bluuebunny/crossref_metadata_2025 huggingface dataset:
Neural Network Approaches to Reconstruct Phytoplankton Time-Series in the Global Ocean
Authors: Martinez Elodie , Brini Anouar , Gorgues Thomas , Drumetz Lucas , Roussillon Joana , Tandeo Pierre , Maze Guillaume , Fablet Ronan
Published: December 2020

Phytoplankton plays a key role in the carbon cycle and supports the oceanic food web. While its seasonal and interannual cycles are rather well characterized owing to the modern satellite ocean color era, its longer time variability remains largely unknown due to the short time-period covered by observations on a global scale. With the aim of reconstructing this longer-term phytoplankton variability, a support vector regression (SVR) approach was recently considered to derive surface Chlorophyll-a concentration (Chl, a proxy of phytoplankton biomass) from physical oceanic model outputs and atmospheric reanalysis. However, those early efforts relied on one particular algorithm, putting aside the question of whether different algorithms may have specific behaviors. Here, we show that this approach can also be applied on satellite observations and can even be further improved by testing performances of different machine learning algorithms, the SVR and a neural network with dense layers (a multi-layer perceptron, MLP). The MLP, thanks to its ability to capture complex non-linear relationships, outperforms the SVR to capture satellite Chl spatial patterns (correlation of 0.75 vs. 0.65 on a global scale, respectively) along with its interannual variability and trend, despite an underestimated amplitude. Among deep learning algorithms, neural network such as MLP models appear to be promising tools to investigate phytoplankton long-term time-series.
Multisensor Thermal Infrared and Microwave Land Surface Temperature Algorithm Intercomparison
Authors: Perry Mike , Ghent Darren J. , Jiménez Carlos , Dodd Emma M. A. , Ermida Sofia L. , Trigo Isabel F. , Veal Karen L.
Published: December 2020

To ensure optimal and consistent algorithm usage within climate studies utilizing satellite-derived Land Surface Temperature (LST) datasets, an algorithm intercomparison exercise was undertaken to assess the various operational and scientific LST retrieval algorithms in use. This study was focused on several LST products including single-sensor products for AATSR, Terra-MODIS, SEVIRI, SSM/I and SSMIS; a Climate Date Record (CDR), which is a combined dataset drawing from AATSR, SLSTR and MODIS; and finally a merged low Earth orbit/geostationary product using data from AATSR, MODIS and SEVIRI. Therefore, the analysis included 14 algorithms: seven thermal infrared algorithms and seven microwave algorithms. The thermal infrared algorithms include five split-window coefficient-based algorithms, one optimal estimation algorithm and one single-channel inversion algorithm, with the microwave focusing on linear regression and neural network methods. The algorithm intercomparison assessed the performance of the retrieval algorithms for all sensors using a benchmark database. This approach was chosen due to the lack of sufficient in situ validation sites globally and the bias this limited set engendered on the training of particular algorithms. A simulated approach has the ability to test all parameters in a consistent, fair manner at a global scale. The benchmark database was constructed from European Centre for Medium-Range Weather Forecasts Re-analysis 5 (ERA5) atmospheric data, Combined ASTER and MODIS Emissivity for Land (CAMEL) infrared emissivity data, and Tool to Estimate Land Surface Emissivities at Microwave frequencies (TELSEM) emissivity data for the period of 2013–2015. The best-performing algorithms had biases of under 0.2 K and standard deviations of approximately 0.7 K. These results were consistent across multiple sensors. Areas of improvement, such as coefficient banding, were found for all algorithms as well as lines for further inquiry that could improve the global and regional performance.
Mean Sea Surface Model over the Sea of Japan Determined from Multi-Satellite Altimeter Data and Tide Gauge Records
Authors: Yuan Jiajia , Guo Jinyun , Niu Yupeng , Zhu Chengcheng , Li Zhen
Published: December 2020

Mean sea surface (MSS) is an important datum for the study of sea-level changes and charting data, and its accuracy in coastal waters has always been the focus of marine geophysics and oceanography. A new MSS model with a grid of 1′ × 1′ over the Sea of Japan and its adjacent ocean (named SJAO2020) (25° N~50° N, 125° E~150° E) was established. It ingested 12 different satellites altimeter data (including TOPEX/Poseidon, Jason-1/2/3, ERS-1/2, Envisat, GFO, HaiYang-2A, SRL/Altika, Sentinel-3A, Cryosat-2) and 24 tide gauge stations’ records and joint GNSS data. The latter were used to correct the sea surface height within 10 km from the coastline by using the Gaussian inverse distance weighting method in SJAO2020. The differences among SJAO2020, CLS15, and DTU18, as well as the differences between them and the altimeter data of HY-2A, Jason-3, and Sentinel-3A were introduced. By comparing with tide gauge records, satellite altimeter data, and other models (DTU18, DTU15, CLS15, CLS11 and WHU13), it was demonstrated that SJAO2020 produces the smallest errors, and its coastal accuracy is relatively reliable.
Damage-Map Estimation Using UAV Images and Deep Learning Algorithms for Disaster Management System
Authors: Tran Dai Quoc , Park Minsoo , Jung Daekyo , Park Seunghee
Published: December 2020

