bluuebunny/crossref_metadata_2025 huggingface dataset:
Mapping Drainage Structures Using Airborne Laser Scanning by Incorporating Road Centerline Information
Authors: Wang Chi-Kuei , Fareed Nadeem
Published: January 2021

Wide-area drainage structure (DS) mapping is of great concern, as many DSs are reaching the end of their design life and information on their location is usually absent. Recently, airborne laser scanning (ALS) has been proven useful for DS mapping through manual methods using ALS-derived digital elevation models (DEMs) and hillshade images. However, manual methods are slow and labor-intensive. To overcome these limitations, this paper proposes an automated DS mapping algorithm (DSMA) using classified ALS point clouds and road centerline information. The DSMA begins with removing ALS ground points within the buffer of the road centerlines; the size of the buffer varies according to different road classes. An ALS-modified DEM (ALS-mDEM) is then generated from the remaining ground points. A drainage network (DN) is derived from the ALS-mDEM. Candidate DSs are then obtained by intersecting the DN with the road centerlines. Finally, a refinement buffer of 15 m is placed around each candidate DS to prevent duplicate DS from being generated in close proximity. A total area of 50 km2, including an urban site and a rural site, in Vermont, USA, was used to assess the DSMA. Based on the road functional classification scheme of the Federal Highway Administration (FHWA), the centerline information regarding FHWA roads was obtained from a public data portal. The centerline information on non-FHWA roads, i.e., private roads and streets, was derived from the impervious surface data of a land cover dataset. A benchmark DS dataset was gathered from the transport agency of Vermont and was further augmented using Google Earth Street View images by the authors. The one-to-one correspondence between the benchmark DS and mapped DS for these two sites was then established. The positional accuracy was assessed by computing the Euclidian distance between the benchmark DS and mapped DS. The mean positional accuracy for the urban site and rural site were 13.5 m and 15.8 m, respectively. F1-scores were calculated to assess the prediction accuracy. For FHWA roads, the F1-scores were 0.87 and 0.94 for the urban site and rural site, respectively. For non-FHWA roads, the F1-scores were 0.72 and 0.74 for the urban site and rural site, respectively.
SMOTE-Based Weighted Deep Rotation Forest for the Imbalanced Hyperspectral Data Classification
Authors: Quan Yinghui , Zhong Xian , Feng Wei , Chan Jonathan Cheung-Wai , Li Qiang , Xing Mengdao
Published: January 2021

Conventional classification algorithms have shown great success in balanced hyperspectral data classification. However, the imbalanced class distribution is a fundamental problem of hyperspectral data, and it is regarded as one of the great challenges in classification tasks. To solve this problem, a non-ANN based deep learning, namely SMOTE-Based Weighted Deep Rotation Forest (SMOTE-WDRoF) is proposed in this paper. First, the neighboring pixels of instances are introduced as the spatial information and balanced datasets are created by using the SMOTE algorithm. Second, these datasets are fed into the WDRoF model that consists of the rotation forest and the multi-level cascaded random forests. Specifically, the rotation forest is used to generate rotation feature vectors, which are input into the subsequent cascade forest. Furthermore, the output probability of each level and the original data are stacked as the dataset of the next level. And the sample weights are automatically adjusted according to the dynamic weight function constructed by the classification results of each level. Compared with the traditional deep learning approaches, the proposed method consumes much less training time. The experimental results on four public hyperspectral data demonstrate that the proposed method can get better performance than support vector machine, random forest, rotation forest, SMOTE combined rotation forest, convolutional neural network, and rotation-based deep forest in multiclass imbalance learning.
Acknowledgment to Reviewers of Sensors in 2020
Authors:   Sensors Editorial Office
Published: January 2021

Peer review is the driving force of journal development, and reviewers are gatekeepers who ensure that Sensors maintains its standards for the high quality of its published papers [...]
HumTouch: Localization of Touch on Semi-Conductive Surfaces by Sensing Human Body Antenna Signal
Authors: Hsia Tzu Hsuan , Okamoto Shogo , Akiyama Yasuhiro , Yamada Yoji
Published: January 2021

HumTouch is a touch sensing technology utilizing the environmental electromagnetic wave. The method can be realized using conductive and semi-conductive materials by simply attaching electrodes to the object’s surface. In this study, we compared three methods for localizing a touch on 20×16cm2 and 40×36cm2 papers, on which four or eight electrodes were attached to record the voltages leaked from the human fingertip. The number and positions of the electrodes and the data processing of the voltages differed according to the localization methods. By constructing a kernel regression analysis model between the electrode outputs and the actual physical locations, the touched locations were estimated. Each of the three methods was tested via leave-one-out cross validation. Out of the three methods discussed, two exhibited superior performances in terms of the estimation errors. Of these two methods, one simply uses the voltages recorded by the four electrodes attached on the middle of paper edges as inputs to the regression system. The other uses differential outputs of electrode pairs as the inputs. The smallest mean location errors were 0.31 cm on 20×16cm2 paper and 0.27 cm on 40×36cm2 paper, which are smaller than the size of a fingertip.
Detecting and Tracking the Positions of Wild Ungulates Using Sound Recordings
Authors: Salem Salem Ibrahim , Fujisao Kazuhiko , Maki Masayasu , Okumura Tadanobu , Oki Kazuo
Published: January 2021

