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Nowadays, various innovative air combat paradigms that rely on unmanned aerial vehicles (UAVs), i.e., UAV swarm and UAV-manned aircraft cooperation, have received great attention worldwide. During the operation, UAVs are expected to perform agile and safe maneuvers according to the dynamic mission requirement and complicated battlefield environment. Deep reinforcement learning (DRL), which is suitable for sequential decision-making process, provides a powerful solution tool for air combat maneuver decision-making (ACMD), and hundreds of related research papers have been published in the last five years. However, as an emerging topic, there lacks a systematic review and tutorial. For this reason, this paper first provides a comprehensive literature review to help people grasp a whole picture of this field. It starts from the DRL itself and then extents to its application in ACMD. And special attentions are given to the design of reward function, which is the core of DRL-based ACMD. Then, a maneuver decision-making method based on one-to-one dogfight scenarios is proposed to enable UAV to win short-range air combat. The model establishment, program design, training methods and performance evaluation are described in detail. And the associated Python codes are available at gitee.com/wangyyhhh, thus enabling a quick-start for researchers to build their own ACMD applications by slight modifications. Finally, limitations of the considered model, as well as the possible future research direction for intelligent air combat, are also discussed.

Organic photodetectors displaying efficient photoelectric response in the near-infrared are typically based on narrow bandgap active materials. Unfortunately, the latter require complex molecular design to ensure sufficient light absorption in the near-infrared region. Here, we show a method combining an unconventional device architecture and ad-hoc supramolecular self-assembly to trigger the emergence of opto-electronic properties yielding to remarkably high near-infrared response using a wide bandgap material as active component. Our optimized vertical phototransistors comprising a network of supramolecular nanowires of N,N′-dioctyl-3,4,9,10-perylenedicarboximide sandwiched between a monolayer graphene bottom-contact and Au nanomesh scaffold top-electrode exhibit ultrasensitive light response to monochromatic light from visible to near-infrared range, with photoresponsivity of 2 × 10 5 A/W and 1 × 10 2 A/W, at 570 nm and 940 nm, respectively, hence outperforming devices based on narrow bandgap materials. Moreover, these devices also operate as highly sensitive photoplethysmography tool for health monitoring. Despite advances in designed supramolecular organic nanowires for optoelectronics, realizing near infrared phototransistors with wide bandgap materials remains a challenge. Here, the authors report high-performance vertical phototransistors featuring supramolecularly engineered organic nanowires.

The parameter uncertainty has an important effect on the motion planning of overhead cranes, especially in relation to its industrial safety of production activities. Thus, a novel uncertain estimation-and-optimization strategy is proposed for motion planning of overhead cranes with uncertainty in this paper. The main work of this paper includes the following aspects. First, the overhead crane is simplified as a double pendulum model and the corresponding motion planning is described as an optimal control problem with uncertainty. Second, uncertainties are expressed as interval parameters where only the upper and lower bounds are required without probability information and a bounds estimation problem for optimal control with uncertainty is established; the solution contains all possible values. Third, the bounds estimation problem is solved by a surrogate model-based method, the motion trajectory intervals of overhead cranes are obtained. Fourth, in order to reduce the influence of uncertainty on the motion planning of overhead cranes, an optimization method is introduced to reduce the sensitivity to uncertainty. Finally, the numerical examples show that high accurate interval estimation results are obtained with a reasonable computational cost, and the sensitivity of motion trajectory to uncertainty is reduced obviously with the help of optimization. The proposed strategy provides a guidance for uncertain analysis and online controller design of overhead cranes.

