bluuebunny/crossref_metadata_2025 huggingface dataset:
Monitoring the Spatial and Temporal Variations in The Water Surface and Floating Algal Bloom Areas in Dongting Lake Using a Long-Term MODIS Image Time Series
Authors: Cao Mengmeng , Mao Kebiao , Shen Xinyi , Xu Tongren , Yan Yibo , Yuan Zijin
Published: November 2020

Significant water quality changes have been observed in the Dongting Lake region due to environmental changes and the strong influence of human activities. To protect and manage Dongting Lake, the long-term dynamics of the water surface and algal bloom areas were systematically analyzed and quantified for the first time based on 17 years of Moderate Resolution Imaging Spectroradiometer (MODIS) observations. The traditional methods (index-based threshold algorithms) were optimized by a dynamic learning neural network (DL-NN) to extract and identify the water surface area and algal bloom area while reducing the extraction complexity and improving the extraction accuracy. The extraction accuracy exceeded 94.5% for the water and algal bloom areas, and the analysis showed decreases in the algal bloom and water surface areas from 2001–2017. Additionally, the variations in the water surface and algal bloom areas are greatly affected by human activities and climatic factors. The results of these analyses can help us better monitor human contamination in Dongting Lake and take measures to control the water quality during certain periods, which is crucial for future management. Moreover, the traditional methods optimized by the DL-NN used in this study can be extended to other inland lakes to assess and monitor long-term temporal and spatial variations in algal bloom areas and can also be used to acquire baseline information for future assessments of the water quality of lakes.
Land Subsidence Susceptibility Mapping in Jakarta Using Functional and Meta-Ensemble Machine Learning Algorithm Based on Time-Series InSAR Data
Authors: Hakim Wahyu , Achmad Arief , Lee Chang-Wook
Published: November 2020

Areas at risk of land subsidence in Jakarta can be identified using a land subsidence susceptibility map. This study evaluates the quality of a susceptibility map made using functional (logistic regression and multilayer perceptron) and meta-ensemble (AdaBoost and LogitBoost) machine learning algorithms based on a land subsidence inventory map generated using the Sentinel-1 synthetic aperture radar (SAR) dataset from 2017 to 2020. The land subsidence locations were assessed using the time-series interferometry synthetic aperture radar (InSAR) method based on the Stanford Method for Persistent Scatterers (StaMPS) algorithm. The mean vertical deformation maps from ascending and descending tracks were compared and showed a good correlation between displacement patterns. Persistent scatterer points with mean vertical deformation value were randomly divided into two datasets: 50% for training the susceptibility model and 50% for validating the model in terms of accuracy and reliability. Additionally, 14 land subsidence conditioning factors correlated with subsidence occurrence were used to generate land subsidence susceptibility maps from the four algorithms. The receiver operating characteristic (ROC) curve analysis showed that the AdaBoost algorithm has higher subsidence susceptibility prediction accuracy (81.1%) than the multilayer perceptron (80%), logistic regression (79.4%), and LogitBoost (79.1%) algorithms. The land subsidence susceptibility map can be used to mitigate disasters caused by land subsidence in Jakarta, and our method can be applied to other study areas.
SAR Target Classification Based on Sample Spectral Regularization
Authors: Liang Wei , Zhang Tengfei , Diao Wenhui , Sun Xian , Zhao Liangjin , Fu Kun , Wu Yirong
Published: November 2020

Synthetic Aperture Radar (SAR) target classification is an important branch of SAR image interpretation. The deep learning based SAR target classification algorithms have made remarkable achievements. But the acquisition and annotation of SAR target images are time-consuming and laborious, and it is difficult to obtain sufficient training data in many cases. The insufficient training data can make deep learning based models suffering from over-fitting, which will severely limit their wide application in SAR target classification. Motivated by the above problem, this paper employs transfer-learning to transfer the prior knowledge learned from a simulated SAR dataset to a real SAR dataset. To overcome the sample restriction problem caused by the poor feature discriminability for real SAR data. A simple and effective sample spectral regularization method is proposed, which can regularize the singular values of each SAR image feature to improve the feature discriminability. Based on the proposed regularization method, we design a transfer-learning pipeline to leverage the simulated SAR data as well as acquire better feature discriminability. The experimental results indicate that the proposed method is feasible for the sample restriction problem in SAR target classification. Furthermore, the proposed method can improve the classification accuracy when relatively sufficient training data is available, and it can be plugged into any convolutional neural network (CNN) based SAR classification models.
Automated Canopy Delineation and Size Metrics Extraction for Strawberry Dry Weight Modeling Using Raster Analysis of High-Resolution Imagery
Authors: Abd-Elrahman Amr , Guan Zhen , Dalid Cheryl , Whitaker Vance , Britt Katherine , Wilkinson Benjamin , Gonzalez Ali
Published: November 2020

