Miracle-dz/UniFAR-FTUs huggingface dataset:
Changes in natural transformation after salt adaptation

The exchange of genes between potentially unrelated bacteria is termed horizontal gene transfer (HGT) and is a driving force in bacterial evolution. Natural transformation is one mechanism of HGT where extracellular DNA (eDNA) from the environment is recombined into a host genome. The widespread conservation of transformation in bacterial lineages implies there is a fitness benefit. However, the nature of these benefits and the evolutionary origins of transformation are still unknown. Here, I examine how ∼330 generations or 100 days of serial passage in either constant or increasing salinities impacts the growth rate and transformation efficiency of Pseudomonas stutzeri. While the growth rate generally improved in response to serial transfer, the transformation efficiency of the evolved lineages varied extensively, with only 39-64% of populations undergoing transformation at the end of adaptive evolution. In comparison, 100% of the ancestral populations were able to undergo natural transformation. I also found that evolving P. stutzeri with different cell lysates (or populations of dead cells) minimally affected the growth rate and transformation efficiency, especially in comparison to the pervasiveness with which transformation capacity was lost across the evolved populations. Taken together, I show that the efficiency of eDNA uptake changes over relatively rapid timescales, suggesting that transformation is an adaptive and selectable trait that could be lost in environments where it is not beneficial.
Non‐Model‐Based Estimation for Velocity and Acceleration by Adaptive Identification Method

The design of an ideal differentiator is a difficult and a challenging task. In this paper, we propose two types of differentiators to estimate the time derivative of an output signal. To begin with, a velocity estimator is proposed on the basis of the adaptive identification method and compared with the exact differentiator proposed by Levant. Then, an acceleration estimator is proposed with the nonpassifiable adaptive schemes. Finally, the proposed differentiators are applied to the estimation of the speed and acceleration of the mass–spring system. The simulation results show that the proposed differentiators are robust to additive noises. Copyright © 2010 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
OPTIONS FOR IMPROVING THE ENERGY EFFICIENCY OF TELECOMMUNCIATION NETWORKS

The energy consumption of telecommunication networks has gained increasing interest throughout the recent past: Besides its environmental implications it has been identified to be a major contributor to operational expenditures of network operators. Targeting at sustainable telecommunication networks, thus, it is important to find appropriate strategies for improving their energy efficiency before the background of rapidly increasing traffic volumes. Besides the obvious benefits of increasing energy efficiency of network elements by leveraging technology progress, load-adaptive network operation is a very promising option, i.e. using network resources only to an extent and for the time they are actually needed. In contrast, current network operation takes almost no advantage of the strongly time-variant behaviour of the network traffic load. Mechanisms for energy-aware load-adaptive network operation can be subdivided in techniques based on local autonomous or per-link decisions and in techniques relying on coordinated decisions incorporating information from several links. For the transformation from current network structures and operation paradigms towards energy-efficient and sustainable networks it will be essential to use energy-optimized network elements as well as including the overall energy consumption in network design and planning phases together with the energy-aware load-adaptive operation. In load-adaptive operation it will be important to establish the optimum balance between local and overarching power management concepts in telecommunication networks.
Effect of lyophilization and spray-drying on cytokine levels and antioxidant capacity in human milk

Abstract Human milk is important for modulating the newborn’s immune and antioxidant response. The applicability of lyophilization and spray-drying processes in human milk donated to human milk banks are alternatives for storage and distribution when breastfeeding is not possible. Therefore, the aim of this work was to detect and quantify cytokines (IFN-ɣ, IL-4, IL-5, IL-10, IL-13, IL-17 A/F, IL-21, and IL-22) and to evaluate the antioxidant capacity in different phases of donated HM submitted to lyophilization and spray-drying processes. The HM donated in natura was submitted to pasteurization, lyophilization and spray-drying processes, then cytokine concentrations were measured using Luminex technology and antioxidant capacity of the superoxide dismutase and catalase enzymes and reduced glutathione levels in vitro. Cytokine profiles, catalase, superoxide dismutase and reduced glutathione profiles were preserved after processing of lyophilization and spray-drying in all phases of donated human milk.
“Slowly But Sure”: A Language Deficit of a Child with Down Syndrome

This is a case study research in this paper reports on findings from the case study (syntactic and morphological) in an individual diagnosed with a genetic disorder, Down Syndrome (DS), that affect language development without getting the help of any official therapists. We perform pragmatic and phonological analyses alongside morphology and syntactic analyses on data collected from studying documents, observation, and interview results. The qualitative descriptive linguistic method is used in this study to provide a detailed description. The result shows some deficits performed by the DS caused by articulatory limitation, difficulty to follow some words in long statements, and difficulty to respond to the topic that he has not known before.
Preventing unnecessary imaging in patients suspect of coronary artery disease through machine learning of electronic health records

