bluuebunny/crossref_metadata_2025 huggingface dataset:
Environmental Assessment of Innovative Paper Recycling Technology Using Product Lifecycle Perspectives
Authors: Ono Yuya , Hayashi Masaaki , Yokoyama Koichiro , Okamura Takehiko , Itsubo Norihiro
Published: March 2020

Paper can be reused to efficiently manage biomass consumption, meaning that it has potential as an environmentally friendly material. On the other hand, because of high energy usage during the recycling process and transportation inefficiencies, there is a call for the development of technologies that can mitigate this environmental burden. This study evaluated, from a lifecycle perspective, a new technology that can collect and recycle paper within the office. This technology can reduce by over 90% the amount of water used compared with the conventional recycled paper that is pulped and bleached once by the dry process. It also eliminates transportation from paper collection facilities to recycling factories, reducing greenhouse gas emissions. This new technology is already in use in Japan, and analyses by user data indicate that evaluation results differ greatly depending on the utilization rate of the machine. In the future, environmental information should be shared by both users and manufacturers, so that users could increase their utilization rate, and manufacturers could develop alternative bonding agents in order to further reduce the total environmental burden.
Prediction of Claims in Export Credit Finance: A Comparison of Four Machine Learning Techniques
Authors: Bärtl Mathias , Krummaker Simone
Published: March 2020

This study evaluates four machine learning (ML) techniques (Decision Trees (DT), Random Forests (RF), Neural Networks (NN) and Probabilistic Neural Networks (PNN)) on their ability to accurately predict export credit insurance claims. Additionally, we compare the performance of the ML techniques against a simple benchmark (BM) heuristic. The analysis is based on the utilisation of a dataset provided by the Berne Union, which is the most comprehensive collection of export credit insurance data and has been used in only two scientific studies so far. All ML techniques performed relatively well in predicting whether or not claims would be incurred, and, with limitations, in predicting the order of magnitude of the claims. No satisfactory results were achieved predicting actual claim ratios. RF performed significantly better than DT, NN and PNN against all prediction tasks, and most reliably carried their validation performance forward to test performance.
Rational Savings Account Models for Backward-Looking Interest Rate Benchmarks
Authors: Macrina Andrea , Skovmand David
Published: March 2020

Interest rate benchmarks are currently undergoing a major transition. The LIBOR benchmark is planned to be discontinued by the end of 2021 and superseded by what ISDA calls an adjusted risk-free rate (RFR). ISDA has recently announced that the LIBOR replacement will most likely be constructed from a compounded running average of RFR overnight rates over a period matching the LIBOR tenor. This new backward-looking benchmark is markedly different when compared with LIBOR. It is measurable only at the end of the term in contrast to the forward-looking LIBOR, which is measurable at the start of the term. The RFR provides a simplification because the cash flows and the discount factors may be derived from the same discounting curve, thus avoiding—on a superficial level—any multi-curve complications. We develop a new class of savings account models and derive a novel interest rate system specifically designed to facilitate a high degree of tractability for the pricing of RFR-based fixed-income instruments. The rational form of the savings account models under the risk-neutral measure enables the pricing in closed form of caplets, swaptions and futures written on the backward-looking interest rate benchmark.
Remote Sensing Applications in Monitoring of Protected Areas: A Bibliometric Analysis
Authors: Duan Peili , Wang Yeqiao , Yin Peng
Published: March 2020

The development of remote sensing platforms and sensors and improvement in science and technology provide crucial support for the monitoring and management of protected areas. This paper presents an analysis of research publications, from a bibliometric perspective, on the remote sensing of protected areas. This analysis is focused on the period from 1991 to 2018. For data, a total of 4546 academic publications were retrieved from the Web of Science database. The VOSviewer software was adopted to evaluate the co-authorships among countries and institutions, as well as the co-occurrences of author keywords. The results indicate an increasing trend of annual publications in the remote sensing of protected areas. This analysis reveals the major topical subjects, leading countries, and most influential institutions around the world that have conducted relevant research in scientific publications; this study also reveals the journals that include the most publications, and the collaborative patterns related to the remote sensing of protected areas. Landsat, MODIS, and LiDAR are among the most commonly used satellites and sensors. Research topics related to protected area monitoring are mainly concentrated on change detection, biodiversity conservation, and climate change impact. This analysis can help researchers and scholars better understand the intellectual structure of the field and identify the future research directions.
Hardware Efficient Solutions for Wireless Air Pollution Sensors Dedicated to Dense Urban Areas
Authors: Banach Marzena , Długosz Rafał , Pauk Jolanta , Talaśka Tomasz
Published: March 2020

