Miracle-dz/UniFAR-FTUs huggingface dataset:
Past Life Experiences and Neurological Recovery: The Role of Cognitive Reserve in the Rehabilitation of Severe Post-Anoxic Encephalopathy and Traumatic Brain Injury

Abstract Objective: Patients with an equivalent clinical background may show unexpected interindividual differences in their outcome. The cognitive reserve (CR) model has been proposed to account for such discrepancies, but its role after acquired severe injuries is still being debated. We hypothesize that inappropriate investigative methods might have been used when dealing with severe patients, which have very likely reduced the possibility of observing meaningful influences in recovery from severe traumas. Methods: To overcome this issue, the potential neuroprotective role of CR was investigated, considering a wider spectrum of clinical symptoms ranging from low-level brain stem functions necessary for life to more complex motor and cognitive skills. In the present study, data from 50 severe patients, 20 suffering from post-anoxic encephalopathy (PAE) and 30 with traumatic brain injury (TBI), were collected and retrospectively analyzed. Results: We found that CR, diagnosis, time of hospitalization, and their interaction had an effect on the clinical indexes. When the predictive power of CR was investigated by means of two machine learning classifier algorithms, CR, together with age, emerged as the strongest factor in discriminating between patients who reached or did not reach successful recovery. Conclusions: Overall, the present study highlights a possible role of CR in shaping the recovery of severe patients suffering from either PAE or TBI. The practical implications underlying the need to routinely considered CR in the clinical practice are discussed.
Unsupervised Multiple-Change Detection in VHR Multisensor Images Via Deep-Learning Based Adaptation

Change Detection (CD) using multitemporal satellite images is an important application of remote sensing. In this work, we propose a Convolutional-Neural-Network (CNN) based unsupervised multiple-change detection approach that simultaneously accounts for the high spatial correlation among pixels in Very High spatial Resolution (VHR) images and the differences in multisensor images. We accomplish this by learning in an unsupervised way a transcoding between multisensor multitemporal data by exploiting a cycle-consistent Generative Adversarial Network (CycleGAN) that consists of two generator CNN networks. After unsupervised training, one generator of the CycleGAN is used to mitigate multisensor differences, while the other is used as a feature extractor that enables the computation of multitemporal deep features. These features are then compared pixelwise to generate a change detection map. Changed pixels are then further analyzed based on multitemporal deep features for identifying different kind of changes (multiple-change detection). Results obtained on multisensor multitemporal dataset consisting of Quickbird and Pleiades images confirm the effectiveness of the proposed approach.
Predictive Real-Time Control Optimization of a Stormwater Management System*

An optimization model of a stormwater pond is developed to improve the performance of the system in terms of water quantity and quality. Nowadays, stormwater management systems play an important role in mitigating the impacts of urbanization on the natural hydrological cycle. These systems can be managed in such a way that they meet smart city needs. An automated dynamically managed system that can adapt itself to ever-changing environmental conditions can be modeled using a mathematical optimization approach. Hence, a Predictive Real Time Control (PRTC) approach is proposed in this paper to optimize the performance of stormwater management basins in terms of minimizing hydraulic shocks during wet periods. Then some generalized rules are designed to control the sedimentation of trapped water in the pond during dry periods to improve the quality of water discharged to the receiving stream. The combination of these two approaches provides a real-time improved performance in comparison to the static strategy.
Adaptive Vision-Based Control for Robotic Tiling with Uncalibrated Cameras and Limited FOV

