Model reduction of fractional impedance spectra for time–frequency analysis of batteries, fuel cells, and supercapacitors

Joint time–frequency analysis is an emerging method for interpreting the underlying physics in fuel cells,batteries,and supercapacitors.To increase the reliability of time–frequency analysis,a theoretical correlation between frequency-domain stationary analysis and time-domain transient analysis is urgently required.The present work formularizes a thorough model reduction of fractional impedance spectra for electrochemical energy devices involving not only the model reduction from fractional-order models to integer-order models and from high-to low-order RC circuits but also insight into the evolution of the characteristic time constants during the whole reduction process.The following work has been carried out:(i)the model-reduction theory is addressed for typical Warburg elements and RC circuits based on the continued fraction expansion theory and the response error minimization technique,respectively;(ii)the order effect on the model reduction of typical Warburg elements is quantitatively evaluated by time–frequency analysis;(iii)the results of time–frequency analysis are confirmed to be useful to determine the reduction order in terms of the kinetic information needed to be captured;and(iv)the results of time–frequency analysis are validated for the model reduction of fractional impedance spectra for lithium-ion batteries,supercapacitors,and solid oxide fuel cells.In turn,the numerical validation has demonstrated the powerful function of the joint time–frequency analysis.The thorough model reduction of fractional impedance spectra addressed in the present work not only clarifies the relationship between time-domain transient analysis and frequency-domain stationary analysis but also enhances the reliability of the joint time–frequency analysis for electrochemical energy devices.

1.42-fold enhancement of formate selectivity by linker conversion on the Zn-based metal organic framework catalyst

Electro-reduction of carbon dioxide(ERCO_(2)) is considered an effective method to alleviate the greenhouse effect and produce value-added chemicals.Achieving the dominant selectivity of Zn-based catalysts for formate remains a challenge.In this article,the ZnIn-E_(12) catalyst is successfully prepared by solvent assisted ligand exchange(SALE) method to convert organic ligands,achieving a Faradaic efficiency of 72.28% for formate at-1.26 V vs.RHE(V_(RHE)),which is 1.42 times higher than the original catalyst.Evidence shows that the successful conversion of organic ligands can transform the catalyst from the original large size polyhedron to cross-linked network of particles with a diameter of about 30 nm.The increased specific surface area can expose more active sites and facilitate the electrocatalytic conversion of CO_(2) to formate.This work is expected to provide inspiration for the regulation of formate selectivity and catalyst size in Zn-based catalysts.

Spatiotemporal variation of land surface temperature and its driving factors in Xinjiang,China

Land surface temperature(LST) directly affects the energy balance of terrestrial surface systems and impacts regional resources, ecosystem evolution, and ecosystem structures. Xinjiang Uygur Autonomous Region is located at the arid Northwest China and is extremely sensitive to climate change. There is an urgent need to understand the distribution patterns of LST in this area and quantitatively measure the nature and intensity of the impacts of the major driving factors from a spatial perspective, as well as elucidate the formation mechanisms. In this study, we used the MOD11C3 LST product developed on the basis of Moderate Resolution Imaging Spectroradiometer(MODIS) to conduct regression analysis and determine the spatiotemporal variation and differentiation pattern of LST in Xinjiang from 2000 to 2020. We analyzed the driving mechanisms of spatial heterogeneity of LST in Xinjiang and the six geomorphic zones(the Altay Mountains, Junggar Basin, Tianshan Mountains, Tarim Basin, Turpan-Hami(Tuha) Basin, and Pakakuna Mountain Group) using geographical detector(Geodetector) and geographically weighted regression(GWR) models. The warming rate of LST in Xinjiang during the study period was 0.24℃/10a, and the spatial distribution pattern of LST had obvious topographic imprints, with 87.20% of the warming zone located in the Gobi desert and areas with frequent human activities, and the cooling zone mainly located in the mountainous areas. The seasonal LST in Xinjiang was at a cooling rate of 0.09℃/10a in autumn, and showed a warming trend in other seasons. Digital elevation model(DEM), latitude, wind speed, precipitation, normalized difference vegetation index(NDVI), and sunshine duration in the single-factor and interactive detections were the key factors driving the LST changes. The direction and intensity of each major driving factor on the spatial variations of LST in the study area were heterogeneous. The negative feedback effect of DEM on the spatial differentiation of LST was the strongest. Lower latitudes, lower vegetation coverage, lower levels of precipitation, and longer sunshine duration increased LST. Unused land was the main heat source landscape, water body was the most important heat sink landscape, grassland and forest land were the land use and land cover(LULC) types with the most prominent heat sink effect, and there were significant differences in different geomorphic zones due to the influences of their vegetation types, climatic conditions, soil types, and human activities. The findings will help to facilitate sustainable climate change management, analyze local climate and environmental patterns, and improve land management strategies in Xinjiang and other arid areas.

