Miracle-dz/UniFAR-FTUs huggingface dataset:
Nosocomial Coronavirus Disease Outbreak Containment, Hanoi, Vietnam, March–April 2020

We report on the public health response generated by an outbreak of coronavirus disease (COVID-19) that occurred during March 2020 at Bach Mai Hospital (BMH) in Hanoi, northern Vietnam’s largest hospital complex. On March 18, a total of 3 distinct clusters of COVID-19 cases were identified at BMH. Diagnosis of the initial 3 COVID-19 cases led to contact tracing, symptom screening, and testing of 495 persons and limited quarantine of affected institutes or departments. When 27 staff members in the catering company tested positive for SARS-CoV-2, the entire BMH staff (7,664 persons) was put under quarantine. Contact tracing in the community resulted in an additional 52,239 persons being quarantined. After 3 weeks, the hospital outbreak was contained; no further spread occurred in the hospital. Rapid screening of cases, extensive testing, prompt quarantine, contact tracing, and social distancing contributed to prevent community transmission in Hanoi and northern Vietnam.
Extended ResNet and Label Feature Vector Based Chromosome Classification

Human chromosome classification is essential to the clinical diagnosis of cytogenetical diseases such as genetic disorders and cancer. This process, however, is time-consuming and requires specialist knowledge. Considerable efforts have been made to automat the process. Recently, methods based on Convolutional Neural Networks achieved state-of-the-art results on the chromosome classification task. Many studies used karyotype images in performance evaluation, few studies have reported the results of human chromosome classification on microscopical images. This paper proposes a novel method to classify single chromosome images into one of 24 types. In the proposed method an extended ResNet was first devised to extract features of single chromosome images. A label feature vector was then extracted for each of 24 chromosome types based on a validation dataset. Hausdorff distance between feature vector of an input image and each of 24 label feature vectors were calculated, and the label feature vector that has minimum hausdorff distance to the feature vector of the input image was selected as the potential label of the input image. To finally allocate the single chromosomes from a same microscopical image into one of 24 types, a Label Redistribution strategy was used to shrink the label space and to increase the efficiency of chromosome classification. Experiments were implemented with 90,624 single chromosome images, 644 of which were randomly picked to form a testing set in advance. The classification accuracy on microscopical images using our proposed method achieved an accuracy of 94.72%.
Lithium niobate film bulk acoustic wave resonator for sub-6 GHz filters

In this work, Film Bulk Acoustic Resonators (FBAR) based on 250 nm-thick Y+163°-cut LiNbO3 films have been fabricated using a layer transfer process (4-inch) including patterned bottom electrodes and a sacrificial layer cavity. As previously reported, this orientation provides a single resonance, whose frequency is here pushed up to 4.78 GHz due to the reduced piezoelectric film thickness. The effective electromechanical coupling factor (kt2) reaches 17.6%, and the quality factor at antiresonance (Qa) is around 200, leading to a Q×f product close to 900, which is slightly higher than previous realizations at 2.5 GHz [1]. This result demonstrates a first step towards introducing LiNbO3 as an alternative to AlN for sub-6 GHz BAW filters.
Optimizing SCAPS model for perovskite solar cell equivalent circuit with utilizing Matlab-based parasitic resistance estimator algorithm

Perovskite solar cells (PSCs) showed a booming trend due to its tunability as well as simplicity in fabrication. Researchers invested in exploring an appropriate equivalent circuit capable of describing the J-V curves of the PSCs as well as illustrating the physical phenomena associated with optical absorption and carrier transportation. In the same context, we propose a modified SCAPS model to demonstrate the optoelectronic behavior of PSCs through estimating the parasitic elements in the form of resistive and capacitive components. A previously reported PSC was selected as a reference where our enhanced model recorded only 4% mismatching. J-V, E-K and C-V curves have been simulated and analyzed where the appearance of the capacitive impact due to E-K charge accumulation has been addressed.
Investigating Effects of Controlled Flights through Fast-Time Simulation

Departure flights at major U.S. airports are often subject to Traffic Management Initiatives to mitigate congestion and delay due to demand-capacity imbalances. These controlled flights can lead to inefficiency and delay on the airport surface. The integrated arrival, departure, and surface traffic management capabilities developed by NASA's Airspace Technology Demonstration 2 (ATD-2) sub-project provide enhanced operational efficiency and predictability of flight operations through data exchange and integration, surface metering, and automated coordination of release time of controlled flights for overhead stream insertion. This paper evaluates the impacts of controlled flights on airport performance and assesses the ATD-2 benefits of pushback hold advisories for both controlled and non-controlled flights using fast-time simulation for Charlotte Douglas International Airport.
Multivariable Adaptive Control Strategy for an Activated Sludge Process Inside a Wastewater Treatment Plant

