Miracle-dz/UniFAR-FTUs huggingface dataset:
Transcatheter Aortic-Valve Replacement in Clinical Practice.

The authors reply: In response to Mahé: the aspect of our results that was unexpected was not the substantial decline in scabies prevalence after mass drug administration, but the further decline (in all three trial groups) between the 3-month and 12-month observation points. To explain this finding, we speculated that the 3-month observation may have been an overestimate of prevalence owing to the persistence of scabetic nodules after successful treatment. We wholeheartedly agree with O’Donnell and colleagues about the importance of improving environmental and living conditions in scabies control, regardless of whether mass drug administration is being considered.
Backhaul Aware User-Specific Cell Association Using Q-Learning

With the advent of network densification and the development of other radio interface technologies, the major bottleneck of future cellular networks is shifting from the radio access network to the backhaul. The future networks are expected to handle a wide range of applications and users with different requirements. In order to tackle the problem of downlink user-cell association, and allocate users to the best cell, an intelligent solution based on reinforcement learning is proposed. A distributed solution based on $Q$ -Learning is developed in order to determine the best cell range extension offsets (CREOs) for each small cell (SC) and the best weights of each user requirement to efficiently allocate users to the most appropriate SC, based on both backhaul constraints and user demands. By optimizing both CREOs and user weights, a user-specific allocation can be achieved, resulting in a better overall quality of service. The results show that the proposed algorithm outperforms current solutions by achieving better user satisfaction, mitigating the total number of users in outage, and minimizing user dissatisfaction when satisfaction is not possible.
Experimental and numerical study of high strain rate property of pure copper processed by ECAE method

ABSTRACT In the present study, the impact strength of refined grain pure copper developed by equal channel angular extrusion, up to eight passes at room temperature following route A with a constant ram speed of 20 mm/min through a die angle of 90° between the die channels was investigated. The impact strength of extruded specimens is evaluated for the as-received and the extruded specimens after four and eight passes at a strain rate of 1200 s−1 using Split–Hopkinson pressure bartechniques by experiment and the finite element method simulation. A total increase in the ultimate strength and yield strength are 128% and 68%, respectively, and observed for the extruded material after eight passes. Optical microscopic examinations show a grain refinement from 32 to 4 µm. The results of the numerical study are consistent with that obtained from the experiment.
Tropifexor‐Mediated Abrogation of Steatohepatitis and Fibrosis Is Associated With the Antioxidative Gene Expression Profile in Rodents

Farnesoid X receptor (FXR) agonism is emerging as an important potential therapeutic mechanism of action for multiple chronic liver diseases. The bile acid‐derived FXR agonist obeticholic acid (OCA) has shown promise in a phase 2 study in patients with nonalcoholic steatohepatitis (NASH). Here, we report efficacy of the novel nonbile acid FXR agonist tropifexor (LJN452) in two distinct preclinical models of NASH. The efficacy of tropifexor at <1 mg/kg doses was superior to that of OCA at 25 mg/kg in the liver in both NASH models. In a chemical and dietary model of NASH (Stelic animal model [STAM]), tropifexor reversed established fibrosis and reduced the nonalcoholic fatty liver disease activity score and hepatic triglycerides. In an insulin‐resistant obese NASH model (amylin liver NASH model [AMLN]), tropifexor markedly reduced steatohepatitis, fibrosis, and profibrogenic gene expression. Transcriptome analysis of livers from AMLN mice revealed 461 differentially expressed genes following tropifexor treatment that included a combination of signatures associated with reduction of oxidative stress, fibrogenesis, and inflammation. Conclusion: Based on preclinical validation in animal models, tropifexor is a promising investigational therapy that is currently under phase 2 development for NASH.
USING TAGUCHI, PCR-TOPSIS AND GENETIC ALGORITHM TO OPTIMISE OF DIAMETRAL TENSILE STRENGTH AND DENSITY OF PMMA-ZIRCONIA COMPOSITE

