Miracle-dz/UniFAR-FTUs huggingface dataset:
Aortic distensibility is associated with both resting and hyperemic coronary blood flow.

BACKGROUND A large body of evidence demonstrates an independent association between arterial stiffness and prospective risk of cardiovascular events. A reduction in coronary perfusion is presumed to underscore this association, however studies confirming this are lacking. This study compared invasive measures of coronary blood flow (CBF) with cardiac magnetic resonance (CMR) derived aortic distensibility (AD). METHODS Following coronary angiography, a Doppler FloWire and infusion microcatheter were advanced into the study vessel. Average peak velocity (APV) was acquired at baseline and following intracoronary adenosine to derive coronary flow velocity reserve (CFVR = hyperemic APV/resting APV), and CBF (π x [Diameter]2 x APV x 0.125). Following angiography, patients underwent CMR to evaluate distensibility at the ascending aorta (AA), proximal descending aorta (PDA) and distal descending aorta (DDA). RESULTS Fifteen subjects (53±13years) with minor epicardial disease (maximum stenosis <30%) were enrolled. Resting CBF was 44.1±11.9mL/min, hyperemic CBF was 143.8±37.4 mL/min and CFVR was 3.15±0.48. Aortic distensibility was 3.89±1.72·10-3mmHg-1 at the AA, 4.08±1.80·10-3mmHg-1 at the PDA and 4.42±1.67·10-3mmHg-1 at the DDA. All levels of distensibility correlated with resting CBF (R2 0.350-0.373, p<0.05), hyperemic CBF (R2 0.453-0.464, p<0.01), and CFVR (R2 0.442-0.511, p<0.01). CONCLUSION This study demonstrates that hyperemic and, to a lesser extent resting coronary blood flow, are significantly associated with measures of aortic stiffness in patients with only minor angiographic disease. These findings provide further mechanistic support for the observed prognostic capacity of large artery function in cardiovascular event prediction.
The adaptation of an evidence-based brief intervention for substance-using adolescents and their caregivers

ABSTRACT Objective: Adolescent substance use is prevalent and associated with additional risk behaviours. Research shows that evidence-based brief interventions with adolescents and their main caregiver may mitigate the progression of substance use. Method: We used the ADAPT-ITT framework to develop a needs-based, culturally-sensitive intervention. Five focus groups with adolescents (n = 30), three focus groups with separate main caregivers of adolescents who use substances (n = 21), key informant interviews and an expert meeting, were conducted. Results: Adolescents described the need to decrease risk behaviours, while adolescents and caregivers requested additional support. The core components of the original intervention were retained, with some additions. The newly adapted RAD-PAL intervention was viewed as acceptable. Conclusion: Despite apparent acceptability, we need to test feasibility and efficacy in a trial.
Pediatric Asthma Care Assessment for the Emergency Department

In the United States alone, approximately 6.2 million children have asthma. Managing the care of pediatric patients presenting in the emergency department with asthma requires clinicians to access multiple locations within the electronic health record. This wealth of information makes it vital to develop methods for sifting through the clutter to information that is relevant to the specific task to be completed. Unfortunately, current information displays have not been developed to maximize provider efficiency when integrating information across a number of disparate locations. Using proven data display theories, we aimed to create a consolidated acute pediatric asthma display to aid in the patient's care with the goal of improving the efficiency and consistency by which they are treated - ultimately reducing the length of stay, complications, and morbidity in these patients.
Robust Droop Control of AC Microgrid Against Nonlinear Characteristic of Inductor

This paper investigates the effect of nonlinear characteristic of filter inductor on power sharing performance of droop-controlled microgrid, and presents a robust droop control strategy to implement proportional reactive power sharing with immunity to nonlinear characteristic of filter inductor. Considering current-dependent soft-saturation natures of filter inductor, time-variant average model of inductor is first established to reveal the nonlinear inductance characteristic. Then, output impedance model of inverters with consideration of nonlinear inductor is derived to analyze the effect of nonlinear inductor on reactive power sharing characteristic. Furthermore, a robust droop control strategy is proposed to mitigate the effect of nonlinear inductor on reactive power sharing and implement proportional reactive power sharing under different load profiles. Simulation and experimental results show that the proposed droop control strategy is able to perform desirable power sharing with a good capability to reject nonlinear characteristic of filter inductor.
Semantic-Emotion Neural Network for Emotion Recognition From Text

