Miracle-dz/UniFAR-FTUs huggingface dataset:
Network Analysis and Tourism: From Theory to Practice

1. Introduction - Noel Scott (University of Queensland, Australia), Rodolfo Baggio (Bocconi University, Italy), Chris Cooper (University of Nottingham, UK) 2. The Historical Development of Network Theories - Noel Scott (University of Queensland, Australia), Rodolfo Baggio (Bocconi University, Italy), Chris Cooper (University of Nottingham, UK) 3. The Network Concept and Tourism - Noel Scott (University of Queensland, Australia), Rodolfo Baggio (Bocconi University, Italy), Chris Cooper (University of Nottingham, UK)QUALITATIVE APPROACHES TO TOURISM NETWORK ANALYSIS 4. Conceptual Tools for Evaluating Tourism Partnerships - Roger March and Ian Wilkinson (University of New South Wales, Australia) 5. Tourism Destination Networks and Knowledge Transfer - Noel Scott (University of Queensland, Australia), Rodolfo Baggio (Bocconi University, Italy), Chris Cooper (University of Nottingham, UK) 6. Policy Networks and Tourism Governance - Dianne Dredge (Griffith University, Australia) and Christof Pforr (Curtin University of Technology, Australia) 7. Network Governance and Connectivity: A Case Study - Kathryn Pavlovich (University of Waikato, New Zealand) 8. The Benefits of Networks to Small and Medium Sized Tourism Enterprises - Carlos Costa, Zelia Breda, Rui Costa and Joana Miguens (University of Aveiro, Portugal) 9. International Tourism Trade Networks: The Case of the Chinese Inbound Travel Trade to Australia - Grace Wen Pan (Griffith University) 10. Power, Destination Branding and the Implications of a Social Network Perspective - Giuseppe Marzano (The University of Queensland, Australia)QUANTITATIVE APPROACHES TO TOURISM NETWORK ANALYSIS 11. Issues in Quantitative Network Analysis - University of Queensland, Australia), Rodolfo Baggio (Bocconi University, Italy), Chris Cooper (University of Nottingham, UK) 12. Visualizing Tourism Networks: Connecting the Dots - University of Queensland, Australia), Rodolfo Baggio (Bocconi University, Italy), Chris Cooper (University of Nottingham, UK) 13. Complex Tourism Networks - University of Queensland, Australia), Rodolfo Baggio (Bocconi University, Italy), Chris Cooper (University of Nottingham, UK) 14. Technological Tourism Networks and Network Simulation - University of Queensland, Australia), Rodolfo Baggio (Bocconi University, Italy), Chris Cooper (University of Nottingham, UK) 15. Conclusions - Noel Scott (University of Queensland, Australia), Rodolfo Baggio (Bocconi University, Italy), Chris Cooper (University of Nottingham, UK)
ffi ciently Learning Metric and Topological Maps with Autonomous Service Robots

Models of the environment are needed for a wide range of robot ic applications, from search and rescue to automated vacuum cleaning. Learning maps has therefore been a majo r research focus in the robotics community over the last decades. In general, one distinguishes between metric and topological maps. Metric maps model the environment based on grids or geometric representations whereas to pological maps model the structure of the environment using a graph. The contribution of this paper is an approach that learns a me tric as well as a topological map based on laser range data obtained with a mobile robot. Our approach c onsists of two steps. First, the robots solves the simultaneous localization and mapping problem using an e ffici nt probabilistic filtering technique. In a second step, it acquires semantic information about the environme nt using machine learning techniques. This semantic information allows the robot to distinguish between di fferent types of places like, e.g., corridors or rooms. This enables the robot to construct annotated metric as well as to pological maps of the environment. All techniques have been implemented and thoroughly tested using real mobi le r ot in a variety of environments.
Combustion and emission characteristics of biofuels in diesel engines