Monitoring wild ungulates such as deer is a highly challenging issue faced by wildlife managers. Wild ungulates are increasing in number worldwide, causing damage to ecosystems. For effective management, the precise estimation of their population size and habitat is essential. Conventional methods used to estimate the population density of wild ungulates, such as the light census survey, are time-consuming with low accuracy and difficult to implement in harsh environments like muddy wetlands. On the other hand, unmanned aerial vehicles are difficult to use in areas with dense tree cover. Although the passive acoustic monitoring of animal sounds is commonly used to evaluate their diversity, the potential for detecting animal positions from their sound has not been sufficiently investigated. This study introduces a new technique for detecting and tracking deer position in the wild using sound recordings. The technique relies on the time lag among three recorders to estimate the position. A sound recording system was also developed to overcome the time drift problem in the internal clock of recorders, by receiving time information from GPS satellites. Determining deer position enables the elimination of repetitive calls from the same deer, thus providing a promising tool to track deer movement. The validation results revealed that the proposed technique can provide reasonable accuracy for the experimental and natural environment. The identification of deer calls in Oze National Park over a period of two hours emphasizes the great potential of the proposed technique to detect repetitive deer calls, and track deer movement. Hence, the technique is the first step toward designing an automated system for estimating the population of deer or other vocal animals using sound recordings.
An Automatic Aggregator of Power Flexibility in Smart Buildings Using Software Based Orchestration
Authors: Sharma Dharmendra , Rehu Jari , Känsälä Klaus , Ailisto Heikki
Published: January 2021

This paper presents a software-based modular and hierarchical building energy management system (BEMS) to control the power consumption in sensor-equipped buildings. In addition, the need of this type of solution is also highlighted by presenting the worldwide trends of thermal energy end use in buildings and peak power problems. Buildings are critical component of smart grid environments and bottom-up BEMS solutions are need of the hour to optimize the consumption and to provide consumption side flexibility. This system is able to aggregate the controls of the all-controllable resources in building to realize its flexible power capacity. This system provides a solution for consumer to aggregate the controls of ‘behind-the-meter’ small loads in short response and provide ‘deep’ demand-side flexibility. This system is capable of discovery, status check, control and management of networked loads. The main novelty of this solution is that it can handle the heterogeneity of the installed hardware system along with time bound changes in the load device network and its scalability; resulting in low maintenance requirements after deployment. The control execution latency (including data logging) of this BEMS system for an external control signal is less than one second per connected load. In addition, the system is capable of overriding the external control signal in order to maintain consumer coziness within the comfort temperature thresholds. This system provides a way forward in future for the estimation of the energy stored in the buildings in the form of heat/temperature and use buildings as temporary batteries when electricity supply is constrained or abundant.
Balanced Leader Distribution Algorithm in Kubernetes Clusters
Authors: Nguyen Nguyen Dinh , Kim Taehong
Published: January 2021

Container-based virtualization is becoming a de facto way to build and deploy applications because of its simplicity and convenience. Kubernetes is a well-known open-source project that provides an orchestration platform for containerized applications. An application in Kubernetes can contain multiple replicas to achieve high scalability and availability. Stateless applications have no requirement for persistent storage; however, stateful applications require persistent storage for each replica. Therefore, stateful applications usually require a strong consistency of data among replicas. To achieve this, the application often relies on a leader, which is responsible for maintaining consistency and coordinating tasks among replicas. This leads to a problem that the leader often has heavy loads due to its inherent design. In a Kubernetes cluster, having the leaders of multiple applications concentrated in a specific node may become a bottleneck within the system. In this paper, we propose a leader election algorithm that overcomes the bottleneck problem by evenly distributing the leaders throughout nodes in the cluster. We also conduct experiments to prove the correctness and effectiveness of our leader election algorithm compared with a default algorithm in Kubernetes.
An iPPG-Based Device for Pervasive Monitoring of Multi-Dimensional Cardiovascular Hemodynamics
Authors: Luo Jingjing , Zhen Junjie , Zhou Peng , Chen Wei , Guo Yuzhu
Published: January 2021

