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Transparent wood (TW) is a promising optical material, which provides a potential choice for windows applications. As a polymer composite material, the mechanical, thermal stability, and flame retardant of TW are crucial properties in practical applications. In this study, the delaminated Ti3C2Tx (MXene) were dispersed into polyvinyl alcohol (PVA) and then infiltrated into delignified wood to prepare MXene-reinforced transparent wood (TW-MXene). The MXene addition leads to an interlocking mechanism between MXene fillers with a polymer matrix (e.g., PVA, cellulose scaffold), thereby improving the tensile strength of TW. Besides, the dynamic mechanical analysis also proved that the tensile strength of TW-MXene is significantly higher than pure transparent wood with changing the temperature at −50 to 150 °C. The TW added with 1 wt% MXene maintained a lower thermal conductivity of 0.31 W m−1 K−1, and it showed better UV resistance than TW. Besides, the thermal decomposition rate of TW-MXene composite slows down, and its weight loss rate decreases with the addition of MXene. Furthermore, the presence of 1 wt% MXene endowed TW with 9.3 and 22.0% reductions for PHRR and THR, respectively. This strategy provides an insight into the development of high-performance transparent wood composites with the potential to be used in building fields.

Adaptive cruise control (ACC) enables efficient, safe, and intelligent vehicle control by autonomously adjusting speed and ensuring a safe following distance from the vehicle in front. This paper proposes a novel adaptive cruise system, namely the Safety-First Reinforcement Learning Adaptive Cruise Control (SFRL-ACC). This system aims to leverage the model-free nature and high real-time inference efficiency of Deep Reinforcement Learning (DRL) to overcome the challenges of modeling difficulties and lower computational efficiency faced by current optimization control-based ACC methods while simultaneously maintaining safety advantages and optimizing ride comfort. Firstly, we transform the ACC problem into a safe DRL formulation Constrained Markov Decision Process (CMDP) by carefully designing state, action, reward, and cost functions. Subsequently, we propose the Projected Constrained Policy Optimization (PCPO)-based ACC Algorithm SFRL-ACC, which is specifically tailored to solve the CMDP problem. PCPO incorporates safety constraints that further restrict the trust region formed by the Kullback–Leibler (KL) divergence, facilitating DRL policy updates that maximize performance while keeping safety costs within their limit bounds. Finally, we train an SFRL-ACC policy and compare its computation time, traffic efficiency, ride comfort, and safety with state-of-the-art MPC-based ACC control methods. The experimental results prove the superiority of the proposed method in the aforementioned performance aspects.

Background: Tumor cells engage in continuous self-replication by utilizing a large number of resources and capabilities, typically within an aberrant metabolic regulatory network to meet their own demands. This metabolic dysregulation leads to the formation of the tumor microenvironment (TME) in most solid tumors. Nanomedicines, due to their unique physicochemical properties, can achieve passive targeting in certain solid tumors through the enhanced permeability and retention (EPR) effect, or active targeting through deliberate design optimization, resulting in accumulation within the TME. The use of nanomedicines to target critical metabolic pathways in tumors holds significant promise. However, the design of nanomedicines requires the careful selection of relevant drugs and materials, taking into account multiple factors. The traditional trial-and-error process is relatively inefficient. Artificial intelligence (AI) can integrate big data to evaluate the accumulation and delivery efficiency of nanomedicines, thereby assisting in the design of nanodrugs. Methods: We have conducted a detailed review of key papers from databases, such as ScienceDirect, Scopus, Wiley, Web of Science, and PubMed, focusing on tumor metabolic reprogramming, the mechanisms of action of nanomedicines, the development of nanomedicines targeting tumor metabolism, and the application of AI in empowering nanomedicines. We have integrated the relevant content to present the current status of research on nanomedicines targeting tumor metabolism and potential future directions in this field. Results: Nanomedicines possess excellent TME targeting properties, which can be utilized to disrupt key metabolic pathways in tumor cells, including glycolysis, lipid metabolism, amino acid metabolism, and nucleotide metabolism. This disruption leads to the selective killing of tumor cells and disturbance of the TME. Extensive research has demonstrated that AI-driven methodologies have revolutionized nanomedicine development, while concurrently enabling the precise identification of critical molecular regulators involved in oncogenic metabolic reprogramming pathways, thereby catalyzing transformative innovations in targeted cancer therapeutics. Conclusions: The development of nanomedicines targeting tumor metabolic pathways holds great promise. Additionally, AI will accelerate the discovery of metabolism-related targets, empower the design and optimization of nanomedicines, and help minimize their toxicity, thereby providing a new paradigm for future nanomedicine development.

