Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Intelligent Method for Bearing Fault Diagnosis Based on Hermitian Scale-Energy Spectrum

Early bearing fault diagnosis can prevent from production accidents and improve safety, so it has an important practical engineering significance. In this paper, an intelligent condition monitoring of rolling bearing based on Hermitian scale-energy spectrum (HSES) and time-domain characteristics is presented. First, aiming to the difficulty of feature extraction for rolling bearing, the HSES of continuous wavelet transform is proposed to analyze the vibration signal from the viewpoint of energy distribution. In order to build robust features, the dimensionless parameters are also used to assist in extracting time-domain characteristics. Then, these characteristics of the time and wavelet domains are mapped into a low-dimensional feature space by using the principal components analysis, which can reduce the redundancy of the features. Within the acquired feature space, support vector machine is used to intelligently determine the existence of bearing failure and classify the type of fault. Moreover, in order to make the model automatically optimize parameters in different working conditions, a designed genetic algorithm is implemented in this paper. Experimental results show that the proposed method can not only accurately recognize fault pattern but also effectively realize the automation during the whole process.
Design of a microstrip antenna with filtering characteristics for wireless communication systems

A novel microstrip antenna with filtering characteristics is designed for wireless communication system. This antenna with filtering characteristics consists of two microstrip open-loop resonators, a coupled line and a microstrip antenna. The coupled line plays a role of admittance inverter, meanwhile it provides energy for antenna. By this way, the antenna performs not only a radiator, but also the last resonator of the bandpass filter. Through this integrated design, it not only reduces the overall size of the filter and the antenna, but also reduces the loss. The simulation results show that the filter-antenna achieves good filtering and impedance bandwidth characteristic.
ToriLLE: Learning Environment for Hand-to-Hand Combat

We present Toribash Learning Environment (To-riLLE), a learning environment for machine learning agents based on the video game Toribash. Toribash is a MuJoCo-like environment of two humanoid characters fighting each other hand-to-hand, controlled by changing actuation modes of the joints. Competitive nature of Toribash as well its focused domain provide a platform for evaluating self-play methods, and evaluating machine learning agents against human players. In this paper we describe the environment with ToriLLE’s capabilities and limitations, and experimentally show its applicability as a learning environment with baseline and human experiments. The source code of the environment and conducted experiments can be found at https://github.com/Miffyli/ToriLLE.
UvA-DARE ( Digital Academic Repository ) Abscisic acid influences tillering by modulation of strigolactones in barley

Strigolactones (SLs) represent a class of plant hormones that are involved in inhibiting shoot branching and in promoting abiotic stress responses. There is evidence that the biosynthetic pathways of SLs and abscisic acid (ABA) are functionally connected. However, little is known about the mechanisms underlying the interaction of SLs and ABA, and the relevance of this interaction for shoot architecture. Based on sequence homology, four genes (HvD27, HvMAX1, HvCCD7, and HvCCD8) involved in SL biosynthesis were identified in barley and functionally verified by complementation of Arabidopsis mutants or by virus-induced gene silencing. To investigate the influence of ABA on SLs, two transgenic lines accumulating ABA as a result of RNAi-mediated down-regulation of HvABA 8’-hydroxylase 1 and 3 were employed. LC-MS/MS analysis confirmed higher ABA levels in root and stem base tissues in these transgenic lines. Both lines showed enhanced tiller formation and lower concentrations of 5-deoxystrigol in root exudates, which was detected for the first time as a naturally occurring SL in barley. Lower expression levels of HvD27, HvMAX1, HvCCD7, and HvCCD8 indicated that ABA suppresses SL biosynthesis, leading to enhanced tiller formation in barley.
Translational control through differential ribosome pausing during amino acid limitation in mammalian cells

