bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimizing the Recognition and Feature Extraction of Wind Turbines through Hybrid Semantic Segmentation Architectures
Authors: Manso-Callejo Miguel-Ángel , Cira Calimanut-Ionut , Alcarria Ramón , Arranz-Justel José-Juan
Published: November 2020

Updating the mapping of wind turbines farms—found in constant expansion—is important to predict energy production or to minimize the risk of these infrastructures during storms. This geoinformation is not usually provided by public mapping agencies, and the alternative sources are usually consortiums or individuals interested in mapping and study. However, they do not offer metadata or genealogy, and their quality is unknown. This article presents a methodology oriented to optimize the recognition and extraction of features (wind turbines) using hybrid architectures of semantic segmentation. The aim is to characterize the quality of these datasets and help to improve and update them automatically at a large-scale. To this end, we intend to evaluate the capacity of hybrid semantic segmentation networks trained to extract features representing wind turbines from high-resolution images and to characterize the positional accuracy and completeness of a dataset whose genealogy and quality are unknown. We built a training dataset composed of 5140 tiles of aerial images and their cartography to train six different neural network architectures. The networks were evaluated on five test areas (covering 520 km2 of the Spanish territory) to identify the best segmentation architecture (in our case, LinkNet as base architecture and EfficientNet-b3 as the backbone). This hybrid segmentation model allowed us to characterize the completeness—both by commission and by omission—of the available georeferenced wind turbine dataset, as well as its geometric quality.
Deep-Learning-Based Classification of Point Clouds for Bridge Inspection
Authors: Kim Hyunsoo , Kim Changwan
Published: November 2020

Conventional bridge maintenance requires significant time and effort because it involves manual inspection and two-dimensional drawings are used to record any damage. For this reason, a process that identifies the location of the damage in three-dimensional space and classifies the bridge components involved is required. In this study, three deep-learning models—PointNet, PointCNN, and Dynamic Graph Convolutional Neural Network (DGCNN)—were compared to classify the components of bridges. Point cloud data were acquired from three types of bridge (Rahmen, girder, and gravity bridges) to determine the optimal model for use across all three types. Three-fold cross-validation was employed, with overall accuracy and intersection over unions used as the performance measures. The mean interval over unit value of DGCNN is 86.85%, which is higher than 84.29% of Pointnet, 74.68% of PointCNN. The accurate classification of a bridge component based on its relationship with the surrounding components may assist in identifying whether the damage to a bridge affects a structurally important main component.
Correction: Zhao, H., et al. Automatic Estimation of Crop Disease Severity Levels Based on Vegetation Index Normalization. Remote Sensing 2020, 12, 1930
Authors: Zhao Hengqian , Yang Chenghai , Guo Wei , Zhang Lifu , Zhang Dongyan
Published: November 2020

The authors wish to make the following correction to this paper [...]
Exploring Appropriate Preprocessing Techniques for Hyperspectral Soil Organic Matter Content Estimation in Black Soil Area
Authors: Xu Xitong , Chen Shengbo , Xu Zhengyuan , Yu Yan , Zhang Sen , Dai Rui
Published: November 2020

Black soil in northeast China is gradually degraded and soil organic matter (SOM) content decreases at a rate of 0.5% per year because of the long-term cultivation. SOM content can be obtained rapidly by visible and near-infrared (Vis–NIR) spectroscopy. It is critical to select appropriate preprocessing techniques for SOM content estimation through Vis–NIR spectroscopy. This study explored three categories of preprocessing techniques to improve the accuracy of SOM content estimation in black soil area, and a total of 496 ground samples were collected from the typical black soil area at 0–15 cm in Hai Lun City, Heilongjiang Province, northeast of China. Three categories of preprocessing include denoising, data transformation and dimensionality reduction. For denoising, Svitzky-Golay filter (SGF), wavelet packet transform (WPT), multiplicative scatter correction (MSC), and none (N) were applied to spectrum of ground samples. For data transformation, fractional derivatives were allowed to vary from 0 to 2 with an increment of 0.2 at each step. For dimensionality reduction, multidimensional scaling (MDS) and locally linear embedding (LLE) were introduced and compared with principal component analysis (PCA), which was commonly used for dimensionality reduction of soil spectrum. After spectral pretreatments, a total of 132 partial least squares regression (PLSR) models were constructed for SOM content estimation. Results showed that SGF performed better than the other three denoising methods. Low-order derivatives can accentuate spectral features of soil for SOM content estimation; as the order increases from 0.8, the spectrum were more susceptible to spectral noise interferences. In most cases, 0.2–0.8 order derivatives exhibited the best estimation performance. Furthermore, PCA yielded the optimal predictability, the mean residual predictive deviation (RPD) and maximum RPD of the models using PCA were 1.79 and 2.60, respectively. The application of appropriate preprocessing techniques could improve the efficiency and accuracy of SOM content estimation, which is important for the protection of ecological and agricultural environment in black soil area.
Examining the Robustness of a Spatial Bootstrap Regional Approach for Radar-Based Hourly Precipitation Frequency Analysis
Authors: Eldardiry Hisham , Habib Emad
Published: November 2020

