bluuebunny/crossref_metadata_2025 huggingface dataset:
Discovery of Substituted (2-Aminooxazol-4-yl)Isoxazole-3-carboxylic Acids as Inhibitors of Bacterial Serine Acetyltransferase in the Quest for Novel Potential Antibacterial Adjuvants
Authors: Magalhães Joana , Franko Nina , Raboni Samanta , Annunziato Giannamaria , Tammela Päivi , Bruno Agostino , Bettati Stefano , Armao Stefano , Spadini Costanza , Cabassi Clotilde Silvia , Mozzarelli Andrea , Pieroni Marco , Campanini Barbara , Costantino Gabriele
Published: February 2021

Many bacteria and actinomycetales use L-cysteine biosynthesis to increase their tolerance to antibacterial treatment and establish a long-lasting infection. In turn, this might lead to the onset of antimicrobial resistance that currently represents one of the most menacing threats to public health worldwide. The biosynthetic machinery required to synthesise L-cysteine is absent in mammals; therefore, its exploitation as a drug target is particularly promising. In this article, we report a series of inhibitors of Salmonella thyphimurium serine acetyltransferase (SAT), the enzyme that catalyzes the rate-limiting step of L-cysteine biosynthesis. The development of such inhibitors started with the virtual screening of an in-house library of compounds that led to the selection of seven structurally unrelated hit derivatives. A set of molecules structurally related to hit compound 5, coming either from the original library or from medicinal chemistry efforts, were tested to determine a preliminary structure–activity relationship and, especially, to improve the inhibitory potency of the derivatives, that was indeed ameliorated by several folds compared to hit compound 5 Despite these progresses, at this stage, the most promising compound failed to interfere with bacterial growth when tested on a Gram-negative model organism, anticipating the need for further research efforts.
Sludge Fiber Waste and Kraft Lignin Powder as Fillers in Polylactic Acid Biocomposites: Physical, Mechanical, and Thermal Properties
Authors: Rosa Thiago Souza da , Trianoski Rosilani , Michaud Franck , Yamashita Fábio , Iwakiri Setsuo
Published: February 2021

In this investigation, sludge fibre waste (SFW) and Kraft lignin powder (KLP) are introduced into polylactic acid (PLA) matrix biocomposites. These alternative materials allow for both the reuse of fibre waste from paper mill sludge and a reduction in the amount of high-cost biopolymer used in the same volume. Proportions from 10 to 40 wt.% of SFW with the addition of 2.5% and 5% of KLP are incorporated in PLA by extrusion and injection moulding. The thermogravimetric properties, water absorption, tensile and flexural properties, and morphology of the fabricated biocomposites were investigated. According to the results, KLP contributes to thermically stabilising the loss resulting from the incorporation of SFW. Flexural and tensile tests reveal a more pronounced decrease in strength with an SFW ratio above 10%. The modulus of elasticity increases significantly with an SFW ratio above 20%. The strength properties are stabilised with the addition of 5% KLP. The addition of KLP presents a tendency to reduce water absorption obtained by the incorporation of SFW into biocomposites. Scanning electron micrographs evidence that KLP improves the interfacial adhesion by reducing the voids between fibres and PLA.
Deep Learning-Based Semantic Segmentation of Urban Features in Satellite Images: A Review and Meta-Analysis
Authors: Neupane Bipul , Horanont Teerayut , Aryal Jagannath
Published: February 2021

Availability of very high-resolution remote sensing images and advancement of deep learning methods have shifted the paradigm of image classification from pixel-based and object-based methods to deep learning-based semantic segmentation. This shift demands a structured analysis and revision of the current status on the research domain of deep learning-based semantic segmentation. The focus of this paper is on urban remote sensing images. We review and perform a meta-analysis to juxtapose recent papers in terms of research problems, data source, data preparation methods including pre-processing and augmentation techniques, training details on architectures, backbones, frameworks, optimizers, loss functions and other hyper-parameters and performance comparison. Our detailed review and meta-analysis show that deep learning not only outperforms traditional methods in terms of accuracy, but also addresses several challenges previously faced. Further, we provide future directions of research in this domain.
Beamforming of LOFAR Radio-Telescope for Passive Radiolocation Purposes
Authors: Droszcz Aleksander , Jędrzejewski Konrad , Kłos Julia , Kulpa Krzysztof , Pożoga Mariusz
Published: February 2021

