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Event crowd management has been a significant research topic with high social impact. When some big events happen such as an earthquake, typhoon, and national festival, crowd management becomes the first priority for governments (e.g., police) and public service operators (e.g., subway/bus operator) to protect people’s safety or maintain the operation of public infrastructures. However, under such event situations, human behavior will become very different from daily routines, which makes prediction of crowd dynamics at big events become highly challenging, especially at a citywide level. Therefore in this study, we aim to extract the “deep” trend only from the current momentary observations and generate an accurate prediction for the trend in the short future, which is considered to be an effective way to deal with the event situations. Motivated by these, we build an online system called DeepUrbanEvent, which can iteratively take citywide crowd dynamics from the current one hour as input and report the prediction results for the next one hour as output. A novel deep learning architecture built with recurrent neural networks is designed to effectively model these highly complex sequential data in an analogous manner to video prediction tasks. Experimental results demonstrate the superior performance of our proposed methodology to the existing approaches. Lastly, we apply our prototype system to multiple big real-world events and show that it is highly deployable as an online crowd management system.

Particle breakage continuously changes the grading of granular materials and has a significant effect on their mechanical behaviors. Revealing the evolution pattern of particle breakage is valuable for development and validation of constitutive models for crushable materials. A series of parallel triaxial compression tests along the same loading paths but stopped at different axial strains were conducted on two coral sands with different particle sizes under drained and undrained conditions. The tested specimens were carefully sieved to investigate the intermediate accumulation of particle breakage during the loading process. The test results showed that under both drained and undrained conditions, particle breakage increases continuously with increasing axial strain but exhibits different accumulating patterns, and higher confining pressures lead to greater particle breakage. Based on the test results, the correlations between particle breakage and the stress state as well as the input energy were examined. The results demonstrated that either the stress state or input energy alone is inadequate for describing the intermediate process of particle breakage evolution. Then, based on experimental observation, a path-dependent model was proposed for particle breakage evolution, which was formulated in an incremental form and reasonably considers the effects of the past breakage history and current stress state on the breakage rate. The path-dependent model successfully reproduced the development of particle breakage during undrained triaxial compression using the parameters calibrated from the drained tests, preliminarily demonstrating its effectiveness for different stress paths.

Rapidly developing location acquisition technologies provide a powerful tool for understanding and predicting human mobility in cities, which is very significant for urban planning, traffic regulation, and emergency management. However, with the existing methodologies, it is still difficult to accurately predict millions of peoples’ mobility in a large urban area such as Tokyo, Shanghai, and Hong Kong, especially when collected data used for model training are often limited to a small portion of the total population. Obviously, human activities in city are closely linked with point-of-interest (POI) information, which can reflect the semantic meaning of human mobility. This motivates us to fuse human mobility data and city POI data to improve the prediction performance with limited training data, but current fusion technologies can hardly handle these two heterogeneous data. Therefore, we propose a unique POI-embedding mechanism, that aggregates the regional POIs by categories to generate an artificial POI-image for each urban grid and enriches each trajectory snippet to a four-dimensional tensor in an analogous manner to a short video. Then, we design a deep learning architecture combining CNN with LSTM to simultaneously capture both the spatiotemporal and geographical information from the enriched trajectories. Furthermore, transfer learning is employed to transfer mobility knowledge from one city to another, so that we can fully utilize other cities’ data to train a stronger model for the target city with only limited data available. Finally, we achieve satisfactory performance of human mobility prediction at the citywide level using a limited amount of trajectories as training data, which has been validated over five urban areas of different types and scales.

AimWe aimed to compare the mortality of individuals at low, moderate, and high risk of diabetic foot disease (DFD) in the context of newly diagnosed type 2 diabetes, before developing active diabetic foot problem.MethodsThis was a population-based cohort study of adults with newly diagnosed type 2 diabetes utilizing IQVIA Medical Research Data. The outcome was all-cause mortality among individuals with low, moderate, and high risk of DFD, and also in those with no record of foot assessment and those who declined foot examination.ResultsOf 225,787 individuals with newly diagnosed type 2 diabetes, 34,061 (15.1%) died during the study period from January 1, 2000 to December 31, 2019. Moderate risk and high risk of DFD were associated with increased mortality risk compared to low risk of DFD (adjusted hazard ratio [aHR] 1.50, 95% CI 1.42, 1.58; aHR 2.01, 95% CI 1.84, 2.20, respectively). Individuals who declined foot examination or who had no record also had increased mortality risk of 75% and 25% vs. those at low risk of DFD, respectively (aHR 1.75, 95% CI 1.51, 2.04; aHR 1.25, 95% CI 1.20, 1.30).ConclusionIndividuals with new-onset type 2 diabetes who had moderate to high risk of DFD were more likely to die compared to those at low risk of DFD. The associations between declined foot examination and absence of foot examinations, and increased risk of mortality further highlight the importance of assessing foot risk as it identifies not only patients at risk of diabetic foot ulceration but also mortality.

