Fracture mechanics global approach concepts applied to creep slow crack growth in a medium density polyethylene (MDPE)

Creep fracture by slow crack growth is studied in a medium density polyethylene at 60 °C and 80 °C. Whereas elastic-plastic fracture mechanics load parameters fail to provide a unique temperature-independent correlation, that of the fracture mechanics for creeping solids C^∗ is proved to be relevant since this parameter correlates very well with the time to failure. Correlation established on both full notched creep tensile and double edge notched tensile tests was validated on cracked gas-pipe samples tested under hydrostatic pressure, extending the use of time to failure versus C^∗ diagram to predict lifetime of engineering components.     

大通河调水对水资源及生态环境的影响

基于生态需水、河流纳污能力、水环境容量等理论,分析研究大通河调水前后可能引起的河道生态环境各要素变化的程度和范围。结果表明,跨流域调水将引起不同河段水资源的剧烈变化,特别是天堂寺、享堂河段会产生断流,进而对河道内生态、水环境及河谷周边陆地生态产生显著影响。综合各方面需水要求,下泄月平均流量至少不小于1.1 m3/s。     

Tributary use and large-scale movements of grass carp in Lake Erie

Infrequent captures of invasive, non-native grass carp (Ctenopharyngodon idella) have occurred in Lake Erie over the last 30+ years, with recent evidence suggesting wild reproduction in the lake’s western basin (WB) is occurring. Information on grass carp movements in the Laurentian Great Lakes is lacking, but an improved understanding of large-scale movements and potential areas of aggregation will help inform control strategies and risk assessment if grass carp spread to other parts of Lake Erie and other Great Lakes. Twenty-three grass carp captured in Lake Erie’s WB were implanted with acoustic transmitters and released. Movements were monitored with acoustic receivers deployed throughout Lake Erie and elsewhere in the Great Lakes. Grass carp dispersed up to 236 km, with approximately 25% of fish dispersing greater than 100 km from their release location. Mean daily movements ranged from <0.01 to 2.49 km/day, with the highest daily averages occurring in the spring and summer. The Sandusky, Detroit, and Maumee Rivers, and Plum Creek were the most heavily used WB tributaries. Seventeen percent of grass carp moved into Lake Erie’s central or eastern basins, although all fish eventually returned to the WB. One fish emigrated from Lake Erie through the Huron-Erie Corridor and into Lake Huron. Based on our results, past assessments may have underestimated the potential for grass carp to spread in the Great Lakes. We recommend focusing grass carp control efforts on Sandusky River and Plum Creek given their high use by tagged fish, and secondarily on Maumee and Detroit Rivers.     

汉江中下游水体重金属时空分布及污染评价

为探明汉江中下游水体中重金属的污染状况,分别于2019年6月(丰水期)和2020年1月(枯水期)在汉江中下游选取20个监测断面采集水样,监测水体中Cu、Zn、Pb、Fe、Mn、As、Se和Cd 8种重金属的含量,分析汉江中下游水体中重金属污染的时空分布特征,并对水体重金属污染状况进行评价。结果表明:汉江中下游水体重金属污染以Fe、Mn为主,其他6种重金属浓度均满足地表水Ⅱ类水标准。汉江中下游水体中Fe和Mn含量沿程波动较大,二者变化规律基本一致。Fe和Mn含量最大值在支流竹皮河,其中Fe浓度超标4.63倍,Mn超标13%;此外,位于汉川市的S17断面Fe元素含量超标3.89倍。结合历史数据分析,发现近年来汉江中下游水体重金属含量总体呈上升趋势。评价结果表明,汉江中下游大部分水体水质良好,钟祥市和汉川市是汉江中下游重金属污染相对较重的两个城市,支流竹皮河的水体重金属污染问题需引起重视。     

基于极限学习机的堤防工程多元风险指标评价方法

为提高堤防工程风险评价的准确性,提出了基于极限学习机的堤防工程多元风险指标评价方法。首先,综合考虑影响堤防风险的28个评价指标,利用层次分析法从预警系统、堤防工程系统、环境系统和社会经济系统这4个方面建立了堤防工程多元风险评价指标体系。接着,基于极限学习机算法对28个指标进行标准化处理及分级标准构建,以风险指标作为输入量,分级隶属度作为输出量,划分风险等级,量化评价指标,估计多元风险评价指标值和判断风险的严重程度。最后,依托鄱阳湖重点堤防——康山大堤,构建多元风险评价指标体系,运用极限学习机算法计算多元风险评价指标值。评价结果表明:康山大堤目前处于基本安全水平,符合康山大堤经过两次加固后的工程实际情况,并与其他方法进行对比,验证了提出方法的可靠性和有效性。该方法可拓展应用到其他重要水工结构工程风险评估中。     

