bluuebunny/crossref_metadata_2025 huggingface dataset:
Dynamic Semi-Quantitative Risk Research in Chemical Plants
Authors: Song Qiusheng , Jiang Peng , Zheng Song , Kong Yaguang , Zhao Ye , Shen Gang
Published: November 2019

When a major accident occurs in a chemical industry park, it directly affects the personal safety of operators and neighboring residents and causes major losses; therefore, we should take measures to strengthen the management of chemical industry parks. This article proposes and analyzes a new dynamic semi-quantitative risk calculation model for chemical plants that can be applied digitally. This model provides a sustainable, standardized, and comprehensive management strategy for the safety management of chemical plants and chemical industry park managers. The model and its determined parameters were applied to the safety management of chemical companies within the chemical industry park of Quzhou, Zhejiang Province. From the point of view of the existing semi-quantitative model, the existing problems of the current model are analyzed, the current model is optimized, and a new dynamic semi-quantitative calculation model scheme is proposed. The new model uses an analytical hierarchy process targeting the factors affecting the risks in chemical plants, and chemical plant semi-quantitative dynamic calculation system consisting of the operator, process/equipment, risk, building environment, safety management, and domino effect, and the comprehensive risk of the chemical plant was calculated. The model is ultimately a real-time quantitative value, but its calculation process can compare and analyze the causes of high risk in a chemical plant as they relate to these six factors. Its implementation requires only software, which will greatly help chemical plant safety management.
Structural Influence and Interactive Binding Behavior of Dopamine and Norepinephrine on the Greek-Key-Like Core of α-Synuclein Protofibril Revealed by Molecular Dynamics Simulations
Authors: Zou Yu , Liu Zhiwei , Zhu Zhiqiang , Qian Zhenyu
Published: November 2019

The pathogenesis of Parkinson’s disease (PD) is closely associated with the aggregation of α-synuclein (αS) protein. Finding the effective inhibitors of αS aggregation has been considered as the primary therapeutic strategy for PD. Recent studies reported that two neurotransmitters, dopamine (DA) and norepinephrine (NE), can effectively inhibit αS aggregation and disrupt the preformed αS fibrils. However, the atomistic details of αS-DA/NE interaction remain unclear. Here, using molecular dynamics simulations, we investigated the binding behavior of DA/NE molecules and their structural influence on αS44–96 (Greek-key-like core of full length αS) protofibrillar tetramer. Our results showed that DA/NE molecules destabilize αS protofibrillar tetramer by disrupting the β-sheet structure and destroying the intra- and inter-peptide E46–K80 salt bridges, and they can also destroy the inter-chain backbone hydrogen bonds. Three binding sites were identified for both DA and NE molecules interacting with αS tetramer: T54–T72, Q79–A85, and F94–K96, and NE molecules had a stronger binding capacity to these sites than DA. The binding of DA/NE molecules to αS tetramer is dominantly driven by electrostatic and hydrogen bonding interactions. Through aromatic π-stacking, DA and NE molecules can bind to αS protofibril interactively. Our work reveals the detailed disruptive mechanism of protofibrillar αS oligomer by DA/NE molecules, which is helpful for the development of drug candidates against PD. Given that exercise as a stressor can stimulate DA/NE secretion and elevated levels of DA/NE could delay the progress of PD, this work also enhances our understanding of the biological mechanism by which exercise prevents and alleviates PD.
Communication on the Science-Policy Interface: An Overview of Conceptual Models
Authors: Sokolovska Nataliia , Fecher Benedikt , Wagner Gert G.
Published: November 2019

This article focuses on scholarly discourse on the science-policy interface, and in particular on questions regarding how this discourse can be understood in the course of history and which lessons we can learn. We aim to structure the discourse, show kinships of different concepts, and contextualize these concepts. For the twentieth century we identify three major phases that describe interactions on the science policy interface: the “linear phase” (1960s–1970s) when science informed policy-making in a unidirectional manner, the “interactive phase” (1970–2000s) when both sides found themselves in a continuous interaction, and the “embedded phase” (starting from the 2000s) when citizens’ voices come to be involved within this dialogue more explicitly. We show that the communicative relationship between science and policy-making has become more complex over time with an increasing number of actors involved. We argue that better skill-building and education can help to improve communication within the science-policy interface.
Appropriation of Caste Spaces in Pakistan: The Theo-Politics of Short Stories in Sindhi Progressive Literature
Authors: Hussain Ghulam
Published: November 2019