Capturing high spatial resolution imagery is becoming a standard operation in many agricultural applications. The increased capacity for image capture necessitates corresponding advances in analysis algorithms. This study introduces automated raster geoprocessing methods to automatically extract strawberry (Fragaria × ananassa) canopy size metrics using raster image analysis and utilize the extracted metrics in statistical modeling of strawberry dry weight. Automated canopy delineation and canopy size metrics extraction models were developed and implemented using ArcMap software v 10.7 and made available by the authors. The workflows were demonstrated using high spatial resolution (1 mm resolution) orthoimages and digital surface models (2 mm) of 34 strawberry plots (each containing 17 different plant genotypes) planted on raised beds. The images were captured on a weekly basis throughout the strawberry growing season (16 weeks) between early November and late February. The results of extracting four canopy size metrics (area, volume, average height, and height standard deviation) using automatically delineated and visually interpreted canopies were compared. The trends observed in the differences between canopy metrics extracted using the automatically delineated and visually interpreted canopies showed no significant differences. The R2 values of the models were 0.77 and 0.76 for the two datasets and the leave-one-out (LOO) cross validation root mean square error (RMSE) of the two models were 9.2 g and 9.4 g, respectively. The results show the feasibility of using automated methods for canopy delineation and canopy metric extraction to support plant phenotyping applications.
Metastable Dark Current in BRITE Nano-Satellite Image Sensors
Authors: Popowicz Adam , Farah Alejandro
Published: November 2020

Dark current in charge-coupled devices (CCDs) is one of the most important sources of impulsive noise present in scientific images. While the dark current originating in the fabrication defects (mainly impurities) is stable and dependent only on temperature, the one present in the proton-irradiated sensors shows a range of metastable states which makes calibration of images almost impossible. In this paper, we show an extended analysis of such metastabilities present in Kodak KAI 11000M CCD sensors employed in the BRITE (BRIghtest Target Explorer) astrophysical mission over 7 years of in-orbit work. Our collection of dark current characteristics has an unprecedented time span, large temperature range and high number of investigated pixels. A special methodology based on the Gaussian mixture model was proposed for identification and characterization of the metastable states in the dark current. We identified several interesting properties of the metastability and found an experimental rule for the dark current in tristable defects. The results shed a new light on the dark current problems, its modeling and the mitigation in an image sensor working in space.
Optical Biosensors for the Detection of Rheumatoid Arthritis (RA) Biomarkers: A Comprehensive Review
Authors: Imas José Javier , Ruiz Zamarreño Carlos , Zubiate Pablo , Sanchez-Martín Lorena , Campión Javier , Matías Ignacio Raúl
Published: November 2020

A comprehensive review of optical biosensors for the detection of biomarkers associated with rheumatoid arthritis (RA) is presented here, including microRNAs (miRNAs), C-reactive protein (CRP), rheumatoid factor (RF), anti-citrullinated protein antibodies (ACPA), interleukin-6 (IL-6) and histidine, which are biomarkers that enable RA detection and/or monitoring. An overview of the different optical biosensors (based on fluorescence, plasmon resonances, interferometry, surface-enhanced Raman spectroscopy (SERS) among other optical techniques) used to detect these biomarkers is given, describing their performance and main characteristics (limit of detection (LOD) and dynamic range), as well as the connection between the respective biomarker and rheumatoid arthritis. It has been observed that the relationship between the corresponding biomarker and rheumatoid arthritis tends to be obviated most of the time when explaining the mechanism of the optical biosensor, which forces the researcher to look for further information about the biomarker. This review work attempts to establish a clear association between optical sensors and rheumatoid arthritis biomarkers as well as to be an easy-to-use tool for the researchers working in this field.
Feasibility of Location-Aware Handover for Autonomous Vehicles in Industrial Multi-Radio Environments
Authors: Lu Yi , Gerasimenko Mikhail , Kovalchukov Roman , Stusek Martin , Urama Jani , Hosek Jiri , Valkama Mikko , Lohan Elena Simona
Published: November 2020

The integration of millimeter wave (mmWave) and low frequency interfaces brings an unique opportunity to unify the communications and positioning technologies in the future wireless heterogeneous networks (HetNets), which offer great potential for efficient handover using location awareness, hence a location-aware handover (LHO). Targeting a self-organized communication system with autonomous vehicles, we conduct and describe an experimental and analytical study on the LHO using a mmWave-enabled robotic platform in a multi-radio environment. Compared to the conventional received signal strength indicator (RSSI)-based handover, the studied LHO not only improves the achievable throughput, but also enhances the wireless link robustness for the industrial Internet-of-things (IIoT)-oriented applications. In terms of acquiring location awareness, a geometry-based positioning (GBP) algorithm is proposed and implemented in both simulation and experiments, where its achievable accuracy is assessed and tested. Based on the performed experiments, the location-related measurements acquired by the robot are not accurate enough for the standalone-GBP algorithm to provide an accurate location awareness to perform a reliable handover. Nevertheless, we demonstrate that by combining the GBP with the dead reckoning, more accurate location awareness becomes achievable, the LHO can therefore be performed in a more optimized manner compared to the conventional RSSI-based handover scheme, and is therefore able to achieve approximately twice as high average throughput in certain scenarios. Our study confirms that the achieved location awareness, if accurate enough, could enable an efficient handover scheme, further enhancing the autonomous features in the HetNets.
Modified Fixed Wall Oedometer When Considering Stress Dependence of Elastic Wave Velocities
Authors: Lee Jong-Sub , Park Geunwoo , Byun Yong-Hoon , Lee Changho
Published: November 2020

