Detection of correlation characteristics between financial time series based on multi-resolution analysis

Interactions between financial time series are complex and changeable in both time and frequency domains. To reveal the evolution characteristics of the time-varying relations between bivariate time series from a multi-resolution perspective, this study introduces an approach combining wavelet analysis and complex networks. In addition, to reduce the influence the phase lag between the time series has on the correlations, we propose dynamic time-warping (DTW) correlation coefficients to reflect the correlation degree between bivariate time series. Unlike previous studies that symbolized the time series only based on the correlation strength, the second-level symbol is set according to the correlation length during the coarse-graining process. This study presents a novel method to analyze bivariate time series and provides more information for investors and decision makers when investing in the stock market. We choose the closing prices of two stocks in China’s market as the sample and explore the evolutionary behavior of correlation modes from different resolutions. Furthermore, we perform experiments to discover the critical correlation modes between the bull market and the bear market on the high-resolution scale, the clustering effect during the financial crisis on the middle-resolution scale, and the potential pseudo period on the low-resolution scale. The experimental results exactly match reality, which provides powerful evidence to prove that our method is effective in financial time series analysis.     

融合时间隐语义填充和子群划分的推荐算法

随着个性化推荐技术的发展，推荐系统面临着越来越多的挑战。传统的推荐算法通常存在数据稀疏性和推荐精度低等问题。针对以上问题，提出了一种融合时间隐语义填充和子群划分的推荐算法[K]-TLFM（Time Based Latent Factor Model Integrated with [k]-means）。该算法利用融合时间因素的隐语义模型对原始用户物品评分矩阵缺失项进行填充，避免了用全局平均值或者用户/物品平均值补全矩阵带来的误差，有效缓解了数据稀疏性问题，同时融合时间因素有效地刻画了用户偏好随时间的变化；完成评分矩阵缺失项填充后，基于二分[k]-means聚类算法将偏好、兴趣特征相似的对象划分到同一个子群中，在目标用户所属的子群中基于选定的协同过滤算法为用户产生推荐列表，提高了推荐效率和准确性。在MovieLens和Netflix数据集上对该算法的推荐性能进行了对比实验，结果表明该算法具有更高的推荐精度。     

跨境电商保税仓库拣货策略研究

针对跨境电商客户订单的多品类且数量不稳定的特点，对跨境电商保税仓库拣货流程进行分析与优化。采用订单动态时间窗分拣策略，并在仓库拣货路径优化的基础上，建立了跨境电商保税仓库拣货模型。最后应用于某公司跨境电商保税仓库管理系统中，取得了较好的预期效果。     

基于孪生神经网络的时间序列相似性度量

在时间序列分类等数据挖掘工作中，不同数据集基于类别的相似性表现有明显不同，因此一个合理有效的相似性度量对数据挖掘非常关键。传统的欧氏距离、余弦距离和动态时间弯曲等方法仅针对数据自身进行相似度公式计算，忽略了不同数据集所包含的知识标注对于相似性度量的影响。为了解决这一问题，提出基于孪生神经网络（SNN）的时间序列相似性度量学习方法。该方法从样例标签的监督信息中学习数据之间的邻域关系，建立时间序列之间的高效距离度量。在UCR提供的时间序列数据集上进行的相似性度量和验证性分类实验的结果表明，与ED/DTW-1NN相比SNN在分类质量总体上有明显的提升。虽然基于动态时间弯曲（DTW）的1近邻（1NN）分类方法在部分数据上表现优于基于SNN的1NN分类方法，但在分类过程的相似度计算复杂度和速度上SNN优于DTW。可见所提方法能明显提高分类数据集相似性的度量效率，在高维、复杂的时间序列的数据分类上有不错的表现。     

Effects of Ni Content and Ball Milling Time on the Hydrogen Storage Thermodynamics and Kinetics Performances of La–Mg–Ni Ternary Alloys