The traditional manual tiling is labor-intensive and now limited by the shortage of skilled labors and the increasing manpower cost. While a few automatic machines have been developed to alleviate the problems, the autonomous capability of existing systems is relatively low, in the sense that humans usually involve to calibrate the spatial relationship between the robot and the target tile, specify the desired position of each tile, or deal with unforeseen changes (e.g., falling of tile, temporary loss of features). This paper proposes a new adaptive vision-based control scheme for robotic tiling, which enables the robot to automatically pick or re-pick the tile then place it to the desired position in the presence of uncalibrated cameras and limited field of view (FOV). The proposed controller improves the autonomous capability of tiling robots in twofold. First, the unknown spatial relationship due to uncalibrated cameras is estimated online, such that the manual calibration is not required. Second, the temporary loss of features due to the limited FOV is addressed with the Cartesian-space regional feedback, such that the manual assistance is also eliminated. The stability of the closed-loop system is rigorously proved with Lyapunov methods, and experimental results are presented to illustrate the performance of the proposed control scheme.
Assessing and Predicting Changes of the Ecosystem Service Values Based on Land Use/Land Cover Changes in Qingdao, China

Ecosystem services are the important link between the ecological system and human welfare. Quantifying and predicting the changes of ecosystem service value (ESV) is an important tool to seek choices to manage natural and human-made capital more effectively and sustainably. This study aims to model and predict the impacts of future Land Use/Land Cover(LULC) changes on ESV in a coastal area suffering rapid process of urbanization of China, Qingdao. Based on Landsat images and random forest algorithm, LULC in 1990, 1995, 2000, 2005, 2010, and 2015 were mapped and classified. A cellular automaton (CA)-Markov model was used to simulate future LULC changes in 2025. ESVs was quantified and predicted based LULC data and a modified coefficient method. The total ESV of Qingdao decreased from 172.72 billion yuan in 1990 to 164.17 billion yuan in 2025, mainly due to the loss of farmland and grassland. The built-up land should be controlled to the occupation of farmland and grassland. The management of LULC should follow ecological protection rules to protect environment and achieve sustainable and balanced development.
DNA stable isotope probing reveals contrasted activity and phenanthrene-degrading bacteria identity in a gradient of anthropized soils.

Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous soil organic pollutants. Although PAH-degrading bacteria are present in almost all soils, their selection and enrichment have been shown in historically high PAH contaminated soils. We can wonder if the effectiveness of PAH biodegradation and the PAH-degrading bacterial diversity differ among soils. The stable isotope probing (SIP) technique with 13C-phenanthrene (PHE) as a model PAH was used to: i) compare for the first time a range of ten soils with various PAH contamination levels, ii) determine their PHE-degradation efficiency, and iii) identify the active PHE-degraders using 16S rRNA gene amplicon sequencing from 13C-labelled DNA. Surprisingly, the PHE degradation rate was not directly correlated to the initial level of total PAHs and phenanthrene in the soils, but was mostly explained by the initial abundance and richness of soil bacterial communities. A large diversity of PAH-degrading bacteria was identified for seven of the soils, with differences among soils. In the soils where the PHE degradation activities were the higher, Mycobacterium species were always the dominant active PHE degraders. A positive correlation between PHE-degradation level and the diversity of active PHE-degraders (Shannon index) supported the hypothesis that cooperation between strains led to a more efficient PAH degradation.
Change Detection and Transfer Learning Approach for Updating the Habitat Maps in UAE

In this work, we aim to establish a framework for short term updates to the existing landcover maps to quickly enable efficient planning and monitoring. This paper presents the work on updating the land cover classification maps in the region of Abu Dhabi, UAE. Experiments are carried out using temporal images acquired from Worldview-2 satellite in 2014 and 2016. In the proposed approach, we first perform an automatic change detection using statistical methods such as principal component analysis. Then we make use of Random Forest algorithm applied on the Fully-Connected features obtained from AlexNet framework using the inputs from un-changed locations. The training labels are generated using the baseline maps. Eventually, our aim is to develop a robust classification approach based on automatic change detection to update the baseline maps in short time intervals.
Thin Film Metamaterial Split Ring Resonators at Microwave Frequencies

