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The incorporation of fluorine atoms within the structure of organic compounds is known to exert a significant impact on their electronic properties, thereby modulating their reactivity in diverse chemical transformations. In the context of our investigation, we observed a striking illustration of this phenomenon. A Michael addition involving gem-difluorovinyl and trifluorovinyl acceptors was successfully achieved, demonstrating high stereoselectivity. This selectivity was further elucidated through theoretical calculations. Using this methodology, a series of new α,β-unsaturated amides, both fluorinated and nonfluorinated, were synthesized.

Abstract We present a methodology for the synthesis of inorganic-organic Janus-type molecules based on mono-T8 and difunctionalized double-decker silsesquioxanes (DDSQs) via hydrosilylation reactions, achieving exceptionally high yields and selectivities. The synthesized compounds were extensively characterized using various spectroscopic techniques, and their sizes and spatial arrangements were predicted through molecular modelling and density functional theory (DFT) calculations. Quantum chemical calculations were employed to examine the interactions among four molecules of the synthesized compounds. These computational results allowed us to determine the propensity for molecular aggregation, identify the functional groups involved in these interactions, and understand the changes in interatomic distances during aggregation. Understanding the aggregation behaviour of silsesquioxane molecules is crucial for tailoring their properties for specific applications, such as nanocomposites, surface coatings, drug delivery systems, and catalysts. Through a combination of experimental and computational approaches, this study provides valuable insights into the design and optimization of silsesquioxane-based Janus-type molecules for enhanced performance across various fields.

Utilising recycled brick aggregate in cementitious mixtures can decrease the overdependency on limited natural resources and improve the sustainability of concrete. This paper presents a potential solution to lower the amount of waste being landfilled and increase the sustainability of 3D concrete printing technology by combining low-carbon ternary blended cement with recycled aggregates. Hence, the effect of incorporating two types of recycled brick aggregate - clay brick (CBA) and engineering brick (EBA) on the properties of 3D printable ternary blended cement were investigated. The natural aggregate in a pre-existing 3D printable blend was substituted by up to 50 wt.-% with two varieties of recycled brick aggregates available throughout Europe. The recycled brick aggregates underwent characterisation to determine their properties. The fresh property evaluation using the green strength test was used to assess the effect of aggregate replacements on the mixture's shape stability. The mechanical performance of mixtures containing CBA and EBA, both cast and 3D printable mixes, was evaluated and compared to that of the control sample. The results indicated that incorporating recycled brick aggregate enhances green strength and Young's modulus significantly. Mechanical strength performance showed significant enhancement when incorporating RBA, which reached up to 67% and 55% for both cast and 3D printing methods, respectively. The suitability of the developed mix formulations for 3D printing was assessed by printing cylindrical objects.

Recently, energy efficiency (EE) has been pointed out as one of the key requirements within mobile networks. The development of intelligent algorithms providing Radio Access Networks (RAN) with EE features is possible when having access to the Key Performance Indicators (KPIs), and proper control actions, e.g., cell on/off switching, or Radio Frequency (RF) channel reconfiguration. These features together with a Machine Learning (ML) framework are available in the O-RAN architecture. This paper provides an overview of the EE framework according to the use cases specified by the O-RAN ALLIANCE. It is followed by the implementation of the Energy Saving rApp (ES-rApp). The rApp utilizes Deep Q-Learning (DQL) to increase EE through intelligent RF channel reconfiguration. Simulation results show up to a 24.8% EE gain over the static RF Channel Configuration (RCC).