Double unmanned surface vehicles (DUSVs) towing a floating rope are more effective at removing large floating garbage on the water’s surface than a single USV. This paper proposes a comprehensive trajectory planner for DUSVs connected with a floating rope for cooperative water-surface garbage collection with dynamic collision avoidance, which takes into account the kinematic constraints and dynamic cooperation constraints of the DUSVs, which reflects the current collection capacity of DUSVs. The optimal travel sequence is determined by solving the TSP problem with an ant colony algorithm. The DUSVs approach the garbage targets based on the guidance of target key points selected by taking into account the dynamic cooperation constraints. An artificial potential field (APF) combined with a leader–follower strategy is adopted so that the each USV passes from different sides of the garbage to ensure garbage capturing. For dynamic obstacle avoidance, an improved APF (IAPF) combined with a leader–follower strategy is proposed, for which a velocity repulsion field is introduced to reduce travel distance. A fuzzy logic algorithm is adopted for adaptive adjustment of the desired velocities of the DUSVs to achieve better cooperation between the DUSVs. The simulation results verify the effectiveness of the algorithm of the proposed planner in that the generated trajectories for the DUSVs successfully realize cooperative garbage collection and dynamic obstacle avoidance while complying with the kinematic constraints and dynamic cooperation constraints of the DUSVs.

Poplar ( Populus adenopoda Maxim.) and radiata pine ( Pinus radiata Don.) woods were treated with an aqueous solution containing styrene-acrylic copolymer (SAC) dispersion and sodium silicate (SS). The modifying effects on the mechanical properties of wood were investigated with 10% SAC and varying concentrations of SS. The SAC and the SS deposition occurred in the cell lumina and condensed under catalysis at elevated temperature, as evidenced by scanning electron microscopy. The wood treated with SAC and SS exhibited a moisture content about 2 times higher than that of the untreated control under 95% relative humidity due to the introduction of hygroscopic silicate. The modulus of rupture (MOR) and modulus of elasticity (MOE) in the bending, compressive strength, surface hardness, tensile strength, and shear strength of the wood were improved up to 83.9, 82.3, 72.7, 48.3, 38.4, and 53.1%, respectively. However, the impact strength decreased by 39.4% due to the treatments. Accordingly, the combined treatment with SAC/SS has a potential application in the improvement of the wood quality, but the reduction in impact strength could limit its utilization in products for which high dynamic strength is required.

Poplar ( Populus adenopoda Maxim.) and radiata pine ( Pinus radiata Don.) wood were treated with an aqueous solution containing styrene-acrylic copolymer (SAC) and sodium silicate (SS). The effects of this treatment on the thermal stability and combustion behavior of the wood were determined. Thermogravimetric (TG) analysis showed that treatment in the presence of SS resulted in earlier thermal degradation compared to samples untreated and treated with SAC alone. Cone calorimetry showed that treatment of wood with SAC alone resulted in increased total heat release, total smoke production, and CO and CO 2 concentration. Wood treated with SAC/SS was more difficult to ignite as evidenced by longer ignition time and higher limiting oxygen index; however, the treatment did not reduce the production of smoke and carbon oxide. Scanning electron microscopy and energy dispersive X-ray analysis of residual char indicated that SS was mainly deposited in the lumina of vessel or tracheid, and SS distribution in wood was not uniform. These findings demonstrate that incorporation of SS retards the flame of SAC-treated wood; however, the fire risk is not reduced due to dense smoke and CO production.

In the domain of automatic parking systems, parking space detection and localization represent fundamental challenges that must be addressed. As a core research focus within the field of intelligent automatic parking, they constitute the essential prerequisite for the realization of fully autonomous parking. Accurate and effective detection of parking spaces is still the core problem that needs to be solved in automatic parking systems. In this study, building upon existing public parking space datasets, a comprehensive panoramic parking space dataset named PSEX (Parking Slot Extended) with complex environmental diversity was constructed by integrating the concept of GAN (Generative Adversarial Network)-based image style transfer. Meanwhile, an improved algorithm based on PP-Yoloe (Paddle-Paddle Yoloe) is used to detect the state (free or occupied) and angle (T-shaped or L-shaped) of the parking space in real-time. For the many and small labels of the parking space, the ResSpp in it is replaced by the ResSimSppf module, the SimSppf structure is introduced at the neck end, and Silu is replaced by Relu in the basic structure of the CBS (Conv-BN-SiLU), and finally an auxiliary detector head is added at the prediction head. Experimental results show that the proposed SimSppf_mepre-Yoloe model achieves an average improvement of 4.5% in mAP50 and 2.95% in mAP50:95 over the baseline PP-Yoloe across various parking space detection tasks. In terms of efficiency, the model maintains comparable inference latency with the baseline, reaching up to 33.7 FPS on the Jetson AGX Xavier platform under TensorRT optimization. And the improved enhancement algorithm can greatly enrich the diversity of parking space data. These results demonstrate that the proposed model achieves a better balance between detection accuracy and real-time performance, making it suitable for deployment in intelligent vehicle and robotic perception systems.

We investigated Alongshan virus infection in reindeer in northeastern China. We found that 4.8% of the animals were viral RNA-positive, 33.3% tested positive for IgG, and 19.1% displayed neutralizing antibodies. These findings suggest reindeer could serve as sentinel animal species for the epidemiologic surveillance of Alongshan virus infection.