Acrylic bone cement of PMMA (poly-methylmethacrylate) have been used for more than 40 years in dental implant and orthopedic surgery for the fixation of artificial joints. Zirconia (ZrO2) has been widely used in medical application because of their chemical and physical properties such as good mechanical strength and good thermal stability. In this study, acrylic bone cement reinforced with bioceramic was prepared by mixing beads of PMMA and ZrO2 powder. Diametral tensile strength and density were tests to determine the mechanical properties of PMMA-ZrO2 composite. Simultaneous optimization of diametral tensile strength and density of PMMA-ZrO2 composite were done by Taguchi method, PCR-TOPSIS and genetic algorithm. Experiments have been carried out using Taguchi L16(45 ) orthogonal arrays. Experimental data have been transformed into new data set using SNR and PCR-TOPSIS. The new data set were used to find simultaneous optimization of DTS and density of PMMA-ZrO2 composite using genetic algorithm. According to the hybrid method, global optimum of DTS and density of PMMA-ZrO2 composite are 8.758 MPa and 68.8 %. This result occurred when 5 wt% ZrO2 in PMMA, sintering temperature at 1570C and holding time at 75 minutes.
Surface Heat Budget over the North Sea in Climate Change Simulations

An ensemble of regional climate change scenarios for the North Sea is validated and analyzed. Five Coupled Model Intercomparison Project Phase 5 (CMIP5) General Circulation Models (GCMs) using three different Representative Concentration Pathways (RCPs) have been downscaled with the coupled atmosphere–ice–ocean model RCA4-NEMO. Validation of sea surface temperature (SST) against different datasets suggests that the model results are well within the spread of observational datasets. The ensemble mean SST with a bias of less than 1 ∘ C is the solution that fits the observations best and underlines the importance of ensemble modeling. The exchange of momentum, heat, and freshwater between atmosphere and ocean in the regional, coupled model compares well with available datasets. The climatological seasonal cycles of these fluxes are within the 95% confidence limits of the datasets. Towards the end of the 21st century the projected North Sea SST increases by 1.5 ∘ C (RCP 2.6), 2 ∘ C (RCP 4.5), and 4 ∘ C (RCP 8.5), respectively. Under this change the North Sea develops a specific pattern of the climate change signal for the air–sea temperature difference and latent heat flux in the RCP 4.5 and 8.5 scenarios. In the RCP 8.5 scenario the amplitude of the spatial heat flux anomaly increases to 5 W/m 2 at the end of the century. Different hypotheses are discussed that could contribute to the spatially non-uniform change in air–sea interaction. The most likely cause for an increased latent heat loss in the central western North Sea is a drier atmosphere towards the end of the century. Drier air in the lee of the British Isles affects the balance of the surface heat budget of the North Sea. This effect is an example of how regional characteristics modulate global climate change. For climate change projections on regional scales it is important to resolve processes and feedbacks at regional scales.
Addressing Couple Violence in Therapy Training Clinics: A Human-Centered Approach to Systems Intervention Design

Abstract This report describes the development and implementation of a multitiered intake-to-treatment pipeline system at a clinical training facility to bridge a gap in the service needs of clients seeking couples’ therapy who experience varying degrees of violence by ensuring that all couples receive some form of assistance. Prospective clients undergo multiple checkpoints for separate screening and assessment protocols and obtain individualized referrals and treatment options. We discuss the application of a human-centered program design and the use of task-shifting to increase adaptability of this system to other clinical settings and lessen the barriers to quality care experienced by couples who seek treatment.
Efficient digital to analog encoding

An important issue in analog circuit design is the problem of digital to analog conversion, namely, the encoding of Boolean variables into a single analog value which contains enough information to reconstruct the values of the Boolean variables. Wegener (1996) proved that [3n-1/2] 2-input arithmetic gates are necessary and sufficient for implementing the encoding function of n Boolean variables. However, the proof of the upper bound is not constructive. We present an explicit construction of a digital to analog encoder that is optimal in the number of 2-input arithmetic gates.
Real-Time Human Detection & Tracking System Incorporating MEMS Technology

