Miracle-dz/UniFAR-FTUs huggingface dataset:
Escherichia coli mediated resistance of Entamoeba histolytica to oxidative stress is triggered by oxaloacetate

Amebiasis, a global intestinal parasitic disease, is due to Entamoeba histolytica. This parasite, which feeds on bacteria in the large intestine of its human host, can trigger a strong inflammatory response upon invasion of the colonic mucosa. Whereas information about the mechanisms which are used by the parasite to cope with oxidative and nitrosative stresses during infection is available, knowledge about the contribution of bacteria to these mechanisms is lacking. In a recent study, we demonstrated that enteropathogenic Escherichia coli O55 protects E. histolytica against oxidative stress. Resin-assisted capture (RAC) of oxidized (OX) proteins coupled to mass spectrometry (OX-RAC) was used to investigate the oxidation status of cysteine residues in proteins present in E. histolytica trophozoites incubated with live or heat-killed E. coli O55 and then exposed to H2O2-mediated oxidative stress. We found that the redox proteome of E. histolytica exposed to heat-killed E. coli O55 is enriched with proteins involved in redox homeostasis, lipid metabolism, small molecule metabolism, carbohydrate derivative metabolism, and organonitrogen compound biosynthesis. In contrast, we found that proteins associated with redox homeostasis were the only OX-proteins that were enriched in E. histolytica trophozoites which were incubated with live E. coli O55. These data indicate that E. coli has a profound impact on the redox proteome of E. histolytica. Unexpectedly, some E. coli proteins were also co-identified with E. histolytica proteins by OX-RAC. We demonstrated that one of these proteins, E. coli malate dehydrogenase (EcMDH) and its product, oxaloacetate, are key elements of E. coli-mediated resistance of E. histolytica to oxidative stress and that oxaloacetate helps the parasite survive in the large intestine. We also provide evidence that the protective effect of oxaloacetate against oxidative stress extends to Caenorhabditis elegans.
MOANOFS: Multi-Objective Automated Negotiation based Online Feature Selection System for Big Data Classification

Feature Selection (FS) plays an important role in learning and classification tasks. The object of FS is to select the relevant and non-redundant features. Considering the huge amount number of features in real-world applications, FS methods using batch learning technique can't resolve big data problem especially when data arrive sequentially. In this paper, we propose an online feature selection system which resolves this problem. More specifically, we treat the problem of online supervised feature selection for binary classification as a decision-making problem. A philosophical vision to this problem leads to a hybridization between two important domains: feature selection using online learning technique (OFS) and automated negotiation (AN). The proposed OFS system called MOANOFS (Multi-Objective Automated Negotiation based Online Feature Selection) uses two levels of decision. In the first level, from n learners (or OFS methods), we decide which are the k trustful ones (with high confidence or trust value). These elected k learners will participate in the second level. In this level, we integrate our proposed Multilateral Automated Negotiation based OFS (MANOFS) method to decide finally which is the best solution or which are relevant features. We show that MOANOFS system is applicable to different domains successfully and achieves high accuracy with several real-world applications. Index Terms: Feature selection, online learning, multi-objective automated negotiation, trust, classification, big data.
Using Program Slicing to Detect the Dead Code

Program code may have useless statements of code. These useless statements called dead code. The dead code affects the code quality negatively. In this research, a new model has been introduced to identify the dead code in a program and remove it. The decomposition slicing technique is used to identify the live code, and refactoring techniques can also help in tide up the code design. As a result, the proposed model succeed to remove the dead code of a program. However, still need some extra work to increase the accuracy of identifying the live code.
Scalable Point Cloud Geometry Coding with Binary Tree Embedded Quadtree

