bluuebunny/crossref_metadata_2025 huggingface dataset:
Assessment of RTK Quadcopter and Structure-from-Motion Photogrammetry for Fine-Scale Monitoring of Coastal Topographic Complexity
Authors: Bertin Stéphane , Stéphan Pierre , Ammann Jérôme
Published: April 2022

Advances in image-based remote sensing using unmanned aerial vehicles (UAV) and structure-from-motion (SfM) photogrammetry continue to improve our ability to monitor complex landforms over representative spatial and temporal scales. As with other water-worked environments, coastal sediments respond to shaping processes through the formation of multi-scale topographic roughness. Although this topographic complexity can be an important marker of hydrodynamic forces and sediment transport, it is seldom characterized in typical beach surveys due to environmental and technical constraints. In this study, we explore the feasibility of using SfM photogrammetry augmented with an RTK quadcopter for monitoring the coastal topographic complexity at the beach-scale in a macrotidal environment. The method had to respond to resolution and time constraints for a realistic representation of the topo-morphological features from submeter dimensions and survey completion in two hours around low tide to fully cover the intertidal zone. Different tests were performed at two coastal field sites with varied dimensions and morphologies to assess the photogrammetric performance and eventual means for optimization. Our results show that, with precise image positioning, the addition of a single ground control point (GCP) enabled a global precision (RMSE) equivalent to that of traditional GCP-based photogrammetry using numerous and well-distributed GCPs. The optimal model quality that minimized vertical bias and random errors was achieved from 5 GCPs, with a two-fold reduction in RMSE. The image resolution for tie point detection was found to be an important control on the measurement quality, with the best results obtained using images at their original scale. Using these findings enabled designing an efficient and effective workflow for monitoring coastal topographic complexity at a large scale.
Few-Shot Learning for Radar Emitter Signal Recognition Based on Improved Prototypical Network
Authors: Huang Jing , Wu Bin , Li Peng , Li Xiao , Wang Jie
Published: April 2022

In recent years, deep learning has been widely used in radar emitter signal identification and has significantly increased recognition rates. However, with the emergence of new institutional radars and an increasingly complex electromagnetic environment, the collection of high-quality signals becomes difficult, leading to a result that the amount of some signal types we own are too few to converge a deep neural network. Moreover, in radar emitter signal identification, most existing networks ignore the signal recognition of unknown classes, which is of vital importance for radar emitter signal identification. To solve these two problems, an improved prototypical network (IPN) belonging to metric-based meta-learning is proposed. Firstly, a reparameterization VGG (RepVGG) net is used to replace the original structure that severely limits the model performance. Secondly, we added a feature adjustment operation to prevent some extreme or unimportant samples from affecting the prototypes. Thirdly, open-set recognition is realized by setting a threshold in the metric module.
Uncertainty Analysis in SAR Sea Surface Wind Speed Retrieval through C-Band Geophysical Model Functions Inversion
Authors: Rana Fabio Michele , Adamo Maria
Published: April 2022

The purpose of the study is to assess the suitability of synthetic aperture radar (SAR) data to provide sea surface wind (SSW) fields along with a spatial distribution of both SSW speed and direction uncertainty. A simple methodology based on geophysical model function (GMF) inversion to obtain a spatial distribution of both SSW speed and its uncertainty is proposed. Exploiting a dataset of Sentinel-1 images, a sensitivity analysis of the SSW speed uncertainty is carried out on both the uncertainties and the mean values of SAR normalised radar cross section (NRCS), incidence angle and SSW direction, at different spatial scales. The results show that SSW speed uncertainty significantly increases with wind vector cell (WVC) dimension. Moreover, the dominant contribution to the SSW speed uncertainty due to both NRCS and SSW direction uncertainty must always be taken into account. A better precision and accuracy in the estimation of SSW speed and its uncertainty is evidenced by C-band model 7 (CMOD7) GMF rather than the C-band model 5.N (CMOD5.N). To evaluate the results of SSW retrievals, wind data from the European Centre for Medium-Range Weather Forecasts (ECMWF) model are also exploited for comparisons. Findings indicate a high correlation between the uncertainty from SAR estimations and that from the comparison of SAR vs. ECMWF.
Determining the Capability of the Tree-Based Pipeline Optimization Tool (TPOT) in Mapping Parthenium Weed Using Multi-Date Sentinel-2 Image Data
Authors: Kiala Zolo , Odindi John , Mutanga Onisimo
Published: April 2022

