This review aims to guide the future development of related research in the field of learning path planning through the analysis of the current research status of learning path planning. Specifically, this review first introduces the definition of learning path planning and the commonly used parameters in learning path planning methods; then, it classifies in detail according to the algorithms used to generate learning path planning and summarizes the advantages and disadvantages of various learning path planning methods. In addition, the data set and evaluation method used by the learning path planning method is introduced. Finally, the challenges faced by the learning path planning method are summarized and the future development trend is predicted.

We designed a switchable bistable rotaxane consisted of the cyclobis (paraquat-p-phenylene) bisradical dicationic (CBPQT^2(·+)) ring and a main chain, concluding the recognition sites 4,4'-bipyridinium radical cationic (BIPY^·+) and 2,6-dioxynaphthalen(DOP). The density functional theory (DFT) was used to analyze the motion mechanism of ring along the main chain A. Quantum mechanics calculations were used to analyze the noncovalent interaction between the CBPQT^2(·+) ring and the two recognition sites. It proves that the generation and dissociation of the trisradical tricationic complex controlled by the redox reaction could drive the reciprocating motion of the CBPQT^2(·+) ring along the main chain between the two recognition sites.

In the face of increasingly serious traffic congestion, intelligent traffic signal control has become an indispensable means to improve the performance of urban road network. In this paper, a spatiotemporal traffic light control (STLight) based on multi-agent reinforcement learning algorithm is proposed. Through the spatiotemporal dependent module (STDM) based on the attention mechanism, STLight can extract the initial traffic observation data as spatiotemporal features, so as to effectively capture the spatiotemporal dependence relationship between intersections. In addition, based on the extracted spatiotemporal characteristics, STLight further introduces global spatiotemporal information to each agent on the basis of the multi-agent reinforcement learning algorithm based on the centralized training decentralized execution framework, so as to further improve the cooperation ability among multi-agents. The experimental results show that STLight has significant advantages in improving the performance of urban road networks, and helps to alleviate the traffic congestion problem of current large-scale urban road networks.

The quadratic assignment problem (QAP) is one of the NP-hard combinatorial optimization problems and is known for its diverse applications in real life. The current relatively mature heuristic algorithms are usually problem-oriented to design customized algorithms and lack the ability to transfer and generalize. In order to provide a unified QAP solution strategy, this paper abstracts the flow matrix and distance matrix of QAP problem into two undirected complete graphs and constructs corresponding correlation graphs, thus transforming the assignment task of facilities and locations into node selection task on the association graph. Based on actor-critic framework, this paper proposes a new algorithm ACQAP(actor-critic for QAP). Firstly, the model uses a multi-headed attention mechanism to construct a policy network to process the node representation vectors from the graph convolutional neural network; Then, the actor-critic algorithm is used to predict the probability of each node being output as the optimal node. Finally, the model outputs an action decision sequence that satisfies the objective reward function within a feasible time. The algorithm is free from manual design and is more flexible and reliable as it is applicable to different sizes of inputs. The experimental results show that on QAPLIB instances, the algorithm has stronger transfer and generalization ability under the premise that the accuracy is comparable to the traditional heuristic algorithm, while the assignment cost for solving is less compared to the latest learning-based algorithms such as NGM, and the deviation is less than 20% in most instances.

With the development of 3D data acquisition technology, point cloud wins the favor of researchers for it's simple but effective representation and it is widely used in the fields of remote sensing, scene reconstruction, 3D modeling, etc. Considering that the data acquisition process is easily disturbed by many factors such as equipment, environment and material, raw point cloud is often corrupted with noise and so it is of great significance to explore robust and efficient denoising algorithms. This paper firstly investigates the relevant research works of point cloud denoising and divides them into traditional algorithms based on the optimization idea and denoising algorithms based on the deep learning idea according to the implementation principles. Secondly, the research progress of each kind of algorithm is discussed and a detailed analysis of representative algorithms is presented. Thirdly, the data sets, the evaluation metrics and experimental results are summarized with an in-depth comparison. Finally, the problems and possible development directions and trends of point cloud denoising are prospected.

