The functioning of each traffic system depends to a great extent on the way the rail transport system operates. Taking into account the aspect of market turbulence and the dependence on adequate delivery when it comes to freight transport and traffic in accordance with a yearly Timetable in passenger traffic, transport policies are changing with time. Therefore, this document is considering the railway management models on the territory of Bosnia and Herzegovina. For the purpose of evaluating these models, a new hybrid model has been applied, i.e. the model which includes a combination of the Delphi, SWARA (Step-Wise Weight Assessment Ratio Analysis) and MABAC (Multi-Attributive Border Approximation Area Comparison) methods. In the first phase of the study, the criteria ranking was determined based on 16 expert grades used in the Delphi Method. After that, a total of 14 decision-makers determined the mutual criteria impact, which is a prerequisite for the application of the SWARA Method used to determine the relative weight values of the criteria. The third phase involves the application of the MABAC Method for evaluating and determining the most suitable variant. In addition, a sensitivity analysis involving the application of the ARAS, WASPAS, SAW and EDAS methods has been performed, thus verifying the previously obtained variant ranking.

High availability of automation systems is one of the main goals for the companies from all industrial branches. To achieve and maintain this high availability, the condition monitoring of the automation systems is an essential building block. However, as automation systems become increasingly equipped with numerous mechanical, electrical, and software components, creating a condition monitoring solution is becoming more and more challenging and requires knowledge from multiple engineering disciplines. Today, creating a condition monitoring solution is mostly based on the experience and preferences of the developers without a systematic and interdisciplinary method. Today, methods and tools supporting an interdisciplinary development exist. However, they do not fully consider condition monitoring relevant information. In addition, tools that increase software productivity and ease the adjustment of condition monitoring software are lacking. The main goal of this paper is to narrow the condition monitoring expertise gap by proposing convenient, systematic, and automated techniques to support the development of condition monitoring solutions from their design to their implementation. To achieve this goal, we propose an extension of the CONSENS systems engineering method to face issues caused in the design phase. By adopting a Model-Driven Development (MDD) approach, we propose a Domain-Specific Language (DSL) for condition monitoring that promotes increased understandability, and automation during the software implementation phase.

The fourth industrial revolution (Industry 4.0) strives for an optimized and flexible production process to reduce configuration costs. To achieve such a production process, first architecture-level decisions (i.e. deployment configuration, scheduling, etc.) are optimized. Following, these optimized architectural configurations are applied to dynamically reconfigure industrial control applications. To calculate software configurations, current research applies Design Space Exploration (DSE) techniques embedded into the IEC 61499 model-based approach. However, this research either applies simple and non-applicable constraints and objectives for real-life problems or considers architectural optimization solely at the design phase. Thus, reconfiguring industrial control applications is still an exhausting and manual task that requires production process' downtime. In this Ph.D. thesis, I handle the automatically optimized reconfiguration of the industrial automation systems. In particular, I propose applying DSE to calculate architectural configurations of IEC 61499-based control applications. To define different configuration problems from real-world situations, I identify domain-specific constraints and objectives. Furthermore, I will tackle the problem of runtime reconfiguration by applying several optimization strategies in various context scenarios and investigating an incremental search for new optimal configurations.

Here we report that density functional theory (DFT) can be used to accurately predict how Au nanoparticle (NP) catalysts cooperate with SnO x ( x = 1.9 or 2.0) supports to carry out the oxygen reduction reaction (ORR). Specifically, dendrimers were used to encapsulate AuNPs and prevent their interactions with the underlying SnO x supports. After removal of the dendrimers, however, the AuNPs are brought into direct contact with the support and hence feel its effect. The results show that both SnO1.9 and SnO2.0 supports strongly enhance the electrocatalytic properties of AuNPs for the ORR. In the case of AuNP interaction with a SnO1.9 support, the number of electrons involved in the ORR ( neff) increases from 2.1 ± 0.2 to 2.9 ± 0.1 following removal of the dendrimers, indicating an increased preference for the desired four-electron product (water), while the overpotential decreases by 0.32 V. Similarly, direct interactions between AuNPs and a SnO2.0 support result in an increase in neff from 2.2 ± 0.1 to 3.1 ± 0.1 and a reduction of the overpotential by 0.28 V. These experimental results are in excellent agreement with the theoretically predicted onset potential shift of 0.30 V. According to the DFT calculations, the observed activity enhancements are attributed to the existence of anionic Au resulting from electron transfer from surface oxygen vacancies within the SnO x supports to the AuNPs. This theoretical finding was confirmed experimentally using X-ray photoelectron spectroscopy. Importantly, the calculations reported here were performed prior to the experiments. In other words, this study represents an unusual case of theory accurately predicting the electrocatalytic manifestation of strong metal support interactions.

