bluuebunny/crossref_metadata_2025 huggingface dataset:
Design Optimization of Resource Allocation in OFDMA-Based Cognitive Radio-Enabled Internet of Vehicles (IoVs)
Authors: Eze Joy , Zhang Sijing , Liu Enjie , Eze Elias
Published: November 2020

Joint optimal subcarrier and transmit power allocation with QoS guarantee for enhanced packet transmission over Cognitive Radio (CR)-Internet of Vehicles (IoVs) is a challenge. This open issue is considered in this paper. A novel SNBS-based wireless radio resource scheduling scheme in OFDMA CR-IoV network systems is proposed. This novel scheduler is termed the SNBS OFDMA-based overlay CR-Assisted Vehicular NETwork (SNO-CRAVNET) scheduling scheme. It is proposed for efficient joint transmit power and subcarrier allocation for dynamic spectral resource access in cellular OFDMA-based overlay CRAVNs in clusters. The objectives of the optimization model applied in this study include (1) maximization of the overall system throughput of the CR-IoV system, (2) avoiding harmful interference of transmissions of the shared channels’ licensed owners (or primary users (PUs)), (3) guaranteeing the proportional fairness and minimum data-rate requirement of each CR vehicular secondary user (CRV-SU), and (4) ensuring efficient transmit power allocation amongst CRV-SUs. Furthermore, a novel approach which uses Lambert-W function characteristics is introduced. Closed-form analytical solutions were obtained by applying time-sharing variable transformation. Finally, a low-complexity algorithm was developed. This algorithm overcame the iterative processes associated with searching for the optimal solution numerically through iterative programming methods. Theoretical analysis and simulation results demonstrated that, under similar conditions, the proposed solutions outperformed the reference scheduler schemes. In comparison to other scheduling schemes that are fairness-considerate, the SNO-CRAVNET scheme achieved a significantly higher overall average throughput gain. Similarly, the proposed time-sharing SNO-CRAVNET allocation based on the reformulated convex optimization problem is shown to be capable of achieving up to 99.987% for the average of the total theoretical capacity.
Distributed Extended Kalman Filtering Based Techniques for 3-D UAV Jamming Localization
Authors: Aldosari Waleed , Moinuddin Muhammad , Aljohani Abdulah Jeza , Al-Saggaf Ubaid M.
Published: November 2020

Wireless networks are vulnerable to jamming attacks. Jamming in wireless communication becomes a major research problem due to ease in Unmanned Aerial Vehicle (UAV) launching and blocking of communication channels. Jamming is a subset of Denial of Service Attack (DoS) and an intentional interference where the malicious node disrupts the wireless communication by increasing the noise at the receiver node through transmission interference signal towards the target channel. In this work, the considered jammer is a UAV hovering around the target area to block the communication channel between two transceivers. We proposed a three-dimensional (3-D) UAV jamming localization scheme to track and detect the jammer position at each time step by employing a single boundary node observer. For this purpose, we developed two distributed Extended Kalman Filter (EKF) based schemes: (1) the Distributed EKF (DEKF) scheme using the information of the received power from the jammer at a single nearby boundary node only and (2) Distance Ratio aided Distributed EKF (DEKF-DR) based scheme utilizing an edge node in addition to a single boundary node. Extensive simulations are conducted in order to evaluate the performance of the proposed distributed algorithms for a 3-D trajectory and compared with that of the conventional Centralized EKF (EKF-Centr) based method (which is also modified for the 3-D scenario). The results show the clear supremacy of the proposed distributed algorithms with much lesser complexity in contrast to the conventional EKF-Centr technique.
Distributed High Temperature Monitoring of SMF under Electrical Arc Discharges Based on OFDR
Authors: Chen Chen , Gao Song , Chen Liang , Bao Xiaoyi
Published: November 2020

The distributed high temperature measurement of an optical fiber subjected to electric arc discharges based on optical frequency-domain reflectometry is experimentally demonstrated. The distributed temperature profile is attained in an open glow regime of a few milliamps with maximum detectable temperature up to 2100 ± 20 °C. The discharge arc-induced softened length of the fiber and mechanical stress are measured and statistically analyzed in terms of the correlation of the Rayleigh spectra. The large wavelength scanning range of OFDR enables much higher accuracy for the delay time measurement with a minimum measured delay of 40 fs. The delay shift over the entire heating range for a single discharge duration is statistically calculated by using a temporal correlation method. The reliability of the thermal sensitivity coefficient as 10 pm/°C for telecom single mode fiber (SMF, @1550 nm) is quantitatively analyzed and evaluated by the correlation coefficient. Lastly, a spectral mapping method is employed in spectrum monitoring for discharge dynamic impact on the optical path length (OPL) and local Rayleigh scatter.
Microscopic Study on Excitation and Emission Enhancement by the Plasmon Mode on a Plasmonic Chip
Authors: Chida Hinako , Tawa Keiko
Published: November 2020