This paper presents the results of investigations on the beamforming of a low-frequency radio-telescope LOFAR which can be used as a receiver in passive coherent location (PCL) radars for aerial and space object detection and tracking. The use of a LOFAR radio-telescope for the passive tracking of space objects can be a highly cost-effective solution due to the fact that most of the necessary equipment needed for passive radiolocation already exists in the form of LOFAR stations. The capability of the radiolocation of planes by a single LOFAR station in Borowiec is considered to be ‘proof of concept’ for future research focused on the localization of space objects. Beam patterns of single sets of LOFAR antennas (known as tiles), as well as for the entire LOFAR station, are presented and thoroughly discussed in the paper. Issues related to grating lobes in LOFAR beam patterns are also highlighted. A beamforming algorithm used for passive radiolocation purposes, exploiting data collected by a LOFAR station, is also discussed. The results of preliminary experiments carried out with real signals collected by the LOFAR station in Borowiec, Poland confirm that the appropriate beamforming can significantly increase the radar’s detection range, as well as the detection’s certainty.
TNNG: Total Nuclear Norms of Gradients for Hyperspectral Image Prior
Authors: Yuzuriha Ryota , Kurihara Ryuji , Matsuoka Ryo , Okuda Masahiro
Published: February 2021

We introduce a novel regularization function for hyperspectral image (HSI), which is based on the nuclear norms of gradient images. Unlike conventional low-rank priors, we achieve a gradient-based low-rank approximation by minimizing the sum of nuclear norms associated with rotated planes in the gradient of a HSI. Our method explicitly and simultaneously exploits the correlation in the spectral domain as well as the spatial domain. Our method exploits the low-rankness of a global region to enhance the dimensionality reduction by the prior. Since our method considers the low-rankness in the gradient domain, it more sensitively detects anomalous variations. Our method achieves high-fidelity image recovery using a single regularization function without the explicit use of any sparsity-inducing priors such as ℓ0, ℓ1 and total variation (TV) norms. We also apply this regularization to a gradient-based robust principal component analysis and show its superiority in HSI decomposition. To demonstrate, the proposed regularization is validated on a variety of HSI reconstruction/decomposition problems with performance comparisons to state-of-the-art methods its superior performance.
The Impact of Shale Oil and Gas Development on Rangelands in the Permian Basin Region: An Assessment Using High-Resolution Remote Sensing Data
Authors: Wang Haoying
Published: February 2021

The environmental impact of shale energy development is a growing concern in the US and worldwide. Although the topic is well-studied in general, shale development’s impact on drylands has received much less attention in the literature. This study focuses on the effect of shale development on land cover in the Permian Basin region—a unique arid/semi-arid landscape experiencing an unprecedented intensity of drilling and production activities. By taking advantage of the high-resolution remote sensing land cover data, we develop a fixed-effects panel (longitudinal) data regression model to control unobserved spatial heterogeneities and regionwide trends. The model allows us to understand the land cover’s dynamics over the past decade of shale development. The results show that shale development had moderate negative but statistically significant impacts on shrubland and grassland/pasture. The effect is more strongly associated with the hydrocarbon production volume and less with the number of oil and gas wells drilled. Between shrubland and grassland/pasture, the impact on shrubland is more pronounced in terms of magnitude. The dominance of shrubland in the region likely explains the result.
Assessing Near Real-Time Satellite Precipitation Products for Flood Simulations at Sub-Daily Scales in a Sparsely Gauged Watershed in Peruvian Andes
Authors: Llauca Harold , Lavado-Casimiro Waldo , León Karen , Jimenez Juan , Traverso Kevin , Rau Pedro
Published: February 2021

This study investigates the applicability of Satellite Precipitation Products (SPPs) in near real-time for the simulation of sub-daily runoff in the Vilcanota River basin, located in the southeastern Andes of Peru. The data from rain gauge stations are used to evaluate the quality of Integrated Multi-satellite Retrievals for GPM–Early (IMERG-E), Global Satellite Mapping of Precipitation–Near Real-Time (GSMaP-NRT), Climate Prediction Center Morphing Method (CMORPH), and HydroEstimator (HE) at the pixel-station level; and these SPPs are used as meteorological inputs for the hourly hydrological modeling. The GR4H model is calibrated with the hydrometric station of the longest record, and model simulations are also verified at one station upstream and two stations downstream of the calibration point. Comparing the sub-daily precipitation data observed, the results show that the IMERG-E product generally presents higher quality, followed by GSMaP-NRT, CMORPH, and HE. Although the SPPs present positive and negative biases, ranging from mild to moderate, they do represent the diurnal and seasonal variability of the hourly precipitation in the study area. In terms of the average of Kling-Gupta metric (KGE), the GR4H_GSMaP-NRT’ yielded the best representation of hourly discharges (0.686), followed by GR4H_IMERG-E’ (0.623), GR4H_Ensemble-Mean (0.617) and GR4H_CMORPH’ (0.606), and GR4H_HE’ (0.516). Finally, the SPPs showed a high potential for monitoring floods in the Vilcanota basin in near real-time at the operational level. The results obtained in this research are very useful for implementing flood early warning systems in the Vilcanota basin and will allow the monitoring and short-term hydrological forecasting of floods by the Peruvian National Weather and Hydrological Service.
Hyperspectral Image Destriping and Denoising Using Stripe and Spectral Low-Rank Matrix Recovery and Global Spatial-Spectral Total Variation
Authors: Yang Fang , Chen Xin , Chai Li
Published: February 2021

