Search

Abstract We report multi-differential measurements of strange hadron production ranging from mid- to target-rapidity in Au+Au collisions at a center-of-momentum energy per nucleon pair of s NN $$ \sqrt{s_{\textrm{NN}}} $$ = 3 GeV with the STAR experiment at RHIC. K S 0 $$ {K}_S^0 $$ meson and Λ hyperon yields are measured via their weak decay channels. Collision centrality and rapidity dependences of the transverse momentum spectra and particle ratios are presented. Particle mass and centrality dependence of the average transverse momenta of Λ and K S 0 $$ {K}_S^0 $$ are compared with other strange particles, providing evidence of the development of hadronic rescattering in such collisions. The 4π yields of each of these strange hadrons show a consistent centrality dependence. Discussions on radial flow, the strange hadron production mechanism, and properties of the medium created in such collisions are presented together with results from hadronic transport and thermal model calculations.

Abstract Fast radio bursts (FRBs) are immensely energetic millisecond-duration radio pulses. Observations indicate that nearby FRBs can be produced by old stellar populations, as suggested by the localization of the repeating source FRB 20200120E in a globular cluster of M81. Nevertheless, the burst energies of FRB 20200120E are significantly smaller than those of other cosmological FRBs. Here, we report the detection of a bright burst from FRB 20200120E in 1.1 – 1.7 GHz, with a fluence of approximately 30 Jy ms, which is more than 42 times larger than the previously detected bursts near 1.4 GHz frequency. It reaches one-third of the energy of the weakest burst from FRB 20121102A and is detectable at a distance exceeding 200 Mpc. Our finding bridges the gap between nearby and cosmological FRBs and indicates that FRBs hosted in globular clusters can be bright enough to be observable at cosmological distances.

The chiral magnetic effect (CME) is a phenomenon that arises from the QCD anomaly in the presence of an external magnetic field. The experimental search for its evidence has been one of the key goals of the physics program of the Relativistic Heavy-Ion Collider. The STAR Collaboration has previously presented the results of a blind analysis of isobar collisions (_{44}^{96}Ru+_{44}^{96}Ru, _{40}^{96}Zr+_{40}^{96}Zr) in the search for the CME. The isobar ratio (Y) of CME-sensitive observable, charge separation scaled by elliptic anisotropy, is close to but systematically larger than the inverse multiplicity ratio, the naive background baseline. This indicates the potential existence of a CME signal and the presence of remaining nonflow background due to two- and three-particle correlations, which are different between the isobars. In this postblind analysis, we estimate the contributions from those nonflow correlations as a background baseline to Y, utilizing the isobar data as well as Heavy Ion Jet Interaction Generator simulations. This baseline is found consistent with the isobar ratio measurement, and an upper limit of 10% at 95% confidence level is extracted for the CME fraction in the charge separation measurement in isobar collisions at sqrt[s_{NN}]=200 GeV.

Abstract We report a new measurement of the production of electrons from open heavy-flavor hadron decays (HFEs) at mid-rapidity (|y| < 0.7) in Au+Au collisions at s NN $$ \sqrt{s_{\textrm{NN}}} $$ = 200 GeV. Invariant yields of HFEs are measured for the transverse momentum range of 3.5 < p T < 9 GeV/c in various configurations of the collision geometry. The HFE yields in head-on Au+Au collisions are suppressed by approximately a factor of 2 compared to that in p + p collisions scaled by the average number of binary collisions, indicating strong interactions between heavy quarks and the hot and dense medium created in heavy-ion collisions. Comparison of these results with models provides additional tests of theoretical calculations of heavy quark energy loss in the quark-gluon plasma.

