Miracle-dz/UniFAR-FTUs huggingface dataset:
The Broad-Spectrum Endolysin LySP2 Improves Chick Survival after Salmonella Pullorum Infection

Salmonella pullorum causes typical “Bacillary White Diarrhea” and loss of appetite in chicks, which leads to the death of chicks in severe cases; thus, it is still a critical issue in China. Antibiotics are conventional medicines used for Salmonella infections; however, due to the extensive long-term use and even abuse of antibiotics, drug resistance becomes increasingly severe, making treating pullorum disease more difficult. Most of the endolysins are hydrolytic enzymes produced by bacteriophages to cleave the host’s cell wall during the final stage of the lytic cycle. A virulent bacteriophage, YSP2, of Salmonella was isolated in a previous study. A Pichia pastoris expression strain that can express the Salmonella bacteriophage endolysin was constructed efficiently, and the Gram-negative bacteriophage endolysin, LySP2, was obtained in this study. Compared with the parental phage YSP2, which can only lyse Salmonella, LySP2 can lyse Salmonella and Escherichia. The survival rate of Salmonella-infected chicks treated with LySP2 can reach up to 70% and reduce Salmonella abundance in the liver and intestine. The treatment group showed that LySP2 significantly improved the health of infected chicks and alleviated organ damage caused by Salmonella infection. In this study, the Salmonella bacteriophage endolysin was expressed efficiently by Pichia pastoris, and the endolysin LySP2 showed good potential for the treatment of pullorum disease caused by Salmonella pullorum.
Robust stabilization of switched non-linear systems with time-varying delays under asynchronous switching

Abstract This paper investigates the problem of robust stabilization for switched non-linear systems with time-varying delays under asynchronous switching. Sufficient conditions for the existence of the robust stabilization problem are developed. First, when the switching instants of the controller experience delays with respect to those of the system, a state feedback controller is proposed to guarantee exponential stability for switched systems with time-varying delays. The dwell-time approach and free weighting matrix scheme are utilized for stability analysis and controller design. Then the approach is extended to take into account switched time-delay systems with Lipschitz non-linearities and structured uncertainties. Finally, a numerical example is given to illustrate the effectiveness of the proposed method.
Turbulent Sprays Evaporating Under “Stabilized Cool Flame” Conditions: Assessment of two CFD Approaches

An “in-house” computational fluid dynamics code implementing a Euler-Lagrange approach is extended by incorporating the Euler-Euler (two-fluid model) approach, to improve prediction capabilities of flow and thermal characteristics of turbulent evaporating sprays. The performance of both approaches is assessed by comparing predictions with experimental data for a variety of evaporating-spray test cases. The applicability of the Euler-Lagrange and Euler-Euler approach is established in an isopropyl alcohol–air turbulent flow, in which characteristic droplet quantity predictions are in satisfactory overall agreement with measurements. The evaporating spray characteristics are then predicted under “stabilized cool flame” conditions and the computational results are compared to experimental data for a nonreactive case and a reactive case. In both cases, the velocity and thermal fields are successfully captured by both approaches. Overall, the article demonstrates an approach toward the development of performance-based computational tools.
On the Optimization of Constellation Tilting for MU-MIM O Tomlinson- Harashima Precoding

Tomlinson-Harashima Precoding (THP), as an alternative algorithm of Dirty Paper Precoding (DPC), can effectively decrease transmitted power through modulo operation. To further reduce the power loss of THP, THP with constellation tilting (THP-CT) was considered by introducing different rotation angles into interfering data streams. In this paper, we propose methods to find the adequate tilting angles for the THP-CT algorithm, including per-stream serial optimization via convex optimization algorithm and global optimization via particle swarm algorithm. The simulation result shows that Block Error Rate (BLER) of these two proposed algorithms is 2-3dB better than that of the conventional THP without constellation tilting.
Dependence in probabilistic modeling, Dempster-Shafer theory, and probability bounds analysis.