Emotion detection and recognition from text is a recent essential research area in Natural Language Processing (NLP) which may reveal some valuable input to a variety of purposes. Nowadays, writings take many forms of social media posts, micro-blogs, news articles, customer review, etc., and the content of these short-texts can be a useful resource for text mining to discover an unhide various aspects, including emotions. The previously presented models mainly adopted word embedding vectors that represent rich semantic/syntactic information and those models cannot capture the emotional relationship between words. Recently, some emotional word embeddings are proposed but it requires semantic and syntactic information vice versa. To address this issue, we proposed a novel neural network architecture, called SENN (Semantic-Emotion Neural Network) which can utilize both semantic/syntactic and emotional information by adopting pre-trained word representations. SENN model has mainly two sub-networks, the first sub-network uses bidirectional Long-Short Term Memory (BiLSTM) to capture contextual information and focuses on semantic relationship, the second sub-network uses the convolutional neural network (CNN) to extract emotional features and focuses on the emotional relationship between words from the text. We conducted a comprehensive performance evaluation for the proposed model using standard real-world datasets. We adopted the notion of Ekman’s six basic emotions. The experimental results show that the proposed model achieves a significantly superior quality of emotion recognition with various state-of-the-art approaches and further can be improved by other emotional word embeddings.
LAB to SOC: Robust Features for Dynamic Malware Detection

Machine learning models regularly achieve more than 95% accuracy in academic literature for dynamic malware detection problems, but the samples providing the data for these models are rarely shared publicly. This not only creates a benchmarking problem for academic and industry practitioners but could fail to reveal the hidden bias of machine learning models towards data from a particular source. This paper simulates "lab" experiments with several filetypes, machine learning algorithms, and features tested using data from two sources to probe the robustness of these models across different test sets. The first source is the same as the training data, the second is a commercial malware dataset provided by an organisation's advanced malware detection methods. These preliminary results indicate that for Windows executable files, widely used API call features are less robust than behavioural metrics such as CPU usage, RAM use, and packets received and transmitted, which give greater consistency in predictive accuracy rates across the different test sets.
Run-time Performance Monitoring, Verification, and Healing of End-to-End Services

Softwarization enables tremendous flexibility for networks as the use of software-defined networking (SDN) and programmable data planes (e.g. P4) together support dynamic reconfiguration of networks in real-time, in response to network conditions and new service requests. In such networks, it is imperative to ensure that end-to-end network services continue to satisfy SLAs, especially performance requirements. However, in the presence of unpredictable dynamics due to network reconfigurations, it is not possible to guarantee prior to deployment that a service will meet such SLAs. Run-time verification - in combination with programmable control and data plane monitoring - can provide a basis for detecting potential performance SLA violations, together with identifying and executing appropriate network mitigations. In this paper, we propose a verification transverse based on formal specifications, that spans performance SLAs across the distributed SDN control and programmable data planes, and can coordinate with both planes to execute dynamic reconfiguration that mitigate the detected issues. We demonstrate a proof-of-concept prototype based on an extension of the Aerial run-time verification tool, together with the Inband Network Telemetry (INT) capability, on a network running distributed ONOS controllers together with a P4 data plane.
Multihop Wireless Controlled Sensor and Actuator Network for Cable Installation System

A Long and heavy cable should be carried out the installation under the construction of plant. The current cable installation system drives the cables to relax the physical stress of workers, although. That system cannot work to pull the long and chained cable sequentially at the timing of tension triggered to the cable head by worker. We propose the multihop wireless controlled system that works to push the cable at the timing of instruction packets delivered by tension applied. This paper explains the adoption of multihop wireless controlled sensor and actuator network into the cable installation system.
Thermal evolution of H-related defects in H implanted Si: from nano-platelets to micro-cracks

Annealing at moderate temperatures (300–450°C) of H implanted silicon leads to the formation of nano-platelets within minutes and of micro-cracks or blisters within hours. Up to now, the mechanism by which they transform from one to another type of H2 filled defect was unknown. We have analyzed the evolution of the characteristics of platelets populations at the onset of micro-cracks formation and found it occurs when and where a group of « large » platelets of 50 nm in diameter are close enough, typically at about 50 nm from each other. In situ TEM confirms that sudden nucleation of micro-cracks in place of groups of platelets occurs. It is concluded that micro-crack nucleation results from elastic interactions between neighboring platelets, favoring the opening of the silicon crystal between them.
Abnormal Behavior Detection Scheme of UAV Using Recurrent Neural Networks