Many applications of point cloud have recently been identified in automobile navigation system, visual communication, and so on. However, the huge data size of point cloud has been a bottleneck for the practical implementations. In this paper, we present a compression scheme that utilizes variable-rate coding of a same point cloud data at different quality. Point cloud is encoded at fixed-rate for highest representation. Encoder, however, can present variable-rate encoded data for any lowest to highest representation to decoder which is then decoded to reconstruct point cloud at different quality. Variable-rate encoding is achieved through the so-called binary tree quadtree (BTQT) scheme. The BTQT scheme made the compression more effective by dividing point cloud frame into blocks using binary-tree and encoding flat surfaces in the blocks by quadtree and non-flat surfaces by octree. Simulation results show that scalable coding solution can efficiently compress point cloud data at variable rate compensating the quality.
How the Home Matches the Person: The Relevance of the Home Environment Questionnaire

Seeking ecological and healthy ways to stimulate one’s well-being and growth is one of the most important trends in today’s psychology (Moser, 2009; Horelli & Prezza, 2004). Ironically, the home as the very basic “ ” has not attracted until now much research focus. Home is the primary, basic and relatively constant space in human life that meets a person’s basic needs, stimulates development and also carries a psychotherapeutic function. Even when a person has no home, the home’s image itself appears to be the source of a person’s support (Bochaver, 2015; Dovey, 1985; Case, 1996; Nartova-Bochaver, Bochaver, Dmitrieva, & Reznichenko, 2016; Abstract In a questionnaire study with N = 1730 participants aged from 11 to 72 years (M = 20.4, Me = 19, SD = 6.96), 60% female, we developed the Relevance of the Home Environment Questionnaire (RHEQ) aimed at measuring parameters of the home environment as the inhabitants’ eco-social resource. Results from exploratory and confirmatory factor analyses showed that the seven-factor structure of the questionnaire was uniquely formulated. The scales were titled as follows: Privacy, Potential, Self-presentation, Ergonomics, Home detachment, Plasticity, and Historicity. Consistency coefficients (Cronbach’s alpha) of scales ranged from . 91 to . 94. Age trends in four out of seven scales were found. Potential scores were higher in women, while Home detachment — in men. Content validity was investigated by comparison of home relevance scores in students living in their own homes or dormitories. Convergent validity was explored by measuring correlations of the scales with the Functionality of the Home Environment Questionnaire and the Home Attachment Scale. All psychometric properties are satisfactory. Data was discussed with regard to possible applications in research, expertise, and psychotherapy.
Vehicle Pose Estimation Using G-Net: Multi-Class Localization and Depth Estimation

In this paper we present a new network architecture, called G-Net, for 3D pose estimation on RGB images which is trained in a weakly supervised manner. We introduce a two step pipeline based on region-based Convolutional neural networks (CNNs) for feature localization, bounding box refinement based on non-maximum-suppression and depth estimation. The G-Net is able to estimate the depth from single monocular images with a self-tuned loss function. The combination of this predicted depth and the presented two-step localization allows the extraction of the 3D pose of the object. We show in experiments that our method achieves good results compared to other state-of-the-art approaches which are trained in a fully supervised manner.
Microgrid Sizing with Environmental and Economic Optimization

Hybrid microgrid is the best system to build for the lower demand mainly in the non-electrified rural area. However, a hybrid system based on PV, wind turbine, diesel and Battery Energy Storage System (BESS) must be optimized. In this paper, Multi-Objective Particle Swarm Optimization (MOPSO) method is used to design the hybrid system. Net Present Cost (NPC) that cover all capital, operation maintenance and replacement cost, and emission cost to evaluate the damage caused by the pollution are the choosen obective functions. Two location area are considered to sizing the hybrid system taking in account reliability with Loss of Power Supply Probability (LPSP), Fraction Renewable (FR) that must be upper to 90% and availability should be upper to 96%. A Pareto-optimal solution using Pareto frontier give the best results with highest reliability and availability of this system.
Oxidized and electronegative low-density lipoprotein as potential biomarkers of cardiovascular risk in obese adolescents

