bluuebunny/crossref_metadata_2025 huggingface dataset:
The COVID-19 Pandemic as a Catalyst for Religious Polarization in Poland
Authors: Bożewicz Marta , Boguszewski Rafał
Published: July 2021

In theory, the coronavirus pandemic, with its wide-ranging implications for the functioning of societies around the world, cannot fail to have an impact on religiosity. We test whether this is really the case and investigate the scope and trend of changes in religious commitment using the example of Polish society. We make use of survey research conducted at various times on representative samples of Poles. Many studies have shown that in the face of destabilization and uncertainty, religious engagement gives hope and support, and therefore religiosity should be expected to increase during a pandemic. On the other hand, it can be assumed that a superficial and traditional religiosity, associated only with customary participation in Sunday religious practices, may weaken or even disappear when churches close. It emerges that both these phenomena can be observed in Polish society and, consequently, in the context of the pandemic, they are leading to religious polarization.
The Influence of Spiritual Transcendence on a Centering Meditation: A Growth Curve Analysis of Resilience
Authors: Dorais Stephanie , Gutierrez Daniel
Published: July 2021

The authors longitudinally examined a spiritual meditation based on centering prayer. The study consisted of two primary aims: (1) to determine the effectiveness of centering meditation on increasing resilience and (2) to examine the temporal dynamics of spiritual transcendence on resilience during the meditation. Participants (n = 150) engaged in a 4-week randomized controlled trial, in which the treatment group practiced the centering meditation twice a day. The growth curve model includes a three-way interaction to determine if there were group effects in the relationship between spiritual transcendence and time. The interaction between treatment group, time, and spiritual transcendence was statistically significant in explaining the trajectory of resilience, p < 0.05. Based on the findings, the centering meditation was effective in statistically significantly increasing resilience in the treatment group compared to the waitlist control group. In addition, spiritual transcendence significantly potentiated the effect of centering meditation on improving resilience over time, p < 0.05. The authors discuss limitations and implications for research and practice of centering meditation.
A Climatological Assessment of Intense Desert Dust Episodes over the Broader Mediterranean Basin Based on Satellite Data
Authors: Gavrouzou Maria , Hatzianastassiou Nikolaos , Gkikas Antonis , Lolis Christos J. , Mihalopoulos Nikolaos
Published: July 2021

A satellite algorithm able to identify Dust Aerosols (DA) is applied for a climatological investigation of Dust Aerosol Episodes (DAEs) over the greater Mediterranean Basin (MB), one of the most climatologically sensitive regions of the globe. The algorithm first distinguishes DA among other aerosol types (such as Sea Salt and Biomass Burning) by applying threshold values on key aerosol optical properties describing their loading, size and absorptivity, namely Aerosol Optical Depth (AOD), Aerosol Index (AI) and Ångström Exponent (α). The algorithm operates on a daily and 1° × 1° geographical cell basis over the 15-year period 2005–2019. Daily gridded spectral AOD data are taken from Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua Collection 6.1, and are used to calculate the α data, which are then introduced into the algorithm, while AI data are obtained by the Ozone Monitoring Instrument (OMI) -Aura- Near-UV aerosol product OMAERUV dataset. The algorithm determines the occurrence of Dust Aerosol Episode Days (DAEDs), whenever high loads of DA (higher than their climatological mean value plus two/four standard deviations for strong/extreme DAEDs) exist over extended areas (more than 30 pixels or 300,000 km2). The identified DAEDs are finally grouped into Dust Aerosol Episode Cases (DAECs), consisting of at least one DAED. According to the algorithm results, 166 (116 strong and 50 extreme) DAEDs occurred over the MB during the study period. DAEDs are observed mostly in spring (47%) and summer (38%), with strong DAEDs occurring primarily in spring and summer and extreme ones in spring. Decreasing, but not statistically significant, trends of the frequency, spatial extent and intensity of DAECs are revealed. Moreover, a total number of 98 DAECs was found, primarily in spring (46 DAECs) and secondarily in summer (36 DAECs). The seasonal distribution of the frequency of DAECs varies geographically, being highest in early spring over the eastern Mediterranean, in late spring over the central Mediterranean and in summer over the western MB.
Corn Residue Covered Area Mapping with a Deep Learning Method Using Chinese GF-1 B/D High Resolution Remote Sensing Images
Authors: Tao Wancheng , Xie Zixuan , Zhang Ying , Li Jiayu , Xuan Fu , Huang Jianxi , Li Xuecao , Su Wei , Yin Dongqin
Published: July 2021