Abstract Partons traversing the strongly interacting medium produced in heavy-ion collisions are expected to lose energy depending on their color charge and mass. We measure the nuclear modification factors for charm- and bottom-decay electrons, defined as the ratio of yields, divided by the number of binary nucleon–nucleon collisions, in $$\sqrt{s_{\textrm{NN}}}=200$$ s NN = 200 GeV Au+Au collisions to p+p collisions ( $$R_{\textrm{AA}}$$ R AA ), or in central to peripheral Au+Au collisions ( $$R_{\textrm{CP}}$$ R CP ). We find the bottom-decay electron $$R_{\textrm{AA}}$$ R AA and $$R_{\textrm{CP}}$$ R CP to be significantly higher than those of charm-decay electrons. Model calculations including mass-dependent parton energy loss in a strongly coupled medium are consistent with the measured data. These observations provide evidence of mass ordering of charm and bottom quark energy loss when traversing through the strongly coupled medium created in heavy-ion collisions.

Halo-structured nuclei are examples of many-body open quantum system. Here the authors use a complete kinematics measurement and find an elastic breakup of proton halo nucleus 8B.

The deconfined quark-gluon plasma (QGP) created in relativistic heavy-ion collisions enables the exploration of the fundamental properties of matter under extreme conditions. Noncentral collisions can produce strong magnetic fields on the order of 10^{18} G, which offers a probe into the electrical conductivity of the QGP. In particular, quarks and antiquarks carry opposite charges and receive contrary electromagnetic forces that alter their momenta. This phenomenon can be manifested in the collective motion of final-state particles, specifically in the rapidity-odd directed flow, denoted as v_{1}(y). Here, we present the charge-dependent measurements of dv_{1}/dy near midrapidities for π^{±}, K^{±}, and p(p[over ¯]) in Au+Au and isobar (_{44}^{96}Ru+_{44}^{96}Ru and _{40}^{96}Zr+_{40}^{96}Zr) collisions at sqrt[s_{NN}]=200 GeV, and in Au+Au collisions at 27 GeV, recorded by the STAR detector at the Relativistic Heavy Ion Collider. The combined dependence of the v_{1} signal on collision system, particle species, and collision centrality can be qualitatively and semiquantitatively understood as several effects on constituent quarks. While the results in central events can be explained by the u and d quarks transported from initial-state nuclei, those in peripheral events reveal the impacts of the electromagnetic field on the QGP. Our data put valuable constraints on the electrical conductivity of the QGP in theoretical calculations.

Rotating radio transients (RRATs) are neutron stars that emit sporadic radio bursts. We detected 1955 single pulses from RRAT J1913+1330 using the 19 beam receiver of the Five-hundred-meter Aperture Spherical radio Telescope. These pulses were detected in 19 distinct clusters, with 49.4% of them occurring with a waiting time of one rotation period. The energy distribution of these individual pulses exhibited a wide range, spanning 3 orders of magnitude, reminiscent of repeating fast radio bursts (FRBs). Furthermore, we observed abrupt variations in pulse profile, width, peak flux, and fluence between adjacent sequential pulses. These findings suggest that this RRAT could be interpreted as a pulsar with extreme pulse-to-pulse modulation. The presence of sequential pulse trains during active phases, along with significant pulse variations in profile, fluence, flux, and width, should be intrinsic to a subset of RRATs. Our results indicate that J1913+1330 represents a peculiar source that shares certain properties with populations of nulling pulsars, giant pulses, and FRBs from different perspectives. The dramatic pulse-to-pulse variation observed in J1913+1330 could be attributed to unstable pair creation above the polar cap region and the variation of the site where streaming pairs emit coherently. Exploring a larger sample of RRATs exhibiting similar properties to J1913+1330 has the potential to significantly advance our understanding of pulsars, RRATs, and FRBs.

Abstract The focal plane camera is the core component of the Wide Field-of-view Cherenkov/fluorescence Telescope Array (WFCTA) of the Large High-Altitude Air Shower Observatory (LHAASO). Because of the capability of working under moonlight without aging, silicon photomultipliers (SiPM) have been proven to be not only an alternative but also an improvement to conventional photomultiplier tubes (PMT) in this application. Eighteen SiPM-based cameras with square light funnels have been built for WFCTA. The telescopes have collected more than 100 million cosmic ray events and preliminary results indicate that these cameras are capable of working under moonlight. The characteristics of the light funnels and SiPMs pose challenges (e.g. dynamic range, dark count rate, assembly techniques). In this paper, we present the design features, manufacturing techniques and performances of these cameras. Finally, the test facilities, the test methods and results of SiPMs in the cameras are reported here.

