bluuebunny/crossref_metadata_2025 huggingface dataset:
Branched-Chain Fatty Acids Alter the Expression of Genes Responsible for Lipid Synthesis and Inflammation in Human Adipose Cells
Authors: Czumaj Aleksandra , Śledziński Tomasz , Mika Adriana
Published: May 2022

Recently, we have demonstrated a decreased level of iso-branched-chain fatty acids (iso-BCFAs) in patients with excessive weight. However, it is still unclear whether BCFAs may influence lipid metabolism and inflammation in lipogenic tissues. To verify this, human visceral adipocytes were cultured with three different concentrations of selected iso-BCFA (14-methylpentadecanoic acid) and anteiso-BCFA (12-methyltetradecanoic acid), and then the expression of genes associated with lipid metabolism (FASN—fatty acid synthase; SREBP1—sterol regulatory element-binding protein 1; SCD1—stearoyl-CoA desaturase; ELOVL4—fatty acid elongase 4; ELOVL6—fatty acid elongase 6; FADS2—fatty acid desaturase 2; FADS1–fatty acid desaturase 1) and inflammation (COX-2—cyclooxygenase 2; ALOX-15—lipoxygenase 15; IL-6—interleukin 6) were determined. This study demonstrates for the first time that incubation with iso-BCFA decreases the expression of adipocyte genes that are associated with lipid metabolism (except FASN) and inflammation. These findings suggest that changes in the iso-BCFA profile in obese patients may contribute to adipose inflammation and dyslipidemia. Further studies should evaluate whether iso-BCFA supplementation in obese patients would be beneficial.
In Silico Prediction of Plasmodium falciparum Cytoadherence Inhibitors That Disrupt Interaction between gC1qR-DBLβ12 Complex
Authors: Hafiz Abdul , Bakri Rowaida , Alsaad Mohammad , Fetni Obadah M. , Alsubaihi Lojain I. , Shamshad Hina
Published: May 2022

Malaria causes about half a million deaths per year, mainly in children below 5 years of age. Cytoadherence of Plasmodium falciparum infected erythrocytes in brain and placenta has been linked to severe malaria and malarial related deaths. Cytoadherence is mediated by binding of human receptor gC1qR to the DBLβ12 domain of a P. falciparum erythrocyte membrane protein family 1 (PfEMP1) protein. In the present work, molecular dynamic simulation was extensively studied for the gC1qR-DBLβ12 complex. The stabilized protein complex was used to study the protein–protein interface interactions and mapping of interactive amino acid residues as hotspot were performed. Prediction of inhibitors were performed by using virtual protein–protein inhibitor database Timbal screening of about 15,000 compounds. In silico mutagenesis studies, binding profile and protein ligand interaction fingerprinting were used to strengthen the screening of the potential inhibitors of gC1qR-DBLβ12 interface. Six compounds were selected and were further subjected to the MAIP analysis and ADMET studies. From these six compounds, the compounds 3, 5, and 6 were found to outperform on all screening criteria from the rest selected compounds. These compounds may provide novel drugs to treat and manage severe falciparum malaria. Additionally. the identified hotspots can be used in future for designing novel interventions for disruption of interface interactions, such as through peptides or vaccines. Futher in vitro and in vivo studies are required for the confirmation of these compounds as potential inhibitors of gC1qR-DBLβ12 interaction.
Enalapril and Enalaprilat Pharmacokinetics in Children with Heart Failure Due to Dilated Cardiomyopathy and Congestive Heart Failure after Administration of an Orodispersible Enalapril Minitablet (LENA-Studies)
Authors: Laeer Stephanie , Cawello Willi , Burckhardt Bjoern B. , Ablonczy László , Bajcetic Milica , Breur Johannes M. P. J. , Dalinghaus Michiel , Male Christoph , de Wildt Saskia N. , Breitkreutz Jörg , Faisal Muhammed , Keatley-Clarke Anne , Klingmann Ingrid , Lagler Florian B.
Published: May 2022

Angiotensin-converting enzyme inhibitors (ACEI), such as enalapril, are a cornerstone of treatment for pediatric heart failure which is still used off-label. Using a novel age-appropriate formulation of enalapril orodispersible minitablets (ODMTs), phase II/III open-label, multicenter pharmacokinetic (PK) bridging studies were performed in pediatric patients with heart failure due to dilated cardiomyopathy (DCM) and congenital heart disease (CHD) in five participating European countries. Children were treated for 8 weeks with ODMTs according to an age-appropriate dosing schedule. The primary objective was to describe PK parameters (area under the curve (AUC), maximal concentration (Cmax), time to reach maximal concentration (t-max)) of enalapril and its active metabolite enalaprilat. Of 102 patients, 89 patients (n = 26, DCM; n = 63 CHD) were included in the primary PK endpoint analysis. Rate and extent of enalapril and its active metabolite enalaprilat were described and etiology and age could be identified as potential PK modifying factors. The dosing schedule appeared to be tolerated well and did not result in any significant drug-related serious adverse events. The PK analysis and the lack of severe safety events supports the applied age-appropriate dosing schedule for the enalapril ODMTs.
Reprocessable Polybenzoxazine Thermosets with High Tgs and Mechanical Strength Retentions Using Boronic Ester Bonds as Crosslinkages
Authors: Wang Xiaoxi , Zhang Sujuan , He Youjun , Guo Wei , Lu Zaijun
Published: May 2022

