The aim of the study was to determine the effectiveness of selected seven commercial essential oils (EsO) (grapefruit, lemongrass, tea tree (TTO), thyme, verbena, cajeput, and Litsea cubeba) on isolates of common Central European parasitic fungal species of Fusarium obtained from infected wheat kernels, and to evaluate the oils as potential natural fungicides. The study was conducted in 2 stages. At each stage, the fungicidal activity of EsO (with concentrations of 0.025; 0.05; 0.125; 0.25; 0.50; 1.0, and 2.0%) against Fusarium spp. was evaluated using the disc plate method and zones of growth inhibition were measured. At the first stage, the fungistatic activity of EsO was evaluated against four species of Fusarium from the Polish population (F. avenaceum FAPL, F. culmorum FCPL, F. graminearum FGPL and F. oxysporum FOPL). The correlation coefficient between the mycelial growth rate index (T) and the fungistatic activity (FA) was calculated. At the second stage, on the basis of the mycelium growth rate index, the effectiveness of the EsO in limiting the development of Fusarium isolates from the German population (F. culmorum FC1D, F. culmorum FC2D, F. graminearum FG1D, F. graminearum FG2D and F. poae FP0D) was assessed. The first and second stage results presented as a growth rate index were then used to indicate essential oils (as potential natural fungicides) effectively limiting the development of various common Central European parasitic species Fusarium spp. Finally, the sensitivity of four Fusarium isolates from the Polish population and five Fusarium isolates from the German population was compared. The data were compiled in STATISTICA 13.0 (StatSoft, Inc, CA, USA) at the significance level of 0.05. Fusarium isolates from the German population were generally more sensitive than those from the Polish population. The sensitivity of individual Fusarium species varied. Their vulnerability, regardless of the isolate origin, in order from the most to the least sensitive, is as follows: F. culmorum, F. graminearum, F. poae, F. avenaceum and F. oxysporum. The strongest fungicidal activity, similar to Funaben T, showed thyme oil (regardless of the concentration). Performance of citral oils (lemongrass and Litsea cubeba) was similar but at a concentration above 0.025%.

An addition of carbon nanostructures to cement paste is problematic due to the difficulties in obtaining homogenous mixtures. The paper reports on a more effective way of mixing carboxylated multi-walled carbon nanotubes (MWCNT-COOH) in cement pastes. The additional biological impact of the studied nanomodified cement was analyzed in the case of two moss species’ vitality. The applied approach of obtaining a homogeneous mixture is based on intense mechanochemical mixing of MWCNT-COOH together with polycarboxylate superplasticizer (SP). As a result, a more homogenous suspension of MWCNT-COOH within a liquid superplasticizer, suitable for addition to hydrophilic cement paste, was obtained. FT-IR/Raman spectroscopy was used for materials’ characterization. To explain the mixing process at the molecular level, systematic theoretical studies using density functional theory (DFT) were performed. The structures, interaction energies and IR/Raman vibrational spectra of model carboxylic acids, mixed with functionalized SWCNTs as simplified models of real MWCNTs, were obtained. Due to the controversial opinions on the environmental hazards of carbon nanostructures, additional in vivo studies were performed. In this case, effects of cement modified by the addition of small amounts of MWCNT-COOH with SP in comparison to the composite without carbon nanostructures and control subsoil on the vitality of mosses Polytrichum formosum and Pseudoscleropodium purum were studied.

It is difficult to obtain efficient flowability of reactive powder concrete (RPC) mix due to a low water/binder ratio. The improvement of material flowability could be achieved by using the latest generation polycarboxylate superplasticizers (SPs), as well as by changing the mixing procedure. This paper presents two different superplasticizers’ effect on a fresh mix and hardened reactive powder concrete properties. Results of systematic experimental studies (including physicochemical and spectroscopic tests) and molecular modelling suggest that superplasticizer chemical structure plays a key role in shaping the properties of the concrete mix. It has been demonstrated that SP containing more carboxylate salt groups -COO⁻ Me⁺ improves fluidity of the RPC mix and causes its better deaeration. In contrast, hardened concrete exhibits lower porosity and consequently greater strength. On the other hand, a change in ingredients mixing from a three-stage to a four-stage procedure increased the mix flowability and the RPC strength. The chemical structure of SP and the mixing procedure had no significant impact on cement hydration progress. Our results could be useful both from the point of view of the basic science of materials and the applied field of planning of cement composites in construction.

There is a widespread belief that VR technologies can provide controlled, multi-sensory, interactive 3D stimulus environments that engage patients in interventions and measure, record and motivate required human performance. In order to investigate state-of-the-art and associated occupations we provided a careful review of 6 leading medical and technical bibliometric databases. Despite the apparent popularity of the topic of VR use in cardiac surgery, only 47 articles published between 2002 and 2022 met the inclusion criteria. Based on them VR-based solutions in cardiac surgery are useful both for medical specialists and for the patients themselves. The new lifestyle required from cardiac surgery patients is easier to implement thanks to VR-based educational and motivational tools. However, it is necessary to develop the above-mentioned tools and compare their effectiveness with Augmented Reality (AR). With the aforementioned reasons, interdisciplinary collaboration between scientists, clinicians and engineers is necessary.

This paper presents a preliminary study delving into the application of machine learning-based methods for optimizing parameter selection in filtering techniques. The authors focus on exploring the efficacy of two prominent filtering methods: smoothing and cascade filters, known for their profound impact on enhancing the quality of brain signals. The study specifically examines signals acquired through functional near-infrared spectroscopy (fNIRS), a noninvasive neuroimaging modality offering valuable insights into brain activity. Through meticulous analysis, the research underscores the potential of machine learning approaches in discerning optimal parameters for filtering, thereby leading to a significant enhancement in the quality and reliability of fNIRS-derived signals. The results demonstrate the effectiveness of machine learning-based methods in optimizing parameter selection for filtering techniques, particularly in the context of fNIRS signals. By leveraging these approaches, the study achieves notable improvements in the quality and reliability of brain signal data. This work sheds light on promising avenues for refining neuroimaging methodologies and advancing the field of signal processing in neuroscience. The successful application of machine learning-based techniques highlights their potential for optimizing neuroimaging data processing, ultimately contributing to a deeper understanding of brain function.