Radar-based Quantitative Precipitation Estimates (QPE) provide rainfall products with high temporal and spatial resolutions as opposed to sparse observations from rain gauges. Radar-based QPE’s have been widely used in many hydrological and meteorological applications; however, using these high-resolution products in the development of Precipitation Frequency Estimates (PFE) is impeded by their typically short-record availability. The current study evaluates the robustness of a spatial bootstrap regional approach, in comparison to a pixel-based (i.e., at site) approach, to derive PFEs using hourly radar-based multi-sensor precipitation estimation (MPE) product over the state of Louisiana in the US. The spatial bootstrap sampling technique augments the local pixel sample by incorporating rainfall data from surrounding pixels with decreasing importance when distance increases. We modeled extreme hourly rainfall data based on annual maximum series (AMS) using the generalized extreme value statistical distribution. The results showed a reduction in the uncertainty bounds of the PFEs when using the regional spatial bootstrap approach compared to the pixel-based estimation, with an average reduction of 10% and 2% in the 2- and 5-year return periods, respectively. Using gauge-based PFE’s as a reference, the spatial bootstrap regional approach outperforms the pixel-based approach in terms of robustness to outliers identified in the radar-based AMS of some pixels. However, the systematic bias inherent to radar-based QPE especially for extreme rainfall cases, appear to cause considerable underestimation in PFEs in both the pixel-based and the regional approaches.
An Automated System for Classification of Chronic Obstructive Pulmonary Disease and Pneumonia Patients Using Lung Sound Analysis
Authors: Naqvi Syed Zohaib Hassan , Choudhry Mohammad Ahmad
Published: November 2020

Chronic obstructive pulmonary disease (COPD) and pneumonia are two of the few fatal lung diseases which share common adventitious lung sounds. Diagnosing the disease from lung sound analysis to design a noninvasive technique for telemedicine is a challenging task. A novel framework is presented to perform a diagnosis of COPD and Pneumonia via application of the signal processing and machine learning approach. This model will help the pulmonologist to accurately detect disease A and B. COPD, normal and pneumonia lung sound (LS) data from the ICBHI respiratory database is used in this research. The performance analysis is evidence of the improved performance of the quadratic discriminate classifier with an accuracy of 99.70% on selected fused features after experimentation. The fusion of time domain, cepstral, and spectral features are employed. Feature selection for fusion is performed through the back-elimination method whereas empirical mode decomposition (EMD) and discrete wavelet transform (DWT)-based techniques are used to denoise and segment the pulmonic signal. Class imbalance is catered with the implementation of the adaptive synthetic (ADASYN) sampling technique.
Optimization of Time Synchronization and Algorithms with TDOA Based Indoor Positioning Technique for Internet of Things
Authors: Zhao Kun , Zhao Tiantian , Zheng Zhengqi , Yu Chao , Ma Difeng , Rabie Khaled , Kharel Rupak
Published: November 2020

To provide high-precision positioning for Internet of Things (IoT) scenarios, we optimize the indoor positioning technique based on Ultra-Wideband (UWB) Time Difference of Arrival (TDOA) equipment. This paper analyzes sources of positioning error and improves the time synchronization algorithm based on the synchronization packet. Then we use the labels of the known position to further optimize the time synchronization performance, and hence improve TDOA measurements. After time synchronization optimization, a Weighted Least Square (WLS) and Taylor coordination algorithm is derived. Experiments show that our optimization reduces the average positioning error from 54.8 cm to 12.6 cm.
Traffic Intersection Re-Identification Using Monocular Camera Sensors
Authors: Xiong Lu , Deng Zhenwen , Huang Yuyao , Du Weixin , Zhao Xiaolong , Lu Chengyu , Tian Wei
Published: November 2020

Perception of road structures especially the traffic intersections by visual sensors is an essential task for automated driving. However, compared with intersection detection or visual place recognition, intersection re-identification (intersection re-ID) strongly affects driving behavior decisions with given routes, yet has long been neglected by researchers. This paper strives to explore intersection re-ID by a monocular camera sensor. We propose a Hybrid Double-Level re-identification approach which exploits two branches of Deep Convolutional Neural Network to accomplish multi-task including classification of intersection and its fine attributes, and global localization in topological maps. Furthermore, we propose a mixed loss training for the network to learn the similarity of two intersection images. As no public datasets are available for the intersection re-ID task, based on the work of RobotCar, we propose a new dataset with carefully-labeled intersection attributes, which is called “RobotCar Intersection” and covers more than 30,000 images of eight intersections in different seasons and day time. Additionally, we provide another dataset, called “Campus Intersection” consisting of panoramic images of eight intersections in a university campus to verify our updating strategy of topology map. Experimental results demonstrate that our proposed approach can achieve promising results in re-ID of both coarse road intersections and its global pose, and is well suited for updating and completion of topological maps.
Path-Tracking of a WMR Fed by Inverter-DC/DC Buck Power Electronic Converter Systems
Authors: Hernández-Guzmán Victor Manuel , Silva-Ortigoza Ramón , Tavera-Mosqueda Salvador , Marcelino-Aranda Mariana , Marciano-Melchor Magdalena
Published: November 2020

