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During development and under normal physiological conditions, biological tissues are continuously subjected to substantial mechanical stresses. In response to large deformations, cells in a tissue must undergo multicellular rearrangements to maintain integrity and robustness. However, how these events are connected in time and space remains unknown. Here, using theoretical modeling, we study the mechanical plasticity of cell monolayers under large deformations. Our results suggest that the jamming-unjamming (solid-fluid) transition can vary significantly depending on the degree of deformation, implying that tissues are highly unconventional materials. We elucidate the origins of this behavior. We also demonstrate how large deformations are accommodated through a series of cellular rearrangements, similar to avalanches in non-living materials. We find that these ‘tissue avalanches’ are governed by stress redistribution and the spatial distribution of “soft” or vulnerable spots, which are more prone to undergo rearrangements. Finally, we propose a simple and experimentally accessible framework to infer tissue-level stress and predict avalanches based on static images. 

Polymers are thermally insulating due to randomly oriented molecular chains, limiting their effectiveness in thermal management. However, when processed into nanofibers, polymers can exhibit significantly higher thermal conductivity, primarily due to enhanced internal structures such as crystallinity and molecular alignment. Characterizing these structural parameters at the single nanofiber level remains a challenge, limiting understanding of thermal transport mechanisms. Here, we investigate the relationship between internal structure and thermal conductivity of single polyethylene oxide (PEO) nanofibers fabricated from near-field electrospinning (NFES). By varying molecular weight and concentration of PEO, their impact on thermal conductivity and internal structure are examined. Crystallinity is examined using conventional Raman spectroscopy, while molecular orientation is assessed through polarized Raman and polarized FTIR spectroscopy. Results reveal that enhanced thermal conductivity in PEO nanofibers is primarily attributed to increased molecular orientation. A maximum thermal conductivity of 2.7 W/m·K is achieved in PEO nanofibers, representing a notable improvement over bulk PEO (0.2 W/m·K). These findings demonstrate the potential of structurally engineered PEO nanofibers for thermal applications including electronic packaging and thermal interface materials. Further, the approach presented in this work can provide a framework for exploring thermal transport mechanisms in other polymer systems. 

Abstract The widespread adoption of conversational LLMs for software development has raised new security concerns regarding the safety of LLM-generated content. Our motivational study outlines ChatGPT’s potential in volunteering context-specific information to the developers, promoting safe coding practices. Motivated by this finding, we conduct a study to evaluate the degree of security awareness exhibited by three prominent LLMs: Claude 3, GPT-4, and Llama 3. We prompt these LLMs with Stack Overflow questions that contain vulnerable code to evaluate whether they merely provide answers to the questions or if they also warn users about the insecure code, thereby demonstrating a degree of security awareness. Further, we assess whether LLM responses provide information about the causes, exploits, and the potential fixes of the vulnerability, to help raise users’ awareness. Our findings show that all three models struggle to accurately detect and warn users about vulnerabilities, achieving a detection rate of only 12.6% to 40% across our datasets. We also observe that the LLMs tend to identify certain types of vulnerabilities related to sensitive information exposure and improper input neutralization much more frequently than other types, such as those involving external control of file names or paths. Furthermore, when LLMs do issue security warnings, they often provide more information on the causes, exploits, and fixes of vulnerabilities compared to Stack Overflow responses. Finally, we provide an in-depth discussion on the implications of our findings, and demonstrated a CLI-based prompting tool that can be used to produce more secure LLM responses. 

ABSTRACT:Creation of a fracture network in a hydraulic fracturing process is essential for subsurface energy extraction and CO2 sequestration. It is facilitated by reactivation of pre-existing intersecting weak layers and cemented cracks in the rock. In this study, a poromechanical model is developed for the hydraulic fracturing process in rocks containing such pre-existing weak layers. Based on the mixture theory, the crack band model is used to simulate the growth of a crack system. The governing equations with the parameters for hydromechanical coupling are derived, to describe the evolution of the opening and branching of cracks caused by water injection. Microplane model M7 is adopted to characterize the deformation and fracturing of the solid skeleton of the rock, and the Poiseuille law is used to characterize fluid flow through the hydraulic fractures. Numerical simulations are performed to reproduce and interpret recently published laboratory-scale hydraulic fracturing experiments conducted at Los Alamos National Laboratory (LANL). In these experiments, the rock was represented by confined plaster slabs containing orthogonal intersecting weak layers of higher porosity. Numerical simulations reveal how poromechanical characteristics such as the Biot coefficient and the fluid injection rate lead to various typical fracture modes observed in the experiments. These modes include formation of one dominant planar crack or various orthogonal fracture networks. 

ABSTRACT:Using the classical pulse decay test to measure the permeability of tight rock such as serpentinized harzburgite can be time-consuming, often requiring hours or even days. This prolonged duration not only complicates experimental control but also introduces difficulties in maintaining stable environmental conditions. To address such challenges, a fast permeability measurement method has been developed based on an analytical solution that approximates the pressure distribution in the test specimen using parabolic arcs. This solution yields a simple linear regression formula, enabling rapid interpretation of rock permeability using data from only the initial stage of the pulse decay test. In this study, the proposed method is validated by numerical simulations using synthesized pulse decay test data. In addition, an experimental validation of this method using a serpentinized harzburgite is also presented. It is shown that the method is not only faster but also more accurate than the classical method, which ignores the storage of the rock specimen. 

Clinician notes are a rich source of patient information, but often contain inconsistencies due to varied writing styles, abbreviations, medical jargon, grammatical errors, and non-standard formatting. These inconsistencies hinder their direct use in patient care and degrade the performance of downstream computational applications that rely on these notes as input, such as quality improvement, population health analytics, precision medicine, clinical decision support, and research. We present a large-language-model (LLM) approach to the preprocessing of 1618 neurology notes. The LLM corrected spelling and grammatical errors, expanded acronyms, and standardized terminology and formatting, without altering clinical content. Expert review of randomly sampled notes confirmed that no significant information was lost. To evaluate downstream impact, we applied an ontology-based NLP pipeline (Doc2Hpo) to extract biomedical concepts from the notes before and after editing. F1 scores for Human Phenotype Ontology extraction improved from 0.40 to 0.61, confirming our hypothesis that better inputs yielded better outputs. We conclude that LLM-based preprocessing is an effective error correction strategy that improves data quality at the level of free text in clinical notes. This approach may enhance the performance of a broad class of downstream applications that derive their input from unstructured clinical documentation.