This paper proposes novel solutions for the application of air pollution monitoring systems in so called ‘smart cities’. A possibility of the implementation of a relatively dense network of wireless air pollution sensors that can collect and process data in real time was the motive behind our research and investigations. We discuss the concept of the wireless sensor network, taking into account the structure of the urban development in cities and we present a novel signal processing algorithm that may be used to control the communication scheme between particular sensors and an external network. We placed a special emphasis on the computational complexity to facilitate the implementation directly at the transistor level of particular sensors. The algorithm was verified using real data obtained from air pollution sensors installed in Krakow, Poland. To ensure sufficient robustness of the variability of input data, we artificially added high amplitude noise to the real data we obtained. This paper demonstrates the performance of the algorithm. This algorithm allows for the reduction of the noise amplitude by 23 dB and enables a reduction of the number of wireless communication sessions with a base station (BS) by 70%–80%. We also present selected measurement results of a prototype current-mode digital-to-analogue converter to be used in the sensors, for signal resolutions up to 7 bits.
Estimating Mangrove Above-Ground Biomass Using Extreme Gradient Boosting Decision Trees Algorithm with Fused Sentinel-2 and ALOS-2 PALSAR-2 Data in Can Gio Biosphere Reserve, Vietnam
Authors: Pham Tien Dat , Le Nga Nhu , Ha Nam Thang , Nguyen Luong Viet , Xia Junshi , Yokoya Naoto , To Tu Trong , Trinh Hong Xuan , Kieu Lap Quoc , Takeuchi Wataru
Published: March 2020

This study investigates the effectiveness of gradient boosting decision trees techniques in estimating mangrove above-ground biomass (AGB) at the Can Gio biosphere reserve (Vietnam). For this purpose, we employed a novel gradient-boosting regression technique called the extreme gradient boosting regression (XGBR) algorithm implemented and verified a mangrove AGB model using data from a field survey of 121 sampling plots conducted during the dry season. The dataset fuses the data of the Sentinel-2 multispectral instrument (MSI) and the dual polarimetric (HH, HV) data of ALOS-2 PALSAR-2. The performance standards of the proposed model (root-mean-square error (RMSE) and coefficient of determination (R2)) were compared with those of other machine learning techniques, namely gradient boosting regression (GBR), support vector regression (SVR), Gaussian process regression (GPR), and random forests regression (RFR). The XGBR model obtained a promising result with R2 = 0.805, RMSE = 28.13 Mg ha−1, and the model yielded the highest predictive performance among the five machine learning models. In the XGBR model, the estimated mangrove AGB ranged from 11 to 293 Mg ha−1 (average = 106.93 Mg ha−1). This work demonstrates that XGBR with the combined Sentinel-2 and ALOS-2 PALSAR-2 data can accurately estimate the mangrove AGB in the Can Gio biosphere reserve. The general applicability of the XGBR model combined with multiple sourced optical and SAR data should be further tested and compared in a large-scale study of forest AGBs in different geographical and climatic ecosystems.
Example of Monte Carlo Method Uncertainty Evaluation for Above-Water Ocean Colour Radiometry
Authors: Białek Agnieszka , Douglas Sarah , Kuusk Joel , Ansko Ilmar , Vabson Viktor , Vendt Riho , Casal Tânia
Published: March 2020

We describe a method to evaluate an uncertainly budget for the in situ Ocean Colour Radiometric measurements. A Monte Carlo approach is chosen to propagate the measurement uncertainty inputs through the measurements model. The measurement model is designed to address instrument characteristics and uncertainty associated with them. We present the results for a particular example when the radiometers were fully characterised and then use the same data to show a case when such characterisation is missing. This, depending on the measurement and the wavelength, can increase the uncertainty value significantly; for example, the downwelling irradiance at 442.5 nm with fully characterised instruments can reach uncertainty values of 1%, but for the instruments without such characterisation, that value could increase to almost 7%. The uncertainty values presented in this paper are not final, as some of the environmental contributors were not fully evaluated. The main conclusion of this work are the significance of thoughtful instrument characterisation and correction for the most significant uncertainty contributions in order to achieve a lower measurements uncertainty value.
A Modified Model for Electromagnetic Scattering of Sea Surface Covered with Crest Foam and Static Foam
Authors: Li Dongfang , Zhao Zhiqin , Zhao Yanwen , Huang Yuan , Nie Zaiping
Published: March 2020

With the increase of sea surface wind speed, whitecaps will appear on the sea surface. Generally, for Electromagnetic (EM) scattering of the foam-covered sea surface, medium-scale waves are used to replace the breaking waves of the real sea surface. Another treatment in computation is to adopt one of the whitecap coverages and fixed foam layer thickness. In fact, the evolution process of a breaking wave goes through two stages: stage A (crest foam) and stage B (static foam). In this paper, a geometric model of the sea surface covered with crest foam and static foam is established. The coverage ratio of stage A and stage B is proposed for the first time for a given sea state. In addition, different foam layer thickness distributions in each foam for various wind speeds are also considered. Based on the facet scattering theory of sea surface, this paper adopts the modified facet-based scattering model to deal with the scattering contribution of the sea surface and the effect of foam. Finally, in order to verify the accuracy of the geometric modeling and the scattering model of the sea surface, the EM backscattering of sea surface under different sea states are calculated. Simulation results show that the results of the proposed model are more consistent with the measured data than the results of the sea surface covered with individual crest foam or the sea surface covered with individual static foam.
Optimized Segmentation Based on the Weighted Aggregation Method for Loess Bank Gully Mapping
Authors: Ding Hu , Liu Kai , Chen Xiaozheng , Xiong Liyang , Tang Guoan , Qiu Fang , Strobl Josef
Published: March 2020

