bluuebunny/crossref_metadata_2025 huggingface dataset:
The LAST Coring Platform You Will Ever Need: Light, Affordable, Stable, and Transportable
Authors: Blarquez Olivier , Aleman Julie
Published: September 2020

Coring lakes and water bodies for paleoecological studies often involves using a coring platform to properly operate a sediment sampling device. In the past, coring platforms have been developed by specific paleoecology laboratories or by private companies. Those coring platforms are generally composed of two boats (inflatable boats, kayaks, etc.) connected together by a metallic and wood structure. While these coring platforms have proven their efficacy, they are not ideal in several coring settings requiring remote transportation, and their cost may be prohibitive for less funded paleoecological laboratories. On this technical note, we describe the Light, Affordable, Stable, and Transportable (LAST) coring platform. Coring platforms based on these principles and on the design presented herein have been extensively tested in various conditions and countries by our research group and collaborators. In the first part of this manuscript, we present the principles and the design of the LAST coring platform; then, we discuss the coring setting for which the LAST coring platform is suitable, and its possible limitations. Associated with this manuscript, we provide a construction and assemblage manual developed without words and with simple illustrations in order to make it easily accessible to speakers of any language.
Co-Evolution of Emerging Multi-Cities: Rates, Patterns and Driving Policies Revealed by Continuous Change Detection and Classification of Landsat Data
Authors: Liu Maochou , Liu Shuguang , Ning Ying , Zhu Yu , Valbuena Rubén , Guo Rui , Li Yuanyuan , Tang Wenxi , Mo Dengkui , Rosa Isabel M.D. , Kutia Mykola , Hu Wenmin
Published: September 2020

The co-evolution of multi-cities has emerged as the primary form of urbanization in China in recent years. However, the processes, patterns, and coordination are not well characterized and understood, which hinders the understanding of the driving forces, consequences, and management of polycentric urban development. We used the Continuous Change Detection and Classification (CCDC) algorithm to integrate all available Landsat 5, 7, and 8 images and map annual land use and land cover (LULC) from 2001 to 2017 in the Chang–Zhu–Tan urban agglomeration (CZTUA), a typical urban agglomeration in China. Results showed that the impervious surface in the study area expanded by 371 km2 with an annual growth rate of 2.25%, primarily at the cost of cropland (169 km2) and forest (206 km2) during the study period. Urban growth has evolved from infilling being the dominant type in the earlier period to mainly edge-expansion and leapfrogging in the core cities, and from no dominant type to mainly leapfrogging in the satellite cities. The unfolding of the “cool center and hot edge” urban growth pattern in CZTUA, characterized by higher expansion rates in the peripheral than in the core cities, may signify a new form of the co-evolution of multi-cities in the process of urbanization. Detailed urban management and planning policies in CZTUA were analyzed. The co-evolution of multi-cities principles need to be studied in more extensive regions, which could help policymakers to promote sustainable and livable development in the future.
Comparing Sentinel-1 and -2 Data and Indices for Agricultural Land Use Monitoring
Authors: Holtgrave Ann-Kathrin , Röder Norbert , Ackermann Andrea , Erasmi Stefan , Kleinschmit Birgit
Published: September 2020

Agricultural vegetation development and harvest date monitoring over large areas requires frequent remote sensing observations. In regions with persistent cloud coverage during the vegetation season this is only feasible with active systems, such as SAR, and is limited for optical data. To date, optical remote sensing vegetation indices are more frequently used to monitor agricultural vegetation status because they are easily processed, and the characteristics are widely known. This study evaluated the correlations of three Sentinel-2 optical indices with Sentinel-1 SAR indices over agricultural areas to gain knowledge about their relationship. We compared Sentinel-2 Normalized Difference Vegetation Index, Normalized Difference Water Index, and Plant Senescence Radiation Index with Sentinel-1 SAR VV and VH backscatter, VH/VV ratio, and Sentinel-1 Radar Vegetation Index. The study was conducted on 22 test sites covering approximately 35,000 ha of four different main European agricultural land use types, namely grassland, maize, spring barley, and winter wheat, in Lower Saxony, Germany, in 2018. We investigated the relationship between Sentinel-1 and Sentinel-2 indices for each land use type considering three phenophases (growing, green, senescence). The strength of the correlations of optical and SAR indices differed among land use type and phenophase. There was no generic correlation between optical and SAR indices in our study. However, when the data were split by land use types and phenophases, the correlations increased remarkably. Overall, the highest correlations were found for the Radar Vegetation Index and VH backscatter. Correlations for grassland were lower than for the other land use types. Adding auxiliary data to a multiple linear regression analysis revealed that, in addition to land use type and phenophase information, the lower quartile and median SAR values per field, and a spatial variable, improved the models. Other auxiliary data retrieved from a digital elevation model, Sentinel-1 orbit direction, soil type information, and other SAR values had minor impacts on the model performance. In conclusion, despite the different nature of the signal generation, there were distinct relationships between optical and SAR indices which were independent of environmental variables but could be stratified by land use type and phenophase. These relationships showed similar patterns across different test sites. However, a regional clustering of landscapes would significantly improve the relationships.
A General Point-Based Method for Self-Calibration of Terrestrial Laser Scanners Considering Stochastic Information
Authors: Zhou Tengfei , Cheng Xiaojun , Lin Peng , Wu Zhenlun , Liu Ensheng
Published: September 2020

