bluuebunny/crossref_metadata_2025 huggingface dataset:
UAV-Based Multispectral Phenotyping for Disease Resistance to Accelerate Crop Improvement under Changing Climate Conditions
Authors: Chivasa Walter , Mutanga Onisimo , Biradar Chandrashekhar
Published: July 2020

Accelerating crop improvement for increased yield and better adaptation to changing climatic conditions is an issue of increasing urgency in order to satisfy the ever-increasing global food demand. However, the major bottleneck is the absence of high-throughput plant phenotyping methods for rapid and cost-effective data-driven variety selection and release in plant breeding. Traditional phenotyping methods that rely on trained experts are slow, costly, labor-intensive, subjective, and often require destructive sampling. We explore ways to improve the efficiency of crop phenotyping through the use of unmanned aerial vehicle (UAV)-based multispectral remotely sensed data in maize (Zea mays L.) varietal response to maize streak virus (MSV) disease. Twenty-five maize varieties grown in a trial with three replications were evaluated under artificial MSV inoculation. Ground scoring for MSV infection was carried out at mid-vegetative, flowering, and mid-grain filling on a scale of 1 (resistant) to 9 (susceptible). UAV-derived spectral data were acquired at these three different phenological stages in multispectral bands corresponding to Green (0.53–0.57 μm), Red (0.64–0.68 μm), Rededge (0.73–0.74 μm), and Near-Infrared (0.77–0.81 μm). The imagery captured was stitched together in Pix4Dmapper, which generates two types of multispectral orthomosaics: the NoAlpha and the transparent mosaics for each band. The NoAlpha imagery was used as input into QGIS to extract reflectance data. Six vegetation indices were derived for each variety: normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), Rededge NDVI (NDVIrededge), Simple Ratio (SR), green Chlorophyll Index (CIgreen), and Rededge Chlorophyll Index (CIrededge). The Random Forest (RF) classifier was used to evaluate UAV-derived spectral and VIs with and without variable optimization. Correlations between the UAV-derived data and manual MSV scores were significant (R = 0.74–0.84). Varieties were classified into resistant, moderately resistant, and susceptible with overall classification accuracies of 77.3% (Kappa = 0.64) with optimized and 68.2% (Kappa = 0.51) without optimized variables, representing an improvement of ~13.3% due to variable optimization. The RF model selected GNDVI, CIgreen, CIrededge, and the Red band as the most important variables for classification. Mid-vegetative was the most ideal phenological stage for accurate varietal phenotyping and discrimination using UAV-derived multispectral data with RF under artificial MSV inoculation. The results provide a rapid UAV-based remote sensing solution that offers a step-change towards data availability at high spatial (submeter) and temporal (daily/weekly) resolution in varietal analysis for quick and robust high-throughput plant phenotyping, important for timely and unbiased data-driven variety selection and release in plant breeding programs, especially as climate change accelerates.
The Potential of the Least-Squares Spectral and Cross-Wavelet Analyses for Near-Real-Time Disturbance Detection within Unequally Spaced Satellite Image Time Series
Authors: Ghaderpour Ebrahim , Vujadinovic Tijana
Published: July 2020

Near-real-time disturbance detection within the remotely sensed time series has become a crucial task in many environmental applications that can help policymakers and responsible authorities to make rapid decisions and proper actions. Although there are several techniques for the near-real-time monitoring of time series, their reliability in regions with higher latitudes are not yet assessed, particularly in regions with consistent data gaps in certain time periods and with large observational uncertainties. A new method is proposed that determines a stable history period from which the least-squares spectral analysis can detect and classify the changes in newly acquired data. To validate the effectiveness of the method, both simulated and real-world vegetation time series obtained for a region in northern Alberta, Canada, are used, where there are consistent data gaps from November to April each year due to the availability of valid Landsat satellite imagery and climate conditions. Furthermore, the least-squares cross-wavelet analysis is applied to demonstrate how the temperature and precipitation time series can be used for assessment of the results. The proposed method is fast, does not rely on any interpolation methods, leaves the data gap as is, considers the observational uncertainties, and does not depend on thresholds.
Threshold Vegetation Greenness under Water Balance in Different Desert Areas over the Silk Road Economic Belt
Authors: Ma Yu-Jun , Shi Fang-Zhong , Hu Xia , Li Xiao-Yan
Published: July 2020

