bluuebunny/crossref_metadata_2025 huggingface dataset:
Gemcitabine-Loaded Magnetically Responsive Poly(ε-caprolactone) Nanoparticles against Breast Cancer
Authors: García-García Gracia , Fernández-Álvarez Fátima , Cabeza Laura , Delgado Ángel V. , Melguizo Consolación , Prados José C. , Arias José L.
Published: November 2020

A reproducible and efficient interfacial polymer disposition method has been used to formulate magnetite/poly(ε-caprolactone) (core/shell) nanoparticles (average size ≈ 125 nm, production performance ≈ 90%). To demonstrate that the iron oxide nuclei were satisfactorily embedded within the polymeric solid matrix, a complete analysis of these nanocomposites by, e.g., electron microscopy visualizations, energy dispersive X-ray spectroscopy, Fourier-transform infrared spectroscopy, electrophoresis, and contact angle goniometry was conducted. The magnetic responsive behaviour of these nanoparticles was quantitatively characterized by the hysteresis cycle and qualitatively investigated by visualization of the colloid under exposure to a 0.4 T magnet. Gemcitabine entrapment into the polymeric shell reported adequate drug loading values (≈11%), and a biphasic and pH-responsive drug release profile (≈four-fold faster Gemcitabine release at pH 5.0 compared to pH 7.4). Cytotoxicity studies in MCF-7 human breast cancer cells proved that the half maximal inhibitory concentration of Gem-loaded nanocomposites was ≈two-fold less than that of the free drug. Therefore, these core/shell nanoparticles could have great possibilities as a magnetically targeted Gemcitabine delivery system for breast cancer treatment.
Surface Characterization of Powdered Cellulose Activated by Potassium Hydroxide in Dry Condition Through Ball Milling
Authors: Azadfar Mohammadali , Wolcott Michael P.
Published: November 2020

The surface chemical compositions of powdered cellulose have been characterized utilizing X-ray photoelectron spectroscopy (XPS) and attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) techniques. Powdered cellulose was prepared by milling of bleached softwood pulp residues through a lab-scale planetary ball mill. Here we show how milling a mixture of the powdered cellulose with potassium hydroxide determines the surface chemical compositions of the obtained powdered cellulose, in a completely dry condition. The XPS analysis indicated the presence of new carbon and oxygen atoms as C4, C5, and O3. In turn, the FTIR analysis showed the stretching vibrations of the carbon–carbon double bond. The results suggest the formation of active oxygenated species on powdered cellulose surfaces.
Automated Test Assembly for Large-Scale Standardized Assessments: Practical Issues and Possible Solutions
Authors: Spaccapanico Proietti Giada , Matteucci Mariagiulia , Mignani Stefania
Published: November 2020

In testing situations, automated test assembly (ATA) is used to assemble single or multiple test forms that share the same psychometric characteristics, given a set of specific constraints, by means of specific solvers. However, in complex situations, which are typical of large-scale assessments, ATA models may be infeasible due to the large number of decision variables and constraints involved in the problem. The purpose of this paper is to formalize a standard procedure and two different strategies—namely, additive and subtractive—for overcoming practical ATA concerns with large-scale assessments and to show their effectiveness in two case studies. The MAXIMIN and MINIMAX ATA methods are used to assemble multiple test forms based on item response theory models for binary data. The main results show that the additive strategy is able to identify the specific constraints that make the model infeasible, while the subtractive strategy is a faster but less accurate process, which may not always be optimal. Overall, the procedures are able to produce parallel test forms with similar measurement precision and contents, and they minimize the number of items shared among the test forms. Further research could be done to investigate the properties of the proposed approaches under more complex testing conditions, such as multi-stage testing, and to blend the proposed approaches in order to obtain the solution that satisfies the largest set of constraints.
Intercomparison of Burned Area Products and Its Implication for Carbon Emission Estimations in the Amazon
Authors: Pessôa Ana Carolina M. , Anderson Liana O. , Carvalho Nathália S. , Campanharo Wesley A. , Junior Celso H. L. Silva , Rosan Thais M. , Reis João B. C. , Pereira Francisca R. S. , Assis Mauro , Jacon Aline D. , Ometto Jean P. , Shimabukuro Yosio E. , Silva Camila V. J. , Pontes-Lopes Aline , Morello Thiago F. , Aragão Luiz E. O. C.
Published: November 2020