Due to the existence of environmental or human factors, and because of the instrument itself, there are many uncertainties in point clouds, which directly affect the data quality and the accuracy of subsequent processing, such as point cloud segmentation, 3D modeling, etc. In this paper, to address this problem, stochastic information of point cloud coordinates is taken into account, and on the basis of the scanner observation principle within the Gauss–Helmert model, a novel general point-based self-calibration method is developed for terrestrial laser scanners, incorporating both five additional parameters and six exterior orientation parameters. For cases where the instrument accuracy is different from the nominal ones, the variance component estimation algorithm is implemented for reweighting the outliers after the residual errors of observations obtained. Considering that the proposed method essentially is a nonlinear model, the Gauss–Newton iteration method is applied to derive the solutions of additional parameters and exterior orientation parameters. We conducted experiments using simulated and real data and compared them with those two existing methods. The experimental results showed that the proposed method could improve the point accuracy from 10−4 to 10−8 (a priori known) and 10−7 (a priori unknown), and reduced the correlation among the parameters (approximately 60% of volume). However, it is undeniable that some correlations increased instead, which is the limitation of the general method.
Machine Learning and the End of Atmospheric Corrections: A Comparison between High-Resolution Sea Surface Salinity in Coastal Areas from Top and Bottom of Atmosphere Sentinel-2 Imagery
Authors: Medina-Lopez Encarni
Published: September 2020

This paper introduces a discussion about the need for atmospheric corrections by comparing data-driven sea surface salinity (SSS) derived from Top- and Bottom-of-Atmosphere imagery. Atmospheric corrections are used to remove the effect of the atmosphere in reflectances acquired by satellite sensors. The Sentinel-2 Level-2A product provides atmospherically corrected Bottom-of-Atmosphere (BOA) imagery, derived from Level-1C Top-of-Atmosphere (TOA) tiles using the Sen2Cor processor. SSS at high resolution in coastal areas (100m) is derived from multispectral signatures using artificial neural networks. These obtain relationships between satellite band information and in situ SSS data. Four scenarios with different input variables are tested for both TOA and BOA imagery, for interpolation (previous information on all platforms is available in the training dataset) and extrapolation (certain platforms are isolated and the network does not have any previous information on these) problems. Results show that TOA always outperforms BOA in terms of higher coefficient of determination (R2), lower mean absolute error (MAE) and lower most common error (μe). The best TOA results are R2=0.99, MAE=0.4PSU and μe=0.2PSU. Moreover, the evaluation of the neural network in all the pixels of Sentinel-2 tiles shows that BOA results are accurate only far away from the coast, while TOA data provides useful information on nearshore mixing patterns, estuarine processes and is able to estimate freshwater salinity values. This suggests that land adjacency corrections could be a relevant source of error. Sun glint corrections appear to be another source of error. TOA imagery is more accurate than BOA imagery when using machine learning algorithms and big data, as there is a clear loss of information in the atmospheric correction process that affects the multispectral–in situ relationships. Finally, the time and computational resources gained by avoiding atmospheric corrections can make the use of TOA imagery interesting in future studies, such as the estimation of chlorophyll or coloured dissolved organic matter.
Electrochemical Affinity Biosensors Based on Selected Nanostructures for Food and Environmental Monitoring
Authors: Campuzano Susana , Yáñez-Sedeño Paloma , Pingarrón José M.
Published: September 2020

The excellent capabilities demonstrated over the last few years by electrochemical affinity biosensors should be largely attributed to their coupling with particular nanostructures including dendrimers, DNA-based nanoskeletons, molecular imprinted polymers, metal-organic frameworks, nanozymes and magnetic and mesoporous silica nanoparticles. This review article aims to give, by highlighting representative methods reported in the last 5 years, an updated and general overview of the main improvements that the use of such well-ordered nanomaterials as electrode modifiers or advanced labels confer to electrochemical affinity biosensors in terms of sensitivity, selectivity, stability, conductivity and biocompatibility focused on food and environmental applications, less covered in the literature than clinics. A wide variety of bioreceptors (antibodies, DNAs, aptamers, lectins, mast cells, DNAzymes), affinity reactions (single, sandwich, competitive and displacement) and detection strategies (label-free or label-based using mainly natural but also artificial enzymes), whose performance is substantially improved when used in conjunction with nanostructured systems, are critically discussed together with the great diversity of molecular targets that nanostructured affinity biosensors are able to quantify using quite simple protocols in a wide variety of matrices and with the sensitivity required by legislation. The large number of possibilities and the versatility of these approaches, the main challenges to face in order to achieve other pursued capabilities (development of antifouling, continuous operation, wash-, calibration- and reagents-free devices, regulatory or Association of Official Analytical Chemists, AOAC, approval) and decisive future actions to achieve the commercialization and acceptance of these devices in our daily routine are also noted at the end.
Case Study—Spiking Neural Network Hardware System for Structural Health Monitoring
Authors: Pang Lili , Liu Junxiu , Harkin Jim , Martin George , McElholm Malachy , Javed Aqib , McDaid Liam
Published: September 2020