Black soil is one of the most productive soils with high organic matter content. Crop residue covering is important for protecting black soil from alleviating soil erosion and increasing soil organic carbon. Mapping crop residue covered areas accurately using remote sensing images can monitor the protection of black soil in regional areas. Considering the inhomogeneity and randomness, resulting from human management difference, the high spatial resolution Chinese GF-1 B/D image and developed MSCU-net+C deep learning method are used to mapping corn residue covered area (CRCA) in this study. The developed MSCU-net+C is joined by a multiscale convolution group (MSCG), the global loss function, and Convolutional Block Attention Module (CBAM) based on U-net and the full connected conditional random field (FCCRF). The effectiveness of the proposed MSCU-net+C is validated by the ablation experiment and comparison experiment for mapping CRCA in Lishu County, Jilin Province, China. The accuracy assessment results show that the developed MSCU-net+C improve the CRCA classification accuracy from IOUAVG = 0.8604 and KappaAVG = 0.8864 to IOUAVG = 0.9081 and KappaAVG = 0.9258 compared with U-net. Our developed and other deep semantic segmentation networks (MU-net, GU-net, MSCU-net, SegNet, and Dlv3+) improve the classification accuracy of IOUAVG/KappaAVG with 0.0091/0.0058, 0.0133/0.0091, 0.044/0.0345, 0.0104/0.0069, and 0.0107/0.0072 compared with U-net, respectively. The classification accuracies of IOUAVG/KappaAVG of traditional machine learning methods, including support vector machine (SVM) and neural network (NN), are 0.576/0.5526 and 0.6417/0.6482, respectively. These results reveal that the developed MSCU-net+C can be used to map CRCA for monitoring black soil protection.
Precision-Aided Partial Ambiguity Resolution Scheme for Instantaneous RTK Positioning
Authors: Castro-Arvizu Juan Manuel , Medina Daniel , Ziebold Ralf , Vilà-Valls Jordi , Chaumette Eric , Closas Pau
Published: July 2021

The use of carrier phase data is the main driver for high-precision Global Navigation Satellite Systems (GNSS) positioning solutions, such as Real-Time Kinematic (RTK). However, carrier phase observations are ambiguous by an unknown number of cycles, and their use in RTK relies on the process of mapping real-valued ambiguities to integer ones, so-called Integer Ambiguity Resolution (IAR). The main goal of IAR is to enhance the position solution by virtue of its correlation with the estimated integer ambiguities. With the deployment of new GNSS constellations and frequencies, a large number of observations is available. While this is generally positive, positioning in medium and long baselines is challenging due to the atmospheric residuals. In this context, the process of solving the complete set of ambiguities, so-called Full Ambiguity Resolution (FAR), is limiting and may lead to a decreased availability of precise positioning. Alternatively, Partial Ambiguity Resolution (PAR) relaxes the condition of estimating the complete vector of ambiguities and, instead, finds a subset of them to maximize the availability. This article reviews the state-of-the-art PAR schemes, addresses the analytical performance of a PAR estimator following a generalization of the Cramér–Rao Bound (CRB) for the RTK problem, and introduces Precision-Driven PAR (PD-PAR). The latter constitutes a new PAR scheme which employs the formal precision of the (potentially fixed) positioning solution as selection criteria for the subset of ambiguities to fix. Numerical simulations are used to showcase the performance of conventional FAR and FAR approaches, and the proposed PD-PAR against the generalized CRB associated with PAR problems. Real-data experimental analysis for a medium baseline complements the synthetic scenario. The results demonstrate that (i) the generalization for the RTK CRB constitutes a valid lower bound to assess the asymptotic behavior of PAR estimators, and (ii) the proposed PD-PAR technique outperforms existing FAR and PAR solutions as a non-recursive estimator for medium and long baselines.
Sinkhole Scanner: A New Method to Detect Sinkhole-Related Spatio-Temporal Patterns in InSAR Deformation Time Series
Authors: Kulshrestha Anurag , Chang Ling , Stein Alfred
Published: July 2021

Sinkholes are sudden disasters that are usually small in size and occur at unexpected locations. They may cause serious damage to life and property. Sinkhole-prone areas can be monitored using Interferometric Synthetic Aperture Radar (InSAR) time series. Defining a pattern using InSAR-derived spatio-temporal deformations, this study presents a sinkhole pattern detector, called the Sinkhole Scanner. The Sinkhole Scanner includes a spatio-temporal mathematical model such as a 2-dimensional time evolving Gaussian function as a kernel, which moves over the study area using a sliding window approach. The scanner attempts to fit the model over deformation time series of Constantly Coherent Scatterers (CCS) intersected by the window and returns the posterior variance as a measure of goodness of fit. In this way, the scanner searches for subsiding regions resembling sinkhole shapes over a sinkhole prone area. It is designed to detect large sinkholes with a high efficiency, and small sinkholes with a lower efficiency. It is tested at four different spatial scales, and on a simulated and real set of deformation data. Real data were obtained from Sentinel-1A SLC data in IW mode, over Ireland where a large sinkhole occurred on 24 September 2018. The Sinkhole Scanner was able to identify a pattern of low posterior variance zones consistent with the simulated set. In case of the real data, it is able to identify significantly low posterior variance zones near the sinkhole area with the lowest value being 51.1% of the maximum value. The results from Sinkhole Scanner over the real sinkhole site were compared with Multiple Hypothesis Testing (MHT), which identifies Breakpoint and Heaviside temporal anomalies in the deformation time series of CCS. MHT was able to identify high likelihood for Heaviside anomalies in deformation time series of CCS near the sinkhole site about 10 epochs before the sinkhole occurrence. We show that the Sinkhole Scanner is efficient in monitoring a large area and search for sinkholes and that MHT can be used successively to identify temporal anomalies in the vicinity of areas detected by the Sinkhole Scanner. Future research may address other Sinkhole shapes whereas the underlying stochastic model may be adjusted. We conclude that the Sinkhole Scanner is important to be applied at different levels of scale to converge on potential sinkhole centers.
Bias in Deep Neural Networks in Land Use Characterization for International Development
Authors: Kim Do-Hyung , López Guzmán , Kiedanski Diego , Maduako Iyke , Ríos Braulio , Descoins Alan , Zurutuza Naroa , Arora Shilpa , Fabian Christopher
Published: July 2021

