Uncertainties

This paper investigates the prescribed-time control(PTC) problem for a class of strict-feedback systems subject to non-vanishing uncertainties. The coexistence of mismatched uncertainties and non-vanishing disturbances makes PTC synthesis nontrivial. In this work, a control method that does not involve infinite time-varying gain is proposed, leading to a practical and global prescribed time tracking control solution for the strict-feedback systems, in spite of both the mismatched and nonvanishing uncertainties. Different from methods based on control switching to avoid the issue of infinite control gain that involves control discontinuity at the switching point, in our method a softening unit is exclusively included to ensure the continuity of the control action. Furthermore, in contrast to most existing prescribed-time control works where the control scheme is only valid on a finite time interval, in this work, the proposed control scheme is valid on the entire time interval. In addition, the prior information on the upper or lower bound of gi is not in need,enlarging the applicability of the proposed method. Both the theoretical analysis and numerical simulation confirm the effectiveness of the proposed control algorithm.

Noise-Tolerant ZNN-Based Data-Driven Iterative Learning Control for Discrete Nonaffine Nonlinear MIMO Repetitive Systems

Aiming at the tracking problem of a class of discrete nonaffine nonlinear multi-input multi-output(MIMO) repetitive systems subjected to separable and nonseparable disturbances, a novel data-driven iterative learning control(ILC) scheme based on the zeroing neural networks(ZNNs) is proposed. First, the equivalent dynamic linearization data model is obtained by means of dynamic linearization technology, which exists theoretically in the iteration domain. Then, the iterative extended state observer(IESO) is developed to estimate the disturbance and the coupling between systems, and the decoupled dynamic linearization model is obtained for the purpose of controller synthesis. To solve the zero-seeking tracking problem with inherent tolerance of noise,an ILC based on noise-tolerant modified ZNN is proposed. The strict assumptions imposed on the initialization conditions of each iteration in the existing ILC methods can be absolutely removed with our method. In addition, theoretical analysis indicates that the modified ZNN can converge to the exact solution of the zero-seeking tracking problem. Finally, a generalized example and an application-oriented example are presented to verify the effectiveness and superiority of the proposed process.

Systemic Inflammation Response Index and weight loss as prognostic factors in metastatic pancreatic cancer: A concept study from the PANTHEIA-SEOM trial

BACKGROUND The prognostic value of the Systemic Inflammation Response Index(SIRI)in advanced pancreatic cancer is recognized,but its correlation with patients´nutritional status and outcomes remains unexplored.AIM To study the prognostic significance of SIRI and weight loss in metastatic pancreatic cancer.METHODS The PANTHEIA-Spanish Society of Medical Oncology(SEOM)study is a multicentric(16 Spanish hospitals),observational,longitudinal,non-interventional initiative,promoted by the SEOM Real World-Evidence work group.This pilot study sought to analyze the association between weight loss and inflammatory status as defined by SIRI.The cohort stems from a proof-of-concept pilot study conducted at one of the coordinating centers.Patients with pathologically confirmed metastatic pancreatic adenocarcinoma,treated from January 2020 to January 2023,were included.The index was calculated using the product of neutrophil and monocyte counts,divided by lymphocyte counts,obtained within 15 days before initiation chemotherapy.This study evaluated associations between overall survival(OS),SIRI and weight loss.RESULTS A total of 50 patients were included.66%of these patients were male and the median age was 66 years.Metastasis sites:36%liver,12%peritoneal carcinomatosis,10%lung,and 42%multiple locations.Regarding the first line palliative chemotherapy treatments:50%received gemcitabine plus nab-paclitaxel;28%,modified fluorouracil,leucovorin,irinotecan and oxaliplatin,and 16%were administered gemcitabine.42%had a weight loss>5%in the three months(mo)preceding diagnosis.21 patients with a SIRI≥2.3×10^(3)/L exhibited a trend towards a lower median OS compared to those with a SIRI<2.3×10^(3)/L(4 vs 18 mo;P<0.000).Among 21 patients with>5%weight loss before diagnosis,the median OS was 6 mo,in contrast to 19 mo for those who did not experience such weight loss(P=0.003).Patients with a weight loss>5%showed higher SIRI levels.This difference was statistically significant(P<0.000).For patients with a SIRI<2.3×10^(3)/L,those who did not lose>5%of their weight had an OS of 20 mo,compared to 11 mo for those who did(P<0.001).No association was found between carbohydrate antigen 19-9 levels≥1000 U/mL and weight loss.CONCLUSION A higher SIRI was correlated with decreased survival rates in patients with metastatic pancreatic cancer and associated with weight loss.An elevated SIRI is suggested as a predictor of survival,emphasizing the need for prospective validation in the upcoming PANTHEIA-SEOM study.