Estimating the damaged area after a forest fire is important for responding to this natural catastrophe. With the support of aerial remote sensing, typically with unmanned aerial vehicles (UAVs), the aerial imagery of forest-fire areas can be easily obtained; however, retrieving the burnt area from the image is still a challenge. We implemented a new approach for segmenting burnt areas from UAV images using deep learning algorithms. First, the data were collected from a forest fire in Andong, the Republic of Korea, in April 2020. Then, the proposed two-patch-level deep-learning models were implemented. A patch-level 1 network was trained using the UNet++ architecture. The output prediction of this network was used as a position input for the second network, which used UNet. It took the reference position from the first network as its input and refined the results. Finally, the final performance of our proposed method was compared with a state-of-the-art image-segmentation algorithm to prove its robustness. Comparative research on the loss functions was also performed. Our proposed approach demonstrated its effectiveness in extracting burnt areas from UAV images and can contribute to estimating maps showing the areas damaged by forest fires.
Comparison of River Basin Water Level Forecasting Methods: Sequential Neural Networks and Multiple-Input Functional Neural Networks
Authors: Wei Chih-Chiang
Published: December 2020

To precisely forecast downstream water levels in catchment areas during typhoons, the deep learning artificial neural networks were employed to establish two water level forecasting models using sequential neural networks (SNNs) and multiple-input functional neural networks (MIFNNs). SNNs, which have a typical neural network structure, are network models constructed using sequential methods. To develop a network model capable of flexibly consolidating data, MIFNNs are employed for processing data from multiple sources or with multiple dimensions. Specifically, when images (e.g., radar reflectivity images) are used as input attributes, feature extraction is required to provide effective feature maps for model training. Therefore, convolutional layers and pooling layers were adopted to extract features. Long short-term memory (LSTM) layers adopted during model training enabled memory cell units to automatically determine the memory length, providing more useful information. The Hsintien River basin in northern Taiwan was selected as the research area and collected relevant data from 2011 to 2019. The input attributes comprised one-dimensional data (e.g., water levels at river stations, rain rates at rain gauges, and reservoir release) and two-dimensional data (i.e., radar reflectivity mosaics). Typhoons Saola, Soudelor, Dujuan, and Megi were selected, and the water levels 1 to 6 h after the typhoons struck were forecasted. The results indicated that compared with linear regressions (REG), SNN using dense layers (SNN-Dense), and SNN using LSTM layers (SNN-LSTM) models, superior forecasting results were achieved for the MIFNN model. Thus, the MIFNN model, as the optimal model for water level forecasting, was identified.
FGATR-Net: Automatic Network Architecture Design for Fine-Grained Aircraft Type Recognition in Remote Sensing Images
Authors: Liang Wei , Li Jihao , Diao Wenhui , Sun Xian , Fu Kun , Wu Yirong
Published: December 2020

Fine-grained aircraft type recognition in remote sensing images, aiming to distinguish different types of the same parent category aircraft, is quite a significant task. In recent decades, with the development of deep learning, the solution scheme for this problem has shifted from handcrafted feature design to model architecture design. Although a great progress has been achieved, this paradigm generally needs strong expert knowledge and rich expert experience. It is still an extremely laborious work and the automation level is relatively low. In this paper, inspired by Neural Architecture Search (NAS), we explore a novel differentiable automatic architecture design framework for fine-grained aircraft type recognition in remote sensing images. In our framework, the search process is divided into several phases. Network architecture deepens at each phase while the number of candidate functions gradually decreases. To achieve it, we adopt different pruning strategies. Then, the network architecture is determined through a potentiality judgment after an architecture heating process. This approach can not only search deeper network, but also reduce the computational complexity, especially for relatively large size of remote sensing images. When all differentiable search phases are finished, the searched model called Fine-Grained Aircraft Type Recognition Net (FGATR-Net) is obtained. Compared with previous NAS, ours are more suitable for relatively large and complex remote sensing images. Experiments on Multitype Aircraft Remote Sensing Images (MTARSI) and Aircraft17 validate that FGATR-Net possesses a strong capability of feature extraction and feature representation. Besides, it is also compact enough, i.e., parameter quantity is relatively small. This powerfully indicates the feasibility and effectiveness of the proposed automatic network architecture design method.
Application-Specific Evaluation of a Weed-Detection Algorithm for Plant-Specific Spraying
Authors: Ruigrok Thijs , van Henten Eldert , Booij Johan , van Boheemen Koen , Kootstra Gert
Published: December 2020