Hyperspectral image (HSI) is easily corrupted by different kinds of noise, such as stripes, dead pixels, impulse noise, Gaussian noise, etc. Due to less consideration of the structural specificity of stripes, many existing HSI denoising methods cannot effectively remove the heavy stripes in mixed noise. In this paper, we classify the noise on HSI into three types: sparse noise, stripe noise, and Gaussian noise. The clean image and different types of noise are treated as independent components. In this way, the image denoising task can be naturally regarded as an image decomposition problem. Thanks to the structural characteristic of stripes and the low-rank property of HSI, we propose to destripe and denoise the HSI by using stripe and spectral low-rank matrix recovery and combine it with the global spatial-spectral TV regularization (SSLR-SSTV). By considering different properties of different HSI ingredients, the proposed method separates the original image from the noise components perfectly. Both simulation and real image denoising experiments demonstrate that the proposed method can achieve a satisfactory denoising result compared with the state-of-the-art methods. Especially, it outperforms the other methods in the task of stripe noise removal visually and quantitatively.
Tracking the Evolution of Riverbed Morphology on the Basis of UAV Photogrammetry
Authors: Gracchi Teresa , Rossi Guglielmo , Tacconi Stefanelli Carlo , Tanteri Luca , Pozzani Rolando , Moretti Sandro
Published: February 2021

Unmanned aerial vehicle (UAV) photogrammetry has recently become a widespread technique to investigate and monitor the evolution of different types of natural processes. Fluvial geomorphology is one of such fields of application where UAV potentially assumes a key role, since it allows for overcoming the intrinsic limits of satellite and airborne-based optical imagery on one side, and in situ traditional investigations on the other. The main purpose of this paper was to obtain extensive products (digital terrain models (DTMs), orthophotos, and 3D models) in a short time, with low costs and at a high resolution, in order to verify the capability of this technique to analyze the active geomorphic processes on a 12 km long stretch of the French–Italian Roia River at both large and small scales. Two surveys, one year apart from each other, were carried out over the study area and a change detection analysis was performed on the basis of the comparison of the obtained DTMs to point out and characterize both the possible morphologic variations related to fluvial dynamics and modifications in vegetation coverage. The results highlight how the understanding of different fluvial processes may be improved by appropriately exploiting UAV-based products, which can thus represent a low-cost and non-invasive tool to crucially support decisionmakers involved in land management practices.
Monitoring the Efficacy of Crested Floatingheart (Nymphoides cristata) Management with Object-Based Image Analysis of UAS Imagery
Authors: Benjamin Adam R. , Abd-Elrahman Amr , Gettys Lyn A. , Hochmair Hartwig H. , Thayer Kyle
Published: February 2021

This study investigates the use of unmanned aerial systems (UAS) mapping for monitoring the efficacy of invasive aquatic vegetation (AV) management on a floating-leaved AV species, Nymphoides cristata (CFH). The study site consists of 48 treatment plots (TPs). Based on six unique flights over two days at three different flight altitudes while using both a multispectral and RGB sensor, accuracy assessment of the final object-based image analysis (OBIA)-derived classified images yielded overall accuracies ranging from 89.6% to 95.4%. The multispectral sensor was significantly more accurate than the RGB sensor at measuring CFH areal coverage within each TP only with the highest multispectral, spatial resolution (2.7 cm/pix at 40 m altitude). When measuring response in the AV community area between the day of treatment and two weeks after treatment, there was no significant difference between the temporal area change from the reference datasets and the area changes derived from either the RGB or multispectral sensor. Thus, water resource managers need to weigh small gains in accuracy from using multispectral sensors against other operational considerations such as the additional processing time due to increased file sizes, higher financial costs for equipment procurements, and longer flight durations in the field when operating multispectral sensors.
Glacier Geometry Changes in the Western Shore of Admiralty Bay, King George Island over the Last Decades
Authors: Pasik Mariusz , Bakuła Krzysztof , Różycki Sebastian , Ostrowski Wojciech , Kowalska Maria Elżbieta , Fijałkowska Anna , Rajner Marcin , Łapiński Sławomir , Sobota Ireneusz , Kejna Marek , Osińska-Skotak Katarzyna
Published: February 2021

This paper presents changes in the range and thickness of glaciers in Antarctic Specially Protected Area (ASPA) No. 128 on King George Island in the period 1956–2015. The research indicates an intensification of the glacial retreat process over the last two decades, with the rate depending on the type of glacier front. In the period 2001–2015, the average recession rate of the ice cliffs of the Ecology Glacier and the northern part of the Baranowski Glacier was estimated to be approximately 15–25 m a−1 and 10–20 m a−1, respectively. Fronts of Sphinx Glacier and the southern part of the Baranowski Glacier, characterized by a gentle descent onto land, show a significantly lower rate of retreat (up to 5–10 m a−1 1). From 2001 to 2013, the glacier thickness in these areas decreased at an average rate of 1.7–2.5 m a−1 for the Ecology Glacier and the northern part of the Baranowski Glacier and 0.8–2.5 m a−1 for the southern part of the Baranowski Glacier and Sphinx Glacier. The presented deglaciation processes are related to changes of mass balance caused by the rapid temperature increase (1.0 °C since 1948). The work also contains considerations related to the important role of the longitudinal slope of the glacier surface in the connection of the glacier thickness changes and the front recession.
A Wearable Navigation Device for Visually Impaired People Based on the Real-Time Semantic Visual SLAM System
Authors: Chen Zhuo , Liu Xiaoming , Kojima Masaru , Huang Qiang , Arai Tatsuo
Published: February 2021

