Crop yield per unit area, efficiency and the value of products are three essential factors of agro−productivity. Since 1949, the level of crop yield per unit area has been always incresed. In recent years, agricultural mechanization extended in rapid speed and raised production efficiecies. However, the levels of agricultural products’ prices have kept almost still. And the upgrading of their values has been proved very difficult. This paper analysed a few demonstrating techniques of themal chemical and microbial transition for biomass, which their technical bottlenecks have already been overcome, and expressing abilities of 'golden tough' by biomass manufacturing,as well as revealed the huge potentials of inducing an agricultural New Quality Productive Forces. Nevertheless, if some agro−products could possess same compositions with several industrial consumer goods, chemicals and special commodity energies, then they could substitute for above metioned goods and values be significantly boosted. The national goals of carbon peaking and carbon neutrality is compulsorily demanding the 'green transition' by means of agro−biomass for the manufacturing of a lot of industrial goads. And the breakthrough of thermal−chemical transition technique for biomass provides the possibility for it.

A deep understanding of the molecular neural mechanisms underlying the loss and emergence of consciousness in the brain is of great importance for the prevention and treatment of disorders of consciousness associated with certain brain diseases, as well as for the development of super artificial intelligence. This article summarizes the progress of research into how the brain loses and regains consciousness in response to anesthetics. Based on advances in the biological mechanisms of anaesthesia−induced loss of consciousness and emergence from anesthesia, a new hypothesis for the emergence of consciousness is proposed. This article concludes with a list of key scientific questions that deserve attention in the context of in−depth research on loss and recovery of consciousness and cognitive dysfunction. This article lists several key scientific questions worthy of attention for in−depth research on the loss and recovery of consciousness and cognitive dysfunction: What are the biological principles underlying the molecular, neuronal, and neural network activities that normal conscious activity relies on; How do anesthetics, by acting on their pharmacological molecular targets to inhibit neuronal activity and interfere with or block information transmission, further lead to disorders of consciousness or loss of consciousness; What are the key nuclei, molecules, and their working mechanisms involved in the molecular neural mechanisms of regained consciousness after its loss, beyond the known ubiquitination−mediated degradation of KCC2 in ventral posteromedial thalamic nucleus (VPM) neurons; How does the intervention in specific neural nuclei and circuits through external forces, such as optogenetics, chemogenetics, electrical stimulation, and drugs, significantly alter the state of consciousness in the overall context of the brain; Unlike waking from normal periodic sleep, why does cognitive dysfunction occur after anesthesia or why does cognitive function not fully recover, and what are the molecular neural mechanisms hindering the recovery of cognitive function; Identifying which nuclei, neurons, their activity patterns, neural networks, and arousal systems are unique or shared mechanisms in the processes of anesthesia, sleep, and their recovery; What are the molecular neural mechanisms hindering the recovery of consciousness under states of anesthesia, coma, and vegetative state.

As one of the world's major food crops, wheat production is severely threatened by soil salinization, as salt stress affects approximately 10% to 20% of the global wheat cultivation area. Salt stress inhibits wheat growth via a triple mechanism including osmotic imbalance, ion toxicity, and oxidative damage. Wheat, being a salt−sensitive crop, possesses a narrow genetic base for salt tolerance, limiting its productivity improvement in salinized lands. In recent years, multi−level studies have elucidated the molecular, physiological, and biochemical mechanisms underlying salt tolerance in wheat. However, coordinated optimization of salt tolerance with agronomic traits such as yield, as well as the complexity of salt stress responses, remain major challenges. This review summarizes advances in understanding the physiological and biochemical mechanisms, molecular regulatory networks, genetic basis of salt tolerance, discovery and utilization of novel salt−tolerant genes, and breeding strategies for developing salt−tolerant wheat varieties. It also analyzes existing challenges in the field, including the paradigm and limitations of conventional breeding, identification and cloning of key salt−tolerant genes, applications of genetic engineering and gene editing technologies, and the systematic integration of multidisciplinary technologies. Due to the mechanism complexities of plant resonese to salinity stress, and the exploring and functional characterization difficulties of salt tolerance related genes in wheat, it is necessary to identify salt tolerance genes from wheat by integrating multiomics techniques, and timely employ the important salt resistance genes excavated in other plant species for wheat improvement. This paper aims to provide some valuable information for genetic improvement of wheat on salt tolerance.

The Trihelix transcription factor GT2A plays a critical regulatory role in plant responses to abiotic stress. This study aimed to investigate the response of the soybean GmGT2A gene to salt stress and explore its physiological mechanisms in soybean salt stress adaptation. Using the 'Williams 82' cultivar as experimental material, the GmGT2A gene was obtained via homologous cloning. A plant overexpression vector was constructed, and transgenic soybean lines were generated using an Agrobacterium−mediated genetic transformation system. Following preliminary screening with Bar test strips, PCR detection, Southern blot single−copy identification, and RT−qPCR validation, physiological response differences between transgenic and wild−type plants under salt stress were systematically compared. Results demonstrated that under salt stress, comparing to control plants, GmGT2A−overexpressing soybean lines exhibited significantly improved germination rates, seedling salt tolerance indices, and fresh weight. The activities of superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT) in leaves increased by 1.28− to 1.44−fold, while malondialdehyde (MDA) content decreased by 38.06%. Ion homeostasis analysis revealed reduced Na⁺ accumulation in transgenic roots, enhanced K⁺ retention capacity, and a decreased Na⁺/K⁺ ratio to 61% of the control. In conclusion, the GmGT2A gene may improve soybean salt tolerance by regulating antioxidant enzyme activities and ion homeostasis. This study provides bases for the genetic improvement of soybean stress tolerance.