The effects of Ni content and ball milling time on the hydrogen storage thermodynamics and kinetics performances of asmilled La_5Mg_(95-x)Ni_x(x = 5, 10, 15) ternary alloys have been investigated.The evolution of microstructure and phase of experimental alloys in the absorption/desorption process has been characterized by XRD, SEM and HRTEM.The hydrogen storage kinetics and thermodynamics performances and PCI curves have been tested using the Sievert apparatus.It is found that the rising of Ni content remarkably improves the hydrogen storage kinetic performance, but reduces hydrogen storage capacity.And with the increase in milling time, hydrogen desorption activation( E_a) value decreases firstly and then increases; the minimum value is 47.6 kJ/mol, and the corresponding milling time is 10 h for La_5Mg_(85)Ni_(10) alloy.As for the thermodynamics properties, the hydrogenation enthalpy(Δ H) and hydrogenation entropy(Δ S) both decrease firstly and then increase with the rising of Ni content and milling time.The composite La_5Mg_(85)Ni_(10)alloy milled for 10 h exhibits the best thermodynamics and kinetics performances, the lowest E_a of 47.6 kJ/mol, absorption of 5.4 wt.% within 5 min and desorption of 5.2 wt.% within 3 min at 360 ℃ and the lowest Δ H and Δ S of 72.1 kJ/mol and 123.2 J/mol/K.     

基于词法特征的恶意域名快速检测算法

针对互联网中恶意域名攻击事件频发，现有域名检测方法实时性不强的问题，提出一种基于词法特征的恶意域名快速检测算法。该算法根据恶意域名的特点，首先将所有待测域名按照长度进行正则化处理后赋予权值；然后利用聚类算法将待测域名划分成多个小组，并利用改进的堆排序算法按照组内权值总和计算各域名小组优先级，根据优先级降序依次计算各域名小组中每一域名与黑名单上域名之间的编辑距离；最后依据编辑距离值快速判定恶意域名。算法运行结果表明，基于词法特征的恶意域名快速检测算法与单一使用域名语义和单一使用域名词法的恶意域名检测算法相比，准确率分别提高1.7%与2.5%，检测速率分别提高13.9%与6.8%，具有更高的准确率和实时性。     

数据驱动的机床等待过程节能方法研究

为了降低机床等待过程中的能耗，提出了一种实时数据驱动的机床等待时间预测与节能控制方法。首先，建立了射频识别驱动的生产进度评估方法，并以生产进度数据作为输入，构建了基于堆栈降噪自编码的机床等待时间预测模型；其次，依据预测的机床等待时间，提出了机床状态切换方法，以降低机床能耗；最后，通过一个电梯零部件制造车间的案例分析，表明该方法的预测误差仅为4.1%，同时将机床等待过程能耗降低了57%，实现了制造车间的节能减排。     

Effect of processing method on vitamin profile,antioxidant properties and total phenolic content of coconut (Cocos nucifera L.) sugar syrup

Coconut (Cocos nucifera L.) sugar is a more nutritious alternative sugar source as compared to sugar palm (Borassus flabellifer) and sugarcane (Saccharum officinarum L.). This work was aimed to investigate the browning index (BI), vitamin profile and antioxidant properties of coconut sap sugar syrups, which were produced by different processing methods: rotary evaporation (RE), microwave evaporation (ME) and open-heat evaporation (OHE). The results obtained showed that coconut sugar syrup produced by RE-60 contained high antioxidant activities [DPPH (36.71%) and ABTS (34.84%), TPC (299.87 mg per 100 g sample) and FRAP (3.74 mm )]. These values were slightly lower than those of ME and OHE. Coconut sugar syrup (RE-60) also contained higher amounts of vitamin C (1587.27 mg L⁻¹), vitamin B1 (97.44 mg L⁻¹) and vitamin B3 (19.84 mg L⁻¹) compared with those of ME and OHE coconut sugar syrups. RE-60 was the best method to produce coconut sugar syrup in a shorter time with lower browning index and higher vitamin contents.     

Emergency egress for the elderly in care home fire situations

Practitioners are continuing to develop egress modelling software for the design of the built environment. These models require data about human behaviour and factors for calibration, validation and verification. This study aims to address the specific data and knowledge gap: emergency egress of the elderly. Such data are difficult to collect given privacy and consent concerns, with strong relationships generally being required between residences and researchers. Through the observation of nine fire drills at six Canadian long-term care (LTC) and retirement homes, specific evacuation actions and behaviour were observed for 37 staff members and information about the evacuation of 56 residents was collected. These drills demonstrated that emergency egress in LTC and retirement homes is highly staff dependent with 72% of residents recorded requiring full assistance at all stages of movement in evacuation, and that the type of announced/unannounced drill and level of resident care will affect the type of data collected. The development of travel speed and pre-movement is discussed subject to limitation with qualitative behavioural insights of residents that were observed. This study provides valuable methodological discussion on how to conduct behavioural studies in similar highly restricted research environments. Specific attention is given to understanding the considerations that must be made when using fire drills as data sources, and the impact that these can have on using such data for modelling. This study may inform the initial setup and programming of evacuation models from an actions and behavioural perspectives of staff members and residents.