The Tree-based Pipeline Optimization Tool (TPOT) is a state-of-the-art automated machine learning (AutoML) approach that automatically generates and optimizes tree-based pipelines using a genetic algorithm. Although it has been proven to outperform commonly used machine techniques, its capability to handle high-dimensional datasets has not been investigated. In vegetation mapping and analysis, multi-date images are generally high-dimensional datasets that contain embedded information, such as phenological and canopy structural properties, known to enhance mapping accuracy. However, without the implementation of a robust classification algorithm or a feature selection tool, the large sets and the presence of redundant variables in multi-date images can impede accurate and efficient landscape classification. Hence, this study sought to test the efficacy of the TPOT on a multi-date Sentinel-2 image to optimize the classification accuracies of a landscape infested by a noxious invasive plant species, the parthenium weed (Parthenium hysterophorus). Specifically, the models created from the multi-date image, using the TPOT and an algorithm system that combines feature selection and the TPOT, dubbed “ReliefF-Svmb-EXT-TPOT”, were compared. The results showed that the TPOT could perform well on data with large feature sets, but at a computational cost. The overall accuracies were 91.9% and 92.6% using the TPOT and ReliefF-Svmb-EXT-TPOT models, respectively. The study findings are crucial for automated and accurate mapping of parthenium weed using high-dimensional geospatial datasets with limited human intervention.
Study of Coastal Effects Relevant for Offshore Wind Energy Using Spaceborne Synthetic Aperture Radar (SAR)
Authors: Djath Bughsin’ , Schulz-Stellenfleth Johannes , Cañadillas Beatriz
Published: April 2022

Coastal wind speed gradients relevant for offshore windfarming are analysed based on synthetic aperture radar (SAR) data. The study concentrates on situations with offshore wind directions in the German Bight using SAR scenes from the European satellites Sentinel-1A and Sentinel-1B. High resolution wind fields at 10 m height are derived from the satellite data set and respective horizontal wind speed gradients are investigated up to about 170 km offshore. The wind speed gradients are classified according to their general shape with about 60% of the cases showing an overall increase of wind speeds with growing distance from the coast. About half of the remaining cases show an overall wind speed decrease and the other half a decrease with a subsequent increase at larger distances from the coast. An empirical model is fitted to the horizontal wind speed gradients, which has three main parameters, namely, the wind speed over land, the equilibrium wind speed over sea far offshore, and a characteristic adjustment length scale. For the cases with overall wind speed increase, a mean absolute difference of about 2.6 m/s is found between wind speeds over land and wind speeds far offshore. The mean normalised wind speed increase with respect to the land conditions is estimated as 40%. In terms of wind power density at 10 m height this corresponds to an absolute average growth by 0.3 kW/m2 and a normalised increase by 160%. The distance over which the wind speed grows to 95% of the maximum wind speed shows large variations with maximum above 170 km and a mean of 67 km. The impact of the atmospheric boundary layer stability on horizontal wind speed gradients is investigated using additional information on air and sea temperature differences. The absolute SAR-derived wind speed increase offshore is usually higher in unstable situations and the respective adjustment distance is shorter. Furthermore, we have found atypical cases with a wind speed decrease offshore to be often connected to stable atmospheric conditions. A particular low-level jet (LLJ) situation is analysed in more detail using vertical wind speed profiles from a wind LIDAR system.
Multi-Frequency Interferometric Coherence Characteristics Analysis of Typical Objects for Coherent Change Detection
Authors: Wang Zhongbin , Wang Yachao , Wang Bingnan , Xiang Maosheng , Wang Rongrong , Xu Weidi , Song Chong
Published: April 2022

This paper focuses on the study of a multi-frequency interferometric coherence characteristics analysis of typical objects for coherent change detection. Coherent change detection utilizes the phase difference between two or more SAR images to detect potential changes in the scene. It makes a difference in civilian and military applications. However, the relationship between the coherence of typical objects and SAR frequency has not been fully studied, which restricts the quality of the detection results. To address this problem, this paper conducts research on the relationship between the coherence of typical objects and SAR frequency, and the coherence characteristics are obtained through statistical analysis. In order to illustrate the relationship more clearly, the actual experimental data obtained by the DVD-InSAR system developed by the Aerospace Information Research Institute, Chinese Academy of Sciences, are utilized. The experimental results show that the coherence characteristics of typical objects are different, and this finding can provide strong support for developing change-detection applications.
Remote Sensing and Spatial Analysis for Land-Take Assessment in Basilicata Region (Southern Italy)
Authors: Santarsiero Valentina , Nolè Gabriele , Lanorte Antonio , Tucci Biagio , Cillis Giuseppe , Murgante Beniamino
Published: April 2022

Land use is one of the drivers of land-cover change (LCC) and represents the conversion of natural to artificial land cover. This work aims to describe the land-take-monitoring activities and analyze the development trend in test areas of the Basilicata region. Remote sensing is the primary technique for extracting land-use/land-cover (LULC) data. In this study, a new methodology of classification of Landsat data (TM–OLI) is proposed to detect land-cover information automatically and identify land take to perform a multi-temporal analysis. Moreover, within the defined model, it is crucial to use the territorial information layers of geotopographic database (GTDB) for the detailed definition of the land take. All stages of the classification process were developed using the supervised classification algorithm support vector machine (SVM) change-detection analysis, thus integrating the geographic information system (GIS) remote sensing data and adopting free and open-source software and data. The application of the proposed method allowed us to quickly extract detailed land-take maps with an overall accuracy greater than 90%, reducing the cost and processing time.
Comparison of Scanning LiDAR with Other Remote Sensing Measurements and Transport Model Predictions for a Saharan Dust Case
Authors: Zhang Hengheng , Wagner Frank , Saathoff Harald , Vogel Heike , Hoshyaripour Gholamali , Bachmann Vanessa , Förstner Jochen , Leisner Thomas
Published: April 2022