Robotic plant-specific spraying can reduce herbicide usage in agriculture while minimizing labor costs and maximizing yield. Weed detection is a crucial step in automated weeding. Currently, weed detection algorithms are always evaluated at the image level, using conventional image metrics. However, these metrics do not consider the full pipeline connecting image acquisition to the site-specific operation of the spraying nozzles, which is vital for an accurate evaluation of the system. Therefore, we propose a novel application-specific image-evaluation method, which analyses the weed detections on the plant level and in the light of the spraying decision made by the robot. In this paper, a spraying robot is evaluated on three levels: (1) On image-level, using conventional image metrics, (2) on application-level, using our novel application-specific image-evaluation method, and (3) on field level, in which the weed-detection algorithm is implemented on an autonomous spraying robot and tested in the field. On image level, our detection system achieved a recall of 57% and a precision of 84%, which is a lower performance than detection systems reported in literature. However, integrated on an autonomous volunteer-potato sprayer-system we outperformed the state-of-the-art, effectively controlling 96% of the weeds while terminating only 3% of the crops. Using the application-level evaluation, an accurate indication of the field performance of the weed-detection algorithm prior to the field test was given and the type of errors produced by the spraying system was correctly predicted.
An Improved Bluetooth Indoor Positioning Method Using Dynamic Fingerprint Window
Authors: Ruan Ling , Zhang Ling , Zhou Tong , Long Yi
Published: December 2020

The weighted K-nearest neighbor algorithm (WKNN) is easily implemented, and it has been widely applied. In the large-scale positioning regions, using all fingerprint data in matching calculations would lead to high computation expenses, which is not conducive to real-time positioning. Due to signal instability, irrelevant fingerprints reduce the positioning accuracy when performing the matching calculation process. Therefore, selecting the appropriate fingerprint data from the database more quickly and accurately is an urgent problem for improving WKNN. This paper proposes an improved Bluetooth indoor positioning method using a dynamic fingerprint window (DFW-WKNN). The dynamic fingerprint window is a space range for local fingerprint data searching instead of universal searching, and it can be dynamically adjusted according to the indoor pedestrian movement and always covers the maximum possible range of the next positioning. This method was tested and evaluated in two typical scenarios, comparing two existing algorithms, the traditional WKNN and the improved WKNN based on local clustering (LC-WKNN). The experimental results show that the proposed DFW-WKNN algorithm enormously improved both the positioning accuracy and positioning efficiency, significantly, when the fingerprint data increased.
The AMIRO Social Robotics Framework: Deployment and Evaluation on the Pepper Robot
Authors: Ghiță Alexandra Ștefania , Gavril Alexandru Florin , Nan Mihai , Hoteit Bilal , Awada Imad Alex , Sorici Alexandru , Mocanu Irina Georgiana , Florea Adina Magda
Published: December 2020

Recent studies in social robotics show that it can provide economic efficiency and growth in domains such as retail, entertainment, and active and assisted living (AAL). Recent work also highlights that users have the expectation of affordable social robotics platforms, providing focused and specific assistance in a robust manner. In this paper, we present the AMIRO social robotics framework, designed in a modular and robust way for assistive care scenarios. The framework includes robotic services for navigation, person detection and recognition, multi-lingual natural language interaction and dialogue management, as well as activity recognition and general behavior composition. We present AMIRO platform independent implementation based on a Robot Operating System (ROS). We focus on quantitative evaluations of each functionality module, providing discussions on their performance in different settings and the possible improvements. We showcase the deployment of the AMIRO framework on a popular social robotics platform—the Pepper robot—and present the experience of developing a complex user interaction scenario, employing all available functionality modules within AMIRO.
Green Communication for Underwater Wireless Sensor Networks: Triangle Metric Based Multi-Layered Routing Protocol
Authors: Khasawneh Ahmad M. , Kaiwartya Omprakash , Lloret Jaime , Abuaddous Hayfa Y. , Abualigah Laith , Shinwan Mohammad Al , Al-Khasawneh Mahmoud Ahmad , Mahmoud Marwan , Kharel Rupak
Published: December 2020

In this paper, we propose a non-localization routing protocol for underwater wireless sensor networks (UWSNs), namely, the triangle metric based multi-layered routing protocol (TM2RP). The main idea of the proposed TM2RP is to utilize supernodes along with depth information and residual energy to balance the energy consumption between sensors. Moreover, TM2RP is the first multi-layered and multi-metric pressure routing protocol that considers link quality with residual energy to improve the selection of next forwarding nodes with more reliable and energy-efficient links. The aqua-sim package based on the ns-2 simulator was used to evaluate the performance of the proposed TM2RP. The obtained results were compared to other similar methods such as depth based routing (DBR) and multi-layered routing protocol (MRP). Simulation results showed that the proposed protocol (TM2RP) obtained better outcomes in terms of energy consumption, network lifetime, packet delivery ratio, and end-to-end delay.
Vibration-Based Diagnostics of Radial Clearances and Bolts Loosening in the Bearing Supports of the Heavy-Duty Gearboxes
Authors: Krot Pavlo , Korennoi Volodymyr , Zimroz Radoslaw
Published: December 2020