Hemodynamic activities, as an essential measure of physiological and psychological characteristics, can be used for cardiovascular and cerebrovascular disease detection. Photoplethysmography imaging (iPPG) can be applied for such purposes with non-contact advances, however, most cardiovascular hemodynamics of iPPG systems are developed for laboratory research, which limits the application in pervasive healthcare. In this study, a video-based facial iPPG detecting equipment was devised to provide multi-dimensional spatiotemporal hemodynamic pulsations for applications with high portability and self-monitoring requirements. A series of algorithms have also been developed for physiological indices such as heart rate and breath rate extraction, facial region analysis, and visualization of hemodynamic pulsation distribution. Results showed that the new device can provide a reliable measurement of a rich range of cardiovascular hemodynamics. Combined with the advanced computing techniques, the new non-contact iPPG system provides a promising solution for user-friendly pervasive healthcare.
Differential Evolution Based Layer-Wise Weight Pruning for Compressing Deep Neural Networks
Authors: Wu Tao , Li Xiaoyang , Zhou Deyun , Li Na , Shi Jiao
Published: January 2021

Deep neural networks have evolved significantly in the past decades and are now able to achieve better progression of sensor data. Nonetheless, most of the deep models verify the ruling maxim in deep learning—bigger is better—so they have very complex structures. As the models become more complex, the computational complexity and resource consumption of these deep models are increasing significantly, making them difficult to perform on resource-limited platforms, such as sensor platforms. In this paper, we observe that different layers often have different pruning requirements, and propose a differential evolutionary layer-wise weight pruning method. Firstly, the pruning sensitivity of each layer is analyzed, and then the network is compressed by iterating the weight pruning process. Unlike some other methods that deal with pruning ratio by greedy ways or statistical analysis, we establish an optimization model to find the optimal pruning sensitivity set for each layer. Differential evolution is an effective method based on population optimization which can be used to address this task. Furthermore, we adopt a strategy to recovery some of the removed connections to increase the capacity of the pruned model during the fine-tuning phase. The effectiveness of our method has been demonstrated in experimental studies. Our method compresses the number of weight parameters in LeNet-300-100, LeNet-5, AlexNet and VGG16 by 24×, 14×, 29× and 12×, respectively.
Linker-Free Magnetite-Decorated Gold Nanoparticles (Fe3O4-Au): Synthesis, Characterization, and Application for Electrochemical Detection of Arsenic (III)
Authors: Sedki Mohammed , Zhao Guo , Ma Shengcun , Jassby David , Mulchandani Ashok
Published: January 2021

Linker-free magnetite nanoparticles (Fe3O4NPs)-decorated gold nanoparticles (AuNPs) were grown using a new protocol that can be used as a new platform for synthesis of other intact metal–metal oxide nanocomposites without the need for linkers. This minimizes the distance between the metal and metal oxide nanoparticles and ensures the optimum combined effects between the two material interfaces. X-ray diffraction (XRD) and Fourier transform infrared (FTIR) spectroscopy confirmed the successful synthesis of the Fe3O4-Au nanocomposite, without any change in the magnetite phase. Characterization, using transmission electron microscopy (TEM), scanning electron microscopy (SEM) and energy dispersive X-ray (EDX) spectroscopy, revealed the composite to consist of AuNPs of 70 ± 10 nm diameter decorated with tiny 10 ± 3 nm diameter Fe3O4NPs in Au:Fe mass ratio of 5:1. The prepared Fe3O4-Au nanocomposite was embedded in ionic liquid (IL) and applied for the modification of glassy carbon electrode (GCE) for the electrochemical detection of As(III) in water. By combining the excellent catalytic properties of the AuNPs with the high adsorption capacity of the tiny Fe3O4NPs towards As(III), as well as the good conductivity of IL, the Fe3O4-Au-IL nanocomposite showed excellent performance in the square wave anodic stripping voltammetry detection of As(III). Under the optimized conditions, a linear range of 1 to 100 μg/L was achieved with a detection limit of 0.22 μg/L (S/N = 3), and no interference from 100-fold higher concentrations of a wide variety of cations and anions found in water. A very low residual standard deviation of 1.16% confirmed the high precision/reproducibility of As(III) analysis and the reliability of the Fe3O4-Au-IL sensing interface. Finally, this proposed sensing interface was successfully applied to analyzing synthetic river and wastewater samples with a 95–101% recovery, demonstrating excellent accuracy, even in complex synthetic river and wastewater samples containing high concentrations of humic acid without any sample pretreatments.
Reactive Self-Collision Avoidance for a Differentially Driven Mobile Manipulator
Authors: Jang Keunwoo , Kim Sanghyun , Park Jaeheung
Published: January 2021

