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Abstract Current therapies for systemic lupus erythematosus that target a particular factor or cell type exhibit limited effectiveness. To address this limitation, our focus was on CD132, a subunit common to six inflammatory factor receptors implicated in SLE. Our study revealed heightened CD132 expression in SLE patients’ lymphocytes, contributing to the production of pro-inflammatory cytokines and immunoglobulins. We developed a novel humanized anti-CD132 monoclonal antibody, named as 2D4. 2D4 efficiently blocked IL-21 and IL-15, with limited effectiveness against IL-2, thereby suppressing T and B cells without disrupting immune tolerance. In the mouse immunization model, 2D4 virtually inhibited T cell-dependent, antigen-specific B-cell response. In lupus murine models, 2D4 mitigated inflammation by suppressing multiple pro-inflammatory cytokines and anti-dsDNA antibody titers, also diminishing proteinuria and glomerulonephritis. Compared to Belimumab, 2D4 exhibited superior efficacy in ameliorating the inflammatory state and preserving renal function. Moreover, 2D4 exhibited the ability to inhibit the production of pro-inflammatory factors and autoantibodies in PBMCs from individuals with SLE, highlighting its therapeutic potential for SLE individuals. Potent, 2D4 has the potential to significantly improve clinical outcomes in SLE and other complex autoimmune disorders.

Cell polarity refers to the orientation of tissue and organelles within a cell and the direction of its function. It is one of the most critical characteristics of metazoans. The development, growth, and functional tissue distribution are closely related to holistic tissue or organ homeostasis. However, the connection between cell polarity and osteoarthritis (OA) is less well-known. In OA, multiple chondrocyte clusters and tissue disorganisation can be observed in the degraded cartilage tissue. The excessive upregulation of the planar cell polarity (PCP) signalling pathway leads to the loss of cell polarity and organisation in OA progression and aetiology. Recent research has become increasingly aware of the importance of cell polarity and its correlation with OA. Several cell polarity-related treatments have shed light on OA. A thorough understanding of cell polarity and OA would provide more insights for future investigations to treat this worldwide disease. The translational potential of this article: Understanding cell polarity, associated signalling pathways, organelle changes, and cell movement in the development of OA could lead to advances in precision medicine and enhanced treatment strategies for OA patients.

Abstract Osteoarthritis (OA) is a debilitating degenerative disease affecting multiple joint tissues, including cartilage, bone, synovium, and adipose tissues. OA presents diverse clinical phenotypes and distinct molecular endotypes, including inflammatory, metabolic, mechanical, genetic, and synovial variants. Consequently, innovative technologies are needed to support the development of effective diagnostic and precision therapeutic approaches. Traditional analysis of bulk OA tissue extracts has limitations due to technical constraints, causing challenges in the differentiation between various physiological and pathological phenotypes in joint tissues. This issue has led to standardization difficulties and hindered the success of clinical trials. Gaining insights into the spatial variations of the cellular and molecular structures in OA tissues, encompassing DNA, RNA, metabolites, and proteins, as well as their chemical properties, elemental composition, and mechanical attributes, can contribute to a more comprehensive understanding of the disease subtypes. Spatially resolved biology enables biologists to investigate cells within the context of their tissue microenvironment, providing a more holistic view of cellular function. Recent advances in innovative spatial biology techniques now allow intact tissue sections to be examined using various -omics lenses, such as genomics, transcriptomics, proteomics, and metabolomics, with spatial data. This fusion of approaches provides researchers with critical insights into the molecular composition and functions of the cells and tissues at precise spatial coordinates. Furthermore, advanced imaging techniques, including high-resolution microscopy, hyperspectral imaging, and mass spectrometry imaging, enable the visualization and analysis of the spatial distribution of biomolecules, cells, and tissues. Linking these molecular imaging outputs to conventional tissue histology can facilitate a more comprehensive characterization of disease phenotypes. This review summarizes the recent advancements in the molecular imaging modalities and methodologies for in-depth spatial analysis. It explores their applications, challenges, and potential opportunities in the field of OA. Additionally, this review provides a perspective on the potential research directions for these contemporary approaches that can meet the requirements of clinical diagnoses and the establishment of therapeutic targets for OA.