Valley transport in Kekulé structures of graphene

Valleytronics is an emergent discipline in condensed matter physics and offers a new way to encode and manipulate information based on the valley degree of freedom in materials. Among the various materials being studied, Kekulé distorted graphene has emerged as a promising material for valleytronics applications. Graphene can be artificially distorted to form the Kekulé structures rendering the valley-related interaction. In this work, we review the recent progress of research on Kekulé structures of graphene and focus on the modified electronic bands due to different Kekulé distortions as well as their effects on the transport properties of electrons. We systematically discuss how the valley-related interaction in the Kekulé structures was used to control and affect the valley transport including the valley generation, manipulation, and detection. This article summarizes the current challenges and prospects for further research on Kekulé distorted graphene and its potential applications in valleytronics.

Geometries and electronic structures of Zr_(n)Cu(n=2–12) clusters: A joint machine-learning potential density functional theory investigation

Zr-based amorphous alloys have attracted extensive attention because of their large glassy formation ability, wide supercooled liquid region, high elasticity, and unique mechanical strength induced by their icosahedral local structures.To determine the microstructures of Zr–Cu clusters, the stable and metastable geometry of Zr_(n)Cu(n=2–12) clusters are screened out via the CALYPSO method using machine-learning potentials, and then the electronic structures are investigated using density functional theory. The results show that the Zr_(n)Cu(n ≥ 3) clusters possess three-dimensional geometries, Zr_(n)Cu(n≥9) possess cage-like geometries, and the Zr_(12)Cu cluster has icosahedral geometry. The binding energy per atom gradually gets enlarged with the increase in the size of the clusters, and Zr_(n)Cu(n=5,7,9,12) have relatively better stability than their neighbors. The magnetic moment of most Zr_(n)Cu clusters is just 1μB, and the main components of the highest occupied molecular orbitals(HOMOs) in the Zr_(12)Cu cluster come from the Zr-d state. There are hardly any localized two-center bonds, and there are about 20 σ-type delocalized three-center bonds.

Glacier area change and its impact on runoff in the Manas River Basin,Northwest China from 2000 to 2020

Understanding the distribution and dynamics of glaciers is of great significance to the management and allocation of regional water resources and socio-economic development in arid regions of Northwest China.In this study,based on 36 Landsat images,we extracted the glacier boundaries in the Manas River Basin,Northwest China from 2000 to 2020 using eCognition combined with band operation,GIS(geographic information system)spatial overlay techniques,and manual visual interpretation.We further analyzed the distribution and variation characteristics of glacier area,and simulated glacial runoff using a distributed degree-day model to explore the regulation of runoff recharge.The results showed that glacier area in the Manas River Basin as a whole showed a downward trend over the past 21 a,with a decrease of 10.86%and an average change rate of–0.54%/a.With the increase in glacier scale,the number of smaller glaciers decreased exponentially,and the number and area of larger glaciers were relatively stable.Glacier area showed a normal distribution trend of increasing first and then decreasing with elevation.About 97.92%of glaciers were distributed at 3700–4800 m,and 48.11%of glaciers were observed on the northern and northeastern slopes.The retreat rate of glaciers was the fastest(68.82%)at elevations below 3800 m.There was a clear rise in elevation at the end of glaciers.Glaciers at different slope directions showed a rapid melting trend from the western slope to the southern slope then to the northern slope.Glacial runoff in the basin showed a fluctuating upward trend in the past 21 a,with an increase rate of 0.03×10^(8) m^(3)/a.The average annual glacial runoff was 4.80×10^(8) m^(3),of which 33.31%was distributed in the ablation season(June–September).The average annual contribution rate of glacial meltwater to river runoff was 35.40%,and glacial runoff accounted for 45.37%of the total runoff during the ablation season.In addition,precipitation and glacial runoff had complementary regulation patterns for river runoff.The findings can provide a scientific basis for water resource management in the Manas River Basin and other similar arid inland river basins.

