bluuebunny/crossref_metadata_2025 huggingface dataset:
Improved Prototypical Network Model for Forest Species Classification in Complex Stand
Authors: Tian Xiaomin , Chen Long , Zhang Xiaoli , Chen Erxue
Published: November 2020

Deep learning has become an effective method for hyperspectral image classification. However, the high band correlation and data volume associated with airborne hyperspectral images, and the insufficiency of training samples, present challenges to the application of deep learning in airborne image classification. Prototypical networks are practical deep learning networks that have demonstrated effectiveness in handling small-sample classification. In this study, an improved prototypical network is proposed (by adding L2 regularization to the convolutional layer and dropout to the maximum pooling layer) to address the problem of overfitting in small-sample classification. The proposed network has an optimal sample window for classification, and the window size is related to the area and distribution of the study area. After performing dimensionality reduction using principal component analysis, the time required for training using hyperspectral images shortened significantly, and the test accuracy increased drastically. Furthermore, when the size of the sample window was 27 × 27 after dimensionality reduction, the overall accuracy of forest species classification was 98.53%, and the Kappa coefficient was 0.9838. Therefore, by using an improved prototypical network with a sample window of an appropriate size, the network yielded desirable classification results, thereby demonstrating its suitability for the fine classification and mapping of tree species.
Lossy Compression of Multichannel Remote Sensing Images with Quality Control
Authors: Lukin Vladimir , Vasilyeva Irina , Krivenko Sergey , Li Fangfang , Abramov Sergey , Rubel Oleksii , Vozel Benoit , Chehdi Kacem , Egiazarian Karen
Published: November 2020

Lossy compression is widely used to decrease the size of multichannel remote sensing data. Alongside this positive effect, lossy compression may lead to a negative outcome as making worse image classification. Thus, if possible, lossy compression should be carried out carefully, controlling the quality of compressed images. In this paper, a dependence between classification accuracy of maximum likelihood and neural network classifiers applied to three-channel test and real-life images and quality of compressed images characterized by standard and visual quality metrics is studied. The following is demonstrated. First, a classification accuracy starts to decrease faster when image quality due to compression ratio increasing reaches a distortion visibility threshold. Second, the classes with a wider distribution of features start to “take pixels” from classes with narrower distributions of features. Third, a classification accuracy might depend essentially on the training methodology, i.e., whether features are determined from original data or compressed images. Finally, the drawbacks of pixel-wise classification are shown and some recommendations on how to improve classification accuracy are given.
Spatio-Temporal Dynamics of Algae and Macrophyte Cover in Urban Lakes: A Remote Sensing Analysis of Bellandur and Varthur Wetlands in Bengaluru, India
Authors: Bareuther Mischa , Klinge Michael , Buerkert Andreas
Published: November 2020

Rapid urbanization processes and indiscriminate disposal of urban wastewaters are major causes for anthropogenic lake-sediment deposition and eutrophication. However, information about the spatial and temporal variation of macrophyte and phytoplankton distribution as indicators for water contamination is limited. To gain insights into the dynamics, we analyzed lake-cover changes of Bellandur and Varthur Lake in the S-Indian megacity of Bengaluru for the post-rainy seasons of the years 2002–2019. Supervised maximum likelihood classifications were conducted on 62 freely available, true-color satellite images in order to distinguish between macrophytes, algae, and free water surface. The image-derived results were verified by supervised classification and manual mapping of two simultaneously recorded multispectral satellite images (Sentinel-2 and WorldView-2). Seasonal interrelations between macrophytes and algae distribution were similar for both lakes. The increase in macrophyte cover during post-rainy season negatively correlated with algal abundance. Macrophyte expansion progressively suppressed algae development at both lakes, reflective of increasing eutrophication caused by on-going wastewater input. Seasonal variation in precipitation, wind direction, and temperature seemed to trigger intra-annual shifts of macrophytes and algae while similar macrophyte spread intensities during the post-monsoon season indicated independence of nutrient loads in the lake water.
An Analytical Framework for Accurate Traffic Flow Parameter Calculation from UAV Aerial Videos
Authors: Brkić Ivan , Miler Mario , Ševrović Marko , Medak Damir
Published: November 2020

Unmanned Aerial Vehicles (UAVs) represent easy, affordable, and simple solutions for many tasks, including the collection of traffic data. The main aim of this study is to propose a new, low-cost framework for the determination of highly accurate traffic flow parameters. The proposed framework consists of four segments: terrain survey, image processing, vehicle detection, and collection of traffic flow parameters. The testing phase of the framework was done on the Zagreb bypass motorway. A significant part of this study is the integration of the state-of-the-art pre-trained Faster Region-based Convolutional Neural Network (Faster R-CNN) for vehicle detection. Moreover, the study includes detailed explanations about vehicle speed estimation based on the calculation of the Mean Absolute Percentage Error (MAPE). Faster R-CNN was pre-trained on Common Objects in COntext (COCO) images dataset, fine-tuned on 160 images, and tested on 40 images. A dual-frequency Global Navigation Satellite System (GNSS) receiver was used for the determination of spatial resolution. This approach to data collection enables extraction of trajectories for an individual vehicle, which consequently provides a method for microscopic traffic flow parameters in detail analysis. As an example, the trajectories of two vehicles were extracted and the comparison of the driver’s behavior was given by speed—time, speed—space, and space—time diagrams.
A Novel Intelligent Classification Method for Urban Green Space Based on High-Resolution Remote Sensing Images
Authors: Xu Zhiyu , Zhou Yi , Wang Shixin , Wang Litao , Li Feng , Wang Shicheng , Wang Zhenqing
Published: November 2020