220kV线路工程滑坡地质灾害危险性分析

文章介绍了喜德县红莫升压站-鲁基升压站220kV线路工程滑坡地质灾害危险性现状评估和预测评估,确定了评估区内滑坡地质灾害对工程的影响,以及工程建设加剧已有滑坡地质灾害的危险性和自身可能遭受已存在滑坡地质灾害的危险性。据此评估结果,为工程在建设过程中提出了具体的防治处理措施建议。     

电力工程中的地质灾害危险性评估

我国幅员辽阔,地质环境条件多样且复杂,导致了地质灾害发育强烈,种类繁多、危害较大。地质灾害与工程建设活动密切相关,既相互作用,相互依存,又相互影响[1]。电力工程项目遍布全国,特别是输电线路大多为人烟稀少的山区分布,线路长,跨越的地质地貌区域复杂,存在的地质灾害多种多样,或多或少面临着各种地质灾害的严重威胁[2]。通过电力工程地质灾害危险性评估工作的现状及评估工作流程的介绍,进一步分析电力工程地质灾害的危险性评估工作中存在的问题并提出了相应的看法。     

No-reference stereoscopic image quality assessment using quaternion wavelet transform and heterogeneous ensemble learning

As the demand for high-quality stereo images has grown in recent years, stereoscopic image quality assessment (SIQA) has become an important research area in modern image processing technology.In this paper, we propose a no-reference stereoscopic image quality assessment (NR-SIQA) model using heterogeneous ensemble learning ‘quality-aware’ features from luminance image, chrominance image, disparity and cyclopean images via quaternion wavelet transform (QWT). Firstly, luminance image and chrominance image are generated by CIELAB color space as monocular perception, and the novel disparity and cyclopean images are utilized to complement with monocular information. Then, a number of ‘quality-aware’ features in the quaternion wavelet domain are discovered, including entropy, texture features, energy features, energy differences features and MSCN coefficients of high frequency sub-band. Finally, a heterogeneous ensemble model via support vector regression (SVR) & extreme learning machine (ELM) & random forest (RF) is proposed to predict quality score, and bootstrap sampling and rotated feature space are used to increase the diversity of data distribution. Comparing with the state-of-the-art NR-SIQA models, experimental results on four public databases prove the accuracy and robustness of the proposed model.     

Maximizing learning without sacrificing the fun: Stealth assessment,adaptivity and learning supports in educational games

In this study, we investigated the validity of a stealth assessment of physics understanding in an educational game, as well as the effectiveness of different game-level delivery methods and various in-game supports on learning. Using a game called Physics Playground, we randomly assigned 263 ninth- to eleventh-grade students into four groups: adaptive, linear, free choice and no-treatment control. Each condition had access to the same in-game learning supports during gameplay. Results showed that: (a) the stealth assessment estimates of physics understanding were valid—significantly correlating with the external physics test scores; (b) there was no significant effect of game-level delivery method on students' learning; and (c) physics animations were the most effective (among eight supports tested) in predicting both learning outcome and in-game performance (e.g. number of game levels solved). We included student enjoyment, gender and ethnicity in our analyses as moderators to further investigate the research questions.     

A Network Security Risk Assessment Method Based on a B_NAG Model

Computer networks face a variety of cyberattacks. Most network attacks are contagious and destructive, and these types of attacks can be harmful to society and computer network security. Security evaluation is an effective method to solve network security problems. For accurate assessment of the vulnerabilities of computer networks, this paper proposes a network security risk assessment method based on a Bayesian network attack graph (B_NAG) model. First, a new resource attack graph (RAG) and the algorithm E-Loop, which is applied to eliminate loops in the B_NAG, are proposed. Second, to distinguish the confusing relationships between nodes of the attack graph in the conversion process, a related algorithm is proposed to generate the B_NAG model. Finally, to analyze the reachability of paths in B_NAG, the measuring indexs such as node attack complexity and node state transition are defined, and an iterative algorithm for obtaining the probability of reaching the target node is presented. On this basis, the posterior probability of related nodes can be calculated. A simulation environment is set up to evaluate the effectiveness of the B_NAG model. The experimental results indicate that the B_NAG model is realistic and effective in evaluating vulnerabilities of computer networks and can accurately highlight the degree of vulnerability in a chaotic relationship.