Understanding the biases in Deep Neural Networks (DNN) based algorithms is gaining paramount importance due to its increased applications on many real-world problems. A known problem of DNN penalizing the underrepresented population could undermine the efficacy of development projects dependent on data produced using DNN-based models. In spite of this, the problems of biases in DNN for Land Use and Land Cover Classification (LULCC) have not been a subject of many studies. In this study, we explore ways to quantify biases in DNN for land use with an example of identifying school buildings in Colombia from satellite imagery. We implement a DNN-based model by fine-tuning an existing, pre-trained model for school building identification. The model achieved overall 84% accuracy. Then, we used socioeconomic covariates to analyze possible biases in the learned representation. The retrained deep neural network was used to extract visual features (embeddings) from satellite image tiles. The embeddings were clustered into four subtypes of schools, and the accuracy of the neural network model was assessed for each cluster. The distributions of various socioeconomic covariates by clusters were analyzed to identify the links between the model accuracy and the aforementioned covariates. Our results indicate that the model accuracy is lowest (57%) where the characteristics of the landscape are predominantly related to poverty and remoteness, which confirms our original assumption on the heterogeneous performances of Artificial Intelligence (AI) algorithms and their biases. Based on our findings, we identify possible sources of bias and present suggestions on how to prepare a balanced training dataset that would result in less biased AI algorithms. The framework used in our study to better understand biases in DNN models would be useful when Machine Learning (ML) techniques are adopted in lieu of ground-based data collection for international development programs. Because such programs aim to solve issues of social inequality, MLs are only applicable when they are transparent and accountable.
Bearings-Only Target Tracking with an Unbiased Pseudo-Linear Kalman Filter
Authors: Huang Zihao , Chen Shijin , Hao Chengpeng , Orlando Danilo
Published: July 2021

In bearings-only target tracking, the pseudo-linear Kalman filter (PLKF) attracts much attention because of its stability and its low computational burden. However, the PLKF’s measurement vector and the pseudo-linear noise are correlated, which makes it suffer from bias problems. Although the bias-compensated PLKF (BC–PLKF) and the instrumental variable-based PLKF (IV–PLKF) can eliminate the bias, they only work well when the target behaves with non-manoeuvring movement. To extend the PLKF to the manoeuvring target tracking scenario, an unbiased PLKF (UB–PLKF) algorithm, which splits the noise away from the measurement vector directly, is proposed. Based on the results of the UB–PLKF, we also propose its velocity-constrained version (VC–PLKF) to further improve the performance. Simulations show that the UB–PLKF and VC–PLKF outperform the BC–PLKF and IV–PLKF both in non-manoeuvring and manoeuvring scenarios.
Elevation Spatial Variation Error Compensation in Complex Scene and Elevation Inversion by Autofocus Method in GEO SAR
Authors: Chang Faguang , Li Dexin , Dong Zhen , Huang Yang , He Zhihua
Published: July 2021

Due to the high altitude of geosynchronous synthetic aperture radar (GEO SAR), its synthetic aperture time can reach up to several hundred seconds, and its revisit cycle is very short, which makes it of great application worth in the remote sensing field, such as in disaster monitoring and vegetation measurements. However, because of the elevation of the target, elevation spatial variation error is caused in the GEO SAR imaging. In this paper, we focus on the compensation of the elevation space-variant error in the fast variant part with the autofocus method and utilize the error to carry out elevation inversing in complex scenes. For a complex scene, it can be broken down into a slow variant slope and the remaining fast variant part. First, the phase error caused by the elevation spatial variation is analyzed. Second, the spatial variant error caused by the slowly variant slope is compensated with the improved imaging algorithm. The error caused by the remaining fast variable part is the focus of this paper. We propose a block map-drift phase gradient autofocus (block-MD-PGA) algorithm to compensate for the random phase error part. By dividing sub-blocks reasonably, the elevation spatial variant error is compensated for by an autofocus algorithm in each sub-block. Because the errors of different elevations are diverse, the proposed algorithm is suitable for the scene where the target elevations are almost the same after the sub-blocks are divided. Third, the phase error obtained by the autofocus method is used to inverse the target elevation. Finally, simulations with dot-matrix targets and targets based on the high-resolution TerraSAR-X image verify the excellent effect of the proposed method and the accuracy of the elevation inversion.
A Building Roof Identification CNN Based on Interior-Edge-Adjacency Features Using Hyperspectral Imagery
Authors: Ye Chengming , Li Hongfu , Li Chunming , Liu Xin , Li Yao , Li Jonathan , Gonçalves Wesley Nunes , Junior José Marcato
Published: July 2021