The integration of low earth orbit （LEO） satellite with 5G in the mobile communication system presents extensive application prospects. To ensure effective establishment of communication links between users and base stations， as well as reliable data transmission， time-frequency synchronization technology plays a crucial role. However， traditional timing and frequency synchronization algorithms face limitations when dealing with millimeter wave frequency bands， large bandwidths， high Doppler frequency shifts and change rates， and low signal-to-noise ratios in low-orbit satellite channels. These limitations result in decreased estimation accuracy. For the uplink service data channel of a 5G-based low orbit satellite communication system， a joint timing and frequency synchronization algorithm based on weighted embedded synchronization sequences is proposed to improve the accuracy of timing and frequency offset estimation. Simulation results demonstrate that the proposed timing and frequency synchronization algorithm outperforms traditional algorithms with comparable complexity in terms of synchronization performance.

Super-resolution technology has become an important tool for reconstructing high-resolution datasets and supplementing the shortage of high-resolution images with its characteristics of flexibility and low cost. Compared with natural images， remote sensing images of real scenes are complex and specific， which make super-resolution tasks more difficult. Meanwhile， for remote sensing images， traditional deep learning models can improve the resolution， but there is still a great deficiency of improvement for the details and textures of the ground objects. Therefore， based on the generative adversarial network model， this paper fuses channel-space attention to enhance the feature learning capability of the network and use an artifact suppression strategy to distinguish smooth regions from detail-rich regions， so that the network can focus more on detail-rich regions and suppress the generation of artifacts. Extensive experiments on GaoFen satellite data show that the quantitative metrics and visual quality of the method proposed in this paper are better than those of the current mainstream methods.

The palm-leaf manuscript is a treasure of human civilization， which is the scripture written or engraved on the processed leaves of palm trees. Originating in ancient India， it was gradually introduced into China in the historical period， and reached a period of great prosperity in the Tang Dynasty. Chinese ancestors had a clear understanding of the materials used to make the palm-leaf manuscript. According to our study， the “Doro” （多罗） tree in ancient books should be talipot（Corypha umbraculifera）， while the “Beiduo” （贝多） tree should be palmyra（Borassus flabellifer）. The word “Beiduo”， which appeared many times in Chinese classical poetry， has become a synonym of Buddhist classics. Material evidence of ancient palm-leaf manuscript has been unearthed in many places in Xinjiang， among which Powell’s manuscript as well as Spitzer’s manuscript unearthed in Kuqa were of great significance， with the latter being considered to be the earliest physical evidence of palm-leaf manuscript all over the world. In addition， ancient palm-leaf manuscripts were also unearthed in the Mogao Caves in Dunhuang， which was also of great importance. To date， palm-leaf manuscripts are mainly preserved in Xizang， as well as Xishuangbanna in Yunnan Province， where they have been well organized and protected.​ The characters on the palm-leaf manuscripts were carved and written， respectively， which should be adapted to their preservation conditions.

Regime shifts and their underlying mechanisms in ecosystems are a critical issue in ecological research， with profound implications for predicting ecological risks under global change. This review systematically synthesizes the theoretical advances in alternative stable state （ASS） of ecosystem multi-stability， focusing on three key dimensions： micro-macro process coupling， mechanisms of threshold response， and the role of regulatory nodes in ecosystem resilience. By integrating methodologies such as ASS theory， potential landscape modeling， and bifurcation analysis， we highlight how climate change and anthropogenic activities are driving critical ecosystems （e.g.， coral reefs， Amazon rainforest， Arctic permafrost） toward tipping points， while hysteresis effects and irreversible potentials exacerbate recovery challenges. Emerging approaches combining network theory and energy （carbon） flux analysis offer novel insights for cross-scale early warning， yet bridging micro-scale mechanisms with macro-scale patterns remains a critical challenge. This review provides a theoretical framework for ecological threshold management and underscores the urgent need for interdisciplinary approaches to address planetary-scale regime shift risks.

