Multi-Granularity Neighborhood Fuzzy Rough Set Model on Two Universes

The two universes multi-granularity fuzzy rough set model is an effective tool for handling uncertainty problems between two domains with the help of binary fuzzy relations. This article applies the idea of neighborhood rough sets to two universes multi-granularity fuzzy rough sets, and discusses the two-universes multi-granularity neighborhood fuzzy rough set model. Firstly, the upper and lower approximation operators are defined in the two universes multi-granularity neighborhood fuzzy rough set model. Secondly, the properties of the upper and lower approximation operators are discussed. Finally, the properties of the two universes multi-granularity neighborhood fuzzy rough set model are verified through case studies.

On Perceptibility and Recognizability of Traditional Settlements' Landscape Genes in Chinese Minority Areas: A Case Study in Yutou Dong Minority Village of Tongdao

Taking Yutou Dong Minority Village in Tongdao County for example, this study carried out questionnaire survey and interview survey from the perspective of environment element perceptibility and culture psychology recognizability, on the basis of identifying 9 landscape genes, namely drum tower, wind-rain bridge, the village gate, Sa altar, overall layout, totem signs, and environmental factors. The survey results showed that 1 Sa altar has the highest perceptibility, and drum tower has the lowest perceptibility; 2 Sa altar and drum tower have the highest recognizability, overall layout, totem signs and environment have the lowest; 3 Location has the highest perceptibility and size has the poorest; 4 Sentiment has the highest perceptibility, function the lowest; 5 Men have higher perception and sense of recognition than women; 6 Education background has greater impact on perception, but no obvious impact on sense of recognition; 7 Length of residence in the village has positive effect on perception and sense of recognition; 8 Distance from the village has positive effect on perception and sense of recognition.

A HYBRID GRANULARITY PARALLEL ALGORITHM FOR PRECISE INTEGRATION OF STRUCTURAL DYNAMIC RESPONSES

Precise integration methods to solve structural dynamic responses and the corresponding time integration formula are composed of two parts： the multiplication of an exponential matrix with a vector and the integration term. The second term can be solved by the series solution. Two hybrid granularity parallel algorithms are designed, that is, the exponential matrix and the first term are computed by the fine-grained parallel algorithra and the second term is computed by the coarse-grained parallel algorithm. Numerical examples show that these two hybrid granularity parallel algorithms obtain higher speedup and parallel efficiency than two existing parallel algorithms.

Granularity of Knowledge Computed by Genetic Algorithms Based on Rough Sets Theory

Rough set philosophy hinges on the granularity of data, which is used to build all its basic concepts, like approximations, dependencies, reduction etc. Genetic Algorithms provides a general frame to optimize problem solution of complex system without depending on the domain of problem.It is robust to many kinds of problems.The paper combines Genetic Algorithms and rough sets theory to compute granular of knowledge through an example of information table. The combination enable us to compute granular of knowledge effectively.It is also useful for computer auto-computing and information processing.

Integrating Coarse Granularity Part-Level Features with Supervised Global-Level Features for Person Re-Identification

Person re-identification(Re-ID)has achieved great progress in recent years.However,person Re-ID methods are still suffering from body part missing and occlusion problems,which makes the learned representations less reliable.In this paper,we pro⁃pose a robust coarse granularity part-level network(CGPN)for person Re-ID,which ex⁃tracts robust regional features and integrates supervised global features for pedestrian im⁃ages.CGPN gains two-fold benefit toward higher accuracy for person Re-ID.On one hand,CGPN learns to extract effective regional features for pedestrian images.On the other hand,compared with extracting global features directly by backbone network,CGPN learns to extract more accurate global features with a supervision strategy.The single mod⁃el trained on three Re-ID datasets achieves state-of-the-art performances.Especially on CUHK03,the most challenging Re-ID dataset,we obtain a top result of Rank-1/mean av⁃erage precision(mAP)=87.1%/83.6%without re-ranking.

