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To tackle problems in the domains of human-machine collaboration and multiagent cooperation (e.g., multiagent sequential decision-making, path planning, and navigation), “legibility” — the agent’s ability to convey its intentions through its behavior — can provides a reliable foundation for efficient and seamless collaboration. This paper systematically reviews the most recent research on legibility in agent-based scenarios, concentrating on its theoretical foundations, core methodologies, evaluation metrics, and future directions. The analysis demonstrates that existing methods (such as reward shaping, inverse reinforcement learning, and network flow optimization) have increased the efficiency of collaboration as well as the speed and success rate of intention inference. These methods hold both theoretical significance and practical value in real-world scenarios like warehouse management. But there are still issues with scalability in dynamic target scenarios and adaptability to partially observable environments. Future research could look at adaptive legibility techniques combined with inverse reinforcement learning as well as legibility coordination mechanisms in multiagent dynamic interactions.

The evaluation of Physical Education Teaching Methods in Colleges and Universities faces two main challenges: an excess of evaluating elements and a lack of assessment framework. Hence, the research proposes the multi-feature fuzzy evaluation model based on artificial intelligence to streamline the evaluation process and provide an efficient framework for accessing teaching methods. The framework integrates natural/human language using fuzzy instructions considering three evaluation perspectives, including the management stage, instructors, and students and employs the enhanced cuckoo search optimization algorithm. After the teaching expert has determined each parameter's ratings, they are fed into the improved cuckoo search algorithm and solved using an unbiased function to assess the assessment's final result. It incorporates the students' mobility mechanism and movement vector deconstruction designed based on functional criteria. A system for evaluating the quality of instruction has been developed utilizing the proposed model with enhanced cuckoo search optimization. The results indicate that the proposed algorithm has achieved the highest scores across multiple assessment categories, average skill performances of 97.01%, learning progress of 87.36%, physical fitness of 93.49%, participation rate of 95.04%, student satisfaction of 95.49%, and physical education of 96.8% teaching efficiency. The usefulness of the proposed framework in enhancing physical education teaching methods has been demonstrated by comparing the results with traditional methods. It contributes to advancing pedagogical practices in the field.

This study aimed to investigate the adaptive mechanisms of mulberry ( Morus alba ) to waterlogged conditions, with a specific focus on the development of adventitious roots (ARs), alteration of growth strategies, and adjustment of chlorophyll fluorescence parameters. To achieve this goal, 4-year-old potted mulberry plants were selected for research, and a waterlogging simulation method was implemented. Four treatments were established to investigate the effects of varying water conditions on leaf waterlogging damage, the number of ARs, plant height, chlorophyll fluorescence parameters, and proton motive force (pmf) parameters in mulberry plants. These treatments included the control group (CK), shallow submerged group (SS), half-submerged group (HS) and deep submerged group (DS). Our results showed that (1) The number of ARs in each group increased with increasing waterlogging time. (2) Waterlogging stress inhibited the height growth of mulberry, and the changes in plant height in the HS and DS groups were significantly lower than those in the CK and SS groups. (3) The maximum photochemical quantum yield (Fv/Fm) in the HS and DS groups decreased significantly under waterlogging stress. The nonphotochemical quenching (NPQ t ) of mulberry leaves in the submergence group increased significantly in the early stage of waterlogging stress, and the NPQ t in the submergence group increased continuously with increasing waterlogging time. (4) Thylakoid conductivity to protons (g H + ) in the leaves of mulberry decreased significantly under waterlogging stress, whereas the steady-state rate of proton flux (v H + ) and total electrochromic shift (ECS t ) increased significantly. The morphological, physiological, and ecological responses of mulberry plants to waterlogging stress include the timely generation of ARs at the stem base, the adjustment of plant growth strategies, and the repair of photosynthetic response centers in leaves through heat dissipation and thylakoid acidification mechanisms.