The Chinese Loess Plateau suffers severe gully erosion. Gully mapping is a fundamental task for gully erosion monitoring in this region. Among the different gully types in the Loess Plateau, the bank gully is usually regarded as the most important source for the generation of sediment. However, approaches for bank gully extraction are still limited. This study put forward an integrated framework, including segmentation optimization, evaluation and Extreme Gradient Boosting (XGBoost)-based classification, for the bank gully mapping of Zhifanggou catchment in the Chinese Loess Plateau. The approach was conducted using a 1-m resolution digital elevation model (DEM), based on unmanned aerial vehicle (UAV) photogrammetry and WorldView-3 imagery. The methodology first divided the study area into different watersheds. Then, segmentation by weighted aggregation (SWA) was implemented to generate multi-level segments. For achieving an optimum segmentation, area-weighted variance (WV) and Moran’s I (MI) were adopted and calculated within each sub-watershed. After that, a new discrepancy metric, the area-number index (ANI), was developed for evaluating the segmentation results, and the results were compared with the multi-resolution segmentation (MRS) algorithm. Finally, bank gully mappings were obtained based on the XGBoost model after fine-tuning. The experiment results demonstrate that the proposed method can achieve superior segmentation compared to MRS. Moreover, the overall accuracy of the bank gully extraction results was 78.57%. The proposed approach provides a credible tool for mapping bank gullies, which could be useful for the catchment-scale gully erosion process.
Inversion and Validation of Improved Marine Gravity Field Recovery in South China Sea by Incorporating HY-2A Altimeter Waveform Data
Authors: Zhang Shengjun , Andersen Ole Baltazar , Kong Xiangxue , Li Hang
Published: March 2020

HaiYang-2A (HY-2A, where ‘Haiyang’ means ‘Ocean’ in Chinese) has provided reliable sea surface height observations for gravity with uniform ocean data coverage on a global scale for more than 8 years, particularly with denser across track sampling during the geodetic mission since March 2016. This paper aims at modeling and evaluating the regional marine gravity field at 1′×1′ resolution by incorporating HY-2A altimeter waveform data from 7 complete 168-day cycles in the geodetic mission phase. Initial evaluation indicates that, firstly, the measurements in the geodetic mission stay at a consistent accuracy level with observations at the start-of-life stage according to statistics of discrepancies at crossover points cycle by cycle. Secondly, range precision improvement can be achieved using a two-pass weighted least-squares retracker. Thirdly, a downsampling procedure combined with a low-pass filter is designed for HY-2A 20 Hz data to obtain 5 Hz measurements with enhanced precision. We calculate the 1′×1′ marine gravity field model over the South China Sea area by using the EGM2008 model as a reference field with the remove/restore method. The verifications with published models and shipborne gravimetric data show that HY-2A GM data is capable of improving marine gravity field modeling. Results show slightly higher accuracy than other models with similar input datasets but not including HY-2A. The accuracy is also compared with the latest DTU17 and SIO V27.1 model.
Annular Neighboring Points Distribution Analysis: A Novel PLS Stem Point Cloud Preprocessing Algorithm for DBH Estimation
Authors: Duanmu Jialong , Xing Yanqiu
Published: March 2020

Personal laser scanning (PLS) has significant potential for estimating the in-situ diameter of breast height (DBH) with high efficiency and precision, which improves the understanding of forest structure and aids in building carbon cycle models in the big data era. PLS collects more complete stem point cloud data compared with the present laser scanning technology. However, there is still no significant advantage of DBH estimation accuracy. Because the error caused by merging different point cloud fragments has not yet been eliminated, overlapping and inaccurate co-registered point cloud fragments are often inevitable, which are usually the leading error sources of PLS-based DBH estimation. In this study, a novel pre-processing algorithm named annular neighboring points distribution analysis (ANPDA) was developed to improve PLS-based DBH estimation accuracy. To reduce the impact of inaccurately co-registered point cloud fragments, ANPDA identified outliers through iterative removal of outermost points and analyzing the distribution of annular neighboring points. Six plots containing 247 trees under different forest conditions were selected to evaluate the ANPDA. Results showed that in the six plots, error reductions of 53.80–87.13% for bias, 38.82–57.30% for mean absolute error (MAE), and 27.17–56.02% for root mean squared error (RMSE) were achieved after applying ANPDA. These results confirmed that ANPDA was generally effective for improving PLS-based DBH estimation accuracy. It appeared that ANPDA could be conveniently fused with an automatic PLS-based DBH estimation process as a preprocessing algorithm. Furthermore, it has the potential to predict and warn operators of potential large errors during hierarchical semi-automatic DBH estimation.
Responses of Seasonal Indicators to Extreme Droughts in Southwest China
Authors: Lai Peiyu , Zhang Miao , Ge Zhongxi , Hao Binfei , Song Zengjing , Huang Jing , Ma Mingguo , Yang Hong , Han Xujun
Published: March 2020