A modified oedometer cell for measuring the applied stresses and elastic waves at the top and bottom of the specimen is developed to evaluate the effect of the side friction on the stress dependence of the elastic wave velocities. In the modified cell, two load cells are installed at the top and bottom plates, respectively. To generate and detect the compressional and shear waves, a pair of piezo disk elements and a pair of bender elements are mounted at both the top and bottom plates. Experimental results show that the stresses measured at the bottom are smaller than those measured at the top during the loading and vice versa during unloading, regardless of the densities and heights of the specimens. Under nearly saturated conditions, the compressional wave velocities remain almost constant for the entire stress state. With plotting stresses measured at top, the shear wave velocities during unloading are greater than those during loading, whereas with plotting stresses measured at bottom, the shear wave velocities during unloading are smaller than those during loading owing to the side friction. The vertical effective stress may be simply determined from the average values of the stresses measured at the top and bottom of the specimens.
Loosely Coupled GNSS and UWB with INS Integration for Indoor/Outdoor Pedestrian Navigation
Authors: Di Pietra Vincenzo , Dabove Paolo , Piras Marco
Published: November 2020

The growth of location-based services (LBS) has increased rapidly in last years, mainly due to the possibility to exploit low-cost sensors installed in portable devices, such as smartphones and tablets. This work aims to show a low-cost multi-sensor platform developed by the authors in which an ultra-wideband (UWB) indoor positioning system is added to a classical global navigation satellite systems–inertial navigation system (GNSS-INS) integration, in order to acquire different synchronized data for further data fusion analysis in order to exploit seamless positioning. The data fusion is based on an extended Kalman filter (EKF) and on a geo-fencing approach which allows the navigation solution to be provided continuously. In particular, the proposed algorithm aims to solve a navigation task of a pedestrian user moving from an outdoor space to an indoor environment. The methodology and the system setup is presented with more details in the paper. The data acquired and the real-time positioning estimation are analysed in depth and compared with ground truth measurements. Particular attention is given to the UWB positioning system and its behaviour with respect to the environment. The proposed data fusion algorithm provides an overall horizontal and 3D accuracy of 35 cm and 45 cm, respectively, obtained considering 5 different measurement campaigns.
Performance Assessment of Portable Optical Particle Spectrometer (POPS)
Authors: Mei Fan , McMeeking Gavin , Pekour Mikhail , Gao Ru-Shan , Kulkarni Gourihar , China Swarup , Telg Hagen , Dexheimer Darielle , Tomlinson Jason , Schmid Beat
Published: November 2020

Accurate representation of atmospheric aerosol properties is a long-standing problem in atmospheric research. Modern pilotless aerial systems provide a new platform for atmospheric in situ measurement. However, small airborne platforms require miniaturized instrumentation due to apparent size, power, and weight limitations. A Portable Optical Particle Spectrometer (POPS) is an emerged instrument to measure ambient aerosol size distribution with high time and size resolution, designed for deployment on a small unmanned aerial system (UAS) or tethered balloon system (TBS) platforms. This study evaluates the performance of a POPS with an upgraded laser heater and additional temperature sensors in the aerosol pathway. POPS maintains its performance under different environmental conditions as long as the laser temperature remains above 25 °C and the aerosol flow temperature inside the optical chamber is 15 °C higher than the ambient temperature. The comparison between POPS and an Ultra-High Sensitivity Aerosol Spectrometer (UHSAS) suggests that the coincidence error is less than 25% when the number concentration is less than 4000 cm−3. The size distributions measured by both of them remained unaffected up to 15,000 cm−3. While both instruments’ sizing accuracy is affected by the aerosol chemical composition and morphology, the influence is more profound on the POPS.
Vision and Deep Learning-Based Algorithms to Detect and Quantify Cracks on Concrete Surfaces from UAV Videos
Authors: Bhowmick Sutanu , Nagarajaiah Satish , Veeraraghavan Ashok
Published: November 2020