This paper is concerned with path-tracking control of a wheeled mobile robot. This robot is equipped with two permanent magnet brushed DC-motors which are fed by two inverter-DC/DC Buck power converter systems as power amplifiers. By taking into account the dynamics of all the subsystems we present, for the first time, a formal stability proof for this control problem. Our control scheme is simple, in the sense that it is composed by four internal classical proportional-integral loops and one external classical proportional-derivative loop for path-tracking purposes. This is the third paper of a series of papers devoted to control different nonlinear systems, which proves that the proposed methodology is a rather general approach for controlling electromechanical systems when actuated by power electronic converters.
A Submillimeter-Level Relative Navigation Technology for Spacecraft Formation Flying in Highly Elliptical Orbit
Authors: Wang Xiaoliang , Gong Deren , Jiang Yifei , Mo Qiankun , Kang Zeyu , Shen Qiang , Wu Shufan , Wang Dengfeng
Published: November 2020

Spacecraft formation flying (SFF) in highly elliptical orbit (HEO) has attracted a great deal of attention in many space exploration applications, while precise guidance, navigation, and control (GNC) technology—especially precise ranging—are the basis of success for such SFF missions. In this paper, we introduce a novel K-band microwave ranging (MWR) equipment for the on-orbit verification of submillimeter-level precise ranging technology in future HEO SFF missions. The ranging technique is a synchronous dual one-way ranging (DOWR) microwave phase accumulation system, which achieved a ranging accuracy of tens of microns in the laboratory environment. The detailed design and development process of the MWR equipment are provided, ranging error sources are analyzed, and relative orbit dynamic models for HEO formation scenes are given with real perturbations considered. Moreover, an adaptive Kalman filter algorithm is introduced for SFF relative navigation design, incorporating process noise uncertainty. The performance of SFF relative navigation while using MWR is tested in a hardware-in-the-loop (HIL) simulation system within a high-precision six degrees of freedom (6-DOF) moving platform. The final range estimation errors from MWR using the adaptive filter were less than 35 μm and 8.5 μm/s for range rate, demonstrating the promising accuracy for future HEO formation mission applications.
The Perception System of Intelligent Ground Vehicles in All Weather Conditions: A Systematic Literature Review
Authors: Mohammed Abdul Sajeed , Amamou Ali , Ayevide Follivi Kloutse , Kelouwani Sousso , Agbossou Kodjo , Zioui Nadjet
Published: November 2020

Perception is a vital part of driving. Every year, the loss in visibility due to snow, fog, and rain causes serious accidents worldwide. Therefore, it is important to be aware of the impact of weather conditions on perception performance while driving on highways and urban traffic in all weather conditions. The goal of this paper is to provide a survey of sensing technologies used to detect the surrounding environment and obstacles during driving maneuvers in different weather conditions. Firstly, some important historical milestones are presented. Secondly, the state-of-the-art automated driving applications (adaptive cruise control, pedestrian collision avoidance, etc.) are introduced with a focus on all-weather activity. Thirdly, the most involved sensor technologies (radar, lidar, ultrasonic, camera, and far-infrared) employed by automated driving applications are studied. Furthermore, the difference between the current and expected states of performance is determined by the use of spider charts. As a result, a fusion perspective is proposed that can fill gaps and increase the robustness of the perception system.
Can We Ditch Feature Engineering? End-to-End Deep Learning for Affect Recognition from Physiological Sensor Data
Authors: Dzieżyc Maciej , Gjoreski Martin , Kazienko Przemysław , Saganowski Stanisław , Gams Matjaž
Published: November 2020

To further extend the applicability of wearable sensors in various domains such as mobile health systems and the automotive industry, new methods for accurately extracting subtle physiological information from these wearable sensors are required. However, the extraction of valuable information from physiological signals is still challenging—smartphones can count steps and compute heart rate, but they cannot recognize emotions and related affective states. This study analyzes the possibility of using end-to-end multimodal deep learning (DL) methods for affect recognition. Ten end-to-end DL architectures are compared on four different datasets with diverse raw physiological signals used for affect recognition, including emotional and stress states. The DL architectures specialized for time-series classification were enhanced to simultaneously facilitate learning from multiple sensors, each having their own sampling frequency. To enable fair comparison among the different DL architectures, Bayesian optimization was used for hyperparameter tuning. The experimental results showed that the performance of the models depends on the intensity of the physiological response induced by the affective stimuli, i.e., the DL models recognize stress induced by the Trier Social Stress Test more successfully than they recognize emotional changes induced by watching affective content, e.g., funny videos. Additionally, the results showed that the CNN-based architectures might be more suitable than LSTM-based architectures for affect recognition from physiological sensors.
Cost Effective Mobile Mapping System for Color Point Cloud Reconstruction
Authors: Peng Cheng-Wei , Hsu Chen-Chien , Wang Wei-Yen
Published: November 2020