Soil salinization severely restricts global agricultural production, and screening salt−tolerant crops is crucial for utilizing saline−alkali lands. This study employed salt−tolerant triticale cultivar Jinsicao 1 (JS−1) and conventional cultivar Jisi 3 (JS−3) as materials, simulating salt stress with 300 mmol/L NaCl solution. Through physiological, biochemical, and metabolomic analyses, we revealed the metabolic regulation mechanisms of triticale in adapting to salt stress. The results showed that salt stress significantly inhibited growth in both cultivars, but JS−1 maintained higher biomass, water content, stronger antioxidant capacity, and lower ion toxicity under stress. Metabolomic analysis identified numerous differential metabolites, with particular focus on metabolites specifically upregulated in JS−1 under salt stress. In leaves, these metabolites were primarily fatty acids, amino acids, and carbohydrates, while in roots they were mainly flavonoids, amino acids, and carbohydrates. Both roots and leaves of the salt−tolerant cultivar accumulated carbohydrates and amino acids for osmoregulation. However, JS−1 relied on the accumulation of unsaturated fatty acids for reactive oxygen species (ROS) scavenging in leaves, whereas flavonoids played this role in roots. These findings demonstrate that salt−tolerant triticale coordinates the accumulation of distinct metabolite types in roots and leaves to mitigate salt−induced damage.

Improvement in water use efficiency, yield potential, winter hardness, lodging resistance and early maturity are the main breeding objectives in northern China winter wheat region. Based on these objectives and assessment of parents, Shimai12 was chosen as the female parent to cross with Shijiazhuang8. The offsprings were selected for water use efficiency by reduced irrigations, seedling growth habit selected by molecular marker assistant selection and phenotype selection, early maturity selected by adjusted plant growing stages, lodging resistance and yield related traits selected in densely populated field by increasing seeding rate, breeding procedure speeded up by sowing in different biological zones. As the result, the new variety Lunxuan103 was released, which has combined high water use efficiency, high yield potential, salt and alkaline tolerance, excellent winter hardness, good lodging resistance and early maturity. In the future, besides the combination of all the characters mentioned above, more afford should be put into quality improvement and the efficiency in breeding for winter hardness.

Cangmai 17 was a new drought−resistant and salt−alkali−tolerant wheat variety developed by Cangzhou Academy of Agriculture and Forestry Sciences using the two−nursery parallel alternating selection breeding method, which was approved by Hebei Province in 2023. This paper outlined the breeding background, strategy, and process of Cangmai 17, analyzed the varietal characteristics, yield traits, resistance performances, and quality attributes of the variety, and established a high−efficiency cultivation technology system of Cangmai 17. The study provided a basis for promoting the cultivation of Cangmai 17 in saline−alkali regions of Hebei Province and other similar areas in China. It is of great significance for ensuring stable wheat yield, green and sustainable production, and national food security in arid and saline−alkali regions of China.

To address the challenges of reconciling high oil content with high yield in Ningxia and neighbouring spring soybean−growing regions, alongside the narrow genetic base of breeding parents, this study harnessed the potential of the ms1 male−sterile recurrent selection technique for polygenic aggregation and ecological adaptability improvement. This trial employed soybean ms1 male−sterile lines as maternal parents and over 70 superior domestic germplasm accessions—including Ninghuang 117 and Chengdou 6—as mixed paternal parents. Through 12 years of selection using the ms1 male−sterile recurrent population breeding method, the new spring soybean variety Ningdou 10 was developed. This variety exhibits Grade 2 salt−alkali tolerance. Quality characteristics include crude protein (39.37%) and crude fat (22.16%). Disease resistance assessment indicates high resistance. In the 2019–2020 Ningxia regional trials, it achieved an average yield of 4197 kg/hm², representing a 4.5% increase over the control variety Chengdou No. 6. In the 2021 production trials, the average yield was 3981 kg/hm², representing a 5.2% increase over the control variety Chengdou No. 6. Demonstrations in Ningxia, Gansu and other regions have consistently yielded high production. In summary, Ningdou No. 10 exhibits characteristics of high oil content, high and stable yields, salt−alkali tolerance, disease resistance and broad adaptability.

