Miracle-dz/UniFAR-FTUs huggingface dataset:
Non-conventional therapeutic technique to replace CRISPR bacteria from biofilm by inducible lysogen

ABSTRACT Bacteriophage can be an effective means of regulating bacterial populations when conditions allow phage invasion of bacterial colonies. Phage can either infect and lyse a host cell, or insert their DNA into the host cell genome; the latter process is called lysogeny. The clustered regularly interspaced short palindromic repeat (CRISPR) system, linked with CRISPR-associated (Cas) genes, is a regulatory system present in a variety of bacteria which confers immunity against bacteriophage. Studies of the group behaviour of bacteria with CRISPR/Cas systems have provided evidence that CRISPR in lysogenized bacteria can cause an inability to form biofilm. This allows CRISPR-immune bacteria in biofilms to effectively resist phage therapy. Our recent work has described a potential therapeutic technique to eradicate CRISPR-immune bacteria from a biofilm by a continuous influx of lysogens carrying an identical phage sequence. However, this model predicted that the CRISPR-immune population could persist for long times before eradication. Our current focus is on the use of diverse lysogens against CRISPR-capable bacterial populations. The goal of this work is to find a suitable strategy which can eradicate bacteria with a CRISPR system through the influx of finite amounts of distinct lysogens over fixed intervals.
A HMM-R Approach to Detect L-DDoS Attack Adaptively on SDN Controller

A data center network is vulnerable to suffer from concealed low-rate distributed denial of service (L-DDoS) attacks because its data flow has the characteristics of data flow delay, diversity, and synchronization. Several studies have proposed addressing the detection of L-DDoS attacks, most of them are only detect L-DDoS attacks at a fixed rate. These methods cause low true positive and high false positive in detecting multi-rate L-DDoS attacks. Software defined network (SDN) is a new network architecture that can centrally control the network. We use an SDN controller to collect and analyze data packets entering the data center network and calculate the Renyi entropies base on IP of data packets, and then combine them with the hidden Markov model to get a probability model HMM-R to detect L-DDoS attacks at different rates. Compared with the four common attack detection algorithms (KNN, SVM, SOM, BP), HMM-R is superior to them in terms of the true positive rate, the false positive rate, and the adaptivity.
Welfare of layers housed in small furnished cages on Danish commercial farms: the condition of keel bone, feet, plumage and skin

ABSTRACT 1. An on-farm assessment of keel bone, feet, plumage and skin damage was conducted in layers in small furnished cages to evaluate welfare implications. 2. Thirteen farms participated, all housing Lohmann LSL layers in systems allowing 8 or 10 hens per cage. 3. Each flock was visited at three different ages: 32, 62 and 77 weeks. During each visit, 100 hens were examined for keel bone fractures and deviations, plumage damage, body wounds, foot pad lesions, bumble feet, missing toes and toe wounds. 4. A deterioration with age of the condition of the plumage and keel bone was found (P < 0.001). At 77 weeks of age, 16% of the birds had poor plumage, and 43% of the birds had keel bone damage. In contrast, foot pad lesions were most prevalent at 32 weeks of age (13%), whereas the other foot injuries and body wounds remained at a relatively low level throughout the three ages examined. 5. Prevention of damage to the keel bone, plumage and foot pads should therefore be considered when aiming to improve welfare of laying hens housed in small furnished cages.
Leader-Following Consensus Control for Complex Heterogeneous Multi-Agent Systems Without Velocity Measurements

This paper considers a leader-following consensus problem for complex heterogeneous multi-agent systems composed of the first-order linear, the second-order linear and the nonlinear Euler-Lagrange agents. First, we design a leader-following consensus control protocol with the active leader for multi-agent systems where the followers cannot obtain the velocity measurements. Then, according to the actual situation, the actuator saturation problem is solved as the special practical nonlinear problem by designing the actuator nonlinear consensus protocol. Based on Graph theory, Lyapunov theory and Barbalat's Lemma, the effectiveness of controller is proved with a second-order dynamic of the leader under the undirected communication topological graph. Some simulation examples verify the validity of the conclusion.
Quantifying Heterogeneous Returns to Genetic Selection: Evidence from Wisconsin Dairies

Estimates of productivity growth in the dairy sector attribute as much as half of observed growth to genetic improvement. Unobserved match quality is an important determinate of genetic selection by dairy farmers that confounds attribution to genetic improvement alone. Using data from a large sample of Wisconsin dairy farms, and national-level data on sire rankings, we develop and estimate a model that accounts for selection behavior, and decompose total productivity change into separate effects for genetic improvement and endogenous selection. We find that selection accounts for as much as 75 percent of the total productivity improvement in our sample. Our results provide evidence for positive assortative matching, whereby farmers who adopt above-average yield genetics also perform better than average for their chosen genetics. Further, we find that management behavior accounts for a significant portion of within-herd cow-level heterogeneity, suggesting that dairy farmers manage their herds at the level of individual cows. Overall, our results indicate that a large portion of productivity growth in dairy farming can be explained by farmers’ ability to identify and select genetics well suited to their production environment.
Emulating a Software Defined LTE Radio Access Network Towards 5G

