Predicting Bitcoin (BTC) Price in the Context of Economic Theories: A Machine Learning Approach

Bitcoin (BTC)—the first cryptocurrency—is a decentralized network used to make private, anonymous, peer-to-peer transactions worldwide, yet there are numerous issues in its pricing due to its arbitrary nature, thus limiting its use due to skepticism among businesses and households. However, there is a vast scope of machine learning approaches to predict future prices precisely. One of the major problems with previous research on BTC price predictions is that they are primarily empirical research lacking sufficient analytical support to back up the claims. Therefore, this study aims to solve the BTC price prediction problem in the context of both macroeconomic and microeconomic theories by applying new machine learning methods. Previous work, however, shows mixed evidence of the superiority of machine learning over statistical analysis and vice versa, so more research is needed. This paper applies comparative approaches, including ordinary least squares (OLS), Ensemble learning, support vector regression (SVR), and multilayer perceptron (MLP), to investigate whether the macroeconomic, microeconomic, technical, and blockchain indicators based on economic theories predict the BTC price or not. The findings point out that some technical indicators are significant short-run BTC price predictors, thus confirming the validity of technical analysis. Moreover, macroeconomic and blockchain indicators are found to be significant long-term predictors, implying that supply, demand, and cost-based pricing theories are the underlying theories of BTC price prediction. Likewise, SVR is found to be superior to other machine learning and traditional models. This research’s innovation is looking at BTC price prediction through theoretical aspects. The overall findings show that SVR is superior to other machine learning models and traditional models. This paper has several contributions. It can contribute to international finance to be used as a reference for setting asset pricing and improved investment decision-making. It also contributes to the economics of BTC price prediction by introducing its theoretical background. Moreover, as the authors still doubt whether machine learning can beat the traditional methods in BTC price prediction, this research contributes to machine learning configuration and helping developers use it as a benchmark.     

澄清对相对论性原理和协变性的误解

近来有些文章断言,在一个惯性参考系里能量守恒的物理系统,在别的参考系看来能量也一定守恒.实际上这些作者混淆了物理方程式的协变性和相对性原理.本文将澄清这一误解.     

Microscopical Evaluation of the Effects of High-Pressure Processing on Milk Casein Micelles

The effect of different high-pressure processing (HPP) treatments on casein micelles was analyzed through scanning electron microscopy (SEM) and a particle size distribution analysis. Raw whole and skim milk samples were subjected to HPP treatments at 400, 500 and 600 MPa for Come-Up Times (CUT) up to 15 min at ambient temperature. Three different phenomena were observed in the casein micelles: fragmentation, alterations to shape and agglomeration. The particle size distribution analysis determined that, as pressure and time treatment increased, the three phenomena intensified. First, the size of the casein micelles began to decrease as their fragmentation occurred. Subsequently, the casein micelles lost roundness, and their shape deformed. Finally, in the most intense treatments (higher pressures and/or longer times), the micelles fragments began to agglomerate, which resulted in an increase in their average diameter. Homogenization and defatting had no significant effect on the casein micelles; however, the presence of fat in whole milk samples was bioprotective, as the effects of the three phenomena appeared faster in treated skim milk samples. Through this study, it was concluded that the size and structure of casein micelles are greatly altered during high-pressure treatment. These results provide information that broadens the understanding of the changes induced on casein micelles by high-pressure treatments at room temperature.     

Water Status and Predictive Models of Moisture Content during Drying of Soybean Dregs Based on LF-NMR

To explore the drying characteristics of soybean dregs and a nondestructive moisture content test method, in this study, soybean dregs were dried with hot air (80 °C), the moisture content was measured using the drying method, water status was analyzed using low-field nuclear magnetic resonance (LF-NMR) and the moisture content prediction models were built and validated. The results revealed that the moisture contents of the soybean dregs were 0.57 and 0.01 g/g(w.b.), respectively, after drying for 5 and 7 h. The effective moisture diffusivity increased with the decrease in moisture content; it ranged from 5.27 × 10⁻⁹ to 6.96 × 10⁻⁸ m²·s⁻¹. Soybean dregs contained bound water (T₂₁), immobilized water (T₂₂) and free water (T₂₃ and T₂₃’). With the proceeding of drying, all of the relaxation peaks shifted left until a new peak (T₂₃’) appeared; then, the structure of soybean dregs changed, and the relaxation peaks reformed, and the peak shifted left again. The peak area may predict the moisture content of soybean dregs, and the gray values of images predict the moisture contents mainly composed of free water or immobilized water. The results may provide a reference for drying of soybean dregs and a new moisture detection method.     

Effect of Different Processing Methods on the Chemical Constituents of Scrophulariae Radix as Revealed by 2D NMR-Based Metabolomics

Scrophulariae Radix (SR) is one of the oldest and most frequently used Chinese herbs for oriental medicine in China. Before clinical use, the SR should be processed using different methods after harvest, such as steaming, “sweating”, and traditional fire-drying. In order to investigate the difference in chemical constituents using different processing methods, the two-dimensional (2D) ¹H-¹³C heteronuclear single quantum correlation (¹H-¹³C HSQC)-based metabolomics approach was applied to extensively characterize the difference in the chemical components in the extracts of SR processed using different processing methods. In total, 20 compounds were identified as potential chemical markers that changed significantly with different steaming durations. Seven compounds can be used as potential chemical markers to differentiate processing by sweating, hot-air drying, and steaming for 4 h. These findings could elucidate the change of chemical constituents of the processed SR and provide a guide for the processing. In addition, our protocol may represent a general approach to characterizing chemical compounds of traditional Chinese medicine (TCM) and therefore might be considered as a promising approach to exploring the scientific basis of traditional processing of TCM.     

