Miracle-dz/UniFAR-FTUs huggingface dataset:
Biomass-Based Porous N-Self-Doped Carbon Framework/Polyaniline Composite with Outstanding Supercapacitance

Composites combining electrostatic charge accumulation and faradic reaction mechanisms are especially attractive high-performance supercapacitor electrodes for electrochemical energy storage. Up to now, it is difficult to prepare low-cost carbon composites from renewable resources. In this work, an outstanding and low-cost composite was fabricated by using sustainable N-self-doped carbon framework as a hierarchical porous carbon substrate from renewable resource. The N-self-doped carbon framework was fabricated from chitosan via a facile yet unique self-assembly and ice template method without any physical or chemical activation, and exhibited hierarchical porous structure. This texture not only allowed the efficient infiltration and uniform coating of polyaniline (PANI) in the inner network but also permitted a rapid penetration and desorption of electrolytes. Due to short diffusion pathway, uniformly coating of PANI, and high accessibility of PANI to electrolytes, the composite electrode had a very high.
High-sensitive bioactivity assay for hybrid nanostructured materials with plasmonic properties

 The binding of biotin to gold nanorods (Au-NRs) conjugated to neutravidin is accompanied by an increase in the fluorescence intensity of protein ́s tryptophan residues. Localized surface plasmon resonance (LSPR), combined with the known affinity of streptavidin for biotin, led to the development of a high-sensitive enzyme activity assay. Keywords Au-NRs/Neutravidin, LSPR, enzyme activity, in-vivo imaging, biological sensing
Automated Thrombin Generation Assay for Rivaroxaban, Apixaban, and Edoxaban Measurements

Background: The thrombin generation assay (TG) is a promising approach to measure the degree of anticoagulation in patients treated with direct oral anticoagulants (DOAC). A strong association with plasma drug concentrations would be a meaningful argument for the potential use to monitor DOAC. Objectives: We aimed to study the correlation of TG with rivaroxaban, apixaban, and edoxaban drug concentrations in a large, prospective multicenter cross-sectional study. Methods: Five-hundred and fifty-nine patients were included in nine tertiary hospitals. The Technothrombin® TG was conducted in addition to an anti-Xa assay; LC-MS/MS was performed as the reference standard. Results: Correlation (rs) between thrombin generation measurements and drug concentrations was −0.72 for peak thrombin generation (95% confidence interval, CI, −0.77, −0.66), −0.55 for area under the curve (AUC; 95% CI −0.61, −0.48), and 0.80 for lag time (95% CI 0.75, 0.84). In contrast, rs was 0.96 with results of the anti-Xa activity (95% CI 0.95–0.97). Sensitivity with regard to the clinically relevant cut-off value of 50 μgL−1 was 49% in case of peak thrombin generation (95% CI, 44, 55), 29% in case of AUC (95% CI, 24, 34), and 64% in case of lag time (95% CI, 58, 69). Sensitivity of the anti-Xa assay was 95% (95% CI, 92, 97). Conclusions: The correlation of thrombin generation measurements with DOAC drug concentrations was weak, and clinically relevant drug levels were not predicted correctly. Our results do not support an application of TG in the monitoring of DOAC.
An Asymmetric Silicon Grating Dual-Narrow-Band Perfect Absorber Based on Dielectric-Metal-Dielectric Structure

With the exhaustion of world energy, new energy has become the most important content of each country’s development strategy. How to efficiently use solar energy has become a research hotspot in current scientific research. Based on surface plasmon resonance and Fabry-Perot (FP) cavity, this paper proposes a design method of asymmetric silicon grating absorber, and uses finite difference time domain (FDTD) method for simulation calculation. By adjusting the geometric parameters, the asymmetric silicon grating absorber realizes two narrow-band absorption peaks with absorption greater than 99% in the optical wavelength range of 3,000–5,000 nm, and the absorption peak wavelengths are λ 1 = 3,780 nm and λ 2 = 4,135 nm, respectively. When the electromagnetic wave is incident on the surface of the metamaterial, it will excite the plasmon resonance of the metal to form a surface plasmon (SP) wave. When the SP wave propagates along the x axis, the silicon grating can reflect the SP wave back and forth. When the frequency of the SP wave and the incident light are equal, it will cause horizontal FP coupling resonance, resulting in different resonance wavelengths. This paper also discusses the influence of geometric parameters, incident angle and polarization angle on the performance of silicon grating absorbers. Finally, the sensing performance of the structure as a refractive index sensor is studied. The absorber can be used for various spectral applications such as photon detection, optical filtering and spectral sensing.
Novel Point-of-Care Diagnostic Method for Neonatal Encephalopathy Using Purine Nucleosides