The problem solved in this research is the diagnosis of the radial clearances in bearing supports and the loosening of fastening bolts due to their plastic elongation (creep) or weak tightening using vibration signals. This is an important issue for the maintenance of the heavy-duty gearboxes of powerful mining machines and rolling mills working in non-stationary regimes. Based on a comprehensive overview of bolted joint diagnostic methods, a solution to this problem based on a developed nonlinear dynamical model of bearing supports is proposed. Diagnostic rules are developed by comparing the changes of natural frequency and its harmonics, the amplitudes and phases of shaft transient oscillations. Then, the vibration signals are measured on real gearboxes while the torque is increasing in the transmission during several series of industrial trials under changing bearings and bolts conditions. In parallel, dynamical torque is measured and its interrelation with vibration is determined. It is concluded that the radial clearances are the most influencing factors among the failure parameters in heavy-duty gearboxes of industrial machines working under impulsive and step-like loading. The developed diagnostics algorithm allows condition monitoring of bearings and fastening bolts, allowing one to undertake timely maintenance actions to prevent failures.
Addressing Noise and Skewness in Interpretable Health-Condition Assessment by Learning Model Confidence
Authors: Zhou Yuxi , Hong Shenda , Shang Junyuan , Wu Meng , Wang Qingyun , Li Hongyan , Xie Junqing
Published: December 2020

Assessing the health condition has a wide range of applications in healthcare, military, aerospace, and industrial fields. Nevertheless, traditional feature-engineered techniques involve manual feature extraction, which are too cumbersome to adapt to the changes caused by the development of sensor network technology. Recently, deep-learning-based methods have achieved initial success in health-condition assessment research, but insufficient considerations for problems such as class skewness, noisy segments, and result interpretability make it difficult to apply them to real-world applications. In this paper, we propose a K-margin-based Interpretable Learning approach for health-condition assessment. In detail, a skewness-aware RCR-Net model is employed to handle problems of class skewness. Furthermore, we present a diagnosis model based on K-margin to automatically handle noisy segments by naturally exploiting expected consistency among the segments associated with each record. Additionally, a knowledge-directed interpretation method is presented to learn domain knowledge-level features automatically without the help of human experts which can be used as an interpretable decision-making basis. Finally, through experimental validation in the field of both medical and aerospace, the proposed method has a better generality and high efficiency with 0.7974 and 0.8005 F1 scores, which outperform all state-of-the-art deep learning methods for health-condition assessment task by 3.30% and 2.99%, respectively.
A Spoof Surface Plasmon Polaritons (SSPPs) Based Dual-Band-Rejection Filter with Wide Rejection Bandwidth
Authors: Farokhipour Ehsan , Mehrabi Mohammad , Komjani Nader , Ding Can
Published: December 2020

This paper presents a novel single-layer dual band-rejection-filter based on Spoof Surface Plasmon Polaritons (SSPPs). The filter consists of an SSPP-based transmission line, as well as six coupled circular ring resonators (CCRRs) etched among ground planes of the center corrugated strip. These resonators are excited by electric-field of the SSPP structure. The added ground on both sides of the strip yields tighter electromagnetic fields and improves the filter performance at lower frequencies. By removing flaring ground in comparison to prevalent SSPP-based constructions, the total size of the filter is significantly decreased, and mode conversion efficiency at the transition from co-planar waveguide (CPW) to the SSPP line is increased. The proposed filter possesses tunable rejection bandwidth, wide stop bands, and a variety of different parameters to adjust the forbidden bands and the filter’s cut-off frequency. To demonstrate the filter tunability, the effect of different elements like number (n), width (WR), radius (RR) of CCRRs, and their distance to the SSPP line (yR) are surveyed. Two forbidden bands, located in the X and K bands, are 8.6–11.2 GHz and 20–21.8 GHz. As the proof-of-concept, the proposed filter was fabricated, and a good agreement between the simulation and experiment results was achieved.
Time Reverse Modeling of Damage Detection in Underwater Concrete Beams Using Piezoelectric Intelligent Modules
Authors: Liang Jiachen , Chen Bo , Shao Chenfei , Li Jianming , Wu Bangbin
Published: December 2020

Underwater cracks in concrete structures are often difficult to detect due to their complexity of the service environment. With numerical and experimental analysis of concrete beams immersed in water, an active monitoring system, based on a cement-based piezoelectric intelligent module array (CPIMA), was developed to locate and quantify the underwater cracks. Time reversal (TR) of the stress wave field is accomplished to focus on the crack area through the concrete beam specimen by the system. First, a piezoelectric actuator is applied to emit the initial propagating wave, which can be reflected, attenuated, and diffracted by the crack, transmitted through water filled in the crack, as well as diffracted by the coarse aggregates. To extract the damage waveforms associated with the crack and analyze the robust time-reversal invariance under the high-order multiple scattering effect, a pair of homogeneous and heterogeneous forward finite element (FE) models is established. Then, the damage waveforms are time-reversed and re-propagated in the inverse numerical model, where an optimal refocusing is achieved on the crack that behaves as an acoustic source. Finally, the damage area is obtained in the form of the stacked energy distribution of each time step. The focus results are represented by cloud images and compared with root-mean-square deviation (RMSD) values. Numerical simulation and experiments show that this method can identify and quantify underwater cracks effectively.
An Improved Calibration Method for Photonic Mixer Device Solid-State Array Lidars Based on Electrical Analog Delay
Authors: Wang Xuanquan , Song Ping , Zhang Wuyang
Published: December 2020

