A Reconfigurable Triple-Notch-Band Antenna Integrated with Defected Microstrip Structure Band-Stop Filter for Ultra-Wideband Cognitive Radio Applications

A printed reconfigurable ultra-wideband (UWB) monopole antenna with triple narrow band-notched characteristics is proposed for cognitive radio applications in this paper. The triple narrow band-notched frequencies are obtained using a defected microstrip structure (DMS) band stop filter (BSF) embedded in the microstrip feed line and an inverted π-shaped slot etched in the rectangular radiation patch, respectively. Reconfigurable characteristics of the proposed cognitive radio antenna (CRA) are achieved by means of four ideal switches integrated on the DMS-BSF and the inverted π-shaped slot. The proposed UWB CRA can work at eight modes by controlling switches ON and OFF. Moreover, impedance bandwidth, design procedures, and radiation patterns are presented for analysis and explanation of this antenna. The designed antenna operates over the frequency band between 3.1 GHz and 14 GHz (bandwidth of 127.5%), with three notched bands from 4.2 GHz to 6.2 GHz (38.5%), 6.6 GHz to 7.0 GHz (6%), and 12.2 GHz to 14 GHz (13.7%). The antenna is successfully simulated, fabricated, and measured. The results show that it has wide impedance bandwidth, multimodes characteristics, stable gain, and omnidirectional radiation patterns

A novel botybirnavirus with a unique satellite dsRNA causes latent infection in Didymella theifolia isolated from tea plants

© 2023 The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/The unique, recently discovered fungus Didymella theifolia specifically infects local varieties of tea plant Camellia sinensis in China, and therefore, the characterization of its mycoviruses is important. Three double-stranded (ds) RNAs (1, 2, and 3, with 6,338, 5,910, and 727 bp in size, respectively) were identified in the avirulent D. theifolia strain CJP4-1, which exhibits normal growth and morphology. Characterization of these double-stranded RNAs (dsRNAs) revealed that the two largest elements are the genomic components of a novel botybirnavirus, tentatively named Didymella theifolia botybirnavirus 1 (DtBRV1). Conversely, dsRNA3 shares no detectable similarity with sequences deposited in public databases but has high similarity with the 5′-terminal regions of dsRNAs 1 and 2 and contains a duplicated region encoding a putative small peptide. All three dsRNAs are encapsidated in isometric virions ca. 40 nm in diameter, supporting the notion that dsRNA3 is a DtBRV1 satellite. SDS-polyacrylamide gel electrophoresis in combination with peptide mass fingerprint analysis revealed that the DtBRV1 capsid protein consists of polypeptides encoded by the 5′-terminal regions of both genomic components dsRNA1 and dsRNA2. Vertical transmission of DtBRV1 through conidia is efficient, while its horizontal transmission from CJP4-1 to other strains was not detected. DtBRV1, with or without dsRNA3, has no obvious effects on fungal growth and virulence, as illustrated following transfection of the virulent D. theifolia strain JYC1-6. In summary, DtBRV1 exhibits unique molecular traits and contributes to our understanding of mycovirus diversity.Peer reviewe

Sedimentary Response to Climate Change in the Central Bay of Bengal since the Last Glacial Maximum

AbstractAs the largest submarine fan, the Bay of Bengal (BoB) captures the abundant environment and climate fingerprints on different time scales. To investigate the sedimentary response to climate change since the Last Glacial Maximum (LGM), an integrated survey was performed to study grain size, major, and trace elements (Al2O3, CaO, K2O, Na2O, TiO2, Sr, and Rb) of core BoB-24 sediments from the central BoB. The (K/Al)-TiO2 (%) relationship of the sediments was taken for the discrimination of provenance, which indicated that sediments from core BoB-24 in 24~6.5 cal ka BP were primarily from terrigenous material input from the Himalayas. In contrast, the material contribution from the Indian subcontinent increased distinctly since 6.5 cal ka BP. The rising sea level severed direct material supply, thus causing the evolution of sediment provenance of the central BoB. Meanwhile, the strengthened Indian summer monsoon (ISM) in the Holocene affected detrital material transport from offshore to the central BoB. After understanding the sediment provenance in the study, we choose the sensitive grain-size fraction to show the evolution of hydrodynamic conditions. The chemical index of alteration (CIA) and Ti/Ca and Rb/Sr ratios are calculated to indicate the change in terrigenous input and weathering intensity. The contents of sediment fraction from 11.05 to 15.63 μm, CIA, and ratios of Ti/Ca and Rb/Sr in core BoB-24 showed the same trends, which were low during the last deglaciation and late Holocene but high in the Early Holocene. The trends were strongly correlated with the variation of the Indian summer monsoon, indicating the possible impact of Indian monsoon on sediment transport in the Bay of Bengal. Alternative indicators such as the contents of ratios of Ti/Ca and Rb/Sr, CIA, and sensitive grain-size content in sediments of core BoB-24 jointly record the evolution history of ISM since 24 ka BP in the Bay of Bengal. Although the sensitivity and response of each indicator to the paleoenvironment and paleoclimate change are slightly different, on the whole, the change trend is the same. Specifically, four warm-cold alternating periods (Heinrich Event 1, Bølling/Allerød, Younger Dryas, and Early Holocene Climatic Optimum) had a strong signal in these proxies that indicated that the millennial-scale climate controls the terrigenous input to the Bay of Bengal, where a high value occurs in warm events and low value in cold events. The sedimentary pattern of the northeastern Indian Ocean provides scientific evidence for an insight into the regional response to global climate change and the long-term climate change trend of the human environment across the monsoon region