In this paper we present a metamaterial Split Ring Resonator (SRR) made of thin films of nanometer thickness working in microwave frequencies. Since the thickness of nano-film used for fabricating metamaterial structures is below the skin depth, unique resonance behavior is observed in comparison to the resonance curves of its conventional counterpart made with thick films. At thickness less than skin depth, the film becomes resistive which in turn results in a wide band magnetic resonance. Silver thin film resonators of thickness 350 nm, 550 nm and 750 nm prepared on glass substrates using RF sputtering technique are used for the study. Absorption characteristics of the SRR and Broad-side Coupled SRR (BCSRR) are analyzed.
Scene Classification of High Resolution Remote Sensing Images Via Self-Paced Deep Learning

Scene classification of high resolution remote sensing (HRRS) images is a fundamental yet challenging problem for remote sensing image analysis. In this paper, we focus on tackling the problem of HRSS scene classification using a small pool of unlabeled images and only a few labeled images per category, namely, few-shot scene classification (FSSC), which is more challenging than common scene classification task. The key challenge arises from selecting trustworthy samples from the pool of unlabeled images that have high confidence. To address this challenge, a novel local manifold constrained self-paced deep learning method is proposed. Specifically, the model is learned by gradually selecting easy samples from the pool of unlabeled images, assigning them with pseudo-labels and further adopting them with labeled images as the new training set. In addition, a local manifold constraint is introduced to enforce that the pseudo-labels assigned by the initial model should be consistent with the local manifold of the labeled samples. In such way, the confidence of the selecting samples is increased and is beneficial to train more robust classifier. Experimental results on a publicly available large scale NWPU-RESISC45 data set demonstrated the effectiveness of our method in achieving competitive performance while significantly reducing manually labeled cost.
Security Mechanisms of Internet of Things (IoT) for Reliable Communication: A Comparative Review

Internet of things (IoT) is a trending technology now a days. Many things are getting shifted to IoT. IoT is related to our daily life actions and hence pose a risk to our privacy if not handled with care. Hence there is need to have secure IoT communication. This paper deals with the various proposed IoT security mechanisms which are capable of authentication, availability, encryption and integrity for secure communication. The paper has been focused on comparative study of emerging security mechanisms of IoT, their strengths and weakness, applications and open research issues. Each and every model has been compared and all the key features of the model have been presented. The IoT has a main requirement which is less power consumption, so the power consumption of various methods used in the proposed models has also been plotted.
Kinetic Optimization of Lysine-Targeting Covalent Inhibitors of HSP72

The covalent inhibition mechanism of action, which overcomes competition with high-affinity, high-abundance substrates of challenging protein targets, can deliver effective chemical probes and drugs. The success of this strategy has centered on exposed cysteine residues as nucleophiles but the low abundance of cysteine in the proteome has limited its application. We have recently reported our discovery that lysine-56 in the difficult-to-drug target HSP72 could form a covalent bond with a small-molecule inhibitor. We now disclose the optimization of these targeted covalent inhibitors using rational design. Essential to our optimization was the development of a new covalent fluorescence polarization assay, which allows for the direct measurement of the key kinetic parameter in covalent inhibitor design, kinact/KI, extrapolation of the underlying parameters, kinact and Ki, and direct comparison to reversible analogues. Using our approach, we demonstrate a >100-fold enhancement in covalent efficiency and key learnings in lysine-selective electrophile optimization.
A Decentralized Sensor Fusion Approach to Human Fatigue Monitoring in Maritime Operations

Human fatigue is one of the main causes of accidents in maritime domain. How to use physiological data to estimate degree of human fatigue without medical domain knowledge is significant to the safety of tasks in maritime operations. In this paper, a decentralized sensor fusion approach is proposed. Various sensor data used to monitor brain wave, heart rate, muscle tension, body temperature, visual focus and head movement, together with subjective measurement of Karolinska Sleepiness Scale (KSS) values are selected as the data source for this study. Convolutional neural networks are adopted in the approach to extract local features of each individual data channel. The local features are further fused into a 5-layer fuzzy neural network for classification of the KSS values. A case study of fatigue monitoring test of ship maneuvering in simulator has been carried out. Through a comparative study with a centralized fusion approach, the proposed method is verified to be able to provide high accuracy up to 96.08% for fatigue level classification, and in particular, robust enough to maintain the accuracy to 88.42% in case of sensor failure.
Multiclass Oriented Ship Localization and Recognition In High Resolution Remote Sensing Images

