Catalytic materials are the greatest challenge for the commercial application of water electrolysis (WEs) and fuel cells (FCs) as clean energy technologies. There is a need to find an alternative to expensive and unavailable platinum group metal (PGM) catalysts. This study aimed to reduce the cost of PGM materials by replacing Ru with RuO2 and lowering the amount of RuO2 by adding abundant and multifunctional ZnO. A ZnO@RuO2 composite in a 10:1 molar ratio was synthesized by microwave processing of a precipitate as a green, low-cost, and fast method, and then annealed at 300°C and 600°C to improve the catalytic properties. The physicochemical properties of the ZnO@RuO2 composites were investigated by X-ray powder diffraction (XRD), Raman and Fourier transform infrared (FTIR) spectroscopy, field emission scanning electron microscopy (FESEM), UV-Vis diffuse reflectance spectroscopy (DRS), and photoluminescence (PL) spectroscopy. The electrochemical activity of the samples was investigated by linear sweep voltammetry in acidic and alkaline electrolytes. We observed good bifunctional catalytic activity of the ZnO@RuO2 composites toward HER and OER in both electrolytes. The improved bifunctional catalytic activity of the ZnO@RuO2 composite by annealing was discussed and attributed to the reduced number of bulk oxygen vacancies and the increased number of established heterojunctions.

The influence of the ageing temperature on the hardness, electrical conductivity, thermal diffusivity and thermal conductivity of the EN AW-7075 aluminium alloy was studied in this paper. After solution treating the alloy at 480 °C for 1 h and quenching it in ice water, the investigated alloy was characterized using Differential Thermal Analysis (DTA) in order to determine the optimal temperatures for the isochronal ageing treatments. Afterwards, isochronal ageing was conducted at the temperature range of 110 °C-250 °C for 30 min The hardness, electrical conductivity, thermal diffusivity, thermal conductivity and microstructural features were investigated during the ageing treatments. Hardness had a peak value after ageing at 150 °C, while other properties gradually increased with the ageing temperature. Microstructural investigation of the aged alloy by SEM-EDS revealed the existence of precipitated phases that appear homogenously distributed in the microstructure.

Naphtho-triazoles and thienobenzo-triazoles have so far proven to be very potent inhibitors of the enzyme butyrylcholinesterase (BChE). Based on these results, in this work, new thienobenzo-thiazoles were designed and synthesized, and their potential inhibitory activity was tested and compared with their analogs, naphtho-oxazoles. The synthesis was carried out by photochemical cyclization of thieno-thiazolostilbenes obtained in the first reaction step. Several thienobenzo-thiazoles and naphtho-oxazoles have shown significant potential as BChE inhibitors, together with the phenolic thiazolostilbene being the most active of all tested compounds. These results are significant as BChE has been attracting growing attention due to its positive role in the treatment of Alzheimer’s disease. Computational examination based on the DFT approach enabled the characterization of the geometry and electronic structure of the studied molecules. Furthermore, the molecular docking study, accompanied by additional optimization of complexes ligand-active site, offered insight into the structure and stabilizing interactions in the complexes of studied molecules and BChE.

Glycans are an essential structural component of Immunoglobulin G (IgG) that modulate its structure and function. However, regulatory mechanisms behind this complex posttranslational modification are not well known. Previous genome-wide association studies (GWAS) identified 29 genomic regions involved in regulation of IgG glycosylation, but only a few were functionally validated. One of the key functional features of IgG glycosylation is the addition of galactose (galactosylation). We performed GWAS of IgG galactosylation (N=13,705) and identified 16 significantly associated loci, indicating that IgG galactosylation is regulated by a complex network of genes that extends beyond the galactosyltransferase enzyme that adds galactose to IgG glycans. Gene prioritization identified 37 candidate genes. Using a recently developed CRISPR/dCas9 system we manipulated gene expression of candidate genes in the in vitro IgG expression system. Up- and downregulation of three genes, EEF1A1, MANBA and TNFRSF13B, changed the IgG glycome composition, which confirmed that these three genes are involved in IgG galactosylation in this in vitro expression system.