This paper is an attempt to understand the appropriation of spaces of Dalits by Sindhi progressive activists and short story writers in Pakistan as they construct, or rather undermine, caste at the anvil of religion and gender to reframe their own theo-political agenda premised on political Sufism or Sufi nationalism. I specifically discuss the narratives emergent of the three popular short stories that are reframed as having exceptional emancipatory potential for the Dalits. Assessing the emancipatory limits of the Sindhi progressive narrative, I argue that while the short stories purport to give fuller expression to religious, gender-based, and class dimensions of the problem, it elides the problem of casteism and the subsequent existential demand of Dalit emancipation. Given the hegemonic influence of local Ashrafia class, the internal caste frictions are glossed over through political Sufism or Sindhi nationalism. This gloss of politicized Sufism hampers Dalit agency and rather facilitates the appropriation of Dalit spaces by the Ashrafia class. This leads to the conclusion that the seemingly progressive literary-political narratives framed in theo-political idiom may offer to the oppressed no more than token sympathy, compassion, self-pity, and false pride in legends. Instead, they allow the appropriation of spaces and events of the oppressed, and the objectification of oppressed bodies by the oppressor.
Hybrid Scene Structuring for Accelerating 3D Radiative Transfer Simulations
Authors: Qi Jianbo , Yin Tiangang , Xie Donghui , Gastellu-Etchegorry Jean-Philippe
Published: November 2019

Three-dimensional (3D) radiative transfer models are the most accurate remote sensing models. However, presently the application of 3D models to heterogeneous Earth scenes is a computationally intensive task. A common approach to reduce computation time is abstracting the landscape elements into simpler geometries (e.g., ellipsoid), which, however, may introduce biases. Here, a hybrid scene structuring approach is proposed to accelerate the radiative transfer simulations while keeping the scene as realistic as possible. In a first step, a 3D description of the Earth landscape with equal-sized voxels is optimized to keep only non-empty voxels (i.e., voxels that contain triangles) and managed using a bounding volume hierarchy (BVH). For any voxel that contains triangles, within-voxel BVHs are created to accelerate the ray–triangle intersection tests. The hybrid scheme is implemented in the Discrete Anisotropic Radiative Transfer (DART) model by integrating the Embree ray-tracing kernels developed at Intel. In this paper, the performance of the hybrid algorithm is compared with the original uniform grid approach implemented in DART for a 3D city scene and a forest scene. Results show that the removal of empty voxels can accelerate urban simulation by 1.4×~3.7×, and that the within-voxel BVH can accelerate forest simulations by up to 258.5×.
A Review of the Applications of Remote Sensing in Fire Ecology
Authors: Szpakowski David , Jensen Jennifer
Published: November 2019

Wildfire plays an important role in ecosystem dynamics, land management, and global processes. Understanding the dynamics associated with wildfire, such as risks, spatial distribution, and effects is important for developing a clear understanding of its ecological influences. Remote sensing technologies provide a means to study fire ecology at multiple scales using an efficient and quantitative method. This paper provides a broad review of the applications of remote sensing techniques in fire ecology. Remote sensing applications related to fire risk mapping, fuel mapping, active fire detection, burned area estimates, burn severity assessment, and post-fire vegetation recovery monitoring are discussed. Emphasis is given to the roles of multispectral sensors, lidar, and emerging UAS technologies in mapping, analyzing, and monitoring various environmental properties related to fire activity. Examples of current and past research are provided, and future research trends are discussed. In general, remote sensing technologies provide a low-cost, multi-temporal means for conducting local, regional, and global-scale fire ecology research, and current research is rapidly evolving with the introduction of new technologies and techniques which are increasing accuracy and efficiency. Future research is anticipated to continue to build upon emerging technologies, improve current methods, and integrate novel approaches to analysis and classification.
Integrating the Continuous Wavelet Transform and a Convolutional Neural Network to Identify Vineyard Using Time Series Satellite Images
Authors: Zhao Longcai , Li Qiangzi , Zhang Yuan , Wang Hongyan , Du Xin
Published: November 2019

Grape is an economic crop of great importance and is widely cultivated in China. With the development of remote sensing, abundant data sources strongly guarantee that researchers can identify crop types and map their spatial distributions. However, to date, only a few studies have been conducted to identify vineyards using satellite image data. In this study, a vineyard is identified using satellite images, and a new approach is proposed that integrates the continuous wavelet transform (CWT) and a convolutional neural network (CNN). Specifically, the original time series of the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and green chlorophyll vegetation index (GCVI) are reconstructed by applying an iterated Savitzky-Golay (S-G) method to form a daily time series for a full year; then, the CWT is applied to three reconstructed time series to generate corresponding scalograms; and finally, CNN technology is used to identify vineyards based on the stacked scalograms. In addition to our approach, a traditional and common approach that uses a random forest (RF) to identify crop types based on multi-temporal images is selected as the control group. The experimental results demonstrated the following: (i) the proposed approach was comprehensively superior to the RF approach; it improved the overall accuracy by 9.87% (up to 89.66%); (ii) the CWT had a stable and effective influence on the reconstructed time series, and the scalograms fully represented the unique time-related frequency pattern of each of the planting conditions; and (iii) the convolution and max pooling processing of the CNN captured the unique and subtle distribution patterns of the scalograms to distinguish vineyards from other crops. Additionally, the proposed approach is considered as able to be applied to other practical scenarios, such as using time series data to identify crop types, map landcover/land use, and is recommended to be tested in future practical applications.
Watson on the Farm: Using Cloud-Based Artificial Intelligence to Identify Early Indicators of Water Stress
Authors: Freeman Daniel , Gupta Shaurya , Smith D. Hudson , Maja Joe Mari , Robbins James , Owen James S. , Peña Jose M. , de Castro Ana I.
Published: November 2019