In order to obtain reprocessable polybenzoxazine thermosets with high heat resistance and mechanical strength retentions, network structures without irreversible parts were constructed via crosslinking benzoxazine oligomers using boronic ester cross-linkers. Firstly, the benzoxazine monomer containing carbon–carbon double bonds was synthesized via the Mannich reaction. After thermal ring-opening polymerization, the benzoxazine oligomer containing carbon–carbon double bonds (OBZ) was yielded. Through the thiol-ene click reaction of the OBZ and dithiol cross-linker bearing boronic ester bonds, the polybenzoxazine thermosets using boronic ester bonds as crosslinkages (OBZ-BDB) were successfully synthesized. The structures of OBZ and OBZ-BDB were characterized by SEC, 1H NMR, and FT-IR measurements. Reprocessing experiments showed that OBZ-BDB has remarkable reprocessability. The retention rates of the tensile strengths through three generations of reprocessing were 98%, 95%, and 84%, respectively. Meanwhile, OBZ-BDB cross-linked by boronic ester bonds had brilliant thermal properties. The Tg of the original OBZ-BDB was 224 °C. With the increase of the reprocessing generations, the Tgs basically remained unchanged.
Microstructure Evolution of Inertia Friction Welded Joints of TC21 Titanium Alloy
Authors: Wang Hongying , Li Zihao , Zhao Shengsheng , Li Zhijun , Tang Weijie , Wu Fayu , Zhou Jun
Published: May 2022

In this paper, TC21 titanium alloy welded joints were successfully formed through inertial friction welding (IFW) processes. Microstructure evolution of IFW joints was investigated by way of different analysis methods including optical microscope (OM), scanning electron microscope(SEM), Electron Back-Scattered Diffraction(EBSD), X-Ray Diffraction(XRD), and Energy Dispersive Spectrometer(EDS). The results indicate that large-sized equiaxial β grains, original α phases, and basketweave structure existing in the BM have completely disappeared in the WZ. Instead, fine equiaxial grains sized at 20–30 μm and very fine α + β lamellar microstructure are formed in the WZ. However, as transition zones, the microstructures of the TMAZ and HAZ are also in transition state while the microstructures existing in the BM partially remain in the TMAZ and completely remain in the HAZ. In addition, second α phases are precipitated and fine α + β lamellar microstructure are formed on the original β base in the TMAZ and HAZ. XRD and EBSD results reveal that the proportion of β phase in the WZ zone decreases greatly. EDS results show that there are aggregations of stabilizing elements of β phase in the BM, but no element aggregation in the WZ. Dynamic recrystallization during the IFW process and element distribution under the rapid cooling condition after the welding process are believed to be responsible for formation of the microstructure in the weld zone of IFW joints.
Significance of Self-Transcendence for Rehabilitation and Relapse Prevention among Patients with Substance Use Disorder: A Qualitative Study
Authors: Sørensen Torgeir , Lien Lars
Published: May 2022

A complex variety of restrictive and promoting factors are in play when people with substance use disorder face challenges concerning rehabilitation and relapse prevention. Self-transcendence sources are strongly associated with meaningfulness, and meaningfulness is found to be associated with less alcohol and drug use severity and relapse prevention. The aim of the present qualitative study was to investigate self-transcendence among patients with substance use disorders and to discuss its significance for treatment and relapse prevention. An exploratory qualitative research design was employed, with individual interviews in a strategic sample of four patients with substance use disorder recruited from a religiously founded rehabilitation clinic in Southeast Norway. The transcribed material was analysed through systematic text condensation. A search for objects beyond immediate needs through self-transcendence was revealed, related to generativity and an unselfish prosocial commitment to family and the surroundings, strengthening the participants’ self-efficacy, confidence, and safety. Spirituality and confidence in a higher power or destiny generated order in life. The use of rituals contributed to tranquillity. Values gave new directions, and private confession helped to start over in life. For patients in rehabilitation aiming at relapse prevention, self-transcendence seems to be significant as part of the recovery process. Possible implications for rehabilitation and relapse prevention are discussed.
Spatio-Temporal Evolution Law of Surface Subsidence Basin with Insufficient Exploitation of Deep Coal Resources in Aeolian Sand Area of Western China
Authors: Du Qiu , Guo Guangli , Li Huaizhan , Gong Yaqiang
Published: May 2022