Immediate assessment of structural integrity of important civil infrastructures, like bridges, hospitals, or dams, is of utmost importance after natural disasters. Currently, inspection is performed manually by engineers who look for local damages and their extent on significant locations of the structure to understand its implication on its global stability. However, the whole process is time-consuming and prone to human errors. Due to their size and extent, some regions of civil structures are hard to gain access for manual inspection. In such situations, a vision-based system of Unmanned Aerial Vehicles (UAVs) programmed with Artificial Intelligence algorithms may be an effective alternative to carry out a health assessment of civil infrastructures in a timely manner. This paper proposes a framework of achieving the above-mentioned goal using computer vision and deep learning algorithms for detection of cracks on the concrete surface from its image by carrying out image segmentation of pixels, i.e., classification of pixels in an image of the concrete surface and whether it belongs to cracks or not. The image segmentation or dense pixel level classification is carried out using a deep neural network architecture named U-Net. Further, morphological operations on the segmented images result in dense measurements of crack geometry, like length, width, area, and crack orientation for individual cracks present in the image. The efficacy and robustness of the proposed method as a viable real-life application was validated by carrying out a laboratory experiment of a four-point bending test on an 8-foot-long concrete beam of which the video is recorded using a camera mounted on a UAV-based, as well as a still ground-based, video camera. Detection, quantification, and localization of damage on a civil infrastructure using the proposed framework can directly be used in the prognosis of the structure’s ability to withstand service loads.
Subtask Segmentation of Timed Up and Go Test for Mobility Assessment of Perioperative Total Knee Arthroplasty
Authors: Hsieh Chia-Yeh , Huang Hsiang-Yun , Liu Kai-Chun , Chen Kun-Hui , Hsu Steen Jun-Ping , Chan Chia-Tai
Published: November 2020

Total knee arthroplasty (TKA) is one of the most common treatments for people with severe knee osteoarthritis (OA). The accuracy of outcome measurements and quantitative assessments for perioperative TKA is an important issue in clinical practice. Timed up and go (TUG) tests have been validated to measure basic mobility and balance capabilities. A TUG test contains a series of subtasks, including sit-to-stand, walking-out, turning, walking-in, turning around, and stand-to-sit tasks. Detailed information about subtasks is essential to aid clinical professionals and physiotherapists in making assessment decisions. The main objective of this study is to design and develop a subtask segmentation approach using machine-learning models and knowledge-based postprocessing during the TUG test for perioperative TKA. The experiment recruited 26 patients with severe knee OA (11 patients with bilateral TKA planned and 15 patients with unilateral TKA planned). A series of signal-processing mechanisms and pattern recognition approaches involving machine learning-based multi-classifiers, fragmentation modification and subtask inference are designed and developed to tackle technical challenges in typical classification algorithms, including motion variability, fragmentation and ambiguity. The experimental results reveal that the accuracy of the proposed subtask segmentation approach using the AdaBoost technique with a window size of 128 samples is 92%, which is an improvement of at least 15% compared to that of the typical subtask segmentation approach using machine-learning models only.
Digital Forensics of Scanned QR Code Images for Printer Source Identification Using Bottleneck Residual Block
Authors: Guo Zhongyuan , Zheng Hong , You Changhui , Xu Xiaohang , Wu Xiongbin , Zheng Zhaohui , Ju Jianping
Published: November 2020

With the rapid development of information technology and the widespread use of the Internet, QR codes are widely used in all walks of life and have a profound impact on people’s work and life. However, the QR code itself is likely to be printed and forged, which will cause serious economic losses and criminal offenses. Therefore, it is of great significance to identify the printer source of QR code. A method of printer source identification for scanned QR Code image blocks based on convolutional neural network (PSINet) is proposed, which innovatively introduces a bottleneck residual block (BRB). We give a detailed theoretical discussion and experimental analysis of PSINet in terms of network input, the first convolution layer design based on residual structure, and the overall architecture of the proposed convolution neural network (CNN). Experimental results show that the proposed PSINet in this paper can obtain extremely excellent printer source identification performance, the accuracy of printer source identification of QR code on eight printers can reach 99.82%, which is not only better than LeNet and AlexNet widely used in the field of digital image forensics, but also exceeds state-of-the-art deep learning methods in the field of printer source identification.
Cooperative Localization and Time Synchronization Based on M-VMP Method
Authors: Deng Zhongliang , Tang Shihao , Jia Buyun , Wang Hanhua , Deng Xiwen , Zheng Xinyu
Published: November 2020