_______________________________________________________________ 2 ACKNOWLEDGEMENTS_____________________________________ _________________3 ABBREVATIONS____________________________________________________________ 4 CONTENTS________________________________________________________________ 6 TABLE OF FIGURES__________________________________________________________ 9 TABLE OF TABLES___________________________________________________________15 1 CHAPTER 1 Introduction _______________________________________________ 16 1.
Improving Hit-and-Run with single observations for continuous simulation optimization

Many algorithms for continuous simulation optimization have been proposed, but the question of the number of replications at a specific point is always an issue. In this paper, instead of averaging replications of the objective function at a specific point (e.g., sample average), we average observed function evaluations from neighboring points. The Improving Hit-and-Run algorithm is modified to accommodate averaging in a ball of fixed radius, thus only sampling any point once. The computational results suggest an efficiency with single observations per sample point that simultaneously improves the estimation of the function value and samples closer to the optimum as the algorithm progresses.
Distinct recruitment of dorsomedial and dorsolateral striatum erodes with extended training

Hypotheses of striatal orchestration of behavior ascribe distinct functions to striatal subregions, with the dorsolateral striatum (DLS) especially implicated in habitual and skilled performance. Thus neural activity patterns recorded from the DLS, but not the dorsomedial striatum (DMS), should be correlated with habitual and automatized performance. Here, we recorded DMS and DLS neural activity in rats during training in a task promoting habitual lever pressing. Despite improving performance across sessions, clear changes in corresponding neural activity patterns were not evident in DMS or DLS during early training. Although DMS and DLS activity patterns were distinct during early training, their activity was similar following extended training. Finally, performance after extended training was not associated with DMS disengagement, as would be predicted from prior work. These results suggest that behavioral sequences may continue to engage both striatal regions long after initial acquisition, when skilled performance is consolidated.
Drug-release system of microchannel transport used in minimally invasive surgery for hemostasis

Background: Sucrose allyl ether (SAE) containing hemostatic drugs and a photoinitiator was established to treat mild postpartum hemorrhage or long-term continuous abnormal uterine bleeding in minimally invasive surgery (MIS) using a photopolymerization method. Real-time infrared spectroscopy and rheological experiments showed that the SAE monomer with shear-thinning characteristics could polymerize rapidly into a transparent membrane. Cytotoxicity experiments in vitro showed that this system could elicit a long-term hemostatic effect. Tissue adhesion was also evaluated. The photo-stability of four delivered antifibrinolytic drugs (6-aminocaproic acid, ethylenediaminediacetic acid, tranexamic acid and p-(aminomethyl) benzoic acid) was tested by ultraviolet-photolysis experiments and illustrated by time-dependent density functional theory. Sustained-release experiments revealed that the formed film could be used as a drug carrier. Molecular docking and molecular dynamics were done to investigate the binding mechanism between hemostatic drugs as ligands and the human plasminogen kringle-1 (1HPK) as a target. Conclusion: It has been suggested that SAE with tranexamic acid could be a drug-release system of microchannel transport used in MIS. This system could tackle the dilemma of fluidity and adhesion in MIS. The photo-stable tranexamic acid was the most suitable drug according to its satisfactory binding energy, good photo-stability, and sustained release.
Optimized machine learning algorithm for intrusion detection

Intrusion detection is mainly achieved by using optimization algorithms. The need for optimization algorithms for intrusion detection is necessitated by the increasing number of features in audit data, as well as the performance failure of the human-based smart intrusion detection system (IDS) in terms of their prolonged training time and classification accuracy. This article presents an improved intrusion detection technique for binary classification. The proposal is a combination of different optimizers, including Rao optimization algorithm, extreme learning machine (ELM), support vector machine (SVM), and logistic regression (LR) (for feature selection & weighting), as well as a hybrid Rao-SVM algorithm with supervised machine learning (ML) techniques for feature subset selection (FSS). The process of selecting the least number of features without sacrificing the FSS accuracy was considered a multi-objective optimization problem. The algorithm-specific, parameter-less concept of the proposed Rao-SVM was also explored in this study. The KDDCup 99 and CICIDS 2017 were used as the intrusion dataset for the experiments, where significant improvements were noted with the new Rao-SVM compared to the other algorithms. Rao-SVM presented better results than many existing works by reaching 100% accuracy for KDDCup 99 dataset and 97% for CICIDS dataset.
Percutaneous coronary intervention vs. coronary artery bypass graft surgery for unprotected left main coronary artery disease in the drug-eluting stents era--an aggregate data meta-analysis of 11,148 patients.

