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.

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 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 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>

scholarly journals Climate-dependent propagation of precipitation uncertainty into the water cycle

2020 ◽  
Author(s):  
Ali Fallah ◽  
Sungmin O ◽  
Rene Orth
Keyword(s):  
Hydrological Model ◽  
Water Cycle ◽  
State Of The Art ◽  
Rain Gauge ◽  
Multiple Sources ◽  
Current State ◽  
Wide Range ◽  
The Impact ◽  
Conceptual Hydrological Model

Abstract. Precipitation is a crucial variable for hydro-meteorological applications. Unfortunately, rain gauge measurements are sparse and unevenly distributed, which substantially hampers the use of in-situ precipitation data in many regions of the world. The increasing availability of high-resolution gridded precipitation products presents a valuable alternative, especially over gauge-sparse regions. Nevertheless, uncertainties and corresponding differences across products can limit the applicability of these data. This study examines the usefulness of current state-of-the-art precipitation datasets in hydrological modelling. For this purpose, we force a conceptual hydrological model with multiple precipitation datasets in > 200 European catchments. We consider a wide range of precipitation products, which are generated via (1) interpolation of gauge measurements (E-OBS and GPCC V.2018), (2) combination of multiple sources (MSWEP V2) and (3) data assimilation into reanalysis models (ERA-Interim, ERA5, and CFSR). For each catchment, runoff and evapotranspiration simulations are obtained by forcing the model with the various precipitation products. Evaluation is done at the monthly time scale during the period of 1984–2007. We find that simulated runoff values are highly dependent on the accuracy of precipitation inputs, and thus show significant differences between the simulations. By contrast, simulated evapotranspiration is generally much less influenced. The results are further analysed with respect to different hydro-climatic regimes. We find that the impact of precipitation uncertainty on simulated runoff increases towards wetter regions, while the opposite is observed in the case of evapotranspiration. Finally, we perform an indirect performance evaluation of the precipitation datasets by comparing the runoff simulations with streamflow observations. Thereby, E-OBS yields the best agreement, while furthermore ERA5, GPCC V.2018 and MSWEP V2 show good performance. In summary, our findings highlight a climate-dependent propagation of precipitation uncertainty through the water cycle; while runoff is strongly impacted in comparatively wet regions such as Central Europe, there are increasing implications on evapotranspiration towards drier regions.

scholarly journals Enhanced Web Log Data Mining using Probability Density Based Fuzzy C Means Clustering

2020 ◽  
Vol 8 (6) ◽  
pp. 2933-2939
Keyword(s):  
World Wide ◽  
Government Agencies ◽  
Fcm Algorithm ◽  
Web Log ◽  
Current State ◽  
The World ◽  
Fuzzy C Means Clustering ◽  
Mass Group ◽  
To Come ◽  
The Right

The World Wide the net is very great place where things are stored and it is growing exponentially. It has in it sizeable amount of news given which is growing and bringing up to the current state quickly. Different organizations, institutes, government agencies and support centers bring up to the current state their news given regularly. The World Wide the net provides its services to the ranges of the net users. The net users may have different interests, needs and back knowledge. Clustering into groups is one of the most important tasks in the action-bound areas of the net record mining. It says without any doubt to grip the trouble of news given over-weight on the net while many users are connected on the meeting thing by which something is done. Clustering into groups is made use of for grouping news given into by comparison way in design for making discovery of person for whom one does work interest. There are two bad points of FCM algorithm, firstly the requirements of no. of clusters C and secondly giving to the first relation matrix. Because of, in relation to these two bad points the FCM algorithm is hard to come to a decision about the right no. of mass, group and this algorithm is unsafe. The strong decision of desirable first stage mass, group is an important hard question, therefore a new expert way called PDFCM algorithm is made, was moving in.

scholarly journals COMMERCIALIZATION OF CARBON CREDITS ON SMALL PROPERTIES

2019 ◽  
Vol 7 (7) ◽  
pp. 156-166
Author(s):  
Jadson Belem De Moura ◽  
Rodrigo Fernandes De Souza ◽  
José Mateus Dos Santos ◽  
Luiz Cesar Lopes Filho ◽  
Willian Marques Pires ◽  
...  
Keyword(s):  
State Of The Art ◽  
Ghg Emissions ◽  
Carbon Credits ◽  
Major Powers ◽  
Average Temperature ◽  
Current State ◽  
Small Producer ◽  
The World ◽  
Reduce Emissions ◽  
Carbon Dioxide Co2