Hyperspectral remote sensing can obtain both spatial and spectral information of ground objects. It is an important prerequisite for a hyperspectral remote sensing application to make good use of spectral and image features. Therefore, we improved the Convolutional Neural Network (CNN) model by extracting interior-edge-adjacency features of building roof and proposed a new CNN model with a flexible structure: Building Roof Identification CNN (BRI-CNN). Our experimental results demonstrated that the BRI-CNN can not only extract interior-edge-adjacency features of building roof, but also change the weight of these different features during the training process, according to selected samples. Our approach was tested using the Indian Pines (IP) data set and our comparative study indicates that the BRI-CNN model achieves at least 0.2% higher overall accuracy than that of the capsule network model, and more than 2% than that of CNN models.
Towards Monitoring Waterlogging with Remote Sensing for Sustainable Irrigated Agriculture
Authors: den Besten Nadja , Steele-Dunne Susan , de Jeu Richard , van der Zaag Pieter
Published: July 2021

Waterlogging is an increasingly important issue in irrigated agriculture that has a detrimental impact on crop productivity. The above-ground effect of waterlogging on crops is hard to distinguish from water deficit stress with remote sensing, as responses such as stomatal closure and leaf wilting occur in both situations. Currently, waterlogging as a source of crop stress is not considered in remote sensing-based evaporation algorithms and this may therefore lead to erroneous interpretation for irrigation scheduling. Monitoring waterlogging can improve evaporation models to assist irrigation management. In addition, frequent spatial information on waterlogging will provide agriculturalists information on land trafficability, assist drainage design, and crop choice. This article provides a scientific perspective on the topic of waterlogging by consulting literature in the disciplines of agronomy, hydrology, and remote sensing. We find the solution to monitor waterlogging lies in a multi-sensor approach. Future scientific routes should focus on monitoring waterlogging by combining remote sensing and ancillary data. Here, drainage parameters deduced from high spatial resolution Digital Elevation Models (DEMs) can play a crucial role. The proposed approaches may provide a solution to monitor and prevent waterlogging in irrigated agriculture.
Hyperspectral and Multispectral Image Fusion Using Coupled Non-Negative Tucker Tensor Decomposition
Authors: Zare Marzieh , Helfroush Mohammad Sadegh , Kazemi Kamran , Scheunders Paul
Published: July 2021

Fusing a low spatial resolution hyperspectral image (HSI) with a high spatial resolution multispectral image (MSI), aiming to produce a super-resolution hyperspectral image, has recently attracted increasing research interest. In this paper, a novel approach based on coupled non-negative tensor decomposition is proposed. The proposed method performs a tucker tensor factorization of a low resolution hyperspectral image and a high resolution multispectral image under the constraint of non-negative tensor decomposition (NTD). The conventional matrix factorization methods essentially lose spatio-spectral structure information when stacking the 3D data structure of a hyperspectral image into a matrix form. Moreover, the spectral, spatial, or their joint structural features have to be imposed from the outside as a constraint to well pose the matrix factorization problem. The proposed method has the advantage of preserving the spatio-spectral structure of hyperspectral images. In this paper, the NTD is directly imposed on the coupled tensors of the HSI and MSI. Hence, the intrinsic spatio-spectral structure of the HSI is represented without loss, and spatial and spectral information can be interdependently exploited. Furthermore, multilinear interactions of different modes of the HSIs can be exactly modeled with the core tensor of the Tucker tensor decomposition. The proposed method is straightforward and easy to implement. Unlike other state-of-the-art approaches, the complexity of the proposed approach is linear with the size of the HSI cube. Experiments on two well-known datasets give promising results when compared with some recent methods from the literature.
The Development of a Combined Satellite-Based Precipitation Dataset across Bolivia from 2000 to 2015
Authors: Ureña Jhonatan , Saavedra Oliver , Kubota Takuji
Published: July 2021

This study proposes the use of satellite-based precipitation (SBP) products in combination with local rain gauges in Bolivia. Using this approach, the country was divided into three major hydrographic basins: the Altiplano, La Plata, and Amazon. The selected SBP products were Global Satellite Mapping of Precipitation (GSMaP) and Climate Hazards Group Infrared Precipitations with Stations (CHIRPS). The correlation coefficients of SBP were found to be from 0.94 to 0.98 at monthly temporal scale. The applied methodology iterates correction factors, taking advantage of surface measurements from the national rain gauge network; five iterations showed stability in the convergence. Once the improved SBP product was obtained, validation was performed by reducing ten percent the number of rain gauges randomly. After applying the correction factors, the combined products improved their correlation coefficient values by up to 0.99. The validation of the methodology showed that with a combination of products using 90% of the rain gauges, correlation coefficients ranged from 0.98 to 0.99. Among the three basins, the Amazon basin presented the poorest results; this fact may be related to low rain gauge density compared to the other two basins. The validation approach shows that the methodology has an acceptable performance. The database generated in this study, now open to the public, is ready to be used for different hydrological applications such as precipitation time-series analysis, water balance, and water assessment at the sub-basin scale within Bolivia.
Aerosol Direct Radiative Effects under Cloud-Free Conditions over Highly-Polluted Areas in Europe and Mediterranean: A Ten-Years Analysis (2007–2016)
Authors: Landi Tony , Bonasoni Paolo , Brunetti Michele , Campbell James , Marquis Jared , Di Girolamo Paolo , Lolli Simone
Published: July 2021

