To enhance the path-tracking performance and stability of autonomous vehicles (AVs), this paper proposes an adaptive robust model predictive control (ARMPC) strategy. First, a path-tracking model is established by integrating a two-degree-of-freedom (2-DoF) vehicle dynamics model with a preview error model. Then, to address model uncertainties caused by variations in tire cornering stiffness, a linear parameter varying (LPV) model with four polytopic vertices is constructed. Subsequently, a stability envelope for vehicle dynamics is defined, specifying the operational boundaries for the sideslip angle and yaw rate. Based on this envelope, a stability index is proposed to quantitatively evaluate vehicle stability, and a weight adaptive mechanism is designed to coordinate the objectives of path tracking and stability control. The min-max optimization problem with adaptive weights and multiple constraints is solved using a robust model predictive control (RMPC) framework based on linear matrix inequality (LMI) to determine the optimal front steering angle. Finally, co-simulation results from Carsim and MATLAB demonstrate that the proposed strategy significantly improves both path-tracking accuracy and vehicle stability under various velocities and road adhesion conditions.

Prescribed-time containment control with self-triggering mechanism for multi-UAVs.

Low-altitude unmanned aerial vehicles (UAVs) often encounter strong turbulence during flight, such as gusts, leading to operational instability. To ensure stable flight of low-altitude UAVs, deep reinforcement learning (DRL) and co-flow jet (CFJ) control are utilized. This paper proposes DRL-based and attention-based CFJ control to obtain appropriate control strategies for a CFJ airfoil in strong turbulence, achieving lift stabilization, lift enhancement, drag reduction, and maintaining jet energy, while improving the visual explanation of intelligent flow control. The cylinder wake flow is used to simulate the actual unsteady working environment of low-altitude UAVs at the Reynolds number of 2.6 × 106. Based on the deep deterministic policy gradient algorithm, first, utilizing the standard fully connected neural network with lift stabilization and enhancement as control objectives, generalization is applied to different initial unsteady flow fields, reducing the standard deviation and increasing the average of the lift coefficient by 74% and 87%, respectively. Then, based on the attention branch network (ABN), while ensuring control effectiveness and generalization performance, the transparency of the agent's decision-making process is improved, showing that the agent primarily focuses on regions with significant pressure changes in the flow field. Finally, based on the ABN, the reward function additionally considers drag and jet energy consumption. The agent is still able to find a reasonable control strategy, achieving the control objective of lift stabilization and a 13% drag reduction, while keeping the jet energy almost unchanged.

Survival outcomes and toxicity profile after CT-guided HDR brachytherapy (interventional radiotherapy) for liver oligometastases in breast cancer patients: The BRALIBREST real world multicenter study.

Microstructures often dictate materials' performance, yet they are rarely treated as an explicit design variable because microstructures are hard to quantify, predict, and optimize. We built an image-centric, closed-loop framework that makes microstructural morphology a controllable objective and demonstrate its use case with precursors for Li- and Mn-rich layered oxide cathodes. This work presents an integrated, AI-driven framework for the predictive design and optimization of lithium-ion battery cathode precursor synthesis. This framework integrates a diffusion-based image generation model, a quantitative image analysis pipeline, and a particle swarm optimization (PSO) algorithm. By extracting key morphological descriptors such as texture, sphericity, and median particle size (D50) from SEM images, the platform accurately predicts SEM-like morphologies resulting from specific co-precipitation conditions, including reaction time-, solution concentration-, and pH-dependent structural changes. We experimentally validated that our optimization pinpoints synthesis parameters yielding user-defined target morphologies of Li- and Mn-rich (LMR) layered oxide materials, with predicted and synthesized structures showing close agreement. This framework offers a practical strategy for data driven materials design, enabling both forward prediction and inverse design of synthesis conditions and paving the way toward autonomous, image-guided microstructure engineering.

The study aims to redefine emotions as a major category for understanding human existence, not merely as psychological responses or objects of control. To this end, by comparing and analyzing Spinoza's theory of affect and Toegye Lee Hwang's theory of human emotions, it is revealed that emotions have been understood as expressions of nature and as opportunities for healing in both Eastern and Western philosophy. In addition, this study critically reviews the emotion-centered model of modern affective science with regard to the limits of the functional reduction of emotions, and argues that the concept of feeling can complement emotions in the dimension of “feeling alive.” This study emphasizes the need to reframe emotions as a path of self-understanding rather than as objects of management, and derives the theoretical implications of a philosophical reconsideration of emotional concepts in healing discourse.

