Azolopyrimidines and pyrimidoquinazolines from 4-chloropyrimidines

5-Cyano-3,4-dihydro-6-phenyl-2-substitutedpyrimidin-4-ones 1a-c reacted with phosphorus oxychloride to give the corresponding 4-chloropyrimidine derivatives IIa-c . Compounds IIa-c reacted with aniline and hydrazine to yield the 4-anilino, IIIa,e , and 4-hydrazino, IIIb-d derivatives. The 4-hydrazino analogues IIIb,c could be converted into the triazolo[4,3-c] and tetrazolo[4,5-c]pyrimidines IV and V by the action of carbon disulphide and nitrous acid, respectively. The reaction of IIb,c with phenylhydrazine afforded directly the 5-amino-4,6-diphenyl-6H-2-substitutedpyrazolo[3,4-d]pyrimidines VIa,b . The 4-chloro derivative IIa reacted with antrhanilic acid to form the 5-cyano-2,4-diphenyl-6-(o-carboxyphenylamino)pyrimidine VIII , which could be cyclised into the 4-cyano-1,3-diphenyl-10H-pyrimido[6,1-b]quinazolin-10-one IX by heating with acetic anhydride.     

Vibrational spectroscopy of the G...C base pair: experiment, harmonic and anharmonic calculations, and the nature of the anharmonic couplings

The results of harmonic and anharmonic frequency calculations on a guanine-cytosine complex with an enolic structure (a tautomeric form with cytosine in the enol form and with a hydrogen at the 7-position on guanine) are presented and compared to gas-phase IR-UV double resonance spectral data. Harmonic frequencies were obtained at the RI-MP2/cc-pVDZ, RI-MP2/TZVPP, and semiempirical PM3 levels of electronic structure theory. Anharmonic frequencies were obtained by the CC-VSCF method with improved PM3 potential surfaces; the improved PM3 potential surfaces are obtained from standard PM3 theory by coordinate scaling such that the improved PM3 harmonic frequencies are the same as those computed at the RI-MP2/cc-pVDZ level. Comparison of the data with experimental results indicates that the average absolute percentage deviation for the methods is 2.6% for harmonic RI-MP2/cc-pVDZ (3.0% with the inclusion of a 0.956 scaling factor that compensates for anharmonicity), 2.5% for harmonic RI-MP2/TZVPP (2.9% with a 0.956 anharmonicity factor included), and 2.3% for adapted PM3 CC-VSCF; the empirical scaling factor for the ab initio harmonic calculations improves the stretching frequencies but decreases the accuracy of the other mode frequencies. The agreement with experiment supports the adequacy of the improved PM3 potentials for describing the anharmonic force field of the G...C base pair in the spectroscopically probed region. These results may be useful for the prediction of the pathways of vibrational energy flow upon excitation of this system. The anharmonic calculations indicate that anharmonicity along single mode coordinates can be significant for simple stretching modes. For several other cases, coupling between different vibrational modes provides the main contribution to anharmonicity. Examples of strongly anharmonically coupled modes are the symmetric stretch and group torsion of the hydrogen-bonded NH2 group on guanine, the OH stretch and torsion of the enol group on cytosine, and the NH stretch and NH out-of-plane bend of the non-hydrogen-bonded NH group on guanine.     

Design and characterization of [(Et)3 N-H]FeCl4 as a nanomagnetic ionic liquid catalyst for the synthesis of xanthene derivatives under solvent-free conditions

The triethylamine-based nanomagnetic ionic liquid, [(Et)₃ N-H]FeCl₄, was synthesized, and its structural and chemical characteristics were detected. The thermogravimetric analysis indicated its high thermal stability with a decomposition temperature higher than 300 °C. Additionally, [(Et)₃ N-H]FeCl₄ was used to efficiently catalyze the synthesis of xanthene derivatives under solvent-free conditions at 120 °C. [(Et)₃ N-H]FeCl₄ was recycled and reused at least five times.

Graphical abstract

     

Four-jet production in e+e−-annihilation

We investigate the four-jet production processes $e^{+}{e}^{?}\to q\overline{q}ggand{e}^{+}{e}^{?}\to q\overline{q}q\overline{q}$ in lowest order QCD perturbation theory. We estimate that four-jet events should be detectable at a rate of about 5% at the highest PETRA energy. The acoplanarity distribution is calculated and compared to nonperturbative effects.     

A novel first-order log-domain allpass filter

This paper proposes a first-order allpass log-domain filter, which is systematically derived using the state-space synthesis procedure. To the best knowledge of the authors, the filter is the first log-domain first-order allpass filter in the literature. The proposed filter has a simple structure and can be electronically tuned. PSPICE simulations are given to confirm the theoretical analysis.     