Localization estimation and clock synchronization are important research directions in the application of wireless sensor networks. Aiming at the problems of low positioning accuracy and slow convergence speed in localization estimation methods based on message passing, this paper proposes a low-complexity distributed cooperative joint estimation method suitable for dynamic networks called multi-Gaussian variational message passing (M-VMP). The proposed method constrains the message to be a multi-Gaussian function superposition form to reduce the information loss in the variational message passing algorithm (VMP). Only the mean, covariance and weight of each message need to be transmitted in the network, which reduces the computational complexity while ensuring the information completeness. The simulation results show that the proposed method is superior to the VMP algorithm in terms of position accuracy and convergence speed and is close to the sum-product algorithm over a wireless network (SPAWN) based on non-parametric belief propagation, but the computational complexity and communication load are significantly reduced.
Default Behaviors of Contractors under Surety Bond in Construction Industry Based on Evolutionary Game Model
Authors: Jing Jiabao , Deng Xiaomei , Maqbool Rashid , Rashid Yahya , Ashfaq Saleha
Published: November 2020

In construction projects, some contractors will take default actions against the contracts to obtain maximum profits and damage the owners’ benefits as a result. In the construction markets where effective supervision is not performed well, contractors have more opportunities to default. Surety bonds were designed to solve the default problems and promote the sustainable development of the construction markets. This paper was proposed to explore the interactions between owners and contractors and investigate the influence of surety bonds (high penalty and low penalty) on the default behavior of contractors based on a static and dynamic evolutionary game analysis model. The results showed that applying the surety bond strategy is effective at decreasing the probability of the contractors’ default behavior when the credit system based on a surety bond system is well developed in the construction industry and the cost of the surety bond is low enough. Therefore, government strategies such as a better development of the credit system driven by surety bonds and the subsidies on surety bonds to reduce the cost can mitigate the contractors’ default behavior and keep the sustainability of the construction markets.
Development of a Two-Stage Pyrolysis Process for the End-Of-Life Nickel Cobalt Manganese Lithium Battery Recycling from Electric Vehicles
Authors: Zhu Lingyun , Chen Ming
Published: November 2020

With the continuous promotion of electric vehicles, the pressure to scrap vehicle batteries is increasing, especially in China, where nickel cobalt manganese lithium (NCM) batteries have gradually come to occupy a dominant position in the battery market. In this study, we propose a two-stage pyrolysis process for vehicle batteries, which aims to effectively deal with the volatilization of organic solvents, the decomposition of lithium salts in the electrolyte and the removal of the separator material and polyvinylidene fluoride (PVDF) during battery recycling. By solving these issues, recycling is more effective, safe. Through thermogravimetric analysis (TGA), the pyrolysis characteristics of the battery’s internal materials are discussed, and 150 °C and 450 °C were determined as the pyrolysis temperatures of the two-stage pyrolysis process. The results show that in the first stage of pyrolysis, organic solvents EC (C4H3O3), DEC (C5H10O3) and EMC (C4H8O3) can be separated from the electrolyte. In the second stage, the pyrolysis can lead to the separator’s thermal decomposition. The gas products are alkane C2-C6, and the tar products are organic hydrocarbons C15-C36. Meanwhile, the solid residue of the battery’s internal material seems to be very homogeneous. Finally, the potential recovery value and pollution control countermeasures of the products and residues from the pyrolysis process are analyzed. Consequently, this method can effectively handle NCM vehicle battery recycling, which provides the basis for the subsequent hydrometallurgical or pyrometallurgical process for element recovery of the battery material.
The Role of Human Operators in Safety Perception of AV Deployment—Insights from a Large European Survey
Authors: Kyriakidis Miltos , Sodnik Jaka , Stojmenova Kristina , Elvarsson Arnór B. , Pronello Cristina , Thomopoulos Nikolas
Published: November 2020

Autonomous vehicles are anticipated to play an important role on future mobility offering encouraging solutions to today’s transport problems. However, concerns of the public, which can affect the AVs’ uptake, are yet to be addressed. This study presents relevant findings of an online survey in eight European countries. First, 1639 responses were collected in Spring 2020 on people’s commute, preferred transport mode, willingness to use AVs and demographic details. Data was analyzed for the entire dataset and for vulnerable road users in particular. Results re-confirm the long-lasting discourse on the importance of safety on the acceptance of AVs. Spearman correlations show that age, gender, education level and number of household members have an impact on how people may be using or allowing their children to use the technology, e.g., with or without the presence of a human supervisor in the vehicle. Results on vulnerable road users show the same trend. The elderly would travel in AVs with the presence of a human supervisor. People with disabilities have the same proclivity, however their reactions were more conservative. Next to safety, reliability, affordability, cost, driving pleasure and household size may also impact the uptake of AVs and shall be considered when designing relevant policies.
Corporate Social Responsibility Influencing Sustainability within the Fashion Industry. A Systematic Review
Authors: Thorisdottir Thorey S , Johannsdottir Lara
Published: November 2020