Module enabled with Micro Electro Mechanical Systems (MEMS) thermal sensor will locate and track intruders, even if they are steady and simultaneously facilitate real time transmission of detected information and requisite coordinates to the far end control station via GSM network. Additionally, the application can also identify the alive or injured bodies in relief operations during disasters/natural calamities. This sensor detects the infrared light emitted by human beings within its field of view (FOV). The detector elements in sensor along with signal processing circuitry unit calculates temperature in °C. On the basis of the temperature values calculated from each detector elements (i.e. thermocouples), the LED's corresponding to the elements are triggered on/off displaying the direction of object being sensed. Simultaneously, GPS module sends real time tracking location in National Marine Electronics Association (NMEA) sentence format to the microcontroller. Both Sensor and GPS data are then transmitted as an SMS script via GSM module to provide tracking information to the control station.
On the Realism of the Rain Microphysics Representation of a Squall Line in the WRF Model. Part II: Sensitivity Studies on the Rain Drop Size Distributions

A comparison between retrieved properties of the rain drop size distributions (DSDs) from multifrequency cloud radar observations and WRF Model results using either the Morrison or the Thompson bulk microphysics scheme is performed in order to evaluate the model’s ability to predict the rain microphysics. This comparison reveals discrepancies in the vertical profile of the rain DSDs for the stratiform region of the squall-line system observed on 12 June 2011 over Oklahoma. Based on numerical sensitivity analyses, this study addresses the bias at the top of the rain layer and the vertical evolution of the DSD properties (i.e., of Dm and N0*). In this way, the Thompson scheme is used to explore the sensitivity to the melting process. Moreover, using the Thompson and Morrison schemes, the sensitivity of the DSD vertical evolution to different breakup and self-collection parameterizations is studied. Results show that the DSDs are strongly dependent on the representation of the melting process in the Thompson scheme. In the Morrison scheme, the simulations with more efficient breakup reproduce the DSD properties with better fidelity. This study highlights how the inaccuracies in simulated Dm and N0* for both microphysics schemes can impact the evaporation rate, which is systematically underestimated in the model.
Genome-wide identification and expression analysis of aquaporins in salt cress (Eutrema salsugineum)

Aquaporins (AQPs) serve as water channel proteins and belong to major intrinsic proteins (MIPs) family, functioned in rapidly and selectively transporting water and other small solutes across biological membranes. Importantly, AQPs have been shown to play critical roles in abiotic stress response of plants. Eutrema salsugineum is close to Arabidopsis thaliana and proposed as a model system for studying plant salt resistance. Here we identified 35 full-length AQP genes in E. salsugineum. Phylogenetic analysis showed EsAQPs were similar with AtAQPs and grouped into four subfamilies including 12 plasma membrane intrinsic proteins (PIPs), 11 tonoplast intrinsic proteins (TIPs), 9 NOD-like intrinsic proteins (NIPs), and 3 small basic intrinsic proteins (SIPs). Gene structure, also the conserved motifs (MEME) of EsAQPs in each subfamily shared high similarities. In detailed sequence analysis, EsAQPs comprised 237-323 amino acids, with a theoretical molecular weight (MW) of 24.31-31.80 kDa and an isoelectric point (pI) value of 4.73-10.49. Functional prediction based on the NPA motif, aromatic/arginine (ar/R) selectivity filter, Froger’s position and specificity-determining position suggested there was a big difference in the specificity of substrate transport between EsAQPs. Gene expression profiles illustrated EsAQP genes could be detected in all organs and appear to play an important role in response salt, cold and drought signals. These results will bring a better understanding on the characterizations of AQPs in E. salsugineum and its complex transport networks in homeostasis control.
Application of Text Mining on the Editorial of a Newspaper of Bangladesh

