When Urban Region Profiling Meets Large Language Models

Urban region profiling from web-sourced data is of utmost importance for urban planning and sustainable development. We are witnessing a rising trend of LLMs for various fields, especially dealing with multi-modal data research such as vision-language learning, where the text modality serves as a supplement information for the image. Since textual modality has never been introduced into modality combinations in urban region profiling, we aim to answer two fundamental questions in this paper: i) Can textual modality enhance urban region profiling? ii) and if so, in what ways and with regard to which aspects? To answer the questions, we leverage the power of Large Language Models (LLMs) and introduce the first-ever LLM-enhanced framework that integrates the knowledge of textual modality into urban imagery profiling, named LLM-enhanced Urban Region Profiling with Contrastive Language-Image Pretraining (UrbanCLIP). Specifically, it first generates a detailed textual description for each satellite image by an open-source Image-to-Text LLM. Then, the model is trained on the image-text pairs, seamlessly unifying natural language supervision for urban visual representation learning, jointly with contrastive loss and language modeling loss. Results on predicting three urban indicators in four major Chinese metropolises demonstrate its superior performance, with an average improvement of 6.1% on R^2 compared to the state-of-the-art methods. Our code and the image-language dataset will be released upon paper notification

Experimental and modeling analysis of thermal runaway propagation over the large format energy storage battery module with Li4Ti5O12 anode

Insight of the thermal characteristics and potential flame spread over lithium-ion battery (LIB) modules is important for designing battery thermal management system and fire protection measures. Such thermal characteristics and potential flame spread are also dependent on the different anode and cathode materials as well as the electrolyte. In the present study, thermal behavior and flame propagation over seven 50 A h Li(Ni1/3Mn1/3Co1/3)O2/Li4Ti5O12 large format LIBs arranged in rhombus and parallel layouts were investigated by directly heating one of the battery units. Such batteries have already been used commercially for energy storage while relatively little is known about its safety features in connection with potential runaway caused fire and explosion hazards. It was found in the present heating tests that fire-impingement resulted in elevated temperatures in the immediate vicinity of the LIBs that were in the range of between 200 °C and 900 °C. Such temperature aggravated thermal runaway (TR) propagation, resulting in rapid temperature rise within the battery module and even explosions after 20 min of “smoldering period”. The thermal runaway and subsequent fire and explosion observed in the heating test was attributed to the violent reduction of the cathode material which coexisted with the electrolyte when the temperature exceeded 260 °C. Separate laboratory tests, which measured the heat and gases generation from samples of the anode and cathode materials using C80 calorimeter, provided insight of the physical-chemistry processes inside the battery when the temperature reaches between 30 °C and 300 °C. The self-accelerating decomposition temperature of the cell, regarded as the critical temperature to trigger TR propagation, was calculated as 126.1 and 139.2 °C using the classical Semenov and Frank-Kamenetskii models and the measurements of the calorimeter with the samples. These are consistent with the measured values in the heating tests in which TR propagated. The events leading to the explosions in the test for the rhombus layout was further analyzed and two possible explanations were postulated and analyzed based on either internal catalytic reactions or Boiling Liquid Expansion Vapor Explosion (BLEVE)

An experimental study on thermal runaway characteristics of lithium-ion batteries with high specific energy and prediction of heat release rate

Understanding the potential thermal hazards of lithium-ion batteries (LIBs) during thermal runaway (TR) is helpful to assess the safety of LIB during storage, transport and use. This paper presents a comprehensive analysis of the thermal runaway (TR) characteristics of type 21700 cylindrical LIBs with a specific energy of 266 W∙h/kg. The batteries with both 30% state of charge (SOC) and 100% SOC were triggered to TR by uniform heating using a flexible heater in a laboratory environment. Three high definition cameras and one high-speed camera were placed to capture TR behavior and flame evolution from different viewpoints. Correlation between the heat release rate (HRR) and the mean flame height of turbulent jet diffusion flame were used to estimate the HRRs of LIBs. Additional characteristics of cell failure (for cells with 100% and 30% SOC) were also noted for comparison, including: number of objects ejected from the cell; sparks and subsequent jet fires. An approach has been developed to estimate the HRRs from TR triggered fires and results compared with previous HRR measurements for type 18650 cylindrical cells with a similar cathode composition

A simplified mathematical model for heating-induced thermal runaway of lithium-ion batteries