As a typical application of indirect-time-of-flight (ToF) technology, photonic mixer device (PMD) solid-state array Lidar has gained rapid development in recent years. With the advantages of high resolution, frame rate and accuracy, the equipment is widely used in target recognition, simultaneous localization and mapping (SLAM), industrial inspection, etc. The PMD Lidar is vulnerable to several factors such as ambient light, temperature and the target feature. To eliminate the impact of such factors, a proper calibration is needed. However, the conventional calibration methods need to change several distances in large areas, which result in low efficiency and low accuracy. To address the problems, this paper presents an improved calibration method based on electrical analog delay. The method firstly eliminates the lens distortion using a self-adaptive interpolation algorithm, meanwhile it calibrates the grayscale image using an integral time simulating based method. Then, the grayscale image is used to estimate the parameters of ambient light compensation in depth calibration. Finally, by combining four types of compensation, the method effectively improves the performance of depth calibration. Through several experiments, the proposed method is more adaptive to multiscenes with targets of different reflectivities, which significantly improves the ranging accuracy and adaptability of PMD Lidar.
Experimental Setup for Investigating the Efficient Load Balancing Algorithms on Virtual Cloud
Authors: Alankar Bhavya , Sharma Gaurav , Kaur Harleen , Valverde Raul , Chang Victor
Published: December 2020

Cloud computing has emerged as the primary choice for developers in developing applications that require high-performance computing. Virtualization technology has helped in the distribution of resources to multiple users. Increased use of cloud infrastructure has led to the challenge of developing a load balancing mechanism to provide optimized use of resources and better performance. Round robin and least connections load balancing algorithms have been developed to allocate user requests across a cluster of servers in the cloud in a time-bound manner. In this paper, we have applied the round robin and least connections approach of load balancing to HAProxy, virtual machine clusters and web servers. The experimental results are visualized and summarized using Apache Jmeter and a further comparative study of round robin and least connections is also depicted. Experimental setup and results show that the round robin algorithm performs better as compared to the least connections algorithm in all measuring parameters of load balancer in this paper.
5G-Enabled Autonomous Driving Demonstration with a V2X Scenario-in-the-Loop Approach
Authors: Szalay Zsolt , Ficzere Dániel , Tihanyi Viktor , Magyar Ferenc , Soós Gábor , Varga Pál
Published: December 2020

Autonomous vehicles are at the forefront of interest due to the expectations of changing transportation for the better. In order to make better decisions on the road, vehicles use information from various sources: their own sensors, messages arriving from surrounding vehicles and objects, as well as from centralized entities—including their own Digital Twin. Certain decisions require the information to arrive with low latency and some of this information (such as video) requires broadband communication. Furthermore, the vehicles can populate an area, so they can represent mass communication endpoints that still need low latency and massive broadband. The mobility of the vehicles obviously requires the complete coverage of the roads with reliable wireless communication technologies fulfilling the previously mentioned needs. The fifth generation of cellular mobile technologies, 5G, addresses these requirements. The current paper presents real-life scenarios—on the M86 highway and the ZalaZONE proving ground in Hungary—for the demonstration of vehicular communication with 5G support, where the cars exchange sensor and control information with each other, their environment, and their Digital Twins. The demonstrations were carried out through the Scenario-in-the-Loop (SciL) methodology, where some of the actionable triggers were not physically present around the vehicles, but sensed or simulated around their Digital Twin. The measurements around the demonstrations aim to reveal the feasibility of the 5G Non-Standalone Architecture for certain communication scenarios, and they mainly aim to reveal the current latency and throughput limitations under real-life conditions.
Effects of Two Types of Straw Biochar on the Mineralization of Soil Organic Carbon in Farmland
Authors: Hu Lening , Li Shuangli , Li Ke , Huang Haiyan , Wan Wenxin , Huang Qiuhua , Li Qiuyan , Li Yafen , Deng Hua , He Tieguang
Published: December 2020

To investigate the effects of biochar on soil carbon composition and transformation, the effects of 1%, 2%, and 5% mass ratios of banana and cassava straw biochar on carbon dioxide release, total organic carbon (TOC), soluble organic carbon (SOC), and enzyme activity in soil were studied in incubation experiments at a constant temperature in the laboratory. The results showed that the cumulative CO2 emissions from cassava straw were 15.82 (1% addition ratio) and 28.14 μg·kg−1 (2%), which were lower than those from banana straw, i.e., 46.77 (1%) and 59.26 μg·kg−1 (2%). After culture, the total organic carbon contents of cassava straw were 8.55 (5%), 5.27 (2%), and 3.98 μg·kg−1 (1%), which were higher than those of banana straw, i.e., 6.31 (5%), 4.23 (2%), and 3.16 μg·kg−1 (1%). The organic carbon mineralization rate in each treatment showed a trend of increasing first, then decreasing, and finally stabilizing. There was a very significant positive correlation between catalase and urease activity in soil with cassava straw biochar and between catalase activity and SOC mineralization with banana straw biochar. It plays an important role in the transformation and decomposition of organic carbon. These results show that the application of biomass carbon can significantly improve the organic carbon content and enzyme activity of farmland soil, increase the cumulative mineralization amount and mineralization rate of SOC, and thus increase the carbon sequestration capacity of soil.
Long-Term City Innovation Trajectories and Quality of Urban Life
Authors: Popescu Alina Irina
Published: December 2020