This paper evaluates the performance of a Software Defined Network (SDN) and the respective OpenFlow (OF) protocol, when supporting a Long Term Evolution (LTE) Radio Access Network (RAN). The main objective is to understand the impact of the new Centralized Radio Access Network (C-RAN) architecture, using a SDN based LTE network (when compared to conventional LTE networks), in a solution towards Fifth Generation (5G) implementation. Several simulations were performed using the SDN emulator Mininet-Wifi platform, which was adapted to produce use cases regarding a SDN based LTE topology. This platform uses WiFi as radio technology. However, and since this is an open source platform, changes were introduced in order to include a LTE RAN parametrization based on LTE technology standards. The latency and throughput are the used Quality of Service (QoS) performance metrics. The throughput results led to spectral efficiency values close to each considered modulation maximum. The obtained average values of latency, 15.72, 16.17 and 16.50 ms for packets of 32, 1000 and 1500 bytes respectively, revealed to be lower than 20 ms, which is the LTE requirement established by 3GPP.
H∞ Control Based on Partitioning the Range of Fuzzy Weights for Uncertain Discrete-Time T-S Fuzzy Systems

This paper proposes a new non-parallel distributed compensation (non-PDC) scheme of $H_{\infty}$ control for uncertain discrete-time Takagi-Sugeno (T-S) fuzzy systems. To utilizing the partition to the range of the fuzzy weights, the proposed scheme sets the variables to be second-order parametric in both the current- and past-time fuzzy weights, which results in a third-order parameterized condition. Then, the scheme uses the elimination lemma, where a decision variable is designed to be constant piecewise by partitioning the range of the parameters, so that the resulting condition is first-order parameterized. Consequently, the switching controller is developed by utilizing the extreme points of each partition. Numerical examples are provided to illustrate the effectiveness of the proposed approach.
The Evolutionary Process of IT Concept Words: A Case Study on IoT

In information technology, new concept words appear every few years and affect the business environment. In several cases, the core technologies and architectures have remained the same despite minor changes in concepts. For example, Bigdata was succeeded by the IoT (Internet of Things). The trend in concept words reveals an evolutionary pattern. In this study, we applied a text mining approach to analyzing all the articles in several popular IT magazines for 15 years, and we proposed a new model of IT concept words transition. The model shows that there are three phases of concept evolution. Some related concepts appeared together in the first phase. In the second phase, such a group of words integrates to form a new concept. Finally, the concept develops into a general word and gains recognition in each industry as a completely specialized concept. This is the third series of research since our papers for PICMET2014 & 2016 focused on Cloud Computing. In this paper, we re-analyze the articles by expanding two more years focusing on Bigdata and IoT in addition to Cloud Computing. As a result, we show that the confidence level of the model is further increased.
Intelligent Inspection System Based on Infrared Vision for Automated Fiber Placement

This paper proposes an intelligent inspection system based on infrared vision for Automated Fiber Placement. It can improve composite materials production quality and production efficiency, shorten process design time, and reduce material waste. The system can realize the defect recognition and defect measurement, which can be used in intelligent decision-making, optimization of multi-parameters and quality traceability. Experiments are conducted to verify the feasibility and effectiveness of the presented system.
ASAR 2018 Competition Page Layout Analysis Using Fully Convolutional Networks

This technical report presents a Fully Convolutional Network based method for layout analysis of benchmarking dataset provided by the competition. The document image is segmented into text and non-text zones by dense pixel prediction. Convolutional part of the network can learn useful features from the document images and is robust to uncontrained layouts. We have evaluated the zone segmentation with average black pixel rate, over-segmentation error, under-segmentation error, correct-segmentation, missed-segmentation error, false alarm error, overall block error rate whereas the zone classification with precision, recall, F1-measure and average class accuracy on both pixel and block levels.
Analysis of Lateral Thermal Coupling for GaN MMIC Technologies