Survey-grade Lidar brands have commercialized Lidar-based mobile mapping systems (MMSs) for several years now. With this high-end equipment, the high-level accuracy quality of point clouds can be ensured, but unfortunately, their high cost has prevented practical implementation in autonomous driving from being affordable. As an attempt to solve this problem, we present a cost-effective MMS to generate an accurate 3D color point cloud for autonomous vehicles. Among the major processes for color point cloud reconstruction, we first synchronize the timestamps of each sensor. The calibration process between camera and Lidar is developed to obtain the translation and rotation matrices, based on which color attributes can be composed into the corresponding Lidar points. We also employ control points to adjust the point cloud for fine tuning the absolute position. To overcome the limitation of Global Navigation Satellite System/Inertial Measurement Unit (GNSS/IMU) positioning system, we utilize Normal Distribution Transform (NDT) localization to refine the trajectory to solve the multi-scan dispersion issue. Experimental results show that the color point cloud reconstructed by the proposed MMS has a position error in centimeter-level accuracy, meeting the requirement of high definition (HD) maps for autonomous driving usage.
Variations in Velocity and Sensitivity of Electromagnetic Waves in Transmission Lines Configured in Model Piles with Necking Defects Containing Soils
Authors: Yu Jung-Doung , Kim Sang Yeob , Lee Jong-Sub
Published: November 2020

This study investigates variations in the velocity and sensitivity of electromagnetic waves in transmission lines configured in defective model piles for the detection of necking defects containing soil. Experiments are performed with model piles containing defects filled with different materials, such as air, sands, and clay. Five different types of transmission lines are configured in model piles. The electromagnetic waves are generated and detected using a time domain reflectometer. The velocity of electromagnetic waves is highest when the defect is filled with air, and it decreases with an increase in the water content. The velocity is lowest when the defect is filled with clay. The sensitivity of transmission lines for detecting defects decreases with an increase in soil water contents. The transmission line with a single electrical wire and epoxy-coated rebar exhibits the highest sensitivity, followed by that with three and two parallel electrical wires. Transmission lines with a single electrical wire and uncoated rebar and those with two parallel electrical wires wrapped with a sheath exhibit poor sensitivity when the defect is filled with clay. This study demonstrates that electromagnetic waves can be effective tools for detecting necking defects with wet and conductive soils in bored piles.
A Comprehensive Dependability Model for QoM-Aware Industrial WSN When Performing Visual Area Coverage in Occluded Scenarios
Authors: Jesus Thiago C. , Portugal Paulo , Costa Daniel G. , Vasques Francisco
Published: November 2020

In critical industrial monitoring and control applications, dependability evaluation will be usually required. For wireless sensor networks deployed in industrial plants, dependability evaluation can provide valuable information, enabling proper preventive or contingency measures to assure their correct and safe operation. However, when employing sensor nodes equipped with cameras, visual coverage failures may have a deep impact on the perceived quality of industrial applications, besides the already expected impacts of hardware and connectivity failures. This article proposes a comprehensive mathematical model for dependability evaluation centered on the concept of Quality of Monitoring (QoM), processing availability, reliability and effective coverage parameters in a combined way. Practical evaluation issues are discussed and simulation results are presented to demonstrate how the proposed model can be applied in wireless industrial sensor networks when assessing and enhancing their dependability.
Potential of Hyperspectral and Thermal Proximal Sensing for Estimating Growth Performance and Yield of Soybean Exposed to Different Drip Irrigation Regimes Under Arid Conditions
Authors: Elmetwalli Adel H. , El-Hendawy Salah , Al-Suhaibani Nasser , Alotaibi Majed , Tahir Muhammad Usman , Mubushar Muhammad , Hassan Wael M. , Elsayed Salah
Published: November 2020

Proximal hyperspectral sensing tools could complement and perhaps replace destructive traditional methods for accurate estimation and monitoring of various morpho-physiological plant indicators. In this study, we assessed the potential of thermal imaging (TI) criteria and spectral reflectance indices (SRIs) to monitor different vegetative growth traits (biomass fresh weight, biomass dry weight, and canopy water mass) and seed yield (SY) of soybean exposed to 100%, 75%, and 50% of estimated crop evapotranspiration (ETc). These different plant traits were evaluated and related to TI criteria and SRIs at the beginning bloom (R1) and full seed (R6) growth stages. Results showed that all plant traits, TI criteria, and SRIs presented significant variations (p < 0.05) among irrigation regimes at both growth stages. The performance of TI criteria and SRIs for assessment of vegetative growth traits and SY fluctuated when relationships were analyzed for each irrigation regime or growth stage separately or when the data of both conditions were combined together. TI criteria and SRIs exhibited a moderate to strong relationship with vegetative growth traits when data from different irrigation regimes were pooled together at each growth stage or vice versa. The R6 and R1 growth stages are suitable for assessing SY under full (100% ETc) and severe (50% ETc) irrigation regimes, respectively, using SRIs. The overall results indicate that the usefulness of the TI and SRIs for assessment of growth, yield, and water status of soybean under arid conditions is limited to the growth stage, the irrigation level, and the combination between them.
Transport Mode Choice for Residents in a Tourist Destination: The Long Road to Sustainability (the Case of Mallorca, Spain)
Authors: Ruiz-Pérez Maurici , Seguí-Pons Joana Maria
Published: November 2020

