Effect of microstructural features on the corrosion behavior of severely deformed Al–Mg–Si alloy

The aim of this study was to determine the influence of severe plastic deformation processing and the changes in microstructure resulting therefrom on the corrosion resistance of an Al–Mg–Si alloy. The alloy was processed using incremental equal channel angular pressing, which caused a reduction in grain size from 15 to 0.9 µm. The grain refinement was accompanied by an increase in the number of grain boundaries and dislocations, and by changes in grain orientation. However, there was no change in the size and number of intermetallic particles, which presumably resulted in a constant number of galvanic couplings. Electrochemical experiments revealed only slight differences between the samples before and after processing. Higher potential transients/oscillations upon immersion and increased corrosion currents in the vicinity of corrosion potential point to slightly higher reactivity of the most refined material. This indicates that intermetallic particles are the most crucial microstructural elements in terms of corrosion resistance. Their impact exceeds that of grain boundaries, in particular, at the stage of corrosion initiation. The development of corrosion attack is controlled more by the microstructure of the matrix as the grain refinement resulted in a less pronounced corrosion attack in comparison with the coarse-grained sample.     

Evaluation of a Novel Plasmid for Simultaneous Gene Electrotransfer-Mediated Silencing of CD105 and CD146 in Combination with Irradiation

Targeting tumor vasculature through specific endothelial cell markers represents a promising approach for cancer treatment. Here our aim was to construct an antibiotic resistance gene-free plasmid encoding shRNAs to simultaneously target two endothelial cell markers, CD105 and CD146, and to test its functionality and therapeutic potential in vitro when delivered by gene electrotransfer (GET) and combined with irradiation (IR). Functionality of the plasmid was evaluated by determining the silencing of the targeted genes using qRT-PCR. Antiproliferative and antiangiogenic effects were determined by the cytotoxicity assay tube formation assay and wound healing assay in murine endothelial cells 2H-11. The functionality of the plasmid construct was also evaluated in malignant melanoma tumor cell line B16F10. Additionally, potential activation of immune response was measured by induction of DNA sensor STING and proinflammatory cytokines by qRT-PCR in endothelial cells 2H-11. We demonstrated that the plasmid construction was successful and can efficiently silence the expression of the two targeted genes. As a consequence of silencing, reduced migration rate and angiogenic potential was confirmed in 2H-11 endothelial cells. Furthermore, induction of DNA sensor STING and proinflammatory cytokines were determined, which could add to the therapeutic effectiveness when used in vivo. To conclude, we successfully constructed a novel plasmid DNA with two shRNAs, which holds a great promise for further in vivo testing.     

Charge Transportation and Chirality in Liquid Crystalline Helical Network Phases of Achiral BTBT-Derived Polycatenar Molecules

First examples of multichain (polycatenar) compounds, based on the π-conjugated [1]benzothieno[3,2-b]benzothiophene unit are designed, synthesized, and their soft self-assembly and charge carrier mobility are investigated. These compounds, terminated by the new fan-shaped 2-brominated 3,4,5-trialkoxybenzoate moiety, form bicontinuous cubic liquid crystalline (LC) phases with helical network structure over extremely wide temperature ranges (>200 K), including ambient temperature. Compounds with short chains show an achiral cubic phase with the double network, which upon increasing the chain length, is at first replaced by a tetragonal 3D phase and then by a mirror symmetry is broken triple network cubic phase. In the networks, the capability of bypassing defects provides enhanced charge carrier mobility compared to imperfectly aligned columnar phases, and the charge transportation is non-dispersive, as only rarely observed for LC materials. At the transition to a semicrystalline helical network phase, the conductivity is further enhanced by almost one order of magnitude. In addition, a mirror symmetry broken isotropic liquid phase is formed beside the 3D phases, which upon chain elongation is removed and replaced by a hexagonal columnar LC phase.     

The influence of brewing method on bioactive compounds residues in spent coffee grounds of different roasting degree and geographical origin

The content of bioactive compounds in spent coffee grounds (SGC) was studied. SGC were obtained from Coffea arabica beans of different roasting degrees (light and dark) and different geographical origins (Nicaragua, Columbia and Mexico) processed using four brewing methods (mocha, filtered, drip and infusion). The highest caffeine and chlorogenic acid contents were determined in filtered spent coffee extracts. All extracts of light roasted spent coffee grounds showed lower browning index levels in comparison to that from dark roasted spent coffee grounds. Generally, the highest content of total polyphenolic compounds and highest antioxidant capacity were determined in extracts prepared in drip. In conclusion, the results obtained in this study indicate that the spent coffee grounds produced of domestic levels, especially those obtained from filter coffeemaker, could be considered as a good source of natural antioxidants.     