Significant impact of extreme droughts on human society and ecosystem has occurred in many places of the world, for example, Southwest China (SWC). Considerable research concentrated on analyzing causes and effects of droughts in SWC, but few studies have examined seasonal indicators, such as variations of surface water and vegetation phenology. With the ongoing satellite missions, more and more earth observation data become available to environmental studies. Exploring the responses of seasonal indicators from satellite data to drought is helpful for the future drought forecast and management. This study analyzed the seasonal responses of surface water and vegetation phenology to drought in SWC using the multi-source data including Seasonal Water Area (SWA), Permanent Water Area (PWA), Start of Season (SOS), End of Season (EOS), Length of Season (LOS), precipitation, temperature, solar radiation, evapotranspiration, the Palmer Drought Severity Index (PDSI), the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), Gross Primary Productivity (GPP) and data from water conservancy construction. The results showed that SWA and LOS effectively revealed the development and recovery of droughts. There were two obvious drought periods from 2000 to 2017. In the first period (from August 2003 to June 2007), SWA decreased by 11.81% and LOS shortened by 5 days. They reduced by 21.04% and 9 days respectively in the second period (from September 2009 to June 2014), which indicated that there are more severe droughts in the second period. The SOS during two drought periods delayed by 3~6 days in spring, while the EOS advanced 1~3 days in autumn. All of PDSI, SWA and LOS could reflect the period of droughts in SWC, but the LOS and PDSI were very sensitive to the meteorological events, such as precipitation and temperature, while the SWA performed a more stable reaction to drought and could be a good indicator for the drought periodicity. This made it possible for using SWA in drought forecast because of the strong correlation between SWA and drought. Our results improved the understanding of seasonal responses to extreme droughts in SWC, which will be helpful to the drought monitoring and mitigation for different seasons in this ecologically fragile region.
Volcanic Hot-Spot Detection Using SENTINEL-2: A Comparison with MODIS–MIROVA Thermal Data Series
Authors: Massimetti Francesco , Coppola Diego , Laiolo Marco , Valade Sébastien , Cigolini Corrado , Ripepe Maurizio
Published: March 2020

In the satellite thermal remote sensing, the new generation of sensors with high-spatial resolution SWIR data open the door to an improved constraining of thermal phenomena related to volcanic processes, with strong implications for monitoring applications. In this paper, we describe a new hot-spot detection algorithm developed for SENTINEL-2/MSI data that combines spectral indices on the SWIR bands 8a-11-12 (with a 20-meter resolution) with a spatial and statistical analysis on clusters of alerted pixels. The algorithm is able to detect hot-spot-contaminated pixels (S2Pix) in a wide range of environments and for several types of volcanic activities, showing high accuracy performances of about 1% and 94% in averaged omission and commission rates, respectively, underlining a strong reliability on a global scale. The S2-derived thermal trends, retrieved at eight key-case volcanoes, are then compared with the Volcanic Radiative Power (VRP) derived from MODIS (Moderate Resolution Imaging Spectroradiometer) and processed by the MIROVA (Middle InfraRed Observation of Volcanic Activity) system during an almost four-year-long period, January 2016 to October 2019. The presented data indicate an overall excellent correlation between the two thermal signals, enhancing the higher sensitivity of SENTINEL-2 to detect subtle, low-temperature thermal signals. Moreover, for each case we explore the specific relationship between S2Pix and VRP showing how different volcanic processes (i.e., lava flows, domes, lakes and open-vent activity) produce a distinct pattern in terms of size and intensity of the thermal anomaly. These promising results indicate how the algorithm here presented could be applicable for volcanic monitoring purposes and integrated into operational systems. Moreover, the combination of high-resolution (S2/MSI) and moderate-resolution (MODIS) thermal timeseries constitutes a breakthrough for future multi-sensor hot-spot detection systems, with increased monitoring capabilities that are useful for communities which interact with active volcanoes.
Improved Winter Wheat Spatial Distribution Extraction Using A Convolutional Neural Network and Partly Connected Conditional Random Field
Authors: Wang Shouyi , Xu Zhigang , Zhang Chengming , Zhang Jinghan , Mu Zhongshan , Zhao Tianyu , Wang Yuanyuan , Gao Shuai , Yin Hao , Zhang Ziyun
Published: March 2020

