Control Issues Research Articles

Cutting-Edge Approaches in Urinary Incontinence Treatment: A Comprehensive Review

Introduction: Urinary incontinence is a prevalent health issue that profoundly impacts the quality of life for patients worldwide. Traditional treatments, including pharmacotherapy and surgical procedures, often fall short, leaving many individuals struggling with bladder control issues that restrict physical and social activities and cause significant psychological distress. Recent advancements in therapeutic methods—encompassing behavioral therapies, cutting-edge medical technologies, innovative pharmacotherapy, and advanced surgical techniques—offer promising alternatives. By exploring the effectiveness and advantages of these novel approaches, we can enhance treatment accessibility and elevate the quality of care for those affected by urinary incontinence, ultimately improving their daily functioning and overall well-being. Abbreviated description of the state of knowledge:Current research on innovative treatments for urinary incontinence indicates significant advancements in enhancing patients' quality of life. These cutting-edge methods encompass behavioral therapies like pelvic floor muscle training, state-of-the-art medical technologies such as nerve stimulation, new pharmacological treatments with high efficacy and minimal side effects, and advanced surgical interventions, including artificial sphincter implants and urinary tract modifications. Conclusions: Urinary incontinence is a prevalent health issue that significantly deteriorates the quality of life, particularly affecting half of women and one-third of men over the age of 80. As our society ages and the number of individuals affected by this condition rises, understanding and advancing treatment methods become increasingly crucial. Ongoing research aims to discover new and improved therapeutic approaches with minimal side effects, offering hope for enhanced patient outcomes and better management of urinary incontinence in the future.

The Role of Formal Schooling, Literacy, and Health Knowledge in Addressing Domestic Violence Against Women in West Africa.

When "education" is cited as a solution for domestic violence, different aspects of knowledge acquisition are often omitted. This study uses 2019 Demographic and Health Surveys from four West African countries (The Gambia, Liberia, Senegal, Sierra Leone) with a combined sample size of 12,480 women and generalized ordered logit regression to examine the effects of types of knowledge (years of schooling, literacy, and health knowledge) on domestic violence (physical abuse, emotional abuse, and control issues). The results suggest that literacy has the most reliable beneficial impact on domestic violence and was consistently associated with decreased odds of abuse. However, greater findings suggest that schooling, literacy, and health knowledge function as separate types of education and that their relationships are complex and context-specific. By neglecting to see these types of knowledge as separate entities that can operate together, it is possible that mitigation strategies for domestic violence are going undiscovered.

Bumpless transfer control of switched systems: A dynamic event-triggering switching control approach

This paper studies the bumpless transfer (BT) control issue for switched systems with event-triggered (ET) control input. The aim is to reduce the jumps in control input at both switching and triggering points, while capturing the stability of the switched systems. First, a dynamic ET program is constructed with the triggering condition based on the level of suppression of input signal jumps. This not only reduces input jumps at triggering points, but also prevents Zeno behavior caused by triggering within the operation time interval of subsystems. Second, a sampled state-dependent switching signal is designed to ensure a dwell time for subsystems to prevent Zeno phenomenon induced by switching. Further, a switched ET controller with alleviated jumps is designed. Third, conditions are obtained to simultaneously reach both stabilization and BT of switched systems. Finally, the effectiveness of the theoretical results is demonstrated through a simulation of an engine model.

Security and Privacy Perspectives of People Living in Shared Home Environments

Security and privacy (S&P) perspectives of people in a multi-user home are a growing area of research, with many researchers reflecting on the complicated power imbalance and challenging access control issues of the devices involved. However, these studies primarily focused on the multi-user scenarios in traditional family home settings, leaving other types of multi-user home environments, such as homes shared by co-habitants without a familial relationship, under-studied. This paper closes this research gap via quantitative and qualitative analysis of results from an online survey and qualitative content analysis of sampled online posts on Reddit. The study explores the complex roles of shared home users, which depend on various factors unique to the shared home environment, e.g., who owns what home devices, how home devices are used by multiple users, and more complicated relationships between the landlord and people in the shared home and among co-habitants. Half (50.7%) of our survey participants thought that devices in a shared home are less secure than in a traditional family home. This perception was found statistically significantly associated with factors such as the fear of devices being tampered with in their absence and (lack of) trust in other co-habitants and their visitors. We observed cyber-physical threats being a prominent topic discussed in Reddit posts. Our study revealed new user types and user relationships in a multi-user environment such as ExternalPrimary-InternalPrimary while analysing the landlord and shared home resident relationship with regard to shared home device use. Based on the results of the online survey and the Reddit data, we propose a threat actor model for shared home environments, which has a focus on possible malicious behaviours of current and past co-habitants of a shared home, as a special type of insider threat in a home environment. We also recommend further research to understand the complex roles co-habitants can play in navigating and adapting to a shared home environment's security and privacy landscape.

