Big Data Research in the Tourism Industry

2020 ◽  
Vol 11 (4) ◽  
pp. 26-41
Author(s):  
Imadeddine Mountasser ◽  
Brahim Ouhbi ◽  
Bouchra Frikh ◽  
Ferdaous Hdioud
Keyword(s):  
Big Data ◽  
State Of The Art ◽  
Tourism Industry ◽  
Data Sources ◽  
Digital Data ◽  
Current State ◽  
Wide Range ◽  
The World ◽  
Data Acquisition Process

Nowadays, people and things are becoming permanently interconnected. This interaction overloaded the world with an incredible digital data deluge—termed big data—generated from a wide range of data sources. Indeed, big data has invaded the domain of tourism as a source of innovation that serves to better understand tourists' behavior and enhance tourism destination management and marketing. Thus, tourism stakeholders have increasingly leveraging tourism-related big data sources to gather abundant information concerning all tourism industry axes. However, big data has several complexity aspects and brings commensurate challenges that go along with its exploitation. It has specifically changed the way data is acquired and managed, which may influence the nature and the quality of the conducted analyses and the made decisions. Thus, this article investigates the big data acquisition process and thoroughly identifies its challenges and requirements. It also reveals its current state-of-the-art protocols and frameworks.

scholarly journals Modern neurooncology – the development of the field and the possibilities of glioma therapy in Warsaw – a neurosurgical perspective

2019 ◽  
Vol 2019 (30) ◽  
pp. 135-141
Author(s):  
Bartosz Czapski ◽  
Adam Warzecha ◽  
Wojciech Górecki ◽  
Tomasz Wójcikiewicz ◽  
Mirosław Ząbek
Keyword(s):  
Brain Tumors ◽  
Brain Cancer ◽  
State Of The Art ◽  
Clinical Problem ◽  
Complex Nature ◽  
Scientific Cooperation ◽  
Malignant Brain Tumors ◽  
Current State ◽  
Wide Range

This article is devoted to discussing the possibility of treating malignant brain tumors in the Mazovia region. The difficult clinical problem confronted by doctors and scientists is discussed in relation to the complex nature of gliomas. The current state-of-the-art treatment along with its limitations is subjected to discussion. The opportunities available to Mazovian doctors, which can be used to significantly prolong and improve the quality of patients’ lives, are presented in detail. Finally, the paper presents the wide range of possibilities for scientific cooperation and the directions it should take in order to learn more about the genesis of brain cancer and make it curable.

scholarly journals Protocol adaptations to conduct Systematic Literature Reviews in Software Engineering: a chronological study

2015 ◽  
Vol 35 (3) ◽  
pp. 84-91
Author(s):  
Samuel Sepúlveda ◽  
Ania Cravero Leal
Keyword(s):  
Software Engineering ◽  
Literature Search ◽  
Data Sources ◽  
Digital Data ◽  
Current Status ◽  
Systematic Literature Search ◽  
Exclusion Criteria ◽  
Current State ◽  
Literature Reviews

<div class="page" title="Page 1"><div class="section"><div class="layoutArea"><div class="column"><p><span>Systematic literature reviews (SLR) have reached a considerable level of adoption in Software Engineering (SE), how-ever protocol adaptations for its implementation remain tangentially addressed. This work provides a chronological framework for the use and adaptation of the SLR protocol, including its current status. A systematic literature search was performed, reviewing a set of twelve articles being selected in accordance with the inclusion and exclusion criteria between 2004 and 2013, using digital data sources recognized by the SE community. A chronological framework is provided that includes the current state of the protocol adaptations to conduct SLR in SE. The results indicate areas where the quantity and quality of investigations needs to be increased and the identi- fication of the main proposals providing adaptations for the protocol conducting SLR in SE. </span></p></div></div></div></div>

scholarly journals Playing (with) Democracy: A Review of Gamified Participation Approaches

