Clinical trials for rare diseases face significant challenges, including scarce patient samples, high heterogeneity, and considerable difficulties in research and development. Patient participatory design, guided by the principles of "how", "for whom" and "with whom", facilitates the transition of patients from passive "subjects" to active "research participants," with the aim of better addressing patient needs, optimizing trial design, and enhancing research quality. Adopting the perspective of design ethics, this article systematically reviews the ethical guidelines and frameworks for patient involvement in clinical trial design proposed by institutions such as the UK National Institute for Health and Care Research and the US Patient−Centered Outcomes Research Institute, offering a comparative analysis of their core principles and content. On this basis, the article critically examines key ethical issues faced by patients with rare diseases during the design process, including equitable participation, the establishment of partnership, privacy protection, and the identification of core needs. It also explores the tensions encountered when applying universal guidelines to the context of rare diseases. The study proposes the establishment of values centered on equitable collaboration, ensuring transparent and accessible information exchange, constructing a lifecycle engagement process, and carefully addressing the conflict between the vulnerability of patient populations and the inherent uncertainties of research. This article aims to provide a theoretical foundation and practical reference for refining the ethical governance system in the design phase of rare disease clinical trials and for advancing the paradigm shift towards "patient−centric" research.

As three−dimensional (3D) in vitro models, organoids recapitulate many aspects of the complex structure and function of the corresponding in vivo tissue. This review summarizes recent advances in organoid biofabrication technology. It identifies key development trends focusing on cutting−edge directions such as multi−process integration, multimodal detection and analysis, and automated intelligent systems. Furthermore, it discusses the application progress of organoid biofabrication in disease modeling and mechanistic insights, regenerative medicine and tissue repair, personalized therapeutics and drug development, as well as research in space microgravity environments. Addressing the current landscape in China, this review proposes improvement strategies focusing on biomaterial innovation, equipment technology advancement, and clinical translation pathways, with the aim of accelerating the innovation and application of organoid biofabrication technology in China.

Grassland health evaluation is a key technical means to measure the structure and function of grassland ecosystems and to support ecological security and sustainable resource utilization. However, traditional evaluation methods have limitations such as strong subjectivity and insufficient spatio−temporal continuity in index system construction and large−scale dynamic monitoring. This paper proposes an intelligent evaluation method for grassland health based on multi−source spatio−temporal data and machine learning, and constructs an intelligent research framework covering "data collection—feature extraction—index construction—health evaluation—management decision−making." By integrating field sampling data with multi−source remote sensing data, this method introduces expert knowledge to construct the Grassland Health Index (GHI) and utilizes machine learning models to achieve pixel−scale quantitative inversion and dynamic monitoring of long−term sequence grassland health conditions. To verify the effectiveness of this method, Ningxia, which has implemented region−wide grazing exclusion for nearly 20 years, was taken as a typical application scenario. The results show that the machine learning method significantly improved the accuracy of various evaluation indicators, with the R² of spatial simulation for grass yield reaching 0.88. From 2012 to 2022, the grasslands in Ningxia were generally at a healthy level (GHI>80), remained stable overall, and the ecosystem showed a recovery trend. There was significant spatial heterogeneity in grassland health; due to differences in land use patterns and precipitation gradients in local areas, degradation risks still require continuous attention. The intelligent evaluation method proposed in this study has good operability and extensibility, providing technical support for grassland ecological health diagnosis, degradation risk early warning, and sustainable management in different regions, as well as providing a scientific basis for the optimization of grassland ecological subsidy policies and resource security decision−making.

Noisy multivariate prediction is a common challenge in fields such as environmental science, transportation, and industry. The core difficulty lies in balancing noise filtering with multi−scale feature capture. To address this, a hybrid model (Kalman−LSTM−Transformer) based on Kalman filter, long short−term memory (LSTM), and Transformer is proposed. This model captures local temporal and global dependencies while filtering noise, and integrates Bayesian optimization to achieve efficient and accurate prediction. Using open−pit mine dust concentration prediction as a case study, experiments based on 1−year of monitoring data demonstrate that the model outperforms baseline models, reducing the root mean square error (RMSE) by 21.70%–27.19% and the mean absolute error (MAE) by 6.68%–18.30%, while achieving a coefficient of determination (R²) of 0.934. Ablation experiments and hyperparameter analysis results further confirm the effectiveness of each module. The model exhibits transferability to similar scenarios, providing support for intelligent early warning and precision management across multiple domains.

