Highly tunable repetition-rate multiplication of mode-locked lasers using all-fibre harmonic injection locking

Higher repetition-rate optical pulse trains have been desired for various applications such as high-bit-rate optical communication, photonic analogue-to-digital conversion, and multi- photon imaging. Generation of multi GHz and higher repetition-rate optical pulse trains directly from mode-locked oscillators is often challenging. As an alternative, harmonic injection locking can be applied for extra-cavity repetition-rate multiplication (RRM). Here we have investigated the operation conditions and achievable performances of all-fibre, highly tunable harmonic injection locking-based pulse RRM. We show that, with slight tuning of slave laser length, highly tunable RRM is possible from a multiplication factor of 2 to >100. The resulting maximum SMSR is 41 dB when multiplied by a factor of two. We further characterize the noise properties of the multiplied signal in terms of phase noise and relative intensity noise. The resulting absolute rms timing jitter of the multiplied signal is in the range of 20 fs to 60 fs (10 kHz - 1 MHz) for different multiplication factors. With its high tunability, simple and robust all-fibre implementation, and low excess noise, the demonstrated RRM system may find diverse applications in microwave photonics, optical communications, photonic analogue-to-digital conversion, and clock distribution networks.Comment: 25 pages, 9 figure

Development and Application of an Electrospray Ionization Ion Mobility-mass Spectrometer Using an RF Ion Funnel and Periodic-focusing Ion Guide

A novel ion mobility-mass spectrometer was designed and built in order to achieve high transmission and high resolution for observing desolvated ion conformations of chemical and biological molecules in the gas phase. The instrument incorporates a home-built electrospray ionization (ESI) source and rf ion funnel(IF) as an ion transfer interface, modular periodic-focusing ion guide (PFIG) as a drift tube (DT) for arrival time measurement, and orthogonally accelerated linear time-of-flight mass spectrometer to maximize analytical figures-of-merit of the instrument including sensitivity and separation of gas-phase ions. The rationales for implementation of each device aforementioned are discussed as enhanced ion focusing to reduce data acquisition time of the instrument, high mobility resolution (R > 56) to separate ions having a small collision cross section (CCS) difference at low-to-intermediate field strength (10 V cm-1 torr-1~19 V cm-1 torr-1), and observation of multiple conformers from isobaric ions at various operation conditions. Gate and transfer ion optic design needed to avoid detrimental gas dynamic effect is presented during hybridization of PFIG into ESI mass spectrometer. The details of instrumentation and electrode/voltage schematics are also discussed along with SIMION calculation results. The characteristic drift motions of ions inside the PFIG require a normalization procedure to compare CCS values from the instrument with the results using conventional uniform field DT ion mobility-mass spectrometry (IM-MS). Through experimental measurements and ion trajectory calculations from well known model peptides and proteins having multiple charge states, a correlation factor which links the results from the two different types of instrumentation was derived and successfully applied (within ±4% difference). Ion heating was also investigated using ubiquitin, insulin, and substance P. Using ESI PFIG-MS, A significant variation of the arrival time distribution at different heated capillary temperature, rf amplitude, and electric field strength was observed for ubiquitin (+7) and (+8) and substance P (+3), while a substantial change was not observed for insulin owing to structural rigidity of disulfide bond cross-linking between two chains. The results imply that ESI PFIG-MS provides a window to monitor conformational variation of gas-phase ions and measure energy barriers of peptide/protein folding processes, which can allow us to reveal pathways in structural energy landscape

MARA-Net: Single Image Deraining Network with Multi-level connections and Adaptive Regional Attentions