In this study, we used ultraviolet-visible (UV-Vis), fluorescence, and circular dichroism (CD) techniques, as well as molecular modeling, to probe the interactions between carbazole derivatives and the G-quadruplex structure formed in the promoter region of gene Bcl-2. This gene is a rational target for anticancer therapy due to its high expression in a variety of tumors as well as resistance to chemotherapy-induced apoptosis. We employed a sequence with a specific dual G-to-T mutation that may form a mixed-type hybrid G-quadruplex structure in the Bcl-2 P1 promoter region. The three tested carbazole compounds differing in substitution on the nitrogen atom of carbazole interact with the Bcl-2 G-quadruplex by the same binding mode with the very comparable binding affinities in the order of 105 M−1. During absorption and fluorescence measurements, large changes in the ligand spectra were observed at higher G4 concentrations. The spectrophotometric titration results showed a two-step complex formation between the ligands and the G-quadruplex in the form of initial hypochromicity followed by hyperchromicity with a bathochromic shift. The strong fluorescence enhancement of ligands was observed after binding to the DNA. All of the used analytical techniques, as well as molecular modeling, suggested the π–π interaction between carbazole ligands and a guanine tetrad of the Bcl-2 G-quadruplex. Molecular modeling has shown differences in the interaction between each of the ligands and the tested G-quadruplex, which potentially had an impact on the binding strength.

Massive Multiple-Input Multiple-Output (MMIMO) is one of the 5G key enablers. Though, most of the works consider MMIMO under assumptions of ideal hardware. It has been shown that Power Amplifiers (PAs) introduce nonlinear distortion while operating close to their saturation power. Moreover, these distortions are in some cases beamformed toward the user, preventing antenna array gain from solving this problem. One of the possible solutions is an adaptive adjustment of the PA operating point, measured by Input Back off (IBO), to find a balance between wanted signal power and nonlinear distortion power. This work proposes a Contextual Bandit-Based IBO Optimization (COBBIO) algorithm to find rate-maximizing IBO for a given user’s radio conditions using learning through interaction. The proposed solution is tested in a realistic analog beamforming MMIMO cell simulator with multiple functional blocks, e.g., precoder, user scheduler, and utilizing an accurate 3D Ray-Tracing radio channel model. COBBIO provides throughput gains both over fixed-IBO schemes and state-of-the-art analytical IBO adjustment algorithms. The highest gains were observed for the so-called cell-edge users, where up to 83% improvement over the state-of-the-art algorithm was observed for the proposed COBBIO algorithm.

Future 6G networks are expected to utilize Massive Multiple-Input Multiple-Output (M-MIMO) and follow a user-centric cell-free (UCCF) architecture. In a UCCF M-MIMO network, the user can be potentially served by multiple surrounding Radio Units (RUs) and Distributed Units (DUs) controlled and coordinated by a single virtualized Centralized Unit (CU). Moreover, in an M-MIMO network, each transmit frontend is equipped with a Power Amplifier (PA), typically with nonlinear characteristics, that can have a significant impact on the throughput achieved by network users. This work evaluates a UCCF M-MIMO network within an advanced system-level simulator considering multicarrier transmission, using Orthogonal Frequency-Division Multiplexing (OFDM), realistic signal-processing steps, e.g., per resource block scheduling, and a nonlinear radio frontend. Moreover, both idealistic independent and identically distributed (i.i.d.) Rayleigh and 3D ray-tracing-based radio channels are evaluated. The results show that under the realistic radio channel, the novel user-centric network architecture can lead to an almost uniform distribution of user throughput and improve the rate of the users characterized by the worst radio conditions by over 3 times in comparison to a classical, network-centric design. At the same time, the nonlinear characteristics of the PA can cause significant degradation of the UCCF M-MIMO network’s performance when operating close to its saturation power.

Each subsequent generation of wireless standards imposes stricter spectral and energy efficiency demands. So far, layered wireless transceiver architectures have been used, allowing for instance to separate channel decoding algorithms from the front-end design. Such an approach may need to be reconsidered in the upcoming 6G era. Especially hardware-originated distortions have to be taken into account while designing other layer algorithms, as high throughput and energy efficiency requirements will push these devices to their limits, revealing their non-linear characteristics. In such a context, this paper will shed some light on the new degrees of freedom enjoyed while cross-layer designing as well as controlling multicarrier and multiantenna transceivers in 6G systems.