Data clustering is the process of identifying natural groupings or clusters based on a certain similarity measure in muti-dimensional data. Aiming at the dynamic clustering problem where the number of clusters cannot be determined in advance, a hybrid dynamic clustering method based on the marine predators algorithm (MPA) and particle swarm optimization (PSO) algorithm was proposed. The position update strategy of the PSO algorithm was used to make up for the lack of MPA in global searching. The fixed-length coding strategy with the real number coding method was used to deal with the variable length clustering optimization problem, and the unfeasible solution processing strategy and the penalty function strategy are adopted to improve the performance of the algorithm and achieve simultaneous optimization of the number of clusters and cluster centers. The proposed MPA-PSO algorithm with PSO algorithm, MPA, Differential Evolution (DE) algorithm, Spotted Hyena Optimizer (SHO), Lightning Searching Algorithm (LSA) and Equilibrium Optimizer (EO) are adopted to carry out the clustering simulation experiments on four artificial data sets and six real data sets (Iris, Wine, Wisconsin breast cancer, Vowel, Seeds, and Wdbc) in UCI databases. Three performance indicators (the number of clusters, ARI and Accuracy) are used to evaluate the clustering results. The experimental results show that the proposed method can not only successfully find the correct number of clusters, but also obtain stable results for most test problems.

Clustering validity function is an index used to judge the accuracy of clustering results. At present, most studies on clustering validity are based on single clustering validity function. Research shows that no clustering validity function can handle any data and always perform better than other indexes. Therefore, a hybrid weighted combination evaluation method based on fuzzy C-means (FCM) clustering validity functions was proposed. The weighting method combines expert weighting with information entropy weighting to improve the subjective factor influence of expert weighting and the shortcoming of information entropy weighting in the value judgment of each clustering validity function. Four clustering validity function combination methods of linear, exponential, logarithm and proportion was studied. Finally, the proposed fuzzy clustering validity evaluation method is verified by experiments on artificial data sets and UCI data sets. The experimental results show that the proposed fuzzy clustering validity evaluation method can overcome the shortcoming of single clustering validity function, and can get the optimal clustering number more accurately for different data sets.

A novel flame retardant containing boron and phosphorus, based on triazine-trione sturcture (TDB) was successfully synthesized, via substitution and esterification reaction between 1,3,5-tris(2-hydroxyethyl)isocyanurate (THEIC), diphenyl phosphoryl chloride (DPCP) and boric acid (BA), and then blended into DGEBA to prepare flame-retardant composites. The structure of TDB was characterized by Fourier transform infrared (FTIR) spectra and nuclear magnetic resonance (NMR). The thermal and flame-retardant properties of epoxy thermosets were systematically investigated. The results showed that the Tg, T5% and Tmax values of EP samples were gradually decreased with the increasing content of TDB, while the char yields at 700 °C increased. With the introduction of 10 wt% TDB, the LOI value of the thermoset was 27.5%, and the UL-94 rating reached V-0. Furthermore, compared with pure EP, the peak heat release rate (pk-HRR) decreased by more than half, as well as the lower total heat release (THR) and total smoke production (TSP) were obtained. The flame retardant mechanism was studied by analyzing the char residue after cone calorimeter (CC) test and the pyrolysis products via scanning electronic microscopy (SEM), laser Raman spectroscopy (LRS), FTIR and Pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS). It revealed that on account of the existence of triazine-trione structure and phosphorus/boron elements, the intumescent and compact phosphorus/boron-rich char layer was formed, meanwhile, the non-flammable gases and phosphorus-containing free radicals from triazine-trione and DPCP structure can develop the flame retardancy in the gas phase.