This study investigates changes in aerosol radiative effects on two highly urbanized regions across the Euro-Mediterranean basin with respect to a natural desert region as Sahara over a decade through space-based lidar observations. The research is based on the monthly-averaged vertically-resolved aerosol optical depth (AOD) atmospheric profiles along a 1∘×1∘ horizontal grid, obtained from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument measurements aboard the Cloud-Aerosol lidar and Infrared Pathfinder Satellite Observation (CALIPSO). To assess the variability of the anthropogenic aerosols on climate, we compared the aerosol vertical profile observations to a one-dimensional radiative transfer model in two metropolitan climate sensible hot-spots in Europe, namely the Po Valley and Benelux, to investigate the variability of the aerosol radiative effects over ten years. The same analysis is carried out as reference on the Sahara desert region, considered subject just to natural local emission. Our findings show the efficacy of emission reduction policies implemented at government level in strongly urbanized regions. The total atmospheric column aerosol load reduction (not observed in Sahara desert region) in Po Valley and Benelux can be associated with: (i) an increase of the energy flux at the surface via direct effects confirmed also by long term surface temperature observations, (ii) a general decrease of the atmospheric column, and likely (iii) an increase in surface temperatures during a ten-year period. Summarizing, the analysis, based on the decade 2007–2016, clearly show an increase of solar irradiation under cloud-free conditions at the surface of +3.6 % and +16.6% for the Po Valley and Benelux, respectively, and a reduction of −9.0% for the Sahara Desert.
Performance Degradation in Cross-Eye Jamming Due to Amplitude/Phase Instability between Jammer Antennas
Authors: Kim Je-An , Lee Joon-Ho
Published: July 2021

Cross-eye gain in cross-eye jamming systems is highly dependent on amplitude ratio and the phase difference between jammer antennas. It is well known that cross-eye jamming is most effective for the amplitude ratio of unity and phase difference of 180 degrees. It is assumed that the instabilities in the amplitude ratio and phase difference can be modeled as zero-mean Gaussian random variables. In this paper, we not only quantitatively analyze the effect of amplitude ratio instability and phase difference instability on performance degradation in terms of reduction in cross-eye gain but also proceed with analytical performance analysis based on the first order and second-order Taylor expansion.
Diagnostic Feature Extraction and Filtering Criterion for Fatigue Crack Growth Using High Frequency Parametrical Analysis
Authors: Angulo Ángela , Mares Cristinel , Gan Tat-Hean
Published: July 2021

Mooring systems are an integral and sophisticated component of offshore assets and are subject to harsh conditions and cyclic loading. The early detection and characterisation of fatigue crack growth remain a crucial challenge. The scope of the present work was to establish filtering and alarm criteria for different crack growth stages by evaluating the recorded signals and their features. The analysis and definition of parametrical limits, and the correlation of their characteristics with the crack, helped to identify approaches to discriminate between noise, initiation, and growth-related signals. Based on these, a filtering criterion was established, to support the identification of the different growth stages and noise with the aim to provide early warnings of potential damage.
Diffusion Model of Preemptive-Resume Priority Systems and Its Application to Performance Evaluation of SDN Switches
Authors: Nycz Tomasz , Czachórski Tadeusz , Nycz Monika
Published: July 2021

The increasing use of Software-Defined Networks brings the need for their performance analysis and detailed analytical and numerical models of them. The primary element of such research is a model of a SDN switch. This model should take into account non-Poisson traffic and general distributions of service times. Because of frequent changes in SDN flows, it should also analyze transient states of the queues. The method of diffusion approximation can meet these requirements. We present here a diffusion approximation of priority queues and apply it to build a more detailed model of SDN switch where packets returned by the central controller have higher priority than other packets.
Performance of Linear Mixed Models to Assess the Effect of Sustained Loading and Variable Temperature on Concrete Beams Strengthened with NSM-FRP
Authors: Perera Ricardo , Torres Lluis , Díaz Francisco J. , Barris Cristina , Baena Marta
Published: July 2021