The IDD Transcription Factors:Their Functions in Plant Development and Environmental Response

INDETERMINATE-DOMAIN proteins(IDDs)are a plant-specific transcription factor family characterized by a conserved ID domain with four zinc finger motifs.Previous studies have demonstrated that IDDs coordinate a diversity of physiological processes and functions in plant growth and development,including floral transition,plant architecture,seed and root development,and hormone signaling.In this review,we especially summarized the latest knowledge on the functions and working models of IDD members in Arabidopsis,rice,and maize,particularly focusing on their role in the regulatory network of biotic and abiotic environmental responses,such as gravity,temperature,water,and pathogens.Understanding these mechanisms underlying the function of IDD proteins in these processes is important for improving crop yields by manipulating their activity.Overall,the review offers valuable insights into the functions and mechanisms of IDD proteins in plants,providing a foundation for further research and potential applications in agriculture.

Preoperative systemic inflammatory response index as a prognostic marker for distal cholangiocarcinoma after pancreatoduodenectomy

BACKGROUND The relationship between preoperative inflammation status and tumorigenesis as well as tumor progression is widely acknowledged.AIM To assess the prognostic significance of preoperative inflammatory biomarkers in patients with distal cholangiocarcinoma(dCCA)who underwent pancreat-oduodenectomy(PD).METHODS This single-center study included 216 patients with dCCA after PD between January 1,2011,and December 31,2022.The individuals were categorized into two sets based on their systemic inflammatory response index(SIRI)levels:A low SIRI group(SIRI<1.5,n=123)and a high SIRI group(SIRI≥1.5,n=93).Inflam-matory biomarkers were evaluated for predictive accuracy using receiver operating characteristic curves.Both univariate and multivariate Cox proportional hazards analyses were performed to estimate SIRI for overall survival(OS)and recurrence-free survival(RFS).RESULTS The study included a total of 216 patients,with 58.3%being male and a mean age of 65.6±9.6 years.123 patients were in the low SIRI group and 93 were in the high SIRI group after PD for dCCA.SIRI had an area under the curve value of 0.674 for diagnosing dCCA,showing better performance than other inflammatory biomarkers.Multivariate analysis indicated that having a SIRI greater than 1.5 independently increased the risk of dCCA following PD,leading to lower OS[hazard ratios(HR)=1.868,P=0.006]and RFS(HR=0.949,P<0.001).Additionally,survival analysis indicated a significantly better prognosis for patients in the low SIRI group(P<0.001).CONCLUSION It is determined that a high SIRI before surgery is a significant risk factor for dCCA after PD.

Systemic inflammatory response index is a predictor of prognosis in gastric cancer patients: Retrospective cohort and meta-analysis