Sustainable mobility policies may encounter social, economic, and cultural barriers to successful implementation that need to be assessed. In this sense, knowledge of the population’s mobility habits and their relationship with transport modes is particularly essential. Along these lines, a study was carried out of the patterns of transport modes chosen concerning various social and territorial variables on the island of Mallorca based on the most recent mobility surveys. The study shows that the choice of mode is influenced by a wide range of factors, such as gender, age group, motive for the trip, occupation, region of residence, duration of the trip, and proximity to Palma, the capital of the island. The results indicate that private vehicles are the most often chosen mode of transport. Private vehicles are mainly used by working men between 30 and 44 years old for journeys between home and work, which do not exceed 30 min and are preferably in areas close to Palma. Sustainable modes are little used, although they are mainly used by women, young people, and retired people for work purposes and for access to educational and health centers. The demand for transport generated by the resident population and tourist activity and the negative externalities generated by mobility in private vehicles are closely related on a municipal level (Pearson’s coefficient 0.84, p = 0.00). However, the modal distribution does not seem to be directly related to these factors. Instead, it develops a more conditioned distribution by access to rail transport infrastructures and other geographical factors. In recent years, the Balearic Islands’ public administration launched the Balearic Islands Sectorial Mobility Plan 2019–2026, which aims to promote sustainable modes and reduce the use of private vehicles. This plan represents a considerable economic investment, but will also require great institutional coordination and cultural changes in the population’s perception of mobility. The study shows that the implementation of sustainable modes on the island requires a global vision of mobility issues that integrates urban planning and tourism planning to make the land-use model more sustainable.
The Right to Urban Streams: Quantitative Comparisons of Stakeholder Perceptions in Defining Adaptive Stream Restoration
Authors: Hong Chang-Yu , Chung Eun-Sung , Chang Heejun
Published: November 2020

Assuring healthy streams in the urban environment is a major goal for restoration scientists, urban planners, and city practitioners around the globe. In South Korea, many urban stream restoration efforts are designed to provide safe water to society and enhance ecological functions. We examined the extent to which the individual interests and different values of multiple stakeholders were considered in previous decision-making in two urban stream restoration projects. The relevant data on stream restoration were collected through the nominal group technique (NGT) and the analytic hierarchy process (AHP) for the two stream cases of a populated inland area and a coastal region in South Korea. The AHP results provide information about the comparative weights of the values of ecological restoration (priority score: 0.487), social restoration (priority score: 0.231), and landscape revitalization (priority score: 0.279) of the Ahn-Yang stream and ecological restoration (priority score: 0.527), social restoration (priority score: 0.182), and landscape revitalization (priority score: 0.290) of the Sahn-Jee stream. The stakeholders of the populated metropolitan area had a relatively high awareness of their role in environmental restoration, thus it was natural for them to place a high value on social restoration.
The Role of Top Management Team Digitalization and Firm Internationalization for Sustainable Business
Authors: Velinov Emil , Maly Milan , Petrenko Yelena , Denisov Igor , Vassilev Vasko
Published: November 2020

This paper aims to investigate the relationship between the existence of firm digitalization and firm internationalization in selected companies in the European Union (EU). The study provides evidence that upper echelon characteristics bring new competences and skills, including digital skills, which are essential for company internationalization in the modern world of turmoil, crisis and uncertainty. The research paper argues that top management team (TMT) characteristics, and particularly digitalization awareness and skills among the senior management, are critical factors for driving success in international markets and for the sustainability of the business. The dataset in this paper includes around one hundred executive and non-executive profiles in selected European countries, distributed in different industrial sectors. The empirical data have been collected from available online secondary sources on private companies in Germany, Sweden, Estonia, Latvia and Lithuania. The paper’s findings suggest that a relationship exists between TMT digitalization and firm internationalization. Additionally, the paper’s results illustrate that the companies with an on board Chief Information Officer or Chief Digitalization Officer tend to be more international in comparison to those which do not possess these two job positions in their top management.
The Science-Policy Nexus: U.S. Policy and International Environmental Governance
Authors: Kimble Melinda L.
Published: November 2020

In June 1991 I was reassigned from the Bureau of Near East Affairs within the U [...]
Performance Evaluation of Photovoltaic/Thermal (PV/T) System Using Different Design Configurations
Authors: Hussain M. Imtiaz , Kim Jun-Tae
Published: November 2020

