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Abstract Tissue damage and aging lead to dysfunction, disfigurement, and trauma, posing significant global challenges. Creating a regenerative microenvironment to resist external stimuli and induce stem cell differentiation is essential. Plant-derived nanovesicles (PDNVs) are naturally bioactive lipid bilayer nanovesicles that contain proteins, lipids, ribonucleic acid, and metabolites. They have shown potential in promoting cell growth, migration, and differentiation into various types of tissues. With immunomodulatory, microbiota regulatory, antioxidant, and anti-aging bioactivities, PDNVs are valuable in resisting external stimuli and facilitating tissue repair. The unique structure of PDNVs provides an optimal platform for drug encapsulation, and surface modifications enhance their stability and specificity. Moreover, by employing synergistic administration strategies, PDNVs can maximize their therapeutic potential. This review summarized the progress and prospects of PDNVs as regenerative tools, provided insights into their selection for repair activities based on existing studies, considered the key challenge for clinical application, and anticipated their continued prominent role in the field of biomedicine. Graphical Abstract

To address the increasing hydrogen demand and carbon emissions of industrial parks, this paper proposes an integrated energy system dispatch strategy considering multi-hydrogen supply and comprehensive demand response. This model adopts power-to-gas technology to produce green hydrogen, replacing a portion of gray hydrogen and incorporates a carbon capture system to effectively reduce the overall carbon emissions of the industrial park. Meanwhile, incentive-based and price-based demand response strategies are implemented to optimize the load curve. A scheduling model is established targeting the minimization of procurement, operation, carbon emission, and wind curtailment costs. The case study of a northern industrial park in China demonstrates that the joint supply of green and gray hydrogen reduces carbon emissions by 40.98% and costs by 17.93% compared to solely using gray hydrogen. The proposed approach successfully coordinates the economic and environmental performance of the integrated energy system. This study provides an effective scheduling strategy for industrial parks to accommodate high shares of renewables while meeting hydrogen needs and carbon reduction targets.

Integrins, a family of adhesion molecules generally exist on the cell surface, are essential for regulating cell growth and its function. As a bi-directional signaling molecule, they mediate cell-cell and cell-extracellular matrix interaction. The recognitions of their key roles in many human pathologies, including autoimmunity, thrombosis and neoplasia, have revealed their great potential as a therapeutic target. This paper focuses on the activation of integrins, the role of integrins in tumorigenesis and progression, and advances of integrin-dependent tumor therapeutics in recent years. It is expected that understanding function and signaling transmission will fully exploit potentialities of integrin as a novel target for tumors.

The shared control system has been a key technology framework and trend, with its advantages in overcoming the performance shortage of safety and comfort in automated vehicles. Understanding human drivers’ driving capabilities and styles is the key to improving system performance, in particular, the acceptance by and adaption of shared control vehicles to human drivers. In this research, personalized shared control considering drivers’ main human factors is proposed. A simulated scenario generation method for human factors was established. Drivers’ driving capabilities were defined and evaluated to improve the rationality of the driving authority allocation. Drivers’ driving styles were analyzed, characterized, and evaluated in a field test for the intention-aware personalized automated subsystem. A personalized shared control framework is proposed based on the driving capabilities and styles, and its evaluation criteria were established, including driving safety, comfort, and workload. The personalized shared control system was evaluated in a human-in-the-loop simulation platform and a field test based on an automated vehicle. The results show that the proposed system could achieve better performances in terms of different driving capabilities, styles, and complex scenarios than those only driven by human drivers or automated systems.

Abstract Surface buckling behaviors of thin-film soft-substrate bilayers have important research value. Recent research has focused on bilayers with infinite-thickness substrates. However, bilayers with finite-thickness substrates widely exist. To study this problem more comprehensively, we extended the stability theory of a beam on an elastic foundation to bilayers and then established a finite element method of bilayers using the neo-Hookean hyperelastic constitutive model. A self-contact analysis method was coupled to the finite element method so that the further buckling evolution of the film surface after folding could be simulated. Based on our analysis of various modulus ratios and thickness ratios, the evolution of the buckling path was significantly influenced by the thickness ratio. Without considering the situation of a prestressed substrate, four new buckling paths were found. Thus, we extended the single buckling path (under infinite thickness substrate) to five types. Our study also found that for path four, the substrate with a certain thickness exhibited a special final stable surface morphology. That is, regardless of the friction, a new periodic morphology after film folding appeared due to the contact slip of the film surface. Finally, further analysis showed that these five buckling paths were all dependent on different modulus ratios and thickness ratios.

Minimum energy consumption with maximum comfort driving experience defines the ideal human mobility. Recent technological advances in most highly automated driving systems on electric vehicles with regenerative braking system not only enhance the safety and comfort level but also present a significant opportunity for automated eco-driving. This research focuses on the longitudinal eco-driving considering the coordinated control for 4WD intelligent electric vehicles. The intelligent electric vehicle framework with 4-wheel hub motors is established and the intention-aware longitudinal automated driving strategy for overall traffic situation levels is proposed. Further, the coordinated control strategy arbitrates the control mode basing on the traffic situation level and distributes braking forces between the electronic hydraulic braking system and the cooperative regenerative auxiliary braking system. The proposed strategy is verified in the co-simulation environment and field test respectively. Test results show optimal control effects in overall traffic situation levels and an enhanced energy recycling efficiency.