The fashion industry, one of the largest industries in the world, is a complicated phenomenon, driven by aspirations of symbolic lifestyle and the creativity of architecture and design. It pushes the use of natural resources to its limits by mass production and a low-cost structure that motivates consumerism at large. The purpose of this study is to explore corporate social responsibility and how it influences sustainability within the fashion industry. A systematic literature review was carried out. This encompassed the academic publications available in two scientific databases focusing on Corporate Social Responsibility (CSR), sustainability, and fashion, covering the period 2003–2019. The findings indicate that the CSR approach taken by managers within the fashion industry is focused on sustainability, business models, and/or supply chain innovation, with commitments undertaken concerning the economy, environment, and/or society, wherein the production of eco-friendly products and workers’ safety are emphasized. Actions that tie CSR and sustainability with companies’ actions are presented in a micro-meso-macro framework, where brand equity, culture, supply chain management, activism, and human rights are evident. The findings of the study are relevant for academia, practitioners, and policymakers, as they provide insight into the operations and impacts of domestic and multinational fashion companies, outlining the most relevant studies on the topic, and also highlighting research trends and gaps in the field.
Assessing Renewable Resources at the Saronikos Gulf for the Development of Multi-Generation Renewable Systems
Authors: Lavidas George , K. Kaldellis John
Published: November 2020

Decarbonisation of any energy system implies that more renewables will have to be incorporated into the grid. This requires a thorough assessment of available resources to properly estimate potential contributions and identify opportunities. This work focuses on the Saronikos Gulf, which is part of the most crowded urban coastline in Greece. Solar, wind and wave resources are analysed, and the long-term characteristics affecting power production are discussed. Solar resources provide ≥250 Wh·m−2 with small long-term changes. Wind resources at coastal and onshore regions are ≥50 W·m−2; however, it has higher annual volatility. Finally, the wave resources of the region are from 130 to 170 W/m with a positive resource rate of change ≈2.5 W·m−1/year. It is expected that multi-generation by different resources, especially with temporal overlaps of wind and waves, will reduce intermittent production, hence accelerating the energy transition.
Towards a Sustainable Society through Emerging Mobility Services: A Case of Autonomous Buses
Authors: Chinen Kenichiro , Sun Yang , Matsumoto Mitsutaka , Chun Yoon-Young
Published: November 2020

The topic of emerging mobility services has quickly received attention from scholars and media in recent years. Mobility services employing autonomous buses in transport systems is one such example. Mobility services using emerging technologies are expected to create social, economic, and environmental benefits. However, the potential benefits of emerging mobility services using autonomous technology will not be realized unless self-driving vehicles are accepted and used by many passengers. The recent worldwide pandemic caused us to recognize the benefits of autonomous technologies. This pretest-and-posttest designed research examines the predictors of willingness to ride autonomous buses in a closed environment. The results of this study indicate that a combination of factors, such as societal benefits, attitude and technology adoption, directly and indirectly influence an individual’s acceptance of autonomous buses. This study finds that passengers’ willingness to use emerging mobility services after a sample riding experience is higher than before having a sample riding experience.
Pro-Environmental Behavior, Connectedness to Nature, and Wellbeing Dimensions among Granada Students
Authors: Ibáñez-Rueda Nazaret , Guillén-Royo Mònica , Guardiola Jorge
Published: November 2020

This paper aims to answer the following research questions: Are there differences between individual and collective pro-environmental behaviors (PEBs) in their relationship with wellbeing? What role does connectedness to nature play in those relationships? We understand individual PEBs as efforts to consume less and to reduce the environmental impact of consumption, whereas we define collaborative PEBs in terms of environmental activism. We consider connectedness to nature as a potential factor moderating the relationship between PEBs and wellbeing. The study incorporates several dimensions of wellbeing: cognitive, affective and eudaimonic. We use regression analysis to study the extent to which individual and collective PEBs explain the three wellbeing dimensions and we explore the moderating role of connectedness to nature using data from a sample of 973 students at the University of Granada (Spain). Results indicate that individual PEBs are positively related to the eudaimonic dimension of wellbeing but they do not explain the cognitive and affective dimensions. In contrast, collaborative PEB is negatively related to life satisfaction, our measure of the cognitive dimension, but not significantly related to the other wellbeing measures. Based on this evidence, we can answer our first research question in the affirmative. As for the second question, our results suggest that connectedness to nature plays a moderating role in the relationship between life satisfaction and collaborative PEBs, as the initially negative relationship is reversed when people feel highly connected to nature.
Wild Food Plant Gathering among Kalasha, Yidgha, Nuristani and Khowar Speakers in Chitral, NW Pakistan
Authors: Abdul Aziz Muhammad , Ullah Zahid , Pieroni Andrea
Published: November 2020