The accurate detection of railway tracks from unmanned aerial vehicle (UAV) images is essential for intelligent railway inspection and the development of electronic railway maps. Traditional computer vision algorithms struggle with the complexities of high-precision track extraction due to challenges such as diverse track shapes, varying angles, and complex background information in UAV images. While deep learning neural networks have shown promise in this domain, they still face limitations in precisely extracting track line edges. To address these challenges, this paper introduces an improved NL-LinkNet network, named NL-LinkNet-SSR, designed specifically for railway track detection. The proposed NL-LinkNet-SSR integrates a Sobel edge detection module and a SimAM attention module to enhance the model’s accuracy and robustness. The Sobel edge detection module effectively captures the edge information of track lines, improving the segmentation and extraction of target edges. Meanwhile, the parameter-free SimAM attention module adaptively emphasizes significant features while suppressing irrelevant information, broadening the model’s perceptual field and improving its responsiveness to target areas. Experimental results show that the NL-LinkNet-SSR significantly outperforms the original NL-LinkNet model across multiple key metrics, including a more than 0.022 increase in accuracy, over a 4% improvement in F1-score, and a more than 3.5% rise in mean Intersection over Union (mIoU). These enhancements suggest that the improved NL-LinkNet-SSR offers a more reliable solution for railway track detection, advancing the field of intelligent railway inspection.

Abstract The osteochondral interface is a thin layer that connects hyaline cartilage to subchondral bone. Subcellular elemental distribution can be visualised using synchrotron X-ray fluorescence microscopy (SR-XFM) (1 μm). This study aims to determine the relationship between elemental distribution and osteoarthritis (OA) progression based on disease severity. Using modified Mankin scores, we collected tibia plates from 9 knee OA patients who underwent knee replacement surgery and graded them as intact cartilage (non-OA) or degraded cartilage (OA). We used a tape-assisted system with a silicon nitride sandwich structure to collect fresh-frozen osteochondral sections, and changes in the osteochondral unit were defined using quantified SR-XFM elemental mapping at the Australian synchrotron's XFM beamline. Non-OA osteochondral samples were found to have significantly different zinc (Zn) and calcium (Ca) compositions than OA samples. The tidemark separating noncalcified and calcified cartilage was rich in zinc. Zn levels in OA samples were lower than in non-OA samples (P = 0.0072). In OA samples, the tidemark had less Ca than the calcified cartilage zone and subchondral bone plate (P 0.05). In conclusion, SR-XFM analysis revealed spatial elemental distribution at the subcellular level during OA development.

Surface longwave radiation (SLR) plays a pivotal role in the Earth’s energy balance, influencing a range of environmental processes and climate dynamics. As the demand for high spatial resolution remote sensing products grows, there is an increasing need for accurate SLR retrieval with enhanced spatial detail. This study focuses on the development and validation of models to estimate SLR using measurements from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor. Given the limitations posed by fewer spectral bands and data products in ASTER compared to moderate-resolution sensors, the proposed approach combines an atmospheric radiative transfer model MODerate resolution atmospheric TRANsmission (MODTRAN) with the Light Gradient Boosting Machine algorithm to estimate SLR. The MODTRAN simulations were performed to construct a representative training dataset based on comprehensive global atmospheric profiles and surface emissivity spectra data. Global sensitivity analyses reveal that key inputs influencing the accuracy of SLR retrievals should reflect surface thermal radiative signals and near-surface atmospheric conditions. Validated against ground-based measurements, surface upward longwave radiation (SULR) and surface downward longwave radiation (SDLR) using ASTER thermal infrared bands and surface elevation estimations resulted in root mean square errors of 17.76 W/m2 and 25.36 W/m2, with biases of 3.42 W/m2 and 3.92 W/m2, respectively. Retrievals show systematic biases related to extreme temperature and moisture conditions, e.g., causing overestimation of SULR in hot humid conditions and underestimation of SDLR in arid conditions. While challenges persist, particularly in addressing atmospheric variables and cloud masking, this work lays a foundation for accurate SLR retrieval from high spatial resolution sensors like ASTER. The potential applications extend to upcoming satellite missions, such as the Landsat Next, and contribute to advancing high-resolution remote sensing capabilities for an improved understanding of Earth’s energy dynamics.