The integration of IT services is a critical challenge for public organizations that seek to modernize their operational ecosystems and strengthen mission-oriented processes. In the field of fiscal oversight, supreme audit institutions (SAIs) increasingly require systematized and interoperable service architectures to ensure transparency, accountability, and effective public resource control. However, existing literature reveals persistent gaps concerning how service integration models can be deployed and validated within complex government environments. This study describes an enterprise architecture-driven service integration model designed and evaluated within the Office of the General Comptroller of the Republic of Colombia (Contraloría General de la República, CGR). The study tests the hypothesis that an Enterprise Architecture-driven integration model provides the necessary structural coupling to align technical IT performance with the legal requirements of fiscal oversight, which is an alignment that typically does not appear in generic governance frameworks. The methodological approach followed in this study combines an IT service management maturity assessment, process analysis, architecture repository review, and iterative validation sessions with institutional stakeholders. The model integrates ITILv4 (Information Technology Infrastructure Library), TOGAF (The Open Group Architecture Framework), COBIT (Control Objectives for Information and Related Technologies), and ISO20000 into a coherent framework tailored to the operational and regulatory requirements of an SAI. Results show that the proposed model reduces service fragmentation, improves process standardization, strengthens information governance, and enables a unified service catalog aligned with fiscal oversight functions. The empirical validation demonstrates measurable improvements in service delivery, transparency, and organizational responsiveness. The study contributes to the field of applied system innovation by: (i) providing an integration model, which is scientifically grounded and evidence-based, (ii) demonstrating how hybrid governance and architecture frameworks can be adapted to complex public-sector environments, and (iii) offering a replicable approach for SAIs that seek to modernize their technological service ecosystems through enterprise architecture principles. Future research directions are also discussed to provide guidelines to advance integrated governance and digital transformation in oversight institutions.

This study presents a novel application of the Golden Jackal Optimization (GJO) algorithm for load frequency control (LFC) in a multi-source, two-area interconnected power system. The LFC model comprises of thermal, hydro, nuclear, gas, wind, and solar PV energy units. Inspired by the cooperative hunting strategies and dynamic adaptability of golden jackals, the GJO algorithm mimics their social behaviour and communication tactics to efficiently solve complex optimisation problems. The proposed LFC framework addresses key control objectives of frequency and tie-line power regulation, achieving acceptable transient performance with reduced computational time and iterations. Comparative analysis with established optimisation algorithms such as particle swarm (PSO), whale optimization (WOA), salp swarm (SSA), ant lion (ALO), harris hawk (HHA), and teaching learning (TLO) reveals the effectiveness of GJO, demonstrating improvements of atleast 21.9% in rise time, 5.04% in peak time, 22.1% in overshoot, and 9.7% in settling time across both areas. The robustness of the design is validated under various scenarios, including variable step load changes, system parameter variation, and the presence of nonlinearities such as generation rate constraints (GRC) and governor dead bands (GDB). Frequency domain stability analysis using Bode plots further validates the system’s stability.

Purpose This paper aims to realize the high-precision guidance and control for an axisymmetric gliding aircraft based on the improved active disturbance rejection control (IADRC). Design/methodology/approach An integral chain state-space model with attitude angle as the control objective is established, and the three-channel state-space models are simplified to the second order systems with similar expressions. The comprehensive design method of IADRC is proposed based on the second order system, which uses same control parameters for three channels to reduce workload of parameter tuning. The proposed IADRC combines active disturbance rejection control (ADRC) and proportional integral derivative control to suppress the disturbance. Findings The control parameters are selected according to the comprehensive design method, and they are taken into the transfer function and six-degree-of-freedom nonlinear dynamic model for simulation verification. Furthermore, IADRC is checked by the Monte Carlo simulation. The simulation results show that the proposed IADRC has strong adaptability with biased parameters of the axisymmetric gliding aircraft. The guidance and control effect of IADRC is compared with that of other modern control methods, such as standard ADRC, optimal control and pole placement control. The simulation results show that the guidance and control effect of IADRC is better than that of other methods. Originality/value Theoretical analysis and simulation results show that the proposed IADRC is feasible for engineering application. The comprehensive design method of IADRC has excellent guidance and control performance.