The documentation of local food resources among linguistic/cultural minorities is essential for fostering measures aimed at sustaining food biocultural heritage. Moreover, interdisciplinary studies on food cultural heritage represent a vital aspect of promoting environmental and social sustainability. The current study aimed to record the traditional foraging of wild food plants (WFPs) among three minority groups (Kalasha, Muslim Ismaili Yidgha, and Muslim Sunni Kamkata-vari speakers) as well as the dominant (Sunni Muslim) Kho/Chitrali people in the Kalasha and Lotkoh valleys, Chitral, NW Pakistan. A field survey recorded fifty-five locally gathered wild food plants and three mycological taxa. Most of the WFPs were used raw as snacks or as cooked vegetables, and Yidgha speakers reported the highest number of WFPs. Although the wild food plant uses of the four considered groups were quite similar, Yidgha speakers exclusively reported the use of Heracleum candicans, Matricaria chamomilla, Seriphidium brevifolium, and Sisymbrium irio. Similarly, Kalasha speakers reported the highest number of use reports, and along with Yidgha speakers they quoted a few WFPs that were frequently used only by them. The results of the study showed a remarkable degree of cultural adaptation of the minority groups to the dominant Kho/Chitrali culture, but also some signs of cultural resilience among those linguistic and religious minorities that were historically more marginalized (Kalasha and Yidgha speakers). The recorded food biocultural heritage should be seriously considered in future development programs aimed at fostering social cohesion and sustainability.
Artificial Intelligence-Enabled Traffic Monitoring System
Authors: Mandal Vishal , Mussah Abdul Rashid , Jin Peng , Adu-Gyamfi Yaw
Published: November 2020

Manual traffic surveillance can be a daunting task as Traffic Management Centers operate a myriad of cameras installed over a network. Injecting some level of automation could help lighten the workload of human operators performing manual surveillance and facilitate making proactive decisions which would reduce the impact of incidents and recurring congestion on roadways. This article presents a novel approach to automatically monitor real time traffic footage using deep convolutional neural networks and a stand-alone graphical user interface. The authors describe the results of research received in the process of developing models that serve as an integrated framework for an artificial intelligence enabled traffic monitoring system. The proposed system deploys several state-of-the-art deep learning algorithms to automate different traffic monitoring needs. Taking advantage of a large database of annotated video surveillance data, deep learning-based models are trained to detect queues, track stationary vehicles, and tabulate vehicle counts. A pixel-level segmentation approach is applied to detect traffic queues and predict severity. Real-time object detection algorithms coupled with different tracking systems are deployed to automatically detect stranded vehicles as well as perform vehicular counts. At each stage of development, interesting experimental results are presented to demonstrate the effectiveness of the proposed system. Overall, the results demonstrate that the proposed framework performs satisfactorily under varied conditions without being immensely impacted by environmental hazards such as blurry camera views, low illumination, rain, or snow.
A Scientific Approach to the Densification Debate in Bergen Centre in Norway
Authors: Koning Remco Elric de , Roald Hans Jacob , Nes Akkelies van
Published: November 2020

The municipality of Bergen in Norway aims to densify fifty per cent of new housing within the city’s central parts. The Ministry of Local Government and Modernisation ordered and financed an investigation to be carried out by the Western Norway University of Applied Sciences and the consulting firm Asplan Viak to give research-based input to the densification strategy debate in Bergen. This article demonstrates how the Space Syntax method can be applied to urban densification strategies in urban planning and policy making. The Geographical Information System (GIS) is used to obtain, select, and aggregate operational information. First, the spatial attributes that constitute an area’s attractiveness were registered. Then, this analysis was modelled after the Spacescape® method. Next, the Space Syntax methodology was applied to predict to-movement and through-movement flow potentials. Finally, through weighting the relevant parameters, including impediments such as land ownership, twelve areas were identified as having major potential for transformation based on their overall score. As it turns out, the spatial structure of the street and road network is the underlying driver for how and where to densify. Now, the challenge is how to apply this knowledge into current planning practice.
A New Generalized Step-Down Single-Stage AC/AC Power Converter
Authors: Uddin Md. Shihab , Biswas Shuvra Prokash , Islam Md. Rabiul , Anower Md. Shamim , Kouzani Abbas Z. , Mahmud M A Parvez
Published: November 2020

Most traditional AC/AC power converters suffer from power quality problems and multi-stage power conversion losses. The rectifier and inverter-based AC/AC converter topology not only increases multi-stage power conversion losses, but also increases the volume, weight, and cost, and decreases the longevity of the converter due to the DC-link capacitor, line filter and electromagnetic interference (EMI) filter. High-frequency (about 10 kHz) switching advanced pulse width modulation techniques are generally used in order to compensate the power quality problems, which increase the switching losses and introduce the EMI problems. In this paper, a new generalized step-down single-stage line-frequency switching AC/AC power converter topology is proposed. The proposed converter uses line-frequency switching, and does not require any pulse width modulation techniques. The proposed topology offers promising performances in terms of lower order harmonics, total harmonic distortion, the elimination of DC-link capacitors and EMI filters, and switching losses. The circuit was designed and simulated in a MATLAB/Simulink environment. A scaled-down laboratory prototype of the proposed topology was developed in order to validate the feasibility. The experimental and simulation results reveal the feasibility of the proposed generalized step-down single-stage converter topology, and its excellent features.
Communication and Information Flow in Polish Construction Projects
Authors: Kania Ewelina , Radziszewska-Zielina Elżbieta , Śladowski Grzegorz
Published: November 2020