The convergence of broadband LEO satellite communication systems with 5G is the development trend of satellite communication. To meet the demand for high-speed data transmission, multibeam technology based on large-scale digital phased arrays is indispensable. The peak-to-average ratio problem of multibeam signals induces the non-linear distortion of onboard power amplifiers, while the non-linear distortion of power amplifiers leads to serious interference between multibeams. A digital pre-distortion structure for multibeam is proposed for low-orbit satellite communication systems using large-scale phased arrays. A pre-distortion model is developed to solve the problems of non-linearity, intermodulation distortion and multibeam interference. Through simulation experiments, it is found that the proposed structure is more advantageous than the traditional pre-distortion structure in terms of performance and complexity balance, providing a feasible solution for the implementation of multibeam and overcoming non-linearity on board.

The ramp event of wind speed is a large increase or decrease in wind speed within a short period, causing a significant change in wind farm power, affecting the safe operation of the grid and even triggering accidents such as frequency reduction and voltage collapse. This paper selects the simultaneous data of wind turbines and meteorological towers in wind farms, identifies the ramp events by the swinging door algorithm (SDA), analyzes the duration, magnitude and change rate of the ramp events, and discusses the influence of mountainous terrain on them. In this paper, the recognition algorithm of the ramp event of wind speed and power is designed based on the SDA, and the algorithm parameters are set as follows: the time threshold 4 h, wind speed threshold 6 m·s^-1, and power threshold 1 000 kW. For the recognition of ramp events in other wind turbines, this paper suggests using 2/3 value of the difference between rated wind speed and cut-in wind speed as the wind speed threshold parameter, and 2/3 value of rated power as the power threshold parameter. The terrain influence on the ramp event is significant, and the ramp event is more related to the altitude and average wind speed at the turbine, and the time proportion of the ramp event under different terrain ranges from 6.5% to 9.8%, with the average value of 7.8%.

The fine particulate matter PM_2.5 could be harmful to human health and the atmospheric environment. Beijing-Tianjin-Hebei is one of the most serious regions in China in terms of atmospheric PM_2.5 pollution. Based on PM_2.5 concentrations data, natural factors data, and human activity factors data, this study used kriging interpolation and statistical analysis to explore the spatial and temporal distribution characteristics of atmospheric PM_2.5 pollution in 13 cities of Beijing-Tianjin-Hebei in 2017 and then used correlation analysis models and factor analysis models to explore its influential factors. The results show that in Beijing-Tianjin-Hebei, 1) PM_2.5 concentrations are low in the north and high in the south. The gradient of annual average concentrations between the southern and northern cities can reach up to 64μg/m³. 2) PM_2.5 concentrations are high in winter and low in summer, high in the morning and evening, and low in the afternoon. PM_2.5 concentration in winter is 1.3-2.8 times higher than in summer, and the daily differences in PM_2.5 concentrations in all seasons are between 11-29μg/m³. 3) Atmospheric PM_2.5 pollution is closely related to natural factors. Terrain and topography affect the processes of PM_2.5 aggregation, transport, and dispersion. Wind speed, sunshine hours, and relative humidity are the dominant meteorological factors affecting atmospheric PM_2.5 pollution, and PM_2.5 concentrations have the strongest correlation with meteorological factors in winter. 4) Atmospheric PM_2.5 pollution is closely related to human activities, which can be summarized into social economy factor, industrial pollutant discharge factor, and urban construction factor. The results of this study will help fill the gaps in air pollution prevention and control in Beijing-Tianjin-Hebei.