The real-time, accurate, and refined monitoring of urban green space status information is of great significance in the construction of urban ecological environment and the improvement of urban ecological benefits. The high-resolution technology can provide abundant information of ground objects, which makes the information of urban green surface more complicated. The existing classification methods are challenging to meet the classification accuracy and automation requirements of high-resolution images. This paper proposed a deep learning classification method for urban green space based on phenological features constraints in order to make full use of the spectral and spatial information of green space provided by high-resolution remote sensing images (GaoFen-2) in different periods. The vegetation phenological features were added as auxiliary bands to the deep learning network for training and classification. We used the HRNet (High-Resolution Network) as our model and introduced the Focal Tversky Loss function to solve the sample imbalance problem. The experimental results show that the introduction of phenological features into HRNet model training can effectively improve urban green space classification accuracy by solving the problem of misclassification of evergreen and deciduous trees. The improvement rate of F1-Score of deciduous trees, evergreen trees, and grassland were 0.48%, 4.77%, and 3.93%, respectively, which proved that the combination of vegetation phenology and high-resolution remote sensing image can improve the results of deep learning urban green space classification.
Investigating the Long-Range Transport of Aerosol Plumes Following the Amazon Fires (August 2019): A Multi-Instrumental Approach from Ground-Based and Satellite Observations
Authors: Bencherif Hassan , Bègue Nelson , Kirsch Pinheiro Damaris , du Preez David Jean , Cadet Jean-Maurice , da Silva Lopes Fábio Juliano , Shikwambana Lerato , Landulfo Eduardo , Vescovini Thomas , Labuschagne Casper , Silva Jonatan João , Anabor Vagner , Coheur Pierre-François , Mbatha Nkanyiso , Hadji-Lazaro Juliette , Sivakumar Venkataraman , Clerbaux Cathy
Published: November 2020

Despite a number of studies on biomass burning (BB) emissions in the atmosphere, observation of the associated aerosols and pollutants requires continuous efforts. Brazil, and more broadly Latin America, is one of the most important seasonal sources of BB, particularly in the Amazon region. Uncertainty about aerosol loading in the source regions is a limiting factor in terms of understanding the role of aerosols in climate modelling. In the present work, we investigated the Amazon BB episode that occurred during August 2019 and made the international headlines, especially when the smoke plumes plunged distant cities such as São Paulo into darkness. Here, we used satellite and ground-based observations at different locations to investigate the long-range transport of aerosol plumes generated by the Amazon fires during the study period. The monitoring of BB activity was carried out using fire related pixel count from the moderate resolution imaging spectroradiometer (MODIS) onboard the Aqua and Terra platforms, while the distribution of carbon monoxide (CO) concentrations and total columns were obtained from the infrared atmospheric sounding interferometer (IASI) onboard the METOP-A and METOP-B satellites. In addition, AERONET sun-photometers as well as the MODIS instrument made aerosol optical depth (AOD) measurements over the study region. Our datasets are consistent with each other and highlight AOD and CO variations and long-range transport of the fire plume from the source regions in the Amazon basin. We used the Lagrangian transport model FLEXPART (FLEXible PARTicle) to simulate backward dispersion, which showed good agreement with satellite and ground measurements observed over the study area. The increase in Rossby wave activity during the 2019 austral winter the Southern Hemisphere may have contributed to increasing the efficiency of large-scale transport of aerosol plumes generated by the Amazon fires during the study period.
Toward Sub-Diffraction Imaging of Single-DNA Molecule Sensors Based on Stochastic Switching Localization Microscopy
Authors: Lee Seungah , Batjikh Indra , Kang Seong Ho
Published: November 2020

The natural characteristics of deoxyribonucleic acid (DNA) enable its advanced applications in nanotechnology as a special tool that can be detected by high-resolution imaging with precise localization. Super-resolution (SR) microscopy enables the examination of nanoscale molecules beyond the diffraction limit. With the development of SR microscopy methods, DNA nanostructures can now be optically assessed. Using the specific binding of fluorophores with their target molecules, advanced single-molecule localization microscopy (SMLM) has been expanded into different fields, allowing wide-range detection at the single-molecule level. This review discusses the recent progress in the SR imaging of DNA nano-objects using SMLM techniques, such as direct stochastic optical reconstruction microscopy, binding-activated localization microscopy, and point accumulation for imaging nanoscale topography. Furthermore, we discuss their advantages and limitations, present applications, and future perspectives.
MW-ACGAN: Generating Multiscale High-Resolution SAR Images for Ship Detection
Authors: Zou Lichuan , Zhang Hong , Wang Chao , Wu Fan , Gu Feng
Published: November 2020

