Miracle-dz/UniFAR-FTUs huggingface dataset:
A Lightweight Policy Preserving EHR Sharing Scheme in the Cloud

Electronic Health Record (EHR) is a digital health documentary. It contains not only the health-related records but also the personal sensitive information. Therefore, how to reliably share EHR through the cloud is a challenging issue. Ciphertext-policy attribute-based encryption (CP-ABE) is a promising cryptography prototype, which can achieve fine-grained access control as well as one-to-many encryption. In CP-ABE, access policy is attached to the ciphertext, and however, the access policy is not protected, which will also cause some privacy leakage. In this paper, we propose a policy preserving EHR system on the basis of CP-ABE. Specifically, we designed an algorithm, which can hide the entire access policy as well as recover the hidden attributes from the access matrix. The subsequent evaluation of element insert, lookup, and recovery shows that our proposed scheme only introduces light-weighted overhead cost. The security analysis indicates that the scheme is selectively secure under $q$ -BDHE assumption.
Presepsin and Procalcitonin Levels as Markers of Adverse Postoperative Complications and Mortality in Cardiac Surgery Patients

Backgound: This study was aimed at evaluating the presepsin and procalcitonin levels to predict adverse postoperative complications and mortality in cardiac surgery patients. Methods: A total of 122 cardiac surgery patients were enrolled for the study. Presepsin and procalcitonin levels were measured 48 h after the procedure. The primary endpoints were adverse renal, respiratory, and cardiovascular outcomes and mortality. Results: Presepsin and procalcitonin levels were significantly higher in patients with adverse renal and respiratory outcome (p < 0.001 and 0.0081). The presepsin levels were significantly higher in patients with adverse cardiovascular outcome (p = 0.023) and the procalcitonin values in patients with sepsis (p = 0.0013). Presepsin levels were significantly higher in patients who died during hospitalization (382 pg/mL, interquartile range [IQR] 243–717.5 vs. 1,848 pg/mL, IQR 998–5,451.5, p = 0.049). In addition, the predictive value for in-hospital, 30-days, and 6-months mortality was higher for presepsin, with a significant difference between the 2 biomarkers (p = 0.025, p = 0.035, p = 0.003; respectively). Presepsin and procalcitonin seem to have comparable predictive value for adverse renal, cardiovascular, and respiratory outcome in cardiac surgery patients. Although a positive trend was notable for presepsin and adverse renal outcome (area under the ROC [receiver operating characteristic] curves [AUC] of 0.760, 95% CI 0.673–0.833 versus procalcitonin: AUC 0.692; 95% CI 0.601–0.773): no statistically significant difference was evident between the AUC of the 2 biomarkers (p = 0.25). Conclusions: Presepsin and procalcitonin seem to have comparable predictive value for adverse renal, cardiovascular, and respiratory outcome in cardiac surgery patients. Also, presepsin possesses a better predictive value for in-hospital, 30-days, and 6-months mortality.
Learning Distance Metrics for Entity Resolution

Entity resolution (ER) is to find database records that refer to the same real-world entity. A key component for ER is to choose a proper distance (similarity) function for each database field to quantify the similarity of records. Most existing ER approaches focus on how to define a proper matching rule based on generic or hand-crafted distance metrics. In this paper, we explore two learnable string distance metrics for two kinds of ER problems by employing the principle component analysis and the largest margin nearest neighbor algorithm for training. Experimental results on real data sets show that our approaches can improve entity resolution accuracy over traditional techniques.
Gradation of diabetic retinopathy on reconstructed image using compressed sensing

