bluuebunny/crossref_metadata_2025 huggingface dataset:
Removal of Scale-Forming Ions and Oil Traces from Oil Field Produced Water Using Graphene Oxide/Polyethersulfone and TiO2 Nanoribbons/Polyethersulfone Nanofiltration Membranes
Authors: Ashraf Tarek , Alfryyan Nada , Nasr Mervat , Ahmed Sayed , Shaban Mohamed
Published: June 2022

Treatment of produced water in oil fields has become a tough challenge for oil producers. Nanofiltration, a promising method for water treatment, has been proposed as a solution. The phase inversion technique was used for the synthesis of nanofiltration membranes of polyethersulfone embedded with graphene oxide nanoparticles and polyethersulfone embedded with titanium nanoribbons. As a realistic situation, water samples taken from the oil field were filtered using synthetic membranes at an operating pressure of 0.3 MPa. Physiochemical properties such as water flux, membrane morphology, flux recovery ratio, pore size and hydrophilicity were investigated. Additionally, filtration efficiency for removal of constituent ions, oil traces in water removal, and fouling tendency were evaluated. The constituent ions of produced water act as the scaling agent which threatens the blocking of the reservoir bores of the disposal wells. Adding graphene oxide (GO) and titanium nanoribbons (TNR) to polyethersulfone (PES) enhanced filtration efficiency, water flux, and anti-fouling properties while also boosting hydrophilicity and porosity. The PES-0.7GO membrane has the best filtering performance, followed by the PES-0.7TNR and pure-PES membranes, with chloride salt rejection rates of 81%, 78%, and 35%; oil rejection rates of 88%, 85%, and 71%; and water fluxes of 85, 82, and 42.5 kg/m2 h, respectively. Because of its higher hydrophilicity and physicochemical qualities, the PES-0.7GO membrane outperformed the PES-0.7TNR membrane. Nanofiltration membranes embedded with nanomaterial described in this work revealed encouraging long-term performance for oil-in-water trace separation and scaling agent removal.
Numerical Simulation of the Deformation Behavior of Softwood Tracheids for the Calculation of the Mechanical Properties of Wood–Polymer Composites
Authors: Hartmann Robert , Puch Florian
Published: June 2022

From a fiber composite point of view, an elongated softwood particle is a composite consisting of several thousand tracheids, which can be described as fiber wound hollow profiles. By knowing their deformation behavior, the deformation behavior of the wood particle can be described. Therefore, a numerical approach for RVE- and FEM-based modelling of the radial and tangential compression behavior of pine wood tracheids under room climate environment is presented and validated with optical and laser-optical image analysis as well as tensile and compression tests on pine sapwood veneer strips. According to the findings, at 23 °C and 12% moisture content, at least 10 MPa must be applied for maximum compaction of the earlywood tracheids. The latewood tracheids can withstand at least 100 MPa compression pressure and would deform elastically at this load by about 20%. The developed model can be adapted for other wood species and climatic conditions by adjusting the mechanical properties of the base materials of the cell wall single layers (cellulose, hemicellulose, lignin), the dimensions and the structure of the vessel elements, respectively.
Development of Narrow-Bore C18 Column for Fast Separation of Peptides and Proteins in High-Performance Liquid Chromatography
Authors: Ali Ashraf , Alharthi Sarah , Al-Shaalan Nora , Santali Eman
Published: June 2022

Separation with high efficiency and good resolution is constantly in demand in the pharmaceutical industry. The fast and efficient separation of complex samples such as peptides and proteins is a challenging task. To achieve high efficiency with good resolution, chromatographers are moving towards small particles packed into narrow-bore columns. Silica monolith particles (sub-2 µm) were derivatized with chlorodimethyl octadecyl silane (C18) and packed into stainless steel columns (100 mm × 1.8 mm i.d) by a slurry-packing method. The developed columns were used for the separation of peptides and proteins. A separation efficiency (N) of 40,000 plates/column (400,000 plates/m) was achieved for the mixture of five peptides. Similarly, the fast separation of the peptides was carried out using a high flow rate, and the separation of the five peptides was achieved in one minute with high efficiency (N ≅ 240,000 plates/m). The limit of detection (DL) and the limit of quantification (QL) for each analyte were determined by developing a linear regression curve with relatively very low concentrations of the target compound. The average values of the QL for the peptide and proteins were 0.55 ng and 0.48 ng, respectively, using short C18 column (1.8 mm × 100 mm) UV (at 214 nm). The fast analysis of peptides and proteins with such high efficiency and good resolution has not been reported in the literature yet. Owing to high efficiency, these home-made columns could be used as an alternative to the expensive commercial columns for peptide and protein separation.
The Effect of Plasma Treatment of Polyethylene Powder and Glass Fibers on Selected Properties of Their Composites Prepared via Rotational Molding
Authors: Ghanem Zoya , Šourkova Hana Jelinek , Sezemsky Jan , Špatenka Petr
Published: June 2022