The objective of this paper is to analyse communication and information flow within construction projects on the basis of a survey study and a review of the literature. The subject of communication and its impact on carrying out a construction project is widely discussed in global academic literature. Many scholars point to problems with communication and information flow while also reporting that it directly affects construction project time and cost. In the literature, communication is also presented as an essential factor that improves safety and partnering in construction projects. There is also a group of studies that points to the lack of effective communication as the cause of delays and modifications. The authors, in reference to global studies, present the results of a survey study performed in Poland on a group of 160 construction industry practitioners. Information about five research areas was collected. These areas included: general information about communication and information flow between construction project participants, problems in carrying out construction projects in relation to the lack of effective communication, the impact of communication on the success of carrying out a construction project, communication costs, and the need to develop a tool (a digital system) that would support construction project managers in the optimal control over communication between project participants. On the basis of the obtained research results and the literature study, it was found that effective communication and information flow within a construction project can improve the construction process and that there exists a need to develop a comprehensive approach that would aid construction project managers in ensuring a more effective information flow.
Mobile Augmented Reality in Electronic Commerce: Investigating User Perception and Purchase Intent Amongst Educated Young Adults
Authors: Haile Tegegne Tesfaye , Kang Mincheol
Published: November 2020

Even though the presence and use of mobile augmented reality (MAR) technology has become increasingly popular in the field of marketing and advertising in recent years, it has largely been neglected in the study of consumer behavior research. This paper utilizes a single-group posttest-only quasi-experimental design to investigate how the feature of mobile augmented reality application influences consumers’ attitude and purchasing intention as explained by the dimensions of persuasion (i.e., consumers’ cognitive, affective, and conative dimensions). Structural Equation Modeling (SEM) with SPSS and AMOS is used to analyze the psychometric survey data collected from 179 participants. The results supported the prediction that MAR application’s real-time interactivity and entertainment increase cognition and affection, respectively; while irritation with MAR application decreases affection. The unsupported hypothesis, which predicted a positive relationship between informativeness and cognition, came as a surprise. The overall result of the study demonstrates the positive influence of MAR application in enhancing consumers’ purchasing intention. Finally, implications and future research directions are discussed.
Human Resource Management Practices and Service Provider Commitment of Green Hotel Service Providers: Mediating Role of Resilience and Work Engagement
Authors: Arasli Huseyin , Nergiz Ahmet , Yesiltas Mehmet , Gunay Tugrul
Published: November 2020

Although research on human resource management practices (HRMPs) has been ongoing for many years, studies have actually paid little attention to HRMPs and their contribution to the emotional side of the bottom line or commitment to the external environment, particularly the serial mediation of HRMPs. Hence, to fill this research void, this study extends social exchange theory, broaden-and-build theory and the conservation of resources (COR) theory in the context of green hospitality by proposing a novel conceptual model to test the mediating effects of resilience and commitment between HRMPs (training, empowerment, and rewards) and service providers’ environmental commitment. A quantitative study was performed involving 557 participants at green hotels. The findings show that the components of HRMPs (training, rewards, and empowerment) were found to be crucial tools in encouraging service providers to engage in environmental tasks while green training, empowerment and reward systems can unlock environmental commitment (EEC) for the setting. In addition, environmental commitment increased by the contribution of two mediators, resilience and engagement; and interestingly, rewards did not contribute to the environmental resilience of service providers, while all three HRMPs had a positive influence on work engagement of service providers in the research context.
Extracting Key Drivers of Air Passenger’s Experience and Satisfaction through Online Review Analysis
Authors: Shadiyar Aralbayeva , Ban Hyun-Jeong , Kim Hak-Seon
Published: November 2020

This study compared the competitiveness of the Commonwealth Independent State Airlines (Azerbaijan Airlines, Air Astana, Aeroflot) with Korean airlines (Asiana Airlines, Korean Air) using customer online reviews through big data analytics. The purpose of this study was to get the understanding of airline issues, especially the relationship between airline traveler experience and satisfaction. This study also shows which group has a better service and is more developed and provides significant and social network-oriented suggestions for another group of airlines. Data were collected from Skytrax and the collected reviews were written from January 2011 to March 2019. The size of the dataset was 1693 reviews, and a total of 199,469 words were extracted. As part of the qualitative analysis method, semantic network analysis through text mining was performed, and linear regression analysis was conducted using SPSS as part of the quantitative analysis method. This study shows which group of airlines has a better service and provides significant and social network-oriented suggestions for another group of airlines. The common concerns, as well as special features for different airlines, can also be extracted from online review data.