Background: Evidence suggests that earlier diagnosis and initiation of treatment immediately after birth is critical for improved neurodevelopmental outcomes following neonatal encephalopathy (NE). Current diagnostic tests are, however, mainly restricted to clinical diagnosis with no molecular tests available. Purines including adenosine are released during brain injury such as hypoxia and are also present in biofluids. Whether blood purine changes can be used to diagnose NE has not been investigated to date. Methods: Blood purines were measured in a mouse model of neonatal hypoxia and infants with NE using a novel point-of-care diagnostic technology (SMARTChip) based on the summated electrochemical detection of adenosine and adenosine metabolites in the blood. Results: Blood purine concentrations were ∼2–3-fold elevated following hypoxia in mice [2.77 ± 0.48 μM (Control) vs. 7.57 ± 1.41 μM (post-hypoxia), p = 0.029]. Data in infants with NE had a 2–3-fold elevation when compared to healthy controls [1.63 ± 0.47 μM (Control, N = 5) vs. 4.87 ± 0.92 μM (NE, N = 21), p = 0.0155]. ROC curve analysis demonstrates a high sensitivity (81%) and specificity (80%) for our approach to identify infants with NE. Moreover, blood purine concentrations were higher in infants with NE and seizures [8.13 ± 3.23 μM (with seizures, N = 5) vs. 3.86 ± 0.56 μM (without seizures, N = 16), p = 0.044]. Conclusion: Our data provides the proof-of-concept that measurement of blood purine concentrations via SMARTChip technology may offer a low-volume bedside test to support a rapid diagnosis of NE.
Decreased IL-6 and NK Cells in Early-Stage Lung Adenocarcinoma Presenting as Ground-Glass Opacity

Background Lung ground-glass opacities (GGOs) are an early manifestation of lung adenocarcinoma. It is of great value to study the changes in the immune microenvironment of GGO to elucidate the occurrence and evolution of early lung adenocarcinoma. Although the changes of IL-6 and NK cells in lung adenocarcinoma have caught global attention, we have little appreciation for how IL-6 and NK cells in the lung GGO affect the progression of early lung adenocarcinoma. Methods We analyzed the RNA sequencing data of surgical specimens from 21 patients with GGO-featured primary lung adenocarcinoma and verified the changes in the expression of IL-6 and other important immune molecules in the TCGA and GEO databases. Next, we used flow cytometry to detect the protein expression levels of important Th1/Th2 cytokines in GGO and normal lung tissues and the changes in the composition ratio of tumor infiltrating lymphocytes (TILs). Then, we analyzed the effect of IL-6 on NK cells through organoid culture and immunofluorescence. Finally, we explored the changes of related molecules and pathway might be involved. Results IL-6 may play an important role in the tumor microenvironment of early lung adenocarcinoma. Further research confirmed that the decrease of IL-6 in GGO tissue is consistent with the changes in NK cells, and there seems to be a correlation between these two phenomena. Conclusion The IL-6 expression status and NK cell levels of early lung adenocarcinoma as GGO are significantly reduced, and the stimulation of IL-6 can up-regulate or activate NK cells in GGO, providing new insights into the diagnosis and pathogenesis of early lung cancer.
Cross-Domain Scene Classification Based on a Spatial Generalized Neural Architecture Search for High Spatial Resolution Remote Sensing Images