In this article, the effect of plasma treatment of polyethylene powder and glass fibers on the adhesion between polyethylene and glass fibers in composites prepared by rotational molding was studied. In contrast to other processing techniques, such as injection molding, the rotational molding process operates at atmospheric pressure, and no pressure is added to ensure mechanical interlocking. This makes reinforcing the rotomolded product very difficult. Therefore, the formation of chemical bonds is necessary for strong adhesion. Different combinations of untreated polyethylene (UT.PE), plasma-treated polyethylene (PT.PE), untreated and plasma-treated glass fibers were manually mixed and processed by rotational molding. The resulting composites were cut and tested to demonstrate the effect of the treatment on the adhesion between the composite components and on the mechanical properties of the final composites. The results showed that the treatment of both powder and fiber improved the adhesion between the matrix and fibers, thus improving the mechanical properties of the resulting composites compared to those of pure polyethylene samples and composites prepared using untreated components. The tensile strength, tensile modulus, and flexural modulus of the composites prepared using 10-min treated powder with 20 wt% of 40-min treated fibers improved by 20%, 82%, and 98%, respectively, compared to the pure polyethylene samples.
Rethinking Guo Xiang’s Concept of “Nothing” in the Perspective of His Reception of Laozi and Zhuangzi
Authors: Gao Yuhan
Published: June 2022

Since Feng Youlan and Tang Yongtong, scholars have mostly understood Guo Xiang’s “supreme nothing” (至無, zhi wu) as “non-existence”, arguing that by denying Dao as the origin of the universe, the philosophical tradition of Laozi, Zhuangzi, and Wang Bi, he strives to prove “self-generation” (自生, zi sheng) of all things. This way of interpretation not only leads to various dilemmas from the perspective of intellectual history, but also diverges from Guo Xiang’s own account of Dao. The purpose of this paper is to argue that Guo Xiang, instead of dismissing it, solidifies the opinion of Laozi and Zhuangzi on the transcendence of Dao through the concept of “supreme nothing”, and that the self-generation of all things is the logical endpoint of this reinforcement. The seemingly opposite viewpoints of transcendence and immanence, “Dao generates all things” and “All things are self-generated”, merge with each other in the context of the proposition “Dao follows nature” (道法自然, dao fa zi ran) in Laozi.
Cold Atom Interferometry for Enhancing the Radio Science Gravity Experiment: A Phobos Case Study
Authors: Plumaris Michael , Dirkx Dominic , Siemes Christian , Carraz Olivier
Published: June 2022

Interplanetary missions have typically relied on Radio Science (RS) to recover gravity fields by detecting their signatures on the spacecraft trajectory. The weak gravitational fields of small bodies, coupled with the prominent influence of confounding accelerations, hinder the efficacy of this method. Meanwhile, quantum sensors based on Cold Atom Interferometry (CAI) have demonstrated absolute measurements with inherent stability and repeatability, reaching the utmost accuracy in microgravity. This work addresses the potential of CAI-based Gradiometry (CG) as a means to strengthen the RS gravity experiment for small-body missions. Phobos represents an ideal science case as astronomic observations and recent flybys have conferred enough information to define a robust orbiting strategy, whilst promoting studies linking its geodetic observables to its origin. A covariance analysis was adopted to evaluate the contribution of RS and CG in the gravity field solution, for a coupled Phobos-spacecraft state estimation incorporating one week of data. The favourable observational geometry and the small characteristic period of the gravity signal add to the competitiveness of Doppler observables. Provided that empirical accelerations can be modelled below the nm/s2 level, RS is able to infer the 6 × 6 spherical harmonic spectrum to an accuracy of 0.1–1% with respect to the homogeneous interior values. If this correlates to a density anomaly beneath the Stickney crater, RS would suffice to constrain Phobos’ origin. Yet, in event of a rubble pile or icy moon interior (or a combination thereof) CG remains imperative, enabling an accuracy below 0.1% for most of the 10 × 10 spectrum. Nevertheless, technological advancements will be needed to alleviate the current logistical challenges associated with CG operation. This work also reflects on the sensitivity of the candidate orbits with regard to dynamical model uncertainties, which are common in small-body environments. This brings confidence in the applicability of the identified geodetic estimation strategy for missions targeting other moons, particularly those of the giant planets, which are targets for robotic exploration in the coming decades.
From Its Core to the Niche: Insights from GPR Applications
Authors: Lombardi Federico , Podd Frank , Solla Mercedes
Published: June 2022

Thanks to its non-destructive, high-resolution imaging possibilities and its sensitivity to both conductive and dielectric subsurface structures, Ground-Penetrating Radar (GPR) has become a widely recognized near-surface geophysical tool, routinely adopted in a wide variety of disciplines. Since its first development almost 100 years ago, the domain in which the methodology has been successfully deployed has significantly expanded from ice sounding and environmental studies to precision agriculture and infrastructure monitoring. While such expansion has been clearly supported by the evolution of technology and electronics, the operating principles have always secured GPR a predominant position among alternative inspection approaches. The aim of this contribution is to provide a large-scale survey of the current areas where GPR has emerged as a valuable prospection methodology, highlighting the reasons for such prominence and, at the same time, to suggest where and how it could be enhanced even more.
High-Resolution and Low Blind Range Waveform for Migratory Insects’ Taking-Off and Landing Behavior Observation
Authors: Wang Rui , Zhang Tianran , Cui Kai , Yu Teng , Jiang Qi , Zhang Rongjing , Li Jiayi , Hu Cheng
Published: June 2022

