Flow Dynamics during the Hydrocarbon Exploitation for Prevention and Management of Water Venues in Oil Field: A Study Case of Crystal Field in Badila/Chad

The southern part of the Lake Chad basin is under the gas and oil petroleum industry due to its hydrocarbon potential for about twenty years. This project stands out as the main challenges of the hydrocarbon production and the management of fluxes particularly the groundwater venues. A comprehensive study is thus conducted to develop a dynamic and analytic model for diagnosing the production performances with a particular view on the management of groundwater venues. The three main concerned reservoirs subdivided on subunits evidence their proper characteristics. The porous media, their densities, the internal flows and the water injection techniques such as water flooding were thus adopted. The oil viscosity variability within the reservoirs creates different levels of mobility between water and oil, highlighting the challenges of water management. The material balance model and the behavior of the well analysis were taken in consideration within the identified aquifer, emphasizing the importance of keeping the pressure through injection. The control of water productions, the management of the reservoir, the well strategical position and the specific completions lead to the model functioning. In addition, the CO log and the Pulsed Neutron indicate their limitations as a result of the water salinity and the porosity of the aquifer. The management of groundwater venues at Badila requires various approaches throughout the lifetime of the Crystal field such as the data acquisition and remediation actions and prevention, under a permanent monitoring of the dynamic fluxes in the reservoirs.

Quantitatively characterizing sandy soil structure altered by MICP using multi-level thresholding segmentation algorithm

The influences of biological,chemical,and flow processes on soil structure through microbially induced carbonate precipitation(MICP)are not yet fully understood.In this study,we use a multi-level thresholding segmentation algorithm,genetic algorithm(GA)enhanced Kapur entropy(KE)(GAE-KE),to accomplish quantitative characterization of sandy soil structure altered by MICP cementation.A sandy soil sample was treated using MICP method and scanned by the synchrotron radiation(SR)micro-CT with a resolution of 6.5 mm.After validation,tri-level thresholding segmentation using GAE-KE successfully separated the precipitated calcium carbonate crystals from sand particles and pores.The spatial distributions of porosity,pore structure parameters,and flow characteristics were calculated for quantitative characterization.The results offer pore-scale insights into the MICP treatment effect,and the quantitative understanding confirms the feasibility of the GAE-KE multi-level thresholding segmentation algorithm.

Scheme Based on Multi-Level Patch Attention and Lesion Localization for Diabetic Retinopathy Grading

Early screening of diabetes retinopathy(DR)plays an important role in preventing irreversible blindness.Existing research has failed to fully explore effective DR lesion information in fundus maps.Besides,traditional attention schemes have not considered the impact of lesion type differences on grading,resulting in unreasonable extraction of important lesion features.Therefore,this paper proposes a DR diagnosis scheme that integrates a multi-level patch attention generator(MPAG)and a lesion localization module(LLM).Firstly,MPAGis used to predict patches of different sizes and generate a weighted attention map based on the prediction score and the types of lesions contained in the patches,fully considering the impact of lesion type differences on grading,solving the problem that the attention maps of lesions cannot be further refined and then adapted to the final DR diagnosis task.Secondly,the LLM generates a global attention map based on localization.Finally,the weighted attention map and global attention map are weighted with the fundus map to fully explore effective DR lesion information and increase the attention of the classification network to lesion details.This paper demonstrates the effectiveness of the proposed method through extensive experiments on the public DDR dataset,obtaining an accuracy of 0.8064.