Coal is one of the fundamental fossil energy supporting the world’s economy. The synergistic development between efficient coal mining and ecological environment protection is the inevitable requirement for the preservation of global harmony. As the world’s largest coal producer, China has conducted a strategic shift from east to west in terms of the exploitation of its energy resources, posing a serious threat to the fragile ecological environment of the western region. In particular, the surface subsidence caused by coal mining is the root of the ecological deteriotation and the destruction of ground structures. However, it is difficult to reveal the law of large-scale surface subsidence in western mining areas merely by conventional measurement methods such as leveling, on account of the high intensity of coal seam mining, the weakness of the lithology of overlying rock and the large thickness of wind-blown sand strata. In view of this, small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technology was used in this study to obtain the time series of surface vertical displacement during the whole mining process of the 2401 working face in the Yingpanhao coal mine, Inner Mongolia. Based on the deformation data, the dynamic evolution characteristics of surface subsidence under high intensity mining in the western mining area were analyzed exhaustively. It was found that the surface subsidence is characterized by an extensive coverage range (48.52 km2) with minimal ground settlement (250 mm) in the study area. Meanwhile, the boundary shape of the subsidence basin followed a “circular-parallelogram-trapezoid” changeable process and the coverage area of the basin experienced three stages: a linear increasing period, a temporary stagnation period, and a re-expansion period. Furthermore, there existed an abnormal uplift phenomenon on the east side of the open-off cut in the 2401 working face. Combined with the structure of overlying strata, this paper carried out a preliminary analysis on the reasons of the abovementioned phenomenon. The research results are of vital realistic significance for ground buildings and ecological environmental protection in the aeolian sand mining area in Western China.
Mineral Exploration Potential Estimation Using 3D Inversion: A Comparison of Three Different Norms
Authors: Chen Tao , Zhang Guibin
Published: May 2022

Gravity data have been frequently used in researching the subsurface to map the 3D geometry of the density structure, which is considered the basis for further interpretations, such as the estimation of exploration potential in mineral exploration. The gravity inversion, practically employed to map the density structure, can be achieved by different methods. The method based on Tikhonov regularization is the most commonly used among them. Usually, the subsurface is discretized into a set of cells or voxels. To recover a stable and reliable solution, constraints are introduced into the Tikhonov regularization. One constrained inversion introduces a quadratic penalty (L2 norm) into the inversion, which imposes smooth features on the recovered model. Another gravity inversion, known as sparse inversion, imposes compactness and sharp boundaries on the recovered density structure. Specifically, the L1 norm and L0 norm are favored for such a purpose. This work evaluates the merits of the gravity data inversion in cooperation with different model norms and their applicability in exploration potential estimation. Because these norms promote different features in the recovered models, the reconstructed 3D density structure reveals different geometric features of the ore deposit. We use two types of synthetic data for evaluating the performances of the inversion with different norms. Numerical results demonstrate that L0 norm-based inversion provides high-resolution recovered models and offers reliable estimates of exploration potential with minimal deviation from theoretical mass compared to inversions equipped with the other two norms. Finally, we use the gravity data collected over the iron ore deposit at the Dida mining area in Jilin province (Northeast China) for the application. It is estimated that the exploration potential of the iron ore deposits is about 3.2 million tons.
An Improved Multi-Baseline Phase Unwrapping Method for GB-InSAR
Authors: Lin Zihao , Duan Yan , Deng Yunkai , Tian Weiming , Zhao Zheng
Published: May 2022

Ground-based interferometric synthetic aperture radar (GB-InSAR) technology can be applied to generate a digital elevation model (DEM) with high spatial resolution and high accuracy. Phase unwrapping is a critical procedure, and unwrapping errors cannot be effectively avoided in the interferometric measurements of terrains with discontinuous heights. In this paper, an improved multi-baseline phase unwrapping (MB PU) method for GB-InSAR is proposed. This method combines the advantages of the cluster-analysis-based MB PU algorithm and the minimum cost flow (MCF) method. A cluster-analysis-based MB PU algorithm (CA-based MB PU) is firstly utilized to unwrap the clustered pixels with high phase quality. Under the topological constraints of a triangulation network, the connectivity graph of any non-clustered pixel is established with its adjacent unwrapped cluster pixels. Then, the absolute phase of these non-clustered pixels can be identified using the MCF method. Additionally, a spatial-distribution-based denoising algorithm is utilized to denoise the data in order to further improve the accuracy of the phase unwrapping. The DEM generated by one GB-InSAR is compared with that generated by light detection and ranging (LiDAR). Both simulated and experimental datasets are utilized to verify the effectiveness and robustness of this improved method.
A Self-Adaptive Optimization Individual Tree Modeling Method for Terrestrial LiDAR Point Clouds
Authors: Hui Zhenyang , Cai Zhaochen , Liu Bo , Li Dajun , Liu Hua , Li Zhuoxuan
Published: May 2022

Individual tree modeling for terrestrial LiDAR point clouds always involves heavy computation burden and low accuracy toward a complex tree structure. To solve these problems, this paper proposed a self-adaptive optimization individual tree modeling method. In this paper, we first proposed a joint neighboring growing method to segment wood points into object primitives. Subsequently, local object primitives were optimized to alleviate the computation burden. To build the topology relation among branches, branches were separated based on spatial connectivity analysis. And then the nodes corresponding to each object primitive were adopted to construct the graph structure of the tree. Furthermore, each object primitive was fitted as a cylinder. To revise the local abnormal cylinder, a self-adaptive optimization method based on the constructed graph structure was proposed. Finally, the constructed tree model was further optimized globally based on prior knowledge. Twenty-nine field datasets obtained from three forest sites were adopted to evaluate the performance of the proposed method. The experimental results show that the proposed method can achieve satisfying individual tree modeling accuracy. The mean volume deviation of the proposed method is 1.427 m3. In the comparison with two other famous tree modeling methods, the proposed method can achieve the best individual tree modeling result no matter which accuracy indicator is selected.
Unambiguous ISAR Imaging Method for Complex Maneuvering Group Targets
Authors: Liu Fengkai , Huang Darong , Guo Xinrong , Feng Cunqian
Published: May 2022

