Miracle-dz/UniFAR-FTUs huggingface dataset:
Gain Enhancement Techniques for mm-Wave On-Chip Antennas on Lossy CMOS Platforms

With the advent of number of applications in the mm-wave bands, antenna sizes are becoming small enough to fit them on mm-scale Integrated Circuits (ICs), commonly known as microchips. Integration of antenna on a chip along with the driving circuits in the standard CMOS process can enable a true RF System-on-Chip (SoC) solution, however, Silicon used in CMOS is very lossy and has a high dielectric constant. This results in low gain and efficiency antennas which also suffer from surface waves issues at mm-wave frequencies. In this paper, we will present Artificial Magnetic Conductor (AMC) surface to increase the gain/efficiency of the on-chip antenna realized in standard CMOS platforms. The gain and radiation efficiency of 1.7dBi and 42.52% are achieved respectively.
Optimization of MAPbI3 Perovskite Solar Cell with Nano Structures

Perovskite solar cell has been a promising low cost high efficiency solar cell and the efficiency increase rapidly. Since the crystal quality issues have been improved significantly, a further numerical optimization in the structure design is needed. We applied FDTD program for optical absorption and use our Poisson and drift-diffusion solver for electrical simulation. The Poisson and drift-diffusion solver is optimized for sovling organic and inorganic materials. Finally, we design a nano-structure that have efficiency of MAPBI3 based solar cell has been improved from 18% to 19.67% That efficiency has be enhanced by 12% compared to the planar structure.
Correlated Topic Modeling for Big Data with MapReduce

Efficient extraction of useful information is a rising problem in Big data, since the amount of information being gathered across various domains grows with an increasing rate. So, it takes more time to understand the underlying themes of the documents collection. To deal with such problem in the context of Big data, the proposed approach implements the correlated topic model (CTM) with MapReduce framework to reveal the thematic information represented by words, to speed up the processing and to increase the scalability of the model. We apply variational Expectation-Maximization (EM) to make inference for CTM. In this paper, academic articles are collected by using a web crawler. Then CTM is exploited to uncover the underlying themes of the collection. The use of CTM with MapReduce implementation improves the accuracy and performance in a reliable and scalable manner.
Automatic Extraction of Abnormalities on Temporal CT Subtraction Images Using Sparse Coding and 3D-CNN

In recent years, the proportion of deaths from cancer tends to increase in Japan, especially the number of deaths from lung cancer is increasing. CT device is effective for early detection of lung cancer. However, there is concern that an increase in burden on doctors will be caused by high performance of CT improving. Therefore, by presenting the “second opinion” by the CAD system, it reduces the burden on the doctor. In this paper, we develop a CAD system for automatic detection of lesion candidate regions such as lung nodules or ground glass opacity (GGO) from 3D CT images. Our proposed method consists of three steps. In the first step, lesion candidate regions are extracted using temporal subtraction technique. In the second step, the image is reconstructed by sparse coding for the extracted region. In the final step, 3D Convolutional Neural Network (3D-CNN) identification using reconstructed images is performed. We applied our method to 51 cases and True Positive rate (TP) of 79.81 % and False Positive rate (FP) of 37.65 % are obtained.
Correlation Analysis between PIK3CA, TP53, CDH1 Genes Mutations and Breast Cancer

In the DNA sequence, accumulation of single nucleotide variation (SNV) changes the amino acid sequence of oncoprotein, and it causes normall cells to turn into cancel' cells. This study uses the 1105 samples of breast cancel' in the cBioPortal Cancel' Database to screen out three key genes, and use the electron-ion interaction pseudopotential (EIIP) of base A, base C, base G and base T to propose the E difference value formula, further verification of the impact of the three key genes on breast cancer patients. The study found that the PIK3CA, TP53 and CDHI are key genes that cause breast cancer, and PIK3CA. TP53 and CDH1 are closely related to the survival time of breast cancer.
Single-Ended-to-Balanced Filtering Power Dividers With Wideband Common-Mode Suppression