In high-resolution Synthetic Aperture Radar (SAR) ship detection, the number of SAR samples seriously affects the performance of the algorithms based on deep learning. In this paper, aiming at the application requirements of high-resolution ship detection in small samples, a high-resolution SAR ship detection method combining an improved sample generation network, Multiscale Wasserstein Auxiliary Classifier Generative Adversarial Networks (MW-ACGAN) and the Yolo v3 network is proposed. Firstly, the multi-scale Wasserstein distance and gradient penalty loss are used to improve the original Auxiliary Classifier Generative Adversarial Networks (ACGAN), so that the improved network can stably generate high-resolution SAR ship images. Secondly, the multi-scale loss term is added to the network, so the multi-scale image output layers are added, and multi-scale SAR ship images can be generated. Then, the original ship data set and the generated data are combined into a composite data set to train the Yolo v3 target detection network, so as to solve the problem of low detection accuracy under small sample data set. The experimental results of Gaofen-3 (GF-3) 3 m SAR data show that the MW-ACGAN network can generate multi-scale and multi-class ship slices, and the confidence level of ResNet18 is higher than that of ACGAN network, with an average score of 0.91. The detection results of Yolo v3 network model show that the detection accuracy trained by the composite data set is as high as 94%, which is far better than that trained only by the original SAR data set. These results show that our method can make the best use of the original data set, improve the accuracy of ship detection.
Synthesis and Characterization of Ru-MOFs on Microelectrode for Trace Mercury Detection
Authors: Xiong Chenyu , Xu Yuhao , Bian Chao , Wang Ri , Xie Yong , Han Mingjie , Xia Shanhong
Published: November 2020

Mercury ions (Hg2+) pollution in the water environment can cause serious harm to human health. Trace Hg2+ detection is of vital importance for environmental monitoring. Herein, we report a novel design of Ru-MOFs modified gold microelectrode for Hg2+ determination. Ru-MOFs are synthesized directly by the cathodic method on gold microelectrode, with the covered area accurately controlled. Cathodic synthesized Ru-MOFs show good conductivity and are suitable to be used as the electrode surface material directly. The synergy of the pre-deposition process and the adsorption process of Ru-MOFs can effectively improves the performance of the sensor. The results show good linearity (R2 = 0.996) from 0.1 ppb to 5 ppb, with a high sensitivity of 0.583 μA ppb−1 mm−2. The limit of detection is found to be 0.08 ppb and the test process is within 6 min. Most importantly, the senor has a good anti-interference ability and the recoveries are satisfactory. This miniature electrochemical sensor has the potential for on-site detection of trace mercury in the field.
Multirate Audio-Integrated Feedback Active Noise Control Systems Using Decimated-Band Adaptive Filters for Reducing Narrowband Noises
Authors: Siswanto Antonius , Chang Cheng-Yuan , Kuo Sen M.
Published: November 2020

Audio-integrated feedback active noise control (AFANC) systems deliver wideband audio signals and cancel low frequency narrowband noises simultaneously. The conventional AFANC system uses single-rate processing with fullband adaptive active noise control (ANC) filter for generating anti-noise signal and fullband audio cancelation filter for audio-interference cancelation. The conventional system requires a high sampling rate for audio processing. Thus, the fullband adaptive filters require long filter lengths, resulting in high computational complexity and impracticality in real-time system. This paper proposes a multirate AFANC system using decimated-band adaptive filters (DAFs) to decrease the required filter lengths. The decimated-band adaptive ANC filter is updated by the proposed decimated filtered-X least mean square (FXLMS) algorithm, and the decimated-band audio cancelation filter can be obtained by the proposed on-line and off-line decimated secondary-path modeling algorithms. The computational complexity can be decreased significantly in the proposed AFANC system with good enough noise reduction and fast convergence speed, which were verified in the analysis and computer simulations. The proposed AFANC system was implemented for an active headrest system, and the real-time performances were tested in real-time experiments.
An Impartial Semi-Supervised Learning Strategy for Imbalanced Classification on VHR Images
Authors: Sun Fei , Fang Fang , Wang Run , Wan Bo , Guo Qinghua , Li Hong , Wu Xincai
Published: November 2020