This paper introduces a reactive self-collision avoidance algorithm for differentially driven mobile manipulators. The proposed method mainly focuses on self-collision between a manipulator and the mobile robot. We introduce the concept of a distance buffer border (DBB), which is a 3D curved surface enclosing a buffer region of the mobile robot. The region has the thickness equal to buffer distance. When the distance between the manipulator and mobile robot is less than the buffer distance, which means the manipulator lies inside the buffer region of the mobile robot, the proposed strategy is to move the mobile robot away from the manipulator in order for the manipulator to be placed outside the border of the region, the DBB. The strategy is achieved by exerting force on the mobile robot. Therefore, the manipulator can avoid self-collision with the mobile robot without modifying the predefined motion of the manipulator in a world Cartesian coordinate frame. In particular, the direction of the force is determined by considering the non-holonomic constraint of the differentially driven mobile robot. Additionally, the reachability of the manipulator is considered to arrive at a configuration in which the manipulator can be more maneuverable. In this respect, the proposed algorithm has a distinct advantage over existing avoidance methods that do not consider the non-holonomic constraint of the mobile robot and push links away from each other without considering the workspace. To realize the desired force and resulting torque, an avoidance task is constructed by converting them into the accelerations of the mobile robot. The avoidance task is smoothly inserted with a top priority into the controller based on hierarchical quadratic programming. The proposed algorithm was implemented on a differentially driven mobile robot with a 7-DOFs robotic arm and its performance was demonstrated in various experimental scenarios.
End-to-End Deep Learning by MCU Implementation: An Intelligent Gripper for Shape Identification
Authors: Hung Chung-Wen , Zeng Shi-Xuan , Lee Ching-Hung , Li Wei-Ting
Published: January 2021

This paper introduces a real-time processing and classification of raw sensor data using a convolutional neural network (CNN). The established system is a microcontroller-unit (MCU) implementation of an intelligent gripper for shape identification of grasped objects. The pneumatic gripper has two embedded accelerometers to sense acceleration (in the form of vibration signals) on the jaws for identification. The raw data is firstly transferred into images by short-time Fourier transform (STFT), and then the CNN algorithm is adopted to extract features for classifying objects. In addition, the hyperparameters of the CNN are optimized to ensure hardware implementation. Finally, the proposed artificial intelligent model is implemented on a MCU (Renesas RX65N) from raw data to classification. Experimental results and discussions are introduced to show the performance and effectiveness of our proposed approach.
When the Tension Is Rising: A Simulation-Based Study on the Effects of Safety Incentive Programs and Behavior-Based Safety Management
Authors: Brandhorst Sebastian , Kluge Annette
Published: January 2021

When an organization’s management creates a goal conflict between workplace safety and the profitability of the organization, workers perceive work-safety tension. This leads to reduced safety-related behavior, culminating in higher rates of occupational injuries. In this study, we explored design components of behavior-based safety programs: audit results and process communication, reward and punishment, and the framing of production goals as gains or losses. This allowed us to directly observe the effects of the goal conflicts and of the countermeasures that we designed in this study. We examined the perceived work-safety tension using a simulated water treatment plant in a laboratory study with 166 engineering students. Participants had the task of conducting a start-up procedure. The operators’ goal conflict was created by a choice between a safe and mandatory (less productive) procedure and an unsafe and forbidden (more productive) one. As participants were told that their payment for the study would depend on their performance, we expected that rule violations would occur. We found acceptance of measures and their design as important for rule related behavior. Work-safety tension emerged as a strong driver for violating safety rules. We conclude that safety incentive programs can become ineffective if goal conflicts create work-safety tension.
Acknowledgment to Reviewers of Soil Systems in 2020
Authors:   Soil Systems Editorial Office
Published: January 2021

Peer review is the driving force of journal development, and reviewers are gatekeepers who ensure that Soil Systems maintains its standards for the high quality of its published papers [...]
Multi-Horizon Financial and Housing Wealth Effects across the U.S. States
Authors: Coskun Yener , Bouras Christos , Gupta Rangan , Wohar Mark E.
Published: January 2021

We investigate for the presence of multi-horizon wealth effects across U.S. states over the period of 1975:Q2 to 2012:Q2 by utilizing multi-horizon non-causality testing and multi-horizon causality measurement. At the state/aggregate level, we document that housing wealth has more statistically significant and persistent impact on private consumption than financial wealth. We also find that state-level housing/financial wealth effects are present at long time horizons and exhibit heterogeneity across the U.S. From a policy perspective, we suggest that state-level policies may specifically utilize the housing market to support consumption and growth.
Groundwater Resources in the Main Ethiopian Rift Valley: An Overview for a Sustainable Development
Authors: Bonetto Sabrina Maria Rita , Caselle Chiara , De Luca Domenico Antonio , Lasagna Manuela
Published: January 2021