With the development of technology and the decreasing of manufacturing costs, unmanned aerial vehicle (UAV) is considered to be one of the most effective relay to expand the communication coverage and improve the performance of cellular networks. However, the communication system of UAV is very susceptible to Global Positioning System (GPS) spoofing, causing it to deviate from the original trajectory and perform abnormal behavior. To address this issue, the abnormal behavior detection scheme of UAV using Recurrent Neural Networks (RNNs) is proposed in this paper. Specifically, the reliable normal behavior models for two different scenarios are established by applying RNNs to avoid the confusion of slight offset and abnormal behavior, so as to improve the accuracy of proposed detection scheme of UAV. Besides, in order to ensure the accuracy of training samples of RNNs, Direction of Arrival (DOA) estimation algorithm is used to obtain a large number of current 2D arrival angle of UAV. Moreover, an appropriate threshold is selected through amounts of experiments to measure the Normalized Root Mean Square Error (NRMSE) between the real position and the position provided by normal behavior models, thus detecting the abnormal behavior of UAV. Experimental results reveal that the proposed abnormal behavior detection scheme is of high accuracy.
CFlam: Cost-effective Flow Latency Monitoring System for Software Defined Networks

Flow latency monitoring is a fundamental task in network measurement. The development of software defined networking enables flexible flow latency monitoring in the control plane. Existing approaches mainly focus on direct probe-based latency measurement, which has a high measurement overhead, especially in high accuracy monitoring systems. In this paper, we revisit software defined latency monitoring framework and explore a cost-effective approach named CFlam to produce flow latency results. We observe large scale latency monitoring generates too many duplicate probe packets. Based on this observation, we attempt to measure only a very small subset of active flows to infer the rest flow latencies. We formulate the monitoring flow selection problem by an algebraic model, and develop an efficient algorithm to generate the optimal probe flow set. We implement and deploy CFlam on a SDN testbed to verify its feasibility and performance. We conduct experiments on a public-available network topology with real packet traces collected from a data center. Experiment results demonstrate that our scheme generates accurate flow latencies at minimum measurement overhead.
Tell Me You Fixed It: Evaluating Vulnerability Notifications via Quarantine Networks

Mechanisms for large-scale vulnerability notifications have been confronted with disappointing remediation rates. It has proven difficult to reach the relevant party and, once reached, to incentivize them to act. We present the first empirical study of a potentially more effective mechanism: quarantining the vulnerable resource until it is remediated. We have measured the remediation rates achieved by a medium-sized ISP for 1, 688 retail customers running open DNS resolvers or Multicast DNS services. These servers can be abused in UDP-based amplification attacks. We assess the effectiveness of quarantining by comparing remediation with two other groups: one group which was notified but not quarantined and another group where no action was taken. We find very high remediation rates for the quarantined users, 87%, even though they can self-release from the quarantine environment. Of those who received the email-only notification, 76% remediated. Surprisingly, over half of the customers who were not notified at all also remediated, though this is tied to the fact that many observations of vulnerable servers are transient. All in all, quarantining appears more effective than other notification and remediation mechanisms, but it is also clear that it can not be deployed as a general solution for Internet-wide notifications.
Automated Frequency and Quality Factor Mismatch Compensation Method for MEMS Rate Integrating Gyroscope

This paper reports an automated mismatch compensation method for a rate integrating gyroscope (RIG). The eigenmodes of an ideal degenerated resonator are clockwise (CW) and counterclockwise (CCW) modes, which are independent each other. However, the frequency and Q-factor mismatches of the resonator, which are generally unavoidable because of fabrication imperfectness, entangles these eigenmodes. This cross-coupling effect between CW and CCW modes deteriorate the linearity and causes the angle locking [1]. Thus, in this paper, a method to detects the cross-coupling terms (i.e. the frequency and Q mismatches) and tunes the driving signals to compensate these mismatches was proposed. The proposed method was experimentally confirmed using an electromagnetic ring resonator. The cross-coupling terms, which come from the frequency and Q-factor mismatches, were reduced to be 3 orders of magnitude by this method.
Biomimetic Micro-Gel Robot Having a Soft-Rigid Hybrid Structure

In recent years, soft robots are attracting attention. The important point of fabrication of soft robot is mimicking soft-rigid hybrid structures like living organisms. Most of soft robots reported are in macro-scale, but soft microrobot has not been reported much. To realize soft microrobot, we propose micro-gel robots having a soft-rigid hybrid structure in this study. The soft parts are made from thermoresponsive gel actuator and it can be actuated by irradiating laser on it. The rigid parts are made of SU-8 that supports entire body of the micro-gel robot. This hybrid structure of the soft and rigid materials can realize complicated motion like living organisms. We succeeded in fabrication of the proposed micro-gel robot by using sacrificial layer process. Furthermore, we performed proof of concept of the actuation of the robot with laser irradiation.
Trapping of Cell-Laden Hyaluronic Acid-Acrylamide Hydrogel Droplets using Bulk Acoustic Waves