In the contemporary era, the exploration of machine learning (ML) has gained widespread attention and is being leveraged to augment traditional methodologies in quantitative structure–activity relationship (QSAR) investigations. The principal objective of this research was to assess the anticancer potential of colchicine-based compounds across five distinct cell lines. This research endeavor ultimately sought to construct ML models proficient in forecasting anticancer activity as quantified by the IC50 value, while concurrently generating innovative colchicine-derived compounds. The resistance index (RI) is computed to evaluate the drug resistance exhibited by LoVo/DX cells relative to LoVo cancer cell lines. Meanwhile, the selectivity index (SI) is computed to determine the potential of a compound to demonstrate superior efficacy against tumor cells compared to its toxicity against normal cells, such as BALB/3T3. We introduce a novel ML system adept at recommending novel chemical structures predicated on known anticancer activity. Our investigation entailed the assessment of inhibitory capabilities across five cell lines, employing predictive models utilizing various algorithms, including random forest, decision tree, support vector machines, k-nearest neighbors, and multiple linear regression. The most proficient model, as determined by quality metrics, was employed to predict the anticancer activity of novel colchicine-based compounds. This methodological approach yielded the establishment of a library encompassing new colchicine-based compounds, each assigned an IC50 value. Additionally, this study resulted in the development of a validated predictive model, capable of reasonably estimating IC50 values based on molecular structure input.

This study presents the comparison of microstructural and durability characteristics of 3D printed concrete (3DPC) depending on its production method (printing or casting). Printed samples with different numbers of layers, as well as a cast specimen with an identical mix composition, were produced and compared, with their microstructural pore and solid characteristics quantitatively and qualitatively investigated. For this purpose, scanning electron microscopy (SEM), mercury intrusion porosimetry (MIP) and X-ray micro-computed tomography (micro-CT) were utilized to evaluate the microstructures of the 3DPC. In particular, quantitative approaches using micro-CT data were newly proposed for a better understanding of the microstructural characteristics of 3DPC. Moreover, their durability-related characteristics and transport properties, including freeze-thaw and thermal resistance, were examined and compared. Despite noticeable differences between the microstructures of the printed and cast specimens, including their anisotropic and inter-layer porosity and heterogeneity, confirmed by MIP, SEM and micro-CT, no significant differences in the transport (capillary water porosity and water sorptivity) or durability-related properties (frost and thermal attack) were found. This was due to the dense and homogenous microstructure of 3DPC, which is attributable to the high binder content and low w/b of the mixture. Moreover, the newly proposed evaluation provided reasonable quantitative and qualitative characteristics, which can be used to demonstrate and predict the material properties of 3DPC.

Coronaviruses are dangerous human and animal pathogens. The newly identified coronavirus SARS-CoV-2 is the causative agent of COVID-19 outbreak, which is a real threat to human health and life. The world has been struggling with this epidemic for about a year, yet there are still no targeted drugs and effective treatments are very limited. Due to the long process of developing new drugs, reposition of existing ones is one of the best ways to deal with an epidemic of emergency infectious diseases. Among the existing drugs, there are candidates potentially able to inhibit the SARS-CoV-2 replication, and thus inhibit the infection of the virus. Some therapeutics target several proteins, and many diseases share molecular paths. In such cases, the use of existing pharmaceuticals for more than one purpose can reduce the time needed to design new drugs. The aim of this review was to analyze the key targets of viral infection and potential drugs acting on them, as well as to discuss various strategies and therapeutic approaches, including the possible use of natural products. We highlighted the approach based on increasing the involvement of human deaminases, particularly APOBEC deaminases in editing of SARS-CoV-2 RNA. This can reduce the cytosine content in the viral genome, leading to the loss of its integrity. We also indicated the nucleic acid technologies as potential approaches for COVID-19 treatment. Among numerous promising natural products, we pointed out curcumin and cannabidiol as good candidates for being anti-SARS-CoV-2 agents.