In arid and semi-arid areas, human health and economic development depend on water availability, which can be greatly compromised by droughts. In some cases, the presence of natural contaminants may additionally reduce the availability of good quality water. This research analyzed the water resources and hydrochemical characteristics in a rural area of the central Main Ethiopian Rift Valley, particularly in the districts of Shashemene, Arsi Negelle, and Siraro. The study was developed using a census of the main water points (springs and wells) in the area and the sampling and physico-chemical analysis of the water, with particular regard to the fluoride concentration. In many cases, fluoride content exceeded the drinking water limits set by the World Health Organization, even in the absence of anthropogenic contamination. Two different aquifers were recognized: A shallow aquifer related to the eastern escarpment and highlands, and a deep aquifer in the lowland areas of the rift valley on the basis of compositional changes from Ca–Mg/HCO3 to Na–HCO3. The distribution of fluoride, as well as pH and EC values, showed a decrease from the center of the lowlands to the eastern highlands, with similar values closely aligned along an NNE/SSW trend. All these data contribute to creating awareness among and sharing information on the risks with rural communities and local governments to support the adequate use of the available water resources and to plan appropriate interventions to increase access to fresh water, aimed at the sustainable human and rural local development of the region.
Does Tourism Affect Economic Growth of China? A Panel Granger Causality Approach
Authors: Su Yong , Cherian Jacob , Sial Muhammad Safdar , Badulescu Alina , Thu Phung Anh , Badulescu Daniel , Samad Sarminah
Published: January 2021

The main purpose of the current study is to investigate if tourism affects economic growth of China. The data set has been acquired from the Beijing Municipal Bureau of Statistics, and the time span of the data set takes into account a 20-year time period, from 2000 to 2019. To determine the strength of the above-mentioned relationship previous models that have been used for this research are mainly VAR (vector auto-regression) and VECM (vector error correction) models. The VAR and VECM models have been conducted together with the Granger causality test. The internal revenue generated from tourism-related activities is taken as being the main indicator for the tourism industry, while economic growth is determined by GDP (gross domestic product). We support the above-mentioned notion, as we found that a strong relationship exists between the development of the tourism industry and economic growth. Moreover, our analysis also indicates that this industry has a major impact on long-term economic growth in the region as well. This study thus provides further support to the existing literature on the topic of tourism and the impact that tourism-related activities have upon economic development and growth. The existence and the impact of tourism-related activities upon long-term economic growth were confirmed by the results of the VAR models. At the same time, the unidirectional results of VECM models have confirmed the existence of economic growth in the short term. In our case, the cardinal relationship between the development of the tourism industry and the economic growth in the Beijing region of China have managed to provide strong empirical support to the earlier stated notions and to the literature alike.
Multi-Attributes Decision-Making for CDO Trajectory Planning in a Novel Terminal Airspace
Authors: Yang Lei , Li Wenbo , Wang Simin , Zhao Zheng
Published: January 2021

Continuous Descent Operations (CDO) has been recognized as an effective way to significantly reduce fuel burn and noise impact. Designing efficient and flexible arrival routes for generating conflict-free and economical trajectories is a cornerstone for fully achieving CDO by high-level automation in high-density traffic scenarios. In this research, inspired by the Point Merge (PM), we design the Inverted Crown-Shaped Arrival Airspace (ICSAA) and its operational procedures to support Omni-directional CDO. In order to generate optimal conflict-free trajectories for upcoming aircraft in an efficient manner, we established a multi-objective trajectory optimization model solved by Non-dominated Sorting Genetic Algorithm with Elitist Strategy (NSGA-II). The Pareto solutions of minimal fuel consumption and trip time were achieved in single aircraft and highly complex multi-aircraft scenarios. Among all the elements of Pareto front, we obtained an unique solution with Entropy-Weights Method and TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) to strike a better trade-off among collision probability, fuel consumption, and trip time, which incorporates both air traffic controller’s and pilot’s interests. The effectiveness of CDO performance improvement and computational efficiency in different scenarios were verified. The ICSAA would be a promising structure that promotes the application of automated and flexible CDO.
Fuzzy Inference Model for Decision Support in Sustainable Production Planning Processes—A Case Study
Authors: Zarte Maximilian , Pechmann Agnes , Nunes Isabel L.
Published: January 2021

Due to crises (e.g., climate crisis, extinction of species, shortage of natural resources, human health crisis), customer requirements for conventionally produced products shift to more sustainably produced products, reducing and avoiding negative environmental and social impacts. Circular thinking in production systems offers new opportunities to meet these new customer expectations. However, it enlarges new challenges for production planning too. Research gaps exist in production planning approaches, considering all three sustainability aspects (economic, environmental, and social) simultaneously. This paper presents a concept of a fuzzy inference model (FIM) to assess the sustainability of production programs. The FIM concept is demonstrated and tested using a single case study considering lab production schedules. The model’s outcome indicates the most significant opportunities to improve production programs’ sustainability using experts’ knowledge.
Optimal Financing Strategy in a Capital-Constrained Supply Chain with Retailer Green Marketing Efforts
Authors: Zhang Xiaoli , Xiu Guoyi , Shahzad Fakhar , Duan Yupeng
Published: January 2021