The performance of traditional building temperature control systems is often suboptimal due to the inherent complexity of the control objects and the challenges associated with establishing accurate mathematical models. These traditional systems frequently result in energy inefficiency and compromised comfort levels for occupants. Addressing these shortcomings requires an innovative approach to temperature regulation. After a thorough review of existing indoor temperature regulation systems both domestically and internationally, it is evident that there is significant room for improvement. Combining insights from control theory with advanced algorithms, this study proposes the use of a fuzzy PID (Proportional-Integral-Derivative) algorithm to establish a more effective temperature control system. The fuzzy PID algorithm integrates the robustness of PID control with the adaptability of fuzzy logic, providing a more responsive and precise method for temperature regulation. This hybrid approach leverages fuzzy logic to handle the uncertainties and nonlinearities in the control process, adjusting the PID parameters in real-time to optimize performance. As a result, the new system can dynamically respond to changing environmental conditions and occupant needs, maintaining a comfortable indoor climate while minimizing energy consumption.

Currently, there are many objects that use input actions to control output. Such objects are called multi-channel. The theory of regulator synthesis for multichannel systems is intensively developing. In general, the model of the control object has a nonlinear form. The equation describing the behavior of a control object contains a sine, cosine, and degrees. In most studies, to solve the problem of managing a multi-channel object, the authors linearize object models. The task of synthesizing a controller, even for a linear object, is a very difficult task. However, the modern world imposes increasingly high requirements on automatic control systems in terms of accuracy, speed, fault tolerance, and protection against external influences. To meet these requirements, it is necessary to take into account nonlinearities. This paper presents examples of multi-channel control objects and methods for constructing controllers for these objects.

With the rapid development of the low-altitude economy, UAV logistics delivery systems have garnered widespread attention due to their flexibility and efficiency. The cooperative delivery mode involving a UAV with a suspended payload and a ground vehicle represents a typical networked distribution scenario, whose performance is constrained by the tight coupling of sensing, communication, and control. In practical applications, sensor measurement noise and sudden disturbances propagate through the closed-loop system, severely degrading velocity synchronization and swing angle stability. To address this challenge, this paper focuses on a quadrotor UAV slung-load system and proposes a three-layer nested networked closed-loop control architecture for simultaneous velocity tracking of a moving ground target and swing angle stabilization. First, by establishing the system’s dynamic model, the mapping relationship between cable tension and the payload swing angle (based on sensor feedback) is revealed. Then, by setting the payload velocity as the outermost control objective and constructing a coupled error to drive a virtual swing angle actuator, the direct impact of noise in the raw sensor data is effectively mitigated. Subsequently, the desired acceleration of the UAV is derived through inverse computation, achieving synchronous optimization of velocity tracking and swing angle suppression. Theoretical analysis using Lyapunov methods demonstrates the stability of the closed-loop system in the presence of bounded delays. Simulation results show that the proposed method effectively suppresses payload swing, controls velocity synchronization error, and exhibits strong robustness against sensor noise and sudden disturbance. This study provides a control solution that improves the precision and robustness of sensor-enabled networked control systems in complex dynamic scenarios

Open complex giant systems (OCGSs) represent some of the most challenging problems in contemporary engineering and governance. Although Qian Xuesen’s OCGS methodology and metasynthesis system approach have provided a rich theoretical foundation, there remains a persistent gap between theory and scalable engineering practice, which we refer to as the entropy-increase dilemma. In this paper, we propose the Entropy-Regulated Layered Multi-Agent Coordination (ER-LMAC) paradigm as a modern, engineering-realizable framework for managing OCGSs. ER-LMAC synthesizes (i) entropy regulation as a cybernetic control objective, (ii) layered multi-agent coordination as a scalable decision mechanism, and (iii) edge intelligence implemented on an end–edge–cloud architecture as the computational substrate. As an empirical validation, we instantiate ER-LMAC in the domain of urban intelligent transportation systems by designing and deploying a decentralized, nonhistorical adaptive traffic signal control (ATSC) system in Xiangyang, China. The system covers 448 intersections in the main urban area and has operated continuously for more than three years. Longitudinal operational data indicate that road traffic efficiency increased by more than 20%, congestion duration decreased by more than 30%, and traffic accident rates were reduced by more than 60%. The city has maintained the lowest congestion index in its province for over 40 consecutive months, despite having one of the highest vehicle ownership levels. These results demonstrate that ER-LMAC can effectively resolve the entropy-increase dilemma in a real-world OCGS and suggest its broader applicability to domains such as smart grids, supply chain logistics, industrial Internet of Things (IIoT), and epidemic prevention and control.

