Macroinvertebrate Community in Streams on the Canary Islands: Gradient Analysis and Stressors

This study describes the gradient analysis of the freshwater macroinvertebrate assemblages in eight streams of Tenerife and La Gomera (Canary Islands) over a 16-year period. During this period, a total of 75 taxa belonging to 34 taxonomic families were found. Endemism has an important presence in the streams on both islands, especially regarding Trichoptera and Coleoptera. The overall status of freshwater macroinvertebrates is rather uncertain as recent data on these communities are scarce and focused on a limited number of sites. Overexploitation of aquifers and the diversion of natural water flows for irrigation have resulted in the drying up of numerous natural streams, inevitably endangering the fauna that inhabits them. A reduction in number and abundance of endemic and sensitive species was observed in the majority of the sampled streams resulting in a lower ecological rating. Therefore, it is proposed that the protection of streams of high conservation value is essential to conserve freshwater macroinvertebrate fauna native to the Canary Islands.

Workout Action Recognition in Video Streams Using an Attention Driven Residual DC-GRU Network

Regular exercise is a crucial aspect of daily life, as it enables individuals to stay physically active, lowers thelikelihood of developing illnesses, and enhances life expectancy. The recognition of workout actions in videostreams holds significant importance in computer vision research, as it aims to enhance exercise adherence, enableinstant recognition, advance fitness tracking technologies, and optimize fitness routines. However, existing actiondatasets often lack diversity and specificity for workout actions, hindering the development of accurate recognitionmodels. To address this gap, the Workout Action Video dataset (WAVd) has been introduced as a significantcontribution. WAVd comprises a diverse collection of labeled workout action videos, meticulously curated toencompass various exercises performed by numerous individuals in different settings. This research proposes aninnovative framework based on the Attention driven Residual Deep Convolutional-Gated Recurrent Unit (ResDCGRU)network for workout action recognition in video streams. Unlike image-based action recognition, videoscontain spatio-temporal information, making the task more complex and challenging. While substantial progresshas been made in this area, challenges persist in detecting subtle and complex actions, handling occlusions,and managing the computational demands of deep learning approaches. The proposed ResDC-GRU Attentionmodel demonstrated exceptional classification performance with 95.81% accuracy in classifying workout actionvideos and also outperformed various state-of-the-art models. The method also yielded 81.6%, 97.2%, 95.6%, and93.2% accuracy on established benchmark datasets, namely HMDB51, Youtube Actions, UCF50, and UCF101,respectively, showcasing its superiority and robustness in action recognition. The findings suggest practicalimplications in real-world scenarios where precise video action recognition is paramount, addressing the persistingchallenges in the field. TheWAVd dataset serves as a catalyst for the development ofmore robust and effective fitnesstracking systems and ultimately promotes healthier lifestyles through improved exercise monitoring and analysis.

Sentiment Drift Detection and Analysis in Real Time Twitter Data Streams

Handling sentiment drifts in real time twitter data streams are a challen-ging task while performing sentiment classifications,because of the changes that occur in the sentiments of twitter users,with respect to time.The growing volume of tweets with sentiment drifts has led to the need for devising an adaptive approach to detect and handle this drift in real time.This work proposes an adap-tive learning algorithm-based framework,Twitter Sentiment Drift Analysis-Bidir-ectional Encoder Representations from Transformers(TSDA-BERT),which introduces a sentiment drift measure to detect drifts and a domain impact score to adaptively retrain the classification model with domain relevant data in real time.The framework also works on static data by converting them to data streams using the Kafka tool.The experiments conducted on real time and simulated tweets of sports,health care andfinancial topics show that the proposed system is able to detect sentiment drifts and maintain the performance of the classification model,with accuracies of 91%,87%and 90%,respectively.Though the results have been provided only for a few topics,as a proof of concept,this framework can be applied to detect sentiment drifts and perform sentiment classification on real time data streams of any topic.