The evolution and the properties of a Saharan dust plume were studied near the city of Karlsruhe in southwest Germany (8.4298°E, 49.0953°N) from 7 to 9 April 2018, combining a scanning LiDAR (90°, 30°), a vertically pointing LiDAR (90°), a sun photometer, and the transport model ICON-ART. Based on this Saharan dust case, we discuss the advantages of a scanning aerosol LiDAR and validate a method to determine LiDAR ratios independently. The LiDAR measurements at 355 nm showed that the dust particles had backscatter coefficients of 0.86 ± 0.14 Mm−1 sr−1, extinction coefficients of 40 ± 0.8 Mm−1, a LiDAR ratio of 46 ± 5 sr, and a linear particle depolarisation ratio of 0.27 ± 0.023. These values are in good agreement with those obtained in previous studies of Saharan dust plumes in Western Europe. Compared to the remote sensing measurements, the transport model predicted the plume arrival time, its layer height, and its structure quite well. The comparison of dust plume backscatter values from the ICON-ART model and observations for two days showed a correlation with a slope of 0.9 ± 0.1 at 355 nm. This work will be useful for future studies to characterise aerosol particles employing scanning LiDARs.
Performance Evaluation of Feature Matching Techniques for Detecting Reinforced Soil Retaining Wall Displacement
Authors: Ha Yong-Soo , Lee Jeongki , Kim Yun-Tae
Published: April 2022

Image registration technology is widely applied in various matching methods. In this study, we aim to evaluate the feature matching performance and to find an optimal technique for detecting three types of behaviors—facing displacement, settlement, and combined displacement—in reinforced soil retaining walls (RSWs). For a single block with an artificial target and a multiblock structure with artificial and natural targets, five popular detectors and descriptors—KAZE, SURF, MinEigen, ORB, and BRISK—were used to evaluate the resolution performance. For comparison, the repeatability, matching score, and inlier matching features were analyzed based on the number of extracted and matched features. The axial registration error (ARE) was used to verify the accuracy of the methods by comparing the position between the estimated and real features. The results showed that the KAZE method was the best detector and descriptor for RSWs (block shape target), with the highest probability of successfully matching features. In the multiblock experiment, the block used as a natural target showed similar matching performance to that of the block with an artificial target attached. Therefore, the behaviors of RSW blocks can be analyzed using the KAZE method without installing an artificial target.
Simulating Future LUCC by Coupling Climate Change and Human Effects Based on Multi-Phase Remote Sensing Data
Authors: Huang Zihao , Li Xuejian , Du Huaqiang , Mao Fangjie , Han Ning , Fan Weiliang , Xu Yanxin , Luo Xin
Published: April 2022

Future land use and cover change (LUCC) simulations play an important role in providing fundamental data to reveal the carbon cycle response of forest ecosystems to LUCC. Subtropical forests have great potential for carbon sequestration, yet their future dynamics under natural and human influences are unclear. Zhejiang Province in China is an important distribution area for subtropical forests. For forest management, it is of great significance to explore the future dynamic changes of subtropical forests in Zhejiang. As a popular LUCC spatial simulation model, the cellular automata (CA) model coupled with machine learning and LUCC quantitative demand models such as system dynamics (SD) can achieve effective LUCC simulation. Therefore, we first integrated a back propagation neural network (BPNN), a CA, and a SD model as a BPNN_CA_SD (BCS) coupled model for future LUCC simulation and then designed a slow development scenario (SD_Scenario), a harmonious development scenario (HD_Scenario), a baseline development scenario (BD_Scenario), and a fast development scenario (FD_Scenario), combining climate change and human disturbance. Thirdly, we obtained future land-use patterns in Zhejiang Province from 2014 to 2084 under multiple scenarios, and finally, we analyzed the temporal and spatial changes of land use and discussed the subtropical forest dynamics of the future. The results showed the following: (1) The overall accuracy was approximately 0.8, the kappa coefficient was 0.75, and the figure of merit (FOM) value was over 28% when using the BCS model to predict LUCC, indicating that the model could predict the consistent change of LUCC accurately. (2) The future evolution of the LUCC under different scenarios varied, with the growth of bamboo forests and the decline of coniferous forests in the FD_Scenario being prominent among the forest dynamics changes. Compared with 2014, the bamboo forest in 2084 will increase by 37%, while the coniferous forest will decrease by 25%. (3) Comparing the area and spatial change of the subtropical forests, the SD_Scenario was found to be beneficial for the forest ecology. These results can provide an important decision-making reference for land-use planning and sustainable forest development in Zhejiang Province.
A New Method for Generating the SMOPS Blended Satellite Soil Moisture Data Product without Relying on a Model Climatology
Authors: Yin Jifu , Zhan Xiwu , Liu Jicheng , Ferraro Ralph R.
Published: April 2022