2016 ◽  
Vol 8 (3) ◽  
pp. 32-60 ◽  
Author(s):  
Sarah-Kristin Thiel ◽  
Michaela Reisinger ◽  
Kathrin Röderer ◽  
Peter Fröhlich
Keyword(s):  
Public Participation ◽  
State Of The Art ◽  
Current State ◽  
Wide Range ◽  
The World ◽  
To Come ◽  
Game Elements

Albeit a wide range of e-participation platforms being already available, the level of public participation remains low. Governments around the world as well as academia are currently exploring new ways to design participation methods that are more engaging to use and will foster participation. One of the strategies is gamification. By adding game elements to e-participation platforms it is hoped to motivate for citizens to engage. This paper reviewed a large number of e-participation platforms, seeking to provide an overview of the current state of the art of so-called gamified participation initiatives. Our results show that while about half of the review projects can be categorized as game-related, only a small amount employs gamification. Moreover, current gamified participation initiatives seem to focus on reward-based gamification, a strategy which is said to come with risks. In this paper we further provide recommendations for future gamified participation projects.

scholarly journals Smoothing additive manufactured parts using ns-pulsed laser radiation

2021 ◽  
Author(s):  
Florian Kuisat ◽  
Fernando Lasagni ◽  
Andrés Fabián Lasagni
Keyword(s):  
Surface Roughness ◽  
Mechanical Performance ◽  
State Of The Art ◽  
Pulsed Laser ◽  
Industrial Applications ◽  
Laser Source ◽  
Pulsed Laser Radiation ◽  
Current State ◽  
The Impact

AbstractIt is well known that the surface topography of a part can affect its mechanical performance, which is typical in additive manufacturing. In this context, we report about the surface modification of additive manufactured components made of Titanium 64 (Ti64) and Scalmalloy®, using a pulsed laser, with the aim of reducing their surface roughness. In our experiments, a nanosecond-pulsed infrared laser source with variable pulse durations between 8 and 200 ns was applied. The impact of varying a large number of parameters on the surface quality of the smoothed areas was investigated. The results demonstrated a reduction of surface roughness Sa by more than 80% for Titanium 64 and by 65% for Scalmalloy® samples. This allows to extend the applicability of additive manufactured components beyond the current state of the art and break new ground for the application in various industrial applications such as in aerospace.

scholarly journals Ensemble-Based Out-of-Distribution Detection

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 567
Author(s):  
Donghun Yang ◽  
Kien Mai Mai Ngoc ◽  
Iksoo Shin ◽  
Kyong-Ha Lee ◽  
Myunggwon Hwang
Keyword(s):  
Detection Method ◽  
State Of The Art ◽  
Metric Learning ◽  
Feature Space ◽  
Confidence Score ◽  
Distance Metric Learning ◽  
Current State ◽  
Overall Performance ◽  
Deep Learning Model

To design an efficient deep learning model that can be used in the real-world, it is important to detect out-of-distribution (OOD) data well. Various studies have been conducted to solve the OOD problem. The current state-of-the-art approach uses a confidence score based on the Mahalanobis distance in a feature space. Although it outperformed the previous approaches, the results were sensitive to the quality of the trained model and the dataset complexity. Herein, we propose a novel OOD detection method that can train more efficient feature space for OOD detection. The proposed method uses an ensemble of the features trained using the softmax-based classifier and the network based on distance metric learning (DML). Through the complementary interaction of these two networks, the trained feature space has a more clumped distribution and can fit well on the Gaussian distribution by class. Therefore, OOD data can be efficiently detected by setting a threshold in the trained feature space. To evaluate the proposed method, we applied our method to various combinations of image datasets. The results show that the overall performance of the proposed approach is superior to those of other methods, including the state-of-the-art approach, on any combination of datasets.

scholarly journals Introduction to redundancy rules: the continuous wavelet transform comes of age

2018 ◽  
Vol 376 (2126) ◽  
pp. 20170258 ◽  
Author(s):  
Paul S. Addison
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
State Of The Art ◽  
Structural Characteristics ◽  
Continuous Wavelet ◽  
Theme Issue ◽  
Current State ◽  
Analysis Methods ◽  
Wide Range ◽  
New Form

