bluuebunny/crossref_metadata_2025 huggingface dataset:
Linking Taxonomic, Phylogenetic and Functional Plant Diversity with Ecosystem Services of Cliffs and Screes in Greece
Authors: Panitsa Maria , Kokkoris Ioannis P. , Kougioumoutzis Konstantinos , Kontopanou Anna , Bazos Ioannis , Strid Arne , Dimopoulos Panayotis
Published: May 2021

Sparsely vegetated habitats of cliffs and screes act as refugia for many regional and local endemic specialized plant taxa most of which have evolved precisely for that type of habitat. The interplay between taxonomic, phylogenetic, and functional plant diversity on rock and scree habitats of extreme environmental conditions, enlightens the relations of plant communities and ecosystems and facilitates management planning for the conservation of biodiversity and ecosystem services. The identification of biodiversity patterns and hotspots (taxonomic, phylogenetic, and functional) contributes to the integration of the ecosystem services (ES) approach for the mapping and assessment of ecosystems and their services (MAES) implementation in Greece and the creation of thematic maps based on the MAES reporting format. The overlap among the protected areas’ network revealed that almost all areas of cliffs and screes of medium, high, and very high taxonomic and phylogenetic plant endemism are included in the Natura 2000 area network. The results of this study provide the baseline information for ES assessments at sparsely vegetated land of cliffs and screes. Our results contribute to the implementation of certain indicators of the national set of MAES indicators in Greece such as (a) floristic diversity and (b) microrefugia of endemic diversity and support of decision-making.
Lessons from Comparison of Hypoxia Signaling in Plants and Mammals
Authors: Doorly Catherine M. , Graciet Emmanuelle
Published: May 2021

Hypoxia is an important stress for organisms, including plants and mammals. In plants, hypoxia can be the consequence of flooding and causes important crop losses worldwide. In mammals, hypoxia stress may be the result of pathological conditions. Understanding the regulation of responses to hypoxia offers insights into novel approaches for crop improvement, particularly for the development of flooding-tolerant crops and for producing better therapeutics for hypoxia-related diseases such as inflammation and cancer. Despite their evolutionary distance, plants and mammals deploy strikingly similar mechanisms to sense and respond to the different aspects of hypoxia-related stress, including low oxygen levels and the resulting energy crisis, nutrient depletion, and oxidative stress. Over the last two decades, the ubiquitin/proteasome system and the ubiquitin-like protein SUMO have been identified as key regulators that act in concert to regulate core aspects of responses to hypoxia in plants and mammals. Here, we review ubiquitin and SUMO-dependent mechanisms underlying the regulation of hypoxia response in plants and mammals. By comparing and contrasting these mechanisms in plants and mammals, this review seeks to pinpoint conceptually similar mechanisms but also highlight future avenues of research at the junction between different fields of research.
Theoretical Characterization of New Frustrated Lewis Pairs for Responsive Materials
Authors: Galdeano Maialen , Ruipérez Fernando , Matxain Jon M.
Published: May 2021

In recent years, responsive materials including dynamic bonds have been widely acclaimed due to their expectation to pilot advanced materials. Within these materials, synthetic polymers have shown to be good candidates. Recently, the so-called frustrated Lewis pairs (FLP) have been used to create responsive materials. Concretely, the activation of diethyl azodicarboxylate (DEAD) by a triphenylborane (TPB) and triphenylphosphine (TPP) based FLP has been recently exploited for the production of dynamic cross-links. In this work, we computationally explore the underlying dynamic chemistry in these materials, in order to understand the nature and reversibility of the interaction between the FLP and DEAD. With this goal in mind, we first characterize the acidity and basicity of several TPB and TPP derivatives using different substituents, such as electron-donating and electron-withdrawing groups. Our results show that strong electron-donating groups increase the acidity of TPB and decrease the basicity of TPP. However, the FLP–DEAD interaction is not mainly dominated by the influence of these substituents in the acidity or basicity of the TPB or TPP systems, but by attractive or repulsive forces between substituents such as hydrogen bonds or steric effects. Based on these results, a new material is proposed based on FLP–DEAD complexes.
Effects of Fabrication Conditions on Structure and Properties of Mechanically Prepared Natural Silk Web and Non-Woven Fabrics
Authors: Bae Yeon-Su , Um In-Chul
Published: May 2021

In this study, natural silk web and natural silk non-woven fabric were prepared mechanically using the binding character of the sericin in silk. The effect of process variables on the preparation, structure, and properties of the silk web and the non-woven fabric was examined. The reeling velocity affected the morphology and mechanical properties of the web but had almost no influence on the crystalline structure of the silk. From the viewpoint of reel-ability and the mechanical properties (work of rupture) of silk web, a reeling velocity of 39.2 m/min represented the optimal processing velocity. The porosity and swelling ratio of the silk web decreased slightly with increasing reeling velocity. Furthermore, the reeling bath temperature had a significant effect on the reel-ability of silk filaments from a silkworm cocoon. Bath temperatures ≥50 °C yielded good reel-ability (>900 m reeling length). The porosity, swelling ratio in water, and mechanical properties of the silk web and silk non-woven fabric changed only slightly with the reeling bath temperature but changed significantly with the hot press treatment. The hot-pressed silk web (i.e., silk non-woven fabric) exhibited higher tensile strength as well as lower elongation at break, porosity, and swelling ratio than the silk web.
Utilizing Light Cure Units: A Concise Narrative Review
Authors: Hasanain Fatin A. , Nassar Hani M.
Published: May 2021