To address the needs for ecological monitoring in arid and semi−arid areas, this study proposes a synergistic inversion and digital representation framework for shrub aboveground biomass (AGB) driven by the fusion of UAV multispectral and LiDAR features. Leveraging the 3D structural sensing advantages of UAV−LiDAR and the spectral−texture features of UAV−MS, the study establishes a technical workflow of "object segmentation−feature selection−synergistic inversion." This framework enables the automatic identification and precise biomass accounting for typical shrubs such as Artemisia ordosica and Salix psammophila. Taking seven typical shrub communities in the Ordos region as the study area, technical validation was conducted based on ground−truth data. Experimental results demonstrate that the proposed method effectively overcomes the limitations of "same spectrum, different objects" in single optical remote sensing and the lack of spectral information in single LiDAR data. The XGBoost model achieves the best comprehensive performance under multi−source feature synergy (R² ranging from 0.7615 to 0.8814). It exhibits good generalization capabilities across different plant types and complex backgrounds, realizing the digital representation of shrub ecological assets and significantly improving the data production efficiency and technical reliability of biomass monitoring in arid and semi−arid areas.

An intelligent prediction framework for deep fluid storage potential is proposed by integrating operational monitoring data with reservoir structural features. By introducing machine learning and time−series feature–driven modeling strategies, the proposed workflow enables automated identification of injection pressure evolution patterns and accurate prediction of storage−stage responses. The method combines long−term high−frequency pressure–flow monitoring data with pore–fracture structural information derived from core−scale analysis, and establishes a stage−labeled, phase−wise prediction mechanism to improve model robustness through multi−model comparison and cross−validation. The framework was validated in a deep injection and storage engineering scenario involving highly saline fluids, using an 18−month field dataset with a cumulative injection volume exceeding 1.5 million tonnes. Results demonstrate that the proposed intelligent workflow can effectively distinguish key operational stages, including breakthrough and filling phases, and significantly enhance pressure prediction accuracy, with the best model achieving a MAPE as low as 0.6. The method reduces dependence on complex mechanistic models and large labeled datasets, and shows strong generalization across different operational periods and stage conditions. Owing to its scalability and transferability, the proposed approach provides technical support for intelligent assessment and safe operation of various deep fluid injection and storage projects.

Accurately acquiring the spatial distribution of open−pit mines is vital for "green mine" development and dynamic geological monitoring. To overcome the inherent challenges of dataset scarcity, drastic intra− and inter−class differences, and complex topological structures in this field, we propose a segmentation framework integrating hybrid semantic prompting and topological awareness. We constructed a specific dataset named Mine Semantic Segmentation (MSS). MSS contains 7,622 finely annotated images of open−pit mines. Based on MSS, we propose an instance segmentation method called Mine Segment Anything Model (Mine−SAM). Mine−SAM employs a dual−encoder structure. It also utilizes multi−scale feature aggregation techniques. The model couples global context from foundation models with local fine−grained features from expert models. Mine−SAM achieved an Average Precision box (P_A,box) of 64.4%. The Average Precision mask (P_A,mask) score reached 65.2%. In addition, we developed a semantic segmentation method named SemMSeg. This method combines graph convolutional networks (GCN) with pixel−level contrastive learning. The GCN captures spatial dependencies among mining elements. It also enforces structural constraints within the model. SemMSeg achieved an Intersection over Union (IoU) of 73.38%. The precision of the method reached 85.03%. These techniques provide a technical path for automatic mine monitoring. The findings contribute to the intelligent interpretation of remote sensing imagery.

Covalent organic frameworks (COFs), featuring designable topological structures and tunable pore architectures, have shown promising potential as cathode materials for high−performance lithium−ion batteries (LIBs). However, the energy density and cycling stability of COFs−based cathodes remain difficult to further improve due to their single type of redox−active centers (n−type or p−type) and intrinsically low electrical conductivity. To address these limitations, a highly conjugated copper porphyrin−based covalent organic framework with bipolar redox−active centers (TBP−COF−Cu) was constructed. The incorporation of Cu²⁺ ions into the porphyrin units significantly enhances the electronic transport capability of the framework, improves the utilization efficiency of active sites, and effectively promotes lithium−ion diffusion kinetics. When employed as a LIB cathode, TBP−COF−Cu delivers a high specific discharge capacity of 288 mA·h/g at 0.1 A/g, corresponding to an energy density of 639 W·h/kg. Even at a high current density of 5 A/g, a capacity of 81 mA·h/g is retained. After 5000 charge–discharge cycles, the capacity decay rate is as low as 0.0038%, with a capacity retention of 81%. In addition, TBP−COF−Cu exhibits fast ion transport behavior, with a lithium−ion diffusion coefficient of 8.02×10⁻¹⁰ cm²/s. This work provides an effective strategy for designing organic LIB cathode materials that simultaneously achieve high energy density and high−rate performance.

