An Open Source Based Data Warehouse Architecture to Support Decision Making in the Tourism Sector

In this paper an alternative Tourism oriented Data Warehousing architecture is proposed which makes use of the most recent free and open source technologies like Java, Postgresql and XML. Such architecture's aim will be to support the decision making process and giving an integrated view of the whole Tourism reality in an established context (local, regional, national, etc.) without requesting big investments for getting the necessary software.Tourism, Data warehousing architecture

Markov chain to analyze web usability of a university website using eye tracking data

Web usability is a crucial feature of a website, allowing users to easily find information in a short time. Eye tracking data registered during the execution of tasks allow to measure web usability in a more objective way compared to questionnaires. In this work, we evaluated the web usability of the website of the University of Cagliari through the analysis of eye tracking data with qualitative and quantitative methods. Performances of two groups of students (i.e., high school and university students) across 10 different tasks were compared in terms of time to completion, number of fixations and difficulty ratio. Transitions between different areas of interest (AOI) were analyzed in the two groups using Markov chain. For the majority of tasks, we did not observe significant differences in the performances of the two groups, suggesting that the information needed to complete the tasks could easily be retrieved by students with little previous experience in using the website. For a specific task, high school students showed a worse performance based on the number of fixations and a different Markov chain stationary distribution compared to university students. These results allowed to highlight elements of the pages that can be modified to improve web usability

Chapter Decomposing tourists’ sentiment from raw NL text to assess customer satisfaction

The importance of the Word of Mouth is growing day by day in many topics. This phenomenon is evident in everyday life, e.g., the rise of influencers and social media managers. If more people positively debate specific products, then even more people are encouraged to buy them and vice versa. This effect is directly affected by the relationship between the potential customer and the reviewer. Moreover, considering the negative reporting bias is evident in how the Word of Mouth analysis is of absolute interest in many fields. We propose an algorithm to extract the sentiment from a natural language text corpus. The combined approach of Neural Networks, with high predictive power but more challenging interpretation, with more simple but informative models, allows us to quantify a sentiment with a numeric value and to predict if a sentence has a positive (negative) sentiment. The assessment of an objective quantity improves the interpretation of the results in many fields. For example, it is possible to identify crucial specific sectors that require intervention, improving the company's services whilst finding the strengths of the company himself (useful for advertising campaigns). Moreover, considering that the time information is usually available in textual data with a web origin, to analyze trends on macro/micro topics. After showing how to properly reduce the dimensionality of the textual data with a data-cleaning phase, we show how to combine: WordEmbedding, K-Means clustering, SentiWordNet, and the Threshold-based Naïve Bayes classifier. We apply this method to Booking.com and TripAdvisor.com data, analyzing the sentiment of people who discuss a particular issue, providing an example of customer satisfaction

Evolutionary Algorithms in Decision Tree Induction

One of the biggest problem that many data analysis techniques have to deal with nowadays is Combinatorial Optimization that, in the past, has led many methods to be taken apart. Actually, the (still not enough!) higher computing power available makes it possible to apply such techniques within certain bounds. Since other research fields like Artificial Intelligence have been (and still are) dealing with such problems, their contribute to statistics has been very significant. This chapter tries to cast the Combinatorial Optimization methods into the Artificial Intelligence framework, particularly with respect Decision Tree Induction, which is considered a powerful instrument for the knowledge extraction and the decision making support. When the exhaustive enumeration and evaluation of all the possible candidate solution to a Tree-based Induction problem is not computationally affordable, the use of Nature Inspired Optimization Algorithms, which have been proven to be powerful instruments for attacking many combinatorial optimization problems, can be of great help. In this respect, the attention is focused on three main problems involving Decision Tree Induction by mainly focusing the attention on the Classification and Regression Tree-CART (Breiman et al., 1984) algorithm. First, the problem of splitting complex predictors such a multi-attribute ones is faced through the use of Genetic Algorithms. In addition, the possibility of growing “optimal” exploratory trees is also investigated by making use of Ant Colony Optimization (ACO) algorithm. Finally, the derivation of a subset of decision trees for modelling multi-attribute response on the basis of a data-driven heuristic is also described. The proposed approaches might be useful for knowledge extraction from large databases as well as for data mining applications. The solution they offer for complicated data modelling and data analysis problems might be considered for a possible implementation in a Decision Support System (DSS). The remainder of the chapter is as follows. Section 2 describes the main features and the recent developments of Decision Tree Induction. An overview of Combinatorial Optimization with a particular focus on Genetic Algorithms and Ant Colony Optimization is presented in section 3. The use of these two algorithms within the Decision Tree Induction Framework is described in section 4, together with the description of the algorithm for modelling multi-attribute response. Section 5 summarizes the results of the proposed method on real and simulated datasets. Concluding remarks are presented in section 6. The chapter also includes an appendix that presents J-Fast, a Java-based software for Decision Tree that currently implements Genetic Algorithms and Ant Colony Optimization

