scholarly journals INVESTIGATING NATURAL TREATMENT FACTORS AND INEQUALITIES OF MEDICINAL WATER INSTITUTIONS IN THE ASPECT OF TOURISM IN HUNGARY

2021 ◽  
Vol 36 (2spl) ◽  
pp. 555-562
Author(s):  
Ágnes Erzsébet HOJCSKA ◽  
◽  
Zoltán SZABÓ ◽  
Keyword(s):  
Medical Tourism ◽  
Secondary Data ◽  
Test Methods ◽  
Spatial Inequality ◽  
Data Set ◽  
Spatial Inequalities ◽  
Natural Treatment ◽  
Wide Range ◽  
Water Institutions ◽  
Medicinal Waters

The aim of the research is to reveal the spatial inequalities of the natural treatment factors in Hungary and the medicinal water institutions built on them, with the help of spatial research methods on the basis of secondary data. Due to the favorable geographical conditions of Central Europe, the Carpathian Basin has a considerable amount of natural resources. With the appreciation of health, in our days they represent significant value because they are becoming increasingly important in tourist services aimed at maintaining and restoring health. In Hungary, there are outstanding opportunities for this in health tourism, which provides a wide range of medical services, including medical tourism based on natural treatment factors. In order to achieve the set research goal, we used the range of the data set (range-ratio), the dispersion range (range), the relative range (relative range) and the dual measure (Éltető–Frigyes-index) as spatial inequality test methods for the geographically based examination of Hungary's natural treatment factors and the system of medicinal water institutions. The research results show that the spatial polarisation of natural treatment factors and the medicinal water institutions based on them show significant inequalities in Hungary. It has been proved that the development of the counties is outstanding in terms of medicinal waters and medicinal bath, and the spatial difference is also the lowest in the case of these treatment factors.

Exploring innovation creation across rural and urban firms

2018 ◽  
Vol 7 (4) ◽  
pp. 357-376 ◽  
Author(s):  
Giri Aryal ◽  
John Mann ◽  
Scott Loveridge ◽  
Satish Joshi
Keyword(s):  
National Survey ◽  
Negative Binomial ◽  
Secondary Data ◽  
Firm Level ◽  
Data Set ◽  
Content Type ◽  
Rural And Urban ◽  
Wide Range ◽  
Rural Firms ◽  
Urban Firms

Purpose The innovation creation literature primarily focuses on urban firms/regions or relies heavily on these data; less studied are rural firms and areas in this regard. The purpose of this paper is to employ a new firm-level data set, national in scale, and analyze characteristics that potentially influence innovation creation across rural and urban firms. Design/methodology/approach The authors use the 2014 National Survey of Business Competitiveness (NSBC) covering multiple firm-level variables related to innovation creation combined with secondary data reflecting the regional business and innovative environments where these firms operate. The number of patent applications filed by these firms measures their innovation creation, and the paper employs a negative binomial regression estimation for analysis. Findings After controlling for industry, county and state factors, rural and urban firms differ in their innovation creation characteristics and behaviors, suggesting that urban firms capitalize on their resources better than rural firms. Other major findings of the paper provide evidence that: first, for rural firms, the influence of university R&D is relevant to innovation creation, but their perception of university-provided information is not significant; and second, rural firms that are willing to try, but fail, in terms of innovation creation have a slight advantage over other rural firms less willing to take on the risk. Originality/value This paper is one of the first to analyze the 2014 NSBC, a firm-level national survey covering a wide range of innovation-related variables. The authors combine it with other regional secondary data, and use appropriate analytical modeling to provide empirical evidence of influencing factors on innovation creation across rural and urban firms.

Demonstration of a Remotely-Controlled Swirl Generator for Simulating Aircraft Inlet Secondary Flows During Turbine Engine Ground Tests

2018 ◽  
Author(s):  
David Beale
Keyword(s):  
Flight Test ◽  
Test Methods ◽  
Secondary Flows ◽  
Engine Test ◽  
Data Set ◽  
Turbine Engine ◽  
Methodology Development ◽  
Engine Control System ◽  
Swirl Generator ◽  
Wide Range