Excitation and emission enhancement by using the plasmon mode formed on a plasmonic chip was studied with a microscope and micro-spectroscope. Surface plasmon resonance wavelengths were observed on one-dimensional (1D) and two-dimensional (2D) plasmonic chips by measuring reflection and transmission spectra, and they were assigned to the plasmon modes predicted by the theoretical resonance wavelengths. The excitation and emission enhancements were evaluated using the fluorescence intensity of yellow–green fluorescence particles. The 2D grating had plasmon modes of kgx45(2) (diagonal direction with m = 2) in addition to the fundamental mode of kgx(1) (direction of a square one side) in the visible range. In epifluorescence detection, the excitation enhancement factors of kgx(2) on the 1D and 2D chips were found to be 1.3–1.4, and the emission enhancement factor of kgx45(2) on the 2D chip was 1.5–1.8, although the emission enhancement was not found on the 1D chip. Moreover, enhancement factors for the other fluorophores were also studied. The emission enhancement factor of kgx(1) was shown to depend on the fluorescence quantum yield. The emission enhancement of 2D was 1.3-fold larger than that of 1D considering all azimuth components, and the 2D pattern was shown to be advantageous for bright fluorescence microscopic observation.
Turning Image Sensors into Position and Time Sensitive Quantitative Colorimetric Data Sources with the Aid of Novel Image Processing/Analysis Software
Authors: Yoo Yeongsik , Yoo Woo Sik
Published: November 2020

Still images and video images acquired from image sensors are very valuable sources of information. From still images, position-sensitive, quantitative intensity, or colorimetric information can be obtained. Video images made of a time series of still images can provide time-dependent, quantitative intensity, or colorimetric information in addition to the position-sensitive information from a single still image. With the aid of novel image processing/analysis software, extraction of position- and time-sensitive quantitative colorimetric information was demonstrated from still image and video images of litmus test strips for pH tests of solutions. Visual inspection of the color change in the litmus test strips after chemical reaction with chemical solutions is typically exercised. Visual comparison of the color of the test solution with a standard color chart provides an approximate pH value to the nearest whole number. Accurate colorimetric quantification and dynamic analysis of chemical properties from captured still images and recorded video images of test solutions using novel image processing/analysis software are proposed with experimental feasibility study results towards value-added image sensor applications. Position- and time-sensitive quantitative colorimetric measurements and analysis examples are demonstrated.
Towards Efficient Building Designing: Heating and Cooling Load Prediction via Multi-Output Model
Authors: Sajjad Muhammad , Khan Samee Ullah , Khan Noman , Haq Ijaz Ul , Ullah Amin , Lee Mi Young , Baik Sung Wook
Published: November 2020

In the current technological era, energy-efficient buildings have a significant research body due to increasing concerns about energy consumption and its environmental impact. Designing an appropriate energy-efficient building depends on its layout, such as relative compactness, overall area, height, orientation, and distribution of the glazing area. These factors directly influence the cooling load (CL) and heating load (HL) of residential buildings. An accurate prediction of these load facilitates a better management of energy consumption and enhances the living standards of inhabitants. Most of the traditional machine learning (ML)-based approaches are designed for single-output (SO) prediction, which is a tedious task due to separate training processes for each output with low performance. In addition, these approaches have a high level of nonlinearity between input and output, which need more enhancement in terms of robustness, predictability, and generalization. To tackle these issues, we propose a novel framework based on gated recurrent unit (GRU) that reliably predicts the CL and HL concurrently. To the best of our knowledge, we are the first to propose a multi-output (MO) sequential learning model followed by utility preprocessing under the umbrella of a unified framework. A comprehensive set of ablation studies on ML and deep learning (DL) techniques is done over an energy efficiency dataset, where the proposed model reveals an incredible performance as compared to other existing models.
Evapotranspiration Estimation with Small UAVs in Precision Agriculture
Authors: Niu Haoyu , Hollenbeck Derek , Zhao Tiebiao , Wang Dong , Chen YangQuan
Published: November 2020

Estimating evapotranspiration (ET) has been one of the most critical research areas in agriculture because of water scarcity, the growing population, and climate change. The accurate estimation and mapping of ET are necessary for crop water management. Traditionally, researchers use water balance, soil moisture, weighing lysimeters, or an energy balance approach, such as Bowen ratio or eddy covariance towers to estimate ET. However, these ET methods are point-specific or area-weighted measurements and cannot be extended to a large scale. With the advent of satellite technology, remote sensing images became able to provide spatially distributed measurements. However, the spatial resolution of multispectral satellite images is in the range of meters, tens of meters, or hundreds of meters, which is often not enough for crops with clumped canopy structures, such as trees and vines. Unmanned aerial vehicles (UAVs) can mitigate these spatial and temporal limitations. Lightweight cameras and sensors can be mounted on the UAVs and take high-resolution images. Unlike satellite imagery, the spatial resolution of the UAV images can be at the centimeter-level. UAVs can also fly on-demand, which provides high temporal imagery. In this study, the authors examined different UAV-based approaches of ET estimation at first. Models and algorithms, such as mapping evapotranspiration at high resolution with internalized calibration (METRIC), the two-source energy balance (TSEB) model, and machine learning (ML) are analyzed and discussed herein. Second, challenges and opportunities for UAVs in ET estimation are also discussed, such as uncooled thermal camera calibration, UAV image collection, and image processing. Then, the authors share views on ET estimation with UAVs for future research and draw conclusive remarks.
A Fast Segmentation Method for Fire Forest Images Based on Multiscale Transform and PCA
Authors: Tlig Lotfi , Bouchouicha Moez , Tlig Mohamed , Sayadi Mounir , Moreau Eric
Published: November 2020