In the research of cooperative adaptive cruise control, the coupling relationship between the communication delay, the following time headway, and the string stability leads to a stringent limit on the minimum allowable following time headway. To deal with this limitation, this paper proposes the Disturbance Observer-Smith Predictor Compensation (DOB-SPC)-based Master-Slave architecture. The SPC is adopted to move the communication delays and actuator delays outside the feedback loop in the Master-Slave architecture. It theoretically realizes decoupling when parameters are estimated accurately and achieves zero-headway-string-stability for arbitrary values of communication delay. Subsequently, the robust performance of SPC with estimation deviation of vehicle model dynamic lag, actuator delay, and communication delay in practical application is discussed through the changing trend of the minimum string-stable time headway. To alleviate this problem, the DOB is designed to compensate for the interference caused by the perturbation of vehicle dynamic parameters and improve the estimation accuracy. The simulation results of a four-vehicular platoon show that the string stability and following accuracy are fully guaranteed by applying the proposed strategy, even with smaller headway in the presence of estimation deviation.

LiDAR-based simultaneous localization and mapping (SLAM) and online localization methods are widely used in autonomous driving, and are key parts of intelligent vehicles. However, current SLAM algorithms have limitations in map drift and localization algorithms based on a single sensor have poor adaptability to complex scenarios. A SLAM and online localization method based on multi-sensor fusion is proposed and integrated into a general framework in this paper. In the mapping process, constraints consisting of normal distributions transform (NDT) registration, loop closure detection and real time kinematic (RTK) global navigation satellite system (GNSS) position for the front-end and the pose graph optimization algorithm for the back-end, which are applied to achieve an optimized map without drift. In the localization process, the error state Kalman filter (ESKF) fuses LiDAR-based localization position and vehicle states to realize more robust and precise localization. The open-source KITTI dataset and field tests are used to test the proposed method. The method effectiveness shown in the test results achieves 5–10 cm mapping accuracy and 20–30 cm localization accuracy, and it realizes online autonomous driving in complex scenarios.

The testing and evaluation system has been the key technology and security with its necessity in the development and deployment of maturing automated vehicles. In this research, the physics–intelligence hybrid theory-based dynamic scenario library generation method is proposed to improve system performance, in particular, the testing efficiency and accuracy for automated vehicles. A general framework of the dynamic scenario library generation is established. Then, the parameterized scenario based on the dimension optimization method is specified to obtain the effective scenario element set. Long-tail functions for performance testing of specific ODD are constructed as optimization boundaries and critical scenario searching methods are proposed based on the node optimization and sample expansion methods for the low-dimensional scenario library generation and the reinforcement learning for the high-dimensional one, respectively. The scenario library generation method is evaluated with the naturalistic driving data (NDD) of the intelligent electric vehicle in the field test. Results show better efficient and accuracy performances compared with the ideal testing library and the NDD, respectively, in both low- and high-dimensional scenarios.

Capillary rise is one of the best-known capillarity phenomena, and some capillary flows rise monotonically, whereas others oscillate, but no criteria have been formulated for this scenario. In this paper, the critical radius of the capillary tube is predicted numerically by Levine’s model. To the best of our knowledge, the phase–space diagram of capillary velocity vs height is obtained for the first time, which shows that the phase transition from oscillating to monotonically rising occurs when the phase trajectory decreases exponentially to somewhere other than the “attractor.” Two general Maple codes are provided as an essential part of this paper.

Inspired by amazing result obtained by Semay (2018), this study revisits generalised Kepler’s third law of an n-body system from the perspective of dimensional analysis. To be compatible with Semay’s quantum n-body result, this letter reports a conjecture which had not be included in author’s early publication (Sun, 2018) but formulated in the author’s research memo. The new conjecture for quantum N-body system is proposed as follows: Tq|Eq|3/2=π2G∑i=1N∑j=i+1Nmimj3∑k=1Nmk1/2. This formulae is, of course, consistent with the Kepler’s third law of 2-body system, and exact same as Semay’s quantum result for identical bodies. Keywords: Kepler’s third law, N-body system, Periodic orbits, Dimensional analysis, Classical and quantum mechanics

This article attempts to clarify an issue regarding the proper definition of plastic dislocation density tensor. This study shows that the Ortiz’s and Berdichevsky’s plastic dislocation density tensors are equivalent with each other, but not with Kondo’s one. To fix the problem, we propose a modified version of Kondo’s plastic dislocation density tensor. Keywords: Elasto-plasticity, Deformation gradient, Plastic dislocation density tensor

This article is mainly focussed on revisited of the two well-known postulates of plasticity, i.e., the Drucker and the Il’iushin postulate, and it describes their rational interpretation within the framework of irreversible thermodynamics and using exterior calculus. It shows that the Il’iushin and the Drucker postulate is the integral form and local form of the irreversible thermodynamics of plastic deformation, respectively. The Drucker and Il’iushin postulate is equivalent for both soft and hardening materials.