Imbalanced learning is a common problem in remote sensing imagery-based land-use and land-cover classifications. Imbalanced learning can lead to a reduction in classification accuracy and even the omission of the minority class. In this paper, an impartial semi-supervised learning strategy based on extreme gradient boosting (ISS-XGB) is proposed to classify very high resolution (VHR) images with imbalanced data. ISS-XGB solves multi-class classification by using several semi-supervised classifiers. It first employs multi-group unlabeled data to eliminate the imbalance of training samples and then utilizes gradient boosting-based regression to simulate the target classes with positive and unlabeled samples. In this study, experiments were conducted on eight study areas with different imbalanced situations. The results showed that ISS-XGB provided a comparable but more stable performance than most commonly used classification approaches (i.e., random forest (RF), XGB, multilayer perceptron (MLP), and support vector machine (SVM)), positive and unlabeled learning (PU-Learning) methods (PU-BP and PU-SVM), and typical synthetic sample-based imbalanced learning methods. Especially under extremely imbalanced situations, ISS-XGB can provide high accuracy for the minority class without losing overall performance (the average overall accuracy achieves 85.92%). The proposed strategy has great potential in solving the imbalanced classification problems in remote sensing.
Synergy Effect of Combined Near and Mid-Infrared Fibre Spectroscopy for Diagnostics of Abdominal Cancer
Authors: Hocotz Thaddäus , Bibikova Olga , Belikova Valeria , Bogomolov Andrey , Usenov Iskander , Pieszczek Lukasz , Sakharova Tatiana , Minet Olaf , Feliksberger Elena , Artyushenko Viacheslav , Rau Beate , Zabarylo Urszula
Published: November 2020

Cancers of the abdominal cavity comprise one of the most prevalent forms of cancers, with the highest contribution from colon and rectal cancers (12% of the human population), followed by stomach cancers (4%). Surgery, as the preferred choice of treatment, includes the selection of adequate resection margins to avoid local recurrences due to minimal residual disease. The presence of functionally vital structures can complicate the choice of resection margins. Spectral analysis of tissue samples in combination with chemometric models constitutes a promising approach for more efficient and precise tumour margin identification. Additionally, this technique provides a real-time tumour identification approach not only for intraoperative application but also during endoscopic diagnosis of tumours in hollow organs. The combination of near-infrared and mid-infrared spectroscopy has advantages compared to individual methods for the clinical implementation of this technique as a diagnostic tool.
Timeliness-Aware On-Site Planning Method for Tour Navigation
Authors: Isoda Shogo , Hidaka Masato , Matsuda Yuki , Suwa Hirohiko , Yasumoto Keiichi
Published: November 2020

In recent years, there has been a growing interest in travel applications that provide on-site personalized tourist spot recommendations. While generally helpful, most available options offer choices based solely on static information on places of interest without consideration of such dynamic factors as weather, time of day, and congestion, and with a focus on helping the tourist decide what single spot to visit next. Such limitations may prevent visitors from optimizing the use of their limited resources (i.e., time and money). Some existing studies allow users to calculate a semi-optimal tour visiting multiple spots in advance, but their on-site use is difficult due to the large computation time, no consideration of dynamic factors, etc. To deal with this situation, we formulate a tour score approach with three components: static tourist information on the next spot to visit, dynamic tourist information on the next spot to visit, and an aggregate measure of satisfaction associated with visiting the next spot and the set of subsequent spots to be visited. Determining the tour route that produces the best overall tour score is an NP-hard problem for which we propose three algorithms variations based on the greedy method. To validate the usefulness of the proposed approach, we applied the three algorithms to 20 points of interest in Higashiyama, Kyoto, Japan, and confirmed that the output solution was superior to the model route for Kyoto, with computation times of the three algorithms of 1.9±0.1, 2.0±0.1, and 27.0±1.8 s.
“I Had No Hope, I Had No Help at All”: Insights from a First Study of Fathers and Recurrent Care Proceedings
Authors: Philip Georgia , Youansamouth Lindsay , Bedston Stuart , Broadhurst Karen , Hu Yang , Clifton John , Brandon Marian
Published: November 2020

This article presents data from the first large-scale study of fathers involved in repeat (or recurrent) care proceedings in England. The project complements important research on mothers and recurrence. It consisted of three elements: an analysis of population-level administrative data from the Child and Family Court Advisory and Support Service (CAFCASS), a survey of fathers in pre-proceedings and care proceedings, and a qualitative longitudinal (QL) study of recurrent fathers. Here we report findings from the survey and the QL study, offering an expanded definition and description of fathers and recurrence. Elsewhere, we reported that a significant number of fathers appear in recurrent care proceedings and that the majority return with the same partner. Alongside this, there is also a notable pattern of “missing” fathers demonstrated by the proportion of lone mothers reappearing before the court. Our survey indicates a certain profile of recurrent fathers, but also that recurrent fathers are not straightforwardly a homogenous group. We report on the significance of recurrent fathers’ early lives, on the phenomenon of enduring couple relationships and on the prevalence of issues affecting parenting, such as poor mental health, substance use and domestic abuse. Insights from the QL study in particular reveal legacies of harm, loss, and a lack of emotional and relational resources in childhood, which have debilitating and far-reaching consequences. We argue the importance of understanding the vulnerabilities of recurrent fathers and of challenging certain assumptions in child welfare and family justice practices. There is much to be learnt from existing services for recurrent mothers, but also a need for bespoke or adapted services that may be more responsive to particular circumstances of recurrent fathers and couples.
Introduction to “Reshaping the World: Rethinking Borders”
Authors: Castañeda Ernesto
Published: November 2020