An Effective Hybrid Model of ELM and Enhanced GWO for Estimating Compressive Strength of Metakaolin-Contained Cemented Materials

This research proposes a highly effective soft computing paradigm for estimating the compressive strength(CS)of metakaolin-contained cemented materials.The proposed approach is a combination of an enhanced grey wolf optimizer(EGWO)and an extreme learning machine(ELM).EGWO is an augmented form of the classic grey wolf optimizer(GWO).Compared to standard GWO,EGWO has a better hunting mechanism and produces an optimal performance.The EGWO was used to optimize the ELM structure and a hybrid model,ELM-EGWO,was built.To train and validate the proposed ELM-EGWO model,a sum of 361 experimental results featuring five influencing factors was collected.Based on sensitivity analysis,three distinct cases of influencing parameters were considered to investigate the effect of influencing factors on predictive precision.Experimental consequences show that the constructed ELM-EGWO achieved the most accurate precision in both training(RMSE=0.0959)and testing(RMSE=0.0912)phases.The outcomes of the ELM-EGWO are significantly superior to those of deep neural networks(DNN),k-nearest neighbors(KNN),long short-term memory(LSTM),and other hybrid ELMs constructed with GWO,particle swarm optimization(PSO),harris hawks optimization(HHO),salp swarm algorithm(SSA),marine predators algorithm(MPA),and colony predation algorithm(CPA).The overall results demonstrate that the newly suggested ELM-EGWO has the potential to estimate the CS of metakaolin-contained cemented materials with a high degree of precision and robustness.

The Expression Characteristics and Potential Functions of Heat Shock Factors in Diatom Phaeodactylum tricornutum

Heat shock transcription factor(HSF)are essential regulators of heat shock protein(HSP)gene expression in plants and algae,contributing to their resilience against biotic and abiotic stresses.However,the localization,structure,phylogenetic relationship,and characteristics of PtHSF genes in microalgae,especially in diatom Phaeodactylum tricornutum,remain largely unexplored.This study presents a comprehensive analysis of the PtHSF gene family in P.tricornutum.A genome-wide analysis identified 68 PtHSF genes,which were classified into two distinct subfamilies:traditional and untraditional.Motif and structure analyses revealed evidence of multiple duplication events within the PtHSF gene family.Expression profiling revealed diurnal patterns,with 34 genes being downregulated during the light period and upregulated during the dark period,while 19 genes exhibited the opposite pattern.These findings suggest that PtHSF genes may have specialized functions during the diurnal cycle and play a crucial role in maintaining cellular homeostasis in response to various stresses.Notably,PtHSF16,30,and 43 genes exhibited higher expression levels,suggesting their potential importance.This study provides a valuable foundation for future investigations into the specific functions of HSFs under different stress conditions and their regulatory mechanisms in P.tricornutum and other microalgae.