Wearable auxiliary devices for visually impaired people are highly attractive research topics. Although many proposed wearable navigation devices can assist visually impaired people in obstacle avoidance and navigation, these devices cannot feedback detailed information about the obstacles or help the visually impaired understand the environment. In this paper, we proposed a wearable navigation device for the visually impaired by integrating the semantic visual SLAM (Simultaneous Localization And Mapping) and the newly launched powerful mobile computing platform. This system uses an Image-Depth (RGB-D) camera based on structured light as the sensor, as the control center. We also focused on the technology that combines SLAM technology with the extraction of semantic information from the environment. It ensures that the computing platform understands the surrounding environment in real-time and can feed it back to the visually impaired in the form of voice broadcast. Finally, we tested the performance of the proposed semantic visual SLAM system on this device. The results indicate that the system can run in real-time on a wearable navigation device with sufficient accuracy.
Nonlinear Ride Height Control of Active Air Suspension System with Output Constraints and Time-Varying Disturbances
Authors: Zhao Rongchen , Xie Wei , Zhao Jin , Wong Pak Kin , Silvestre Carlos
Published: February 2021

This paper addresses the problem of nonlinear height tracking control of an automobile active air suspension with the output state constraints and time-varying disturbances. The proposed control strategy guarantees that the ride height stays within a predefined range, and converges closely to an arbitrarily small neighborhood of the desired height, ensuring uniform ultimate boundedness. The designed nonlinear observer is able to compensate for the time-varying disturbances caused by external random road excitation and perturbations, achieving robust performance. Simulation results obtained from the co-simulation (AMESim-Matlab/Simulink) are given and analyzed, demonstrating the efficiency of the proposed control methodology.
Development and Performance Evaluation of a Bevameter for Measuring Soil Strength
Authors: Kim Ji-Tae , Im Dong-U , Choi Hyuek-Jin , Oh Jae-Won , Park Young-Jun
Published: February 2021

The driving performance of an off-road vehicle is closely related to soil strength. A bevameter is used to measure the soil strength, and it usually consists of two independent devices: a pressure–sinkage test device and a shear test device. However, its development and measurement processes have not been standardized; thus, researchers apply it in various fields according to their own discretion. In this study, a new bevameter was developed, and experiments were conducted to clarify the factors that affect the measurement performance of the bevameter. The pressure–sinkage test device was tested with circular plates of different sizes, and the results confirmed that the pressure–sinkage parameters decreased with the plate size. For the shear-test device, normal pressure was applied using a dead load to prevent normal-pressure variation due to displacement and speed. In addition, a spline was installed on top of the shaft connected to the shear ring to measure slip sinkage during the shear test. The results showed that the slip sinkage increased in proportion to the normal pressure and slip displacement, but the increase gradually decreased and converged to a certain point.
Planar Phase-Variation Microwave Sensors for Material Characterization: A Review and Comparison of Various Approaches
Authors: Muñoz-Enano Jonathan , Coromina Jan , Vélez Paris , Su Lijuan , Gil Marta , Casacuberta Pau , Martín Ferran
Published: February 2021

Planar phase-variation microwave sensors have attracted increasing interest in recent years since they combine the advantages of planar technology (including low cost, low profile, and sensor integration with the associated circuitry for post-processing and communication purposes, among others) and the possibility of operation at a single frequency (thereby reducing the costs of the associated electronics). This paper reviews and compares three different strategies for sensitivity improvement in such phase-variation sensors (devoted to material characterization). The considered approaches include line elongation (through meandering), dispersion engineering (by considering slow-wave artificial transmission lines), and reflective-mode sensors based on step-impedance open-ended lines. It is shown that unprecedented sensitivities compatible with small sensing regions are achievable with the latter approach.
Sensor Modeling for Underwater Localization Using a Particle Filter
Authors: Martínez-Barberá Humberto , Bernal-Polo Pablo , Herrero-Pérez David
Published: February 2021