This paper presents a study of the lateral heat propagation in an aluminum gallium nitride/gallium nitride (AlGaN/GaN) heterostructure grown on a silicon carbide substrate. The study is enabled by the design of a temperature sensor that utilizes the temperature-dependent <inline-formula> <tex-math notation="LaTeX">$I$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">$V$ </tex-math></inline-formula> characteristic of a semiconductor resistor, making it suitable for integration in GaN monolithic microwave integrated circuit technologies. Using the sensor, we are able to characterize the thermal transient response and extract lateral thermal time constants from the measurements. Time constants in the range from 25 <inline-formula> <tex-math notation="LaTeX">$\mu \text{s}$ </tex-math></inline-formula> to 1.2 ms are identified. Furthermore, the heat propagation properties are characterized for heat source-to-sensor distances of 86–<inline-formula> <tex-math notation="LaTeX">$484~\mu \text{m}$ </tex-math></inline-formula>, resulting in delay times from 3.5 to <inline-formula> <tex-math notation="LaTeX">$111~\mu \text{s}$ </tex-math></inline-formula>. It is shown that both the time constants and propagation delay increase with temperature. An empirical model of the sensor current versus temperature and voltage is proposed and used to predict the junction temperature of the sensor. The study provides knowledge for heat management design and proposes an integrated temperature measurement solution for future highly integrated GaN applications.
The Impact of Chronic Ozone and Particulate Air Pollution on Mortality in Patients With Sepsis Across the United States

Objective: The impact of chronic exposure to air pollution on mortality in patients with sepsis is unknown. We attempted to quantify the relationship between air pollution, notably excess ozone, and particulate matter (PM), with in-hospital mortality in patients with sepsis nationwide. Methods: The 2011 Nationwide Inpatient Sample (NIS) was linked with ambient air pollution data from the Environmental Protection Agency for both 8-hour ozone exposure and annual mean 2.5-micron PM (PM2.5) pollution levels. A validated severity of illness model for sepsis using administrative data was used to control for sepsis severity. Results: The records of 8 023 590 hospital admissions from the 2011 NIS sample were analyzed. Of these, there were 444 928 patients who met the Angus definition of sepsis, treated in hospitals for which air pollution data were available. The cohort had an overall mortality of 11.2%. After adjustment for severity of sepsis, increasing exposure to ozone pollution was associated with increased risk of mortality (odds ratio [OR]: 1.04 for each 0.01 ppm increase, 95% confidence interval [CI]: 1.03-1.05; P < .01). Particulate matter was not associated with mortality (OR: 0.99 for each 5 µg/m3 increase, 95% CI: 0.97-1.01; P = .28). When stratified by sepsis source, ozone pollution had a higher impact on patients with pneumonia (OR: 1.06, 95% CI: 1.04-1.08; P < .01) compared to those patients without pneumonia (OR: 1.02, 95% CI: 1.01-1.03; P < .01). Conclusion: Exposure to increased levels of ozone but not particulate air pollution was associated with higher risk of mortality in patients with sepsis. This association was strongest in patients with pneumonia but persisted in all sources of sepsis. Further work is needed to understand the relationship between ambient ozone air pollution and sepsis outcomes.
Sexual activity and life satisfaction in older adults

Based on the concept of successful ageing and research reports on the benefits of maintaining sexual activity in mid‐ to late life, the purpose of the current study was to examine the relationship between global life satisfaction and sexual satisfaction in adults older than 60 years. The study also analyzed the relationship between various forms of sexual activity (genital and non‐genital) and both types of satisfaction.
Multisynchronization of Interconnected Memristor-Based Impulsive Neural Networks With Fuzzy Hybrid Control

This paper studies a class of heterogeneous delayed impulsive neural networks with memristors and their collective evolution for multisynchronization. The multisynchronization represents a diversified collective behavior that is inspired by multitasking as well as observations of heterogeneity and hybridity arising from system models. In view of memristor, the memristor-based impulsive neural network is first represented by an impulsive differential inclusion. According to the memristive and impulsive mechanism, a fuzzy logic rule is introduced, and then, a new fuzzy hybrid impulsive and switching control method is presented correspondingly. It is shown that using the proposed fuzzy hybrid control scheme, multisynchronization of interconnected memristor-based impulsive neural networks can be guaranteed with a positive exponential convergence rate. The heterogeneity and hybridity in system models, thus, can be indicated by the obtained error thresholds that contribute to the multisynchronization. Numerical examples are presented and compared to demonstrate the effectiveness of the developed theoretical results.
Data-Based Parameter Calibration Method of the Computed Tomography System

This paper mainly studies data-based parameter calibration of the computed tomography (CT) system. By establishing a least squares algorithm model, CT system parameters were calibrated. First, the attenuation coefficient is calculated from the known data, and the relationship between the received information and the pitch of the X-ray transmission template is obtained by using the Lambert-beers principle. Then, by using the geometric relationship between the ray and the two-dimensional graph, the relationship model between the pitch and the variable to be sought is established. On this basis, we can get the multivariate linear equations which present the received information and the variables to be solved. Finally, the least square method is used to establish the target programming model which is based on the minimum square sum of the fitting value and the actual value of the received information. By using the genetic algorithm, the multiple linear equations are solved. Meanwhile, the deflection center and receiver unit spacing can be obtained.
Discovering Fuzzy Structural Patterns for Graph Analytics