This study addresses an innovative multivariable control strategy for a wastewater treatment bioprocess with activated sludge. This bioprocess takes place in a continuous reactor located at the Wastewater Treatment Plant Făcăi, Romania. The proposed adaptive control structure is designed under realistic assumptions: several process variables are unavailable, the reaction kinetics is time-varying and unknown, the influent pollutant flow rate varies in large limits and much more its concentration is totally unknown. The novelty of the approach consists in the insertion of a sliding mode observer (SMO) in overall control structure. The SMO provides the estimates of the unknown influent pollutant concentration. The performance of the adaptive multivariable control algorithm is assessed via numerical simulations based on data provided from the plant.
Patrones locales de alimentación del león marino sudamericano Otaria byronia: el caso de Punta Patache, Norte de Chile

Mediante análisis de fecas se estudiaron los hábitos alimentarios del león marino sudamericano (Otaria byronia) (LMS) en Punta Patache, norte de Chile durante 2 periodos El Niño (1997/98 y 2009/10). Las muestras fecales (n= 183) fueron colectadas en la lobera de Punta Patache, sur de Iquique (20º48’63’’S). Se identificaron 10 especies de presas neríticas (incluyendo 4 peces, 2 cefalópodos y 3 crustáceos). Los más importantes para el conjunto de ambos períodos fueron en términos de abundancia la anchoveta (Engraulis ringens) (7,4%), el langostino (Pleuroncodes monodon) (83,0%) y el calamar (Loligo gahi) (6,9%). En términos de biomasa consumida destacaron el calamar (35,7%) y el langostino (21,9%). En términos de aportes energéticos a la dieta, los más importantes fueron el langostino (19,9-32,7%) y el calamar (24,9-40,5%), en lípidos la anchoveta (31,8-36,4%) y en proteína el calamar (20,9-41,0%) y el langostino (22,5-29,9%). La comparación con resultados de estudios anteriores bajo condiciones ENOS neutrales/frías (1996 y 2015) destaca mayor consumo de anchoveta (82,8% y 23,3-48,4%, respectivamente). Sobre esta base se concluye que frente a la falta de anchoveta durante el desarrollo de eventos ENOS, el LMS ajustaría su dieta a otros recursos, situación que determina un ‘estrés nutricional’ derivado de una nutrición inadecuada, cuyas consecuencias son reducción del crecimiento, bajas en la tasa de natalidad, enfermedades, desnutrición y en muchos casos muerte, tal como se ha reportado para los ENOS fuertes 1982/83 y 1997/98.
Analytical Geometry Calibration for Acoustic Transceiver Arrays

There are many intelligent devices around us that have the ability to emit and receive audio signals, such as smartphones, smartwatches, and tablets. If they constitute an acoustic transceiver network, it can boost the performance of many audio processing tasks. The speaker localization and tracking algorithms using such a network require the prior information of node positions. To acquire this knowledge, we derive an analytical solution for geometry calibration of acoustic transceiver networks where each node consists of a microphone array and a loudspeaker. Specifically, a linear cost function for node orientations is first established using the delivered direction-of-arrival (DoA) measurements, and its analytical solution is derived. Next, based on the DoA and time-of-arrival (ToA) measurements, another cost function for node positions is formulated, which can also be solved in the closed form. Experimental results show that the proposed method can successfully estimate the geometry structure of acoustic transceiver networks in noisy and reverberant environments. In contrast to most state-of-the-art geometry calibration algorithms, which iteratively solve the calibration problem by optimization methods, the proposed method can decrease the computational complexity greatly.
THE EFFECT OF BOARD OF DIRECTOR, AUDIT COMMITTEE, INSTITUTIONAL OWNERSHIP TO FIRM VALUE, WITH FIRM SIZE, FINANCIAL LEVERAGE AND INDUSTRIAL SECTOR AS CONTROL VARIABLES (Study on Listed Companies in Indonesian Stock Exchange Period 2011-2015)

This study aims to examine the effect of corporate governance’s mechanism to firm value which is proxied by Tobin’s Q. This mechanism is divided into two, internal mechanism which is proxied by board of director and audit committee and external mechanism which is proxied by institutional ownership. This study is using control variables such as firm size, financial leverage and industrial sector.Sample used in this study are 40 companies which incorporated in several industrial sector in Indonesia which are listed in Indonesia Stock Exchange during 2011-2015. Hypothesis testing in this study is using multiple regression analysis.The result of this study shows that board of director has positive but insignificant effect to firm value in Indonesia, audit committee has positive and significant effect to firm value in Indonesia and institutional ownership has negative but insignificant effect to firm value in Indonesia. The control variables used in this study provide mixed results, where firm size has negative and insignificant effect to firm value, financial leverage has positive and significant effect to firm value and the whole industrial sector in Indonesia has no effect to firm value. From simultaneous test, this study shows that simultaneously independent variables significantly affect to firm value.
Olive Leaves Extract Effects on Sperm Quality Following Experimentally-Induced Diabetes in Rats

