Miracle-dz/UniFAR-FTUs huggingface dataset:
Identifying biofilm forming bacteria in cow milk in Mekong Delta, Viet Nam

Bacterial biofilms are agglomeration of bacterial cells, stuck to the material surfaces of material in wet environments and formed by a self-produced matrix. The formation of bacterial biofilms is a great risk for the milk processing industry, as the survival of many bacterial species in cow milk may lead to many problems such as microbial spoilage, deterioration in quality, and consumer health risks. This study aimed to identify biofilm formation bacteria from cow milk. The experiment included isolation; biofilm forming assay in 96-well microtiter plates and the identification of microbial isolates using classical and molecular biological methods. A total of 14 bacterial isolates from 10 cow milk samples were evaluated for their biofilm formatting ability. Among them, four isolates were identified as moderate and strong biofilm producers. These four isolates belong to the genera Serratia and Aeromonas. Out of the 4 isolates, Serratia marcescens VL41 was classed as a strong biofilm producer while Aeromonas veronii ST15, Aeromonas sp. ST17, Serratia marcescens VL13 were classed as moderate biofilm producers respectively. The findings of this study suggest that it is necessary to discover the contamination causes and prevention of genera Serratia, and Aeromonas into cow milk.
Comparison of Classical Linear Regression and Orthogonal Regression According to the Sum of Squares Perpendicular Distances

Regression analysis is a statistical technique for investigating and modeling the relationship between variables. The purpose of this study was the trivial presentation of the equation for orthogonal regression (OR) and the comparison of classical linear regression (CLR) and OR techniques with respect to the sum of squared perpendicular distances. For that purpose, the analyses were shown by an example. It was found that the sum of squared perpendicular distances of OR is smaller. Thus, it was seen that OR line has appeared to present a much better fit for the data than CLR line. Depending on those results, the OR is thought to be a regression technique to obtain more accurate results than CLR at simple linear regression studies.
Academic Motivation, Perceived Creativity Fostering Teacher Behavior, and Student Engagement in Online Statistics Course of Indonesian Undergraduates: An Investigation of Relations and Predictability

This study aims to analyze the relationship and influence of academic motivation and perceived creativity fostering teacher behavior (P-CFTB) on student engagement in Statistics online learning courses in Indonesia. The research used a cross-sectional design study with a quantitative approach. Three standardized scales (academic motivation scale, perceived creativity fostering teacher index, and university student engagement inventory) were filled out by 533 psychology undergraduates who participated. All data were analyzed statistically by descriptive, correlational, and multiple linear regression tests. The results show that academic motivation and student engagement, P-CFTB and student engagement were related moderately and positively (r = 0.582, r = 0.593; p < .001, respectively). Furthermore, academic motivation and P-CFTB simultaneously affected students’ learning engagement by 48%. The result shows that when students’ academic motivation is combined with creativity fostering teacher behavior, student engagement in online learning increases.
Development of loop-mediated isothermal amplification for rapid detection of sporotrichosis caused by Sporothrix schenckii

Background and Aim: Sporothrix schenckii is the causative agent of sporotrichosis, which most commonly causes lymphocutaneous infections in immunocompromised hosts. This pathogen infects dogs, cats, cattle, and buffaloes and can potentially infect humans. Diagnosis by fungal culture is lengthy, and although there are several clinical diagnoses and molecular methods, these are complicated and time-consuming for veterinarians. This study aimed to develop a visual diagnostic assay that is less time-consuming and can be used by veterinarians to screen for sporotrichosis. Materials and Methods: To develop a loop-mediated isothermal amplification (LAMP) assay for sporotrichosis, primers specific for fragments of the 18S rRNA gene of S. schenckii were designed. Then, the time and temperature were optimized to successfully achieve LAMP. Ten-fold serial dilutions of DNA were used to determine the detection limit using both LAMP and nested polymerase chain reaction (nPCR) assays. Results: The optimal LAMP conditions were incubation at 73°C for 30 min. Agarose gel electrophoresis revealed a ladder-like pattern of the LAMP product, and a sky-blue color indicated a positive result. A comparison of the LAMP assay with nPCR revealed that it was 10 times more sensitive than nPCR, with a detection limit of 10 pg. The use of a heat box compared with a thermocycler gave the same results. Conclusion: Loop-mediated isothermal amplification gives good results and may represent a future alternative diagnostic tool for screening fungal pathogens before the results of conventional fungal cultures are received. However, this method should be further studied to clarify its use with clinical samples.
APSO-TA-LSTM: a long and short term memory model combining time attention and adaptive particle swarm optimization for stock forecasting