Limitation for amino acids is thought to regulate translation in mammalian cells primarily by signaling through the kinases mTORC1 and GCN2. We find that limitation for the amino acid arginine causes a selective loss of tRNA charging, which regulates translation through ribosome pausing at two of six arginine codons. Surprisingly, limitation for leucine, an essential and abundant amino acid in protein, results in little or no ribosome pausing. Chemical and genetic perturbation of mTORC1 and GCN2 signaling revealed that their robust response to leucine limitation prevents ribosome pausing, while an insufficient response to arginine limitation led to loss of arginine tRNA charging and ribosome pausing. Codon-specific ribosome pausing decreased protein production and triggered premature ribosome termination without significantly reducing mRNA levels. Together, our results suggest that amino acids which are not optimally sensed by the mTORC1 and GCN2 pathways still regulate translation through an evolutionarily conserved mechanism based on codon-specific ribosome pausing.
Intercept Probability Analysis of Wireless Powered Relay System in kappa-mu Fading

Energy harvesting relays are predicted to play a pivotal role in large scale networks. This paper evaluates the secrecy performance of a system that employs a two-way decode-and-forward relay assisting transmission between two nodes. The relay has RF energy harvesting capability and it can receive energy from the RF signal and the transmission of the system can be overheard by an eavesdropper. More specifically, we derive an exact expression for the interception probability when the main and wiretap links experience generalized κ-μ fading. The impacts of the power- splitting factor at the relay and the fading parameters on the secrecy performance of the considered system are also assessed. Numerical and simulation results are presented to verify the derived results.
3D kidney segmentation from abdominal diffusion MRI using an appearance-guided deformable boundary

A new technique for more accurate automatic segmentation of the kidney from its surrounding abdominal structures in diffusion-weighted magnetic resonance imaging (DW-MRI) is presented. This approach combines a new 3D probabilistic shape model of the kidney with a first-order appearance model and fourth-order spatial model of the diffusion-weighted signal intensity to guide the evolution of a 3D geometric deformable model. The probabilistic shape model was built from labeled training datasets to produce a spatially variant, independent random field of region labels. A Markov-Gibbs random field spatial model with up to fourth-order interactions was adequate to capture the inhomogeneity of renal tissues in the DW-MRI signal. A new analytical approach estimated the Gibbs potentials directly from the DW-MRI data to be segmented, in order that the segmentation procedure would be fully automatic. Finally, to better distinguish the kidney object from the surrounding tissues, marginal gray level distributions inside and outside of the deformable boundary were modeled with adaptive linear combinations of discrete Gaussians (first-order appearance model). The approach was tested on a cohort of 64 DW-MRI datasets with b-values ranging from 50 to 1000 s/mm2. The performance of the presented approach was evaluated using leave-one-subject-out cross validation and compared against three other well-known segmentation methods applied to the same DW-MRI data using the following evaluation metrics: 1) the Dice similarity coefficient (DSC); 2) the 95-percentile modified Hausdorff distance (MHD); and 3) the percentage kidney volume difference (PKVD). High performance of the new approach was confirmed by the high DSC (0.95±0.01), low MHD (3.9±0.76) mm, and low PKVD (9.5±2.2)% relative to manual segmentation by an MR expert (a board certified radiologist).
Analysis of Exercise-Induced Periodic Breathing Using an Autoregressive Model and the Hilbert-Huang Transform

Evaluation of exercise-induced periodic breathing (PB) in cardiopulmonary exercise testing (CPET) is one of important diagnostic evidences to judge the prognosis of chronic heart failure cases. In this study, we propose a method for the quantitative analysis of measured ventilation signals from an exercise test. We used an autoregressive (AR) model to filter the breath-by-breath measurements of ventilation from exercise tests. Then, the signals before reaching the most ventilation were decomposed into intrinsic mode functions (IMF) by using the Hilbert-Huang transform (HHT). An IMF represents a simple oscillatory pattern which catches a part of original ventilation signal in different frequency band. For each component of IMF, we computed the number of peaks as the feature of its oscillatory pattern denoted by Δi. In our experiment, 61 chronic heart failure patients with or without PB pattern were studied. The computed peaks of the third and fourth IMF components, Δ3 and Δ4, were statistically significant for the two groups (both p values < 0.02). In summary, our study shows a close link between the HHT analysis and level of intrinsic energy for pulmonary ventilation. The third and fourth IMF components are highly potential to indicate the prognosis of chronic heart failure.
Molecular Structure and Decomposition Kinetics of Kaolinite/Alkylamine Intercalation Compounds