To provide users with high throughputs, the fifth generation (5G) and beyond networks are expected to utilize the Massive Multiple-Input Multiple-Output technology (MMIMO), i.e., large antenna arrays. However, additional antennas require the installation of dedicated hardware. As a result the power consumption of a 5G MMIMO network grows. This implies, e.g., higher operator costs. From this angle, the improvement of Energy Efficiency (EE) is identified as one of the key challenges for the 5G and beyond networks. EE can be improved through intelligent antenna switching, i.e., disabling some of the antennas installed at a 5G MMIMO Base Station (BS) when there are few User Equipments (UEs) within the cell area. To improve EE in this scenario we propose to utilize a sub-class of Machine Learning techniques named Reinforcement Learning (RL). Because 5G and beyond networks are expected to come with accurate UE localization, the proposed RL algorithm is based on UE location information stored in an intelligent database named a Radio Environment Map (REM). Two approaches are proposed: first EE is maximized independently for every set of UEs’ positions. After that the process of learning is accelerated by exploiting similarities between data in REM, i.e., REM-Empowered Action Selection Algorithm (REASA) is proposed. The proposed RL algorithms are evaluated with the use of a realistic simulator of the 5G MMIMO network utilizing an accurate 3D-Ray-Tracing radio channel model. The utilization of RL provides about 18.5% EE gains over algorithms based on standard optimization methods. Moreover, when REASA is used the process of learning can be accomplished approximately two times faster.

Abstract In 5G networks information about localization of a user equipment (UE) can be used not only for emergency calls or location-based services, but also for the network optimization applications, e.g., network management or dynamic spectrum access by using Radio Environment Maps (REM). However, some of these applications require much better localization accuracy than currently available in 4G systems. One promising localization method is Global Navigation Satellite System (GNSS)-based Real-Time Kinematics (RTK). While the signal received from satellites is the same as in traditional GNSS, a new reception method utilizing real-time data from a nearby reference station (e.g., 5G base station) results in cm-level positioning accuracy. The aim of this paper is to obtain a model of the RTK localization error for smartphone-grade GNSS antenna under open-sky conditions, that can be used in 5G network simulators. First, a tutorial-style overview of RTK positioning, and satellite orbits prediction is provided. Next, an RTK localization simulator is implemented utilizing GNSS satellites constellations. Results are investigated statistically to provide a simple, yet accurate RTK localization error framework, which is based on two Gauss-Markov process generators parametrized by visible satellites geometry, UE motion, and UE-satellite distance error variance.

Due to their biological activity and structural analogy to corresponding α-amino acids, α-aminophosphonates and their fluorinated derivatives provide an important source for drug discovery. Therefore convenient access to this class of compounds is still desirable. Four series of novel phosphonate analogues of fluorinated phenylalanine containing variable number of fluorine atoms in different positions of the phenyl ring were synthesized and subjected to solid state characterization by single-crystal X-ray diffraction analysis, and to studies with the use of NMR, HRMS and DFT methods. Such an approach provided valuable information in regard to preferable conformation, hydrogen bonds and also weak intermolecular interactions present in the crystals investigated. As analogues of naturally occurring compounds, the obtained α-aminophosphonates have a big potential for biological activity. Formation of some indolinylphosphonates as minor products arisen from intramolecular SNAr reactions show that aminophosphonates exhibiting an electronically depleted aromatic group, and possessing a fluorine atom in ortho position of the phosphonoalkyl substituent may give an entrance to further derivatives that may exhibit entirely new properties. Keywords: Fluorinated aminophosphonates, Phenylalanine, Phosphonate analogues, DFT calculations, X-ray analysis, Hirshfeld surface analysis, Cluster analysis

Herein, we present the application of fluorinated carbohydrate-derived building blocks for α-hydroxy β-fluoro/β-trifluoromethyl and unsaturated phosphonates synthesis. Pudovik and Horner–Wadsworth–Emmons reactions were applied to achieve this goal. The proposed pathway of the key reactions is supported by the experimental results, as well as quantum chemical calculations. The structure of the products was established by spectroscopic (1D, 2D NMR) and spectrometric (MS) techniques. Based on our data received, we claim that the progress of the Pudovik and HWE reactions is significantly influenced by the acidic protons present in the molecules as assessed by pKa values of the reagent.