Forests provide various important things to human life. Fire is one of the main disasters in the world. Nowadays, the forest fire incidences endanger the ecosystem and destroy the native flora and fauna. This affects individual life, community and wildlife. Thus, it is essential to monitor and protect the forests and their assets. Nowadays, image processing outputs a lot of required information and measures for the implementation of advanced forest fire-fighting strategies. This work addresses a new color image segmentation method based on principal component analysis (PCA) and Gabor filter responses. Our method introduces a new superpixels extraction strategy that takes full account of two objectives: regional consistency and robustness to added noises. The novel approach is tested on various color images. Extensive experiments show that our method obviously outperforms existing segmentation variants on real and synthetic images of fire forest scenes, and also achieves outstanding performance on other popular benchmarked images (e.g., BSDS, MRSC). The merits of our proposed approach are that it is not sensitive to added noises and that the segmentation performance is higher with images of nonhomogeneous regions.
Business Model as a Base for Building Firms’ Competitiveness
Authors: Koprivnjak Tihana , Oberman Peterka Sunčica
Published: November 2020

Designing and creating a business model is crucial for a successful firm’s operation in today’s market in a complex and changing environment. A business model is the factor that differentiates one firm from another—it defines the distinctions of the firm, how the firm deals with the competition, the firms’ partnerships, and customer relations. This paper explores the role of the business model in the creation of sustainable competitive advantage. The empirical part of the paper presents the business model of three small companies from the Information and Communications Technology (ICT) industry in Croatia using the Business Model Canvas. Additionally, business model components were also analyzed using a framework for evaluating a business model. The results of the analysis point to a few crucial components of the business model, on which small ICT firms in Croatia build their competitive advantage by creating significant distinctions of those components. These distinctions are essential for their longevity and sustainability.
Sustainable Reciprocity Mechanism of Social Initiatives in Sport: The Mediating Effect of Gratitude
Authors: Lee Seung Pil
Published: November 2020

The study aims to explore a conceptual model for the sustainable reciprocity relationships in sport-based initiatives and empirically test the model and its underlying mechanism in the context of a real sport-based national initiative. Adapting a seminal work from social work literature as a theoretical framework and the following measurement for the social impact of sport from sport management literature, a conceptual model addressing sport participation, gratitude, social benefits, and prosocial behaviors is presented. Two separate surveys were conducted through face-to-face interviews with independent and random samples representing the Singaporean residents in October 2014 for Study 1 (n = 500) and February 2015 for Study 2 (n = 501). The results demonstrate that the frequency of participation in a range of daily sport activities of a national sport initiative positively influences the perceived value of social capital and health literacy through the mediation effect of gratitude. The study also demonstrates that participation in sport activities positively influences prosocial behavioral intention through the serial mediation effect of gratitude and social capital. The findings implies how we better understand and utilize the dynamic power of gratitude to sustain the win–win relationships to multi-stakeholders in the contexts of sport-based initiatives based on the nature of reciprocity.
Comparative Analysis of Data Detection Techniques for 5G Massive MIMO Systems
Authors: Albreem Mahmoud A. , Kumar Arun , Alsharif Mohammed H. , Khan Imran , Choi Bong Jun
Published: November 2020

Massive multiple-input multiple-output (MIMO) is a backbone technology in the fifth-generation (5G) and beyond 5G (B5G) networks. It enhances performance gain, energy efficiency, and spectral efficiency. Unfortunately, a massive number of antennas need sophisticated processing to detect the transmitted signal. Although a detector based on the maximum likelihood (ML) is optimal, it incurs a high computational complexity, and hence, it is not hardware-friendly. In addition, the conventional linear detectors, such as the minimum mean square error (MMSE), include a matrix inversion, which causes a high computational complexity. As an alternative solution, approximate message passing (AMP) algorithm is proposed for data detection in massive MIMO uplink (UL) detectors. Although the AMP algorithm is converging extremely fast, the convergence is not guaranteed. A good initialization influences the convergence rate and affects the performance substantially together and the complexity. In this paper, we exploit several free-matrix-inversion methods, namely, the successive over-relaxation (SOR), the Gauss–Seidel (GS), and the Jacobi (JA), to initialize the AMP-based massive MIMO UL detector. In other words, hybrid detectors are proposed based on AMP, JA, SOR, and GS with an efficient initialization. Numerical results show that proposed detectors achieve a significant performance enhancement and a large reduction in the computational complexity.
How Could Cooperatives Successfully Develop Their Social Responsibility: The Perspective of Life Cycle
Authors: Tang Jin , Sipiläinen Timo , Fu Gang
Published: November 2020