Carbon (C) emissions from forest fires in the Amazon during extreme droughts may correspond to more than half of the global emissions resulting from land cover changes. Despite their relevant contribution, forest fire-related C emissions are not directly accounted for within national-level inventories or carbon budgets. A fundamental condition for quantifying these emissions is to have a reliable estimation of the extent and location of land cover types affected by fires. Here, we evaluated the relative performance of four burned area products (TREES, MCD64A1 c6, GABAM, and Fire_cci v5.0), contrasting their estimates of total burned area, and their influence on the fire-related C emissions in the Amazon biome for the year 2015. In addition, we distinguished the burned areas occurring in forests from non-forest areas. The four products presented great divergence in the total burned area and, consequently, total related C emissions. Globally, the TREES product detected the largest amount of burned area (35,559 km2), and consequently it presented the largest estimate of committed carbon emission (45 Tg), followed by MCD64A1, with only 3% less burned area detected, GABAM (28,193 km2) and Fire_cci (14,924 km2). The use of Fire_cci may result in an underestimation of 29.54 ± 3.36 Tg of C emissions in relation to the TREES product. The same pattern was found for non-forest areas. Considering only forest burned areas, GABAM was the product that detected the largest area (8994 km2), followed by TREES (7985 km2), MCD64A1 (7181 km2) and Fire_cci (1745 km2). Regionally, Fire_cci detected 98% less burned area in Acre state in southwest Amazonia than TREES, and approximately 160 times less burned area in forests than GABAM. Thus, we show that global products used interchangeably on a regional scale could significantly underestimate the impacts caused by fire and, consequently, their related carbon emissions.
A Machine Learning Approach for Remote Sensing Data Gap-Filling with Open-Source Implementation: An Example Regarding Land Surface Temperature, Surface Albedo and NDVI
Authors: Sarafanov Mikhail , Kazakov Eduard , Nikitin Nikolay O. , Kalyuzhnaya Anna V.
Published: November 2020

Satellite remote sensing has now become a unique tool for continuous and predictable monitoring of geosystems at various scales, observing the dynamics of different geophysical parameters of the environment. One of the essential problems with most satellite environmental monitoring methods is their sensitivity to atmospheric conditions, in particular cloud cover, which leads to the loss of a significant part of data, especially at high latitudes, potentially reducing the quality of observation time series until it is useless. In this paper, we present a toolbox for filling gaps in remote sensing time-series data based on machine learning algorithms and spatio-temporal statistics. The first implemented procedure allows us to fill gaps based on spatial relationships between pixels, obtained from historical time-series. Then, the second procedure is dedicated to filling the remaining gaps based on the temporal dynamics of each pixel value. The algorithm was tested and verified on Sentinel-3 SLSTR and Terra MODIS land surface temperature data and under different geographical and seasonal conditions. As a result of validation, it was found that in most cases the error did not exceed 1 °C. The algorithm was also verified for gaps restoration in Terra MODIS derived normalized difference vegetation index and land surface broadband albedo datasets. The software implementation is Python-based and distributed under conditions of GNU GPL 3 license via public repository.
Multi-Sensor Analysis of a Weak and Long-Lasting Volcanic Plume Emission
Authors: Scollo Simona , Boselli Antonella , Corradini Stefano , Leto Giuseppe , Guerrieri Lorenzo , Merucci Luca , Prestifilippo Michele , Sanchez Ricardo Zanmar , Sannino Alessia , Stelitano Dario
Published: November 2020

Volcanic emissions are a well-known hazard that can have serious impacts on local populations and aviation operations. Whereas several remote sensing observations detect high-intensity explosive eruptions, few studies focus on low intensity and long-lasting volcanic emissions. In this work, we have managed to fully characterize those events by analyzing the volcanic plume produced on the last day of the 2018 Christmas eruption at Mt. Etna, in Italy. We combined data from a visible calibrated camera, a multi-wavelength elastic/Raman Lidar system, from SEVIRI (EUMETSAT-MSG) and MODIS (NASA-Terra/Aqua) satellites and, for the first time, data from an automatic sun-photometer of the aerosol robotic network (AERONET). Results show that the volcanic plume height, ranging between 4.5 and 6 km at the source, decreased by about 0.5 km after 25 km. Moreover, the volcanic plume was detectable by the satellites up to a distance of about 400 km and contained very fine particles with a mean effective radius of about 7 µm. In some time intervals, volcanic ash mass concentration values were around the aviation safety thresholds of 2 × 10−3 g m−3. Of note, Lidar observations show two main stratifications of about 0.25 km, which were not observed at the volcanic source. The presence of the double stratification could have important implications on satellite retrievals, which usually consider only one plume layer. This work gives new details on the main features of volcanic plumes produced during low intensity and long-lasting volcanic plume emissions.
Spatio-Temporal Patterns of Drought and Impact on Vegetation in North and West Africa Based on Multi-Satellite Data
Authors: Henchiri Malak , Liu Qi , Essifi Bouajila , Javed Tehseen , Zhang Sha , Bai Yun , Zhang Jiahua
Published: November 2020