As demand for freshwater increases while supply remains stagnant, the critical need for sustainable water use in agriculture has led the EPA Strategic Plan to call for new technologies that can optimize water allocation in real-time. This work assesses the use of cloud-based artificial intelligence to detect early indicators of water stress across six container-grown ornamental shrub species. Near-infrared images were previously collected with modified Canon and MAPIR Survey II cameras deployed via a small unmanned aircraft system (sUAS) at an altitude of 30 meters. Cropped images of plants in no, low-, and high-water stress conditions were split into four-fold cross-validation sets and used to train models through IBM Watson’s Visual Recognition service. Despite constraints such as small sample size (36 plants, 150 images) and low image resolution (150 pixels by 150 pixels per plant), Watson generated models were able to detect indicators of stress after 48 hours of water deprivation with a significant to marginally significant degree of separation in four out of five species tested (p < 0.10). Two models were also able to detect indicators of water stress after only 24 hours, with models trained on images of as few as eight water-stressed Buddleia plants achieving an average area under the curve (AUC) of 0.9884 across four folds. Ease of pre-processing, minimal amount of training data required, and outsourced computation make cloud-based artificial intelligence services such as IBM Watson Visual Recognition an attractive tool for agriculture analytics. Cloud-based artificial intelligence can be combined with technologies such as sUAS and spectral imaging to help crop producers identify deficient irrigation strategies and intervene before crop value is diminished. When brought to scale, frameworks such as these can drive responsive irrigation systems that monitor crop status in real-time and maximize sustainable water use.
Evaporation-Rate Control of Water Droplets on Flexible Transparent Heater for Sensor Application
Authors: Park Jaesoung , Lee Suhan , Kim Dong-Ik , Kim Young-You , Kim Samsoo , Kim Han-Jung , Kim Yoonkap
Published: November 2019

To develop high-performance de- or anti-frosting/icing devices based on transparent heaters, it is necessary to study the evaporation-rate control of droplets on heater surfaces. However, almost no research has been done on the evaporation-rate control of liquid droplets on transparent heaters. In this study, we investigate the evaporation characteristics of water droplets on transparent heater surfaces and determine that they depend upon the surface wettability, by modifying which, the complete evaporation time can be controlled. In addition, we study the defrosting and deicing performances through the surface wettability, by placing the flexible transparent heater on a webcam. The obtained results can be used as fundamental data for the transparent defrosting and deicing systems of closed-circuit television (CCTV) camera lenses, smart windows, vehicle backup cameras, aircraft windows, and sensor applications.
Kinetic Energy Harvesting for Wearable Medical Sensors
Authors: Gljušćić Petar , Zelenika Saša , Blažević David , Kamenar Ervin
Published: November 2019

The process of collecting low-level kinetic energy, which is present in all moving systems, by using energy harvesting principles, is of particular interest in wearable technology, especially in ultra-low power devices for medical applications. In fact, the replacement of batteries with innovative piezoelectric energy harvesting devices can result in mass and size reduction, favoring the miniaturization of wearable devices, as well as drastically increasing their autonomy. The aim of this work is to assess the power requirements of wearable sensors for medical applications, and address the intrinsic problem of piezoelectric kinetic energy harvesting devices that can be used to power them; namely, the narrow area of optimal operation around the eigenfrequencies of a specific device. This is achieved by using complex numerical models comprising modal, harmonic and transient analyses. In order to overcome the random nature of excitations generated by human motion, novel excitation modalities are investigated with the goal of increasing the specific power outputs. A solution embracing an optimized harvester geometry and relying on an excitation mechanism suitable for wearable medical sensors is hence proposed. The electrical circuitry required for efficient energy management is considered as well.
Mounting Behaviour Recognition for Pigs Based on Deep Learning
Authors: Li Dan , Chen Yifei , Zhang Kaifeng , Li Zhenbo
Published: November 2019