The development of superior combat aircraft demands the complex integration of the airframe, engine, control system, avionics, and on-board weapon systems. The integration of the engine and the inlet is tantamount to prevailing in an engagement due to the thrust required to execute combat maneuvers. For this reason, test and evaluation methods have been developed to help ensure inlet-engine compatibility by design. The most commonly used methodology characterizes inlet distortion in terms of total-pressure descriptors and correlations. The method includes ground tests employing both wind tunnel and engine test facilities, to acquire the information needed to establish inlet-engine compatibility prior to flight test. Advanced aircraft employing evolving technologies never seen in legacy systems have introduced new challenges to the methodology, and to the ground test methods employed by the methodology. One such challenge arises from the significant flow angularity, or swirl, often found in advanced inlet systems. This paper focuses on the simulation of aircraft inlet swirl during direct-connect turbine engine ground tests. To meet the engine test challenges introduced by advanced aircraft, the Arnold Engineering Development Complex (AEDC) embarked on the development of a swirl generator capable of simulating the different types of swirl expected in future inlet systems over a wide range of swirl angles, and with the ability to remotely set steady-state or transient swirl patterns. The development progressed through a five-step process that culminated in the validation and demonstration of a fully-functional prototype. This paper focuses on the prototype swirl generator and the progression from the establishment of simulation requirements through the prototype validation. Following summaries of each development step, the results of the validation test are presented. The paper also summarizes a recent application of the prototype which not only demonstrated the device in an engine test, but which provided a data set to support swirl methodology development.

Pengaruh Reputasi Underwriter, Umur Perusahaan, dan Persentase Penawaran Saham 53 kepada Publik terhadap Underpricing

2016 ◽  
Vol 8 (1) ◽  
pp. 53-74
Author(s):  
Maria Jeanne ◽  
Chermian Eforis
Keyword(s):  
Initial Public Offering ◽  
Stock Price ◽  
Stock Exchange ◽  
Secondary Data ◽  
Secondary Market ◽  
Financial Statement ◽  
Data Set ◽  
Multiple Regression Method ◽  
Underwriter Reputation ◽  
Public Offering

The objective of this research is to obtain empirical evidence about the effect of underwriter reputation, company age, and the percentage of share’s offering to public toward underpricing. Underpricing is a phenomenon in which the current stock price initial public offering (IPO) was lower than the closing price of shares in the secondary market during the first day. Sample in this research was selected by using purposive sampling method and the secondary data used in this research was analyzed by using multiple regression method. The samples in this research were 72 companies conducting initial public offering (IPO) at the Indonesian Stock Exchange in the period January 2010 - December 2014; perform initial offering of shares; suffered underpricing; has a complete data set forth in the company's prospectus, IDX monthly statistics, financial statement and stock price site (e-bursa); and use Rupiah currency. Results of this research were (1) underwriter reputation significantly effect on underpricing; (2) company age do not effect on underpricing; and (3) the percentage of share’s offering to public do not effect on undepricing. Keywords: company age, the percentage of share’s offering to public, underpricing, underwriter reputation.

Structure Guided Molecular Docking Assisted Alignment Dependent 3DQSAR Study on Steroidal Aromatase Inhibitors (SAIs) as Anti-breast Cancer Agents

2019 ◽  
Vol 16 (7) ◽  
pp. 808-817 ◽  
Author(s):  
Laxmi Banjare ◽  
Sant Kumar Verma ◽  
Akhlesh Kumar Jain ◽  
Suresh Thareja
Keyword(s):  
Breast Cancer ◽  
Molecular Docking ◽  
Aromatase Inhibitors ◽  
Direct Synthesis ◽  
3D Qsar ◽  
Chemotherapeutic Agents ◽  
Modeling Tools ◽  
Data Set ◽  
Wide Range ◽  
Steroidal Aromatase Inhibitors

Background: In spite of the availability of various treatment approaches including surgery, radiotherapy, and hormonal therapy, the steroidal aromatase inhibitors (SAIs) play a significant role as chemotherapeutic agents for the treatment of estrogen-dependent breast cancer with the benefit of reduced risk of recurrence. However, due to greater toxicity and side effects associated with currently available anti-breast cancer agents, there is emergent requirement to develop target-specific AIs with safer anti-breast cancer profile. Methods: It is challenging task to design target-specific and less toxic SAIs, though the molecular modeling tools viz. molecular docking simulations and QSAR have been continuing for more than two decades for the fast and efficient designing of novel, selective, potent and safe molecules against various biological targets to fight the number of dreaded diseases/disorders. In order to design novel and selective SAIs, structure guided molecular docking assisted alignment dependent 3D-QSAR studies was performed on a data set comprises of 22 molecules bearing steroidal scaffold with wide range of aromatase inhibitory activity. Results: 3D-QSAR model developed using molecular weighted (MW) extent alignment approach showed good statistical quality and predictive ability when compared to model developed using moments of inertia (MI) alignment approach. Conclusion: The explored binding interactions and generated pharmacophoric features (steric and electrostatic) of steroidal molecules could be exploited for further design, direct synthesis and development of new potential safer SAIs, that can be effective to reduce the mortality and morbidity associated with breast cancer.