Regular exercise improves health, modulating the immune system and impacting inflammatory status. Immunoglobulin G (IgG) N-glycosylation reflects changes in inflammatory status; thus, we investigated the impact of regular exercise on overall inflammatory status by monitoring IgG N-glycosylation in a previously inactive, middle-aged, overweight and obese population (50.30 ± 9.23 years, BMI 30.57 ± 4.81). Study participants (N = 397) underwent one of three different exercise programs lasting three months with blood samples collected at baseline and at the end of intervention. After chromatographically profiling IgG N-glycans, linear mixed models with age and sex adjustment were used to investigate exercise effects on IgG glycosylation. Exercise intervention induced significant changes in IgG N-glycome composition. We observed an increase in agalactosylated, monogalctosylated, asialylated and core-fucosylated N-glycans (padj = 1.00 × 10−4, 2.41 × 10−25, 1.51 × 10−21 and 3.38 × 10−30, respectively) and a decrease in digalactosylated, mono- and di-sialylated N-glycans (padj = 4.93 × 10−12, 7.61 × 10−9 and 1.09 × 10−28, respectively). We also observed a significant increase in GP9 (glycan structure FA2[3]G1, β = 0.126, padj = 2.05 × 10−16), previously reported to have a protective cardiovascular role in women, highlighting the importance of regular exercise for cardiovascular health. Other alterations in IgG N-glycosylation reflect an increased pro-inflammatory IgG potential, expected in a previously inactive and overweight population, where metabolic remodeling is in the early stages due to exercise introduction.

Smart traffic lights systems (STLSs) are a promising approach to improve traffic efficiency at intersections. They rely on the information sent by vehicles via C2X communication (like in cooperative awareness messages (CAMs)) at the managed intersection. While there exists a large body of work on privacy-enhancing technologies (PETs) for cooperative Intelligent Transport Systems (cITS) in general, such PETs like changing pseudonyms often impact the performance of cITS applications. This paper analyzes the extent to which different PETs affect the performance of two types of STLSs, a phase-based and a reservation-based STLS. These are implemented in SUMO and combined with four different PETs. Through extensive simulations we then investigate the impact of those PETs on STLS performance metrics like time loss, waiting time, fuel consumption, and average velocity. Our analysis shows that the impact of PETs on performance varies greatly depending on the type of STLS. Finally, we propose a hybrid STLS which is a combination of the two STLS types as a potential solution for limiting the negative impact of PETs on performance.

Museums are traditionally considered learning environments and are ordinarily used for non-formal education. Physical museums, while being irreplaceable, are limited to a physical space, requiring mobility and physical presence. In addition, traditional exhibitions are not designed for interaction and physical exploration of artefacts. With the focus being shifted from museum exhibits to visitors’ experience, utilization of emerging technologies and co-creation of virtual museums not only helps in preservation of cultural heritage, but enhances the dissemination, engagement, and experience, while addressing the mobility and the plurality of voices and perspectives represented. In this work, we designed and developed the School House Virtual Museum with tangible user interfaces based on participatory, interdisciplinary, and co-creative methods with students and a larger community of researchers, artists, and practitioners working on heritage and memory. In a user study with 62 participants, usability and user experience were explored and the potential contribution of such virtual museums to learning, based on critical, cross-disciplinary, and participatory dialogue, both in cultural and educational institutions/programs has been investigated. The results have confirmed that the system has been well designed and developed, and the user experience was largely positive. The responses from educators and students confirmed that the application holds potential as a learning and education tool in either museums, schools, or when used independently.

The application of Industry 4.0 (I4.0) in the field of logistics leads to the emergence and development of the concept of logistics 4.0. Many I4.0 technologies have been applied in the field of logistics. The goal of this research is to analyze the applicability of nine key I4.0 technologies in logistics centers (LC). For this purpose, an integrated MEREC (MEthod based on the Removal Effects of Criteria)—fuzzy MARCOS (Measurement of Alternatives and Ranking according to COmpromise Solution) model was developed. The applicability of nine I4.0 technologies was evaluated based on 15 subcriteria within three main groups of criteria, namely, technological, social and political, and economic and operative. Using the MEREC method, the weight values of the criteria and subcriteria were determined, while the technologies were ranked using the fuzzy MARCOS method. Based on the results obtained by applying this integrated MCDM (multicriteria decision-making) model, CC was identified as the best alternative, i.e., the technology that is most applicable in logistics centers, followed by IoT and big data. An analysis of the sensitivity of the obtained results to the change in the importance of the criteria was carried out, which shows certain changes in the ranking when the importance of the most important criterion changes.

