Dynamic Scheme Research Articles

Dynamic overmodulation strategy based on torque and flux optimal tracking for DTC‐SVM of surface‐mounted PMSM drives

AbstractDirect torque control with space vector modulation has been increasingly attracting emphasis for permanent‐magnet synchronous machine control, benefiting from a high dynamic response and low torque ripple. However, the rapid tracking performance of torque and stator flux linkage is gradually deteriorating as the machine enters the overmodulation region because of the output‐voltage limitation of the inverter. Therefore, to enhance the system tracking performance in the overmodulation region, an innovative overmodulation method based on torque and flux optimal tracking is proposed. In contrast to the conventional overmodulation method where only one of the torque and stator flux linkage tracking is considered, the proposed strategy first constructs the weightless cost function to achieve a trade‐off control between the torque and stator flux linkage. Then, by using an equivalent geometric path to intuitively express the weightless cost function, the minimum cost function can be easily solved and the optimal output voltage vector can be determined correspondingly, to achieve the fast‐tracking of both the torque and stator flux linkage. Additionally, the voltage utilization of different overmodulation schemes is also analysed. Finally, the experimental results demonstrate the efficiency and practicality of the proposed strategy.

MDSA: A Dynamic and Greedy Approach to Solve the Minimum Dominating Set Problem

The graph theory is one of the fundamental structures in computer science used to model various scientific and engineering problems. Many problems within the graph theory are categorized as NP-hard and NP-complete. One such problem is the minimum dominating set (MDS) problem, which seeks to identify the minimum possible subsets in a graph such that every other node in the subset is directly connected to a node in this subset. Due to its inherent complexity, developing an efficient polynomial-time method to address the MDS problem remains a significant challenge in graph theory. This paper introduces a novel algorithm that utilizes a centrality measure known as the Malatya Centrality to effectively address the MDS problem. The proposed algorithm, called the Malatya Dominating Set Algorithm (MDSA), leverages centrality values to identify dominating sets within a graph. It extends the Malatya centrality by incorporating a second-level centrality measure, which enhances the identification of dominating nodes. Through a systematic and algorithmic approach, these centrality values are employed to pinpoint the elements of the dominating set. The MDSA uniquely integrates greedy and dynamic programming strategies. At each step, the algorithm selects the most optimal (or near-optimal) node based on the centrality values (greedy approach) while updating the neighboring nodes’ criteria to influence subsequent decisions (dynamic programming). The proposed algorithm demonstrates efficient performance, particularly in large-scale graphs, with time and space requirements scaling proportionally with the size of the graph and its average degree. Experimental results indicate that our algorithm outperforms existing methods, especially in terms of time complexity when applied to large datasets, showcasing its effectiveness in addressing the MDS problem.

Dynamic strategies and optimal control analysis for hepatitis C management: non-invasive liver fibrosis diagnosis

This study proposes a novel model employing nonlinear ordinary differential equations to dissect HCV dynamics. Six distinct population groups are delineated: Susceptible, Treatment, Responder, Non-Responder, Cured, and Fibrosis. A detailed numerical analysis of this model was conducted, tracking the predicted trends over a span of 20 years. The primary objective of this analysis is to assess and confirm the model’s predictive accuracy and its potential to supplant invasive diagnostic methods in monitoring the progression of liver fibrosis. By incorporating various control parameters, namely u 1 ( t ) , u 2 ( t ) , and u 3 ( t ) , the model offers a nuanced perspective on disease progression and treatment outcomes. Parameter u 1 ( t ) modulates treatment-induced fibrosis progression, providing a crucial lever for mitigating treatment-related side effects. u 2 ( t ) reflects treatment effectiveness, capturing the proportion of responders within the treatment cohort. Meanwhile, u 3 ( t ) governs fibrosis progression in non-responders, shedding light on the disease’s natural trajectory without effective treatment.

Customs Intelligence Strategy Encountering Smuggling Threats in a Changing World

This research analyzes the intelligence strategies employed by the Directorate General of Customs and Excise (DGCE) in Indonesia to combat the evolving landscape of smuggling in the era of Industry 4.0. The study aims to identify and analyze current intelligence practices and subsequently formulate effective strategies for early detection, prevention, and suppression of smuggling activities. Employing a qualitative research methodology, the study utilizes NVivo 14 software to analyze data gathered from relevant sources. The findings reveal a significant increase in the complexity of smuggling operations, characterized by the involvement of transnational organized crime syndicates, sophisticated trade-based money laundering schemes, and the exploitation of electronic commerce (e-commerce) platforms. Despite these challenges, the research indicates that customs intelligence has not been fully maximized as a tool for economic security. Therefore, this study proposes the adoption of a dynamic and adaptive customs intelligence strategy. This strategy encompasses crucial aspects such as strengthening legal frameworks, optimizing organizational structure, enhancing human resource capabilities, and modernizing intelligence facilities and infrastructure. With implementing these recommendations, the DGCE can optimize the role of customs intelligence in safeguarding national economic security and effectively enforcing customs and excise laws in the face of increasingly sophisticated smuggling threats.