Although some extended studies about the short-term behavior of NSM FRP strengthened beams have been carried out, there is a lack of knowledge about the behavior of this kind of strengthening under sustained loads and high service temperatures. Electromechanical impedance method formulated from measurements obtained from PZT patches gives the ability for monitoring the performance and changes experienced by these strengthened beams at a local level, which is a key aspect considering its possible premature debonding failure modes. This paper presents an experimental testing program aimed at investigating the long-term performance of a concrete beam strengthened with a NSM CFRP laminate. Long term performance under different levels of sustained loading and temperature conditions is correlated with EMI signatures processed using Linear Mixed-effects models. These models are very powerful to process datasets that have a multilevel or hierarchical structure as those yielded by our tests. Results have demonstrated the potential of these techniques as health monitoring methodology under different conditions in an especially complex problem such as NSM-FRP strengthened concrete structures.
Multi-Disciplinary Analysis of Light Shelves Application within a Student Dormitory Refurbishment
Authors: Ruggiero Silvia , Assimakopoulos Margarita-Niki , De Masi Rosa Francesca , de Rossi Filippo , Fotopoulou Anastasia , Papadaki Dimitra , Vanoli Giuseppe Peter , Ferrante Annarita
Published: July 2021

The achievement of sustainable cities and communities is closely linked to an accurate design of the buildings. In this context, the transparent elements of the building envelope have a crucial role since, on one hand, they are a bottleneck in regards to heat and mass transfers and sound propagation, while, on the other hand, they must allow daylight penetration. Thus, they are responsible for occupants’ thermal and visual comfort and their health. Considering passive solutions for windows, the light shelves can improve natural light penetration, reducing the lights’ electricity demand and controlling windows’ related thermal aspects. The scientific literature is characterized by several studies that analyze this topic, which, however, focus only on the daylight field and sometimes the energy saving for lights. Moreover, they often refer to fixed sky type for the simulations. The aim of the present study is to analyze the application of the light shelves with a multi-disciplinary approach, by means of dynamic simulations, in the EnergyPlus engine, for a whole year. A new methodological approach is presented in order to investigate the technology under different fields of interest: daylight, lighting energy, cooling and heating needs, and thermo-hygrometric comfort. The case study chosen is an existing building, a student dormitory belonging to the University of Athens. It is subject to a deep energy renovation to conform to the “nearly Zero Energy Building” target, in the frame of a European research project called Pro-GET-onE (G.A No. 723747). By means of the calibrated numerical model of this HVAC–building system, ten different configurations of light shelves have been investigated. The best solution is given by the application of an internal horizontal light shelf placed at 50 cm from the top of the window with a depth of 90 or 60 cm. It has been found that despite the reduction in electricity demand for lighting, the variation in heating and cooling needs does not always lead to a benefit.
Canopy Index Evaluation for Precision Management in an Intensive Olive Orchard
Authors: Assirelli Alberto , Romano Elio , Bisaglia Carlo , Lodolini Enrico Maria , Neri Davide , Brambilla Massimo
Published: July 2021

The evaluation of the canopy in orchard cultivation is a key aspect for the main cultivation techniques, such as pruning, thinning, harvesting, production and improved fruit quality. The possibility of having a periodic screening of the state of development of the vegetation can be of practical support to growers. Research on the application of precision agriculture has provided tools for reading and interpreting crops, and the resulting information is potentially useful. Many of the systems under study provide after monitoring information processing systems that reduce the timeliness of intervention. Especially in intensive systems such as olive groves, knowing the precise intervention points is often essential. In the present work, a multi-parameter instrument was used for field monitoring on the agricultural tractor to analyse the canopy. The system allows measuring various indicators such as height and density of the canopy and the temperature and humidity of the ambient air and at the leaf level. The first evaluation of the data made it possible to identify areas with greater vegetative concentration and greater or lesser development. The system made it possible to identify with good approximation the homogeneous areas, based on the Canopy Index (CI) evaluation to be subjected to subsequent and specific management efforts, dividing them into low, ordinary, and high vegetative growth. The results highlight the possibility of directly combining operators able to intervene with the same passage, selecting based on differences in growth, typical varietal specificities, and areas of deficient development or that are affected by plant diseases, confirming the objective of defining the areas of the orchard that require different management and workload techniques.
Multi-Objective Optimization for Healthcare Waste Management Network Design with Sustainability Perspective
Authors: Torkayesh Ali Ebadi , Vandchali Hadi Rezaei , Tirkolaee Erfan Babaee
Published: July 2021

Healthcare Waste Management (HWM) is considered as one of the important urban decision-making problems due to its potential environmental, economic, and social risks and damages. The network of the HWM system involves important decisions such as facility locating, inventory management, and transportation management. Moreover, with growing concerns towards sustainable development objectives, HWM systems should address its environmental and social aspects as well as its economic and technical characteristics. In this regard, this paper formulates a novel multi-objective optimization model to empower companies in making optimized decisions considering the economic, environmental, and social aspects. Within the proposed model, the first objective function aims to minimize the transportation costs, processing costs, and establishment costs. The second objective function aims to minimize environmental risks and emissions related to the transportation of waste between facilities. The third objective function aims to maximize job creation opportunities. Formulating these three functions, an Improved Multi-Choice Goal Programing (IMCGP) approach is proposed to solve the multi-objective optimization model, which is then compared with the Goal Attainment Method (GAM). Finally, to show the applicability and feasibility of the proposed model, an illustrative example of healthcare waste management is analyzed, and the results are discussed.
Data-Driven Scenario Specification for AV–VRU Interactions at Urban Roundabouts
Authors: Keler Andreas , Malcolm Patrick , Grigoropoulos Georgios , Hosseini Seyed Abdollah , Kaths Heather , Busch Fritz , Bogenberger Klaus
Published: July 2021