Four novel architectures of wideband five-port single-ended-to-balanced power dividers with broadband common-mode suppression are proposed in this paper. The bandpass filtering capability with/without close-to-passband transmission zeros are also incorporated in some of them to obtain multifunctional single-ended-to-differential-mode filtering/power division operation. Two ideal 180° phase inverters are used to replace the conventional half-wavelength transmission line-based inverters in the basic power divider structure so that wideband common-mode rejection and a smaller circuit size are achieved. Quarter-wavelength transmission lines and coupled-line sections are exploited to simultaneously realize the bandpass filtering and wideband power division functionalities. Each five-port power divider exhibits broadband 3-dB single-ended-to-balanced power division, high in-band input–output power-matching levels, high in-band power isolation levels between the two balanced outputs and wideband common-mode rejection. The theoretical analysis, parametric study, and design procedure for the conceived single-ended-to-differential-mode power dividers are provided. Furthermore, for experimental and demonstration purposes, four microstrip prototypes designed at 1 GHz are manufactured and characterized.
The influence of different inositol stereoisomers supplementation in pregnancy on maternal gestational diabetes mellitus and fetal outcomes in high-risk patients: a randomized controlled trial

Abstract Objective: To identify the effects of different dietary inositol stereoisomers on insulin resistance and the development of gestational diabetes mellitus (GDM) in women at high risk for this disorder. Design: A preliminary, prospective, randomized, placebo controlled clinical trial. Participants: Nonobese singleton pregnant women with an elevated fasting glucose in the first or early second trimester were studied throughout pregnancy. Intervention: Supplementation with myo-inositol, d-chiro-inositol, combined myo- and d-chiro-inositol or placebo. Main outcome measure: Development of GDM on a 75 grams oral glucose tolerance test at 24–28 weeks’ gestation. Secondary outcome measures were increase in BMI, need for maternal insulin therapy, macrosomia, polyhydramnios, neonatal birthweight and hypoglycemia. Results: The group of women allocated to receive myo-inositol alone had a lower incidence of abnormal oral glucose tolerance test (OGTT). Nine women in the control group (C), one of the myo-inositol (MI), five in d-chiro-inositol (DCI), three in the myo-inositol/D-chiro-inositol group (MI/DCI) required insulin (p = .134). Basal, 1-hour, and 2 hours glycemic controls were significantly lower in exposed groups (p < .001, .011, and .037, respectively). The relative risk reduction related to primary outcome was 0.083, 0.559, and 0.621 for MI, DCI, and MI/DCI groups. Conclusions: This study compared the different inositol stereoisomers in pregnancy to prevent GDM. Noninferiority analysis demonstrated the largest benefit in the myo-inositol group. The relevance of our findings is mainly related to the possibility of an effective approach in GDM. Our study confirmed the efficacy of inositol supplementation in pregnant women at risk for GDM.
Complete Decongestive Therapy Is an Option for the Treatment of Rosacea Lymphedema (Morbihan Disease): Two Cases.

BACKGROUND AND PURPOSE Morbihan disease, also known as rosacea lymphedema, is a rare persistent form of lymphedema that is associated with the disease rosacea. Even though acne rosacea responds well to standard medical treatment, the lymphedema component of the disease is resistant to both medical and surgical therapy. Complete decongestive therapy (CDT) can be considered as a conservative alternative option for treatment of rosacea lymphedema. To date, there is no report on the use of CDT in treating facial lymphedema secondary to acne rosacea. CASE DESCRIPTION We present 2 cases of women with a diagnosis of Morbihan disease and chronic facial lymphedema that remained resistant to drug treatment for many years before CDT was offered. The treatment program included 4 components: manual lymphatic drainage, compression bandaging, exercises to enhance lymphatic drainage, and patient education. OUTCOMES Following 10 to 15 sessions of CDT, the first patient's facial edema had almost completely resolved. The second patient's response to treatment was assessed as moderate. DISCUSSION To the best of our knowledge, these 2 cases of Morbihan disease treated with CDT are the first of their kind to be presented in the literature. As the treatment options for Morbihan disease remain inadequate, we believe that CDT should be considered as a treatment option in those patients who do not benefit from or refuse drug treatment, before moving on to more invasive procedures. Prospective studies should be designed to demonstrate the efficacy of CDT and provide management details.
Use of Deep Learning for Position Estimation and Control of Soft Glove

