高分辨MRI在肛瘘诊断及Parks分型中的应用价值

目的 探讨高分辨MRI在肛瘘诊断及Parks分型中的应用价值。方法 选取肛瘘接受手术治疗患者108例,均行MRI检查,以手术结果为金标准,分析患者MRI特征及Parks分型。结果 108例中总计检出内口112个、瘘管124个、外口112个、肛周脓肿35个,与手术确诊结果对比差异无统计学意义(P>0.05),其中括约肌间型64例、经括约肌型30例、括约肌上型11例、括约肌外型3例,与手术确诊结果对比差异无统计学意义(P>0.05)。结论 MRI应用于肛瘘诊断并进行Parks分型,能清晰显示出内口、外口、瘘管、脓肿位置以及数目信息。

Assessment of Wet Season Precipitation in the Central United States by the Regional Climate Simulation of the WRFG Member in NARCCAP and Its Relationship with Large-Scale Circulation Biases

Assessment of past-climate simulations of regional climate models(RCMs)is important for understanding the reliability of RCMs when used to project future regional climate.Here,we assess the performance and discuss possible causes of biases in a WRF-based RCM with a grid spacing of 50 km,named WRFG,from the North American Regional Climate Change Assessment Program(NARCCAP)in simulating wet season precipitation over the Central United States for a period when observational data are available.The RCM reproduces key features of the precipitation distribution characteristics during late spring to early summer,although it tends to underestimate the magnitude of precipitation.This dry bias is partially due to the model’s lack of skill in simulating nocturnal precipitation related to the lack of eastward propagating convective systems in the simulation.Inaccuracy in reproducing large-scale circulation and environmental conditions is another contributing factor.The too weak simulated pressure gradient between the Rocky Mountains and the Gulf of Mexico results in weaker southerly winds in between,leading to a reduction of warm moist air transport from the Gulf to the Central Great Plains.The simulated low-level horizontal convergence fields are less favorable for upward motion than in the NARR and hence,for the development of moist convection as well.Therefore,a careful examination of an RCM’s deficiencies and the identification of the source of errors are important when using the RCM to project precipitation changes in future climate scenarios.

显微镜下改良Parks切口斜视矫正术治疗水平共同性斜视患儿的效果及其对泪膜功能的影响

目的探探讨显微镜下改良Parks切口斜视矫正术治疗水平共同性斜视患儿的效果及其对泪膜功能的影响。方法选取我科2017年6月至2019年11月期间95例水平共同性斜视患儿作为研究对象,按照随机数字表法分成两组,均行斜视矫正术,对照组47例给予术中作角膜缘梯形结膜瓣切口,观察组48例给予显微镜下改良Parks切口,1个月后对比两组患儿视功能改善效果、手术效果、泪膜功能。结果观察组治疗后总有效率95.83%高于对照组89.36%(χ^(2)=0.6632,P=0.4154);观察组水平共同性斜视患儿手术时间明显短于对照组(t=11.9892,P=0.0000);观察组患儿治疗后泪液分泌量高于对照组(t=15.0051,P=0.0000),泪膜破裂时间较对照组上升(t=9.2228,P=0.0000),角膜荧光染色评分较对照组降低(t=10.7815,P=0.0000)。结论显微镜下改良Parks切口斜视矫正术治疗水平共同性斜视效果确切,能够显著矫正眼部,改善患儿视功能及泪膜功能,安全可靠,值得应用。

A Two-Layer Encoding Learning Swarm Optimizer Based on Frequent Itemsets for Sparse Large-Scale Multi-Objective Optimization

Traditional large-scale multi-objective optimization algorithms(LSMOEAs)encounter difficulties when dealing with sparse large-scale multi-objective optimization problems(SLM-OPs)where most decision variables are zero.As a result,many algorithms use a two-layer encoding approach to optimize binary variable Mask and real variable Dec separately.Nevertheless,existing optimizers often focus on locating non-zero variable posi-tions to optimize the binary variables Mask.However,approxi-mating the sparse distribution of real Pareto optimal solutions does not necessarily mean that the objective function is optimized.In data mining,it is common to mine frequent itemsets appear-ing together in a dataset to reveal the correlation between data.Inspired by this,we propose a novel two-layer encoding learning swarm optimizer based on frequent itemsets(TELSO)to address these SLMOPs.TELSO mined the frequent terms of multiple particles with better target values to find mask combinations that can obtain better objective values for fast convergence.Experi-mental results on five real-world problems and eight benchmark sets demonstrate that TELSO outperforms existing state-of-the-art sparse large-scale multi-objective evolutionary algorithms(SLMOEAs)in terms of performance and convergence speed.