OBJECTIVES: To evaluate biomarkers associated with early cardiometabolic risk in obese adolescents. METHODS: This cross-sectional study included 137 adolescents of both sexes aged 10 to 19 years divided into a normal weight group (NW) (n=69) and an obese group (OB) (n=68). RESULTS: As expected, obesity showed positive associations with homeostatic model assessment for insulin resistance (HOMA-IR), triacylglycerol, insulin, plasma levels of non-esterified fatty acids, and cholesterol ester transfer protein activity and negative associations with plasma antioxidant levels. Plasma oxidized low-density lipoprotein (oxLDL) and electronegative low-density lipoprotein [LDL(-)] levels were significantly higher in the OB group. Higher tertiles of oxLDL were associated with increased values of body mass index; waist circumference; fatty mass percentage (%FM); and the atherogenic lipids non-high-density-lipoprotein cholesterol (non-HDL-C), low-density lipoprotein cholesterol (LDL-C), apolipoprotein B and triacylglycerol. Higher tertiles of LDL(-) were robustly associated with body mass index and waist circumference. Logistic regression models (odds ratios) confirmed that increased values of lipids and apolipoprotein B were associated with increased risk of oxLDL. For LDL(-), these associations were not significant, suggesting that another mechanism is involved in generating this particle in obese adolescents. CONCLUSIONS: Obese adolescents showed increased plasma LDL(-) and oxLDL, and obese girls had more LDL(-) than obese boys. Therefore, oxLDL is strongly and independently associated with classical cardiovascular risk factors, while increased levels of LDL(-) were influenced by body mass index, waist circumference and demographic parameters in obese adolescents.
Densely Stacked Generative Adversarial Networks

Recent work has shown that GANs can be stacked to generate plausible images, with each GAN generating lower-level representations conditioned on higher-level ones. However, the gap between generated representations and the real ones grows rapidly as the number of stacked GANs increases. In this paper, we aim to alleviate this problem and propose a novel generative model named DSGAN, which densely connects all stacks of GANs in order to provide enhanced conditional information for individual GANs. Besides, we introduce a weighted feature loss during training, which encourages representations reuse and provides multi-level supervision for each generator. Compared to existing architectures, our model maximizes internal information flow and prominently improves the discriminability of generated representations. We evaluate DSGAN on three public datasets: MNIST, SVHN and CIFAR-10. Experiments show that DSGAN outperforms current stacked GANs in generating realistic images, which has achieved obvious improvements on quantitative measures like Inception Score.
Joint Transmit Beamforming and Antenna Selection in MIMO Systems

This letter investigates joint beamforming and antenna selection (JBAS) for a spatial diversity multiple-input multiple-output system, where the transmitter equipped with multiple antennas performs both antenna selection and beamforming optimization to maximize the received signal-to-noise ratio (SNR). The JBAS problem is NP-hard to solve due to a sparsity constraint. In this letter, we propose smooth approximation-based iterative algorithms to address the JBAS problem. The simulation results validate the effectiveness of the proposed algorithms in terms of the achieved SNR and the computational complexity.
Combining an Internal SMC with an External MTPA Control Loop for an Interior PMSM

This paper deals with control strategies in Permanent Magnet Synchronous Machines (PMSMs). An internal loop control is proposed in which a Sliding Mode Control (SMC) works to control the optimal desired current calculated by the Maximum Torque per Ampere (MTPA) strategy which works in the external loop of control. The main contribution of the paper is to show how disturbance rejection is provided together with a load compensation. Simulation results are shown comparing, with the same control scheme, the performances obtained with SMC and Pronortional-Intezral controllers (PI).
Multiband Antenna Deploying Swastika Slotted Fractal for WLAN Utilities

The Swastika slotted fractal antenna is unveiled in this article that exhibits multiband operation that can used for different WLAN applications like Bluetooth and Wi-Fi, The proposed antenna has been designed on substrate of material FR4 with 1.6 mm thickness, 0.02 loss tangent and 4.4 dielectric constant. The simulation is done using IE3D simulator and experimentally verified through Rohde & Schwarz ZVL Vector Network Analyzer. Antenna parameters like S-Parameter (S11), Voltage Standing Wave Ratio (VSWR) and Directivity are investigated. The values of measured S-Parameter (S11) are −26.59 dB, −24.31 dB and −12.39 dB for frequencies 2.45 GHz, 2.77 GHz and 2.99 GHz respectively. The results of designed and fabricated prototype are in good coalescence with each other.
Is the turbulence caused by the dynamic environment straining start-ups' agility? Exploring the pragmatic solution of open innovation while capturing the role of formal and informal protection mechanisms