Studying the significant impacts of drought on vegetation is crucial to understand its dynamics and interrelationships with precipitation, soil moisture, and temperature. In North and West Africa regions, the effects of drought on vegetation have not been clearly stated. Therefore, the present study aims to bring out the drought fluctuations within various types of Land Cover (LC) (Grasslands, Croplands, Savannas, and Forest) in North and West Africa regions. The drought characteristics were evaluated by analyzing the monthly Self-Calibrating Palmer Drought Severity Index (scPDSI) in different timescale from 2002 to 2018. Then, the frequency of droughts was examined over the same period. The results have revealed two groups of years (dry years and normal years), based on drought intensity. The selected years were used to compare the shifting between vegetation and desert. The Vegetation Condition Index (VCI), the Temperature Condition Index (TCI), the Precipitation Condition Index (PCI), and the Soil Moisture Condition Index (SMCI) were also used to investigate the spatiotemporal variation of drought and to determine which LC class was more vulnerable to drought risk. Our results revealed that Grasslands and Croplands in the West region, and Grasslands, Croplands, and Savannas in the North region are more sensitive to drought. A higher correlation was observed among the Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), Tropical Rainfall Measuring Mission (TRMM), and Soil Moisture (SM). Our findings suggested that NDVI, TRMM, and SM are more suitable for monitoring drought over the study area and have a reliable accuracy (R2 > 0.70) concerning drought prediction. The outcomes of the current research could, explicitly, contribute progressively towards improving specific drought mitigation strategies and disaster risk reduction at regional and national levels.
How Far Can We Classify Macroalgae Remotely? An Example Using a New Spectral Library of Species from the South West Atlantic (Argentine Patagonia)
Authors: Olmedo-Masat O. Magalí , Raffo M. Paula , Rodríguez-Pérez Daniel , Arijón Marianela , Sánchez-Carnero Noela
Published: November 2020

Macroalgae have attracted the interest of remote sensing as targets to study coastal marine ecosystems because of their key ecological role. The goal of this paper is to analyze a new spectral library, including 28 macroalgae from the South-West Atlantic coast, in order to assess its use in hyperspectral remote sensing. The library includes species collected in the Atlantic Patagonian coast (Argentina) with representatives of brown, red, and green algae, being 22 of the species included in a spectral library for the first time. The spectra of these main groups are described, and the intraspecific variability is also assessed, considering kelp differentiated tissues and depth range, discussing them from the point of view of their effects on spectral features. A classification and an independent component analysis using the spectral range and simulated bands of two state-of-the-art drone-borne hyperspectral sensors were performed. The results show spectral features and clusters identifying further algae taxonomic groups, showing the potential applications of this spectral library for drone-based mapping of this ecological and economical asset of our coastal marine ecosystems.
Graphene and Perovskite-Based Nanocomposite for Both Electrochemical and Gas Sensor Applications: An Overview
Authors: Chen Tse-Wei , Ramachandran Rasu , Chen Shen-Ming , Anushya Ganesan , Ramachandran Kumarasamy
Published: November 2020

Perovskite and graphene-based nanocomposites have attracted much attention and been proven as promising candidates for both gas (H2S and NH3) and electrochemical (H2O2, CH3OH and glucose) sensor applications. In this review, the development of portable sensor devices on the sensitivity, selectivity, cost effectiveness, and electrode stability of chemical and electrochemical applications is summarized. The authors are mainly focused on the common analytes in gas sensors such as hydrogen sulfide, ammonia, and electrochemical sensors including non-enzymatic glucose, hydrazine, dopamine, and hydrogen peroxide. Finally, the article also addressed the stability of composite performance and outlined recent strategies for future sensor perspectives.
Multi-Modal Explicit Sparse Attention Networks for Visual Question Answering
Authors: Guo Zihan , Han Dezhi
Published: November 2020

Visual question answering (VQA) is a multi-modal task involving natural language processing (NLP) and computer vision (CV), which requires models to understand of both visual information and textual information simultaneously to predict the correct answer for the input visual image and textual question, and has been widely used in smart and intelligent transport systems, smart city, and other fields. Today, advanced VQA approaches model dense interactions between image regions and question words by designing co-attention mechanisms to achieve better accuracy. However, modeling interactions between each image region and each question word will force the model to calculate irrelevant information, thus causing the model’s attention to be distracted. In this paper, to solve this problem, we propose a novel model called Multi-modal Explicit Sparse Attention Networks (MESAN), which concentrates the model’s attention by explicitly selecting the parts of the input features that are the most relevant to answering the input question. We consider that this method based on top-k selection can reduce the interference caused by irrelevant information and ultimately help the model to achieve better performance. The experimental results on the benchmark dataset VQA v2 demonstrate the effectiveness of our model. Our best single model delivers 70.71% and 71.08% overall accuracy on the test-dev and test-std sets, respectively. In addition, we also demonstrate that our model can obtain better attended features than other advanced models through attention visualization. Our work proves that the models with sparse attention mechanisms can also achieve competitive results on VQA datasets. We hope that it can promote the development of VQA models and the application of artificial intelligence (AI) technology related to VQA in various aspects.
Decoding Attempted Hand Movements in Stroke Patients Using Surface Electromyography
Authors: Jochumsen Mads , Niazi Imran Khan , Zia ur Rehman Muhammad , Amjad Imran , Shafique Muhammad , Gilani Syed Omer , Waris Asim
Published: November 2020

