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.     

Infrared-Visible Image Fusion Based on Semantic Guidance and Visual Perception

Infrared-visible fusion has great potential in night-vision enhancement for intelligent vehicles. The fusion performance depends on fusion rules that balance target saliency and visual perception. However, most existing methods do not have explicit and effective rules, which leads to the poor contrast and saliency of the target. In this paper, we propose the SGVPGAN, an adversarial framework for high-quality infrared-visible image fusion, which consists of an infrared-visible image fusion network based on Adversarial Semantic Guidance (ASG) and Adversarial Visual Perception (AVP) modules. Specifically, the ASG module transfers the semantics of the target and background to the fusion process for target highlighting. The AVP module analyzes the visual features from the global structure and local details of the visible and fusion images and then guides the fusion network to adaptively generate a weight map of signal completion so that the resulting fusion images possess a natural and visible appearance. We construct a joint distribution function between the fusion images and the corresponding semantics and use the discriminator to improve the fusion performance in terms of natural appearance and target saliency. Experimental results demonstrate that our proposed ASG and AVP modules can effectively guide the image-fusion process by selectively preserving the details in visible images and the salient information of targets in infrared images. The SGVPGAN exhibits significant improvements over other fusion methods.     

Practical asymmetric amine nucleophilic approach for the modular construction of protected α-quaternary amino acids

We report the first amine nucleophilic approach for the modular construction of enantioenriched protected α-quaternary amino acids. The key to success is the use of an alcohol solvent, which makes a rationally designed COOMe-bonded Cu-allenylidene electrophilic intermediate stable enough to couple with amine nucleophiles before its decomposition. The reaction features wide functional group tolerance with high enantioselectivity, typically >90% ee, and is amenable to the modification of commercially available bioactive molecules. The resultant protected α-amino acids could be readily converted into a number of precious enantioenriched amines featuring α-hindered tertiary carbon centers, which are otherwise synthetically quite challenging, including those of α-amino aldehyde, peptides or α-vinyl amino ester with >92% ee in excellent yields. This protocol could be utilized for the synthesis of the protected bioactive α-ethylnorvaline in 3 steps, a significant advancement in comparison to an 11-step sequence reported previously.

We report the first amine nucleophilic approach for the modular construction of enantioenriched protected α-quaternary amino acids.     

“Click handle”-modified 2′-deoxy-2′-fluoroarabino nucleic acid as a synthetic genetic polymer capable of post-polymerization functionalization

The functions of natural nucleic acids such as DNA and RNA have transcended genetic information carriers and now encompass affinity reagents, molecular catalysts, nanostructures, data storage, and many others. However, the vulnerability of natural nucleic acids to nuclease degradation and the lack of chemical functionality have imposed a significant constraint on their ever-expanding applications. Herein, we report the synthesis and polymerase recognition of a 5-(octa-1,7-diynyl)uracil 2′-deoxy-2′-fluoroarabinonucleic acid (FANA) triphosphate. The DNA-templated, polymerase-mediated primer extension using this “click handle”-modified FANA (cmFANA) triphosphate and other FANA nucleotide triphosphates consisting of canonical nucleobases efficiently generated full-length products. The resulting cmFANA polymers exhibited excellent nuclease resistance and the ability to undergo efficient click conjugation with azide-functionalized molecules, thereby becoming a promising platform for serving as a programmable and evolvable synthetic genetic polymer capable of post-polymerization functionalization.

Polymerase-mediated incorporation of a “click handle”-modified fluoroarabinonucleic acid (cmFANA) triphosphate produces a new class of nuclease-resistant, evolvable genetic polymers that can be functionalized with azide-containing molecules.     

苯乙烯/丙烯酸正丁酯乳液聚合反应过程中残余单体含量的实时监测

采用近红外光谱分析技术在线测量苯乙烯(St)/丙烯酸正丁酯(BA)乳液聚合体系中残余单体的含量. 共设计9个半连续方式的St/BA乳液共聚反应, 在反应过程中实时取样测量其残余单体含量, 并记录取样时刻对应的聚合体系的近红外光谱. 采用多元散射校正法(MSC)处理光谱, 有效地克服了乳胶粒子散射效应对近红外光谱分析的影响. 采用主成分分析法(PCA)对乳液体系的近红外光谱数据进行了解析. 选取6个聚合反应对应不同反应时间的72个样品, 用于建立校正模型, 另外3个聚合反应共取36个样品用于校正模型的验证, 并在反应设计上体现了乳化剂用量的变化, 从而使校正模型对乳化剂用量的变化具有一定的适应性. 研究结果表明, 所得模型对残余单体St和BA含量的预测结果标准差(SEP)分别为0.08026和0.05305.     

电活性聚芳醚酮类环状分子的合成和性质

将电活性苯胺四聚体链段引入聚芳醚酮类刚性大环分子中, 得到了含电活性苯胺链段的聚芳醚酮类大环状分子低聚物C-1, 并通过循环伏安和紫外滴定法证明了这种新型环状分子具有电活性.     

焊后热处理对聚变堆用奥氏体不锈钢MAG焊焊缝金属冲击韧性的影响

对聚变堆用316LN奥氏体不锈钢熔化极活性气体保护电弧焊(MAG焊)接接头进行不同温度的热处理,并在液氮温度下进行夏比冲击试验。利用光学显微镜、扫描电镜、EDS分析等研究了热处理温度对接头微观组织、断口形貌及析出物的影响。结果表明,873K热处理可以显著提高焊缝金属冲击韧性,但随着热处理温度的上升,焊缝金属逐渐出现沿着晶界分布的析出物,韧性逐渐下降。断口均为延性断裂,但随着热处理温度的升高,韧窝变浅、数量变少。韧窝底部存在球状析出和不规则状析出,球状析出在焊接过程中产生,不因热处理温度而变化,不规则析出随着热处理温度的升高逐渐增多。焊材中的Mo含量过高导致焊缝金属中Mo在晶界大量偏聚,促进了σ相的析出,当σ相在晶界形成连续分布后,焊缝金属冲击韧性显著下降。     

斜圈弹簧力学性能的有限元分析

斜圈弹簧在医疗器械、高压开关、汽车工业等领域应用广泛. 但其结构特殊, 受力复杂, 目前尚无有效的理论计算方法得出其载荷位移曲线, 只能通过数值仿真的方法研究其力学性能. 利用有限元软件ANSYS参数化建立环形斜圈弹簧有限元模型, 通过对模型的分析求解, 得出了其主要参数线径、节距、倾斜角与斜圈弹簧力学性能之间的关系. 研究了斜圈弹簧工作状态下的受力变形情况, 计算分析结果为斜圈弹簧的优化设计和合理应用提供了理论依据.     

成像型速度干涉仪散斑成因的研究

在冲击波过程中,利用成像型速度干涉仪探测样品自由面的速度时,散斑的叠加使得干涉曲线被严重干扰,影响了速度信息的精密提取。在理论上从光路的角度分析了系统中可能造成散斑的两种因素：靶面粗糙度和多模光纤。在实验上设计了三种照明方式分别对这两种因素进行验证。离线实验的结果表明,多模光纤的引入对散斑的产生起了主要的作用,这对抑制乃至遏制散斑的产生具有很好的指导性意义。