This report summarizes methods to incorporate information (or lack of information) about inter-variable dependence into risk assessments that use Dempster-Shafer theory or probability bounds analysis to address epistemic and aleatory uncertainty. The report reviews techniques for simulating correlated variates for a given correlation measure and dependence model, computation of bounds on distribution functions under a specified dependence model, formulation of parametric and empirical dependence models, and bounding approaches that can be used when information about the intervariable dependence is incomplete. The report also reviews several of the most pervasive and dangerous myths among risk analysts about dependence in probabilistic models.
Joint Channel Estimation and Symbol Detection for UAV-Assisted Systems Using Tensor Framework

In this paper, we focus on the unmanned aerial vehicle (UAV)-assisted systems and present three joint channel estimation and symbol detection algorithms using tensor framework. Specifically, the UAVs amplify and forward the received signals from the vehicle to the base station (BS) transmitted through time-varying channels, and the signals received at the BS are constructed to the PARAFAC model or the PARATUCK2 model. Then, three different algorithms are proposed to fit the tensor models, corresponding to different communication scenarios. In particular, a tensor decomposition algorithm that can convert the PARATUCK2 model to the parallel factor (PARAFAC) model is proposed. Our simulation results show that the proposed algorithm can effectively estimate channels and detect symbols.
Microstructure and Mechanical Properties of Gradient Interfaces in Wire Arc Additive Remanufacturing of Hot Forging Die Steel

Hot forging dies are subjected to periodic thermal stress and often fail in the forms of thermal fatigue, wear, plastic deformation, and fracture. A gradient multi-material wire arc additive remanufacturing method for hot forging dies was proposed to extend the service life of hot forging dies and reduce total production costs. The properties of multi-material gradient interfaces play a critical role in determining the overall performance of the final products. In this study, the remanufacturing zone of a hot forging die was divided into three deposition layers: the transition layer, the intermediate layer, and the strengthening layer. Experiments of wire arc additive manufacturing with gradient material were conducted on a 5CrNiMo hot forging die steel. The microstructure, microhardness, bonding strength, and impact property of gradient interfaces were characterized and analyzed. The results revealed that the gradient additive layers and their interfaces were defect-free and that the gradient interfaces had obtained a high-strength metallurgical bonding. The microstructure of the gradient additive layers presented a gradient transformation process of bainite-to-martensite from the bottom to the top layer. The microhardness gradually increased from the substrate layer to the surface-strengthening layer, forming a three-level gradient in the range of 100 HV. The impact toughness values of the three interfaces were 46.15 J/cm2, 54.96 J/cm2, and 22.53 J/cm2, and the impact fracture morphology ranged from ductile fracture to quasi-cleavage fracture. The mechanical properties of the gradient interfaces showed a gradient increase in hardness and strength, and a gradient decrease in toughness. The practical application of hot forging die remanufactured by the proposed method had an increase of 37.5% in average lifespan, which provided scientific support for the engineering application of the gradient multi-material wire arc additive remanufacturing of hot forging dies.
Indoor Localization-based Energy Management for Smart Home

With the development of the Internet of Things (IoT) industry, many services and applications have been developed, such as indoor localization, proximity detection, smart home, and so on. However, many services are severely limited due to energy constraints. Many researchers have proposed solutions to reduce energy consumption in these applications and improve energy utilization. For instance, Bluetooth low energy (BLE)-based data transmission scheme and sensor-based energy management scheme. In this paper, we combine BLE and sensors to propose an indoor localization-based energy management system for smart homes, which can effectively solve the energy waste caused by users forgetting to turn off the device's power. The analysis results show that the proposed scheme can save energy effectively.
Association of the Systemic Immune-Inflammation Index with Outcomes in Acute Coronary Syndrome Patients with Chronic Kidney Disease

Background The systemic immune-inflammation index (SII; neutrophil × platelet/lymphocyte) is a novel marker for immune and inflammatory status and is associated with adverse prognosis in cardiovascular disease. Methods In total, 744 patients diagnosed with acute coronary syndrome (ACS) and chronic kidney disease (CKD) were included in our study, received standard therapies, and were followed up. Patients were divided into high and low SII groups according to the baseline SII. The primary endpoint was major cardiovascular events (MACEs), defined as cardiovascular death, nonfatal myocardial infarction, and nonfatal stroke. Results During a median follow-up of 2.5 years, a total of 185 (24.9%) MACEs were recorded. Analysis of the ROC curve revealed that the best cutoff value of SII was 1159.84×109/L for predicting MACEs. The Kaplan–Meier analysis showed that those patients in the low SII group had higher survival rates than those in the high SII group (p < 0.001). Compared to those in the low SII group, patients in the high SII group were at significantly higher risk of MACEs (134 (38.8%) vs 51 (12.8%), p < 0.001). Univariate and multivariable Cox regression analyses revealed that a high SII level was independently associated with MACEs in ACS patients with CKD (adjusted hazard ratio [HR]: 1.865, 95% confidence interval [CI]: 1.197–2.907, p = 0.006). Conclusion The present study showed that an elevated SII is associated with adverse cardiovascular outcomes in ACS with CKD patients, suggesting that SII may be a valuable predictor of poor prognosis in ACS with CKD patients. Further studies are needed to confirm our findings.
On the Effect of Channel Knowledge in Underwater Acoustic Communications: Estimation, Prediction and Protocol