Background: The experimental model of streptozotocine (STZ)-induced diabetes explains changes in the male reproductive system as part of the disease. Objective: The aim of the present study was to evaluate the olive leaf extract effects on STZ-induced diabetes and to examine its modulatory effects on sperm quality. Methods: Twenty adult male Sprague–Dawley rats were divided into four equal groups: the first group served as untreated control. Groups 2, 3 and 4 of rats were injected STZ (65 mg/kg). The animals which exhibited blood glucose levels higher than 250 mg/dl by days 4-6, were considered as diabetic rats. Groups 3 and 4 received olive leaf extract (100 and 150 mg/kg, orally) and vehicle to the control and diabetic rats (group 2) for 10 consecutive days. Results: Glycated haemoglobin percentage (%HbA1c) as a diabetic index significantly decreased in the animals ingested by the 150 mg/kg of the extract compared to the diabetic group (P
A Solution to Monitor Environmental Parameters in Industrial Areas

Monitoring the environmental parameters in active industrial areas where there are potential sources of pollution and, especially in the area of decommissioned or closed mining activities, is very important for the prevention of environmental accidents and for ensuring a suitable working environment for workers in one factory located in that area. In this paper, we propose a solution for monitoring the environmental parameters regarding air quality. After the data is acquired with the help of air quality sensors, they are sent using a LoRa type radio communication to a higher hierarchical level where it is processed and potential alerts for exceeding certain critical thresholds of the values can be issued. The whole autonomous system can be located in an industrial hall and in the test phase it has an autonomy of about eight hours. The monitoring equipment consists of sensors connected to LoRa modules. The communication between the sensors and these modules is done through a standard serial protocol. LoRa modules send data to a gateway system using a LoRaWAN protocol. This flexible structure represents a concept of hardware and software development that presents the possibility of being integrated into the national air quality monitoring network.
SIR Analysis in 3D UAV Networks: A Stochastic Geometry Approach

In this work, a mathematical framework to evaluate the performance of a finite, three dimensional (3D) unmanned aerial vehicle (UAV) network in the presence of interference is developed. The framework builds upon stochastic geometry tools and specifically the binomial point process (BPP) for the spatial distribution of the UAVs. A UAV base station (UAV-BS) reference receiver is located at the center of a sphere and communicates with the nearest transmitting UAV node, whose distance from the UAV-BS reference receiver is either fixed or random. The reverse link suffers from the presence of a single dominant interferer, whose location is random within the sphere. Closed-form expressions are derived for statistical metrics of the signal-to-interference ratio (SIR) for two scenarios, namely for fixed or random location of the desired transmitting node. Then, the impact of the location of the transmitting node on the coverage probability (CP) is studied while the average error probability and the ergodic capacity have been also analytically investigated. Finally, the theoretical results are numerically evaluated and compared to simulation to reveal some useful insights.
A Discriminative Person Re-Identification Model With Global-Local Attention and Adaptive Weighted Rank List Loss

At present, occlusion and appearance similarity pose severe challenges to person re-identification tasks. Although many robust deep convolutional neural networks alleviate these problems, convolutional layers with limited receptive fields cannot model global semantic information well. In addition, in the person re-identification model, many metric losses ignore or destroy the intra-class structure of the sample, which makes the model difficult to be optimized. Therefore, we design a discriminative Re-identification model with global-local attention and adaptive weighted rank list loss (GLWR). Specifically, our global-local attention (GL-Attention) learns the semantic context in the channel and spatial dimensions. By learning the dependencies between features, GL-Attention integrates global semantic information into local features to extract discriminative features. Unlike rank list loss, our adaptive weighted rank list loss (WRLL) adaptively assigns weights according to the metric distance between the negative sample and the input image, which further improves the performance of the model. Experimental studies on three public datasets (Market-1501, DukeMTMC-ReID and CUHK03) indicate that the performance of our GLWR is significantly superior to many of the latest algorithms.
Self-Supervised Learning of a Deep Neural Network for Ultrafast Ultrasound Imaging as an Inverse Problem

Compared with conventional delay-and-sum (DAS) beamforming method, sparse regularization (SR) techniques can reconstruct higher quality ultrasound (US) images from plane wave (PW) transmission. However, these methods suffer from long reconstruction time, owing to the high computational complexity inherently associated with its iterative process. In this work, a deep neural network (DNN) is trained to reconstruct plane wave ultrasound (PWUS) images from RF channel data with significantly reduced computational time. To overcome the lack of gold standard of ideal US images, a self-supervised learning scheme is employed to utilize the RF channel data as both the inputs and the labels during the training process. Using simulation data, the proposed method achieves comparable spatial resolution and 3.2-dB higher CNR, compared with the SR method.
A suggestion for effective shepherding models with two sheepdogs