This paper provides some historical context to understand border formations. By comprehending how our present system of borders and exclusions function, we can gain a new appreciation for migration. Moreover, it presents arguments for open borders to counter anti-immigrant policies, includes short summaries of relevant research, as well as for each article included in this Special Issue. Together, these articles show how more welcoming policies towards immigrants, refugees, and asylum seekers do not threaten popular sovereignty but, conversely, strengthen both democracy and local rights.
Social Capital and Prosocial Behavior among German Children
Authors: Barton Helen , Thorpe Jared , Dufur Mikaela
Published: November 2020

A robust literature connects children’s and adolescents’ social capital to a range of desirable outcomes, including increased academic achievement and decreased delinquency. We extend this research by extending possible associations with child social capital to positive behaviors, measured here as prosocial behaviors. We examine data on 6th graders in Germany. We select the German context in part because one important source of child social capital, participation in religious congregations, is not as prevalent in modern Germany as in the US samples from which many social capital studies are derived. We use data from the German National Educational Panel Study (NEPS) and measures of child social capital, including parent–child interactions, family activities, and religious participation, to predict prosocial behavior. Results indicate that social capital in the form of parent-child interactions in the home and child religiosity is associated weakly with greater prosocial behavior. These results suggest that adults can help children develop stronger prosocial norms by increasing interaction with their children and by exposing their children to network ties in religious settings, but also that social capital can be derived different ways in different contexts.
Removal of Arsenate and Arsenite in Equimolar Ferrous and Ferric Sulfate Solutions through Mineral Coprecipitation: Formation of Sulfate Green Rust, Goethite, and Lepidocrocite
Authors: Su Chunming , Wilkin Richard T.
Published: November 2020

An improved understanding of in situ mineralization in the presence of dissolved arsenic and both ferrous and ferric iron is necessary because it is an important geochemical process in the fate and transformation of arsenic and iron in groundwater systems. This work aimed at evaluating mineral phases that could form and the related transformation of arsenic species during coprecipitation. We conducted batch tests to precipitate ferrous (133 mM) and ferric (133 mM) ions in sulfate (533 mM) solutions spiked with As (0–100 mM As(V) or As(III)) and titrated with solid NaOH (400 mM). Goethite and lepidocrocite were formed at 0.5–5 mM As(V) or As(III). Only lepidocrocite formed at 10 mM As(III). Only goethite formed in the absence of added As(V) or As(III). Iron (II, III) hydroxysulfate green rust (sulfate green rust or SGR) was formed at 50 mM As(III) at an equilibrium pH of 6.34. X-ray analysis indicated that amorphous solid products were formed at 10–100 mM As(V) or 100 mM As(III). The batch tests showed that As removal ranged from 98.65–100%. Total arsenic concentrations in the formed solid phases increased with the initial solution arsenic concentrations ranging from 1.85–20.7 g kg−1. Substantial oxidation of initially added As(III) to As(V) occurred, whereas As(V) reduction did not occur. This study demonstrates that concentrations and species of arsenic in the parent solution influence the mineralogy of coprecipitated solid phases, which in turn affects As redox transformations.
How (Un)sustainable Environments Are Related to the Diffusion of COVID-19: The Relation between Coronavirus Disease 2019, Air Pollution, Wind Resource and Energy
Authors: Coccia Mario
Published: November 2020

The pandemic caused by novel coronavirus disease 2019 (COVID-19) is generating a high number of cases and deaths, with negative effects on public health and economic systems. One of the current questions in the contemporary environmental and sustainability debate is how high air pollution and reduced use of renewable energy can affect the diffusion of COVID-19. This study endeavors to explain the relation between days of air pollution, wind resources and energy, and the diffusion of COVID-19 to provide insights into sustainable policy to prevent future epidemics. The statistical analysis here focuses on a case study of Italy, one of the first countries to experience a rapid increase in confirmed cases and deaths. The results reveal two main findings: (1) cities with high wind speed and high wind energy production have a lower number of cases of COVID-19 in the context of a more sustainable environment; (2) cities located in hinterland zones with high air pollution, low wind speed and less wind energy production have a greater number of cases and total deaths. The results presented here suggest that the pandemic caused by novel coronavirus (SARS-CoV-2) and future epidemics similar to COVID-19 cannot be solved only with research in medicine but the solution also needs advanced capabilities and technologies for supporting sustainable development based on the reduction of air pollution and increase of production in renewable energy to improve air quality and as a consequence public health.
A Framework for Capacity and Operations Planning in Services Organizations Employing Workers with Intellectual Disabilities
Authors: Garcia-Sabater Jose Pedro , Maheut Julien , Ruiz Angel , Garcia-Sabater Julio Juan
Published: November 2020