Many real-world datasets can be represented as attributed graphs that contain vertices, each of which is associated with a set of attribute values. Discovering clusters, or communities, which are structural patterns in these graphs, are one of the most important tasks in graph analysis. To perform the task, a number of algorithms have been proposed. Some of them detect clusters of particular topological properties, whereas some others discover them mainly based on attribute information. Also, most of the algorithms discover disjoint clusters only. As a result, they may not be able to detect more meaningful clusters hidden in the attributed graph. To do so more effectively, we propose an algorithm, called FSPGA, to discover fuzzy structural patterns for graph analytics. FSPGA performs the task of cluster discovery as a fuzzy-constrained optimization problem, which takes into consideration both the graph topology and attribute values. FSPGA has been tested with both synthetic and real-world graph datasets and is found to be efficient and effective at detecting clusters in attributed graphs. FSPGA is a promising fuzzy algorithm for structural pattern detection in attributed graphs.
Model Parameters Estimation and Robust Roll Control with Transient Performance Constrains for Small UAV Systems

In this paper, we proposed a scheme which can online amend the controller gains caused by the inaccurate model parameters. In order to obtain accurate model parameters, we introduce an online parameter estimator. The idea of prescribed performance function (PPF) is incorporated into the control and estimation design, which can strictly guarantee both the steady-state and transient performance. Finally, comparative simulations are conducted based on rolling channel of UAV systems to validate the improved response and the robustness of the proposed scheme.
A Zero Phase Delay Power Divider Using Zero Phase Impedance Transformer

The 3-dB power divider is one of the most common three-port device in microwave systems, which equally splits the signal at its two output ports. In previous Wilkinson power divider design, the phase delay between the input and output ports is not considered into the design requirements. However, the zero-phase shifting power divider attracts great interest in modern microwave systems. In this letter, a 3-dB power divider with zero-phase delay is designed based on a zero-phase impedance transformer. Instead of metamaterial transmission line, a transmission line stub is added to connect the isolation resistor, and a capacitor is insert between the output ports and the end of main branch to cancel out the imaginary part of even–odd mode impedances. With this new design, it releases the layout restriction that the two branches route around to connect to lumped element resistor. Closed-form design equations are derived and a prototype device is then simulated and measured. The measurement results agree well with the simulation results.
Quantum-Inspired Optimization of Echo State Networks Applied to System Identification

Quantum-Inspired Evolutionary Algorithms (QIEA) represent an efficient alternative to the traditional genetic algorithms, being capable of finding good solutions with smaller populations. Echo State Networks (ESNs) are a simple and efficient implementation of the Reservoir Computing framework. The use of this kind of networks in system identification is advantageous due to its intrinsic dynamic behavior and fast training procedure. However, ESNs have global parameters that should be tuned in order to improve their performance in a determined task. Besides, the random generation of the reservoir weights of these networks may not be ideal in terms of performance. Thus, this work presents a method that automatically defines an ESN for system identification problems by using a real coded QIEA (QIEA-R) in a two-phase optimization procedure. The QIEA-R firstly searches for the best global parameters of an ESN; then, on a second stage, optimizes some of its reservoir weights. In two benchmark problems for system identification, the proposed method overcame the performance of a randomly generated ESN with the same global parameters and has presented comparable and, in most cases, better accuracy results in comparison to some methods which were applied to the same datasets.
Dactyl Alphabet Modeling and Recognition Using Cross Platform Software

We target the problem of providing 5G network connectivity in rural zones by means of Base Stations (BSs) carried by Unmanned Aerial Vehicles (UAVs). Our goal is to schedule the UAVs missions to: i) limit the amount of energy consumed by each UAV, ii) ensure the coverage of selected zones over the territory, ii) decide where and when each UAV has to be recharged in a ground site, iii) deal with the amount of energy provided by Solar Panels (SPs) and batteries installed in each ground site. We then formulate the RURALPLAN optimization problem, a variant of the unsplittable multicommodity flow problem defined on a multiperiod graph. After detailing the objective function and the constraints, we solve RURALPLAN in a realistic scenario. Results show that RURALPLAN is able to outperform a solution ensuring coverage but not considering the energy management of the UAVs.
A Summary on Some Typical Adaptive Dynamic Programming Schemes

This paper sums up four typical schemes of adaptive dynamic programming (ADP). The diagrams are provided and the algorithms of various schemes are described, which is convenient for comparison. Some schemes in this paper belong to the group of action-dependent (AD) adaptive critic designs, which features without a model network in the design. For simplicity of notation, we do not use the prefix AD. The learning process of ADP is accomplished by updating the weights of the networks. The weight updating processes of some networks in GDHP scheme are introduced.