Deep neural network based on multi-level wavelet and attention for structured illumination microscopy

Structured illumination microscopy(SIM)is a popular and powerful super-resolution(SR)technique in biomedical research.However,the conventional reconstruction algorithm for SIM heavily relies on the accurate prior knowledge of illumination patterns and signal-to-noise ratio(SNR)of raw images.To obtain high-quality SR images,several raw images need to be captured under high fluorescence level,which further restricts SIM’s temporal resolution and its applications.Deep learning(DL)is a data-driven technology that has been used to expand the limits of optical microscopy.In this study,we propose a deep neural network based on multi-level wavelet and attention mechanism(MWAM)for SIM.Our results show that the MWAM network can extract high-frequency information contained in SIM raw images and accurately integrate it into the output image,resulting in superior SR images compared to those generated using wide-field images as input data.We also demonstrate that the number of SIM raw images can be reduced to three,with one image in each illumination orientation,to achieve the optimal tradeoff between temporal and spatial resolution.Furthermore,our MWAM network exhibits superior reconstruction ability on low-SNR images compared to conventional SIM algorithms.We have also analyzed the adaptability of this network on other biological samples and successfully applied the pretrained model to other SIM systems.

An Expert System to Detect Political Arabic Articles Orientation Using CatBoost Classifier Boosted by Multi-Level Features

The number of blogs and other forms of opinionated online content has increased dramatically in recent years.Many fields,including academia and national security,place an emphasis on automated political article orientation detection.Political articles(especially in the Arab world)are different from other articles due to their subjectivity,in which the author’s beliefs and political affiliation might have a significant influence on a political article.With categories representing the main political ideologies,this problem may be thought of as a subset of the text categorization(classification).In general,the performance of machine learning models for text classification is sensitive to hyperparameter settings.Furthermore,the feature vector used to represent a document must capture,to some extent,the complex semantics of natural language.To this end,this paper presents an intelligent system to detect political Arabic article orientation that adapts the categorical boosting(CatBoost)method combined with a multi-level feature concept.Extracting features at multiple levels can enhance the model’s ability to discriminate between different classes or patterns.Each level may capture different aspects of the input data,contributing to a more comprehensive representation.CatBoost,a robust and efficient gradient-boosting algorithm,is utilized to effectively learn and predict the complex relationships between these features and the political orientation labels associated with the articles.A dataset of political Arabic texts collected from diverse sources,including postings and articles,is used to assess the suggested technique.Conservative,reform,and revolutionary are the three subcategories of these opinions.The results of this study demonstrate that compared to other frequently used machine learning models for text classification,the CatBoost method using multi-level features performs better with an accuracy of 98.14%.

The world's widest avenue

Activity 1.Think about the following questions and write down your answers before reading the text.1.If you were to design the widest avenue in the world,what features would you include to make it unique and functional?2.Suppose there is a very wide avenue in your hometown,how do you think it would change peoples daily lives and social interactions?

Construction of a Multi-Level Strategic System for Cultivating Cultural Industry Management Talents in Colleges and Universities

Through SWOT(strengths,weaknesses,opportunities,and threats)and PEST(political,economic,social,and technological)analysis,this study discusses the construction of a multi-level strategic system for the cultivation of cultural industry management talents in colleges and universities.First of all,based on SWOT analysis,it is found that colleges and universities have rich educational resources and policy support,but they face challenges such as insufficient practical teaching and intensified international competition.External opportunities come from the rapid development of the cultivation of cultural industry management talents and policy promotion,while threats come from global market competition and talent flow.Secondly,PEST analysis reveals the key factors in the macro-environment:at the political level,the state vigorously supports the cultivation of cultural industry management talents;at the economic level,the market demand for cultural industries is strong;at the social level,the public cultural consumption is upgraded;at the technological level,digital transformation promotes industry innovation.On this basis,this paper puts forward a multi-level strategic system covering theoretical education,practical skill improvement,interdisciplinary integration,and international vision training.The system aims to solve the problems existing in talent training in colleges and universities and cultivate high-quality cultural industry management talents with theoretical knowledge,practical skills,and global vision,so as to adapt to the increasingly complex and diversified cultural industry management talents market demand and promote the long-term development of the industry.