The observation of taking-off and landing behaviors of migratory insects is important for pest early monitoring and forecasting. Entomological radar, which can measure the ascent or descent rates remotely, has been proved to be the most effective way to observe the above behaviors. For the current entomological radars, the highest range resolution of 7.5 m and smallest blind range of 150 m make it difficult to distinguish individual insects in a swarm and observe the migratory behaviors at low altitudes. In this paper, based on the outfield data acquired from a high-resolution radar of 0.2 m, the spatial spacing distribution of migratory insects is presented for the first time and waveform design requirements are analyzed, proving the necessity of a higher resolution. Secondly, for the high-resolution and low blind range observation of insects, the stepped frequency train of LFM pulses (also called frequency-jumped burst, FJB) waveform is discussed as the optimal choice. In order to resolve the high grating-lobe problem in the FJB waveform with the low blind range, the precise spectrum model and its approximation of the LFM subpulse are first derived in detail. Then, according to the obtained spectrum characteristics, the high-resolution and low blind range FJB waveform design methods based on spectrum fluctuation period and Fresnel integral windowing are proposed to reduce the grating-lobe number to more than 50% and suppress the highest grating lobe level by at least 4 dB. Finally, based on the high-resolution and low blind range VLR adopting the proposed waveform, several typical taking-off and landing behavior observation results are presented, which proves that, in their migratory pattern, insects usually take off around sunset.
Geolocation Assessment and Optimization for OMPS Nadir Mapper: Methodology
Authors: Wang Likun , Pan Chunhui , Yan Banghua , Beck Trevor , Chen Junye , Zhou Lihang , Kalluri Satya , Goldberg Mitch
Published: June 2022

Onboard both the Suomi National Polar-orbiting Partnership and Joint Polar Satellite System (JPSS) series of satellites, the Ozone Mapping and Profiler Suite Nadir Mapper (OMPS-NM) is a new generation of a total ozone column sensor and is used to generate total column ozone products. This study presents a method for efficiently assessing OMPS-NM geolocation accuracy using spatially collocated radiance measurements from the Visible Infrared Imaging Radiometer Suite (VIIRS) Moderate Band M1 by taking advantage of its high spatial resolution (750 m at nadir) and accurate geolocation. The basic idea is to find the best collocation position with maximum correlation between VIIRS collocated and real OMPS-NM radiances by perturbing OMPS-NM line-of-sight (LOS) vectors in the cross-track and along-track directions with small steps in the spacecraft coordinate. The perturbation angles at the best collocation position where OMPS-NM and VIIRS are optimally aligned are used to characterize OMPS-NM geolocation accuracy. In addition, the assessment results can be used to optimize the OMPS-NM field view angle lookup table in the Sensor Data Record (SDR) processing software to improve its geolocation accuracy. To demonstrate the methodology, the proposed method is successfully employed to evaluate OMPS-NM geolocation accuracy with different spatial resolutions. The results indicate that, after the view angle table was updated, the geolocation accuracy for both SNPP and NOAA-20 OMPS-NM is on the sub-pixel level (less than ¼ pixel size) along all the scan positions in both cross-track and along-track directions and the performance is very stable with time. The method proposed in this study lays down the framework for assessing the geolocation accuracy of future high-resolution OMPS-NM measurements.
Using Deep Learning and Very-High-Resolution Imagery to Map Smallholder Field Boundaries
Authors: Mei Weiye , Wang Haoyu , Fouhey David , Zhou Weiqi , Hinks Isabella , Gray Josh M. , Van Berkel Derek , Jain Meha
Published: June 2022

The mapping of field boundaries can provide important information for increasing food production and security in agricultural systems across the globe. Remote sensing can provide a viable way to map field boundaries across large geographic extents, yet few studies have used satellite imagery to map boundaries in systems where field sizes are small, heterogeneous, and irregularly shaped. Here we used very-high-resolution WorldView-3 satellite imagery (0.5 m) and a mask region-based convolutional neural network (Mask R-CNN) to delineate smallholder field boundaries in Northeast India. We found that our models had overall moderate accuracy, with average precision values greater than 0.67 and F1 Scores greater than 0.72. We also found that our model performed equally well when applied to another site in India for which no data were used in the calibration step, suggesting that Mask R-CNN may be a generalizable way to map field boundaries at scale. Our results highlight the ability of Mask R-CNN and very-high-resolution imagery to accurately map field boundaries in smallholder systems.
Extending the National Burned Area Composite Time Series of Wildfires in Canada
Authors: Skakun Rob , Castilla Guillermo , Metsaranta Juha , Whitman Ellen , Rodrigue Sebastien , Little John , Groenewegen Kathleen , Coyle Matthew
Published: June 2022