The increasing incidence of retrogressive thaw slumps (RTSs) in permafrost regions underscores the need for detailed spatial and temporal analysis using InSAR technology to monitor and predict dynamic changes in the process of RTSs. Nevertheless, current InSAR deformation forecasting methods employing deep learning strategies such as the traditional long short-term memory (LSTM) and recent transformer models encounter difficulties in effectively capturing temporal features. Moreover, they are limited in their ability to directly integrate spatial information. In this paper, an innovative deep learning approach named Spacetimeformer is proposed for predicting medium- and short-term InSAR deformation of RTSs in the Chumar River area. This method employs a transformer architecture with a spatiotemporal attention mechanism, which enhances the long-term prediction capabilities of time series models and dynamic spatial modeling. It is applicable to multivariate InSAR spatiotemporal deformation prediction problems. The findings include a list of 72 RTSs compiled based on derived InSAR deformation maps and Sentinel-2 optical images, of which 64 have an average deformation rate exceeding 10 mm/year, indicating signs of permafrost degradation. The density distribution of the displacement maps predicted by the Spacetimeformer model aligned well with the InSAR deformation maps obtained from the small baseline subset (SBAS) method, with the overall prediction deviation controlled within 20 mm. In addition, the point-scale prediction results were compared with LSTM and transformer models. This study indicates that the Spacetimeformer network achieved good results in predicting the deformation of RTSs, with a root mean square error of 1.249 mm. The Spacetimeformer method for deformation prediction with the spacetime mechanism presented in this study can serve as a general framework for multivariate deformation prediction based on InSAR results. It can also quantitatively assess the spatial deformation characteristics and deformation trends of RTSs.

Seismic disaster risk assessment results are an important way to reduce disaster losses and casualties. In this article, a seismic disaster risk assessment method is constructed based on the lethal level, and a quantitative assessment of the earthquake disaster risk in Guangzhou is carried out. There are four typical building types in this study area: brick-wood structures, brick-concrete structures, reinforced concrete structures, and civil structures. Typical building structural components (vertical load-bearing component, ring beam, mortar of infilled wall, roof or floor structure, foundation, height of building, nonstructural component, anti-seismic joint or other anti-seismic measures) are selected to build the lethal level index system of buildings. On the basis of the field survey, a lethal level of 431 survey points is obtained. The average lethal level in urban areas is 0.3476, and in rural areas is 0.4198, the overall lethal level of each township is 0.389, and the errors between the urban and rural areas are 0.0414 and 0.0308, and the fitting results coefficient of determination (R2) values are 0.6803 and 0.6714, respectively, the results is good. Furthermore, considering the seismic hazard (peak ground acceleration, PGA), population exposure and lethal level, the seismic disaster risk distribution of the Guangzhou area is determined. Under different intensities, the risk of death has shown obvious characteristics, as the intensity increases, the number of possible deaths increases rapidly, ranging from a few to several thousand, the higher risk areas are relatively fixed, generally mainly located in the central city, the quantitative assessment results of disaster risk also have obvious directivity. The areas with the highest risk are located in most areas of Tianhe District, Liwan District and Haizhu District, the highest grid death result is more than 1; the lethal level of Baiyun District and Huangpu District is low, but the death risk is also higher, generally between 0.5 and 1, in rural areas in the southern and northern regions of Guangzhou, the risk is relatively low, generally between 0 and 0.5. The distribution map of seismic disaster risk can provide solid scientific and technological support for the government to investigate potential risk points.