Brain- and muscle-triggered exoskeletons have been proposed as a means for motor training after a stroke. With the possibility of performing different movement types with an exoskeleton, it is possible to introduce task variability in training. It is difficult to decode different movement types simultaneously from brain activity, but it may be possible from residual muscle activity that many patients have or quickly regain. This study investigates whether nine different motion classes of the hand and forearm could be decoded from forearm EMG in 15 stroke patients. This study also evaluates the test-retest reliability of a classical, but simple, classifier (linear discriminant analysis) and advanced, but more computationally intensive, classifiers (autoencoders and convolutional neural networks). Moreover, the association between the level of motor impairment and classification accuracy was tested. Three channels of surface EMG were recorded during the following motion classes: Hand Close, Hand Open, Wrist Extension, Wrist Flexion, Supination, Pronation, Lateral Grasp, Pinch Grasp, and Rest. Six repetitions of each motion class were performed on two different days. Hudgins time-domain features were extracted and classified using linear discriminant analysis and autoencoders, and raw EMG was classified with convolutional neural networks. On average, 79 ± 12% and 80 ± 12% (autoencoders) of the movements were correctly classified for days 1 and 2, respectively, with an intraclass correlation coefficient of 0.88. No association was found between the level of motor impairment and classification accuracy (Spearman correlation: 0.24). It was shown that nine motion classes could be decoded from residual EMG, with autoencoders being the best classification approach, and that the results were reliable across days; this may have implications for the development of EMG-controlled exoskeletons for training in the patient’s home.
Bioimpedance Sensor and Methodology for Acute Pain Monitoring
Authors: Ghita Mihaela , Neckebroek Martine , Juchem Jasper , Copot Dana , Muresan Cristina I. , Ionescu Clara M.
Published: November 2020

The paper aims to revive the interest in bioimpedance analysis for pain studies in communicating and non-communicating (anesthetized) individuals for monitoring purpose. The plea for exploitation of full potential offered by the complex (bio)impedance measurement is emphasized through theoretical and experimental analysis. A non-invasive, low-cost reliable sensor to measure skin impedance is designed with off-the-shelf components. This is a second generation prototype for pain detection, quantification, and modeling, with the objective to be used in fully anesthetized patients undergoing surgery. The 2D and 3D time–frequency, multi-frequency evaluation of impedance data is based on broadly available signal processing tools. Furthermore, fractional-order impedance models are implied to provide an indication of change in tissue dynamics correlated with absence/presence of nociceptor stimulation. The unique features of the proposed sensor enhancements are described and illustrated here based on mechanical and thermal tests and further reinforced with previous studies from our first generation prototype.
Parental Non-Engagement within Child Protection Services—How Can Understandings of Complex Trauma and Epistemic Trust Help?
Authors: Mason Claire , Taggart Danny , Broadhurst Karen
Published: November 2020

Building on the findings from the national study of mothers in recurrent care proceedings in England, this paper proposes that the concepts of complex trauma and epistemic trust may help explain parents’ difficulties in engaging with child protection services. With the aim of advancing theoretical knowledge, qualitative data drawn from interviews with 72 women who have experienced repeat care proceedings are revisited, with a focus on women’s developmental histories and accounts of engagement with professionals, to probe the issue of service engagement. The article starts with a succinct review of the literature on parental non-engagement in child protection, highlighting strengths and potential limitations of current knowledge. This is followed by an introduction to the theoretical concepts of complex trauma and epistemic trust, outlining how these concepts provide an alternative framing of the reasons why parents may resist, or are reluctant to engage with, professionals. Drawing on women’s first-person accounts, we argue that high levels of maltreatment and adversity in women’s own childhoods shape adult relationships, particularly in relation to vulnerability to harm in adult lives but also mistrust of professional help. Extracts from women’s first-person accounts, chosen for their typicality against the core themes derived from the data, indicate that acts of resistance or rejection of professional help can be seen as adaptive—given women’s childhoods and relationship histories. The authors conclude that parents’ social histories need to be afforded far closer attention in child protection practice, if preventative services are to reach those with histories of developmental trauma.
Recovery of Municipal Solid Waste Recyclables under Different Incentive Schemes in Tamale, Ghana
Authors: Asare Wilhemina , Oduro Kwarteng Sampson , Donkor Emmanuel A. , Rockson Mizpah A.D.
Published: November 2020

The recovery of recyclables from the waste stream is a vital factor to consider in any recycling scheme since it enhances the quality of recyclables and ensures efficient recycling schemes. This study was conducted in the Tamale Metropolis of Ghana to investigate incentive options that can promote municipal solid waste resource recovery to support policy decisions on the formalization of recycling scheme in the Metropolis. Solid waste source separation scheme was set up in four different waste management service zones in the Metropolis. The quantity of separated materials, set-out rate, as well as separation efficiency of the targeted materials in the scheme were also assessed. The result of the study indicated a high recovery factor for dry recyclables (53.97%) as against food waste (10.03%). There was a significant effect of incentives on waste material quantities. Significant differences also occurred in the set-out rates and separation efficiency. Moreover, the study showed that employing prize as a reward scheme in addition to the provision of a waste storage facility had a high propensity for waste material recovery. The study demonstrated that the quantity of waste materials that may be retrieved in a recycling scheme in different solid waste management service zones depends on the type of incentive employed in the scheme and the service zone considered.
Analysing Consumer Preferences, Characteristics, and Behaviour to Identify Energy-Efficient Consumers
Authors: Dolšak Janez , Hrovatin Nevenka , Zorić Jelena
Published: November 2020