The use of photo-curable resin composite restorations is an essential treatment modality in modern dental practice. The success and longevity of these restorations depend on achieving predictable and effective polymerization. Understanding the dynamics of the polymerization and the effect of light cure units (LCUs) on this process is paramount. The goal of this concise narrative review is to provide a simplified presentation of basic principles of composite chemistry, polymerization reactions, and photo-curing with relevant terminologies. Clinical guidelines for choosing and maintaining LCUs, as well as safety precautions and factors under the control of the clinician are listed. Finally, clinical recommendations of LCUs’ usage and monitoring are included to aid practitioners in achieving predictable polymerization during the placement of direct resin composite restorations.
Classification and Production of Polymeric Foams among the Systems for Wound Treatment
Authors: Trucillo Paolo , Di Maio Ernesto
Published: May 2021

This work represents an overview on types of wounds according to their definition, classification and dressing treatments. Natural and synthetic polymeric wound dressings types have been analyzed, providing a historical overview, from ancient to modern times. Currently, there is a wide choice of materials for the treatment of wounds, such as hydrocolloids, polyurethane and alginate patches, wafers, hydrogels and semi-permeable film dressings. These systems are often loaded with drugs such as antibiotics for the simultaneous delivery of drugs to prevent or cure infections caused by the exposition of blood vessel to open air. Among the presented techniques, a focus on foams has been provided, describing the most diffused branded products and their chemical, physical, biological and mechanical properties. Conventional and high-pressure methods for the production of foams for wound dressing are also analyzed in this work, with a proposed comparison in terms of process steps, efficiency and removal of solvent residue. Case studies, in vivo tests and models have been reported to identify the real applications of the produced foams.
Advances in Polymer Based Composite Coatings
Authors: Groza Andreea
Published: May 2021

Polymer based composite coatings represent an important class of materials for various applications [...]
Properties and Interfacial Bonding Enhancement of Oil Palm Bio-Ash Nanoparticles Biocomposites
Authors: Abdullah C. K. , Ismail I. , Nurul Fazita M. R. , Olaiya N. G. , Nasution H. , Oyekanmi A. A. , Nuryawan Arif , H. P. S. Abdul Khalil
Published: May 2021

The effect of incorporating different loadings of oil palm bio-ash nanoparticles from agriculture waste on the properties of phenol-formaldehyde resin was investigated in this study. The bio-ash filler was used to enhance the performance of phenol-formaldehyde nanocomposites. Phenol-formaldehyde resin filled with oil palm bio-ash nanoparticles was prepared via the in-situ polymerization process to produce nanocomposites. The transmission electron microscope and particle size analyzer result revealed that oil palm bio-ash nanoparticles had a spherical geometry of 90 nm. Furthermore, X-ray diffraction results confirmed the formation of crystalline structure in oil palm bio-ash nanoparticles and phenol-formaldehyde nanocomposites. The thermogravimetric analysis indicated that the presence of oil palm bio-ash nanoparticles enhanced the thermal stability of the nanocomposites. The presence of oil palm bio-ash nanoparticles with 1% loading in phenol-formaldehyde resin enhanced the internal bonding strength of plywood composites. The scanning electron microscope image revealed that phenol-formaldehyde nanocomposites morphology had better uniform distribution and dispersion with 1% oil palm bio-ash nanoparticle loading than other phenol-formaldehyde nanocomposites produced. The nanocomposite has potential use in the development of particle and panel board for industrial applications.
Effect of Moisture Content on the Processing and Mechanical Properties of a Biodegradable Polyester
Authors: Titone Vincenzo , Correnti Antonio , La Mantia Francesco Paolo
Published: May 2021

This work is focused on the influence of moisture content on the processing and mechanical properties of a biodegradable polyester used for applications in injection molding. The pellets of the biodegradable polyester were exposed under different relative humidity conditions at a constant temperature before being compression molded. The compression-molded specimens were again placed under the above conditions before the mechanical testing. With all these samples, it is possible to determine the effect of moisture content on the processing and mechanical properties separately, as well as the combined effect of moisture content on the mechanical properties. The results obtained showed that the amount of absorbed water—both before processing and before mechanical testing—causes an increase in elongation at break and a slight reduction of the elastic modulus and tensile strength. These changes have been associated with possible hydrolytic degradation during the compression molding process and, in particular, with the plasticizing action of the moisture absorbed by the specimens.
Connective Tissue: Embracing Fluidity and Subverting Boundaries in European Iron Age and Roman Provincial Images
Authors: Aldhouse-Green Miranda
Published: May 2021