Multiview clustering (MVC) is a proven, effective approach to boosting the various downstream tasks given by unlabeled data. In contemporary society, domain-specific multiview data, such as multiphase postoperative liver tumor contrast-enhanced computed tomography (CECT) images, may be vulnerable to exploitation by illicit organizations or may not be comprehensively collected due to patient privacy concerns. Thus, these can be modeled as incomplete multiview clustering (IMVC) problems. Most existing IMVC methods have three issues: (1) most methods rely on paired views, which are often unavailable in clinical practice; (2) directly predicting the features of missing views may omit key features; and (3) recovered views still have subtle differences from the originals. To overcome these challenges, we proposed a novel framework named fuzzy clustering combined with information theory arithmetic based on feature reconstruction (FCITAFR). Specifically, we propose a method for reconstructing the characteristics of prevailing perspectives for each sample. Based on this, we utilized the reconstructed features to predict the missing views. Then, based on the predicted features, we used variational fuzzy c-means clustering (FCM) combined with information theory to learn the mutual information among views. The experimental results indicate the advantages of FCITAFR in comparison to state-of-the-art methods, on both in-house and external datasets, in terms of accuracy (ACC) (77.5%), normalized mutual information (NMI) (37.9%), and adjusted rand index (ARI) (29.5%).

Because of its high flooding tolerance, in recent years, mulberry has become a tree species that is used in plant restoration in impact zones in reservoir areas. Therefore, 3-year-old potted forage mulberry seedlings were used to investigate the mechanism of mulberry adaptation to flooding stress. An indoor simulated flooding method was adopted to study the morphology of mulberry seedling leaves and the changes in leaf chlorophyll fluorescence parameters and fluorescence imaging under different flooding times and depths. The results showed that the leaves of mulberry seedlings treated with shallow submergence remained healthy during the flooding period, while the leaves of mulberry seedlings treated with half submergence and full submergence showed different degrees of waterlogging symptoms in the middle and late flooding periods and formed adventitious roots at the base of the stem. Most of the chlorophyll fluorescence parameters decreased at the beginning of flooding, but the steady-state degree of closure of PS II reaction centres (1-qP_Lss) increased significantly. In the later stage of flooding, the fluorescence parameters showed relatively stable trends. Based on these results, we conclude that mulberry has high flooding tolerance due to a combination of morphological and physiological responses.

Lithium cobalt oxide, as a popular cathode in portable devices, delivers only half of its theoretical capacity in commercial lithium-ion batteries. When increasing the cut-off voltage to release more capacity, solubilization of cobalt in the electrolyte and structural disorders of lithium cobalt oxide particles are severe, leading to rapid capacity fading and limited cycle life. Here, we show a class of ternary lithium, aluminum, fluorine-modified lithium cobalt oxide with a stable and conductive layer using a facile and scalable hydrothermal-assisted, hybrid surface treatment. Such surface treatment hinders direct contact between liquid electrolytes and lithium cobalt oxide particles, which reduces the loss of active cobalt. It also forms a thin doping layer that consists of a lithium-aluminum-cobalt-oxide-fluorine solid solution, which suppresses the phase transition of lithium cobalt oxide when operated at voltages >4.55 V. LiCoO 2 is a cathode material widely used in lithium-ion batteries but suffers from solubilization of cobalt and structural disorder when the voltage is increased to release more capacity. Here the authors show a ternary Li, Al and F-modified LiCoO 2 and battery cells with stable cycling behavior over 4.55 V.

Solid polymer electrolytes with large-scale processability and interfacial compatibility are promising candidates for solid-state lithium metal batteries. Among various systems, poly(vinylidene fluoride)-based polymer electrolytes with residual solvent are appealing for room-temperature battery operations. However, their porous structure and limited ionic conductivity hinder practical application. Herein, we propose a phase regulation strategy to disrupt the symmetry of poly(vinylidene fluoride) chains and obtain the dense composite electrolyte through the incorporation of MoSe 2 sheets. The electrolyte with high dielectric constant can optimize the solvation structures to achieve high ionic conductivity and low activation energy. The in-situ reactions between MoSe 2 and Li metal generate Li 2 Se fast conductor in solid electrolyte interphase, which improves the Coulombic efficiency and interfacial kinetics. The solid-state Li||Li cells achieve robust cycling at 1 mA cm −2 , and the Li||LiNi 0.8 Co 0.1 Mn 0.1 O 2 full cells show practical performance at high rate (3C), high loading (2.6 mAh cm −2 ) and in pouch cell. Polymer electrolytes based on poly(vinylidene fluoride) with residual solvents are appealing for room-temperature battery operations. Here, the authors present a phase regulation approach to achieve a dense electrolyte and enhance ionic conductivity through the incorporation of MoSe 2 sheets.

