bluuebunny/crossref_metadata_2025 huggingface dataset:
Integration of Geospatial Tools and Multi-source Geospatial Data to Evaluate the Tropical Forest Cover Change in Central America and Its Methodological Replicability in Brazil and the DRC
Authors: Jiménez Abner , Hernández Alexander J. , Rodríguez-Espinosa Víctor M
Published: August 2020

Satellite monitoring of forests plays a relevant role in the agendas of tropical countries, mainly in the framework of international negotiations to implement a mechanism that ensures a reduction in global CO2 emissions from deforestation. An efficient way to approach this monitoring is to avoid duplication of efforts, generating products in a regional context that are subsequently adopted at the national level. In this effort, you should take advantage of the different data sources available by integrating geospatial tools and satellite image classification algorithms. In this research, a methodological framework was developed to generate cost-efficient national maps of forest cover and its dynamics for the countries of Central America, and its scalability and replicability was explored in the Democratic Republic of the Congo (DRC) and the State of Pará in Brazil. The maps were generated from Landsat images from the years 2000, 2012, and 2017. New geoprocessing elements have been incorporated into the digital classification procedures for satellite images, such as the automated extraction of training samples from secondary sources, the use of official national reference maps that respond to nationally adopted forest definitions, and automation of post-classification adjustments incorporating expert criteria. The applied regional approach offers advantages in terms of reducing costs and time, as well as improving the consistency and coherence of reports at different territorial levels (regional and national), reducing duplication of efforts and optimizing technical and financial resources. In Central America, the percentage of forest area decreased from 44% in 2000 to 38% in 2017. Average deforestation in the 2000–2012 period was 197,443 ha/year and that of 2012–2017 was 332,243 ha/year. Average deforestation for the complete period 2000–2017 was 264,843 ha/year. The tropical forests in both the State of Pará, Brazil, and the DRC have decreased over time.
Spatiotemporal Dynamics and Environmental Controlling Factors of the Lake Tana Water Hyacinth in Ethiopia
Authors: Worqlul Abeyou W. , Ayana Essayas K. , Dile Yihun T. , Moges Mamaru A. , Dersseh Minychl G. , Tegegne Getachew , Kibret Solomon
Published: August 2020

The largest freshwater lake in Ethiopia, Lake Tana, has faced ecological disaster due to water hyacinth (Eichhornia crassipes) infestation. The water hyacinth is a threat not only to the ecology but also to the socioeconomic development of the region and cultural value of the lake, which is registered as a UNESCO reserve. This study aims to map the spatiotemporal dynamics of the water hyacinth using high-resolution PlanetScope satellite images and assesses the major environmental variables that relate to the weed spatial coverage dynamics for the period August 2017 to July 2018. The plausible environmental factors studied affecting the weed dynamics include lake level, water and air temperature, and turbidity. Water temperature and turbidity were estimated from the moderate resolution imaging spectroradiometer (MODIS) satellite image and the water level was estimated using Jason-1 altimetry data while the air temperature was obtained from the nearby meteorological station at Bahir Dar station. The results indicated that water hyacinth coverage was increasing at a rate of 14 ha/day from August to November of 2017. On the other hand, the coverage reduced at a rate of 6 ha/day from December 2017 to June 2018. However, the length of shoreline infestation increased significantly from 4.3 km in August 2017 to 23.4 km in April 2018. Lake level and night-time water temperatures were strongly correlated with water hyacinth spatial coverage (p < 0.05). A drop in the lake water level resulted in a considerable reduction of the infested area, which is also related to decreasing nutrient levels in the water. The water hyacinth expansion dynamics could be altered by treating the nutrient-rich runoff with best management practices along the wetland and in the lake watershed landscape.
New JAXA High-Resolution Land Use/Land Cover Map for Vietnam Aiming for Natural Forest and Plantation Forest Monitoring
Authors: Hoang Thanh Tung , Truong Van Thinh , Hayashi Masato , Tadono Takeo , Nasahara Kenlo Nishida
Published: August 2020

Highly detailed and accurate forest maps are important for various applications including forest monitoring, forestry policy, climate change, and biodiversity loss. This study demonstrates a comprehensive and geographically transferable approach to produce a 12 category high-resolution land use/land cover (LULC) map over mainland Vietnam in 2016 by remote sensing data. The map included several natural forest categories (evergreen broadleaf, deciduous (mostly deciduous broadleaf), and coniferous (mostly evergreen coniferous)) and one category representing all popular plantation forests in Vietnam such as acacia (Acacia mangium, Acacia auriculiformis, Acacia hybrid), eucalyptus (Eucalyptus globulus), rubber (Hevea brasiliensis), and others. The approach combined the advantages of various sensor data by integrating their posterior probabilities resulting from applying a probabilistic classifier (comprised of kernel density estimation and Bayesian inference) to each datum individually. By using different synthetic aperture radar (SAR) images (PALSAR-2/ScanSAR, PALSAR-2 mosaic, Sentinel-1), optical images (Sentinel-2, Landsat-8) and topography data (AW3D30), the resultant map achieved 85.6% for the overall accuracy. The major forest classes including evergreen broadleaf forests and plantation forests had a user’s accuracy and producer’s accuracy ranging from 86.0% to 95.3%. Our map identified 9.55 × 106 ha (±0.16 × 106 ha) of natural forests and 3.89 × 106 ha (±0.11 × 106 ha) of plantation forests over mainland Vietnam, which were close to the Vietnamese government’s statistics (with differences of less than 8%). This study’s result provides a reliable input/reference to support forestry policy and land sciences in Vietnam.
Comparative Analysis of Landsat-8, Sentinel-2, and GF-1 Data for Retrieving Soil Moisture over Wheat Farmlands
Authors: Wang Qi , Li Jiancheng , Jin Taoyong , Chang Xin , Zhu Yongchao , Li Yunwei , Sun Jiaojiao , Li Dawei
Published: August 2020