Identification,characteristics and classification of cryogenic block streams

Cryogenic block streams consist of a stream of rocks superficially resembling a stream deposit but lacking a matrix, usually occurring on a valley or gully floor or on slopes that are less steep than the maximum angle of repose of coarse sediments. They are usually formed on perennially frozen ground, but can also occur as relict landforms. There are three main active kinds forming today, viz., Siberian and Tibetan dynamic rock streams and lag block streams. During their formation, the blocks in the active Siberian and Tibetan dynamic block streams move downslope at up to 1 rn/a. They are forming today on the Tibetan Plateau and in the more arid parts of south-central Siberia, although the processes involved in the movement are different. In the case of the Tibetan type, individual blocks slide downslope over the substrate in winter on an icy coating in areas of minimal winter precipitation. The Siberian type develops in areas of 15-80 cm of winter snow cover and an MAAT （mean annual air temperature） of-4 ~C to -17 ~C. The movement is due to creep of snow and ice and collapse of the blocks downslope during thawing. Lag block streams are formed by meltwater flowing over the surface of sediment consisting primarily of larger blocks with a limited amount of interstitial sediment. The erosion of the matrix is primarily in the spring in areas of higher winter precipitation on 10^-30~ slopes. The blocks remain stationary, but the interstitial sediment is washed out by strong seasonal flows of meltwater or rain to form an alluvial fan. The boulders undergo weathering and become more rounded in the process. Lag block streams can also develop without the presence of permafrost in areas with cold climates or glaciers. Block streams also occur as relict deposits in older deposits under various climatic regimes that are unsuitable for their formation today. An example of relict lag block streams with subangular to subrounded blocks occurs in gullies on the forested mountainsides at Felsen in Germany, and is the original ＂felsenmeer＂. Similar examples occur near Vitosha Mountain in Bulgaria. The ＂stone runs＂ in the Falkland Islands are examples of the more angular relict lag block streams. In both Tasmania and the Falkland Islands, they mask a more complex history, the underlying soils indicating periods of tropical and temperate soil formation resulting from weathering during and since the Tertiary Period. Block streams have also been reported from beneath cold-based glaciers in Sweden, and below till in Canada, and when ex- humed, can continue to develop.

Subspace Clustering in High-Dimensional Data Streams:A Systematic Literature Review

Clustering high dimensional data is challenging as data dimensionality increases the distance between data points,resulting in sparse regions that degrade clustering performance.Subspace clustering is a common approach for processing high-dimensional data by finding relevant features for each cluster in the data space.Subspace clustering methods extend traditional clustering to account for the constraints imposed by data streams.Data streams are not only high-dimensional,but also unbounded and evolving.This necessitates the development of subspace clustering algorithms that can handle high dimensionality and adapt to the unique characteristics of data streams.Although many articles have contributed to the literature review on data stream clustering,there is currently no specific review on subspace clustering algorithms in high-dimensional data streams.Therefore,this article aims to systematically review the existing literature on subspace clustering of data streams in high-dimensional streaming environments.The review follows a systematic methodological approach and includes 18 articles for the final analysis.The analysis focused on two research questions related to the general clustering process and dealing with the unbounded and evolving characteristics of data streams.The main findings relate to six elements:clustering process,cluster search,subspace search,synopsis structure,cluster maintenance,and evaluation measures.Most algorithms use a two-phase clustering approach consisting of an initialization stage,a refinement stage,a cluster maintenance stage,and a final clustering stage.The density-based top-down subspace clustering approach is more widely used than the others because it is able to distinguish true clusters and outliers using projected microclusters.Most algorithms implicitly adapt to the evolving nature of the data stream by using a time fading function that is sensitive to outliers.Future work can focus on the clustering framework,parameter optimization,subspace search techniques,memory-efficient synopsis structures,explicit cluster change detection,and intrinsic performance metrics.This article can serve as a guide for researchers interested in high-dimensional subspace clustering methods for data streams.

Clustering algorithm for multiple data streams based on spectral component similarity

A new algorithm for clustering multiple data streams is proposed.The algorithm can effectively cluster data streams which show similar behavior with some unknown time delays.The algorithm uses the autoregressive （AR） modeling technique to measure correlations between data streams.It exploits estimated frequencies spectra to extract the essential features of streams.Each stream is represented as the sum of spectral components and the correlation is measured component-wise.Each spectral component is described by four parameters,namely,amplitude,phase,damping rate and frequency.The ε-lag-correlation between two spectral components is calculated.The algorithm uses such information as similarity measures in clustering data streams.Based on a sliding window model,the algorithm can continuously report the most recent clustering results and adjust the number of clusters.Experiments on real and synthetic streams show that the proposed clustering method has a higher speed and clustering quality than other similar methods.

