Recently, large models have shown significant potential for smart healthcare. However, the deployment of Large Vision-Language Models (LVLMs) for clinical services is currently hindered by three critical challenges: a tendency to hallucinate answers not grounded in visual evidence, the inefficiency of fixed-depth reasoning, and the difficulty of multi-institutional collaboration. To address these challenges, in this paper, we develop MedAlign, a novel framework to ensure visually accurate LVLM responses for Medical Visual Question Answering (Med-VQA). Specifically, we first propose a multimodal Direct Preference Optimization (mDPO) objective to explicitly align preference learning with visual context. We then design a Retrieval-Aware Mixture-of-Experts (RA-MoE) architecture that utilizes image and text similarity to route queries to a specialized and context-augmented LVLM (i.e., an expert), thereby mitigating hallucinations in LVLMs. To achieve adaptive reasoning and facilitate multi-institutional collaboration, we propose a federated governance mechanism, where the selected expert, fine-tuned on clinical datasets based on mDPO, locally performs iterative Chain-of-Thought (CoT) reasoning via the local meta-cognitive uncertainty estimator. Extensive experiments on three representative Med-VQA datasets demonstrate that MedAlign achieves state-of-the-art performance, outperforming strong retrieval-augmented baselines by up to 11.85% in F1-score, and simultaneously reducing the average reasoning length by 51.60% compared with fixed-depth CoT approaches.

Smart contracts play a significant role in automating blockchain services. Nevertheless, vulnerabilities in smart contracts pose serious threats to blockchain security. Currently, traditional detection methods primarily rely on static analysis and formal verification, which can result in high false-positive rates and poor scalability. Large Language Models (LLMs) have recently made significant progress in smart contract vulnerability detection. However, they still face challenges such as high inference costs and substantial computational overhead. In this paper, we propose ParaVul, a parallel LLM and retrievalaugmented framework to improve the reliability and accuracy of smart contract vulnerability detection. Specifically, we first develop Sparse Low-Rank Adaptation (SLoRA), a technique for efficient LLM fine-tuning tailored to smart contract vulnerability detection. Distinct from existing LoRA methods, SLoRA inserts parallel sparse and low-rank branches after the attention projection and the feed-forward block, enabling LLMs to capture both global code semantics and localized vulnerability patterns while maintaining low training overhead. We then construct a vulnerability contract knowledge base and develop a hybrid Retrieval-Augmented Generation (RAG) system that integrates Okapi BM25 with dense retrieval to provide complementary lexical and semantic evidence for smart contract vulnerability verification. Furthermore, we propose a meta-learner-based gated verification module to fuse the outputs of the SLoRA detector and the two RAG-based detectors, thereby generating the final detection results. After completing vulnerability detection, we design chain-of-thought prompts to guide LLMs to generate comprehensive vulnerability detection reports. Simulation results demonstrate the superiority of ParaVul, especially in terms of F1 scores, achieving 0.9398 for single-label detection and 0.9930 for multi-label detection.

Snow-covered forests are widely distributed in middle- and high-latitude regions of the Northern Hemisphere and have significant impacts on global climate change and albedo feedback. However, knowledge of the radiative transfer mechanism in snow–canopy–atmosphere systems is insufficient. Existing bidirectional reflectance models often oversimplify, assuming that snow only persists on the floor, and neglect the interactions between the atmosphere and snow-covered forests. In our previous snow-covered forest bidirectional reflectance (SFBR) model, we considered ground snow, the concentration of soot pollution, needle leaf characteristics, and discontinuous canopy distributions. Furthermore, this study proposed an improved snow-covered forest bidirectional reflectance (SFBR2) model by considering canopy-intercepted snow (CIS) and atmospheric effects. Specifically, the SFBR2 model was constructed by a series of analytical models for CIS, ground snow (asymptotic radiative transfer (ART) snow model), soil [brightness shape moisture (BSM)], needle leaf [leaf incorporating biochemistry exhibiting reflectance and transmittance yields (LIBERTY)], canopy [two-layer version of four-stream scattering by arbitrarily inclined leaves (4SAIL2)], and atmosphere [simplified method for atmospheric correction (SMAC)], in which the CIS is parameterized by snow optical properties and two-stream theory, and the interaction between the atmosphere and snow-covered forests is optimized by a four-stream theory. It makes the model able to simulate the reflectance at the top of the canopy/atmosphere (TOC/TOA). Validations against 3-D large-scale remote sensing data and image simulation framework over heterogeneous 3-D scenes (LESS) model, unmanned aerial vehicle (UAV) observations, and MODIS data indicated a good consistency with SFBR2 model in the reflectance simulation. The established model has the ability to simulate the bidirectional reflectance of different snow-covered forest scenes whether CIS exists or not. Potential applications include satellite signal simulation, radiation mechanism analysis, and parameter inversion in snow-covered forests.

It is promising to improve MODIS snow cover mapping by training effective machine learning models. However, considering the strong spatiotemporal heterogeneity of snow cover, the representativeness of training samples becomes a critical issue to ensure the model effectiveness over large areas and long terms. To deal with this issue, we propose them-day dynamic training strategy. The core of this strategy is to divide a long-term snow cover mapping task into multiple short-term tasks with consecutivemdays (mis greater or equal to the number of days that higher-resolution remote sensing images used as equivalent ground references cover the study area once). It can ensure the spatial representativeness of training samples by utilizing all available equivalent ground references data, and reduce the issue caused by snow cover changing over time. We apply this strategy to random forest for both binary (BSC) and fractional (FSC) snow cover mapping using Landsat-8 data as equivalent ground reference and validate its effect on the Tibetan Plateau. Results show that this strategy achieves higher accuracies than other training strategies, withF1of 0.9596 for BSC mapping,RandMSEof 0.9037 and 0.0260 for FSC mapping, respectively. In addition, spatiotemporal analysis of results further demonstrates that this strategy holds advantages for snow cover mapping in areas of complex terrain and in periods when snow cover changes rapidly. In conclusion, this strategy can effectively ensure the representativeness of training samples generated from Landsat-8 data and thus improve MODIS snow cover mapping through machine learning.