Soil moisture is an important variable in ecological, hydrological, and meteorological studies. An effective method for improving the accuracy of soil moisture retrieval is the mutual supplementation of multi-source data. The sensor configuration and band settings of different optical sensors lead to differences in band reflectivity in the inter-data, further resulting in the differences between vegetation indices. The combination of synthetic aperture radar (SAR) data with multi-source optical data has been widely used for soil moisture retrieval. However, the influence of vegetation indices derived from different sources of optical data on retrieval accuracy has not been comparatively analyzed thus far. Therefore, the suitability of vegetation parameters derived from different sources of optical data for accurate soil moisture retrieval requires further investigation. In this study, vegetation indices derived from GF-1, Landsat-8, and Sentinel-2 were compared. Based on Sentinel-1 SAR and three optical data, combined with the water cloud model (WCM) and the advanced integral equation model (AIEM), the accuracy of soil moisture retrieval was investigated. The results indicate that, Sentinel-2 data were more sensitive to vegetation characteristics and had a stronger capability for vegetation signal detection. The ranking of normalized difference vegetation index (NDVI) values from the three sensors was as follows: the largest was in Sentinel-2, followed by Landsat-8, and the value of GF-1 was the smallest. The normalized difference water index (NDWI) value of Landsat-8 was larger than that of Sentinel-2. With reference to the relative components in the WCM model, the contribution of vegetation scattering exceeded that of soil scattering within a vegetation index range of approximately 0.55–0.6 in NDVI-based models and all ranges in NDWI1-based models. The threshold value of NDWI2 for calculating vegetation water content (VWC) was approximately an NDVI value of 0.4–0.55. In the soil moisture retrieval, Sentinel-2 data achieved higher accuracy than data from the other sources and thus was more suitable for the study for combination with SAR in soil moisture retrieval. Furthermore, compared with NDVI, higher accuracy of soil moisture could be retrieved by using NDWI1 (R2 = 0.623, RMSE = 4.73%). This study provides a reference for the selection of optical data for combination with SAR in soil moisture retrieval.
A Security Concept Based on Scaler Distribution of a Novel Intrusion Detection Device for Wireless Sensor Networks in a Smart Environment
Authors: Boni Kenneth Rodolphe Chabi , Xu Lizhong , Chen Zhe , Baddoo Thelma Dede
Published: August 2020

Following the significant improvement of technology in terms of data collection and treatment during the last decades, the notion of a smart environment has widely taken an important pedestal in the science industry. Built in order to better manage assets, smart environments provide a livable environment for users or citizens through the deployment of sensors responsible for data collection. Much research has been done to provide security to the involved data, which are extremely sensitive. However, due to the small size and the memory constraint of the sensors, many of these works are difficult to implement. In this paper, a different concept for wireless sensor security in smart environments is presented. The proposed security system, which is based on the scaler distribution of a novel electronic device, the intrusion detection system (IDS), reduces the computational functions of the sensors and therefore maximizes their efficiency. The IDS also introduces the concept of the feedback signal and “trust table” used to trigger the detection and isolation mechanism in case of attacks. Generally, it ensures the whole network security through cooperation with other IDSs and, therefore, eliminates the problem of security holes that may occur while adopting such a security technique.
Obstacle Detection and Safely Navigate the Autonomous Vehicle from Unexpected Obstacles on the Driving Lane
Authors: Haris Malik , Hou Jin
Published: August 2020

Nowadays, autonomous vehicle is an active research area, especially after the emergence of machine vision tasks with deep learning. In such a visual navigation system for autonomous vehicle, the controller captures images and predicts information so that the autonomous vehicle can safely navigate. In this paper, we first introduced small and medium-sized obstacles that were intentionally or unintentionally left on the road, which can pose hazards for both autonomous and human driving situations. Then, we discuss Markov random field (MRF) model by fusing three potentials (gradient potential, curvature prior potential, and depth variance potential) to segment the obstacles and non-obstacles into the hazardous environment. Since the segment of obstacles is done by MRF model, we can predict the information to safely navigate the autonomous vehicle form hazardous environment on the roadway by DNN model. We found that our proposed method can segment the obstacles accuracy from the blended background road and improve the navigation skills of the autonomous vehicle.
End-to-End Training for Compound Expression Recognition
Authors: Li Hongfei , Li Qing
Published: August 2020

