Gravitational wave trispectrum in the axion-SU(2) model

We study the trispectrum of the gravitational waves (GWs) generated through the dynamics of an axionic spectator field and SU(2) gauge fields during inflation. In non-Abelian gauge theory, the gauge fields have four-point self-interactions, which induce the tree-level GW trispectrum. We formulate this type of the GW trispectrum including the non-dynamical contributions and evaluate it in the equilateral limit as a unique signal of this model. We find that the ratio of the GW trispectrum to the cube of the scalar power spectrum can be as large as 𝒪(106) in the viable parameter space, which could be captured in the CMB observations.

Novel 8-Port Network

The proposed 8-port network which consists of 4-coupler combined in unique way to produce 8-port network. This network with the proper terminations will find many applications in microwave and millimeter wave networks. This proposed 8-port network (with the proper termination) can be used as power combiner divider, six-port reflect meter in order to measure both the amplitude and the phase of the network under test. The proposed 8-port also can be used as phase comparator in a monopulse radar system for determining both the azimuth and the elevation of the target (K. Chang, et al, 1987). In addition, the circuit can be utilized as a 4-way power combiner/divider with the sub-arms isolated from one another. Generally, this 8-port network enjoys a compact structure which contains slots in sections the ground plane and between the coupled lines in order to improve the performance of this networks. Also this circuit can be utilized with proper termination as a network synthesize (G. Matthaei, et al, 1980). In the theoretical analysis of the proposed 8-port network, unique signal flow has been developed for this proposed. The purpose of the 8-port has been fabricated on Roger substrate of dielectric constant (ɛṛ = 3.38) and (thickness of the substrate is =0.2).

Incorporating Sentinel-1 SAR imagery with the MODIS MCD64A1 burned area product to improve burn date estimates and reduce burn date uncertainty in wildland fire mapping

Wildland fires result in a unique signal detectable by multispectral remote sensing and synthetic aperture radar (SAR). However, in many regions, such as Southeast Asia, persistent cloud cover and aerosols temporarily obstruct multispectral satellite observations of burned area, including the MODIS MCD64A1 Burned Area Product (BAP). Multiple days between cloud free pre- and postburn MODIS observations result in burn date uncertainty. We incorporate cloud-penetrating, C-band SAR-with the MODIS MCD64A BAP in Southeast Asia, to exploit the strengths of each dataset to better estimate the burn date and reduce the potential burn date uncertainty range. We incorporate built-in quality control using MCD64A1 to reduce erroneous pixel updating. We test the method over part of Laos and Thailand during April 2016 and found average uncertainty reduction of 4.5 d, improving 15% of MCD64A1 pixels. A new BAP could improve monitoring temporal trends of wildland fires, air quality studies and monitoring post-fire vegetation dynamics.

The Classification of Taekwondo Kicks Via Machine Learning: A feature selection investigation

Martial art strike classification by machine learning has drawn more attention over the past decade. The unique signal from each technique makes it harder to be recognized. Thus, this paper proposed an SVM, Random Forest, k-NN, and Naïve Bayes classification method applied to the time-domain signal to classify the three type of taekwondo technique. Data collected from the total of five participant and statistical features such as mean, median, minimum, maximum, standard deviation, variance, skewness, kurtosis, and standard error mean were extracted from the signal. After that, the data will be trained using selected rank features and hold-out method with k-fold cross-validation applied to the training and testing data. Therefore, with ANOVA test as features selection and 60:40 ratio of a hold-out method, Random Forest classifier score the highest accuracy of 86.7%..

Isolation of a member of the candidate phylum ‘Atribacteria’ reveals a unique cell membrane structure

A key feature that differentiates prokaryotic cells from eukaryotes is the absence of an intracellular membrane surrounding the chromosomal DNA. Here, we isolate a member of the ubiquitous, yet-to-be-cultivated phylum ‘Candidatus Atribacteria’ (also known as OP9) that has an intracytoplasmic membrane apparently surrounding the nucleoid. The isolate, RT761, is a subsurface-derived anaerobic bacterium that appears to have three lipid membrane-like layers, as shown by cryo-electron tomography. Our observations are consistent with a classical gram-negative structure with an additional intracytoplasmic membrane. However, further studies are needed to provide conclusive evidence for this unique intracellular structure. The RT761 genome encodes proteins with features that might be related to the complex cellular structure, including: N-terminal extensions in proteins involved in important processes (such as cell-division protein FtsZ); one of the highest percentages of transmembrane proteins among gram-negative bacteria; and predicted Sec-secreted proteins with unique signal peptides. Physiologically, RT761 primarily produces hydrogen for electron disposal during sugar degradation, and co-cultivation with a hydrogen-scavenging methanogen improves growth. We propose RT761 as a new species, Atribacter laminatus gen. nov. sp. nov. and a new phylum, Atribacterota phy. nov.

In silico characterization and expression profiles of zinc transporter-like (LOC100037509) gene of tomato