The sustainability of dryland vegetation growth over the Silk Road Economic Belt is under threat of water shortage, and the determination of water carrying capacity for vegetation is critically essential to balance water supply and water demand for the maintenance of existing ecosystems. To better understand how and why vegetation growth varies in different desert areas, this study first analyzed the spatiotemporal variation of the normalized difference vegetation index (NDVI). Then, we investigated the relationship between NDVI and climatic factors (precipitation, soil water content, air temperature, evapotranspiration), and estimated the threshold NDVI under water balance in different desert areas. Results showed that the higher NDVI was mainly distributed in Kazakhstan, Russia, and Azerbaijan, and it increased in approximately 53% of desert areas from 1982 to 2015 in the whole study region. The mean annual NDVI showed a simultaneous increasing trend in all desert areas from 1982 to 1994, and decreased significantly only in the cold arid desert area (p < 0.01, −0.0067 decade−1) or had no significant change in other desert areas after 1994 (p > 0.01). The climate condition generally appeared as a warming and drying trend in the past 34 years, with varied changing rates in different desert areas. NDVI presented a strong positive relationship with both precipitation and evapotranspiration in most desert areas. The threshold values of the mean annual NDVI under water balance between 1982 and 2015 were approximately 0.1041 (hot arid desert), 0.1337 (cold arid desert), 0.1346 (cold arid semi-desert), 0.0951 (hot arid desert semi-desert), 0.0776 (polar desert tundra), 0.1071 (hot arid desert shrub), 0.1377 (cold arid desert steppe), and 0.0701 (polar desert steppe), respectively. The responses of these threshold values to precipitation were all positive in different desert areas. These results provide an enhanced understanding of vegetation dynamics and ecological conservation, which are of great importance to implementing adaptation and mitigation measures for terrestrial ecosystems over the Silk Road Economic Belt.
A Plant-by-Plant Method to Identify and Treat Cotton Root Rot Based on UAV Remote Sensing
Authors: Wang Tianyi , Thomasson J. Alex , Isakeit Thomas , Yang Chenghai , Nichols Robert L.
Published: July 2020

Cotton root rot (CRR), caused by the fungus Phymatotrichopsis omnivora, is a destructive cotton disease that mainly affects the crop in Texas. Flutriafol fungicide applied at or soon after planting has been proven effective at protecting cotton plants from being infected by CRR. Previous research has indicated that CRR will reoccur in the same regions of a field as in past years. CRR-infected plants can be detected with aerial remote sensing (RS). As unmanned aerial vehicles (UAVs) have been introduced into agricultural RS, the spatial resolution of farm images has increased significantly, making plant-by-plant (PBP) CRR classification possible. An unsupervised classification algorithm, PBP, based on the Superpixel concept, was developed to delineate CRR-infested areas at roughly the single-plant level. Five-band multispectral data were collected with a UAV to test these methods. The results indicated that the single-plant level classification achieved overall accuracy as high as 95.94%. Compared to regional classifications, PBP classification performed better in overall accuracy, kappa coefficient, errors of commission, and errors of omission. The single-plant fungicide application was also effective in preventing CRR.
Demodulation Method of F-P Sensor Based on Wavelet Transform and Polarization Low Coherence Interferometry
Authors: Cui Jiwen , Niu Yizhao , Dang Hong , Feng Kunpeng , Sun Xun , Tan Jiubin
Published: July 2020

Polarized low-coherence interferometry (PLCI) is widely used for the demodulation of Fabry–Perot (F-P) sensors. To avoid the influence of noise and dispersion on interference fringes, this paper proposes a data processing method in which the wavelet tools are applied to extract useful information from the extremum locations and envelope center of the fringes. Firstly, the wavelet threshold denoising (WTD) algorithm is used to remove electrical noise, and the complex Morlet wavelet is used to extract the fringe envelope. Based on this, the envelope center is used to predict the extremum locations of the specified order in its adjacent interval, the predicted locations are used as references to track the exact extremum locations, and the middle location of the peak and valley values is obtained to demodulate the F-P cavity accurately. The validity of this demodulation theory is verified by an air F-P cavity whose cavity length varies from 17 to 20 μm. With a sampling interval of 30 nm, the experimental results indicate that the repeatability accuracy is higher than 6.04 nm, and the resolution is better than 4.0 nm.
The Diverging Understandings of Quality by Coffee Chain Actors—Insights from Colombian Producers and Austrian Roasters
Authors: Quiñones-Ruiz Xiomara F.
Published: July 2020

This paper addresses the understandings of quality recognized by diverse actors along the coffee chain with focus on specialty coffee producers. In the specialty coffee niche, the assessment of (physical) quality acts as a coordination device among chain actors that enables the identification of divergences on how quality is practiced. This paper aims to uncover how quality attributes (e.g., physical, symbolic) are understood (perceived) by value chain actors and are interwoven with quality conventions (e.g., market, green). Explorative interviews were carried out with Colombian producers located in selected coffee regions. A cupping exercise (tasting of coffee) was organized with producers. Afterwards, another informal cupping was done with roasters in Vienna, Austria. By coupling the concepts of quality attributes and conventions, the paper proposes a theoretical framework that connects quality attributes, conventions and the links to value addition (i.e., from parchment coffee to roasted coffee). Results show that the understandings of quality produce risky battles and ruptures among producers to follow certain conventions. The divergences, risky battles and ruptures in the understandings of quality acknowledged by producers are not only based on “roaster-driven” quality definitions penetrating the producers’ community, or the (individual) knowledge gained by producers, but also on the regulatory framework pursued by producers in a country with a strong coffee federation aiming to legitimize and safeguard the product’s reputation. To what extent is sound experimentation allowed, and direct exchange and interaction between producers, buyers and roasters desirable? Specialty coffee represents a differentiation alternative for small producers located in remote rural areas. Therefore, a transparent and inclusive dialogue between chain actors is required to reduce the divergences in the understandings of quality to balance the value appropriation along the chain.
The Spatial Externalities of Tourism Activities in Poverty Reduction
Authors: Ponce Pablo , Aguirre-Padilla Nathalie , Oliveira Cristiana , Álvarez-García José , del Río-Rama María de la Cruz
Published: July 2020