Lessons Learnt Delivering a Novel Infectious Diseases National Training Programme to Timor‑Leste's Primary Care Workforce.

Background and Objectives: Timor‑Leste is a lower‑middle‑income country in Southeast Asia. To control the significant local threat from infectious diseases, it is imperative to strengthen the knowledge and practice capabilities of the primary care workforce. Methods: We report and reflect on the development and delivery of a national training programme in infectious diseases called the Advancing Surveillance and Training to Enhance Recognition of Infectious Diseases (ASTEROID) programme, developed by the medical non‑governmental organisation (NGO) Maluk Timor and other Timorese stakeholders. The 1-week training course delivered by local doctors is multi‑modal, combining lectures with educational videos, interactive sessions and a mobile application. The ongoing training was delivered to every Timorese municipality in the participants' place of work and involved 540 healthcare professionals from 37 facilities. Training covered infectious diseases most relevant to the Timorese workforce, and focused on disease detection, management, prevention (including infection prevention and control issues) and notification. Findings: Multiple choice question (MCQ) assessment during the training has shown an average improvement in test scores from 45% to 64%, improving to 71% and 79% at 3- and 12-month follow‑up respectively. The programme has been well‑received, with participants appreciating the use of local specialists in video content, the tailoring of content to the local context and the variety of educational methods. Difficulties have been faced when it comes to delivering adequate content in a week‑long format to a workforce which has not previously received significant professional development. Conclusions: This approach could provide a model for delivering training to national healthcare workforces in low- and middle‑income countries (LMIC) and could be further refined on the basis of the lessons detailed here.

Prescribed-Time Dynamic Positioning Control for USV with Lumped Disturbances, Thruster Saturation and Prescribed Performance Constraints

This work studies the dynamic positioning (DP) control issue of unmanned surface vessels subjected to thruster saturation, error constraints, and lumped disturbances composed of time-varying marine environmental disturbances and model parameter uncertainties. Combining the disturbance-accurate estimation technique and the prescribed performance control strategy, a novel prescribed-time DP control scheme is established to address this challenging problem. In particular, the prescribed-time lumped disturbance observer is designed to accurately estimate external marine disturbances, which guarantees that the estimation error converges to zero within a prescribed time. Subsequently, a prescribed performance control strategy is proposed to guarantee that the positioning errors of DP surface vessels with thruster saturation constraints meet the error constraints requirements within a prescribed time. Furthermore, an anti-windup compensator is presented to mitigate the thruster saturation and improve the robustness of the DP control system. The stability analysis demonstrates that all positioning errors of the closed-loop system can converge to predefined performance constraints within a prescribed time. Finally, the numerical simulation confirms the efficacy and superiority of the proposed PTDP scheme.

Event-triggered iterative learning containment control for one-sided Lipschitz nonlinear singular switched multi-agent systems with multiple leaders and error quantization

In this work, under a repeatable control environment, the iterative learning containment control issue for multi-agent systems with one-sided Lipschitz nonlinear singular switched dynamics is addressed. It focuses on the output containment for one-sided Lipschitz nonlinear singular switched multi-agent systems (SWMASs) with multiple leaders, for which an event-triggered containment quantized iterative learning control (QILC) scheme is designed. Due to the limited system resources, SWMASs are often restricted in their communication capabilities and the frequency of controller updates in many applications, the proposed event-triggering mechanism can greatly restrict the use of communication and computation resources throughout the iterative learning process. When the triggering requirement is met, the event-triggered QILC input is repeatedly updated; alternatively, the control input stays unchanged. To conquer the nonidentical initial states of followers, an initial state learning with error quantization and event-triggering is also utilized. By virtue of some appropriate learning gains and sufficient convergent conditions, the containment error convergence can be accomplished for one-sided Lipschitz SWMASs. Finally, some numerical examples via Matlab are provided to demonstrate the efficacy of the event-triggered QILC scheme, it indicates that followers’ outputs can converge to the convex hull formed by leaders and the containment errors gradually converge to the 10−3 - vicinity of the origin along the iteration axis, where the communication resources can save more than 50% in the transmission channel.