Thermal barrier coatings (TBCs) are one of the core thermal protection technologies for the hot components of advanced aeroengines. Under high−temperature service conditions, TBCs of engine blades are eroded and damaged by environmental deposits mainly composed of CaO−MgO−Al₂O₃−SiO₂ (CMAS), leading to early spalling and failure of the blade coatings, which has attracted extensive attention in the field of high−temperature protection among the researchers. Especially for the commonly used TBCs material−yttria−stabilized zirconia (YSZ) prepared by electron beam−physical vapor deposition (EB−PVD) method, molten CMAS can easily penetrate into the coatings through the columnar crystal gaps and microcracks, causing severe corrosion. This article focused on the urgent issue of CMAS corrosion in the high−temperature service process of TBCs for advanced aeroengines. A proper amount of Al₂O₃ was doped into the YSZ material by means of composition modification to form an Al₂O₃−YSZ composite coating (AYSZ coating). YSZ coating and AYSZ/YSZ coating were fabricated on the surface of alumina ceramic plates by EB−PVD technique. The phase composition and microstructure evolution of the coatings were studied. The comparisions of the two coatings were made on their thermal conductivity, high−temperature thermal stability and resistance to molten CMAS. The results show that in the AYSZ/YSZ coating system, YSZ possesses feather structure while AYSZ exhibits "micro columnar crystal" structure at the microscopic level. Compared to YSZ coating, the porosity of AYSZ coating decreased by 12.6%, indicating that AYSZ is denser layer. The thermal conductivity of AYSZ coating at 1200℃ is only 0.94 W/(m·K), which is better than that of YSZ coating at the same temperature. Moreover, it maintains phase stability for a long time at 1400℃ and has excellent high−temperature stability. AYSZ coating exhibits certain resistance to melting CMAS corrosion, which is because of its dense "micro columnar crystal" structure, as well as the reaction sacrificial layer containing high melting point compounds such as CaAl₂Si₂O₈, MgAl₂O₄, and CaAl₄Si₂O₁₁ formed by the reaction between AYSZ coating and CMAS, hindering the penetration of CMAS into the interior of the coatings. The novel developed AYSZ coating has achieved efficient insulation, high−temperature stability, and resistance to molten CMAS corrosion, providing theoretical and technical guidance for the development of long−life and corrosion−resistant TBCs for aeroengines.

216−type metal halide perovskites (A₂BX₆) have garnered significant attention in recent years due to their environmental friendliness and excellent optoelectronic properties. However, large bandgaps, high production costs, and suboptimal photoluminescence (PL) limit the further applications in optoelectronics. To overcome these limitations, cation synergistic substitution at both the A−site and B−site has emerged as an effective strategy to tune and optimize their optical properties. In this study, Bi³⁺−doped Cs₂SnCl₆ and (BTP)₂SnCl₆ (BTP⁺=C₂₅H₂₂P⁺) were successfully synthesized via a cost−effective and high−yield solution crystallization method under varying temperature conditions. Structural and optical characterizations reveal that both Cs₂SnCl₆: xBi³⁺ and (BTP)₂SnCl₆: xBi³⁺ exhibit bright blue self−trapped excitons (STE) emission. Their luminescence properties can be effectively tuned by tuning Bi³⁺ doping concentrations, with excellent STE emission achieved at 5%. Notably, distinct differences in luminescence features, including Stokes shift, full width at half maximum (FWHM), PL decay time, and chromaticity coordinates, are observed in Cs₂SnCl₆: xBi³⁺ and (BTP)₂SnCl₆: xBi³⁺. These differences originate from the lattice isolation effects induced by the different sizes and functionalities of A−site cations, further influencing the dynamic behaviors of exited−state carrier. Based on these findings, a temperature−responsive dual−color fluorescent anti−counterfeiting seal was designed, demonstrating the practical application potential of these materials. Our strategy on cation synergistic substitution provides a theoretical and experimental guidance to develop novel tin−based perovskites.

The sustainable operation of wearable /implantable medical devices is crucial for the next generation of personalized medicine. However, limited battery capacity is a critical challenge for most wearable /implantable medical electronics. The human body is rich in mechanical and chemical energy (such as respiration, exercise, blood circulation, oxidation and reduction of glucose, etc.), so it is considered a feasible method to obtain mechanical energy from the body to supply power for wearable /implantable medical electronics. A variety of new methods for developing in vivo energy harvesters have been proposed to power wearable /implantable medical electronics. Based on this background, we here focus on the recent research progress of energy harvesters based on piezoelectric or triboelectric effects, with an emphasis on the fabrication, materials design, energy output, durability, as well as their typical applications in biomedicine and evaluation criteria. Finally, according to the actual needs of wearable /implantable medical electronics, the prospects and challenges of nanogenerators are discussed.

Taking western China as the research object, based on provincial panel data from 2013 to 2022, this study employs the "top−down" method to estimate transportation carbon emissions. Using the natural breaks method, standard deviational ellipse, and global Moran’s index, the evolution characteristics of transportation carbon emissions in western China are systematically analyzed from three dimensions: spatiotemporal distribution, center−of−gravity migration, and spatial correlation. The results show that transportation carbon emissions in western China generally exhibit a slow growth trend, with a spatially uneven distribution pattern characterized by "high in the north and south, low in the middle". The center of carbon emissions has shifted from the northwest to the southwest, demonstrating a clear centripetal aggregation trend. The findings provide a scientific basis for formulating differentiated and coordinated transportation carbon reduction policies in western China.

To scientifically assess the effectiveness of safety development in China's transportation sector, this study employs the SBM model and the GTWR model to evaluate the transport safety development efficiency across 30 provinces in China from 2005 to 2017. The results reveal a steady increase in overall efficiency, but with pronounced regional disparities, exhibiting a spatial pattern of "higher in the east and lower in the west." The evolution of efficiency also demonstrates significant spatial clustering and a "club convergence" effect. Factors such as road density and per capita vehicle ownership contribute positively to efficiency improvement, whereas education level, income, and industrial structure exert a negative influence in most regions. These findings indicate that improvements in traffic safety do not automatically accompany socioeconomic development. Accordingly, this study recommends advancing intelligent governance systems, establishing regional coordination mechanisms, and fostering public participation to enhance the overall effectiveness of traffic safety governance.