Poverty is one of the main indicators of economic development worldwide, in such a way that one of the Sustainable Development Goals is to eradicate poverty in all its forms worldwide. The objective of this research was to examine the effect of the gross value added (GVA) of tourism on poverty in the 198 contiguous Ecuadorian cantons. The methodology used was the application of a set of spatial econometric models to capture the regional effect of tourism on poverty. Data were obtained from the Central Bank of Ecuador and the National Survey of Employment, Unemployment and Under-Employment of the National Institute of Statistics and Census (INEC). The results show that tourism activities and regional poverty are negatively related; thus, a 1% increase in tourism-related economic activity decreases the regional poverty of the canton itself by 4.31%, and that of neighboring cantons by between 0.7% and 2.4%. The inclusion of the control variables shows that schooling and the mestizo population contribute to reducing the canton’s poverty, since the regions with a high GVA of tourism have high levels of schooling and a mestizo population. Thus, regional poverty increases when the level of schooling increases in neighboring cantons. On the other hand, in cantons with a high Mestizo population, compared to the African-American population, the poverty of the canton and its neighboring regions decreases. Public policy measures which aim at reducing poverty must take into account spatial spills from tourist activity in the cantons.
Enhancing City Sustainability through Smart Technologies: A Framework for Automatic Pre-Emptive Action to Promote Safety and Security Using Lighting and ICT-Based Surveillance
Authors: Vogiatzaki Maria , Zerefos Stelios , Hoque Tania Marzia
Published: July 2020

The scope of the present paper is to promote social, cultural and environmental sustainability in cities by establishing a conceptual framework and the relationship amongst safety in urban public space (UPS), lighting and Information and Communication Technology (ICT)-based surveillance. This framework uses available technologies and tools, as these can be found in urban equipment such as lighting posts, to enhance security and safety in UPS, ensuring protection against attempted criminal activity. Through detailed literary research, publications on security and safety concerning crime and lighting can be divided into two periods, the first one pre-1994, and the second one from 2004–2008. Since then, a significant reduction in the number of publications dealing with lighting and crime is observed, while at the same time, the urban nightscape has been reshaped with the immersion of light-emitting diode (LED) technologies. Especially in the last decade, where most municipalities in the EU28 (European Union of all the member states from the accession of Croatia in 2013 to the withdrawal of the United Kingdom in 2020) are refurbishing their road lighting with LED technology and the consideration of smart networks and surveillance is under development, the use of lighting to deter possible attempted felonies in UPS is not addressed. To capitalize on the potential of lighting as a deterrent, this paper proposes a framework that uses existing technology, namely, dimmable LED light sources, presence sensors, security cameras, as well as emerging techniques such as artificial intelligence (AI)-enabled image recognition algorithms and big data analytics and presents a possible system that could be developed as a stand-alone product to alert possible dangerous situations, deter criminal activity and promote the perception of safety thus linking lighting and ICT-based surveillance towards safety and security in UPS.
Hybrid Deep Learning Algorithm with Open Innovation Perspective: A Prediction Model of Asthmatic Occurrence
Authors: Kim Min-Seung , Lee Jeong-Hee , Jang Yong-Ju , Lee Chan-Ho , Choi Ji-Hye , Sung Tae-Eung
Published: July 2020

Due to recent advancements in industrialization, climate change and overpopulation, air pollution has become an issue of global concern and air quality is being highlighted as a social issue. Public interest and concern over respiratory health are increasing in terms of a high reliability of a healthy life or the social sustainability of human beings. Air pollution can have various adverse or deleterious effects on human health. Respiratory diseases such as asthma, the subject of this study, are especially regarded as ‘directly affected’ by air pollution. Since such pollution is derived from the combined effects of atmospheric pollutants and meteorological environmental factors, and it is not easy to estimate its influence on feasible respiratory diseases in various atmospheric environments. Previous studies have used clinical and cohort data based on relatively a small number of samples to determine how atmospheric pollutants affect diseases such as asthma. This has significant limitations in that each sample of the collections is likely to produce inconsistent results and it is difficult to attempt the experiments and studies other than by those in the medical profession. This study mainly focuses on predicting the actual asthmatic occurrence while utilizing and analyzing the data on both the atmospheric and meteorological environment officially released by the government. We used one of the advanced analytic models, often referred to as the vector autoregressive model (VAR), which traditionally has an advantage in multivariate time-series analysis to verify that each variable has a significant causal effect on the asthmatic occurrence. Next, the VAR model was applied to a deep learning algorithm to find a prediction model optimized for the prediction of asthmatic occurrence. The average error rate of the hybrid deep neural network (DNN) model was numerically verified to be about 8.17%, indicating better performance than other time-series algorithms. The proposed model can help streamline the national health and medical insurance system and health budget management in South Korea much more effectively. It can also provide efficiency in the deployment and management of the supply and demand of medical personnel in hospitals. In addition, it can contribute to the promotion of national health, enabling advance alerts of the risk of outbreaks by the atmospheric environment for chronic asthma patients. Furthermore, the theoretical methodologies, experimental results and implications of this study will be able to contribute to our current issues of global change and development in that the meteorological and environmental data-driven, deep-learning prediction model proposed hereby would put forward a macroscopic directionality which leads to sustainable public health and sustainability science.
Peer-to-Peer Accommodation in Rural Areas of Catalonia: Defining Typologies of Rural Municipalities
Authors: Cors-Iglesias Martí , Gómez-Martín María Belén , Armesto-López Xosé Antón
Published: July 2020