CRF:A Scheduling of Multi-Granularity Locks in Object-Oriented Database Systems

This paper introduces a multi-granularity locking model (MGL) for concurrency control in object-oriented database system briefiy, and presents a MGL model formally. Four lockingscheduling algorithms for MGL are proposed in the paper. The ideas of single queue scheduling(SQS) and dual queue scheduling (DQS) are proposed and the algorithm and the performance evaluation for these two scheduling are presented in some paper. This paper describes a new idea of thescheduling for MGL, compatible requests first (CRF). Combining the new idea with SQS and DQS,we propose two new scheduling algorithms called CRFS and CRFD. After describing the simulationmodel, this paper illustrates the comparisons of the performance among these four algorithms. Asshown in the experiments, DQS has better performance than SQS, CRFD is better than DQS, CRFSperforms better than SQS, and CRFS is the best one of these four scheduling algorithms.

On Granularity in Information Systems Based on Binary Relation

In this paper, some important issues of granularity are discussed mainly in information systems (ISs) based on binary relation. Firstly, the vector representation method of knowledge granules is proposed in an infor-mation system based on binary relation to eliminate limitations of set representation method. Secondly, operators among knowledge granularity are introduced and some important properties of them are studied carefully. Thirdly, distance between two knowledge granules is established and granular space is constructed based on it. Fourthly, axiomatic definition of knowledge granularity is investigated, and one can find that some existed knowledge granularities are special cases under the definition. In addition, as an application of knowledge granular space, an example is employed to validate some results in our work.

Research on the Development Trend and General Applications of the Fuzzy Rough Granularity Computing Theory

In this paper, we conduct research on the development trend and general applications of the fuzzy rough granular computing theory. Granular computing is a new concept of general information processing and computing paradigm which covers all the granularity the study of the theory, methods, techniques and the tools. In many areas are the basic ideas of granular computing, such as the interval analysis, rough set theory, clustering analysis and information retrieval, machine learning, database, etc. With the theory of domain known division of target concept and rule acquisition, in knowledge discovery, data mining and the pattern recognition is widely used. Under this basis, in this paper, we propose the fuzzy rough theory based computing paradigm that gains ideal performance.

Dynamic Distribution Model with Prime Granularity for Parallel Computing

Dynamic distribution model is one of the best schemes for parallel volume rendering. How- ever, in homogeneous cluster system.since the granularity is traditionally identical, all processors communicate almost simultaneously and computation load may lose balance. Due to problems above, a dynamic distribution model with prime granularity for parallel computing is presented. Granularities of each processor are relatively prime, and related theories are introduced. A high parallel performance can be achieved by minimizing network competition and using a load balancing strategy that ensures all processors finish almost simultaneously. Based on Master-Slave-Gleaner ( MSG) scheme, the parallel Splatting Algorithm for volume rendering is used to test the model on IBM Cluster 1350 system. The experimental results show that the model can bring a considerable improvement in performance, including computation efficiency, total execution time, speed, and load balancing.

Research on the Effects of the Contents of Radioactive Elements on Granularity and Climate Change:An Example of SZK1 and SZK2 Drill Holes Strata in the Fuzhou Basin

Based on the content of radioactive elements (U, Th, K) of strata in two drill holes in the Fuzhou basin, and combined with the result of spore_pollen analysis, the relationship between radioactivity and lithology and deposit environments is discussed and the results show that the content of radioactive substances is related to the granularity and lithology in sediment, and it is higher in argillaceous sediment (e.g. silt and clay), lower in sand sediment and in the middle in gravels between the above two kinds of sediment. The content of radioactive substances is also related to paleoclimate. A warm and humid environment is propitious to the deposition of radioactive substances, while a cool and dry climate is just the reverse.

Parsing Objects at a Finer Granularity: A Survey

Fine-grained visual parsing, including fine-grained part segmentation and fine-grained object recognition, has attracted considerable critical attention due to its importance in many real-world applications, e.g., agriculture, remote sensing, and space technologies. Predominant research efforts tackle these fine-grained sub-tasks following different paradigms, while the inherent relations between these tasks are neglected. Moreover, given most of the research remains fragmented, we conduct an in-depth study of the advanced work from a new perspective of learning the part relationship. In this perspective, we first consolidate recent research and benchmark syntheses with new taxonomies. Based on this consolidation, we revisit the universal challenges in fine-grained part segmentation and recognition tasks and propose new solutions by part relationship learning for these important challenges. Furthermore, we conclude several promising lines of research in fine-grained visual parsing for future research.