Reinforcement learning-adaptive fault-tolerant IGC method for a class of aircraft with non-affine characteristics and multiple uncertainties

Abstract In this paper, a brand-new adaptive fault-tolerant non-affine integrated guidance and control method based on reinforcement learning is proposed for a class of skid-to-turn (STT) missile. Firstly, considering the non-affine characteristics of the missile, a new non-affine integrated guidance and control (NAIGC) design model is constructed. For the NAIGC system, an adaptive expansion integral system is introduced to address the issue of challenging control brought on by the non-affine form of the control signal. Subsequently, the hyperbolic tangent function and adaptive boundary estimation are utilised to lessen the jitter due to disturbances in the control system and the deviation caused by actuator failures while taking into account the uncertainty in the NAIGC system. Importantly, actor-critic is introduced into the control framework, where the actor network aims to deal with the multiple uncertainties of the subsystem and generate the control input based on the critic results. Eventually, not only is the stability of the NAIGC closed-loop system demonstrated using Lyapunov theory, but also the validity and superiority of the method are verified by numerical simulations.

Intelligent sliding mode fault-tolerant control for aircraft engines with actuator dynamics and faults based on adaptive dynamic programming

Abstract The fault-tolerant control issue of aircraft engines with actuator dynamics and faults is investigated in this paper. By proposing a novel intelligent sliding mode fault-tolerant control (ISMFTC) method, which combines an adaptive dynamic programming (ADP) sliding surface with Grey Wolf Optimizer (GWO) for controller parameter optimisation, the goal is to achieve quality steady-state and dynamic performance in aircraft engines while maintaining strong fault-tolerance properties. Firstly, by considering not only actuator dynamics but also actuator faults, an uncertain nonlinear cascaded model of aircraft engines is developed according to characteristic of aircraft engines and their actuators. Secondly, an ADP-based sliding surface is proposed for considered aircraft engine uncertain nonlinear cascaded system. It can obtain a certain sense of optimised performance, and could be solved by ADP strategy off-line as well. Thirdly, fault-tolerant controller is obtained on the basis of sliding mode theory and adaptive fault estimation law, namely, ADP-based ISMFTC controller. Meanwhile, GWO is integrated into the investigation of ADP-based ISMFTC controller, optimised designable control parameters are obtained subsequently. Besides, robustness analysis is elaborated according to Lyapunov theory, fault estimation error is bounded and states of closed-loop system are uniformly ultimately bounded. Simulation on a twin-shaft turbofan aircraft engine, indicates the effect of proposed ADP-based ISMFTC method.

Resilient Event‐Triggered Control for Switched Systems With Reconstructed Switching Signals Under DoS Attacks

ABSTRACTIn this article, the resilient event‐triggered control issue for switched linear systems with the bounded disturbance is explored under the aperiodic denial‐of‐service (DoS) attacks. Unlike the discussions on asynchronous behavior caused by network transmission/DoS attacks, configure a copy of predictor at controller module to avert the negative impacts of asynchronous switching. Subsequently, by establishing a resilient event‐triggered strategy (RETS) to eliminate invalid sampling during DoS attacks intervals, the waste of network resources is reduced. Besides, a sufficient condition is derived to ensure the input‐to‐state stability (ISS) for the close‐loop switched systems under DoS attacks. The results indicate that the switched systems can resist a certain degree of DoS attacks under the average dwell time (ADT) constrained by DoS attacks frequency and duration. In addition, Zeno behavior is eliminated by calculating the lower bound of the triggering intervals. Finally, the availability of theoretical approach is verified through a numerical simulation.

Research on online deformation monitoring of thin-walled parts driven by digital twin

Thin-walled parts are widely used in aerospace. Because thin-walled parts have thin walls, weak stiffness, and are easy to deform and other characteristics, it is easy to produce deformation in the processing, affecting the quality and accuracy of the workpiece processing. Therefore, accurate and efficient online real-time monitoring of the deformation of thin-walled parts has become a vital issue in process control. The visualization, real-time monitoring, and iterative optimization of digital twin technology can effectively solve the problem of online monitoring of thin-walled parts processing. Therefore, this paper proposes a digital twin driven deformation monitoring method for thin-walled parts, which realizes the effective monitoring of deformation during the processing of thin-walled parts. Firstly, the digital twin architecture for online deformation monitoring of thin-walled parts is established, and the critical technologies of the digital twin for online deformation monitoring of thin-walled parts are proposed. Secondly, the Support Vector Regression (SVR) finite element surrogate model is established to realize the rapid simulation of thin-walled parts cutting. At the same time, a One-Dimensional Convolutional Neural Network (1DCNN) combined with the Self-Attention Mechanism (SAM) deformation prediction model was established based on sensor data. The real-time monitoring and accurate prediction of the deformation of thin-walled components are realized through the parallel guidance of the deep learning and surrogate model. Finally, an experimental analysis was carried out by machining thin-walled plates of aluminum alloy 7075, and the fit of the monitoring models was above 95%, thus verifying the validity of the proposed method.