A new stock forecasting model that combines time attention and adaptive particle swarm optimization with LSTM (APSO-TA-LSTM) is proposed to improve the forecasting ability of neural networks for financial time series. The model uses a two-layer LSTM network to encode stock information within the time window and employs time attention to strategically focus on dependencies among time series features for more accurate feature representations. Additionally, the proposed adaptive particle swarm optimization algorithm is used to pick out the key parameters of the network structure and enhance the overall prediction performance. Finally, the experimental results on three stock datasets validate the innovation and effectiveness of our method, and this work will have a broad application prospect in the study of financial time series.
Influence of flow constraints on the properties of the critical endpoint of symmetric nuclear matter

We propose a novel family of equations of state for symmetric nuclear matter based on the induced surface tension concept for the hard-core repulsion. It is shown that having only four adjustable parameters the suggested equations of state can, simultaneously, reproduce not only the main properties of the nuclear matter ground state, but the proton flow constraint up its maximal particle number densities. Varying the model parameters we carefully examine the range of values of incompressibility constant of normal nuclear matter and its critical temperature which are consistent with the proton flow constraint. This analysis allows us to show that the physically most justified value of nuclear matter critical temperature is 15.5-18 MeV, the incompressibility constant is 270-315 MeV and the hard-core radius of nucleons is less than 0.4 fm.
Survival modeling of first birth interval after marriage

This is a data exploratory analysis of retrospective cross-sectional study of pattern of first birth interval after marriage in Nigeria. The data for the study are extracted from the published reports of the National Demographic and Health Survey 2009 edition. Fertility as a major component of population change is influenced by first birth interval after marriage, since the interval is positively correlated with the cumulative number of children a woman would have at the end of her reproductive life. Studies have described this interval using non-parametric methods which lack features to project the estimates further. This paper is designed to fill the gap by attempting to fit a parametric model to data on the first birth interval among women of reproductive age in Nigeria. Four parametric models whose various curves and estimates are compared with non-parametric values are considered, namely Inverse Gaussian, Log-logistic, Weibull and Burr Type XII. The best model appears to be Inverse Gaussian based on the Akaike Information Criterion of lowest value of 116617.6. Quantile-quantile plots also identify Inverse Gaussian as model whose data points clustered much around straight line. All other curves give credence to the Inverse Gaussian model as the one that describes the data better than the rest. However, the study does not rule out adaptability of Log-logistic distribution to model waiting time to first birth after marriage since it also behaves similarly to Inverse Gaussian distribution.
Heating neutral beams for ITER: negative ion sources to tune fusion plasmas

Neutral beam injection (NBI) based on a negative ion source is one of the basic heating and current drive systems designed for ITER required to reach its goals of the operation with high fusion power, Pfus ∼ 500 MW with fusion gain, Q = 10 for 400 s in a baseline scenario, and Pfus > 250 MW, Q = 5 operation for 3600 s in an advanced scenario. A total power of 33 MW from the two heating neutral beam (HNB) injectors is envisaged in the present scenario. The scope of the present paper is to provide an overview of the main aspects of the interaction of the HNBs with the ITER plasma. Various operational scenarios with different mixtures of the main ion species, He, H, DD and DT, foreseen at different phases of the ITER operation are considered.
SAR image de-noising based on texture strength and weighted nuclear norm minimization