Foreign object intrusion detection is vital to ensure the safety of railway transportation. Recently, object detection algorithms based on deep learning have been applied in a wide range of fields. However, in complex and volatile railway environments, high false detection, missed detection, and poor timeliness still exist in traditional object detection methods. To address these problems, an efficient railway foreign object intrusion detection approach SDRC-YOLO is proposed. First, a hybrid attention mechanism that fuses local representation ability is proposed to improve the identification accuracy of small targets. Second, DW-Decoupled Head is proposed to construct a mixed feature channel to improve localization and classification ability. Third, a large convolution kernel is applied to build a larger receptive field and improve the feature extraction capability of the network. In addition, the lightweight universal upsampling operator CARAFE is employed to sample the size and proportion of the intruding foreign body features in order to accelerate the convergence speed of the network. Experimental results show that, compared with the baseline YOLOv5s algorithm, SDRC-YOLO improved the mean average precision (mAP) by 2.8% and 1.8% on datasets RS and Pascal VOC 2012, respectively.

ObjectivesThe use of medications among pregnant women has been rising over the past few decades but the reporting of polypharmacy has been sporadic. The objective of this review is to identify literature reporting the prevalence of polypharmacy among pregnant women, the prevalence of multimorbidity in women taking multiple medications in pregnancy and associated effects on maternal and offspring outcomes.DesignMEDLINE and Embase were searched from their inception to 14 September 2021 for interventional trials, observational studies and systematic reviews reporting on the prevalence of polypharmacy or the use of multiple medications in pregnancy were included.Data on prevalence of polypharmacy, prevalence of multimorbidity, combinations of medications and pregnancy and offspring outcomes were extracted. A descriptive analysis was performed.ResultsFourteen studies met the review criteria. The prevalence of women being prescribed two or more medications during pregnancy ranged from 4.9% (4.3%–5.5%) to 62.4% (61.3%–63.5%), with a median of 22.5%. For the first trimester, prevalence ranged from 4.9% (4.7%–5.14%) to 33.7% (32.2%–35.1%). No study reported on the prevalence of multimorbidity, or associated pregnancy outcomes in women exposed to polypharmacy.ConclusionThere is a significant burden of polypharmacy among pregnant women. There is a need for evidence on the combinations of medications prescribed in pregnancy, how this specifically affects women with multiple long-term conditions and the associated benefits and harms.Tweetable abstractOur systematic review shows significant burden of polypharmacy in pregnancy but outcomes for women and offspring are unknown.PROSPERO registration numberCRD42021223966.

As a decisive part in the success of Mobility-as-a-Service (MaaS), spatio-temporal predictive modeling for crowd movements is a challenging task particularly considering scenarios where societal events drive mobility behavior deviated from the normality. While tremendous progress has been made to model high-level spatio-temporal regularities with deep learning, most, if not all of the existing methods are neither aware of the dynamic interactions among multiple transport modes nor adaptive to unprecedented volatility brought by potential societal events. In this paper, we are therefore motivated to improve the canonical spatio-temporal network (ST-Net) from two perspectives: (1) design a heterogeneous mobility information network (HMIN) to explicitly represent intermodality in multimodal mobility; (2) propose a memory-augmented dynamic filter generator (MDFG) to generate sequence-specific parameters in an on-the-fly fashion for various scenarios. The enhanced event-aware spatio-temporal network, namely EAST-Net, is evaluated on several real-world datasets with a wide variety and coverage of societal events. Both quantitative and qualitative experimental results verify the superiority of our approach compared with the state-of-the-art baselines. Code and data are published on https://github.com/underdoc-wang/EAST-Net., Accepted by AAAI 2022