In inverse synthetic-aperture radar (ISAR) imaging, it is essential to deal with the Doppler ambiguity of group targets with complex maneuvers in order to avoid the bias of target position towards the actual value. Simultaneously, migration through resolution cell (MTRC) under the Doppler ambiguity is unable to be compensated for as a preprocessing. Traditional ISAR imaging methods for maneuvering targets, however, are undesirable to handle the severe deformation and defocusing in the imaging results induced by the Doppler ambiguity and MTRC. In this paper, we propose a novel and effective ISAR imaging method to improve the imaging quality by removing the Doppler ambiguity and compensating for the MTRC. Specifically, we first model the echo as a multi-component cubic phase signal (m-CPS) and design a high-order instantaneous autocorrelation function–generalized scaled Fourier transform (HIAF–GSCFT) to process the echo. This is to estimate the rotational parameters without MTRC compensation. Then, a maximum weighted contrast algorithm is used to remove the Doppler ambiguity, followed by reconstructing the echo. Compared with the existing method, the proposed method can accurately estimate the rotational parameters under the existing MTRCs and achieves a high-quality ISAR image for group targets, with complex maneuvers without Doppler ambiguity. Experiments of simulated and measured datasets validate its effectiveness and robustness for single target and group targets.
Hyperspectral Image Classification Based on Class-Incremental Learning with Knowledge Distillation
Authors: Xu Meng , Zhao Yuanyuan , Liang Yajun , Ma Xiaorui
Published: May 2022

By virtue of its large-covered spatial information and high-resolution spectral information, hyperspectral images make lots of mapping-based fine-grained remote sensing applications possible. However, due to the inconsistency of land-cover types between different images, most hyperspectral image classification methods keep their effectiveness by training on every image and saving all classification models and training samples, which limits the promotion of related remote sensing tasks. To deal with the aforementioned issues, this paper proposes a hyperspectral image classification method based on class-incremental learning to learn new land-cover types without forgetting the old ones, which enables the classification method to classify all land-cover types with one final model. Specially, when learning new classes, a knowledge distillation strategy is designed to recall the information of old classes by transferring knowledge to the newly trained network, and a linear correction layer is proposed to relax the heavy bias towards the new class by reapportioning information between different classes. Additionally, the proposed method introduces a channel attention mechanism to effectively utilize spatial–spectral information by a recalibration strategy. Experimental results on the three widely used hyperspectral images demonstrate that the proposed method can identify both new and old land-cover types with high accuracy, which proves the proposed method is more practical in large-coverage remote sensing tasks.
Fast Tree Skeleton Extraction Using Voxel Thinning Based on Tree Point Cloud
Authors: Sun Jingqian , Wang Pei , Li Ronghao , Zhou Mei , Wu Yuhan
Published: May 2022

Tree skeletons play an important role in tree structure analysis and 3D model reconstruction. However, it is a challenge to extract a skeleton from a tree point cloud with complex branches. In this paper, an automatic and fast tree skeleton extraction method (FTSEM) based on voxel thinning is proposed. In this method, a wood–leaf classification algorithm was introduced to filter leaf points for the reduction of the leaf interference on tree skeleton generation, tree voxel thinning was adopted to extract a raw tree skeleton quickly, and a breakpoint connection algorithm was used to improve the skeleton connectivity and completeness. Experiments were carried out in Haidian Park, Beijing, in which 24 trees were scanned and processed to obtain tree skeletons. The graph search algorithm (GSA) was used to extract tree skeletons based on the same datasets. Compared with the GSA method, the FTSEM method obtained more complete tree skeletons. The time cost of the FTSEM method was evaluated using the runtime and time per million points (TPMP). The runtime of FTSEM was from 1.0 s to 13.0 s, and the runtime of GSA was from 6.4 s to 309.3 s. The average value of TPMP was 1.8 s for FTSEM and 22.3 s for GSA, respectively. The experimental results demonstrate that the proposed method is feasible, robust, and fast with good potential for tree skeleton extraction.
Rescaling-Assisted Super-Resolution for Medium-Low Resolution Remote Sensing Ship Detection
Authors: Zou Huanxin , He Shitian , Cao Xu , Sun Li , Wei Juan , Liu Shuo , Liu Jian
Published: May 2022

Medium-low resolution (M-LR) remote sensing ship detection is a challenging problem due to the small target sizes and insufficient appearance information. Although image super resolution (SR) has become a popular solution in recent years, the ability of image SR is limited since much information is lost in input images. Inspired by the powerful information embedding ability of the encoder in image rescaling, in this paper, we introduce image rescaling to guide the training of image SR. Specifically, we add an adaption module before the SR network, and use the pre-trained rescaling network to guide the optimization of the adaption module. In this way, more information is embedded in the adapted M-LR images, and the subsequent SR module can utilize more information to achieve better performance. Extensive experimental results demonstrate the effectiveness of our method on image SR. More importantly, our method can be used as a pre-processing approach to improve the detection performance.
Monitoring 2019 Drought and Assessing Its Effects on Vegetation Using Solar-Induced Chlorophyll Fluorescence and Vegetation Indexes in the Middle and Lower Reaches of Yangtze River, China
Authors: Li Meng , Chu Ronghao , Sha Xiuzhu , Xie Pengfei , Ni Feng , Wang Chao , Jiang Yuelin , Shen Shuanghe , Islam Abu Reza Md. Towfiqul
Published: May 2022