Wildfires are a major natural disturbance in Canada that are postulated to increase under a warming climate. To derive accurate trends in burned area and to quantify the effects of fire frequency, duration, and extent, a sufficiently long time series of reliable burned area maps is required. With that in mind, we extended Canada’s National Burned Area Composite (NBAC) dataset from its previous start year (2004) back to 1986. NBAC consists of annual maps in polygon format where the area burned in each fire event is represented by the best available delineation among various mapping methods and data sources of varying quality. Ordered from more to less reliability, in the new 35-year time series (1986 to 2020), 10% of the total burned area was derived from airborne and high-resolution (<5 m) satellite imagery, 81% from change detection methods using 30 m Landsat satellite imagery, and the remaining 9% was largely from aerial surveys. Total (Canada-wide) annual burned area estimates ranged from 215,797 ha in 2020 to 6.7 million ha (Mha) in 1989. We computed 95% confidence intervals for the estimate of each year from 1986 to 2020 based on the accuracy and relative contribution in that year of the different data sources, for both the new NBAC time series and the polygon version of the Canadian National Fire Database (CNFDB), a commonly used source of spatially explicit data on burned area in Canada. NBAC confidence intervals were on average ±9.7% of the annual figure, about one-third the width of the confidence intervals derived for CNFDB. The NBAC time series also included nearly 5000 fire events (totalling 4 Mha, with the largest event being 120,661 ha in size) that are missing in the CNFDB. In a regional analysis for the Northwest Territories, retroactive fire mapping from Landsat imagery reduced historical estimates by 3 Mha (16%), which would result in a 1.6 Mha increase in the reported undisturbed critical habitat for threatened woodland caribou. The NBAC dataset is freely downloadable from the Canadian Wildland Fire Information System.
STF-EGFA: A Remote Sensing Spatiotemporal Fusion Network with Edge-Guided Feature Attention
Authors: Cheng Feifei , Fu Zhitao , Tang Bohui , Huang Liang , Huang Kun , Ji Xinran
Published: June 2022

Spatiotemporal fusion in remote sensing plays an important role in Earth science applications by using information complementarity between different remote sensing data to improve image performance. However, several problems still exist, such as edge contour blurring and uneven pixels between the predicted image and the real ground image, in the extraction of salient features by convolutional neural networks (CNNs). We propose a spatiotemporal fusion method with edge-guided feature attention based on remote sensing, called STF-EGFA. First, an edge extraction module is used to maintain edge details, which effectively solves the boundary blurring problem. Second, a feature fusion attention module is used to make adaptive adjustments to the extracted features. Among them, the spatial attention mechanism is used to solve the problem of weight variation in different channels of the network. Additionally, the problem of uneven pixel distribution is addressed with a pixel attention (PA) mechanism to highlight the salient features. We transmit the different features extracted by the edge module and the encoder to the feature attention (FA) module at the same time after the union. Furthermore, the weights of edges, pixels, channels and other features are adaptively learned. Finally, three remote sensing spatiotemporal fusion datasets, Ar Horqin Banner (AHB), Daxing and Tianjin, are used to verify the method. Experiments proved that the proposed method outperformed three typical comparison methods in terms of the overall visual effect and five objective evaluation indexes: spectral angle mapper (SAM), peak signal-to-noise ratio (PSNR), spatial correlation coefficient (SCC), structural similarity (SSIM) and root mean square error (RMSE). Thus, the proposed spatiotemporal fusion algorithm is feasible for remote sensing analysis.
Optimizing the Assimilation of the GOES-16/-17 Atmospheric Motion Vectors in the Hurricane Weather Forecasting (HWRF) Model
Authors: Lim Agnes H. N. , Nebuda Sharon E. , Jung James A. , Daniels Jaime M. , Bailey Andrew , Bresky Wayne , Bi Li , Mehra Avichal
Published: June 2022

Hourly and 15 min GOES-16 and -17 atmospheric motion vectors (AMVs) are evaluated using the 2020 version of the operational HWRF to assess their impact on tropical cyclone forecasting. The evaluation includes infrared (IR), visible (VIS), shortwave (SWIR), clear air, and cloud top water vapor (CAWV and CTWV) AMVs derived from the ABI imagery. Several changes are made to optimize the assimilation of these winds. The observational error profile is inflated to avoid overweighting of the AMVs. The range of allowable AMV wind speeds entering the assimilation system is increased to include larger wind speeds observed in tropical cyclones. Two data quality checks, commonly used for rejecting AMVs, namely QI and PCT1, have been removed. These changes resulted in a 20–40% increase in the number of AMVs assimilated. One additional change, specific to infrared AMVs, is narrowing the atmospheric layer where IR AMVs are rejected from 400–800 hPa to 400–600 hPa. The AMVs’ impact on forecast skill is assessed using storms from the North Atlantic and the Eastern Pacific, respectively. Overall, GOES-16 and -17 AMVs are beneficial for improving tropical cyclone forecasting. Positive analysis and forecast impact are obtained for track error, intensity error, minimum central pressure error, and storm size.
Forest Height Estimation Approach Combining P-Band and X-Band Interferometric SAR Data
Authors: Xu Kunpeng , Zhao Lei , Chen Erxue , Li Kun , Liu Dacheng , Li Tao , Li Zengyuan , Fan Yaxiong
Published: June 2022