Deep reinforcement learning based multi-level dynamic reconfiguration for urban distribution network:a cloud-edge collaboration architecture

With the construction of the power Internet of Things(IoT),communication between smart devices in urban distribution networks has been gradually moving towards high speed,high compatibility,and low latency,which provides reliable support for reconfiguration optimization in urban distribution networks.Thus,this study proposed a deep reinforcement learning based multi-level dynamic reconfiguration method for urban distribution networks in a cloud-edge collaboration architecture to obtain a real-time optimal multi-level dynamic reconfiguration solution.First,the multi-level dynamic reconfiguration method was discussed,which included feeder-,transformer-,and substation-levels.Subsequently,the multi-agent system was combined with the cloud-edge collaboration architecture to build a deep reinforcement learning model for multi-level dynamic reconfiguration in an urban distribution network.The cloud-edge collaboration architecture can effectively support the multi-agent system to conduct“centralized training and decentralized execution”operation modes and improve the learning efficiency of the model.Thereafter,for a multi-agent system,this study adopted a combination of offline and online learning to endow the model with the ability to realize automatic optimization and updation of the strategy.In the offline learning phase,a Q-learning-based multi-agent conservative Q-learning(MACQL)algorithm was proposed to stabilize the learning results and reduce the risk of the next online learning phase.In the online learning phase,a multi-agent deep deterministic policy gradient(MADDPG)algorithm based on policy gradients was proposed to explore the action space and update the experience pool.Finally,the effectiveness of the proposed method was verified through a simulation analysis of a real-world 445-node system.

Influencing Factors of the Elderly’s Preference for Sports Venues in a University of the North

From the aspects of time characteristics,spatial characteristics and subjective will of sports activities,the preference of sports venues for the elderly in colleges and universities is investigated through questionnaires and interviews,and further analysis and studies are conducted by using the abstract method and discrete selection model,so as to explore the factors affecting the preference of sports venues for the elderly in colleges and universities and their importance.The results show that the factors affecting preference of the elderly in colleges and universities for sports venues from high to low are site greening,site amenities,site shade facilities,site rest seats,site lighting facilities,site size,site fitness and leisure facilities,site walking distance and site quietness degree.Among them,the walking distance of the site is negatively correlated,and the quiet degree of the site has no significant effect,and the other 7 items are positively correlated.The research on the preference of sports venues for the elderly in colleges and universities is helpful to the location and construction of sports venues,so as to provide comfortable and satisfactory sports space for the elderly,which has important practical significance.

FPGA based hardware platform for trapped-ion-based multi-level quantum systems

We report a design and implementation of a field-programmable-gate-arrays(FPGA)based hardware platform,which is used to realize control and signal readout of trapped-ion-based multi-level quantum systems.This platform integrates a four-channel 2.8 Gsps@14 bits arbitrary waveform generator,a 16-channel 1 Gsps@14 bits direct-digital-synthesisbased radio-frequency generator,a 16-channel 8 ns resolution pulse generator,a 10-channel 16 bits digital-to-analogconverter module,and a 2-channel proportion integration differentiation controller.The hardware platform can be applied in the trapped-ion-based multi-level quantum systems,enabling quantum control of multi-level quantum system and highdimensional quantum simulation.The platform is scalable and more channels for control and signal readout can be implemented by utilizing more parallel duplications of the hardware.The hardware platform also has a bright future to be applied in scaled trapped-ion-based quantum systems.

Neighborhood Effects and Political Trust: A Multi-level Analysis of Chinese Rural-to-Urban Migrants’ Trust in County Government

Massive rural-to-urban migration in China is consequential for political trust: rural-to-urban migrants have been found to hold lower levels of trust in local government than their rural peers who choose to stay in the countryside (mean 4.92 and 6.34 out of 10, respectively, p < 0.001). This article explores why migrants have a certain level of political trust in their county-level government. Using data of rural-to-urban migrants from the China Family Panel Survey, this study performs a hierarchical linear modeling (HLM) to unpack the multi-level explanatory factors of rural-to-urban migrants’ political trust. Findings show that the individual-level socio-economic characteristics and perceptions of government performance (Level-1), the neighborhood-level characteristics-the physical and social status and environment of neighborhoods (Level-2), and the objective macroeconomic performance of county-level government (Level-3), work together to explain migrants’ trust levels. These results suggest that considering the effects of neighborhood-level factors on rural-to-urban migrants’ political trust merits policy and public management attention in rapidly urbanizing countries.