BACKGROUND Patients with unprotected left main coronary artery (LMCA) disease are increasingly treated with percutaneous coronary intervention (PCI) using drug-eluting stents (DES), but its benefits compared with coronary artery bypass grafting (CABG) remain controversial. We hypothesized that PCI with DES for unprotected LMCA disease is safe and effective compared with CABG. METHODS AND RESULTS We performed aggregate data meta-analyses of clinical outcomes [death; non-fatal myocardial infarction (MI); stroke; repeat revascularization; and major adverse cardiac and cerebrovascular events (MACCE)] in studies comparing PCI with DES vs. CABG in patients with LMCA disease. A comprehensive literature search (01/01/2003 to 12/01/2011) identified 27 studies comparing PCI and CABG (11,148 patients). Summary odds ratios (OR) were calculated using a random-effects model. At 30 days, PCI for unprotected LMCA disease was associated with lower MACCE [odds ratio (OR) 0.57, 95% confidence interval (CI) 0.36-0.89) and stroke rates (OR 0.22, 95% CI 0.11-0.44) compared with CABG. At 12 months, the PCI group experienced higher rates of repeat revascularization (OR 3.72, 95% CI 2.75-5.03), but lower rates of stroke (OR 0.25, 95% CI 0.14-0.44) and all-cause death (OR 0.69, 95% CI 0.49-0.97). At the longest follow-up of 60 months, PCI was associated with equivalent mortality, lower rates of stroke (OR 0.42, 95% CI 0.28-0.62) and higher rates of MACCE (OR 1.30, 95% CI 1.10-1.55) and repeat revascularization (OR 3.54, 95% CI 2.75-4.54). CONCLUSIONS In the DES era, PCI for unprotected LMCA disease is associated with equivalent mortality and MI, lower stroke rates and higher rates of repeat revascularization compared with CABG.
Collaboration between a human group and artificial intelligence can improve prediction of multiple sclerosis course: a proof-of-principle study [version 2; peer review: 1 approved, 2 approved with reservations]

Background: Multiple sclerosis has an extremely variable natural course. In most patients, disease starts with a relapsing-remitting (RR) phase, which proceeds to a secondary progressive (SP) form. The duration of the RR phase is hard to predict, and to date predictions on the rate of disease progression remain suboptimal. This limits the opportunity to tailor therapy on an individual patient's prognosis, in spite of the choice of several therapeutic options. Approaches to improve clinical decisions, such as collective intelligence of human groups and machine learning algorithms are widely investigated. Methods: Medical students and a machine learning algorithm predicted the course of disease on the basis of randomly chosen clinical records of patients that attended at the Multiple Sclerosis service of Sant'Andrea hospital in Rome. Results: A significant improvement of predictive ability was obtained when predictions were combined with a weight that depends on the consistence of human (or algorithm) forecasts on a given clinical record. Conclusions: In this work we present proof-of-principle that humanmachine hybrid predictions yield better prognoses than machine learning algorithms or groups of humans alone. To strengthen and Open Peer Review
Discriminative Tandem Features for HMM-based EEG Classification

We investigate the use of discriminative feature extractors in tandem configuration with generative EEG classification system. Existing studies on dynamic EEG classification typically use hidden Markov models (HMMs) which lack discriminative capability. In this paper, a linear and a non-linear classifier are discriminatively trained to produce complementary input features to the conventional HMM system. Two sets of tandem features are derived from linear discriminant analysis (LDA) projection output and multilayer perceptron (MLP) class-posterior probability, before appended to the standard autoregressive (AR) features. Evaluation on a two-class motor-imagery classification task shows that both the proposed tandem features yield consistent gains over the AR baseline, resulting in significant relative improvement of 6.2% and 11.2% for the LDA and MLP features respectively. We also explore portability of these features across different subjects.
Strength pareto evolutionary algorithm based gene subset selection