Luminescence regulation of Sb^(3+)in 0D hybrid metal halides by hydrogen bond network for optical anti-counterfeiting

The Sb^(3+) doping strategy has been proven to be an effective way to regulate the band gap and improve the photophysical properties of organic-inorganic hybrid metal halides(OIHMHs).However,the emission of Sb^(3+) ions in OIHMHs is primarily confined to the low energy region,resulting in yellow or red emissions.To date,there are few reports about green emission of Sb^(3+)-doped OIHMHs.Here,we present a novel approach for regulating the luminescence of Sb^(3+) ions in 0D C_(10)H_(2)_(2)N_(6)InCl_(7)·H_(2)O via hydrogen bond network,in which water molecules act as agents for hydrogen bonding.Sb^(3+)-doped C_(10)H_(2)2N_(6)InCl_(7)·H_(2)O shows a broadband green emission peaking at 540 nm and a high photoluminescence quantum yield(PLQY)of 80%.It is found that the intense green emission stems from the radiative recombination of the self-trapped excitons(STEs).Upon removal of water molecules with heat,C_(10)H_(2)_(2)N_(6)In_(1-x)Sb_(x)Cl_(7) generates yellow emis-sion,attributed to the breaking of the hydrogen bond network and large structural distortions of excited state.Once water molecules are adsorbed by C_(10)H_(2)_(2)N_(6)In_(1-x)Sb_(x)Cl_(7),it can subsequently emit green light.This water-induced reversible emission switching is successfully used for optical security and information encryption.Our findings expand the under-standing of how the local coordination structure influences the photophysical mechanism in Sb^(3+)-doped metal halides and provide a novel method to control the STEs emission.

Implications of the Stellar Mass Density of High-z Massive Galaxies from JWST on Warm Dark Matter

A significant excess of the stellar mass density at high redshift has been discovered from the early data release of James Webb Space Telescope(JWST),and it may require a high star formation efficiency.However,this will lead to large number density of ionizing photons in the epoch of reionization(EoR),so that the reionization history will be changed,which can arise tension with the current EoR observations.Warm dark matter(WDM),via the free streaming effect,can suppress the formation of small-scale structure as well as low-mass galaxies.This provides an effective way to decrease the ionizing photons when considering a large star formation efficiency in high-z massive galaxies without altering the cosmic reionization history.On the other hand,the constraints on the properties of WDM can be derived from the JWST observations.In this work,we study WDM as a possible solution to reconcile the JWST stellar mass density of high-z massive galaxies and reionization history.We find that,the JWST high-z comoving cumulative stellar mass density alone has no significant preference for either CDM or WDM model.But using the observational data of other stellar mass density measurements and reionization history,we obtain that the WDM particle mass with mw=0.51_(-0.12)^(+0.22) keV and star formation efficiency parameter f_(*)^(0)> 0.39 in 2σ confidence level can match both the JWST high-z comoving cumulative stellar mass density and the reionization history.

Probing the Gamma-Ray Emission Region of Five TeV Flat Spectrum Radio Quasars

The location ofγ-ray emission of blazars remains a contested topic,inspiring the development of numerous investigative techniques to address this issue.In this work,we analyzed Fermiγ-ray light curves in the GeV and MeV bands,employing the discrete cross-correlation function method to discern time lags between the two bands.For 4C+21.35,Ton 599,B21420+32,and PKS 1510-089,we identified a time lag spanning several days,while for PKS 1441+25,the time lag was not statistically found.The results imply that the soft photons necessary for inverse Compton scattering predominantly originate from the dusty torus in the first four sources,whereas for PKS1441+25,they seem to be sourced mainly from the broad-line region.Further analysis of the opacity(τγγ)and the GeV spectra study supports the conclusion that the location of the dissipation region must be beyond the BLR to avoid significant absorption.Notably,for PKS 1441+25,the emission region is also posited to lie outside yet proximate to the BLR.The parameters of describing the emission region were obtained by fitting broadband spectral energy distribution with contemporaneous observation data.Our findings suggest that for the five TeV FSRQs,during Te V flaring events,the jet appears to maintain an equilibrium between the energy density of the magnetic field and that of the particles for all investigated sources,with the exceptions of 4C+21.35 and PKS1441+25.In terms of the overall jet power,particle energy is the dominant contributor,and the observed blazar radiation cannot be solely attributed to the magnetic field,except in the case of 4C+21.35.Consequently,magnetic reconnection is unlikely to be the primary mechanism behind particle acceleration in these systems.