According to the T/CEEIA 415 standard, taking the drive motor slot insulating material as the research object, three kinds of interstitial aramid insulating papers with 0.25 mm thickness are selected for the oil compatibility test, and based on the changes of the physical, chemical, electrical, and mechanical properties of the insulating paper surfaces in different cycles, the degree of damage and the degradation factors of the materials are elaborated in detail. The results indicate that in the oil compatibility test, the porous structure and surface capillarity enhance the densification of the paper-based structure in the short term, which is beneficial to the electrical performance. However, with the increase of the test time, the diffusion of moisture in the automatic transmission fluid in the oil-paper insulating system is intensified, which triggers the formation of insulating defects and results in the attenuation of the mechanical strength. When screening insulation materials, changes in mechanical properties are more intuitive and accurate. In addition, due to the destruction of the hydrogen bond stabilizing configuration, the amide bond and the C＝O bond strength are changed, and the dielectric properties of the lower density paper deteriorate significantly.

Based on the perspective of human-earth relationships, firstly, this article analyzes the theoretical connotation of “Lucid waters and lush mountains are invaluable assets” from the perspectives of philosophical meaning, geographical logic, and economic thinking. Secondly, from the three aspects of serving the rural revitalization, building a beautiful China, and building a community of human destiny, the era value of “Lucid waters and lush mountains are invaluable assets”. Subsequently, from the perspective of coordinated development of human-earth relations, from the four aspects of highlighting the participation of people, the value of excavation land, maintaining human-land balance, and the standardized human and ground behavior, it proposed the development of the transformation from “lucid waters and lush mountains” to “gold and silver mountains”. Finally, according to the key issues that have not been resolved in the practical path, it is pointed out that future research should be strengthened on the selection of transformation path and model, the valuation of ecological products, and the evaluation of transformation efficiency of the “Two Mountains”.

Stellar mass and star formation rate are essential properties of galaxies. They are also an important basis for understanding the evolution of baryonic matter distribution on the cosmic time scale. Astronomers have developed many methods to estimate the stellar mass and star formation rate of a single galaxy using multi-waveband data. This paper evaluates the variations of the results from different stellar mass and star formation rate estimation methods using multi-wavelength sky survey data of a sample of normal galaxies from the Salon Digital Sky Survey. Our study shows that the results of various stellar mass estimation methods are not significantly different. However, the degree of deviation between the estimated star formation rates from different methods is significant and critical. Therefore, to avoid inaccurate interpretation caused by the discrepancy of different estimation methods, a similar observation data set and the same star formation rate estimation method should be used for each galaxy when comparing star formation rates between galaxies or analyzing star formation rates of a large galaxy sample.

The production and supply of food are core components of sustainable development. Ensuring the sustainability of global food production and supply is crucial for maintaining human survival and socioeconomic stability, and it holds significant importance in advancing the “Zero Hunger” goal within the framework of global sustainable development. This paper selects the five key cereal crops, including wheat, barley, maize, oats, and rice, as the subjects of study, focusing on the Central Asian region. It analyzes the variations in yield per hectare, total production, and cultivated area for these cereals from 1992 to 2021, investigates regional disparities in food production fluctuations within Central Asia, and employs the ARIMA model to forecast future grain production in Central Asia. The results showed that: 1) From 1992 to 2021, the grain yield, total output and sown area in Central Asia showed a trend of first decreasing and then increasing, and the three changes ranged from 0.79～1.96 t/hm², (0.14～0.37)×10⁸ t and (0.14～0.23)×10⁸ hm², respectively. Grain yield and total production reached their peaks in 2011 at 1.96 t/hm², and 0.37×10⁸ t, respectively, while the cultivated grain area peaked in 1993 at 0.23×10⁸ hm². 2) The grain volatility in Central Asia is characterized by frequent fluctuations in grain production, with a significant proportion of years experiencing fluctuations exceeding 5%. The amplitude of these fluctuations is substantial, and the average fluctuation cycle is 2-4 years, indicating a short-term cyclical pattern dominated by classical rather than growth-oriented fluctuations. 3) In the coming years, Central Asia is projected to experience an upward trend in wheat, barley, maize, and oats production, while rice production is expected to decline. Compared to the year 2021, by 2030, Central Asia’s wheat, barley, maize, and oats production is estimated to increase by (410.15, 91.6, 795.26, and 8.91)×10⁴ t, respectively, representing growth rates of 20.1%, 31%, 299.2%, and 37.1%. Conversely, rice production may decrease by 15.99×10⁴ t, with a decline of 15.5%.

