Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of a decision aid on the choice of pregnant women whether to have epidural anesthesia or not during labor

Objective Decision aids (DAs) are useful in providing information for decision-making on using epidural anesthesia during birth. To date, there has been little development of DAs for Japanese pregnant women. Herein, we investigated the effect of a DA on the decision of pregnant women whether to have epidural anesthesia or not for labor during vaginal delivery. The primary outcome was changes in mean decision conflict score. Methods In this non-randomized controlled trial, 300 low-risk pregnant women in an urban hospital were recruited by purposive sampling and assigned to 2 groups: DA (intervention) and pamphlet (control) groups. Control enrollment was started first (until 150 women), followed by intervention enrollment (150 women). Pre-test and post-test scores were evaluated using the Decision Conflict Scale (DCS) for primary outcome, knowledge of epidural anesthesia and satisfaction with decision making for secondary outcomes, and decision of anesthesia usage (i.e., with epidural anesthesia, without epidural anesthesia, or undecided). Results Women in the DA group (n = 149: 1 excluded because she did not return post-test questionnaire) had significantly lower DCS score than those in the pamphlet group (n = 150) (DA: -8.41 [SD 8.79] vs. pamphlet: -1.69 [SD 5.91], p < .001). Knowledge of epidural anesthesia and satisfaction with decision-making scores of women who used the DA were significantly higher than those of women who used the pamphlet (p < .001). Women in the DA group showed a significantly lower undecided rate than those in the pamphlet group. The number of undecided women in the DA group significantly decreased from 30.2% to 6.1% (p < .001), whereas that in the pamphlet group remained largely unchanged from 40.7% to 38.9%. Conclusion This study indicates that a DA can be useful in helping women make a decision whether to have epidural anesthesia or not for labor during vaginal delivery.
Influenza A viruses limit NLRP3‐NEK7‐complex formation and pyroptosis in human macrophages

Pyroptosis is a fulminant form of macrophage cell death, contributing to release of pro‐inflammatory cytokines. In humans, it depends on caspase 1/4‐activation of gasdermin D and is characterized by the release of cytoplasmic content. Pathogens apply strategies to avoid or antagonize this host response. We demonstrate here that a small accessory protein (PB1‐F2) of contemporary H5N1 and H3N2 influenza A viruses (IAV) curtails fulminant cell death of infected human macrophages. Infection of macrophages with a PB1‐F2‐deficient mutant of a contemporary IAV resulted in higher levels of caspase‐1 activation, cleavage of gasdermin D, and release of LDH and IL‐1β. Mechanistically, PB1‐F2 limits transition of NLRP3 from its auto‐repressed and closed confirmation into its active state. Consequently, interaction of a recently identified licensing kinase NEK7 with NLRP3 is diminished, which is required to initiate inflammasome assembly.
Supplementary Damping Control Design for Large Scale PV Power Plant at Transmission Level Interconnection

The deployment of large scale photovoltaic (PV) power generation has been witnessed in several countries worldwide with different installed capacities. Accordingly, codes and regulations to ensure secure and economical operation have been revised to address the challenges related with PV integration into electrical networks. This paper presents an H∞ mixed sensitivity robust control design for enhancing the overall damping of low frequency oscillations. The presented architecture will implement the output signal of the power oscillator damper (POD) at the control loop of the PV-based solar power plant. The effectiveness of the proposed approach is tested using the New-England, 10machines test system.
Multi-user Cooperative Spectrum Sensing Based on the Mean Value of Cumulative Power Spectral Density

In the practical application of cognitive radio systems, spectrum sensing requires accurate signal detection without any prior knowledge and in complex channel background. Energy detection algorithms perform poorly in complex channel environments, and single-user spectrum sensing is susceptible to problems such as multipath effects, shadow fading, and hidden terminals. This paper analyzes the spectrum sensing algorithm based on the mean value of the cumulative power spectral density, and compares it with the traditional energy detection algorithm. In view of the limitations of single-user spectrum sensing, this paper applies multi-user cooperative spectrum sensing to the algorithm, which improves the performance of spectrum sensing, such as detection probability.
Studies for Designing Stereotypes in a Mobile Recommendation System for Environmental Awareness

Centuries-Old Olive Groves is a natural monument that may of interest to a great variety of users, with different knowledge, interests and need. A recommendation system may customize access to information. In this paper, we focus on the design of a recommendation system for environmental awareness, which actually provides information about Centuries-Old Olive Groves. More specifically, we describe the expert-based and user-based studies that have been implemented in the early stages of the software’s life cycle. These studies help in the implementation of a combination of user modeling techniques and multi-criteria decision making methods, used for personalizing recommendations in the recommendation system.
Derivation and validation of a simple nomogram prediction model for all-cause mortality among middle-aged and elderly general population.