The main aims of this paper are to examine the technological trajectories of city innovation, to provide a picture of the current state in the most significant technologies, and to propose an explanation for the long-run evolutionary trajectories of technological developments that contribute to the quality of urban life through innovation. In the conceptual part of the paper, we develop the argument that the explanation may rest on the interrelationships between the concept of urban transformative capacity and the theory of path dependence. In the empirical part, we analyze patent data on city-related innovations to examine the trajectories of technological developments over the period 1980–2020. Our main findings at a technological field level (i) confirm the path dependence theory in general and the institutional approach in particular, (ii) acknowledge the rapid transformation towards ‘smart cities’ through the explosive growth of digital technologies, and (iii) confirm the environmental sustainability concerns when developing new technologies. In our study, we focus particularly on the technological sectors (‘clusters’) that provide a significant contribution to quality of urban life, namely environment, public services, and leisure and participation. Our findings provide theoretical, managerial, and policy implications for future research activities on the technological developments that benefit quality of urban life.
RETRACTED: Guo et al. Improvement in the Poverty Status of Ecological Migrants under the Urban Resettlement Model: An Empirical Study in China. Sustainability 2020, 12, 2084
Authors: Guo Shijie , Liu Guichang , Zhang Qi , Zhao Fang , Ding Guomin
Published: December 2020

The journal retracts the article “Improvement in the Poverty Status of Ecological Migrants under the Urban Resettlement Model: An Empirical Study in China” by Guo et al. [...]
Human Perceptions of Recycled Textiles and Circular Fashion: A Systematic Literature Review
Authors: Wagner Melissa Monika , Heinzel Tincuta
Published: December 2020

The textile and fashion industry has had significant technological developments but is currently criticised for its environmental and social impacts and for being a major contributor to waste. The rise of the circular economy (CE) has promoted more sustainable concepts, including the trending of recycling strategies to add value to the textile and plastic waste. However, adding value to products and for users implies technical upscaling and clear communication about the benefits of recycling. This paper presents a systematic literature review (SLR) and explores the state of the art of recycled textiles (RT) and circular fashion (CF) as perceived by humans. The literature review was performed on the basis of journal articles, book chapters, and conference papers using the ScienceDirect (SD) and Web of Science (WoS) databases. The review identified that a significant proportion of consumers from different countries have a basic understanding of sustainable products but that there have been differences in consumers’ attitudes towards sustainable purchase, use, consumption, and post-purchase behaviour. Diverse and even contradictory results occurred concerning the relationships between RT and CF and their perceived product attributes in terms of quality and functionality, as well as social-cultural factors. Manufacturers’ and brands’ perceived values of RT were observed to be influenced by different factors on the basis of the recycling system and cultural values, while designer attitudes towards sustainability were observed to be influenced by external factors. This review contributes to the creation of three main implications in terms of environmental impact and awareness, including actions and concrete proposals for RT and CF.
The Environmental Impacts of the Grassland Agricultural System and the Cultivated Land Agricultural System: A Comparative Analysis in Eastern Gansu
Authors: Lin Huilong , Pu Yanfei , Ma Xueni , Wang Yue , Nyandwi Charles , Nzabonakuze Jean de Dieu
Published: December 2020

“Introducing grass into fields”, the major approach to modern grassland agriculture, is the crucial direction of agricultural structure adjustment in the farming-pastoral zone of Northern China. However, there have been few studies on the environmental impacts of agricultural production in this pattern. We used the life cycle assessment (LCA) method for the first time from the perspective of the entire industry chain from agricultural material production to livestock marketing, which involves the combination of planting and breeding. A comparative analysis of the environmental impact processes of beef and pork, the main products of the two existing agricultural systems in Eastern Gansu, was conducted. The findings showed that based on the production capacity of the 1 ha land system, the comprehensive environmental impact benefit of the grassland agricultural system (GAS) in the farming-pastoral zone was 21.82%, higher than that of the cultivated land agricultural system (CLAS). On Primary energy demand (PED) and environmental acidification potential (AP), the GAS needs improvement because those values were 38.66% and 22.01% higher than those of the CLAS, respectively; on global warming potential (GWP), eutrophication potential (EP), and water use (WU), the GAS performed more environment-friendlily because those values were 25.00%, 68.37%, and 11.88% lower than those of the CLAS, respectively. This indicates that a change in land use will lead to a change in environmental impacts. Therefore, PED and AP should be focused on the progress of grassland agriculture modernization by “introducing grass into fields” and new agricultural technologies.
Application of Factor Analysis for Characterizing the Relationships between Groundwater Quality and Land Use in Taiwan’s Pingtung Plain
Authors: Liang Ching-Ping , Wang Chia-Hui , Wang Sheng-Wei , Chang Ta-Wei , Chen Jui-Sheng
Published: December 2020