Although the development of clay/polymer nanocomposites and their applications have attracted much attention in recent years, a thorough understanding of the structure and the decomposition mechanism of clay/polymer nanocomposites is still lacking. In this research, the intercalation of kaolinite (Kaol) with different alkylamines were investigated by X-ray diffracion (XRD), Fourier-transform infrared spectroscopy (FTIR), and thermogravimetry and differential scanning calorimetry (TG-DSC). The results showed that the intercalation of Kaol/methanol compound with hexylamine (HA), dodecylamine (DA), and octadecylamine (OA) led to the expansion of the interlayer distance and resulted in the dominant basal diffraction at 2.86, 4.08, and 5.66 nm. The alky chains of HA, DA, and OA are tilted toward the Kaol surface in bilayer with an inclination angle of ~40°. The most probable mechanism function, activation energy E, and pre-exponential factor A were obtained by mutual authentication using KAS and Ozawa methods, itrative and Satava integral method. The average activation energy E of the three intercalation compounds are 104.44, 130.80, and 154.59 kJ mol−1, respectively. It shows a positive correlation with the alkyl chain length. The pre-exponential factor A was estimated to be 1.09 × 1015, 1.15 × 108, and 4.17 × 1021 s−1, respectively. The optimized mechanism function for the decomposition of alkylamine is G(α) = [(1-α) −1/3−1]2.
Anomaly Detection in Vehicle-to-Infrastructure Communications

This paper presents a neural network-based anomaly detection system for vehicular communications. The proposed system is able to detect in-vehicle data tampering in order to avoid the transmission of bogus or harmful information. We investigate the use of Long Short-term Memory (LSTM) and Multilayer Perceptron (MLP) neural networks to build two prediction models. For each model, an efficient architecture is designed based on appropriate hardware requirements. Then, a comparative performance analysis is provided to recommend the most efficient neural network model. Finally, a set of metrics are selected to show the accuracy of the proposed detection system under several types of security attacks.
Shared-Memory Parallel Maximal Clique Enumeration

We present shared-memory parallel methods for Maximal Clique Enumeration (MCE) from a graph. MCE is a fundamental and well-studied graph analytics task, and is a widely used primitive for identifying dense structures in a graph. Due to its computationally intensive nature, parallel methods are imperative for dealing with large graphs. However, surprisingly, there do not yet exist scalable and parallel methods for MCE on a shared-memory parallel machine. In this work, we present efficient shared-memory parallel algorithms for MCE, with the following properties: (1) the parallel algorithms are provably work-efficient relative to a state-of-the-art sequential algorithm (2) the algorithms have a provably small parallel depth, showing that they can scale to a large number of processors, and (3) our implementations on a multicore machine shows a good speedup and scaling behavior with increasing number of cores, and are substantially faster than prior shared-memory parallel algorithms for MCE.
Comparative study of various machine learning classifiers on medical data

Data classification is an important task to label the class of data. Attributes or feature is a portion of information which is applicable to the task of computation. Our task is to predict and prevent cardiac arrest which is one of the biggest challenges of cardiology using a machine learning classifier. Since a particular classifier may or may not work well for such datasets so it is important to do a comparative study of classifiers in order to achieve maximum performance in such critical predictions of cardiac arrest. The UCI dataset is chosen for the purpose of comparison, and a comparative study of various classifiers is provided on the same dataset. Results are given as accuracy of different classifiers. The various classifier methods include the KNN classifier, Nave Bayes classifier, Support Vector Machine, Neural Network, Gaussian Mixture Model and Decision Tree classifier.
Targeting Resident Memory T Cells for Cancer Immunotherapy