There is a mounting body of evidence for somatic exchange in burial practices within later British prehistory. The title of the present paper was sparked by a recent article in The Times (Tuesday 1 September 2020), which contained a description of human bone curation and body mingling clearly present in certain Bronze Age funerary depositional rituals. The practice of mixing up bodies has been identified at several broadly coeval sites, a prime example being Cladh Hallan in the Scottish Hebrides, where body parts from different individuals were deliberately mingled, not just somatically but also chronologically. This paper’s arguments rest upon the premise that somatic boundary crossing is reflected in Iron Age and later art, especially in the blending of human and animal imagery and of one animal species with another. Such themes are endemic in La Tène decorative metalwork and in western Roman provincial sacred imagery. It is possible, indeed likely, that such fluidity is associated with deliberate subversion of nature and with the presentation of ‘shamanism’ in its broadest sense. Breaking ‘natural’ rules and orders introduces edge blurring between material and spiritual worlds, representing, perhaps, the ability of certain individuals (shamans) to break free from human-scapes and to wander within the realms of the divine.
“Pray Aggressively for a Higher Goal—The Unification of All Christianity”: U.S. Catholic Charismatics and Their Ecumenical Relationships in the Late 1960s and 1970s
Authors: Ciciliot Valentina
Published: May 2021

In July 1977, 50,000 Christians from different backgrounds and traditions converged on Kansas City to participate in the Conference on Charismatic Renewal in the Christian Churches. Catholic charismatics played a key role in its organization, relying on all their ecumenical contacts built since the origins of the Catholic Charismatic Renewal (CCR) in 1967 at Duquesne University in Pittsburgh (PA). If the Kansas City conference represented the zenith of a shared unified vision for all charismatic Christianity, it also showed the emergence of the crisis which affected Catholic charismatic communities and their connection with Rome. This paper will explore U.S. Catholic charismatics’ relationships with other Christian denominations and groups in the initial development of the CCR, particularly in structuring Catholic charismatic communities, and their ecumenical perspectives in the tension between needs for legitimization (by the Vatican) and needs for self-expression.
Regional GNSS-Derived SPCI: Verification and Improvement in Yunnan, China
Authors: Ma Xiongwei , Yao Yibin , Zhao Qingzhi
Published: May 2021

From the aspect of global drought monitoring, improving the regional drought monitoring method is becoming increasingly important for the sustainable development of regional agriculture and the economy. The standardized precipitation conversion index (SPCI) calculated by the Global Navigation Satellite System (GNSS) observation is a new means for drought monitoring that has the advantages of simple calculation and real-time monitoring. However, only SPCI with a 12-month scale has been verified on a global scale, while its capability and applicability for monitoring drought at a short time scale in regional areas have never been investigated. Therefore, this study aims to evaluate the performance of SPCI at other time scales in Yunnan, China, and propose an improved method for SPCI. The data of six GNSS stations were selected to calculate SPCI; the standardized precipitation evapotranspiration index (SPEI) and composite meteorological drought index (CI) are introduced to evaluate the SPCI at a short time scale in Yunnan Province. In addition, a modified CI (MCI) was proposed to calibrate the SPCI because of its large bias in Yunnan. Experimental results show that (1) SPCI exhibits better agreement with CI in Yunnan Province when compared to SPEI; (2) the capability of SPCI for drought monitoring is superior to that of SPEI in Yunnan; and (3) the improved SPCI is more suitable for drought monitoring in Yunnan, with a relative bias of 5.43% when compared to the MCI. These results provide a new means for regional drought monitoring in Yunnan, which is significant for dealing with drought disasters and formulating related disaster prevention and mitigation policies.
Ocean–Atmosphere Interactions during Hurricanes Marco and Laura (2020)
Authors: Eley Emily N. , Subrahmanyam Bulusu , Trott Corinne B.
Published: May 2021

During August of the 2020 Atlantic Hurricane Season, the Gulf of Mexico (GoM) was affected by two subsequent storms, Hurricanes Marco and Laura. Hurricane Marco entered the GoM first (22 August) and was briefly promoted to a Category 1 storm. Hurricane Laura followed Marco closely (25 August) and attained Category 4 status after a period of rapid intensification. Typically, hurricanes do not form this close together; this study aims to explain the existence of both hurricanes through the analysis of air-sea fluxes, local thermodynamics, and upper-level circulation. The GoM and its quality of warm, high ocean heat content waters proved to be a resilient and powerful reservoir of heat and moisture fuel for both hurricanes; however, an area of lower ocean heat content due to circulation dynamics was crucial in the evolution of both Marco and Laura. An analysis of wind shear further explained the evolution of both hurricanes. Furthermore, a suite of satellite observations and ocean model outputs were used to evaluate the biophysical modulations in the GoM. The cold core eddy (CCE) and Mississippi River surface plume had the greatest biophysical oceanic responses; the oceanic modulations were initialized by Marco and extended temporally and spatially by Laura. Reduced sea surface temperatures (SST), changes in sea surface salinity (SSS), and changes in Chlorophyll-a (Chl-a) concentrations are related to translation speeds, and respective contributions of hurricane winds and precipitation are evaluated in this work.
Using Landsat Imagery to Assess Burn Severity of National Forest Inventory Plots
Authors: Pelletier Flavie , Eskelson Bianca N.I. , Monleon Vicente J. , Tseng Yi-Chin
Published: May 2021