This Paper emphasizes the role of market dynamics in determining the extent of openness for knowledge sourcing measured on the basis of breadth and depth for startups. The backdrop was formulated on the padding of the resource-based view, knowledge-based view, network theory, organizational learning, opportunistic behaviour and trust, each of which serve as a valuable theoretical underpinning to encapsulate fragmented branches. Protection mechanisms play a critical role when opening up the system to match and align knowledge and resources. Open innovation is contingent on the format used to bring resources together which according to our results is sequenced as divergence (breadth) leading to convergence (depth).
Performance of a Non-Orthogonal Multiple Access System With Full-Duplex Relaying

In this letter, we study a power-domain non-orthogonal multiple access (NOMA) system in which a base station (BS) superimposes the transmit signals to the users. To enhance spectral efficiency and link reliability for the far-distance user, a full-duplex (FD) relay assists the BS while the near-distance user is reached over the direct link. For this setting, we analyze outage probability and sum rate of the NOMA system over Nakagami-<inline-formula> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> fading with integer fading severity parameter <inline-formula> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula>. Numerical results are provided for outage probability and sum rate to show the effect of system parameters on the performance of the FD NOMA system over Nakagami-<inline-formula> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> fading.
Role of Functional Beverages on Sport Performance and Recovery

Functional beverages represent a palatable and efficient way to hydrate and reintegrate electrolytes, carbohydrates, and other nutrients employed and/or lost during physical training and/or competitions. Bodily hydration during sporting activity is one of the best indicators of health in athletes and can be a limiting factor for sport performance. Indeed, dehydration strongly decreases athletic performance until it is a risk to health. As for other nutrients, each of them is reported to support athletes’ needs both during the physical activity and/or in the post-workout. In this study, we review the current knowledge of macronutrient-enriched functional beverages in sport taking into account the athletes’ health, sports performance, and recovery.
Multi-Class Wheat Moisture Detection with 5GHz Wi-Fi: A Deep LSTM Approach

Moisture content of cereal grains is a highly important factor in safe storage and food processing. The existing detection methods are either time-consuming, sensitive to the environment, or have a high cost. In this paper, we propose DeepWMD, a deep LSTM network based system for multi-class wheat moisture detection. We first collect CSI amplitude and phase difference data to detect wheat moisture content. Then, we design the DeepWMD system with commodity Wi-Fi devices in the 5GHz band, including data preprocessing of collected CSI data, offline training, and online testing. Our experimental results verify the efficacy of the proposed DeepWMD system, and demonstrates that DeepWDM can achieve high-precision multi-class wheat moisture detection in different indoor storage environments.
Optimal User Grouping for Downlink NOMA

In this letter, we study the user grouping for the downlink nonorthogonal multiple access system. The optimal user grouping problem is formulated to maximize the sum rate. Due to the combinatorial nature, the computational complexity to find the optimal solution is generally very high. To address the complexity issue, we propose faster optimal and suboptimal schemes for the user grouping problem. We then obtain the useful and important insight of the optimal user grouping. Numerical results demonstrate that the proposed schemes significantly outperform the existing schemes and the proposed suboptimal algorithm works very close to the optimal algorithm.
An Order-based Algorithm for Learning Structure of Bayesian Networks