For both pigs in commercial farms and biological experimental pigs at breeding bases, mounting behaviour is likely to cause damage such as epidermal wounds, lameness and fractures, and will no doubt reduce animal welfare. The purpose of this paper is to develop an efficient learning algorithm that is able to detect the mounting behaviour of pigs based on the data characteristics of visible light images. Four minipigs were selected as experimental subjects and were monitored for a week by a camera that overlooked the pen. The acquired videos were analysed and the frames containing mounting behaviour were intercepted as positive samples of the dataset, and the images with inter-pig adhesion and separated pigs were taken as negative samples. Pig segmentation network based on Mask Region-Convolutional Neural Networks (Mask R-CNN) was applied to extract individual pigs in the frames. The region of interest (RoI) parameters and mask coordinates of each pig, from which eigenvectors were extracted, could be obtained. Subsequently, the eigenvectors were classified with a kernel extreme learning machine (KELM) to determine whether mounting behaviour has occurred. The pig segmentation presented considerable accuracy and mean pixel accuracy (MPA) with 94.92% and 0.8383 respectively. The presented method showed high accuracy, sensitivity, specificity and Matthews correlation coefficient with 91.47%, 95.2%, 88.34% and 0.8324 respectively. This method can be an efficient way of solving the problem of segmentation difficulty caused by partial occlusion and adhesion of pig bodies, even if the pig body colour was similar to the background, in recognition of mounting behaviour.
Data Anomaly Detection for Internet of Vehicles Based on Traffic Cellular Automata and Driving Style
Authors: Ding Nan , Ma Haoxuan , Zhao Chuanguo , Ma Yanhua , Ge Hongwei
Published: November 2019

The data validity of safe driving in the Internet of Vehicles (IoV) is the basis of improving the safety of vehicles. Different from a traditional information systems, the data anomaly analysis of vehicle safety driving faces the diversity of data anomaly and the randomness and subjectivity of the driver’s driving behavior. How to combine the characteristics of the IOV data with the driving style analysis to provide effective real-time anomaly data detection has become an important issue in the IOV applications. This paper aims at the critical safety data analysis, considering the large computing cost generated by the real-time anomaly detection of all data in the data package. We preprocess it through the traffic cellular automata model which is built to achieve the ideal abnormal detection effect with limited computing resources. On the basis of this model, the Anomaly Detection based on Driving style (ADD) algorithm is proposed to realize real-time and online detection of anomaly data related to safe driving. Firstly, this paper designs the driving coefficient and proposes a driving style quantization model to represent the driving style of the driver. Then, based on driving style quantization model and vehicle driving state information, a data anomaly detection algorithm is developed by using Gaussian mixture model (GMM). Finally, combining with the application scenarios of multi-vehicle collaboration in the Internet of Vehicles, this paper uses real data sets and simulation data sets to analyze the effectiveness of the proposed ADD algorithm.
Erratum: Martins, M.S., et al. Wideband and Wide Beam Polyvinylidene Difluoride (PVDF) Acoustic Transducer for Broadband Underwater. Sensors 2019, 19, 3991
Authors: Martins Marcos S. , Faria Carlos L. , Matos Tiago , Goncalves Luís M. , Cabral José , Silva António , Jesus Sérgio M.
Published: November 2019

The authors wish to make the following erratum to this paper [...]
Mobility Aware Duty Cycling Algorithm (MADCAL) A Dynamic Communication Threshold for Mobile Sink in Wireless Sensor Network
Authors: Thomson Craig , Wadhaj Isam , Tan Zhiyuan , Al-Dubai Ahmed
Published: November 2019

The hotspot issue in wireless sensor networks, with nodes nearest the sink node losing energy fastest and degrading network lifetime, is a well-referenced problem. Mobile sink nodes have been proposed as a solution to this. They do not completely remove the hotspot problem though, with nodes the sink passes most closely still expending more energy than others. This study proposes a lightweight algorithm, located in the media access control (MAC) layer of static nodes and utilising knowledge of predictable sink node mobility. This is in order to create a dynamic communication threshold between static nodes and the sink, within which static nodes awaken, lessening competition for sink communication between nodes. In utilising predictable mobility and factors already known to the static node, such as location and interference range, there is no need for energy-consuming messaging. Analysis and simulation results, tested on a lightweight implementation of a carrier-sense multiple-access-based MAC protocol, show a significant improvement in energy consumption in both controlled and random environments, with frame delivery improved to the point where sink speed is negated. This is when compared to the existing duty cycling approach.
Synthesis, Photophysics, and Solvatochromic Studies of an Aggregated-Induced-Emission Luminogen Useful in Bioimaging
Authors: Espinar-Barranco Laura , Meazza Marta , Linares-Perez Azahara , Rios Ramon , Paredes Jose Manuel , Crovetto Luis
Published: November 2019

Biological samples are a complex and heterogeneous matrix where different macromolecules with different physicochemical parameters cohabit in reduced spaces. The introduction of fluorophores into these samples, such as in the interior of cells, can produce changes in the fluorescence emission properties of these dyes, caused by the specific physicochemical properties of cells. This effect can be especially intense with solvatofluorochromic dyes, where changes in the polarity environment surrounding the dye can drastically change the fluorescence emission. In this article, we studied the photophysical behavior of a new dye and confirmed the aggregation-induced emission (AIE) phenomenon with different approaches, such as by using different solvent proportions, increasing the viscosity, forming micelles, and adding bovine serum albumin (BSA), through analysis of the absorption and steady-state and time-resolved fluorescence. Our results show the preferences of the dye for nonpolar media, exhibiting AIE under specific conditions through immobilization. Additionally, this approach offers the possibility of easily determining the critical micelle concentration (CMC). Finally, we studied the rate of spontaneous incorporation of the dye into cells by fluorescence lifetime imaging and observed the intracellular pattern produced by the AIE. Interestingly, different intracellular compartments present strong differences in fluorescence intensity and fluorescence lifetime. We used this difference to isolate different intracellular regions to selectively study these regions. Interestingly, the fluorescence lifetime shows a strong difference in different intracellular compartments, facilitating selective isolation for a detailed study of specific organelles.
The Optimal Image Date Selection for Evaluating Cultivated Land Quality Based on Gaofen-1 Images
Authors: Xia Ziqing , Peng Yiping , Liu Shanshan , Liu Zhenhua , Wang Guangxing , Zhu A-Xing , Hu Yueming
Published: November 2019