This paper presents a framework for processing, modeling, and fusing underwater sensor signals to provide a reliable perception for underwater localization in structured environments. Submerged sensory information is often affected by diverse sources of uncertainty that can deteriorate the positioning and tracking. By adopting uncertain modeling and multi-sensor fusion techniques, the framework can maintain a coherent representation of the environment, filtering outliers, inconsistencies in sequential observations, and useless information for positioning purposes. We evaluate the framework using cameras and range sensors for modeling uncertain features that represent the environment around the vehicle. We locate the underwater vehicle using a Sequential Monte Carlo (SMC) method initialized from the GPS location obtained on the surface. The experimental results show that the framework provides a reliable environment representation during the underwater navigation to the localization system in real-world scenarios. Besides, they evaluate the improvement of localization compared to the position estimation using reliable dead-reckoning systems.
Accuracy Analysis of International Reference Ionosphere 2016 and NeQuick2 in the Antarctic
Authors: Guo Zihuai , Yao Yibin , Kong Jian , Chen Gang , Zhou Chen , Zhang Qi , Shan Lulu , Liu Chen
Published: February 2021

Global navigation satellite system (GNSS) can provide dual-frequency observation data, which can be used to effectively calculate total electron content (TEC). Numerical studies have utilized GNSS-derived TEC to evaluate the accuracy of ionospheric empirical models, such as the International Reference Ionosphere model (IRI) and the NeQuick model. However, most studies have evaluated vertical TEC rather than slant TEC (STEC), which resulted in the introduction of projection error. Furthermore, since there are few GNSS observation stations available in the Antarctic region and most are concentrated in the Antarctic continent edge, it is difficult to evaluate modeling accuracy within the entire Antarctic range. Considering these problems, in this study, GNSS STEC was calculated using dual-frequency observation data from stations that almost covered the Antarctic continent. By comparison with GNSS STEC, the accuracy of IRI-2016 and NeQuick2 at different latitudes and different solar radiation was evaluated during 2016–2017. The numerical results showed the following. (1) Both IRI-2016 and NeQuick2 underestimated the STEC. Since IRI-2016 utilizes new models to represent the F2-peak height (hmF2) directly, the IRI-2016 STEC is closer to GNSS STEC than NeQuick2. This conclusion was also confirmed by the Constellation Observing System for Meteorology Ionosphere and Climate (COSMIC) occultation data. (2) The differences in STEC of the two models are both normally distributed, and the NeQuick2 STEC is systematically biased as solar radiation increases. (3) The root mean square error (RMSE) of the IRI-2016 STEC is smaller than that of the NeQuick2 model, and the RMSE of the two modeling STEC increases with solar radiation intensity. Since IRI-2016 relies on new hmF2 models, it is more stable than NeQuick2.
Fully Automated DCNN-Based Thermal Images Annotation Using Neural Network Pretrained on RGB Data
Authors: Ligocki Adam , Jelinek Ales , Zalud Ludek , Rahtu Esa
Published: February 2021

One of the biggest challenges of training deep neural network is the need for massive data annotation. To train the neural network for object detection, millions of annotated training images are required. However, currently, there are no large-scale thermal image datasets that could be used to train the state of the art neural networks, while voluminous RGB image datasets are available. This paper presents a method that allows to create hundreds of thousands of annotated thermal images using the RGB pre-trained object detector. A dataset created in this way can be used to train object detectors with improved performance. The main gain of this work is the novel method for fully automatic thermal image labeling. The proposed system uses the RGB camera, thermal camera, 3D LiDAR, and the pre-trained neural network that detects objects in the RGB domain. Using this setup, it is possible to run the fully automated process that annotates the thermal images and creates the automatically annotated thermal training dataset. As the result, we created a dataset containing hundreds of thousands of annotated objects. This approach allows to train deep learning models with similar performance as the common human-annotation-based methods do. This paper also proposes several improvements to fine-tune the results with minimal human intervention. Finally, the evaluation of the proposed solution shows that the method gives significantly better results than training the neural network with standard small-scale hand-annotated thermal image datasets.
Indirect Estimation of Vertical Ground Reaction Force from a Body-Mounted INS/GPS Using Machine Learning
Authors: Sharma Dharmendra , Davidson Pavel , Müller Philipp , Piché Robert
Published: February 2021

Vertical ground reaction force (vGRF) can be measured by force plates or instrumented treadmills, but their application is limited to indoor environments. Insoles remove this restriction but suffer from low durability (several hundred hours). Therefore, interest in the indirect estimation of vGRF using inertial measurement units and machine learning techniques has increased. This paper presents a methodology for indirectly estimating vGRF and other features used in gait analysis from measurements of a wearable GPS-aided inertial navigation system (INS/GPS) device. A set of 27 features was extracted from the INS/GPS data. Feature analysis showed that six of these features suffice to provide precise estimates of 11 different gait parameters. Bagged ensembles of regression trees were then trained and used for predicting gait parameters for a dataset from the test subject from whom the training data were collected and for a dataset from a subject for whom no training data were available. The prediction accuracies for the latter were significantly worse than for the first subject but still sufficiently good. K-nearest neighbor (KNN) and long short-term memory (LSTM) neural networks were then used for predicting vGRF and ground contact times. The KNN yielded a lower normalized root mean square error than the neural network for vGRF predictions but cannot detect new patterns in force curves.
Vulnerability and Impact Analysis of the IEC 61850 GOOSE Protocol in the Smart Grid
Authors: Reda Haftu Tasew , Ray Biplob , Peidaee Pejman , Anwar Adnan , Mahmood Abdun , Kalam Akhtar , Islam Nahina
Published: February 2021