Forecasting incident occurrences (e.g. crime, EMS, traffic accident) is a crucial task for emergency service providers and transportation agencies in performing response time optimization and dynamic fleet management. However, such events are by nature rare and sparse, which causes the label imbalance problem and inferior performance of models relying on data sufficiency. The existing studies circumvent, instead of truly solving, this issue by defining the incident prediction problem in a coarse-grained temporal (e.g. daily) setting, which leaves the proposed models unrobust to fine-grained dynamics and trivial for the real-world decision making. In this paper, we tackle the temporally fine-grained incident prediction problem in a sparse setting by explicitly exploiting the behind-the-scene chainlike triggering mechanism. Moreover, this chain effect roots in multiple domains (i.e. spatial, categorical), which further entangles with the temporal dimension and happens to be time-variant. To be specific, we propose a novel deep learning framework, namely Spatio-Temporal-Categorical Graph Neural Networks (STC-GNN), to handle the multidimensional and dynamic chain effect for performing fine-grained multi-incident co-prediction. Extensive experiments on three real-world city-level incident datasets verify the insightfulness of our perspective and effectiveness of the proposed model.

Nowadays, with the rapid development of IoT (Internet of Things) and CPS (Cyber-Physical Systems) technologies, big spatiotemporal data are being generated from mobile phones, car navigation systems, and traffic sensors. By leveraging state-of-the-art deep learning technologies on such data, urban traffic prediction has drawn a lot of attention in AI and Intelligent Transportation System community. The problem can be uniformly modeled with a 3D tensor (T, N, C), where T denotes the total time steps, N denotes the size of the spatial domain (i.e., mesh-grids or graph-nodes), and C denotes the channels of information. According to the specific modeling strategy, the state-of-the-art deep learning models can be divided into three categories: grid-based, graph-based, and multivariate time-series models. In this study, we first synthetically review the deep traffic models as well as the widely used datasets, then build a standard benchmark to comprehensively evaluate their performances with the same settings and metrics. Our study named DL-Traff is implemented with two most popular deep learning frameworks, i.e., TensorFlow and PyTorch, which is already publicly available as two GitHub repositories https://github.com/deepkashiwa20/DL-Traff-Grid and https://github.com/deepkashiwa20/DL-Traff-Graph. With DL-Traff, we hope to deliver a useful resource to researchers who are interested in spatiotemporal data analysis., This paper has been accepted by CIKM 2021 Resource Track

In the present study, we developed a highly sensitivity electrochemical acetylcholinesterase (AChE, E.C.3.1.1.7) biosensor for organophosphorous pesticides (OPs) detection on the basis of three dimensional graphene-copper oxide nanoflowers nanocomposites (3DG-CuO NFs). The 3DG-CuO NFs nanocomposites with network-like structure not only increase the effective specific surface area, but also provide a favorable microenvironment for AChE loading, which could improve the biosensor performance. The electrochemical performance of the AChE-CS/3DG-CuO NFs/GCE biosensor was thoroughly investigated by cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), amperometry (i–t) and square wave voltammetry (SWV). Under the optimal detection conditions, the AChE-CS/3DG-CuO NFs/GCE biosensor exhibits advantages such as a wide linear relationship to malathion ranging from 1 ppt to 15.555 ppb (3 pM-46.665 nM). The theoretical detection limit was calculated to be 0.31 ppt (0.92 pM) with good selectivity and ideal stability. Most importantly, satisfactory recoveries were achieved in real samples analysis, indicating that our developed biosensor has great potential to be an effective platform for pesticides detection.

Forecasting regional ambulance demand plays a fundamental part in dynamic fleet allocation and redeployment. This topic has been gaining increasing significance, as virtually every country is experiencing an aging population, with generally higher level of vulnerability and demand for the emergency medical service (EMS). Although exploring the spatial and temporal correlations in EMS historical records, the existing methods principally consider the former time-invariant, which does not necessarily hold in reality. Moreover, this assumption ignores the fact that the behind-the-scenes dynamics are people, whose demographic profiles and activity patterns could be determinants of regional EMS demands. In this paper, we are therefore motivated to mine the collective daily routines in human mobility, to further represent the evolving spatial correlations. Particularly, we model profiled mobility groups as multiple random walkers and propose a novel bicomponent neural network, including a heterogeneous multi-graph convolution layer and spatio-temporal interlacing attention module, to perform the prediction task. Experimental results on the real-world data verify the effectiveness of introducing dynamic human mobility and the advantage of our approach over the state-of-the-art models.