In this paper, we study the problem learning structure of Bayesian networks from data. The problem of Bayesian networks structure learning (BNSL) takes a dataset as input and produces a directed acyclic graph (DAG) as the output. This problem is known to be NP-hard which is commonly solved using the heuristic methods. There are generally three main approaches to the BNSL problem: score-based , constraint-based and hybrid learning. We propose a new simple and fast algorithm for addressing BNSL problem. The proposed hybrid algorithm is based on a partial ordering learned from data. We reduce the super-exponential search space of structures to the smaller ordering space of nodes. We evaluate the proposed algorithm using some standard benchmark datasets and compare the results with those of some state-of-the-art algorithms. Finally, we show that our algorithm is competitive with recent algorithms.
Smart Soil Parameters Estimation System Using an Autonomous Wireless Sensor Network With Dynamic Power Management Strategy

This paper presents the design of a wireless sensor network (WSN) system for smart estimation of soil conditions. The WSN is formed by very low-power autonomous sensor nodes and employs Internet of Things and cloud service communication protocols to generate spatial distribution maps of soil parameters at two different levels below ground. An artificial neural network is used to analyze the measured data and estimate the levels of phosphorus (P) in the soil. This feature eliminates the need to perform time-consuming laboratory analysis to continuously monitor the value of this nutrient. Considering the changing rate of soil phenomena throughout a day, a dynamic power management strategy is applied, allowing the system to establish an adaptive balance between its energy consumption and the accuracy of phosphorus estimation. The proposed precision agriculture structure allows the implementation of a flexible methodology that can be adapted to different type of crops and agricultural regions. Experimental results obtained in the lab and on the field corroborate the system’s performance and reliability.
Microstrip Patch Antenna for WLAN and RAS Applications

This paper presents a microstrip patch antenna for WLAN and radio astronomy services (RAS). By embedding a trapezoidal and two rectangular notches with a sorting pin, the designed antenna resonates at two distinct resonating frequencies of 4.8 GHz and 5.8 GHz. It is shown that the proposed antenna has good gain and radiation characteristics.
Relay-Assisted Proactive Eavesdropping With Cooperative Jamming and Spoofing

In this paper, we consider a legitimate proactive eavesdropping scenario where a distant central monitor covertly wiretaps the communication between a pair of suspicious users via several multi-antenna full-duplex spoofing relays and a multi-antenna cooperative jammer. Assuming an adaptive transmission policy at the suspicious users, the spoofing relays not only intercept but also forward the manipulated information to control the data rate of the suspicious users in collaboration with the jammer. We provide a technique which jointly optimizes the receive combining vector at the central monitor, the precoders at the relays, and the transmit covariance matrix at the jammer for maximizing the eavesdropping rate. To reveal some fundamental properties of the optimal operation, we first study a single-relay system. It is shown that when the central monitor experiences a poor eavesdropping channel link, the spoofing relay and the jammer should transmit destructive signals and jamming signals, respectively. In this case, the suspicious users are forced to decrease their data rate, while the intercepted information can be successfully decoded by the central monitor. On the other hand, when the eavesdropping channel condition is favorable, the spoofing relay forwards constructive signals to further increase the data rate at the suspicious users in a way that more information can be intercepted from the suspicious users. We then formulate a general eavesdropping rate maximization problem for multiple relays and present a semi-definite relaxation approach. A low-complexity design is also proposed based on our analysis for the single-relay system. Simulation results verify the efficiency of the proposed solutions compared to other baseline schemes in various practical setups.
Robust Tensor Principal Component Analysis in All Modes

Robust tensor principal component analysis extracts the low rank and sparse component of multi-dimensional data by tensor singular value decomposition (t-SVD), which can be used for many data analysis problems. However, the current t-SVD based methods cannot fully extract the low rank component in tensor data, and low rank structure still exists in the core tensor, because t-SVD does not decompose data in the third mode. To fully exploit the low rank structure, we further extract the low rank component using low rank plus sparsity for the core matrix whose entries are from the diagonal elements of the frontal slices in the core tensor. The proposed method is applied to three groups of numerical experiments on image denoising, illumination normalization for face images and motion separation for surveillance videos, respectively, and the results show that the proposed method outperforms state-of-the-art methods in terms of both accuracy and computational complexity.