It is well known that earthquake brings about gigantic direct and indirect economic losses, such as building and lifeline system damages, every year at the global scales. Thus, efficient and accurately estimate the economic loss after earthquake is one of the important factors for the post-earthquake reconstruction and the recovery activity of ordinary life. To estimate the earthquake-induced direct economic losses (DEL), a regression empirical method based on the earthquake-induced direct building damage economic losses (DBDEL) is proposed in this study. Firstly, a linear model between DBDEL of the entire earthquake impact areas and the DBDEL of areas with earthquake intensity greater than VII are acquired based six historical earthquakes occurred at the mainland China. Then, another linear model between the DEL and the entire DBDEL are obtained based on 16 historical earthquakes. The DBDEL and DEL of the historical earthquake records are acquired from field investment. To evaluate the accuracy of the proposed method, the DEL of 2013 Lushan M 7.0 earthquake estimated. Using the field invested and high-resolution remote sensing image estimated DBDEL at the intensity greater than VII areas, the corresponding DELs are estimated based on the proposed method, respectively, with the relative error of 3.2% and 57.0%. In addition, an empirical DEL method based on earthquake magnitude and intensity is also used to estimate the Lushan earthquake DEL, with the relative error of 90.0%. The result indicates that the proposed method can properly estimate DEL based on the field invested or remote sensed DBDEL.

To acquire daytime land surface temperature (LST) in thin cirrus cloudy skies, we have developed a three-channel LST retrieval algorithm based on a widely used two-channel LST retrieval algorithm for the clear-sky conditions. In this algorithm, the LST is expressed as a multiple linear function of MODIS channels 29, 31 and 32 with the coefficients of the linear function dependent on the cirrus optical depth (COD) and cirrus effective radius (R). The influences from land surface emissivities (LSEs) are also considered in this algorithm. The simulated dataset shows that the LST could be estimated using the proposed algorithm with the root mean squire error (RMSE) less than 2.2 K in thin cirrus cloudy skies (COD less than 0.7) when viewing zenith angle (VZA) equivalent to 0°. As VZA is equivalent to 60°, the maximum RMSE are 2.7 K. The widely used generalized split-window (GSW) algorithm proposed for clear-sky conditions are used in cirrus cloudy skies, and the RMSEs of GSW algorithm estimated LST are 16.89 K and 22.32 K for VZA =0° and VZA =60° respectively when COD is 0.7. It indicates that the proposed three-channel algorithm can significantly improve the LST retrieval accuracy using thermal infrared data in cirrus cloudy skies. To estimate the LST errors caused by the uncertainties of COD, R, LSE and instrument noise, a sensitivity analysis was performed. It shows that the accuracy of cirrus COD is more important for the retrieval of LST compared with other parameters. The maximum total LST errors, taking into account all the input parameters’ uncertainty and algorithm error itself, are 3.8 K and 4.3 K when VZA =0° and VZA =60° respectively.

Osteoarthritis (OA) remains a prevalent disease affecting more than 20% of the global population, resulting in morbidity and lower quality of life for patients. The study of OA pathophysiology remains predominantly in animal models due to the complexities of mimicking the physiological environment surrounding the joint tissue. Recent development in microfluidic organ-on-chip (OoC) systems have demonstrated various techniques to mimic and modulate tissue physiological environments. Adaptations of these techniques have demonstrated success in capturing a joint tissue’s tissue physiology for studying the mechanism of OA. Adapting these techniques and strategies can help create human-specific in vitro models that recapitulate the cellular processes involved in OA. This review aims to comprehensively summarise various demonstrations of microfluidic platforms in mimicking joint microenvironments for future platform design iterations.