Enhancing Evolutionary Algorithms With Pattern Mining for Sparse Large-Scale Multi-Objective Optimization Problems

Sparse large-scale multi-objective optimization problems(SLMOPs)are common in science and engineering.However,the large-scale problem represents the high dimensionality of the decision space,requiring algorithms to traverse vast expanse with limited computational resources.Furthermore,in the context of sparse,most variables in Pareto optimal solutions are zero,making it difficult for algorithms to identify non-zero variables efficiently.This paper is dedicated to addressing the challenges posed by SLMOPs.To start,we introduce innovative objective functions customized to mine maximum and minimum candidate sets.This substantial enhancement dramatically improves the efficacy of frequent pattern mining.In this way,selecting candidate sets is no longer based on the quantity of nonzero variables they contain but on a higher proportion of nonzero variables within specific dimensions.Additionally,we unveil a novel approach to association rule mining,which delves into the intricate relationships between non-zero variables.This novel methodology aids in identifying sparse distributions that can potentially expedite reductions in the objective function value.We extensively tested our algorithm across eight benchmark problems and four real-world SLMOPs.The results demonstrate that our approach achieves competitive solutions across various challenges.

Large-scale model testing of high-pressure grouting reinforcement for bedding slope with rapid-setting polyurethane

Bedding slope is a typical heterogeneous slope consisting of different soil/rock layers and is likely to slide along the weakest interface.Conventional slope protection methods for bedding slopes,such as retaining walls,stabilizing piles,and anchors,are time-consuming and labor-and energy-intensive.This study proposes an innovative polymer grout method to improve the bearing capacity and reduce the displacement of bedding slopes.A series of large-scale model tests were carried out to verify the effectiveness of polymer grout in protecting bedding slopes.Specifically,load-displacement relationships and failure patterns were analyzed for different testing slopes with various dosages of polymer.Results show the great potential of polymer grout in improving bearing capacity,reducing settlement,and protecting slopes from being crushed under shearing.The polymer-treated slopes remained structurally intact,while the untreated slope exhibited considerable damage when subjected to loads surpassing the bearing capacity.It is also found that polymer-cemented soils concentrate around the injection pipe,forming a fan-shaped sheet-like structure.This study proves the improvement of polymer grouting for bedding slope treatment and will contribute to the development of a fast method to protect bedding slopes from landslides.

Online identification and extraction method of regional large-scale adjustable load-aggregation characteristics

This article introduces the concept of load aggregation,which involves a comprehensive analysis of loads to acquire their external characteristics for the purpose of modeling and analyzing power systems.The online identification method is a computer-involved approach for data collection,processing,and system identification,commonly used for adaptive control and prediction.This paper proposes a method for dynamically aggregating large-scale adjustable loads to support high proportions of new energy integration,aiming to study the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction methods.The experiment selected 300 central air conditioners as the research subject and analyzed their regulation characteristics,economic efficiency,and comfort.The experimental results show that as the adjustment time of the air conditioner increases from 5 minutes to 35 minutes,the stable adjustment quantity during the adjustment period decreases from 28.46 to 3.57,indicating that air conditioning loads can be controlled over a long period and have better adjustment effects in the short term.Overall,the experimental results of this paper demonstrate that analyzing the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction algorithms is effective.

A semantic vector map-based approach for aircraft positioning in GNSS/GPS denied large-scale environment

Accurate positioning is one of the essential requirements for numerous applications of remote sensing data,especially in the event of a noisy or unreliable satellite signal.Toward this end,we present a novel framework for aircraft geo-localization in a large range that only requires a downward-facing monocular camera,an altimeter,a compass,and an open-source Vector Map(VMAP).The algorithm combines the matching and particle filter methods.Shape vector and correlation between two building contour vectors are defined,and a coarse-to-fine building vector matching(CFBVM)method is proposed in the matching stage,for which the original matching results are described by the Gaussian mixture model(GMM).Subsequently,an improved resampling strategy is designed to reduce computing expenses with a huge number of initial particles,and a credibility indicator is designed to avoid location mistakes in the particle filter stage.An experimental evaluation of the approach based on flight data is provided.On a flight at a height of 0.2 km over a flight distance of 2 km,the aircraft is geo-localized in a reference map of 11,025 km~2using 0.09 km~2aerial images without any prior information.The absolute localization error is less than 10 m.