To meet the needs of beamforming in broadband phased−array radar systems, optical beamforming technology achieves mechanical−scan−free operation by dynamically controlling the phase distribution of optical beams. It offers many advantages such as high precision, rapid response, and integration, demonstrating broad application potential across radar, communications, electronic warfare, and other fields. As a critical component in optical phased−array beamforming systems, the amplitude and time−delay accuracy of optical true−time−delay channels directly impact radar beam quality. Optical delay lines not only introduce different random amplitude and time delay inconsistencies, but also introduce inherent errors of the system. By analyzing the principles of phased arrays, the effect of ±2 dB amplitude and ±3 ps delay errors on beamforming was simulated. The result shows that the sidelobe suppression degrades by 13 dB under 30 dB channel weighting, beamwidth broadens, and the beam pointing deviates from the design value. Additionally, the beam distortion caused by accumulated nonlinear dispersion effect in dispersion−based delay line architectures is analyzed. The test system is constructed, and the tested results are consistent with the simulated results.

The low−altitude economy, as a national strategic emerging industry, is becoming a new engine driving future economic development. It is characterized by heterogeneity, high density, high frequency, and high complexity. In response to this trend, low−altitude security urgently needs to shift from key area protection to comprehensive security management, addressing not only unauthorized drone flights but also the integrated supervision of cooperative and non−cooperative targets. However, domestic capabilities in detecting and controlling UAVs remain inadequate, posing serious challenges to both public safety and national security. This paper first analyzes the key challenges in detecting low, slow, and small (LSS) targets, including coping with agile and unpredictable targets, swarm−flying targets, as well as new types of drones such as fully autonomous and infrared/fiber−optic guided models. Then the paper compares the advantages and disadvantages of existing detection technologies, noting current problems such as the difficulty of adapting to different scenarios when using a single detection technology, and the inefficiency of coordination when combining multiple detection devices. Based on this analysis, it proposes measures for detecting and managing LSS targets, including focusing on the development of technologies such as wideband imaging and integrated sensing−communication, as well as using multi−source and multi−platform information fusion to build a networked collaborative detection system, thereby achieving real−time awareness of the low−altitude airspace. Furthermore, it explores integrated approaches combining technological tools with regulatory frameworks to enable efficient supervision of LSS targets through a combination of technological and policy measures. These include the formulation of laws and regulations, the enforcement of penalties for violations, and the establishment of regulatory systems. Finally, the paper proposes starting from pilot programs, with a planned and phased approach to the development and utilization of low−altitude airspace, all aimed at ensuring the healthy development of the low−altitude economy and maintaining the security and controllability of low−altitude airspace.

Shen Guofang, a renowned Chinese forestry scientist, ecologist, and forestry educator, is an academician of the Chinese Academy of Engineering and a founding figure of silviculture in China. His research career has kept pace with the development of the China's forestry sector. By reviewing his seventy−year journey from his studies in the former Soviet Union to his lifelong dedication to China's forestry, this paper outlines his academic contributions and illuminates his embodiment of the scientific spirit characterized by patriotism, innovation, pragmatism, dedication, collaboration, and mentorship. Upon returning to China, he conducted extensive research in the rocky mountainous areas of North China, laying the foundation for the theory of "site−species matching principle". He founded the discipline of silviculture and led the compilation of China's first domestically−focused textbook Silviculture, driving the discipline’s shift from the mere adoption of foreign models to independent innovation. He also proposed the iconic "3−5−7" index system for fast−growing, high−yield plantations, advocated for the Natural Forest Protection Program, and contributed to the holistic and systematic governance of mountains, rivers, forests, farmlands, lakes, and grasslands. In all his work, he lived out the ethos of "crafting the finest chapters in the book of the motherland". His academic life epitomizes the history of modern Chinese forestry and vividly reflects the patriotic devotion of a generation of Chinese scientists.