Removing rain streaks from single images is an important problem in various computer vision tasks because rain streaks can degrade outdoor images and reduce their visibility. While recent convolutional neural network-based deraining models have succeeded in capturing rain streaks effectively, difficulties in recovering the details in rain-free images still remain. In this paper, we present a multi-level connection and adaptive regional attention network (MARA-Net) to properly restore the original background textures in rainy images. The first main idea is a multi-level connection design that repeatedly connects multi-level features of the encoder network to the decoder network. Multi-level connections encourage the decoding process to use the feature information of all levels. Channel attention is considered in multi-level connections to learn which level of features is important in the decoding process of the current level. The second main idea is a wide regional non-local block (WRNL). As rain streaks primarily exhibit a vertical distribution, we divide the grid of the image into horizontally-wide patches and apply a non-local operation to each region to explore the rich rain-free background information. Experimental results on both synthetic and real-world rainy datasets demonstrate that the proposed model significantly outperforms existing state-of-the-art models. Furthermore, the results of the joint deraining and segmentation experiment prove that our model contributes effectively to other vision tasks

Investigating Influence of Hydrological Regime on Organic Matters Characteristic in a Korean Watershed

Source tracking of dissolved organic matter (DOM) is important to manage water quality in rivers. However, it is difficult to find the source of this DOM because various DOMs can be added from the river watershed. Moreover, the DOM composition can be changed due to environmental conditions. This study investigated the change of organic matter characteristics in the Taewha River of Ulsan City, Korea, before and after rainfall. A Soil and Water Assessment Tool (SWAT) was used to simulate water flow from various sources, and dissolved organic matter characterization was conducted in terms of molecular size distribution, hydrophobicity, fluorescence excitation and emission, and molecular composition. From the results, it was found that lateral flow transported hydrophobic and large-molecule organic matter after rainfall. According to the orbitrap mass spectrometer analysis, the major molecular compound of the DOM was lignin. Coupling the SWAT model with organic matter characterization was an effective approach to find sources of DOM in river

Targeted and non-targeted liquid chromatography-mass spectrometric workflows for identification of transformation products of emerging pollutants in the aquatic environment

Identification of transformation products (TPs) of emerging pollutants is challenging, due to the vast number of compounds, mostly unknown, the complexity of the matrices and their often low concentrations, requiring highly selective, highly sensitive techniques.Wecompile background information on biotic and abiotic formation of TPs and analytical developments over the past five years. We present a database of biotic or abiotic TPs compiled fromthose identified in recent years.We discuss mass spectrometric (MS) techniques and workflows for target, suspect and non-target screening of TPs with emphasis on liquid chromatography coupled to MS (LC-MS). Both low- and high-resolution (HR) mass analyzers have been applied, but HR-MS is the technique of choice, due to its high confirmatory capabilities, derived fromthe high resolving power and the mass accuracy in MS and MS/MS modes, and the sophisticated software developed

Chronic Inflammation in the Epidermis: A Mathematical Model

The epidermal tissue is the outmost component of the skin that plays an important role as a first barrier system in preventing the invasion of various environmental agents, such as bacteria. Recent studies have identified the importance of microbial competition between harmful and beneficial bacteria and the diversity of the skin surface on our health. We develop mathematical models (M1 and M2 models) for the inflammation process using ordinary differential equations and delay differential equations. In this paper, we study microbial community dynamics via transcription factors, protease and extracellular cytokines. We investigate possible mechanisms to induce community composition shift and analyze the vigorous competition dynamics between harmful and beneficial bacteria through immune activities. We found that the activation of proteases from the transcription factor within a cell plays a significant role in the regulation of bacterial persistence in the M1 model. The competition model (M2) predicts that different cytokine clearance levels may lead to a harmful bacteria persisting system, a bad bacteria-free state and the co-existence of harmful and good bacterial populations in Type I dynamics, while a bi-stable system without co-existence is illustrated in the Type II dynamics. This illustrates a possible phenotypic switch among harmful and good bacterial populations in a microenvironment. We also found that large time delays in the activation of immune responses on the dynamics of those bacterial populations lead to the onset of oscillations in harmful bacteria and immune activities. The mathematical model suggests possible annihilation of time-delay-driven oscillations by therapeutic drugs.ope

Developing a deep learning model for the simulation of micro-pollutants in a watershed