This case study provides feasibility analysis of adapting Spiking Neural Networks (SNN) based Structural Health Monitoring (SHM) system to explore low-cost solution for inspection of structural health of damaged buildings which survived after natural disaster that is, earthquakes or similar activities. Various techniques are used to detect the structural health status of a building for performance benchmarking, including different feature extraction methods and classification techniques (e.g., SNN, K-means and artificial neural network etc.). The SNN is utilized to process the sensory data generated from full-scale seven-story reinforced concrete building to verify the classification performances. Results show that the proposed SNN hardware has high classification accuracy, reliability, longevity and low hardware area overhead.
A Ship Trajectory Prediction Framework Based on a Recurrent Neural Network
Authors: Suo Yongfeng , Chen Wenke , Claramunt Christophe , Yang Shenhua
Published: September 2020

Ship trajectory prediction is a key requisite for maritime navigation early warning and safety, but accuracy and computation efficiency are major issues still to be resolved. The research presented in this paper introduces a deep learning framework and a Gate Recurrent Unit (GRU) model to predict vessel trajectories. First, series of trajectories are extracted from Automatic Identification System (AIS) ship data (i.e., longitude, latitude, speed, and course). Secondly, main trajectories are derived by applying the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. Next, a trajectory information correction algorithm is applied based on a symmetric segmented-path distance to eliminate the influence of a large number of redundant data and to optimize incoming trajectories. A recurrent neural network is applied to predict real-time ship trajectories and is successively trained. Ground truth data from AIS raw data in the port of Zhangzhou, China were used to train and verify the validity of the proposed model. Further comparison was made with the Long Short-Term Memory (LSTM) network. The experiments showed that the ship’s trajectory prediction method can improve computational time efficiency even though the prediction accuracy is similar to that of LSTM.
UAV-Assisted Low-Consumption Time Synchronization Utilizing Cross-Technology Communication
Authors: Tan Ziyi , Yang Xu , Pang Mingzhi , Gao Shouwan , Li Ming , Chen Pengpeng
Published: September 2020

Wireless sensor networks (WSNs) have been used in many fields due to its wide applicability. In this kind of network, each node is independent of each other and has its own local clock and communicates wirelessly. Time synchronization plays a vital role in WSNs and it can ensure accuracy requirements for coordination and data reliability. However, two key challenges exist in large-scale WSNs that are severe resource constraints overhead and multihop time synchronization errors. To address these issues, this paper proposes a novel unmanned aerial vehicle (UAV)-assisted low-consumption time synchronization algorithm based on cross-technology communication (CTC) for a large-scale WSN. This algorithm uses a UAV to send time synchronization data packets for calibration. Moreover, to ensure coverage and a high success rate for UAV data transmission, we use CTC for time synchronization. Without any relays, a high-power time synchronization packet can be sent by a UAV to achieve the time synchronization of low-power sensors. This algorithm can achieve accurate time synchronization with almost zero energy consumption for the sensor nodes. Finally, we implemented our algorithm with 30 low-power RF-CC2430 ZigBee nodes and a Da Jiang Innovations (DJI) M100 UAV on a 1 km highway and an indoor site. The results show that time synchronization can be achieved accurately with almost zero energy consumption for the sensor nodes, and the time synchronization error is less than 30 μs in 99% of cases.
Research Progress of Automated Visual Surface Defect Detection for Industrial Metal Planar Materials
Authors: Fang Xiaoxin , Luo Qiwu , Zhou Bingxing , Li Congcong , Tian Lu
Published: September 2020

The computer-vision-based surface defect detection of metal planar materials is a research hotspot in the field of metallurgical industry. The high standard of planar surface quality in the metal manufacturing industry requires that the performance of an automated visual inspection system and its algorithms are constantly improved. This paper attempts to present a comprehensive survey on both two-dimensional and three-dimensional surface defect detection technologies based on reviewing over 160 publications for some typical metal planar material products of steel, aluminum, copper plates and strips. According to the algorithm properties as well as the image features, the existing two-dimensional methodologies are categorized into four groups: statistical, spectral, model, and machine learning-based methods. On the basis of three-dimensional data acquisition, the three-dimensional technologies are divided into stereoscopic vision, photometric stereo, laser scanner, and structured light measurement methods. These classical algorithms and emerging methods are introduced, analyzed, and compared in this review. Finally, the remaining challenges and future research trends of visual defect detection are discussed and forecasted at an abstract level.
A Vision-Based Driver Assistance System with Forward Collision and Overtaking Detection
Authors: Lin Huei-Yung , Dai Jyun-Min , Wu Lu-Ting , Chen Li-Qi
Published: September 2020

