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.     

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.     

Excited singlet state absorption spectrum with tunable dye lasers

A technique for measuring absorption from excited states with nanosecond lifetimes utilizing pulsed tunable dye laser probes is described. The method features a simple experimental set-up, and high signal-to-noise ratio. The excited singlet state absorption spectrum of one transition in acridine red is presented.     

The preparation and structural studies in the (80 − x)TeO2–20ZnO–(x)Er2O3 glass system

Er³⁺-doped tellurite glasses of the (80 ? x)TeO₂–20ZnO–(x)Er₂O₃ system (0.5 mol% ? x ? 2.5 mol%) have successfully been made by melt-quenching technique and their structure has been investigated by means of DTA and Raman spectroscopy. The DTA results show the thermal parameters; such as the glass transition temperature (T_g) and crystallization temperature (T_c) were determined. It is found that this system provides a stable and wide glass formation range in which the glass stability around 99–140 °C may be obtained. The Raman spectroscopy used the structural studies in the glass system. Two Raman shift peaks were observed around 640–670 cm^?1 and 720–740 cm^?1, which correspond to the stretching vibration mode of TeO₄ tbp and TeO₃ tp, respectively. It is found that the spectral shift in Raman spectra is depending on the Er₂O₃ content. This evolution is an indication of the changes in the basic unit of the glass structure.     

Electrocatalytic oxidation of salicylic acid at a carbon paste electrode impregnated with cerium-doped zirconium oxide nanoparticles as a new sensing approach for salicylic acid determination

Cerium-doped zirconium oxide (Ce/ZrO₂) was introduced as a highly efficient electrocatalyst for electrooxidation of salicylic acid (SA). The electrocatalyst material was synthesized via co-precipitation of cerium and zirconium ions, and then the resulting solid was heat-treated at high temperature to create crystallized cerium-doped zirconium oxide nanoparticles. The obtained material was characterized by scanning electron microscopy and X-ray diffraction methods. The Ce/ZrO₂-modified carbon paste electrode (Ce/ZrO₂-CPE) exhibited a distinct oxidative peak for SA, whereas no signal was observed for SA at unmodified carbon paste electrode at the same experimental conditions. Cyclic voltammetry and electrochemical impedance spectroscopy were applied to investigate the electrocatalytic performance of the electrode and SA electrooxidation mechanism. Square wave voltammetry was used to capture the analytical signal of SA. The electrode composition was optimized to increase the SA signal. Using the optimized electrode, it became possible to determine SA in the concentration range of 5.0–1000.0 μM with detection limit of 1.1 μM (3S_b/m). The electrode showed very high sensitivity of 1013.5 μA mM^?1 cm^?2 which is remarkably better than the previously reported SA sensors. The proposed method was successfully applied for the determination of SA in human serum, milk, and pharmaceutical samples.     

An Intricate Review on Nutritional and Analytical Profiling of Coconut,Flaxseed, Olive,and Sunflower Oil Blends

Vegetable oils (VOs), being our major dietary fat source, play a vital role in nourishment. Different VOs have highly contrasting fatty acid (FA) profiles and hence possess varying levels of health protectiveness. Consumption of a single VO cannot meet the recommended allowances of various FA either from saturated FA (SFA), monounsaturated FA (MUFA), polyunsaturated FA (PUFA), Ω-3 PUFAs, and medium-chain triglycerides (MCTs). Coconut oil (CO), flaxseed oil (FO), olive oil (OO), and sunflower oil (SFO) are among the top listed contrast VOs that are highly appreciated based on their rich contents of SFAs, Ω-3 PUFAs, MUFAs, and Ω-6 PUFA, respectively. Besides being protective against various disease biomarkers, these contrasting VOs are still inappropriate when consumed alone in 100% of daily fat recommendations. This review compiles the available data on blending of such contrasting VOs into single tailored blended oil (BO) with suitable FA composition to meet the recommended levels of SFA, MUFA, PUFA, MCTs, and Ω-3 to Ω-6 PUFA ratios which could ultimately serve as a cost-effective dietary intervention towards the health protectiveness and improvement of the whole population in general. The blending of any two or more VOs from CO, FO, OO, and SFO in the form of binary, ternary, or another type of blending was found to be very conclusive towards balancing FA composition; enhancing physiochemical and stability properties; and promising the therapeutic protectiveness of the resultant BOs.     

The Physical Properties of P2O5–Sm2O3–MnO2 Glass System

Phosphate glasses doped with samarium oxide have been made using the melt quenching technique. The density of the glass was determined using the Archimedes method while the Vickers hardness was measured using a micro-vicker tester. Both density and hardness have shown an increasing trend with the addition of a small amount of Sm₂O₃. The refractive index was characterized using UV-visible spectroscopy and the Sellmeier fitting equation was used to verify the result. The refractive index was found to vary from 1.972 to 1.807 depending on the wavelength and well agreed with the fitting curve