This paper presents findings relating to the territorial processes underlying the distribution and location of P2P accommodation in rural parts of Catalonia by establishing a system for classifying rural municipalities based on the volume and growth rate of the supply of this tourist accommodation, its share of the total supply of tourist accommodation, the pressure on the local population, and the function of this supply of accommodation in the regional tourism context. According to the findings, there are three distinct types of rural municipality: (a) Rural municipalities where the volume and growth of the supply of this accommodation are striking, these municipalities are located in the coastal hinterland and in high mountain areas, both of which enjoy high levels of tourism. This supply of accommodation plays a key role in the expansion of the areas of influence of well-established tourist destinations, (b) rural municipalities where this supply of accommodation presents an average volume and growth rate, located in inland areas of Catalonia and in high mountain regions with non-mass tourism development models specializing in rural tourism. This supply is helping boost tourism by expanding and diversifying the range of accommodation, and (c) rural municipalities where no tourist accommodation was available before the arrival of P2P accommodation. These are municipalities in inland areas with a strong agricultural presence, where P2P accommodation is paving the way for tourism.
The Effects of Team-Game-Tournaments Application towards Learning Motivation and Motor Skills in College Physical Education
Authors: Luo Yu-Jy , Lin Mei-Ling , Hsu Chien-Huei , Liao Chun-Chin , Kao Chun-Chieh
Published: July 2020

Physical education (PE) helps form lifelong learning and exercise habits; therefore, PE courses should be designed to enhance student motivation. Team-game tournaments (TGTs) enable learning in heterogeneous groups and involve positive interdependence, individual accountability, social skills, face-to-face interaction, group processing, and equal opportunities. Therefore, this quasi-experimental pre-test–post-test study investigated the effects of the TGT on learning motivation and motor skills. In this study, 108 students who enrolled in an advanced basketball course from two classes in a Taiwanese university were recruited as participants. Experimental teaching was implemented based on the class patterns, during which the students were divided into experimental and control groups. The control group, consisting of 56 students (46 male and 10 female), received conventional PE. In the TGT experimental group, constituting 52 students (40 male and 12 female), the TGT learning program was implemented. After a 12-week basketball teaching session, the TGT teaching strategy significantly improved student motivation but not motor skill acquisition. Competency level, however, did not significantly affect motivation but was significantly related to motor skill acquisition. Interaction effects between teaching strategy and competency level were non-significant. Despite TGTs enhancing learning motivation, PE teachers are still responsible for teaching rules, knowledge, and skills, engaging team members, and ensuring sufficient time for skill practice.
Recommendations for Developing a BIM for the Purpose of LCA in Green Building Certifications
Authors: Veselka Jakub , Nehasilová Marie , Dvořáková Karolína , Ryklová Pavla , Volf Martin , Růžička Jan , Lupíšek Antonín
Published: July 2020

Building information modeling (BIM) and life cycle assessment (LCA) are two methods that can be helpful when designing buildings with lower environmental impacts. One of the most significant examples of environmental impact assessments in construction is green building certification. Certified buildings have improved performance and greater asset value. In this study, four certification systems were investigated for their potential interconnections with BIM and LCA. The main tasks were (1) to review a BIM-based workflow, (2) assess its usage as an input for the LCA within green certifications, and (3) provide suggestions for developing building models. Building models can be helpful during the design process, but the best results are expected when the specifically described steps are followed. These suggestions aim at improving building models in terms of their usage for green building certifications and particularly for LCA. All the investigated results were clarified and adjusted using a model of a recently finished building in Zug. As reference tools, One Click LCA and a manual process were selected. The outcomes were aligned with those of other studies and confirmed the necessity of good data and management quality for building projects.
A Multi-Class Classification Model for Technology Evaluation
Authors: Lee Juhyun , Kang Jiho , Park Sangsung , Jang Dongsik , Lee Junseok
Published: July 2020

