Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact on binocular visual function of small-incision lenticule extraction for high myopia.

PURPOSE To assess if small-incision lenticule extraction (SMILE) for high myopia reduces the binocular visual function assessed by stereoacuity, binocular visual acuity, binocular contrast sensitivity, and binocular summation. SETTING University hospital DESIGN:: Prospective case series METHODS:: Patients with a myopic spherical equivalent of at least 6 diopters (D) scheduled for SMILE aimed at emmetropia were included. Psychophysical testing was done with correction before surgery but no correction after surgery. Stereoacuity was assessed with the Randot Circles test and the near Frisby test, visual acuity (monocular and binocular) was assessed with high-contrast Early Treatment Diabetic Retinopathy Study charts, and contrast sensitivity (monocular and binocular) was assessed with the Pelli-Robson chart and the Freiburg Acuity and Contrast Test. Binocular summation was calculated by comparing the binocular score against the best monocular score. RESULTS 138 eyes of 69 patients were included. Mean spherical equivalent changed from -7.46 D±1.06 (SD) to -0.23 D±0.40 after surgery. Stereoacuity did not change significantly following surgery; median change (IQR) was -0.32 (-6.21 to 1.55) seconds of arc with the Frisby test and 0.00 (-7.5 to 5.0) seconds of arc with the Randot test (P≥0.06). Binocular postoperative uncorrected distance visual acuity was not different from the preoperative corrected distance visual acuity (P=0.40). Contrast sensitivity declined slightly monocularly with both tests of contrast sensitivity, but was unaltered binocularly (P≥0.08). Binocular summation for visual acuity and contrast sensitivity was unaltered following surgery (P≥0.09). CONCLUSIONS SMILE for high myopia did not reduce the binocular function assessed by stereoacuity, binocular visual acuity, binocular contrast sensitivity, and binocular summation.
Elastography imaging: the 30 year perspective

From the development of x-ray imaging in the late 19th century, the field of medical imaging developed an impressive array of modalities. These can measure and image a variety of physical parameters from absorption coefficients to spin–spin relaxations. However, throughout most of the 20th century, the intrinsic biomechanical properties of tissues remained hidden from conventional radiology. This changed around 1990 when it was demonstrated that medical ultrasound systems with their fast pulse repetition rate and high sensitivity to motion could create images related to the stiffness of tissues and their shear wave properties. From there, vigorous development efforts towards imaging the elastic properties of tissues were launched across different modalities. These progressed from the research phase, through implementation on clinical scanners, through extensive clinical trials of selected diagnostic tasks, to government approvals, payer approvals, international standards statements, and into routine clinical practice around the globe. This review covers highlights of some major topics of the technical and clinical developments over the last 30 years with brief pointers to some of the remaining issues for the next decade of development.
The psychological states of people after Wuhan eased the lockdown

It has been two months since Wuhan eased the lockdown and the people of Wuhan have been under great pressure during COVID-19. The psychological status among healthcare workers and residents were barely know due to the lack of research after Wuhan eased of the lockdown. The purpose of this study was to assess people’s mental health and the changes after Wuhan eased the lockdown. A cross-sectional online study among citizens in Wuhan was conducted. Among 1417 participants, 387(27.0%) were frontline healthcare workers and 1035(73.0%) were residents from the general public. Their COVID-19 psychological status was evaluated using Patient Health Questionnaire-9(PHQ-9), Generalized Anxiety Disorder 7-item (GAD-7), and the PTSD Checklist-Civilian Version (PCL-C). Results show that 16.1%,22.3% and 17.2% healthcare workers and 21.2%, 16.7% and 17.2% general public had symptoms of depression, anxiety and PTSD ranging from moderate to severe. Anxiety levels were not significantly different between healthcare workers and the general public. The decreased income and the frequent social media exposure are the risk factors for general public. Compared to the early COVID-19 epidemic period, the proportion of anxiety and depression among both the general public and health workers decreased after Wuhan eased the lockdown. Our finding can be used to help the government of Wuhan to develop psychological interventions to improve the mental health of the population and work as a reference of public health guidelines for other cities with severe COVID-19 outbreak.
Comparative Performance Assessment of Hysteresis and Constant Switching Frequency DTC over AC Machines