For a long time, expressions have been something that human beings are proud of. That is an essential difference between us and machines. With the development of computers, we are more eager to develop communication between humans and machines, especially communication with emotions. The emotional growth of computers is similar to the growth process of each of us, starting with a natural, intimate, and vivid interaction by observing and discerning emotions. Since the basic emotions, angry, disgusted, fearful, happy, neutral, sad and surprised are put forward, there are many researches based on basic emotions at present, but few on compound emotions. However, in real life, people’s emotions are complex. Single expressions cannot fully and accurately show people’s inner emotional changes, thus, exploration of compound expression recognition is very essential to daily life. In this paper, we recommend a scheme of combining spatial and frequency domain transform to implement end-to-end joint training based on model ensembling between models for appearance and geometric representations learning for the recognition of compound expressions in the wild. We are mainly devoted to digging the appearance and geometric information based on deep learning models. For appearance feature acquisition, we adopt the idea of transfer learning, introducing the ResNet50 model pretrained on VGGFace2 for face recognition to implement the fine-tuning process. Here, we try and compare two minds, one is that we utilize two static expression databases FER2013 and RAF Basic for basic emotion recognition to fine tune, the other is that we fine tune the model on the input three channels composed of images generated by DWT2 and WAVEDEC2 wavelet transforms based on rbio3.1 and sym1 wavelet bases respectively. For geometric feature acquisition, we firstly introduce a densesift operator to extract facial key points and their histogram descriptions. After that, we introduce deep SAE with a softmax function, stacked LSTM and Sequence-to-Sequence with stacked LSTM and define their structures by ourselves. Then, we feed the salient key points and their descriptions into three models to train respectively and compare their performances. When the model training for appearance and geometric features learning is completed, we combine the two models with category labels to achieve further end-to-end joint training, considering that ensembling models, which describe different information, can further improve recognition results. Finally, we validate the performance of our proposed framework on an RAF Compound database and achieve a recognition rate of 66.97%. Experiments show that integrating different models, which express different information, and achieving end-to-end training can quickly and effectively improve the performance of the recognition.
Skymask Matching Aided Positioning Using Sky-Pointing Fisheye Camera and 3D City Models in Urban Canyons
Authors: Lee Max Jwo Lem , Lee Shang , Ng Hoi-Fung , Hsu Li-Ta
Published: August 2020

3D-mapping-aided (3DMA) global navigation satellite system (GNSS) positioning that improves positioning performance in dense urban areas has been under development in recent years, but it still faces many challenges. This paper details a new algorithm that explores the potential of using building boundaries for positioning and heading estimation. Rather than applying complex simulations to analyze and correct signal reflections by buildings, the approach utilizes a convolutional neural network to differentiate between the sky and building in a sky-pointing fisheye image. A new skymask matching algorithm is then proposed to match the segmented fisheye images with skymasks generated from a 3D building model. Each matched skymask holds a latitude, longitude coordinate and heading angle to determine the precise location of the fisheye image. The results are then compared with the smartphone GNSS and advanced 3DMA GNSS positioning methods. The proposed method provides degree-level heading accuracy, and improved positioning accuracy similar to other advanced 3DMA GNSS positioning methods in a rich urban environment.
NO2 Sensing with SWCNT Decorated by Nanoparticles in Temperature Pulsed Mode: Modeling and Characterization
Authors: Panzardi Enza , Lo Grasso Anna , Vignoli Valerio , Mugnaini Marco , Lupetti Pietro , Fort Ada
Published: August 2020

In this paper, NO2 sensing by means of single-wall carbon nanotubes (SWCNT) networks, decorated with nanoparticles of TiO2 and Au, is proposed. In particular, it is shown that the performance of these materials can be enhanced using pulsed temperature mode. This sensing strategy effectiveness is theoretically and experimentally assessed. In this paper, in fact, a dynamic model for conductive gas sensors formed by networks of nanowires, considering the junctions between different wires as the main contribution to sensor conductance, and in the presence of the target gas, is presented and validated. The model accounts for variable temperature and gas concentration and sheds some light on the mechanisms leading to the sensor response improvement related to temperature pulsed working mode. It is also shown how the addition of a different material can be modeled through different surface adsorption kinetics.
3D Printable Dry EEG Electrodes with Coiled-Spring Prongs
Authors: Kimura Masaya , Nakatani Shintaro , Nishida Shin-Ichiro , Taketoshi Daiju , Araki Nozomu
Published: August 2020

Various dry electroencephalography (EEG) electrodes have been developed. Dry EEG electrodes need to be pressed onto the scalp; therefore, there is a tradeoff between keeping the contact impedance low and maintaining comfort. We propose an approach to solve this tradeoff through the printing of complex-shaped electrodes by using a stereolithography 3D printer. To show the feasibility of our approach, we fabricated electrodes that have flexible fingers (prongs) with springs. Although dry electrodes with flexible prongs have been proposed, a suitable spring constant has not been obtained. In this study, the spring constant of our electrodes was determined from a contact model between the electrodes and the scalp. The mechanical properties and reproductivity of the electrodes were found to be sufficient. Finally, we measured the alpha waves when a participant opened/closed his eyes by using our electrodes.
Establishment of the Complete Closed Mesh Model of Rail-Surface Scratch Data for Online Repair
Authors: Guo Yanbin , Huang Lulu , Liu Yingbin , Liu Jun , Wang Guoping
Published: August 2020