Soil moisture operational product system (SMOPS) is developed by National Oceanic and Atmospheric Administration (NOAA) to provide the real-time blended soil moisture (SM) for numeric weather prediction and national water model applications. However, all individual satellite SM data ingested into the current operational SMOPS are scaled to global land data assimilation system (GLDAS) 0–10 cm SM climatology before the combination. As a result, the useful information from the original microwave SM retrievals could be lost, and the GLDAS model errors could be brought into the final SMOPS blended product. In this paper, we propose to scale the individual SM retrievals to the soil moisture active passive (SMAP) data through building regression models. The rescaled individual SM data and the SMAP observations then have similar climatology and dynamics, which allows producing the SMOPScdr (distinguishing with the current operational SMOPSopr) data using an equal-weight averaging approach. With respect to the in situ SM measurements, the developed SMOPScdr is more successful tracking the surface SM status than the individual satellite SM products with significantly decreased errors. The proposed method also preserves the climatology of the reference SMAP data for the period when SMAP is not available, allowing us to produce a long-term SMOPScdr data product.
Adaptive Machine Learning Based Distributed Denial-of-Services Attacks Detection and Mitigation System for SDN-Enabled IoT
Authors: Aslam Muhammad , Ye Dengpan , Tariq Aqil , Asad Muhammad , Hanif Muhammad , Ndzi David , Chelloug Samia Allaoua , Elaziz Mohamed Abd , Al-Qaness Mohammed A. A. , Jilani Syeda Fizzah
Published: April 2022

The development of smart network infrastructure of the Internet of Things (IoT) faces the immense threat of sophisticated Distributed Denial-of-Services (DDoS) security attacks. The existing network security solutions of enterprise networks are significantly expensive and unscalable for IoT. The integration of recently developed Software Defined Networking (SDN) reduces a significant amount of computational overhead for IoT network devices and enables additional security measurements. At the prelude stage of SDN-enabled IoT network infrastructure, the sampling based security approach currently results in low accuracy and low DDoS attack detection. In this paper, we propose an Adaptive Machine Learning based SDN-enabled Distributed Denial-of-Services attacks Detection and Mitigation (AMLSDM) framework. The proposed AMLSDM framework develops an SDN-enabled security mechanism for IoT devices with the support of an adaptive machine learning classification model to achieve the successful detection and mitigation of DDoS attacks. The proposed framework utilizes machine learning algorithms in an adaptive multilayered feed-forwarding scheme to successfully detect the DDoS attacks by examining the static features of the inspected network traffic. In the proposed adaptive multilayered feed-forwarding framework, the first layer utilizes Support Vector Machine (SVM), Naive Bayes (NB), Random Forest (RF), k-Nearest Neighbor (kNN), and Logistic Regression (LR) classifiers to build a model for detecting DDoS attacks from the training and testing environment-specific datasets. The output of the first layer passes to an Ensemble Voting (EV) algorithm, which accumulates the performance of the first layer classifiers. In the third layer, the adaptive frameworks measures the real-time live network traffic to detect the DDoS attacks in the network traffic. The proposed framework utilizes a remote SDN controller to mitigate the detected DDoS attacks over Open Flow (OF) switches and reconfigures the network resources for legitimate network hosts. The experimental results show the better performance of the proposed framework as compared to existing state-of-the art solutions in terms of higher accuracy of DDoS detection and low false alarm rate.
Impedance Technique-Based Label-Free Electrochemical Aptasensor for Thrombin Using Single-Walled Carbon Nanotubes-Casted Screen-Printed Carbon Electrode
Authors: Park Kyungsoon
Published: April 2022

An impedance technique-based aptasensor for the detection of thrombin was developed using a single-walled carbon nanotube (SWCNT)-modified screen-printed carbon electrode (SPCE). In this work, a thrombin-binding aptamer (TBA) as probe was used for the determination of thrombin, and that was immobilized on SWCNT through π–π interaction. In the presence of thrombin, the TBA on SWCNT binds with target thrombin, and the amount of TBA on the SWCNT surface decreases. The detachment of TBA from SWCNT will be affected by the concentration of thrombin and the remaining TBA on the SWCNT surface can be monitored by electrochemical methods. The TBA-modified SWCNT/SPCE sensing layer was characterized by cyclic voltammetry (CV). For the measurement of thrombin, the change in charge-transfer resistance (Rct) of the sensing interface was investigated using electrochemical impedance spectroscopy (EIS) with a target thrombin and [Fe(CN)6]3− as redox maker. Upon incubation with thrombin, a decrease of Rct change was observed due to the decrease in the repulsive interaction between the redox marker and the electrode surface without any label. A plot of Rct changes vs. the logarithm of thrombin concentration provides the linear detection ranges from 0.1 nM to 1 µM, with a ~0.02 nM detection limit.
A Robust and Accurate Indoor Localization Using Learning-Based Fusion of Wi-Fi RTT and RSSI
Authors: Rizk Hamada , Elmogy Ahmed , Yamaguchi Hirozumi
Published: April 2022