Acute respiratory distress syndrome (ARDS), a devastating complication of numerous conditions, is often associated with high mortality. It is well known that endothelial cell (EC) damage and inflammation are vital processes in the pathogenesis of ARDS. Nevertheless, the mechanisms of EC damage are largely unknown. In the present study, we investigated the role of pyroptosis in the initiation of ARDS and demonstrated that endothelial pyroptosis might play a pivotal role in the pathophysiology of ARDS. Metformin, an antidiabetic drug, exhibited a protective effect in lipopolysaccharide (LPS)-induced lung injury, and we hypothesized that metformin alleviated LPS-induced lung injury via inhibiting ECs pyroptosis. In vivo , male ICR mice were intratracheally injected with LPS, and metformin was previously administered intraperitoneally. Morphological properties of lung tissues were detected. We showed that metformin inhibited NLRP3 inflammasome activation and NLRP3-stimulated pyroptosis induction, as shown by decreased levels of cleaved caspase-1, N-terminal fragment of GSDMD, and protein contents of IL-1β in lung tissues of mice exposed to LPS. LPS-induced expression of vascular adhesion molecules was also reduced after the treatment with metformin. In vitro , exposure of pulmonary ECs to LPS resulted in increased expression of NLRP3 and pyroptosis-associated indicators. By inhibiting the expression of NLRP3 with NLRP3 inhibitor MCC950, pyroptosis-related markers and vascular adhesion molecules were ameliorated. Moreover, metformin treatment significantly inhibited the NF-κB signaling pathway and increased the expression of sirtuin 1 (SIRT1) both in LPS-stimulated lung tissues and pulmonary ECs. Administration of the selective SIRT1 inhibitor nicotinamide significantly reversed the protective effect of metformin against endothelial pyroptosis and lung injury in LPS-treated ECs and LPS-induced acute lung injury (ALI). Thus, these findings demonstrated that metformin alleviated LPS-induced ALI by inhibiting NF-κB-NLRP3–mediated ECs pyroptosis, possibly by upregulating the expression of SIRT1.

The monitoring of vegetation via remote sensing has been widely applied in various fields, such as crop diseases and pests, forest coverage and vegetation growth status, but such monitoring activities were mainly carried out in the daytime, resulting in limitations in sensing the status of vegetation at night. In this article, with the aim of monitoring the health status of outdoor plants at night by remote sensing, a polarized multispectral low-illumination-level imaging system (PMSIS) was established, and a fusion algorithm was proposed to detect vegetation by sensing the spectrum and polarization characteristics of the diffuse and specular reflection of vegetation. The normalized vegetation index (NDVI), degree of linear polarization (DoLP) and angle of polarization (AOP) are all calculated in the fusion algorithm to better detect the health status of plants in the night environment. Based on NDVI, DoLP and AOP fusion images (NDAI), a new index of night plant state detection (NPSDI) was proposed. A correlation analysis was made for the chlorophyll content (SPAD), nitrogen content (NC), NDVI and NPSDI to understand their capabilities to detect plants under stress. The scatter plot of NPSDI shows a good distinction between vegetation with different health levels, which can be seen from the high specificity and sensitivity values. It can be seen that NPSDI has a good correlation with NDVI (coefficient of determination R2 = 0.968), PSAD (R2 = 0.882) and NC (R2 = 0.916), which highlights the potential of NPSDI in the identification of plant health status. The results clearly show that the proposed fusion algorithm can enhance the contrast effect and the generated fusion image will carry richer vegetation information, thereby monitoring the health status of plants at night more effectively. This algorithm has a great potential in using remote sensing platform to monitor the health of vegetation and crops.

The standing long jump (SLJ) is widely used for large-scale fitness assessment, yet existing distance measurement solutions remain labor-intensive or hardware-dependent. We frame SLJ distance estimation as a markerless monocular imaging-metrology problem and present a reusable vision pipeline comprising key-frame selection, person detection, semantic segmentation of the heel and jump-mat, homography-based plane mapping, and distance computation with polynomial compensation for perspective-edge bias. Field tests with a high-frame-rate camera demonstrate real-time performance (≈ 23 FPS, ~ 42.7 ms per frame) and centimeter-level accuracy. Using manual tape measurement as the reference, the overall system attains an MAE of 0.71 cm; ablations show that removing key modules sharply degrades accuracy (e.g., 25.07 cm without incomplete-athlete handling; 4.35 cm without single-view perspective mapping; 2.31 cm without convex-hull/curvature-based heel extraction). Minimal calibration files and inference scripts are provided to support reproducibility and deployment in school testing.