This paper presents an integrated framework for capacity and operations planning in Spanish sheltered employment centers (SECs). Employment provides socio-economic opportunities for people with disability. Well functioning SECs that provide opportunities for people with and without disability to work alongside each other are an important component of Spain’s current labor market. To be economically sustainable, SECs need to satisfy their clients expectations in terms of price, flexibility and performance, whilst taking into account and strengthening the diverse skills and abilities of their workforce like specific learning and forgetting processes. The SEC studied herein, a non-for-profit organization whose mission is to create employment for people with disabilities its workforce is deployed directly on clients’ premises. Efficient management across this multi-site environment, whilst supporting the diverse employment needs of its staff, is of paramount importance. This paper contributes an integrated framework to support SEC capacity and operations planning, which prioritizes the explicit training needs of workers with disability at all levels (strategic, tactical, and operational) as a lever for achieving the organization’s goals.
Blockchain Technology for Smart Tourism Destinations
Authors: Tyan Inessa , Yagüe Mariemma I. , Guevara-Plaza Antonio
Published: November 2020

This conceptual paper discusses the potential of blockchain technology for Smart Tourism Destinations. The main focus is placed on Smart Tourism Destinations’ four major goals that can be achieved by using blockchain technology, namely: enhancing tourism experience, rewarding sustainable behaviour, ensuring benefits for local communities, and reducing privacy concerns. The paper also outlines the major challenges that need to be overcome to successfully implement this innovative technology. This paper attempts to further advance the current knowledge about the possible implications of blockchain technology within the smart tourism domain, and especially Smart Tourism Destinations.
Factors Influencing Adoption of Sustainable Farming Practices in Europe: A Systemic Review of Empirical Literature
Authors: Serebrennikov Dmytro , Thorne Fiona , Kallas Zein , McCarthy Sinéad N.
Published: November 2020

Modern practices of industrial farming, such as mineral fertilization, caused a widespread degradation of agricultural land and water bodies in Europe. Different farm management strategies exist to reduce the impact of mineral fertilization while preserving soil productivity. The aim of this paper is to provide a thorough systemic review of contemporary literature exploring factors and conditions affecting EU farmers’ adoption of sustainable farming practices. The specific focus is on widely adopted and empirically explored measures, such as organic farming, manure treatment technologies and manure fertilization, as well as soil and water conservation methods. In total, 23 peer-reviewed studies were extracted by means of Google Scholar covering the time period between 2003 and 2019. The main findings show that farmers’ environmental and economic attitudes in addition to their sources of information have a strong effect on the adoption of organic farming, although there is a lack of evidence of their impact on adopting manure treatment and conservation measures. Similarly, farmers’ age and education are found to systemically influence organic farming adoption, but not adoption of other reviewed technologies. While other factors, such as farm physical characteristics or technological attributes, may be important determinants of adoption, it is hard to recognize definite patterns of their impact across technologies given a shortage of empirical evidence. More research utilizing standardized surveys and methods of analysis is needed to formulate qualified guidelines and recommendations for policymakers.
Accessibility and Inclusive Tourism Development: Current State and Future Agenda
Authors: Gillovic Brielle , McIntosh Alison
Published: November 2020

Accessibility constitutes one important consideration in the field of scholarship relating to inclusive tourism development because it is fundamentally about the inclusion of people with disabilities in tourism and in society. This conceptual paper maps how accessible tourism is currently positioned against an established framework of inclusive tourism development and gives examples of relevant accessible tourism studies to recommend a future agenda for more inclusive outcomes that move towards sustainability. The seven elements of Scheyvens and Biddulph’s (2018) conceptual framework for inclusive tourism development form an appropriate and useful tool upon which to examine the current state of accessible tourism. The application of this framework reveals that we still have some way to go. We conclude this paper with a future agenda that posits attention to all seven elements of the inclusive tourism framework for accessible tourism, notably, to increase the involvement of people with disabilities as tourism producers and consumers; increase their self-representation and participation in decision-making; transform power relations; reimagine tourism places and people; and break down social barriers. We especially urge researchers to examine the dominant ableist discourse, to consider how our inquiry can be more participatory and inclusive, and to seek to bridge inquiry, industry and community.
Pore Pressure Analysis for Distinguishing Earthquakes Induced by CO2 Injection from Natural Earthquakes
Authors: Chhun Chanmaly , Tsuji Takeshi
Published: November 2020

It is important to distinguish between natural earthquakes and those induced by CO2 injection at carbon capture and storage sites. For example, the 2004 Mw 6.8 Chuetsu earthquake occurred close to the Nagaoka CO2 storage site during gas injection, but we could not quantify whether the earthquake was due to CO2 injection or not. Here, changes in pore pressure during CO2 injection at the Nagaoka site were simulated and compared with estimated natural seasonal fluctuations in pore pressure due to rainfall and snowmelt, as well as estimated pore pressure increases related to remote earthquakes. Changes in pore pressure due to CO2 injection were clearly distinguished from those due to rainfall and snowmelt. The simulated local increase in pore pressure at the seismogenic fault area was much less than the seasonal fluctuations related to precipitation and increases caused by remote earthquakes, and the lateral extent of pore pressure increase was insufficient to influence seismogenic faults. We also demonstrated that pore pressure changes due to distant earthquakes are capable of triggering slip on seismogenic faults. The approach we developed could be used to distinguish natural from injection-induced earthquakes and will be useful for that purpose at other CO2 sequestration sites.
Multicase Study Comparison of Different Types of Flood-Resilient Buildings (Elevated, Amphibious, and Floating) at the Vistula River in Warsaw, Poland
Authors: Piątek Łukasz , Wojnowska-Heciak Magdalena
Published: November 2020