Soft wearable robots comprised of deformable materials have recently attracted much attention in the field of applications for its lightness and elasticity. However, there exist some limitation to the control of wearable robots due to the complexity of the model and the conditions of the wearer. In this paper, we propose a learning-based position control method of soft wearable glove using a deep neural network (DNN). To analyze our proposed method, we fabricated a soft pneumatic glove and a control board for the glove based on open hardware platform data. With our developed system, we collected the pressure and position data of the soft glove using a Leap Motion sensor to train our soft glove position network (SGPN). Along with our proposed DNN model, we could enable open-loop control on the joint positions of the soft glove by supplying pressure to the actuator without prior knowledge of the wearer or the wearable robot such as hand size of the wearer or the stiffness of pneumatic actuator.
Active Phased-Array Antennas for Ka/K Mobile Satellite Communications

This paper presents the measured results of two sub-arrays for mobile satellite communication systems operating in Ka (Tx) and K (Rx) bands. A 4 × 2 Ka-band (30 GHz) circularly polarized active phased-array antenna is fabricated and tested for different steering angles. Measurements show a high purity right hand circularly polarized (RHCP) radiation pattern over the elevation angle range 0° -±25 ° with axial ratio ≤1.1 dB. Furthermore, a 4 × 1 (20 GHz) dual fed active phased-array antenna is designed and tested. It shows a RHCP radiation pattern with high cross-polarization discrimination ≥27 dB (> 0.7 dB AR) over the elevation range 0° -±25°.
Leading Sectors and Energy Consumption in China

With China's deepening industrialization process, research on economic structure and leading sectors contributes to grasp the industrialization law. Based on the intersectoral linkage theory, this paper utilizes input-output table to calculate IPD (index of power of dispersion) and ISD (index of sensitivity of dispersion), selects the leading sectors in different phases, and explores their changing tendency. Moreover, the total energy consumption and consumption structure of the leading sectors are also analyzed. The emphasis is placed on the intersectoral linkage and energy consumption structure of the largest energy consumer-iron and steel industry. The analysis on the leading sectors and their energy consumption provides a feasible solution for studying industry structure change and energy security in the late phase of China's industrialization and post-industrialization process.
A Study on Sentiment Analysis of Product Reviews

Sentiment analysis is an intellectual way of identifying users' sentiments towards certain entities. Natural Language Processing (NLP) has the process of analyzing sentiments as one of its prominent fields. NLP is a branch of computer science and artificial intelligence that deals with human-computer language interaction. The growth in e-commerce has led to increasing customer reviews about various products which are available on the internet. These product reviews offer a massive amount of valuable information which can be used for various decision- making purposes. Several methods have been developed in recent years in order to accomplish this task. In this paper, we discuss various levels of sentiment analysis followed by comparison among different approaches to sentiment analysis.
UIT-VSFC: Vietnamese Students’ Feedback Corpus for Sentiment Analysis

Students’ feedback is a vital resource for the interdisciplinary research combining of two fields: sentiment analysis and education. To strengthen the sentiment analysis of the Vietnamese language which is a low-resource language, we build a Vietnamese Students’ Feedback Corpus (UIT-VSFC), a free and high-quality corpus for research on two different tasks: sentiment-based and topic-based classifications. In this paper, we present the methods of building annotation guidelines and ensure the annotation accuracy and consistency of this corpus. The resource consists of over 16,000 sentences which are human-annotated on the two tasks. To assess the quality of our corpus, we measure the inter-annotator agreements and classification accuracies on our UIT-VSFC. As a result, we achieved 91.20% of the inter-annotator agreement for the sentiment-based task and 71.07% of that for the topic-based task. In addition, the best results are of baseline model as the Maximum Entropy classifier with 87.94% and 84.03% of the overall F1-score of the sentiment-based and topic-based tasks respectively. These results illustrate that the corpus is reliable and helpful resource for research.
Cyber-Attacks Related to Intelligent Electronic Devices and Their Countermeasures: A Review

