Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative Study between Sentinel Lymph Node Biopsy and Axillary Dissection in Patients with One or Two Lymph Node Metastases

Purpose Sentinel lymph node biopsy (SLNB) is a standard axillary surgery in early breast cancer. If the SLNB result is positive, subsequent axillary lymph node dissection (ALND) is a routine procedure. In 2011, the American College of Surgeons Oncology Group Z0011 trial revealed that ALND may not be necessary in early breast cancer with one or two positive sentinel lymph nodes. The purpose of this study was to compare outcomes among Korean patients with one or two positive axillary lymph nodes in the final pathology who did and did not undergo ALND. Methods A total of 131,717 patients from the Korea Breast Cancer Society registry database received breast cancer surgery from January 1995 to December 2014. Inclusion criteria were T stage 1 or 2, one or two positive lymph nodes, and having received breast-conserving surgery (BCS), whole breast radiation therapy, and no neoadjuvant therapy. We analyzed the differences in disease-specific survival (DSS) and overall survival (OS) between patients who received SLNB only and those who underwent SLNB+ALND. Results A total 4,442 patients met the inclusion criteria, with 1,268 (28.6%) in the SLNB group and 3,174 (71.4%) in the SLNB+ALND group. There were no differences in DSS and OS between the two groups (p=0.378 and p=0.925, respectively). The number of patients who underwent SLNB alone for one or two positive lymph nodes increased continuously from 2004 to 2014. Conclusion Korean patients with early breast cancer and 1 or 2 positive axillary lymph nodes who received BCS plus SLNB showed no significant difference in DSS and OS regardless of whether they received ALND. The findings of this retrospective study demonstrate that omitting ALND can be considered when treating selected patients with early breast cancer who have one or two positive lymph nodes.
Adaptive Deep Sparse Semantic Modeling Framework for High Spatial Resolution Image Scene Classification

High spatial resolution (HSR) imagery scene classification, which involves labeling an HSR image with a specific semantic class according to the geographical properties, has received increased attention, and many algorithms have been proposed for this task. The employment of the probabilistic topic model to acquire latent topics and the convolutional neural networks (CNNs) to capture deep features for representing HSR images has been an effective ways to bridge the semantic gap. However, the midlevel topic features are usually local and significant, whereas the high-level deep features convey more global and detailed information. In this paper, to discover more discriminative semantics for HSR images, the adaptive deep sparse semantic modeling (ADSSM) framework combining sparse topics and deep features is proposed for HSR image scene classification. In ADSSM, the fully sparse topic model and a CNN are integrated. To exploit the multilevel semantics for HSR scenes, the sparse topic features and deep features are effectively fused at the semantic level. Based on the difference between the sparse topic features and the deep features, an adaptive feature normalization strategy is proposed to improve the fusion of the different features. The experimental results obtained with four HSR image classification data sets confirm that the proposed method significantly improves the performance when compared with the other state-of-the-art methods.
Neurosteroids in Pain Management: A New Perspective on an Old Player

Since the discovery of the nervous system’s ability to produce steroid hormones, numerous studies have demonstrated their importance in modulating neuronal excitability. These central effects are mostly mediated through different ligand-gated receptor systems such as GABAA and NMDA, as well as voltage-dependent Ca2+ or K+ channels. Because these targets are also implicated in transmission of sensory information, it is not surprising that numerous studies have shown the analgesic properties of neurosteroids in various pain models. Physiological (nociceptive) pain has protective value for an organism by promoting survival in life-threatening conditions. However, more prolonged pain that results from dysfunction of nerves (neuropathic pain), and persists even after tissue injury has resolved, is one of the main reasons that patients seek medical attention. This review will focus mostly on the analgesic perspective of neurosteroids and their synthetic 5α and 5β analogs in nociceptive and neuropathic pain conditions.
A 100-MS/s–5-GS/s, 13–5-bit Nyquist-Rate Reconfigurable Time-Domain ADC