Rail surface scratching occurs with increasing frequency, seriously threatening the safety of vehicles and humans. Online repair of rail-surface scratches on damaged rails with scratch depths >1 mm is of increased importance, because direct rail-replacement has the disadvantages of long operation time, high manpower and high material costs. Advanced online repair of rail-surface scratch using three-dimensional (3D) metal printing technology such as laser cladding has become an increasing trend, desperately demanding a solution for the fast and precise establishment of a complete closed mesh model of rail-surface scratch data. However, there have only been limited studies on the topic so far. In this paper, the complete closed mesh model is well established based on a novel triangulation algorithm relying on the topological features of the point-cloud model (PCM) of scratch-data, which is obtained by implementing a scratch-data-computation process following a rail-geometric-feature-fused algorithm of random sample consensus (RANSAC) performed on the full rail-surface PCM constructed by 3D laser vision. The proposed method is universal for all types of normal-speed rails in China. Experimental results show that the proposed method can accurately acquire the complete closed mesh models of scratch data of one meter of 50 Kg/m-rails within 1 min.
Physiological and Race Pace Characteristics of Medium and Low-Level Athens Marathon Runners
Authors: Myrkos Aristides , Smilios Ilias , Kokkinou Eleni Maria , Rousopoulos Evangelos , Douda Helen
Published: August 2020

This study examined physiological and race pace characteristics of medium- (finish time < 240 min) and low-level (finish time > 240 min) recreational runners who participated in a challenging marathon route with rolling hills, the Athens Authentic Marathon. Fifteen athletes (age: 42 ± 7 years) performed an incremental test, three to nine days before the 2018 Athens Marathon, to determine maximal oxygen uptake (VO2 max), maximal aerobic velocity (MAV), energy cost of running (ECr) and lactate threshold velocity (vLTh), and were analyzed for their pacing during the race. Moderate- (n = 8) compared with low-level (n = 7) runners had higher (p < 0.05) VO2 max (55.6 ± 3.6 vs. 48.9 ± 4.8 mL·kg−1·min−1), MAV (16.5 ± 0.7 vs. 14.4 ± 1.2 km·h−1) and vLTh (11.6 ± 0.8 vs. 9.2 ± 0.7 km·h−1) and lower ECr at 10 km/h (1.137 ± 0.096 vs. 1.232 ± 0.068 kcal·kg−1·km−1). Medium-level runners ran the marathon at a higher percentage of vLTh (105.1 ± 4.7 vs. 93.8 ± 6.2%) and VO2 max (79.7 ± 7.7 vs. 68.8 ± 5.7%). Low-level runners ran at a lower percentage (p < 0.05) of their vLTh in the 21.1–30 km (total ascent/decent: 122 m/5 m) and the 30–42.195 km (total ascent/decent: 32 m/155 m) splits. Moderate-level runners are less affected in their pacing than low-level runners during a marathon route with rolling hills. This could be due to superior physiological characteristics such as VO2 max, ECr, vLTh and fractional utilization of VO2 max. A marathon race pace strategy should be selected individually according to each athlete’s level.
Integrated Development of Information Technology and the Real Economy in China Based on Provincial Panel Data
Authors: Zhang Hui , Qian Yuyao , Yu Liying , Wang Zheng
Published: August 2020

The deep integration of information technology (IT) and the real economy contributes to China’s high-quality economic sustainable development. From the perspective of efficiency, this paper aims to build an evaluation index system, with 30 provinces in China from 2011 to 2017 being taken as the research objects, conduct static and dynamic analysis by using the super-efficiency data envelopment analysis (DEA)-Malmquist index, and draw a spatial distribution diagram to reflect its changes. This study reveals that the integrated development of information technology and the real economy is unbalanced in China. The eastern region performs better with a development slowdown, while the central and western regions lag behind with a lot of room for improvement. The effect of regional agglomeration is obvious, which can be evidenced by the fact that those provinces with a high-efficiency value produce a radiation effect on their neighboring provinces. On the whole, the total factor productivity is increasing annually, with the technical progress being the main factor that affects the total efficiency. According to the results, the countermeasures and suggestions are put forward in this paper to provide reference for a better sustainability development of information technology and the real economy.
Touching Down in Cities: Territorial Planning Instruments as Vehicles for the Implementation of SDG Strategies in Cities of the Global South
Authors: Mejía-Dugand Santiago , Pizano-Castillo Marcela
Published: August 2020

We discuss municipal physical-spatial planning instruments as vehicles for the implementation of Sustainable Development Goals (SDGs) in cities in the Global South. We do this by focusing on Medellin, Colombia, a city that has endured significant challenges–mainly related to poverty and violence–, but has attracted significant international attention due to its approach to territorial planning and its innovative application of new and existing legal tools to transform realities and repay historical debts with the urban poor. We performed a review of the most important documents related to SDG implementation in the country and the city, as well as Municipal Development Plans and legal planning instruments issued from 1 January 2016. The article maps active planning instruments and suggests the analysis, already from the diagnosis and formulation phases, of the linkage among strategies and projects, and SDGs, and the inclusion of SDG considerations in citizen participation instruments such as so-called Local Development Plans.
Human Impact Promotes Sustainable Corn Production in Hungary
Authors: Marton Tibor András , Kis Anna , Zubor-Nemes Anna , Kern Anikó , Fodor Nándor
Published: August 2020

