Structural integrity assessment of nuclear containment through fracture characterization

The paper deals with the study of structural response of partially cracked nuclear containment model structure in over pressurized condition with the simulated experiments conducted under severe accidents analysis program for Indian nuclear containment structures. In this research, the fracture characterization of concrete containment structure is also investigated through the over pressure experiments on the BARC Containment (BARCOM) test model structure, which represents 1:4 scale of the prototype 540 MWe Tarapur pre‐stressed nuclear containment structure. In addition to the surface‐type electrical resistance, strain gauges conventionally and commonly deployed for containment proof‐test and ultimate load capacity evaluation of containment models, embedded vibratory wire strain gauges (VWSGs), the digital image correlation (DIC) technique, and soap bubble tests are employed in this study. For fracture characterization, an optical crack profile (OCP) technique is developed through DIC full‐field experiment conducted at the identified critical locations with conventional strain gauges to evaluate the fracture energy and the characteristics of the fracture process zone of concrete containment model structure subjected to the over‐pressure condition for its performance assessment in the case of the beyond design basis accidents. The combination of conventional sensors and full‐field DIC deployed for the first time on the largest scale containment model along with the associated analysis is shown to be effective in fracture characterization and improved structural integrity assessment of the containment model.     

融合重检测机制的卷积回归网络目标跟踪算法

针对基于人工特征的背景感知相关滤波（CACF）算法在形变、运动模糊、低分辨率情形跟踪效果较差以及跟踪器遇到严重遮挡等情形容易陷入局部最优而导致跟踪失败的问题，提出一种融合重检测机制的卷积回归网络（CRN）目标跟踪算法。在训练阶段，将相关滤波作为CRN层融入进深度神经网络，使网络成为一个整体进行端到端训练；在跟踪阶段，通过残差连接融合不同网络层及其响应值，同时引入重检测机制使算法从潜在的跟踪失败中恢复，当响应值低于给定阈值时激活检测器。在数据集OTB-2013上的实验表明，所提算法在50个视频序列上精确度达到88.1%，相比原始CACF算法提高9.7个百分点，在具有形变、运动模糊等属性的视频序列上相比原始算法表现更优秀。     

Estimation of Antenna Correlation Coefficient of N-Port Lossy MIMO Array

This paper proposes a simple yet accurate method for estimating the antenna correlation coefficient (ACC) of a high-order multiple-input multiple-output (MIMO) antenna. The conventional method employed to obtain the ACC from three-dimensional radiation patterns is costly and difficult to measure. An alternate method is to use the S-parameters, which can be easily measured using a network analyzer. However, this method assumes that the antennas are highly efficient, and it is therefore not suitable for lossy MIMO antenna arrays. To overcome this limitation, we define and utilize the non-coupled radiation efficiency in the S-parameter-based ACC formula. The accuracy of the proposed method is verified by the simulation results of a 4-port highly coupled lossy MIMO array. Further, the proposed method can be applied to N-port arrays by expanding the calculation matrix.     

不同熄焦方式对40kg焦炉焦炭质量的影响

不同熄焦方式对焦炭的冷态强度、热态强度以及生产焦炉的工艺特点均有影响,为了更好地指导40 kg试验焦炉的生产,需对比分析水熄焦、干熄焦方式对焦炭质量的影响。选用4种不同炼焦煤和1种生产配合煤,利用40 kg级实验焦炉炼制焦炭,分别采用蒸汽熄焦与水熄焦方式对40 kg焦炉炼制的焦炭进行冷却,检测熄焦后焦炭的工业分析、机械强度及热性质等性能指标,对比分析2种熄焦方式对焦炭质量的影响,并研究2种熄焦方式下焦炭与生产干熄焦的焦炭质量关系。结果表明,相对水熄焦,蒸汽熄焦的焦炭水分较稳定(小于1%)、成焦率有所提升,改善了焦炭质量,其中1/3焦煤冷强度的改善最为明显,气煤与1/3焦煤的热性质改善最为明显,肥煤的成焦率提升最大;蒸汽熄焦与生产干熄焦的焦炭质量指标更接近,机械强度M 40一致、M 10数据高出2.5个百分点,热性质CRI数据高出8.1个百分点、CSR低出10.4个百分点。焦化企业可参照试验结果,进一步优化40 kg实验焦炉熄焦工艺及调整生产与试验焦炉的相关性。     

组合赋权的TOPSIS在冲击地压危险性评价中的应用

为了合理准确地评价煤矿冲击地压危险性,在现有评价冲击地压危险性实例的基础上,从煤矿地质因素和开采因素2个方面,选取10个主要的影响指标,利用熵权法和指标相关性权重法分别计算影响冲击地压危险性的各评价指标权重,再根据组合赋权的方法得出各指标的综合权重,构建基于逼近理想解理论的冲击地压危险性评价模型。以重庆砚石台煤矿为例,选取20组评价冲击地压危险性样本数据进行分析,确定评价冲击地压危险性的最优和最劣状况,然后计算出各个评价方案与最优和最劣状况的距离,最后综合最优与最劣距离,确定评价对象的相对贴近度。应用结果表明:评价结果与实际情况一致,且该模型相较其他模型的评价结果更加科学准确,表明该方法有良好的实用价值。     

Genetic (co-)variation of methane emissions,efficiency, and production traits in Danish Holstein cattle along and across lactations