This paper investigates preference heterogeneity among Slovenian energy consumers and attempts to ascertain how different consumer groups value various attributes of energy products and services. More specifically, it aims to establish whether a consumer segment can be identified that shows a preference for additional energy services—in particular services, associated with energy-efficient and green behaviour. A latent class analysis is employed to classify consumers on the basis of their preferences for energy services. Additionally, information about their attitudes and behaviour toward green energy and energy efficiency, energy consumption, and usage of energy services together with socio-economic characteristics is used in the latent class regression to explain differences between latent consumer classes. Three classes are identified: the largest class of regular consumers, energy-efficient consumers, and dissatisfied consumers. In contrast to regular and dissatisfied consumers, energy-efficient consumers show a significantly higher interest in additional services, energy efficiency, and green energy. In line with the found heterogeneity of consumer preferences, suppliers should customise marketing strategies to meet the needs of specific segments. Energy policymakers also need to pay more attention to consumer heterogeneity and behavioural changes to increase the effectiveness of energy efficiency policies.
Towards Understanding and Sustaining Natural Resource Systems through the Systems Perspective: A Systematic Evaluation
Authors: Nyam Yong S. , Kotir Julius H. , Jordaan Andries J. , Ogundeji Abiodun A. , Adetoro Adetoso A. , Orimoloye Israel R.
Published: November 2020

A bibliometric and network analysis was performed to explore global research publication trends and to investigate relevant policy recommendations in the field of sustainability of natural resources, system dynamics, and systems thinking, to solve water resources issues and enhance water resource management. Overall, 1674 academic research articles data were generated from the Web of Science and Scopus databases, from 1981 to 2019. The findings of this study revealed that system dynamics and systems thinking research has significantly increased over the last decade (from 40 to 250 articles). Countries such as the USA (20%), China (18%), the United Kingdom (5%), Canada, Iran, Australia, and India (4% each) have the most publications and strongest collaborative networks. Sterman (2000) and Forrester (1961) had the most co-cited research while Zhang X had the highest citations, respectively. Results also showed that system theory which includes systems thinking and system dynamics were the most used keywords. The Journal of Cleaner Production was found to have published the highest number of systems thinking and system dynamics related studies, perhaps due to scope relevance. Despite the exponential rise in natural resource sustainability research globally, the result of this study shows that developing countries especially in Africa have low numbers of research publications in the field. Thus, the result of this study serves as a signal for policymakers to increase attention on research publications that could enhance natural resource sustainability, particularly in less developed countries in Africa where the application of systems thinking to natural resource management is limited.
Analyzing Waste Management System Alternatives for Kabul City, Afghanistan: Considering Social, Environmental, and Economic Aspects
Authors: Azimi Ahmad Nadim , Dente Sébastien M. R. , Hashimoto Seiji
Published: November 2020

Our earlier work identified social issues of stakeholders who are highly exposed to poor social performance in the current waste management system (WMS) of Kabul city, Afghanistan. The present work builds on earlier findings to elaborate four alternative scenarios with better social outcomes. For each scenario of the current system, greenhouse gas (GHG) and economic assessments were conducted. Results show that Scenario 2, considering increase waste collection coverage, recycling, unsanitary landfilling, and integration of informal workers, was found as the best alternative. Scenario 3, which added a source-separated system to Scenario 2, was the second-best alternative. These two scenarios address social issues and can reduce GHG emissions, save costs, and provide more jobs than the current system. In contrast, the absence of recycling in Scenario 1, and the conversion of unsanitary landfill into sanitary landfill in scenario 4 result in higher costs and GHG emissions, even though they deal with social issues and generate higher jobs to the existing waste management practice.
Augmented Reality (AR)-Based Intervention to Enhance Awareness of Fine Dust in Sustainable Environments
Authors: Huh Jung Rim , Park In-Jo , Sunwoo Young , Choi Hyo Jeong , Bhang Kon Joon
Published: November 2020