Since the 21st century, the Qian Xuesen Question has been a phenomenon − level event in the field of education. From when Qian Xuesen put forward the question in to when his relevant remarks were summarized as the Qian Xuesen Question, and then to the heated discussions it triggered, different versions of the Qian Xuesen Question have emerged. This paper focuses on sorting out the origins and development of various versions, and clarifies the false "additions". On this basis, it points out that the significance of the Qian Xuesen Question lies in guiding people to focus on the innovation of China's training model for outstanding talents, and promoting the education sector to take actions.At the same time, the Qian Xuesen Question is an open issue, which will be accompanied by the Chinese nation's desire for outstanding talents, and will not come to an end in the short term.

The rapid development of artificial intelligence (AI) is propelling scientific research toward the fifth paradigm, characterized by intelligence−driven inquiry, following the earlier paradigms of experiment, theory, computation, and data−driven science. This paper reviews the historical evolution of research paradigms, analyzes the logic of transformation from "human−centered" to "human–AI collaborative" research, discusses the development and current state of the large model era, and examines the applications of AI in research management, hypothesis generation, and academic writing and projects. The findings indicate that AI not only reshapes research processes and improves efficiency but also facilitates cross−disciplinary knowledge recombination and innovation, gradually forming an intelligent research ecosystem centered on large models and generative AI. At the same time, challenges such as hallucinations, poor interpretability, and ethical risks cannot be overlooked. Accordingly, this paper proposes building a trustworthy AI system for scientific research, advancing innovative models of human–AI collaboration, improving governance and policy frameworks, strengthening interdisciplinary integration, and enhancing mechanisms of research ethics and social responsibility, in order to ensure the sustainable development and orderly evolution of AI−driven research paradigms.

With the development of wireless communication and artificial intelligence technology, the number of small mobile devices is increasing rapidly, and the traditional wired power supply model can no longer meet people's requirements for portability and mobility. Radio frequency and microwave wireless energy transfer technology can get rid of the limitation of wired power, and has a greater application potential in wireless devices. Metasurface is a new kind of artificial material, which has unique advantages like regulating electromagnetic parameters and two−dimensional compact conformal. It is expected to play a key role in radio frequency and microwave wireless energy transfer. In wireless energy transfer systems, metasurface can generate energy beam at the transmitting terminal, enhance coupling resonance in the transmission path, and improve AC−DC conversion efficiency at the receiving terminal. So this paper took stock of the hot spots of this technology, and introduced the advanced scientific progress of wireless energy transfer integration wireless communication, wireless sensing, reconfigurable intelligent surface and target recognition & localization. The hot spots of wireless energy transfer metasurface technology, including wireless power transfer, wireless energy harvesting, simultaneous wireless information and power transfer, and reconfigurable wireless energy transfer technology.

Geographic Intelligence System (GeoIS), an emerging technological framework that integrates geographic science and artificial intelligence, is rapidly becoming a key driver for the reconstruction of spatial cognition and intelligent spatiotemporal decision−making. This paper systematically reviews the development trajectory of GeoIS and the latest international research advances. Focusing on the three core capabilities of perception, analysis, and decision−making, it identifies major shortcomings in China's GeoIS ecosystem—particularly in sensor development, algorithmic foundations, platform engines, and data governance. Based on this analysis, the study proposes a development pathway centered on "core technological breakthroughs–cross−domain integration–application−driven scenarios", and offers policy recommendations including standards development, security governance, and institutional support. The goal is to provide a reference for building an autonomous, secure, and co−evolving GeoIS system.

In recent years, large models have made breakthroughs in natural language processing and computer vision by virtue of their powerful sequence modeling capability, excellent representation learning potential, and flexible pre-training–fine-tuning paradigm, which also bring new development opportunities for time-series and spatio-temporal data intelligent analysis and are expected to revolutionize the analysis paradigm. This paper provides a systematic review of research progress on large models for time series and spatio-temporal data analysis, focusing on two major directions: empowering large language models and building dedicated foundation models. The former leverages prompt engineering, tokenization, and parameter-efficient fine-tuning to adapt large models to time series and spatio-temporal tasks, while the latter employs large-scale cross-domain pre-training to establish unified dynamic representations. The path of empowering large language models offers advantages such as low development costs and flexible zero-shot/few-shot transfer learning, while specialized foundational models demonstrate superior cross-domain generalization capabilities. At the same time, both approaches still face challenges including insufficient interpretability and difficulties in multimodal semantic alignment. Future research urgently requires breakthroughs in enhancing interpretability, advancing multimodal joint modeling, and innovating model architectures.

Object−oriented modeling is a fundamental paradigm in the field of geographic information system (GIS), offering distinct advantages in representing spatial structures, semantic relationships, and dynamic behaviors. This paper first provides a systematic review of the theoretical foundations and developmental trajectory of object−oriented spatial modeling, highlighting its central role and evolution within spatial cognition modeling frameworks. It then analyzes the technical progress and practical applications of this approach in key scenarios such as 3D urban modeling, watershed management, and landslide analysis. In response to the spatial intelligence demands of the foundation model era, this study proposes a pathway to empower geographic cognition through object−oriented spatial modeling, focusing on four key aspects: structured spatial semantic embedding, computable spatial relational logic, graph−based cognitive execution frameworks, and unified intermediate representation languages. The findings suggest that integrating object−oriented cognitive structures and dynamic process models can significantly enhance the capabilities of pre−trained models in spatial semantic understanding, causal reasoning, and complex task execution. This research provides a theoretical perspective and key element analysis for integrating GIS modeling with large model technologies, offering conceptual support and an exploratory framework for building spatially intelligent foundation models.