This study summarizes the performance of a photovoltaic/thermal (PV/T) system integrated with a glass-to-PV backsheet (PVF film-based backsheet) and glass-to-glass photovoltaic (PV) cells protections. A dual-fluid heat exchanger is used to cool the PV cells in which water and air are operated simultaneously. The proposed PV/T design brings about a higher electric output while producing sufficient thermal energy. A detailed numerical study was performed by calculating real-time heat transfer coefficients. Energy balance equations across the dual-fluid PV/T system were solved using an ordinary differential equation (ODE) solver in MATLAB software. The hourly and annual energy and exergy variations for both configurations were evaluated for Cheonan City, Korea. In the case of a PV/T system with a glass-to-glass configuration, a larger heat exchange area causes the extraction of extra solar heat from the PV cells and thus improving the overall efficiency of the energy transfer. Results depict that the annual electrical and total thermal efficiencies with a glass-to-glass configuration were found to be 14.31% and 52.22%, respectively, and with a glass-to-PV backsheet configuration, the aforementioned values reduced to 13.92% and 48.25%, respectively. It is also observed that, with the application of a dual-fluid heat exchanger, the temperature gradient across the PV panel is surprisingly reduced.
A Meta-Analysis of Industrial Security Research for Sustainable Organizational Growth
Authors: Yu Harang , Chang Hangbae
Published: November 2020

As the world enters a fourth Industrial Revolution, organizations worldwide face challenges in dealing with important assets such as industrial technology. Leaking these assets can not only damage organizations economically but also negatively affect customer relationships and brand image. This has led to an increased awareness of industrial security in both the business and academic spheres and a focus on identifying and implementing countermeasures against security incidents, as future computing environments will continuously face security threats. This study first examines the literature on industrial security and its current status in South Korea, which is considered an active industrial security research environment. Subsequently, meta-analyses are conducted on South Korea and abroad to compare their status and research trends in the industrial security field. The results confirm that South Korea has more actively conducted relevant studies compared to international research. This study contributes to the current literature by not only increasing the awareness of industrial security but also encouraging future studies in the field to facilitate a safe and sustainable computing environment.
Navigating Climate Change: Rethinking the Role of Buildings
Authors: Cole Raymond J.
Published: November 2020

This paper focuses on the design of buildings as part of society’s response to the climate crisis in the aftermath of the COVID-19 pandemic. It draws on a broad literature to address two interrelated goals—first, to align regenerative development and design with the necessary bottom-up adaptation strategies and human agency, and second, to identify new, broader possible roles of buildings and responsibilities of design professionals. This required a comparison of current green building and emerging regenerative approaches and identifying the relevant characteristics of top-down and bottom-up mechanisms. The paper accepts that adaptation to climate change will, to a large extent, depend on people’s day-to-day actions in the places they live, and argues that the built environment will have to be infused with the capability to enable inhabitants’ greater agency. Viewing buildings as playing a connective role in the existing urban fabric seriously challenges the primacy of the individual building as the focus of environmental strategies. The roles of building design professionals will likely expand to include mediating between top-down imposed government controls and increasing bottom-up neighborhood-scale social activism.
Conceptualizing Smart Disaster Governance: An Integrative Conceptual Framework
Authors: Shahat Ehab , Hyun Chang T. , Yeom Chunho
Published: November 2020

The discourse of disaster governance is focused on the arrangements, relationships, and roles of the various actors involved in disaster governance. However, due to the lack of research on utilizing emerging technologies in disaster governance systems, this paper addresses the prospective benefits of utilizing Internet of Things (IoT) technologies in smart disaster governance systems. The authors employed a conceptual analysis of the previous research on disaster governance and utilizing IoT in disaster management. The basic concepts and constructs were abstracted from the literature to conceptualize a smart disaster governance system and its processes. As a result, six spheres were portrayed to form the characteristics of the smart disaster governance system, and an integrative conceptual framework for smart disaster governance was developed. The conceptual framework encompasses IoT technologies’ capabilities and disaster governance functions, in addition to the foreseen enhancements in disaster coping capacities and an overall rise in disaster resilience. A hypothetical case study was conducted to investigate the conceptual framework’s plausibility, and it showed a probable enhancement of the disaster governance of the 2018 Kerala floods if the smart disaster governance framework was utilized. This research provides a novel conceptualization of smart disaster governance. It can deepen the understanding of prospective benefits of integrating IoT technologies with disaster governance functions, contribute to disaster governance policy formulations, and construct a basis for future research on smart disaster governance for a sustainable society.
Evaluation of Reductions in Fume Emissions (VOCs and SVOCs) from Warm Mix Asphalt Incorporating Natural Zeolite and Reclaimed Asphalt Pavement for Sustainable Pavements
Authors: Espinoza Javier , Medina Cristian , Calabi-Floody Alejandra , Sánchez-Alonso Elsa , Valdés Gonzalo , Quiroz Andrés
Published: November 2020