The present study aims to develop a simplified mathematical model for the evolution of heating-induced thermal runaway (TR) of lithium-ion batteries (LIBs). This model only requires a minimum number of input parameters, and some of these unknown parameters can be obtained from accelerating rate calorimeter (ARC) tests and previous studies, removing the need for detailed measurements of heat flow of cell components by differential scanning calorimetry. The model was firstly verified by ARC tests for a commercial cylindrical 21700 cell for the prediction of the cell surface temperature evolution with time. It was further validated by uniform heating tests of 21700 cells conducted with flexible and nichrome-wire heaters, respectively. The validated model was finally used to investigate the critical ambient temperature that triggers battery TR. The predicted critical ambient temperature is between 127 °C and 128 °C. The model has been formulated as lumped 0D, axisymmetric 2D and full 3D to suit different heating and geometric arrangements and can be easily extended to predict the TR evolution of other LIBs with different geometric configurations and cathode materials. It can also be easily implemented into other computational fluid dynamics (CFD) code

Assessing Reproducibility of Inherited Variants Detected With Short-Read Whole Genome Sequencing

Background: Reproducible detection of inherited variants with whole genome sequencing (WGS) is vital for the implementation of precision medicine and is a complicated process in which each step affects variant call quality. Systematically assessing reproducibility of inherited variants with WGS and impact of each step in the process is needed for understanding and improving quality of inherited variants from WGS. Results: To dissect the impact of factors involved in detection of inherited variants with WGS, we sequence triplicates of eight DNA samples representing two populations on three short-read sequencing platforms using three library kits in six labs and call variants with 56 combinations of aligners and callers. We find that bioinformatics pipelines (callers and aligners) have a larger impact on variant reproducibility than WGS platform or library preparation. Single-nucleotide variants (SNVs), particularly outside difficult-to-map regions, are more reproducible than small insertions and deletions (indels), which are least reproducible when \u3e 5 bp. Increasing sequencing coverage improves indel reproducibility but has limited impact on SNVs above 30×. Conclusions: Our findings highlight sources of variability in variant detection and the need for improvement of bioinformatics pipelines in the era of precision medicine with WGS

Assessing reproducibility of inherited variants detected with short-read whole genome sequencing

Background: Reproducible detection of inherited variants with whole genome sequencing (WGS) is vital for the implementation of precision medicine and is a complicated process in which each step affects variant call quality. Systematically assessing reproducibility of inherited variants with WGS and impact of each step in the process is needed for understanding and improving quality of inherited variants from WGS. Results: To dissect the impact of factors involved in detection of inherited variants with WGS, we sequence triplicates of eight DNA samples representing two populations on three short-read sequencing platforms using three library kits in six labs and call variants with 56 combinations of aligners and callers. We find that bioinformatics pipelines (callers and aligners) have a larger impact on variant reproducibility than WGS platform or library preparation. Single-nucleotide variants (SNVs), particularly outside difficult-to-map regions, are more reproducible than small insertions and deletions (indels), which are least reproducible when > 5 bp. Increasing sequencing coverage improves indel reproducibility but has limited impact on SNVs above 30x. Conclusions: Our findings highlight sources of variability in variant detection and the need for improvement of bioinformatics pipelines in the era of precision medicine with WGS.Peer reviewe

Constructing Co3O4/La2Ti2O7 p-n Heterojunction for the Enhancement of Photocatalytic Hydrogen Evolution

Layered perovskite-type semiconductor La2Ti2O7 has attracted lots of attention in photocatalytic hydrogen evolution, due to the suitable energy band position for water splitting, high specific surface area, and excellent physicochemical stability. However, the narrow light absorption range and the low separation efficiency of photogenerated carriers limit its photocatalytic activity. Herein, plate-like La2Ti2O7 with uniform crystal morphology was synthesized in molten NaCl salt. A p-n heterojunction was then constructed through the in situ hydrothermal growth of p-type Co3O4 nanoparticles on the surface of n-type plate-like La2Ti2O7. The effects of Co3O4 loading on photocatalytic hydrogen evolution performance were investigated in detail. The results demonstrate that composite Co3O4/La2Ti2O7 possesses much better photocatalytic activity than the pure component. The composite photocatalyst with 1 wt% Co3O4 exhibits the highest hydrogen evolution rate of 79.73 μmol·g−1·h−1 and a good cycling stability. The photoelectrochemistry characterizations illustrate that the improvement of photocatalytic activity is mainly attributed to both the enhanced light absorption from the Co3O4 ornament and the rapid separation of photogenerated electron-hole pairs driven by the built-in electric field close to the p-n heterojunction. The results may provide further insights into the design of high-efficiency La2Ti2O7-based heterojunctions for photocatalytic hydrogen evolution