The purpose of this research is to examine the green supply chain (GSC) financing decisions of manufacturers and capital-constrained retailers in order to establish a Stackelberg game model under decentralized and centralized decision-making. This paper studies the influence of retailers’ choice of trade credit or bank loan financing strategy on a GSC’s performance and analyzes their decision-making tendency. The results show that manufacturers should provide trade credit and participate in retailers’ financing decisions to avoid double marginal effects under both centralized and decentralized decision-making. Interestingly, the optimal value of green marketing effort and retailer order quantity was twice as high as the decentralized under the centralized decision, indicating that the centralized decision could better improve GSC’s financing efficiency. Especially when the trade credit financing strategy is feasible, this effect is more significant. Finally, the outcomes are verified through numerical simulation, which references GSC practitioners in management decisions.
The Probability Distribution of Worldwide Forest Areas
Authors: González-Val Rafael
Published: January 2021

This paper analyses the probability distribution of worldwide forest areas. We find moderate support for a Pareto-type distribution (power law) using FAO data from 1990 to 2015. Power laws are common features of many complex systems in nature. A power law is a plausible model for the world probability distribution of forest areas in all examined years, although the log-normal distribution is a plausible alternative model that cannot be rejected. The random growth of forest areas could generate a power law or log-normal distribution. We study the change in forest coverage using parametric and non-parametric methods. We identified a slight convergence of forest areas over the time reviewed; however, random forest area growth cannot be rejected for most of the distribution of forest areas. Therefore, our results give support to theoretical models of stochastic forest growth.
The Role of Collaboration Breadth Attributes in Research Project and Innovation: The Example of National Funded Projects in China
Authors: Yang (Alamo) Zhenhua , Xu Yanmei
Published: January 2021

Collaboration is regarded as an effective approach to improve the efficiency of research resources and reduce risks in innovation activities. Collaboration breadth is an important feature of collaborative extent. Therefore, it is necessary to analyse its role between R&D subsidy and innovation and to explore its direct and indirect ties to innovation. The paper is based on government-funded research projects, and the results show that R&D subsidy and collaboration breadth of organizations have positive direct effect on innovation output. Meanwhile, R&D subsidy has positive effect to collaboration breadth. However, the linkage of collaboration breadth of researchers on innovation output is uncertainty. That is, the direct effect is positive, while the indirect effect is negative. It may suggest that collaboration breadth of researchers and innovation output may be affected by other factors, such as knowledge sharing or diffusion. Our contribution is to extend the theory of collaboration breadth and to suggest discussion of the role of knowledge in collaboration breadth.
A Logical-Mathematical Approach for the Implementation of Ecologically Equipped Productive Urban Areas
Authors: Sessa Maria Rosaria , Esposito Benedetta , Sica Daniela , Malandrino Ornella
Published: January 2021

Sustainability is a complex phenomenon that refers to economic, environmental, and social aspects. Any concept of sustainable urban development must incorporate sectoral concepts; these must be well integrated into the overarching urban, regional, and governance policies. One sectoral policy of great importance is the redevelopment processes of disused industrial areas into Sustainable Industrial Areas (AIS), Ecologically Equipped Productive Areas (APEA), or Eco-Industrial Parks (EIP). These territories, as socioeconomic systems that are being observed in the framework of the development of sustainability monitoring, are complex objects for evaluation due to the presence of a large number of interconnections between the constituent elements and hierarchical levels (sectors and spheres). For this reason, it is necessary that a new interpretation of economical, natural, and social phenomena, following a systemic and integrated approach, is able to reinterpret them for the dissemination of an ecologically and socially sustainable economy. The purpose of this work is to analyse the state of realisation of APEA on the Italian national territory, in order to understand the real benefits of production areas managed through eco-efficiency standards and to guarantee an integrated management system of environmental aspects. An additional aim is to consider a logical-mathematical model that would be able to support territorial policies in the identification of suitable areas to be converted into APEA, in order to promote sustainable development of the territory.
Multi-Risk Assessment and Management—A Comparative Study of the Current State of Affairs in Chile and Ecuador
Authors: Greiving Stefan , Schödl Leonie , Gaudry Karl-Heinz , Quintana Miralles Iris Katherine , Prado Larraín Benjamín , Fleischhauer Mark , Jácome Guerra Myriam Margoth , Tobar Jonathan
Published: January 2021