An Efficient Supervised Deep Hashing Method for Image Retrieval

In recent years, searching and retrieving relevant images from large databases has become an emerging challenge for the researcher. Hashing methods that mapped raw data into a short binary code have attracted increasing attention from the researcher. Most existing hashing approaches map samples to a binary vector via a single linear projection, which restricts the flexibility of those methods and leads to optimization problems. We introduce a CNN-based hashing method that uses multiple nonlinear projections to produce additional short-bit binary code to tackle this issue. Further, an end-to-end hashing system is accomplished using a convolutional neural network. Also, we design a loss function that aims to maintain the similarity between images and minimize the quantization error by providing a uniform distribution of the hash bits to illustrate the proposed technique’s effectiveness and significance. Extensive experiments conducted on various datasets demonstrate the superiority of the proposed method in comparison with state-of-the-art deep hashing methods.     

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.     

Trusted Threat Intelligence Sharing in Practice and Performance Benchmarking through the Hyperledger Fabric Platform

Historically, threat information sharing has relied on manual modelling and centralised network systems, which can be inefficient, insecure, and prone to errors. Alternatively, private blockchains are now widely used to address these issues and improve overall organisational security. An organisation’s vulnerabilities to attacks might change over time. It is utterly important to find a balance among a current threat, the potential countermeasures, their consequences and costs, and the estimation of the overall risk that this provides to the organisation. For enhancing organisational security and automation, applying threat intelligence technology is critical for detecting, classifying, analysing, and sharing new cyberattack tactics. Trusted partner organisations can then share newly identified threats to improve their defensive capabilities against unknown attacks. On this basis, organisations can help reduce the risk of a cyberattack by providing access to past and current cybersecurity events through blockchain smart contracts and the Interplanetary File System (IPFS). The suggested combination of technologies can make organisational systems more reliable and secure, improving system automation and data quality. This paper outlines a privacy-preserving mechanism for threat information sharing in a trusted way. It proposes a reliable and secure architecture for data automation, quality, and traceability based on the Hyperledger Fabric private-permissioned distributed ledger technology and the MITRE ATT&CK threat intelligence framework. This methodology can also be applied to combat intellectual property theft and industrial espionage.     

Synthesis,Leishmanicidal, Trypanocidal,Antiproliferative Assay and Apoptotic Induction of (2-Phenoxypyridin-3-yl)naphthalene-1(2H)-one Derivatives

The coexistence of leishmaniasis, Chagas disease, and neoplasia in endemic areas has been extensively documented. The use of common drugs in the treatment of these pathologies invites us to search for new molecules with these characteristics. In this research, we report 16 synthetic chalcone derivatives that were investigated for leishmanicidal and trypanocidal activities as well as for antiproliferative potential on eight human cancers and two nontumor cell lines. The final compounds 8–23 were obtained using the classical base-catalyzed Claisen–Schmidt condensation. The most potent compounds as parasiticidal were found to be 22 and 23, while compounds 18 and 22 showed the best antiproliferative activity and therapeutic index against CCRF-CEM, K562, A549, and U2OS cancer cell lines and non-toxic VERO, BMDM, MRC-5, and BJ cells. In the case of K562 and the corresponding drug-resistant K562-TAX cell lines, the antiproliferative activity has shown a more significant difference for compound 19 having 10.3 times higher activity against the K562-TAX than K562 cell line. Flow cytometry analysis using K562 and A549 cell lines cultured with compounds 18 and 22 confirmed the induction of apoptosis in treated cells after 24 h. Based on the structural analysis, these chalcones represent new compounds potentially useful for Leishmania, Trypanosoma cruzi, and some cancer treatments.     

Effect of ZnO Nanofiller on Structural and Electrochemical Performance Improvement of Solid Polymer Electrolytes Based on Polyvinyl Alcohol–Cellulose Acetate–Potassium Carbonate Composites

In this study, a solution casting method was used to prepare solid polymer electrolytes (SPEs) based on a polymer blend comprising polyvinyl alcohol (PVA), cellulose acetate (CA), and potassium carbonate (K₂CO₃) as a conducting salt, and zinc oxide nanoparticles (ZnO-NPs) as a nanofiller. The prepared electrolytes were physicochemically and electrochemically characterized, and their semi-crystalline nature was established using XRD and FESEM. The addition of ZnO to the polymer–salt combination resulted in a substantial increase in ionic conductivity, which was investigated using impedance analysis. The size of the semicircles in the Cole–Cole plots shrank as the amount of nanofiller increased, showing a decrease in bulk resistance that might be ascribed to an increase in ions due to the strong action of the ZnO-NPs. The sample with 10 wt % ZnO-NPs was found to produce the highest ionic conductivity, potential window, and lowest activation energy (E_a) of 3.70 × 10^–3 Scm^–1, 3.24 V, and 6.08 × 10^–4 eV, respectively. The temperature–frequency dependence of conductivity was found to approximately follow the Arrhenius model, which established that the electrolytes in this study are thermally activated. Hence, it can be concluded that, based on the improved conductivity observed, SPEs based on a PVA-CA-K₂CO₃/ZnO-NPs composite could be applicable in all-solid-state energy storage devices.