Intelligent electronic devices (IEDs) are deployed in power systems to facilitate advanced automation for controlling critical equipment. Once an IED is compromised, attackers can seize the control of the physical entities in substations. They may interrupt the normal functioning of switch devices, sabotage primary equipment and manipulate measurements to impact the stability of power supply. If multiple correlative attacks are launched simultaneously, attackers may even cause cascading failures, whose consequences are often catastrophic. In this paper, a systematic review of cyber-attacks related to IEDs and their countermeasures is provided. By retrieving relevant research, the information including potential threat vectors, attack types, and defense countermeasures are analyzed. The primary purpose of this paper is to remind the researchers and engineers of up-to-date attacks and to inspire studies on countermeasures to protect IEDs and ensure power system cybersecurity.
Connection of the SUMO Microscopic Traffic Simulator and the Unity 3D Game Engine to Evaluate V2X Communication-Based Systems

In-vehicle applications that are based on Vehicle-to-Everything (V2X) communication technologies need to be evaluated under lab-controlled conditions before performing field tests. The need for a tailored platform to perform specific research on the cooperative Advanced Driving Assistance System (ADAS) to assess the effect on driver behavior and driving performance motivated the development of a driver-centric traffic simulator that is built over a 3D graphics engine. The engine creates a driving situation as it communicates with a traffic simulator as a means to simulate real-life traffic scenarios. The TraCI as a Service (TraaS) library was implemented to perform the interaction between the driver-controlled vehicle and the Simulation of Urban MObility (SUMO). An extension of a previous version, this work improves simulation performance and realism by reducing computational demand and integrating a tailored scenario with the ADAS to be tested. The usability of the implemented simulation platform was evaluated by means of an experiment related to the efficiency of a Traffic Light Assistant (TLA), showing the analysis of the answer that 80% of the participants were satisfied with the simulator and the TLA system implemented.
A Receiver for Inductive Ear-to-Ear Communication

This paper proposes a receiver circuit for inductive ear-to-ear communication. Its input resonance structure, an LCR front-end, is optimized for both a data transmission rate of 100 kbit/s and maximum voltage excess. Following an analytical study, the optimal quality factor $Q_{\text {LCR}}$ is found to be 12.8. Subsequent amplification of ON-OFF keyed bits is performed with a four-stage JFET amplifier, introducing a total amplification of 68.6 dB at a sensitivity of $31.6~\mu \text{V}$ . Each stage is implemented in common-source JFET topology containing a current source in its drain path instead of the conventional drain resistor. This modification allows for a per-stage increase in voltage amplification by a factor of 1.87, avoiding extra quiescent current (detailed circuit theory is added). A hardware realization for signal demodulation reconstructs the baseband signal. In combination with a dedicated transmitter, a Hartley Oscillator, the inductive ear-to-ear transmission system demonstrates a reliable functionality over a distance of 18 cm. Operation is possible with a standard 1.5 V battery cell requiring low currents on a discrete benchtop prototype [transmitter: 0.8 mA; receiver: 4.0 mA]. Applying a carrier frequency of 3.175 MHz, the system is fully compliant with ITU regulation 5.115.
Development of IoT-Based Data Shoes for Daily Life Step Count

During the past few years, Internet of Things (IoT) has unprecedentedly become a buzz word in most areas of business and industry. In this paper, we have developed an IoT solution for human gait monitoring based on a data shoe system over the internet via 3G/4G LTE or internet router. A private java application server displays the walking information via graphs and widgets together with Blynk which is a platform on android and iOS mobile apps. To observe gait information, we have adopted force sensitive resistors (FSRs) installed under the insole of the shoes for collecting walking data. ESP8266 Wi-Fi modules were used as low cost data transfer. Bridge widgets function is used for device-to-device communication between both shoes. Step countering, as one of the most important gait parameters, was chosen for evaluating this solution with a simple walking algorithm. The accuracy of step countering is compared to iPhone apps in difference walking styles. The integration of sensors, wireless technology and IoT could improve the data shoe system in terms of facilitating the users and administrators to access the gait monitoring, primarily in the step count.
Link Prediction in Bipartite Nested Networks