In this paper an acoustofluidic system to trap hydrogel droplets is shown. The presented trapping method is label-free, biocompatible and operated in non-contact mode. The results show that the droplets can be trapped at flow rates up to 76 µL/min which corresponds to an average flow speed of 3.2 mm/s. Moreover, it is shown that the droplets can be trapped for several hours, thus allowing for studies of the encapsulated cells over time. An application of the system is shown by performing on-chip cell nuclei staining.
Evaluation of Mental Stress and Heart Rate Variability Derived from Wrist-Based Photoplethysmography

Heart Rate Variability (HRV) is a significant indicator of physical fitness and mental stresses. Electrocardiography (ECG) is the most common means to detect HRV. However, the nature of the ECG detection limits its convenience and usage time. While wrist-based Photoplethysmography (PPG) provides the possibility of convenient 24/7 HRV monitoring, its accuracy has not been systematically analyzed. We experimented with six healthy subjects and measured PPG from their wrist and ECG from their chest. The result shows that ten HRV parameters have significant differences between stress and non-stress states. Furthermore, the 10-fold accuracy of stress state detection within subjects is 98% and the Leave-One-Participant-Out F1score reaches 80%. The results demonstrate that wrist-based PPG can provide HRV measurements that enable the recognition of mental stress as accurately as ECG, even for a short three-minute temporal window.
Detecting Malware on X86-Based IoT Devices in Autonomous Driving

X86-based software runs on some mainstream autonomous driving systems to perform intelligent operations and help to significantly improve driving safety. However, vulnerabilities of software in autonomous driving can lead to vehicle components and systems being attacked, which ultimately affects the work of the autonomous vehicle. Although many scholars have detected malware on X86-based Internet of Things (IoT) devices through static or dynamic analysis, these methods cannot be directly ported to X86-based IoT devices in autonomous driving because of hardware and software capabilities and real-time requirements. In this work, we propose the detection of malware on X86-based IoT devices in an autonomous driving approach combining fusion features from static analysis and machine learning to solve problems of resource overhead for dynamic analysis and low accuracy of the static analysis. First, a feature extraction model based on the level of operation code is designed. Then fusion features at three different levels are extracted through unpacking programs. Finally, a detection model based on Extreme Gradient Boosting (XGBoost) is used to discover malware on X86-based IoT devices in autonomous driving. On this basis, a malware detection approach based on fusion features is demonstrated. After that, we compare our approach with other identification approaches on a dataset consisting of 4169 samples, which includes 2379 malware and 1790 benign programs. Experimental results show that fusion features can significantly increase the recognition rate. Moreover, XGBoost has a higher recognition accuracy than other mainstream classification algorithms.
DASH-based Streaming Client for View Switching in Free-viewpoint Video Systems

Image processing and communications networking technologies are evolving at a rapid pace. Users are demanding high-quality and realistic services to enjoy cultural contents such as sports and concerts. Free-viewpoint video (FVV) streaming is an active field of research that enables realistic services. FVV streaming service augments the user experience by providing different viewpoints of the same scene. The user can navigate freely and continuously to any viewpoint. In order to provide FVV streaming services, the transmission method of various on-demand user viewpoints is important. In this paper, we propose and implement a DASH-based FVV streaming client to reduce the view switching latency by receiving a number of adjacent images. We verify the performance of the proposed client and analyze the switching time and transmission data rate depending on the number of simultaneously received videos.
A Novel Impact Analysis Approach for SDN-based Networks

Risk assessment comprises a series of processes for evaluating the extent to which a system is threatened by cyber-attacks. One important aspect of risk assessment is to determine the magnitude of impact of cyber-attacks. In this paper we propose a novel impact analysis approach which adopts both qualitative and quantitative elements for determining the impact value. As use case, we consider a Software-Defined Networking (SDN)-based communication system and three types of Distributed Denial of Service (DDoS) attacks, namely ICMP Flood, UDP Flood, and TCP Syn Flood attacks. By applying the impact analysis approach, we are able to calculate the impact of the aforementioned DDoS attack types on each network component and identify the most impacted component. We are also able to calculate the impact on the whole SDN network and thus, to figure out the most severe DDoS attack type.
PCR Amplified Dnazyme-Amplicons for Generic Solid-Phase Antimicrobial Resistence Screening

Fiber optic surface plasmon resonance (FO-SPR) has shown its potential for the detection of nucleic acids and more recently the technology has been combined with catalytic active strands such as DNAzymes. In this work, an innovative, generic solid-phase DNA sensor concept is presented, based on FO-SPR and PCR amplified DNAzyme activity. Improved levels of specificity and sensitivity were obtained down to picomolar concentrations. Moreover, the FO-SPR sensor concept enables AuNP amplified DNA target detection, independent of the target sequence length. The FO-SPR sensor was demonstrated for the screening of the mobile colistin resistance (MCR-2) gene, a gene important for the antimicrobial resistance in Gram-negative species such as E. Coli.