We numerically investigate the dynamic behaviors of a droplet impacting onto the non-volatile superheated liquid pool, and focuse on the influence of the Marangoni effect resulted from the inhomogeneous temperature distribution at the surface of liquid pool. Particularly, the time evolution of the flow field and the temperature field inside the pool during and after the impacting are studied. We find that the pool surface, made of oil, is cooled quickly as the ethanol droplet approaches, and a large temperature gradient is produced along the radial direction. Correspondingly, the Marangoni force directs radially inwards to resist the radial outward vapor stress, and a “jet” like structure is formed at the position where the vapor layer becomes thinnest. As the time advances, the Marangoni force becomes dominant and the “jet” is pushed radially inwards until the rear of the droplet and form the downward plume flow. In addition, as the viscosity of the liquid pool increases, the flow induced by the vapor stress is suppressed which further reduce the heat exchange efficiency near the interface.

With the rapid development of broadband low-orbit satellite systems, communication frequency bands such as Ku and Ka tend to be saturated gradually, and non-geostationary orbit (NGSO) satellites will inevitably cause interference to geostationary orbit (GSO) satellites operating at the same frequency. At present, a spatial isolation strategy is often adopted to avoid interference. NGSO satellites always produce the strongest interference to the collinear area. Increasing the isolation angle can reduce the interference, but it will greatly lose the coverage of the LEO satellite. This paper proposes an interference avoidance strategy based on sidelobe nulling of the transmit beam. The antenna array is divided into row and column elements by establishing the LEO satellite coordinate system. In the dimension of column elements, the robust LCMV algorithm is used to realize wide nulling. In the dimension of row elements, it is expanded in combination with beam direction, and finally forms a “null band” in the direction of the collinear area. Through simulation analysis, the proposed strategy can effectively reduce the interference avoidance isolation area of LEO satellites while avoiding collinear interference. The algorithm has low complexity and is easy to implement on satellites.

In recent years, multi-agent cooperation under partially observable conditions has attracted extensive attention. As a general paradigm to deal with such tasks, centralized training with decentralized execution faces the core problem of credit assignment. Value decomposition is a representative method within this paradigm. Through the mixing network, the joint state action-value function is decomposed into multiple local observation action-value functions to realize credit assignment, which performs well in many problems. However, the single point estimation of the mixing network parameters maintained by these methods lacks the representation of uncertainty and is thus difficult to effectively deal with the random factors in the environment, resulting in convergence to the suboptimal strategy. To alleviate this problem, this paper performs Bayesian analysis on the mixing network and proposes a method based on uncertainty for multi-agent credit assignment, which guides the credit assignment by explicitly quantifying the uncertainty of parameters. Considering the complex interactions among agents, this paper utilizes the Bayesian hypernetwork to implicitly model the arbitrary complex posterior distribution of the mixing network parameters, to avoid falling into the local optima by specifying the distribution type a priori. This paper compares and analyzes the performance of representative algorithms on multiple maps in StarCraft multi-agent challenge (SMAC) and verifies the effectiveness of the proposed algorithm.

Astana Cemetery is located in Turpan Basin, Xinjiang. Due to the local extremely arid climate, large numbers of plant remains and documents were well preserved in Astana Cemetery, providing valuable materials for exploring the agricultural production and plant utilization of Turpan people during the Jin to Tang dynasties. Archaeobotanical method was applied on the Chinese jujube (Ziziphus jujuba Mill.) remains unearthed from the Astana Cemetery. Combined with relevant documents, the cultivation and utilization of Chinese jujube during the Jin to Tang dynasties were discussed. It showed that Chinese jujube was a common and important characteristic cash crop in ancient Turpan. The indigenous people used Chinese jujube in various ways, including eating in fresh and further processing it into dried jujube, jujube cake or dry powder of jujube. In addition, the medicinal value of Chinese jujube and the excellent characteristics of jujube wood were also recognized and utilized by ancient Turpan people.