Modeling and predicting human mobility are of great significance to various application scenarios such as intelligent transportation system, crowd management, and disaster response. In particular, in a severe pandemic situation like COVID-19, human movements among different regions are taken as the most important point for understanding and forecasting the epidemic spread in a country. Thus, in this study, we collect big human GPS trajectory data covering the total 47 prefectures of Japan and model the daily human movements between each pair of prefectures with time-series Origin-Destination (OD) matrix. Then, given the historical observations from past days, we predict the countrywide OD matrices for the future one or more weeks by proposing a novel deep learning model called Origin-Destination Convolutional Recurrent Network (ODCRN). It integrates the recurrent and 2-dimensional graph convolutional components to deal with the highly complex spatiotemporal dependencies in sequential OD matrices. Experiment results over the entire COVID-19 period demonstrate the superiority of our proposed methodology over existing OD prediction models. Last, we apply the predicted countrywide OD matrices to the SEIR model, one of the most classic and widely used epidemic simulation model, to forecast the COVID-19 infection numbers for the entire Japan. The simulation results also demonstrate the high reliability and applicability of our countrywide OD prediction model for a pandemic scenario like COVID-19.

The results of a series of drained and undrained triaxial compression tests terminated at various axial strains were reported to show the gradual accumulation process of particle breakage o...

Global warming and urbanization have made heatstroke a serious emergency disease especially for large cities in summer daytime. Although a lot of studies have focused on the heat-related analysis or on developing general routing applications for pedestrians, few have aimed at providing routing services for pedestrians specifically to reduce their heatstroke risk. In this research, we propose a novel routing system that can recommend pedestrian routes based on the estimated heatstroke risk using heterogeneous data and we conduct a detailed system design for the proposed application.

Osteoarthritis (OA) is a disease characterized by cartilage damage and abnormal remodeling of subchondral bone. Our previous study showed that in the early stage of OA, knee loading exerts protective effects by suppressing osteoclastogenesis through Wnt signaling, but little is known about loading effects at the late OA stage. Endoplasmic reticulum (ER) stress and autophagy are known to be involved in the late OA stage. We determined the effects of mechanical loading on ER stress and autophagy in OA mice. One hundred seventy-four mice were used for a surgery-induced OA model. In the first set of experiments, 60 mice were devoted to evaluation of the role of ER stress and autophagy in the development of OA. In the second set, 114 mice were used to assess the effect of knee loading on OA. Histologic, cellular, microcomputed tomography, and electron microscopic analyses were performed to evaluate morphologic changes, ER stress, and autophagy. Mechanical loading increased phosphorylation of eukaryotic translation initiation factor 2α (eIF2α) and regulated expressions of autophagy markers LC3II/I and p62. Osteoarthritic mice also exhibited an elevated ratio of calcified cartilage to total articular cartilage (CC/TAC), and synovial hyperplasia with increased lining cells was found. At the early disease stage, subchondral bone plate thinning and reduced subchondral bone volume fraction (B.Ar/T.Ar) were observed. At the late disease stages, subchondral bone plate thickened concomitant with increased B.Ar/T.Ar. Mice subjected to mechanical loading exhibited resilience to cartilage destruction and a correspondingly reduced Osteoarthritis Research Society International score at 4 and 8 wk, as well as a decrease in synovitis and CC/TAC. While chondrocyte numbers in the OA group was notably decreased, mechanical loading restored chondrogenic differentiation. These results demonstrate that mechanical loading can retard the pathologic progression of OA at its early and late stages. The observed effects of loading are associated with the regulations of ER stress and autophagy.-Zheng, W., Li, X., Liu, D., Li, J., Yang, S., Gao, Z., Wang, Z., Yokota, H., Zhang, P. Mechanical loading mitigates osteoarthritis symptoms by regulating endoplasmic reticulum stress and autophagy.