Abstract Aims With the ageing European population, the incidence of coronary artery disease (CAD) is expected to rise. This will likely result in an increased imaging use. Symptom recognition can be complicated, as symptoms caused by CAD can be atypical, particularly in women. Early CAD exclusion may help to optimize use of diagnostic resources and thus improve the sustainability of the healthcare system. To develop sex-stratified algorithms, trained on routinely available electronic health records (EHRs), raw electrocardiograms, and haematology data to exclude CAD in patients upfront. Methods and results We trained XGBoost algorithms on data from patients from the Utrecht Patient-Oriented Database, who underwent coronary computed tomography angiography (CCTA), and/or stress cardiac magnetic resonance (CMR) imaging, or stress single-photon emission computerized tomography (SPECT) in the UMC Utrecht. Outcomes were extracted from radiology reports. We aimed to maximize negative predictive value (NPV) to minimize the false negative risk with acceptable specificity. Of 6808 CCTA patients (31% female), 1029 females (48%) and 1908 males (45%) had no diagnosis of CAD. Of 3053 CMR/SPECT patients (45% female), 650 females (47%) and 881 males (48%) had no diagnosis of CAD. On the train and test set, the CCTA models achieved NPVs and specificities of 0.95 and 0.19 (females) and 0.96 and 0.09 (males). The CMR/SPECT models achieved NPVs and specificities of 0.75 and 0.041 (females) and 0.92 and 0.026 (males). Conclusion Coronary artery disease can be excluded from EHRs with high NPV. Our study demonstrates new possibilities to reduce unnecessary imaging in women and men suspected of CAD.
INMI1 Zika Virus NS4B Antagonizes the Interferon Signaling by Suppressing STAT1 Phosphorylation

The evasion of the Interferon response has important implications in Zika virus (ZIKV) disease. Mutations in ZIKV viral protein NS4B, associated with modulation of the interferon (IFN) system, have been linked to increased pathogenicity in animal models. In this study, we unravel ZIKV NS4B as antagonist of the IFN signaling cascade. Firstly, we reported the genomic characterization of NS4B isolated from a strain of the 2016 outbreak, ZIKV Brazil/2016/INMI1, and we predicted its membrane topology. Secondly, we analyzed its phylogenetic correlation with other flaviviruses, finding a high similarity with dengue virus 2 (DEN2) strains; in particular, the highest conservation was found when NS4B was aligned with the IFN inhibitory domain of DEN2 NS4B. Hence, we asked whether ZIKV NS4B was also able to inhibit the IFN signaling cascade, as reported for DEN2 NS4B. Our results showed that ZIKV NS4B was able to strongly inhibit the IFN stimulated response element and the IFN-γ-activated site transcription, blocking IFN-I/-II responses. mRNA expression levels of the IFN stimulated genes ISG15 and OAS1 were also strongly reduced in presence of NS4B. We found that the viral protein was acting by suppressing the STAT1 phosphorylation and consequently blocking the nuclear transport of both STAT1 and STAT2.
Driver Distraction Detection and Classification using Machine Learning

: One of the most critical problem overcome in India is death caused by the road accidents. Almost 80% of accidents are happen due to driver distraction. We attempt to develop robust system for detecting Driver distraction. There are some methods to detect it but that consumes more time. It is important to early detect the distraction, inform to the driver about the distraction. So, computer-based application needs to be developed to detect this distraction as early as possible and minimize the risk of accidents. The aim is to develop a simple and capable method to detect the distraction. There are some proposed methods that contain following stages, preprocessing, feature extraction and classification. To increase the accuracy of the result we use two layer neural network. ELM, softmax are used for feature extraction and SVM-ELM and method is used for classification. The accuracy of proposed method is 97.2% which shows its reliability.
Automated CNN Based Coral Reef Classiﬁcation Using Image Augmentation and Deep Learning

A critical issue faced by the marine scientist is to classify underwater images describing coral benthic cover. Typically, scientists take underwater imagery using high-resolution cameras and further analysis on these corals and marine species is done on land (preferably a laboratory) and by visual inspection. However, the analysis is time consuming, since the ﬁrst step, which is the classiﬁcation of corals, is an intensive activity by taxonomic experts. This traditional manual classiﬁcation method is difﬁcult to automate or quicken which is problematic given the high volume of images. In this work, the fundamental analysis is discussed by using available techniques such as deep learning (DL) and Convolutional Neural Network (CNN). It is required to ﬁnd an easier, efﬁcient and faster way to automate the classiﬁcation of corals. This task is complicated, since most of the common coral species look similar to one another. For reasons of structural diversity, it is easier to differentiate other forms of marine life such as ﬁsh and stingrays. This paper is based on the difﬁcult but important Scleractinian (Stony) corals only. A technique recommended is investigated further at structural level such as branching corals. Veriﬁcation result proves that the training and testing data are almost similar, thus the proposed technique is capable to learn and predict correctly.
Performance Analysis of IRS-Aided NOMA Communications in the Presence of Imperfect SIC