Deep Learning for Distinguishing Normal versus Abnormal Chest Radiographs and Generalization to Unseen Diseases

Chest radiography (CXR) is the most widely-used thoracic clinical imaging modality and is crucial for guiding the management of cardiothoracic conditions. The detection of specific CXR findings has been the main focus of several artificial intelligence (AI) systems. However, the wide range of possible CXR abnormalities makes it impractical to build specific systems to detect every possible condition. In this work, we developed and evaluated an AI system to classify CXRs as normal or abnormal. For development, we used a de-identified dataset of 248,445 patients from a multi-city hospital network in India. To assess generalizability, we evaluated our system using 6 international datasets from India, China, and the United States. Of these datasets, 4 focused on diseases that the AI was not trained to detect: 2 datasets with tuberculosis and 2 datasets with coronavirus disease 2019. Our results suggest that the AI system generalizes to new patient populations and abnormalities. In a simulated workflow where the AI system prioritized abnormal cases, the turnaround time for abnormal cases reduced by 7-28%. These results represent an important step towards evaluating whether AI can be safely used to flag cases in a general setting where previously unseen abnormalities exist

Measurement of differential cross sections for top quark pair production using the lepton plus jets final state in proton-proton collisions at 13 TeV

National Science Foundation (U.S.

Identification of heavy-flavour jets with the CMS detector in pp collisions at 13 TeV

Many measurements and searches for physics beyond the standard model at the LHC rely on the efficient identification of heavy-flavour jets, i.e. jets originating from bottom or charm quarks. In this paper, the discriminating variables and the algorithms used for heavy-flavour jet identification during the first years of operation of the CMS experiment in proton-proton collisions at a centre-of-mass energy of 13 TeV, are presented. Heavy-flavour jet identification algorithms have been improved compared to those used previously at centre-of-mass energies of 7 and 8 TeV. For jets with transverse momenta in the range expected in simulated $\mathrm{t}\overline{\mathrm{t}}$ events, these new developments result in an efficiency of 68% for the correct identification of a b jet for a probability of 1% of misidentifying a light-flavour jet. The improvement in relative efficiency at this misidentification probability is about 15%, compared to previous CMS algorithms. In addition, for the first time algorithms have been developed to identify jets containing two b hadrons in Lorentz-boosted event topologies, as well as to tag c jets. The large data sample recorded in 2016 at a centre-of-mass energy of 13 TeV has also allowed the development of new methods to measure the efficiency and misidentification probability of heavy-flavour jet identification algorithms. The heavy-flavour jet identification efficiency is measured with a precision of a few per cent at moderate jet transverse momenta (between 30 and 300 GeV) and about 5% at the highest jet transverse momenta (between 500 and 1000 GeV)

Particle-flow reconstruction and global event description with the CMS detector

The CMS apparatus was identified, a few years before the start of the LHC operation at CERN, to feature properties well suited to particle-flow (PF) reconstruction: a highly-segmented tracker, a fine-grained electromagnetic calorimeter, a hermetic hadron calorimeter, a strong magnetic field, and an excellent muon spectrometer. A fully-fledged PF reconstruction algorithm tuned to the CMS detector was therefore developed and has been consistently used in physics analyses for the first time at a hadron collider. For each collision, the comprehensive list of final-state particles identified and reconstructed by the algorithm provides a global event description that leads to unprecedented CMS performance for jet and hadronic tau decay reconstruction, missing transverse momentum determination, and electron and muon identification. This approach also allows particles from pileup interactions to be identified and enables efficient pileup mitigation methods. The data collected by CMS at a centre-of-mass energy of 8 TeV show excellent agreement with the simulation and confirm the superior PF performance at least up to an average of 20 pileup interactions

Search for heavy resonances decaying to a top quark and a bottom quark in the lepton+jets final state in proton–proton collisions at 13 TeV

info:eu-repo/semantics/publishe

Evidence for the Higgs boson decay to a bottom quark–antiquark pair

info:eu-repo/semantics/publishe

Pseudorapidity and transverse momentum dependence of flow harmonics in pPb and PbPb collisions

info:eu-repo/semantics/publishe