This article provides an overview of the domestic and international development of remote sensing multidimensional data formats, especially multidimensional spatiotemporal spectral data, the latest research results, current technical difficulties, and future development directions. This paper mainly introduces the multi−dimensional data format MDD (Multi Dimensional Dataset) proposed by the Aerospace Information Innovation Institute of the Chinese Academy of Sciences for the first time in the world, as well as the theory and technical system of multi−dimensional remote sensing data synthesis and representation, which fills the original gap in data organization in China and has a positive impact on international research. With the rapid development of remote sensing technology, multidimensional spatiotemporal spectral remote sensing data continues to emerge. This article aims to provide an overview of the domestic and international development of remote sensing multidimensional data formats, expound on the latest research achievements, analyze current technical difficulties, and look forward to future development directions, providing comprehensive references for researchers in related fields.

Due to technological limitations in early archaeological work, a large number of sites were documented only with vague textual descriptions, lacking precise geographic coordinates. This has posed significant challenges for subsequent archaeological investigations, site protection, and research. Traditional field survey methods are time−consuming and labor−intensive, making them unsuitable for large−scale or high−throughput site localization tasks. To address this issue, this study proposes an intelligent localization framework for archaeological sites based on large language models (LLMs). By designing tailored natural language prompts, the framework guides LLMs to automatically extract geographic descriptions—such as landmarks, directions, and distances—from online archaeological literature and records. It then integrates this information with high−resolution satellite imagery analysis to infer and calculate the precise geographic coordinates of the target site. This pipeline achieves full−process automation, covering text−based information extraction, remote sensing data retrieval, and spatial reasoning, thereby significantly improving the efficiency and intelligence level of site localization. Validation experiments conducted on the Laohushan Site, Sanxingdui Site, and Liao Zhongjing Site show that the deviation between the automatically inferred locations and actual surveyed coordinates is within one kilometer, with the best accuracy reaching approximately 10 meters—meeting the basic precision requirements of archaeological applications. Localization errors primarily stem from ambiguities in textual descriptions and uncertainties in image interpretation. Compared with traditional manual methods, this approach greatly reduces labor costs and offers enhanced scalability and application potential. The proposed method provides a novel technical pathway and tool support for the digitalization of archaeological information, site conservation, and further research.

The construction of well−facilitated farmland in China has imposed higher requirements on the digital and refined management of farmland, posing new challenges for the automatic extraction of multi−element information from remote sensing data. This study proposes a framework for the automatic extraction of multiple key farmland elements by integrating prompt engineering with remote sensing feature knowledge. Leveraging the general segmentation capabilities of the vision foundation model, combined with open−source data and feature−driven specialized algorithms, the proposed approach enables efficient automatic identification of critical farmland elements, including plots, field roads, shelterbelts, and irrigation and drainage facilities. Taking the well−facilitated farmland construction area in Shouguang City, Shandong Province as a case study, we conducted technical validation using high−resolution domestic satellite remote sensing imagery. Experimental results demonstrate that the proposed method can achieve batch automatic processing within farmland project areas, significantly reducing dependence on large amounts of high−quality training samples and manual workload. Moreover, it shows good generalization capabilities across images with different acquisition times, spatial resolutions, and regions, thus greatly improving data production efficiency and practical application potential. This research provides a novel approach for the intelligent interpretation of digitalized farmland areas and offers strong technical support for the application of remote sensing in post−construction supervision of agricultural engineering projects.

Land resource type is the basis for evaluating land suitability and development potential. The geomorphic pattern of "three mountains sandwiching two basins" and the significant landscape distribution characteristics of "mountain−oasis−desert" pose great challenges to the classification and utilization of land resources in Xinjiang. The paper aims to propose a grid−based fuzzy self−organizing feature maps (GF−SOFM) coupling classification method through the following steps: 1) A four−tier classification system was established based on dominant factors (climate+topography), stable factors (soil), relatively stable factors (vegetation), and dynamic factors (land use), comprising five factors with multiple indicators. 2) The study area was partitioned into 1 km×1 km grid units, where all indicators were spatially quantified. After fuzzy processing, the indicator data were input into SOFM model with dominant factors as control boundaries, enabling automated land resource type identification. 3) Area consistency test was conducted to compare the classification results of GF−SOFM method with those of the traditional thematic overlay method. The results indicate that Xinjiang was classified into 133 dominant factor types, 1906 stable factor types, 6054 relatively stable factor types, and 38493 dynamic factor types, achieving an average overall accuracy of 86%. The classification results of GF−SOFM method exhibited high spatial consistency with those of the traditional hierarchical overlay method, with 128 dominant factor types showing area consistency exceeding 92.35%. By refining topography classification and land use status, the GF−SOFM method effectively enables fine−scale land resource classification, accurately capturing the spatial patterns of climate−landform−soil−vegetation−land use. This approach serves as an ideal integrative classification method for physical geography, effectively indicating regional differentiation in ecological and geographical studies across varying climatic and landform regions. This research can provide a scientific basis for rational land development and utilization.