Machine Learning-Aided 3D Dynamic SERS Strategy for Physiological Mapping: Biotoxicity of Environmentally Dimensional Aged Nanoplastics and Corresponding Protein Corona Complexes.

Nanoplastics (NPs) are emerging pollutants that undergo inevitable aging in the environment, raising concerns about human exposure and health hazards. Research on the cytotoxicity of various polymer types of NPs, aged nanoplastics (aNPs), and their interactions with proteins (aNPs-protein corona) is still nascent. Traditional cytotoxicity detection methods often rely on end point assays with restricted temporal resolution and analysis of single or multiple biomarkers. Here, we propose a novel approach integrating the 3D dynamic SERS strategy (DSS) with machine learning to rapidly analyze the cell fate and death modes induced by NPs, aNPs, and aNPs-protein corona complexes at the molecular level. PS, PVC, PMMA, and PC products from the water environment were used to prepare the corresponding NPs, and the impact of UV irradiation on their physicochemical properties was examined. DSS systematically maps the molecular changes in the cellular secretome caused by these NPs. Machine learning effectively extracts information from complex spectra, differentiating between biological samples. Results show prolonged UV exposure increases cell sensitivity to ferroptosis and cytotoxicity in various aNPs, while the protein corona on aNPs significantly mitigates toxicity associated with surface oxygen-containing functional groups, resulting in a reduced similarity to ferroptosis signatures. 3D DSS with machine learning technique analyzes the overall metabolite profile at the molecular level rather than individual biomarkers. This study is the first attempt to compare the biotoxicity of diverse polymer NPs, aNPs, and aNPs-protein coronas at cellular and molecular levels in human hepatocytes, enhancing our understanding of the complex biological impacts of NPs.

Exploring Soliton Solutions and Chaotic Dynamics in the (3+1)-Dimensional Wazwaz–Benjamin–Bona–Mahony Equation: A Generalized Rational Exponential Function Approach

This paper investigates the explicit, accurate soliton and dynamic strategies in the resolution of the Wazwaz–Benjamin–Bona–Mahony (WBBM) equations. By exploiting the ensuing wave events, these equations find applications in fluid dynamics, ocean engineering, water wave mechanics, and scientific inquiry. The two main goals of the study are as follows: Firstly, using the dynamic perspective, examine the chaos, bifurcation, Lyapunov spectrum, Poincaré section, return map, power spectrum, sensitivity, fractal dimension, and other properties of the governing equation. Secondly, we use a generalized rational exponential function (GREF) technique to provide a large number of analytical solutions to nonlinear partial differential equations (NLPDEs) that have periodic, trigonometric, and hyperbolic properties. We examining the wave phenomena using 2D and 3D diagrams along with a projection of contour plots. Through the use of the computational program Mathematica, the research confirms the computed solutions to the WBBM equations.

Second-Order Mass-Weighting Scheme for Atom-Centered Density Matrix Propagation Molecular Dynamics.

The atom-centered density matrix propagation (ADMP) method is an extended Lagrangian approach to ab initio molecular dynamics, which includes the density matrix in an orthonormalized atom-centered Gaussian basis as additional, fictitious, electronic degrees of freedom, classically propagated along with the nuclear ones. A high adiabaticity between the nuclear and electronic subsystems is mandatory in order to keep the trajectory close to the Born-Oppenheimer (BO) surface. In this regard, the fictitious electronic mass μ, being a symmetric, nondiagonal matrix in its most general form, represents a free parameter, exploitable to optimize the propagation of the electronic density. Although mass-weighting schemes in ADMP exist, a systematic procedure to define an optimal value of the fictitious masses is not available yet. In this work, in order to rationally evaluate the electronic mass, fictitious electronic normal modes are defined through the diagonalization of the Hessian of the electronic density matrix. If the same frequency is imposed on all such modes (compatible with the chosen integration time step), then the corresponding μ matrix can be calculated and then employed for the following propagation. Analysis of several ADMP test simulations reveals that such Hessian-based mass-weighting approach is able to ensure, together with a 0.1/0.2 fs time steps, a high separation between the (real) nuclear and the (fictitious) electronic frequencies, which determines a high adiabaticity. This high, unprecedented, accuracy in the propagation leads, in turn, to low errors in the estimated nuclear vibrational frequencies, making the ADMP method totally comparable to a fully converged BO molecular dynamics simulation but more computationally efficient. This work, therefore, contributes to a further development of the ADMP ab initio molecular dynamics method, aimed at improving its accuracy through a more rational evaluation of the fictitious electronic mass parameter.