Many studies have confirmed that DNA methylation is highly correlated with the occurrence and development of various diseases including cancers. In this work, we developed a simple, sensitive, selective, and universal electrochemical biosensor for detection of DNA methylation and assay of DNA methyltransferase (MTase) activity using M.SssI MTase as an example. The thiolated single-stranded DNA S1 was self-assembled on the surface of gold nanoparticles deposition modified glassy carbon electrode via Au S bonding, then hybridization between the DNA S1 and its complementary DNA S2 formed a double-stranded target sequence for both M.SssI MTase and restriction endonuclease HpaII. HpaII could not cleave the target sequence after it was methylated by M.SssI MTase, while the sequence without methylation could be cleaved. Here, we used methylene blue (MB) as electrochemical indicator. The electrochemical signal of MB increased linearly with increasing M.SssI MTase concentration from 0.5 to 25 U/mL and from 25 to 400 U/mL with a detection limit of 0.04 U/mL. Moreover, screening of M.SssI MTase inhibitors 5-azacytidine (5-Aza) and 5-Aza-2′-deoxycytidine (5-Aza-dC) were successfully investigated using the fabricated electrochemical biosensor and showed that the two classic drugs could both inhibit the M.SssI MTase activity with the IC50 of 2.8 μM and 0.37 μM, respectively, indicating potential application in discovery of new anticancer drugs.

Purpose This study aims to examine the impact of various dimensions of product market competition on accounting conservatism particularly in the wake of regulatory changes and varying ownership structures in China. Design/methodology/approach This study examines impact of product market competition on accounting conservatism by using conservatism measure of Khan and Watts (2009) and measures for important dimensions of competition such as competition intensity, non-price competition and competition from existing rivals and potential entrants. Findings The findings suggest that competition intensity and non-price competition result in higher conservatism. This study also advocates that industry leaders exhibit lower conservatism as compared to industry followers. Moreover, the authors document positive association between competition from existing/potential rivals and accounting conservative. These findings reveal that regulatory changes (International Financial Reporting Standards adoption) influence the effect of various dimensions of competition on conservatism. The authors also propose that financial reporting practices of state-owned enterprises are not influenced by competition. However, competition affects financial reporting (conservatism) when institutional or managerial ownership is higher. Originality/value The authors document that strategic considerations shape conservative financial reporting decisions of the managers. This study also advocates that when regulatory changes affect the influence of competitive pressure on the conservative reporting decisions of the mangers. Findings also suggest that unlike state ownership, institutional as well as managerial ownership affects the influence of competition on managerial decisions like conservative financial reporting. These results are robust to various alternative measures of conservatism.

Cardiac catheterization procedures are performed on about 20,000 children with congenital heart disease (CHD) annually in China. The procedure, which involves exposure to ionizing radiation, causes DNA damage and may lead to increased cancer risk. We have studied chromosomal aberrations (CA) in peripheral lymphocytes of CHD children. CA frequencies were assessed in an interventional group of 70 children who underwent cardiac catheterization and a control group of 51 children receiving open-heart surgery. Total CA and all chromosome-type aberrations were higher in the exposed children than in the control group. With respect to the type of septal defect, the translocation frequency was higher in patients with ventricular rather than atrial defects. Cardiac catheterization procedures increase CA frequencies and may also increase the risk of cancer.

Event crowd management has been a significant research topic with high social impact. When some big events happen such as an earthquake, typhoon, and national festival, crowd management becomes the first priority for governments (e.g. police) and public service operators (e.g. subway/bus operator) to protect people's safety or maintain the operation of public infrastructures. However, under such event situations, human behavior will become very different from daily routines, which makes prediction of crowd dynamics at big events become highly challenging, especially at a citywide level. Therefore in this study, we aim to extract the deep trend only from the current momentary observations and generate an accurate prediction for the trend in the short future, which is considered to be an effective way to deal with the event situations. Motivated by these, we build an online system called DeepUrbanEvent which can iteratively take citywide crowd dynamics from the current one hour as input and report the prediction results for the next one hour as output. A novel deep learning architecture built with recurrent neural networks is designed to effectively model these highly-complex sequential data in an analogous manner to video prediction tasks. Experimental results demonstrate the superior performance of our proposed methodology to the existing approaches. Lastly, we apply our prototype system to multiple big real-world events and show that it is highly deployable as an online crowd management system.