This paper presents a 65-nm CMOS Nyquist-rate reconfigurable time-based analog-to-digital converter (ADC). The time-domain nature of the ADC allows for efficient hardware reuse and a wide range of reconfigurability. The ADC employs a reconfigurable time-to-digital converter (TDC) that can be configured as a high-resolution 1-ps correlated double sampling SAR-TDC for the high-resolution modes or as a $4\times $ asynchronous time-interleaved flash-TDC for the high sampling rate modes. The Nyquist-rate ADC supports continuous sampling rate variations from 100 MS/s to 5 GS/s providing 13–5-bit resolution with exponential power scaling from 8.4 to 22.3 mW, respectively. A programmable highly linear 1-bit folded voltage-controlled oscillator is employed to provide the appropriate tuning characteristics for different sampling rate modes with high linearity. The ADC linearity is further enhanced using foreground digital calibration achieving figure-of-merit ranging from 14.6 to 196 fJ/conv.
The Experiences of Younger Individuals Living With Heart Failure

Background: Heart failure (HF) impacts 1.7 million Americans between the ages of 20 and 59 years, but limited research exists addressing the issues unique to this age group. Objective: The aim of this study was to investigate the experiences of younger persons living with HF. Methods: Semistructured interviews (n = 18) were analyzed using a qualitative thematic analysis. Results: Six themes emerged: (1) role fulfillment: participants who perceived their roles as a parent, caregiver, spouse, employee, and friend have been negatively affected by HF; (2) autonomy: participants frequently linked their well-being to a sense of independence, productivity, and purpose; (3) financial impact: most of the sample expressed financial instability further complicated by existing disability policies; (4) perception of self: recurrent statements revealed participants feeling judged because of their HF diagnosis and younger age; (5) retrospective life changes: participants reflected on past decisions and often blamed themselves for their current health; and (6) symptom burden: participants reported higher levels of psychosocial symptoms, such as depression, anxiety, and fatigue. Conclusions: Younger individuals living with HF reported overwhelmingly poor health-related quality of life that was linked to the 6 themes identified previously. The effect of HF was seen on numerous life events that traditionally occur earlier in the life course such as establishing a career, meaningful relationships, family, and financial security. This directly impacted health-related quality of life by disrupting what each individual perceived as important to them, which took a toll on their overall mental health. To provide holistic care to younger patients living with HF, interventions that address the heavy burden of psychosocial symptoms and policy changes associated with financial burden and disability laws must be developed.
A New Three-Port Non-Isolated DC-DC Converter for Renewable Energy Sources Application

A new three-port DC-DC converter for renewable energy systems with battery storage is proposed in this paper. The proposed converter is employed to manage the energy supplied from different sources, here the photovoltaic solar arrays and the battery, and deliver a continuous power to the output loads in the best suited form. Traditionally, three different DC-DC converters should have been used for this purpose. However, the proposed converter not only retains the major desirable features of three DC-DC converters, but also reduces the cost and improves the power density of the power system. It has many advantages such as high voltage gain in low duty cycles, low inductor current ripple and conduction losses, high efficiency and also the low number of switches. Three different scenarios are simulated to corroborate the performance of the proposed converter. The simulation results show that the proposed converter has the capability to track the maximum power point (MPP) of the PV system and also control the battery charging/discharging according to the load and the PV system power.
High Density Fixed Valued Impulse Noise Removal Using Improved Decision Based Hybrid Median Filter and its Application on Medical Images