Structural, Magnetic and Heating Efficiency of Ball Milled γ-Fe2O3/Gd2O3 Nanocomposite for Magnetic Hyperthermia

The preparation of γ-Fe2O3/Gd2O3 nanocomposite for possible use in magnetic hyperthermia application was done by ball milling technique. The nanocomposite was characterized by X-ray diffraction (XRD) and vibrating sample magnetometer (VSM). The heating efficiency and the effect of milling time (5 h and 30 h) on the structural and magnetic properties of the nanocomposite were reported. XRD analysis confirms the formation of the nanocomposite, while magnetization measurements show that the milled sample present hysteresis with low coercivity and remanence. The specific absorption rate (SAR) under an alternating magnetic field is investigated as a function of the milling time. A mean heating efficiency of 68 W/g and 28.7 W/g are obtained for 5 h and 30 h milling times respectively at 332 kHz and 170 Oe. The results showed that the obtained nanocomposite for 5 h milling time is a promising candidate for magnetic hyperthermia due to his properties which show an interesting magnetic behavior and high specific absorption rate.

The FRB-searching Pipeline of the Tianlai Cylinder Pathfinder Array

This paper presents the design,calibration,and survey strategy of the Fast Radio Burst(FRB)digital backend and its real-time data processing pipeline employed in the Tianlai Cylinder Pathfinder Array.The array,consisting of three parallel cylindrical reflectors and equipped with 96 dual-polarization feeds,is a radio interferometer array designed for conducting drift scans of the northern celestial semi-sphere.The FRB digital backend enables the formation of 96 digital beams,effectively covering an area of approximately 40 square degrees with the 3 dB beam.Our pipeline demonstrates the capability to conduct an automatic search of FRBs,detecting at quasi-realtime and classifying FRB candidates automatically.The current FRB searching pipeline has an overall recall rate of88%.During the commissioning phase,we successfully detected signals emitted by four well-known pulsars:PSR B0329+54,B2021+51,B0823+26,and B2020+28.We report the first discovery of an FRB by our array,designated as FRB 20220414A.We also investigate the optimal arrangement for the digitally formed beams to achieve maximum detection rate by numerical simulation.

The electrical design of a membrane antenna for a lunar-based low-frequency radio telescope

Detecting primordial fluctuations from the cosmic dark ages requires extremely large low-frequency radio telescope arrays deployed on the far side of the Moon.The antenna of such an array must be lightweight,easily storable and transportable,deployable on a large scale,durable,and capable of good electrical performance.A membrane antenna is an excellent candidate to meet these criteria.We study the design of a low-frequency membrane antenna for a lunar-based low-frequency(<30 MHz)radio telescope constructed from polyimide film widely used in aerospace applications,owing to its excellent dielectric properties and high stability as a substrate material.We first design and optimize an antenna in free space through dipole deformation and coupling principles,then simulate an antenna on the lunar surface with a simple lunar soil model,yielding an efficiency greater than 90%in the range of 12-19 MHz and greater than 10%in the range of 5-35 MHz.The antenna inherits the omni-directional radiation pattern of a simple dipole antenna in the 5-30 MHz frequency band,giving a large field of view and allowing detection of the 21 cm global signal when used alone.A demonstration prototype is constructed,and its measured electrical property is found to be consistent with simulated results using|S11|measurements.This membrane antenna can potentially fulfill the requirements of a lunar low-frequency array,establishing a solid technical foundation for future large-scale arrays for exploring the cosmic dark ages.

