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.     

镁质熔剂性球团矿相的多重分形谱

 球团矿相微观结构决定其冶金物理化学性能，为透视球团矿微观结构与其宏观冶金物理化学性能之间的依赖关系，需要解决的首要关键问题是矿相微观视觉特征的提取与定量表征。以镁质熔剂性球团微观矿相为研究对象，在直观分析矿相视觉特征的基础上，采用多重分形谱分析方法，提取矿相的微观多重分形谱定量表征值f（α），并分别深度挖掘由f（α）衍生出的对称度特征值Δf、谱宽特征值Δα、容量维特征值D（0）随碱度变化的迁移规律和随矿相检测位置变化的迁移规律。研究结果表明，矿相微观视觉特征在HSV颜色空间上呈现的颜色特征未在碱度变化和位置变化上存在显著差异性；矿相微观多重分形谱分析中，Δf、Δα、D（0） 3个特征值在碱度变化和位置变化上有显著的差异性，可以很好地表征碱度迁移规律和位置迁移规律。     

Ergonomic mismatch between students anthropometry and university classroom furniture

University students spend most of their time in classrooms, mostly in a sitting position. Prolonged sitting on ill-fitted furniture and the resulting bad posture is making students suffer from different musculoskeletal disorders (MSDs). This study evaluates the potential mismatch between furniture dimensions and anthropometric measurements. To determine potential mismatch, 13 anthropometric measurements of 550 students and 11 dimensions of two types of classroom furniture (mounted-desktop and chair-with-table) were measured and then compared. Additionally, chi-square test was performed to compare the relation between anthropometry and relevant furniture dimensions. Results showed that a significant number of mismatches emerged between anthropometric measurements and furniture dimensions. For both types of furniture, seat height was too high and seat depth was too deep. Moreover, for mounted desktop, desk height is almost appropriate for all students whereas for chair and table, it was too high for the males. These circumstances may lead to increase discomfort and increase MSDs problems among all students. It can be concluded that the dimensions of both types of classroom furniture were not appropriate for users according to anthropometric measurements. This analysis recommends the measurements of furniture dimensions based on participants’ anthropometric measurements to avoid or minimise discomfort and MSDs problems.     

The effect of economic policy uncertainty on stock-commodity correlations and its implications on optimal hedging

Motivated by previous studies documenting significant return and volatility effects of economic policy uncertainty (EPU) on the stock market, this study examines whether EPU has an effect on the dynamic conditional correlations between stock and commodity returns. Our findings point to a positive and significant effect of EPU on stock-commodity correlations with particularly stronger effects in the case of energy and industrial metals. The EPU effect is stronger during weak economic conditions, while VIX as a proxy of market uncertainty is generally found to be insignificant. Finally, we show that the EPU effect on correlations has investment implications as well, implied by a significant effect on optimal hedge ratios in commodities in order to mitigate stock market risks. Our results underscore the importance of selective hedging strategies in which risk managers base the timing and size of their hedging programs on future price expectations, conditional on the level of policy uncertainty state and prevalent economic conditions.     

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。     

Quantitative Analysis of Crime Incidents in Chicago Using Data Analytics Techniques

In this paper we aim to identify certain social factors that influence, and thus can be used to predict, the occurrence of crimes. The factors under consideration for this analytic are social demographics such as age, sex, poverty, etc., train ridership, traffic density and the number of business licenses per community area in Chicago, IL. A factor will be considered pertinent if there is high correlation between it and the number of crimes of a particular type in that community area.     

一种新的多渐消因子容积卡尔曼滤波

将强跟踪思想引入容积卡尔曼滤波（cubature Kalman filter，CKF），建立强跟踪CKF能有效克服CKF在模型不确定、状态突变等情况下，滤波性能下降的问题。通过分析现有多渐消因子计算方法，发现它们均只利用了协方差矩阵的对角线元素，并没有考虑各个状态之间的相关性，不能充分发挥多渐消因子的优势。为此，本文提出渐消因子矩阵，基于正交原理推导渐消因子矩阵的求解方法，提出多渐消因子强跟踪CKF算法。多渐消因子强跟踪CKF算法突破了传统多渐消因子为向量的限制，也不再要求渐消因子取值要大于1。仿真验证了算法具有更好的滤波精度何鲁棒性，能更好的满足工程应用的要求。     

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。