By labelling high spatial resolution (HSR) images with specific semantic classes according to geographical properties, scene classification has been proven to be an effective method for HSR remote sensing image semantic interpretation. Deep learning is widely applied in HSR remote sensing scene classification. Most of the scene classification methods based on deep learning assume that the training datasets and the test datasets come from the same datasets or obey similar feature distributions. However, in practical application scenarios, it is difficult to guarantee this assumption. For new datasets, it is time-consuming and labor-intensive to repeat data annotation and network design. The neural architecture search (NAS) can automate the process of redesigning the baseline network. However, traditional NAS lacks the generalization ability to different settings and tasks. In this paper, a novel neural network search architecture framework—the spatial generalization neural architecture search (SGNAS) framework—is proposed. This model applies the NAS of spatial generalization to cross-domain scene classification of HSR images to bridge the domain gap. The proposed SGNAS can automatically search the architecture suitable for HSR image scene classification and possesses network design principles similar to the manually designed networks. To obtain a simple and low-dimensional search space, the traditional NAS search space was optimized and the human-the-loop method was used. To extend the optimized search space to different tasks, the search space was generalized. The experimental results demonstrate that the network searched by the SGNAS framework with good generalization ability displays its effectiveness for cross-domain scene classification of HSR images, both in accuracy and time efficiency.
Exploiting High Geopositioning Accuracy of SAR Data to Obtain Accurate Geometric Orientation of Optical Satellite Images

Accurate geopositioning of optical satellite imagery is a fundamental step for many photogrammetric applications. Considering the imaging principle and data processing manner, SAR satellites can achieve high geopositioning accuracy. Therefore, SAR data can be a reliable source for providing control information in the orientation of optical satellite images. This paper proposes a practical solution for an accurate orientation of optical satellite images using SAR reference images to take advantage of the merits of SAR data. Firstly, we propose an accurate and robust multimodal image matching method to match the SAR and optical satellite images. This approach includes the development of a new structural-based multimodal applicable feature descriptor that employs angle-weighted oriented gradients (AWOGs) and the utilization of a three-dimensional phase correlation similarity measure. Secondly, we put forward a general optical satellite imagery orientation framework based on multiple SAR reference images, which uses the matches of the SAR and optical satellite images as virtual control points. A large number of experiments not only demonstrate the superiority of the proposed matching method compared to the state-of-the-art methods but also prove the effectiveness of the proposed orientation framework. In particular, the matching performance is improved by about 17% compared with the latest multimodal image matching method, namely, CFOG, and the geopositioning accuracy of optical satellite images is improved, from more than 200 to around 8 m.
Application of Sentinel-1B Polarimetric Observations to Soil Moisture Retrieval Using Neural Networks: Case Study for Bare Siberian Chernozem Soil

Sentinel-1 is currently the only synthetic-aperture radar, which radar measurements of the earth’s surface to be carried out, regardless of weather conditions, with high resolution up to 5–40 m and high periodicity from several to 12 days. Sentinel-1 creates a technological platform for the development of new globally remote sensing algorithms of soil moisture, not only for hydrological and climatic model applications, but also on a single field scale for individual farms in precision farming systems used. In this paper, the potential of soil moisture remote sensing using polarimetric Sentinel-1B backscattering observations was studied. As a test site, the fallow agricultural field with bare soil near the Minino village (56.0865°N, 92.6772°E), Krasnoyarsk region, the Russian Federation, was chosen. The relationship between the cross-polarized ratio, reflectivity, and the soil surface roughness established Oh used as a basis for developing the algorithm of soil moisture retrieval with neural networks (NNs) computational model. Two NNs is used as a universal regression technique to establish the relationship between scattering anisotropy, entropy and backscattering coefficients measured by the Sentinel-1B on the one hand and reflectivity on the other. Finally, the soil moisture was found from the soil reflectivity in solving the inverse problem using the Mironov dielectric model. During the field campaign from 21 May to 25 August 2020, it was shown that the proposed approach allows us to predict soil moisture values in the layer thickness of 0.00–0.05 m with the root-mean-square error and determination coefficient not worse than 3% and 0.726, respectively. The validity of the proposed approach needs additional verification on a wider dataset using soils of different textures, a wide range of variations in soil surface roughness, and moisture.
A Systematic Survey on the Role of Cloud, Fog, and Edge Computing Combination in Smart Agriculture