Social responsibility is a natural obligation of cooperatives, and fulfilling social responsibility is of great meaning to the sustainable development of cooperatives and society. This article constructs a “life cycle-cooperative social responsibility framework (LC-CoopSRF)” and analyzes the framework with the case of Chongxin Apiculture Specialized Cooperative of Sichuan Province, China. The research results show that cooperatives should respect the law of life cycle, consider conditions such as operational capabilities and ethical expectations, and fulfill social responsibility in a reasonable manner. The successful cooperative highlights the bottom-line responsibility in the establishment phase, internal responsibility in the growth phase, system responsibility in the maturity phase, and the differentiation phase is the phase of system responsibility.
Outdoor Thermal Comfort at a University Campus: Studies from Personal and Long-Term Thermal History Perspectives
Authors: Xue Jiao , Hu Xiao , Sani Shu Nuke , Wu Yuanyuan , Li Xinyu , Chai Liang , Lai Dayi
Published: November 2020

Thermally comfortable outdoor spaces have contributed to high-quality urban living. In order to provide a further understanding of the influences of gender and long-term thermal history on outdoor thermal comfort, this study conducted field surveys at a university campus in Shanghai, China by carrying out microclimatic monitoring and subjective questionnaires from May to October, 2019. The analysis of collected data found that, during our survey, 57% of the occupants felt comfortable overall and 40–60% of them perceived the microclimate variables (air temperature, humidity, solar radiation, and wind speed) as “neutral”. The universal thermal climate index (UTCI) provided a better correlation with occupant thermal sensation than the physiologically equivalent temperature (PET). Females were more sensitive to the outdoor thermal environment than males. Older age led to lower thermal sensation, but the thermal sensitivities for age groups of <20, 20–50, and >50 were similar. Occupants who had resided in Shanghai for a longer period showed higher overall comfort rating and lower thermal sensation. Interviewees who came from hot summer and cold winter climate regions were less effected by the change of UTCI than those from severe cold or cold climate regions.
Envisaging Mitigation Action Can Induce Lower Discounting toward Future Environmental Gains and Promote Pro-Environmental Behavior
Authors: Ho Liang-Chu , Sung Yu-Hsien , Wu Chia-Chun , Lee Pei-Shan , Chiou Wen-Bin
Published: November 2020

Low engagement with climate change may stem from the tendency to discount the distant benefits of mitigation action. Hence, a reduced tendency to discount the future should be associated with increased involvement in climate change mitigation. Prior research has demonstrated that episodic future thinking (EFT; i.e., envisioning future events that involve self-projection) can reduce discounting. In two laboratory studies, we showed that engaging in EFT about mitigation action was associated with a lower discounting tendency toward future environmental gains (Experiments 1 and 2) and a greater tendency to act pro-environmentally, as manifested by using air conditioning in an energy-saving manner (Experiment 1), choosing a meal with less environmental impact (Experiment 2), and willingness to participate in beach cleaning (Experiment 2). The present findings suggest that engagement in EFT about mitigation action may represent a promising strategy for improving personal involvement in climate change.
Sea Buckthorn and Grape Extract Might Be Helpful and Sustainable Phyto-Resources as Associated Hypolipidemic Agents—Preliminary Study
Authors: Mohamed Erieg A. , Tulcan Camelia , Alexa Ersilia , Morar Doru , Dumitrescu Eugenia , Muselin Florin , Radulov Isidora , Puvača Nikola , Cristina Romeo T.
Published: November 2020

Phytotherapy can enhance the beneficial health outcomes in the prevention of obesity and is able to improve the function of the metabolic organs, like the liver and kidneys. Since sea buckthorn (SBT) and grape extracts are known as abundant sources of polyphenol, we assumed that the extracts of these two plants might have a hypolipidemic effect and an improved metabolic function in obese rats treated with atorvastatin. One hundred and twelve white Wistar rats were divided equally into seven groups (G.I–VII) and orally treated as follows: G.I, atorvastatin 20 mg × kg·bw−1; G.II, atorvastatin 20 mg × kg·bw−1 + SBT 100 mg × kg·bw−1; G.III, atorvastatin 20 mg × kg·bw−1 + grape extract 100 mg × kg·bw−1; G.IV, grape extract 100 mg × kg·bw−1; G.V, SBT 100 mg × kg·bw−1; G.VI, high-fat diet (HFD); group VII was considered the control group. After two and six months of administration, the rats were sacrificed, and blood samples were taken for biochemical analyses. The statistical results (analysis of variance (ANOVA)) showed that a combination of SBT and grape extracts with atorvastatin significantly reduced (p ˂ 0.001) the lipid parameters. After six months, the liver and kidneys improved their functioning, showing a statistically significant change (p ˂ 0.001) in the grape and sea buckthorn groups compared to the other groups. In addition, grape extract and SBT combined with atorvastatin proved to be potent hypolipidemic agents, so associations with phytodietary supplements can be considered as a valuable means of combating hypolipidemia and decreasing risk factors.
Small-Scale Hybrid Photovoltaic-Biomass Systems Feasibility Analysis for Higher Education Buildings
Authors: Alfonso-Solar David , Vargas-Salgado Carlos , Sánchez-Díaz Carlos , Hurtado-Pérez Elías
Published: November 2020