BACKGROUND A simple clinical model that can predict all-cause mortality in the middle-aged and older adults in general population based on demographics and physical measurement indicators. The aim of this study was to develop a simple nomogram prediction model for all-cause mortality in middle-aged and elderly general population based on demographics and physical measurement indicators. METHODS This was a prospective cohort study. We used data from the 1999-2006 National Health and Nutrition Examination Survey (NHANES), which included adults aged ≥40 years with mortality status updated through 31 December 2015. Cox proportional hazards regression, nomogram and least absolute shrinkage and selection operator (LASSO) binomial regression model were performed to evaluate the prediction model in the derivation and validation cohort. RESULTS A total of 13,026 participants (6,414 men, mean age was 61.59±13.80 years) were included, of which 6,671 (3,263 men) and 6,355 (3,151 men) were included in the derivation cohort and validation cohort, respectively. During an average follow-up period of 129.23±9.62 months, 4,321 died. We developed a 9-item nomogram mode included age, gender, smoking, alcohol intake, diabetes, hypertension, marriage status, education and poverty to income ratio (PIR). The area under the curve (AUC) was 0.842 and had good calibration. Internal validation showed good discrimination of the nomogram model with AUC of 0.849 and good calibration. Application of the LASSO regression model in the validation cohort also revealed good discrimination (AUC =0.854) and good calibration. A time-dependent and optimism-corrected AUC value for the model showed no significant relationship with the change of follow-up time. CONCLUSIONS A simple nomogram model, including age, gender, smoking, alcohol intake, diabetes, hypertension, marriage, education and PIR, could predict all-cause mortality well in middle-aged and elderly general population.
Small Cell Deployment Challenges in Ultradense Networks: Architecture and Resource Management

The industries today, are undergoing transformational changes as a result of the growing demand for ubiquitous connectivity. To meet the increasing demand every day, loads of reengineering is required to achieve best-fitted architecture to accommodate more devices, ways to manage available resources, enhanced radio technologies, etc. to deliver more coverage and capacity. This paper aims to study and present different critical features of small cell and ultradense networks. The scope is to summarize various deployment scenarios and challenges. Broadly, the challenges w.r. t resource management, developing flexible architecture, managing the available spectrum and also the use of unlicensed spectrum, etc. will be addressed. The shortcomings in available resource management, interference awareness techniques, spectrum management, etc. will be summarized with different potential technologies. For small cells, it is necessary to understand its interaction with the macro network to demonstrate the overall user experience. Thus, this work will present an elaborated picture of small cell evolution followed by the radio access network (RAN) architecture to achieve a full-heterogeneous and centralized network.
Global Fast Terminal Sliding Mode Control for a Quadrotor UAV

A control method based on global fast terminal sliding mode control (GFTSMC) technique is proposed to track desired trajectories of a quadrotor UAV. By taking into account the high-order nonholonomic constraints to control the six degrees of freedom system with four inputs. The whole system control can be divided into position control unit and attitude control unit, adopt GFTSMC to achieve reference position and attitude. And because of the characteristic of GFTSMC, the robustness and timeliness of the convergence process of the quadrotor system can be guaranteed.
Umbra: A Visual Analysis Approach for Defense Construction Against Inference Attacks on Sensitive Information

Collecting and analyzing anonymous personal information is required as a part of data analysis processes, such as medical diagnosis and restaurant recommendation. Such data should ostensibly be stored so that specific individual information cannot be disclosed. Unfortunately, inference attacks—integrating background knowledge and intelligent models—hinder classic sanitization techniques like syntactic anonymity and differential privacy from exhaustively protecting sensitive information. As a solution, we introduce a three-stage approach empowered within a visual interface, which depicts underlying inference behaviors via a Bayesian Network and supports a customized defense against inference attacks from unknown adversaries. In particular, our approach visually explains the process details of the underlying privacy preserving models, allowing users to verify if the results sufficiently satisfy the requirements of privacy preservation. We demonstrate the effectiveness of our approach through two case studies and expert reviews.
Comparison of 4 sinus augmentation techniques for implant placement with residual alveolar bone height ≤3 mm

Abstract This study compared implant outcomes following maxillary sinus floor augmentation (MSFA) in edentulous patients with a residual alveolar bone height ≤3 mm. Four techniques were evaluated: 1-stage bone-added osteotome sinus floor elevation procedure (BAOSFE) with simultaneous implant placement; 2-stage BAOSFE with delayed implant placement; 1-stage lateral window sinus floor elevation with simultaneous implant placement; and 2-stage lateral window sinus floor elevation with delayed implant placement. Patients were followed for 18 to 72 months (mean: 52.5 months) after prosthesis placement. Data were analyzed with cone-beam computed tomography. A total of 96 implants from 71 patients were analyzed; pretreatment, there were no significant differences between patients. Total implant survival was 98.9%. The mean residual bone height was significantly higher in the 1-stage BAOSFE group than the other groups (P < .01); 1 implant in this group failed at 3 months. There was no significant difference in total bone height gain between groups. However, the bone height gain of 1st sinus lifting with 2-stage BAOSFE was significantly lower than the 2-stage lateral window procedure (P < .01). There was no prosthesis failure. The favorable implant outcomes suggest these 1-stage and 2-stage MSFA procedures should be considered as alternative treatment options for patients with extremely atrophic posterior maxilla.
BMP signaling: at the gate between activated melanocyte stem cells and differentiation