Great attention has been paid to indoor localization due to its wide range of associated applications and services. Fingerprinting and time-based localization techniques are among the most popular approaches in the field due to their promising performance. However, fingerprinting techniques usually suffer from signal fluctuations and interference, which yields unstable localization performance. On the other hand, the accuracy of time-based techniques is highly affected by multipath propagation errors and non-line-of-sight transmissions. To combat these challenges, this paper presents a hybrid deep-learning-based indoor localization system called RRLoc which fuses fingerprinting and time-based techniques with a view of combining their advantages. RRLoc leverages a novel approach for fusing received signal strength indication (RSSI) and round-trip time (RTT) measurements and extracting high-level features using deep canonical correlation analysis. The extracted features are then used in training a localization model for facilitating the location estimation process. Different modules are incorporated to improve the deep model’s generalization against overtraining and noise. The experimental results obtained at two different indoor environments show that RRLoc improves localization accuracy by at least 267% and 496% compared to the state-of-the-art fingerprinting and ranging-based-multilateration techniques, respectively.
Analysis of Marine-Pilot Biometric Data Recordings during Port-Approach Using a Full-Mission Simulator
Authors: Žagar Dejan , Svetina Matija , Brcko Tanja , Perkovič Marko , Dimc Franc , Košir Andrej
Published: April 2022

The purpose of this study is to analyse data from the marine pilots’ bio-sensor readings to determine how experience affects their biometrical response during the port approach. The experiences play a significant role in the participant’s decision-making process and correlate with the repetitions. Through the repetitions of the experimental task, the participants gain experience, which correlates with the biometrical response, e.g., heart rate, electrodermal activity, etc. After exposing the two experience-distinct groups of participants to the same simulated port-approaching task, their collected biometric data is analysed and discussed. The results show that biometrical readings of the less experienced participants typically vary compared to that of the experienced participants, who take the simulated task more seriously. The study also yields insight into the workload process, involving disturbing factors during the task.
Emotion Self-Regulation in Neurotic Students: A Pilot Mindfulness-Based Intervention to Assess Its Effectiveness through Brain Signals and Behavioral Data
Authors: Izhar Lila Iznita , Babiker Areej , Rizki Edmi Edison , Lu Cheng-Kai , Abdul Rahman Mohammad
Published: April 2022

Neuroticism has recently received increased attention in the psychology field due to the finding of high implications of neuroticism on an individual’s life and broader public health. This study aims to investigate the effect of a brief 6-week breathing-based mindfulness intervention (BMI) on undergraduate neurotic students’ emotion regulation. We acquired data of their psychological states, physiological changes, and electroencephalogram (EEG), before and after BMI, in resting states and tasks. Through behavioral analysis, we found the students’ anxiety and stress levels significantly reduced after BMI, with p-values of 0.013 and 0.027, respectively. Furthermore, a significant difference between students in emotion regulation strategy, that is, suppression, was also shown. The EEG analysis demonstrated significant differences between students before and after MI in resting states and tasks. Fp1 and O2 channels were identified as the most significant channels in evaluating the effect of BMI. The potential of these channels for classifying (single-channel-based) before and after BMI conditions during eyes-opened and eyes-closed baseline trials were displayed by a good performance in terms of accuracy (~77%), sensitivity (76–80%), specificity (73–77%), and area-under-the-curve (AUC) (0.66–0.8) obtained by k-nearest neighbor (KNN) and support vector machine (SVM) algorithms. Mindfulness can thus improve the self-regulation of the emotional state of neurotic students based on the psychometric and electrophysiological analyses conducted in this study.
Association between Neighborhood and Racial Composition of Victims on Fatal Police Shooting and Police Violence: An Integrated Review (2000–2022)
Authors: Zare Hossein , Meyerson Nicholas S. , Delgado Paul , Spencer Michelle , Gaskin Darrell J. , Thorpe Roland J.
Published: April 2022