BACKGROUND The systemic inflammatory response index(SIRI)has been demonstrated to make a significant difference in assessing the prognosis of patients with different solid neoplasms.However,research is needed to ascertain the accuracy and reliability of applying the SIRI to patients who undergo robotic radical gastric cancer sur-gery.AIM To validate the applicability of the SIRI in assessing the survival of gastric cancer patients and evaluate the clinical contribution of preoperative SIRI levels to predicting long-term tumor outcomes in patients,who received robotic radical gastric cancer surgery.METHODS Initially,an exhaustive retrieval was performed in the PubMed,the Cochrane Library,EMBASE,Web of Science,and Scopus databases to identify relevant studies.Subsequently,a meta-analysis was executed on 6 cohort studies iden-tifying the value of the SIRI in assessing the survival of gastric cancer patients.Additionally,the clinical data of 161 patients undergoing robotic radical gastric cancer surgery were retrospectively analyzed to evaluate their clinicopathological characteristics and relevant laboratory indicators.The association between preoperative SIRI levels and 5-year overall survival(OS)and disease-free survival(DFS)was assessed.RESULTS The findings demonstrated an extensive connection between SIRI values and the outcome of patients with gastric cancer.Preoperative SIRI levels were identified as an independent hazard feature for both OS and DFS among those who received robotic surgery for gastric cancer.SIRI levels in gastric cancer patients were observed to be associated with the presence of comorbidities,T-stage,carcinoembryonic antigen levels,the development of early serious postoperative complications,and the rate of lymph node metastasis.CONCLUSION SIRI values are correlated with adverse in the gastric cancer population and have the potential to be utilized in predicting long-term oncological survival in patients who undergo robotic radical gastric cancer surgery.

Efficient computation for dynamic responses of systems with time-varying characteristics

Based on Neumman series and epsilon-algorithm, an efficient computation for dynamic responses of systems with arbitrary time-varying characteristics is investigated. Avoiding the calculation for the inverses of the equivalent stiffness matrices in each time step, the computation effort of the proposed method is reduced compared with the full analysis of Newmark method. The validity and applications of the proposed method are illustrated by a 4-DOF spring-mass system with periodical time-varying stiffness properties and a truss structure with arbitrary time-varying lumped mass. It shows that good approximate results can be obtained by the proposed method compared with the responses obtained by the full analysis of Newmark method.

Multi-Time Scale Operation and Simulation Strategy of the Park Based on Model Predictive Control

Due to the impact of source-load prediction power errors and uncertainties,the actual operation of the park will have a wide range of fluctuations compared with the expected state,resulting in its inability to achieve the expected economy.This paper constructs an operating simulation model of the park power grid operation considering demand response and proposes a multi-time scale operating simulation method that combines day-ahead optimization and model predictive control(MPC).In the day-ahead stage,an operating simulation plan that comprehensively considers the user’s side comfort and operating costs is proposed with a long-term time scale of 15 min.In order to cope with power fluctuations of photovoltaic,wind turbine and conventional load,MPC is used to track and roll correct the day-ahead operating simulation plan in the intra-day stage to meet the actual operating operation status of the park.Finally,the validity and economy of the operating simulation strategy are verified through the analysis of arithmetic examples.

Cyber Resilience through Real-Time Threat Analysis in Information Security

This paper examines how cybersecurity is developing and how it relates to more conventional information security. Although information security and cyber security are sometimes used synonymously, this study contends that they are not the same. The concept of cyber security is explored, which goes beyond protecting information resources to include a wider variety of assets, including people [1]. Protecting information assets is the main goal of traditional information security, with consideration to the human element and how people fit into the security process. On the other hand, cyber security adds a new level of complexity, as people might unintentionally contribute to or become targets of cyberattacks. This aspect presents moral questions since it is becoming more widely accepted that society has a duty to protect weaker members of society, including children [1]. The study emphasizes how important cyber security is on a larger scale, with many countries creating plans and laws to counteract cyberattacks. Nevertheless, a lot of these sources frequently neglect to define the differences or the relationship between information security and cyber security [1]. The paper focus on differentiating between cybersecurity and information security on a larger scale. The study also highlights other areas of cybersecurity which includes defending people, social norms, and vital infrastructure from threats that arise from online in addition to information and technology protection. It contends that ethical issues and the human factor are becoming more and more important in protecting assets in the digital age, and that cyber security is a paradigm shift in this regard [1].

