The influence of the political environment and destination governance on sustainable tourism development: a study of Bled, Slovenia

In the context of sustainable tourism development, there are many studies about the exchange process between residents and tourism, yet this issue is practically unexplored with respect to the political environment of tourism. Therefore, this paper introduces and posits that the political environment is a necessary enabler for implementing sustainable tourism. The authors extend the established three-pillar sustainability concept by adding in the political dimension. Then they surveyed how residents' positive and negative perceptions of tourism impacts determine their satisfaction with life in the tourism destination and thus their support for tourism in their community. The model was empirically tested within the context of the long-established Alpine destination of Bled in Slovenia. The findings confirm the importance of the political environment and question the sustainability of Bled's tourism development. It is suggested that the community has relatively weak destination governance due to the underdeveloped political environment. The survey expands and deepens the tourism sustainability debate by adding in the political environment and how it relates to the emerging growth of research on destination governance. The proposed model can be adapted and applied to any destination in order to improve its governance, including the implementation of sustainable tourism development

New national and regional bryophyte records, 69

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New national and regional bryophyte records, 52

Marchantia paleacea is a new species for the Umbria Region and is rare in central and southern Italy. This record is in a Site of Community Importance (SCI) IT5220017 and a Special Area of Conservation (SAC) of the Natura 2000 EU-wide network due to the presence of the 7220* ‘Petrifying springs with tufa formation (Cratoneurion)’ Annexe I priority habitat. The particular environment, with a gorge and waterfall, created a very special microclimate that allowed the establishment of interesting liverworts and mosses

Phylogeographic Analysis Elucidates the Influence of the Ice Ages on the Disjunct Distribution of Relict Dragonflies in Asia

Unusual biogeographic patterns of closely related groups reflect events in the past, and molecular analyses can help to elucidate these events. While ample research on the origin of disjunct distributions of different organism groups in the Western Paleartic has been conducted, such studies are rare for Eastern Palearctic organisms. In this paper we present a phylogeographic analysis of the disjunct distribution pattern of the extant species of the strongly cool-adapted Epiophlebia dragonflies from Asia. We investigated sequences of the usually more conserved 18 S rDNA and 28 S rDNA genes and the more variable sequences of ITS1, ITS2 and CO2 of all three currently recognised Epiophlebia species and of a sample of other odonatan species. In all genes investigated the degrees of similarity between species of Epiophlebia are very high and resemble those otherwise found between different populations of the same species in Odonata. This indicates that substantial gene transfer between these populations occurred in the comparatively recent past. Our analyses imply a wide distribution of the ancestor of extant Epiophlebia in Southeast Asia during the last ice age, when suitable habitats were more common. During the following warming phase, its range contracted, resulting in the current disjunct distribution. Given the strong sensitivity of these species to climatic parameters, the current trend to increasing global temperatures will further reduce acceptable habitats and seriously threaten the existences of these last representatives of an ancient group of Odonata

Bottom-up approach in understanding tourism destination resilience : the case of SMEs in Ljubljana

202305 bckwOther VersionSelf-funde

Automatic evaluation of the lung condition of COVID-19 patients using X-ray images and convolutional neural networks

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. COVID-19 represents one of the greatest challenges in modern history. Its impact is most noticeable in the health care system, mostly due to the accelerated and increased influx of patients with a more severe clinical picture. These facts are increasing the pressure on health systems. For this reason, the aim is to automate the process of diagnosis and treatment. The research presented in this article conducted an examination of the possibility of classifying the clinical picture of a patient using X-ray images and convolutional neural networks. The research was conducted on the dataset of 185 images that consists of four classes. Due to a lower amount of images, a data augmentation procedure was performed. In order to define the CNN architecture with highest classification performances, multiple CNNs were designed. Results show that the best classification performances can be achieved if ResNet152 is used. This CNN has achieved AUCmacro and AUCmicro up to 0.94, suggesting the possibility of applying CNN to the classification of the clinical picture of COVID-19 patients using an X-ray image of the lungs. When higher layers are frozen during the training procedure, higher AUCmacro and AUCmicro values are achieved. If ResNet152 is utilized, AUCmacro and AUCmicro values up to 0.96 are achieved if all layers except the last 12 are frozen during the training procedure

Estimation of covid-19 epidemiology curve of the united states using genetic programming algorithm

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. Estimation of the epidemiology curve for the COVID-19 pandemic can be a very computationally challenging task. Thus far, there have been some implementations of artificial intelligence (AI) methods applied to develop epidemiology curve for a specific country. However, most applied AI methods generated models that are almost impossible to translate into a mathematical equation. In this paper, the AI method called genetic programming (GP) algorithm is utilized to develop a symbolic expression (mathematical equation) which can be used for the estimation of the epidemiology curve for the entire U.S. with high accuracy. The GP algorithm is utilized on the publicly available dataset that contains the number of confirmed, deceased and recovered patients for each U.S. state to obtain the symbolic expression for the estimation of the number of the aforementioned patient groups. The dataset consists of the latitude and longitude of the central location for each state and the number of patients in each of the goal groups for each day in the period of 22nd January 2020–3rd December 2020. The obtained symbolic expressions for each state are summed up to obtain symbolic expressions for estimation of each of the patient groups (confirmed, deceased and recovered). These symbolic expressions are combined to obtain the symbolic expression for the estimation of the epidemiology curve for the entire U.S. The obtained symbolic expressions for the estimation of the number of confirmed, deceased and recovered patients for each state achieved R2 score in the ranges 0.9406–0.9992, 0.9404–0.9998 and 0.9797–0.99955, respectively. These equations are summed up to formulate symbolic expressions for the estimation of the number of confirmed, deceased and recovered patients for the entire U.S. with achieved R2 score of 0.9992, 0.9997 and 0.9996, respectively. Using these symbolic expressions, the equation for the estimation of the epidemiology curve for the entire U.S. is formulated which achieved R2 score of 0.9933. Investigation showed that GP algorithm can produce symbolic expressions for the estimation of the number of confirmed, recovered and deceased patients as well as the epidemiology curve not only for the states but for the entire U.S. with very high accuracy

Application of artificial intelligence-based regression methods in the problem of covid-19 spread prediction: A systematic review

COVID-19 is one of the greatest challenges humanity has faced recently, forcing a change in the daily lives of billions of people worldwide. Therefore, many efforts have been made by researchers across the globe in the attempt of determining the models of COVID-19 spread. The objectives of this review are to analyze some of the open-access datasets mostly used in research in the field of COVID-19 regression modeling as well as present current literature based on Artificial Intelligence (AI) methods for regression tasks, like disease spread. Moreover, we discuss the applicability of Machine Learning (ML) and Evolutionary Computing (EC) methods that have focused on regressing epidemiology curves of COVID-19, and provide an overview of the usefulness of existing models in specific areas. An electronic literature search of the various databases was conducted to develop a comprehensive review of the latest AI-based approaches for modeling the spread of COVID-19. Finally, a conclusion is drawn from the observation of reviewed papers that AI-based algorithms have a clear application in COVID-19 epidemiological spread modeling and may be a crucial tool in the combat against coming pandemics

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