Police violence is a multidimensional issue that requires consideration of the violent events and how these events reflect systemic oppression. Violence and policing practices are influenced by race and ethnicity, place/neighborhood, structural inequality, and racism. We performed an integrated literature review to critically evaluate the current evidence, focusing on the racial composition of communities and neighborhoods and its association with police-involved violence and fatal shootings between 2000 and 2022. We used Scopus and Web of Science to include peer-reviewed articles in English that studied racial/ethnic differences in police-involved violence in the United States between January 2000 and February 2022. We excluded prior systematic reviews, meta-analyses, and articles on drug-related arrests. Using a PRISMA approach from 651 identified articles, we included 37 articles. Our findings showed that racial/ethnic minorities are disproportionately stopped, experience a higher probability of arrest, and are more commonly subjected to police-involved fatal shootings. Victims are more likely to live in neighborhoods with lower income and distressed communities of color, higher poverty ratios, and the highest levels of criminal violence. Citizens reporting of negative interactions with police is strongly associated with race/ethnicity. Maintaining the highest standards of professional practice consistent with the law and protections guaranteed by the Constitution may reduce police violence. In addressing police violence, policymakers not only need to consider the multidisciplinary nature of vulnerability to address the needs of vulnerable populations and create a collaborative environment but also to control police violence. Considering community-based approaches, encouraging training to interact with minority individuals, and adjusting the racial composition of the police officers by the racial composition of communities are other strategies; more importantly, prioritizing strategies to reduce social inequality and structural racism are crucial.
The Central Role of Soil Organic Matter in Soil Fertility and Carbon Storage
Authors: Gerke Jörg
Published: April 2022

The aim of the paper is to give an overview on the chemistry of soil organic carbon (SOC) affecting nutrient availability, the emission of greenhouse gases and detoxifying harmful substances in soil. Humic substances represent the stable part of SOC, accounting for between 50 and more than 80% of organically bound carbon in soil. Humic substances strongly affect the soil solution concentration of several plant nutrients and may increase P-, Fe-, and Cu- solubility, thereby increasing their plant availability. Soil organic carbon, mainly humic substances, can detoxify monomeric Al in acid soils, can strongly bind toxic heavy metals, making them unavailable to the plant roots, and may strongly bind a vast variety of harmful organic pollutants. Increasing SOC is an important goal in agriculture. The inclusion of mixtures of semi-perennial plant species and cultivars may strongly increase SOC and humic substance content in soils. To increase SOC, farmyard manure and its rotted or composted forms are superior compared to the separate application of straw and slurry to soil. The storage of carbon, mainly in organic form, in soils is very important in the context of the emission of greenhouse gases. Worldwide, soils release about 10 times more greenhouse gases compared to fossil fuel combustion. Small increments in SOC worldwide will strongly affect the concentration of atmospheric CO2. The public discussion on soil fertility and greenhouse gas emissionshas been politically controlled in a way that leaves the important and positive contribution of soil organic carbon and mainly humic substances partly misinterpreted and partly underestimated.
Comparison between Dry-Land and Swimming Priming on 50 m Crawl Performance in Well-Trained Adolescent Swimmers
Authors: Zaras Nikolaos , Apostolidis Andreas , Kavvoura Angeliki , Hadjicharalambous Marios
Published: April 2022

The purpose of the study was to investigate the effect of dry-land priming (DLP) versus swimming priming (SP) on the 50 m crawl performance of well-trained adolescent swimmers. Thirteen adolescent swimmers were randomly assigned to perform either a DLP or SP 24 h prior to a 50 m sprint crawl time-trial. Baseline measurements included a 50 m sprint crawl time-trial as a control (C) condition, the evaluation of body composition, countermovement jump (CMJ), isometric peak torque (IPT), and rate of torque development (RTD). Rating of perceived exertion (RPE) was obtained following the DLP and SP programs. Both DLP and SP significantly decreased the 50 m crawl time-trial, by −2.51 ± 2.43% and −2.59 ± 1.89% (p < 0.01), respectively, compared with the C time-trial. RPE was not different between DLP and SP (p = 0.919). CMJ performance remained unchanged after DLP and SP programs compared with the C trial (p > 0.05). The percentage decrease in the 50 m crawl after DLP was significantly correlated with the percentage decrease in the 50 m crawl following SP (r = 0.720, p = 0.006). CMJ power, lean body mass, IPT, and RTD were significantly correlated with 50 m crawl performance. These results suggest that both DLP and SP strategies, when applied 24 h prior to a 50 m crawl time-trial, may enhance performance in well-trained adolescent swimmers.
Guidelines for the Implementation of Mass Customization in Affordable House-Building Projects
Authors: T. Formoso Carlos , Tillmann Patrícia A. , Hentschke Cynthia dos S.
Published: April 2022

Mass customization (MC) is a business strategy that stands for the ability to develop high value-added products within short time frames and at relatively low costs. It emerged from the manufacturing industry and has been applied to several industrial sectors, including housing. However, the segment of affordable housing programs in developing countries has been criticized for having a high degree of product standardization, and failure to meet customers’ specific needs. The aim of this paper is to propose guidelines for implementing mass customization in affordable house-building projects. It is based on a design science research study carried out in a small-sized company, which explored the possibility of adopting mass customization ideas to offer some flexibility for customers while maintaining low costs. The main contributions of this research study are concerned with expanding the current understanding of the components that enable MC to be implemented in the context of house-building companies that adopt traditional construction technologies. From a practical perspective, this investigation outlines several practices that can be introduced for the implementation of MC strategies at a relatively low cost by small-sized house-building companies.
Enabling Sustainable Lifestyles in New Urban Areas: Evaluation of an Eco-Development Case Study in the UK
Authors: Oreskovic Luka , Gupta Rajat
Published: April 2022