Towards POI-based large-scale land use modeling: spatial scale, semantic granularity, and geographic context

The combination of spatial distribution,semantic characteristics,and sometimes temporal dynamics of POIs inside a geographic region can capture its unique land use characteristics.Most previous studies on POI-based land use modeling research focused on one geographic region and select one spatial scale and semantic granularity for land use characterization.There is a lack of understanding on the impact of spatial scale,semantic granularity,and geographic context on POI-based land use modeling,particularly large-scale land use modeling.In this study,we developed a scalable POI-based land use modeling framework and examined the impact of these three factors on POI-based land use characterization using data from three geographic regions.We developed a unified semantic representation framework for POI semantics that can help fuse heterogeneous POI data sources.Then,by combining POIs with a neural network language model,we developed a spatially explicit approach to learn the embedding representation of POIs and AOIs.We trained multiple supervised classifiers using AOI embeddings as input features to predict AOI land use at different semantic granularities.The classification performance of different land use classes was analyzed and compared across three geographic regions to identify the semantic representativeness of POI-based AOI embedding and the impact of geographic context.

The Multisource Time Series Data Granularity Conversion Method

Granular information has emerged as a potent tool for data represen-tation and processing across various domains.However,existing time series data granulation techniques often overlook the influence of external factors.In this study,a multisource time series data granularity conversion model is proposed that achieves granularity conversion effectively while maintaining result consis-tency and stability.The model incorporates the impact of external source data using a multivariate linear regression model,and the entropy weighting method is employed to allocate weights andfinalize the granularity conversion.Through experimental analysis using Beijing’s 2022 air quality dataset,our proposed method outperforms traditional information granulation approaches,providing valuable decision-making insights for industrial system optimization and research.

白藜芦醇改善多囊卵巢综合征模型大鼠卵巢颗粒细胞线粒体功能和抑制凋亡的作用

目的:观察白藜芦醇对多囊卵巢综合征(polycystic ovary syndrome,PCOS)模型大鼠卵巢颗粒细胞增殖和凋亡的影响以及线粒体的形态和功能的改变,并探讨其可能的保护机制。方法:32只雌性SD大鼠幼鼠随机分为2组,PCOS模型组(n=24)和模型对照组(n=8)。成功提取PCOS组颗粒细胞并体外培养,然后用不同浓度的白藜芦醇处理颗粒细胞,CCK-8法检测细胞活力的变化;Mito Tracker Deep Red染色观察细胞线粒体的形态变化;JC-1检测线粒体膜电位的变化;ATP检测试剂盒检测ATP的变化;分光光度计检测与凋亡密切相关casppase-3和caspase-9酶活性变化;最后,蛋白免疫印迹法检测颗粒细胞内caspase-3和caspase-9蛋白水平的变化。结果:白藜芦醇处理PCOS模型大鼠卵巢颗粒细胞后,颗粒细胞的增殖活性明显增强(P=0.000);Mito Tracker Deep Read染色结果显示线粒体质量明显增加,形状呈管状或长条状;颗粒细胞内ATP产量和线粒体膜电位也明显增加(P=0.000、P=0.000)。另外,白藜芦醇也降低了caspase-3和caspase-9酶活性和蛋白水平的表达(P=0.000、P=0.000)。结论:白藜芦醇抑制了PCOS模型大鼠卵巢颗粒细胞的凋亡,也促进了细胞的增殖,其机制与白藜芦醇改善颗粒细胞线粒体的形态和功能有关。

颗粒活性炭对亚甲基蓝的吸附与再生研究

为推进颗粒活性炭在染料废水治理中的应用,采用自制的木质素基颗粒活性炭吸附亚甲基蓝(MB),考察了MB初始质量浓度、吸附温度和溶液pH值对吸附过程的影响,并利用吸附等温线和动力学对吸附过程进行了探讨,最后探索了活性炭的再生性能。研究结果表明:颗粒活性炭更适合高浓度MB吸附,吸附速率随溶液初始质量浓度的降低而降低;平衡吸附量受吸附初始质量浓度、吸附温度的影响较大,最佳吸附温度25℃,受溶液pH值影响较小。颗粒活性炭对MB的吸附等温线遵循Langmiur模型,吸附过程符合准二级动力学模型,颗粒内扩散模型分析表明内扩散及外扩散都是吸附的限速因素。吸附后活性炭在300℃热再生30 min,颗粒活性炭的MB吸附值可达240 mg/g,恢复达初始样品的94.12%,反复吸附再生4次后,颗粒活性炭的MB吸附值仍保持在85%以上。