Methane emissions in ruminant livestock has become a hot topic, given the pressure to reduce greenhouse gas emissions drastically in the European Union over the next 10 to 30 yr. During the 2021 United Nations Climate Change conference, countries also made collective commitments to curb methane emissions by 2050. Genetic selection for low-methane-emitting animals, particularly dairy cows, is one possible strategy for mitigation. However, it is essential to understand how methane emissions in lactating animals vary along lactation and across lactations. This understanding is useful when making decisions for future phenotyping strategies, such as the frequency and duration of phenotyping within and across lactations. Therefore, the objectives of this study were to estimate (1) genetic parameters for 2 methane traits: methane concentration (MeC) and methane production (MeP) at 2 parity levels in Danish Holstein cows across the entire lactation using random regression models; (2) genetic correlations within and between methane traits across the entire lactation; and (3) genetic correlations between the methane traits and economically important traits throughout first lactation. Methane concentration (n = 19,639) records of 575 Danish Holstein cows from a research farm measured between 2013 and 2020 were available. Subsequently, CH₄ production in grams/day (MeP; n = 13,866) was calculated; MeP and MeC for first and second lactation (L1 and L2) were analyzed as separate traits: MeC_L1, MeP_L1, MeC_L2, and MeP_L2. Heritabilities, variance components, and genetic correlations within and between the 4 CH₄ traits were estimated using random regression models with Legendre polynomials. The additive genetic and permanent environmental effects were modeled using second-order Legendre polynomial for lactation weeks. Estimated heritabilities for MeP_L1 ranged between 0.11 and 0.49, for MeC_L1 between 0.10 and 0.28, for MeP_L2 between 0.14 and 0.36, and for MeC_L2 between 0.13 and 0.29. In general, heritability estimates of MeC traits were lower and more stable throughout lactation and were similar between lactations compared with MeP. Genetic correlations (within trait) at different lactation weeks were generally highly positive (0.7) for most of the first lactation, except for the correlation of early lactation (<10 wk) with late lactation (>40 wk) where the correlation was the lowest (<0.5). Genetic correlations between methane traits were moderate to highly correlated during early and mid lactation. Finally, MeP_L1 has stronger genetic correlations with energy-corrected milk and dry matter intake compared with MeC_L1. In conclusion, both traits are different along (and across) lactation(s) and they correlated differently with production, maintenance, and intake traits, which is important to consider when including one of them in a future breeding objective.     

Predicting hydrogen adsorption and desorption rates in cylindrical metal hydride beds: Empirical correlations and machine learning

A major limitation in chemisorptive hydrogen storage in metal hydrides is the long time required for the adsorption reaction during charging. This study investigates how the shape and material of the reaction chamber influences the adsorption and desorption rates. Numerical simulations of hydrogen storage in a cylindrical reaction chamber filled with LaNi₅ hydride are conducted for a range of chamber thermal conductivities and aspect ratios. The results show that adsorption and desorption processes are limited by thermal diffusion in the hydride bed and storage chamber. A storage efficiency is proposed based on an ideal isothermal process and used to evaluate the impact of chamber thermal conductivity and aspect ratio on the adsorption and desorption rates. Empirical correlations are proposed for predicting the adsorption and desorption efficiency of cylindrical LaNi₅ hydride beds. Finally, a machine-learning based data model for predicting storage efficiency in metal hydride chambers is presented. Comparison against the empirical correlations highlights that the machine learning-based data model can predict the storage efficiency more accurately.     

A combination of near-infrared hyperspectral imaging with two-dimensional correlation analysis for monitoring the content of biogenic amines in mutton

The study investigated the feasibility of using a combination of near-infrared hyperspectral imaging (NIR-HSI) with two-dimensional correlation (2DCOS) analysis for rapid and non-destructive determination of the content of biogenic amines in mutton during refrigerated storage. Total contents of biogenic amines (TBA) were used as the perturbation. By analysing the synchronous and asynchronous two-dimensional correlation spectra, sensitive variables that were closely related to TBA contents were obtained. The results showed that the wavelengths in the spectra range of 1002–1335 nm were the research area for the detection of TBA contents in mutton. The least-squares support vector machines (LSSVM) model based on effective wavelengths selected by competitive adaptive reweighted sampling (CARS) from 2DCOS analysis showed excellent results, with correlation coefficient in prediction (Rp) of 0.91, root mean square error in prediction (RMSEP) of 1.67 mg kg⁻¹ and the ratio of performance deviation (RPD) of 2.76. The research demonstrated that the combination of NIR-HSI and 2DCOS could be used as an effective method for monitoring the content of biogenic amines in mutton.     

广州市老年人使用空气调节设备状况及对健康影响调查

老人的健康与建筑室内环境存在重要联系，为了给适老建筑的暖通系统优化设计提供理论依据，有必要探究室内空气调节设备与老人健康的关系。对广州市104位本地老年人进行了问卷调查，收集了老年人的基本健康信息和家庭使用空气调节设备的情况，包括制冷、除湿、采暖和空气净化4种设备及其使用时长，使用习惯。收集到的信息用SPSS20.0进行了统计分析。结果包括广州市老年人家中四种设备拥有率、设备平均使用龄、设备使用时间以及常见日均使用时长；所调查的老年人健康比例达到55.8%。患病老人中心血管疾病的比例最高，其余为风湿性疾病和关节炎等疾病。相关性分析表明，广州地区夏天家庭采取制冷措施的老人患病可能性更小；制冷设备常见日均使用时长与身处空调区域时长的比值越大，老人患病的可能性越小。