Multi-UAV Cooperative Target Assignment Method Based on Reinforcement Learning

To overcome the problems of traditional distributed target allocation algorithms in terms of lack of target strategic priority, poor scalability, and robustness, this paper proposes a proximal strategy optimization algorithm that combines threat assessment and attention mechanism (TAPPO). Based on the distributed training framework, the algorithm integrates a threat assessment and dynamic attention strategy and designs a dynamic reward function based on the current hit rate of the drone and the missile benefit ratio to improve the algorithm’s exploration ability and scalability. Through an 8vs8 multi-UAV confrontation experiment in a digital twin simulation environment, the results show that the agent using the TAPPO algorithm for target allocation defeats the state machine with an 85% winning rate and is significantly better than other current mainstream target allocation algorithms, verifying the effectiveness of the algorithm.

Synchronization of Euler-Lagrangian networks: a periodic intermittent dynamic event-triggered control strategy

In this paper, the synchronization problem of Euler–Lagrange networks is studied by designing periodic intermittent dynamic event-triggered control strategy. The synchronization of all agents is successfully realized by injecting negative comments into the discontinuous interval of the agents described by the Euler–Lagrange systems. Different from the traditional intermittent event-triggered control strategy, the control strategy proposed in this study introduces internal dynamic variables to expand the sampling interval of the event-triggered mechanism, thereby improving resource utilization efficiency. In addition, the event trigger instants of each agent are asynchronous. By using graph theory and Lyapunov method, the synchronization criteria of Euler–Lagrange networks are derived. The effectiveness of the proposed control scheme is verified by simulations for the Euler-Lagrangian network composed of six two-link rotating manipulators.

IMOABC: An efficient multi-objective filter–wrapper hybrid approach for high-dimensional feature selection

With the development of data science, the challenge of high-dimensional data has become increasingly prevalent. High-dimensional data contains a significant amount of redundant information, which can adversely affect the performance and effectiveness of machine learning algorithms. Therefore, it is necessary to select the most relevant features from the raw data and perform feature selection on high-dimensional data. In this paper, a novel filter–wrapper feature selection method based on an improved multi-objective artificial bee colony algorithm (IMOABC) is proposed to address the feature selection problem in high-dimensional data. This method simultaneously considers three objectives: feature error rate, feature subset ratio, and distance, effectively improving the efficiency of obtaining the optimal feature subset on high-dimensional data. Additionally, a novel Fisher Score-based initialization strategy is introduced, significantly enhancing the quality of solutions. Furthermore, a new dynamic adaptive strategy is designed, effectively improving the algorithm’s convergence speed and enhancing its global search capability. Comparative experimental results on microarray cancer datasets demonstrate that the proposed method significantly improves classification accuracy and effectively reduces the size of the feature subset when compared to various traditional and state-of-the-art multi-objective feature selection algorithms. IMOABC improves the classification accuracy by 2.27% on average compared to various multi-objective feature selection methods, while reducing the number of selected features by 88.76% on average. Meanwhile, IMOABC shows an average improvement of 4.24% in classification accuracy across all datasets, with an average reduction of 76.73% in the number of selected features compared to various traditional methods.

Enabling data-driven process dynamic modeling for extractive leaching and chemical precipitation

To address the limitations of static models and gain insight into the processes of extractive leaching and chemical precipitation, a data-driven dynamic modeling strategy is proposed using a Lithium-ion battery recycling case study. The data correlations among pH, temperature, redox potential, conductivity and system state are investigated. Predictive models are then developed to describe the system state online and are employed as surrogate models for time-intensive offline chemical analyses. This enables further process optimization, such as time-saving measures and improved process efficiency through dynamic parameter studies. The proposed strategy serves as a guideline for dynamic modeling and integrates big data methodologies into chemical engineering.