Terrain or climate factor dominates vegetation resilience?Evidence from three national parks across different climatic zones in China

Vegetation resilience(VR),providing an objective measure of ecosystem health,has received considerable attention,however,there is still limited understanding of whether the dominant factors differ across different climate zones.We took the three national parks(Hainan Tropical Rainforest National Park,HTR;Wuyishan National Park,WYS;and Northeast Tiger and Leopard National Park,NTL)of China with less human interference as cases,which are distributed in different climatic zones,including tropical,subtropical and temperate monsoon climates,respectively.Then,we employed the probabilistic decay method to explore the spatio-temporal changes in the VR and their natural driving patterns using Geographically Weighted Regression(GWR)model as well.The results revealed that:(1)from 2000 to 2020,the Normalized Difference Vegetation Index(NDVI)of the three national parks fluctuated between 0.800 and 0.960,exhibiting an overall upward trend,with the mean NDVI of NTL(0.923)>HTR(0.899)>WYS(0.823);(2)the positive trend decay time of vegetation exceeded that of negative trend,indicating vegetation gradual recovery of the three national parks since 2012;(3)the VR of HTR was primarily influenced by elevation,aspect,average annual temperature change(AATC),and average annual precipitation change(AAPC);the WYS'VR was mainly affected by elevation,average annual precipitation(AAP),and AAPC;while the terrain factors(elevation and slope)were the main driving factors of VR in NTL;(4)among the main factors influencing the VR changes,the AAPC had the highest proportion in HTR(66.7%),and the AAP occupied the largest area proportion in WYS(80.4%).While in NTL,elevation served as the main driving factor for the VR,encompassing 64.2%of its area.Consequently,our findings indicated that precipitation factors were the main driving force for the VR changes in HTR and WYS national parks,while elevation was the main factors that drove the VR in NTL.Our research has promoted a deeper understanding of the driving mechanism behind the VR.

Low-Density Microplastics in Recreational Parks of Al Ain, United Arab Emirates: Abundance, Composition, and Potential Effects on Soil Health

Microplastics (MPs) have been an emerging concern due to their harmful effects on the ecosystem and are ubiquitous in various habitats, from marine to terrestrial environments. However, studies on the presence of MPs in recreational areas are limited. One of the previous works has reported that urban recreational parks are considered “sinks” for plastic debris, including MPs. In this study, low-density MPs (LD-MPs) in soil samples collected from recreational parks of Al Ain, United Arab Emirates (UAE) were isolated by density flotation method. Results showed that these parks have varying levels of LD-MPs caused by various anthropogenic activities, such as sludge use and application of reclaimed water from wastewater treatment facilities in those areas. These plastic particles were isolated in 87% of the soil samples, with an average concentration of 1550 ± 340 MPs/kg. Predominantly, these comprised large LD-MPs (300 - 5000 μm), with red and blue being the most common colors. Fourier transform infrared (FTIR) spectroscopy identified possible synthetic polymers, including polyethylene and polypropylene. Additionally, a negative correlation was observed between LD-MP concentration and soil pH and moisture content, indicating potential adverse effects on soil health. These findings highlight the need for monitoring and managing microplastic pollution in urban recreational areas to mitigate its ecological impacts.

A Large-Scale Group Decision Making Model Based on Trust Relationship and Social Network Updating

With the development of big data and social computing,large-scale group decisionmaking(LGDM)is nowmerging with social networks.Using social network analysis(SNA),this study proposes an LGDM consensus model that considers the trust relationship among decisionmakers(DMs).In the process of consensusmeasurement:the social network is constructed according to the social relationship among DMs,and the Louvain method is introduced to classify social networks to form subgroups.In this study,the weights of each decision maker and each subgroup are computed by comprehensive network weights and trust weights.In the process of consensus improvement:A feedback mechanism with four identification and two direction rules is designed to guide the consensus of the improvement process.Based on the trust relationship among DMs,the preferences are modified,and the corresponding social network is updated to accelerate the consensus.Compared with the previous research,the proposedmodel not only allows the subgroups to be reconstructed and updated during the adjustment process,but also improves the accuracy of the adjustment by the feedbackmechanism.Finally,an example analysis is conducted to verify the effectiveness and flexibility of the proposed method.Moreover,compared with previous studies,the superiority of the proposed method in solving the LGDM problem is highlighted.

