聚变科学与工程资源库
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Code for: Phenological Fluctuations Induce Seasonal Asymmetric Warming in Boreal Forests
ZenodoCode accompanying the manuscript "Phenological Fluctuations Induce Seasonal Asymmetric Warming in Boreal Forests" (Nature Communications, 2026). The repository contains the Google Earth Engine JavaScript scripts for seasonal compositing of the MODIS products, quality masking, the persistent-forest mask and computation of the leaf area change and change-rate layers, and the Python and R scripts for the paired-grid matching, the inverse distance weighting climate-background removal, the moving-window Huber regression, the asymmetry index, the structural equation modelling and the robustness analyses (block bootstrap, baseline stratification and truncation sensitivity). All input data are public MODIS products and the FLUXNET2015 dataset, as listed in the Data Availability statement of the paper.
Sparse Quotient-Threshold Transform (SQTT): benchmark data, reference code, and reproducibility package
University of PhayaoThis version 1.0.0 research compendium supports the computational evaluation of the Sparse Quotient-Threshold Transform (SQTT), an exact method for computing all outputs of a knapsack-threshold transform and its additive generalized-assignment extension. The compendium contains the MATLAB reference implementation, declared exact comparators, instance generators, correctness checks, the frozen primary experimental protocol, analysis scripts, preservation-friendly data tables, figure-source data, rendered figures, and SHA-256 artifact manifests. The empirical data comprise 1,788 completed full-benchmark case-method trials over 610 generated cases and a balanced 30-run scalar scalability panel for k = 18 and k = 20. The balanced panel is a post-inspection exploratory supplement and is not part of the prespecified primary benchmark. Eight accepted k = 22 observations are supplied in a separate exploratory resource-boundary dataset: three completed outcomes and five verified memory-limit outcomes. These k = 22 observations are not included in the primary runtime comparisons, and no missing runtime is imputed. The statistical unit is the generated case rather than an individual timing repeat. Timing repeats are summarized within each case. Paired log-speed ratios are defined as ln(baseline time / SQTT time), and uncertainty is reported using stratified case-level percentile bootstrap 95% confidence intervals. The archive includes the derived case-level dataset, summary tables, performance-profile curves, scaling summaries, resource-boundary summaries, and an independent correctness gate covering 2,000 scalar and 500 generalized-assignment cases with zero failures. Code is released under the MIT License. Data, tables, figures, protocols, and documentation are released under Creative Commons Attribution 4.0 International. File-level provenance and SHA-256 checksums are provided in the package manifest.
Vision-Language Models for Low-Vision Navigation Assistance in Cluttered Environments
Birla Institute of Technology and Science, PilaniNavigating busy, unstructured roads is a daily challenge for people with visual impairments, while many assistive systems do not explicitly account for the clutter and heterogeneous traffic found in Indian street environments. We present an end-to-end vision-to-audio navigation pipeline that transforms monocular video frames into priority-ordered spoken directional commands. The reported system combines a YOLOv8n detector fine-tuned on the Indian Driving Dataset (IDD), MiDaS DPT-LeViT monocular depth estimation, DeepSORT tracking, field-of-view-aware spatial reasoning, temporal smoothing, and an eight-layer rule-based navigation planner. The planner maps perceived hazards to concise commands such as STOP, AVOID, MOVE LEFT/RIGHT, and CONTINUE FORWARD, while a smart text-to-speech gate suppresses passive descriptions when an urgent command is pending. An optional BLIP-based visual validation stage is used in image mode. On the reported 60-frame manually annotated Indian-street evaluation set, the complete pipeline achieves 97.9% hazard recall, 92.8% distance accuracy, 0.407 walkable-corridor IoU, and 84.6 ms average latency per frame. On the IDD detection test set, fine-tuning improves mAP@50 from 0.389 for the COCO- pretrained baseline to 0.542, with mAP@50–95 increasing from 0.224 to 0.351. Ablations show that field-of-view-aware depth calibration is critical for proximity estimation, while temporal tracking is important for instruction stability and motion-aware warnings. The system is deliberately modular: the perception and spatial reasoning stack supplies grounded state information, while deterministic planning converts that state into actionable audio rather than relying on unconstrained generative narration.