Improving the accuracy of edge pixel classification is crucial for extracting the winter wheat spatial distribution from remote sensing imagery using convolutional neural networks (CNNs). In this study, we proposed an approach using a partly connected conditional random field model (PCCRF) to refine the classification results of RefineNet, named RefineNet-PCCRF. First, we used an improved RefineNet model to initially segment remote sensing images, followed by obtaining the category probability vectors for each pixel and initial pixel-by-pixel classification result. Second, using manual labels as references, we performed a statistical analysis on the results to select pixels that required optimization. Third, based on prior knowledge, we redefined the pairwise potential energy, used a linear model to connect different levels of potential energies, and used only pixel pairs associated with the selected pixels to build the PCCRF. The trained PCCRF was then used to refine the initial pixel-by-pixel classification result. We used 37 Gaofen-2 images obtained from 2018 to 2019 of a representative Chinese winter wheat region (Tai’an City, China) to create the dataset, employed SegNet and RefineNet as the standard CNNs, and a fully connected conditional random field as the refinement methods to conduct comparison experiments. The RefineNet-PCCRF’s accuracy (94.51%), precision (92.39%), recall (90.98%), and F1-Score (91.68%) were clearly superior than the methods used for comparison. The results also show that the RefineNet-PCCRF improved the accuracy of large-scale winter wheat extraction results using remote sensing imagery.
Learning to Describe: A New Approach to Computer Vision Based Ancient Coin Analysis
Authors: Cooper Jessica , Arandjelović Ognjen
Published: March 2020

In recent years, a range of problems under the broad umbrella of computer vision based analysis of ancient coins have been attracting an increasing amount of attention. Notwithstanding this research effort, the results achieved by the state of the art in published literature remain poor and far from sufficiently well performing for any practical purpose. In the present paper we present a series of contributions which we believe will benefit the interested community. We explain that the approach of visual matching of coins, universally adopted in existing published papers on the topic, is not of practical interest because the number of ancient coin types exceeds by far the number of those types which have been imaged, be it in digital form (e.g., online) or otherwise (traditional film, in print, etc.). Rather, we argue that the focus should be on understanding the semantic content of coins. Hence, we describe a novel approach—to first extract semantic concepts from real-world multimodal input and associate them with their corresponding coin images, and then to train a convolutional neural network to learn the appearance of these concepts. On a real-world data set, we demonstrate highly promising results, correctly identifying a range of visual elements on unseen coins with up to 84% accuracy.
Study of the Oxidative Forced Degradation of Glutathione in Its Nutraceutical Formulations Using Zone Fluidics and Green Liquid Chromatography
Authors: Tsiasioti Apostolia , Zacharis Constantinos K. , Zotou Anastasia-Stella , Tzanavaras Paraskevas D.
Published: March 2020

In the present study, we report the results of our investigation of the oxidative forced degradation of glutathione in its nutraceutical formulations by two validated analytical methods. The first is based on the reaction of glutathione with o-phthalaldehyde through an automated zone fluidics flow platform and fluorimetric detection (λex/λem = 340/425 nm). The second is based on the separation of glutathione and its oxidation product by a green reversed-phase HPLC method coupled to direct UV detection, at 210 nm. A solution of 3% w/v H2O2 provided fast oxidation of more than 95% of glutathione to yield oxidized glutathione in a time period of 180 min. The mechanism of the oxidation was proved to follow pseudo-first order kinetics. The k, t90 and t1/2 values were calculated.
Border Residents’ Perceptions of Crime and Security in El Paso, Texas
Authors: Castañeda Ernesto , Chiappetta Casey
Published: March 2020

Research has continued to show the overall safety of the U.S. border region contrary to the widespread belief about the insecurity of the U.S.-Mexico border and frequent claims for the need to secure the border in order to prevent the spread of violence into the rest of the country. Rarely do we ask how border residents feel about safety and crime, which could shed significant light on the claims that the border is an insecure warzone posing a threat to the entire country. While calls to secure national borders are common, outsiders’ perceptions of an unsafe border are not supported by official crime rates and statistics, Border Patrol apprehensions, or the everyday experiences of people in American cities along the U.S.-Mexico border. This paper investigates the perception of crime and security, as expressed by the residents of El Paso, Texas, a large city located along the U.S.-Mexico border and directly across from Ciudad Juárez, Mexico. Data come from a National Institutes of Health (NIH) funded survey that asked 919 residents about their perceptions of crime, sense of security and safety in their neighborhood and the city in general. The results show that the overwhelming majority of border city residents feel safe and that those who are undocumented and raised in El Paso are the most likely to report feeling safe or very safe. We also find that the foreign-born population had a statistically significant lower felony conviction rate than those who were U.S.-born, an important qualifier in discussions over immigration and its connection with violence and crime. Contrary to sensationalized claims about border violence, residents of El Paso do not display any of the sense of insecurity experienced in neighboring Ciudad Juárez, Mexico. We present hypotheses about possible causes for these low levels of violence in the U.S.-side of the border and discuss the dissonance between the reality on the border and perception outside of the border region.
Culinary Tourism as An Effective Strategy for a Profitable Cooperation between Agriculture and Tourism
Authors: Di-Clemente Elide , Hernández-Mogollón José Manuel , López-Guzmán Tomás
Published: March 2020