A number of imines, including 12 new compounds, previously not reported in the literature, derived from variously fluorinated benzaldehydes and different anilines or chiral benzylamines were synthesized by a solvent-free mechanochemical method, which was based on the manual grinding of equimolar amounts of the substrates at the room temperature. In a very short reaction time of only 15 min, the method produced the expected products with good-to-excellent yields. The yields were comparable or significantly higher than those reported in the literature for the imines synthesized by other methods. Importantly, the conditions used for the reactions with aniline derivatives also resulted in the high yields of imines obtained from chiral benzylamines, and can be extended to the synthesis with other similar amines. Structures of all imines were confirmed by NMR spectroscopy: 1H, 13C and 19F. For four compounds, X-ray structures were also obtained. The synthetic approach presented in this paper contributes to the prevention of environmental pollution and can be easily extended for larger-scale syntheses. The mechanochemical solvent-free method provides a convenient strategy particularly useful for the preparation of fluorinated imines being versatile intermediates or starting material in the synthesis of drugs and other fine chemicals.

The application of DFT computational method (B3LYP/6-311++G(d,p)) to mono- and poly(CF3)substituted naphthalene derivatives helps to study changes in the electronic properties of these compounds under the influence of 11 substituents (-Br, -CF3, -CH3, -CHO, -Cl, -CN, -F, -NH2, -NMe2, -NO2, and -OH) to confront substituent effects in naphthalene with an analogous situation in benzene. This paper shows the dependencies of theoretically calculated SESE (Substituent Effect Stabilization Energy) values on empirically determined, well-defined Hammett-type constants (σp, σm, R, and F). Described poly(CF3)substituted derivatives of naphthalene are, so far, the most sensitive molecular probes for the substituent effects in the aromatic system. The presence of the trifluoromethyl groups of such an expressive nature significantly increases the sensitivity of the SESE to changes caused by another substitution. Further, the more -CF3 groups are attached to the naphthalene ring, the more sensitive the probe is. Certain groups of probes show additivity of sensitivity: the obtained sensitivity relates to the sum of the sensitivities of the mono(CF3)substituted probes.

This article discusses the problem of vibrations during machining. The manufacturing process of generator turbine blades is highly complex. Machining using Computerized Numerical Control (CNC) requires low cutting parameters in order to avoid vibration problems. However, even under these conditions, the surface quality and accuracy of the manufactured objects suffer from high levels of vibrations. Hence, the aim of this research is to counteract this phenomenon. Basic issues related to vibration problems will also be also discussed and a short review of currently available solutions for both active and passive vibration monitoring during machining will be presented. The authors developed a method which does not require any additional equipment other than modified CNC code. The proposed method can be applied to any CNC machine, and is especially suitable for lathes. The method seeks to eradicate the phenomenon of vibrations by providing enhanced control through Input Shaping Control (ISC). For this purpose, the authors present a method for modeling the machining process and design an ISC filter; the model is then implemented in the Matlab and Simulink environment. The last part of the article presents the results, together with a discussion, and includes a brief summary.

Despite the rapid development of 3D printing technology for cement composites, there are still a number of unsolved issues related to extrusion printing. One of them is proper mix design that allows for meeting criteria related to the printing of cementitious materials, such as pumpability, buildability, consistency on the materials, flowability and workability, simultaneously incorporating sustainable development ideas. In the case of mixes for 3D printing, the modification of the composition which increases the overall performance does not always go hand in hand with the reduction of negative environmental impact. The article presents the results of tests of eight mixtures modified with reactive and inert mineral additives designed for 3D printing. The mixes were evaluated in terms of their rheological and mechanical properties as well as environmental impact. Initial test results were verified by printing hollow columns up until collapse. Later, the differences between the compressive strength of standard samples and printed columns were determined. In order to summarize the results, a multi-faceted analysis of the properties of the mixes was carried out, introducing assessment indicators for its individual parameters. The article proves that appropriate material modification of mixes for 3D printing can significantly reduce the negative impact on the environment without hindering required 3D printing properties.