This study proposes a method for determining the optimal image date to improve the evaluation of cultivated land quality (CLQ). Five vegetation indices: leaf area index (LAI), difference vegetation index (DVI), enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), and ratio vegetation index (RVI) are first retrieved using the PROSAIL model and Gaofen-1 (GF-1) images. The indices are then introduced into four regression models at different growth stages for assessing CLQ. The optimal image date of CLQ evaluation is finally determined according to the root mean square error (RMSE). This method is tested and validated in a rice growth area of Southern China based on 115 sample plots and five GF-1 images acquired at the tillering, jointing, booting, heading to flowering, and milk ripe and maturity stage of rice in 2015, respectively. The results show that the RMSEs between the measured and estimated CLQ from four vegetation index-based regression models at the heading to flowering stage are smaller than those at the other growth stages, indicating that the image date corresponding with the heading to flowering stage is optimal for CLQ evaluation. Compared with other vegetation index-based models, the LAI-based logarithm model provides the most accurate estimates of CLQ. The optimal model is also driven using the GF-1 image at the heading to flowering stage to map CLQ of the study area, leading to a relative RMSE of 14.09% at the regional scale. This further implies that the heading to flowering stage is the optimal image time for evaluating CLQ. This study is the first effort to provide an applicable method of selecting the optimal image date to improve the estimation of CLQ and thus advanced the literature in this field.
Development of an AI Model to Measure Traffic Air Pollution from Multisensor and Weather Data
Authors: Ly Hai-Bang , Le Lu Minh , Phi Luong Van , Phan Viet-Hung , Tran Van Quan , Pham Binh Thai , Le Tien-Thinh , Derrible Sybil
Published: November 2019

Gas multisensor devices offer an effective approach to monitor air pollution, which has become a pandemic in many cities, especially because of transport emissions. To be reliable, properly trained models need to be developed that combine output from sensors with weather data; however, many factors can affect the accuracy of the models. The main objective of this study was to explore the impact of several input variables in training different air quality indexes using fuzzy logic combined with two metaheuristic optimizations: simulated annealing (SA) and particle swarm optimization (PSO). In this work, the concentrations of NO2 and CO were predicted using five resistivities from multisensor devices and three weather variables (temperature, relative humidity, and absolute humidity). In order to validate the results, several measures were calculated, including the correlation coefficient and the mean absolute error. Overall, PSO was found to perform the best. Finally, input resistivities of NO2 and nonmetanic hydrocarbons (NMHC) were found to be the most sensitive to predict concentrations of NO2 and CO.
MEMS-Based Sensor for Simultaneous Measurement of Pulse Wave and Respiration Rate
Authors: Nguyen Thanh-Vinh , Ichiki Masaaki
Published: November 2019

The continuous measurements of vital signs (body temperature, blood pressure, pulse wave, and respiration rate) are important in many applications across various fields, including healthcare and sports. To realize such measurements, wearable devices that cause minimal discomfort to the wearers are highly desired. Accordingly, a device that can measure multiple vital signs simultaneously using a single sensing element is important in order to reduce the number of devices attached to the wearer’s body, thereby reducing user discomfort. Thus, in this study, we propose a device with a microelectromechanical systems (MEMS)-based pressure sensor that can simultaneously measure the blood pulse wave and respiration rate using only one sensing element. In particular, in the proposed device, a thin silicone tube, whose inner pressure can be measured via a piezoresistive cantilever, is attached to the nose pad of a pair of eyeglasses. On wearing the eyeglasses, the tube of sensor device is in contact with the area above the angular artery and nasal cavity of the subject, and thus, both pulse wave and breath of the subject cause the tube’s inner pressure to change. We experimentally show that it is possible to extract information related to pulse wave and respiration as the low-frequency and high-frequency components of the sensor signal, respectively.
Subterranean Detention and Sanctuary from below: Canada’s Carceral Geographies
Authors: Bagelman Jen , Kovalchuk Sasha
Published: November 2019