Cloud Computing is a well-established paradigm for building service-centric systems. However, ultra-low latency, high bandwidth, security, and real-time analytics are limitations in Cloud Computing when analysing and providing results for a large amount of data. Fog and Edge Computing offer solutions to the limitations of Cloud Computing. The number of agricultural domain applications that use the combination of Cloud, Fog, and Edge is increasing in the last few decades. This article aims to provide a systematic literature review of current works that have been done in Cloud, Fog, and Edge Computing applications in the smart agriculture domain between 2015 and up-to-date. The key objective of this review is to identify all relevant research on new computing paradigms with smart agriculture and propose a new architecture model with the combinations of Cloud–Fog–Edge. Furthermore, it also analyses and examines the agricultural application domains, research approaches, and the application of used combinations. Moreover, this survey discusses the components used in the architecture models and briefly explores the communication protocols used to interact from one layer to another. Finally, the challenges of smart agriculture and future research directions are briefly pointed out in this article.
Random Forest for Automatic Feature Importance Estimation and Selection for Explainable Postural Stability of a Multi-Factor Clinical Test

Falling is a common incident that affects the health of elder adults worldwide. Postural instability is one of the major contributors to this problem. In this study, we propose a supplementary method for measuring postural stability that reduces doctor intervention. We used simple clinical tests, including the timed-up and go test (TUG), short form berg balance scale (SFBBS), and short portable mental status questionnaire (SPMSQ) to measure different factors related to postural stability that have been found to increase the risk of falling. We attached an inertial sensor to the lower back of a group of elderly subjects while they performed the TUG test, providing us with a tri-axial acceleration signal, which we used to extract a set of features, including multi-scale entropy (MSE), permutation entropy (PE), and statistical features. Using the score for each clinical test, we classified our participants into fallers or non-fallers in order to (1) compare the features calculated from the inertial sensor data, and (2) compare the screening capabilities of the multifactor clinical test against each individual test. We use random forest to select features and classify subjects across all scenarios. The results show that the combination of MSE and statistic features overall provide the best classification results. Meanwhile, PE is not an important feature in any scenario in our study. In addition, a t-test shows that the multifactor test of TUG and BBS is a better classifier of subjects in this study.
Depth-Camera-Aided Inertial Navigation Utilizing Directional Constraints

This paper presents a practical yet effective solution for integrating an RGB-D camera and an inertial sensor to handle the depth dropouts that frequently happen in outdoor environments, due to the short detection range and sunlight interference. In depth drop conditions, only the partial 5-degrees-of-freedom pose information (attitude and position with an unknown scale) is available from the RGB-D sensor. To enable continuous fusion with the inertial solutions, the scale ambiguous position is cast into a directional constraint of the vehicle motion, which is, in essence, an epipolar constraint in multi-view geometry. Unlike other visual navigation approaches, this can effectively reduce the drift in the inertial solutions without delay or under small parallax motion. If a depth image is available, a window-based feature map is maintained to compute the RGB-D odometry, which is then fused with inertial outputs in an extended Kalman filter framework. Flight results from the indoor and outdoor environments, as well as public datasets, demonstrate the improved navigation performance of the proposed approach.
Variation Tendency of Coastline under Natural and Anthropogenic Disturbance around the Abandoned Yellow River Delta in 1984-2019