One major concern in the development of intelligent vehicles is to improve the driving safety. It is also an essential issue for future autonomous driving and intelligent transportation. In this paper, we present a vision-based system for driving assistance. A front and a rear on-board camera are adopted for visual sensing and environment perception. The purpose is to avoid potential traffic accidents due to forward collision and vehicle overtaking, and assist the drivers or self-driving cars to perform safe lane change operations. The proposed techniques consist of lane change detection, forward collision warning, and overtaking vehicle identification. A new cumulative density function (CDF)-based symmetry verification method is proposed for the detection of front vehicles. The motion cue obtained from optical flow is used for overtaking detection. It is further combined with a convolutional neural network to remove repetitive patterns for more accurate overtaking vehicle identification. Our approach is able to adapt to a variety of highway and urban scenarios under different illumination conditions. The experiments and performance evaluation carried out on real scene images have demonstrated the effectiveness of the proposed techniques.
A Study on Determining Time-Of-Flight Difference of Overlapping Ultrasonic Signal: Wave-Transform Network
Authors: Li Zhipeng , Wu Tong , Zhang Wei , Gao Xuyang , Yao Zhenqiu , Li Yanjun , Shi Yibing
Published: September 2020

An ultrasonic sensors system is commonly used to measure the wall thickness of buried pipelines in the transportation of oil and gas. The key of the system is to precisely measure time-of-flight difference (TOFD) produced by the reflection of ultrasonic on the inner and outer surfaces of the pipelines. In this paper, based on deep learning, a novel method termed Wave-Transform Network is proposed to tackle the issues. The network consists of two parts: part 1 is designed to separate the potential overlapping ultrasonic echo signals generated from two surfaces, and part 2 is utilized to divide the sample points of each signal into two types corresponding to before and after the arrival time of ultrasonic echo, which can determine the time-of-flight (TOF) of each signal and calculate the thickness of pipelines. Numerical simulation and actual experiments are carried out, and the results show satisfactory performances.
GPS Trajectory Completion Using End-to-End Bidirectional Convolutional Recurrent Encoder-Decoder Architecture with Attention Mechanism
Authors: Nawaz Asif , Huang Zhiqiu , Wang Senzhang , Akbar Azeem , AlSalman Hussain , Gumaei Abdu
Published: September 2020

GPS datasets in the big data regime provide rich contextual information that enable efficient implementation of advanced features such as navigation, tracking, and security in urban computing systems. Understanding the hidden patterns in large amount of GPS data is critically important in ubiquitous computing. The quality of GPS data is the fundamental key problem to produce high quality results. In real world applications, certain GPS trajectories are sparse and incomplete; this increases the complexity of inference algorithms. Few of existing studies have tried to address this problem using complicated algorithms that are based on conventional heuristics; this requires extensive domain knowledge of underlying applications. Our contribution in this paper are two-fold. First, we proposed deep learning based bidirectional convolutional recurrent encoder-decoder architecture to generate the missing points of GPS trajectories over occupancy grid-map. Second, we interfaced attention mechanism between enconder and decoder, that further enhance the performance of our model. We have performed the experiments on widely used Microsoft geolife trajectory dataset, and perform the experiments over multiple level of grid resolutions and multiple lengths of missing GPS segments. Our proposed model achieved better results in terms of average displacement error as compared to the state-of-the-art benchmark methods.
Modeling of the MET Sensitive Element Conversion Factor on the Intercathode Distance
Authors: Ryzhkov Maksim , Agafonov Vadim
Published: September 2020

MET sensors for measuring motion parameters are used in many scientific and technical fields. Meanwhile, the geometries of the transforming cell applied practically are far from optimal, and the influence of many geometric parameters on the sensitivity has not been studied. These parameters include the intercathode distance in a four-electrode conversion cell. In this paper, a mathematical model that allows calculating the behavior of the conversion coefficient depending on the frequency for a cell with flat electrodes at different intercathode distances is constructed. The stationary current is shown to decrease monotonically with the decreasing intercathode distance at the constancy of other system parameters. At the same time, the signal current decreases in the low-frequency region and increases in the high-frequency range. Taking into account the results obtained, practically speaking, it is advisable to reduce the intercathode distance to the technologically possible minimum, which makes the frequency response more uniform and reduces the current consumed by the sensitive element.
Thermo-Optical Tuning Cascaded Double Ring Sensor with Large Measurement Range
Authors: Yang Zhiping , Wang Yanlu , Su Chang , Shao Liyang , He Jian-Jun , Li Mingyu
Published: September 2020