As the frequency and size of wildfires increase, accurate assessment of burn severity is essential for understanding fire effects and evaluating post-fire vegetation impacts. Remotely-sensed imagery allows for rapid assessment of burn severity, but it also needs to be field validated. Permanent forest inventory plots can provide burn severity information for the field validation of remotely-sensed burn severity metrics, although there is often a mismatch between the size and shape of the inventory plot and the resolution of the rasterized images. For this study, we used two distinct datasets: (1) ground-based inventory data from the United States national forest inventory to calculate ground-based burn severity; and (2) remotely-sensed data from the Monitoring Trends in Burn Severity (MTBS) database to calculate different remotely-sensed burn severity metrics based on six weighting scenarios. Our goals were to test which MTBS metric would best align with the burn severity of national inventory plots observed on the ground, and to identify the superior weighting scenarios to extract pixel values from a raster image in order to match burn severity of the national inventory plots. We fitted logistic and ordinal regression models to predict the ground-based burn severity from the remotely-sensed burn severity averaged from six weighting scenarios. Among the weighting scenarios, two scenarios assigned weights to pixels based on the area of a pixel that intersected any parts of a national inventory plot. Based on our analysis, 9-pixel weighted averages of the Relative differenced Normalized Burn Ratio (RdNBR) values best predicted the ground-based burn severity of national inventory plots. Finally, the pixel specific weights that we present can be used to link other Landsat-derived remote sensing metrics with United States forest inventory plots.
CscGAN: Conditional Scale-Consistent Generation Network for Multi-Level Remote Sensing Image to Map Translation
Authors: Liu Yuanyuan , Wang Wenbin , Fang Fang , Zhou Lin , Sun Chenxing , Zheng Ying , Chen Zhanlong
Published: May 2021

Automatic remote sensing (RS) image to map translation is a crucial technology for intelligent tile map generation. Although existing methods based on a generative network (GAN) generated unannotated maps at a single level, they have limited capacity in handling multi-resolution map generation at different levels. To address the problem, we proposed a novel conditional scale-consistent generation network (CscGAN) to simultaneously generate multi-level tile maps from multi-scale RS images, using only a single and unified model. Specifically, the CscGAN first uses the level labels and map annotations as prior conditions to guide hierarchical feature learning with different scales. Then, a multi-scale discriminator and two multi-scale generators are introduced to describe both high-resolution and low-resolution representations, aiming to improve the similarity of generated maps and thus produce high-quality multi-level tile maps. Meanwhile, a level classifier is designed for further exploring the characteristics of tile maps at different levels. Moreover, the CscGAN is optimized by jointly multi-scale adversarial loss, level classification loss, and scale-consistent loss in an end-to-end manner. Extensive experiments on multiple datasets and study areas demonstrate that the CscGAN outperforms the state-of-the-art methods in multi-level map translation, with great robustness and efficiency.
Monitoring the Structure of Regenerating Vegetation Using Drone-Based Digital Aerial Photogrammetry
Authors: Nuijten Rik J. G. , Coops Nicholas C. , Watson Catherine , Theberge Dustin
Published: May 2021

Measures of vegetation structure are often key within ecological restoration monitoring programs because a change in structure is rapidly identifiable, measurements are straightforward, and structure is often a good surrogate for species composition. This paper investigates the use of drone-based digital aerial photogrammetry (DAP) for the characterization of the structure of regenerating vegetation as well as the ability to inform restoration programs through spatial arrangement assessment. We used cluster analysis on five DAP-derived metrics to classify vegetation structure into seven classes across three sites of ongoing restoration since linear disturbances in 2005, 2009, and 2014 in temperate and boreal coniferous forests in Alberta, Canada. The spatial arrangement of structure classes was assessed using land cover maps, mean patch size, and measures of local spatial association. We observed DAP heights of short-stature vegetation were consistently underestimated, but strong correlations (rs > 0.75) with field height were found for juvenile trees, shrubs, and perennials. Metrics of height and canopy complexity allowed for the extraction of relatively tall and complex vegetation structures, whereas canopy cover and height variability metrics enabled the classification of the shortest vegetation structures. We found that the boreal site disturbed in 2009 had the highest cover of classes associated with complex vegetation structures. This included early regenerative (22%) and taller (13.2%) wood-like structures as well as structures representative of tall graminoid and perennial vegetation (15.3%), which also showed the highest patchiness. The developed tools provide large-scale maps of the structure, enabling the identification and assessment of vegetational patterns, which is challenging based on traditional field sampling that requires pre-defined location-based hypotheses. The approach can serve as a basis for the evaluation of specialized restoration objectives as well as objectives tailored towards processes of ecological succession, and support prioritization of future inspections and mitigation measures.
A Universal Automatic Bottom Tracking Method of Side Scan Sonar Data Based on Semantic Segmentation
Authors: Zheng Gen , Zhang Hongmei , Li Yuqing , Zhao Jianhu
Published: May 2021