Large-Scale Multi-Objective Optimization Algorithm Based on Weighted Overlapping Grouping of Decision Variables

The large-scale multi-objective optimization algorithm(LSMOA),based on the grouping of decision variables,is an advanced method for handling high-dimensional decision variables.However,in practical problems,the interaction among decision variables is intricate,leading to large group sizes and suboptimal optimization effects;hence a large-scale multi-objective optimization algorithm based on weighted overlapping grouping of decision variables(MOEAWOD)is proposed in this paper.Initially,the decision variables are perturbed and categorized into convergence and diversity variables;subsequently,the convergence variables are subdivided into groups based on the interactions among different decision variables.If the size of a group surpasses the set threshold,that group undergoes a process of weighting and overlapping grouping.Specifically,the interaction strength is evaluated based on the interaction frequency and number of objectives among various decision variables.The decision variable with the highest interaction in the group is identified and disregarded,and the remaining variables are then reclassified into subgroups.Finally,the decision variable with the strongest interaction is added to each subgroup.MOEAWOD minimizes the interactivity between different groups and maximizes the interactivity of decision variables within groups,which contributed to the optimized direction of convergence and diversity exploration with different groups.MOEAWOD was subjected to testing on 18 benchmark large-scale optimization problems,and the experimental results demonstrate the effectiveness of our methods.Compared with the other algorithms,our method is still at an advantage.

Large-Scale Carbon Dioxide Storage in Salt Caverns:Evaluation of Operation,Safety,and Potential in China

Underground salt cavern CO_(2) storage(SCCS)offers the dual benefits of enabling extensive CO_(2) storage and facilitating the utilization of CO_(2) resources while contributing the regulation of the carbon market.Its economic and operational advantages over traditional carbon capture,utilization,and storage(CCUS)projects make SCCS a more cost-effective and flexible option.Despite the widespread use of salt caverns for storing various substances,differences exist between SCCS and traditional salt cavern energy storage in terms of gas-tightness,carbon injection,brine extraction control,long-term carbon storage stability,and site selection criteria.These distinctions stem from the unique phase change characteristics of CO_(2) and the application scenarios of SCCS.Therefore,targeted and forward-looking scientific research on SCCS is imperative.This paper introduces the implementation principles and application scenarios of SCCS,emphasizing its connections with carbon emissions,carbon utilization,and renewable energy peak shaving.It delves into the operational characteristics and economic advantages of SCCS compared with other CCUS methods,and addresses associated scientific challenges.In this paper,we establish a pressure equation for carbon injection and brine extraction,that considers the phase change characteristics of CO_(2),and we analyze the pressure during carbon injection.By comparing the viscosities of CO_(2) and other gases,SCCS’s excellent sealing performance is demonstrated.Building on this,we develop a long-term stability evaluation model and associated indices,which analyze the impact of the injection speed and minimum operating pressure on stability.Field countermeasures to ensure stability are proposed.Site selection criteria for SCCS are established,preliminary salt mine sites suitable for SCCS are identified in China,and an initial estimate of achievable carbon storage scale in China is made at over 51.8-77.7 million tons,utilizing only 20%-30%volume of abandoned salt caverns.This paper addresses key scientific and engineering challenges facing SCCS and determines crucial technical parameters,such as the operating pressure,burial depth,and storage scale,and it offers essential guidance for implementing SCCS projects in China.

Exploring impacts of COVID-19 on spatial and temporal patterns of visitors to Canadian Rocky Mountain National Parks from social media big data

COVID-19 posed challenges for global tourism management.Changes in visitor temporal and spatial patterns and their associated determinants pre-and peri-pandemic in Canadian Rocky Mountain National Parks are analyzed.Data was collected through social media programming and analyzed using spatiotemporal analysis and a geographically weighted regression(GWR)model.Results highlight that COVID-19 significantly changed park visitation patterns.Visitors tended to explore more remote areas peri-pandemic.The GWR model also indicated distance to nearby trails was a significant influence on visitor density.Our results indicate that the pandemic influenced tourism temporal and spatial imbalance.This research presents a novel approach using combined social media big data which can be extended to the field of tourism management,and has important implications to manage visitor patterns and to allocate resources efficiently to satisfy multiple objectives of park management.