开发网络通信服务的理想工具──STREAMS

ＳＴＲＥＡＭＳ机制为开发各种不同网络体系结构的通信服务提供了一种框架，本文叙述了ＳＴＲＥＡＭＳ的组成及基本流操作，并以Ｘ．２５协议为例，讨论了ＳＴＲＥＡＭＳ在开发计算机网络通信服务中的应用。

Oracle Data Guard与Oracle Streams技术对比

Oracle Data Guard和Oracle Streams是提高数据库可用性,构建灾难备份系统以及实现数据库分布的理想的技术解决方案。探讨Oracle Data Guard和Oracle Streams技术的实现原理以及技术特点。

Data partitioning based on sampling for power load streams

A novel data streams partitioning method is proposed to resolve problems of range-aggregation continuous queries over parallel streams for power industry.The first step of this method is to parallel sample the data,which is implemented as an extended reservoir-sampling algorithm.A skip factor based on the change ratio of data-values is introduced to describe the distribution characteristics of data-values adaptively.The second step of this method is to partition the fluxes of data streams averagely,which is implemented with two alternative equal-depth histogram generating algorithms that fit the different cases：one for incremental maintenance based on heuristics and the other for periodical updates to generate an approximate partition vector.The experimental results on actual data prove that the method is efficient,practical and suitable for time-varying data streams processing.

Vegetation dynamics of ephemeral and perennial streams in mountainous headwater catchments

Ephemeral and perennial streams of mountainous catchments in Sabaragamuwa Province of Sri Lanka and Hong Kong of China were studied for two years on vegetation dynamics.Each year,sampling was conducted during a period when ephemeral streams had low surface flows.Sampling was realized contiguously using belt transects.The standing crop biomass(hereafter biomass)of herbaceous vegetation in ephemeral channels was comparatively lower than perennials and so was the herb diversity.Herb diversity showed a peak from 1.5 to 4.5 m from the centerline/thalweg of ephemeral and perennial streams.Out of 24 herbs,only three were common for both.A peak herb biomass zone was observed in perennials in the same region where diversity peaked.In ephemerals,herb biomass increased laterally up to^1.5 m,and was constant thereafter.Seedling experiment results tallied with the field diversity observations of both stream types,and suggested that seed dispersion was the main reason for herb colonization.Furthermore,it showed sapling emergence to be significantly higher in perennials than ephemerals.Return period of annual maximum monthly rainfall was a strong indicator of age of trees in ephemeral streams,and elucidated the possibility of hindcasting past flow episodes.Electrical conductivity was significantly high in ephemeral streams among all the water quality parameters.The contents of the water nutrients were approximately the same in both stream types.While recommending further studies on eco-hydrology of ephemerals,we recognize ephemeral streams to be valuable references in climate change studies due to their responsiveness and representativeness in long term hydrological changes.

Min-wise hash function-based sampling over distributed data streams

In order to avoid the redundant and inconsistent information in distributed data streams, a sampling method based on min-wise hash functions is designed and the practical semantics of the union of distributed data streams is defined. First, for each family of min-wise hash functions, the data with the minimum hash value are selected as local samples and the biased effect caused by frequent updates in a single data stream is filtered out. Secondly, for the same hash function, the sample with the minimum hash value is selected as the global sample and the local samples are combined at the center node to filter out the biased effect of duplicated updates. Finally, based on the obtained uniform samples, several aggregations on the defined semantics of the union of data streams are precisely estimated. The results of comparison tests on synthetic and real-life data streams demonstrate the effectiveness of this method.