ABSTRACTZinc (Zn) is an essential microelement for plants. ZIP transporters play a critical role in Zn homeostasis in plants. This in silico study characterizes different characteristics of putative Zn transporter of tomato (Solyc07g065380) and its homologs. A total of 10 ZIP protein homologs were identified across nine plant species by protein BLAST. All these ZIP protein homologs located at chromosome 7 showed 305-350 amino acid residues, 7-8 transmembrane helices, and stable instability index. Further, these ZIP protein homologs are localized in the plasma membrane at the subcellular level corresponding to the ZIP zinc transporter (PF02535) domain. Gene organization analysis reveals the presence of 3 exon along with the position of the promoter, TATA-box, transcriptional start site, and splice sites in these ZIP transporter homologs, in which tomato ZIP transporter (NM_001247420.1) contains a promoter, TATA-box, transcriptional start site at 500, 911 and 946 bp, respectively along with several splice sites, which may be useful for targeting binding sites and transcription factor analysis. Further, the cutting sites and restriction enzymes of each ZIP gene homologs might be helpful for future transgenic studies underlying Zn homeostasis. MEMO displayed five conserved motifs associated with the ZIP zinc transporter, N-glycosylation site, and phosphorylation site. Phylogenetic studies reveal a closet relationship of Solyc07g065380 with Solanum pennellii homolog, while ZIP transporter of Nicotiana sylvestris and Nicotiana tabacum predicted to be in close connection. The Solyc07g065380 transporter is predominantly linked to several uncharacterized zinc metal ion transporters and expressed in diverse anatomical part, developmental stage, and subjected to pathogen and heat stress. The secondary structural prediction reveals unique signal peptide in the ZIP protein homologs of S. lycopersicum and S. pennellii along with extended alpha-helix. These bioinformatics analyses might provide essential background to perform wet-lab experiments and to understand Zn homeostasis for the development of Zn-biofortified crops.Key message♦ ZIP protein homologs are localized in the plasma membrane and are linked to ZIP zinc transporter (PF02535) domain at chromosome 7.♦ ZIP protein motifs are associated with the ZIP zinc transporter, N-glycosylation site, and phosphorylation site.♦ Phylogenetic studies reveal a closet relationship of Solyc07g065380 with Solanum pennellii homolog.♦ ZIP protein homologs of S. lycopersicum and S. pennellii show unique signal peptide along with extended alpha-helix.

A Mathematical Analysis of Memory Lifetime in a Simple Network Model of Memory

We study the learning of an external signal by a neural network and the time to forget it when this network is submitted to noise. The presentation of an external stimulus to the recurrent network of binary neurons may change the state of the synapses. Multiple presentations of a unique signal lead to its learning. Then, during the forgetting time, the presentation of other signals (noise) may also modify the synaptic weights. We construct an estimator of the initial signal using the synaptic currents and in this way define a probability of error. In our model, these synaptic currents evolve as Markov chains. We study the dynamics of these Markov chains and obtain a lower bound on the number of external stimuli that the network can receive before the initial signal is considered forgotten (probability of error above a given threshold). Our results are based on a finite-time analysis rather than large-time asymptotic. We finally present numerical illustrations of our results.

Narcolepsy type 1: what have we learned from genetics?

Type-1 narcolepsy is a severe neurological disorder with distinct characteristic of loss of hypocretin neurotransmitter. Genetic analysis in type-1 narcolepsy have revealed a unique signal pointing toward autoimmune, rather than psychiatric origin. While type-1 narcolepsy has been intensively studied, the other subtypes of hypersomnolence, narcolepsy, and hypersomnia are less thoroughly understood. This review summarizes the latest breakthroughs in the field in narcolepsy. The goal of this article is to help the reader to understand better the risk from genetic factors and their interplay with immune, genetic, and epidemiological aspects in narcolepsy.

Detection of tar brown carbon with a single particle soot photometer (SP2)

We investigate the possibility that the refractory, infrared-light-absorbing carbon particulate material known as “tarballs” or tar brown carbon (tar brC) generates a unique signal in the scattering and incandescent detectors of a single particle soot photometer (SP2). As recent studies have defined tar brC in different ways, we begin by reviewing the literature and proposing a material-based definition of tar. We then show that tar brC results in unique SP2 signals due to a combination of complete or partial evaporation, with no or very little incandescence. Only a subset of tar brC particles exhibited detectable incandescence (70 % by number); for these particles the ratio of incandescence to light scattering was much lower than that of soot black carbon (BC). At the time of incandescence the ratio of light scattering to incandescence from these particles was up to 2-fold greater than from soot (BC). In our sample, where the mass of tar was 3-fold greater than the mass of soot, this led to a bias of <5 % in SP2-measured soot mass, which is negligible relative to calibration uncertainties. The enhanced light scattering of tar is interpreted as being caused by tar being more amorphous and less graphitic than soot BC. The fraction of the tar particle which does incandesce was likely formed by thermal annealing during laser heating. These results indicate that laser-induced incandescence, as implemented in the SP2, is the only BC measurement technique which can quantify soot BC concentrations separately from tar while also potentially providing real-time evidence for the presence of tar. In contrast, BC measurement techniques based on thermal–optical (EC: elemental carbon) and absorption (eBC: equivalent BC) measurements cannot provide such distinctions. The optical properties of our tar particles indicate a material similarity to the tar particles previously reported in the literature. However, more- and less-graphitized tar samples have also been reported, which may show stronger and weaker SP2 responses, respectively.

Hand Gesture Recognition System using Deep Learning

Hand motion acknowledgment is a characteristic method for human PC association and a zone of dynamic research in PC vision and AI. This is a zone with a wide range of conceivable applications, giving clients an easier and increasingly normal approach to speak with robots/frameworks interfaces, without the requirement for additional gadgets. Along these lines, the essential objective of signal acknowledgment explore connected to Human-Computer Interaction (HCI) is to make frameworks, which can distinguish explicit human motions and use them to pass on data or controlling gadgets. For that, vision-based hand signal interfaces require quick and incredibly strong hand discovery, and motion acknowledgment continuously. This paper introduces an answer, sufficiently nonexclusive, with the assistance of deep learning, permitting its application in a wide scope of human-PC interfaces, for ongoing motion acknowledgment. Investigations did demonstrated that the framework had the capacity to accomplish a precision of 99.4% as far as hand act acknowledgment and a normal exactness of 93.72% as far as unique signal acknowledgment.