Quadcopters are attracting widespread attention due to their growing demand for use in various applications. Since wired communication would severely restrict a quadcopter’s range, maneuverability, and applications, quadcopters usually communicate via wireless networks. Although wireless communication allows the freedom of movement necessary for a wide array of quadcopter applications, it is subject to bandwidth constraints. When multiple quadcopters operate simultaneously, the bandwidth of a wireless network will not meet the requirements. To address this issue, we propose an event-triggered fuzzy-networked control system for 3-DOF quadcopters that reduces the bandwidth requirement. We utilized a fuzzy-networked controller to control a 3-DOF quadcopter. After that, we adopted an event-triggered control approach to reduce the bandwidth requirement. Using the proposed method, one only needs to translate the signals while the event-triggering condition is satisfied, thus reducing the amount of data transmitted over the network. Also, to analyze the stability of the overall system, the Lyapunov stability theorem was adopted. Finally, the proposed method was validated through a 3-DOF quadcopter simulation model. The computer simulations are presented to demonstrate that the proposed control strategy enables a 75.2% (without external disturbance) reduction in bandwidth, which is sufficient to achieve the control objective. This reflects the fact that the proposed control scheme can achieve good control performance with relatively little bandwidth resources and indicates its potential to allow scalable deployment of UAVs.

ABSTRACT Sponge city policies in China primarily focus on micro-scale low-impact development (LID) facilities, yet their influence on macro-scale urban design remains insufficiently understood. This study develops a parametric “form – performance” framework that links quantitative policy requirements to key urban morphological elements. The framework integrates automated scheme generation, policy-based runoff calculations, and sensitivity analysis to clarify how stormwater control objectives interact with land-use intensity and underlying surface configurations. Results show that stormwater management strategies depend strongly on development density. High-plot-ratio sites rely more on green roofs to meet total and peak runoff control requirements, whereas low-density contexts benefit more from permeable pavements as a cost-effective alternative. Sunken green space consistently appears as the most sensitive indicator across scenarios, underscoring its disproportionate influence on runoff control. However, the findings also suggest that current policies may inadvertently encourage the overuse of certain high-performance LID technologies, potentially reducing spatial diversity and weakening resilience under extreme rainfall. This study demonstrates the value of integrating policy-based evaluation with parametric tools in early-stage urban design. The proposed framework offers a systematic approach for aligning policy targets with spatial design strategies, supporting more balanced and resilient stormwater management in future sponge city planning.

The paper is devoted to the analysis of the influence of the method of determining the permissible sampling period in digital controllers of automatic control systems of industrial electric drives on their quality indicators. The possibility of determining the permissible sampling period based on the frequency of the bandwidth of the amplitude-frequency characteristic of both the open and the closed system was studied. It is shown that the use of the bandwidth of an open system to determine the permissible sampling period does not allow taking into account such properties of the control object as delay and astaticity. These are quantitative recommendations for determining the bandwidth of a closed system for determining the permissible sampling period of digital controllers of industrial electric drives. Ref. 13, fig. 12.

This article examines the management of physical, chemical, biological, social, and technical systems. The properties and capabilities of such systems are determined by a set of interrelated characteristics: discrete or continuous control, the presence of feedback, and the type of stability of the control object. The most important factor determining the implementation of a control system is the complexity of the control object. The article considers the specifics of discrete control of complex systems with a large number of relationships between the parameters characterizing the control object. Various discrete control options are identified, including hybrid ones that possess some of the properties of continuous control. Conclusion obtained as a result of the analysis of various particular control system implementation options are compared with an ontological assessment of the problem of managing complex systems, which allows us to state the exceptionally discrete nature of man-aging complex systems, both natural and artificial. The methodology for studying control systems for complex objects is based on a comprehensive analysis of the characteristics that determine the control implementation option, as well as an ontological understanding of the existing fundamental limitation on the continuous control of complex systems. The main scientific results obtained by the authors in the article are: the formation of a representative presentation of a variety of control system implementation options; a statement of the impossi-bility of constructing complex systems, including systems with feedback, with continuous control; a reasoned choice of discrete control as the only possible one for complex control objects; a description, functional and algorithmic positioning of hybrid control options based on discrete control, but endowed with some properties of continuous control. Analysis of the feasibility of implementing various control options within the ontological (maximum in width) representation reveals the existence of a limitation in the implementation of continuous control for complex objects. For artificial systems that are epistemological in their genesis, and therefore are a special case of systems defined within the framework of ontology, this limitation is also relevant.