Forest height is an essential parameter used to derive important information about forest ecosystems, such as forest above-ground biomass. In this article, a forest height estimation approach combining P-band and X-band interferometric synthetic aperture radar (InSAR) was introduced. The forest height was estimated using the difference in the penetration of long- and short-wavelength radars to the forest. That is, the P-band and X-band InSAR data were used to extract the digital terrain model (DTM) and digital surface model (DSM), respectively. For the DTM, an improved time-frequency (TF) analysis method was used to reduce the effect of forest scatterers on the extraction of a pure understory terrain phase based on P-band InSAR. For the DSM, a novel compensation algorithm based on a multi-layer model (MLM) was proposed to remove the penetration bias of the X-band. Compared to the existing method based on the infinitely deep uniform volumes (IDUV) model, the MLM-based method is more in line with the characteristics of forest structure and the scattering mechanism for X-band InSAR. The airborne P-band repeat-pass InSAR and spaceborne X-band (TanDEM-X) single-pass InSAR data were used to verify the proposed method over the study area in the Saihanba Forest Farm in Hebei, China. The results demonstrated that the improved TF method can achieve high-precision DTM extraction based on P-band InSAR data, and the root mean square error (RMSE) was 0.94 m. The proposed MLM-based compensation method of the DSM achieved a smaller error (RMSE: 1.67 m) compared to the IDUV-based method (RMSE: 3.01 m). Under the same DTM extracted by P-band InSAR, the estimation accuracy of forest height based on the MLM method was 86.58% (RMSE: 1.81 m), which was 8.49% higher than that of the IDUV-based method (RMSE: 2.98 m).
Different Responses of Solar-Induced Chlorophyll Fluorescence at the Red and Far-Red Bands and Gross Primary Productivity to Air Temperature for Winter Wheat
Authors: Chen Jidai , Liu Xinjie , Yang Guijun , Han Shaoyu , Ma Yan , Liu Liangyun
Published: June 2022

Solar-induced chlorophyll fluorescence (SIF) is closely related to the light-reaction process and has been recognized as a good indicator for tracking gross primary productivity (GPP). Nevertheless, it has not been widely examined how SIF and GPP respond to temperature. Here, we explored the linkage mechanisms between SIF and GPP in winter wheat based on continuous measurements of canopy SIF (cSIF), GPP, and meteorological data. To separately explore the structural and physiological mechanisms underlying the SIF–GPP relationship, we studied the temperature responses of the estimated light use efficiency (LUEp), canopy-level chlorophyll fluorescence yield (cSIFyield) and photosystem-level chlorophyll fluorescence yield (ΦF) estimated using canopy-scale remote sensing measurements. We found that GPP, red canopy SIF (cSIF688) and far-red canopy SIF (cSIF760) all exhibited a decreasing trend during overwintering periods. However, GPP and cSIF688 showed relatively more obvious changes in response to air temperature (Ta) than cSIF760 did. In addition, the LUEp responded sensitively to Ta (the correlation coefficient, r = 0.83, p-value < 0.01). The cSIFyield_688 and ΦF_688 (ΦF at 688 nm) also exhibited significantly positive correlations with Ta (r > 0.7, p-value < 0.05), while cSIFyield_760 and ΦF_760 (ΦF at 760 nm) were weakly correlated with Ta (r < 0.3, p-value > 0.05) during overwintering periods. The results also show that LUEp was more sensitive to Ta than ΦF, which caused changes in the LUEp/ΦF ratio in response to Ta. By considering the influence of Ta, the GPP estimation based on the total SIF emitted at the photosystem level (tSIF) was improved (with R2 increased by more than 0.12 for tSIF760 and more than 0.05 for tSIF688). Therefore, our results indicate that the LUEp/ΦF ratio is affected by temperature conditions and highlights that the SIF–GPP model should consider the influence of temperature.
A Hierarchical Approach for Traffic Sign Recognition Based on Shape Detection and Image Classification
Authors: Lu Eric Hsueh-Chan , Gozdzikiewicz Michal , Chang Kuei-Hua , Ciou Jing-Mei
Published: June 2022

In recent years, the development of self-driving cars and their inclusion in our daily life has rapidly transformed from an idea into a reality. One of the main issues that autonomous vehicles must face is the problem of traffic sign detection and recognition. Most works focusing on this problem utilize a two-phase approach. However, a fast-moving car has to quickly detect the sign as seen by humans and recognize the image it contains. In this paper, we chose to utilize two different solutions to solve tasks of detection and classification separately and compare the results of our method with a novel state-of-the-art detector, YOLOv5. Our approach utilizes the Mask R-CNN deep learning model in the first phase, which aims to detect traffic signs based on their shapes. The second phase uses the Xception model for the task of traffic sign classification. The dataset used in this work is a manually collected dataset of 11,074 Taiwanese traffic signs collected using mobile phone cameras and a GoPro camera mounted inside a car. It consists of 23 classes divided into 3 subclasses based on their shape. The conducted experiments utilized both versions of the dataset, class-based and shape-based. The experimental result shows that the precision, recall and mAP can be significantly improved for our proposed approach.
A Machine Learning Model for Predicting Sit-to-Stand Trajectories of People with and without Stroke: Towards Adaptive Robotic Assistance
Authors: Bennett Thomas , Kumar Praveen , Garate Virginia Ruiz
Published: June 2022