IEC 61850 is one of the most prominent communication standards adopted by the smart grid community due to its high scalability, multi-vendor interoperability, and support for several input/output devices. Generic Object-Oriented Substation Events (GOOSE), which is a widely used communication protocol defined in IEC 61850, provides reliable and fast transmission of events for the electrical substation system. This paper investigates the security vulnerabilities of this protocol and analyzes the potential impact on the smart grid by rigorously analyzing the security of the GOOSE protocol using an automated process and identifying vulnerabilities in the context of smart grid communication. The vulnerabilities are tested using a real-time simulation and industry standard hardware-in-the-loop emulation. An in-depth experimental analysis is performed to demonstrate and verify the security weakness of the GOOSE publish-subscribe protocol towards the substation protection within the smart grid setup. It is observed that an adversary who might have familiarity with the substation network architecture can create falsified attack scenarios that can affect the physical operation of the power system. Extensive experiments using the real-time testbed validate the theoretical analysis, and the obtained experimental results prove that the GOOSE-based IEC 61850 compliant substation system is vulnerable to attacks from malicious intruders.
Sensors for Structural Health Monitoring and Condition Monitoring
Authors: Pozo Francesc , Tibaduiza Diego A. , Vidal Yolanda
Published: February 2021

Structural control and health monitoring as condition monitoring are some essential areas that allow for different system parameters to be designed, supervised, controlled, and evaluated during the system’s operation in different processes, such as those used in machinery, structures, and different physical variables in mechanical, chemical, electrical, aeronautical, civil, electronics, mechatronics, and agricultural engineering applications, among others [...]
“Sometimes You Just Need People around You Who Understand You”: A Qualitative Study of Everyday Life at a Residential Care Unit for Young People with Diabetes
Authors: Johansen Lise Bro , Grabowski Dan
Published: February 2021

Solglimt is the only long-term residential care facility in Denmark for young people with diabetes who, for various reasons, are particularly exposed or vulnerable. This target group is characterized by poorly regulated diabetes and psychosocial challenges. As this unique setting and specific target group has not been studied before, we designed a qualitative sociological study of the experiences and perceptions of everyday life from the perspectives of current and former residents as well as from those of the residential care social workers (RCSWs). We also studied the specific care practices at Solglimt. The dataset consists of nine days of participatory observations, 29 semi-structured individual interviews with three target groups (9 current residents, 8 former residents and 12 RCSWs) and three participatory workshops. All data were analyzed using radical hermeneutics. The analysis produced four main themes: (1) In it together, like a family—feeling different among peers, (2) Social workers as substitutes for parents and healthcare professionals, (3) Individualization—Increased self-confidence and autonomy, and (4) Tacit knowledge and illness behavior automation. The findings show that the residential care facility is a family-like setting with a strong focus on individualization, which enables new illness behaviors and perceptions. The results offer important steps towards developing improved diabetes care strategies through individualized informal knowledge sharing. Furthermore, the results are applicable to general clinical diabetes care for vulnerable or low-resource children and young people with diabetes.
Resilience of Urban Economic Structures Following the Great Recession
Authors: Shutters Shade T. , Kandala Srinivasa S. , Wei Fangwu , Kinzig Ann P.
Published: February 2021

The future sustainability of cities is contingent on economic resilience. Yet, urban resilience is still not well understood, as cities are frequently disrupted by shocks, such as natural disasters, economic recessions, or changes in government policies. These shocks can significantly alter a city’s economic structure. Yet the term economic structure is often used metaphorically and is often not understood sufficiently by those having to implement policies. Here, we operationalized the concept of economic structure as a weighted network of interdependent industry sectors. For 938 U.S. urban areas, we then quantified the magnitude of change in the areas’ economic structures over time, focusing on changes associated with the 2007–2009 global recession. The result is a novel method of analyzing urban change over time as well as a typology of U.S. urban systems based on how their economic structures responded to the recession. We further compared those urban types to changes in economic performance during the recession to explore each structural type’s adaptive capacity. Results suggest cities that undergo constant but measured change are better positioned to weather the impacts of economic shocks.
Coordinator Perceptions When Assessing the Impact of Citizen Science towards Sustainable Development Goals
Authors: Sprinks James , Woods Sasha Marie , Parkinson Stephen , Wehn Uta , Joyce Hannah , Ceccaroni Luigi , Gharesifard Mohammad
Published: February 2021