This paper begins with an account of Lucía Vega Jimenez, a Mexican woman who lived and worked in Metro Vancouver, Coast Salish Territories (Canada) and who died while held in detention in British Columbia’s Immigration Holding Centre. This article argues that Lucía’s story exposes a number of critical aspects regarding the geographies and politics of migration in Canada today. First, Lucia’s story points to the ways in which Canada’s determination process invisibilises certain forms of violence and, as such, serves as a highly restrictive and exclusionary mechanism. Second, it shows how this exclusionary mechanism extends like ‘capillaries’ throughout urban space. In this context city services (like transit) increasingly become less spaces of refuge, and more privatized border checkpoints. Third, following Lucia’s story reveals how city checkpoints funnel people with precarious status into remote detention, akin to Foucault’s ‘carceral archipelago.’ While expanding on carceral literature, this paper departs from existing scholarship that tends to think about remoteness horizontally. The paper argues that it is below the surface where carceral regimes become particularly hostile and—as such—the paper calls for deepened engagement with questions of verticality. Finally, the article illustrates how subterranean carceral dimensions are being politicized, agonistically, through sanctuary practices.
Real-World Sustainable Citizenship between Political Consumerism and Material Practices
Authors: Zorell Carolin  V. , Yang Mundo
Published: November 2019

While the number of theoretical concepts surrounding sustainable citizenship, political consumerism and ethical lifestyles is rising continuously, this article is interested in how citizens themselves delineate sustainable citizenship through their practices. Asking which contours real-world sustainable citizenship has, we draw on the practice turn. From this perspective, sustainable citizenship might be an empirical nexus of material practices, like buying organic products or sharing goods. These practices rely on dispositions that include practical rules, attitudes and political values. With survey data from Germany (N = 1350) and using principle component analysis, we reconstruct sustainable citizenship through stable and widespread real-world patterns. The results suggest that sustainable citizenship is a relatively coherent, nonetheless hybrid bundle of performances and dispositions. Real-world sustainable citizenship most resembles political consumerism, but consists overall of three distinct practices: sustainable purchasing, reduced consumption, and green mobility. All three are shown to be connected to engaged citizenship norms and the intention to advance social-ecological change. However, social class seems to prevent some citizens particularly from applying sustainable purchasing, while age and infrastructures constrain green mobility. Altogether, our results show that citizens from all social backgrounds practice sustainable citizenship. Yet they do so through different forms of practices, adjusted to their capabilities.
Maximal Heart Rate for Swimmers
Authors: Olstad Bjørn , Bjørlykke Veronica , Olstad Daniela
Published: November 2019

The main purpose of this study was to identify whether a different protocol to achieve maximal heart rate should be used in sprinters when compared to middle-distance swimmers. As incorporating running training into swim training is gaining increased popularity, a secondary aim was to determine the difference in maximal heart rate between front crawl swimming and running among elite swimmers. Twelve elite swimmers (4 female and 8 male, 7 sprinters and 5 middle-distance, age 18.8 years and body mass index 22.9 kg/m2) swam three different maximal heart rate protocols using a 50 m, 100 m and 200 m step-test protocol followed by a maximal heart rate test in running. There were no differences in maximal heart rate between sprinters and middle-distance swimmers in each of the swimming protocols or between land and water (all p ≥ 0.05). There were no significant differences in maximal heart rate beats-per-minute (bpm) between the 200 m (mean ± SD; 192.0 ± 6.9 bpm), 100 m (190.8 ± 8.3 bpm) or 50 m protocol (191.9 ± 8.4 bpm). Maximal heart rate was 6.7 ± 5.3 bpm lower for swimming compared to running (199.9 ± 8.9 bpm for running; p = 0.015). We conclude that all reported step-test protocols were suitable for achieving maximal heart rate during front crawl swimming and suggest that no separate protocol is needed for swimmers specialized on sprint or middle-distance. Further, we suggest conducting sport-specific maximal heart rate tests for different sports that are targeted to improve the aerobic capacity among the elite swimmers of today.
A Study on the Sustainable Development for Senior Learners
Authors: Lee Zne-Jung , Huang Hsiang , Lee Chou-Yuan , Jiang Yi-Huo , Chang Chun-Yen
Published: November 2019

In an aging population with a changing demographic structure, the government aims to ensure that elderly people receive care. In the concept of lifelong learning, education opportunities are available to senior learners, not just children and young people. The sustainable development for senior learners becomes a very important issue because it promotes a variety of learning activities for senior learners. Many universities have started to offer education for senior learners in Taiwan. Positive experiences for senior learners in senior universities can be fostered by ensuring the sustainable development of senior education. In this paper, a study on sustainable development for senior learners is proposed. This study aims to explore potential tools or approaches in evaluating the sustainable development for senior learners for decision making. In this study, two approaches are applied to analyze the sustainable development for senior learners. The first is a statistical analysis, and the second is the random forest model. The methodology of statistical analysis focuses on three aspects such as social assistance, inspiration, and the learning fulfillment for senior learners in senior universities. The random forest model is used to generate decision rules to support decision making. The random forest in this study obtained 22 decision rules. The results suggest that the items in the questionnaire and the decision rules from random forest could provide useful information that allows decision-makers to analyze the sustainable development of senior learners.
Flights Dominate Travel Emissions of Young Urbanites
Authors: Czepkiewicz Michał , Árnadóttir Áróra , Heinonen Jukka
Published: November 2019

