MOTIVATION: Enhancer-promoter interactions (EPIs) are essential for gene regulation and disease progression. Recent studies have shown that distal enhancers can regulate target genes through interactions with nearby promoters, providing important insights into transcriptional regulation mechanisms. Although high-throughput experimental techniques have enabled large-scale identification of EPIs, these methods are often costly and time-consuming. In addition, existing computational approaches still face challenges in effectively integrating heterogeneous feature representations from different cell lines. RESULTS: We propose a stacked ensemble framework for EPI prediction that integrates feature representations from diverse cell line datasets using multiple machine learning algorithms. The extracted complementary patterns are further combined by an XGBoost classifier to improve robustness against overfitting. Experiments on six independent datasets show that the proposed method achieves superior accuracy and generalization compared with existing EPI prediction models, with an average AUROC of 0.909 while maintaining computational efficiency. AVAILABILITY: The source code and its archived release are available at GitHub and Zenodo. The Zenodo archive provides a versioned snapshot of the repository: https://zenodo.org/records/19952998.

This article investigates the distributed optimal output consensus problem of heterogeneous linear multiagent systems over weight-unbalanced directed networks. A novel distributed continuous-time state feedback controller is proposed to steer the outputs of all the agents to converge to the optimal solution of the global cost function. Under the standard condition that the unbalanced digraph is strongly connected and the local cost functions are strongly convex with global Lipschitz gradients, the exponential convergence of the closed-loop multiagent system is established. Then, the proposed state feedback control law is extended to an observer-based output feedback setting. Two examples are finally provided to illustrate the effectiveness of the proposed control schemes.

This paper investigates the modeling and control of an aerial flight platform built with a quadrotor for assisting in the landing of fixed-wing UAVs. During the process of landing on the aerial flight platform, the lift force of the fixed-wing UAV decreases with the decrease in airspeed. Therefore, the aerial platform will bear additional force and torque disturbance, which seriously affects the attitude stability of the aerial flight platform. The existing methods are difficult to maintain the stability of the aerial flight platform under such strong disturbances. To solve this problem, we propose a novel modeling and control method for the aerial flight platform. First, we consider the aerial flight platform and the fixed-wing

This paper studies the distributed continuous-time nonconvex optimization problem of multi-agent systems over unbalanced digraphs. Each agent is endowed with a local cost function, which is privately known to the agent but not necessarily convex. We aim to drive all the agents to cooperatively converge to the optimal solution of the sum of all local cost functions. Based on the adaptive control approach, a fully distributed algorithm is developed for each agent in the case that neither prior global information concerning network connectivity nor convexity of local cost functions is available. A key feature of the algorithm is that it removes the dependence on the smallest strong convexity constant of local cost functions, and the left eigenvector corresponding to the zero eigenvalue of the Laplacian matrix of unbalanced digraphs.

Insulator visual aiming is difficult for washing drone due to the complex washing environment, strong dis-turbance, lack of debugging environment, and other factors. Conventional visual servo control methods often fail to consider these complex factors adequately and fall short in reliable insulator visual aiming. To address these problems, we propose a novel multi-feature fusion-based drone visual servo control method for accurate insulator visual aiming. A multi-feature fusion neural network (MFFNet) is proposed to map the dif-ferent input modalities into an embedding space spanned by the learned deep features. Suitable control commands are generated by the simple combination of learned deep features. These deep features represent the intrinsic structural properties of the insulator and the motion pattern of the drones. Particularly, our method is trained purely in simulation and transferred to a real drone directly. Moreover, accurate visual aiming is guaranteed even in strong disturbance environments. Simulation and experimental results verify the high accurate insulator aiming, anti-disturbance, and sim-to-real transfer capabilities of the proposed method. Video: https://youtu.be/Ptlajzvp46A.