Predicting the density and flow of the crowd or traffic at a citywide level becomes possible by using the big data and cutting-edge AI technologies. It has been a very significant research topic with high social impact, which can be widely applied to emergency management, traffic regulation, and urban planning. In particular, by meshing a large urban area to a number of fine-grained mesh-grids, citywide crowd and traffic information in a continuous time period can be represented with 4D tensor (Timestep, Height, Width, Channel). Based on this idea, a series of methods have been proposed to address grid-based prediction for citywide crowd and traffic. In this study, we revisit the density and in-out flow prediction problem and publish a new aggregated human mobility dataset generated from a real-world smartphone application. Comparing with the existing ones, our dataset holds several advantages including large mesh-grid number, fine-grained mesh size, and high user sample. Towards this large-scale crowd dataset, we propose a novel deep learning model called DeepCrowd by designing pyramid architectures and high-dimensional attention mechanism based on Convolutional LSTM. Lastly, thorough and comprehensive performance evaluations are conducted to demonstrate the superiority of the proposed DeepCrowd comparing to multiple state-of-the-art methods.

Properly modelling the particle breakage characteristics is important for crushable soils such as carbonate sands, rockfills and rail ballasts. According to the generation mechanism, particle breakage was decomposed into two parts: compression-induced particle breakage associated with the increase of mean effective stress and shear-induced particle breakage related to the change of shear stress ratio. The accumulation rate of the compression-induced particle breakage can be well correlated to the current stress state; while the accumulative rate of the shear-induced particle breakage depends on both the current stress state and the accumulated amount of particle breakage during past loading histories. Within the framework of the critical state plasticity, a double yield surfaces constitutive model incorporating the two particle breakage components was developed. By employing a breakage critical state surface with particle breakage being an extra dimension, the effect of particle breakage on the critical state, dilatancy, strength and modulus were involved. The drained and undrained triaxial test results of two carbonate sands and one silica sand of different densities were selected to validate the proposed model. It was shown that the proposed model is capable to reproduce well the stress-strain behavior of the crushable soils.

Osteoporosis is a major skeletal disease with low bone mineral density, which leads to an increased risk of bone fracture. Salubrinal is a synthetic chemical that inhibits dephosphorylation of eukaryotic translation initiation factor 2 alpha (eIF2α) in response to endoplasmic reticulum (ER) stress. To understand possible linkage of osteoporosis to ER stress, we employed an unloading mouse model and examined the effects of salubrinal in the pathogenesis of disuse osteoporosis. The results presented several lines of evidence that osteoclastogenesis in the development of osteoporosis was associated with ER stress, and salubrinal suppressed unloading-induced bone loss. Compared to the age-matched control, unloaded mice reduced the trabecular bone area/total area (B.Ar/T.Ar) as well as the number of osteoblasts, and they increased the osteoclasts number on the trabecular bone surface in a time-dependent way. Unloading-induced disuse osteoporosis significantly increased the expression of Bip, p-eIF2α and ATF4 in short-term within 6h of tail suspension, but time-dependent decreased in HU2d to HU14d. Furthermore, a significant correlation of ER stress with the differentiation of osteoblasts and osteoclasts was observed. Administration of salubrinal suppressed the unloading-induced decrease in bone mineral density, B.Ar/T.Ar and mature osteoclast formation. Salubrinal also increased the colony-forming unit-fibroblasts and colony-forming unit-osteoblasts. It reduced the formation of mature osteoclasts, suppressed their migration and adhesion, and increased the expression of Bip, p-eIF2α and ATF4. Electron microscopy showed that rough endoplasmic reticulum expansion and a decreased number of ribosomes on ER membrane were observed in osteoblast of unloading mice, and the abnormal ER expansion was significantly improved by salubrinal treatment. A TUNEL assay together with CCAAT/enhancer binding protein homologous protein (CHOP) expression indicated that ER stress-induced osteoblast apoptosis was rescued by salubrinal. Collectively, the results support the notion that ER stress plays a key role in the pathogenesis of disuse osteoporosis, and salubrinal attenuates unloading-induced bone loss by altering proliferation and differentiation of osteoblasts and osteoclasts via eIF2α signaling.

This paper focuses on the landing region of Chang E-3 (the satellite that will soon be sent to the Moon by Chinese government in 2013). The topographical and thermal factors are considered in our addressing model. Regions with a relatively lower slope and shadowed by hills several times a day are thought to be much easier for satellites to land and work on because these kinds of regions have a proper surface temperature and lower moving difficulty. Through our calculation, a difficult level map is given for proper landing and working regions.