有机负荷率对厌氧颗粒污泥处理畜禽养殖废水效能的影响探究

针对高浓度畜禽养殖废水厌氧处理效率低,构建了外循环厌氧反应器(EGSB),开展了进水有机负荷率(OLR)对EGSB处理畜禽养殖废水过程内污染物去除效能及颗粒污泥形成规律的影响,并解析相关作用机制。结果表明进水OLR提高至9.0 kg/(m^(3)·d),COD、氨氮及总磷去除维持在91.2%~95.3%、89.6%和76.9%,产气及甲烷产率分别提高至4.64~4.94 L/(L·d)和2.96~3.40 L/(L·d)。适宜提高OLR提高了污泥颗粒化,在工况III内,污泥浓度提高至6.1~6.5 g/L,大粒径污泥质量浓度增加。OLR提高增加了颗粒污泥内胞外聚合物(EPS)含量,并影响了EPS内蛋白质/多糖比值。此外,OLR能影响EGSB内微生物群落特征,且当OLR为9.0kg/(m^(3)·d)时,Firmicutes、Bacteroidetes和Proteobacteria是主要的门级别微生物,且相对丰度分别高达25.6%、16.5%和15.2%。研究结果为畜禽养殖废水高效处理提供一定理论依据。

好氧颗粒污泥的解体机制及修复策略研究进展

好氧颗粒污泥(AGS)技术被誉为有望替代活性污泥法的下一代污水生物法处理技术,但其存在长期运行下颗粒易解体而造成系统失稳的瓶颈问题,未能大规模化工程应用.通过文献分析和整理,系统归纳了造成AGS解体的各类不利因素及其危害阈值与作用机制,梳理了解体AGS的现有修复策略,成效及其再颗粒化机理,并基于现有研究的局限性对未来的研究方向进行展望,以期实现AGS系统a解体-再颗粒化”的动态稳定运行,推动污水厂采纳AGS技术而低碳化转型.

融合领域要素知识的多粒度法律文本匹配方法

法律文本匹配的目标是快速提炼对比要素信息并发现关联案件,保障法律适用的统一性同案同判.现有方法未能充分利用特定类型案件的先验知识,其核心要素提取准确率低,仅进行词向量的权重计算,忽略字义、句义、句法的向量信息,影响匹配效果.提出一种融合领域要素知识的多粒度法律文本匹配方法,通过建立特定案件类型领域知识库准确提取法律要素,引入字、词、句3个粒度的注意力机制计算不同文本向量的权重提升匹配模型效果.实验结果表明,该方法在公开数据集上可达到最好效果.

AOA后置短时低氧曝气实现短程硝化反硝化除磷

为培养亚硝酸盐型反硝化聚磷菌实现好氧颗粒污泥(AGS)短程硝化内源反硝化除磷,设置3组同规格以厌氧/好氧/缺氧后置短时曝气(AO_(1)A-O_(2))模式运行的SBR,各反应器好氧段/后置好氧段(O_(1)/O_(2))的曝气强度和曝气时间均不同,通过对比3组反应器60 d的运行情况,探究各系统污染物处理性能和功能菌活性。结果表明,后置短时低氧曝气10 min且O_(1)、O_(2)的曝气强度分别为5、2.5 L/(h·L)的R2脱氮除磷效果最佳,其COD、TP、NH^(+)_(4)-N、TN去除率达95.49%、95.57%、100%、95.52%。通过短时好氧饥饿和低溶解氧可以创造出短程硝化内源反硝化除磷的最适环境,R2中约60%的除磷菌为DPAOs,且亚硝酸盐型聚磷菌最多,可达38.76%,其反应器好氧段的亚硝酸盐积累率(R_(NA))为74.19%,实现了较高的NO^(-)_(2)-N积累,游离亚硝酸(FNA)为1.03μg/L,可抑制PAOs和NOB,同时富集出更多的AOB和DPAOs。