Real-time monitoring and protection strategies for dense granular flow spallation target in Accelerator-Driven System

An innovative concept of a high-power, gravity-driven, dense granular spallation target has been integrated into the prototype design of the China initiative Accelerator-Driven System (CiADS), which is envisioned as a promising nuclear demonstration facility aimed at converting long-lived fission products and minor actinides into short-lived isotopes. This spallation target system, bridging the proton beam accelerator and the subcritical reactor, plays a crucial role in ensuring safety control and facilitating dynamic operational strategies. In this study, we have developed a system design for an online detector array, incorporating several neutron fission chambers and thermocouples installed in the gap between the spallation target and the subcritical reactor to monitor operations. Furthermore, we have devised real-time monitoring and protection methods to tackle the challenges of measuring beam position and the blockage condition associated with granular flow, considering the distribution of neutron flux and heat deposition under abnormal irradiation conditions. Monte Carlo simulations have demonstrated the applicability and feasibility of the control system for the dense granular spallation target in the accelerator-driven system. The numerical results confirm that the proposed control system meets the requirements for stable measurement with acceptable accuracy.

Static and Dynamic Regulation of Precursor Supply Pathways to Enhance Raspberry Ketone Synthesis from Glucose in Escherichia coli.

Raspberry ketone (RK), a natural product derived from raspberry fruit, is commonly utilized as a flavoring agent in foods and as an active component for weight loss. Metabolic engineering has enabled microorganisms to produce RK more efficiently and cost-effectively. However, the biosynthesis of RK is hindered by an unbalanced synthetic pathway and a deficiency of precursors, including tyrosine and malonyl-CoA. In this study, we constructed and optimized the RK synthetic pathway in Escherichia coli using a static metabolic engineering strategy to enhance the biosynthesis of tyrosine from glucose, thereby achieving the de novo production of RK. Additionally, the synthetic and consumption pathways of malonyl-CoA were dynamically regulated by p-coumaric acid-responsive biosensor to balance the metabolic flux distribution between cell growth and RK biosynthesis. Following pathway optimization, the medium components and fermentation conditions were further refined, resulting in a significant increase in the RK titer to 415.56 mg/L. The optimized strain demonstrated a 32.4-fold increase in the RK titer while maintaining a comparable final OD600 to the initial strain. Overall, the implemented static and dynamic regulatory strategies provide a novel approach for the efficient production of RK, taking into account cell viability and growth.

Joint segmentation of tumors in 3D PET-CT images with a network fusing multi-view and multi-modal information

Objective. Joint segmentation of tumors in positron emission tomography-computed tomography (PET-CT) images is crucial for precise treatment planning. However, current segmentation methods often use addition or concatenation to fuse PET and CT images, which potentially overlooks the nuanced interplay between these modalities. Additionally, these methods often neglect multi-view information that is helpful for more accurately locating and segmenting the target structure. This study aims to address these disadvantages and develop a deep learning-based algorithm for joint segmentation of tumors in PET-CT images.Approach. To address these limitations, we propose the Multi-view Information Enhancement and Multi-modal Feature Fusion Network (MIEMFF-Net) for joint tumor segmentation in three-dimensional PET-CT images. Our model incorporates a dynamic multi-modal fusion strategy to effectively exploit the metabolic and anatomical information from PET and CT images and a multi-view information enhancement strategy to effectively recover the lost information during upsamping. A Multi-scale Spatial Perception Block is proposed to effectively extract information from different views and reduce redundancy interference in the multi-view feature extraction process.Main results. The proposed MIEMFF-Net achieved a Dice score of 83.93%, a Precision of 81.49%, a Sensitivity of 87.89% and an IOU of 69.27% on the Soft Tissue Sarcomas dataset and a Dice score of 76.83%, a Precision of 86.21%, a Sensitivity of 80.73% and an IOU of 65.15% on the AutoPET dataset.Significance. Experimental results demonstrate that MIEMFF-Net outperforms existing state-of-the-art models which implies potential applications of the proposed method in clinical practice.

19F-Labeled NMR Probes for the Detection and Discrimination of Nitrogen-Containing Analytes

The development of 19F-labeled NMR probes has become a pivotal tool in analytical chemistry. Recent advancements in probe design enable precise identification of nitrogen-containing analytes, significantly enhancing the analysis of these biologically important analytes in complex mixtures. This review highlights recent progress with probes based on covalent derivatization and dynamic exchange strategies, which yield distinct 19F NMR signals for each nitrogen-containing analyte. These strategies facilitate separation-free multicomponent analysis and chiral discrimination. Discussions will cover design principles, scope, limitations, and strategies to enhance sensitivity and resolving ability. By addressing current challenges, 19F-labeled NMR probes hold the potential to revolutionize the detection of biologically relevant molecules, catalyzing new discoveries in chemical and biochemical research.