This study explores neovascularisation and lesion detection in an integrated framework for gradation in diabetic retinopathy (DR). Imaging is assumed to be done from sub-sample measurements following compressed sensing. Blind estimation of the scale of the matched filter (MF) followed by fuzzy entropy maximisation is done for extraction and classification of the thick and the thin vessels. Mutual information (MI) between vessel density and tortuosity of the thin vessel class is maximised in two dimensions (2D) for neovascularisation detection. For lesion detection, MI between the maximum MF response and the maximum Laplacian of Gaussian filter response is jointly maximised in 2D. The outcomes are then combined in a common platform for gradation in DR. Simulation results demonstrate that 95% images of each of DRIVE, STARE and DIARETDB1 databases and 94% images of MESSIDOR database are correctly graded by the proposed method when 80% measurement space is considered.
Bilinear Adaptive Generalized Vector Approximate Message Passing

This paper considers the generalized bilinear recovery problem, which aims to jointly recover the vector b and the matrix X from componentwise nonlinear measurements <inline-formula> <tex-math notation="LaTeX">${\text {Y}}\sim p({\text {Y}}|{\text {Z}})=\prod \limits _{i,j}p(Y_{ij}|Z_{ij})$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">${\text {Z}}={\text {A}}({\text {b}}){\text {X}}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">${\text {A}}(\cdot)$ </tex-math></inline-formula> is a known affine linear function of b, and <inline-formula> <tex-math notation="LaTeX">$p(Y_{ij}|Z_{ij})$ </tex-math></inline-formula> is a scalar conditional distribution that models the general output transform. A wide range of real-world applications, e.g., quantized compressed sensing with matrix uncertainty, blind self-calibration and dictionary learning from nonlinear measurements, one-bit matrix completion, and joint channel and data decoding, can be cast as the generalized bilinear recovery problem. To address this problem, we propose a novel algorithm called the Bilinear Adaptive Generalized Vector Approximate Message Passing (BAd-GVAMP), which extends the recently proposed Bilinear Adaptive Vector AMP algorithm to incorporate arbitrary distributions on the output transform. The numerical results on various applications demonstrate the effectiveness of the proposed BAd-GVAMP algorithm.
Design and Characterization of a Single-Phase Main Exciter for Aircraft Wound-Rotor Synchronous Starter–Generator

This paper proposes and analyzes the design philosophies of the single-phase main exciter (ME) for an aircraft wound-rotor synchronous machine and investigates its operating characteristics in both generating and starting modes. The fundamental structure and starting–generating system are discussed. The load features of single-phase ME are summarized. The ME has the characteristics of linear current amplifier and approximately constant current source the generating mode, which can be used in the wide range of working temperature. Detailed waveforms and finite-element analysis verify that excitation current $I_{\mathrm {ef}}$ is almost irrelevant to speed but in direct proportion to frequency in the starting mode. Besides, simulated data indicate that the energy transferring efficiency between ME stator and rotor can reach about 55% by the single-phase ac excitation method. Finally, the experimental verification is carried out, and the simulated and experimental data and waveforms of single-phase ME agree well with each other.
HyINT: Signature-Anomaly Intrusion Detection System

Information plethora being generated in an organization tends to the evolution of cloud computing technologies for efficient management and accessibility of voluminous data. With increased utilization of cloud computing by organizations working in different domains, several new security threats have been encountered. In recent time, ensuring security to the cloud platforms is one of the key challenges. In case of highly distributed systems, the threat of malicious content or behavior form network side itself is considered to be the most challenging one. In particular, we have proposed a hybrid intrusion detection algorithm for host-based intrusion detection. Based on proposed algorithm, a hybrid intrusion system has been developed namely, HyINT, which uses both signature and anomaly-based detection methodologies. Also, different parameters have been discussed for evaluation of the system.
Automatic Diagnosis Method for Rolling Bearing Using Measured Signal from Distant Points