Tracking progress towards the United Nations’ Sustainable Development Goals (SDGs) requires high-quality, timely, and accessible data, often in areas where data are rarely available. Problems exist due to socioeconomic variations between countries and the qualitative nature of certain indicators in their definition. Citizen science has the potential to contribute to several SDGs. However, whilst citizen science’s potential to contribute towards SDGs is well documented, limitations exist when measuring the impact that citizen science has made toward SDG progress. To better understand the issues and prospective solutions surrounding impact assessment towards SDG progress, this work presents the outcomes of semi-structured interviews with citizen science project coordinators. They reveal the complex nature of impact assessment within a citizen science context. Coordinators demonstrate greater confidence when the project is easier to relate to the SDGs, and the project methodology can objectively measure indicators. Issues exist, however, when considering SDGs with a broader, global context, those more difficult to link to project goals and when the project’s impact on them happens at timescales beyond the funding period. If the full potential of citizen-science contributions to the SDGs is to be realised, approaches are needed to fully consider practitioners’ needs and motivations.
On the Celebration of the International Day of Women and Girls in Science: Assessment of the Factors Mediating Women’s Empowerment in Scientific Research in Saudi Arabia
Authors: Al shammary Saud E. , Zrieq Rafat , Ibrahem Usama M. , Altamimi Ahmed B. , Diab Hanan M.
Published: February 2021

We present this study as a contribution to celebrate “the International Day of Women and Girls in Science”. Women’s empowerment in scientific research is a crucial issue in the United Nations “2030 Agenda for Sustainable Development”. This study aimed to investigate the obstacles facing women researchers in scientific research in Saudi Arabia. A cross-sectional survey was used to assess women researchers’ perceptions towards the roles of universities, donors and women researchers. Descriptive statistics, correlation and regression analysis were used for data analysis. Findings showed that the overall assessment of women’s empowerment in scientific research is moderate and faces a mixture of obstacles mainly due to dissatisfaction with the role of donors. Moreover, the role of universities has not yet reached a satisfactory level, while the role of women researchers was satisfactory. The results demonstrated that the role of the university enhances the role of the donor institutions, while the role of researchers has a significant positive influence on the role of the university. No significant association between the role of the researchers and donors was achieved. In conclusion, despite the great achievement by women researchers in empowering themselves as scientific researchers, they still need more support from the university and donors.
Corporate Social Responsibility and Firm Performance in China’s Manufacturing: A Global Perspective of Business Models
Authors: Hu Qianqian , Zhu Tianlun , Lin Chien-Liang , Chen Tiejun , Chin Tachia
Published: February 2021

In a globalized and digital world, manufacturing firms have used internet technology to conduct value appropriation (VA). However, during the COVID-19 crisis, export-led manufacturing firms around the world, particularly those in developing countries, have been forced to lay off workers and cope with VA-related problems, and serious survival problems have resulted in critical corporate social responsibility (CSR)-related challenges. Whereas limited research has discussed relevant issues in nonwestern contexts, we adopt a global perspective of business model and transactional cost theory, aiming to fill this gap by investigating the mechanisms among different dimensions of CSR implementation, firm performance, and VA herein. Based on a sample of listed Chinese manufacturing firms, the results show that the CSR technique dimension is negatively related to firm performance, that the CSR content dimension is positively related to firm performance, and that VA positively moderates the relationships of all three CSR dimensions to firm performance. The main contribution here is providing a more comprehensive understanding of how different CSR dimensions reflect firms’ multiple ethical behaviors, which influence their sustainable performance, respectively, thus enriching the existing knowledge of CSR studies in a new digital era riddled with uncertainties and complexities. We also offer practical implications for other export-led manufacturing firms in developing countries facing turbulent times.
Prospective Methodologies in Hybrid Renewable Energy Systems for Energy Prediction Using Artificial Neural Networks
Authors: Rahman Md Mijanur , Shakeri Mohammad , Tiong Sieh Kiong , Khatun Fatema , Amin Nowshad , Pasupuleti Jagadeesh , Hasan Mohammad Kamrul
Published: February 2021

This paper presents a comprehensive review of machine learning (ML) based approaches, especially artificial neural networks (ANNs) in time series data prediction problems. According to literature, around 80% of the world’s total energy demand is supplied either through fuel-based sources such as oil, gas, and coal or through nuclear-based sources. Literature also shows that a shortage of fossil fuels is inevitable and the world will face this problem sooner or later. Moreover, the remote and rural areas that suffer from not being able to reach traditional grid power electricity need alternative sources of energy. A “hybrid-renewable-energy system” (HRES) involving different renewable resources can be used to supply sustainable power in these areas. The uncertain nature of renewable energy resources and the intelligent ability of the neural network approach to process complex time series inputs have inspired the use of ANN methods in renewable energy forecasting. Thus, this study aims to study the different data driven models of ANN approaches that can provide accurate predictions of renewable energy, like solar, wind, or hydro-power generation. Various refinement architectures of neural networks, such as “multi-layer perception” (MLP), “recurrent-neural network” (RNN), and “convolutional-neural network” (CNN), as well as “long-short-term memory” (LSTM) models, have been offered in the applications of renewable energy forecasting. These models are able to perform short-term time-series prediction in renewable energy sources and to use prior information that influences its value in future prediction.
Bikeability and Emerging Phenomena in Cycling: Exploratory Analysis and Review
Authors: Castañon Ugo N. , Ribeiro Paulo J. G.
Published: February 2021