AC machines and drives are widely used in industrial applications. This prioritizes that AC drives operates with high efficiency and high performance in general. In this respect, one of the hottest topics in the literature is Direct Torque Control (DTC) since it is inherently mechanical sensorless, strong robust, driving method. Furthermore, it is a generic method applicable different type of machines such as permanent magnet and the new synchronous reluctance motors. DTC is classified as Hysteresis based DTC (HDTC), and Space Vector Pulse Width Modulation based DTC (SVPWM-DTC) which is also called as constant switching frequency DTC. In this study, Induction Motor (IM), Interior Permanent Magnet Synchronous Motor (IPMSM), Surface Mounted Permanent Magnet Synchronous Motor (SPMSM), and Synchronous Reluctance Motor (SynRM) are driven by HDTC and SVPWM-DTC, and results are compared to each other in terms of performance indexes. Consequently, it is concluded that the flux and torque ripples become lower with SVPWM-DTC. However, it is inferred that greatly increasing torque response and robustness against external disturbances are obtained by HDTC.
Model-Driven Fault Injection in Java Source Code

The injection of software faults in source code requires accurate knowledge of the programming language, both to craft faults and to identify injection locations. As such, fault injection and code mutation tools are typically tailored for a specific language and have limited extensibility. In this paper we present a model-driven approach to craft and inject software faults in source code. While its concrete application is presented for Java, the workflow we propose does not depend on a specific programming language. Following Model-Driven Engineering principles, the faults and the criteria to select injection locations are described using structured, machine-readable specifications based on a domain-specific language. Then, automated transformations craft artifacts based on OCL and Java, which represent the faults to be injected and are able to select the candidate injection locations. Finally, artifacts are executed against the target source code, performing the injection in the desired locations. We devise a supporting tool and exercise the approach injecting 13 different kinds of software faults in the Java source code of six different projects.
A Stacking Ensemble Learning Model for Mobile Traffic Prediction

Mobile traffic prediction has been the foundation to enable effective network design and intelligent management. Machine learning (ML) methods have drawn extensive concern in this field. However, many existing methods fail to reach a satisfactory outcome, due to the fact that the performance of a single ML model is not always good. In this paper, we develop a novel mobile traffic prediction model based on the stacking ensemble learning method. This model consists of two parts, i.e., a base learner with distributed multilayer perceptrons (MLPs) and a meta learner called the self-adaptive support vector regression model (SSVR). On real-world mobile traffic flows of various mobile applications at different base stations, we demonstrate that the proposed model is significantly superior to some other advanced network models in prediction performance. Besides, the statistical analysis method verifies this effectiveness.
Estimation and Validation of Enhanced Resolution Brightness Temperature Products of SCATSAT-1

The brightness temperature (Tb) is considered a fundamental climate data record that governs the radiance traveling towards the satellite from the top of the atmosphere. It is used to derive the wind speed, cloud liquid water and sea surface temperature. The optical sensors play a vital role in the retrieval of Tb but also affected by the clouds and different solar illuminations. On the other hand, scatterometer offers the day/night data delivery even in cloudy conditions and insensitive towards solar illuminations. Scatterometer Satellite (SCATSAT-1) based on Ku-band (13.5 GHz), is launched by ISRO (Indian Space Research Organization) on 26th September 2016. Besides backscattering coefficients (sigma-naught and gamma-naught), SCATSAT-1 also provides the Tb for slices and footprints that are derived from scatterometer noise measurement, expressed in units of the temperature (deg. K). The SCATSAT-1 Level 4 Tb product is available at two different polarization modes i.e. HH and VV. The main objective of this study is to retrieve the Tb at HH and VV over a part of Western Himalayas (Himachal Pradesh, India) using the SCATSAT-1 backscattering and validating the results using MODIS (Moderate Resolution Imaging Spectroradiometer) data. Based on similarity measures, it is found that SCATSAT-1 achieved the well-correlation [R squared 0.8311 (for VV) and 0.8343 (for HH)] with MODIS Tb. This study can be utilized in many remote sensing applications in flood detection or other natural disasters.
A Novel Three-Port NPC Converter for Grid-Tied Photovoltaic Systems with Integrated Battery Energy Storage