The intelligent reflecting surface (IRS) is expected to be a promising technique to achieve a robust spectrum and energy efficiency. This paper investigates the advantages of IRS in enhancing performance of non-orthogonal multiple access (NOMA) communications in the presence of imperfect successive-interference-cancellation (SIC) and phase distortion (PD) caused by a non-ideal IRS. Specifically, average achievable rates (AARs) of the users are the target performance metrics. For performance evaluation, the probabilistic characterizations of signal-to-interference-plus-noise ratios (SINRs) at the users are studied. These results allow for deriving the theoretical formulas for the AAR. Monte Carlo simulations are adopted to verify the accuracy of these theoretical results. The numerical results show the effects of various key system parameters, such as source transmit power, NOMA power allocation (PA) factors, reflecting tile (RTs) allocation, the SIC imperfection factor, and the PD factor, on the AAR that provide useful information for the system’s design.
Managerial Input and Firm Performance. Evidence from a Policy Experiment∗

We study the effects of a subsidy program designed to boost the export capabilities of SMEs by hiring of a consultant (a Temporary Export Manager—TEM), who works for at least 6 months to provide advice and guidance on how reach foreign markets. Firms applied online for the subsidy, and vouchers to hire TEMs were allocated on a first-come, first-served basis. We use a local difference-in-differences design to compare the performances of firms who nearly got the subsidy with those that barely did not. Eligible firms experienced an increase in revenues, ROE, profits and value added per employee. These performances were accompanied by a significant growth in export in extra-EU markets in the fourth year after receiving the subsidy. Before starting to export, TEMs were also effective in stimulating ‘good’ labor demand, with an overall increase in their workforce of about 13%, mainly composed by full time and permanent employees. J.E. L. codes: L2, L38, 040, F14, H2, F2
Exponential Separation between Two Learning Models and Adversarial Robustness

We prove an exponential separation for the sample/query complexity between the standard PAC-learning model and a version of the Equivalence-Query-learning model. In the PAC model all samples are provided at the beginning of the learning process. In the Equivalence-Query model the samples are acquired through an interaction between a teacher and a learner, where the teacher provides counterexamples to hypotheses given by the learner. It is intuitive that in an interactive setting fewer samples are needed. We make this formal and prove that in order to achieve an error ✏ exponentially (in ✏ ) fewer samples sufﬁce than what the PAC bound requires. It was shown experimentally by Stutz, Hein, and Schiele that adversarial training with on-manifold adversarial examples aids generalization (compared to standard training). If we think of the adversarial examples as counterexamples to the current hypothesis then our result can be thought of as a theoretical conﬁrmation of those ﬁndings. We also discuss how our result relates to adversarial robustness. In the standard adversarial model one restricts the adversary by introducing a norm constraint. An alternative was pioneered by Goldwasser et. al. Rather than restricting the adversary the learner is enhanced. We pursue a third path. We require the adversary to return samples according to the Equivalance-Query model and show that this leads to robustness. Even though our model has its limitations it provides a fresh point of view on adversarial robustness.
Distributed First-Order Optimization with Tamed Communications

—Many machine learning and signal processing applications involve high-dimensional nonsmooth optimization problems. The nons-moothness is essential as it brings a low-dimensional structure to the optimal solutions, as (block, rank, or variation) sparsity. In this work, we exploit this nonsmoothness to reduce the communication cost of optimization algorithms solving these problems in a distributed setting. We introduce two key ideas: i) a random subspace descent algorithm; ii) an adaptative subspace selection based on sparsity identiﬁcation of the proximal operator. We get signiﬁcant performance improvements in terms of convergence with respect to data exchanged.
Design of flextensional transducers using homogenization design method