This paper proposes a multi-class classification model for technology evaluation (TE) using patent documents. TE is defined as converting technology quality to its present value; it supports efficient research and development using intellectual property rights–research & development (IP–R&D) and decision-making by companies. Through IP–R&D, companies create their patent portfolios and develop technology management strategies. They protect core patents and use those patents to cooperate with other companies. In modern society, as conversion technology has been rapidly developed, previous TE methods became difficult to apply to technology. This is because they relied on expert-based qualitative methods. Qualitative results are difficult to use to guarantee objectivity. Many previous studies have proposed models for evaluating technology based on patent data to address these limitations. However, those models can lose contextual information during the preprocessing of bibliographic information and require a lexical analyzer suitable for processing terminology in patents. This study uses a lexical analyzer produced using a deep learning structure to overcome this limitation. Furthermore, the proposed method uses quantitative information and bibliographic information of patents as explanatory variables and classifies the technology into multiple classes. The multi-class classification is conducted by sequentially evaluating the value of a technology. This method returns multiple classes in order, enabling class comparison. Moreover, it is model-agnostic, enabling diverse algorithms to be used. We conducted experiments using actual patent data to examine the practical applicability of the proposed methodology. Based on the experiment results, the proposed method was able to classify actual patents into an ordered multi-class. In addition, it was possible to guarantee the objectivity of the results. This is because our model used the information in the patent specification. Furthermore, the model using both quantitative and bibliographic information exhibited higher classification performance than the model using only quantitative information. Therefore, the proposed model can contribute to the sustainable growth of companies by classifying the value of technology into more detailed categories.
The Influence of the Knowledge-Based Network Structure Hole on Enterprise Innovation Performance: The Threshold Effect of R&D Investment Intensity
Authors: Hao Jiajia , Li Chunling , Yuan Runsen , Ahmed Masood , Khan Muhammad Asif , Oláh Judit
Published: July 2020

The purpose of innovation is to consume fewer natural resources in order to create sustainable performance; therefore, innovation can ease the pressure of the ecological load and promote the sustainable development of the economy. Taking the 269 enterprises listed on the main board of the electronic information industry from 2010 to 2019 as samples, using the threshold panel data model, the nonlinear relationship between the knowledge-based network structure hole and the short-term and long-term innovation performance of the enterprises were studied, and the threshold effect of R&D investment intensity was discussed. When the R&D investment intensity is from 1.96% to 15.96%, the knowledge-based network structure hole has a significant positive impact on short-term innovation performance. When the R&D investment intensity is from 5.72% to 10.64%, the knowledge-based network structure hole has a significant positive effect on long-term innovation performance. Lower R&D investment intensity can make the knowledge-based network structure hole promote the increase of short term innovation performance, but to make the knowledge-based network structure hole have a positive impact on long term innovation performance, the R&D investment intensity should be increased by more than 5.72%. When R&D investment intensity is not higher than 15.96%, the knowledge-based network structure hole has a significant positive impact on short term innovation performance, but to make the knowledge-based network structure hole maintain the positive effect on long term innovation performance, R&D investment intensity should not exceed 10.64%. Therefore, enterprises should be guided to optimize the knowledge-based network structure according to the R&D investment intensity in order to improve the short term and long-term innovation performance of an enterprise. These research results can help enterprises to save resources and promote the sustainable development of the economy.
Sustaining International Trade with China: Does ACFTA Improve ASEAN Export Efficiency?
Authors: Alleyne Antonio , Zhang Zhaoyong , Mu Yifei
Published: July 2020

The business of international trade has never been identified as zero-sum. Yet, dependence on regional agreements to provide alternate means for countries to overlook shortcomings persists. China’s recent rise up the trade value-chain has fostered an assumption that the regional agreement provides various advantages to exports from Association of Southeast Asian Nations (ASEAN) member countries. This paper provides an empirical assessment of the ASEAN-China Free Trade Area (ACFTA) impact on ASEAN export efficiency into the Chinese market, in pre and post eras. Using a structured gravity approach to estimate potential export pattern at aggregate and industry level, this research indicates that though not consistent across years, the ACFTA offers improvements to ASEAN exports to China. No clear effects of the ACFTA can be extrapolated from the years surrounding the initial signing (Article 8—2004). However, following its full enactment (2010), the ACFTA resulted in more sustainable trade from ASEAN members towards China, at both the industry and country levels. While seeing improvements in export efficiency during its era, ACFTA’s facilitation of efficiency measures with China came at no expense to other major export destinations.
More Democratic Sustainability Governance through Participatory Knowledge Production? A Framework and Systematic Analysis
Authors: de Hoop Evelien
Published: July 2020

This paper investigates how participatory knowledge production may contribute to more democratic sustainability governance. It develops an analytical framework in order to perform a systematic analysis of the GammaSense project in the Netherlands, on the measurement of gamma radiation by citizens. The paper first of all concludes that the way in which participation takes place throughout each and every stage of the knowledge production process, including technically complex issues such as the design of the measurement system and analytical toolset, has consequences for (a) which aspects of the gamma radiation decision-making process can potentially be democratized; (b) who gains a voice on the issue; (c) which form of democratization process is potentially facilitated. Secondly, the democratizing effects of setting the purpose of knowledge production, defining the research object and developing the methodological toolset are closely intertwined. Finally, providing space for multiple epistemologies and being attentive towards the role of material objects—the issue at hand and the methodological devices—are of crucial importance to realize the democratizing ambitions that the GammaSense project aimed to contribute towards.
Researches of the Impact of the Nominal Power Ratio and Environmental Conditions on the Efficiency of the Photovoltaic System: A Case Study for Poland in Central Europe
Authors: Sarniak Mariusz T.
Published: July 2020