Robots are expected to become ubiquitous in the near future, working with people in various environments, including homes, schools, hospitals, and offices. As physically and socially interactive technologies, robots present new opportunities for embodied interaction and active as well as passive sensing in these contexts. They have also been shown to psychologically impact individuals, affect group and organizational dynamics, and modify our concepts and experiences of work, care, and social relationships. Designing robots for increasingly ubiquitous everyday use requires understanding how robots are perceived, and can be adopted and supported in open-ended, natural social circumstances. This, in turn, calls for design and evaluation methodologies that go beyond the dyadic and small group interactions in laboratories that have largely been the focus of research in human-robot interaction. In this talk, I will present alternative perspectives on the design and evaluation of socially interactive robotic technologies in real-world contexts, focusing on several case studies of socially assistive robots in eldercare. I will first discuss how older adults make sense of robots for use in their homes, in relation to the broader social contexts in which they live, as part of collaborative design activities, and in the course of month-long implementations of robots in their homes. These in-home studies bring up various issues relating to the types of data older adults and the clinicians who work with them would like to collect, related privacy concerns, impacts on other people in the home, and how robot designs can support the relationships older adults hope to have with and through robots. Secondly, I will explore the institutional and community-based use and design of robots in different eldercare facilities, including a nursing home, a retirement community, and an intergenerational daycare. These studies bring out how robots fit into and affect the institutional and group dynamics of interaction, and also allow us to explore how robots might be envisioned as technologies that can support not only individual, but community-level goals. Through these case studies of robots, as emerging ubiquitous interactive technologies, I will bring out themes that can inform the design and study of pervasive systems more broadly, including collaborative design, the use of data collected during social interactions with and around technologies, related ethical concerns, and the need for incorporating the aims of groups, institutions, and communities in the design of intelligent interactive technologies.

Human-Robot Interaction (HRI) research in public spaces often encounters delays and restrictions due to several factors, including the need for sophisticated technology, regulatory approvals, and public or community support. To remedy these concerns, we suggest HRI can apply the core philosophy of Tactical Urbanism, a concept from urban planning, to catalyze HRI in public spaces, provide community feedback and information on the feasibility of future implementations of robots in the public, and also create social impact and forge connections with the community while spreading awareness about robots as a public resource. As a case study, we share tactics used and strategies followed to conduct a pop-up style study of 'A robotic mailbox to support and raise awareness about homelessness.' We discuss benefits and challenges of the pop-up approach and recommend using it to enable the social studies of HRI not only to match but to precede, the fast-paced technological advancement and deployment of robots.

The study examined the psychometric characteristics of three automated working memory span tasks: operational, reading, and symmetry span task, administered in groups of participants. For each task, the metric characteristics of six scoring procedures were evaluated: partial, absolute, partial non-weighted, absolute non-weighted, partial weighted, and absolute weighted scoring. Metric characteristics of all measures were compared across two parallel analyses: with and without application of a typical 85% accuracy criterion on the processing component of the tasks. The study demonstrates that the group administration of span tasks does not compromise their psychometric characteristics. All the tasks had an adequate internal consistency with Cronbach’s αs equal to or above .70; the exception being all types of the symmetry span task absolute scores with α values close to .60. Furthermore, all tasks have satisfactory convergent construct validity as well as criterion validity estimated in relation to measures of fluid intelligence. Omitting the 85% accuracy criterion on the processing component of the span tasks did not impair their psychometric properties. Thus, it is recommended that researchers discard this accuracy criterion as a criterion for filtering the results for further statistical analyses.

BackgroundThe focus of this study is to identify particular microRNA (miRNA) signatures in exosomes derived from plasma of 435 human epidermal growth factor receptor 2 (HER2)-positive and triple-negative (TN) subtypes of breast cancer (BC).MethodsFirst, miRNA expression profiles were determined in exosomes derived from the plasma of 15 TNBC patients before neoadjuvant therapy using a quantitative TaqMan real-time PCR-based microRNA array card containing 384 different miRNAs. Forty-five miRNAs associated with different clinical parameters were then selected and mounted on microRNA array cards that served for the quantification of exosomal miRNAs in 435 BC patients before therapy and 20 healthy women. Confocal microscopy, Western blot, and ELISA were used for exosome characterization.ResultsQuantification of 45 exosomal miRNAs showed that compared with healthy women, 10 miRNAs in the entire cohort of BC patients, 13 in the subgroup of 211 HER2-positive BC, and 17 in the subgroup of 224 TNBC were significantly deregulated. Plasma levels of 18 exosomal miRNAs differed between HER2-positive and TNBC subtypes, and 9 miRNAs of them also differed from healthy women. Exosomal miRNAs were significantly associated with the clinicopathological and risk factors. In uni- and multivariate models, miR-155 (p = 0.002, p = 0.003, respectively) and miR-301 (p = 0.002, p = 0.001, respectively) best predicted pathological complete response (pCR).ConclusionOur findings show a network of deregulated exosomal miRNAs with specific expression patterns in exosomes of HER2-positive and TNBC patients that are also associated with clinicopathological parameters and pCR within each BC subtype.