Reliability and reproducibility of the new AO/OTA 2018 classification system for proximal humeral fractures: a comparison of three different classification systems

Background: The classification systems for proximal humeral fractures routinely used in clinical practice include the Neer and Arbeitsgemeinschaft für Osteosynthesefragen/Orthopaedic Trauma Association (AO/OTA) 2007 systems. Currently used systems have low inter- and intraobserver reliability. In 2018, AO/OTA introduced a new classification system with the aim of simplifying the coding process, in which the Neer four-part classification was integrated into the fracture description. The aim of the present work is to assess the inter- and intraobserver agreement of the new AO/OTA 2018 compared with the Neer and AO/OTA 2007 classifications. Materials and methods: A total of 116 radiographs of consecutive patients with proximal humeral fracture were selected and classified by three observers with different levels of experience. All three observers independently reviewed and classified the images according to the Neer, AO/OTA 2007, and new AO/OTA 2018 systems. To determine the intraobserver agreement, the observers reviewed the same set of radiographs after an interval of 8 weeks. The inter- and intraobserver agreement were determined through Cohen’s kappa coefficient analysis. Results: The new AO/OTA 2018 classification showed substantial mean inter- (k=0.67) and intraobserver (k=0.75) agreement. These results are similar to the reliability observed for the Neer classification (interobserver, k=0.67; intraobserver, k=0.85) but better than those found for the AO/OTA 2007 system, which showed only moderate inter- (k=0.57) and intraobserver (k=0.58) agreement. The two more experienced observers showed better overall agreement, but no statistically significant difference was found. No differences were found between surgical experience and agreement regarding specific fracture types or groups. Conclusions: The results showed that the Neer system still represents the more reliable and reproducible classification. However, the new AO/OTA 2018 classification improved the agreement among observers compared with the AO/OTA 2007 system, while still maintaining substantial descriptive power and simplifying the coding process. The universal modifiers and qualifications, despite their possible complexity, allowed a more comprehensive fracture definition without negatively affecting the reliability or reproducibility of the classification system. Level of evidence: Level III, diagnostic studie

Italian Version of the Risk Assessment and Prediction Tool: Properties and Usefulness of a Decision-Making Tool for Subjects' Discharge after Total Hip and Knee Arthroplasty

Background: Growing attention is being given to standardized outcome measures to improve interventions for total hip arthroplasty (THA) and total knee arthroplasty (TKA). We culturally adapt and validate the Italian version of the Risk Assessment and Prediction Tool (RAPT-I) to allow its predictive use after THA and TKA. Methods: The RAPT-I was adapted by forward-backward translation, a final review by an expert committee and a test of the pre-final version to establish its correspondence with the original version. The psychometric testing included test-retest reliability (intraclass correlation coefficient, ICC). The RAPT score was used to predict the subjects-destination (<6: rehabilitation unit; 6-9: additional intervention before discharging home; or >9: discharge directly at home) by comparing the actual discharge destination with the predicted destination. The predictive effects of RAPT items on the discharge destination were further described by a logistic regression model (repeated leave-one-out bootstrap procedure). Results: The questionnaire was administered to 78 subjects with THA and 70 subjects with TKA and proven to be acceptable. The questionnaire showed excellent test–retest reliability (ICC = 0.839; with 95% confidence interval (CI) of 0.725–0.934 for THA; ICC = 0.973, with 95% CI of 0.930–0.997 for TKA). The RAPT-I overall predictive validity was 87.2%, and the discharge destination was directly related to living condition (odds ratio (OR) = 2.530), mobility (OR = 2.626) and age (OR = 1.332) and inversely related to gait aids (OR = 0.623) and gender (OR = 0.474). Conclusions: The RAPT-I was successfully adapted into Italian and proven to exhibit satisfactory properties, including predictive validity in determining discharge destination