In this paper, a thermo-optic tuning optical waveguide sensor system based on a cascaded double micro-ring resonator is investigated. The system consists of a micro-ring resonator with the microheater as a reference ring and a micro-ring resonator with removing the upper cladding layers as a sensing ring, combined with a microfluidic control. The refractive index change of the sample is measured by the electric power change of the microheater. The experimental results show that the sensitivity of the thermo-optic tuning is 34.231 W/RIU (refractive index units), and the measurement range is 4.325 × 10−3 RIU, almost eight times larger than that of the cascaded double micro-ring resonator without thermo-optic tuning for the intensity interrogation.
Lasers for Satellite Uplinks and Downlinks
Authors: Dmytryszyn Mark , Crook Matthew , Sands Timothy
Published: September 2020

The use of Light Amplification by Stimulated Emission of Radiation (i.e., LASERs or lasers) by the U.S. Department of Defense is not new and includes laser weapons guidance, laser-aided measurements, even lasers as weapons (e.g., Airborne Laser). Lasers in support of telecommunications is also not new. The use of laser light in fiber optics shattered thoughts on communications bandwidth and throughput. Even the use of lasers in space is no longer new. Lasers are being used for satellite-to-satellite crosslinking. Laser communication can transmit orders-of-magnitude more data using orders-of-magnitude less power and can do so with minimal risk of exposure to the sending and receiving terminals. What is new is using lasers as the uplink and downlink between the terrestrial segment and the space segment of satellite systems. More so, the use of lasers to transmit and receive data between moving terrestrial segments (e.g., ships at sea, airplanes in flight) and geosynchronous satellites is burgeoning. This manuscript examines the technological maturation of employing lasers as the signal carrier for satellite communications linking terrestrial and space systems. The purpose of the manuscript is to develop key performance parameters (KPPs) to inform U.S. Department of Defense initial capabilities documents (ICDs) for near-future satellite acquisition and development. By appreciating the history and technological challenges of employing lasers rather than traditional radio frequency sources for satellite uplink and downlink signal carrier, this manuscript recommends ways for the U.S. Department of Defense to employ lasers to transmit and receive high bandwidth, large-throughput data from moving platforms that need to retain low probabilities of detection, intercept, and exploitation (e.g., carrier battle group transiting to a hostile area of operations, unmanned aerial vehicle collecting over adversary areas). The manuscript also intends to identify commercial sector early-adopter fields and those fields likely to adapt to laser employment for transmission and receipt.
Salience, Credibility and Legitimacy in a Rapidly Shifting World of Knowledge and Action
Authors: Cash David W. , Belloy Patricio G.
Published: September 2020

We are in a rapidly changing world where new dynamics are stressing the knowledge-action landscape: a greater understanding that cross-scale interactions are critical; increasing pressure to more fully address issues of equity in sustainable development challenges; rapidly transforming digital technologies; and the emergence of a “post-truth world”. These stressors are ripening at a time in which there is increased urgency in linking knowledge to action to solve some of the earth’s most pressing human-environment problems. This paper explores to what degree one model of knowledge-action may be useful in the face of these stressors. This model relies on co-production of knowledge across boundaries, and the importance of knowledge in meeting criteria of salience, credibility and legitimacy. Tentative explorations suggest utility of this model in responding to the changing knowledge-action landscape.
Disinfection Methods and Survival of SARS-CoV-2 in the Environment and Contaminated Materials: A Bibliometric Analysis
Authors: Al-Gheethi Adel , Al-Sahari Mohammed , Abdul Malek Marlinda , Noman Efaq , Al-Maqtari Qais , Mohamed Radin , Talip Balkis A. , Alkhadher Sadeq , Hossain Md. Sohrab
Published: September 2020

The presence of SARS-CoV-2 in sewage and water resources has been used as an indication for the possible occurrence of the virus among communities and for its potential of transmission among humans through the surrounding environment or water resources. In order to reduce the transmission of SARS-CoV-2, contaminated surfaces should be disinfected frequently by using an effective disinfectant. The present review discusses a bibliometric analysis of the global SARS-CoV-2 research and focuses mainly on reviewing the efficiency of the most traditional disinfection technologies. The disinfection methods reviewed include those for hospitals’ or medical facilities’ wastewater, contaminated surfaces, and contaminated masks. The elimination of the virus based on the concept of sterility assurance level (SAL) is also discussed. In addition, the chemical disinfectants that are currently used, as well as their temporary efficiency, are also reviewed. The different technologies that are globally used for disinfection processes during the COVID-19 pandemic are shown. However, more advanced technologies, such as nanotechnology, might have more potential for higher inactivation effectiveness against SARS-CoV-2.
Evaluating the Global State of Ecosystems and Natural Resources: Within and Beyond the SDGs
Authors: Dickens Christopher , McCartney Matthew , Tickner David , Harrison Ian J. , Pacheco Pablo , Ndhlovu Brown
Published: September 2020