Integrative Data Analysis from a Unifying Research Synthesis Perspective

2018 ◽  
Author(s):  
Eun-Young Mun ◽  
Anne E. Ray
Keyword(s):  
Data Analysis ◽  
Large Scale ◽  
Research Synthesis ◽  
Alcohol Intervention ◽  
Data Set ◽  
Integrative Data Analysis ◽  
Level Data ◽  
Model Complex ◽  
Wide Range ◽  
Individual Participant

Integrative data analysis (IDA) is a promising new approach in psychological research and has been well received in the field of alcohol research. This chapter provides a larger unifying research synthesis framework for IDA. Major advantages of IDA of individual participant-level data include better and more flexible ways to examine subgroups, model complex relationships, deal with methodological and clinical heterogeneity, and examine infrequently occurring behaviors. However, between-study heterogeneity in measures, designs, and samples and systematic study-level missing data are significant barriers to IDA and, more broadly, to large-scale research synthesis. Based on the authors’ experience working on the Project INTEGRATE data set, which combined individual participant-level data from 24 independent college brief alcohol intervention studies, it is also recognized that IDA investigations require a wide range of expertise and considerable resources and that some minimum standards for reporting IDA studies may be needed to improve transparency and quality of evidence.

Immiserizing Growth

2019 ◽  
Keyword(s):  
Political Economy ◽  
Secondary Data ◽  
Household Survey ◽  
Data Set ◽  
Republic Of China ◽  
The Poor ◽  
Poverty Measures ◽  
Entry Points ◽  
Country Analysis ◽  
Cross Country

Immiserizing Growth occurs when growth fails to benefit, or harms, those at the bottom. It is not a new concept, appearing such figures as Malthus, Ricardo and Marx. It is also not empirically insignificant, occurring in between 10% and 35% of cases, depending on the data set and the growth and poverty measures used. In spite of this, it has not received its due attention in the academic literature, dominated by the prevailing narrative that ‘growth is good for the poor’. The chapters in this volume aim to arrive at a better understanding of when, why and how growth fails the poor. They combine discussion of mechanisms of Immiserizing Growth with empirical data on trends in growth, poverty and related welfare indicators. In terms of mechanisms, politics and political economy are chosen as useful entry points to explain IG episodes. The disciplinary focus is diverse, drawing on economics, political economy, applied social anthropology, and development studies. A number of methodological approaches are represented including statistical analysis of household survey and cross-country data, detailed ethnographic work and case study analysis drawing on secondary data. Geographical coverage is wide including Bolivia, the Dominican Republic, Ecuador, India, Indonesia, Mexico, Nigeria, the People’s Republic of China, Singapore, and South Korea, in addition to cross-country analysis. As the first book-length treatment of Immiserizing Growth in the literature, we believe that this volume constitutes an important step in redirecting attention to this issue.

scholarly journals A Self-Spatial Adaptive Weighting Based U-Net for Image Segmentation

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 348
Author(s):  
Choongsang Cho ◽  
Young Han Lee ◽  
Jongyoul Park ◽  
Sangkeun Lee
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Feature Maps ◽  
Data Set ◽  
Feature Map ◽  
Adaptive Weighting ◽  
Spatially Adaptive ◽  
Wide Range ◽  
Decoder Architecture

Semantic image segmentation has a wide range of applications. When it comes to medical image segmentation, its accuracy is even more important than those of other areas because the performance gives useful information directly applicable to disease diagnosis, surgical planning, and history monitoring. The state-of-the-art models in medical image segmentation are variants of encoder-decoder architecture, which is called U-Net. To effectively reflect the spatial features in feature maps in encoder-decoder architecture, we propose a spatially adaptive weighting scheme for medical image segmentation. Specifically, the spatial feature is estimated from the feature maps, and the learned weighting parameters are obtained from the computed map, since segmentation results are predicted from the feature map through a convolutional layer. Especially in the proposed networks, the convolutional block for extracting the feature map is replaced with the widely used convolutional frameworks: VGG, ResNet, and Bottleneck Resent structures. In addition, a bilinear up-sampling method replaces the up-convolutional layer to increase the resolution of the feature map. For the performance evaluation of the proposed architecture, we used three data sets covering different medical imaging modalities. Experimental results show that the network with the proposed self-spatial adaptive weighting block based on the ResNet framework gave the highest IoU and DICE scores in the three tasks compared to other methods. In particular, the segmentation network combining the proposed self-spatially adaptive block and ResNet framework recorded the highest 3.01% and 2.89% improvements in IoU and DICE scores, respectively, in the Nerve data set. Therefore, we believe that the proposed scheme can be a useful tool for image segmentation tasks based on the encoder-decoder architecture.

scholarly journals The IDEAL household energy dataset, electricity, gas, contextual sensor data and survey data for 255 UK homes

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Martin Pullinger ◽  
Jonathan Kilgour ◽  
Nigel Goddard ◽  
Niklas Berliner ◽  
Lynda Webb ◽  
...  
Keyword(s):  
Survey Data ◽  
Energy Demand ◽  
Secondary Data ◽  
Hot Water ◽  
Power Measurement ◽  
Sensor Data ◽  
Energy Awareness ◽  
Household Energy ◽  
Wide Range ◽  
The Ideal