Applications of renewable electricity in cities are mostly limited to photovoltaics, and they need other renewable sources, batteries, and the grid to guarantee reliability. This paper proposes a hybrid system, combining biomass and photovoltaics, to supply electricity to educational buildings. This system is reliable and provides at least 50% of electricity based on renewable sources. Buildings with small (<500 kW) installed power based on renewables, mainly biomass, are usually expensive. Besides, in urban areas, photovoltaic capacity is limited due to roof availability. This paper analyzes different configurations, meeting these constraints to obtain an economically feasible solution based on photovoltaic-biomass modelling of small size hybrid systems. The technology used for biomass energy valorization is a fluidized bed gasification power plant, which has been modelled with real data obtained from experimental tests and previous research projects. Thereby, real costs and electric efficiency are included in the model. The techno-economic feasibility analysis using HOMER software with metered real load curves from an educational building has been modelled. The results of the model show that hybrid renewable systems are very feasible in the scenario of 50% of electricity contribution, however, higher contribution (>70%) implies high electricity costs.
Unified Energy Agents for Combined District Heating and Electrical Network Simulation
Authors: Loose Nils , Thommessen Christian , Mehlich Jan , Derksen Christian , Eicker Stefan
Published: November 2020

A sustainable and climate-friendly energy supply needs flexible and efficient distribution systems. Key factors to implement this kind of systems are intelligent coordination (smart grid approaches) and the integration of different energy sectors. This article introduces the unified energy agent as an agent-based approach for a comprehensive modelling and control of energy conversion systems. This approach enables both the simulation and optimization of coupled energy networks, and then in a next step, the development of corresponding smart grid solutions to be applied in the field. Its applicability for the simulation of coupled networks is presented by a real-world use-case of an innovative combined heat and electrical network, which was implemented for the city of Lemgo, Germany. Preliminary results from the project are discussed and an outlook on future work is given.
Enhanced Plant Restoration in Open-Pit Mines Using Maize Straw and Ultrasonically Pre-Treated Coal Fly Ash
Authors: Lu Xiang , Zhou Wei , Qi Chongchong , Yang Meng
Published: November 2020

The rehabilitation of the post-mining landscape is one of the intractable bottlenecks faced by the mining industry. Though plant restoration has been regarded as an efficient way, its application is often restricted by the physicochemical properties of the soil, i.e., macronutrient deficiencies. In this work, a novel plant restoration method was proposed that employs maize straw and ultrasonically pre-treated coal fly ash (UCFA) for soil amelioration. Seed germination experiments were performed to investigate the effect of maize straw leachates (MSLs) on the growth of maize, alfalfa, and soybean. Then, the influence of MSLs and UCFA on the physiological attributes, chlorophyll, and trace element concentrations of maize was studied by plant growth experiments. Our results show that: (i) the MSLs concentration had a significant effect on the plant growth and the optimum concentration was 20%; (ii) considering the physiological attributes of maize plants, MSLs and UCFA benefited its growth in most cases. The UCFA proportion should be less than 20 v.% for optimum performance; (iii) the mixture of MSLs and UCFA could generally increase the chlorophyll and decrease trace element concentrations; and (iv) the optimum proportion was found to be soil:UCFA:MSLs = 70:20:10 and soil:UCFA:MSLs = 60:20:20, which achieved satisfactory performance during engineering applications.
Identification of Vacant and Emerging Technologies in Smart Mobility Through the GTM-Based Patent Map Development
Authors: Yu Jiwon , Hwang Jong-Gyu , Hwang Jumi , Jun Sung Chan , Kang Sumin , Lee Chulung , Kim Hyundong
Published: November 2020

With the development of the online platforms and the Internet of Things (IoT), various transportation services have been provided, and the lifestyle of the general public has changed significantly. However, the speed of development of technologies and services for the mobility handicapped has been relatively slow. Accordingly, in this paper, the smart mobility patent data for the mobility handicapped is subdivided through clustering to derive the mobility handicapped-related vacant technologies, and the prospect of the vacant technology is verified. For each cluster, a technology level map is generated in consideration of the technology growth level and the scope of authority of the vacant technology derived through the generative topographic map (GTM) patent map, and the level of the vacant technology is checked in terms of quantity and quality. Both indicators perform time series analyses on superior technology to predict technology trends and determine the technology’s promisingness. Unlike the precedent studies that focused only on quantitative analysis methods, this paper identified the usefulness of the technology through clustering and various verification processes and materialized it as a vacant technology that is applicable to actual R&D. Accordingly, through this empirical paper, it is possible to understand the current level of vacant technology in smart mobility for the mobility handicapped and establish an R&D strategy to prevent monopoly in technology in the future market and maintain competitiveness. It can also be utilized for new technology development in consideration of convergence with currently developed technology.
Sustainable Development Goals in Early Childhood Education. Empowering Young Girls to Bridge the Gender Gap in Science
Authors: Mérida-Serrano Rosario , González-Alfaya María E. , Olivares-García María A. , Rodríguez-Carrillo Julia , Muñoz-Moya Miguel
Published: November 2020