Monitoring drought precisely and evaluating drought effects quantitatively can establish a scientific foundation for understanding drought. Although solar-induced chlorophyll fluorescence (SIF) can detect the drought stress in advance, the performance of SIF in monitoring drought and assessing drought-induced gross primary productivity (GPP) losses from lush to senescence remains to be further studied. Taking the 2019 drought in the middle and lower reaches of the Yangtze River (MLRYR) as an example, this study aims to monitor and assess this drought by employing a new global, OCO-2-based SIF (GOSIF) and vegetation indexes (VIs). Results showed that the GPP, GOSIF, and VIs all exhibited significant increasing trends during 2000–2020. GOSIF was most consistent with GPP in spatial distribution and was most correlated with GPP in both annual (linear correlation, R2 = 0.87) and monthly (polynomial correlation, R2 = 0.976) time scales by comparing with VIs. During July–December 2019, the precipitation (PPT), soil moisture, and standardized precipitation evapotranspiration index (SPEI) were generally below the averages during 2011–2020 and reached their lowest point in November, while those of air temperature (Tem), land surface temperature (LST), and photosynthetically active radiation (PAR) were the contrary. For drought monitoring, the spatial distributions of standardized anomalies of GOSIF and VIs were consistent during August–October 2019. In November and December, however, considering vegetation has entered the senescence stage, SIF had an obvious early response in vegetation physiological state monitoring compared with VIs, while VIs can better indicate meteorological drought conditions than SIF. For drought assessment, the spatial distribution characteristics of GOSIF and its standardized anomaly were both most consistent with that of GPP, especially the standardized anomaly in November and December. All the above phenomena verified the good spatial consistency between SIF and GPP and the superior ability of SIF in capturing and quantifying drought-induced GPP losses. Results of this study will improve the understanding of the prevention and reduction in agrometeorological disasters and can provide an accurate and timely method for drought monitoring.
Establishing the Capabilities of the Murchison Widefield Array as a Passive Radar for the Surveillance of Space
Authors: Hennessy Brendan , Rutten Mark , Young Robert , Tingay Steven , Summers Ashley , Gustainis Daniel , Crosse Brian , Sokolowski Marcin
Published: May 2022

This paper describes the use of the Murchison Widefield Array, a low-frequency radio telescope at a radio-quiet Western Australian site, as a radar receiver forming part of a continent-spanning multistatic radar network for the surveillance of space. This paper details the system geometry employed, the orbit-specific radar signal processing, and the orbit determination algorithms necessary to ensure resident space objects are detected, tracked, and propagated. Finally, the paper includes the results processed after a short collection campaign utilising several FM radio transmitters across the country, up to a maximum baseline distance of over 2500 km. The results demonstrate the Murchison Widefield Array is able to provide widefield and persistent coverage of objects in low Earth orbit.
Experimental Analysis of Atmospheric Ducts and Navigation Radar Over-the-Horizon Detection
Authors: Huang Li-Feng , Liu Cheng-Guo , Wang Hong-Guang , Zhu Qing-Lin , Zhang Li-Jun , Han Jie , Zhang Yu-Sheng , Wang Qian-Nan
Published: May 2022

Since the height of sea detection radar antenna and ship targets is relatively low, it is generally believed that its over-the-horizon detection is mainly caused by the evaporation duct at sea. To fully understand the influence of atmospheric ducts on radar over-the-horizon detection, a shore-based navigation radar was used to carry out over-the-horizon detection experiments; radiosondes were used to measure the atmospheric profile and evaporation duct monitoring equipment was used to measure the evaporation duct. Based on experimental data and model simulation, a comparative analysis of a navigation radar’s over-the-horizon detection, the evaporation duct, and the lower atmospheric duct is presented in this study. The results show that the atmospheric duct can affect the signal propagation of the navigation radar, thus resulting in over-the-horizon detection. The long-range over-the-horizon detection of the navigation radar is caused by the strong lower atmospheric duct, while the evaporation duct can generally only form weak over-the-horizon detection, which is different from the general cognition.
Sentinel-1 to NDVI for Agricultural Fields Using Hyperlocal Dynamic Machine Learning Approach
Authors: Pelta Ran , Beeri Ofer , Tarshish Rom , Shilo Tal
Published: May 2022