The coast around the Abandoned Yellow River Delta underwent significant changes under anthropogenic disturbance. This study aims to reveal the variation of the coastline, tidal flat area, and intertidal zone slope before, during, and after extensive reclamation during the period of 1984–2019 using satellite remote sensing images. In order to eliminate the influence of the varying water level, a new coastline correction algorithm had been proposed under the condition of insufficient accurate slope and water level data. The influence of seawalls on slope estimation were considered in it. The spatiotemporal evolution of coast had been analyzed and confirmed to be reasonable by comparing with the observed data. The results show that the coast can be roughly divided into a north erosion part and a south deposition part. Affected by reclamation, their tidal flat area in 2019 is reduced to only 43 and 27% of original area in 1984, respectively, which results in a continuous decrease in the tidal flat width. The adjustment of the tidal flat profile makes the slopes steeper in the erosion part, while the slopes in the deposition part remain stable. The reclamation has stimulated a cumulative effect as the disappearance of the intertidal zone, which may lead to the destruction of biological habitats.
Deep-Learning-Based Wi-Fi Indoor Positioning System Using Continuous CSI of Trajectories

In a Wi-Fi indoor positioning system (IPS), the performance of the IPS depends on the channel state information (CSI), which is often limited due to the multipath fading effect, especially in indoor environments involving multiple non-line-of-sight propagation paths. In this paper, we propose a novel IPS utilizing trajectory CSI observed from predetermined trajectories instead of the CSI collected at each stationary location; thus, the proposed method enables all the CSI along each route to be continuously encountered in the observation. Further, by using a generative adversarial network (GAN), which helps enlarge the training dataset, the cost of trajectory CSI collection can be significantly reduced. To fully exploit the trajectory CSI’s spatial and temporal information, the proposed IPS employs a deep learning network of a one-dimensional convolutional neural network–long short-term memory (1DCNN-LSTM). The proposed IPS was hardware-implemented, where digital signal processors and a universal software radio peripheral were used as a modem and radio frequency transceiver, respectively, for both access point and mobile device of Wi-Fi. We verified that the proposed IPS based on the trajectory CSI far outperforms the state-of-the-art IPS based on the CSI collected from stationary locations through extensive experimental tests and computer simulations.
Small-Sized Ship Detection Nearshore Based on Lightweight Active Learning Model with a Small Number of Labeled Data for SAR Imagery

Marine ship detection by synthetic aperture radar (SAR) is an important remote sensing technology. The rapid development of big data and artificial intelligence technology has facilitated the wide use of deep learning methods in SAR imagery for ship detection. Although deep learning can achieve a much better detection performance than traditional methods, it is difficult to achieve satisfying performance for small-sized ships nearshore due to the weak scattering caused by their material and simple structure. Another difficulty is that a huge amount of data needs to be manually labeled to obtain a reliable CNN model. Manual labeling each datum not only takes too much time but also requires a high degree of professional knowledge. In addition, the land and island with high backscattering often cause high false alarms for ship detection in the nearshore area. In this study, a novel method based on candidate target detection, boundary box optimization, and convolutional neural network (CNN) embedded with active learning strategy is proposed to improve the accuracy and efficiency of ship detection in nearshore areas. The candidate target detection results are obtained by global threshold segmentation. Then, the strategy of boundary box optimization is defined and applied to reduce the noise and false alarms caused by island and land targets as well as by sidelobe interference. Finally, a lightweight CNN embedded with active learning scheme is used to classify the ships using only a small labeled training set. Experimental results show that the performance of the proposed method for small-sized ship detection can achieve 97.78% accuracy and 0.96 F1-score with Sentinel-1 images in complex nearshore areas.
Artificial Vision Algorithms for Socially Assistive Robot Applications: A Review of the Literature

Today, computer vision algorithms are very important for different fields and applications, such as closed-circuit television security, health status monitoring, and recognizing a specific person or object and robotics. Regarding this topic, the present paper deals with a recent review of the literature on computer vision algorithms (recognition and tracking of faces, bodies, and objects) oriented towards socially assistive robot applications. The performance, frames per second (FPS) processing speed, and hardware implemented to run the algorithms are highlighted by comparing the available solutions. Moreover, this paper provides general information for researchers interested in knowing which vision algorithms are available, enabling them to select the one that is most suitable to include in their robotic system applications.