体育场馆促进城市更新:驱动场景、中美比较及中国方案

体育场馆是城市更新的重要内容,也是实施城市更新行动的重要抓手。通过文献资料、实地考察、专家访谈、比较研究等方法,就体育场馆促进城市更新问题进行了研究。研究总结了体育场馆促进城市更新的5类驱动场景:大型体育场馆新建与升级、工业遗产通过体育再利用、老旧小区补齐体育场馆短板、体育业态盘活商业闲置空间、体育场景激活城市金边银角。通过对中美两国体育场馆促进城市更新的背景、目的、方式、资金、主体、效益进行较为全面的比较分析,研究提出了体育场馆促进城市更新的中国方案,以经济为导向的更新项目应充分发挥触媒效应:融入城市规划,带动区域共同更新;拓宽投资渠道,吸引社会力量参与;提升更新效益,建设体育服务综合体;走微更新之路,保护传承文化遗产;多元主体参与,提升社会治理水平;以民生为导向的更新项目应补齐公共服务短板:坚持政府引导,纳入民生实事工程;财政资金兜底,鼓励社会资本投入;加强居民自治,实现共同缔造目标;借力城市更新,补齐健身设施短板,以期为探索体育场馆促进城市更新的中国道路提供理论依据与参考。

中小学校园立体运动场馆下部的采光模拟与优化分析

在我国大中城市学位不足与建设用地紧缺的大背景下,对中小学校园运动场进行立体化设计能够形成空间的纵向叠加,从而释放大量建设用地,但运动场立体化同样也会产生一些新的设计难题,其中运动场地下部空间的自然采光问题又最为复杂,既面临照度是否足够的问题,又面临光线分布是否均匀的问题。国内外文献研究显示,当前国内外的立体运动场馆采光研究几乎空白,使得当前的建设存在一定的盲目性。基于以上考虑,该研究拟利用基于Rhino、Grasshopper、Ladybugtools三者构建的采光模拟平台的参数化光环境模拟技术,对不同形式的校园立体运动场馆下部区域的自然采光环境进行模拟,提炼出影响立体运动场下部空间采光的因素,包括运动场尺寸、架空层数、空间进深、功能布局、采光口位置、遮阳反光措施。通过对影响因素进行模拟分析,得出不同因素的影响机制,进一步提炼营造良好自然光环境的立体运动场设计对策,并以广州实验中学项目的学生活动中心的自然光环境优化实践形成研究反馈,从而验证了研究的有效性。

多分隔受限场景空间火灾疏散安全性分析

为了优化密室逃脱类场所的复杂空间布局,基于该场所剧情场景下空间多重分隔、空间狭小、封闭且组合多样等结构特点,分析归纳该场所3种典型的受限场景空间类型,以西安市某密室逃脱类场所建筑布局为例,运用Pathfinder和PyroSim软件对其展开火灾情景下的疏散模拟仿真,进而对比分析不同场景空间的火灾特性指标以及疏散安全性。研究结果表明:密室逃脱类场所3种场景空间中,串联式场景空间人员疏散时间最长(141.53 s),单元式次之(115.53 s),并联式最短(112.28 s);从火灾烟气模拟结果看,串联式场景空间安全出口的温度、CO体积分数以及能见度始终都处于安全临界范围内,而其他2种场景空间均超过临界值,并且能见度降至0.06 m,因此从安全性判定,该密室逃脱类场所只有串联式场景空间人员疏散过程是安全的,并联式和单元式场景空间人员疏散过程中均存在一定危险性。对于密室逃脱类场所这种特定狭小空间,烟气的危害对人员安全疏散的影响尤为显著,因此要在防排烟和火灾荷载方面进行重点防控。研究结果可为密室逃脱类场所的空间结构优化提供消防指导。