Impact of Tea Processing on Tryptophan,Melatonin, Phenolic and Flavonoid Contents in Mulberry (Morus alba L.) Leaves: Quantitative Analysis by LC-MS/MS

Mulberry (Morus alba L.) leaves from two cultivars, Yai-Burirum (YB) and Khunphai (KP), were prepared into green tea (GT) and black tea (BT). Compared to fresh leaf (FL) extract, GT and BT extracts were evaluated for their total phenolic and total flavonoid contents. Total phenolic content (TPCs) in all samples ranged between 129.93 and 390.89 mg GAE/g extract. The processing of tea decreased the levels of TPC when compared to FL extracts in both cultivars. The total flavonoid content (TFCs) in all samples was found in the range of 10.15–39.09 mg QE/g extract and TFCs in GT and BT extracts were higher than FL extracts. The change in tryptophan, melatonin, phenolic and flavonoid contents was investigated by liquid chromatography–mass spectroscopy (LC-MS). The results exhibited that tryptophan contents in all samples were detected in the range 29.54–673.72 µg/g extract. Both GT and BT extracts increased tryptophan content compared to FL extracts. BT extracts presented the highest amounts of tryptophan among others in both cultivars. Phenolic compounds were found in mulberry leaf extracts, including gallic acid, caffeic acid, gentisic acid, protocatechuic acid and chlorogenic acid. Chlorogenic acid presented the highest amount in all samples. Almost all phenolic acids were increased in the processed tea extracts except chlorogenic acid. Rutin was the only flavonoid that was detected in all extracts in the range 109.48–1009.75 mg/g extract. The change in phenolic and flavonoid compounds during tea processing resulted in the change in antioxidant capacities of the GT and BT extracts. All extracts presented acetylcholinesterase enzyme (AChE) inhibitory activity with IC₅₀ in the range 146.53–165.24 µg/mL. The processing of tea slightly increased the AChE inhibitory effect of GT and BT extracts. In conclusion, processed tea from mulberry leaves could serve as a new alternative functional food for health-concerned consumers because it could be a promising source of tryptophan, phenolics and flavonoids. Moreover, the tea extracts also had antioxidative and anti-AChE activities.     

On the Spatial Distribution of Temporal Complexity in Resting State and Task Functional MRI

Measuring the temporal complexity of functional MRI (fMRI) time series is one approach to assess how brain activity changes over time. In fact, hemodynamic response of the brain is known to exhibit critical behaviour at the edge between order and disorder. In this study, we aimed to revisit the spatial distribution of temporal complexity in resting state and task fMRI of 100 unrelated subjects from the Human Connectome Project (HCP). First, we compared two common choices of complexity measures, i.e., Hurst exponent and multiscale entropy, and observed a high spatial similarity between them. Second, we considered four tasks in the HCP dataset (Language, Motor, Social, and Working Memory) and found high task-specific complexity, even when the task design was regressed out. For the significance thresholding of brain complexity maps, we used a statistical framework based on graph signal processing that incorporates the structural connectome to develop the null distributions of fMRI complexity. The results suggest that the frontoparietal, dorsal attention, visual, and default mode networks represent stronger complex behaviour than the rest of the brain, irrespective of the task engagement. In sum, the findings support the hypothesis of fMRI temporal complexity as a marker of cognition.     

Community Detection in Semantic Networks: A Multi-View Approach

The semantic social network is a complex system composed of nodes, links, and documents. Traditional semantic social network community detection algorithms only analyze network data from a single view, and there is no effective representation of semantic features at diverse levels of granularity. This paper proposes a multi-view integration method for community detection in semantic social network. We develop a data feature matrix based on node similarity and extract semantic features from the views of word frequency, keyword, and topic, respectively. To maximize the mutual information of each view, we use the robustness of L21-norm and F-norm to construct an adaptive loss function. On this foundation, we construct an optimization expression to generate the unified graph matrix and output the community structure with multiple views. Experiments on real social networks and benchmark datasets reveal that in semantic information analysis, multi-view is considerably better than single-view, and the performance of multi-view community detection outperforms traditional methods and multi-view clustering algorithms.     

离散梯度法在基于图像的计算生物力学中的应用1)

介绍一种可用于计算生物力学的离散梯度方法,此方法可利用离散的点云模型直接进行数值模拟分析而不需要传统的几何模型。将离散梯度法应用于点云模型需要首先确定模型中点之间的相邻关系和每个点所分配的材料体积,然后通过用广义的有限差分的形式定义了梯度插值向量,并以此向量来近似函数在每个离散点上的梯度。从弱形式出发,推导建立了适用于弹性固体大变形问题的求解器,并具有和有限元法中双线性四边形单元一致的准确性和收敛性。着重描述了一种可以从医学图像中快速提取材料点并建立点云模型的方法,以及利用三角划分和重心划分确定材料点之间的相邻关系和每个材料点体积的具体过程。通过腹主动脉瘤膨胀的静力学模拟分析,展示了离散梯度法的实用性和准确性。该算法实现了基于医学图像进行生物力学分析的过程自动化,为病体特异性的研究和治疗提供便利和实用的工具。     

大挠度直梁二类混合变量变分原理及其应用

本文在功的互等定理的基础上,利用位移和应力作为变分变量的二类混合变量的最小势能原理和最小势作用量原理来求解大挠度直梁变形稳定问题,将所得结果与有限元模拟结果进行对比分析,验证了给出的方法的可行性和计算结果的准确性。给出的方法简单灵活,结果准确,为解决大挠度直梁问题提供了新的解决途径,不仅具有一定的理论意义,而且可以直接应用于实际工程中。