Determining the altitude of side-scan sonar (SSS) above the seabed is critical to correct the geometric distortions in the sonar images. Usually, a technology named bottom tracking is applied to estimate the distance between the sonar and the seafloor. However, the traditional methods for bottom tracking often require pre-defined thresholds and complex optimization processes, which make it difficult to achieve ideal results in complex underwater environments without manual intervention. In this paper, a universal automatic bottom tracking method is proposed based on semantic segmentation. First, the waterfall images generated from SSS backscatter sequences are labeled as water column (WC) and seabed parts, then split into specific patches to build the training dataset. Second, a symmetrical information synthesis module (SISM) is designed and added to DeepLabv3+, which not only weakens the strong echoes in the WC area, but also gives the network the capability of considering the symmetry characteristic of bottom lines, and most importantly, the independent module can be easily combined with any other neural networks. Then, the integrated network is trained with the established dataset. Third, a coarse-to-fine segmentation strategy with the well-trained model is proposed to segment the SSS waterfall images quickly and accurately. Besides, a fast bottom line search algorithm is proposed to further reduce the time consumption of bottom tracking. Finally, the proposed method is validated by the data measured with several commonly used SSSs in various underwater environments. The results show that the proposed method can achieve the bottom tracking accuracy of 1.1 pixels of mean error and 1.26 pixels of standard deviation at the speed of 2128 ping/s, and is robust to interference factors.
A Multi-Branch Feature Fusion Strategy Based on an Attention Mechanism for Remote Sensing Image Scene Classification
Authors: Shi Cuiping , Zhao Xin , Wang Liguo
Published: May 2021

In recent years, with the rapid development of computer vision, increasing attention has been paid to remote sensing image scene classification. To improve the classification performance, many studies have increased the depth of convolutional neural networks (CNNs) and expanded the width of the network to extract more deep features, thereby increasing the complexity of the model. To solve this problem, in this paper, we propose a lightweight convolutional neural network based on attention-oriented multi-branch feature fusion (AMB-CNN) for remote sensing image scene classification. Firstly, we propose two convolution combination modules for feature extraction, through which the deep features of images can be fully extracted with multi convolution cooperation. Then, the weights of the feature are calculated, and the extracted deep features are sent to the attention mechanism for further feature extraction. Next, all of the extracted features are fused by multiple branches. Finally, depth separable convolution and asymmetric convolution are implemented to greatly reduce the number of parameters. The experimental results show that, compared with some state-of-the-art methods, the proposed method still has a great advantage in classification accuracy with very few parameters.
Improving the Image Quality of Moving Ships for GF-3NG Based on Simultaneous AIS Information
Authors: Lv Yini , Zhong Lihua , Qiu Xiaolan , Yuan Xinzhe , Yang Junying , Hu Yuxin , Ding Chibiao
Published: May 2021

The synthetic aperture radar (SAR) is an important means of ship surveillance, but the motion of the ship leads to azimuth position offset, false targets, and azimuth defocusing for the spaceborne high-resolution and wide-swath (HRWS) SAR system, causing the degradation of imaging quality. The automatic identification system (AIS) can provide real-time information of the ships, which is an important auxiliary method for ship surveillance. Up to now, the traditional fusion of SAR and AIS mainly has focused on location matching and auxiliary recognition, and the next generation of GaoFen-3 (GF-3NG) satellite is equipped with both a SAR sensor and an AIS sensor to obtain the SAR images and simultaneous AIS information of ships. Consequently, this paper proposes a novel scheme to improve the imaging quality of moving ships for GF-3NG using AIS information. In this paper, through introducing the virtual stationary target, the slant range derivation (SRD) algorithm is proposed to estimate the radial velocity (RV) and the radial acceleration (RA) between the ship and the SAR platform relative to the stationary scene. According to the calculated RV, the azimuth position offset can be estimated and the ship can be repositioned on the image. After that, the traditional method is conducted to suppress the false targets. Finally, the method of using the RA to refocus ship slices is proposed. Additionally, the experiment results based on real data illustrate the effectiveness of the proposed methods.
Multiple Pedestrians and Vehicles Tracking in Aerial Imagery Using a Convolutional Neural Network
Authors: Azimi Seyed Majid , Kraus Maximilian , Bahmanyar Reza , Reinartz Peter
Published: May 2021