The relationships between tourism and agriculture have traditionally been studied due to the positive impacts they can potentially have on the development of rural economies. This research puts forward a new interpretation of the cooperation between these two sectors, involving culinary tourism as a key factor. An empirical study was carried out and 720 tourists were interviewed while visiting the city of Cáceres, Extremadura, in south-western Spain. Results show that (i) food and traditional gastronomy are the main motivators of modern tourists, (ii) their income level is above average, (iii) tourists who travel with a culinary motivation are more likely to consume, in their daily diet, traditional products from the visited destination, generating positive impacts to the destination over the long term. The paper reaches significant conclusions on the management and marketing of destinations and the development of traditional rural economies.
The Influence of the Objective and Subjective Physical Neighbourhood Environment on the Physical Activity of Older Adults: A Case Study in the Malaysian Neighbourhoods of Johor Bahru
Authors: Nordin Nadhirah , Nakamura Hitoshi
Published: March 2020

Physical neighbourhood environments (PNE) can affect the active ageing and physical wellbeing of older residents. This paper examined the PNE objective (PNEO): land use mix entropy, population density, traffic intersection density and distance to facilities; and PNE subjective (PNES): the Neighbourhood Environment Walkability Scale-Abbreviated (NEWS-A): and their relationship with the physical activity (PA) levels of older residents. The PA was measured using an IPAQ questionnaire on 280 older residents in neighbourhoods in the city of Johor Bahru, Johor, Malaysia. Cross-tabulations and correlation analyses were conducted to analyse the significant relationships. It was found that PNEO and PNES in the different housing environments influenced the PA levels of the late middle-aged and young-old adults, that the distance to facilities such as mosques and recreation areas should be less than 200 metres, and that high population density areas in Malaysian neighbourhoods are unsuitable for active ageing lifestyles because of the construction of physical barriers. This research identified the PNEO and PNES indicators for the promotion of an active ageing lifestyle in older residents, which could assist in improving existing housing policies and guidelines on active ageing in Malaysian neighbourhoods.
Variability of Soil Water Heat and Energy Transfer Under Different Cover Conditions in a Seasonally Frozen Soil Area
Authors: Meng Fanxiang , Hou Renjie , Li Tianxiao , Fu Qiang
Published: March 2020

In a seasonally frozen soil area, there is frequent energy exchange between soil and environment, which changes the hydrological cycle process, and then has a certain impact on the prediction and management of agricultural soil moisture. To reveal the effects of different modes of regulation on the energy budget of soil in a region with seasonally frozen soil, four treatments, including the regulation of bare land (BL), biochar (CS), and straw (JS), and the combined regulation of biochar and straw (CJS), were used in field experiments. The variations in the soil temperature, liquid water content, and total water content were analyzed, the energy budget of the soil was calculated, the response functions of the soil energy were determined, and the mechanism of soil energy transfer was elucidated. The results showed that, during the freezing period, the JS treatment reduced the amplitudes of the variations in the soil temperature and liquid water content and increased the water content at the soil surface. During the thawing period, the CJS treatment effectively improved the soil hydrothermal conditions. During the freezing period, the heat absorbed by the CS and JS treatments reduced the fluctuation of the soil energy budget. At a soil depth of 10 cm, the spectral entropy of a time series of the soil net energy was 0.837 under BL treatment, and the CS, JS, and CJS treatments decreased by 0.015, 0.059, and 0.045, respectively, compared to the BL treatment. During the thawing period, the CS treatment promoted energy exchange between the soil and the external environment, and the spectral entropy of a time series of the soil net energy was increased; the JS treatment had the opposite effect. The CJS treatment weakened the impact of environmental factors on the soil energy budget during the freezing period, while it enhanced the energy exchange between the soil and the environment during the thawing period. This study can provide important theoretical and technical support for the efficient utilization of soil hydrothermal resources on farmland in cold regions.
Serious Gaming for Climate Adaptation—Assessing the Potential and Challenges of a Digital Serious Game for Urban Climate Adaptation
Authors: Neset Tina-Simone , Andersson Lotta , Uhrqvist Ola , Navarra Carlo
Published: March 2020

Serious gaming has gained increasing prominence in climate change communication, and provides opportunity to engage new audiences and new platforms for knowledge co-creation and dialogues. This paper presents the design and evaluation of a serious game on climate adaptation, primarily targeted towards high school students, practitioners and politicians. The game aims to provide an experience of the impact of climate adaptation measures, and illustrates links with selected Agenda 2030 goals, which the player has to consider, while limiting impacts of hazardous climate events. The game design builds on the key goals in Education for Sustainable Development combining comprehensive views, action competence, learner engagement and pluralism. This study draws on game sessions and surveys with high school students in Sweden, and aims to assess to what extent different aspects of the game can support an increased understanding of the needs and benefits of adaptation actions. The results of this study indicate that the game can engage players to reflect upon challenges related to climate adaptation decision making, but also point towards the challenge of including a high degree of complexity which can make it difficult to grasp consequences of individual measures, as well as to link these to the natural variability of the occurrence of extreme climatic events.
A Resilient and Sustainable Water Sector: Barriers to the Operationalisation of Resilience
Authors: Lawson Elizabeth , Farmani Raziyeh , Woodley Ewan , Butler David
Published: March 2020