The Sustainable Development Goals (SDGs) purport to report holistically on progress towards sustainability and do so using more than 231 discrete indicators, with a primary objective to achieve a balance between the environment, social and economic aspects of development. The research question underpinning the analyses presented in this paper is: are the indicators in the SDGs sufficient and fit for purpose to assess the trajectory of natural resources towards sustainability? We extracted the SDG indicators that monitor the state of natural resources, or alternately support policy or governance for their protection, and determined whether these are adequate to provide the essential data on natural resources to achieve the aims of the SDGs. The indicators are clustered into four natural resource categories—land, water (both marine and freshwater), air and biodiversity. Indicators for monitoring land resources show that the most comprehensive land resource indicator for degraded land is not fully implemented and that missing from land monitoring is an evaluation of vegetation health outside of forests and mountains, the condition of soils, and most importantly the overall health of terrestrial ecosystems. Indicators for monitoring water resources have substantial gaps, unable to properly monitor water quality, water stress, many aspects of marine resources and, most significantly, the health of fresh and salt water ecosystems. Indicators for monitoring of air have recently become more comprehensive, but linkage to IPCC results would benefit both programs. Monitoring of biodiversity is perhaps the greatest weakness of the SDG Agenda, having no comprehensive assessment even though narrow aspects are monitored. Again, deliberate linkages to other global biodiversity programs (e.g., CBD and the Post-2020 Biodiversity Framework, IPBES, and Living Planet) are recommended on condition that data can be defined at a country level. While the SDG list of indicators in support of natural resource is moderately comprehensive, it lacks holistic monitoring in relation to evaluation of ecosystems and biodiversity to the extent that these missing but vital measures of sustainability threaten the entire SDG Agenda. In addition, an emerging issue is that even where there are appropriate indicators, the amount of country-level data remains inadequate to fully evaluate sustainability. This signals the delicate balance between the extent and complexity of the SDG Agenda and uptake at a country level.
Consumer Preferences for Sustainable Product Attributes and Farm Program Features
Authors: Yue Chengyan , Lai Yufeng , Wang Jingjing , Mitchell Paul
Published: September 2020

Previous literature primarily focused on consumers’ preference for specific sustainable attributes, such as a product being organic, eco-friendly, locally grown, and fair trade. Little is known about consumers’ preference for sustainable program features. We conduct two online choice experiments with U.S. consumers and find that consumers consistently care about farmers’ engagements in sustainable programs, and they are willing to pay a price premium for products from such programs. Consumers also value promoting science in sustainability, establishing concrete measurements of sustainability, and communicating sustainable practices with consumers and downstream industries. We apply the latent class logit model to investigate the potential segmentation of consumers. Three consumer segments are identified based on participants’ heterogeneity in preferences. Our research provides useful information for designing new sustainability programs.
Setbacks to IoT Implementation in the Function of FMCG Supply Chain Sustainability during COVID-19 Pandemic
Authors: Končar Jelena , Grubor Aleksandar , Marić Radenko , Vučenović Sonja , Vukmirović Goran
Published: September 2020

One of the basic measures of the World Health Organization (WHO) in the fight against the COVID-19 pandemic is a lockdown policy with reduced contacts and physical distance. This presents a challenge, especially for fast-moving-consumer-goods (FMCG) supply chains, which are characterized by a large number of physical contacts between employees in production, physical distribution, wholesale, and retail. One of the ways to comply with the prescribed measures with the smooth functioning of the supply chain is the complete digitalization and automation of all business activities and operations based on the application of the Internet of Things (IoT). In this regard, this paper aims to analyze the setbacks to the digitalization of business processes and the sustainability of the FMCG supply chain based on the implementation of IoT. The research has been conducted among the participants in the standardization chain in the sectors of production, physical distribution, wholesale, and retail of FMCG in the Western Balkans region during the COVID-19 pandemic. The results showed significant differences between business sectors in terms of the intensity of setbacks to successful IoT implementation. Based on the obtained results, a set of measures and incentives was proposed that the competent institutions and the management of the FMCG supply chain should apply to encourage the digitalization process. Suggestions for future research are given in the paper.
Development of Sustainable Heterogeneous Catalysts for the Photocatalytic Treatment of Effluents
Authors: Sales Herbet B. , Menezes Romualdo R. , Neves Gelmires A. , Souza João J. N. de , Ferreira Jailson M. , Chantelle Laís , Menezes de Oliveira André L. , Lira Hélio de L.
Published: September 2020