Microarray gene expression data is voluminous and very few genes in the dataset are informative for disease analysis. Selecting those genes from the whole dataset is a very challenging task. There are many optimization techniques used by the researchers for gene subset selection but none of them provides global optimal solution for all gene datasets. In the paper, we have proposed a strength pareto evolutionary algorithm based gene subset selection technique to select the informative gene subset for analyzing and identifying the disease efficiently. It is a multi-objective optimization algorithm that provides a non-dominated pareto front exploring the search space to obtain an optimal gene subset. The external cluster validation index and number of genes in a sample are considered as two objective functions of the algorithm and based on this two objective functions the chromosomes in the population are evaluated and after the convergence of the algorithm, chromosomes in the non-dominated pareto front gives the important gene subset. The experimental result on selected gene subset proves the usefulness of the method.
Unsupervised anomaly detection using an evolutionary extension of k-means algorithm

In this paper, we propose a new unsupervised anomaly detection framework for network intrusions. The framework consists of a new clustering algorithm named I-means and new anomalousness metrics named IP Weights. I-means is an evolutionary extension of k means algorithm that estimates automatically the number of clusters for a set of data. IP Weights allow the automatic conversion of regular packet features into a 3-dimensional numerical feature space. Online and offline evaluations show not only strong detection effectiveness, but also strong runtime efficiency, with response times falling within a few seconds ranges.
Ecological Stoichiometry: What Role Does it Play in the Competition and Spatial Distribution Patterns of <i>Aedes Albopictus</i> and <i>Aedes Aegypti</i>?

ii ACKNOWLEDGMENTS . iii LIST OF TABLES . vi LIST OF ILLUSTRATIONS . vii CHAPTER I – INTRODUCTION . 1 1.1 Ecological Stoichiometry . 1 1.2 Study Organisms . 4 1.3 Container Systems . 5 CHAPTER II – EXPERIMENT 1: CEMETERY VASES . 8 2.
Smartphones in Mental Health : Detecting Depressive and Manic Episodes

As we study human nature, we’re realizing that successful disease treatment outcomes depend on incorporating individuals’ phenotype into the treatment model. This realization has given rise to personalized medicine, which aims to integrate data on the genetic makeup of individuals as well as environmental and lifestyle factors. Personalized medicine will greatly benefit from genomics [1], but to build a complete picture of individuals’ phenotype, we also need to better understand environmental and behavioral factors. Specifically, behavioral data will have a significant impact on our understanding of mental disorders. Because symptoms of most mental disorders are manifested as changes in an individual’s behavior, analyzing such changes could lead to a better understanding of these types of diseases and possible treatments. Given that we don’t yet have strong biomarkers for diagnosing most mental disorders, behavior evaluation based on subjective information remain the primary means of diagnosis. Recently, behavior understanding has been given a boost from rapid increases in the capabilities and miniaturization of sensing devices. Specifically, the evolution of smartphones from communication-centric devices toward devices with multimodal sensing capabilities promises to provide unprecedented insights into human behavior (see the “Smartphone Sensing” sidebar for more information). Motivated by the advantages of using smartphones for behavior monitoring in general and monitoring of symptoms of neurological disorders in particular, my collaborators and I from the MONARCA project (http://www.monarca-project.eu) carried out an observational study with patients diagnosed with bipolar disorder. Bipolar disorder is a common and severe form of mental illness characterized by repeated relapses of mania and depression. Patients suffering from the disorder thus can experience—often in rapid succession—periods of manic, normal, and depressive states. The current standard for diagnosing bipolar disorder uses subjective clinical rating scales based on selfreports. Scales such as the Hamilton Depression Rating Scale (HAMD) were developed in the early 1960s, and there are also more recent variations, such as the Bipolar Spectrum Diagnostic Scale (BSDS). Although the efficacy of these scales has been proven in diagnosing bipolar disorder, they have their drawbacks because of the potential for subjectivity in the diagnosis. To address this issue, the aim of our study was to investigate whether data from smartphone sensors could be used to recognize bipolar disorder episodes and to detect behavior changes that could signal the onset of an episode using objective, sensor data.
Probing the Binding Pathway of BRACO19 to a Parallel-Stranded Human Telomeric G-Quadruplex Using Molecular Dynamics Binding Simulation with AMBER DNA OL15 and Ligand GAFF2 Force Fields