Consensus Control for Stochastic Multi-Agent Systems with Markovian Switching via Periodic Dynamic Event-Triggered Strategy

The consensus problem in stochastic multi-agent systems (MASs) with Markovian switching is addressed by proposing a novel distributed dynamic event-triggered (DDET) technique based on periodic sampling to reduce information transmission. Unlike traditional event-triggered control, the proposed periodic sampling-based DDET method is characterized by the following three advantages: (1) The need for continuous monitoring of the event trigger is eliminated. (2) Zeno behavior in stochastic MASs is effectively prevented. (3) Communication costs are significantly reduced. Based on this, sufficient conditions for achieving consensus in the mean-square sense are derived using Lyapunov–Krasovskii functions, providing a solid theoretical foundation for the proposed strategy. The effectiveness of the proposed DDET control is validated through two numerical examples.

Portfolio optimization with feedback strategies based on artificial neural networks

Dynamic portfolio optimization has significantly benefited from a wider adoption of deep learning (DL). While existing research has focused on how DL can applied to solving the Hamilton–Jacobi–Bellman (HJB) equation, some very recent developments propose to forego the derivation of HJB in favor of empirical utility maximization over dynamic allocation strategies expressed through artificial neural networks. In addition to simplicity and transparency, this approach is universally applicable, as it is essentially agnostic about market dynamics. We apply it to optimal portfolio allocation between cash account and risky asset following Heston model. The results appear on par with theoretical ones.

A self-supervised assisted label-efficient method for online remaining useful life prediction

Remaining Useful Life (RUL) prediction is crucial for safe and efficient production in industrial equipment. Conventional methods struggle in online environments, facing challenges like data distribution differences across working conditions, model cold start problem under new equipment, and lack of labels in online scenarios. To address these challenges, we propose a novel framework for online RUL prediction that leverages knowledge from self-supervised tasks via metric learning. First, a label-efficient self-supervised learning method incorporating soft triplet loss is proposed to transfer degradation knowledge. Second, we develop a two-stage method to distinguish different degradation stages of the equipment for better alignment of data distribution. Third, we introduce a pseudo-label dynamic self-updating strategy and a sample entropy-based data replay strategy, fine-tuning the offline model with newly acquired data. Our method outperforms state-of-the-art unsupervised approaches, reducing RMSE and MAPE by 16.02% and 8.40%, respectively, and achieves performance comparable to supervised methods.

Key internal drivers for an SME’s dynamic ambidextrous growth strategy: A case study of a Norwegian seafood group

This qualitative, explorative case study presents a small and medium-sized enterprise (SME) in Norway that successfully pursued a dynamic ambidextrous growth strategy. The study applies a micro-foundation perspective and focuses on identifying and describing key internal drivers behind the group’s ambidextrous strategy. The empirical findings underscore ambidextrous owner-managers' pivotal role in facilitating strategic realignment, structural adaptation, and knowledge management to enable the case group’s long-term growth. This study contributes to the strategy literature by proposing a framework for theorizing how the identified key internal drivers can be employed to form a dynamic ambidextrous growth strategy in SMEs.

Synthesis of dynamic modelling framework and optimal control strategy for virtually coupled train sets with guaranteed safety

Virtual coupling represents a novel railway operational mechanism, wherein several trains share state information and adjust their behaviours based on control objectives, which include safety (collision avoidance) assurance and stability (uniform velocity and constant spacing between trains) maintenance. However, the conflict between safety and stability requirements becomes more apparent, as the closer spacing between trains. Moreover, the hybrid nature of the train platoon operation, which involves both discrete events and continuous dynamics, also poses a significant barrier to the control strategy synthesis. This paper presents the Virtually Coupled Train Sets (VCTS) dynamic modelling framework and strategy synthesis approach to address these problems. A Hybrid Markov Decision Process (HMDP) is adopted for relative longitudinal dynamics modelling of trains, considering the influence of the uncertainty dynamic of trains ahead. A Stackelberg game-based strategy synthesis algorithm is applied for the safe guarantee by overcoming the uncertainty, and a Q-learning method is used for stability strategy selection from the safe strategy. The results showed a 17.95% expansion in the safe state space when compared to the general relative braking scheme (which assumes maximal acceleration during the delay horizon). In a four-train tracking operation scenario, compared with Q-learning without safe constraints, our approach gives similar stability performance; compared with Q-learning with general relative braking safe constraints, our approach not only assurance safety but has a better stability performance.