Detailed specifications of urban traffic from different perspectives and scales are crucial for understanding and predicting traffic situations from the view of an autonomous vehicle (AV). We suggest a data-driven specification scheme for maneuvers at different design elements of the built infrastructure and focus on urban roundabouts in Germany. Based on real observations, we define classes of maneuvers, interactions and driving strategies for cyclists, pedestrians and motorized vehicles and define a matrix for merging different maneuvers, resulting in more complex interactions. The sequences of these interactions, which partially consist of explicit communications, are extracted from real observations and adapted into microscopic traffic flow simulations. The simulated maneuver sequences are then visualized in 3D environments and experienced by bicycle simulator test subjects. Using trajectory segments (in fictional space) from two conducted simulator studies, we relate the recorded movement patterns of test subjects with observed cyclists in reality.
Dynamics of Implementation of SDG 7 Targets in EU Member States 5 Years after the Adoption of the Paris Agreement
Authors: Firoiu Daniela , Ionescu George H. , Pîrvu Ramona , Cismaș Laura Mariana , Tudor Sorin , Patrichi Ioana C.
Published: July 2021

The European Union, by adopting the European Green Deal, aims at an extremely ambitious goal to become climate neutral by 2050. This objective implies a massive investment plan to reduce disparities between EU Member States and to support their transformation efforts in order to reshape the Union into a modern, resource-efficient and competitive economy. The objective of this paper is to analyze the dynamics of implementation of SDG 7 targets in EU Member States 5 years after the adoption of the Paris Agreement. Using hierarchical clustering analysis to reveal hidden associative structures, EU countries were grouped in 2015 and 2019 based on Eurostat data in order to identify and analyze key characteristics, but also to evaluate their evolution over time. The results of this research revealed clusters of high-performing countries, as well as countries that require increased attention and support to ease the transition to a greener economy. If in 2015 the cluster of the best performing countries consisted of four EU countries, in 2019, their number increased to eight EU countries, simultaneously with an improvement of the indicators, proving a real concern and involvement regarding the achievement of SDG 7 targets.
District Drinking Water Planning for Sustainability in Maharashtra: Between Local and Global Scales
Authors: Wescoat Jr. James L. , Murty Jonnalagadda V. R.
Published: July 2021

Sustainable rural drinking water is a widespread aim in India, and globally, from the household to district, state, and national scales. Sustainability issues in the rural drinking water sector range from increasing water demand to declining groundwater levels, premature deterioration of village schemes and services, inadequate revenues for operations and maintenance, weak capacity of water operators, frequently changing state and national policies, and destabilizing effects of climate change. This paper focuses on the special role of district-scale drinking water planning, which operates at the intersection between bottom-up water demand and top-down water programs. After surveying the challenges associated with bottom-up and top-down planning approaches, we present recent efforts to strengthen district and block drinking water planning in the state of Maharashtra. A combination of district interviews, institutional history, village surveys, GIS visualization, and planning workshops were used to advance district planning goals and methods. Results assess bottom-up processes of water demand; top-down water programs and finance; and intermediate-level planning at the district and block scales. Discussion focuses on potential improvements in district planning methods in Maharashtra.
The Role of Digital Technology Platforms in the Context of Changes in Consumption and Production Patterns
Authors: Łobejko Stanisław , Bartczak Krzysztof
Published: July 2021

The article focuses on issues relating to achieving sustainable development by promoting new consumption and production patterns based, for example, on sharing resources and doing business exclusively in a digital environment. This topic is extremely important because sustainable development is a fundamental concept aimed at improving the functioning of the present and future generations. The main focus of the article is to show the role played by digital technology platforms within this concept, including in relation to consumption and production patterns. The article is based on two research methods—CATI (i.e., computer-aided telephone interviews) and regression analysis for CATREG quality variables. It has been established that digital technology platforms significantly influence the creation and development of modern business models and increase the quality and intensity of relations between various company stakeholders, which is the basis for promoting new consumption and production patterns—including those based on the sharing economy, subscription to various products and services or the functioning of virtual markets, enabling purchase and sale transactions.
Sustainable and Affordable Prefabricated Construction: Developing a Natural, Recycled, and Recyclable Mobile Home
Authors: Berizzi Carlo , Nirta Salvatore , Terlicher Gaia Nerea , Trabattoni Luca
Published: July 2021