Lipids and Antiplatelet Therapy: Important Considerations and Future Perspectives

Lipids play an essential role in platelet functions. It is known that polyunsaturated fatty acids play a role in increasing platelet reactivity and that the prothrombotic phenotype plays a crucial role in the occurrence of major adverse cardiovascular events. The ongoing increase in cardiovascular diseases’ incidence emphasizes the importance of research linking lipids and platelet function. In particular, the rebound phenomenon that accompanies discontinuation of clopidogrel in patients receiving dual antiplatelet therapy has been associated with changes in the lipid profile. Our many years of research underline the importance of reduced HDL values for the risk of such a rebound effect and the occurrence of thromboembolic events. Lipids are otherwise a heterogeneous group of molecules, and their signaling molecules are not deposited but formed “on-demand” in the cell. On the other hand, exosomes transmit lipid signals between cells, and the profile of such changes can be monitored by lipidomics. Changes in the lipid profile are organ-specific and may indicate new drug action targets.     

ZnO Nanoplatelets with Controlled Thickness: Atomic Insight into Facet-Specific Bimodal Ligand Binding Using DNP NMR

Colloidal nanoplatelets (NPLs) and nanosheets with controlled thickness have recently emerged as an exciting new class of quantum-sized nanomaterials with substantially distinct optical properties compared to 0D quantum dots. Zn-based NPLs are an attractive heavy-metal-free alternative to the so far most widespread cadmium chalcogenide colloidal 2D semiconductor nanostructures, but their synthesis remains challenging to achieve. The authors describe herein, to the best of their knowledge, the first synthesis of highly stable ZnO NPLs with the atomically precise thickness, which for the smallest NPLs is 3.2 nm (corresponding to 12 ZnO layers). Furthermore, by means of dynamic nuclear polarization-enhanced solid-state ¹⁵N NMR, the original role of the benzamidine ligands in stabilizing the surface of these nanomaterials is revealed, which can bind to both the polar and non-polar ZnO facets, acting either as X- or L-type ligands, respectively. This bimodal stabilization allows obtaining hexagonal NPLs for which the surface energy of the facets is modulated by the presence of the ligands. Thus, in-depth study of the interactions at the organic–inorganic interfaces provides a deeper understanding of the ligand–surface interface and should facilitate the future chemistry of stable-by-design nano-objects.     

Energy Efficiency Trade-Off Between Duty-Cycling and Wake-Up Radio Techniques in IoT Networks

Wireless Personal Communications - For robust monitoring, control and proper energy management of renewable energy sources (RES), wireless sensing networks (WSNs) are proved to be a vital solution....     

Effectiveness of strategic environmental assessment in Poland

The paper presents the results of an evaluation of the effectiveness of SEA obtained as a result of a survey with the participation of stakeholders. In Poland, several hundred SEAs are conducted annually at the local and regional levels and several at the national level. Although the survey demonstrated that SEA is evaluated as effective or quite effective, the respondents pointed to several irregularities in the non-procedural dimension of effectiveness. The main ones in terms of substantive effectiveness are that SEA is rarely used to help develop plans/programmes and procedures are highly politicised. Moreover, low effectiveness in terms of variant assessment and cumulative impacts and nearly non-existent monitoring of the actual effects of implementing plans/programmes influence substantive effectiveness significantly. The respondents pointed to the problem resulting from the fact that plan-makers are unwilling to take into account the SEA recommended changes. They also emphasised that there are certain attempts to put pressure on SEA consultants to make the conclusions less stringent. Respondents indicate that in Poland the costs associated with conducting SEA often outweigh the profits. The research shows that the society is properly informed about SEA, but public involvement is still low.     

Machine Learning-Based Predictions of Customers’ Decisions in Car Insurance

Predicting customer decisions allows companies to obtain higher profits due to better resource management. The accuracy of those predictions can be currently boosted by the application of machine learning algorithms.

We propose a new method to predict a car driver’s decision about taking a replacement car after a vehicle accident happens. We use feature engineering to create attributes of high significance. The generated attributes are related to time (e.g., school holidays), place of collision (e.g., distance from home), time and conditions (e.g., weather), vehicles (e.g., vehicle value), addresses of both the victim and the perpetrator. Feature engineering involves external sources of data.

Five machine learning methods of classification are considered: decision trees, multi-layer perceptrons, AdaBoost, logistic regression and gradient boosting. Algorithms are tested on real data from a Polish insurance company. Over 80% accuracy of prediction is achieved. Significance of the attributes is calculated using the linear vector quantization method.

Presented work shows the applicability of machine learning in the car insurance market.