A novel population of memory CD8+ T cells called resident memory T cells (TRM) has been identified based on their phenotype (CD103, CD69) and on their local tissue residency without recirculating in the blood. These cells have been implicated in protective immune response against pathogens in both animal models and humans. Their role in cancer is just emerging as a key player in tumor immunosurveillance. Many properties of these cells suggest that they could control tumor growth: (i) they respond much faster to reexposure to cognate antigen than circulating memory cells, (ii) they express high levels of cytotoxic molecules, and (iii) they are enriched in tumor-specific T cells in close contact with tumor cells. TRM are present in many human cancers and are associated with a good clinical outcome independently of the infiltration of CD8+ T cells. It has been recently shown that the efficacy of cancer vaccines depends on their ability to elicit TRM. In adoptive cell therapy, the transfer of cells with the ability to establish TRM at the tumor site correlates with the potency of this approach. Interestingly, TRM express immune checkpoint molecules and preliminary data showed that they could expand early during anti-PD-1 treatment, and thus be considered as a surrogate marker of response to immunotherapy. Some cues to better expand these cells in vivo and improve the success of cancer immunotherapy include using mucosal routes of immunization, targeting subpopulations of dendritic cells as well as local signal at the mucosal site to recruit them in mucosal tissue.
Angular Based Beamforming and Power Allocation Framework in a Multi-User Millimeter-Wave Massive MIMO System

Millimeter-Wave (mmWave) systems offer extremely large Band-Width (BW) and highly Line-of-Sight (LOS) dominant channels. Many beamforming and power allocation techniques have recently emerged to leverage such channel characteristics in order to enhance the sum capacity and the coverage for multi-user transmissions. However, most of the existing algorithms are practically limited due to full channel knowledge and high complexity requirements. In this paper we introduce a novel, low complexity, angular based beamforming and power allocation framework, that requires the knowledge of the main contributive Directions of Arrival/Departure (DoAs/DoDs) of the propagation channel only. Exploiting the fact that propagation conditions are highly driven by the geometrical structure of the channel in mmWave scenarios, our method relies on the estimation of the leakage caused by each User Equipment (UE) on all the other UEs, approximated from the DoAs contributions. We prove with the simulation results that this approach, in addition to be practically plausible, can enhance the network Spectral Efficiency (SE) alongside with maintaining acceptable data rates for the cell edge UEs.
Spectrum Allocation for mmWave Backhaul Networks: An Approach Based on Matching Game

Millimeter wave backhaul (mmW BH) network has attracted the great interest of both academia and industry for providing high capacity, low cost and easy installation. In this regard, we formulate a dynamic spectrum allocation optimization problem for mmW BH network, which maximizes the spectrum efficiency of the network and characterizes the associated traffic demand of each small cell base station (SBS). To solve the problem, a spectrum allocation algorithm based on matching game (SAMG) is proposed, which yields a two-sided stable and Pareto optimal matching between the sub-channels and SBSs. Simulations show that our SAMG algorithm can effectively improve the spectrum efficiency of the mmW BH network, with the dynamic traffic demand of each SBS satisfied.
Optimal Time Allocation in Relay Assisted Backscatter Communication Systems

In this paper, we consider a relay assisted backscatter communication (RaBackCom) system, where a user backscatters incident signals from a carrier emitter (CE) to a relay and a receiver simultaneously, and then the relay forwards the user's information to the receiver for throughput improvement. We consider two cases that the relay is with/without an embedded energy source. Specifically, if the relay does not have an energy source, it first harvests energy from the signals from the CE and then uses its harvested energy for information forwarding. For both cases, we formulate time allocation problems on the user's information backscattering, the user's information forwarding, or the relay's energy harvesting to maximize the system throughput, and then derive closed-form solutions. Simulation results demonstrate the advantages of the proposed relay cooperation scheme with the optimal time allocation in terms of system throughput.
Iterative MRC and EGC Receivers for MIMO-OFDM Systems

This paper proposes a way of turning the equalisation of multiple- input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) signals simpler through the employment of low complexity iterative frequency domain equalisation (FDE) receivers based on equal gain combining (EGC) and maximum ratio combining (MRC) concepts, that do not require computing the inverses of high dimensional channel matrices. Performance results show that MIMO- OFDM schemes employing this type of techniques can achieve excellent bit error rate (BER) performances over severe time- dispersive channels and, therefore, approach or even outperform receivers that require inverting channel matrices, such as zero forcing (ZF) and minimum mean squared error (MMSE), after a few iterations.
Predictive Model for the Incidence of Hyperkalemia for Congestive Heart Failure Patients on Spironolactone