Human telomeric DNA G-quadruplex has been identified as a good therapeutic target in cancer treatment. G-quadruplex-specific ligands that stabilize the G-quadruplex have great potential to be developed as anticancer agents. Two crystal structures (an apo form of parallel stranded human telomeric G-quadruplex and its holo form in complex with BRACO19, a potent G-quadruplex ligand) have been solved, yet the binding mechanism and pathway remain elusive. In this study, we simulated the binding of a free BRACO19 molecule to the apo form of the G-quadruplex using the latest AMBER DNA (OL15) and ligand (GAFF2) force fields. Three binding modes have been identified: top stacking, bottom intercalation, and groove binding. Bottom intercalation (51% of the population) resembles the bottom binding pose in the complex crystal structure very well. The groove binding mode is less stable than the bottom binding mode and is likely to be an intermediate state leading to the bottom binding mode. A flip-insertion mechanism was observed in the bottom intercalation mode, during which flipping of the bases outward makes space for ligand insertion, after which the bases flip back to increase the stability of the complex. In addition to reproducing the base-flipping behavior for some loop residues upon ligand binding, the direct alignment type of the ATAT-tetrad was observed in our simulations for the first time. These successes provide initial support for using this combination of the OL15 and GAFF2 force fields to study quadruplex-ligand interactions.
Single-cell RNA-sequencing of differentiating iPS cells reveals dynamic genetic effects on gene expression

Recent developments in stem cell biology have enabled the study of cell fate decisions in early human development that are impossible to study in vivo. However, understanding how development varies across individuals and, in particular, the influence of common genetic variants during this process has not been characterised. Here, we exploit human iPS cell lines from 125 donors, a pooled experimental design, and single-cell RNA-sequencing to study population variation of endoderm differentiation. We identify molecular markers that are predictive of differentiation efficiency, and utilise heterogeneity in the genetic background across individuals to map hundreds of expression quantitative trait loci that influence expression dynamically during differentiation and across cellular contexts.
Elevated DNA methylation across a 48kb region spanning the HOXA gene cluster is 2 associated with Alzheimer’s disease neuropathology.

methylated (DMP); Differentially methylated region (DMR); Down Entorhinal cortex (EC); Epigenome-wide association study (EWAS); Illumina Infinium Human Methylation 450K BeadChip (450K array); Prefrontal cortex (PFC); control (QC); Reference Sequence (RefSeq); Single nucleotide polymorphism (SNP); Superior temporal gyrus (STG) Abstract 3 INTRODUCTION: Alzheimer’s disease is a neurodegenerative disorder that is hypothesized 4 to involve epigenetic dysregulation of gene expression in the brain. 5 METHODS: We performed an epigenome-wide association study to identify differential 6 DNA methylation associated with neuropathology in prefrontal cortex and superior temporal 7 gyrus samples from 147 individuals, replicating our findings in two independent datasets 8 (N=117 and 740). 9 RESULTS: We identify elevated DNA methylation associated with neuropathology across a 10 48kb region spanning 208 CpG sites within the HOXA gene cluster. A meta-analysis of the 11 top-ranked probe within the HOXA3 gene (cg22962123) highlighted significant 12 hypermethylation across all three cohorts ( P = 3.11 X 10 -18 ). 13 DISCUSSION: We present robust evidence for elevated DNA methylation associated with 14 Alzheimer’s disease neuropathology spanning the HOXA gene cluster on chromosome 7. 15 These data add to the growing evidence highlighting a role for epigenetic variation in 16 Alzheimer’s disease, implicating the HOX gene family as a target for future investigation. 17 18 19