In Chile and Ecuador, multiple hazards and dynamic processes in vulnerability pose a high risk. Spatial planning and emergency management can contribute to disaster risk management but they follow different goals. However, global goals, such as from UN-ISDR (United Nations International Strategy for Disaster Risk Reduction) and UN SDGs (Sustainable Development Goals) can potentially support cities and regions in defining concerted action. This paper aims at measuring the performance of Chile and Ecuador in regard to the aforementioned policy goals. Although both countries show considerable progresses in the implementation of the UN strategies, it is doubtful that the existing global monitoring approach is appropriately designed for measuring the real situation on the ground. Our paper is based on a desktop research combined with stakeholder workshops and expert interviews. Overall, both countries made considerable progress in regard to disaster preparedness and monitoring. However, multi-risks are rarely considered and there is still increasing vulnerability due to the expansion of informal settlements. The risk management is characterized by an imbalanced distribution of financial resources and institutional capacities between the metropolitan regions and smaller municipalities, and by low public participation and hardly community-based approaches. The paper underlines the importance for more qualitative, in-depth studies on the root causes of disaster risk which could complement the global monitoring which is very much focused on quantitative data and shows inconsistency between input and output indicators.
Research on Enterprises’ Intention to Adopt Green Technology Imposed by Environmental Regulations with Perspective of State Ownership
Authors: Xia De , Chen Wenhua , Gao Qinglu , Zhang Rui , Zhang Yundong
Published: January 2021

Environmental regulations (ER) affect enterprise behaviors. Nevertheless, whether the state ownership influences the relationship between environmental regulations and enterprises’ green intentions and behaviors need to be explored further. In this paper, the effects of environmental regulations on enterprises’ intentions to adopt green technologies, especially the moderating role of state ownership between environmental regulations and green technologic adoption intentions (GTAI), are proposed. An empirical study is carried out with the questionnaire data collected from 207 Chinese managers and executives in order to explore the influence of environmental regulations. With the perspective of ownership, the results confirm that the three kinds of environmental regulations (command-and-control (CAC), market-based incentives (MBI) and voluntary environmental (VER) regulations) have positive effects on enterprise green technology adoption intention. Furthermore, the state ownership of enterprise plays a positive moderating role in the relationship between command-and-control environmental regulations and green technology adoption intentions, but plays a negative moderating role in the relationship between voluntary environmental regulations and green technology adoption intentions. It generates no significant moderate effect on the relationship between market-based incentives environmental regulations and green technology adoption intentions. The work verifies that the differences of ownership would lead to varying effects on the intentions of enterprise green technology adoption imposed by regulations. Managerial implications, as well as the limitation of the work, are concluded at the end of this paper.
The Price of Sustainability of a Traditional Irrigation System in Northern Thailand
Authors: Mungsunti Arriya , Parton Kevin A.
Published: January 2021

In recent years in northern Thailand, the traditional surface-water irrigation system known as muang fai has been challenged by the introduction of small-scale, groundwater pumping technology. This trend presents concerns about the sustainability of the system, as the new technology uses more water but produces lower-quality agricultural outputs. In this paper, we provide evidence that farmers who use relatively modern irrigation technology (ground water pumping systems) are willing to switch to a more traditional (hundreds of years old) and more sustainable surface water irrigation system. In the Sop Rong region in northern Thailand, we surveyed 570 longan farmers, approximately half being muang fai members and half using pumped groundwater. We designed an experiment for the second group to check whether they were interested in becoming muang fai members in a scenario where they have access to the canal system. We found that almost half of them were willing to pay fees to become members and that the negative relationship between membership fees and the willingness to join is robust after controlling for all other relevant factors. Despite this positive result for sustainability, suggesting that there is a price at which many farmers would be willing to switch to a more water-saving system, few farmers are making the shift. We conclude that there are strong social pressures that discourage them from doing so. Such social influences are probably an important and often overlooked determinant of efforts to achieve sustainability.
Inflows and Outflows from Material Stocks of Buildings and Networks and their Space-Differentiated Drivers: The Case Study of the Paris Region
Authors: Augiseau Vincent , Kim Eunhye
Published: January 2021

Urbanization causes massive flows of construction materials and waste, which generates environmental impacts and land-use conflicts. Circular economy strategies at a local scale and in coordination with urban planning could respond to those issues. Implementing these strategies raises challenges as it requires a better knowledge of flows and their space-differentiated drivers. This article focuses on the case of the Paris region (Ile-de-France) in 2013. Construction materials inflows and outflows to and from anthropogenic stocks of buildings and networks are estimated and located though a bottom-up approach based on the collection and processing of geolocalized data. Flow analysis focuses on the relationship between urbanization and flows with a view to establishing context-specific circular economy strategies. Results show that regional inflows of construction materials to stocks in 2013 reach between 1.8 and 2.1 t/capita while outflows are between 1.0 and 1.5 t/capita. Both inflows and outflows are mainly driven by building construction and demolition as well as by road renewal. The region is composed of three sub-urban areas and flows per capita in the dense central city of Paris are significantly lower than in the low-density outskirt area of Grande Couronne (GC). Road renewal accounts for a larger share of flows in GC. Future research will address methodological limits.
Potential Carbon Footprint Reduction for Reclaimed Asphalt Pavement Innovations: LCA Methodology, Best Available Technology, and Near-Future Reduction Potential
Authors: Bizarro Diana Eliza Godoi , Steinmann Zoran , Nieuwenhuijse Isabel , Keijzer Elisabeth , Hauck Mara
Published: January 2021