Redundancy: it is a word heavy with connotations of lacking usefulness. I often hear that the rationale for not using the continuous wavelet transform (CWT)—even when it appears most appropriate for the problem at hand—is that it is ‘redundant’. Sometimes the conversation ends there, as if self-explanatory. However, in the context of the CWT, ‘redundant’ is not a pejorative term, it simply refers to a less compact form used to represent the information within the signal. The benefit of this new form—the CWT—is that it allows for intricate structural characteristics of the signal information to be made manifest within the transform space, where it can be more amenable to study: resolution over redundancy. Once the signal information is in CWT form, a range of powerful analysis methods can then be employed for its extraction, interpretation and/or manipulation. This theme issue is intended to provide the reader with an overview of the current state of the art of CWT analysis methods from across a wide range of numerate disciplines, including fluid dynamics, structural mechanics, geophysics, medicine, astronomy and finance. This article is part of the theme issue ‘Redundancy rules: the continuous wavelet transform comes of age’.

Sir Charles Parsons and Electrical Power Generation—a Turbine Designer's Perspective

1994 ◽  
Vol 208 (3) ◽  
pp. 159-176 ◽  
Author(s):  
J R Bolter
Keyword(s):  
Power Generation ◽  
Steam Turbine ◽  
Gas Turbine ◽  
Electrical Power Generation ◽  
Analytical Methods ◽  
State Of The Art ◽  
Electrical Power ◽  
Turbine Generator ◽  
Current State ◽  
The World

Sir Charles Parsons died some three years after the author was born. In this paper the author looks back at the pioneering work of Parsons in the field of power generation. It shows how he was able to increase output of the steam turbine generator from 7.5 kW in 1884 to 50000 kW in 1930 while increasing efficiency from 1.6 to 36 per cent, and relates these achievements to the current state of the art. Blading design, rotor construction and other aspects of turbine engineering are considered. The conclusion is that Parsons and his associates charted the course which manufacturers and utilities throughout the world have continued to follow, although increasingly sophisticated design and analytical methods have succeeded the intuitive approach of Parsons. His constant search for improved efficiency was and is highly relevant to today's concern for the environment. Finally, although it did not become a practical proposition in his lifetime, the paper reviews Parsons' vision of, and continuing interest in, the gas turbine, first mentioned in his 1884 patents.

scholarly journals Echinococcus spp.: Tapeworms that Pose a Danger to Both Animals and Humans – a Review

2017 ◽  
Vol 48 (4) ◽  
pp. 193-201 ◽  
Author(s):  
A. Brožová ◽  
I. Jankovská ◽  
V. Bejček ◽  
S. Nechybová ◽  
P. Peřinková ◽  
...  
Keyword(s):  
Small Intestine ◽  
Life Cycle ◽  
Echinococcus Multilocularis ◽  
Alveolar Echinococcosis ◽  
Sensu Stricto ◽  
Mammal Species ◽  
Current State ◽  
Wide Range ◽  
The World ◽  
Echinococcus Spp

Abstract Species of the genus Echinococcus (Cestoda; Taeniidae) are minute tapeworms of carnivores. Their larvae are known as hydatids (metacestode), which proliferate asexually in various mammals. Like the majority of cestodes, Echinococcus spp. require two different host species to complete their life cycle. Definitive hosts harbouring the adult cestodes in the small intestine are exclusively carnivores of the Canidae and Felidae families. A wide range of mammal species including humans is susceptible to infection by the metacestode of Echinococcus spp., which develops in their viscera. The disease, caused by species of the genus Echinococcus, is called echinococcosis, and it is one of the most dangerous zoonoses in the world. The traditional species Echinococcus granulosus and Echinococcus multilocularis are agents of significant diseases due to the high number of cases and the wide geographical species range. The taxonomy of the genus is controversial; in the current state of ongoing complex revisions, the agent of cystic echinococcosis E. granulosus sensu lato is divided into five species (E. granulosus sensu stricto, E. felidis, E. equinus, E. ortleppi, E. canadensis), in addition to the agents of alveolar echinococcosis (E. multilocularis, E. shiquicus) and polycystic/unicystic echinococcosis (E. vogeli, E. oligarthrus). Here we provide an overview of the current situation, which continues to develop.