Kinodynamic planning of tensegrity robots is a thorny problem, and there are few works that have been reported on this subject, especially for tensegrity manipulators. In this study, a symplectic instantaneous optimal control (IOC) method for the obstacle-avoiding kinodynamic planning of a spinal tensegrity manipulator driven by sliding cables is first developed. This tensegrity mechanism can imitate the basic operations of the humanoid spine, such as bending, scoliosis, contraction and rotation. The actuation of sliding cables is treated as the kinematic constraints of the system inspired by the concept of multibody dynamics, so that a general dynamic model of the sliding cable-driven tensegrity robots is constructed by differential algebraic equations (DAEs). Subsequently, based on the discrete variational principle and Lagrange–d’Alembert principle, an IOC planner coupled with a symplectic penalty iteration is proposed to solve the kinodynamic planning problem of DAE systems. The proposed algorithm provides a novel unified control framework for the kinodynamic planning of tensegrity manipulators with fewer sliding cable actuators. A suboptimal collision-free path with input saturation can be planned in a complex dynamic environment where the target or the obstacles are moving. Finally, certain numerical experiments on the kinodynamic planning of a spinal tensegrity manipulator are carried out to demonstrate the effectiveness and advantages of the proposed symplectic IOC approach.

For computational multibody system dynamics, contact and friction problems are very complex and important problems. Therefore, this paper proposes a novel nonsmooth method for flexible multibody systems with contact and friction in 3D space. Considering the nonsmooth effect of contact and friction on the state variable of the multibody systems, the proposed method is divided into two parts: (i) the change of state variables under the action of smooth force and (ii) the change of state variables caused by contact and friction. Furthermore, for the contact part, the Newton’s impact law and the classical Coulomb friction model are employed to deal with normal impact contact impulse and tangential friction contact impulse, respectively. In addition, Fischer–Burmeister function and Karush–Kuhn–Tucker conditions are also used to solve the normal impact contact impulse and tangential friction contact impulse. What’s more, since the symplectic discrete format performs well robustness to numerical results, the discrete format of the proposed method is based on the symplectic discreteness, and it is expected to obtain the robust numerical results. Finally, several numerical examples are tested by the proposed nonsmooth method, and the numerical simulation results verify the effectiveness of the proposed approach.

Flexible multibody system plays an important role for the simulation of mechanism system. Due to the requirement of precision or high complexity of the model, the number of the finite elements of flexible multibody system will increase rapidly, which will lead to the decrease in the computational efficiency. In order to save the computational cost for simulating flexible multibody system, a novel model order reduction strategy based on the idea of data-driven model is proposed. In addition, the proposed method which is called symplectic model order reduction is in light of proper symplectic decomposition and symplectic Galerkin projection. At first, the snapshot matrix is obtained by an empirical data ensemble of the full-order model, and the transfer symplectic matrix of high dimension to low dimension is obtained by reduced-order bases using the method of cotangent lift. Then, the discrete governing equations of reduced-order model (ROM) are derived by symplectic discretization. Furthermore, a systematic study of model order reduction in system level and component level is provided in the paper. In addition, for adaption of ROM to parameter variation, a parameter interpolation method is offered to obtain the ROM. Eventually, several examples are used to verify the effectiveness of the proposed method, and the results show that the proposed method has better numerical accuracy and higher computational efficiency with respect to classic proper orthogonal decomposition-based ROM.

Peatland vegetation takes up mercury (Hg) from the atmosphere, typically contributing to net production and export of neurotoxic methyl-Hg to downstream ecosystems. Chemical reduction processes can slow down methyl-Hg production by releasing Hg from peat back to the atmosphere. The extent of these processes remains, however, unclear. Here we present results from a comprehensive study covering concentrations and isotopic signatures of Hg in an open boreal peatland system to identify post-depositional Hg redox transformation processes. Isotope mass balances suggest photoreduction of Hg II is the predominant process by which 30% of annually deposited Hg is emitted back to the atmosphere. Isotopic analyses indicate that above the water table, dark abiotic oxidation decreases peat soil gaseous Hg 0 concentrations. Below the water table, supersaturation of gaseous Hg is likely created more by direct photoreduction of rainfall rather than by reduction and release of Hg from the peat soil. Identification and quantification of these light-driven and dark redox processes advance our understanding of the fate of Hg in peatlands, including the potential for mobilization and methylation of Hg II . Mercury isotope signatures in groundwater, soil gas, solid peat, and atmosphere suggest that dark abiotic reduction of peat soil Hg II to volatile Hg 0 does not play a significant role in mobilizing Hg during peat mass loss