The binding affinities of three carbazole derivatives to the intramolecular G-quadruplex (GQ) DNA formed by the sequence 5′-AGGGAGGGCGCTGGGAGGAGGG-3′, derived from the c-KIT 1 oncogene region, were investigated. All carbazole cationic ligands that differed in the substituents on the nitrogen atom were able to stabilize G-quadruplex, as demonstrated using UV-Vis, fluorescence and CD spectroscopic techniques as well as molecular modeling. The spectrophotometric titration results showed spectral features characteristic of these ligands-bathochromic shifts and initial hypochromicity followed by hyperchromicity at higher GQ concentrations. All free carbazole ligands exhibited modest fluorescent properties, but after binding to the DNA the fluorescence intensity increased significantly. The binding affinities of carbazole ligands to the c-KIT 1 DNA were comparable showing values in the order of 105 M−1. Molecular modeling highlights the differences in interactions between each particular ligand and studied G-quadruplex, which potentially influenced binding strength. Obtained results relevant that all three investigated ligands have stabilization properties on studied G-quadruplex.

The DNA ligases, enzymes that seal breaks in the backbones of DNA, are essential for all organisms, however bacterial ligases essential for DNA replication use β-nicotinamide adenine dinucleotide as their co-factor, whereas those that are essential in eukaryotes and viruses use adenosine-5′-triphosphate. This fact leads to the conclusion that NAD+-dependent DNA ligases in bacteria could be targeted by their co-factor specific inhibitors. The development of novel alternative medical strategies, including new drugs, are a top priority focus areas for tuberculosis research due to an increase in the number of multi-drug resistant as well as totally drug resistant tubercle bacilli strains. Here, through the use of a virtual high-throughput screen and manual inspection of the top 200 records, 23 compounds were selected for in vitro studies. The selected compounds were evaluated in respect to their Mycobacterium tuberculosis NAD+ DNA ligase inhibitory effect by a newly developed assay based on Genetic Analyzer 3500 Sequencer. The most effective agents (e.g., pinafide, mitonafide) inhibited the activity of M. tuberculosis NAD+-dependent DNA ligase A at concentrations of 50 µM. At the same time, the ATP-dependent (phage) DNA LigT4 was unaffected by the agents at concentrations up to 2 mM. The selected compounds appeared to also be active against actively growing tubercle bacilli in concentrations as low as 15 µM.

Two questions are asked: the first is if the lack/presence of methoxyl moiety at aromatic ring essentially affects the stability of curcuminoid-metal complexes, and the second is if it is possible that in the metal complexes one of the possible demethoxycurcumin structures predominates. To answer the first question, the ESI-MS/MS spectra were taken of ions containing demethoxycurcumin, curcumin, and metal cation (e.g., ion [dCurc + Curc-H + Pb]+), and in order to answer the second question the ESI-MS/MS spectra were recorded of ions containing demethoxycurcumin and metal cation (e.g., ion [dCurc-H + Zn]+). An interpretation of the mass spectra has indicated that (i) for some metals, curcumin-metal complexes are more stable than demethoxycurcumin-metal complexes and for some metals vice versa, and (ii) in demethoxycurcumin-metal complexes structure B1 [(1E,4Z,6E)-5-hydroxy-7-(4-hydroxy-3-methoxyphenyl)-1-(4-hydroxyphenyl)hepta-1,4,6-trien-3-one] is more stable than structure B2 [(1E,4Z,6E)-5-hydroxy-1-(4-hydroxy-3-methoxyphenyl)-7-(4-hydroxyphenyl)hepta-1,4,6-trien-3-one].