The study aims to present, compare, and assess three different types of buildings considered as flood-resilient construction: building on piles (also called static elevation), amphibious building (also called can-float), and floating building in terms of their performance in the context of a semiwild river in a large city. The comparative multiple-case study covers three objects realized between 2014 and 2017 at the Vistula riverbanks in Warsaw, Poland: pile founded Beach Pavilion, amphibious Boulevard Pavilion, and floating Water Tram Terminal. The research was based on the blueprints analysis as well as on on-site observations in the phase of construction and operation and interviews. The general characteristic of the three resilient typologies has been confirmed in the study. The pile building has an almost unlimited range of operation regarding the water level on the cost of a relatively remote location from the river, but during an exceptionally extreme flood, it will be flooded. Floating buildings provide the best visual and physical contact with water, cannot be flooded, and may be relocated but access to them from land is hampered, especially during very low and very high water levels. Amphibious buildings seem to be a compromise of water proximity and reliability of operation in all circumstances. A closer look reveals technical problems with buoyant structures. In the case of the floating terminals, problems with mooring on the semiwild freezing river remain a challenge. The amphibious buildings are still in the phase of implementation, waiting for their first test in natural flooding conditions.
Multivariable Analysis of Transport Network Seismic Performance: Mexico City
Authors: Román-de la Sancha Azucena , Silva Rodolfo
Published: November 2020

In densely populated urban areas, predicting the post-earthquake performance of a transport network is a particularly challenging task that requires the integration of modeled structural seismic response, damage scenarios, and resulting traffic behavior. Previous approaches assessing the vulnerability and performance of networks after earthquakes have not succeeded in capturing and estimating the interdependencies between seismic risk parameters and key traffic behavior variables. This paper presents a methodology, based on data analysis and optimization, where the dynamic traffic modeling and probabilistic seismic hazard assessment are coupled, to link and characterize key network performance variables after extreme earthquakes and establish a multivariable seismic performance measure. The methodology is used to study the transport network in the southern part of Mexico City for a set of scenarios. The seismic environment is established through uniform hazard spectra derived for firm soil. Damage to structures is estimated considering site response and using fragility functions. Dynamic traffic modeling is developed to simulate damage-induced road closures and resulting in traffic variations. Post-earthquake network performance is evaluated through data envelopment analyses, obtaining sets of seismic performance boundaries, and seismic performance maps. The methodology offers a quantitative tool with applications in the planning of urban areas that are sustainable and seismic resilient.
Application of Biostimulants Containing Amino Acids to Tomatoes Could Favor Sustainable Cultivation: Implications for Tyrosine, Lysine, and Methionine
Authors: Alfosea-Simón Marina , Simón-Grao Silvia , Zavala-Gonzalez Ernesto A. , Cámara-Zapata Jose María , Simón Inmaculada , Martínez-Nicolás Juan José , Lidón Vicente , Rodríguez-Ortega Wilbert M. , García-Sánchez Francisco
Published: November 2020

Tomato (Solanum lycopersicum L.) is one of the most important crops worldwide as per its production and the surface cultivated. The use of biostimulant products plays a fundamental role in mitigating the negative effects of climate change and reducing the use of conventional fertilizers. Many of these products are formulated with amino acids (AAs). This study was conducted to elucidate the effects of the foliar application of tyrosine (Tyr) (15 mM), lysine (Lys) (15 mM), methionine (Met) (15 mM), and a Tyr + Lys + Met (15 mM + 15 mM + 15 mM) mixture on the physiological and metabolic processes, vegetative growth, and nutritional state of Optima variety tomato plants. The results showed that application of the AAs, individually and combined, was beneficial for the growth of the aerial part, net assimilation of CO2, and water use efficiency (WUE). Application of Tyr resulted in the best WUE. The metabolomics study revealed that AA treatments increased the concentration of proline, fructose, and glucose, whose role was to stimulate glycolysis and the Krebs cycle. Thus, the plants could have greater reduction power and energy, as well as more carbon molecules for their growth processes.
Acoustic Comfort as a Salutogenic Resource in Learning Environments—A Proposal for the Design of a System to Improve the Acoustic Quality of Classrooms
Authors: Laurìa Antonio , Secchi Simone , Vessella Luigi
Published: November 2020