Outdoor tourism is a form of outdoor holiday that is growing rapidly today, and that stands out from other forms of tourism for its immediate relationship with the landscape which becomes for the tourist the main attraction of the holiday intended as a break from ordinary urban life. Outdoor tourism today represents a growing percentage in the tourism sector, in which mobile homes are the real players. Despite the considerable use of this product in open-air accommodations located in relevant landscapes, there is still no sensitivity in the constructive approach and in the choice of materials in terms of sustainability. In the open-air tourism sector, the lack of ecological sensitivity results from two levels of application: one regarding the whole settlement and the public spaces of outdoor accommodations and one regarding the mobile unit from the design to the production process. This paper will provide some practical strategies to introduce the ecological theme in the mobile home for the tourism sector. The research aims to analyze the production system of mobile homes in order to introduce alternative materials within the existing assembly line. The research demonstrates the possibility of a product being sustainable both economically and environmentally, healthy, and well-integrated with landscape by adopting an approach that makes it possible to use the same assembly line currently in use.
Corporate Payments for Ecosystem Services in Theory and Practice: Links to Economics, Business, and Sustainability
Authors: Thompson Benjamin S.
Published: July 2021

Few Payments for Ecosystem Services (PES) schemes are financed voluntarily by corporations. This is perhaps unsurprising, given that limited literature on the theory and practice of PES has a dedicated focus on businesses. This article unifies the PES and business literatures in order to address the awareness and management challenges that corporations face in engaging in PES. First, it shows how corporations fit into the economic theory that underpins PES, demonstrating that corporate-financed PES schemes can exhibit a diversity and hybridity of Coasean and Pigouvian characteristics. Second, it shows how PES fits into corporate sustainability theory, demonstrating how PES can help companies achieve synergies across the economic, environmental, and social tenets of the triple bottom line; for example, by helping gain social license to operate from adjacent communities, or by using PES to meet sustainability reporting requirements related to emissions and water management. Third, it shows the different PES options available to firms based on their industrial sector, operating practices, and business strategies. The options with higher potential are maintenance and enhancement of production inputs across the supply chain, and carbon offsetting and insetting to help meet climate change mitigation regulations and avoid fines. Fourth, it identifies lessons learned when transitioning from theory to practice by synthesising the latest empirical research on corporate-financed PES schemes—considering exactly what these ‘should’ or ‘could’ resemble, for example, in terms of their additionality, conditionality, permanence, co-benefits, budgeting, and bargaining. Examples are drawn from corporate-financed schemes in forests and watersheds across Africa, Asia, Europe, Latin America, and North America. The article concludes that these schemes remain small in number and size, but have significant potential to increase—and this can be aided by future research on corporate motives, understandings, and actions on PES.
Incentivizing Innovation: The Causal Role of Government Subsidies on Lithium-Ion Battery Research and Development
Authors: Buravleva Yana , Tang Decai , Bethel Brandon J.
Published: July 2021

Governments design and implement policies to achieve a variety of goals, but perhaps none are as pressing as shifting national economies away from non-renewable fuels and towards more sustainable, environmentally-friendly technologies. To incentivize such transitions, governments provide subsidies to private and public companies to innovate, i.e., to engage in research and development (R&D) to develop those technologies. However, the question of the companies is using government subsidies (GS) to perform R&D and its answer determines the effectiveness of government policies. Consequently, this paper seeks to answer this question through investigating Chinese lithium-ion battery (LiB) firms and the GS they receive through novel usage of information flow (IF). Hausman tests, fixed- and random-effects models confirmed a weak, though positive correlation between GS and R&D as determined by patent output (PO), but interestingly, observations of IF intimated that GS also affected other variables such as net profit (NP) and main business income (MBI). This suggests that firms are being awarded GS for higher PO, but a corresponding increase in R&D and its expected growth in company performance is not occurring. Thus, it is suggested that performance variables other than PO be used as firms may ab (use) this metric to apply for more GS, rather than performing R&D that leads to technological breakthroughs.
The Sharing Economy in the Framework of Sustainable Development Goals: Case of European Union Countries
Authors: Karobliene Vilma , Pilinkiene Vaida
Published: July 2021

The aim of this research study was to establish a framework for the relationships between the sharing economy and the Sustainable Development Goals (hereinafter, SDGs) set by the United Nations. There are 17 SDGs with 169 targets, which, in the scientific literature, are classified into sustainability dimensions: economic, social, and environmental. Thus, the objective of the current research was to perform an economic assessment of the sharing economy in the context of SDGs by analyzing European Union countries with a particular emphasis on their economic growth. Although the sharing economy has been analyzed from different aspects in recent scientific articles, the impact of this phenomenon on national economies in the framework of SDGs is lacking. Firstly, based on the latest research on the sharing economy from the perspective of sustainable development, a theoretical model of the sharing economy was developed in this study. Secondly, SDG indicators in the economic dimension and other key economic growth variables for European Union countries were collected. Thirdly, a cluster analysis was performed to determine the impact of the sharing economy on European Union countries in terms of SDGs in the economic dimension. The current study contributes to the existing research by analyzing the sharing economy from the perspective of sustainable economic development and highlights that this business model positively impacts countries’ economic sustainability in terms of SDGs.