Underwater acoustic communications are limited by the following channel impairments: time variability, narrow bandwidth, multipath, frequency selective fading and the Doppler effect. Orthogonal Frequency Division Modulation (OFDM) is recognized as an effective solution to such impairments, especially when optimally designed according to the propagation conditions. On the other hand, OFDM implementation requires accurate channel knowledge atboth transmitter and receiver sides. Long propagation delay may lead to outdated channel information. In this work, we present an adaptive OFDM scheme where channel state information is predicted through a Kalman-like filter so as to optimize communication parameters, including the cyclic prefix length. This mechanism aims to mitigate the variability of channel delay spread. This is cast in a protocol where channel estimation/prediction are jointly considered, so as to allow efficiency. The performance obtained through extensive simulations using real channels and interference show the effectiveness of the proposed scheme, both in terms of rate and reliability, at the expense of an increasing complexity. However, this solution is significantly preferable to the conventional mechanism, where channel estimation is performed only at the receiver, with channel coefficients sent back to the transmit node by means of frequent overhead signaling.
Community Detection using Unsupervised Learning Approach

A community is referred to as a set of nodes in a network that has a high degree of connectivity with each other and a low degree of connectivity with other nodes in the same network. Community Detection is a renowned research problem for the past many years. The applications of Community Detection is spread across several domains like social networks, transportation networks, genetic networks, citation networks, web networks etc. In this work, several unsupervised learning techniques namely Louvain Algorithm, K-means clustering Algorithm and Gaussian Mixture Model have been examined to identify communities in social networks. The results demonstrated that the Louvain Algorithm outperforms the other two unsupervised learning techniques.
Evaluation of a time-frequency domain interferometer for simultaneous group-velocity dispersion measurements in multimode photonic crystal fibers

Fiber optical applications are primarily limited by group-velocity dispersion. In single-mode fibers, material and waveguide dispersion yield a total chromatic dispersion where no modal influences occur. However, modal dispersion plays a significant role in multimode fibers, whereas waveguide dispersion is negligible. The concern of this report is the investigation of the dispersion properties of photonic crystal fibers, with both single-mode and multimode operation, using a time-frequency domain white-light Mach-Zehnder interferometer. The results demonstrate the possibility to separate different transverse fiber modes and, hence, to measure the material, waveguide, and modal dispersion of optical fibers.
Face Recognition Application of Blur-Robust

Understanding the effect of blur is an important problem in unconstrained visual analysis. We concentrate on this problem in the context of image-based recognition, by a fusion of image-formation models, as well as differential geometric tools. First, we talk about the space spanned by blurred versions of an image and then under certain assumptions, present a differential geometric analysis of that space. More exclusively, we create a subspace resulting from convolution of an image with a complete set of orthonormal basis functions of a pre-specified maximum size (that can represent an arbitrary blur kernel within that size), and explain that the equivalent subspaces created from a clean image and its blurred versions are equal under the ideal case of zero noise, and some assumptions on the properties of blur kernels. We then learn the practical utility of this subspace representation for the problem of direct recognition of blurred faces and by viewing the subspaces as points on the Grassmann manifold and present methods to perform recognition for cases where the blur is both homogenous and spatially varying. We empirically evaluate the effect of noise, as well as the presence of other facial variations between the gallery and probe images, and give comparisons with existing approaches on usual datasets.
Security Shading Generation of Logo Image Based on Neural Style Transfer

With the development of economical society, the problem of product piracy security is becoming more and more serious. In order to protect the copyright of brands, based on the image neural style transfer, this paper proposes an automatic generation algorithm of anti-counterfeiting logo with security shading, which increases the difficulty of illegal copying and packaging production. VGG19 deep neural network is used to extract image features and calculate content response loss and style response loss. Based on the original neural style transfer algorithm, the content loss is added, and the generated security shading is fused with the original binary logo image to generate the anti-counterfeiting logo image with higher recognition rate. In this paper, the global loss function is composed of content loss, content response loss and style response loss. The L-BFGS optimization algorithm is used to iteratively reduce the global loss function, and the relationship between the weight adjustment, the number of iterations and the generated anti-counterfeiting logo among the three losses is studied. The secret keeping of shading style image used in this method increases the anti-attack ability of the algorithm. The experimental results show that, compared with the original logo, this method can generate the distinguishable logo content, complex security shading, and has convergence and withstand the attacks.
Anti-windup Design for Uncertain Systems based on Guaranteed Cost Control Approach