Formation of image is affected by image capturing device characteristics and intensity of light. Therefore inferior quality of image capturing device and inadequate lighting conceals particulars and significant details associated with image. In order to squeeze out the hidden features, image enhancement (noise removal) is mandatory. Hence noise removal is realized as pre-processing step in image study. In this research paper a Decision Based Hybrid Median Filter is suggested for the renovation of gray scale images corrupted by fixed valued Impulse noise. This suggested filtering technique offers superior results than the earlier known enhancement techniques like standard median filter (MF), adaptive median filter (AMF), fast and efficient median filter (FEMF), new adaptive weighted mean filter (AWMF), noise adaptive fuzzy switching median filter (NAFSMF). The foremost objective of the suggested technique is to enhance visual perception and boost peak signal to noise ratio (PSNR). In the suggested technique if current pixel is found noisy then it is substituted by median value achieved after eliminating pixels with intensity values 0 and 255 from the window. When all the pixels in the window are 0 and 255 then the window size is increased. If 80 to 90% pixels are 0 and 255 in the new window, then substitute the pixel with the previously processed pixel value else substitute it with mean of the designated window. The suggested technique is verified against standard as well as medical images. Comparisons and experimental results shows better visual and quantitative results with reduced mean square error (MSE), increased image enhancement factor (IEF) and peak signal to noise ratio (PSNR).
Mid-Term Load Forecasting for Iran Power System Using Seasonal Autoregressive Integrated Moving Average Model (SARIMA)

This paper analyses and characterizes the nonindustrial demand of Iran power system in the last decade and proposes a seasonal autoregressive integrated moving average model (SARIMA) for its mid-term load forecasting (MTLF). Detrending an observed growth and a seasonal additive, the orders of the SARIMA's components are determined by autocorrelation and partial autocorrelation functions. The model residuals are explored for normality by statistical moments including mean, variance, skewness and kurtosis indices. Moreover, the predictive performance of the model is validated with distinct real data. The results proved the applicability of the model for MTLF in power systems.
Mimo-OFDM Compressed Channel Estimation Using Forward-Backward Pursuit

Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO-OFDM) channel estimation is considered recently utilizing Compressed Sensing (CS) based methods. Here, we proposed to use the joint sparsity of MIMO-OFDM channels using Forward Backward Pursuit (FBP) algorithm. In order to increase the accuracy of estimation, we proposed to take into account the common sparsity of MIMO channels in each step and to exploit common sparsity in the system model. Furthermore, the backward steps improve the accuracy by omitting evil previously gathered atoms. Simulation results represent the superiority of the proposed FBP-based channel estimation approach rather than the conventional CS-based approaches.
Computational Intelligence Approach for Predicting Ischemic Stroke using Brain MRI

Stroke occurs when the vessels that supply blood to the brain either blocked by a clot or ruptures. Magnetic resonance imaging (MRI) is widely used in diagnosis process of stroke, assist physicians in finding the etiology for the incident, and to predict the functional outcome. Computer-aided diagnosis is essential for fast, accurate diagnosis and monitoring the progress of the disease. In this paper, we propose an automated process to segment the stroke lesions in diffusion-weighted imaging (DWI) modality of brain MRI. The lesion area was segmented from brain images using expectation-maximization (EM) algorithm. The statistical features were extracted and classified for stroke type according to The Oxfordshire Community Stroke Project (OCSP) using Random Forest (RF) classifier. The proposed method was implemented in 42 MR images having stroke lesions collected from hospital. The method efficiently segmented the stroke lesion and the result was evaluated with measured parameters that predicts the accuracy of the classifier. It achieved the sensitivity of 0.804, accuracy 0.835, dice coefficient of 0. 809 and Jaccard index of 0.702, which are better than to earlier published results.
Short-Term Effects of Ambient Air Pollution on Hospitalization for Respiratory Disease in Taiyuan, China: A Time-Series Analysis