基于多源数据的公共体育场馆空间活力评价与提升策略研究

公共体育场馆是供给全民健身公共服务的重要物质载体,其空间活力是衡量场馆运营管理水平的重要指标。运用GIS空间分析法,对湖北省160个公共体育场馆的空间活力进行测量评价和空间特征分析;对不同类型、级别、运营主体的场馆空间活力进行比较分析;引入多尺度地理加权回归模型和地理探测器,分别从宏观尺度和微观尺度探究周边建成环境和场馆内部因素对场馆空间活力的影响。研究表明:1)湖北省公共体育场馆空间活力存在空间分异特征,场馆资源与空间活力之间存在错位现象;2)场馆空间活力呈现出省级高于市级、县级,体育馆、游泳馆高于体育场、全民健身中心,事业单位运营场馆高于公司企业运营场馆的特征;3)场馆空间活力受到建成环境因素中人口密度、路网密度的正向影响,受经济密度负向影响,与场馆的固定座位数、场地面积、接待人次、体育赛事数量存在显著相关。研究建议:优化场馆空间布局,融入城市整体规划;加强硬件条件建设,提升场馆服务环境;提高运营管理水平,助推场馆高效利用;推进基本公共体育服务均等化,促进区域协调发展;协调社会效益和经济效益,实现平衡、共赢发展。

红色场馆数字化建设的动力机制与实践路径

红色场馆作为我国极具重要文化价值的空间载体和传播基地,在红色文化的传播和红色基因的赓续中发挥着举足轻重的作用。随着数字经济时代的到来,数字化转型升级成为旅游业的时代主旋律,红色场馆也逐渐开始进行数字化建设,但依然存在一些制约其高质量发展的现实困境。文章旨在探讨红色旅游场馆数字化建设的关键问题,明晰红色场馆向数字化方向转型的动力机制和实践路径。

‘维纳斯黄金’苹果在辽宁省的引种试栽表现

在辽宁省绥中县、庄河市和辽宁省果树科学研究所选择3个果园试栽‘维纳斯黄金’苹果,果实10月上旬成熟。均在定植后第4年进入盛果期,每666.7 m2产量1270~1688 kg。该品种高桩,果肉淡黄色,香气浓郁。2023年10月测定,3个果园的平均单果重246.6~292.4 g,果型指数0.92~0.93,果实硬度7.98~8.23 kg/cm2,可溶性固形物含量15.4%~17.6%。表现出优质、高产、抗逆性强等优异性状,值得推广。

《维纳斯》历史意义的再生机制研究——以洛特曼的文化符号学为视角

当代美国非裔女剧作家苏珊-洛里·帕克斯的代表作《维纳斯》是一部基于真实历史改编的戏剧。该剧通过丰富的文化符号,再现了南非土著妇女巴特曼的一生,重新诠释了历史文本。运用洛特曼的文化符号学理论,跨语境解读《维纳斯》中独特的符号体系,揭示出动态文本观的重要性,有助于系统理解该剧的历史意义衍生机制。戏剧文本的创造功能与记忆功能的相互作用实现了历史意义的挖掘,创新的交际模式在信息传递与创造的过程中有效修订了原有历史,空间模拟复现了非裔群体作为边缘人的双重困境。《维纳斯》不仅激发了读者和观众的历史责任感和社会意识,还鼓励人们审视和思考历史的真实性,从而实现了历史书写的更深层次意义。

公共体育场馆运营商评价指标体系构建研究

公共体育场馆运营商评价是我国公共体育场馆经营权改革的重要理论与实践问题。运用文献研究法、访谈法、德尔菲法、层次分析法、数理统计法等方法,依据委托代理等理论,以信息不对称为逻辑起点,从吸引力、管理力及影响力3个维度构建了包含3个一级指标、11个二级指标和41个三级指标的公共体育场馆运营商评价指标体系。构建公共体育场馆运营商评价指标体系,既可以为公共体育场馆委托方遴选、激励及监管场馆运营商提供参考,又可以为场馆运营商提高经营管理水平提供参照,同时也是消除公共体育场馆委托管理中各个利益相关方之间信息不对称弊端的重要举措。