Removal of 17β-Estradiol by Activated Charcoal Supported Titanate Nanotubes (TNTs@AC) through Initial Adsorption and Subsequent Photo-Degradation: Intermediates, DFT calculation, and Mechanisms

A low-cost composite of activated charcoal supported titanate nanotubes (TNTs@AC) was developed via the facile hydrothermal method to remove the 17β-estradiol (E2, a model of pharmaceutical and personal care products) in water matrix by initial adsorption and subsequent photo-degradation. Characterizations indicated that the modification occurred, i.e., the titanate nanotubes would be grafted onto the activated charcoal (AC) surface, and the micro-carbon could modify the tubular structure of TNTs. E2 was rapidly adsorbed onto TNTs@AC, and the uptake reached 1.87 mg/g from the dual-mode model fitting. Subsequently, the adsorbed E2 could be degraded 99.8% within 2 h under ultraviolet (UV) light irradiation. TNTs@AC was attributed with a unique hybrid structure, providing the hydrophobic effect, π−π interaction, and capillary condensation for E2 adsorption, and facilitating the electron transfer and then enhancing photocatalytic ability for E2-degradation. In addition, the removal mechanism of E2 was elucidated through the density functional theory calculation. Our study is expected to provide a promising material for environmental application

Low sensitivity of net primary productivity to climatic factors in three karst provinces in southwest China from 1981 to 2019

Karst areas are sensitive to environmental changes due to their fragile ecological environments. It is imperative to explore the temporal and spatial variations in the characteristics of vegetation net primary productivity (NPP) and evaluate the influence of different driving factors on NPP. Taking three karst provinces in southwest China as the study area, where the historical vegetation changes and the mechanisms influencing these changes remain unclear; therefore, the present study used vegetation NPP data from 1981 to 2019 to analyze the temporal and spatial variations in the characteristics of vegetation NPP using slope trend analyses and the sequential Mann-Kendall test. The influence of each driving factor on vegetation NPP and the interaction between each driving factor were investigated using the optimal parameters-based geographic detector (OPGD) model. The results were as follows: (1) The annual variation of the total NPP in the study area from 1981 to 2019 showed a fluctuating growth trend, with a slow growth rate before 2005 followed by a rapid increase. (2) The annual fluctuations in the seasonal NPP in the study area were generally upward, and the total NPP of vegetation in summer was the highest, but the growth rate of NPP in autumn was the fastest, and the area with the largest increase in NPP in autumn was the largest. (3) NPP was related to natural factors, with slope being the primary influencing factor at the annual scale. However, at the seasonal scale, there were differences in the explanatory power of the influencing factors on vegetation NPP. The highest explanatory power was observed for surface soil moisture in spring, air temperature in summer, and precipitation in autumn and winter. (4) The spatiotemporal evolution of NPP characteristics was affected by the interaction among various natural factors. At the annual and seasonal scales, the interaction between each climatic factor and slope greatly enhanced the explanatory power of individual climatic factors. Taken together, the interaction between slope and precipitation contributes the most to NPP

Research on Electric Vehicle Braking Intention Recognition Based on Sample Entropy and Probabilistic Neural Network

The accurate identification of a driver’s braking intention is crucial to the formulation of regenerative braking control strategies for electric vehicles. In this paper, a braking intention recognition model based on the sample entropy of the braking signal and a probabilistic neural network (PNN) is proposed to achieve the accurate recognition of different braking intentions. Firstly, the brake pedal travel signal is decomposed to extract the effective components via variational modal decomposition (VMD); then, the features of the decomposed signal are extracted using sample entropy to obtain the multidimensional feature vector of the braking signal; finally, the sparrow search algorithm (SSA) and probabilistic neural network are combined to optimize the smoothing factor with the sparrow search algorithm and the cross-entropy loss function as the fitness function to establish a braking intention recognition model. The experimental validation results show that combining the sample entropy features of the braking signal with the probabilistic neural network can effectively identify the braking intention, and the SSA-PNN algorithm has higher recognition accuracy compared with the traditional machine learning algorithm