The present study is evaluative research on the program INFACIENCIA: From the girls of today to the women scientists of tomorrow. Its aim is to increase knowledge about relevant women scientists among the entire educational community and, specifically, to help children in gaining an inclusive image of science, i.e., one that values and shows female talent. The evaluation of this program is based upon qualitative research techniques, such as semi-structured interviews, in order to give voice to everyone involved. In this paper we focus on teachers’ and families’ perspectives. Findings show that when young children actively learn science, they develop positive attitudes towards it. At the same time, it is revealed how learning about women scientists boosts a positive socialization context where egalitarian knowledge and attitudes towards science can be acquired. Participating teachers value the experience since there are still very few coeducational school interventions focused on young children. Participating families have increased their knowledge of women scientists they barely knew before and believe that these women deserve public appreciation. To conclude, INFACIENCIA has been regarded as a very positive experience both by teachers and families, thus claiming for it to be carried out again.
Functional Unit for Impact Assessment in the Mining Sector—Part 1
Authors: Bongono Julien , Elevli Birol , Laratte Bertrand
Published: November 2020

More and more efforts are directed towards the standardization of the methods of determining the functional unit (FU) in a Life Cycle Assessment (LCA). These efforts concern the development of theories and detailed methodological guides, but also the evaluation of the quality of the FU obtained. The objective of this article is to review this work in order to propose, using a multiscale approach, a method for defining the FU in the mining sector, which takes into account all the dimensions of the system under study. In this first part, the emphasis is on identifying the shortcomings of the FU. The absence of a precise normative framework specific to each sector of activity, as well as the complex, multifunctional and hard-to-scale nature of the systems concerned, are at the origin of the flexibility in the selection of the FU. This lack of a framework, beyond generating a heterogeneous definition of the FU for the same system, most often leads to an incomplete formulation of this sensitive concept of LCA. It has been found that key parameters such as the end-use of a product or process, as well as the interests of stakeholders, are hardly taken into account in the specification of the FU.
Modelling Variation in Petroleum Products’ Refining Footprints
Authors: Johnson Eric , Vadenbo Carl
Published: November 2020

Energy-related greenhouse gas emissions dominate the carbon footprints of most product systems, where petroleum is one of the main types of energy sources. This is consumed as a variety of refined products, most notably diesel, petrol (gasoline) and jet fuel (kerosene). Refined product carbon footprints are of great importance to regulators, policymakers and environmental decision-makers. For instance, they are at the heart of current legislation, such as the European Union’s Renewable Energy Directive or the United States’ Renewable Fuels Standard. This study identified 14 datasets that report footprints for the same system, namely, petroleum refinery operations in Europe. For the main refined products, i.e., diesel, petrol and jet fuel, footprints vary by at least a factor of three. For minor products, the variation is even greater. Five different organs of the European Commission have estimated the refining footprints, where for the main products, these are relatively harmonic; for minor products, much less so. The observed variation in carbon footprints is due mainly to differing approaches to refinery modelling, especially regarding the rationale and methods applied to assign shares of the total burden from the petroleum refinery operation to the individual products. Given the economic/social importance of refined products, a better harmony regarding their footprints would be valuable to their users.
Machine Learning Technologies for Sustainability in Smart Cities in the Post-COVID Era
Authors: De Las Heras Ana , Luque-Sendra Amalia , Zamora-Polo Francisco
Published: November 2020

The unprecedented urban growth of recent years requires improved urban planning and management to make urban spaces more inclusive, safe, resilient and sustainable. Additionally, humanity faces the COVID pandemic, which especially complicates the management of Smart Cities. A possible solution to address these two problems (environmental and health) in Smart Cities may be the use of Machine Learning techniques. One of the objectives of our work is to thoroughly analyze the link between the concepts of Smart Cities, Machine Learning techniques and their applicability. In this work, an exhaustive study of the relationship between Smart Cities and the applicability of Machine Learning (ML) techniques is carried out with the aim of optimizing sustainability. For this, the ML models, analyzed from the point of view of the models, techniques and applications, are studied. The areas and dimensions of sustainability addressed are analyzed, and the Sustainable Development Goals (SDGs) are discussed. The main objective is to propose a model (EARLY) that allows us to tackle these problems in the future. An inclusive perspective on applicability, sustainability scopes and dimensions, SDGs, tools, data types and Machine Learning techniques is provided. Finally, a case study applied to an Andalusian city is presented.
A DPSIR Assessment on Ecosystem Services Challenges in the Mekong Delta, Vietnam: Coping with the Impacts of Sand Mining
Authors: S. Naveedh Ahmed , Hung Anh Le , Schneider Petra
Published: November 2020