Disturbance mainly caused by recoil force in-evitably makes washing drone seriously deviate from the desired position, thereby reducing the cleaning efficiency. It is neces-sary to develop an effective anti-disturbance control method. Although some progresses have been made, the position error thereof is still large, rendering existing methods inapplicable in washing drone. In this paper, we propose a learning-based anti-disturbance control (LADC) method to significantly reduce the position error by combining robust nonlinear control and partial differential equation network (PDENet). Taking data noise into account, we use differential spectral normalization in the training of the PDENet. A distinguishing feature of our method is to directly learn PDENet parameters from flight logs without installing extra sensors. Experimental results indicate that the proposed method outperforms classical PD method and extended state observer (ESO) based control method with 70 % and 50 % reduced position error, respectively, and can be further applied in variable scenarios. Video: https: / / youtu.be/gNfLFAXalkI

When multiple quadrotors fly in a cluttered environment, collision-free flight must be assured. In this paper, we propose a novel elastic safety clearance based model predictive control (ESC-MPC) for multiple maneuverable quadrotors to avoid collisions in the presence of disturbance. This is accomplished through leveraging tube based model predictive control to maintain the quadrotor in a tube of trajectories. Exponential control barrier function (ECBF) is integrated to realize the elastic safety clearance mechanism which offers a dynamic safety margin in maneuverable flight. We validate the superiority of our approach with laboratory experiments.

Automatic 3D navigation control of microrobot in large microenvironment is one of the primary challenges hindering its applications. In this article, we present a systematic approach to use an electromagnetic manipulation system to deal with this challenge. Two movable orthogonal microscopic cameras scan the microenvironment on the top view and side view to build a stereo occupancy map automatically. The initial position of microrobot can be located on the basis of the similarity curves generated by the scanning sequences. In accordance with the distribution characteristic of electromagnetic fields in workspace, an enhanced rapidly exploring random tree algorithm is proposed to avoid obstacles in the complex environment. To ensure continuous visual servo in wide range, a field-of-view tracking method is developed using a specific image definition evaluation algorithm. A prescribed performance controller with disturbance observer is designed, which guarantees that the microrobot can remain in the view of microscopic cameras, and the transient and steady-state performance of the system can satisfy the expected requirements. A set of experiments are performed to verify the effectiveness of the proposed automatic 3D navigation strategy. Experimental results show that the microrobot can navigate automatically in a$\boldsymbol{12}\,\times 10\,\times \text{10-mm}$microenvironment containing obstacles, and achieve reliable field-of-view tracking and path following at high speed.

This article focuses on the distributed quantized optimization problem of continuous-time multiagent systems (MASs) over switching graphs. By proposing a dynamic encoding–decoding scheme, a distributed protocol via sampled and quantized data is developed, which can obtain an exact optimal solution, rather than an approximate optimal solution. Compared with existing works on quantized distributed optimization of MASs, the protocol presented in this article does not require the global information on the communication graph or the initial state. Besides, in this article, the gradients of the cost functions are not required to be bounded functions. A simulation example is finally presented to illustrate the effectiveness of the proposed distributed protocol.

In this paper, we study the distributed Nash equilibrium (NE) seeking problem for a class of aggregative games with players described by uncertain perturbed nonlinear dynamics. To seek the NE, each player needs to construct a distributed algorithm based on information of its cost function and the exchanging information obtained from its neighbors. By combining the internal model principle and the average consensus technique, we propose a distributed gradient-based algorithm for the players. This paper not only assures the NE seeking of aggregative games but also achieves the disturbance rejection of external disturbances.

This paper investigates the quantized consensus problem of general linear multiagent systems (MASs) by event-triggered control. A novel event-triggered distributed control protocol is proposed based on a new dynamic quantizer. Compared with periodical sampling for most existing works on quantized consensus, the proposed event-triggered control approach can significantly save communication and thus energy resource. It is shown that with the proposed distributed control protocol quantized consensus of the concerned MAS can be achieved, and the Zeno behavior and continuous monitoring of the neighbors' states are avoided. Moreover, it is shown that the required number of the quantization levels of the new quantizer remains small even if the number of the agents in the MAS is large. Three simulation examples are given to illustrate the effectiveness of the proposed consensus protocol.