The paper analyzes a case study of the impact of changing the nominal power ratio (NPR) on the efficiency of a PV (photovoltaic) system located in Poland. In the first stage of the research, the acceptable range of variability for NPR was determined based on simulation calculations, taking into account the parameters of PV modules, inverter, and climatic conditions. The second stage was verification tests for two acceptable extreme cases, carried out based on the analysis of detailed data from the monitoring of PV installations. The results of the verification tests for the two considered periods of operation of the PV system with the change of the NPR coefficient from 82% to 98% resulted in an increase in the annual energy yield by 446.2 kWh. On the other hand, higher relative values of generated energy were obtained only for the months with the lowest insolation in December and in January by 8.2 and 6.04 kWh/kWp, respectively. Higher oversizing of the PV generator (for NPR = 82%) also resulted in an increase by 6.4% in the frequency of operation of the PV inverter in the largest power range (2250–2500 W) and a decrease by 3.7% in the frequency in the lowest power range (0–250 W) for the whole year.
Real-Time Measurement of Indoor PM Concentrations on Daily Change of Endocrine Disruptors in Urine Samples of New Mothers
Authors: Kim Dohyeong , Kim Ju Hee , Seo SungChul
Published: July 2020

The recent innovation of IoT-based sensor technologies facilitates real-time monitoring of indoor air pollutants, such as particulate matter (PM), but its dynamic impacts on the level of endocrine disruptors in human body remain understudied. This feasibility study analyzed if the constant measurements of indoor PM concentrations collected at every five minutes are meaningfully associated with the levels of 15 types of endocrine disruptors in urine samples collected three times a day from nine new breastfeeding mothers in Seoul, Korea. Some promising results are observed in terms of detecting cumulative effects of PM10 and PM2.5 on some phthalate metabolites (MnBP, MiBP, MiNP, MCOP, MEOHP and MEHHP), BPA and TCS, at least for some participants. The findings from this study are expected to provide valuable directions for guiding future studies that discover potential associations between indoor PM concentrations and exposure to endocrine disruptors, which is still far from the consensus in the literature. Such efforts should offer empirical and scientific evidences for designing technology-based early warning/alarm services and evidence-based interventions to mitigate the level of exposure to PM and endocrine disruptors in their living environments.
Research Issues in the Process of Cutting Straw into Pieces
Authors: Zastempowski Marcin , Bochat Andrzej
Published: July 2020

Comparative studies of the classical and new design of a chaff cutter’s cutting drum have been conducted within the frames of the study in order to determine their functional characteristics at the time of cutting rye straw into pieces of specific length (into chaff). A classical design of a cutting drum is of the shape of a roller and is suitable for material layer cross-cutting. However, the new drum design has the shape of a double truncated cone and, with the use of it, diagonal cuts in two directions can be realized. The studies were conducted for four designs of cutting drums with cutting angles α = 0° (classical design) and α = 15°, α = 30°, and α = 45° (new design). Three indicators were applied for the assessment: capacity, unit energy consumption, and the unit cutting resistance. The obtained results show that the use of a new drum design makes it possible, depending on the assumed parameters of the cutting process and the material’s compaction degree, to increase the process’s efficiency by up to 25%, lower the unit energy consumption up to 34%, and lower the unit cutting resistance by up to 8%. The conducted experimental studies have unequivocally shown that the use of a new cutting drum design, as compared to the classical one, results in a considerable increase in the cutting process’s performance. Studies on the new design of a chaff cutter’s cutting drum, of the shape of a double truncated cone, belong to original ones that so far have not been described in the available literature.
A Novel Extension of the TOPSIS Method Adapted for the Use of Single-Valued Neutrosophic Sets and Hamming Distance for E-Commerce Development Strategies Selection
Authors: Karabašević Darjan , Stanujkić Dragiša , Zavadskas Edmundas Kazimieras , Stanimirović Predrag , Popović Gabrijela , Predić Bratislav , Ulutaş Alptekin
Published: July 2020

Neutrosophic sets have been recognized as an effective approach in solving complex decision-making (DM) problems, mainly when such problems are related to uncertainties, as published in numerous articles thus far. The use of the three membership functions that can be used to express accuracy, inaccuracy, and indeterminacy during the evaluation of alternatives in multiple-criteria DM can be said to be a significant advantage of these sets. By utilizing these membership functions, neutrosophic sets provide an efficient and flexible approach to the evaluation of alternatives, even if DM problems are related to uncertainty and predictions. On the other hand, the TOPSIS method is a prominent multiple-criteria decision-making method used so far to solve numerous decision-making problems, and many extensions of the TOPSIS method are proposed to enable the use of different types of fuzzy as well as neutrosophic sets. Therefore, a novel extension of the TOPSIS method adapted for the use of single-valued neutrosophic sets was considered in this paper.
Mycotoxins in Feed and Food and the Role of Ozone in Their Detoxification and Degradation: An Update
Authors: Conte Giuseppe , Fontanelli Marco , Galli Francesca , Cotrozzi Lorenzo , Pagni Lorenzo , Pellegrini Elisa
Published: July 2020