Automatic inshore ship recognition, including target localization and type classification, is an important and challenging problem. However, arbitrarily rotated ships are always moored inshore densely. This makes it very difficult to locate ship targets. To resolve this problem, we proposes a multiclass oriented ship localization and recognition framework based on a cascade region convolutional neural network (R-CNN). First, Cascade R-CNN is adopted to localize and classify the positive regions – a set of bounding boxes (BBox). Second, a novel procedure which transforms a bounding box to a rotated bounding box (B2RB) is designed and applied to each BBox to regress a rotated BBox (RBox) and non-maximum suppression (NMS) is adopted to remove redundant RBoxes. Extensive experimental results conducted on the dataset collected from Google Earth demonstrate the effectiveness of our proposed approach, compared to two other state-of-the-art approaches.
Automatic Cloud Removal from Multi-Temporal Landsat Collection 1 Data Using Poisson Blending

In this paper, an automatic cloud removal algorithm based on Poisson blending is proposed. For Landsat Collection 1 Level-1 surface reflectance products generated from Landsat 8 Operational Land Imager (OLI), the algorithm first builds a cloud mask from the Quality Assessment (QA) band and then reconstructs cloud-contaminated portions based on temporal similarity of multi-temporal Landsat images. A Poisson blending algorithm is used to eliminate radiation differences between cloud-free region and cloud removed/reconstructed region. Besides, a quad-tree approach is used to improve the efficiency of gradient-domain compositing. This automatic cloud removal algorithm yields better results in terms of radiometric accuracy and consistency for experimental images with cloud coverage less than 85%.
Evaluation of the inertial dissipation method within boundary layers using numerical simulations

We evaluated the accuracy of the inertial dissipation method to estimate the rate of dissipation of turbulent kinetic energy within boundary layers by performing well‐resolved numerical simulations of turbulent channel flows and comparing the dissipation calculated directly from the data, with that deduced from the frequency spectra. The convection velocity, commonly used to convert frequency spectra into wave number spectra is found to be larger than the local mean velocity by approximately a factor of 2 near the bed and about 10% in the logarithmic layer and beyond. Usage of the standard Kolmogorov constants (particularly in the vertical and spanwise directions) also leads to significant errors (50% or more) in computation of dissipation. Usage of the optimal Kolmogorov constants and the convection velocity, obtained from the simulation results could result in improved accuracy in dissipation calculation with the inertial method.
Research of Backscattering Properties of Vegetation Fire Based On Ground-Based Scatterometer Measurement

This paper investigates the backscattering properties of vegetation fire based on ground-based scatterometer measurement in the combustion period. According to the different states of combustion during and after vegetation fire, respectively, X-band and C-band scattering coefficient of full polarization (HH, HV, VH, and VV ) are measured to exploiting the spatial and temporal responses of fire effect on vegetation. Meanwhile, the relevant influencing factors, which include the parameters of vegetation, ground and environment, are obtained in observation area. Besides the process before and after fire, it is discussed in detail for analysis of microwave scattering coefficients changing with different burning degrees in the fire development process. Based on the experimental result, the distinction through combustion is shown by the microwave method effectively, which proves its potential in information extraction from vegetation fire.
Adaptive User Grouping Based on EVM Prediction for Efficient & Robust Massive MIMO in TDD