Rapid precise point positioning (PPP) over wide areas breaks through the dependency of traditional differential techniques on dense reference network, and serves as one of the key techniques to establish and maintain autonomous, wide-area, high-precision spatiotemporal framework using Global Navigation Satellite Systems (GNSS). This work provides a comprehensive review of the current development of satellite constellations, ground station infrastructure, and associated precise satellite products. Key technical advances are summarized in four respects, including multi-frequency and multi−GNSS integration, ambiguity resolution, atmospheric augmentation, and Low Earth Orbit (LEO) augmentation. All the analysis concentrate on their contributions to improving PPP positioning accuracy and convergence speed. Recent progress in PPP commercialization is discussed, alongside the latest developments in satellite-based PPP services, including BDS PPP-B2b. Finally, we outline persistent challenges and future research directions confronting wide-area rapid PPP, highlighting deep integration of high and low orbit constellations, multi-source fusion for positioning enhancement, and the evolution of next-generation satellite-based PPP service architectures. The study aims to support the large-scale deployment and service system development of high-precision positioning based on BDS/GNSS.

This article empirically studies the spatiotemporal differentiation and dynamic transfer characteristics of the open innovation level of 147 national high−tech zones from 2015 to 2020. Research has found that: the level of openness and innovation in world−class high−tech parks is higher than that in innovative technology parks, and innovative technology parks are better than innovative characteristic parks; the overall regional gap in the level of open innovation in the eight major national high−tech zones is widening, the gap in the level of open innovation in the southern coastal, northern coastal, and eastern coastal national high−tech zones is the largest, while the gap in the northwest national high−tech zone is the smallest; the regional gap in the level of open innovation in the eight major national high−tech zones has shown an expanding trend, and the growth rate of the regional gap has obvious regional heterogeneity, the regional gap has gradually become the main source of regional gap; without considering spatial correlation effects, its spatial transfer characteristics exhibit trends such as "asymmetric upward", "club convergence", and "stage differences".

In the early stages of Sino−American high−energy physics collaboration, gaps in cooperation demands, talent resources, and goal alignment posed significant challenges. Addressing these required effective communication channels. Due to his own characteristics, Tsung−Dao Lee has the control advantage of guiding the flow of elements of Sino−US scientific and technological cooperation. During the Beijing Electron−Positron Collider project, he acted as a trust, talent, and knowledge intermediary, facilitating alignment in policy−making, goal−setting and talent development between the two countries, thus effectively advancing Sino−American high−energy physics collaboration. This paper emphasizes the significance of intermediary roles in international scientific and technological cooperation under the situation of asymmetric and offers insights for future collaborations.

Due to their excellent physical and chemical properties, two−dimensional materials have become potential stocks in the fields of integrated circuits, wearable technology, medical monitoring, etc. in the "post−Moore era", and have become a research hotspot in the current academic and industrial circles. Firstly, the bottlenecks and challenges faced in the post−Moore era are introduced. Secondly, the development history, preparation methods, ultra−thinness, adjustable band gap and ultra−high mobility of two−dimensional materials are introduced, and the application prospects of two−dimensional materials in chips, flexible sensors, energy storage, optoelectronic devices and other fields are analyzed. Then, the problems faced by two−dimensional materials in practical applications, such as large−scale preparation, high process complexity, and large differences between actual tests and expected theoretical values, are discussed. It is proposed to make up for these shortcomings through new atomic catalysts, roll−to−roll transfer technology, hydrogen passivation and other means to explore the potential of two−dimensional materials. Finally, the direction and path of the development of two−dimensional materials are summarized, emphasizing the importance of silicon−based transformation of two−dimensional materials, multi−dimensional innovation and industrialization promotion.

This paper reviews corrosion big data technology and its applications in the intelligent engineering, in systematically. Corrosion serves as an essential factor, which threatening the service safety and service life of engineering materials. Corrosion data exhibits complicated characteristics, such as multi−source heterogeneity, long duration, cross−scale, and non−linearity. These characteristics, corrosion big data technology integrate various sensor technologies and establishing multi−dimensional intelligent correlation databases, which can support the mining and visualizing of material corrosion big data to achieving the construction of corrosion big data sharing platform and engineering applications services. In intelligent engineering applications, corrosion big data technology possesses three core functions. First, real−time monitoring technologies had been applied on recording the corrosion status of facilities such as bridge steel structures and oil−gas transmission pipeline sin dynamically, combined with high−throughput collection of multi−source heterogeneous corrosion data, to instantly record corrosion rates and environmental parameters for systematic data gathering; Analyzing the coupling laws between corrosion data, environmental factors, and operational conditions through multi−source data mining technology to support dynamic optimization of anti−corrosion strategies; Achieving precise prediction of the service life of engineering, based on artificial intelligence models (e.g., neural networks), combined with accumulated corrosion big data and machine learning algorithms, providing quantitative basis for engineering safety operation and maintenance. Furthermore, by integrating with digital twin technology, corrosion big data constructs a 3D virtual model of corrosion process to achieve visual warning of engineering maintenance status. The joint construction of corrosion big data sharing platforms is promoted and advancing the traditional anti−corrosion technology towards to closed−loop management system, "perception−diagnosis−decision−execution". Corrosion big data technology supports and promotes the intelligent and precise transformation of traditional anti−corrosion technologies, and support the safety operation and maintenance system of smart engineering, demonstrating broad application prospects in fields such as marine engineering and energy internet.