Sit-to-stand and stand-to-sit transfers are fundamental daily motions that enable all other types of ambulation and gait. However, the ability to perform these motions can be severely impaired by different factors, such as the occurrence of a stroke, limiting the ability to engage in other daily activities. This study presents the recording and analysis of a comprehensive database of full body biomechanics and force data captured during sit-to-stand-to-sit movements in subjects who have and have not experienced stroke. These data were then used in conjunction with simple machine learning algorithms to predict vertical motion trajectories that could be further employed for the control of an assistive robot. A total of 30 people (including 6 with stroke) each performed 20 sit-to-stand-to-sit actions at two different seat heights, from which average trajectories were created. Weighted k-nearest neighbours and linear regression models were then used on two different sets of key participant parameters (height and weight, and BMI and age), to produce a predicted trajectory. Resulting trajectories matched the true ones for non-stroke subjects with an average R2 score of 0.864±0.134 using k = 3 and 100% seat height when using height and weight parameters. Even among a small sample of stroke patients, balance and motion trends were noticed along with a large within-class variation, showing that larger scale trials need to be run to obtain significant results. The full dataset of sit-to-stand-to-sit actions for each user is made publicly available for further research.
A Machine Learning Approach for Predicting Non-Suicidal Self-Injury in Young Adults
Authors: Marti-Puig Pere , Capra Chiara , Vega Daniel , Llunas Laia , Solé-Casals Jordi
Published: June 2022

Artificial intelligence techniques were explored to assess the ability to anticipate self-harming behaviour in the mental health context using a database collected by an app previously designed to record the emotional states and activities of a group of subjects exhibiting self-harm. Specifically, the Leave-One-Subject-Out technique was used to train classification trees with a maximum of five splits. The results show an accuracy of 84.78%, a sensitivity of 64.64% and a specificity of 85.53%. In addition, positive and negative predictive values were also obtained, with results of 14.48% and 98.47%, respectively. These results are in line with those reported in previous work using a multilevel mixed-effect regression analysis. The combination of apps and AI techniques is a powerful way to improve the tools to accompany and support the care and treatment of patients with this type of behaviour. These studies also guide the improvement of apps on the user side, simplifying and collecting more meaningful data, and on the therapist side, progressing in pathology treatments. Traditional therapy involves observing and reconstructing what had happened before episodes once they have occurred. This new generation of tools will make it possible to monitor the pathology more closely and to act preventively.
Spatial Knowledge via Auditory Information for Blind Individuals: Spatial Cognition Studies and the Use of Audio-VR
Authors: Afonso-Jaco Amandine , Katz Brian F. G.
Published: June 2022

Spatial cognition is a daily life ability, developed in order to be able to understand and interact with our environment. Even if all the senses are involved in mental representation of space elaboration, the lack of vision makes it more difficult, especially because of the importance of peripheral information in updating the relative positions of surrounding landmarks when one is moving. Spatial audio technology has long been used for studies of human perception, particularly in the area of auditory source localisation. The ability to reproduce individual sounds at desired positions, or complex spatial audio scenes, without the need to manipulate physical devices has provided researchers with many benefits. We present a review of several studies employing the power of spatial audio virtual reality for research in spatial cognition with blind individuals. These include studies investigating simple spatial configurations, architectural navigation, reaching to sounds, and sound design for improved acceptability. Prospects for future research, including those currently underway, are also discussed.
Toward Mapping an NGSI-LD Context Model on RDF Graph Approaches: A Comparison Study
Authors: Abid Ahmed , Lee Jieun , Le Gall Franck , Song JaeSeung
Published: June 2022

A considerable number of Internet of Things deployments are isolated from specific solutions, from devices to data platforms. Standardized data models were proposed to overcome the interoperability gap between these deployments. Next generation service interfaces-linked data (NGSI-LD) is one of the proposed platforms that exploits linked data and proposes an information model and an application programming interface (API) for easy use and standard management of context information. The NGSI-LD information model is based on JSON for Linked Data (JSON-LD) as a serialization format for context information. This efficiently exploits the potential of semantics and linked open data. However, the NGSI-LD graph API and query language are still theoretically defined and limited to some preliminary works. Consequently, current NGSI-LD implementations are mainly based on traditional databases, where the JSON-LD serialization is supported but not exploited owing to the difficulties in defining and implementing new NGSI-LD based Graph APIs. One of the basic solutions is the use of an RDF store for NGSI-LD payloads because these types of databases are well defined and maintained and will not need any added effort for JSON-LD based payloads. However, the main complication at this level is the use of reification to annotate relationships. This study focused on both aspects of exploiting the semantics of NGSI-LD by proposing standardized mapping mechanisms to RDF graphs without reifying JSON-LD payloads and with respect to the NGSI-LD context model and API. Our main proposals highlight that exploiting the RDF store for processing NGSI-LD data semantically is feasible and uncomplicated. We illustrated the proposed mapping approaches with real use-case examples and a possible exploitation of semantic approaches.
Influence of Analog and Digital Crease Lines on Mechanical Parameters of Corrugated Board and Packaging
Authors: Garbowski Tomasz , Gajewski Tomasz , Knitter-Piątkowska Anna
Published: June 2022