Massive multiple-input, multiple-output (Ma-MIMO) offers significant capacity improvements for sub-6GHz wireless access. Evaluating the practicalities for real-world deployments and identifying solutions is the next critical step in the roll-out of this technology. Here novel adaptive user grouping algorithms for a single cell Ma-MIMO scenario are proposed and shown to further enhance performance. For the first time, the methodology addresses the interference caused by the user channel vectors as well as hardware impairments. Here, this is uniquely achieved by extracting the Error Vector Magnitude (EVM), and three different methodologies are applied to address specific wireless connectivity objectives. The first, maximizes the spectral efficiency which is more desirable for cellular networks. The second, maximizes the number of users for low data rate bearers, which is suitable for the Internet of Things (IoT). Whilst, the third approach focuses on specific quality per user, for applications such as wireless cameras at major cultural or sporting events. Importantly, real-world experimental data-sets have been used to evaluate the proposed user grouping algorithms.
Nurse Practitioner Scope of Practice Regulations and Nurse Practitioner Supply

Adopting full scope of practice (SOP) for nurse practitioners (NPs) is associated with improved access to care. One possible mechanism for these improvements is increased NP supply. Using county-level data, we fit cross-sectional and panel regression models to estimate the association between adopting full NP SOP and NP supply in general, and in rural and health professional shortage area—designated counties in particular. In cross-sectional analyses, we estimated positive associations between NP SOP and NP supply, though these relationships were only statistically significant when analyzing health professional shortage areas. In the panel regression models with county fixed effects, the estimated effects were attenuated toward zero and sometimes switched signs. Our findings suggest that improvements in access to care following adoption of full SOP may not be driven by increased NP supply but rather by increased capacity of NPs and physicians to provide care.
A Global Point-Sift Attention Network for 3D Point Cloud Semantic Segmentation

The existing 3D point cloud classification/segmentation networks directly use Convolutional Neural Networks (C-NNs) to extract features from indoor data and have no advantages handling complex outdoor scenes. This is mainly due to the segmentation of large scale outdoor scenes depends on global context information. Inspired by Global Attention (GA) mechanism, we design a Global Point Attention module (GPA) by regarding high-level features, which usually contain rich semantic information, as a guidance to low-level features. In this paper, we embed GPA in PointSIFT to accomplish segmentation and call this new network PointSIFT-GPA. Experimental results on the US3D dataset demonstrate the performance of GPA and the superior performance of PointSIFT-GPA. In particular, PointSIFT-GPA ranks the 2-nd place on 2019 IEEE GRSS Data Fusion Contest 3D Point Cloud Classification Challenge with mIoU 0.9454.
A New Automatic Approach to Distinguish Myocardial Infarction Based on LSTM

Myocardial Infarction (MI) has the characteristics of rapid development and poor prognosis. Early intervention is of great significance in relieving pain and preventing death. For reducing the misdiagnosis rate of MI, a novel classification approach of MI based on a long short term memory (LSTM) is proposed in this paper. Firstly, the original electrocardiogram (ECG) signal is preprocessed, and then it is divided into a heartbeat sequence. Then the heartbeat sequence is input into the deep neural network model for training and learning. Finally, the validity of the method is verified on the Physikalisch-Technische Bundesanstalt (PTB) ECG database. The accuracy of the method is 99.91%. The experimental results show that the classification accuracy of the proposed method is superior to the other methods.
Study on Neutral Networks of Ionosphere Delay Corrections of Satellite Altimeters

Ionosphere is part of the upper atmosphere of the Earth, research of ionosphere model is meaningful to reduce ionosphere delay on microwave signals. In this paper, the Back Propagation (BP) model was designed with data of Jason-2 altimeter from 2008 to 2018. The inputs of the model are year, day of the year, hour of the day, latitude and longitude, and the output is the double-frequency correction of the ionosphere. The best model is obtained by repeated training. The selected best model is validated with four cycles of Jason-3. The results show that the mean differences between the model and target of four cycles are between -0.2 and 0.06cm, the standard deviations are less than 1cm, the differences of 95% are located between ± 2cm, the correlation coefficients are all above 0.7. The result indicates that BP model is efficient for the correction of ionosphere delay and with a high accuracy.