River sand mining has been a concerning problem for the southern Asian developing nations. The rampant growth of urbanisation in developing countries has led to an extensive need for and consumption of sand. The Mekong River and its delta are an essential part of southern Vietnam, and also a global biodiversity hub that is currently being exhausted by intensive sand mining. The understanding of the cause–effect of the sand mining over the Mekong delta region and river, from a systems-thinking perspective, is lacking, not only with Vietnam but also with other countries along the Mekong River. The DPSIR framework (Driver–Pressure–State–Impact–Response) is a useful tool to assess and describe the cause–effect within an ecosystem to aid in a better systems-thinking approach for stakeholders, policy makers, and governance managers to draft response measures. This study used the DPSIR framework to assess the different effects of sand mining on the ecosystem services and human well-being in the Mekong River and delta region of Vietnam. Rapid population growth, urbanisation, and infrastructure development needs remain as primary drivers for the sand consumption. The DPSIR study showed a holistic view of several interlinked pressures and state changes in Vietnam’s Mekong, along with some potential responses, to form systematic, sustainable approaches for mitigating and adapting the impacts caused by extensive river sand mining.
Sustainable Digital Transformation of Disaster Risk—Integrating New Types of Digital Social Vulnerability and Interdependencies with Critical Infrastructure
Authors: Fekete Alexander , Rhyner Jakob
Published: November 2020

This article explores the relationship between digital transformation and disaster risk. Vulnerability studies aim at differentiating impacts and losses by using fine-grained information from demographic, social, and personal characteristics of humans. With ongoing digital development, these characteristics will transform and result in new traits, which need to be identified and integrated. Digital transformations will produce new social groups, partly human, semi-human, or non-human—some of which already exist, and some which can be foreseen by extrapolating from recent developments in the field of brain wearables, robotics, and software engineering. Though involved in the process of digital transformation, many researchers and practitioners in the field of Disaster Risk Reduction or Climate Change Adaptation are not yet aware of the repercussions for disaster and vulnerability assessments. Emerging vulnerabilities are due to a growing dependency on digital services and tools in the case of a severe emergency or crisis. This article depicts the different implications for future theoretical frameworks when identifying novel semi-human groups and their vulnerabilities to disaster risks. Findings include assumed changes within common indicators of social vulnerability, new indicators, a typology of humans, and human interrelations with digital extensions and two different perspectives on these groups and their dependencies with critical infrastructure.
A Framework of Key Growth Factors for Small Enterprises Operating at the Base of the Pyramid
Authors: van der Merwe Michael D. , Grobbelaar Sara S. , Meyer Isabel A. , Schutte Cornelius S.L. , von Leipzig Konrad H.
Published: November 2020

Enterprises operating at the Base of the Pyramid (BoP) find it difficult to simultaneously achieve economic (profitable growth) and social (poverty alleviation) outcomes. This study builds on a previously published systematic literature review that identified the key growth-promoting factors of the Small, Medium, and Micro Enterprises (SMMEs) operating in this space. A Grounded Theory Approach was followed to identify six core focus areas, which form the basis of a practical framework to evaluate SMMEs at the BoP, namely: Business modelling; business strategy; innovation; marketing; strategic alliances; and sustainable development. Expert screening interviews were conducted to evaluate the framework for credibility and confirmability. Thereafter, 57 for-profit South African enterprises were surveyed to empirically explore how SMMEs view and implement these framework components. The outcome of the study is a validated high growth-promoting BoP portfolio framework against which SMME owners can evaluate their enterprises to identify areas of growth and development.
Modeling and Predictive Mapping of Soil Organic Carbon Density in a Small-Scale Area Using Geographically Weighted Regression Kriging Approach
Authors: Liu Tao , Zhang Huan , Shi Tiezhu
Published: November 2020

Different natural environmental variables affect the spatial distribution of soil organic carbon (SOC), which has strong spatial heterogeneity and non-stationarity. Additionally, the soil organic carbon density (SOCD) has strong spatial varying relationships with the environmental factors, and the residuals should keep independent. This is one hard and challenging study in digital soil mapping. This study was designed to explore the different impacts of natural environmental factors and construct spatial prediction models of SOC in the junction region (with an area of 2130.37 km2) between Enshi City and Yidu City, Hubei Province, China. Multiple spatial interpolation models, such as stepwise linear regression (STR), geographically weighted regression (GWR), regression kriging (RK), and geographically weighted regression kriging (GWRK), were built using different natural environmental variables (e.g., terrain, environmental, and human factors) as auxiliary variables. The goodness of fit (R2), root mean square error, and improving accuracy were used to evaluate the predicted results of the spatial interpolation models. Results from Pearson correlation coefficient analysis and STR showed that SOCD was strongly correlated with elevation, topographic position index (TPI), topographic wetness index (TWI), slope, and normalized difference vegetation index (NDVI). GWRK had the highest simulation accuracy, followed by RK, whereas STR was the weakest. A theoretical scientific basis is, therefore, provided for exploring the relationship between SOCD and the corresponding environmental variables as well as for modeling and estimating the regional soil carbon pool.
ISO Standards: A Platform for Achieving Sustainable Development Goal 2
Authors: Zhao Xiaoli , Castka Pavel , Searcy Cory
Published: November 2020