AbstractThe IDEAL household energy dataset described here comprises electricity, gas and contextual data from 255 UK homes over a 23-month period ending in June 2018, with a mean participation duration of 286 days. Sensors gathered 1-second electricity data, pulse-level gas data, 12-second temperature, humidity and light data for each room, and 12-second temperature data from boiler pipes for central heating and hot water. 39 homes also included plug-level monitoring of selected electrical appliances, real-power measurement of mains electricity and key sub-circuits, and more detailed temperature monitoring of gas- and heat-using equipment, including radiators and taps. Survey data included occupant demographics, values, attitudes and self-reported energy awareness, household income, energy tariffs, and building, room and appliance characteristics. Linked secondary data comprises weather and level of urbanisation. The data is provided in comma-separated format with a custom-built API to facilitate usage, and has been cleaned and documented. The data has a wide range of applications, including investigating energy demand patterns and drivers, modelling building performance, and undertaking Non-Intrusive Load Monitoring research.

scholarly journals The smart grid as a security device: Electricity infrastructure and urban governance in Kingston and Rio de Janeiro

Urban Studies ◽  
2021 ◽  
pp. 004209802098571
Author(s):  
Francesca Pilo’
Keyword(s):  
Smart Grid ◽  
Low Income ◽  
Smart Grids ◽  
Rio De Janeiro ◽  
Urban Governance ◽  
Spatial Inequality ◽  
Technical Failure ◽  
Spatial Inequalities ◽  
Urban Security ◽  
Security Device

This article aims to contribute to recent debates on the politics of smart grids by exploring their installation in low-income areas in Kingston (Jamaica) and Rio de Janeiro (Brazil). To date, much of this debate has focused on forms of smart city experiments, mostly in the Global North, while less attention has been given to the implementation of smart grids in cities characterised by high levels of urban insecurity and socio-spatial inequality. This article illustrates how, in both contexts, the installation of smart metering is used as a security device that embeds the promise of protecting infrastructure and revenue and navigating complex relations framed along lines of socio-economic inequalities and urban sovereignty – here linked to configurations of state and non-state (criminal) territorial control and power. By unpacking the political workings of the smart grid within changing urban security contexts, including not only the rationalities that support its use but also the forms of resistance, contestation and socio-technical failure that emerge, the article argues for the importance of examining the conjunction between urban and infrastructural governance, including the reshaping of local power relations and spatial inequalities, through globally circulating devices.

scholarly journals Prediction of Wide Range Two-Dimensional Refractivity Using an IDW Interpolation Method from High-Altitude Refractivity Data of Multiple Meteorological Observatories

2021 ◽  
Vol 11 (4) ◽  
pp. 1431
Author(s):  
Sungsik Wang ◽  
Tae Heung Lim ◽  
Kyoungsoo Oh ◽  
Chulhun Seo ◽  
Hosung Choo
Keyword(s):  
High Altitude ◽  
Korean Peninsula ◽  
Interpolation Method ◽  
Atmospheric Condition ◽  
Propagation Path ◽  
Two Dimensional ◽  
Data Set ◽  
Wide Range ◽  
Atmospheric Data ◽  
Terrain Surface

This article proposes a method for the prediction of wide range two-dimensional refractivity for synthetic aperture radar (SAR) applications, using an inverse distance weighted (IDW) interpolation of high-altitude radio refractivity data from multiple meteorological observatories. The radio refractivity is extracted from an atmospheric data set of twenty meteorological observatories around the Korean Peninsula along a given altitude. Then, from the sparse refractive data, the two-dimensional regional radio refractivity of the entire Korean Peninsula is derived using the IDW interpolation, in consideration of the curvature of the Earth. The refractivities of the four seasons in 2019 are derived at the locations of seven meteorological observatories within the Korean Peninsula, using the refractivity data from the other nineteen observatories. The atmospheric refractivities on 15 February 2019 are then evaluated across the entire Korean Peninsula, using the atmospheric data collected from the twenty meteorological observatories. We found that the proposed IDW interpolation has the lowest average, the lowest average root-mean-square error (RMSE) of ∇M (gradient of M), and more continuous results than other methods. To compare the resulting IDW refractivity interpolation for airborne SAR applications, all the propagation path losses across Pohang and Heuksando are obtained using the standard atmospheric condition of ∇M = 118 and the observation-based interpolated atmospheric conditions on 15 February 2019. On the terrain surface ranging from 90 km to 190 km, the average path losses in the standard and derived conditions are 179.7 dB and 182.1 dB, respectively. Finally, based on the air-to-ground scenario in the SAR application, two-dimensional illuminated field intensities on the terrain surface are illustrated.

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