Education on fine dust is a challenging field, as it must explain abstract phenomena and accompany the demands for behavioral changes. This research studied whether the adoption of augmented reality (AR) technology can effect change in student perspectives with a view to improving the attitudes of passive students regarding fine dust-related matters. With regard to student awareness, 94.0% of students responded that the issue of fine dust in Korea is severe; the higher the degree of interest they had in fine dust, the more severe they perceived the issue to be. On the perceived severity of fine dust in schools, 47.8% viewed it as severe; again, the level differed by the degree of interest. Students (80%) who received a traditional education on fine dust expressed dissatisfaction. The effect of using AR in experiential learning in fine dust environmental education has shown enhanced results compared to before receiving the education, notably in the group with a low degree of interest in fine dust and the group without prior experience in fine dust environmental education. Likewise, in the satisfaction rate for AR-based education on fine dust, 72.5% of participants responded that they had enjoyed the class, 75.9% showed active participation, and 80.0% felt that the content of the education was useful. Considering that the objective of fine dust environmental education is not based on understanding a theory but on maintaining individual health through practice in real life, the use of AR in fine dust environmental education as applied in this study may be interpreted as having a certain level of effect. Additionally, our results demonstrate that the use of AR in classes is effective in arousing students’ interest and inducing participation for better engagement in learning activities, which is, consequently, effective in enhancing the understanding of learning materials by students.
Correction: Russo, B., et al. Assessment of Urban Flood Resilience in Barcelona for Current and Future Scenarios. The RESCCUE Project. Sustainability 2020, 12, 5638
Authors: Russo Beniamino , Velasco Marc , Locatelli Luca , Sunyer David , Yubero Daniel , Monjo Robert , Martínez-Gomariz Eduardo , Forero-Ortiz Edwar , Sánchez-Muñoz Daniel , Evans Barry , Gonzalez Gómez Andoni
Published: November 2020

The authors would like to make the following corrections about the published paper [...]
Core Stability and Electromyographic Activity of the Trunk Musculature in Different Woman’s Sports
Authors: Esteban-García Paula , Rubio-Arias Jacobo Á. , Abián-Vicen Javier , Sánchez-Infante Jorge , Jiménez-Díaz José Fernando
Published: November 2020

Volleyball players and gymnasts need strength training to achieve their optimum sport performance. The aims of this study were to describe body composition, strength, performance, and characteristics of trunk muscle activation in volleyball players and gymnasts, and to analyze the differences between the sports. The sample consisted of 40 female athletes: rhythmic gymnasts (n = 24; age 13.95 ± 2.77 years) and volleyball players (n = 16; age 19.81 ± 5.55 years). Body composition, maximum voluntary contraction (MVC) of isometric tests in an isokinetic dynamometer, McGill core endurance test, and surface electromyography (EMGrms) of the trunk muscle during the McGill test and isometric tests were recorded. Rhythmic gymnasts presented lower body composition values than volleyball players (p < 0.05). The volleyball players presented higher isometric strength than rhythmic gymnasts in terms of MVC in trunk flexion (p < 0.05, d = 1.3) and trunk extension (p < 0.001, d = 1.3). EMGrms from the rhythmic gymnasts were greater for trunk flexor muscles (p < 0.01, d = 0.7) and trunk extensor muscles (p < 0.001, d = 1.3) during McGill endurance tests compared to the volleyball players. In the isometric test, EMGrms from the rhythmic gymnasts were greater for trunk flexor muscles in flexion (p < 0.01, d = 0.9) and extension tests (p < 0.05, d = 0.7). In conclusion, the volleyball players exhibited higher peak strength, despite the fact that the gymnasts showed greater muscle activity during the maximum voluntary contraction.
Increased Urban Resilience to Climate Change—Key Outputs from the RESCCUE Project
Authors: Velasco Marc , Russo Beniamino , Monjo Robert , Paradinas César , Djordjević Slobodan , Evans Barry , Martínez-Gomariz Eduardo , Guerrero-Hidalga Maria , Cardoso Maria Adriana , Brito Rita Salgado , Pacheco David
Published: November 2020

RESCCUE is an H2020 research project that aims to help cities around the world to become more resilient to physical, social, and economic challenges, using the water sector as the central point of the approach. Since 2016, RESCCUE has been developing methodologies and tools to support cities increase their resilience. The three RESCCUE cities, Barcelona, Bristol, and Lisbon, have become a testing platform for the cutting-edge technologies developed in RESCCUE but these are also ready to be deployed to different types of cities, with different climate change pressures. This paper presents some of the main outputs generated by RESCCUE. From climate change scenarios to dissemination tools, and from sectorial models to Resilience Action Plans (RAPs), the outputs that have been produced are very diverse, but special focus is put on the urban water cycle and urban floods. All the project results have a common goal: to increase the resilience of cities from around the world, by offering the methodologies and tools so anyone can take advantage of using them and replicate the RESCCUE results.
Intelligent Rework Process Management System under Smart Factory Environment
Authors: Jo Da-Seol , Kim Tae-Woong , Kim Jun-Woo
Published: November 2020

Rework for defective items is very common in practical shopfloors; however, it generally causes unnecessary energy consumptions and operational costs. In order to address this problem, we propose a novel approach called the intelligent rework process management (i-RPM) system. The proposed system is based on intelligent rework policy, which provides a preventive rework procedure for items with latent defects. Such items can be detected before quality tests by applying conventional classification techniques. Moreover, training sets for the classification algorithms can be collected by using modern information and communications technology (ICT) infrastructures. Items with latent defects are not allowed to proceed to the following processes under intelligent rework policy. Instead, they are returned to the preceding processes for rework in order to avoid unnecessary losses on the shopfloor. Consequently, the proposed system helps to achieve a sustainable manufacturing system. Nevertheless, misclassification by the classification model can degrade the performance of intelligent rework policy. Therefore, the i-RPM system is designed to compare rework policies based on classification accuracy and choose the best one of them. For illustration, we applied the i-RPM system to the rework procedure of a steel manufacturer located in Busan, South Korea, and our experiment results revealed that the cost reduction effect of the intelligent rework policy is affected by several input parameters.
Attention to Global Warming and the Success of Environmental Initial Coin Offerings: Empirical Evidence
Authors: Guzmán Alexander , Pinto-Gutiérrez Cristian , Trujillo María-Andrea
Published: November 2020