Though acceleration sensors must be set as near the diagnosed bearings as possible, it is difficult to set acceleration sensors near the diagnosed bearings in some field equipment, and in some cases bearing faults are diagnosed at distant points from the diagnosed bearings. So, in this research, to solve the problem described above, we investigated the basic method by which bearing faults can be automatically diagnosed simply and precisely using the signals measured by the acceleration sensors at distant points from the diagnosed bearings. In this paper, first we proposed the method by which the optimal cut-off frequency of high-pass-filter is automatically searched and decided by genetic algorithm and tabu search to extract vibration signals of the abnormal bearings, and the method by which bearing faults can be automatically diagnosed by decision tree. Then, these methods were applied to bearing diagnosis using the vibration signals measured at distant points from the diagnosed bearings rolling at middle speed, and the efficiency of these methods have been verified by the results of the automatic bearing faults diagnosis.
Properties of Low-Frequency Electromagnetic Wave Across Different Rough Sea Surfaces

A lossy Drude dispersive medium is used to analyze the propagation properties of the low-frequency electromagnetic wave under the rough sea surface. Numerical results in f=150Hz are illustrated for two different rough sea surfaces. It is noted that the rough sea surface affects the phase and amplitude distributions of the low-frequency electromagnetic wave.
An Automatic Liver Segmentation Algorithm for CT Images U-Net with Separated Paths of Feature Extraction

In this paper, a fully convolutional neural network based on U-net is proposed to segment the liver in CT images. Two modifications are made to the original U-net structure. Firstly, an extra path is added to the original net structure to extract the global features and detail features separately. Secondly, the number of convolutional channels of the original contraction path, the original expansion path and the new path is reduced. These two modifications make the training more rapid and improve the efficiency of the convolution kernel extraction feature. Then, the segmentation results before and after modification is compared in terms of performance, including recall rate and precision rate, to ensure that the modified network can reach even higher than the original network precision. After that, the paper analyzes the reasons why our network can maintain good segmentation effect and summarizes the application prospect of the modified network.
Effect of Including Transient Virtual Impedance in Droop-Controlled Microgrids

Droop control has shown promising results for decentralized power sharing in microgrids. However, the basic scheme can suffer from unequal reactive power sharing power due to differing line impedances between converters and due to the predominantly resistive nature of low voltage networks. Virtual impedances are able to mitigate these drawbacks, and are mostly implemented using a quasi-stationary approach. This approach replicates an impedance in steady state, but transients of the load current are only partly reproduced. This paper discusses the effect of including the transient term of the virtual impedance. A state-space small-signal model is derived in order to show the effect of including the transient virtual impedance. Simulations of a two-inverter microgrid are also shown to test the model and offer a numerical example. Both modal analysis on the small-signal model and the results from the simulation indicate improved damping when including the transient virtual impedance.
A Broadband Hybrid Continuous Power Amplifier

A highly efficient and broadband 10W GaN HEMT power amplifier (PA) is presented, which employs the hybrid PA mode, transferring between continuous Class-F and continuous inverse Class-F. A GaN PAis designed and realized based on this mode-transferring operation using the multisection matching transformers. Specifically, the multisection matching transformers consisting of stepped transmission-line sections with different characteristic impedances are preferred for its impedance matching. In this letter, the stepped transmission-line transformer is employed to optimize fundamental impedance and control the harmonic impedance directly. The proposed design strategy is experimentally verified by a 0.6-2.5 GHz PA design with 73.4% maximum power-added efficiency and 10 W output power. The footprint of the fabricated PAis ${55}\ \text{mm}\times {35}\ \text{mm}$.
Impacts of Operators’ Behavior on Reliability of Power Grids During Cascading Failures

Human operators play a key role in the reliable operation of critical infrastructures. However, human operators may take actions that are far from optimum. This can be due to various factors affecting the operators’ performance in time-sensitive and critical situations such as reacting to contingencies with significant monetary and social impacts. In this paper, an analytic framework is proposed based on Markov chains for modeling the dynamics of cascading failures in power grids. The model captures the effects of operators’ behavior quantified by the probability of human error under various circumstances. In particular, the observations from historical data and information obtained from interviews with power-system operators are utilized to develop the model as well as identify its parameters. In light of the proposed model, the noncritical regions of power-system's operating characteristics with human-factor considerations are characterized under which the probability of large cascading failures is minimized.
DTARP: A Dynamic Traffic Aware Routing Protocol for Wireless Sensor Networks