The variable behaviour of the photovoltaic (PV) generation arises as the main drawback of this technology. Therefore, Battery Energy Storage (BES) units appear as a promising solution to overcome the inherent intermittent generation profile of PV systems. BES can be implemented separated from the PV generation units or integrated within a single power system. The latter option gives a better dynamic response, as the variable generation can be directly complemented with the BES, controlling the power flow between both elements with the grid. Thus, there is a need for power converters with capability of interface both PV and BES with the grid in a reliable and efficient way. This paper proposes a Three-Port NPC converter to connect a PV array and a BES unit with the ac grid using a single power processing stage. The converter is based in the H-NPC inverter and allows the connection of a low voltage battery to act as a power buffer ensuring a firm energy dispatch to the grid. Furthermore, simulation results show that the proposed topology maintains the good performance of the H-NPC in terms of efficiency, voltage harmonic distortion and reduced leakage current.
Dynamic Spectrum Access Scheme of Joint Power Control in Underlay Mode Based on Deep Reinforcement Learning

With the increasing complexity of wireless networks and the increasing shortage of spectrum resources, a novel dynamic spectrum access (DSA) solution is urgently needed. For complex and dynamic cognitive radio networks (CRN), this paper proposes a joint DSA and power control scheme based on deep reinforcement learning (DRL). In order to improve the convergence speed of the algorithm, the DRL is improved to a hierarchical DRL, centralized DSA is implemented through CBS, and distributed power control is implemented at each secondary user (SU). Sufficient simulation experiments show that the proposed algorithm has faster convergence speed and lower packet loss.
Design of Ku-Band Flat Luneburg Lens Using Ceramic 3-D Printing

A flat Luneburg lens antenna for Ku-band (12–18 GHz) applications is proposed and designed using ceramic 3-D printing. Metamaterial structures and transformation optics technology are used to realize the gradient dielectric constant, compress the height, and reduce the weight of the Luneburg lens. The proposed 3-D printed Luneburg lens, combined with a 10 dB standard gain horn antenna, obtained an antenna gain up to 19.1 dBi over the operating frequency and the thickness reduction of 78%. In addition, the ability of the flat Luneburg lens to perform passive beam scanning with a limited power loss is achieved. This exploration of ceramic 3-D printing can serve as a reference for the development of ceramic components with complex structures.
Data-driven dose calculation algorithm based on deep U-Net