Global threats such as climate change, increasing urbanisation, and rapid population growth will continue to pose major challenges for the water sector over the coming decades. Questions over supply, delivery and demand, all form a central part of this argument with the themes of sustainability and resilience often included in the response. Recent events, along with reactive changes to national legislation and policy, have resulted in a need for the notion of resilience to develop from a theoretical concept to a tangible operational method. This commentary discusses barriers to the operationalisation of resilience in the water sector of England and Wales. The current privatised governance structure of the water sector is first discussed before the three main barriers to operationalisation—lack of agreed definition, metrics and the measuring of resilience—and the need to further acknowledge the ‘socio’ in socio-technical systems, are further explored. A deeper understanding of the notion of resilience in the context of the water sector, and how it can be successfully and effectively applied and implemented at an operational level, are crucial if the sector is to manage and respond to the aforementioned global challenges.
The Impact of Exogenous Aerobic Bacteria on Sustainable Methane Production Associated with Municipal Solid Waste Biodegradation: Revealed by High-Throughput Sequencing
Authors: Ge Sai , Ma Jun , Liu Lei , Yuan Zhiming
Published: March 2020

In this work, the impact of exogenous aerobic bacteria mixture (EABM) on municipal solid waste (MSW) is well evaluated in the following aspects: biogas production, leachate analysis, organic waste degradation, EABM population, and the composition of microbial communities. The study was designed and performed as follows: the control bioreactor (R1) was filled up with MSW and the culture medium of EABM and the experimental bioreactor (R2) was filled up with MSW and EABM. The data suggests that the composition of microbial communities (bacterial and methanogenic) in R1 and R2 were similar at day 0, while the addition of EABM in R2 led to a differential abundance of Bacillus cereus, Bacillus subtilis, Staphylococcus saprophyticus, Staphlyoccus xylosus, and Pantoea agglomerans in two bioreactors. The population of exogenous aerobic bacteria in R2 greatly increased during hydrolysis and acidogenesis stages, and subsequently increased the degradation of volatile solid (VS), protein, lipid, and lignin by 59.25%, 25.68%, 60.47%, and 197.62%, respectively, compared to R1. The duration of hydrolysis and acidogenesis in R2 was 33.33% shorter than that in R1. At the end of the study, the accumulative methane yield in R2 (494.4 L) was almost three times more than that in R1 (187.4 L). In addition, the abundance of acetoclasic methanogens increased at acetogenesis and methanogenesis stages in both bioreactors, which indicates that acetoclasic methanogens (especially Methanoseata) could contribute to methane production. This study demonstrates that EABM can accelerate organic waste degradation to promote MSW biodegradation and methane production. Moreover, the operational parameters helped EABM to generate 20.85% more in accumulative methane yield. With a better understanding of how EABM affects MSW and the composition of bacterial community, this study offers a potential practical approach to MSW disposal and cleaner energy generation worldwide.
Experimental Investigation on the Discharge of Pollutants from Tunnel Fires
Authors: Zhai Lihua , Nong Zhongxing , He Guanhong , Xie Baochao , Xu Zhisheng , Zhao Jiaming
Published: March 2020

Many pollutants are generated during tunnel fires, such as smoke and toxic gases. How to control the smoke generated by tunnel fires was focused on in this paper. A series of experiments were carried out in a 1:10 model tunnel with dimensions of 6.0 m × 1.0 m × 0.7 m. The purpose was to investigate the smoke layer thickness and the heat exhaust coefficient of the tunnel mechanical smoke exhaust mode under longitudinal wind. Ethanol was employed as fuel, and the heat release rates were set to be 10.6 kW, 18.6 kW, and 31.9 kW. The exhaust velocity was 0.32–3.16 m/s, and the longitudinal velocity was 0–0.47 m/s. The temperature profile in the tunnel was measured, and the buoyant flow stratification regime was visualized by a laser sheet. The results showed that the longitudinal ventilation leads to a secondary stratification of the smoke flow. In the ceiling extract tunnel under longitudinal ventilation, considering the research results of the smoke layer height and the heat exhaust coefficient, a better scheme for fire-producing pollutants was that an exhaust velocity of 1.26–2.21 m/s (corresponding to the actual velocity of 4.0–7.0 m/s) should be used. The longitudinal velocity should be 0.16–0.32 m/s (corresponding to the actual velocity of 0.5–1.0 m/s).
Spatial Agglomeration Characteristics of Rural Settlements in Poor Mountainous Areas of Southwest China
Authors: Luo Guanglian , Wang Bin , Luo Dongqi , Wei Chaofu
Published: March 2020