Transport is a key sector in reducing greenhouse gas (GHG) emissions. A consensus prevails on a causal relationship between distance to the city center and emissions from private transport, which has led to an emphasis on density in urban planning. However, several studies have reported a reverse association between the level of urbanity and emissions from long-distance leisure travel. Studies have also suggested that pro-environmental attitudes and climate change concerns are unrelated or positively related to emissions from long-distance travel. The goals of this case study were to find out the structure, levels, distribution, and predictors of GHG emissions from the local, domestic, and international travel of young adults of the Reykjavik Capital Region. A life cycle assessment (LCA) approach was utilized to calculate emissions, and the materials were collected with a map-based online survey. International leisure travel dominated the overall GHG emissions from personal travel regardless of residential location, modality style, or income level. A highly unequal distribution of emissions was found. A higher climate change awareness was found to predict higher GHG emissions from trips abroad. Emissions from leisure travel abroad were the highest in the city center, which was related to cosmopolitan attitudes among downtown dwellers.
Application of a Adaptive Neuro-Fuzzy Technique for Projection of the Greenhouse Gas Emissions from Road Transportation
Authors: Alhindawi Reham , Abu Nahleh Yousef , Kumar Arun , Shiwakoti Nirajan
Published: November 2019

In the past, different forecasting models have been proposed to predict greenhouse gas (GHG) emissions. However, most of these models are unable to handle non-linear data. One of the most widely known techniques, the Adaptive Neuro-fuzzy inference system (ANFIS), can deal with nonlinear data. Its ability to predict GHG emissions from road transportation is still unexplored. This study aims to fulfil that gap by adapting the ANFIS model to predict GHG emissions from road transportation by using the ratio between vehicle-kilometers and number of transportation vehicles for six transportation modes (passenger cars, motorcycle, light trucks, single-unit trucks, tractors, and buses) from the North American Transportation Statistics (NATS) online database over a period of 22 years. The results show that ANFIS is a suitable method to forecast GHG emissions from the road transportation sector.
Numerical Study on Nitrogen Oxide and Black Carbon Reduction of Marine Diesel Engines Using Emulsified Marine Diesel Oil
Authors: Choi Iksoo , Lee Changhee
Published: November 2019

In this study, the exhaust gas characteristics of marine diesel oil (MDO) and emulsion fuels, which are currently used to reduce nitrogen oxides and particulate matters emitted from ship engines, were investigated through experimental and numerical analyses. The moisture included in the emulsion fuel primarily promotes the atomization of fuel due to microexplosion, and lowers the combustion temperature due to the latent heat of evaporation from the evaporation of moisture, thus reducing nitrogen oxides and particulate matter. In the case of emulsion fuel containing a water content of 16%, the combustion temperature was lowered, and the reduction rate of nitrogen oxide and black carbon was about 60% and 15%, respectively. The proposed method is a combustion control technology that can reduce particulate matter as well as nitrogen oxides by using emulsion fuel.
A Novel Effective Indicator of Weighted Inter-City Human Mobility Networks to Estimate Economic Development
Authors: Jiang Jincheng , Chen Jinsong , Tu Wei , Wang Chisheng
Published: November 2019

Estimation of economic development in advance is benefit to test the validity of economic policy or to take timely remedial measures for economic recession. Due to the inevitable connections between human mobility and economic status, estimation of economic trend in advance from easily observable big data in human mobility has the superiority of authenticity, timeliness, and convenience. However, high-precision quantitative relations between human mobility and economic growth remain an outstanding question. To this issue, we firstly analyzed and compared the general patterns of human mobility and economic development; then, a novel, simple, and effective hybrid human mobility indicator ( H H M I i ) of weighted human mobility networks was proposed to quantitatively estimate economic growth. H H M I i contained two parts, that is, the interaction volumes of a given city with all participation cities and only top hub cities, respectively. This implied that the economic growth of a city is affected by not only its own strength, but also the cooperation with hub cities. Several empirical experiments demonstrated that the proposed H H M I i had an exceedingly high estimation ability of economic growth, especially for the tertiary industry. Compared with other complex network indicators, H H M I i had a distinct advantage and its best accuracy reached 0.9543. These results can provide policy-making supports for inter-city sustainable coordinated development.
Stability Analysis of the Entry in a New Mining Approach Influenced by Roof Fracture Position
Authors: Yang Jun , Wang Hongyu , Wang Yajun , Liu Binhui , Hou Shilin , Cheng Yu
Published: November 2019