Novel Dynamic Event-Triggered Consensus Control of Multiagent Systems With Markovian Switching Topologies Under DoS Attacks.

This article focuses on the issue of novel dynamic event-triggered consensus control of multiagent systems (MASs) with denial-of-service (DoS) attacks. Different from the conventional Markovian switching topologies, the generally uncertain semi-Markovian (GUSM) switching topologies with partially unknown elements and time-dependent uncertainties are constructed for the leader-following MASs by considering the equipment performance and external uncertain environment influence. To save communication resources, the novel dynamic memory event-triggered strategy (DMETS) is presented to decrease the frequency of communication between agents. Some secure consensus control criteria are established for the MASs with GUSM switching topologies and DoS attacks due to the potential system communication disruption caused by attackers. Finally, two physical system examples are designed to prove the effectiveness of the presented method.

Prescribed Fixed-Time Control for Constrained Uncertain Nonlinear Cyber-Physical Systems Against Deception Attacks.

In this note, a novel prescribed fixed-time adaptive tracking control scheme is developed to cope with the fixed-time tracking control issue for a category of constrained MIMO nonlinear cyber-physical systems (CPSs) with exogenous perturbations, which suffer from deception attacks started in controller-actuator (C-A) channel. Distinguished from the conservative dynamic surface control (DSC) schemes with a linear filter, a novel nonlinear filter is designed in our strategy, which can tackle the intrinsic issue of explosion of computational complexity and promote the system performance. Besides, a new barrier Lyapunov function (BLF) is designed to ulteriorly enhance the tracking performance on the basis of the prescribed performance function (PPF) approach. Prominently, the proposed control strategy could accommodate the exogenous interferences and deception attacks simultaneously. Furthermore, we have substantiated that the developed approach can not only make certain that all the tracking errors of the resulting closed-loop system, including output tracking errors and virtual tracking errors, enter a prespecified small region near equilibrium point with fixed-time convergence rate, but also guarantee them obey the corresponding constraints throughout the entire control operation, where the regulation time and the tracking accuracy level keep prior known and could be prespecified arbitrarily. Finally, the validity and effectiveness of the proposed control scheme are illustrated through a representative application instance.

[formula omitted] fault-tolerant fuzzy intermittent control for nonlinear hyperbolic PDE systems with multiple delays and actuator failures

This study presents an H∞ fault-tolerant fuzzy intermittent control approach for the nonlinear hyperbolic partial differential equation (PDE) systems with multiple delays and actuator failures (MDAFs). Firstly, the nonlinear hyperbolic PDE systems with MDAFs are characterized by the Takagi–Sugeno (T-S) fuzzy delayed hyperbolic PDE model. Next, by employing the Lyapunov direct method, this paper demonstrates the robust exponential stability using spatial linear matrix inequalities (SLMIs) based on a new switching Lyapunov functional (LF). Furthermore, the H∞ fault-tolerant fuzzy intermittent control issue for nonlinear hyperbolic PDE systems with MDAFs is transformed into the LMI feasibility problem to deal with the SLMIs. Lastly, the feasibility of the constructed control strategy is demonstrated by two illustrative examples.

Forced convection heat transfer control for cylinder via closed-loop continuous goal-oriented reinforcement learning

Forced convection heat transfer control offers considerable engineering value. This study focuses on a two-dimensional rapid temperature control problem in a heat exchange system, where a cylindrical heat source is immersed in a narrow cavity. First, a closed-loop continuous deep reinforcement learning (DRL) framework based on the deep deterministic policy gradient (DDPG) algorithm is developed. This framework swiftly achieves the target temperature with a temperature variance of 0.0116, which is only 5.7% of discrete frameworks. Particle tracking technology is used to analyze the evolution of flow and heat transfer under different control strategies. Due to the broader action space for exploration, continuous algorithms inherently excel in addressing delicate control issues. Furthermore, to address the deficiency that traditional DRL-based active flow control (AFC) frameworks require retraining with each goal changes and cost substantial computational resources to develop strategies for varied goals, the goal information is directly embedded into the agent, and the hindsight experience replay (HER) is employed to improve the training stability and sample efficiency. Then, a closed-loop continuous goal-oriented reinforcement learning (GoRL) framework based on the HER-DDPG algorithm is first proposed to perform real-time rapid temperature transition control and address multiple goals without retraining. Generalization tests show the proposed GoRL framework accomplishes multi-goal tasks with a temperature variance of 0.0121, which is only 5.8% of discrete frameworks, and consumes merely 11% of the computational resources compared with frameworks without goal-oriented capability. The GoRL framework greatly enhances the ability of AFC systems to handle multiple targets and time-varying goals.