Binocular stereoscopic matching is an essential method in computer vision, imitating human binocular technology to obtain distance information. Among plentiful stereo matching algorithms, Semi-Global Matching (SGM) is recognized as one of the most popular vision algorithms due to its relatively low power consumption and high accuracy, resulting in many excellent SGM-based hardware accelerators. However, vision algorithms, including SGM, are still somewhat inaccurate in actual long-range applications. Therefore, this paper proposes a disparity improvement strategy based on subpixel interpolation and disparity optimization post-processing using an area optimization strategy, hardware-friendly divider, split look-up table, and the clock alignment multi-directional disparity occlusion filling, and depth acquisition based on floating-point operations. The hardware architecture based on optimization algorithms is on the Stratix-IV platform. It consumes about 5.6 K LUTs, 12.8 K registers, and 2.5 M bits of on-chip memory. Meanwhile, the non-occlusion error rate of only 4.61% is about 1% better than the state-of-the-art works in the KITTI2015 dataset. The maximum working frequency can reach up to 98.28 MHz for the 640 × 480 resolution video and 128 disparity range with the power dissipation of 1.459 W and 320 frames per second processing speed.

Building damage after seismic disasters is one of the most vital factors threatening people's lives. The seismic vulnerability of buildings over large scales at the regional or country level is a key parameter for the mitigation of seismic disaster risk and rapid assessment of casualties after seismic events. To acquire the seismic vulnerability of buildings over large areas, a machine learning method based on mid-resolution satellite optical images is proposed. Taking field-investigated building vulnerability at the satellite pixel scale as a reference, the 15 most correlated features are calculated based on VIIRS nighttime light, MODIS vegetation index and surface reflectance, and texture data from Landsat-8 OLI surface reflectance products. Taking Yancheng, Jiangsu Province, China, as the study area, where 401 pixel-level seismic vulnerabilities (PLSVs) of the building environment are acquired based on field investigations, support vector regression (SVR) and random forest (RF) models are proposed using the 15 features calculated from satellite optical images. The results show that the proposed method can be used to estimate the PLSV with a root mean square error of approximately 0.1, with the PLSV normalized between 0 and 1. The machine learning model proposed in this study has a better accuracy for PLSV estimation than spatial interpolation.

Automatic building extraction based on high-resolution aerial images has important applications in urban planning and environmental management. In recent years advances and performance improvements have been achieved in building extraction through the use of deep learning methods. However, the design of existing models focuses attention to improve accuracy through an overflowing number of parameters and complex structure design, resulting in large computational costs during the learning phase and low inference speed. To address these issues, we propose a new, efficient end-to-end model, called ARC-Net. The model includes residual blocks with asymmetric convolution (RBAC) to reduce the computational cost and to shrink the model size. In addition, dilated convolutions and multi-scale pyramid pooling modules are utilized to enlarge the receptive field and to enhance accuracy. We verify the performance and efficiency of the proposed ARC-Net on the INRIA Aerial Image Labeling dataset and WHU building dataset. Compared to available deep learning models, the proposed ARC-Net demonstrates better segmentation performance with less computational costs. This indicates that the proposed ARC-Net is both effective and efficient in automatic building extraction from high-resolution aerial images.

This article focuses on the rapid assessment of possible numbers of casualties after an earthquake. We use data on earthquake casualties to study and verify the relationship between casualties and building types, building anti-lethal levels, seismic intensity and other influencing factors. A rapid assessment method for casualties based on buildings’ anti-lethal level is proposed. The method first uses the anti-lethal level and proportions of various types of buildings to obtain the overall anti-lethal level of the study area. Second, according to historical casualty data, the casualty rate of each intensity in the study area is obtained. On this basis, a rapid assessment matrix of casualties is constructed based on the overall anti-lethal level of the study area and the casualty rate of each intensity. Finally, we make a rapid assessment of casualties based on a matrix model. Through the verification of 7 selected historical earthquakes, we find that the data on casualties obtained based on this method are close to the actual number of casualties. The resulting casualty parameters are described against the overall casualty data from several historical earthquakes in China, and the error rates of earthquakes in Lancang-Gengma, Yushu, Lushan Min county-Zhang county and Changning are 4.1%, 25.27%, 12.75%, 2% and 7.69%, respectively. The error is relatively small, within 50%, and a more accurate evaluation result is obtained. Our findings illustrate the effectiveness and practicability of a rapid assessment method for casualties based on anti-lethal levels.