Introduction. In the context of active reform of state control and supervision, rapid digitalization of control and supervisory mechanisms, relatively new approaches to the study of state control and supervisory activities, their types and directions are being formed. One of these scientific approaches is the ecosystem approach, the approach based on positioning control and supervision as a digital ecosystem. The article attempts to consider modern customs control as a digital ecosystem. Theoretical analysis. The concept of an ecosystem comes from the theory of natural scientific knowledge, but is firmly embedded in the field of social sciences. It is beginning to be used by specialists in relation to state control and supervision. The use of the ecosystem concept in relation to customs control is fully justified from the point of view of the level of digitalization of customs control. The digital ecosystem of customs control includes: the regulatory framework of customs control; the system of customs authorities; principles of legal regulation and implementation of customs control; control and supervisory procedures and information systems. Empirical analysis. It was revealed that the digital ecosystem of customs control is manifested in the use of a risk management system, the positioning of digital profiles as objects of customs control, the development of digital services, and the emergence of new customs control tools (customs monitoring). Results. Based on the conducted analysis, several interpretations of the digital ecosystem of customs control were identified: the digital ecosystem of customs control as a reflection of the level of digital maturity of customs control; the ecosystem approach as a modern principle of customs control; the concept of a digital ecosystem as the basis for a methodological approach to studying customs control in modern conditions; the digital ecosystem as a criterion for assessing the level of interaction between participants in control and supervisory legal relations; the digital ecosystem as an indicator of the degree of openness and trust within a given system.

As e-cigarette use rates continue to increase globally, people in different countries have developed varying perceptions of e-cigarette use. Understanding these perceptions could potentially help policymakers develop regulations that align with their tobacco control objectives. Existing research on the reasons for not using e-cigarettes among adults who smoke cigarettes has been limited to specific countries. This study broadens the scope and reports reasons for not using e-cigarettes among adults who smoke cigarettes in 20 leading nicotine markets worldwide. We performed a secondary analysis of data from the 2019-2021 Euromonitor International's Voice of Consumer: Nicotine Survey. The study sample consisted of adults who had never used e-cigarettes or had used them more than one year ago, and who currently smoked combustible cigarettes monthly or more often in 20 leading nicotine-consuming countries, including Canada, China, Czech Republic, France, Germany, Greece, Israel, Italy, Japan, Kazakhstan, Netherlands, Poland, Romania, Russia, Slovakia, South Korea, Spain, Ukraine, the United Kingdom, and the United States. Reasons for not using e-cigarettes were measured with a multiple-response question. Weighted percentages for each reason overall and by age, sex, and country were reported. The top reasons for not using e-cigarettes among adults who smoked cigarettes across the 20 countries were "never considered/not enough information," "inauthentic/not 'the real thing,'" and "too expensive," closely followed by "safety concerns." The top reasons were similar in most countries, with some variations. Greece had "do not want to quit smoking" and France had "prolonging addiction/substituting to another format" among the top reasons. In Poland, South Korea, Romania, and Slovakia, "unaware of the product" was one of the top reasons. Tobacco control practitioners and policymakers can use the findings on reasons for not using e-cigarettes both domestically and globally to inform public health campaigns and policies that align with the specific tobacco control objectives in each country to ultimately reduce the burden of tobacco-related disease.

In the face of climate pressure and threats to biodiversity, intercropping cereals with legumes and using biostimulants can increase feed yield and quality. This research evaluated a two-year intercropping system of maize and climbing beans for silage in central Poland, comparing four sowing schemes 90,000 ha−1 maize with 90,000 (90 + 90); 45,000 (90 + 45) or 27,500 (90 + 27.5) climbing beans ha−1 and sole maize, as well as five biostimulant application: control object, liquid microelement fertilizer (Zn-8.0%) containing zinc acetate, liquid extract from Ecklonia maxima algae, Methylobacterium symbioticum bacteria, Bacillus halotolerans bacteria. The aim of the field research was to evaluate the biomass components, yields, and crude protein content in silage. The intercropping pattern and biostimulants had a significant effect on dry matter and yields, with limited interactions. Single maize plant weight and yield were highest in the single crop and 90 + 27.5 treatments, while total intercrop yield peaked at 90 + 45, exceeding single maize by 14%. Biostimulants increased maize yields by 3–8% and intercrop yields by up to 6%, but reduced bean yields compared to controls. The crude protein content of silage was lowest for maize alone and highest for 90 + 45; biostimulants increased protein content by 5–9%, mainly for Methylobacterium symbioticum. Overall, the combination of 90 + 45 with Ecklonia maxima or Methylobacterium symbioticum optimized silage biomass and protein. The presented research is the first to evaluate the intercropping of maize with runner beans in orderly sowing and under the influence of biostimulants. It may constitute an important step in improving the efficiency of intercropping for implementation in agricultural practice. Further research should evaluate reduced mineral fertilization in this system.