Flextensional transducers consist of a piezoceramic (or a stack of piezoceramics) connected to a flexible mechanical structure that converts and amplifies the output displacement of the piezoceramic. Among the applications, they can be used as actuators and sonars. The transducer performance in these application depends on the distribution of mass, stiffness, and flexibility in the coupling structure domain, which is related to the coupling structure topology. By designing other types of coupling structures connected to the piezoceramic, we can obtain new types of flextensional transducers with enhanced performance for a desired application. In this work, we extended the method for designing flextensional transducers in static and low-frequency applications to dynamic applications by introducing the inertia effect in the optimization problem. The method applies topology optimization technique based on the homogenization design method, which consists of finding the optimal material distribution in a perforated design domain with infinite microscale voids. The problem is posed as the design of a flexible structure coupled to the piezoceramic that produces high output displacements in a specified point of the domain and direction, in a specified frequency. Therefore, it consists of designing a flexible structure with a specified mode shape in a desired resonance frequency. As a result, designs of flextensional transducers are presented.
Performance limitations of free space optical communication satellite networks due to vibrations: heterodyne detection

Free space optical communication between satellites networked together can permit high data rates between different places on earth. In order to establish optical communication between two satellites, the line of sight of their optics must be aligned during the entire communication time. Due to the large distance between the satellites and the required accuracy, the pointing from one satellite to another is complicated. The problem is further complicated due to vibrations of the pointing system caused by two fundamental stochastic mechanisms: 1) tracking noise created by the electro-optic tracker and 2) vibrations deriving from mechanical mechanisms. Vibration of the transmitter beam in the receiver plane causes a decrease in the received optical power. Vibrations of receiver telescope relative to the received beam decrease the heterodyne mixing efficiency. These two factors increase the bit error rate (BER) of the network. In this work we derive simple mathematical models of the network BER versus the system parameters, and the transmitter and receiver vibration statistics. Considering this simple model, we propose four methods to decrease the effects of the vibration on the network performance and to converge to desired performance requirements. An example of a practical optical heterodyne free space satellite optical communication network is presented. From this research it is clear that even low amplitude vibration of the satellite pointing systems decreases dramatically network performance.
A Personalized Intelligent Recommender System Based on Agent Technology

The paper introduces a personalized intelligent recommender system. The system characters include: (1) integrating Agent technology to improve the recommender system reactivity, proactivity and autonomy; (2) giving a mixed recommendation method with content-based recommendation; (3) proposing the personalized user model, which can describe long-term interests and short-term interests, effectively deal with the user interests drift problem.
New formula to calculate the skin effect in isolated tubular conductors at low frequencies

The exact solution of the AC/DC resistance ratio of an isolated tubular conductor involves the calculation of values from 12 Kelvin functions. Previous approximate solutions are complicated, and graphical solutions require interpolation from sets of curves. A simplified solution, based on a new formula, is presented. The values of the resistance ratio derived from this formula are within /spl plusmn/1%, of those derived from the exact solution in the practical ranges 0.05/spl les/(thickness/diameter)/spl les/0.40 and 0 2000. For practical tubular conductors, the formula can be used for frequencies up to about 10 kHz, depending on the thickness t and the outer diameter D/sub 2/. The application of the skin effect formula to a steel-cored conductor can result in significant error.
Estimation of the Local and Global Coherence of Ukrainian Texts Using Transformer-Based, LSTM, and Graph Neural Networks

In this paper, the different models for the estimation of both local and global coherence of Ukrainian-languagetextshavebeenconsidered. Inordertoevaluatethelocalcoherenceofadocument, Transformer-based and LSTM neural networks have been proposed with further training on a Ukrainian-language news corpus. It has been shown that the LSTM-based approach outperforms the corresponding network based on the Transformer architecture according to the accuracy metrics while solving typical tasks on both test datasets. In order to investigate the connection between sentences revealed by the neural network, the Uniform Manifold Approximation and Projection dimension reduction technique has been utilized for the projection of sentences’ embedding into 2D space. The clusters obtained may indicate the consideration of both the structure of a sentence and different types of connections between them by the designed model. In order to estimate the global coherence of a document, a model based on a graph convolutional neural network has been suggested. The appropriateness of taking into account the connection between all sentences despite their positions has been shown. The results obtained for the designed and trained global coherence estimation model may indicate the different aspects of the analysis of a text by the designed models that can lead to the usage of both local and global coherence estimation models according to an assigned task.
Winglet Toe-Out Angle Optimization for the Gates Learjet Longhorn Wing

The Gates Learjet Model 28 Longhorn was the first turbojet to be certified with winglets. It has now been joined by the stand-up cabin Model 55 in applying NASA advanced technology to American business jet aircraft. This paper describes the effect of tailoring toe-out (incidence) angle for best winglet performance. A winglet with little or no twist must be toed out to reduce separation at the root on the upper surface because of the large inflow angles near the upper surface of the wing at the tip. The need for toe out disappears rapidly with radial distance from the chord of the wing tip due to the reduction in local flow angles to freestream flow angles. Results of wind tunnel testing and flight testing will be presented and their relative merits weighed. Some criteria for useful winglet design will be suggested.