The development in the fields of web, digital libraries, technical documentation, and medical data has made it easier to access a larger amount of a textual document. Owing to the increasing demands to obtain knowledge from a large number of textual documents accessible on the web, text mining is gaining significant importance. The aim of this paper is to find the topmost ten frequent words of the whole writing of the Editorial writing appeared in the most popular Bangladeshi Daily English newspaper entitled “The Daily Star” over the period 01 January 2018 to 30 June 2018. Finally, we representation of text data visually with the help of word cloud. The results indicate that the most frequent word highlighted is ‘government’ in the writing of all months considered in this study. Also, the words “Bangladesh”, “Myanmar”, “people”, “must” and “will” emerge in the analysis.
On the suitability of Data Selection for Cross-building Knowledge Transfer

Supervised deep learning has achieved remarkable success in various applications. Such advances were mainly attributed to the rise of computational powers and the amounts of training data made available. Therefore, accurate large-scale data collection services are often needed. Once representative data is retrieved, it becomes possible to train the supervised machine learning predictor. However, a model trained on existing data, that generally comes from multiple datasets, might generalize poorly on the unseen target data. This problem is referred to as domain shift. In this paper, we explore the suitability of data selection to tackle the domain shift challenge in the context of domain generalization. We perform our experimental study on the use case of building energy consumption prediction. Experimental results suggest that minimal building description is capable of improving cross-building generalization performances when used to select data.
Demonstration of an Ultraprecise Optical Bench for the Taiji Space Gravitational Wave Detection Pathfinder Mission

For the Taiji space gravitational wave detection and its pathfinder mission, ultraprecise technology for optical assembly must be adopted to satisfy the high measurement sensitivities required. In this paper, we construct and evaluate an optical bench to assess its precision in optical assembly and bonding. We opted for hydroxide-catalysis bonding technology and designed a corresponding adjustment scheme to achieve an ultraprecise assembly for the optical bench. After alignment, the angular deviation between the interference beam and its ideal position in the global coordinate system is no more than 45 μrad, and positional deviation is less than 10 μm. The final experimental results indicate that the measurement precision of the evaluated board is 6 pm/√Hz, which meets the specifications required for the Taiji pathfinder.
Cost-effective deadbeat current control for wind-energy inverter application with LCL filter

This paper proposes analytical expressions of a deadbeat control system for the output LCL filter in a grid-connected inverter with a settling time of three sampling periods. Unlike the conventional analysis, the proposed control algorithm is derived in the descritised time-domain instead of the s-domain. Furthermore, not only integrated active power but also reactive power can be controlled independently. Experimental results of the proposed deadbeat control system indicate that the feedback of both grid and converter currents have the same highest capabilities of attenuating switching frequency components and damping resonance. This is due to the undetected two remained control variables are possible to be estimated in the DSP.
Blockchain Enabled E-Voting System

- The term “vote” is referred as to opt among the eligible candidates. The prime objective of registering a vote is to pick the best among the capable candidates who can serve the people in better way. Many nations like India have several obstacles during the elections. Several obstacles includes turn-over of votes at the time of election, uncertain or isolated polling terminals, poor polling stuff and lack of workforce skills. Developing a secure smart electing approach to offers simplicity & confidentiality over existing voting systems. It is challenging to provide flexibility through present voting systems. Using block chain as technique to enact this approach. Blockchain have few drawbacks in current approach and assess major block chain architecture for implementing a blockchain-enabled electing approach. This approach enhances safety and minimizes price of conducting elections.
LTR_FINDER: an efficient tool for the prediction of full-length LTR retrotransposons

Long terminal repeat retrotransposons (LTR elements) are ubiquitous eukaryotic transposable elements. They play important roles in the evolution of genes and genomes. Ever-growing amount of genomic sequences of many organisms present a great challenge to fast identifying them. That is the first and indispensable step to study their structure, distribution, functions and other biological impacts. However, until today, tools for efficient LTR retrotransposon discovery are very limited. Thus, we developed LTR_FINDER web server. Given DNA sequences, it predicts locations and structure of full-length LTR retrotransposons accurately by considering common structural features. LTR_FINDER is a system capable of scanning large-scale sequences rapidly and the first web server for ab initio LTR retrotransposon finding. We illustrate its usage and performance on the genome of Saccharomyces cerevisiae. The web server is freely accessible at http://tlife.fudan. edu.cn/ltr_finder/.
Convex relaxation for optimal power flow problem: Mesh networks