In this study, we estimated the short-term effects of ambient air pollution on respiratory disease hospitalization in Taiyuan, China. Daily data of respiratory disease hospitalization, daily concentration of ambient air pollutants and meteorological factors from 1 October 2014 to 30 September 2017 in Taiyuan were included in our study. We conducted a time-series study design and applied a generalized additive model to evaluate the association between every 10-μg/m3 increment of air pollutants and percent increase of respiratory disease hospitalization. A total of 127,565 respiratory disease hospitalization cases were included in this study during the present period. In single-pollutant models, the effect values in multi-day lags were greater than those in single-day lags. PM2.5 at lag02 days, SO2 at lag03 days, PM10 and NO2 at lag05 days were observed to be strongly and significantly associated with respiratory disease hospitalization. No significant association was found between O3 and respiratory disease hospitalization. SO2 and NO2 were still significantly associated with hospitalization after adjusting for PM2.5 or PM10 into two-pollutant models. Females and younger population for respiratory disease were more vulnerable to air pollution than males and older groups. Therefore, some effective measures should be taken to strengthen the management of the ambient air pollutants, especially SO2 and NO2, and to enhance the protection of the high-risk population from air pollutants, thereby reducing the burden of respiratory disease caused by ambient air pollution.
AKT and AMP-activated protein kinase regulate TBC1D1 through phosphorylation and its interaction with the cytosolic tail of insulin-regulated aminopeptidase IRAP

In skeletal muscle, the Rab GTPase-activating (GAP) protein TBC1D1 is phosphorylated by AKT and AMP-activated protein kinase (AMPK) in response to insulin and muscle contraction. Genetic ablation of Tbc1d1 or mutation of distinct phosphorylation sites impairs intracellular GLUT4 retention and GLUT4 traffic, presumably through alterations of the activation state of downstream Rab GTPases. Previous studies have focused on characterizing the C-terminal GAP domain of TBC1D1 that lacks the known phosphorylation sites, as well as putative regulatory domains. As a result, it has been unclear how phosphorylation of TBC1D1 would regulate its activity. In the present study, we have expressed, purified, and characterized recombinant full-length TBC1D1 in Sf9 insect cells via the baculovirus system. Full-length TBC1D1 showed RabGAP activity toward GLUT4-associated Rab8a, Rab10, and Rab14, indicating similar substrate specificity as the truncated GAP domain. However, the catalytic activity of the full-length TBC1D1 was markedly higher than that of the GAP domain. Although in vitro phosphorylation of TBC1D1 by AKT or AMPK increased 14-3-3 binding, it did not alter the intrinsic RabGAP activity. However, we found that TBC1D1 interacts through its N-terminal PTB domains with the cytoplasmic domain of the insulin-regulated aminopeptidase, a resident protein of GLUT4 storage vesicles, and this binding is disrupted by phosphorylation of TBC1D1 by AKT or AMPK. In summary, our findings suggest that other regions outside the GAP domain may contribute to the catalytic activity of TBC1D1. Moreover, our data indicate that recruitment of TBC1D1 to GLUT4-containing vesicles and not its GAP activity is regulated by insulin and contraction-mediated phosphorylation.
Pulsed Schlieren Imaging of Ultrasonic Haptics and Levitation using Phased Arrays

Ultrasonic acoustic fields have recently been used to generate haptic effects on the human skin as well as to levitate small sub-wavelength size particles. Schlieren imaging and background-oriented schlieren techniques can be used for acoustic wave pattern and beam shape visualization. These techniques exploit variations in the refractive index of a propagation medium by applying refractive optics or cross-correlation algorithms of photographs of illuminated background patterns. Here both background-oriented and traditional schlieren systems are used to visualize the regions of the acoustic power involved in creating dynamic haptic sensations and dynamic levitation traps. We demonstrate for the first time the application of back-ground-oriented schlieren for imaging ultrasonic fields in air. We detail our imaging apparatus and present improved algorithms used to visualize these phenomena that we have produced using multiple phased arrays. Moreover, to improve imaging, we leverage an electronically controlled, high-output LED which is pulsed in synchrony with the ultrasonic carrier frequency.
Identification, Model Validation and Control of an Octorotor Unmanned Aerial Vehicle

