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.     

Limoniastrum monopetalum–Mediated Nanoparticles and Biomedicines: In Silico Study and Molecular Prediction of Biomolecules

An in silico approach applying computer-simulated models helps enhance biomedicines by sightseeing the pharmacology of potential therapeutics. Currently, an in silico study combined with in vitro assays investigated the antimicrobial ability of Limoniastrum monopetalum and silver nanoparticles (AgNPs) fabricated by its aid. AgNPs mediated by L. monopetalum were characterized using FTIR, TEM, SEM, and DLS. L. monopetalum metabolites were detected by QTOF–LCMS and assessed using an in silico study for pharmacological properties. The antibacterial ability of an L. monopetalum extract and AgNPs was investigated. PASS Online predictions and the swissADME web server were used for antibacterial activity and potential molecular target metabolites, respectively. Spherical AgNPs with a 68.79 nm average size diameter were obtained. Twelve biomolecules (ferulic acid, trihydroxy-octadecenoic acid, catechin, pinoresinol, gallic acid, myricetin, 6-hydroxyluteolin, 6,7-dihydroxy-5-methoxy 7-O-β-d-glucopyranoside, methyl gallate, isorhamnetin, chlorogenic acid, 2-(3,4-dihydroxyphenyl)-5,7-dihydroxy-4-oxo-4H-chromen-3-yl 6-O-(6-deoxy-β-l-mannopyranosyl)-β-d-glucopyranoside) were identified. The L. monopetalum extract and AgNPs displayed antibacterial effects. The computational study suggested that L. Monopetalum metabolites could hold promising antibacterial activity with minimal toxicity and an acceptable pharmaceutical profile. The in silico approach indicated that metabolites 8 and 12 have the highest antibacterial activity, and swissADME web server results suggested the CA II enzyme as a potential molecular target for both metabolites. Novel therapeutic agents could be discovered using in silico molecular target prediction combined with in vitro studies. Among L. Monopetalum metabolites, metabolite 12 could serve as a starting point for potential antibacterial treatment for several human bacterial infections.     

Macrophage-Targeted Punicalagin Nanoengineering to Alleviate Methotrexate-Induced Neutropenia: A Molecular Docking,DFT, and MD Simulation Analysis

Punicalagin is the most bioactive pomegranate polyphenol with high antioxidant and free-radical scavenging activity and can potentially cure different ailments related to the cardiovascular system. The current research work was envisioned to predict the targeting efficiency of punicalagin (PG) nanoparticles to the macrophages, more specifically to bone marrow macrophages. For this, we selected mannose-decorated PLGA-punicalagin nanoparticles (Mn-PLGA-PG), and before formulating this nanocarrier in laboratory settings, we predicted the targeting efficiency of this nanocarrier by in silico analysis. The analysis proceeded with macrophage mannose receptors to be acquainted with the binding affinity and punicalagin-based nanocarrier interactions with this receptor. In silico docking studies of macrophage mannose receptors and punicalagin showed binding interactions on its surface. PG interacted with hydrogen bonds to the charged residue ASP668 and GLY666 and polar residue GLN760 of the Mn receptor. Mannose with a docking score of −5.811 Kcal/mol interacted with four hydrogen bonds and the mannose receptor of macrophage, and in PLGA, it showed a −4.334 Kcal/mol docking score. Further, the analysis proceeded with density functional theory analysis (DFT) and HOMO–LUMO analysis, followed by an extensive 100 ns molecular dynamics simulation to analyse the trajectories showing the slightest deviation and fluctuation. While analysing the ligand and protein interaction, a wonderful interaction was found among the atoms of the ligand and protein residues. This computational study confirms that this nanocarrier could be a promising lead molecule to regulate the incidence of drug-induced neutropenia. Furthermore, experimental validation is required before this can be stated with complete confidence or before human use.     

Asymptotic normality of recursive estimators under strong mixing conditions

The main purpose of this paper is to estimate the regression function by using a recursive nonparametric kernel approach. We derive the asymptotic normality for a general class of recursive kernel estimate of the regression function, under strong mixing conditions. Our purpose is to extend the work of Roussas and Tran (Ann Stat 20:98–120, 1992) concerning the Devroye–Wagner estimate.     

Synthesis and characterization of two new fluorescent macrocycles: A novel fluorescent chemosensor for zinc ion

Two new macrocyclic systems (L¹, L²) containing two emissive naphthalene were synthesized and characterized. The macrocycles were studied by ¹H NMR, ¹³C NMR, COSY, HMQC, DEPT, microanalysis and mass spectroscopy. The influence of metal cations Na⁺, Cr³⁺, Mn²⁺, Fe³⁺, Co²⁺, Ni²⁺, Cu²⁺, Zn²⁺, Cd²⁺, and Hg²⁺ on the spectroscopic properties of the macrocyclic systems in acetonitrile/DMF 9:1 (v/v) mixtures were investigated by means of absorption and emission spectrophotometry. The macrocycle L¹ was found to be as an effective fluorescence sensor for Zn²⁺ ions. Zn²⁺ and Cd²⁺ ions show the most effects on the fluorescence intensity of L².     

New formulation and relaxation to solve a concave-cost network flow problem

In this paper we study a minimum cost, multicommodity network flow problem in which the total cost is piecewise linear, concave of the total flow along the arcs. Specifically, the problem can be defined as follows. Given a directed network, a set of pairs of communicating nodes and a set of available capacity ranges and their corresponding variable and fixed cost components for each arc, the problem is to select for each arc a range and identify a path for each commodity between its source and destination nodes so as to minimize the total costs. We also extend the problem to the case of piecewise nonlinear, concave cost function. New mathematical programming formulations of the problems are presented. Efficient solution procedures based on Lagrangean relaxations of the problems are developed. Extensive computational results across a variety of networks are reported. These results indicate that the solution procedures are effective for a wide range of traffic loads and different cost structures. They also show that this work represents an improvement over previous work made by other authors. This improvement is the result of the introduction of the new formulations of the problems and their relaxations.     

Reactions between isocyanides and dialkyl acetylenedicarboxylates in the presence of 1,2-diacylhydrazines. One-pot synthesis of highly functionalized pyrazoles

The reactive 1:1 intermediate produced in the reaction between isocyanides and dialkyl acetylenedicarboxylates was trapped with 1,2-diacylhydrazines to yield highly functionalized pyrazoles in good yields.     

Second-harmonic generation from monolayer Langmuir–Blodgett films of various push–pull pyridine and terpyridine metal complexes

The new ligands (E)-4-[2-(4-(N-methyl-N-hexadecylaminophenyl)ethenyl]pyridine (L¹) and 4′-(C₆H₄-p-N(Me)(hexadecyl))-2,2′:6′,2″-terpyridine (L²) were prepared along with their complexes [cis-Ir(CO)₂ClL¹], [fac-Os(CO)₃Cl₂L¹], [ZnCl₂L²] and [IrCl₃L²]. Whereas these complexes show a large second-order nonlinear optical (NLO) response at the molecular level, similar to that of related organic alkylated salts as evidenced by the Electric Field Induced Second-Harmonic (EFISH) generation technique, their Langmuir–Blodgett (LB) film susceptibility is lower than that of the salts.     

Highly chemoselective reductive amination-coupling by one-pot reaction of aldehydes, HMDS and NaBH4

An efficient and highly chemoselective synthesis of symmetrical secondary amines via reductive amination of aldehydes with inexpensive and commercially available HMDS and sodium borohydride in high to quantitative yields is reported.