In this paper, we address various challenges in multi-pedestrian and vehicle tracking in high-resolution aerial imagery by intensive evaluation of a number of traditional and Deep Learning based Single- and Multi-Object Tracking methods. We also describe our proposed Deep Learning based Multi-Object Tracking method AerialMPTNet that fuses appearance, temporal, and graphical information using a Siamese Neural Network, a Long Short-Term Memory, and a Graph Convolutional Neural Network module for more accurate and stable tracking. Moreover, we investigate the influence of the Squeeze-and-Excitation layers and Online Hard Example Mining on the performance of AerialMPTNet. To the best of our knowledge, we are the first to use these two for regression-based Multi-Object Tracking. Additionally, we studied and compared the L1 and Huber loss functions. In our experiments, we extensively evaluate AerialMPTNet on three aerial Multi-Object Tracking datasets, namely AerialMPT and KIT AIS pedestrian and vehicle datasets. Qualitative and quantitative results show that AerialMPTNet outperforms all previous methods for the pedestrian datasets and achieves competitive results for the vehicle dataset. In addition, Long Short-Term Memory and Graph Convolutional Neural Network modules enhance the tracking performance. Moreover, using Squeeze-and-Excitation and Online Hard Example Mining significantly helps for some cases while degrades the results for other cases. In addition, according to the results, L1 yields better results with respect to Huber loss for most of the scenarios. The presented results provide a deep insight into challenges and opportunities of the aerial Multi-Object Tracking domain, paving the way for future research.
Predicting Exact Valence and Arousal Values from EEG
Authors: Galvão Filipe , Alarcão Soraia M. , Fonseca Manuel J.
Published: May 2021

Recognition of emotions from physiological signals, and in particular from electroencephalography (EEG), is a field within affective computing gaining increasing relevance. Although researchers have used these signals to recognize emotions, most of them only identify a limited set of emotional states (e.g., happiness, sadness, anger, etc.) and have not attempted to predict exact values for valence and arousal, which would provide a wider range of emotional states. This paper describes our proposed model for predicting the exact values of valence and arousal in a subject-independent scenario. To create it, we studied the best features, brain waves, and machine learning models that are currently in use for emotion classification. This systematic analysis revealed that the best prediction model uses a KNN regressor (K = 1) with Manhattan distance, features from the alpha, beta and gamma bands, and the differential asymmetry from the alpha band. Results, using the DEAP, AMIGOS and DREAMER datasets, show that our model can predict valence and arousal values with a low error (MAE < 0.06, RMSE < 0.16) and a strong correlation between predicted and expected values (PCC > 0.80), and can identify four emotional classes with an accuracy of 84.4%. The findings of this work show that the features, brain waves and machine learning models, typically used in emotion classification tasks, can be used in more challenging situations, such as the prediction of exact values for valence and arousal.
A New Adaptive Method for the Extraction of Steel Design Structures from an Integrated Point Cloud
Authors: Burdziakowski Pawel , Zakrzewska Angelika
Published: May 2021

The continuous and intensive development of measurement technologies for reality modelling with appropriate data processing algorithms is currently being observed. The most popular methods include remote sensing techniques based on reflected-light digital cameras, and on active methods in which the device emits a beam. This research paper presents the process of data integration from terrestrial laser scanning (TLS) and image data from an unmanned aerial vehicle (UAV) that was aimed at the spatial mapping of a complicated steel structure, and a new automatic structure extraction method. We proposed an innovative method to minimize the data size and automatically extract a set of points (in the form of structural elements) that is vital from the perspective of engineering and comparative analyses. The outcome of the research was a complete technology for the acquisition of precise information with regard to complex and high steel structures. The developed technology includes such elements as a data integration method, a redundant data elimination method, integrated photogrammetric data filtration and a new adaptive method of structure edge extraction. In order to extract significant geometric structures, a new automatic and adaptive algorithm for edge extraction from a random point cloud was developed and presented herein. The proposed algorithm was tested using real measurement data. The developed algorithm is able to realistically reduce the amount of redundant data and correctly extract stable edges representing the geometric structures of a studied object without losing important data and information. The new algorithm automatically self-adapts to the received data. It does not require any pre-setting or initial parameters. The detection threshold is also adaptively selected based on the acquired data.
On the Wireless Microwave Sensing of Bacterial Membrane Potential in Microfluidic-Actuated Platforms
Authors: Jofre Marc , Jofre Lluís , Jofre-Roca Luis
Published: May 2021

The investigation of the electromagnetic properties of biological particles in microfluidic platforms may enable microwave wireless monitoring and interaction with the functional activity of microorganisms. Of high relevance are the action and membrane potentials as they are some of the most important parameters of living cells. In particular, the complex mechanisms of a cell’s action potential are comparable to the dynamics of bacterial membranes, and consequently focusing on the latter provides a simplified framework for advancing the current techniques and knowledge of general bacterial dynamics. In this work, we provide a theoretical analysis and experimental results on the microwave detection of microorganisms within a microfluidic-based platform for sensing the membrane potential of bacteria. The results further advance the state of microwave bacteria sensing and microfluidic control and their implications for measuring and interacting with cells and their membrane potentials, which is of great importance for developing new biotechnologically engineered systems and solutions.
Identifying and Estimating the Location of Sources of Industrial Pollution in the Sewage Network
Authors: Buras Magdalena Paulina , Solano Donado Fernando
Published: May 2021