When it comes to choosing the best option among multiple alternatives with criteria of different importance, it makes sense to use multi criteria decision making (MCDM) methods with more than 200 variations. However, because the algorithms of MCDM methods are different, they do not always produce the same best option or the same hierarchical ranking. At this point, it is important how and according to which MCDM methods will be compared, and the lack of an objective evaluation framework still continues. The mathematical robustness of the computational procedures, which are the inputs of MCDM methods, is of course important. But their output dimensions, such as their capacity to generate well-established real-life relationships and rank reversal (RR) performance, must also be taken into account. In this study, we propose for the first time two criteria that confirm each other. For this purpose, the financial performance (FP) of 140 listed manufacturing companies was calculated using nine different MCDM methods integrated with step-wise weight assessment ratio analysis (SWARA). İn the next stage, the statistical relationship between the MCDM-based FP final results and the simultaneous stock returns of the same companies in the stock market was compared. Finally, for the first time, the RR performance of MCDM methods was revealed with a statistical procedure proposed in this study. According to the findings obtained entirely through data analytics, Faire Un Choix Adéquat (FUCA) and (which is a fairly new method) the compromise ranking of alternatives from distance to ideal solution (CRADIS) were determined as the most appropriate methods by the joint agreement of both criteria.

In recent times the chiral semimetal cobalt monosilicide (CoSi) has emerged as a prototypical, nearly ideal topological conductor hosting giant, topologically protected Fermi arcs. Exotic topological quantum properties have already been identified in CoSi bulk single crystals. However, CoSi is also known for being prone to intrinsic disorder and inhomogeneities, which, despite topological protection, risk jeopardizing its topological transport features. Alternatively, topology may be stabilized by disorder, suggesting the tantalizing possibility of an amorphous variant of a topological metal, yet to be discovered. In this respect, understanding how microstructure and stoichiometry affect magnetotransport properties is of pivotal importance, particularly in case of low-dimensional CoSi thin films and devices. Here we comprehensively investigate the magnetotransport and magnetic properties of ≈25 nm Co1–xSix thin films grown on a MgO substrate with controlled film microstructure (amorphous vs textured) and chemical composition (0.40 < x < 0.60). The resistivity of Co1–xSix thin films is nearly insensitive to the film microstructure and displays a progressive evolution from metallic-like (dρxx/dT > 0) to semiconducting-like (dρxx/dT < 0) regimes of conduction upon increasing the silicon content. A variety of anomalies in the magnetotransport properties, comprising for instance signatures consistent with quantum localization and electron–electron interactions, anomalous Hall and Kondo effects, and the occurrence of magnetic exchange interactions, are attributable to the prominent influence of intrinsic structural and chemical disorder. Our systematic survey brings to attention the complexity and the challenges involved in the prospective exploitation of the topological chiral semimetal CoSi in nanoscale thin films and devices.

With the advent of Artificial Intelligence for healthcare, data synthesis methods present crucial benefits in facilitating the fast development of AI models while protecting data subjects and bypassing the need to engage with the complexity of data sharing and processing agreements. Existing technologies focus on synthesising real-time physiological and physical records based on regular time intervals. Real health data are, however, characterised by irregularities and multimodal variables that are still hard to reproduce, preserving the correlation across time and different dimensions. This paper presents two novel techniques for synthetic data generation of real-time multimodal electronic health and physical records, (a) the Temporally Correlated Multimodal Generative Adversarial Network and (b) the Document Sequence Generator. The paper illustrates the need and use of these techniques through a real use case, the H2020 GATEKEEPER project of AI for healthcare. Furthermore, the paper presents the evaluation for both individual cases and a discussion about the comparability between techniques and their potential applications of synthetic data at the different stages of the software development life-cycle.