As synthetic aperture radar (SAR) has been widely used nearly in every field, SAR image de-noising became a very important research field. A new SAR image de-noising method based on texture strength and weighted nuclear norm minimization (WNNM) is proposed. To implement blind de-noising, the accurate estimation of noise variance is very important. So far, it is still a challenge to estimate SAR image noise level accurately because of the rich texture. Principal component analysis (PCA) and the low rank patches selected by image texture strength are used to estimate the noise level. With the help of noise level, WNNM can be expected to SAR image de-noising. Experimental results show that the proposed method outperforms many excellent de-noising algorithms such as Bayes least squares-Gaussian scale mixtures (BLS-GSM) method, non-local means (NLM) filtering in terms of both quantitative measure and visual perception quality.
Mitigate IR-Drop Effect by Modulating Neuron Activation Functions for Implementing Neural Networks on Memristor Crossbar Arrays

The line resistance (LR) in a large-scale memristor crossbar array can cause serious IR-drop problem, degrading the hardware deployment capability of neural networks (NNs). In this work, two innovation schemes from the level of software are proposed to mitigate the hardware IR-drop problem by intentionally modulating the NN activation function before deploying. The methods are evaluated over typical activation functions and various line resistances on MLP and LeNet-5 for MNIST recognition. Results show the methods can significantly improve the tolerance of NNs to IR-drop and recover the accuracy in some extent. The methods require no extra hardware overhead and reduce the complexity of peripheral circuits, which make them more achievable and attractive.
Opportunistic Block Validation for IoT Blockchain Networks

The blockchain network architecture is a promising technology for constructing highly secure Internet of Things (IoT) networks. IoT networks typically consist of many sensors and actuators. Blockchain network technology can be applied to secure control robots in smart factories or for reliable drone delivery in smart cities. The distributed ledger and data block validation across blockchain networks guarantee the ultimate data security. However, the current blockchain technology is restricted in terms of its overall deployment across IoT networks. A general permissionless blockchain technology typically targets high-performance network nodes with sufficient computing power and memory space. A blockchain node with less computing power and memory, such as an IoT sensor or actuator, cannot employ blockchain technology as a fully functional node. A lightweight blockchain provides practical blockchain availability to IoT networks. We propose an operational advance to develop a lightweight blockchain for IoT networks. The opportunistic block validation optimizes the block validation process. It measures the network vulnerability based on the node reputation, block validation degree, number of fraudulent messages, and network stability. The reinforcement learning mechanism employed in the opportunistic block validation determines whether block validation is to be performed. Two separately developed reinforcement learning methods can increase the processing performance while maintaining the transaction data integrity. In addition, the proposed blockchain technology is easily implementable because it adopts a Hyperledger development environment. Directly embedding the proposed blockchain middleware platform in small computing devices proves the practicability of the proposed block validation mechanism.
Optimization of an enhanced ceramic micro-filter for concentrating E.coli in water

Recently lower limit of detection (LOD) is necessary for rapid bacteria detection and analysis applications in clinical practices and daily life. A critical pre-conditioning step for these applications is bacterial concentration, especially for low level of pathogens. Sample volume can be largely reduced with an efficient pre-concentration process. Some approaches such as hollow-fiber ultra-filtration and electrokinetic technique have been applied to bacterial concentration. Since none of these methods can provide a concentrating method with a stable recovery efficiency, bacterial concentration still remains challenging Ceramic micro- filter can be used to concentrate the bacteria but the cross flow system keeps the bacteria in suspension. Similar harvesting bacteria using ultra-filtration showed an average recovery efficiency of 43% [1] and other studies achieved recovery rates greater than 50% [2]. In this study, an enhanced ceramic micro-filter with 0.14 μm pore size was proposed and demonstrated to optimize the concentration of E.coli. A high recovery rate (mean value >90%) and a high volumetric concentration ratio (>100) were achieved. Known quantities (104 to 106 CFU/ml) of E.coli cells were spiked to different amounts of phosphate buffered saline (0.1 to 1 L), and then concentrated to a final retentate of 5 ml to 10 ml. An average recovery efficiency of 95.3% with a standard deviation of 5.6% was achieved when the volumetric con- centration ratio was 10. No significant recovery rate loss was indicated when the volumetric concentration ratio reached up to 100. The effects of multiple parameters on E.coli recovery rate were also studied. The obtained results indicated that the optimized ceramic micro- filtration system can successfully concentrate E.coli cells in water with an average recovery rate of 90.8%.
Preliminary experimental study on solid-fuel rocket scramjet combustor