Accurate and efficient dose calculation is an important prerequisite to ensure the success of radiation therapy. However, all the dose calculation algorithms commonly used in current clinical practice have to compromise between calculation accuracy and efficiency, which may result in unsatisfactory dose accuracy or highly intensive computation time in many clinical situations. The purpose of this work is to develop a novel dose calculation algorithm based on the deep learning method for radiation therapy. In this study we performed a feasibility investigation on implementing a fast and accurate dose calculation based on a deep learning technique. A two-dimensional (2D) fluence map was first converted into a three-dimensional (3D) volume using ray traversal algorithm. 3D U-Net like deep residual network was then established to learn a mapping between this converted 3D volume, CT and 3D dose distribution. Therefore an indirect relationship was built between a fluence map and its corresponding 3D dose distribution without using significantly complex neural networks. Two hundred patients, including nasopharyngeal, lung, rectum and breast cancer cases, were collected and applied to train the proposed network. Additional 47 patients were randomly selected to evaluate the accuracy of the proposed method through comparing dose distributions, dose volume histograms and clinical indices with the results from a treatment planning system (TPS), which was used as the ground truth in this study. The proposed deep learning based dose calculation algorithm achieved good predictive performance. For 47 tested patients, the average per-voxel bias of the deep learning calculated value and standard deviation (normalized to the prescription), relative to the TPS calculation, is 0.17%±2.28%. The average deep learning calculated values and standard deviations for relevant clinical indices were compared with the TPS calculated results and the t-test p-values demonstrated the consistency between them. In this study we developed a new deep learning based dose calculation method. This approach was evaluated by the clinical cases with different sites. Our results demonstrated its feasibility and reliability and indicated its great potential to improve the efficiency and accuracy of radiation dose calculation for different treatment modalities.
Radio-Frequency Interference Shielding Gasket Based Frequency Reconfigurable Antenna

In this research, by using radiofrequency interference (RFI) shielding gasket, a novel frequency reconfigurable antenna developed. RFI shielding gasket presented in this research is composed of Ni-Cu coated fabric wrapped onto a conductive foam. The gasket placed at various locations on the microstrip (MS) patch antenna; to alter the bandwidth of the antenna. This technique does not need to change the circuit or hardware of the antenna, and any fabricated MS-antenna can make reconfigurable antenna using this technique. A potential application of this method includes the adjustment or tuning of central frequencies of any pre-fabricated antenna whose central frequency not desired as simulated.
The Development of Electromechanical Actuator Mathematical Model for Fault Identification Using Data Mining Methods

General data analysis scheme is proposed for algorithms development for assessing the technical state of an electromechanical actuator (EMA) using data mining (DM) techniques. The EMA model is considered, which is proposed to be used to assess the technical state of EMA and form a base of study cases for the application of DM techniques.
Effects of Acquisition Parameter Modifications and Field Strength on the Reproducibility of Brain Perfusion Measurements Using Arterial Spin-Labeling

BACKGROUND AND PURPOSE: Although the added diagnostic value of arterial spin-labeling is shown in various cerebral pathologies, its use in clinical practice is limited. To encourage clinical adoption of ASL, we investigated the reproducibility of CBF measurements and the effects of variations in acquisition parameters compared to the recommended ASL implementation. MATERIALS AND METHODS: Thirty-four volunteers (mean age, 57.8 ± 17.0 years; range, 22–80 years) underwent two separate sessions (1.5T and 3T scanners from a single vendor) using a 15-channel head coil. Both sessions contained repeated 3D and 2D pseudocontinuous arterial spin-labeling scans using vendor-recommended acquisition parameters (recommendation paper–based), followed by three 3D pseudocontinuous arterial spin-labeling scans, two with postlabeling delays of 1600 and 2000 ms and one with increased spatial resolution. All scans were single postlabeling delay. Intrasession (identical acquisitions, scanned five minutes apart) and intersession (first 2D and 3D acquisitions of two sessions) reproducibility was examined as well as the effect of parameter variations on CBF. RESULTS: Intrasession CBF reproducibility was similar across image readouts and field strengths (within-subject coefficient of variation between 4.0% and 6.7%). Intersession within-subject coefficient of variation ranged from 6.6% to 14.8%. At 3T, the 3D acquisition with a higher spatial resolution resulted in less mixing of GM and WM signal, thus decreasing the bias in GM CBF between the 2D and 3D acquisitions (ΔCBF = 2.49 mL/100g/min [P < .001]). Postlabeling delay variations caused a modest bias (ΔCBF between −3.78 [P < .001] and 2.83 [P < .001] mL/100g/min). CONCLUSIONS: Arterial spin-labeling imaging is reproducible at both field strengths, and the reproducibility is not significantly correlated with age. Furthermore, 3T tolerates more acquisition parameter variations and allows more extensive optimizations so that 3D and 2D acquisitions can be compared.
Multi-Access Edge Computing: A Survey

