10179 papers
This category explores the dynamic relationship between constant pressure and volume changes in thermodynamic systems. By examining how gases behave when held at steady pressure, researchers uncover fundamental insights into energy transfer, heat capacity, and the mechanical work performed by expanding or contracting matter. These studies are essential for understanding everything from engine efficiency to atmospheric physics.
On Gist.Science, we monitor the arXiv preprint server to bring you the very latest research in this specific field. For every new submission, our team processes the raw science into two distinct formats: a clear, plain-language explanation for general understanding and a detailed technical summary for those needing the full mathematical depth. This dual approach ensures that complex findings on isobaric processes are accessible to everyone, regardless of their background.
Below are the most recent papers in this category, freshly processed and ready for you to explore.
This paper proposes a Green AI-guided adaptive encoder freezing strategy for federated learning in MRI-to-CT conversion that significantly reduces energy consumption and CO2 emissions by up to 23% while maintaining or improving model performance, thereby promoting equitable and sustainable healthcare AI.
This study presents a YOLO-based deep learning framework integrated with High-Resolution Class Activation Mapping (HiResCAM) to achieve over 96% accuracy in the automated, interpretable identification of Ichneumonoidea wasp families from high-resolution images, thereby addressing the challenges of manual taxonomic identification in biodiversity and biological control programs.
The paper introduces ToolAnchor, a framework that mitigates behavioral inertia in tool-augmented agents by using teacher models to generate and verify counterfactual anchor contexts, thereby enabling scalable adaptation to new toolsets without full retraining.
The paper introduces scVision, a vision foundation model that transforms single-cell transcriptomes into continuous images by spatially arranging genes based on co-expression, enabling state-of-the-art zero-shot cell-type annotation and gene program recovery without fine-tuning by leveraging the biological signal inherent in gene layout rather than just the neural network architecture.
This paper proposes a novel "gradient vector field surgery" that linearly scales gradient magnitudes with prediction error to effectively mitigate the overconfidence and miscalibration issues inherent in region-based segmentation loss functions, thereby improving model reliability for critical medical imaging applications without compromising accuracy.
This paper demonstrates that frozen DINOv3 vision foundation models, when combined with a face-part labeling interface, enable strong zero-shot region-level facial correspondence and temporal tracking across identities without specialized training, with intermediate feature layers proving most effective for dense anatomical alignment.
This paper presents DS@GT ARC's third-place solution for the PlantCLEF 2026 challenge, which achieves robust multi-species plant identification in high-resolution quadrat images by combining a multi-scale DINOv2 ViT classifier with FAISS kNN retrieval, geographic habitat priors, and temporal fusion, while demonstrating that alternative training-centric approaches yielded no performance gains.
This paper proposes a GPU-friendly compression method for 3D Gaussian Splatting that leverages hardware-accelerated texture coding and primitive reordering to efficiently compress spherical harmonic color coefficients with negligible visual quality loss while enabling large-scale parallel decoding.
This paper presents a novel dual-guidance framework for Stable Diffusion that combines a fine-tuned sequence-to-sequence LLM for automatic negative prompt optimization with a CNN-RNN hybrid classifier for latent-space guidance to reduce artifacts and enhance semantic fidelity.
This paper proposes Test-Space Training (TST), a self-supervised approach that leverages cross-modal learning on multimodal data collected exclusively from a specific test environment to develop specialized models that achieve competitive performance against generalist models pre-trained on large-scale internet datasets, thereby reducing reliance on external data.