After earthquake, the correct and efficient earthquake emergency evacuation of pedestrian is crucial for reducing casualties. Based on the research on the quantitative of panic coefficient and the calibration of initial desired speed, this research has constructed the revised social force model which could accurately simulate the post-earthquake evacuation process of the crowed. Using the revised social force model, the effects of classroom layout, seismic intensity, the number of safeties exits and the number of students in the classroom on the evacuation efficiency are further studied. And then, according to the research results, the classroom layout has been further optimized to improve the students’ earthquake emergency evacuation efficiency. These research results are scientific bases for the targeted earthquake emergency safety education and the design of classroom layout.

Identifying building function type (BFT) is vital for many studies and applications, such as urban planning, disaster risk assessment and management, and traffic control. Traditional remote sensing methods are commonly used for land use/cover classification, but they have some limitations in BFT identification. Considering that the dynamic variations of social sensing mobile signaling (MS) data at diurnal and daily scales are directly related to BFT, in this paper, we propose a method to infer BFT using MS data obtained from mobile devices. First, based on the different patterns of population dynamics within different building types, we propose a BFT classification scheme with five categories: residential (R), working (W), entertainment (E), visiting (V), and hospital (H). Then, a random forest (RF) classification model is constructed based on two days (one workday and one weekend) of MS data with a temporal resolution of one hour to identify the BFT. According to the cross-validation method, the overall classification accuracy is 84.89%, and the Kappa coefficient is 0.78. Applying the MS data-constructed RF model to the central areas of Beijing Dongcheng and Xicheng Districts, the overall detection rate is 97.35%. In addition, to verify the feasibility of the MS data, the Sentinel-2 (S2) remote sensing data are used for comparison, with a classification accuracy of 73.33%. The better performance of the MS method shows its excellent potential for BFT identification, as the spatial and temporal population dynamics reviewed based on MS data are more correlated with BFT than geometric or spectral features in remote sensing images. This is an innovative attempt to identify BFT with MS data, and such a method compensates for the scarcity of BFT studies driven by population dynamics. Overall, in this study, we show the feasibility of using time series MS data to identify BFT and we provide a new path for building function mapping at large scales.

Osteoarthritis (OA) is a long-term condition that causes joint pain and reduced movement. Notably, the same pathways governing cell growth, death, and differentiation during the growth and development of the body are also common drivers of OA. The osteochondral interface is a vital structure located between hyaline cartilage and subchondral bone. It plays a critical role in maintaining the physical and biological function, conveying joint mechanical stress, maintaining chondral microenvironment, as well as crosstalk and substance exchange through the osteochondral unit. In this review, we summarized the progress in research concerning the area of osteochondral junction, including its pathophysiological changes, molecular interactions, and signaling pathways that are related to the ultrastructure change. Multiple potential treatment options were also discussed in this review. A thorough understanding of these biological changes and molecular mechanisms in the pathologic process will advance our understanding of OA progression, and inform the development of effective therapeutics targeting OA.