Here, Infarinato et al. investigated the signaling factors regulating activation of quiescent melanocyte stem cells (McSCs) to generate proliferative progeny that differentiate into pigment-producing melanocytes. Using conditional lineage-specific genetic ablation studies in mice, they found that loss of BMP signaling in the lineage leads to hair graying due to a block in melanocyte maturation, and furthermore show that following WNT-mediated activation of McSCs, BMP, and WNT pathways collaborate to trigger the commitment of proliferative progeny by fueling LEF1- and MITF-dependent differentiation.
A Review with New Approaches of Reflector Antenna

Reflector antennas are broadly utilized antenna in the communication world for various applications like microwave communications, radio space science, radar and satellite applications. This paper dedicated for presently ongoing advancement in reflector antenna for microwave applications. The primary goal of this paper is to explore on recent reflector antenna plans to defeat the challenges brought by the developing communication frameworks. It illustrates about the techniques used in recent years to improve the parameters of antennas such as gain, return loss. The bandwidth improvement is very necessary for advanced applications, some of techniques and data are also presented.
A Bio-inspired Spiking Neural Network for Control of A 4-DoF Robotic Arm

This paper explores the control task of a 4-DoF robotic arm via a Spiking Neural Network (SNN). Inspired from the biological neuron control mechanism, the SNN is proposed and analyzed for the robotic arm control. The SNN adopts a data-driven way to estimate the kinematic properties of the robotic arm and further spares the difficulty of analytical model building. Biologically, the desired target position and sensory information are processed into the network, and the patterns of motor commands are able to extract from the readout layer of the SNN. Finally, numerical studies are conducted to verify the effectiveness of the proposed SNN.
Resistive RAM SET and RESET Switching Voltage Evaluation as an Entropy Source for Random Number Generation

The intrinsic variability of the switching parameters in resistive memories has been a major wall that limits their adoption as the next generation memories. In contrast, this natural stochasticity can be beneficial for other applications such as Random Number Generators (RNGs). This paper presents two RNG approaches relying on a 130nm HfO2-based Resistive RAM (RRAM) memory array. The memory array is programmed with a voltage close to the median value of the SET (resp. RESET) voltage distribution to benefit from the SET (resp. RESET) voltage variability. In both cases, only a subset of the memory array is programmed, resulting in a stochastic distribution of cell resistance values. Resistance values are next converted into a bit stream and confronted to National Institute of Standards and Technology (NIST) test benchmarks.
Quad-Beam Octa Cross-Slotted Pattern Reconfigurable Antenna for 5.8 GHz Band Application

In this paper, a quad-beam pattern reconfigurable antenna is presented. To produce four simultaneous beams, a total number of 8-cross slots are cut from the circular edge of the antenna. The slot is fed from the backside through the microstrip feed line. To reconfigure the direction of the beams, another slot with an interval of 45° angles to the first slot is fed. At last, p-i-n diode switching network is provided between these two consecutive slots. Shifting of the quad-beams in both conditions is achieved up to 23°. The gain in all four beams varies from 2.9 to 4.33 dBi and the efficiency is always better than 95 %.
Humanoid Muscle-Skeleton Robot Arm Design and Control Based on Reinforcement Learning

Muscle-skeleton robots share similar appearances and functions with humans, making these robots more adaptive in human interaction scenarios. In this paper, a new muscle-skeleton robot arm driven by artificial muscles is proposed. First, we design a new multifilament McKibben muscle and measure its properties. Then a humanoid robot arm referred to the anatomy of the human arm is presented, while the configuration of muscle is adjusted to reduce the complexity of manufacturing and controlling. Muscle-skeleton robot arms with different muscle configurations are controlled using the reinforcement learning method in the simulation environment, and different arm models’ movement ranges are obtained to find the best muscle configuration. The experimental results show that the model with the best muscle configuration achieves 79.8% of the whole movement range.
Cryptocurrencies Emerging Threats and Defensive Mechanisms: A Systematic Literature Review