The normalized difference vegetation index (NDVI) is a key parameter in precision agriculture. It has been used globally since the 1970s as a proxy to monitor crop growth and correlates to the crop coefficient (Kc), leaf area index (LAI), crop cover, and more. Yet, it is susceptible to clouds and other atmospheric conditions that might alter the crop’s real NDVI value. Synthetic Aperture Radar (SAR), on the other hand, can penetrate clouds and is hardly affected by atmospheric conditions, but it is sensitive to the physical structure of the crop and therefore does not give a direct indication of the NDVI. Several SAR indices and methods have been suggested to estimate NDVIs via SAR; however, they tend to work for local spatial and temporal conditions and do not work well globally. This is because they are not flexible enough to capture the changing NDVI–SAR relationship throughout the crop-growing season. This study suggests a new method for converting Sentinel-1 to NDVIs for Agricultural Fields (SNAF) by utilizing a hyperlocal machine learning approach. This method generates multiple on-the-fly disposal field- and time-specific models for every available Sentinel-1 image across 2021. Each model learns the field-specific NDVI (from Sentinel-2 and Landsat-8) –SAR (Sentinel-1) relationship based on recent NDVI and SAR time series and consequently estimates the optimal NDVI value from the current SAR image. The SNAF was tested on 548 commercial fields from 18 countries with 28 crop types and, based on 6880 paired NDVI–SAR images, achieved an RMSE, bias, and R2 of 0.06, 0.00, and 0.92, respectively. The outcome of this study aspires to a persistent seamless stream of NDVI values, regardless of the atmospheric conditions, illumination, or local conditions, which can assist in agricultural decision making.
Evolution Analysis of Ecological Networks Based on Spatial Distribution Data of Land Use Types Monitored by Remote Sensing in Wuhan Urban Agglomeration, China, from 2000 to 2020
Authors: Lu Yanchi , Liu Yaolin , Huang Dan , Liu Yanfang
Published: May 2022

Construction and protection of ecological networks (ENs) is considered to be an effective means to curb habitat fragmentation and strengthen landscape connectivity. In this study, a complete evaluation framework of ENs based on “quality–function–structure” was proposed to support the formulation of protection strategies for ENs. First, we built the ENs of Wuhan urban agglomeration (WUA) from 2000 to 2020 based on the advantages of circuit theory and remote sensing data of land use monitoring. The results showed that land development activities are an important driving force for the temporal and spatial evolution of global ENs. Forest fragmentation, transitional urban expansion, and agricultural reclamation were important inducements for the shrinkage of ecological sources. They may also increase the resistance of species migration, which will lead to qualitative change and even fracture of ecological corridors. Second, circuit theory, centrality index, and complex network theory were applied to evaluate the quality defects, functional connectivity, and topology characteristics of ENs in WUA, respectively, from 2000 to 2020. The results showed that the antagonism between ecological corridors and land development activities led to ecological quality defects (ecological barriers and pinchpoints). Different land development models had differential effects on centrality indexes. Moreover, the main trunk in the northern Dabie Mountains and the southern Mufu mountains was developed, while the secondary trunks were abundant in the middle of WUA. Finally, we proposed protection strategies for ENs based on the coupling of the “quality–function–structure” of WUA in 2020. It is suggested that all ecological sources must be included in nature reserves to prevent natural or manmade erosion. The key areas to be repaired were determined through the quality evaluation of ecological corridors. The priority of construction and protection of ecological corridors was determined by coupling two topological structures and functions. We argue that specific protection strategies and directions can be determined according to the construction objectives of local ENs.
Challenges of Labelling Unknown Seabed Munition Dumpsites from Acoustic and Optical Surveys: A Case Study at Skagerrak
Authors: Bryan Oscar , Hansen Roy Edgar , Haines Tom S. F. , Warakagoda Narada , Hunter Alan
Published: May 2022

The disposal of unexploded ordnance (UXOs) at sea is a global problem. The mapping and remediation of historic UXOs can be assisted by autonomous underwater vehicles (AUVs) carrying sensor payloads such as synthetic aperture sonar (SAS) and optical cameras. AUVs can image large areas of the seafloor in high resolution, motivating an automated approach to UXO detection. Modern methods commonly use supervised machine learning which requires labelled examples from which to learn. This work investigates the often-overlooked labelling process and resulting dataset using an example historic UXO dumpsite at Skagerrak. A counterintuitive finding of this work is that optical images cannot be relied on for ground truth as a significant number of UXOs visible in SAS images are not in optical images, presumed buried. Given the lack of ground truth, we use an ordinal labelling scheme to incorporate a measure of labeller uncertainty. We validate this labelling regime by quantifying label accuracy compared to optical labels with high confidence. Using this approach, we explore different taxonomies and conclude that grouping objects into shells, bombs, debris, and natural gave the best trade-off between accuracy and discrimination.
Application of Improved Instance Segmentation Algorithm Based on VoVNet-v2 in Open-Pit Mines Remote Sensing Pre-Survey
Authors: Zhao Lingran , Niu Ruiqing , Li Bingquan , Chen Tao , Wang Yueyue
Published: May 2022

The traditional mine remote sensing information pre-survey is mainly based on manual interpretation, and interpreters delineate the mine boundary shape. This work is difficult and susceptible to subjective judgment due to the large differences in the characteristics of mining complex within individuals and small differences between individuals. CondInst-VoV and BlendMask-VoV, based on VoVNet-v2, are two improved instance segmentation models proposed to improve the efficiency of mine remote sensing pre-survey and minimize labor expenses. In Hubei Province, China, Gaofen satellite fusion images, true-color satellite images, false-color satellite images, and Tianditu images are gathered to create a Key Open-pit Mine Acquisition Areas (KOMMA) dataset to assess the efficacy of mine detection models. In addition, regional detection was carried out in Daye Town. The result shows that the performance of improved models on the KOMMA dataset exceeds the baseline as well as the verification accuracy of manual interpretation in regional mine detection tasks. In addition, CondInst-VoV has the best performance on Tianditu image, reaching 88.816% in positioning recall and 98.038% in segmentation accuracy.
Distributed Airflow Sensing Based on High-Spatial-Resolution BOTDA and a Self-Heated High-Attenuation Fiber
Authors: Zhang Hongying , Lei Yanyang , Zhou Jinzhe , Dong Yongkang
Published: May 2022