scholarly journals DeepMAsED: evaluating the quality of metagenomic assemblies

2020 ◽  
Vol 36 (10) ◽  
pp. 3011-3017 ◽  
Author(s):  
Olga Mineeva ◽  
Mateo Rojas-Carulla ◽  
Ruth E Ley ◽  
Bernhard Schölkopf ◽  
Nicholas D Youngblut
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Ground Truth ◽  
Supplementary Information ◽  
Learning Approach ◽  
Wide Range ◽  
Metagenome Assembly ◽  
Model Training ◽  
Reference Genomes

Abstract Motivation Methodological advances in metagenome assembly are rapidly increasing in the number of published metagenome assemblies. However, identifying misassemblies is challenging due to a lack of closely related reference genomes that can act as pseudo ground truth. Existing reference-free methods are no longer maintained, can make strong assumptions that may not hold across a diversity of research projects, and have not been validated on large-scale metagenome assemblies. Results We present DeepMAsED, a deep learning approach for identifying misassembled contigs without the need for reference genomes. Moreover, we provide an in silico pipeline for generating large-scale, realistic metagenome assemblies for comprehensive model training and testing. DeepMAsED accuracy substantially exceeds the state-of-the-art when applied to large and complex metagenome assemblies. Our model estimates a 1% contig misassembly rate in two recent large-scale metagenome assembly publications. Conclusions DeepMAsED accurately identifies misassemblies in metagenome-assembled contigs from a broad diversity of bacteria and archaea without the need for reference genomes or strong modeling assumptions. Running DeepMAsED is straight-forward, as well as is model re-training with our dataset generation pipeline. Therefore, DeepMAsED is a flexible misassembly classifier that can be applied to a wide range of metagenome assembly projects. Availability and implementation DeepMAsED is available from GitHub at https://github.com/leylabmpi/DeepMAsED. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Mosaic Super-resolution via Sequential Feature Pyramid Networks

2019 ◽  
Author(s):  
Mehrdad Shoeiby ◽  
Mohammad Ali Armin ◽  
Sadegh Aliakbarian ◽  
Saeed Anwar ◽  
Lars petersson
Keyword(s):  
State Of The Art ◽  
Super Resolution ◽  
Autonomous Driving ◽  
Single Shot ◽  
Current State ◽  
Wide Range ◽  
Feature Pyramid ◽  
Novel Method ◽  
Convolutional Lstm ◽  
Mosaic Images

<div>Advances in the design of multi-spectral cameras have</div><div>led to great interests in a wide range of applications, from</div><div>astronomy to autonomous driving. However, such cameras</div><div>inherently suffer from a trade-off between the spatial and</div><div>spectral resolution. In this paper, we propose to address</div><div>this limitation by introducing a novel method to carry out</div><div>super-resolution on raw mosaic images, multi-spectral or</div><div>RGB Bayer, captured by modern real-time single-shot mo-</div><div>saic sensors. To this end, we design a deep super-resolution</div><div>architecture that benefits from a sequential feature pyramid</div><div>along the depth of the network. This, in fact, is achieved</div><div>by utilizing a convolutional LSTM (ConvLSTM) to learn the</div><div>inter-dependencies between features at different receptive</div><div>fields. Additionally, by investigating the effect of different</div><div>attention mechanisms in our framework, we show that a</div><div>ConvLSTM inspired module is able to provide superior at-</div><div>tention in our context. Our extensive experiments and anal-</div><div>yses evidence that our approach yields significant super-</div><div>resolution quality, outperforming current state-of-the-art</div><div>mosaic super-resolution methods on both Bayer and multi-</div><div>spectral images. Additionally, to the best of our knowledge,</div><div>our method is the first specialized method to super-resolve</div><div>mosaic images, whether it be multi-spectral or Bayer.</div><div><br></div>

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