The inadequate discharge of effluents from different sources without prior treatment can impact the characteristics of soil and water, which reflect serious environmental problems. Advanced oxidative processes (AOP) appear as a viable alternative for environmental remediation, including wastewater treatment. Herein, α-MoO3 and α-Fe2O3 semiconductors were synthesized at low temperature by a Pechini-based method and then applied in photocatalysis. The catalytic efficiency was performed under visible light toward the degradation of an organic persistent pollutant (Rhodamine B dye, RhB), commonly present in industries wastewater. The results indicated that the synthesized α-MoO3 or α-Fe2O3 photocatalysts presented a pronounced activity and promoted an efficient RhB degradation after 15 min of reaction. α-MoO3 had a degradation efficiency of 93% and 98%, while α-Fe2O3 showed 67% and 100% RhB degradation without and with the addition of H2O2, respectively. These results suggest that the synthesized oxides have high oxi-reductive capacity, which can be used for a fast and effective photodegradation of RhB and other organic persistent pollutants to minimize environmental impacts.
On the Potential Impacts of Smart Traffic Control for Delay, Fuel Energy Consumption, and Emissions: An NSGA-II-Based Optimization Case Study from Dhahran, Saudi Arabia
Authors: Al-Turki Mohammed , Jamal Arshad , Al-Ahmadi Hassan M. , Al-Sughaiyer Mohammed A. , Zahid Muhammad
Published: September 2020

Intelligent traffic control at urban intersections is vital to ensure efficient and sustainable traffic operations. Urban road intersections are hotspots of congestion and traffic accidents. Poor traffic management at these locations could cause numerous issues, such as longer travel time, low travel speed, long vehicle queues, delays, increased fuel consumption, and environmental emissions, and so forth. Previous studies have shown that the mentioned traffic performance measures or measures of effectiveness (MOEs) could be significantly improved by adopting intelligent traffic control protocols. The majority of studies in this regard have focused on mono or bi-objective optimization with homogenous and lane-based traffic conditions. However, decision-makers often have to deal with multiple conflicting objectives to find an optimal solution under heterogeneous stochastic traffic conditions. Therefore, it is essential to determine the optimum decision plan that offers the least conflict among several objectives. Hence, the current study aimed to develop a multi-objective intelligent traffic control protocol based on the non-dominated sorting genetic algorithm II (NSGA-II) at isolated signalized intersections in the city of Dhahran, Kingdom of Saudi Arabia. The MOEs (optimization objectives) that were considered included average vehicle delay, the total number of vehicle stops, average fuel consumption, and vehicular emissions. NSGA-II simulations were run with different initial populations. The study results showed that the proposed method was effective in optimizing considered performance measures along the optimal Pareto front. MOEs were improved in the range of 16% to 23% compared to existing conditions. To assess the efficacy of the proposed approach, an optimization analysis was performed using a Synchro traffic light simulation and optimization tool. Although the Synchro optimization resulted in a relatively lower signal timing plan than NSGA-II, the proposed algorithm outperformed the Synchro optimization results in terms of percentage reduction in MOE values.
Human-Centered Design as an Approach to Create Open Educational Resources
Authors: Garcia-Lopez Carles , Mor Enric , Tesconi Susanna
Published: September 2020

Open educational resources (OER) play an important role in teaching and learning, especially in lifelong learning. Educational resources should be created in a way that addresses lifelong learners’ needs. Human-centered design (HCD) is a design perspective and an iterative process that involves users in all phases of the process. Thus, an HCD approach can provide relevant advantages when creating OER for lifelong learning. This work presents the Design Toolkit as a case study of digital open educational contents for design education that has been created following an HCD process. The orientation of the Design Toolkit is to provide users OER in a tool format rather than in a traditional manner. The main goal of this research is to contribute to the understanding of how HCD impacts OER creation. The research focuses on teachers, assessing the Design Toolkit content organization and analyzing teacher adoption and usage of the resources. The HCD approach fosters teachers’ satisfaction, promotes OER adoption and provides new design requirements for a future iteration of the HCD process. The results show that designing OER involving users through an HCD approach sets the focus adequately on their needs and limitations. Teachers feel satisfied with the Design Toolkit, fostering the adoption of OER in different educational contexts. Finally, users’ involvement in the whole HCD process points out design and educational requirements for future work.
A Digital Coach Promoting Healthy Aging among Older Adults in Transition to Retirement: Results from a Qualitative Study in Italy
Authors: Santini Sara , Galassi Flavia , Kropf Johannes , Stara Vera
Published: September 2020

Global aging and increasing multimorbidity are questioning the sustainability of healthcare systems. Healthy aging is at the top of the world political agenda, as a possible means for hindering the collapse of care systems. In the aging process, the transition to retirement can lead to an improvement or a deterioration of physical and psychological health. Digital health coaching technology can support older adults at this stage, but what must be the role of such a solution in promoting healthy aging and shaping sustainable care? This qualitative study, carried out in Italy in 2019, involved 15 older workers, retirees, and colleagues. Based on a user-centered design approach, this study aims at gathering older adults’ feelings on a digital health coaching technology for exploring this solution’s potential in promoting healthy aging. Findings highlighted that the digital health coach may help older adults improve several health determinants, e.g., physical activity, cognitive capabilities, and social life, but it can also entail the risk of stigma and break people’s privacy. The latter can be guaranteed by technology customization and codesign. Further research on the digital health coach benefits to boost healthy aging is needed to understand its potential for shaping future sustainable healthcare.
Towards Circular Economy in the Agri-Food Sector. A Systematic Literature Review
Authors: Esposito Benedetta , Sessa Maria Rosaria , Sica Daniela , Malandrino Ornella
Published: September 2020