The thermal barrier coatings (TBC) and environmental barrier coatings (EBC) are essential for advanced gas turbine engines, and their development history is briefly reviewed in this article. In order to champion the enormous challenge from much harsher operating conditions in next generation engines, numerous innovations of coating materials and novel designs of coating microstructures have been investigated. The most potential paths to develop new generation TBC and EBC are now becoming increasingly clear. The best novel TBC system may be based bond coat of nanocrystalline γ' phase and top coat of low thermal conductivity La₂Zr₂O₇ or YTaO₄, because the former is excellent chemically and mechanically compatible to single crystal Ni−base superalloy substrates, and leads to lower thermal stresses, and the latter is structurally stable at much higher temperatures, and has superior resistance against CMAS attack and high CTE similar to YSZ. One of the best ceramic candidates for thermal / environmental barrier coatings is the high−entropy rare earth silicates based on β−Yb₂Si₂O₇, as it is resistant against CMAS and steam corrosion, and has extremely low thermal conductivity and good CTE match with CMC. A further topic of concern is dual−phase ceramics technologies, which are effective in fracture toughness enhancement and capable of improving corrosion resistance and thermal barrier capability.

Classical corrosion electrochemistry based on mixed potential theory has played an important role in advancing research on corrosion and protection. However, it is also essential to recognize the multi−reaction coupling, non−equilibrium, and irreversible nature of corrosion processes, which often leads to excessive simplifications in the Butler–Volmer and Nernst–Planck equations and a weakened focus on the individual electrode reactions that constitute corrosion. Starting from the fundamental corrosion equation, this work clarifies the connotation of corrosion electrochemistry and reviews the advantages and recent progress of four representative scanning probe techniques—scanning electrochemical microscopy (SECM), scanning vibrating electrode technique (SVET), localized electrochemical impedance spectroscopy (LEIS), and scanning electrochemical cell microscopy (SECCM)—in probing corrosion reaction kinetics, monitoring spatially distributed species, and mapping corrosion activity. High−resolution scanning probe methods have been shown to detect corrosion sites as small as a few nanometers and corrosion currents at the picoampere level, enabling in−situ monitoring of the spatial heterogeneity and kinetics of corrosion processes. When further combined with computational modeling, these techniques allow for quantitative comparative analysis of corrosion data. Finally, the paper summarizes and discusses future trends in modern corrosion electrochemistry, suggesting that further research should be rooted in the intrinsic nature of multi−reaction−coupled, non−equilibrium, irreversible corrosion processes, deeply integrating multi−scale characterization, and establishing a dialectical unity between macroscopic and microscopic perspectives.

High−entropy alloys (HEAs) exhibit significant application potential in extreme service environments due to their outstanding overall properties, with corrosion resistance being a critical factor determining their service life and reliability. This review systematically summarizes recent advances in the corrosion behavior and mechanisms of HEAs, highlighting the influence of alloy composition and atomic ratio adjustments on corrosion performance, as well as the effects of thermomechanical processing, such as heat treatment and rolling, on microstructure and passive film characteristics. Studies indicate that compositional design and process optimization can substantially alter the corrosion response and passivation behavior of HEAs, thereby affecting their overall corrosion resistance. Future research should focus on further elucidating localized corrosion mechanisms and the evolution of passivation films, integrating machine learning and multiscale simulations for intelligent alloy design, and establishing comprehensive evaluation frameworks that balance mechanical properties, corrosion resistance, and cost−effectiveness. Collectively, this review provides a systematic overview and reference for the design and application of corrosion−resistant HEAs.

Engineering equipments serving in extremely harsh deep−sea environments will withstand complex environmental factors such as low temperature, low dissolved oxygen, low pH, pollutants, and microorganisms, as well as the effects of hydrothermal, ocean currents, hydrostatic pressure, and complex loads, which pose serious threats to the safety of these equipments in service. Titanium alloy, as a highly corrosion−resistant marine structural material, plays a crucial role in the manufacturing of deep−sea engineering equipments. However, the service behavior and failure mechanism of titanium alloy in extreme deep−sea environments are still unclear, leading to a lack of scientific basis for the selection, design, and protection of titanium alloy structures in deep−sea equipment. This paper analyzes the corrosion resistance and basic corrosion electrochemical characteristics of titanium alloys, systematically reviews the problems of microbial corrosion, crevice corrosion, galvanic corrosion, hydrogen embrittlement and stress corrosion, corrosion fatigue, and multi−factor coupled corrosion damage faced by titanium alloy materials in extreme deep−sea environments, and proposes the research focus and development direction of titanium alloy corrosion in deep−sea environments.

This paper reviews the latest research advancements and hot applications of Metal Additive Manufacturing in 2024, covering areas such as the development and application of new materials, breakthroughs in manufacturing processes, improvements in automation and intelligence, new progress in software development, the latest applications in key industries, and new dynamics in industry standards and policies. With the continuous emergence of new metal alloy materials, metal additive manufacturing has been widely applied in industries such as aerospace, automotive, and healthcare. Innovations in manufacturing processes and the integration of intelligent technologies have significantly improved production efficiency and quality control. Despite challenges such as high material costs and low production efficiency, metal additive manufacturing shows promising prospects in the ongoing development of technology, standards, and markets, especially in the fields of intelligent manufacturing, green manufacturing, and personalized customization, with the potential for broader applications in the future.