An all-fiber distributed airflow sensing method based on a differential pulse-width pair Brillouin optical time domain analysis (DPP-BOTDA) and a self-heated high-attenuation fiber (HAF) is proposed and demonstrated. The HAF heated the sensing fiber, producing a gradient temperature distribution in it through physical contact, where the temperature distribution was obtained by DPP-BOTDA with a spatial resolution of 5 cm. The heat loss caused by the airflow was reflected in the decrease in the Brillouin frequency shift and spatially resolved by DPP-BOTDA. Distributed airflow sensing was experimentally demonstrated for measurements of airflow movement, multiple airflow sources and the deflection angle of the airflow. The positioning error of the airflow was no larger than ~2.2 cm; for the deflection angle measurements of the airflow, the maximum demodulation error was 2.5° within the angle range of 0–30°.
SELWAK: A Secure and Efficient Lightweight and Anonymous Authentication and Key Establishment Scheme for IoT Based Vehicular Ad hoc Networks
Authors: Jan Sagheer Ahmed , Amin Noor Ul , Shuja Junaid , Abbas Assad , Maray Mohammed , Ali Mazhar
Published: May 2022

In recent decades, Vehicular Ad Hoc Networks (VANET) have emerged as a promising field that provides real-time communication between vehicles for comfortable driving and human safety. However, the Internet of Vehicles (IoV) platform faces some serious problems in the deployment of robust authentication mechanisms in resource-constrained environments and directly affects the efficiency of existing VANET schemes. Moreover, the security of the information becomes a critical issue over an open wireless access medium. In this paper, an efficient and secure lightweight anonymous mutual authentication and key establishment (SELWAK) for IoT-based VANETs is proposed. The proposed scheme requires two types of mutual authentication: V2V and V2R. In addition, SELWAK maintains secret keys for secure communication between Roadside Units (RSUs). The performance evaluation of SELWAK affirms that it is lightweight in terms of computational cost and communication overhead because SELWAK uses a bitwise Exclusive-OR operation and one-way hash functions. The formal and informal security analysis of SELWAK shows that it is robust against man-in-the-middle attacks, replay attacks, stolen verifier attacks, stolen OBU attacks, untraceability, impersonation attacks, and anonymity. Moreover, a formal security analysis is presented using the Real-or-Random (RoR) model.
Home Environment Augmented Reality System Based on 3D Reconstruction of a Single Furniture Picture
Authors: Wei Hongtao , Tang Lei , Wang Wenshuo , Zhang Jiaming
Published: May 2022

With the popularization of the concept of “metaverse”, Augmented Reality (AR) technology is slowly being applied to people’s daily life as its underlying technology support. In recent years, rapid 3D reconstruction of interior furniture to meet AR shopping needs has become a new method. In this paper, a virtual home environment system is designed and the related core technologies in the system are studied. Background removal and instance segmentation are performed for furniture images containing complex backgrounds, and a Bayesian Classifier and GrabCut (BCGC) algorithm is proposed to improve on the traditional foreground background separation technique. The reconstruction part takes the classical occupancy network reconstruction algorithm as the network basis and proposes a precise occupancy network (PONet) algorithm, which can reconstruct the structural details of furniture images, and the model accuracy is improved. Because the traditional 3D registration model is prone to the problems of model position shift and inaccurate matching with the scene, the AKAZE-based tracking registration algorithm is improved, and a Multiple Filtering-AKAZE (MF-AKAZE) based on AKAZE is proposed to remove the matching points. The matching accuracy is increased by improving the RANSAC filtering mis-matching algorithm based on further screening of the matching results. Finally, the system is verified to realize the function of the AR visualization furniture model, which can better complete the reconstruction as well as registration effect.
A Novel Collision-Free Homotopy Path Planning for Planar Robotic Arms
Authors: Velez-Lopez Gerardo C. , Vazquez-Leal Hector , Hernandez-Martinez Luis , Sarmiento-Reyes Arturo , Diaz-Arango Gerardo , Huerta-Chua Jesus , Rico-Aniles Hector D. , Jimenez-Fernandez Victor M.
Published: May 2022

Achieving the smart motion of any autonomous or semi-autonomous robot requires an efficient algorithm to determine a feasible collision-free path. In this paper, a novel collision-free path homotopy-based path-planning algorithm applied to planar robotic arms is presented. The algorithm utilizes homotopy continuation methods (HCMs) to solve the non-linear algebraic equations system (NAES) that models the robot’s workspace. The method was validated with three case studies with robotic arms in different configurations. For the first case, a robot arm with three links must enter a narrow corridor with two obstacles. For the second case, a six-link robot arm with a gripper is required to take an object inside a narrow corridor with two obstacles. For the third case, a twenty-link arm must take an object inside a maze-like environment. These case studies validated, by simulation, the versatility and capacity of the proposed path-planning algorithm. The results show that the CPU time is dozens of milliseconds with a memory consumption less than 4.5 kB for the first two cases. For the third case, the CPU time is around 2.7 s and the memory consumption around 18 kB. Finally, the method’s performance was further validated using the industrial robot arm CRS CataLyst-5 by Thermo Electron.
Fear Detection in Multimodal Affective Computing: Physiological Signals versus Catecholamine Concentration
Authors: Gutiérrez-Martín Laura , Romero-Perales Elena , de Baranda Andújar Clara Sainz , F. Canabal-Benito Manuel , Rodríguez-Ramos Gema Esther , Toro-Flores Rafael , López-Ongil Susana , López-Ongil Celia
Published: May 2022