Over the last decade, the unsustainability of the current economic model, based on the so-called take-make-dispose paradigm, has emerged. In particular, the agro-food sector (AFS) has been severely affected by such problems as resource scarcity and food loss and waste generation along the supply chain. In addition, climate change and biodiversity loss have helped to define an imperative paradigm shift towards a circular economy. Recently, with the publication of Sustainable Development Goals (SDGs), the scientific research examining the adoption of circular economy (CE) models and tools has increased. In this context, the importance of shifting towards a circular economy has become urgent. In this paper, a systematic literature review (SLR) was performed to investigate the state-of-the-art research related to the adoption of circular economy models and tools along the agro-food supply chain. Furthermore, this review highlights that, due to the complexity of the agri-food supply chain, it is almost utopian to define a unique circular economy model for the whole sector. In addition, it emerges that future researches should be concentrated on the integration of different stages of the supply chain with circular economy models and tools in order to create a closed-loop agri-food system.
Economic Analysis of Geopolymer Brick Manufacturing: A French Case Study
Authors: Youssef Nicolas , Lafhaj Zoubeir , Chapiseau Christophe
Published: September 2020

This paper presents an economic analysis of manufacturing geopolymer bricks for use in the construction sector. The manufacturing processes of both geopolymer bricks and traditional fired bricks were investigated. For this study, we collected and analyzed all phases of geopolymer brick production from the extraction of raw materials to storage. Seven formulations of geopolymer bricks based on clay and waste bricks were analyzed. We considered the cost of raw materials and logistics operations in the production line of brick manufacturing. The results of this study prove that the manufacturing cost of geopolymer bricks based on clay provides an economic gain of 5% compared to fired bricks for the same compressive strength of 20 MPa. In the case of waste bricks, for the same production cost, the compressive strength of the geopolymer bricks is double that of fired bricks. Hence, this study shows the economic interest in the industrial production of geopolymer bricks. It also confirms that future research is needed that focuses on necessary changes to the current industrial production chain required for the manufacture of geopolymer bricks.
Long-Term Temporal Analysis of Psychoacoustic Parameters of the Acoustic Environment in a University Campus Using a Wireless Acoustic Sensor Network
Authors: Montoya-Belmonte José , Navarro Juan M.
Published: September 2020

Higher education institution campuses have been growing in size to become small cities that encompass all the infrastructures and offer many services to the university community. In order to achieve sustainable growth, it is necessary to take into account different environmental aspects, including the acoustic environment. In this paper, the application of a wireless acoustic sensor network is proposed for the long-term analysis of psychoacoustic parameters of the acoustic environment in a university campus. A network of acoustic nodes is designed and four nodes are deployed at both indoor and outdoor locations of the campus for environmental noise monitoring. A measurement campaign has been running continuously for nine months, including three months in which the state of emergency due to COVID-19 pandemic was established and the university switched to online classes. Acquired and stored data include not only the basic sound level parameters, e.g., equivalent or percentile sound pressure levels, but also psychoacoustic parameters, that are more related with noise assessment of the environment, e.g., loudness or sharpness sound levels. After analysing the acoustic environment of the campus through the complete set of nodes, results show two zones with higher noise indicators where some action plan should be developed. A detailed analysis of the temporal evolution of noise levels in these acoustically saturated zones is done, comparing values between a period of regular activity and the period of state of emergency. Different frequency distributions of sound levels are detected between day and evening periods providing a better insight into the noise sources. The results also show that binaural psychoacoustic annoyance values are correlated with loudness values in these locations, however sharpness values have minor effects.
Social Innovation, Societal Change, and the Role of Policies
Authors: Lukesch Robert , Ludvig Alice , Slee Bill , Weiss Gerhard , Živojinović Ivana
Published: September 2020

Political frameworks and policies have a strong influence on the institutional ecosystem and on governance patterns, which in turn shape the operational space of civil society initiatives. This article aims to explore the social and institutional conditions and policy initiatives that foster or hinder social innovation and the pathways leading from social innovation to institutional change through to actual impacts on policies and political frameworks, in order to understand how policymakers can encourage and enable social innovation. The article builds on an extensive empirical background to develop a heuristic model to facilitate decision making for a policy environment propitious for the emergence of social innovation. The resulting model sets up a triadic configuration of (i) a committed core of key actors, (ii) the benevolent shadow of hierarchy represented by public actors, and (iii) multifunctional and malleable intermediary support structures for a successful development of social innovation initiatives. The model is discussed and validated by reference to three in-depth case studies from differing institutional settings. We conclude that policy should recognize that social innovation will achieve most when the triadic relationships between the state, intermediary organizations, and local actors are working together synergistically.