The rural settlements in poverty-stricken mountainous areas are the "living fossils" of an economic society with the characteristics of spatial dispersion and are slowly changing. Spatial agglomeration is the development direction of rural settlements. In-depth exploration of the spatial agglomeration characteristics and influencing factors of rural settlements in poverty-stricken mountainous areas is a way to provide a basis for rural settlement restructuring. We selected Pengshui County, a national poverty-stricken county in the southwestern mountainous area of China, as the research area. Spatial buffer and kernel density analysis were used to analyze the agglomeration characteristics of rural settlements and influencing factors. The results show that: (1) The rural settlements are small in scale and the space is evenly dispersed. 55.63% of the rural settlements’ sizes are less than 1000 m2, 84.15% of the rural settlements’ sizes are less than 2500 m2, and 92.81% of the rural settlements are within 200 m. (2) The elevation and slope of topographic factors have a significant agglomeration effect on rural settlements. However, the slope direction has no agglomeration effect. 85.41% of rural settlements (52.75% of rural settlements are gathered between 400 and 800 m above sea level) are gathered at an altitude of 1000 m or less, and 77.59% of rural settlements are gathered with a slope of 6~25°. Additionally, there are few rural settlements with a slope of 0~2°. Moreover, the distribution of residential areas has no agglomeration effect on rural settlements. (3) The cultivated land exerts the most significant effect on rural settlements followed by roads and water sources, while the role of urban land is weak. 99.48% of rural settlements are concentrated in the 100 m area of cultivated land. Therefore, in the poverty-stricken mountainous areas in the southwestern mountainous areas of China, convenient farming is the primary condition for production and living. Rural settlements are highly correlated with cultivated land. Rural settlements are scattered and concentrated with the scattered cultivated land. The rural settlements were leaded by the distribution of cultivated land. Less high-quality cultivated land with less slope were occupied or not by rural residential areas’ people.
Role of Business Incubators as a Tool for Entrepreneurship Development: The Mediating and Moderating Role of Business Start-Up and Government Regulations
Authors: Li Cai , Ahmed Naveed , Qalati Sikandar Ali , Khan Asadullah , Naz Shumaila
Published: March 2020

Business incubators create value by combining the entrepreneurial spirit of start-ups with the resources that are typically available to new businesses. It is widely recognized that knowledge-based entrepreneurial companies are the main creators of economic growth, and such enterprises require special business development services. Therefore, the study aims to examine the role of business incubators in providing greater services (networking services, capital support, and training programs) in entrepreneurship development. Secondly, it also examines the mediating and moderating role of business start-up and government regulations for entrepreneurship. Using a quantitative methodology, we examine 567 samples through structural equation modeling. We find that the business incubators are playing an effective mediating role in providing networking services, capital support, and training programs to individuals and entrepreneurs, which are significant for entrepreneurship development, whereas business start-up positively mediates the relationship between networking services, capital support, training programs, and entrepreneurship development. Government regulations for entrepreneurship have a direct effect on entrepreneurship development. More importantly, government regulations for entrepreneurship have a positive moderating effect between business start-up and entrepreneurship development. Our study identifies the critical resources needed to improve the quality of business incubators and to ensure the availability of such resources to improve entrepreneurship development.
Effects of Architectural Space Layouts on Energy Performance: A Review
Authors: Du Tiantian , Jansen Sabine , Turrin Michela , van den Dobbelsteen Andy
Published: March 2020

As one of the most important design tasks of building design, space layout design affects the building energy performance (BEP). In order to investigate the effect, a literature review of relevant papers was performed. Ten relevant articles were found and reviewed in detail. First, a methodology for studying the effects of space layouts on BEP were proposed regarding design variables, energy indicators and BEP calculation methods, and the methodologies used in the 10 articles were reviewed. Then, the effects of space layouts on energy use and occupant comfort were analysed separately. The results show that the energy use for heating, cooling, lighting and ventilation is highly affected by space layouts, as well as thermal and visual comfort. The effects of space layouts on energy use are higher than on occupant comfort. By changing space layouts, the resulting reductions in the annual final energy for heating and cooling demands were up to 14% and 57%, respectively, in an office building in Sweden. The resulting reductions in the lighting demand of peak summer and winter were up to 67% and 43%, respectively, for the case of an office building in the UK, and the resulting reduction in the air volume supplied by natural ventilation was 65%. The influence of other design parameters, i.e., occupancy and window to wall ratio, on the effects of space layouts on BEP was also identified.
Sustainable Economic Growth Based on R&D Amplification and Technological Content of Exports. Evidences from Romania and The V4 Economies
Authors: Burciu Aurel , Kicsi Rozalia , Bostan Ionel , Condratov Iulian , Hapenciuc Cristian Valentin
Published: March 2020

A vast body of literature recognizes the importance of exports as a driver of economic growth. The increasing share of more sophisticated exports gave birth to the idea that the composition of exports, and not just exports per se, could be crucial for economic dynamics. The aim of this paper was to estimate the relative importance of exports with different technological content on economic growth in Romania and V4 economies, considering R&D (Research and Development) expenditure as a predictor of the pattern of these exports. We used a panel analysis on a data set covering the period of 1995–2017 for Romania and V4 economies. The overall results outline significant differences among selected countries in terms of how the composition of exports influenced economic growth in selected countries. In addition, significant differences were found among selected countries in how R&D expenditure influenced the composition of exports. This paper has clearly shown that the composition of exports has different effects on economic growth in selected countries, mostly explainable by country-specific factors. At the same time, the R&D activity, as the main input in the innovation process, can determine the pattern of the structure of exports. The conclusions of such research could become useful tools in shaping macroeconomic policies focused on sustained growth and long-term economic development, especially in countries concerned about improving their status in the global value chain.