arXiv subjects

Anye Shi

Publications and source records attributed to Anye Shi.

4 recordsLinked to original sources

arXiv2026

From Accessibility to Allocation: An Integrated Workflow for Land-Use Assignment and FAR Estimation

Urban land use and building intensity are often planned without a direct, auditable link to network accessibility, limiting ex-ante policy evaluation. This study asks whether multi-radius street centralities can be elevated from diagnosis to design lever to allocate land use and floor area in a transparent, optimization-ready workflow. We introduce a three-stage pipeline that connects configuration to program and intensity. First, multi-radius accessibility is computed on the street network and translated to blocks to provide scale-legible measures of reach. Second, these measures structure nested service basins that guide a rule-based placement of land uses with explicit priorities and minimum parcel footprints, ensuring reproducibility. Third, within each use, floor-area ratio (FAR) is assigned by an accessibility-weighted linear model that satisfies global construction totals while anchoring the average FAR, thereby tilting height toward better-connected blocks without pathological extremes. The framework supports multi-objective policy search via sampling and Pareto screening. Applied to a real urban district, the workflow reproduces corridor-biased commercial siting and industrial belts while concentrating intensity on highly connected blocks. Policy sampling via multi-objective screening yields Pareto-efficient plans that reconcile accessibility gains with deviations from target land-share and construction-share structures. The contribution is twofold: methodologically, it translates familiar space-syntax measures into cluster-aware, rule-governed land-use and FAR assignment with explicit guarantees (scale-legible radii, parcel minima, and an average-FAR anchor). Practically, it offers planners a transparent instrument for counterfactual testing and negotiated trade-offs at neighborhood/district/city scales.

stat.CO↗

arXiv2025

A Review of Urban Resilience Frameworks: Transferring Knowledge to Enhance Pandemic Resilience

Urbanization is rapidly increasing, with urban populations expected to grow significantly by 2050, particularly in developing regions. This expansion brings challenges related to chronic stresses and acute shocks, such as the COVID-19 pandemic, which has underscored the critical role of urban form in a city's capacity to manage public health crises. Despite the heightened interest in urban resilience, research examining the relationship between urban morphology and pandemic resilience remains limited, often focusing solely on density and its effect on disease transmission. This work aims to address this gap by evaluating existing frameworks that analyze the relationship between urban resilience and urban form. By critically reviewing these frameworks, with a particular emphasis on theoretical and quantitative approaches, this study seeks to transfer the knowledge gained to better understand the relationship between pandemic resilience and urban morphology. The work also links theoretical ideas with quantitative frameworks, offering a cohesive analysis. The anticipated novelty of this study lies in its comprehensive assessment of urban resilience frameworks and the identification of the current gaps in integrating resilience to pandemic thinking into urban planning and design. The goal is not only to enhance the understanding of urban resilience but also to offer practical guidance for developing more adaptive and effective frameworks for assessing resilience to pandemics in urban environments, thereby preparing cities to better withstand and recover from future crises.

physics.soc-ph↗

arXiv2024

Simulated patient systems powered by large language model-based AI agents offer potential for transforming medical education

Background: Simulated patient systems are important in medical education and research, providing safe, integrative training environments and supporting clinical decision making. Advances in artificial intelligence (AI), especially large language models (LLMs), can enhance simulated patients by replicating medical conditions and doctor patient interactions with high fidelity and at low cost, but effectiveness and trustworthiness remain open challenges. Methods: We developed AIPatient, a simulated patient system powered by LLM based AI agents. The system uses a retrieval augmented generation (RAG) framework with six task specific agents for complex reasoning. To improve realism, it is linked to the AIPatient knowledge graph built from de identified real patient data in the MIMIC III intensive care database. Results: We evaluated electronic health record (EHR) based medical question answering (QA), readability, robustness, stability, and user experience. AIPatient reached 94.15 percent QA accuracy when all six agents were enabled, outperforming versions with partial or no agent integration. The knowledge base achieved an F1 score of 0.89. Readability scores showed a median Flesch Reading Ease of 68.77 and a median Flesch Kincaid Grade of 6.4, indicating accessibility for most medical trainees and clinicians. Robustness and stability were supported by non significant variance in repeated trials (analysis of variance F value 0.61, p greater than 0.1; F value 0.78, p greater than 0.1). A user study with medical students showed that AIPatient provides high fidelity, usability, and educational value, comparable to or better than human simulated patients for history taking. Conclusions: LLM based simulated patient systems can deliver accurate, readable, and reliable medical encounters and show strong potential to transform medical education.

cs.CL↗

arXiv2022

Liquid Crystals as Multifunctional Interfaces for Trapping and Characterizing Microplastics

Identifying and removing microplastics (MPs) from the environment is a global challenge. This study explores how the colloidal fraction of common MPs behave at aqueous interfaces of liquid crystal (LC) films. We observed polyethylene (PE) and polystyrene (PS) microparticles to be captured at the LC interface and exhibit distinct two-dimensional aggregation patterns. The addition of low concentrations of surfactant (sodium dodecylsulfate (SDS)) was found to further amplify the differences in PS/PE aggregation patterns, with PS changing from a linear chain-like morphology to a singly dispersed state with increasing SDS concentration and PE forming dense clusters at all SDS concentrations. Statistical characterization of assembly patterns using fractal geometric theory-based machine learning and a deep learning image recognition model yielded highly accurate classification of PE vs PS (>99%). Additionally, by performing feature importance analysis on our deep learning model, dense, multi-branched assemblies were confirmed to be unique features of PE relative to PS. To obtain additional insight into the origin of these key features, we performed microscopic characterization of LC ordering at the microparticle surfaces. These observations led us to predict that both microparticle types should generate LC-mediated interactions (due to elastic strain) with a dipolar symmetry, a prediction consistent with the observed interfacial organization of PS but not PE. We conclude that the non-equilibrium organization of the PE microparticles arises from their polycrystalline nature, which leads to rough particle surfaces and weakened LC elastic interactions and enhanced capillary forces. Overall, our results highlight the potential utility of LC interfaces for surface-sensitive characterization of colloidal MPs.

cond-mat.soft↗