Mycotoxins are secondary metabolites produced by some filamentous fungi, which can cause toxicity in animal species, including humans. Because of their high toxicological impacts, mycotoxins have received significant consideration, leading to the definition of strict legislative thresholds and limits in many areas of the world. Mycotoxins can reduce farm profits not only through reduced crop quality and product refusal, but also through a reduction in animal productivity and health. This paper briefly addresses the impacts of mycotoxin contamination of feed and food on animal and human health, and describes the main pre- and post-harvest systems to control their levels, including genetic, agronomic, biological, chemical, and physical methods. It so highlights (i) the lack of effective and straightforward solutions to control mycotoxin contamination in the field, at pre-harvest, as well as later post-harvest; and (ii) the increasing demand for novel methods to control mycotoxin infections, intoxications, and diseases, without leaving toxic chemical residues in the food and feed chain. Thus, the broad objective of the present study was to review the literature on the use of ozone for mycotoxin decontamination, proposing this gaseous air pollutant as a powerful tool to detoxify mycotoxins from feed and food.
Formation Dynamics of Black- and White-Hole Horizons in an Analogue Gravity Model
Authors: Tettamanti Manuele , Parola Alberto
Published: July 2020

We investigate the formation dynamics of sonic horizons in a Bose gas confined in a (quasi) one-dimensional trap. This system is one of the most promising realizations of the analogue gravity paradigm and has already been successfully studied experimentally. Taking advantage of the exact solution of the one-dimensional, hard-core, Bose model (Tonks–Girardeau gas), we show that by switching on a step potential, either a sonic, black-hole-like horizon or a black/white hole pair may form, according to the initial velocity of the fluid. Our simulations never suggest the formation of an isolated white-hole horizon, although a stable stationary solution of the dynamical equations with those properties is analytically found. Moreover, we show that the semiclassical dynamics, based on the Gross–Pitaevskii equation, conforms to the exact solution only in the case of fully subsonic flows while a stationary solution exhibiting a supersonic transition is never reached dynamically.
Dengue: A Minireview
Authors: Harapan Harapan , Michie Alice , Sasmono R. Tedjo , Imrie Allison
Published: July 2020

Dengue, caused by infection of any of four dengue virus serotypes (DENV-1 to DENV-4), is a mosquito-borne disease of major public health concern associated with significant morbidity, mortality, and economic cost, particularly in developing countries. Dengue incidence has increased 30-fold in the last 50 years and over 50% of the world’s population, in more than 100 countries, live in areas at risk of DENV infection. We reviews DENV biology, epidemiology, transmission dynamics including circulating serotypes and genotypes, the immune response, the pathogenesis of the disease as well as updated diagnostic methods, treatments, vector control and vaccine developments.
The Vaginal Virome—Balancing Female Genital Tract Bacteriome, Mucosal Immunity, and Sexual and Reproductive Health Outcomes?
Authors: Happel Anna-Ursula , Varsani Arvind , Balle Christina , Passmore Jo-Ann , Jaspan Heather
Published: July 2020

Besides bacteria, fungi, protists and archaea, the vaginal ecosystem also contains a range of prokaryote- and eukaryote-infecting viruses, which are collectively referred to as the “virome”. Despite its well-described role in the gut and other environmental niches, the vaginal virome remains understudied. With a focus on sexual and reproductive health, we summarize the currently known components of the vaginal virome, its relationship with other constituents of the vaginal microbiota and its association with adverse health outcomes. While a range of eukaryote-infecting viruses has been described to be present in the female genital tract (FGT), few prokaryote-infecting viruses have been described. Literature suggests that various vaginal viruses interact with vaginal bacterial microbiota and host immunity and that any imbalance thereof may contribute to the risk of adverse reproductive health outcomes, including infertility and adverse birth outcomes. Current limitations of vaginal virome research include experimental and analytical constraints. Considering the vaginal virome may represent the missing link in our understanding of the relationship between FGT bacteria, mucosal immunity, and adverse sexual and reproductive health outcomes, future studies evaluating the vaginal microbiome and its population dynamics holistically will be important for understanding the role of the vaginal virome in balancing health and disease.
Protective Immunity Induced by Virus-Like Particle Containing Merozoite Surface Protein 9 of Plasmodium berghei
Authors: Lee Su-Hwa , Kang Hae-Ji , Chu Ki-Back , Basak Swarnendu , Lee Dong-Hun , Moon Eun-Kyung , Quan Fu-Shi
Published: July 2020