We aim to predict Hungarian corn yields for the period of 2020–2100. The purpose of the study was to mutually consider the environmental impact of climate change and the potential human impact indicators towards sustaining corn yield development in the future. Panel data regression methods were elaborated on historic observations (1970–2018) to impose statistical inferences with simulated weather events (2020–2100) and to consider developing human impact for sustainable intensification. The within-between random effect model was performed with three generic specifications to address time constant indicators as well. Our analysis on a gridded Hungarian database confirms that rising temperature and decreasing precipitation will negatively affect corn yields unless human impact dissolves the climate-induced challenges. We addressed the effect of elevated carbon dioxide (CO2) as an important factor of diverse human impact. By superposing the human impact on the projected future yields, we confirm that the negative prospects of climate change can be defeated.
Managerial Pro-Social Rule Breaking in the Chinese Organizational Context: Conceptualization, Scale Development, and Double-Edged Sword Effect on Employees’ Sustainable Organizational Identification
Authors: Lv Yanyan , Liu Xiaoguang , Li Guomin , Choi Yongrok
Published: August 2020

Managerial Pro-Social Rule Breaking (MPSRB) is a prevalent leadership behavior in China, characterized by conflict between favor and rule. Despite emerging interest in this behavior, two theoretical questions remain unsolved. First, its definition, dimensions, and measurement in the Chinese context are still lacking or improper; second, its double-edged sword effect on employees’ attitude is rarely empirically examined. This paper conducts three studies to solve these questions. In study 1, based on an analysis of the Chinese traditional culture, three dimensions of MPSRB (i.e., benevolence-based, pragmatic-based, and justice-based) were identified. In study 2, a scale of MPSRB containing 12 items was developed through an interview, preliminary, and formal questionnaire survey. In study 3, employees’ sustainable organizational identification perception (SOIDP) was studied as the dependent variable, to analyze and verify the double-edged sword effect of MPSRB by hierarchical regression and structural equation modeling (SEM) methods on the data gathered through the three stages from 380 employees. The results show that the three dimensions of MPSRB have a direct positive impact on employees’ SOIDP and, simultaneously, they have an indirect negative impact through the mediating role of procedural justice perception.
Tyre Weights an Overlooked Diffuse Source of Lead and Antimony to Road Runoff
Authors: Taylor Matthew , Kruger Niklas
Published: August 2020

Lead (Pb) remains elevated in road runoff and roadside dust, which has been attributed to legacy lead in surface soils from leaded petrol. However, “lead” tyre weights, an alloy of 95% Pb and 5% Sb, may be a relatively unrecognised diffuse source of Pb and Sb as they are still used in many countries. An unknown number of these weights drop off tyre rims and deposit on the road where they are abraded and dispersed, potentially causing adverse environmental effects. The type, number and weight of tyre weights lost from motor vehicles were characterised for a range of roading infrastructures and motor vehicle intensities in a 38 month long study of a 6.9 km length of road in Hamilton City, New Zealand. Overall, 1070 tyre weights with a combined mass of 18.6 kg were collected. About 96.4% of the collected weights were made of “lead”, which is an alloy of 95% Pb and 5% Sb, indicating tyre weights can be a major source of Pb and Sb in urban areas. The tyre weight distribution on roads used in this study depended mainly on traffic density and the prevalence of “start stop” patterns in traffic flow influenced by roundabouts and intersections. “Lead” tyre weights should be phased out and replaced with environmentally benign materials.
Machine Learning and Algorithmic Pairs Trading in Futures Markets
Authors: Baek Seungho , Glambosky Mina , Oh Seok Hee , Lee Jeong
Published: August 2020

This study applies machine learning methods to develop a sustainable pairs trading market-neutral investment strategy across multiple futures markets. Cointegrated pairs with similar price trends are identified, and a hedge ratio is determined using an Error Correction Model (ECM) framework and support vector machine algorithm based upon the two-step Engle–Granger method. The study shows that normal backwardation and contango do not consistently characterize futures markets, and an algorithmic pairs trading strategy is effective, given the unique predominant price trends of each futures market. Across multiple futures markets, the pairs trading strategy results in larger risk-adjusted returns and lower exposure to market risk, relative to an appropriate benchmark. Backtesting is employed and results show that the pairs trading strategy may hedge against unexpected negative systemic events, specifically the COVID-19 pandemic, remaining profitable over the period examined.
Improvement and Application of Key Pasture Theory for the Evaluation of Forage–Livestock Balance in the Seasonal Grazing Regions of China’s Alpine Desert Grasslands
Authors: Liu Hui , Song Xiaoyu , Qin Lin , Wen Wang , Liu Xiaodi , Hu Zhiqiang , Liu Yu
Published: August 2020