The importance of Spironolactone in congestive heart failure (CHF) treatment has been well established. The prediction of the hyperkalemia in the patient using Spironolactone is still not clearly defined. The aim of this study is to develop an accurate prediction model of hyperkalemia incidence in CHF patients on Spironolactone using machine learning techniques. A classification and prediction process have been applied on patients' data of the cardiac center of National Guard Health Affairs, King Abdulaziz Medical City (KAMC). The study was conducted on the records of 1533 patients representing the CHF patient's during the period of 2011-2016. Our experiments show that the JRip classifier achieves the best performance for the Precision (0.983), Recall (0.983), F-measure (0.976) and Accuracy (98.27) metrics while the Naiive Bayesian classifier achieves the best performance for the Specificity (0.652) and AUC (0.93) metrics.
Host-Derived Microvesicles Carrying Bacterial Pore-Forming Toxins Deliver Signals to Macrophages: A Novel Mechanism of Shaping Immune Responses

Bacterial infectious diseases are a leading cause of death. Pore-forming toxins (PFTs) are important virulence factors of Gram-positive pathogens, which disrupt the plasma membrane of host cells and can lead to cell death. Yet, host defense and cell membrane repair mechanisms have been identified: i.e., PFTs can be eliminated from membranes as microvesicles, thus limiting the extent of cell damage. Released into an inflammatory environment, these host-derived PFTs-carrying microvesicles encounter innate immune cells as first-line defenders. This study investigated the impact of microvesicle- or liposome-sequestered PFTs on human macrophage polarization in vitro. We show that microvesicle-sequestered PFTs are phagocytosed by macrophages and induce their polarization into a novel CD14+MHCIIlowCD86low phenotype. Macrophages polarized in this way exhibit an enhanced response to Gram-positive bacterial ligands and a blunted response to Gram-negative ligands. Liposomes, which were recently shown to sequester PFTs and so protect mice from lethal bacterial infections, show the same effect on macrophage polarization in analogy to host-derived microvesicles. This novel type of polarized macrophage exhibits an enhanced response to Gram-positive bacterial ligands. The specific recognition of their cargo might be of advantage in the efficiency of targeted bacterial clearance.
Bandwidth-enhanced of a very simple circularly polarized square slot antenna with offset feed and modified ground plane

In this paper, a simple wideband circularly polarized (CP) square slot antenna is presented. This antenna is made up of an offset-center 50-Ω microstrip-fed port and a modified ground plane. Both the impedance bandwidth (IBW) and the 3-dB axial ratio bandwidth (ARBW) are greatly improved after adjusting the position of microstrip-fed port. By appropriately adding and cutting on the ground plane, the ARBW is greatly broadened. To verify this design, the CP antenna is fabricated and measured. The measured results of the antenna exhibit an IBW of 101.5% (1.35∼4.13 GHz), an ARBW of 85.7% (1.6∼4.0 GHz). And the measured peak gain can reach 4.2 dBic. The proposed antenna is very suitable for wideband CP communication applications.
Joint Bandwidth and Power Allocation of Hybrid Spectrum Sharing in Cognitive Radio - Invited Paper

As an effective approach to alleviate the spectrum scarcity problem, cognitive radio (CR) has recently attracted an increasing amount of attention. In this paper, an optimization algorithm that joints bandwidth and power allocation of hybrid spectrum sharing is proposed in CR. According to the location variation of cognitive user (CU) that adopt the random waypoint based mobility models, the state of CU can switch between Underlay spectrum sharing model and Overlay spectrum one. Simultaneously, this algorithm can maximize the channel capacity through jointly optimizing power and bandwidth of CU when the Primary User's (PU's) interference temperature and the CU's transmission can be satisfied. Simulation results show that the proposed algorithm can more effectively improve the channel capacity than the single Underlay system and traditional solutions that the bandwidth is evenly allocated.