We analyze the effects of attention to global warming on the success of environmental initial coin offerings (ICOs) measured by the total funding amount raised in the actual ICOs and the long-term survival of the projects. Using a database featuring 324 environmental initial coin offerings between 2017 and 2019, we find that attention to global warming increases the total funding raised in an environmental ICO. Moreover, we find that environmental offerings that occur during periods of greater attention to global warming are significantly less likely to fail in the long term. Our results are consistent with the idea that investor attention as a market-discipline mechanism increases the likelihood of survival for environmental ICOs.
Crash Risk Assessment for Heterogeneity Traffic and Different Vehicle-Following Patterns Using Microscopic Traffic Flow Data
Authors: Shen Jiajun , Yang Guangchuan
Published: November 2020

This paper investigates the impacts of heavy vehicles (HV) on speed variation and assesses the rear-end crash risk for four vehicle-following patterns in a heterogeneous traffic flow condition using three surrogate safety measures: speed variation, time-to-collision (TTC), and deceleration rate to avoid a crash (DRAC). A video-based data collection approach was employed to collect the speed of each individual vehicle and vehicle-following headway; a total of 3859 vehicle-following pairs were identified. Binary logistic regression modeling was employed to assess the impacts of HV percentage on crash risk. TTCs and DRACs were calculated based on the collected traffic flow data. Analytical models were developed to estimate the minimum safe vehicle-following headways for the four vehicle-following patterns. Field data revealed that the variation of speed first increased with HV percentage and reached the maximum when HV percentage was at around 0.35; then, it displayed a decreasing trend with HV percentage. Binary logistic regression modeling results suggest that a high risk of rear-end collision is expected when HV percentage is between 0.19 and 0.5; while, when HV percentage is either below 0.19 or exceed 0.5, a low risk of rear-end collision is anticipated. Analytical modeling results show that the passenger car (PC)-HV vehicle-following pattern requires the largest minimum safe space headway, followed by HV-HV, PC-PC, and HV-PC vehicle-following patterns. Findings from this research present insights to transportation engineers regarding the development of crash mitigation strategies and have the potential to advance the design of real-time in-vehicle forward collision warnings to minimize the risk of rear-end crash.
Multilevel Analysis of Civic Engagement and Effectiveness of Energy Transition Policy in Seoul: The Seoul Eco-Mileage Program
Authors: Kim Jaewan , Jung Tae Yong , Kim Yong Gun
Published: November 2020

The Seoul Eco-mileage Program is a representative project of the One Less Nuclear Power Plant (OLNPP) scheme, which addresses the energy transition policy of Seoul aiming to reduce CO2 emissions. Unlike other governmental projects, the success of this program requires the direct participation of citizens. This study investigates the influence of civic engagement in participant towns (Level 1: 423 administrative towns) and districts (Level 2: 25 administrative districts) on CO2 emissions reduction (kg CO2/membership) throughout 2017 in Seoul via multilevel modeling. Our results show that town-level civic engagement, as measured by voter turnout, is positively correlated with the performance of the Eco-mileage Program. Moreover, when civic engagement factors (social trust, volunteer activities, and citizen participation) at Level 1 interact with Level 2 factors (housing prices, motivation of district leaders, and energy-saving practices) in each district, we observed cross-level interactive effects on CO2 reduction. Among Level 2 factors, the representative institutional capacities, such as financial independence and ordinance, showed positive correlations with the performance efficacy of the energy transition policy of Seoul.
Metric of Highlighting the Synchronicity of Time Series and Its Application in Analyzing the Fundamental Frequencies of the Speaker’s Speech Signal
Authors: Kataeva Elena , Yakimuk Alexey , Konev Anton , Shelupanov Alexander
Published: November 2020

As part of the study, the problem of developing and applying a metric for assessing the degree of similarity of time series is considered, which makes it possible to consider the assumptions about the model of a series when comparing, as well as to compare the values of the corresponding characteristics of the series. Characteristics can be values that describe the structure of a series, or directly the values of the approximating function, which can be obtained using nonparametric statistics methods. One of the directions in which this approach can be applied to assessing the similarity of time series is the study of vocal performances. In practice, the degree of similarity in the performance of melodies by several speakers was analyzed. It was determined that, using the synchronicity metric, it is possible to implement an exercise in which students need to repeat the melody after the teacher. In addition, this approach was applied in the segment identification module with an abrupt change in the sounding of the fundamental frequency. This work is devoted to the modification of the program complex for vocal recognition in order to identify notes with a sharp change in the fundamental frequency. The complex is aimed at carrying out additional independent training in teaching vocals. The use of the software package will allow, in real time, providing feedback to the user with an assessment of the quality of their singing. This should allow students to study not only under the supervision of a teacher, but also independently in the early stages of learning. The basic algorithm of the program recognizes notes without sharp changes in frequencies with high accuracy, which is confirmed by experiments. In order to recognize by the algorithms of the program notes sung vibrato and glissando in singing, a new analysis method based on the metric of time series synchronicity is proposed.
Blockchain-Based Diversion-Point System for Balancing Customer Flow in Shopping Mall
Authors: Wang Liang , Liu Jiayan , Liu Wenyuan , Wang Changwu
Published: November 2020