Liquid or gaseous fuel scramjet technology has made great progress, and there has been some research attention to solid-fuel scramjet. A new scramjet configuration using solid fuel as propellant, namely solid-fuel rocket scramjet, is tested experimentally. It consists of two combustors. One is a rocket combustor used as gas generator, and the other is a supersonic combustor used for secondary combustion. The experiment simulates a flight Mach number of 4 at high altitude (stagnation temperature and pressure are 1170 K and 1.16 MPa, respectively), and metalized solid fuel is used as propellant. The results reveal that fuel-rich gas from the gas generator can burn with air in the supersonic combustor. Preliminary evaluation results show that the combustion efficiency of the propellant is about 90%, and the total pressure recovery coefficient in the supersonic combustor is about 0.6. These results indicate that the configuration of solid-fuel rocket scramjet is feasible. 摘要目的通过发动机直连式实验,验证燃气发生器产生的 富燃燃气可以在超声速气流中二次燃烧,进而证 明固体火箭超燃冲压发动机方案的可行性,并初 步评估固体火箭超燃冲压发动机燃烧室的工作 性能。 创新点1. 提出固体火箭超燃冲压发动机构型方案,并开 展固体火箭超燃冲压发动机燃烧室直连式实验 研究;2. 验证了固体火箭超燃冲压发动机构型可 行;3. 初步评估了固体火箭超燃冲压发动机燃烧 室的工作性能。 方法1. 通过直连式实验测定固体火箭超燃冲压发动机 燃烧室的工作参数(图2、3 和4);2. 通过实验 现象(图8)和数据处理,确定燃气发生器产生 的富燃燃气可以在超声速燃烧室中燃烧,进而确 定固体火箭超燃冲压发动机方案的可行性;3. 初 步确定发动机燃烧室的工作性能(公式(6)和 (7))结论1. 燃气发生器中产生的富燃燃气可以在超声速燃 烧室中燃烧,固体火箭超燃冲压发动机构型方案 可行;2. 初步评估了固体火箭超燃冲压发动机燃 烧室的工作性能,总压恢复系数约为0.6,燃烧 效率约为90%;3. 燃气发生器产生的部分一次燃 气沉积于燃气发生器喉部,使燃气发生器的工作 压力增加,进而引起富燃燃气质量流量的增加; 4. 燃烧室中的总压损失主要集中在富燃燃气入 口处,总压损失主要由射流引起的激波和燃气二次燃烧引起。
From sparse SLAM to dense mapping for UAV autonomous navigation

Autonomous or semi-autonomous navigation of UAVs is of great interest in the Defense and Security domains, as it significantly improves their efficiency and responsiveness during operations. The perception of the environment and in particular the dense and metric 3D mapping in real time is a priority for navigation and obstacle avoidance. We therefore present our strategy to jointly estimate a dense 3D map by combining a sparse map estimated by a state-of-the-art Simultaneous Localization and Mapping (SLAM) system and a dense depth map predicted by a monocular self-supervised method. Then, a lightweight and volumetric multi-view fusion solution is used to build and update a voxel map.
Electron Heating at Kinetic Scales in Magnetosheath Turbulence

We present a statistical study of coherent structures at kinetic scales, using data from the Magnetospheric Multiscale mission in the Earth’s magnetosheath. We implemented the multi-spacecraft partial variance of increments (PVI) technique to detect these structures, which are associated with intermittency at kinetic scales. We examine the properties of the electron heating occurring within such structures. We find that, statistically, structures with a high PVI index are regions of significant electron heating. We also focus on one such structure, a current sheet, which shows some signatures consistent with magnetic reconnection. Strong parallel electron heating coincides with whistler emissions at the edges of the current sheet.
A robust control system scheme based on model predictive controller (MPC) for continuous‐time systems