This article addresses the topic of environmental comfort from a salutogenic and pedagogical point of view. It begins by presenting a wide framework aimed at describing the complexity and specificity of the acoustic issue and the need to integrate decibel-based metrics with knowledge and reflections which are inherent to non-measurable factors. The article then focuses its attention on educational spaces and presents the results of an investigation carried out in 52 classrooms of 19 primary schools in Florence. From this research and keeping in mind the current Italian legislation, the following results are deduced: (1) the layout of a typical classroom, (2) the average reverberation time and (3) the sound-absorbing surface required to improve the acoustic quality of the typical classroom with polystyrene fibre panels. Subsequently, after having briefly described the more appropriate typology of sound-absorbing solutions, a system for the acoustic correction of classrooms is presented. This system is composed of two parts. The first part is fixed and its realisation is entrusted to specialised personnel; the second, based on the concept of personalisation and transformation of the educational space, is modifiable through time and designed and applied by the students themselves.
Maintenance Decision-Making of an Urban Rail Transit System in a Regionalized Network-Wide Perspective
Authors: Sun Baofeng , Liu Jiaojiao , Hao Junyi , Shen Xiuxiu , Mao Xinhua , Song Xianmin
Published: November 2020

The networked operation of Urban Rail Transit (URT) brings the new challenge of network-wide maintenance. This research focuses on the URT Network-Wide Maintenance Decision-Making Problem (URT-NMDP), including regionalized maintenance network design and maintenance resource allocation. In this work, we proposed a bi-objective integer programming model that integrates the characteristics of set coverage and P-median models, resulting in the regionalized maintenance network design model. Some critical factors are considered in the model, such as the importance of node, the maximum failure response time, and maintenance guarantee rules. We designed a NSGA-II based algorithm to solve the model. Moreover, due to the uncertainty of failures in the URT network, we developed the method of allocating maintenance resources based on Monte Carlo simulation to strengthen the reliability of the regionalized maintenance network. With the model and algorithm presented in this work, we obtained Pareto optimal solutions of URT-NMDP, i.e., URT network-wide maintenance planning schemes, which include the number and location of maintenance points, the allocation of demand points, and the amount of maintenance units. Finally, a real-world case is studied to evaluate the operating performance of these schemes for verifying the method in our paper. The results of the case study demonstrate that the reasonable and tested-in-practice maximum failure response time is the precondition for the efficient URT maintenance network. The maintenance scheme considered the weighted importance of node shows the optimal performance, with the shortest overall maintenance path and the minimum average failure response time and investment cost on maintenance resources.
Measuring Urban Resilience to Climate Change in Three Chinese Cities
Authors: Zhang Mingshun , Yang Yaguang , Li Huanhuan , van Dijk Meine Pieter
Published: November 2020

Building an urban resilience index results in developing an increasingly popular tool for monitoring progress towards climate-proof cities. This paper develops an urban resilience index in the context of urban China, which helps planners and policy-makers at city level to identify whether urban development is leading to more resilience. The urban resilience index (URI) suggested in this research uses data on 24 indicators distributed over six URI component indices. While no measure of such a complex phenomenon can be perfect, the URI proved to be effective, useful and robust. Our findings show that the URI ensures access to integrated information on urban resilience to climate change. It allows comparisons of cities in a systematic and quantitative way, and enables identification of strong and weak points related to urban resilience. The URI provides tangible measures of not only overall measures of urban resilience to climate change, but also urban resilience components and related indicators. Therefore, it could meet a wide range of policy and research needs. URI is a helpful tool for urban decision-makers and urban planners to quantify goals, measure progress, benchmark performance, and identify priorities for achieving high urban resilience to climate change.
Financial Inclusion, Socioeconomic Disaster Risks and Sustainable Mountain Development: Empirical Evidence from the Karakoram Valleys of Pakistan
Authors: Ullah Kifayat , Mohsin Abdul Qayyum , Saboor Abdul , Baig Saranjam
Published: November 2020

Does financial inclusion contribute to sustainable mountain development by providing access to financial resources and creating economic opportunities for poor mountain people? Keeping this question in mind, the present study aimed to investigate the nexus between financial inclusion and improvement in the living standards of mountain people, and reduction in socioeconomic disaster risks (economic poverty, multidimensional poverty and income inequality). For empirical investigation, the study employed Quasi Experimental Designs, Foster, Greer and Thorbecke poverty measures, Alkire et al. methodology, Gini Index and Quintile technique to assess the impact of financial inclusion on the living standards and reduction of economic poverty, multidimensional poverty and income inequality, respectively. We used the Logistic Regression technique to identify major drivers of socioeconomic disaster risks in the study area. The study collected quantitative and qualitative household level data from 424 households through structured questionnaires using multistage sampling technique for analysis. The findings of the study revealed a positive synergy among inclusive finance and living standards and a negative connection between financial inclusion and socioeconomic disaster risks in the Karakoram valleys of Pakistan. The logistic regression results also recognized financial inclusion as a potential determinant of economic poverty reduction. However, financial inclusion as a potential tool to eradicate multidimensional poverty in the study area showed insignificant results. These findings can help policy-makers and other stakeholders to understand the dynamics of socioeconomic disaster risks and the role of financial inclusion in their reduction to accomplish sustainable mountain development in the Karakoram valleys of Pakistan.