Harsh pollutants that are illegally disposed in the sewer network may spread beyond the sewer network—e.g., through leakages leading to groundwater reservoirs—and may also impair the correct operation of wastewater treatment plants. Consequently, such pollutants pose serious threats to water bodies, to the natural environment and, therefore, to all life. In this article, we focus on the problem of identifying a wastewater pollutant and localizing its source point in the wastewater network, given a time-series of wastewater measurements collected by sensors positioned across the sewer network. We provide a solution to the problem by solving two linked sub-problems. The first sub-problem concerns the detection and identification of the flowing pollutants in wastewater, i.e., assessing whether a given time-series corresponds to a contamination event and determining what the polluting substance caused it. This problem is solved using random forest classifiers. The second sub-problem relates to the estimation of the distance between the point of measurement and the pollutant source, when considering the outcome of substance identification sub-problem. The XGBoost algorithm is used to predict the distance from the source to the sensor. Both of the models are trained using simulated electrical conductivity and pH measurements of wastewater in sewers of a european city sub-catchment area. Our experiments show that: (a) resulting precision and recall values of the solution to the identification sub-problem can be both as high as 96%, and that (b) the median of the error that is obtained for the estimation of the source location sub-problem can be as low as 6.30 m.
Improved Single Inertial-Sensor-Based Attitude Estimation during Walking Using Velocity-Aided Observation
Authors: Dang Duc Cong , Suh Young Soo
Published: May 2021

This paper presents a Kalman filter-based attitude estimation algorithm using a single body-mounted inertial sensor consisting of a triaxial accelerometer and triaxial gyroscope. The proposed algorithm has been developed for attitude estimation during dynamic conditions such as walking and running. Based on the repetitive properties of the velocity signal of human gait during walking, a novel velocity-aided observation is used as a measurement update for the filter. The performance has been evaluated in comparison to two standard Kalman filters with different measurement update methods and a smoother algorithm which is formulated in the form of a quadratic optimization problem. Whereas two standard Kalman filters give maximum 5 degrees in both pitch and roll error for short walking case, their performance gradually decrease with longer walking distance. The proposed algorithm shows the error of about 3 degrees in 15 m walking case, and indicate the robustness of the method with the same performance in 75 m trials. As far as the accuracy of the estimation is concerned, the proposed method achieves advantageous results due to its periodic error correction capability in both short and long walking cases at varying speeds. In addition, in terms of practicality and stability, with simple parameter settings and without the need of all-time data, the algorithm can achieve smoothing-algorithm-performance level with lower computational resources.
Novel Conformal Skin Patch with Embedded Thin-Film Electrodes for Early Detection of Extravasation
Authors: Lim Ruiqi , Damalerio Ramona B. , Bong Choon Looi , Tan Swee Kim , Cheng Ming-Yuan
Published: May 2021

Extravasation is a complication of intravenous (IV) cannulation in which vesicant drugs leak from a vein into the surrounding subcutaneous tissue. The severity of extravasation depends on the type, concentration, and volume of drugs that accumulate in the subcutaneous tissue. Rapid detection of extravasation can facilitate prompt medical intervention, minimizing tissue damage, and preventing adverse events. In this study, we present two portable sensor patches, namely gold- and carbon-based sensing patches, for early detection of extravasation. The gold-based sensor patch detected extravasated fluid of volume as low as 2 mL in in vivo animal models and human clinical trials; the patch exhibited a resistance change of 41%. The carbon-based sensor patch exhibited a resistance change of 51% for 2 mL of extravasated fluid, and fabrication throughput and cost-effectiveness are superior for this patch compared with the gold-based sensing patch.
Calibration-Free Single-Anchor Indoor Localization Using an ESPAR Antenna
Authors: Groth Mateusz , Nyka Krzysztof , Kulas Lukasz
Published: May 2021

In this paper, we present a novel, low-cost approach to indoor localization that is capable of performing localization processes in real indoor environments and does not require calibration or recalibration procedures. To this end, we propose a single-anchor architecture and design based on an electronically steerable parasitic array radiator (ESPAR) antenna and Nordic Semiconductor nRF52840 utilizing Bluetooth Low Energy (BLE) protocol. The proposed algorithm relies on received signal strength (RSS) values measured by the receiver equipped with the ESPAR antenna for every considered antenna radiation pattern. The calibration-free concept is achieved by using inexpensive BLE nodes installed in known positions on the walls of the test room and acting as reference nodes for the positioning algorithm. Measurements performed in the indoor environment show that the proposed approach can successfully provide positioning results better than those previously reported for single-anchor ESPAR antenna localization systems employing the classical fingerprinting method and relying on time-consuming calibration procedures.
Dynamic Model of a Humanoid Exoskeleton of a Lower Limb with Hydraulic Actuators
Authors: Glowinski Sebastian , Obst Maciej , Majdanik Sławomir , Potocka-Banaś Barbara
Published: May 2021