Elephant trunks can rapidly vary their stiffness over a broad range, seamlessly switching between soft states for dexterous operation and rigid states for load-bearing tasks. Despite extensive efforts to mimic this stiffness variability using various approaches, such as jamming structures and phase-change materials, existing bionic robots are limited to narrow tunable stiffness ranges and/or slow switching frequencies. In this work, we present a bionic robotic trunk with a cable-driven tensegrity skeleton, leveraging synergistic and antagonistic muscle-mimicking mechanisms to achieve dynamic stiffness regulation. Through coordinated contraction of motor-actuated cables (i.e., antagonistic action), the robotic trunk achieves a stiffness range of 23.94 to 542.47 N/m and a switching frequency of 1.06 Hz, matching the adaptability of elephant trunks. This rapid and large-scale stiffness variation enables dexterous navigation in unstructured environments and powerful manipulation of heavy objects. Incorporated into an electric wheelchair with the human-machine interface, the robotic trunk assists a post-stroke individual with daily activities, such as opening cabinet doors, retrieving milk from refrigerators, and watering flowers. This work advances bio-inspired robotics and highlights the potential of stiffness-tunable robotic trunks in assistive applications. Authors present a bionic robotic trunk with a cable-driven tensegrity skeleton, leveraging synergistic and antagonistic muscle-mimicking mechanisms to achieve rapid and large-range stiffness modulation comparable to that of an elephant trunk.

Inflatable membrane antennas have been extensively applied in space missions; however, the simulation methods are not perfect, and many simulation methods still have many difficulties in accuracy, efficiency, and stability. Therefore, the extended position-based dynamics (XPBD) method is employed and improved for the simulation of folded inflatable structures in this paper. To overcome the problem that the original XPBD method with only geometric constraints does not contain any mechanical information and cannot reflect the mechanical characteristics of the structure, we improve the XPBD method by introducing the strain energy constraint. Due to the complicated nonlinear characteristics of the membrane structures, the results with the traditional finite element method (Abaqus) cannot converge, while the tension field theory (TFT) can, but some pretreatments are needed. Compared with them, the method in this paper is simple and has better stability to accurately predict the displacement, stress, and wrinkle region of the membrane structure. In addition, the present method is also compared with the experiment in the reference to verify the feasibility of the folded tube simulation. Finally, the present method is applied to simulate inflatable membrane antennas and analyze the deployable driving force and deployable process sequence of each component.

Background Heart failure (HF) is a prevalent global health concern characterized by elevated mortality and morbidity rates. Non-invasive transcutaneous vagus nerve stimulation (t-VNS) has emerged as a potential therapeutic intervention for improving cardiac function and alleviating clinical symptoms in HF patients by modulating autonomic nervous system equilibrium. Methods We conducted a comprehensive and systematic search across various scholarly databases, including PubMed, the Cochrane Library, CNKI, and Web of Science, to identify RCTs examining the therapeutic benefits of t-VNS in patients with HF. Statistical analysis of the results was carried out using the random-effects model in Review Manager 5.3 software. The research protocol has been registered in the PROSPERO registry with the registration number CRD42024570589. Results The analysis included ten RCTs involving a collective sample of 374 patients with HF. The meta-analysis results revealed that t-VNS treatment significantly increased LVEF (MD 3.21, 95% CI 1.46 to 4.95, P  = 0.0003), improved GLS (MD -2.31, 95% CI -3.52 to -1.10, P  = 0.0002), enhanced 6-MWD (MD 86.6 m, 95% CI 59.55 to 113.65, P  < 0.00001), reduced MLHFQ score (MD -13.32, 95% CI -19.38 to -7.26, P  < 0.00001), lowered TNF-α levels (MD -1.47, 95% CI -2.36 to -0.59, P  = 0.001), and Significantly reduced HR (MD -4.24, 95% CI -8.27 to -0.22, P  = 0.04), t-VNS exhibited good tolerability with no reported adverse events. Conclusion t-VNS represents a promising therapeutic modality for heart failure, providing advantages such as improved cardiac function, enhanced well-being, reduced inflammatory marker levels, reduced HR and favorable safety profile.