When producing packaging from corrugated board, material weakening often occurs both during the die-cutting process and during printing. While the analog lamination and/or printing processes that degrade material can be easily replaced with a digital approach, the die-cutting process remains overwhelmingly analog. Recently, new innovative technologies have emerged that have begun to replace or at least supplement old techniques. This paper presents the results of laboratory tests on corrugated board and packaging made using both analog and digital technologies. Cardboard samples with digital and analog creases are subject to various mechanical tests, which allows for an assessment of the impact of creases on the mechanical properties of the cardboard itself, as well as on the behavior of the packaging. It is proven that digital technology is not only more repeatable, but also weakens the structure of corrugated board to a much lesser extent than analog. An updated numerical model of boxes in compression tests is also discussed. The effect of the crushing of the material in the vicinity of the crease lines in the packaging arising during the analog and digital finishing processes is taken into account. The obtained enhanced computer simulation results closely reflect the experimental observations, which prove that the correct numerical analysis of corrugated cardboard packaging should be performed with the model taking into account the crushing.
MEIoT 2D-CACSET: IoT Two-Dimensional Cartesian Coordinate System Educational Toolkit Align with Educational Mechatronics Framework
Authors: Carrasco-Navarro Rocío , Luque-Vega Luis F. , Nava-Pintor Jesús Antonio , Guerrero-Osuna Héctor A. , Carlos-Mancilla Miriam A. , Castañeda-Miranda Celina Lizeth
Published: June 2022

The educational sector has made extraordinary efforts to neutralize the impact of the pandemic caused by COVID-19, forcing teachers, scholars, and academic personnel to change the way education is delivered by developing creative and technological solutions to improve the landscape for education. The Internet of Things (IoT) is crucial for the educational transition to digital and virtual environments. This paper presents the integration of IoT technology in the Two-Dimensional Cartesian Coordinate System Educational Toolkit (2D-CACSET), to transform it into MEIoT 2D-CACSET; which includes educational mechatronics and the IoT. The Educational Mechatronics Conceptual Framework (EMCF) is extended to consider the virtual environment, enabling knowledge construction in virtual concrete, virtual graphic, and virtual abstract levels. Hence, the students acquire this knowledge from a remote location to apply it further down their career path. Three instructional designs are designed for this work using the MEIoT 2D-CACSET to learn about coordinate axes, quadrants, and a point in the 2D Coordinate Cartesian System. This work is intended to provide an IoT educational technology to offer an adequate response to the educational system’s current context.
Low-Complexity Adaptive Sampling of Block Compressed Sensing Based on Distortion Minimization
Authors: Chen Qunlin , Chen Derong , Gong Jiulu
Published: June 2022

Block compressed sensing (BCS) is suitable for image sampling and compression in resource-constrained applications. Adaptive sampling methods can effectively improve the rate-distortion performance of BCS. However, adaptive sampling methods bring high computational complexity to the encoder, which loses the superiority of BCS. In this paper, we focus on improving the adaptive sampling performance at the cost of low computational complexity. Firstly, we analyze the additional computational complexity of the existing adaptive sampling methods for BCS. Secondly, the adaptive sampling problem of BCS is modeled as a distortion minimization problem. We present three distortion models to reveal the relationship between block sampling rate and block distortion and use a simple neural network to predict the model parameters from several measurements. Finally, a fast estimation method is proposed to allocate block sampling rates based on distortion minimization. The results demonstrate that the proposed estimation method of block sampling rates is effective. Two of the three proposed distortion models can make the proposed estimation method have better performance than the existing adaptive sampling methods of BCS. Compared with the calculation of BCS at the sampling rate of 0.1, the additional calculation of the proposed adaptive sampling method is less than 1.9%.
A Framework for Trajectory Prediction of Preceding Target Vehicles in Urban Scenario Using Multi-Sensor Fusion
Authors: Zou Bin , Li Wenbo , Hou Xianjun , Tang Luqi , Yuan Quan
Published: June 2022

Preceding vehicles have a significant impact on the safety of the vehicle, whether or not it has the same driving direction as an ego-vehicle. Reliable trajectory prediction of preceding vehicles is crucial for making safer planning. In this paper, we propose a framework for trajectory prediction of preceding target vehicles in an urban scenario using multi-sensor fusion. First, the preceding target vehicles historical trajectory is acquired using LIDAR, camera, and combined inertial navigation system fusion in the dynamic scene. Next, the Savitzky–Golay filter is taken to smooth the vehicle trajectory. Then, two transformer-based networks are built to predict preceding target vehicles’ future trajectory, which are the traditional transformer and the cluster-based transformer. In a traditional transformer, preceding target vehicles trajectories are predicted using velocities in the X-axis and Y-axis. In the cluster-based transformer, the k-means algorithm and transformer are combined to predict trajectory in a high-dimensional space based on classification. Driving data from the real-world environment in Wuhan, China, are collected to train and validate the proposed preceding target vehicles trajectory prediction algorithm in the experiments. The result of the performance analysis confirms that the proposed two transformers methods can effectively predict the trajectory using multi-sensor fusion and cluster-based transformer method can achieve better performance than the traditional transformer.
UAT: Universal Attention Transformer for Video Captioning
Authors: Im Heeju , Choi Yong-Suk
Published: June 2022