Although the average municipal water coverage in Taiwan is quite high, at 93.91%, only around half of the residents in the Pingtung Plain use tap water originating from the Taiwan Water Corporation to meet their needs. This means the exploitation of a substantial amount of groundwater as a source of water to meet drinking, agriculture, aquaculture, and industry requirements. Long-term groundwater quality surveys in Taiwan have revealed obvious contamination of the groundwater in several locations in the Pingtung Plain, with measured concentration levels of some groundwater quality parameters in excess of the permissible levels specified by the Taiwan Environmental Protection Administration. Clearly, establishing a sound plan for groundwater quality protection in this area is imperative for maximizing the protection of human health. The inappropriate use of hazardous chemicals and poor management of land use have allowed pollutants to permeate through unsaturated soil and ultimately reach the underlying shallow unconfined groundwater system. Thus, the quality of the water stored in shallow aquifers has been significantly affected by land use. This study is designed to characterize the relationship between groundwater quality and land use in the Pingtung Plain. This goal is achieved by the application of factor analysis to characterize the measured concentrations of 14 groundwater quality parameters sampled from 46 observation wells, the area percentages for nine land use categories in the neighborhood of these 46 observation wells, and the thicknesses of four unsaturated types of soil based on core samples obtained during the establishment of 46 observation wells. The results show that a four-factor model can explain 56% of the total variance. Factor 1 (seawater salinization), which includes the groundwater quality parameters of EC, SO42−, Cl−, Ca2+, Mg2+, Na+, and K+, shows a moderate correlation to land used for water conservation. Factor 2 (nitrate pollution), which includes the groundwater quality parameters of NO3−-N and HCO3−, shows a strong correlation to land used for fruit farming and a moderate correlation to the thickness of the gravel comprising unsaturated soil. Factor 3 (arsenic pollution), which is composed of groundwater quality parameters of total organic carbon (TOC) and As, is very weakly affected by land use. Factor 4 (reductive dissolution of Fe3+ and Mn2+), which involves Mn2+ and Fe3+, is weakly impacted by land use. Based on a geographic visualization of the scores for the four different factors and the patterns for land use, we can demarcate the areas where the groundwater in shallow unconfined aquifers is more vulnerable to being polluted by specific contaminants. We can then prioritize the areas where more intensive monitoring might be required, evaluate current land use practices, and adopt new measures to better prevent or control groundwater pollution.
Sustainability and Kaizen: Business Model Trends in Healthcare
Authors: Morell-Santandreu Oscar , Santandreu-Mascarell Cristina , García-Sabater Julio
Published: December 2020

Kaizen, or continuous improvement, is a management tool that allows the identification of activities that have no value in the processes examined. This identification leads to the improvement of these processes within any organization and promotes economic and social sustainability, and to a lesser extent environmental sustainability. Kaizen, already widely and successfully employed in the industrial sector, is now being applied in the health sector. However, the health sector tends to publish only the results of how processes have been improved in finely focused areas and the resulting benefits. The majority of the benefits focus on time and cost reduction. In this study, the authors carried out a bibliometric analysis using the Scimat program, which maps the thematic evolution of Kaizen in the health sector and its relationship with sustainability, in order to promote the interest of the health sector for this type of process improvement. The findings confirm that the implementation of Kaizen is recent and constantly evolves and grows, and that it can help economic and social sustainability, and to a lesser extent environmental sustainability.
Towards Innovative Governance of Nature Areas
Authors: Krozer Yoram , Coenen Frans , Hanganu Jenica , Lordkipanidze Maia , Sbarcea Madalina
Published: December 2020

How can the governance of nature areas foster the sustainability of ecosystems? This is discussed with regard to larger threats on ecosystems despite larger global nature areas that reach 19 million km2 of land along with larger costs per area unit. Moreover, monetization of nature with payments for ecosystem services is sometimes demanded for justification of the nature areas; however, this does not resolve the threats but faces scientific and ethical scrutiny. An alternative is the governance that incubates sustainable innovations in the nature areas for broad dissemination which generates interests in the sustainability of ecosystems. Opportunities are due to demands for ethical products, ecotourism and images of nature which generate USD 1100 billion in global markets. Sustainable innovations of using reed for insulation walls, furniture panels and upholstery in the EU can generate a few hundred million dollars in addition to present roof thatching, fodder and fuels if good functional qualities of the reed products are developed. Their functionalities can be supported by the inclusive economics, CO2 storage, treatment of water pollution, richer biodiversity, and other ethical qualities. The governance of nature areas can prevent the present deadlock but needs the development of technical and entrepreneurial capabilities.
Urban Vitality, Urban Form, and Land Use: Their Relations within a Geographical Boundary for Walkers
Authors: Kim Suji
Published: December 2020