Conventional asphalt mixtures used for road paving require high manufacturing temperatures and therefore high energy expenditure, which has a negative environmental impact and creates risk in the workplace owing to high emissions of pollutants, greenhouse gases, and toxic fumes. Reducing energy consumption and emissions is a continuous challenge for the asphalt industry. Previous studies have focused on the reduction of emissions without characterizing their composition, and detailed characterization of volatile organic compounds (VOCs) and semi-volatile organic compounds (SVOCs) in asphalt fumes is scarce. This communication describes the characterization and evaluation of VOCs and SVOCs from asphalt mixtures prepared at lower production temperatures using natural zeolite; in some cases, reclaimed asphalt pavement (RAP) was used. Fumes were extracted from different asphalt mix preparations using a gas syringe and then injected into hermetic gas sample bags. The compounds present in the fumes were sampled with a fiber and analyzed by gas-liquid chromatography coupled to mass spectrometry (GC/MS). In general, the preparation of warm mix asphalts (WMA) using RAP and natural zeolite as aggregates showed beneficial effects, reducing VOCs and SVOCs compared to hot mix asphalts (HMA). The fumes captured presented a similar composition to those from HMA, consisting principally of saturated and unsaturated aliphatic hydrocarbons and aromatic compounds but with few halogenated compounds and no polycyclic aromatic hydrocarbons. Thus, the paving mixtures described here are a friendlier alternative for the environment and for the health of road workers, in addition to permitting the re-use of RAP.
D. magna in Combination with M. aquaticum Inhibited the Bacterioplankton in Eutrophic Water
Authors: Yang Bo , Wei Xiaocheng , Wang Hanyang , Li Jiarui , Zheng Xiangqun , Zhang Chunxue , Li Bo
Published: November 2020

D. magna can affect the composition of planktonic bacteria, and can also significantly inhibit the growth of Cyanobacteria at high density. In this study, the inhibitory effects of low-density D. magna groups were stronger than high-density groups due to increases in Acidobacteria abundance in water. Meanwhile, D. magna can inhibit the growth of Planktothrix and Microcystis, but especially the growth of the latter. Alternatively, M. aquaticum and C. demersum can change the community structure of planktonic bacteria. Among them, the inhibitory effect of M. aquaticum on Microcystis and Planktothrix is strong, but it also increases the relative abundance of Chlamydia in water and the risk of pathogenic bacteria. In contrast, the inhibitory effect of C. demersum was more significant on Microcystis than on Planktothrix. Therefore, the combination of “submerged plants-Daphnia”, especially the combination of low density D. magna with M. aquaticum, had a significant inhibitory effect on Planktothrix and Microcystis.
Scenarios for Life Cycle Studies of Bridge Concrete Structure Maintenance
Authors: Kim Hyunsik , Tae Sungho , Ahn Yonghan , Yang Jihwan
Published: November 2020

The sustainability of structures during their construction and service life has become a widespread topic of interest. To ensure the sustainability of bridges, maintenance databases can be analyzed to determine the status changes and required maintenance of existing bridges. The results of this analysis can then be used to predict the environmental impacts and costs incurred during ongoing maintenance of new bridges to prepare accordingly for the future. To prepare for future events, this study utilizes the US National Bridge Inventory to analyze changes in the condition rating of bridge decks and substructures according to their service years, and suggests maintenance scenarios for the service life of bridge deck and substructure concrete by investigating the maintenance activities according to service years. The factors for applying the scenarios in Korea and conceptual equations for life cycle studies which apply the scenarios are discussed for further study in the life cycle assessment field of bridges.
An Optimal Metro Design for Transit Networks in Existing Square Cities Based on Non-Demand Criterion
Authors: Owais Mahmoud , Ahmed Abdou S. , Moussa Ghada S. , Khalil Ahmed A.
Published: November 2020

The overall purpose of this study is to enhance existing transit systems by planning a new underground metro network. The design of a new metro network in the existing cities is a complex problem. Therefore, in this research, the study idea arises from the prerequisites to get out of conventional metro network design to develop a future scheme for forecasting an optimal metro network for these existing cities. Two models are proposed to design metro transit networks based on an optimal cost–benefit ratio. Model 1 presents a grid metro network, and Model 2 presents the ring-radial metro network. The proposed methodology introduces a non-demand criterion for transit system design. The new network design aims to increase the overall transit system connectivity by minimizing passenger transfers through the transit network between origin and destination. An existing square city is presented as a case study for both models. It includes twenty-five traffic analysis zones, and thirty-six new metro stations are selected at the existing street intersection. TransCAD software is used as a base for stations and the metro network lines to coordinate all these data. A passenger transfer counting algorithm is then proposed to determine the number of needed transfers between stations from each origin to each destination. Thus, a passenger Origin/Destination transfer matrix is created via the NetBeans program to help in determining the number of transfers required to complete the trips on both proposed networks. Results show that Model 2 achieves the maximum cost–benefit ratio (CBR) of the transit network that increases 41% more than CBR of Model 1. Therefore, it is found that the ring radial network is a more optimal network to existing square cities than the grid network according to overall network connectivity.
Sustainability and Interoperability: An Economic Study on BIM Implementation by a Small Civil Engineering Firm
Authors: Aranda José Ángel , Martin-Dorta Norena , Naya Ferran , Conesa-Pastor Julián , Contero Manuel
Published: November 2020