Non-coal pillar mining with roadway formed automatically (RFANM) is a new mining approach, which demonstrates revolutionary significance because it does not require making roadway before mining and coal pillar retaining. In order to explore the stability of the surrounding rock structure in RFANM, the deformation of the surrounding rock was theoretically analyzed and simulated based on three different fracture positions of the main roof. It was concluded that reasonable control of temporary support strength in roadway is of great importance to control the deformation of the entry. The deformation process of surrounding rock under different fracture positions in RFANM was simulated by using the Universal Discrete Element Code (UDEC). The results of the numerical simulation showed that the main roof was fractured at the solid coal side or gob side; the deformation of the roadway was small. The fracture condition of the main roof at the gob side required a higher effect of roof slitting or temporary support from the roadway. Through drilling and peeping at the retained roadway, it was judged that the main roof was broken inside the coal wall. Field monitoring results revealed that the deformation of the roadway can be effectively controlled.
Multidimensionality of Sustainable Public Procurement (SPP)—Exploring Concepts and Effects in Sub-Saharan Africa and Europe
Authors: Stoffel Tim , Cravero Carol , La Chimia Annamaria , Quinot Geo
Published: November 2019

Strategic objectives in public procurement, such as environmental or social considerations, are being increasingly referred to under the umbrella term of sustainable public procurement (SPP). The concept of sustainability is intrinsically multidimensional, encompassing environmental, social, and economic aspects. However, the existing literature on SPP highlights the generalization that the regulation and practices of public procurement are biased toward the environmental dimension. There is conflicting evidence from countries in Sub-Saharan Africa (SSA) that calls for further investigation. Analyzing how SPP is actually constituted in SSA and contrasting it with the situation in the European Union (EU), as a spotlight on the Global South and North, contributes to a better understanding of sustainability in public procurement. The comparative analysis will help with understanding processes related to the integration or disintegration of sustainability dimensions in SPP. Our results indicate a contrary orientation on the environmental and the social dimensions in the EU and SSA. Although there is no sign of a comprehensive integration of all dimensions in SPP, there are developments toward the integration of the ‘missing’ dimension in the respective regional setting. Thus, at the moment, achieving a multidimensional implementation of SPP appears to be more a matter of expanding SPP practices of the ‘missing’ dimension than of pushing for integrated concepts.
Probability of Deriving a Yearly Transition Probability Matrix for Land-Use Dynamics
Authors: Hasegawa Shigeaki F. , Takada Takenori
Published: November 2019

Takada’s group developed a method for estimating the yearly transition matrix by calculating the mth power roots of a transition matrix with an interval of m years. However, the probability of obtaining a yearly transition matrix with real and positive elements is unknown. In this study, empirical verification based on transition matrices from previous land-use studies and Monte-Carlo simulations were conducted to estimate the probability of obtaining an appropriate yearly transition probability matrix. In 62 transition probability matrices of previous land-use studies, 54 (87%) could provide a positive or small-negative solution. For randomly generated matrices with differing sizes or power roots, the probability of obtaining a positive or small-negative solution is low. However, the probability is relatively large for matrices with large diagonal elements, exceeding 90% in most cases. These results indicate that Takada et al.’s method is a powerful tool for analyzing land-use dynamics.
Energy Benchmarking of Existing Office Stock in Spain: Trends and Drivers
Authors: Gangolells Marta , Casals Miquel , Ferré-Bigorra Jaume , Forcada Núria , Macarulla Marcel , Gaspar Kàtia , Tejedor Blanca
Published: November 2019

Buildings play a central role in the clean energy transition, which is why it is vital to understand how energy is consumed in this sector. Energy performance certificate databases are considered a key source of information on the characteristics of built building stock. Despite a growing portfolio of studies based on information from such databases, little is known about energy consumption in offices. This paper explores the modelled energy performance of existing offices in Spain, using data from 13,701 energy performance certificates collected by the Catalan Institute of Energy (ICAEN) in 2013–2018. Offices were found to consume between 202.66 and 212.10 kWhp/m2·year and were mostly ranked in classes C and D (~64%). Offices with E, F or G labels represent ~28% of the sample while A and B energy ratings are very scarce (~8%). Key drivers of energy consumption variation were found to be the office type, construction period, climate zone, renewable energy use, energy certification procedure and motivation for obtaining an energy performance certificate. Ownership was not found to affect average calculated energy consumption. The results will help policy makers to plan future energy conservation strategies.
Electric Car Purchase Price as a Factor Determining Consumers’ Choice and their Views on Incentives in Europe
Authors: Gómez Vilchez Jonatan J. , Smyth Austin , Kelleher Luke , Lu Hui , Rohr Charlene , Harrison Gillian , Thiel Christian
Published: November 2019

The deployment of zero-emission vehicles has the potential to drastically reduce air pollution and greenhouse gas emissions from road transport. The purpose of this study is to provide evidence on, and quantify the factors that influence, the European market for electric and fuel cell car technologies. The paper reports the results of a stated preference survey among 1,248 car owners in France, Germany, Italy, Poland, Spain and the United Kingdom. The variables that influence powertrain choice are quantified in a nested multinomial logit model. We find that the electric car purchase price continues to be a major deterrent to sales in the surveyed countries. The majority of the respondents considered government incentives as fundamental or important for considering an electric car purchase. Because of the differences in the socio-economic characteristics of consumers in each country, the effectiveness of government incentives may vary across Europe.