Introducing the nth-Order Features Adjoint Sensitivity Analysis Methodology for Nonlinear Systems (nth-FASAM-N): II. Illustrative Example

This work highlights the unparalleled efficiency of the “nth-Order Function/ Feature Adjoint Sensitivity Analysis Methodology for Nonlinear Systems” (nth-FASAM-N) by considering the well-known Nordheim-Fuchs reactor dynamics/safety model. This model describes a short-time self-limiting power excursion in a nuclear reactor system having a negative temperature coefficient in which a large amount of reactivity is suddenly inserted, either intentionally or by accident. This nonlinear paradigm model is sufficiently complex to model realistically self-limiting power excursions for short times yet admits closed-form exact expressions for the time-dependent neutron flux, temperature distribution and energy released during the transient power burst. The nth-FASAM-N methodology is compared to the extant “nth-Order Comprehensive Adjoint Sensitivity Analysis Methodology for Nonlinear Systems” (nth-CASAM-N) showing that: (i) the 1st-FASAM-N and the 1st-CASAM-N methodologies are equally efficient for computing the first-order sensitivities;each methodology requires a single large-scale computation for solving the “First-Level Adjoint Sensitivity System” (1st-LASS);(ii) the 2nd-FASAM-N methodology is considerably more efficient than the 2nd-CASAM-N methodology for computing the second-order sensitivities since the number of feature-functions is much smaller than the number of primary parameters;specifically for the Nordheim-Fuchs model, the 2nd-FASAM-N methodology requires 2 large-scale computations to obtain all of the exact expressions of the 28 distinct second-order response sensitivities with respect to the model parameters while the 2nd-CASAM-N methodology requires 7 large-scale computations for obtaining these 28 second-order sensitivities;(iii) the 3rd-FASAM-N methodology is even more efficient than the 3rd-CASAM-N methodology: only 2 large-scale computations are needed to obtain the exact expressions of the 84 distinct third-order response sensitivities with respect to the Nordheim-Fuchs model’s parameters when applying the 3rd-FASAM-N methodology, while the application of the 3rd-CASAM-N methodology requires at least 22 large-scale computations for computing the same 84 distinct third-order sensitivities. Together, the nth-FASAM-N and the nth-CASAM-N methodologies are the most practical methodologies for computing response sensitivities of any order comprehensively and accurately, overcoming the curse of dimensionality in sensitivity analysis.

Event-Triggered Finite-Time H∞ Filtering for Discrete-Time Nonlinear Stochastic Systems

This paper addresses the problem of event-triggered finite-time H∞ filter design for a class of discrete-time nonlinear stochastic systems with exogenous disturbances. The stochastic Lyapunov-Krasoviskii functional method is adopted to design a filter such that the filtering error system is stochastic finite-time stable (SFTS) and preserves a prescribed performance level according to the pre-defined event-triggered criteria. Based on stochastic differential equations theory, some sufficient conditions for the existence of H∞ filter are obtained for the suggested system by employing linear matrix inequality technique. Finally, the desired H∞ filter gain matrices can be expressed in an explicit form.

Finite-Time H∞ Control of Switched Nonlinear Systems under State-Dependent Switching

This paper investigates the finite-time H∞ control problem of switched nonlinear systems via state-dependent switching and state feedback control. Unlike the existing approach based on time-dependent switching strategy, in which the switching instants must be given in advance, the state-dependent switching strategy is used to design switching signals. Based on multiple Lyapunov-like functions method, several criteria for switched nonlinear systems to be finite-time H∞ control are derived. Finally, a numerical example with simulation results is provided to show the validity of the conclusions.

Analysis of response time probability distribution of workflows

An evaluation approach for the response time probability distribution of workflows based on the fluid stochastic Petri net formalism is presented. Firstly, some problems about stochastic workflow net modeling are discussed. Then how to convert a stochastic workflow net model into a fluid stochastic Petri net model is described. The response time distribution can be obtained directly upon the transient state solution of the fluid stochastic Petri net model. In the proposed approach, there are not any restrictions on the structure of workflow models, and the processing times of workflow tasks can be modeled by using arbitrary probability distributions. Large workflow models can be efficiently tackled by recursively using a net reduction technique.