This study evaluated the actual environmental behaviours in an eco-development case study in the UK, which was designed to enable more sustainable lifestyles. Data analysis was based on the resident responses to a development-wide questionnaire survey (n = 89), household interviews (n = 12) and waste measurements. Reported energy- and water-saving behaviours were fairly common. The mean waste recycling rates (45% to 60%) were similar to local and national averages, and were below the target of 80%. The mean rates of purchasing organic food (37%), growing food (31%) and meat consumption (in 36% of all meals) indicated that the food behaviours were not more pro-environmental. Car-based modes of transportation were used for 71% of all the reported trips on average, which was higher than the national average, and the target of 55%. Despite these reported behaviours, most of the residents regarded their new lifestyles as more sustainable. This was related to the notion of energy efficiency and low-carbon technologies, rather than changes in behaviour. The findings of this study and similar studies indicated that enabling environmental behaviours in new developments is challenging. New policies need to be more holistic and support the delivery of not only well-performing buildings, but also developments that make sustainable urban living a reality.
Bibliometric Analysis of Multi-Level Perspective on Sustainability Transition Research
Authors: Wang Cheng , Lv Tao , Cai Rongjiang , Xu Jianfeng , Wang Liya
Published: April 2022

The multi-level perspective (MLP) is a prominent framework for transition research. However, few studies have used bibliometrics for conducting a global picture of the MLP research. This study identifies the worldwide trends at three levels: sources, authors, and documents, and uses the bibliometrix based on 757 articles published in WOS and Scopus from 2002 to 2020. The results show that the MLP research literature is proliferating, and the number of journals and countries concerned in this field is increasing. MLP research has mainly focused on transition, sustainability transition, socio-technical transition, energy transition, innovation, and governance; and will increase focus on agency, power, and policy. MLP research will focus on multi-niche, multi-regime, and multi-landscape interactions at the hierarchy levels. The results assist scholars in systematically understanding the current research status, research frontiers, and future trends of MLP from a macro perspective.
Physiological and Biochemical Behaviours and Antioxidant Response of Helianthus annuus under Lanthanum and Cerium Stress
Authors: Dridi Nesrine , Brito Pedro , Bouslimi Houda , Ferreira Renata , Martins-Dias Susete , Caçador Isabel , Sleimi Noomene
Published: April 2022

The continuous progress of global manufacturing and anthropogenic activities has resulted in excessive environmental metallic pollution, particularly with rare earth elements (REEs) which have become a prevalent issue of global concern due to their high toxicity and widespread existence. REEs-contaminated soils could ruin agriculture by inducing plant physiology disturbances in various crops that are considered the principal link of the human food chain. The main purpose of the present work is to assess the phytotoxicity of two light REEs, lanthanum (La) and cerium (Ce), in Helianthus annuus after 14 days of exposure to different concentrations of La and Ce (0, 1, 2.5, 5, and 10 µM). Plants showed different variations in shoot and root lengths at the end of the trial period. The accumulation of photosynthetic pigments, such as chlorophylls and carotenoids, as well as the photosynthetic efficiency, the non-photochemical quenching, the photosynthetically active radiation, and the electron transport rate, increased in the two REE treatments. Hydrogen peroxide significantly increased in all applied concentrations of La and Ce. A significant increase in malondialdehyde content was noticed only when plants were exposed to 2.5 µM La and 10 µM Ce. Results also demonstrated that La and Ce induced an increase in the activity of superoxide dismutase, peroxidase, and catalase (only the highest concentration of La decreased catalase activity). The exposure to different REE concentrations induced the accumulation of La and Ce in the plants, mainly in roots. Helianthus annuus showed an effective resistance behaviour facing La- and Ce-induced stresses.
Reducing NOx Emissions through Microwave Heating of Aftertreatment Systems for Sustainable Transport in the Inland Waterway Sector
Authors: Savu Sorin Vasile , Marin Robert Cristian , David Andrej , Olei Adrian Bebe , Dumitru Ilie , Tarnita Daniela , Maternova Andrea , Savu Ionel Dănuț
Published: April 2022

The inland navigation sector makes a significant contribution to the growth of the global economy as well as to climate change due to pollutants emitted by diesel engines. NOx emissions are very high in port areas where, due to traffic, the ships run at idling regimes. Selective catalytic reduction (SCR) represents one of the most suitable technologies, in terms of cost effectiveness, but does not perform well if the temperature during vessel operation is lower than 180 °C. Microwave technology can support preheating of the ceramic core of SCR in order to increase the temperature towards the optimal interval for the best NOx reduction. Research has focused on coupling a magnetron head to a SCR device in order to evaluate to what extent the technology can meet the requirements of Stage V of the European Directive related to NOx emissions. Measurements of NOx emitted have been performed on engines with 603.5 kW nominal power and 1500 rpm that operate at a lower engine speed (700–1200 rpm) and output power (58–418 kW). The values recorded for emissions using microwave heating of ceramic core of SCR have decreased by 89% for a constant load of engine and idling engine speed.
A Systems Thinking Approach Investigating the Estimated Environmental and Economic Benefits and Limitations of Industrial Hemp Cultivation in Ireland from 2017–2021
Authors: Madden Sinéad , Ryan Alan , Walsh Patrick
Published: April 2022