The calculation of theoretical carrying capacity (TCC) is one of the most fundamental tasks for the evaluation of the forage–livestock balance on grassland pastures. At present, the main methods for calculating TCC are the traditional theory (TT) and key pasture theory (KPT), but they both have obvious limitations in practical applications for the seasonal grazing regions in the alpine desert grasslands of China. In this study, the pastures in Wulan County (PWC) were selected as the research area. The unique features of the research area as well as the faulty applications of TT and KPT were fully analyzed, and then a new method named dynamic key pasture theory (DKPT) was established for calculating TCC by improving KPT with the introduction of the two dynamic factors of the livestock slaughter rate (α) and coefficient of grassland productivity (β). TT, KPT and DKPT were respectively used to calculate the TCC of the PWC under different precipitation scenarios. The forage–livestock balance in the PWC determined using DKPT was assessed by the forage–livestock balance index (FLBI). The results showed that the natural processes of grassland supply and livestock demand were significantly imbalanced in time and space and formed a dynamic cycle with four subprocesses, which was the supporting basis of DKPT; DKPT effectively improved the rationality of TCC and offered greater guidance for the evaluation of the forage–livestock balance in the seasonal grazing regions of China’s alpine desert grasslands. In the PWC, the TCCs of different pastures calculated by DKPT were clearly different from those calculated by TT and KPT; the areas of the pastures divided were extremely imbalanced, with a huge surplus of more than 50% in cool-season pastures; in the representative year of 2016, the pastures in the Xisai Basin were underloaded (FLBI = −35.19%) on the whole, while the pastures in the Chaka Basin were overloaded (FLBI = 24.34%).
The Art of Living as a Community: Profiguration, Sustainability, and Social Development in Rapa Nui
Authors: Molina-Luque Fidel
Published: August 2020

This article analyzes the challenges faced by the inhabitants of the island of Rapa Nui in connection with climate change-related environmental and socio-economic problems, and the survival of the islanders’ cultural identity and their very sustainability. A qualitative research methodology was adopted, using observation and in-depth interviews within a life course approach. An innovative and creative methodology was employed, cross-referencing and comparing data from 2011 and 2020. This methodology has led to the further strengthening of a new concept in sociology and the social sciences in general: profiguration (intergenerational and interdependent socialization). Based on the results of this study, some analytically robust descriptions were made of the socio-cultural and environmental situation in Rapa Nui, and of an increasingly sustainable social development model. It is a model of social development that is on the way to being sustainable, intercultural, intergenerational, and promoted by the community.
The Impact of CSR and Financial Distress on Financial Performance—Evidence from Chinese Listed Companies of the Manufacturing Industry
Authors: Wu Liu , Shao Zhen , Yang Changhui , Ding Tao , Zhang Wan
Published: August 2020

This paper explores the impact of corporate social responsibility (CSR) and financial distress on corporate financial performance (CFP) in Chinese listed companies of the manufacturing industry. Covering a total of 1445 manufacturing observations from 2013 to 2018 by matching the China Stock Market & Accounting Research Database (CSMAR) and Ranking CSR Ratings (RKS) database and regression models, we find that CSR has a significant positive impact on CFP, and the relationship is more pronounced for firms that are more stable. Further, the win-win relationship of CSR and CFP is also stronger in state-owned enterprises (SOEs). These empirical results suggest that enterprises should actively embrace CSR in response to the call of the country. At the same time, corporate stability should be increased to enhance the role of CSR in promoting CFP. We provide a quantitative analysis of the CSR, CFP, and financial distress of listed firms, and help to alleviate managers’ concern of CSR fulfillment and risk control.
Sustainability Assessment of Investments Based on a Multiple Criteria Methodological Framework
Authors: Ovezikoglou Paraskevi , Aidonis Dimitrios , Achillas Charisios , Vlachokostas Christos , Bochtis Dionysis
Published: August 2020

The assessment of an investment is currently carried out by using mainly financial tools. This work presents a new model for the assessment of the sustainability of an industrial investment and focuses on the development of a holistic framework with the use of indicators. With the use of multi-criteria decision analysis, the framework evaluates a total of eighteen (18) alternative indicators in order to select the optimal bundle to be used for the assessment of future industrial investments. The proposed indicators are selected based on relevant data from the literature, taking into account the principles of prevention, planning and designing. The alternatives are assessed over four (4) criteria, namely environment, society, economy and technology, which are grounded on the principles of sustainable development. Depending on the special characteristics of the programme that is foreseen to fund the potential investments, the decision-maker is provided with a hierarchized set of indicators over which the alternative investments could be optimally assessed in parallel with widely used indicators that strictly assess economic performance. In the present work, twelve (12) different scenarios are examined, incorporating different values in the coefficients of the criteria. For the majority of the scenarios examined (a sensitivity analysis is also provided), the alternative indicator that is assessed with the highest score is “Resource Savings”, followed by “Recycling” and “Research, Innovation, Development”.
Citizens’ Involvement in E-Government in the European Union: The Rising Importance of the Digital Skills
Authors: Rodriguez-Hevía Luisa Fernanda , Navío-Marco Julio , Ruiz-Gómez Luis Manuel
Published: August 2020

This paper studies what factors influence European citizens in their e-government adoption and use, and whether there are differences within the European Union. A binary logistic regression model has been used to determine the profile of e-government users. The article highlights especially the influence of digital skills, as an increasingly influential factor, and opens up new areas of research such as the uses of e-government depending on the population density or sociodemographic factors. Given a certain lack of e-government studies at a pan-European level, this article provides an empirical comparative analysis in the EU and provides insights into the factors influencing the behaviour of European e-citizens.
Residents’ Spatial Perceptions of Urban Gardens Based on Soundscape and Landscape Differences
Authors: Zhao Zheng , Wang Yanbin , Hou Yilei
Published: August 2020