基于STREAMS机制的IP/X.25软件的设计与实现

ＳＴＲＥＡＭＳ机制是ＵＮＩＸ系统为开发通信服务而提供的一套通用、灵活的开发工具，ＩＰ／Ｘ．２５软件则是ＴＣＰ／ＩＰ网络与Ｘ．２５公用数据网互连以及通过Ｘ．２５公用数据网实现ＴＣＰ／ＩＰ网络之间的互连的重要途径．文中在介绍基于ＳＴＲＥＡＭＳ机制的通信软件开发的一般思路后，着重叙述了ＩＰ／Ｘ．２５软件的功能和结构以及基于ＳＴＲＥＡＭＳ机制的设计与实现，并对基于ＳＴＲＥＡＭＳ机制的通信软件开发方法作了简单评价．

基于STREAMS机制的UNIX内核网络协议软件开发

STREAMS机制是UNIX提供的一种开发工具。本文在对STREAMS机制进行介绍和分析的基础上,提出了基于STREAMS机制的开发UNIX内核网络协议软件的通用抽象模型。

利用STREAMS机制开发远动系统的通信程序

介绍了ＵＮＩＸＳＹＳＴＥＭＶ的ＳＴＲＥＡＭＳ机制，并论述了利用该ＳＴＲＥＡＭＳ机制开发远动通讯程序的过程及实现方法，得出利用ＳＴＲＥＡＭＳ机制后，程序具有接口简单、易移植且实时性能好的结论。

UNIX系统V中的STREAMS机制

STREAMS机制是操作系统设计和实现中使用的一种重要新技术.本文说明这种机制的主要优点、它的基本结构和工作原理,包括:队列、消息、流首、模块、流量控制和基于流的管理等.

CORBA A/V Streams服务的实现及其在远程教育中的应用

文章详细介绍了CORBAA/VStreams规范,提出了基于ORBUS系统和RSVP资源预留的实现模型,并依据该模型实现了CORBAA/VStreams原型系统。利用该原型系统,设计并实现了一个实用的远程教育系统。

UNIX System V中基于STREAMS的伪终端子系统及其应用

UNIX System V提供一种基于STREAMS机制的伪终端子系统。本文将介绍这种伪终端子系统及采用客户机/服务器模式设计的一种远程登录。

Oracle STREAMS数据同步复制技术应用

简要介绍Oracle STREAMS技术原理和同步过程,以华中科技大学同济医学院附属同济医院为例,阐述HLR综合数据库的构建及Oracle STREAMS同步实现过程,总结其实施与维护经验,HLR数据库既提供综合查询和报表功能,又能满足灾难备份的要求。

基于Oracle Streams的数据库实时备份技术研究

在数据库的故障恢复中,实现无数据丢失的数据库备份与恢复是极为重要的研究内容,运用数据库的实时备份,当有数据库操作时,可以实时地对数据库发生的改变进行备份,从而有效地防止数据丢失。基于Oracle Streams技术,提出了一个数据库的实时备份方案。该方案利用Oracle Streams复制技术,配置捕获进程自动捕获数据库的所有改变,并将这些改变储存在高级队列中,传播至目标数据库,实现数据库的实时备份,恢复时从备份中读取这些改变信息,应用于目标数据库,可以得到一个处于一致性状态的无数据丢失的数据库。在Linux环境下技进行测试实验,结果表明本方案是可行的。

Phytic Acid Contents of Wheat Flours from Different Mill Streams

The phytic acid contents of eight fractions of wheat flours from different mill streams and those in wheat brans, which were separated by different sieves into various sizes, were determined and analyzed. A rapid method for phytic acid assay by adding thioglycolic acid （mercapto acetic acid） with 2,2-bipyridine was used, with an acidic iron-Ⅲ-solution of known iron content. The amount ofphytate was indicated by the decrease in iron in the supernatant. Significant differences were observed in phytic acid content among different milling streams and different cultivars （P〈0.05）. On an average, coarse bran had the highest phytic acid content （53.85 mg g^-1）, and the shorts had 28.48 mg g^-1. The B5 break flour had a higher phytic acid content （4.8 mg g^-1） than the B7 （2.75 mg g^-1） and B8 （2.03 mg g^-1） reduction flours. Lower values were found in the B3, B6 and B7 flours （1.07, 0.79, and 0.76 mg g^-1, respectively）. The phytic acid contents of bran decreased with smaller bran sizes, ranging from 54 to 5.09 mg g^-1.