Cryptocurrencies have been a target for cybercriminal activities because of the pseudo-anonymity and privacy they offer. Researchers have been actively working on analyzing and developing innovative defensive mechanisms to prevent these activities. A significant challenge facing researchers is collecting datasets to train defensive systems to detect and analyze these cyberattacks. Our aims in this systematic review are to explore and aggregate the state of the art threats that have emerged with cryptocurrencies and the defensive mechanisms that have been proposed. We also discuss the threats type, scale, and how efficient the defensive mechanisms are in providing early detection and prevention. We also list out the resources that have been used to collect datasets, and we identify the publicly available ones. In this study, we extracted 1,221 articles from four top scientific and engineering databases and libraries in Computer Science: IEEE Xplore, ACM Digital Library, Elsevier’s Scopus, and Crarivate’s Web of Science. We defined inclusion, exclusion, and quality of assessment criteria, and after a detailed review process, 66 publications were included in the final review. Our analysis revealed that the literature contains a significant amount of research to detect and analyze several attack types, such as the high yield investment programs and pump and dump. These attacks have been used to steal millions of USD, abuse millions of connected devices, and have created even more significant loss in denial of services and productivity losses. We have found that the researchers use various sources to collect training datasets. Many authors have made their dataset publicly available. We have created a list of these datasets, which we have made available along with other supplementary websites, tools, and libraries that can be used in the data collection and analysis process.
Evaluation of Plant-Derived Promoters for Constitutive and Tissue-Specific Gene Expression in Potato

Various plant-derived promoters can be used to regulate ectopic gene expression in potato. In the present study, four promoters derived from the potato genome have been characterized by the expression of identical cassettes carrying the fusion with the reporter β-glucuronidase (gusA) gene. The strengths of StUbi, StGBSS, StPat, and StLhca3 promoters were compared with the conventional constitutive CaMV 35S promoter in various organs (leaves, stems, roots, and tubers) of greenhouse-grown plants. The final amount of gene product was determined at the post-transcriptional level using histochemical analysis, fluorometric measurements, and Western blot analysis. The promoter strength comparison demonstrated that the StUbi promoter generally provided a higher level of constitutive β-glucuronidase accumulation than the viral CaMV 35S promoter. Although the StLhca3 promoter was predominantly expressed in a green tissue-specific manner (leaves and stems) while StGBSS and StPat mainly provided tuber-specific activity, a “promoter leakage” was also found. However, the degree of unspecific activity depended on the particular transgenic line and tissue. According to fluorometric data, the functional activity of promoters in leaves could be arranged as follows: StLhca3 > StUbi > CaMV 35S > StPat > StGBSS (from highest to lowest). In tubers, the higher expression was detected in transgenic plants expressing StPat-gusA fusion construct, and the strength order was as follows: StPat > StGBSS > StUbi > CaMV 35S > StLhca3. The observed differences between expression patterns are discussed considering the benefits and limitations for the usage of each promoter to regulate the expression of genes in a particular potato tissue.
Hybrid-GA based multiple solutions for Selective Harmonic Elimination in Bipolar PWM waveforms

The abolishment of principal low-order dominant harmonics through Selective Harmonic Elimination (SHE-PWM) approach is restricted by the total switching pulses allowed in a quarter of the inverter fundamental-cycle due to constraints imposed on the high-power inverter device switching loss. The paper evaluates and analyzes multiple solution sets pertaining to bipolar waveform pattern for angles of switching $\mathrm{N}=2$ and switching frequency equal to five times the fundamental switching frequency i.e. pulse-number $(\mathrm{P}=5)$. The performances of each solution set are examined considering the most significant nontriplen 5th harmonic order voltage and also overall weighted harmonic distortion $(V_{WTHD})$ in line voltage three-phase waveform patterns. A combination of Genetic algorithm (GA) and Newton Raphson (N-R) hybrid method is adopted to derive the optimum switching-angle sets of distinct solution for modulationindex (M) range $0 \lt M \lt 1$. Finally, the obtained simulation results using MATLAB/Simulink package clearly supports and confirms the analytical outcomes.
Efficient Transfer Learning for Spectral Image Reconstruction from RGB Images

Spectral image reconstruction from RGB images has emerged as a hot topic in the computer vision community due to easy-access and low-cost acquisition of the latter. The goal is to learn a non-linear mapping from 3-RGB bands to L spectral bands. With the growth of the available spectral datasets, this mapping has been learned using deep convolutional representations. However, these methods demand a large number of spectral images to train the net to obtain a good recovery. In contrast, the proposed process consists of a pre-training step where the weights of a convolutional neural network fit with a large amount of available RGB datasets without spectral mapping, taking into account the RGB system acquisition as a layer. Then, some layers of this pre-trained network are frozen to retrain it with the available spectral dataset to generate a spectral image with L bands. The proposed training scheme can be used with any pre-existing deep network that maps RGB to spectral images and it is here evaluated with a “U-net” architecture, and the RGB sensing is based on the Bayer filter pattern. The simulated and experimental data demonstrate the effectiveness of the proposed approach compared to training without transfer learning, showing a gain of up to 4 dB, with less spectral data.