The aim of this study was to exmine the influence of combined urban form and land use on the vibrancy in urban areas within a geographical boundary for walkers. A geographical boundary is defined as a block group surrounded by expressways and arterials, based on findings in previous studies. Spatial regression was performed with mobile signal data representing the degree of vitality within the defined areal unit as a dependent variable, and explanatory variables measured by urban form hierarchy were used to consider both natural and built environments. The outcome helps comprehend the physical and functional forms of vibrant neighborhood environments. The result implies the importance of highly desirable features for walking- or transit-friendly neighborhoods. It also indicates the right combination of land uses needed to support the daily lives of local residents: little lost space, short blocks, well-connected streets, short distances to transit stations, and proximity to essential facilities. This study suggests a new way of defining a spatial unit for vitality analysis and shows the critical roles of both natural and built environments in activating local vitality. These findings establish the groundwork for designing better neighborhoods, especially for an area composed of local streets and collector roads.
Does More Mean Better? Exploring the Relationship between Report Completeness and Environmental Sustainability
Authors: Calabrese Armando , Costa Roberta , Levialdi Nathan , Menichini Tamara , Villazon Montalvan Roberth Andres
Published: December 2020

The reliability of sustainability reporting can impact sustainable development and should provide relevant information to financial analysts, investors, and other stakeholders by reducing information asymmetry between them and management. Nevertheless, its utility is often undermined by a lack of the disclosure information’s trustability. This paper aims to evaluate if the completeness of the sustainability report’s environmental quantitative information is a reliable indicator of the company’s real commitment to environmental sustainability. The paper analyzes the relationship between the report’s completeness and the environmental performance evaluated by data of an independent third party. Fifty Italian companies that have submitted complete data on CO2 emissions to the European Union Emissions Trade Scheme (EU ETS) in the six years from 2008–2013 and published sustainability reports have been evaluated. Results indicate that reporting completeness is not correlated with better environmental performance, and consequently with greater commitment to environmental sustainability, thus suggesting the potential existence of credibility gaps.
The Development of a Security Evaluation Model Focused on Information Leakage Protection for Sustainable Growth
Authors: Kim Jawon , Lee Chanwoo , Chang Hangbae
Published: December 2020

This research establishes a security evaluation model from the insider leakage perspective and suggests an objective evaluation measurement. Organizational security risks are fused and compounded both inside and outside the organization. Although multiple security controls are implemented to minimize an organization’s security risk, effective security control requires management to preemptively check the organization’s security level. Existing criteria for evaluating security level are limited to external security risks and have improper limit points for dealing with security risks that are fused and compounded within an organization. The focus of this study is the prevention of technical information leakage. Furthermore, we propose a method for measuring the level at which the objectivity of certain items is secured. We compiled 26 detailed evaluation items, considering the security requirements to prevent technical information leakage. We not only performed suitability, reliability, and factor analyses and statistical validation, but also established a method to measure the security level. This measurement method ensures the effectiveness and objectivity of the evaluation of security level, mitigating the risks of security incidents caused by insiders. The results serve as a reference for organizations when designing security evaluation criteria and automated tools based on our evaluation model for future research.
Novel Magnetic Nano Silica Synthesis Using Barley Husk Waste for Removing Petroleum from Polluted Water for Environmental Sustainability
Authors: Akhayere Evidence , Vaseashta Ashok , Kavaz Doga
Published: December 2020

Water contamination by petroleum and its byproducts presents a major challenge worldwide. It is critical that sustainable treatment methods be employed for the removal of such contaminants from polluted water. For this investigation, magnetic nano silica (M-NS) was synthesized using agricultural waste from barley husk using a two-step process that is environmentally friendly and uses green chemistry synthesis. The barley husk waste was used as a precursor for the synthesis of nano-silica following a low energy and sustainable method of acid reflux and heat treatment. Nano-silica was then used for the synthesis of M-NS, with the addition of a magnetic solution of Fe3O4 nanoparticles. The magnetic nano-silica particles were characterized using scanning electron microscopy (SEM), Fourier transform infrared (FTIR), Zeta potential analysis (ZETA) and X-Ray Diffraction (XRD). Magnetic nano-silica particles were observed to have an average diameter of 162 nm and appeared to be hydrophobic, with a large surface area of ~120 m2/gm. Due to these characteristics, magnetic nano-silica was used as an adsorbent for the removal of petrol contaminants from water. The experimental procedure showed that only 0.6 gm. of M-NS was used on 40 mg/L concentration of petroleum and the experiments recorded a high uptake efficiency of 85%. The sorption was shown to be an effective process since a high amount of petroleum was removed. The study further demonstrates that as the amount of sorbent is increased, the sorption capacity also increases until an equilibrium is reached. The results of this study establish that synthesis of M-NS, using environmentally sustainable processes, has the required characteristics to serve as sorbent for petroleum and its byproducts from contaminated water, thus enhancing environmental sustainability.