Automatic extraction of buildings from remote sensing imagery plays a significant role in many applications, such as urban planning and monitoring changes to land cover. Various building segmentation methods have been proposed for visible remote sensing images, especially state-of-the-art methods based on convolutional neural networks (CNNs). However, high-accuracy building segmentation from high-resolution remote sensing imagery is still a challenging task due to the potentially complex texture of buildings in general and image background. Repeated pooling and striding operations used in CNNs reduce feature resolution causing a loss of detailed information. To address this issue, we propose a light-weight deep learning model integrating spatial pyramid pooling with an encoder-decoder structure. The proposed model takes advantage of a spatial pyramid pooling module to capture and aggregate multi-scale contextual information and of the ability of encoder-decoder networks to restore losses of information. The proposed model is evaluated on two publicly available datasets; the Massachusetts roads and buildings dataset and the INRIA Aerial Image Labeling Dataset. The experimental results on these datasets show qualitative and quantitative improvement against established image segmentation models, including SegNet, FCN, U-Net, Tiramisu, and FRRN. For instance, compared to the standard U-Net, the overall accuracy gain is 1.0% (0.913 vs. 0.904) and 3.6% (0.909 vs. 0.877) with a maximal increase of 3.6% in model-training time on these two datasets. These results demonstrate that the proposed model has the potential to deliver automatic building segmentation from high-resolution remote sensing images at an accuracy that makes it a useful tool for practical application scenarios.

Simulated data showed that cirrus clouds could lead to a maximum land surface temperature (LST) retrieval error of 11.0 K when using the generalized split-window (GSW) algorithm with a cirrus optical depth (COD) at 0.55 μm of 0.4 and in nadir view. A correction term in the COD linear function was added to the GSW algorithm to extend the GSW algorithm to cirrus cloudy conditions. The COD was acquired by a look up table of the isolated cirrus bidirectional reflectance at 0.55 μm. Additionally, the slope k of the linear function was expressed as a multiple linear model of the top of the atmospheric brightness temperatures of MODIS channels 31–34 and as the difference between split-window channel emissivities. The simulated data showed that the LST error could be reduced from 11.0 to 2.2 K. The sensitivity analysis indicated that the total errors from all the uncertainties of input parameters, extension algorithm accuracy, and GSW algorithm accuracy were less than 2.5 K in nadir view. Finally, the Great Lakes surface water temperatures measured by buoys showed that the retrieval accuracy of the GSW algorithm was improved by at least 1.5 K using the proposed extension algorithm for cirrus skies.

With the booming development of evacuation simulation software, developing an extensive database in indoor scenarios for evacuation models is imperative. In this paper, we conduct a qualitative and quantitative analysis of the collected videotapes and aim to provide a complete and unitary database of pedestrians' earthquake emergency response behaviors in indoor scenarios, including human-environment interactions. Using the qualitative analysis method, we extract keyword groups and keywords that code the response modes of pedestrians and construct a general decision flowchart using chronological organization. Using the quantitative analysis method, we analyze data on the delay time, evacuation speed, evacuation route and emergency exit choices. Furthermore, we study the effect of classroom layout on emergency evacuation. The database for indoor scenarios provides reliable input parameters and allows the construction of real and effective constraints for use in software and mathematical models. The database can also be used to validate the accuracy of evacuation models.

After an earthquake, the important task of emergency rescue work is to minimize casualties, but due to the suddenness of earthquake disasters, it is difficult to obtain enough disaster information immediately, especially personnel distribution and movement information. The traditional methods of obtaining disaster data are through reports from the disaster area or field investigations by the emergency rescue team; this work lags, and its efficiency is low. This paper analyzes the feasibility of using mobile phone location signal data in earthquake emergency rescue work in several respects, such as quantity, location, change rate, and epicentral distance. The results show that mobile phone location signal data can quickly obtain the situation of personnel distribution and quantity after an earthquake, and we find the change rate, distance, etc., can determine the approximate range of the earthquake impact field. Through the data distribution in different time periods, the movement of personnel after the earthquake can be obtained. Based on several situations, we can determine the basic situation of the disaster-stricken areas in times after the earthquake, especially the personnel relevant to the situation, and these data can provide a scientific basis for emergency rescue decision making., Competing Interests: The authors have declared that no competing interests exist.