The International Organization for Standardization (ISO) has recently begun promoting the linkages between its standards and the United Nations’ Sustainable Development Goals (SDGs). However, further research is needed to explore how ISO standards can serve as a platform for achieving the SDGs. In this paper, we discuss the interlinkage between ISO standards and SDG 2 (i.e., Zero hunger—End hunger, achieve food security and improved nutrition, and promote sustainable agriculture). We review the literature on a set of 77 ISO standards related to SDG 2 and study existing evidence pertinent to SDG 2 and its targets. Specifically, we review research in four key areas of interest: agricultural productivity of ISO certified firms, adoption of ISO standards amongst small scale producers, ISO standards development, and governance of standards. We found implicit evidence in the literature that ISO standards have the potential to contribute to two SDG 2 targets, namely targets 2.3 and 2.4. Some aspects of ISO standards, however, such as low levels of adoption amongst small scale farmers or a lack of multi-stakeholder standard development, contradict key aspects of the SDG agenda. We outline key areas for future research in the four areas of interest noted above.
Potential of Octanol and Octanal from Heracleum sosnowskyi Fruits for the Control of Fusarium oxysporum f. sp. lycopersici
Authors: Hpoo May Khaing , Mishyna Maryia , Prokhorov Valery , Arie Tsutomu , Takano Akihito , Oikawa Yosei , Fujii Yoshiharu
Published: November 2020

The antifungal activity of volatile compounds from the fruit, leaf, rhizome and root of 109 plant species was evaluated against Fusarium oxysporum f. sp. lycopersici (FOL) race 1—the tomato wilt pathogen—by using the modified dish pack method. Eighty-eight plant samples inhibited mycelial growth, including volatiles from fruits of Heracleum sosnowskyi, which exhibited the strongest antifungal activity, showing 67% inhibition. Two volatile compounds from the fruits of H. sosnowskyi (octanol and octanal) and trans-2-hexenal as a control were tested for their antifungal activities against FOL race 1 and race 2. In terms of half-maximal effective concentration (EC50) values, octanol was found to be the most inhibitory compound for both pathogenic races, with the smallest EC50 values of 8.1 and 9.3 ng/mL for race 1 and race 2, respectively. In the biofumigation experiment, the lowest disease severity of tomato plants and smallest conidial population of race 1 and race 2 were found in trans-2-hexenal and octanol treated soil, while octanal had an inhibitory effect only on race 2. Therefore, our study demonstrated the effectiveness of volatile octanol and trans-2-hexenal on the control of the mycelial growth of two races of Fusarium oxysporum f. sp. lycopersici and may have potential for the future development of novel biofumigants.
Nitrogen Fertilizer Effects on Pea–Barley Intercrop Productivity Compared to Sole Crops in Denmark
Authors: Cowden Reed John , Shah Ambreen Naz , Lehmann Lisa Mølgaard , Kiær Lars Pødenphant , Henriksen Christian Bugge , Ghaley Bhim Bahadur
Published: November 2020

Cereal–legume intercropping increases the nitrogen (N) input from biological nitrogen fixation (BNF) and improves the exploitation of fertilizer and soil N, often leading to higher grain N content and higher productivity per unit land area compared to monocrops. Previous studies have found that these effects are more tangible under low soil and fertilizer N conditions compared to high N availability, and there is a need to assess the N uptake at critical crop development stages in order to time the N application for maximum uptake and use efficiency. The objective of this study was to assess the productivity of pea–barley intercropping compared to monocropping under 0 kg N ha−1 (0 N) and 100 kg N ha−1 (100 N). In 2017, a split plot experimental design was implemented with pea (Pisum sativum) sole crop (SC pea), barley (Hordeum vulgare) sole crop (SC barley), and pea–barley intercrop (IC total) as the main plots and 100 N applications in two 50 kg N ha−1 splits at 30 and 60 days after emergence as subplots within the main plots. The Land Equivalent Ratio (LER), based on grain dry matter (GDM) yields in the pea–barley intercrop (IC total), was higher (1.14 at 0 N and 1.10 at 100 N), indicating 10–14% greater radiation, nutrient, and water use efficiency compared to the sole crops and 4% greater resource use efficiency at 0 N compared to the 100 N; this illustrated greater total intercrop productivity compared to sole crops. The 100 N treatment decreased the SC pea and pea in intercrop (IC pea) GDM and grain dry matter N (GDMN) and increased the GDM and GDMN in SC barley and barley in the intercrop (IC barley). Intercropping increased the grain N content and therefore the protein content of the grains in 0 N and 100 N treatments. The highest fertilizer N yield, % nitrogen derived from fertilizer (%NDFF), and % nitrogen use efficiency (%NUE) were achieved in SC barley followed by IC total, indicating that intercropping improved the soil and fertilizer N use compared to SC pea. The IC pea increased the % nitrogen derived from atmosphere (%NDFA) from 67.9% in SC pea to 70.1% in IC pea. IC total increased the share of %NDFF, %NDFS, and %NDFA compared to the SC pea, which indicated a significant advantage of intercropping due to the complementarity of the component species under limited N supply in the field.