In this paper, an LMI framework based on model predictive strategy is addressed to design a robust dynamical control law in a typical control system. In the proposed method, instead of traditional static controller, a dynamic control law is used. With a suitable matrix transformation, the controller parameters selection are translated into an optimization problem with some LMI constraints. The plant input and output constraints are also handled with another LMIs. The controller is represented in state space form, and its parameters are computed in real‐time operation. For achieving this goal, by solving an optimization problem, a dynamic controller is designed, which meets the required plant performances. These results are used in 2 numerical examples to demonstrate the effectiveness of the proposed approach.
Time reversal of linear and nonlinear water waves

A. Chabchoub,a,b A. Maurel,c V. Pagneux,d P. Petitjeans,e A. Przadka,e and M. Finkc aDepartment of Ocean Technology Policy and Environment, Graduate School of Frontier Sciences, University of Tokyo, Kashiwa, Chiba 277-8563, Japan bDepartment of Mechanical Engineering, School of Engineering, Aalto University, FI-02150 Espoo, Finland cInstitut Langevin; ESPCI Paris, PSL Research University; CNRS; UPMC-Paris 6, Sorbone University; Univ. UPD-Paris 7; 1 rue Jussieu, 75005 Paris, France dLaboratoire d’Acoustique de l’Université du Maine, UMR CNRS 6613, Avenue Olivier Messiaen, 72085 Le Mans, France ePhysique et Mécanique des Milieux Hétérogènes; ESPCI Paris, PSL Research University; CNRS; UPMC-Paris 6, Sorbone University; Univ. UPD-Paris 7; 10 rue Vauquelin, 75005 Paris, France
Improve Performance of Double Pipe Heat Exchanger by Using Zno/Water Nanofluid

The heat transfer of double tube heat exchanger under counter flow is experimentally investigated. Nanofluid and the pure water are used as cold and hot fluids respectively. ZnO nanoparticles of 30 nm diameter are dispersed in water to prepare nanofluid with mass concentrations of 0.5 and 1%. Cold nanofluid is flowing through the inner tube heat exchanger with 20°C temperature under 2, 4 and 6 lpm volume flow rate. The hot water enters the annular space of the heat exchanger at a temperature of 65°C and 4 lpm volume flow rate. To improve the performance of the heat exchanger, the experimental findings achieved using this sort of nanofluid will be compared to those obtained using pure water. The outcomes showed that employing nanofluid as the working fluid improved performance. When employing nanofluid, the highest heat exchanger effectiveness is 40 % for nanoparticles concentration of 0.5 % per mass and 54 % (with a mass concentration of 1 %) with a volume flow rate of 2 lpm.
Algorithms for Learning the Structure of Monotone and Nonmonotone Sum-Product Networks

Algorithms for Learning the Structure of Monotone and Nonmonotone Sum-Product Networks Aaron W. Dennis Department of Computer Science, BYU Doctor of Philosophy The sum-product network (SPN) is a recently-proposed generative, probabilistic model that is guaranteed to compute any joint or any marginal probability in time linear in the size of the model. An SPN is represented as a directed, acyclic graph (DAG) of sum and product nodes, with univariate probability distributions at the leaves. It is important to learn the structure of this DAG since the set of distributions representable by an SPN is constrained by it. We present the first batch structure learning algorithm for SPNs and show its advantage over learning the parameters of an SPN with fixed architecture. We propose a search algorithm for learning the structure of an SPN and show that its ability to learn a DAG-structured SPN makes it better for some tasks than algorithms that only learn tree-structured SPNs. We adapt the structure search algorithm to learn the structure of an SPN in the online setting and show that two other methods for online SPN structure learning are slower or learn models with lower likelihood. We also propose to combine SPNs with an autoencoder to model image data; this application of SPN structure learning shows that both models benefit from being combined. We are also the first to propose a distinction between nonmonotone and monotone SPNs, or SPNs with negative edge-weights and those without, respectively. We prove several important properties of nonmonotone SPNs, propose algorithms for learning a special class of nonmonotone SPN called the twin SPNs, and show that allowing negative edge-weights can help twin SPNs model some distributions more compactly than monotone SPNs.