This paper investigates the issue of Anti-windup (AW) control designed for uncertain continuous-time (CT) systems using guaranteed cost control (GCC) approach. AW compensator is added to a previously designed dynamic output feedback controller to sustain the above system in utter lack of saturation to eliminate the effects of input saturation. By utilizing the quadratic Lyapunov function, the condition of stability for closed-loop (CL) system is established. The proposed method confirms asymptotic stability of the system and also provides the upper-bound of performance index for CL systems. A convex optimization problem has been exacerbated by an effective AW compensator gain including guaranteed cost design difficulty. To demonstrate the utility of the proposed technique, an acceptable example is used.
Receiving Social Support Motivates Proximal and Distant Prosocial Behaviors

Prosocial behaviors –actions aimed to benefit other individuals, groups, or communities– are important for promoting and maintaining a healthy society. Extant research on the contextual factors that make individuals more likely to engage in prosocial behaviors has mainly looked at their short-term effects taking place in the immediate environment, potentially overlooking their impact in more distant domains. Building on attachment theory, we theorize that an interpersonal factor—receiving social support—can foster prosocial behaviors both in the short-and in the long-term. Moreover, receiving social support can have spillover effects beyond the environment in which the support was received. Receiving social support increases felt security—a sense of care, esteem, love, and safety—which in turn increases motivation to engage in behaviors that benefit others. We test our hypotheses with cross-sectional, longitudinal, and experimental data. In Study 1, data from a sample of international business school alumni show a significant positive relationship between receiving social support and engaging in prosocial behaviors both within and beyond the environment in which support was received. Study 2 leverages data of US adults in a multi-wave study to show that receiving social support predicts prosocial activities several years later. Study 3 experimentally manipulates social support with a sample of working US adults and finds that receiving social support fosters prosocial behaviors through boosting felt security which in turn increases prosocial motivation. Overall, our findings show that receiving social support can affect short-and long-term prosocial behaviors with spillover effect beyond the immediate environment.
A Target Identification Method Based on Uncertainty Estimation and Evidence Theory

Aiming at the problem that the accuracy of target identity recognition is easily affected by environment, target state and hardware limitations in surveillance scenes, a target identity recognition method based on uncertainty estimation and evidence theory is proposed, which fuses face and pedestrian information. Firstly, an uncertainty estimation module based on Dirichlet distribution is proposed to realize the reflection of the influence degree of various complex factors on single-mode characteristics in a monitoring scene by numerically simulating and expressing the subjective logic of single-mode decision. Secondly, a combination decision-making method based on DS (Dempster-Shafer) evidence theory is proposed to realize multi-modal dynamic fusion decision-making and eliminate the possible decision-making conflicts among single modes. Finally, a Bayesian risk-based classification loss method is proposed to realize the generation of evidence-level information under multi-modality through constraint network, and the test is carried out on the data set under the urban surveillance scene, and the accuracy rate reaches 87.87%, which is superior to the single-modality recognition and splicing fusion method.
A new species of Metaprosekia Leistikow, 2000 (Oniscidea, Philosciidae) from caves of the State of Mato Grosso, Brazil

ABSTRACT The present work aims to describe a new species of Metaprosekia from siliciclastic caves from Caverna Nova and Caverna Ponte de Pedra, Utiariti Formation, Municipality of Diamantino, State of Mato Grosso. Metaprosekia utiariti n. sp. easily differs from the congeneric species in the shape of male pleopods 1 and 2. Moreover, a map illustrating the lithology and hydrographic characteristics where the specimens were collected and conservation remarks are given. http://www.zoobank.org/urn:lsid:zoobank.org:pub:F0B93B6A-0FBF-4B66-B6F0-3E5B67DD9C53.
Collisional Excitation Rate Coefficients for Lithium-Like Ions

This report takes all the available good quality quantal calculations of excitation cross-sections by electron collision for lithium-like ions and intercompares them. There is a comparison also with the small amount of experimental data of 2s 2S-2p 2P cross-sections. On the basis of all of these data, a choice is made of the best cross-sections and these are integrated over Maxwellians to give excitation rate coefficients. In general data are available for up to seven transitions in five or six ions. When the results are compared along the iso-electronic sequence, trends are established which allow estimates to be made of the rate coefficients for these seven transitions for any lithium-like ion of nuclear charge greater than boron. The results are presented graphically and as simple formulae. The formulae reproduce the source data at various levels of accuracy from about ± 1% for individual ions to universal formulae of accuracy better than ± 15% in the relevant temperature ranges. The latter are contained on one page and represent an enormous condensation of the original source data without seriously compromising the authors' claimed accuracy.