This paper presents an experimental procedure for the identification of parameters of an octorotor unmanned aerial vehicle (UAV), as well as the obtained model validation via control. The octorotor UAV is a highly nonlinear, multivariable and strongly coupled system. The mathematical model of used UAV includes rigid body dynamics, the Gyroscopic effect and motor dynamics. In order to estimate eleven unknown parameters, the experiments are specially prepared and conducted on the custom made apparatus. Therefore, on basis of obtained measurements, some modifications of the octorotor model are made.
On the additional error of a nonlinear hydraulic measuring circuit with pressure pulsations at the inlet

In power plant and test equipment control systems pressure sensors are often connected by pressure transmission lines for conditions of high temperatures and vibrations. In the presence of pressure pulsations in the monitored object, a restrictor is installed at the pressure transducer inlet. Restrictors serially produced for pressure transducers have a nonlinear flow-drop characteristic which leads to the additional measurement error of the medium or slowly changing component of the pulsating pressure. This distortion can significantly exceed the error of the transducer itself. The results of theoretical and experimental studies of the additional error depending on the parameters of the measurement circuit and pressure pulsations (amplitude, frequency, spectrum and phase shift between components) are presented. Recommendations on the choice of damper parameters for which the additional error can be neglected are given. The methodology developed by the authors can be used to create measurement circuit for an average or slowly changing component of the pulsating pressure measurement in power plant monitored measurement and control systems.
Simplified Neural Network Based on Auxiliary Layer and Adaptive Pruning Rate

Using of neural networks has increased in many applications by its increasing power to model, analysis and solve the problems in many different areas. The performance of neural networks is growing and so is the complexity of their structure. Although in recent years pruning and other similar works have been introduced as an appropriate solution to decrease their complexity, they are still very complex. In this paper, a method is proposed to reduce the complexity of neural networks by inserting an auxiliary layer and employing adaptive pruning rate. By insertion of an auxiliary layer after pruning, the network can be fitted on the given problem with less computational nodes. Simulation results on MNIST dataset represent a reduction of %22 in the number of network weights in comparison with the base pruning method. Also, an energy analysis performed on this dataset shows that it can be implemented with low energy consumption.
Design & Performance Analysis of Area Optimized in BPSK on NCO and High Speed 64 Bit-QAM with Gardner Algorithm

This project used its reconfigurable ability and high throughput performance, using Field Programmable Gate Array (FPGA). Our aim of this paper, BPSK system is designed in verilog for optimized area (as a single block to generate two signals). In order to reduced area have to using numerically controlled oscillator (NCO). By using this NCO system we can controlling the phase of the each carrier signal. The designs have been successfully verified by simulation and implementation. The drawback of BPSK system speed is very slow and this system is not suitable for high bit data rate. So that having to plan design and implementation of 64QAM (Quadrature Amplitude Modulation) for increasing the speed, flexibility, accuracy and configurability in designing and implementation. We can implemented gray code symbol mapping and differential code symbol mapping on 64QAM system.
Smart Power Meter for the IoT

Advanced Metering Infrastructure (AMI) today represents the backbone of Smart City intended as urban area where the most of public services are smart. Smart devices allow cities to be smart, enabling communication also among things and creating a new system nowadays known as IoT (Internet of Things). In particular the smarter the meters, the smarter the City, but the concept of smartness is not univocal. In urban area, smart meters must be accessible, collaborative and accurate. The paper faces this topic, mainly with reference to power measurements and wireless networks. In particular, it describes an innovative solution for electrical power, gas and water metering integration in the same infrastructure. Then it describes metrics and hardware details of a smart electrical power meter and, finally it shows the experimental results of the smart meter metrological characterization.
Natural Language Processing for Requirements Engineering: The Best Is Yet to Come

As part of the growing interest in natural language processing for requirements engineering (RE), RE researchers, computational linguists, and industry practitioners met at the First Workshop on Natural Language Processing for Requirements Engineering (NLP4RE 18). This article summarizes the workshop and presents an overview of the discussion held on the field’s future. This article is part of a theme issue on software engineering’s 50th anniversary.