In recent years, as agricultural activities and types of crops have become diverse, the occurrence of micro-pollutants has been reported more frequently in rural areas. These pollutants have detrimental effects on human health and ecological systems; thus, it is important to manage and monitor their presence in the environment. The modeling approach could be an effective way to understand and manage these pollutants. This study predicts the concentrations of micro-pollutants (MPs) using deep learning (DL) models, and the results are then compared with simulation results obtained from the soil water assessment tool (SWAT) model. The SWAT model showed an unacceptable performance owing to the resulting negative NasheSutcliffe efficiency (NSE) values for the simulations. This may be caused by the limitations of SWAT, which pertains to adopting simplified equations to simulate micro-pollutants. In addition, the ambiguous plan of pesticide application increased the model uncertainty, thereby deteriorating the model result. Here, we developed two different DL models: long short-term memory (LSTM) and convolutional neural network (CNN). LSTM exhibited the highest model performance, with NSE values of 0.99 and 0.75 for the training and validation steps, respectively. In the multi-target MP model, the error decreased as the number of simulated pollutants increased. The simulation of the four pollutants had the highest error, while the six-target simulation had the lowest error. In conclusion, this study demonstrated that the LSTM model has the potential to improve the prediction of MPs in aquatic systems. (c) 2021 Elsevier Ltd. All rights reserved

Flexible Cu2ZnSn(S,Se)4 solar cells with over 10% efficiency and methods of enlarging the cell area

For kesterite copper zinc tin sulfide/selenide (CZTSSe) solar cells to enter the market, in addition to efficiency improvements, the technological capability to produce flexible and large-area modules with homogeneous properties is necessary. Here, we report a greater than 10% efficiency for a cell area of approximately 0.5 cm2 and a greater than 8% efficiency for a cell area larger than 2 cm2 of certified flexible CZTSSe solar cells. By designing a thin and multi-layered precursor structure, the formation of defects and defect clusters, particularly tin-related donor defects, is controlled, and the open circuit voltage value is enhanced. Using statistical analysis, we verify that the cell-to-cell and within-cell uniformity characteristics are improved. This study reports the highest efficiency so far for flexible CZTSSe solar cells with small and large areas. These results also present methods for improving the efficiency and enlarging the cell area. © 2019, The Author(s).1

Measurement of the top quark forward-backward production asymmetry and the anomalous chromoelectric and chromomagnetic moments in pp collisions at √s = 13 TeV

Abstract The parton-level top quark (t) forward-backward asymmetry and the anomalous chromoelectric (d̂ t) and chromomagnetic (μ̂ t) moments have been measured using LHC pp collisions at a center-of-mass energy of 13 TeV, collected in the CMS detector in a data sample corresponding to an integrated luminosity of 35.9 fb−1. The linearized variable AFB(1) is used to approximate the asymmetry. Candidate t t ¯ events decaying to a muon or electron and jets in final states with low and high Lorentz boosts are selected and reconstructed using a fit of the kinematic distributions of the decay products to those expected for t t ¯ final states. The values found for the parameters are AFB(1)=0.048−0.087+0.095(stat)−0.029+0.020(syst),μ̂t=−0.024−0.009+0.013(stat)−0.011+0.016(syst), and a limit is placed on the magnitude of | d̂ t| < 0.03 at 95% confidence level. [Figure not available: see fulltext.

MUSiC : a model-unspecific search for new physics in proton-proton collisions at root s=13TeV

Results of the Model Unspecific Search in CMS (MUSiC), using proton-proton collision data recorded at the LHC at a centre-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 35.9 fb(-1), are presented. The MUSiC analysis searches for anomalies that could be signatures of physics beyond the standard model. The analysis is based on the comparison of observed data with the standard model prediction, as determined from simulation, in several hundred final states and multiple kinematic distributions. Events containing at least one electron or muon are classified based on their final state topology, and an automated search algorithm surveys the observed data for deviations from the prediction. The sensitivity of the search is validated using multiple methods. No significant deviations from the predictions have been observed. For a wide range of final state topologies, agreement is found between the data and the standard model simulation. This analysis complements dedicated search analyses by significantly expanding the range of final states covered using a model independent approach with the largest data set to date to probe phase space regions beyond the reach of previous general searches.Peer reviewe