Electromagnetically actuated microparticles can be widely applied in the field of biomedicine, for its advantages of minimally invasive feature and approachability to complex microenvironments. In this paper, we propose a robust feedback control approach for precise 3D manipulation of a microparticle actuated by a self-constructed electromagnetic coil system. Model uncertainties, environmental disturbances as well as actuator energy loss problem are all taken into account in the controller design. It is shown that this proposed control scheme can enable the entire system to maintain the input-to-state stability in presence of various perturbations. Experimental results have demonstrated the effectiveness of the proposed control approach. Success of the current study will benefit the precise motion control with high throughput in applications of the targeted material delivery.

In this paper, a distributed optimization problem is studied for continuous-time multiagent systems with unknown-frequency disturbances. A distributed gradient-based control is proposed for the agents to achieve the optimal consensus with estimating unknown frequencies and rejecting the bounded disturbance in the semi-global sense. Based on convex optimization analysis and adaptive internal model approach, the exact optimization solution can be obtained for the multiagent system disturbed by exogenous disturbances with uncertain parameters.

The paper studies the distributed optimization problem for a class of nonlinear multiagent systems in the presence of external disturbances. To solve the problem, we need to achieve the optimal multiagent consensus based on local cost function information and neighboring information and meanwhile to reject local disturbance signals modeled by an exogenous system. With convex analysis and the internal model approach, we propose a distributed optimization controller for heterogeneous and nonlinear agents in the form of continuous-time minimum-phase systems with unity relative degree. We prove that the proposed design can solve the exact optimization problem with rejecting disturbances.

Precise manipulation of microparticles has received considerable attention for its great potential applications to clinical medicine. Among the existing manipulation techniques, the method of magnetic force based manipulation exhibits great advantages for its minimally-invasive feature and insensitivity to biological substance, making it ideally suitable to in vivo environment. On the other hand, increasing demand for accurate and high throughput magnetic manipulation highlights the need of incorporating automation technology in the manipulation. In this paper, we propose an automated control approach to manipulating a magnetic microparticle (bead) with a home-designed electromagnetic coil system. A simplified two-order dynamic model for a microparticle suspended in fluidic environment is established first. A closed-loop controller with utilizing visual feedback is then developed based on input-to-state stability and backstepping methodology. The proposed controller guarantees that the microparticle follows the desired trajectory even in presence of environmental uncertainties and disturbances. Experiments are performed to demonstrate the effectiveness of the proposed approach.

This paper investigates the stabilization problem of spacecraft rendezvous with target spacecraft in an arbitrary elliptical orbit. A linearized dynamic model, obtained from the Hill-Clohessy-Wiltshire (HCW) equations, is used to describe the relative motion of two spacecrafts. An inverse optimal method is introduced to deal with the stabilization problem in presence of external disturbances. With Lyapunov analysis, A group of inverse optimal control laws is presented, which guarantees the input-to-state stabilization of the whole system, and at the same time, is optimal with respect to a performance index incorporating a penalty on the states, the disturbance acceleration, and the control effort. Simulation results are presented to elucidate the effectiveness of the control strategy.

In this paper, we consider distributed consensus of linear discrete-time agents whose states are transmitted through logarithmic quantizer. A distributed consensus protocol is proposed based on the states and outputs of encoders and decoders. Even though every single agent's system is stabilized, the consensus of whole multi-agent system may still not be achieved. We prove that for some linear discrete-time systems, by selecting the parameters of logarithmic quantizer properly, the asymptotic consensus can be achieved. We also derive the upper bound for the convergence rate of consensus algorithm. A simulation is given to show the feasibility of the proposed consensus protocol.