GaN Schottky diodes offer significant advantages, including high electron mobility, low on−resistance, and high integration capabilities. These characteristics make them well−suited for applications in power electronics and microwave radio frequency (RF) fields, positioning them as a key enabler for advancing cutting−edge technologies. This paper provides an overview of recent research progress on GaN Schottky diodes, with a particular emphasis on how device structure influences performance. Through continuous structural optimization, the performance of GaN Schottky diodes has been substantially enhanced, demonstrating improvements in both forward and reverse characteristics. Their application scope has expanded from high−voltage environments to RF circuits. Meanwhile, it is recommended that future efforts focus on overcoming existing technical limitations by improving material quality, enhancing device reliability, and reducing manufacturing costs. With the sustained growth of China's integrated circuit industry, GaN Schottky diodes, benefiting from their superior high−frequency and high−voltage performance, are expected to become core components in next−generation electronic devices. In the future, these diodes will find broader applications across various domains and play a more significant role in addressing practical challenges and advancing industrial innovation.

Recently, the XR industry has demonstrated a thriving development trend in all aspects and at multiple levels, achieving remarkable progress in the fields of hardware, software, and content. At the hardware level, pivotal breakthroughs have been made in micro−display technology, significantly enhancing the visual experience indicators of XR devices. Meanwhile, multi−modal intelligent interaction has greatly expanded the dimensions and precision of the interaction between users and the XR environment. In the software aspect, the competition in the open−sourcing of XR operating systems has intensified, accelerating the evolution of the industry ecosystem towards a more open and diversified direction. In the field of content, diversified cultural and tourism large−scale space projects have instilled substantial vitality into the XR content creation ecosystem. The research reveals that during the deep integration of core software, content creation, and industry applications, bottleneck issues such as inconsistent technical standards and poor compatibility exist. Based on these findings, this paper proposes that it is necessary to increase investment in research and development in key areas such as chip and display technologies, enrich the content creation ecosystem, deepen and expand industry application scenarios, strengthen the collaboration between the upstream and downstream of the industrial chain, and improve the relevant standard system. Finally, it is concluded that the XR industry is moving towards the coordinated development of the entire industrial chain and is expected to establish a high−quality industrial development pattern featuring advanced technologies, diverse products, excellent services, and extensive applications.

Glioblastoma (GBM), the most aggressive and lethal form of brain cancer, remains a formidable clinical challenge due to its molecular heterogeneity, pronounced invasiveness, the restrictive nature of the blood−brain barrier (BBB), and an immunosuppressive tumor microenvironment. Conventional therapeutic modalities comprising surgical resection, radiotherapy, and chemotherapy, offer limited efficacy, with the 5−year survival rate remaining below 10%. Recent advances in nanotechnology have enabled the rational design of nanocomposite drug systems capable of penetrating the BBB, enabling site−specific drug delivery, and reducing systemic toxicity. These multifunctional nanoplatforms not only enhance the efficacy of chemotherapeutics but also allow integration with immunomodulators, genetic tools, and imaging agents for synergistic multimodal therapies. This review critically examines the clinical and biological landscape of GBM, highlights recent breakthroughs in nanocomposite drug design, and discusses the translational hurdles and future directions toward clinical implementation. Together, these insights offer a forward−looking perspective on leveraging nanotechnology for precision therapy in GBM.

Unsustainable modern−day agriculture has emerged as a crucial driving force behind the deterioration of the global ecological environment. Existing research is predominantly confined to single−disciplinary perspectives and lacks a comprehensive framework for systematically evaluating the feedback mechanisms between agricultural−induced environmental issues and climate change. Based on the planetary boundaries framework theory, the study systematically assesses agricultural ecological−environmental challenges and their multidimensional manifestations under climate change. It thoroughly examines action mechanisms, impact magnitudes, and geographical distribution patterns while analyzing interactive feedback effects among key elements. Furthermore, development pathways and policy recommendations are proposed to address climate challenges, encompassing: promoting smart agriculture initiatives, advancing green agricultural supply chain transformation, guiding sustainable dietary transitions, enhancing scientific innovation and Research and Development investment, and improving policy systems for agricultural green transition. Additionally, five critical research directions are identified to advance this field, aiming to provide theoretical foundations and practical guidance for sustainable agricultural development under climate change. These proposals seek to facilitate agriculture's transformation into a climate−resilient and sustainable system while offering strategic references for global stakeholders in agricultural sustainability.

The 2024 Nobel Prize in Physiology or Medicine was awarded to Victor Ambros and Gary Ruvkun "for the discovery of miRNA and its role in post−transcriptional gene regulation". The article reviews the discovery journey of miRNA and its role of regulation, proposing that always pursuing one's own interests and constantly delving deeper, not afraid to pause temporarily, learning to simplify complex research are all crucial factor for supporting researchers to climb the peak of scientific research.

AI+钢铁下一阶段的主要目标是全流程一体化的AIGC+钢铁，数字换脑，模型换代，登顶RS（ROBOTSTEEL），完成钢铁工业中国式现代化的光荣任务。

日地空间是当前航天活动、空间开发利用的主要区域，被认为是陆、海、空环境之外，人类活动的“第四环境”。太阳活动引起的日地空间环境在短时间尺度上的变化，被称为空间天气。灾害性的空间天气会对卫星、通信、导航、电力系统等造成不良影响，亟需联合全球空间天气监测与研究力量开展科学攻关。