Towards an integrated model for predictive well control using real-time drilling fluid data

This paper explores the development of an integrated, real-time predictive model for well control that leverages data from drilling fluids to enhance operational safety and efficiency. Traditional well control methods, often reactive in nature, present significant limitations in managing the risks associated with well control issues, such as blowouts and kicks. By utilizing real-time data from drilling fluids—such as pressure, temperature, flow rate, and mud weight—predictive models enable early detection of potential well control problems, allowing for faster decision-making and proactive management of well stability. Key components of the model include data acquisition systems, real-time analytics, and decision-making algorithms that work together to minimize risks and reduce non-productive time (NPT). The paper also highlights the advantages of predictive well control in improving safety, mitigating operational risks, and its potential future applications in broader drilling operations.

Skyward Bound: The HAL Odyssey - Innovations, Alliances, and Trials

Hindustan Aeronautics Limited (HAL) is a cornerstone of India's aerospace and defence sector, significantly influencing the industry landscape since its inception. Established in 1940, HAL has evolved from manufacturing foreign aircraft to developing indigenous technologies, including the Light Combat Aircraft (LCA) Tejas and the Advanced Light Helicopter (ALH) Dhruv. The organization has faced numerous challenges, such as quality control issues, bureaucratic hurdles, and the need for effective marketing strategies, impacting its operational efficiency and reputation. Nevertheless, HAL's commitment to innovation, skill development, and strategic collaborations with small and medium enterprises (SMEs) has fostered a robust aerospace ecosystem in Bangalore. As HAL continues to expand its portfolio, including projects like the Fifth Generation Fighter Aircraft (FGFA) and the Multirole Transport Aircraft (MTA), it remains a pivotal player in enhancing India's defence capabilities and positioning the nation as a formidable force in the global aerospace arena.

Enantioseparation strategies for sertraline by instrumental and crystallization-based techniques: An important issue in quality control.

Enantiomers of a chiral active pharmaceutical ingredient (API) often exhibit different physicochemical, pharmacokinetic, and biological properties. Therefore, enantioseparation becomes a critical aspect of pharmaceutical development. Sertraline, one of the most widely prescribed antidepressant medications, requires purification from its chiral impurities, and this is recommended and essential for its quality control. This perspective highlights the current established research on the separation and quantification of sertraline's chiral impurities, with a focus on instrumental and crystallization-based techniques.

A new fusion neural network model and credit card fraud identification.

Credit card fraud identification is an important issue in risk prevention and control for banks and financial institutions. In order to establish an efficient credit card fraud identification model, this article studied the relevant factors that affect fraud identification. A credit card fraud identification model based on neural networks was constructed, and in-depth discussions and research were conducted. First, the layers of neural networks were deepened to improve the prediction accuracy of the model; second, this paper increase the hidden layer width of the neural network to improve the prediction accuracy of the model. This article proposes a new fusion neural network model by combining deep neural networks and wide neural networks, and applies the model to credit card fraud identification. The characteristic of this model is that the accuracy of prediction and F1 score are relatively high. Finally, use the random gradient descent method to train the model. On the test set, the proposed method has an accuracy of 96.44% and an F1 value of 96.17%, demonstrating good fraud recognition performance. After comparison, the method proposed in this paper is superior to machine learning models, ensemble learning models, and deep learning models.

Rent controls in comparative perspective: Reflections on an international symposium

In Scotland, the policy landscape around rent controls is rapidly evolving and full of uncertainty. At this critical moment, an international symposium in Edinburgh brought together activists and academics in similar contexts living with or fighting for rent controls from across Europe and North America. This contribution is organised around reflections from the participants on (1) the history of rent controls in their area; (2) the current rent regulations in operation and their effectiveness; (3) the role of tenants’ organisations and (4) landlord and other pro-real estate lobbies in shaping the discourse around rent controls; and (5) reflections on what a satisfactory resolution on the rent control issue might look like and how it relates to other dimensions of the housing system. Our collective hope is that these reflections contribute to sharpening and clarifying the re-emergence of demands for rent control in Europe and North America as a powerful tool––if insufficient by itself––for shaping a decommodified housing system.