The challenge of efficient point-to-point communication in meshed Wireless Sensor and Actuator Networks (WSANs) can be addressed by Software Defined Networking (SDN). In contrast to the traditional routing protocols for Wireless Sensor Networks (WSNs) that use distributed local information for routing decisions, SDN based routing protocols can access global topology information for routing decisions. The global topology information enables more efficient routing decisions. Accordingly, this paper proposes the SDN based innovative Dynamic Traffic Aware Routing Protocol (DTARP) that uses up-to-date traffic information from the WSAN and topology metrics from graph theory to increase the overall energy efficiency of the WSAN and to avoid traffic congestion in the network. The proposed routing algorithm has been prototyped and compared to other routing algorithms. The evaluation shows a clear advantage of SDN-based routing for peer-to-peer IoT communication.
Developmental Regulation of Nuclear Factor Erythroid-2 Related Factors (nrfs) by AHR1b in Zebrafish (Danio rerio)

Interactions between regulatory pathways allow organisms to adapt to their environment and respond to stress. One interaction that has been recently identified occurs between the aryl hydrocarbon receptor (AHR) and the nuclear factor erythroid-2 related factor (NRF) family. Each transcription factor regulates numerous downstream genes involved in the cellular response to toxicants and oxidative stress; they are also implicated in normal developmental pathways. The zebrafish model was used to explore the role of AHR regulation of nrf genes during development and in response to toxicant exposure. To determine if AHR1b is responsible for transcriptional regulation of 6 nrf genes during development, a loss-of-function experiment using morpholino-modified oligonucleotides was conducted followed by a chromatin immunoprecipitation study at the beginning of the pharyngula period (24 h postfertilization). The expression of nrf1a was AHR1b dependent and its expression was directly regulated through specific XREs in its cis-promoter. However, nrf1a expression was not altered by exposure to 2, 3, 7, 8-tetrachlorodibenzo-p-dioxin (TCDD), a toxicant and prototypic AHR agonist. The expression of nrf1b, nrf2a, and nfe2 was induced by TCDD, and AHR1b directly regulated their expression by binding to cis-XRE promoter elements. Last, nrf2b and nrf3 were neither induced by TCDD nor regulated by AHR1b. These results show that AHR1b transcriptionally regulates nrf genes under toxicant modulation via binding to specific XREs. These data provide a better understanding of how combinatorial molecular signaling potentially protects embryos from embryotoxic events following toxicant exposure.
RPSA, a candidate gene for isolated congenital asplenia, is required for pre-rRNA processing and spleen formation in Xenopus

ABSTRACT A growing number of tissue-specific inherited disorders are associated with impaired ribosome production, despite the universal requirement for ribosome function. Recently, mutations in RPSA, a protein component of the small ribosomal subunit, were discovered to underlie approximately half of all isolated congenital asplenia cases. However, the mechanisms by which mutations in this ribosome biogenesis factor lead specifically to spleen agenesis remain unknown, in part due to the lack of a suitable animal model for study. Here we reveal that RPSA is required for normal spleen development in the frog, Xenopus tropicalis. Depletion of Rpsa in early embryonic development disrupts pre-rRNA processing and ribosome biogenesis, and impairs expression of the key spleen patterning genes nkx2-5, bapx1 and pod1 in the spleen anlage. Importantly, we also show that whereas injection of human RPSA mRNA can rescue both pre-rRNA processing and spleen patterning, injection of human mRNA bearing a common disease-associated mutation cannot. Together, we present the first animal model of RPSA-mediated asplenia and reveal a crucial requirement for RPSA in pre-rRNA processing and molecular patterning during early Xenopus development. Summary: Mutations in RPSA cause isolated congenital asplenia in humans; this paper presents the first animal model of RPSA-mediated asplenia and explores the role of RPSA in vertebrate spleen formation.
Modeling and Stability Analysis of CCIs- and VCIs-Based Hybrid Microgrids Operating in Grid-Connected Modes