Gene silencing by RNAi in mouse Sertoli cells

<p>Abstract</p> <p>Background</p> <p>RNA interference (RNAi) is a valuable tool in the investigation of gene function. The purpose of this study was to examine the availability, target cell types and efficiency of RNAi in the mouse seminiferous epithelium.</p> <p>Methods</p> <p>The experimental model was based on transgenic mice expressing EGFP (enhanced green fluorescent protein). RNAi was induced by in vivo transfection of plasmid vectors encoding for short hairpin RNAs (shRNAs) targeting EGFP. shRNAs were transfected in vivo by microinjection into the seminiferous tubules via the rete testis followed by square wave electroporation. As a transfection reporter, expression of red fluorescent protein (HcRed 1) was used. Cell types, the efficiency of both transfections and RNAi were all evaluated.</p> <p>Results</p> <p>Sertoli cells were the main transfected cells. A reduction of about 40% in the level of EGFP protein was detected in cells successfully transfected both in vivo and in vitro. However, the efficiency of in vivo transfection was low.</p> <p>Conclusion</p> <p>In adult seminiferous epithelial cells, in vivo post-transcriptional gene silencing mediated by RNAi via shRNA is efficient in Sertoli cells. Similar levels of RNAi were detected both in vivo and in vitro. This also indicates that Sertoli cells have the necessary silencing machinery to repress the expression of endogenous genes via RNAi.</p

Advances in geometrical parametrization and reduced order models and methods for computational fluid dynamics problems in applied sciences and engineering: overview and perspectives

Several problems in applied sciences and engineering require reduction techniques in order to allow computational tools to be employed in the daily practice, especially in iterative procedures such as optimization or sensitivity analysis. Reduced order methods need to face increasingly complex problems in computational mechanics, especially into a multiphysics setting. Several issues should be faced: stability of the approximation, efficient treatment of nonlinearities, uniqueness or possible bifurcations of the state solutions, proper coupling between fields, as well as offline-online computing, computational savings and certification of errors as measure of accuracy. Moreover, efficient geometrical parametrization techniques should be devised to efficiently face shape optimization problems, as well as shape reconstruction and shape assimilation problems. A related aspect deals with the management of parametrized interfaces in multiphysics problems, such as fluid-structure interaction problems, and also a domain decomposition based approach for complex parametrized networks. We present some illustrative industrial and biomedical problems as examples of recent advances on methodological developments. \ua9 The author

MicroRNA co-expression networks exhibit increased complexity in pancreatic ductal compared to Vater’s papilla adenocarcinoma

iRNA expression abnormalities in adenocarcinoma arising from pancreatic ductal system (PDAC) and Vater’s papilla (PVAC) could be associated with distinctive pathologic features and clinical cancer behaviours. Our previous miRNA expression profiling data on PDAC (n=9) and PVAC (n=4) were revaluated to define differences/ similarities in miRNA expression patterns. Afterwards, in order to uncover target genes and core signalling pathways regulated by specific miRNAs in these two tumour entities, miRNA interaction networks were wired for each tumour entity, and experimentally validated target genes underwent pathways enrichment analysis. One hundred and one miRNAs were altered, mainly over-expressed, in PDAC samples. Twenty-six miRNAs were deregulated in PVAC samples, where more miRNAs were down-expressed in tumours compared to normal tissues. Four miRNAs were significantly altered in both subgroups of patients, while 27 miRNAs were differentially expressed between PDAC and PVAC. Although miRNA interaction networks were more complex and dense in PDAC than in PVAC, pathways enrichment analysis uncovered a functional overlapping between PDAC and PVAC. However, shared signalling events were influenced by different miRNA and/or genes in the two tumour entities. Overall, specific miRNA expression patterns were involved in the regulation of a limited core signalling pathways in the biology landscape of PDAC and PVAC