In this study, four types of urban gardens, namely modern commercial, cultural heritage, historical preservation, and natural and ecological areas, were comprehensively described and analyzed based on soundscape and landscape differences, and a multinomial logit model was used to explore potential influencing factors shaping residents’ preferences regarding urban gardens. The results suggest that variations in soundscape are closely related to the characteristics of the garden areas and the timing of residents’ activities; the noise level measurements at different observation points varied considerably according to the time of day and displayed different trends. Residents strongly preferred natural sounds, and traffic noise was the most disliked category of sound. Further, a negative correlation was found between residents’ subjective perception of loudness and the favorability of the area, while a clear positive correlation existed between the degree of favorability and the degree of landscape-soundscape coordination. Regarding landscape differences, residents generally preferred urban gardens without artificial transformation and prioritized the maintenance of natural landscaping and the historical and cultural heritage of urban gardens. In particular, residents’ preferences reflected the trade-offs between natural and artificial forms of urban gardens. These results offer useful information for planners, developers, and governmental organizations related to urban gardens by providing data to design urban gardens with increased practical value and natural benefits.
Economic Segregation Under the Action of Trading Uncertainties
Authors: Ballante Elena , Bardelli Chiara , Zanella Mattia , Figini Silvia , Toscani Giuseppe
Published: August 2020

We study the distribution of wealth in a market economy in which the trading propensity of the agents is uncertain. Our approach is based on kinetic models for collective phenomena, which, at variance with the classical kinetic theory of rarefied gases, has to face the lack of fundamental principles, which are replaced by empirical social forces of which we have at most statistical information. The proposed kinetic description allows recovering emergent wealth distribution profiles, which are described by the steady states of a Fokker–Planck-type equation with uncertain parameters. A statistical study of the stationary profiles of the Fokker–Planck equation then shows that the wealth distribution can develop a multimodal shape in the presence of observable highly stressful economic situations.
Microwave-Assisted Industrial Scale Cannabis Extraction
Authors: Radoiu Marilena , Kaur Harmandeep , Bakowska-Barczak Anna , Splinter Steven
Published: August 2020

Cannabis is a flowering plant that has long been used for medicinal, therapeutic, and recreational purposes. Cannabis contains more than 500 different compounds, including a unique class of terpeno-phenolic compounds known as cannabinoids. Δ9-tetrahydrocannabinol (THC) and cannabidiol (CBD) are the most extensively studied cannabinoids. They have been associated with the therapeutic and medicinal properties of the cannabis plant and also with its popularity as a recreational drug. In this paper, an industrial method for cannabis extraction using 915 MHz microwaves coupled with continuous flow operation is presented. The main advantages of the microwave-assisted extraction (MAE) are associated to the continuous-flow operation at atmospheric pressure which allows for higher volumes of biomass to be processed in less time than existing extraction methods, with improved extraction efficiency leading to increased final product yields, improved extract consistency and quality because the process does not require stopping and restarting material flows, and ease of scale-up to industrial scale without the use of pressurised batch vessels. Moreover, due to the flexibility of changing the operation conditions, MAE eliminates additional steps required in most extraction methods, such as biomass decarboxylation or winterisation, which typically adds at least a half day to the extraction process. Another factor that sets MAE apart is the ability to achieve high extraction efficiency, i.e., up to 95% of the active compounds from cannabis biomass can be recovered at industrial scale.
Dihydrodinophysistoxin-1 Produced by Dinophysis norvegica in the Gulf of Maine, USA and Its Accumulation in Shellfish
Authors: Deeds Jonathan R. , Stutts Whitney L. , Celiz Mary Dawn , MacLeod Jill , Hamilton Amy E. , Lewis Bryant J. , Miller David W. , Kanwit Kohl , Smith Juliette L. , Kulis David M. , McCarron Pearse , Rauschenberg Carlton D. , Burnell Craig A. , Archer Stephen D. , Borchert Jerry , Lankford Shelley K.
Published: August 2020