Spatial-temporal Patterns of Urban Parks’Effects on the Sentiments and Their Associated Factors Based on Social Media Data——a Case Study in Beijing

As the pivotal green space,urban parks play an important role in urban residents’daily activities.Thy can not only bring people physical health,but also can be more likely to elicit positive sentiment to those who visit them.Recently,social media big data has provided new data sources for sentiment analysis.However,there was limited researches that explored the connection between urban parks and individual’s sentiments.Therefore,this study firstly employed a pre-trained language model(BERT,Bidirectional Encoder Representations from Transformers)to calculate sentiment scores based on social media data.Secondly,this study analysed the relationship between urban parks and individual’s sentiment from both spatial and temporal perspectives.Finally,by utilizing structural equation model(SEM),we identified 13 factors and analyzed its degree of the influence.The research findings are listed as below:①It confirmed that individuals generally experienced positive sentiment with high sentiment scores in the majority of urban parks;②The urban park type showed an influence on sentiment scores.In this study,higher sentiment scores observed in Eco-parks,comprehensive parks,and historical parks;③The urban parks level showed low impact on sentiment scores.With distinctions observed mainly at level-3 and level-4;④Compared to internal factors in parks,the external infrastructure surround them exerted more significant impact on sentiment scores.For instance,number of bus and subway stations around urban parks led to higher sentiment scores,while scenic spots and restaurants had inverse result.This study provided a novel method to quantify the services of various urban parks,which can be served as inspiration for similar studies in other cities and countries,enhancing their park planning and management strategies.

Practical Experience of Management of Urban Parks at Home and Abroad

By systematically reviewing the practical experience of management of urban parks at home and abroad,the advanced experience in organization,management system,operation mechanism and social participation were summarized,and the optimization and improvement measures for the construction and management of urban parks in Chengdu were proposed to provide reference for the high-level construction,high-quality experience and efficient management of urban parks in the construction of park city demonstration zone in Chengdu.

Large-scale spatial data visualization method based on augmented reality

Background A task assigned to space exploration satellites involves detecting the physical environment within a certain space.However,space detection data are complex and abstract.These data are not conducive for researchers'visual perceptions of the evolution and interaction of events in the space environment.Methods A time-series dynamic data sampling method for large-scale space was proposed for sample detection data in space and time,and the corresponding relationships between data location features and other attribute features were established.A tone-mapping method based on statistical histogram equalization was proposed and applied to the final attribute feature data.The visualization process is optimized for rendering by merging materials,reducing the number of patches,and performing other operations.Results The results of sampling,feature extraction,and uniform visualization of the detection data of complex types,long duration spans,and uneven spatial distributions were obtained.The real-time visualization of large-scale spatial structures using augmented reality devices,particularly low-performance devices,was also investigated.Conclusions The proposed visualization system can reconstruct the three-dimensional structure of a large-scale space,express the structure and changes in the spatial environment using augmented reality,and assist in intuitively discovering spatial environmental events and evolutionary rules.

Parks松挂线术与分次紧线挂线法治疗高位复杂性肛瘘的效果比较

目的对比Parks松挂线术与分次紧线挂线法治疗高位复杂性肛瘘的效果。方法120例高位复杂性肛瘘患者随机分为两组,对照组采用Parks松挂线术进行治疗,研究组采用分次紧线挂线法进行治疗,比较两组的治疗效果、伤口愈合时间以及肛门功能。结果研究组的治疗总有效率高于对照组,伤口愈合时间长于对照组(P<0.05)。术后1个月,两组的Wexner评分均较治疗前降低(P<0.05),但组间差异无统计学意义(P>0.05)。结论Parks松挂线术与分次紧线挂线法均能有效改善高位复杂性肛瘘患者的肛门功能;相较于Parks松挂线术,分次紧线挂线法的疗效更好,但患者恢复时间相对较长。

Planning and Design Practices for Coping with Climate Change in Urban Parks in Berlin,Germany:A Case Study of EVFBS Cemetery Park

Urban parks,as a vital component of green infrastructure,significantly contribute to alleviating the impacts of climate change and enhancing the urban ecosystem.This paper examines the EVFBS Cemetery Park in Berlin as a case study to elucidate the significant role of centralized urban greening,exemplified by urban cemetery parks,in addressing the challenges posed by urban climate change.Through field research,data collection,and various methodologies,this study examines the specific planning and design measures employed to address climate-related challenges,including air purification,temperature regulation,and mitigation of the urban heat island effect.Additionally,it explores the multifaceted roles of urban parks in enhancing the quality of the urban ecological environment,promoting biodiversity,and providing recreational spaces for residents.The aim is to elucidate the significance of diverse urban parks within the climate regulation strategies of contemporary metropolises.By comparing and discussing relevant practices and successful experiences in climate-adaptive design from Berlin,and summarizing advanced concepts and planning experiences that may serve as valuable references for the development of urban green spaces in China,it examines the significance of urban park greening for climate regulation in contemporary cities.