There may be unrecognised environmental and economic benefits in cultivating industrial hemp for CO2 sequestration in Ireland. By using a Systems Thinking approach, this study aims to answer how industrial hemp, which can sequester between 10 tonnes (t) to 22 t of CO2 emissions per hectare, has been helpful towards carbon sequestration efforts in Ireland. A mixed-methods design combining qualitative and quantitative secondary material is used to inform Behaviour over Time Graphs (BoTGs) to illustrate the data from 2017 to 2021. In 2019 at its peak of hemp cultivation in Ireland the total CO2 emissions from agriculture was 21,156.92 kilotonnes, and the total land cultivated with hemp was 547 hectares which represented an estimated 0.0079% of total land use and 0.011% of agricultural land use. Based on a sequestration rate of between 10 t and 22 t of CO2, industrial hemp had the potential to remove between 5470 t and 24,068 t of CO2 in 2019. The total amount of estimated CO2 sequestrated between 2017 and 2021 was between 14,660 t and 64,504 t of CO2. This represents an estimated contribution in carbon tax equivalent of between €348,805 and €1,534,742, respectively.
Modelling Land Use and Land Cover in the Transboundary Mono River Catchment of Togo and Benin Using Markov Chain and Stakeholder’s Perspectives
Authors: Thiam Sophie , Salas Eric Ariel L. , Hounguè Nina Rholan , Almoradie Adrian Delos Santos , Verleysdonk Sarah , Adounkpe Julien G. , Komi Kossi
Published: April 2022

Integrating both modeling approach and stakeholders’ perspectives to derive past and future trends of land use land cover (LULC) is a key to creating more realistic results on LULC change trajectories and can lead to the implementation of appropriate management measures. This article assessed the past changes of LULC in the Mono River catchment using Landsat images from the years 1986, 2000, 2010, and 2020 by performing Machine Learning Classification Method Random Forest (RF) technique, and using Markov chain method and stakeholder’s perspective to simulate future LULC changes for the years 2030 and 2050. LULC was classified as savanna, cropland, forest, water bodies, and settlement. The results showed that croplands and forests areas declined from 2020 to 2050 with decreases of −7.8% and −1.9%, respectively, a modest increase in settlement (1.3%), and savanna was the dominant LULC in the study region with an increase of 8.5%. From stakeholders’ perspective, rapid population growth, deforestation, rainfall variability/flood, urbanization, and agricultural expansion were the most important drivers associated with the observed LULC changes in the area. Other factors, such as lack of political commitment, distance to river, and elevation were also mentioned. Additionally, most the land-use scenarios identified by stakeholders would intensify land degradation and reduce ecosystem services in the area. By considering all of these potential LULC changes, decision-makers need to develop and implement appropriate solutions (e.g., land use planning strategies, reforestation campaigns, forest protection measures) in order to limit the negative effects of future LULC changes.
Evaluating the COVID-19 Impacts on Sustainable Procurement: Experiences from the Australian Built Environment Sector
Authors: Caldera Savindi , Mohamed Sherif , Feng Yingbin
Published: April 2022

The COVID-19 pandemic has brought global economies to a standstill and created challenges for a variety of sectors, including housing, building and infrastructure. Many business and government organisations have experienced some form of supply chain disruption—either through suppliers going offline, a sudden spike in demand or both. While embedding sustainability in procurement is a powerful tool for bringing about positive change in an organisation’s supply chains, this global pandemic has had a myriad of impacts on these procurement processes. Through focus group discussions with industrial practitioners and government decision makers from the Australian built environment sector, this study presents their lived experiences related to COVID-19 impacts on sustainable procurement. The emergent themes are: (1) the effects of COVID-19 on sustainable procurement, (2) the rapid development of reactive procurement responses and (3) levers for post-COVID-19 sustainable procurement. In order to learn from the challenges related to COVID-19, both government and industry need to re-assess their supply chain risks and determine the supply chain design that will deliver the most resiliency in the event of another large-scale disruption. There are several key levers, including developing reliable, transparent and local supply chains, leveraging innovative tools and digital engineering approaches, creating a coalition between government and industry and assessing risks at multiple levels. This study is the first of its kind to evaluate the COVID-19 impacts on sustainable procurement in the Australian building and construction industries. Government and industry practitioners can immediately apply these actionable recommendations to overcome the impacts of the pandemic and other disruptions on sustainable procurement activities.