Sustainability and interoperability are two closely related concepts. By analyzing the three fundamental facets of sustainability—economic, ecological and ethical/social—it is easier to address their connection with the concept of interoperability. This paper focuses on the economic aspect, in the field of civil engineering. In this area, due to the local nature of many of the software tools used, interoperability problems are frequent, with few studies addressing the economic impact of this, especially in small engineering firms. The main contribution of this paper is a design methodology for linear works based on the federation of building information modelling (BIM) models created with different software tools, conceived to break the interoperability issues between these applications. As an example, this methodology is applied to a mountain road widening project. A detailed economic analysis of the application of this methodology by an engineering Spanish firm reveals the important cost reductions that the integration of the software tools provides versus the prior practices.
A Comparative Cradle-to-Gate Life Cycle Study of Bio-Energy Feedstock from Camelina sativa, an Italian Case Study
Authors: Masella Piernicola , Galasso Incoronata
Published: November 2020

Growing energy needs and medium-term weakening of fossil energy reserves are driving forces towards the exploitation of alternative and renewable energy sources, such as biofuels from energy crops. In recent years, Camelina sativa (L.) Crantz has been rediscovered and is gaining popularity worldwide. The present work reports the results of a study on the life cycle, from cradle-to-gate, of C. sativa oil as a raw material for the production of biofuels in northern Italy, considering two scenarios, namely, the production of biodiesel (BD) and the extraction of pure vegetable oil (PVO). The functional unit was 1 megajoule of biofuel. A life cycle impact assessment (LCIA) was calculated according to the ILCD2011 procedure. Focusing on the global warming potential, the PVO scenario performs better than the BD scenario, with around 30 g CO2eq MJ−1. The net energy ratio (NER) exceeds unity for BD (approximately 1.4) or PVO (approximately 2.5). The same general trend was recorded for all calculated LCIA indicators; the common evidence is a generalized worse performance of the BD scenario, with indicators always scoring higher than the PVO. In particular, the two human toxicity indicators—carcinogenic and fresh water—eutrophication represent a significant difference, attributable to the refining process. Uncertainty and sensitivity analyses, respectively, underline the generalized importance of agricultural performances in the field and of allocation choices. Specifically, the importance of the grain yield and seed oil content in determining the environmental performance of the two scenarios was evident. As far as allocation is concerned, mass allocation provides the most favorable results, while on the other hand, the expansion of the system was the most penalizing alternative.
Patient-Reported Expectations, Outcome and Satisfaction in Thoracic and Lumbar Spine Stabilization Surgery: A Prospective Study
Authors: Krauss Philipp , Sonnleitner Clara , Reinartz Feline , Meyer Bernhard , Meyer Hanno S.
Published: November 2020

Patient-reported outcome measures (PROMs) have become an important aspect of quality control in modern healthcare. In this prospective observational study on 199 patients undergoing thoracolumbar stabilization surgery, we quantified preoperative expectations and PROMs at six and twelve months after surgery, and we investigated what constitutes patient satisfaction with the outcome. We used the visual analogue scale (VAS) for pain and the Oswestry Disability Index (ODI). Preoperative expectations were high (expected ODI: 9 ± 13%; leg pain: 1.0 ± 1.4; back pain: 1.3 ± 1.5). Pain and disability improved substantially, but expectations were mostly unrealistic (ODI expectation fulfilled after six months: 28% of patients; back pain: 48%). However, satisfaction was high (70% at six months after surgery). Satisfied patients had significantly better pain and disability outcomes and higher rates of expectation fulfillment than non-satisfied patients. Patients undergoing revision stabilization had worse outcomes than all other diagnosis groups. Prior stabilization surgery was identified as an independent risk factor for dissatisfaction. There were no preoperative pain or disability levels that predicted dissatisfaction. The data presented in this study can provide benchmarks for diagnosis-specific PROM targets in thoracolumbar stabilization surgery. Future studies should investigate whether satisfaction can be influenced, e.g., by discussing realistic outcome targets with patients ahead of surgery.
Slashed Lomax Distribution and Regression Model
Authors: Li Huihui , Tian Weizhong
Published: November 2020

In this article, the slashed Lomax distribution is introduced, which is an asymmetric distribution and can be used for fitting thick-tailed datasets. Various properties are explored, such as the density function, hazard rate function, Renyi entropy, r-th moments, and the coefficients of the skewness and kurtosis. Some useful characterizations of this distribution are obtained. Furthermore, we study a slashed Lomax regression model and the expectation conditional maximization (ECM) algorithm to estimate the model parameters. Simulation studies are conducted to evaluate the performances of the proposed method. Finally, two sets of data are applied to verify the importance of the slashed Lomax distribution.