Merozoite surface protein 9 (MSP-9) from Plasmodium has shown promise as a vaccine candidate due to its location and possible role in erythrocyte invasion. In this study, we generated virus-like particles (VLPs) targeting P. berghei MSP-9, and investigated the protection against lethal doses of P. berghei in a mouse model. We found that VLP vaccination induced a P. berghei-specific IgG antibody response in the sera and CD4+ and CD8+ T cell populations in blood compared to a naïve control group. Upon challenge infection with P. berghei, vaccinated mice showed a significant increase in CD4+ and CD8+ effector memory T cell and memory B cell populations. Importantly, MSP-9 VLP immunization inhibited levels of the pro-inflammatory cytokines IFN-γ and IL-6 in the spleen and parasite replication in blood, resulting in significantly prolonged survival time. These results suggest that the MSP-9 VLP vaccine may constitute an effective malaria vaccine.
Movimento Escola Sem Partido:
Authors: Rinaldini Guidotti Vitor Hugo
Published: July 2020

Pretendeu-se neste artigo indagar a respeito dos objetivos e efeitos do Movimento Escola Sem Partido (MESP), à luz de contribuições teóricas e analíticas.  Como resultado, percebe-se que não se pode considerar os interesses desse movimento apenas como circunscritos ao âmbito educacional, pois suas ações e as teias que compõem suas alianças ensejam um olhar mais amplo, referente a um projeto que busca, em essência, manter intacta os determinantes da ordem social estabelecida pelo capital.
Produção Artística de um Bebê
Authors: Côrtes Loureiro Bárbara
Published: July 2020

Este é um breve ensaio interessado em discutir implicações da atribuição de lugar social ao feto humano. Nas próximas páginas, debruçando-me sobre um modelo de abordagem da gestação e do feto, investigarei algumas leituras ocidentais de etapas do processo reprodutivo e suas decorrências, atravessadas por possíveis discussões acerca das noções de corpo, coisa e pessoa nesse contexto. Para tanto, tomarei especialmente o exemplo da prática da “Pintura do Ventre Materno”, como trabalhada por Mata & Shimo no campo da Saúde, como objeto inicial para propor reflexões.
Maquegua (Maquewe) y sus habitantes en la Araucanía del siglo XVI: geopolítica y economía aurífera en los términos de la Ciudad Imperial de Chile
Authors: Zavala José Manuel , Medianero Francisco Javier , Zeballos Mirentxu
Published: July 2020

Este artículo muestra el papel jugado por el territorio de Maquegua (Maquewe) y sus habitantes en el marco de la geopolítica y economía aurífera implementadas por los conquistadores españoles en la Araucanía del siglo XVI a partir del estudio de documentación relativa a encomiendas y conflictos armados complementada con un análisis geoarqueológico del territorio. Se obtiene como resultado una localización geográfica de dicho territorio y un análisis casuístico del funcionamiento de la sociedad de conquista española en relación con la población originaria en un área específica.
A WebGIS portal for exploration of deep geothermal energy based on geological and geophysical data
Authors: Vosgerau Henrik , Mathiesen Anders , Sparre Andersen Morten , Boldreel Lars Ole , Hjuler Morten Leth , Kamla Elina , Kristensen Lars , Brogaard Pedersen Christian , Pjetursson Bjarni , Nielsen Lars Henrik
Published: July 2020

The Danish subsurface contains deep geothermal resources which may contribute for hundreds of years to the mixed Danish energy supply (Mathiesen et al. 2009). At present only a limited fraction of these resources are utilised in three existing geothermal power plants in Thisted, Margretheholm and Sønderborg (Fig. 1) where warm formation water is pumped to the surface from a production well and, after heat extraction, returned to the subsurface in injection wells (Fig. 2). Deep geothermal energy has the advantage of being a sustainable and environmentally friendly energy source which is furthermore independent of climate and seasonal variations, in contrast to wind and solar energy. The implementation of deep geothermal energy for district heating replacing conventional energy sources, especially coal and oil, may thus lead to a considerable reduction in the emission of greenhouse gases. There are therefore good reasons to include geothermal energy as a central component in Denmark’s future supply of energy for district heating. Furthermore, heat-demanding industries may consider the possibility to integrate geothermal energy and energy storage in their production process. In order to facilitate the use of geothermal energy, a broad majority in the Danish parliament has granted financial support for initiatives within the geothermal field (Energy policy agreement of March 22, 2012). The present paper deals with one of the outcomes of this agreement, namely a WebGIS portal with an overview of existing and interpreted geological and geophysical data. This will be relevant for all stakeholders in the exploration of deep geothermal resources in the Danish subsurface. The portal focuses on geothermal reservoirs within the 800–3000 m depth interval and provides an overview of the amount and quality of existing geodata, the geological composition of the subsurface, and interpreted thematic products such as geological maps of potential geothermal reservoirs. A comprehensive map from the portal showing onshore and nearoffshore locations where the geological conditions are potentially suitable for extraction of deep geothermal energy in Denmark is shown in Fig. 1. Many of the thematic maps are outcomes of the project The geothermal energy potential in Denmark – reservoir properties, temperature distribution and models for utilization under the programme Sustainable Energy and Environment funded by the Danish Agency for Science, Technology and Innovation.