Affective computing through physiological signals monitoring is currently a hot topic in the scientific literature, but also in the industry. Many wearable devices are being developed for health or wellness tracking during daily life or sports activity. Likewise, other applications are being proposed for the early detection of risk situations involving sexual or violent aggressions, with the identification of panic or fear emotions. The use of other sources of information, such as video or audio signals will make multimodal affective computing a more powerful tool for emotion classification, improving the detection capability. There are other biological elements that have not been explored yet and that could provide additional information to better disentangle negative emotions, such as fear or panic. Catecholamines are hormones produced by the adrenal glands, two small glands located above the kidneys. These hormones are released in the body in response to physical or emotional stress. The main catecholamines, namely adrenaline, noradrenaline and dopamine have been analysed, as well as four physiological variables: skin temperature, electrodermal activity, blood volume pulse (to calculate heart rate activity. i.e., beats per minute) and respiration rate. This work presents a comparison of the results provided by the analysis of physiological signals in reference to catecholamine, from an experimental task with 21 female volunteers receiving audiovisual stimuli through an immersive environment in virtual reality. Artificial intelligence algorithms for fear classification with physiological variables and plasma catecholamine concentration levels have been proposed and tested. The best results have been obtained with the features extracted from the physiological variables. Adding catecholamine’s maximum variation during the five minutes after the video clip visualization, as well as adding the five measurements (1-min interval) of these levels, are not providing better performance in the classifiers.
Efficacy of Electromyographic Biofeedback in Muscle Recovery after Meniscectomy in Soccer Players
Authors: Morales-Sánchez Verónica , Falcó Coral , Hernández-Mendo Antonio , Reigal Rafael E.
Published: May 2022

Electromyographic biofeedback (EMG-BF) is a therapeutic technique that has been used successfully in the rehabilitation of injuries. Although it has been applied to athletes, its use in this field is not very widespread. The objective of this study is to analyze its effectiveness in the recovery of electromyographic activity of the quadriceps after meniscectomy, evaluated through isometric contraction of the vastus lateralis. The sample comprised ten professional footballers in the Spanish League (2nd Division A) who had previously suffered a meniscus injury in their knee and had undergone a meniscectomy. The intervention consisted of EMG-BF treatment lasting between 6 and 10 sessions. The electromyographic signal was recorded using a Thought Technology ProComp Infiniti 8-channel biofeedback unit with a sampling rate of 2048 samples/second. For each session, a within-subject ABA design of 6 or 10 trials per session was used, with three pre- and three post-measures, which determined the gain for each session. The results indicated (1) improvements in all cases, (2) EMG-BF was effective, (3) the working model was statistically significant with an explained variance of between 67% and 75%, and (4) the generalizability analysis showed that the results are reliable and generalizable. The results indicate that EMG-BF is effective in neuromuscular rehabilitation after this type of intervention.
Inertial Data-Based AI Approaches for ADL and Fall Recognition
Authors: Martins Luís M. , Ribeiro Nuno Ferrete , Soares Filipa , Santos Cristina P.
Published: May 2022

The recognition of Activities of Daily Living (ADL) has been a widely debated topic, with applications in a vast range of fields. ADL recognition can be accomplished by processing data from wearable sensors, specially located at the lower trunk, which appears to be a suitable option in uncontrolled environments. Several authors have addressed ADL recognition using Artificial Intelligence (AI)-based algorithms, obtaining encouraging results. However, the number of ADL recognized by these algorithms is still limited, rarely focusing on transitional activities, and without addressing falls. Furthermore, the small amount of data used and the lack of information regarding validation processes are other drawbacks found in the literature. To overcome these drawbacks, a total of nine public and private datasets were merged in order to gather a large amount of data to improve the robustness of several ADL recognition algorithms. Furthermore, an AI-based framework was developed in this manuscript to perform a comparative analysis of several ADL Machine Learning (ML)-based classifiers. Feature selection algorithms were used to extract only the relevant features from the dataset’s lower trunk inertial data. For the recognition of 20 different ADL and falls, results have shown that the best performance was obtained with the K-NN classifier with the first 85 features ranked by Relief-F (98.22% accuracy). However, Ensemble Learning classifier with the first 65 features ranked by Principal Component Analysis (PCA) presented 96.53% overall accuracy while maintaining a lower classification time per window (0.039 ms), showing a higher potential for its usage in real-time scenarios in the future. Deep Learning algorithms were also tested. Despite its outcomes not being as good as in the prior procedure, their potential was also demonstrated (overall accuracy of 92.55% for Bidirectional Long Short-Term Memory (LSTM) Neural Network), indicating that they could be a valid option in the future.