Changing the store layout of a shopping mall is usually costly in terms of time, resources, and money. Balancing customer flow is obviously an economical way to rationalize the store layout without displacing stores or changing their locations. However, it has long been a big challenge for managers of shopping malls, because it is difficult to build trust among stores for the sake of regulating customer flow. This trust depends on a multi-party cooperation model, of which the agreements are implemented on asymmetric information. Unfortunately, any form of endorsement with human intervention cannot support building trust on asymmetric information. To solve this problem technically, this paper proposes a diversion-point system to dynamically divert part of customer flow from popular stores to less popular ones. The system operates diversion-points and -vouchers on an asymmetric basis. It also employs a Blockchain subsystem to replace the centralized endorsement and preserve the information asymmetry, thereby building trust into the cooperation among customers, the shopping mall, and the stores therein. The evaluation shows that the proposed system is effective in remedying imperfect store layout of the shopping mall.
Generating Optimal Eighth Order Methods for Computing Multiple Roots
Authors: Kumar Deepak , Kumar Sunil , Sharma Janak Raj , d’Amore Matteo
Published: November 2020

There are a few optimal eighth order methods in literature for computing multiple zeros of a nonlinear function. Therefore, in this work our main focus is on developing a new family of optimal eighth order iterative methods for multiple zeros. The applicability of proposed methods is demonstrated on some real life and academic problems that illustrate the efficient convergence behavior. It is shown that the newly developed schemes are able to compete with other methods in terms of numerical error, convergence and computational time. Stability is also demonstrated by means of a pictorial tool, namely, basins of attraction that have the fractal-like shapes along the borders through which basins are symmetric.
Walking Together: Cross-Protection, Genome Conservation, and the Replication Machinery of Citrus tristeza virus
Authors: Folimonova Svetlana Y. , Achor Diann , Bar-Joseph Moshe
Published: November 2020

“Cross-protection”, a nearly 100 years-old virological term, is suggested to be changed to “close protection”. Evidence for the need of such change has accumulated over the past six decades from the laboratory experiments and field tests conducted by plant pathologists and plant virologists working with different plant viruses, and, in particular, from research on Citrus tristeza virus (CTV). A direct confirmation of such close protection came with the finding that “pre-immunization” of citrus plants with the variants of the T36 strain of CTV but not with variants of other virus strains was providing protection against a fluorescent protein-tagged T36-based recombinant virus variant. Under natural conditions close protection is functional and is closely associated both with the conservation of the CTV genome sequence and prevention of superinfection by closely similar isolates. It is suggested that the mechanism is primarily directed to prevent the danger of virus population collapse that could be expected to result through quasispecies divergence of large RNA genomes of the CTV variants continuously replicating within long-living and highly voluminous fruit trees. This review article provides an overview of the CTV cross-protection research, along with a discussion of the phenomenon in the context of the CTV biology and genetics.
Cost-Effectiveness Analysis of BCG Vaccination against Tuberculosis in Indonesia: A Model-Based Study
Authors: Machlaurin Afifah , Dolk Franklin Christiaan Karel , Setiawan Didik , van der Werf Tjipke Sytse , Postma Maarten J.
Published: November 2020

Bacillus Calmette–Guerin (BCG), the only available vaccine for tuberculosis (TB), has been applied for decades. The Indonesian government recently introduced a national TB disease control programme that includes several action plans, notably enhanced vaccination coverage, which can be strengthened through underpinning its favourable cost-effectiveness. We designed a Markov model to assess the cost-effectiveness of Indonesia’s current BCG vaccination programme. Incremental cost-effectiveness ratios (ICERs) were evaluated from the perspectives of both society and healthcare. The robustness of the analysis was confirmed through univariate and probabilistic sensitivity analysis (PSA). Using epidemiological data compiled for Indonesia, BCG vaccination at a price US$14 was estimated to be a cost-effective strategy in controlling TB disease. From societal and healthcare perspectives, ICERs were US$104 and US$112 per quality-adjusted life years (QALYs), respectively. The results were robust for variations of most variables in the univariate analysis. Notably, the vaccine’s effectiveness regarding disease protection, vaccination costs, and case detection rates were key drivers for cost-effectiveness. The PSA results indicated that vaccination was cost-effective even at US$175 threshold in 95% of cases, approximating the monthly GDP per capita. Our findings suggest that this strategy was highly cost-effective and merits prioritization and extension within the national TB programme. Our results may be relevant for other high endemic low- and middle-income countries.