Multi-access Edge Computing (MEC) is a key solution that enables operators to open their networks to new services and IT ecosystems to leverage edge-cloud benefits in their networks and systems. Located in close proximity from the end users and connected devices, MEC provides extremely low latency and high bandwidth while always enabling applications to leverage cloud capabilities as necessary. In this article, we illustrate the integration of MEC into a current mobile networks’ architecture as well as the transition mechanisms to migrate into a standard 5G network architecture. We also discuss SDN, NFV, SFC and network slicing as MEC enablers. Then, we provide a state-of-the-art study on the different approaches that optimize the MEC resources and its QoS parameters. In this regard, we classify these approaches based on the optimized resources and QoS parameters (i.e., processing, storage, memory, bandwidth, energy and latency). Finally, we propose an architectural framework for a MEC-NFV environment based on the standard SDN architecture.
A Review on Internet of Things (IoT) Based Pulse Rate, Blood Pressure, Body Temperature Monitoring System

Health plays a significant role in our everyday life. IoT provides patients and physicians with improved medical services. This prediction's fundamental explanation is the nature of a networked patient monitoring framework, identifying and analyzing the patient's condition. It stores information about the patient's condition and helps the doctor diagnose the condition of the patient. IoT is commonly used to connect medical resources and provide patients with uncomplicated, efficient, and relevant health care services. In this project, we have presented a web-based patient management framework for health care applications. This project's primary goal is to create a health monitoring system that can be made available with easily accessible sensors by discussing the different works available on the market.
An Entropy-based Evaluation for Sentiment Analysis of Stock Market Prices using Twitter Data

Stock markets prediction is considered a considerably demanding task due to its notable returns as well as due to the high randomness within the stock market. Moreover, stock price alternations are primarily related to the capital circumstances and hot occasions/events. Nowadays, researchers have sufficiently improved prediction accuracy by taking into consideration news and social media. However, the existing strategies do not employ the different impacts that events may pose. Streaming data proves to be a perpetual real-time source of data analysis as information from different web sources can be carried. In this paper, we explore whether estimations, in terms of sentiment analysis derived from Twitter posts, can be correlated to the stock market prices. Initially, the daily Twitter posts are analyzed and different n-grams along with two strategies that are utilized to increase the accuracy of the classification, are applied. Spark streaming has been employed for the processing of Twitter data, while Apache Flume has been utilized for the analysis.
Development of Onboard Decision Supporting System for Ship Docking Operations

Maritime operations are inevitably influenced by the wind, wave, sea currents, and other perturbations at sea. Providing decision support for these operations based on historical and real-time data of ship status is thus of great concern in terms of ship safety. However, it is challenging for collecting and analysing large quantities of ship data in real operations. Moreover, the development of an onboard decision support system (DSS) will be a gradual and iterative process subject to extensive testing and simulation. Consequently, the paper presents an integrated simulation framework which provides testing and simulation environment for the DSS development. The system enables navigation data transmission from a well-designed simulator and automatic determining of the safe maneuver of a ship within the framework. The development of DSS is divided into three steps. The ship maneuvering data from simulator is collected; the data is then classified and fed into an imitation learning (IL) algorithm to learn an initial policy; the result is further applied to a reinforcement learning (RL) algorithm for safe decision making of the operation. In this way, it could speed up the learning efficiency by extracting more information from available experience. To verify the effectiveness of the proposed integrated simulation framework, in this study, we implemented the proposed DSS in ship docking operation under various environmental disturbances. It is interacted with the simulator to obtain data. By processing these data, it provides the shipmaster with the information about the consequences of the ship maneuvering decisions. The simulation results demonstrate that the proposed DSS could assist the shipmaster in deciding policies and increase the efficiency of decision making.