Converters serving as power interfaces between distributed generation (DG) sources and loads can operate in current-controlled modes (CCMs) and voltage-controlled modes (VCMs) together in a microgrid. This paper studies the stability issues in a grid-connected hybrid microgrid represented by a current-controlled inverter (CCI) and a voltage-controlled inverter (VCI), whose inner control loops would interact with each other because of the grid line impedance. The impedance and admittance-based passivity stability criterion is applied to analyze the stability issues, so output admittances of the CCI and output impedances of the VCI are modeled and analyzed, respectively. Additionally, a stability analysis model is also developed for multiparallel grid-connected inverters to reveal the instability phenomena between the inverters and the grid. Theoretical and simulation results show that the studied VCI can help to mitigate near- synchronous oscillations and cannot damp harmonic oscillations in higher frequencies.
Transport Mode Detection when Fine-grained and Coarse-grained Data Meet

Transport Mode Detection (TDM) algorithms in principle are developed for fine-grained data which is either high frequent accurate GPS data with/or further optional data such as accelerometer from mobile phones. The main drawback of using high frequent GPS data is the battery issue which makes it very expensive experiment to be employed for large scale data. Besides, GPS can not cover underground trajectories and some additional resource is required for such multi-modal trajectories. In this work we investigate the TDM algorithms using a combination of fine-grained (GPS) and coarse-grained (GSM) data with lower frequency compared to existing studies. We first provide a comprehensive overview of transport mode detection for such data by exploring both segment based and sequence-based machine learning approaches and then we use the collected heterogeneous mobility dataset to compare different mode detection algorithms. With the obtained results, we show that TDM algorithms are still effective approach for noisy and sparse heterogeneous data. The obtained decent performance provides the opportunity of extracting precious data from a large population of users in an inexpensive approach.
Self‐reported health and quality of life outcomes of heart failure patients in the aftermath of a national economic crisis: a cross‐sectional study†

There are indications that economic crises can affect public health. The aim of this study was to describe characteristics, health status, and socio‐economic status of outpatient heart failure (HF) patients several years after a national economic crisis and to assess whether socio‐economic factors were associated with patient‐reported outcome measures (PROMs).
EMG pattern recognition compared to foot control of the DEKA Arm

Introduction EMG pattern recognition control (EMG-PR) is a promising option for control of upper limb prostheses with multiple degrees of freedom (DOF). The purposes of this study were to 1) evaluate outcomes of EMG-PR and inertial measurement units (IMU) control of the DEKA Arm as compared to personal prosthesis; and 2) compare outcomes of EMG-PR to IMU control of DEKA Arm. Methods This was a quasi-experimental, multi-site study with repeated measures that compared non-randomized groups using two types of controls: EMG-PR and IMUs. Subjects (N = 36) were transradial (TR) and transhumeral (TH) amputees. Outcomes were collected at Baseline (using personal prosthesis), and after in-laboratory training (Part A), and home use (Part B). Data was compared to personal prosthesis, stratified by amputation level and control type. Outcomes were also compared by control type. Results The EMG-PR group had greater prosthesis use after Part A, but worse dexterity, lower satisfaction, and slower activity performance compared to Baseline; the IMU group had slower activity performance. After Part B, the EMG-PR group had less perceived activity difficulty; the IMU group had improved activity performance, improved disability and activity difficulty, but slower performance. No differences were observed for TH group by control type in Part A or B. The TR group using EMG-PR had worse dexterity (Parts A & B), and activity performance (Part A) as compared to IMU users. Discussion/Conclusion Findings suggest that for the TR group that IMUs are a more effective control method for the DEKA Arm as compared to the EMG-PR prototypes employed in this study. Further research is needed to refine the EMG-PR systems for multi-DOF devices. Future studies should include a larger sample of TH amputees. Trial registration ClinicalTrials.gov NCT01551420.