Real networks typically studied in various research fields—ecology and economic complexity, for example—often exhibit a nested topology, which means that the neighborhoods of high-degree nodes tend to include the neighborhoods of low-degree nodes. Focusing on nested networks, we study the problem of link prediction in complex networks, which aims at identifying likely candidates for missing links. We find that a new method that takes network nestedness into account outperforms well-established link-prediction methods not only when the input networks are sufficiently nested, but also for networks where the nested structure is imperfect. Our study paves the way to search for optimal methods for link prediction in nested networks, which might be beneficial for World Trade and ecological network analysis.
Optimizing Data Intensive Flows for Networks on Chips

A novel framework is proposed to find efficient data intensive flow distributions on Networks on Chip (NoC). Voronoi diagram techniques are used to divide a NoC array of homogeneous processors and links into clusters. A new mathematical tool, named the flow matrix, is proposed to find the optimal flow distribution for individual clusters. Individual flow distributions on clusters are reconciled to be more evenly distributed. This leads to an efficient makespan and a significant savings in the number of cores actually used. The approach here is described in terms of a mesh interconnection but is suitable for other interconnection topologies.
Lactose Intolerance, Dairy Avoidance, and Treatment Options

Lactose intolerance refers to symptoms related to the consumption of lactose-containing dairy foods, which are the most common source for this disaccharide. While four causes are described, the most common is the genetically-determined adult onset lactose maldigestion due to loss of intestinal lactase governed by control of the gene by a 14,000 kb promoter region on chromosome 2. Gastrointestinal symptoms from lactose have expanded to include systemic effects and have also been confounded by other food intolerances or functional gastrointestinal disorders. Partly because lactose maldigestion is often interpreted as lactose intolerance (symptoms), focus of therapy for these symptoms starts with lactose restriction. However, withholding of dairy foods completely is not appropriate due to a more favorable impact on health. Industrial efforts to substitute with plant-based products is not completely successful at this time. This narrative article reviews the complexities of the perception of lactose intolerance, its epidemiology, and pathogenesis. Treatments are discussed, including the inappropriateness of dairy avoidance. In conjunction, effects of dairy products on 19 common diseases are reviewed. Different methods of treatment, lactose-reduced products, plant-based dairy substitutes, adaptation, prebiotics, exogenous lactase, probiotics, and some other dietary interventions are further discussed.
Masking Release for Speech in Modulated Maskers: Electrophysiological and Behavioral Measures.

OBJECTIVES The purpose of this study was to obtain an electrophysiological analog of masking release using speech-evoked cortical potentials in steady and modulated maskers and to relate this masking release to behavioral measures for the same stimuli. The hypothesis was that the evoked potentials can be tracked to a lower stimulus level in a modulated masker than in a steady masker and that the magnitude of this electrophysiological masking release is of the same order as that of the behavioral masking release for the same stimuli. DESIGN Cortical potentials evoked by an 80-ms /ba/ stimulus were measured in two steady maskers (30 and 65 dB SPL), and in a masker that modulated between these two levels at a rate of 25 Hz. In each masker, a level series was undertaken to determine electrophysiological threshold. Behavioral detection thresholds were determined in the same maskers using an adaptive tracking procedure. Masking release was defined as the difference between signal thresholds measured in the steady 65-dB SPL masker and the modulated masker. A total of 23 normal-hearing adults participated. RESULTS Electrophysiological thresholds were uniformly elevated relative to behavioral thresholds by about 6.5 dB. However, the magnitude of masking release was about 13.5 dB for both measurement domains. CONCLUSIONS Electrophysiological measures of masking release using speech-evoked cortical auditory evoked potentials correspond closely to behavioral estimates for the same stimuli. This suggests that objective measures based on electrophysiological techniques can be used to reliably gauge aspects of temporal processing ability.