A Review of In‑Situ Techniques for Probing Active Sites and Mechanisms of Electrocatalytic Oxygen Reduction Reactions

Electrocatalytic oxygen reduction reaction(ORR)is one of the most important reactions in electrochemical energy technologies such as fuel cells and metal–O2/air batteries,etc.However,the essential catalysts to overcome its slow reaction kinetic always undergo a complex dynamic evolution in the actual catalytic process,and the concomitant intermediates and catalytic products also occur continuous conversion and reconstruction.This makes them difficult to be accurately captured,making the identification of ORR active sites and the elucidation of ORR mechanisms difficult.Thus,it is necessary to use extensive in-situ characterization techniques to proceed the real-time monitoring of the catalyst structure and the evolution state of intermediates and products during ORR.This work reviews the major advances in the use of various in-situ techniques to characterize the catalytic processes of various catalysts.Specifically,the catalyst structure evolutions revealed directly by in-situ techniques are systematically summarized,such as phase,valence,electronic transfer,coordination,and spin states varies.In-situ revelation of intermediate adsorption/desorption behavior,and the real-time monitoring of the product nucleation,growth,and reconstruction evolution are equally emphasized in the discussion.Other interference factors,as well as in-situ signal assignment with the aid of theoretical calculations,are also covered.Finally,some major challenges and prospects of in-situ techniques for future catalysts research in the ORR process are proposed.

Selective sulfur conversion with surface engineering of electrocatalysts in a lithium-sulfur battery

The sluggish kinetics of multiphase sulfur conversion with homogeneous and heterogeneous electrochemical processes,causing the“shuttle effect”of soluble polysulfide species(PSs),is the challenges in terms of lithium-sulfur batteries(LSBs).In this paper,a Mn_(3)O_(4-x) catalyst,which has much higher activity for heterogeneous reactions than for homogeneous reactions(namely,preferentialactivity catalysts),is designed by surface engineering with rational oxygen vacancies.Due to the rational design of the electronic structure,the Mn_(3)O_(4-x) catalyst prefers to accelerate the conversion of Li2S4 into Li_(2)S_(2)/Li_(2)S and optimize Li_(2)S deposition,reducing the accumulation of PSs and thus suppressing the“shuttle effect.”Both density functional theory calculations and in situ X-ray diffraction measurements are used to probe the catalytic mechanism and identify the reaction intermediates of MnS and Li_(y)Mn_(z)O_(4-x) for fundamental understanding.The cell with Mn_(3)O_(4-x) delivers an ultralow attenuation rate of 0.028% per cycle over 2000 cycles at 2.5 C.Even with sulfur loadings of 4.93 and 7.10mg cm^(-2) in a lean electrolyte(8.4μL mg s^(-1)),the cell still shows an initial areal capacity of 7.3mAh cm^(-2).This study may provide a new way to develop preferential-activity heterogeneous-reaction catalysts to suppress the“shuttle effect”of the soluble PSs generated during the redox process of LSBs.

Precipitation scale effect of the TRMM satellite in Tianshan,China

High-resolution precipitation data is conducive to objectively describe the spatial-temporal variability of regional precipitation,and the study of downscaling techniques and spatial scale effects can provide technical and theoretical support to improve the spatial resolution and accuracy of satellite precipitation data.In this study,we used a machine learning algorithm combined with a regression algorithm RF-PLS(Random Forest-Partial Least Squares)to construct a downscaling model to obtain three types of high-resolution TRMM(Tropical Rainfall Measuring Mission)downscaled precipitation data for the years 2000-2017 at 250 m,500 m,and 1km.The scale effects with topographic and geomorphological features in the study area were analysed.Finally,we described the spatial and temporal variation of precipitation based on the optimal TRMM downscaled precipitation data.The results showed that:1)The linear relationships between the TRMM downscaled precipitation data obtained by each of the three downscaled models(PLS,RF,and RF-PLS)and the precipitation at the observation stations were improved compared to the linear relationships between the original TRMM data and the precipitation at the observation stations.The accuracy of the RF-PLS model was better than the other two models.2)Based on the RF-PLS model,the resolution of the TRMM data was increased to three different scales(250 m,500 m,and 1 km),considering the scale effects with topographic and geomorphological features.The precipitation simulation effect with a spatial resolution of 500 m was better than the other two scales.3)The annual precipitation was the highest in the areas with extremely high mountains,followed by the mediumhigh mountain,high mountain,medium mountain,medium-low mountain,plain,low mountain,and basin.