Video captioning via encoder–decoder structures is a successful sentence generation method. In addition, using various feature extraction networks for extracting multiple features to obtain multiple kinds of visual features in the encoding process is a standard method for improving model performance. Such feature extraction networks are weight-freezing states and are based on convolution neural networks (CNNs). However, these traditional feature extraction methods have some problems. First, when the feature extraction model is used in conjunction with freezing, additional learning of the feature extraction model is not possible by exploiting the backpropagation of the loss obtained from the video captioning training. Specifically, this blocks feature extraction models from learning more about spatial information. Second, the complexity of the model is further increased when multiple CNNs are used. Additionally, the author of Vision Transformers (ViTs) pointed out the inductive bias of CNN called the local receptive field. Therefore, we propose the full transformer structure that uses an end-to-end learning method for video captioning to overcome this problem. As a feature extraction model, we use a vision transformer (ViT) and propose feature extraction gates (FEGs) to enrich the input of the captioning model through that extraction model. Additionally, we design a universal encoder attraction (UEA) that uses all encoder layer outputs and performs self-attention on the outputs. The UEA is used to address the lack of information about the video’s temporal relationship because our method uses only the appearance feature. We will evaluate our model against several recent models on two benchmark datasets and show its competitive performance on MSRVTT/MSVD datasets. We show that the proposed model performed captioning using only a single feature, but in some cases, it was better than the others, which used several features.
Optimal Task Allocation Algorithm Based on Queueing Theory for Future Internet Application in Mobile Edge Computing Platform
Authors: Katayama Yukiko , Tachibana Takuji
Published: June 2022

For 5G and future Internet, in this paper, we propose a task allocation method for future Internet application to reduce the total latency in a mobile edge computing (MEC) platform with three types of servers: a dedicated MEC server, a shared MEC server, and a cloud server. For this platform, we first calculate the delay between sending a task and receiving a response for the dedicated MEC server, shared MEC server, and cloud server by considering the processing time and transmission delay. Here, the transmission delay for the shared MEC server is derived using queueing theory. Then, we formulate an optimization problem for task allocation to minimize the total latency for all tasks. By solving this optimization problem, tasks can be allocated to the MEC servers and cloud server appropriately. In addition, we propose a heuristic algorithm to obtain the approximate optimal solution in a shorter time. This heuristic algorithm consists of four algorithms: a main algorithm and three additional algorithms. In this algorithm, tasks are divided into two groups, and task allocation is executed for each group. We compare the performance of our proposed heuristic algorithm with the solution obtained by three other methods and investigate the effectiveness of our algorithm. Numerical examples are used to demonstrate the effectiveness of our proposed heuristic algorithm. From some results, we observe that our proposed heuristic algorithm can perform task allocation in a short time and can effectively reduce the total latency in a short time. We conclude that our proposed heuristic algorithm is effective for task allocation in a MEC platform with multiple types of MEC servers.
Influence of the Shod Condition on Running Power Output: An Analysis in Recreationally Active Endurance Runners
Authors: Jaén-Carrillo Diego , Roche-Seruendo Luis E. , Molina-Molina Alejandro , Cardiel-Sánchez Silvia , Cartón-Llorente Antonio , García-Pinillos Felipe
Published: June 2022

Several studies have already analysed power output in running or the relation between VO2max and power production as factors related to running economy; however, there are no studies assessing the difference in power output between shod and barefoot running. This study aims to identify the effect of footwear on the power output endurance runner. Forty-one endurance runners (16 female) were evaluated at shod and barefoot running over a one-session running protocol at their preferred comfortable velocity (11.71 ± 1.07 km·h−1). The mean power output (MPO) and normalized MPO (MPOnorm), form power, vertical oscillation, leg stiffness, running effectiveness and spatiotemporal parameters were obtained using the Stryd™ foot pod system. Additionally, footstrike patterns were measured using high-speed video at 240 Hz. No differences were noted in MPO (p = 0.582) and MPOnorm (p = 0.568), whereas significant differences were found in form power, in both absolute (p = 0.001) and relative values (p < 0.001), running effectiveness (p = 0.006), stiffness (p = 0.002) and vertical oscillation (p < 0.001). By running barefoot, lower values for contact time (p < 0.001) and step length (p = 0.003) were obtained with greater step frequency (p < 0.001), compared to shod running. The prevalence of footstrike pattern significantly differs between conditions, with 19.5% of runners showing a rearfoot strike, whereas no runners showed a rearfoot strike during barefoot running. Running barefoot showed greater running effectiveness in comparison with shod running, and was consistent with lower values in form power and lower vertical oscillation. From a practical perspective, the long-term effect of barefoot running drills might lead to increased running efficiency and leg stiffness in endurance runners, affecting running economy.