In order for a tourist to visit a location, it has to be attractive. Destination attractiveness can be examined in several ways. One of them is offered by this study, which has examined destination attractiveness based on resources available in certain rural settlements. Based on a case study carried out in the Brčko District of Bosnia and Herzegovina (Brčko District), a model will be developed to test for rural tourism destination attractiveness. Examination of tourist destination attractiveness in the area of the Brčko District was conducted with a decision model based on the Decision EXpert method and expert decision-making. For that purpose, six rural settlements in the area of the Brčko District were examined with respect to destination attractiveness. Results obtained using this model showed that rural settlement Bijela has a “very good” attractiveness, rural settlements Brezik, Brezovo Polje, and Gornji Zovik have “good” attractiveness, while rural settlements Ražljevo and Maoča have “middle” attractiveness. The results obtained by applying this model have shown the need for improvement of touristic offer in order to make it more attractive. In order to improve attractiveness of a tourist destination, we need to strengthen human potential in this area and improve tourist infrastructure and make more effort to preserve the environment. The application of the used model has given good results in examination of tourist destination attractiveness and it should be applied for other branches of tourism in the future studies.

The Internet-of-Things (IoT) has enabled Industry 4.0 as a new manufacturing paradigm. The envisioned future of Industry 4.0 and Smart Factories is to be highly configurable and composed mainly of the ‘Things’ that are expected to come with some, often partial, assurance guarantees. However, many factories are categorised as safety-critical, e.g. due to the use of heavy machinery or hazardous substances. As such, some of the guarantees provided by the ‘Things’, e.g. related to performance and availability, are deemed as necessary in order to ensure the safety of the manufacturing processes and the resulting products. In this paper, we explore key safety challenges posed by Industry 4.0and identify the characteristics that its safety assurance should exhibit. We propose a modular safety assurance model by combination of the different actor responsibilities, e.g. system integrators, cloud service providers and “Things” suppliers. Besides the desirable modularity of such a safety assurance approach, our model provides a basis for cooperative, on-demand and continuous reasoning in order to address the reconfigurable nature of Industry 4.0 architectures and services. We illustrate our approach based on a smart factory use case.

For certain industrial control applications an explicit function capturing the non-trivial trade-off between competing objectives in closed loop performance is not available. In such scenarios it is common practice to use the human innate ability to implicitly learn such a relationship and manually tune the corresponding controller to achieve the desirable closed loop performance. This approach has its deficiencies because of individual variations due to experience levels and preferences in the absence of an explicit calibration metric. Moreover, as the complexity of the underlying system and/or the controller increase, in the effort to achieve better performance, so does the tuning time and the associated tuning cost. To reduce the overall tuning cost, a tuning framework is proposed herein, whereby a supervised machine learning is used to extract the human-learned cost function and an optimisation algorithm that can efficiently deal with a large number of variables, is used for optimising the extracted cost function. Given the interest in the implementation across many industrial domains and the associated high degree of freedom present in the corresponding tuning process, a Model Predictive Controller applied to air path control in a diesel engine is tuned for the purpose of demonstrating the potential of the framework.

Abstract Let R be an S-graded ring inducing S, that is, a ring which is the direct sum of a family of its additive subgroups indexed by a nonempty set S, under the assumption that the product of homogeneous elements is again homogeneous. We introduce a graded version of the subring and discuss its homogeneity, where U(R) denotes the group of units of R. Communicated by Pavel Kolesnikov

Abstract We study the graded isoradical of a ring graded by a group. In particular, we compare the graded isoradical and the classical isoradical of a graded ring, examine the question of how the (graded) isoradical of a graded ring depends on the classical isoradical of a ring which corresponds to the identity element of the grading group, and we also give some sufficient conditions under which the classical isoradical of a graded ring is homogeneous.

Motivation Recent advances in single cell sequencing (SCS) offer an unprecedented insight into tumor emergence and evolution. Principled approaches to tumor phylogeny reconstruction via SCS data are typically based on general computational methods for solving an integer linear program (ILP), or a constraint satisfaction program (CSP), which, although guaranteeing convergence to the most likely solution, are very slow. Others based on Monte Carlo Markov Chain (MCMC) or alternative heuristics not only offer no such guarantee, but also are not faster in practice. As a result, novel methods that can scale up to handle the size and noise characteristics of emerging SCS data are highly desirable to fully utilize this technology. Results We introduce PhISCS-BnB, a Branch and Bound algorithm to compute the most likely perfect phylogeny (PP) on an input genotype matrix extracted from a SCS data set. PhISCS-BnB not only offers an optimality guarantee, but is also 10 to 100 times faster than the best available methods on simulated tumor SCS data. We also applied PhISCS-BnB on a large melanoma data set derived from the sub-lineages of a cell line involving 24 clones with 3574 mutations, which returned the optimal tumor phylogeny in less than 2 hours. The resulting phylogeny also agrees with bulk exome sequencing data obtained from in vivo tumors growing out from the same cell line. Availability https://github.com/algo-cancer/PhISCS-BnB

Abstract We present a small collection of examples and counterexamples for selected problems, mostly in spectral graph theory, that have occupied our minds over a number of years without being completely resolved.

In reinforcement learning, we can learn a model of future observations and rewards, and use it to plan the agent's next actions. However, jointly modeling future observations can be computationally expensive or even intractable if the observations are high-dimensional (e.g. images). For this reason, previous works have considered partial models, which model only part of the observation. In this paper, we show that partial models can be causally incorrect: they are confounded by the observations they don't model, and can therefore lead to incorrect planning. To address this, we introduce a general family of partial models that are provably causally correct, yet remain fast because they do not need to fully model future observations.

Contemporary living is marked by powerful presence and all present use of new technologies. We might boldly state that people might not function well without new media. We heedlessly witness large part of contemporary adolescent’s social and emotional development occurring while on the Internet and on cell phones. Many parents and caregivers today use technology incredibly well and feel comfortable and capable with the programs and online venues that their children and adolescents are using. Nevertheless, some parents and adults are concerned about adolescent’s overuse of new media due to their potential risks and negative impact on adolescent’s psycho-social development. Some parents and caregivers may find it difficult to relate to their digitally savvy youngsters online for valid reasons. Such people may lack some basic understanding of adolescents and the new forms of socialization which is happening online, which are integral to their children's lives. Adolescent’s limited capacity for self-regulation and susceptibility to peer pressure make youth particularly vulnerable and at risk for various risks as they navigate and experiment with social media. Primary aim of this paper is to shed some light on adolescent’s online behavior and choices given their physical, cognitive, emotional, social, and behavioral characteristics and discuss potential negative and positive impact of new media on youth, family and social participation.