The Efficacy of Deep Learning-Based Mixed Model for Speech Emotion Recognition

Human speech indirectly represents the mental state or emotion of others.The use of Artificial Intelligence(AI)-based techniques may bring revolution in this modern era by recognizing emotion from speech.In this study,we introduced a robust method for emotion recognition from human speech using a well-performed preprocessing technique together with the deep learning-based mixed model consisting of Long Short-Term Memory(LSTM)and Convolutional Neural Network(CNN).About 2800 audio files were extracted from the Toronto emotional speech set(TESS)database for this study.A high pass and Savitzky Golay Filter have been used to obtain noise-free as well as smooth audio data.A total of seven types of emotions;Angry,Disgust,Fear,Happy,Neutral,Pleasant-surprise,and Sad were used in this study.Energy,Fundamental frequency,and Mel Frequency Cepstral Coefficient(MFCC)have been used to extract the emotion features,and these features resulted in 97.5%accuracy in the mixed LSTM+CNN model.This mixed model is found to be performed better than the usual state-of-the-art models in emotion recognition from speech.It also indicates that this mixed model could be effectively utilized in advanced research dealing with sound processing.

Low light intensity elongates period and defers peak time of photosynthesis: a computational approach to circadian-clock-controlled photosynthesis in tomato

Photosynthesis is involved in the essential process of transforming light energy into chemical energy.Although the interaction between photosynthesis and the circadian clock has been confirmed,the mechanism of how light intensity affects photosynthesis through the circadian clock remains unclear.Here,we propose a first computational model for circadian-clock-controlled photosynthesis,which consists of the light-sensitive protein P,the core oscillator,photosynthetic genes,and parameters involved in the process of photosynthesis.The model parameters were determined by minimizing the cost function(δ=8.56),which is defined by the errors of expression levels,periods,and phases of the clock genes(CCA1,PRR9,TOC1,ELF4,GI,and RVE8).The model recapitulates the expression pattern of the core oscillator under moderate light intensity(100μmol m^(-2) s^(-1)).Further simulation validated the dynamic behaviors of the circadian clock and photosynthetic outputs under low(62.5μmol m^(-2) s^(-1))and normal(187.5μmol m^(-2) s^(-1))intensities.When exposed to low light intensity,the peak times of clock and photosynthetic genes were shifted backward by 1–2 hours,the period was elongated by approximately the same length,and the photosynthetic parameters attained low values and showed delayed peak times,which confirmed our model predictions.Our study reveals a potential mechanism underlying the circadian regulation of photosynthesis by the clock under different light intensities in tomato.

Detecting HI Galaxies with Deep Neural Networks in the Presence of Radio Frequency Interference

In the neutral hydrogen(H I)galaxy survey,a significant challenge is to identify and extract the H I galaxy signal from the observational data contaminated by radio frequency interference(RFI).For a drift-scan survey,or more generally a survey of a spatially continuous region,in the time-ordered spectral data,the H I galaxies and RFI all appear as regions that extend an area in the time-frequency waterfall plot,so the extraction of the H I galaxies and RFI from such data can be regarded as an image segmentation problem,and machine-learning methods can be applied to solve such problems.In this study,we develop a method to effectively detect and extract signals of H I galaxies based on a Mask R-CNN network combined with the PointRend method.By simulating FAST-observed galaxy signals and potential RFI impact,we created a realistic data set for the training and testing of our neural network.We compared five different architectures and selected the best-performing one.This architecture successfully performs instance segmentation of H I galaxy signals in the RFI-contaminated time-ordered data,achieving a precision of 98.64%and a recall of 93.59%.