Exoskeletons are the mechanical systems whose operation is carried out in close cooperation with the human body. In this paper, the authors describe a mathematical model of the hydraulic exoskeleton of a lower limb. The coordinates of characteristic points of the exoskeleton in the sagittal plane as a function of user height are presented. The mathematical models, kinematics, and kinetics equations were determined. The masses of the actuators and their dimensions were selected based on catalog data. The force distribution in the wearable system during the squat is shown. The proposed models allowed us to determine the trajectory of individual points of the exoskeleton and to determine the forces in hydraulic cylinders that are necessary to perform a specific displacement. The simulation results show that the joint moments depend linearly on actuator forces. The dynamics equations of the wearable system are non-linear. The inertia of the system depends on the junction variables and it proves that there are dynamic couplings between the individual axes of the exoskeleton.
Torque Curve Optimization of Ankle Push-Off in Walking Bipedal Robots Using Genetic Algorithm
Authors: Ji Qiaoli , Qian Zhihui , Ren Lei , Ren Luquan
Published: May 2021

Ankle push-off occurs when muscle–tendon units about the ankle joint generate a burst of positive power at the end of stance phase in human walking. Ankle push-off mainly contributes to both leg swing and center of mass (CoM) acceleration. Humans use the amount of ankle push-off to induce speed changes. Thus, this study focuses on determining the faster walking speed and the lowest energy efficiency of biped robots by using ankle push-off. The real-time-space trajectory method is used to provide reference positions for the hip and knee joints. The torque curve during ankle push-off, composed of three quintic polynomial curves, is applied to the ankle joint. With the walking distance and the mechanical cost of transport (MCOT) as the optimization goals, the genetic algorithm (GA) is used to obtain the optimal torque curve during ankle push-off. The results show that the biped robot achieved a maximum speed of 1.3 m/s, and the ankle push-off occurs at 41.27−48.34% of the gait cycle. The MCOT of the bipedal robot corresponding to the high economy gait is 0.70, and the walking speed is 0.54 m/s. This study may further prompt the design of the ankle joint and identify the important implications of ankle push-off for biped robots.
Non-Orthogonal Multiple Access for Unicast and Multicast D2D: Channel Assignment, Power Allocation and Energy Efficiency
Authors: Hmila Mariem , Fernández-Veiga Manuel , Rodríguez-Pérez Miguel , Herrería-Alonso Sergio
Published: May 2021

Non-orthogonal multiple access (NOMA) techniques have emerged in the past years as a solution to approximate the throughput performance of wireless communications systems to their theoretical capacity region. We consider in this paper an optimization-based model for multicast device-to-device (MD2D) communications where the channels are not orthogonal and may be (partially or fully) shared among the transmitters in each cluster. This setting leads naturally to the introduction of NOMA transmitters and receivers who use successive interference cancellation (SIC) to separate the superposed signals. To analyze the role of NOMA in MD2D, its performance impact, potential performance gains and possible shortcomings, we formulate a model that includes SIC operations in the decoders, so that higher rates can be attained when several sources transmit on the same channel(s). We also investigate the energy efficiency of the network (global and max-min) through a dynamic power control algorithm and present a centralized and a semi-distributed solution to these optimization problems. Through numerical simulations, we show that NOMA is able to improve both the sum-rate and the max-min rate of a MD2D network even from a small degree of resource sharing. Furthermore, these gains also improve the global energy efficiency on the network, but not always the max-min energy efficiency of the devices.
Improved In-Flight Estimation of Inertial Biases through CDGNSS/Vision Based Cooperative Navigation
Authors: Causa Flavia , Fasano Giancarmine
Published: May 2021

This paper discusses the exploitation of a cooperative navigation strategy for improved in-flight estimation of inertial sensors biases on board unmanned aerial vehicles. The proposed multi-vehicle technique is conceived for a “chief” Unmanned Aerial Vehicle (UAV) and relies on one or more deputy aircrafts equipped with Global Navigation Satellite System (GNSS) antennas for differential positioning which also act as features for visual tracking. Combining carrier-phase differential GNSS and visual estimates, it is possible to retrieve accurate inertial-independent attitude information, thus potentially enabling improved bias estimation. Camera and carrier-phase differential GNSS measurements are integrated within a 15 states extended Kalman filter. Exploiting an ad hoc developed numerical environment, the paper analyzes the performance of the cooperative approach for inertial biases estimation as a function of number of deputies, formation geometry and distances, and absolute and relative dynamics. It is shown that exploiting two deputies it is possible to improve biases estimation, while a single deputy can be effective if changes of relative geometry and dynamics are also considered. Experimental proofs of concept based on two multi-rotors flying in formation are presented and discussed. The proposed framework is applicable beyond the domain of small UAVs.