Today the world has turned its attention to the phenomena of climate change on the planet. Excess emissions of greenhouse gases (GHG) from human activity are responsible for this change. Gases such as carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) are part of GHG and have a large capacity to store the energy of the sun in the form of heat, increasing the average temperature in the atmosphere. After the Kyoto treaty, major powers pledged to reduce GHG emissions into the atmosphere, but this practice implies a slowdown in the industrialization and profits of these countries. Countries that can not or do not opt to reduce emissions can buy credits from nations that can generate extra emission reduction values, thus resulting in trade in these credits. Small properties have the potential for credit production, which can generate extra income for the small producer. Therefore, this work aimed at evaluating the potential of commercialization of carbon credits by small properties. The methodology of the work consisted in a systematic literature review on the current state of the art of the proposed theme.

scholarly journals Peritoneal Carcinomatosis: Current State of the Art and Schools of Thought

2021 ◽  
Vol 11 (1) ◽  
pp. 85-91
Author(s):  
Sh. Kh. Gantsev ◽  
M. V. Zabelin ◽  
K. Sh. Gantsev ◽  
A. A. Izmailov ◽  
Sh. R. Kzyrgalin
Keyword(s):  
Peritoneal Carcinomatosis ◽  
State Of The Art ◽  
Global Perspective ◽  
Basic Principles ◽  
Global Challenge ◽  
Current State ◽  
Emerging Therapies ◽  
The World ◽  
The Usa ◽  
Scientific Questions

Peritoneal carcinomatosis (PC) is a global challenge of modern oncology representing the most unfavourable scenario in diverse-locality tumourisation. Despite certain attention by the oncological community, the management of PC patients is currently palliative, which weakly promotes research into the basic principles of this morbidity. This literature review attempts to comprehensively cover the PC problematic from a global perspective and presents a key evidence on the world schools of thought in this area. Briefly taking, peritoneal carcinomatosis is viewed today as a local process in the conventional implantation theory, which imposes a locoregional character on all current or emerging therapies, such as cytoreductive surgery and hyperthermic intraperitoneal chemotherapy. Their inadequate efficacy is largely due to pronounced gaps in our understanding of PC logistics and signalling. PSOGI is a key organisation for centralising the specialty effort in peritoneal carcinomatosis. Despite its global geography and approach to PC discussion, a multitude of scientific questions remain unanswered impeding the establishment of novel effective therapies. The seven countries that nurtured distinguished schools of thought in PC studies are the USA, UK, Japan, China, Italy, France and Germany. Taking peritoneal carcinomatosis in a global perspective, an insufficient attention to its problematic in Russia should be addressed. The founding and fostering of national PC institutions will benefit cancer patients and progress in oncological science.

scholarly journals AI-driven deep CNN approach for multi-label pathology classification using chest X-Rays

2021 ◽  
Vol 7 ◽  
pp. e495
Author(s):  
Saleh Albahli ◽  
Hafiz Tayyab Rauf ◽  
Abdulelah Algosaibi ◽  
Valentina Emilia Balas
Keyword(s):  
Neural Networks ◽  
Data Augmentation ◽  
State Of The Art ◽  
Synthetic Data ◽  
X Rays ◽  
Deep Convolutional Neural Networks ◽  
Current State ◽  
Pathology Classification ◽  
Wide Range ◽  
Multi Class Classification

Artificial intelligence (AI) has played a significant role in image analysis and feature extraction, applied to detect and diagnose a wide range of chest-related diseases. Although several researchers have used current state-of-the-art approaches and have produced impressive chest-related clinical outcomes, specific techniques may not contribute many advantages if one type of disease is detected without the rest being identified. Those who tried to identify multiple chest-related diseases were ineffective due to insufficient data and the available data not being balanced. This research provides a significant contribution to the healthcare industry and the research community by proposing a synthetic data augmentation in three deep Convolutional Neural Networks (CNNs) architectures for the detection of 14 chest-related diseases. The employed models are DenseNet121, InceptionResNetV2, and ResNet152V2; after training and validation, an average ROC-AUC score of 0.80 was obtained competitive as compared to the previous models that were trained for multi-class classification to detect anomalies in x-ray images. This research illustrates how the proposed model practices state-of-the-art deep neural networks to classify 14 chest-related diseases with better accuracy.

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