Recent strategies to improve the performance of the cycling mode of transport are based on infrastructural, behavioral, and multimodal measures, which are related to the concept of bikeability. A literature review on “bikeability indexes” was conducted focusing on indicators, using a four-step systematic process. Fourteen studies were included for the final analysis and provided 138 indicators, 17 criteria, and four domains. The exploratory analysis evidenced limited application of indicators related to pollution, scarce use of indicators related to bicycle sharing systems (BSS), absence of indicators related to electric bicycles, lack of indicators related to digital solutions, and the need of a calibration and validation process for bikeability indexes. Considering the changes and opportunities created by emerging innovations (namely BSS and electric bicycles) and the health trade-off related to pollution reduction, this research reveals that the current bikeability indexes do not fully address the real potential of a cycle network, limiting its use as a comprehensive tool for the promotion of sustainable mobility.
Identifying a Sustainable Food Procurement Strategy in Healthcare Systems: A Scoping Review
Authors: Alberdi Goiuri , Begiristain-Zubillaga Mirene
Published: February 2021

The healthcare system’s climate footprint is equivalent to 4.4% of global net emission. The food service offered, with subsequent food waste production and energy consumption, falls within the spectrum of environmentally harmful activities. The development of a Sustainable Food Procurement Strategy is an opportunity to counteract these negative effects. This article aims to identify the nature and extent of the evidence found in the literature on the processes related to food procurement within healthcare systems and analyse them from the perspective of sustainability dimensions. A scoping review is carried out using online databases to identify scientific and grey literature published in English during the period 2000–2019. An analytical-synthetic approach is used for charting the data. Twenty-six studies are included; 65% of them published in the last five years. These include research articles (n 11), an opinion article (n 1), policy handbooks and guides (n 2), project reports (n 4) and technical reports (n 3), policy forums (n 1), factsheet documents (n 3), and legislative directives (n 1). The outcomes framework highlights multilevel governance, a sustainable food supply system, and healthy and sustainable food services as the main action areas for a sustainable food procurement strategy, along with six transversal features: long-term commitment, investment, evaluation, communication, gender, and a holistic approach.
Challenges of Applying Simplified LCA Tools in Sustainable Design Pedagogy
Authors: Suppipat Suphichaya , Teachavorasinskun Kulthida , Hu Allen H.
Published: February 2021

The growing recognition of the Sustainable Development Goals (SDGs) has been integrated globally into product design and business activities. Life cycle assessment (LCA) is considered a useful tool for designers to apply in the early stages of product design to mitigate the environmental impact. The study aims to identify the challenges of applying simplified LCA tools to improve the eco-efficiency of products and achieve a higher level of sustainable innovation. The study was conducted in a sustainable design course at Chulalongkorn University, Bangkok, for four consecutive years. All challenges and opportunities by using ECO-it, Eco-indicators, and the Materials, Energy use, and Toxic emissions (MET) matrix to assess the environmental impact in each phase of 11 home appliances are presented and discussed. Results show the positive potential of applying the tools to achieve function innovation in design for sustainable innovation. The needs for guided instruction, the availability of the database, the complexity of a study product, and the overlooking of social dimensions are four major challenges in applying the tools in the early stages of product redesign. Further study in testing the tools and developing a database in collaboration with industries should be conducted to compare and validate the results.
The Effect of Different Types of Internal Curing Liquid on the Properties of Alkali-Activated Slag (AAS) Mortar
Authors: Zhang Guang-Zhu , Wang Xiao-Yong , Kim Tae-Wan , Lim Jong-Yeon , Han Yi
Published: February 2021

This study shows the effect of different types of internal curing liquid on the properties of alkali-activated slag (AAS) mortar. NaOH solution and deionized water were used as the liquid internal curing agents and zeolite sand was the internal curing agent that replaced the standard sand at 15% and 30%, respectively. Experiments on the mechanical properties, hydration kinetics, autogenous shrinkage (AS), internal temperature, internal relative humidity, surface electrical resistivity, ultrasonic pulse velocity (UPV), and setting time were performed. The conclusions are as follows: (1) the setting times of AAS mortars with internal curing by water were longer than those of internal curing by NaOH solution. (2) NaOH solution more effectively reduces the AS of AAS mortars than water when used as an internal curing liquid. (3) The cumulative heat of the AAS mortar when using water for internal curing is substantially reduced compared to the control group. (4) For the AAS mortars with NaOH solution as an internal curing liquid, compared with the control specimen, the compressive strength results are increased. However, a decrease in compressive strength values occurs when water is used as an internal curing liquid in the AAS mortar. (5) The UPV decreases as the content of zeolite sand that replaces the standard sand increases. (6) When internal curing is carried out with water as the internal curing liquid, the surface resistivity values of the AAS mortar are higher than when the alkali solution is used as the internal curing liquid. To sum up, both NaOH and deionized water are effective as internal curing liquids, but the NaOH solution shows a better performance in terms of reducing shrinkage and improving mechanical properties than deionized water.