This paper is concerned with a fundamental resource allocation problem for electrical power networks. This problem, named optimal power flow (OPF), is nonconvex due to the nonlinearities imposed by the laws of physics, and has been studied since 1962. We have recently shown that a convex relaxation based on semidefinite programming (SDP) is able to find a global solution of OPF for IEEE benchmark systems, and moreover this technique is guaranteed to work over acyclic (distribution) networks. The present work studies the potential of the SDP relaxation for OPF over cyclic (transmission) networks. Given an arbitrary weakly-cyclic network with cycles of size 3, it is shown that the injection region is convex in the lossless case and that the Pareto front of the injection region is convex in the lossy case. It is also proved that the SDP relaxation of OPF is exact for this type of network. Moreover, it is shown that if the SDP relaxation is not exact for a general mesh network, it would still have a low-rank solution whose rank depends on the structure of the network. Finally, a heuristic method is proposed to recover a rank-1 solution for the SDP relaxation whenever the relaxation is not exact.
Deep Auto‐encoded Clustering Algorithm for Community Detection in Complex Networks

The prevalence of deep learning has inspired innovations in numerous research fields including community detection, a cornerstone in the advancement of complex networks. We propose a novel community detection algorithm called the Deep auto-encoded clustering algorithm (DAC), in which unsupervised and sparse single autoencoders are trained and piled up one after another to embed key community information in a lower-dimensional representation, such that it can be handled easier by clustering strategies. Extensive comparison tests undertaken on synthetic and real world networks reveal two advantages of the proposed algorithm: on the one hand, DAC shows higher precision than the k -means community detection method benefiting from the integration of sparsity constraints. On the other hand, DAC runs much faster than the spectral community detection algorithm based on the circumvention of the time-consuming eigenvalue decomposition procedure.
Mediated experts for deep convolutional networks

We present a new supervised architecture termed Mediated Mixture-of-Experts (MMoE) that allows us to improve classification accuracy of Deep Convolutional Networks (DCN). Our architecture achieves this with the help of expert networks: A network is trained on a disjoint subset of a given dataset and then run in parallel to other experts during deployment. A mediator is employed if experts contradict each other. This allows our framework to naturally support incremental learning, as adding new classes requires (re-)training of the new expert only. We also propose two measures to control computational complexity: An early-stopping mechanism halts experts that have low confidence in their prediction. The system allows to trade-off accuracy and complexity without further retraining. We also suggest to share low-level convo-lutional layers between experts in an effort to avoid computation of a near-duplicate feature set. We evaluate our system on a popular dataset and report improved accuracy compared to a single model of same configuration.
Allocation of invasive plant management expenditures for conservation: Lessons from Florida, USA

Although the ecological impacts of biological invasions are well studied, comprehensive analyses of spending on invasive species management are lacking. Such analyses could inform both effective resource allocation and management planning. We evaluated long‐term invasive plant management expenditures and their potential geographic, economic, and ecological drivers for freshwater and terrestrial conservation areas in Florida, USA. Average expenditures for managing invaders were approximately US$45M annually, with over 90% of funding provided by the state. Our model showed that expenditures were best predicted by the prevalence of waterways and abundance of invaders, indicating that funding was allocated towards asset protection in highly invaded aquatic and terrestrial habitats. Higher spending was directly correlated with reduced area invaded for the costliest invader (Hydrilla verticillata, ~$10M/year), demonstrating management efficacy and constructive use of resources. Our study highlights that significant funding is required to manage plant invaders in Florida and that greater funding would likely limit the extent of invasions. Additional analyses of management cost‐effectiveness for Florida and other regions would benefit from consistent collection and reporting of high‐resolution management data. Given the exponential rate of spread of many invaders, additional and sustained management funding is needed for early detection and rapid, effective control of invasive species.