The shepherding problem is to collect several sheep and guide them to some destinations and is possible to be applied to some situations such as evacuation guidance at facilities in which a few people need to guide many people to evacuation doors. The present paper aims to clarify methods in which two sheepdogs guide a herd of sheep effectively through the shepherding problem. Especially, we focus on the effectiveness of cooperative behavior of sheepdogs. To show them, we introduce three controlling models depending on the degree of interference in sheepdogs' behaviors. The effectiveness of sheepdog behavior is evaluated by the success rate, number of steps, moving distance of sheepdogs, and dispersion of sheep. It is shown that cooperative and uncooperative behavior have respective advantages in those results.
Integrating Photovoltaics and Battery Energy Storage to Grid Using Triple Active Bridge and Voltage Source Converters

Triple active bridge converters are an extension of dual active bridge converters and used to combine multiple energy sources. The advantages of using a triple active bridge converter are its high power density, providing isolation along with elimination of low frequency transformer and high efficiency power conversion. To address the intermittent nature of solar energy, it is always appropriate to include a energy storage device along with it. Triple active bridge converter acts a viable option in integrating these two energy sources in the most efficient manner. Further, this energy can be fed to the grid by using a voltage source converter cascaded with this triple active bridge converter. This paper discusses the cascaded converter with its control algorithms and different operating modes. A laboratory prototype of a triple active bridge converter integrated with the grid through a voltage source converter is developed and the different control and operating modes are verified, which are also included in this paper.
Development of a diagnosis tool, based on deep learning algorithms and infrared images, applicable to condition monitoring of induction motors under transient regime

Infrared thermography can be a very useful technique for condition monitoring because the most common faults suffered by induction motors cause a temperature rise in the motor’s frame. Moreover, this technique is non-intrusive, affordable and very sensitive due to the substantial technical progress in the design and development of new thermal cameras. However, data interpretation and decision making from the resulting infrared images is one of the major limitations of this technique, because it is directly dependent on the operator’s experience. Several automated expert systems have been developed using machine learning and, to a lesser extent, with deep learning algorithms. The objective of this paper is to develop a diagnosis tool, based on infrared imaging and deep learning algorithms, applicable to induction motors working in transient conditions. The developed classifier, after training, presents high accuracy levels, classifying the images into one of the five considered scenarios and even at the early stages of the transient state. This methodology can be applied in a broad variety of scenarios with substantial cost saving and offering high-safety standards.
PWM-based Multiple Surface Sliding Control of Boost DC-DC Converters

This paper deals with a multiple surface sliding control to regulate the output voltage of boost dc-dc converters. The input voltage and load resistance are considered as disturbances and they are estimated using disturbance observer. The estimate is used in the design of multiple surface sliding control to accomplish a high performance. The proposed control algorithm is an equivalent control approach which can fulfill regulatory and servo requirements. Theoretical analysis is presented to show closed loop behavior of the proposed scheme. The efficacy of the proposed control scheme is assessed by simulation under uncertainties in input voltage and load resistance. The proposed control scheme is compared with an existing disturbance observer based integral sliding mode control and it is shown that the proposed scheme outperforms the existing one.
Probabilistic harmonization and annotation of single‐cell transcriptomics data with deep generative models

As single-cell transcriptomics becomes a mainstream technology, the natural next step is to integrate the accumulating data in order to achieve a common ontology of cell types and states. However, owing to various nuisance factors of variation, it is not straightforward how to compare gene expression levels across data sets and how to automatically assign cell type labels in a new data set based on existing annotations. In this manuscript, we demonstrate that our previously developed method, scVI, provides an effective and fully probabilistic approach for joint representation and analysis of cohorts of single-cell RNA-seq data sets, while accounting for uncertainty caused by biological and measurement noise. We also introduce single-cell ANnotation using Variational Inference (scANVI), a semi-supervised variant of scVI designed to leverage any available cell state annotations — for instance when only one data set in a cohort is annotated, or when only a few cells in a single data set can be labeled using marker genes. We demonstrate that scVI and scANVI compare favorably to the existing methods for data integration and cell state annotation in terms of accuracy, scalability, and adaptability to challenging settings such as a hierarchical structure of cell state labels. We further show that different from existing methods, scVI and scANVI represent the integrated datasets with a single generative model that can be directly used for any probabilistic decision making task, using differential expression as our case study. scVI and scANVI are available as open source software and can be readily used to facilitate cell state annotation and help ensure consistency and reproducibility across studies.