The carbon footprints of asphalt mixtures with increasing reclaimed asphalt pavement (RAP) content were estimated using a life-cycle assessment methodology. Three asphalt mixtures with different applications and technical requirements, namely porous asphalt (PA), stone mastic asphalt (SMA), and asphalt concrete (AC), were included. The technology leaps needed to achieve asphalt mixtures containing up to 93% RAP were modelled. Mixtures containing up to 57% RAP were hot-mix asphalts (175 °C), while mixtures containing more RAP were produced at 135 °C and 105 °C. The energy requirements and their respective carbon footprints were calculated based on the heat capacity of the aggregates, RAP, and other bituminous materials. Furthermore, the effects of changing the country’s electricity mix were also evaluated. A potential carbon footprint reduction of between 55% and 64% was found for one tonne of asphalt containing 93% RAP and produced at 105 °C compared to the 0% RAP mixture produced at 175 °C. Considering the uncertainty of this technology at its early stage of development, the reduction could be as low as 45% or as high as 79%. Changing the electricity mix to one that is likely to be implemented until 2030 in the Netherlands further reduces the footprint by 10%.
Tissue Engineering in Musculoskeletal Tissue: A Review of the Literature
Authors: Bove Mary , Carlucci Annalisa , Natale Giovanni , Freda Chiara , Noro Antonio , Ferrara Vincenzo , Opromolla Giorgia , Martone Mario , Cascone Roberto , Messina Gaetana , Izzo Anna , Vicidomini Giovanni , Santini Mario , Fiorelli Alfonso
Published: January 2021

Tissue engineering refers to the attempt to create functional human tissue from cells in a laboratory. This is a field that uses living cells, biocompatible materials, suitable biochemical and physical factors, and their combinations to create tissue-like structures. To date, no tissue engineered skeletal muscle implants have been developed for clinical use, but they may represent a valid alternative for the treatment of volumetric muscle loss in the near future. Herein, we reviewed the literature and showed different techniques to produce synthetic tissues with the same architectural, structural and functional properties as native tissues.
Induced Currents and Aharonov–Bohm Effect in Effective Fermion Models and in Spaces with a Compact Dimension
Authors: Zhukovsky Vladimir Ch.
Published: January 2021

We consider fermion models in 3D- and 5D-space-time with an Aharonov–Bohm potential and a domain wall. Induced current is calculated, which is due to vacuum effects in the topologically nontrivial space-time. Violation of chiral symmetry and appearance of induced current is demonstrated in a simple example of quantum mechanical violation of symmetry in a model of a massless Dirac fermion moving in a background vector field and domain walls as barriers for the electron propagation. The effective Dirac equation for massless electrons modeling monolayer graphene is used. One of the solutions to the problem of describing domain walls in planar systems is reduced to finding exact analytic solutions. In this paper, we consider appearance of induced current in two-fermion model with a compact dimension as a result of vacuum polarization in the field of the external gauge field in the 4 + 1 and the 2 + 1 dimensional models with one type of fermions and with two types of fermions living in the brane and in the bulk. Two different approaches (Kaluza–Klein and Aharonov–Bohm) to the problem of induced current are used. Production of an induced current in a planar model with a thin solenoid is also studied.
Influenza A Viruses in Whistling Ducks (Subfamily Dendrocygninae)
Authors: Carter Deborah L. , Link Paul , Tan Gene , Stallknecht David E. , Poulson Rebecca L.
Published: January 2021

As compared to other Anseriformes, data related to influenza A virus (IAV) detection and isolation, and IAV antibody detection in whistling ducks (Dendrocygna spp. and Thalassornis leuconotus; subfamily Dendrocygninae) are limited. To better evaluate the potential role of whistling ducks in the epidemiology of IAV, we (1) conducted surveillance for IAV from black-bellied whistling ducks (BBWD, Dendrocygnaautumnalis) sampled in coastal Louisiana, USA, during February 2018 and 2019, and (2) reviewed the published literature and Influenza Resource Database (IRD) that reported results of IAV surveillance of whistling ducks. In the prospective study, from 166 BBWD sampled, one H10N7 IAV was isolated (0.6% prevalence), and overall blocking enzyme-linked immunosorbent assay (bELISA) antibody seroprevalence was 10%. The literature review included publications and data in the IRD from 1984 to 2020 that reported results from nearly 5000 collected samples. For any given collection, the IAV isolation rate never exceeded 5.5%, and seroprevalence estimates ranged from 0 to 42%. Results from our prospective study in Louisiana are consistent with this historic literature; however, although all data consistently demonstrated a low prevalence of infection, the potential role of this species in the epidemiology of IAV should not be totally discounted. In sum, whistling ducks can be infected with IAV, they represent important species on many areas where waterfowl winter, and their distribution across the globe appears to be changing.