Dihydrodinophysistoxin-1 (dihydro-DTX1, (M-H)−m/z 819.5), described previously from a marine sponge but never identified as to its biological source or described in shellfish, was detected in multiple species of commercial shellfish collected from the central coast of the Gulf of Maine, USA in 2016 and in 2018 during blooms of the dinoflagellate Dinophysis norvegica. Toxin screening by protein phosphatase inhibition (PPIA) first detected the presence of diarrhetic shellfish poisoning-like bioactivity; however, confirmatory analysis using liquid chromatography-tandem mass spectrometry (LC-MS/MS) failed to detect okadaic acid (OA, (M-H)−m/z 803.5), dinophysistoxin-1 (DTX1, (M-H)−m/z 817.5), or dinophysistoxin-2 (DTX2, (M-H)−m/z 803.5) in samples collected during the bloom. Bioactivity-guided fractionation followed by liquid chromatography-high resolution mass spectrometry (LC-HRMS) tentatively identified dihydro-DTX1 in the PPIA active fraction. LC-MS/MS measurements showed an absence of OA, DTX1, and DTX2, but confirmed the presence of dihydro-DTX1 in shellfish during blooms of D. norvegica in both years, with results correlating well with PPIA testing. Two laboratory cultures of D. norvegica isolated from the 2018 bloom were found to produce dihydro-DTX1 as the sole DSP toxin, confirming the source of this compound in shellfish. Estimated concentrations of dihydro-DTX1 were >0.16 ppm in multiple shellfish species (max. 1.1 ppm) during the blooms in 2016 and 2018. Assuming an equivalent potency and molar response to DTX1, the authority initiated precautionary shellfish harvesting closures in both years. To date, no illnesses have been associated with the presence of dihydro-DTX1 in shellfish in the Gulf of Maine region and studies are underway to determine the potency of this new toxin relative to the currently regulated DSP toxins in order to develop appropriate management guidance.
Molecular Characterization of Newcastle Disease Viruses Isolated from Chickens in Tanzania and Ghana
Authors: da Silva Ana P. , Aston Emily J. , Chiwanga Gaspar H. , Birakos Ashley , Muhairwa Amandus P. , Kayang Boniface B. , Kelly Terra , Zhou Huaijun , Gallardo Rodrigo A.
Published: August 2020

Newcastle disease (ND) is one of the most challenging infectious diseases affecting poultry production in Africa, causing major economic losses. To date, Newcastle disease virus isolates from several African countries have been grouped into class II NDV genotypes I, IV, V, VI, VII, XI, XIII, XIV, XVII, XVIII and XXI. Although ND is endemic in many African countries, information on circulating genotypes is still scarce. In Tanzania, outbreaks with genotypes V and XIII have been reported. In West and Central Africa, genotypes XIV, XVII, and XVIII are the most predominant. To investigate other genotypes circulating in Tanzania and Ghana, we performed molecular genotyping on isolates from Tanzania and Ghana using the MinION, a third-generation portable sequencing device from Oxford Nanopore Technologies. Using the MinION, we successfully sequenced the NDV F gene hypervariable region of 24 isolates from Tanzania and four samples from Ghana. In Tanzania, genotypes V, VII and XIII were detected. All isolates from Ghana belonged to genotype XVIII. The data obtained in this study reflect the genetic diversity of NDV in Africa and highlight the importance of surveillance for monitoring the distribution of NDV genotypes and viral evolution.
High-Yield Expression and Purification of Recombinant Influenza Virus Proteins from Stably-Transfected Mammalian Cell Lines
Authors: Ecker Jeffrey W. , Kirchenbaum Greg A. , Pierce Spencer R. , Skarlupka Amanda L. , Abreu Rodrigo B. , Cooper R. Ethan , Taylor-Mulneix Dawn , Ross Ted M. , Sautto Giuseppe A.
Published: August 2020

Influenza viruses infect millions of people each year, resulting in significant morbidity and mortality in the human population. Therefore, generation of a universal influenza virus vaccine is an urgent need and would greatly benefit public health. Recombinant protein technology is an established vaccine platform and has resulted in several commercially available vaccines. Herein, we describe the approach for developing stable transfected human cell lines for the expression of recombinant influenza virus hemagglutinin (HA) and recombinant influenza virus neuraminidase (NA) proteins for the purpose of in vitro and in vivo vaccine development. HA and NA are the main surface glycoproteins on influenza virions and the major antibody targets. The benefits for using recombinant proteins for in vitro and in vivo assays include the ease of use, high level of purity and the ability to scale-up production. This work provides guidelines on how to produce and purify recombinant proteins produced in mammalian cell lines through either transient transfection or generation of stable cell lines from plasmid creation through the isolation step via Immobilized Metal Affinity Chromatography (IMAC). Collectively, the establishment of this pipeline has facilitated large-scale production of recombinant HA and NA proteins to high purity and with consistent yields, including glycosylation patterns that are very similar to proteins produced in a human host.
A User-Friendly Software Package to Develop Storm Water Management Model (SWMM) Inputs and Suggest Low Impact Development Scenarios
Authors: Lee Hanyong , Woo Wonhee , Park Youn Shik
Published: August 2020

Increases in imperviousness due to urbanization and industrialization increases stormwater runoff and nonpoint source pollution. Approaches reducing these hydrological impacts include low impact development (LID) methods. Various methods have been developed and applied to date, and an evaluation of stormwater runoff and a reduction of non-point source pollution has been conducted. The Storm Water Management Model (SWMM) is capable of simulating various LID approaches, although selecting and implementing a suitable method for a specific target area, when considering the cost of various low impact development approaches, requires significant time and effort. A software program called Storm Water Management Model–low impact development design program (SWMM-ING), that can be optimally applied to deal with the cost of low impact development methods, was developed in this study. For SWMM-ING, an optimization process was conducted for low impact development, which can reduce stormwater runoff by 10%, suspended solid by 15%, and total phosphorus by 15%. The spatial arrangement and the area of the permeable pavement, bioretention cells, infiltration trenches, and green roofs were determined. Because SWMM-ING has a user-friendly graphical interface, and the optimization process of the low impact development approach is simple and straightforward, it has the advantage of not requiring specialized knowledge.