The 13C isotopic ratio of methane, δ13C of CH4, provides additional constraints on the CH4 budget to complement the constraints from CH4 observations. The interpretation of δ13C observations is complicated, however, by uncertainties in the methane sink. The reaction of CH4 with Cl is highly fractionating, increasing the relative abundance of 13CH4, but there is currently no consensus on the strength of the tropospheric Cl sink. Global model simulations of halogen chemistry differ strongly from one another in terms of both the magnitude of tropospheric Cl and its geographic distribution. This study explores the impact of the intermodel diversity in Cl fields on the simulated δ13C of CH4. We use a set of GEOS global model simulations with different predicted Cl fields to test the sensitivity of the δ13C of CH4 to the diversity of Cl output from chemical transport models. We find that δ13C is highly sensitive to both the amount and geographic distribution of Cl. Simulations with Cl providing 0.28 % or 0.66 % of the total CH4 loss bracket the δ13C observations for a fixed set of emissions. Thus, even when Cl provides only a small fraction of the total CH4 loss and has a small impact on total CH4, it provides a strong lever on δ13C. Consequently, it is possible to achieve a good representation of total CH4 using widely different Cl concentrations, but the partitioning of the CH4 loss between the OH and Cl reactions leads to strong differences in isotopic composition depending on which model's Cl field is used. Comparing multiple simulations, we find that altering the tropospheric Cl field leads to approximately a 0.5 ‰ increase in δ13CH4 for each percent increase in how much CH4 is oxidized by Cl. The geographic distribution and seasonal cycle of Cl also impacts the hemispheric gradient and seasonal cycle of δ13C. The large effect of Cl on δ13C compared to total CH4 broadens the range of CH4 source mixtures that can be reconciled with δ13C observations. Stronger constraints on tropospheric Cl are necessary to improve estimates of CH4 sources from δ13C observations.

Abstract We report on the measurement of the Central Exclusive Production of charged particle pairs h + h − (h = π, K, p) with the STAR detector at RHIC in proton-proton collisions at s $$ \sqrt{s} $$ = 200 GeV. The charged particle pairs produced in the reaction pp → p′ + h + h − + p′ are reconstructed from the tracks in the central detector and identified using the specific energy loss and the time of flight method, while the forward-scattered protons are measured in the Roman Pot system. Exclusivity of the event is guaranteed by requiring the transverse momentum balance of all four final-state particles. Differential cross sections are measured as functions of observables related to the central hadronic final state and to the forward-scattered protons. They are measured in a fiducial region corresponding to the acceptance of the STAR detector and determined by the central particles’ transverse momenta and pseudorapidities as well as by the forward-scattered protons’ momenta. This fiducial region roughly corresponds to the square of the four-momentum transfers at the proton vertices in the range 0.04 GeV2 < −t 1 , −t 2 < 0.2 GeV2, invariant masses of the charged particle pairs up to a few GeV and pseudorapidities of the centrally-produced hadrons in the range |η| < 0.7. The measured cross sections are compared to phenomenological predictions based on the Double Pomeron Exchange (DPE) model. Structures observed in the mass spectra of π + π − and K + K − pairs are consistent with the DPE model, while angular distributions of pions suggest a dominant spin-0 contribution to π + π − production. For π + π − production, the fiducial cross section is extrapolated to the Lorentz-invariant region, which allows decomposition of the invariant mass spectrum into continuum and resonant contributions. The extrapolated cross section is well described by the continuum production and at least three resonances, the f 0(980), f 2(1270) and f 0(1500), with a possible small contribution from the f 0(1370). Fits to the extrapolated differential cross section as a function of t 1 and t 2 enable extraction of the exponential slope parameters in several bins of the invariant mass of π + π − pairs. These parameters are sensitive to the size of the interaction region.

Ant Lion Optimizer (ALO) is a new meta-heuristic algorithm that simulates the ant lion predator mechanism in nature. Five main steps of hunting include: random walks of ants, building traps, trapping in antlion's pits, sliding ants towards antlion, catching prey and re-building pits. As the predator radius of antlion decreases with the number of iterations, there is an unbalanced between the ant lion optimizer between exploration and exploitation, and it is easy to fall into the local optimal solution. An improved ant lion optimizer based on spiral complex path searching pattern is proposed, where eight spiral paths (Hypotrochoid, Rose spiral curve, Logarithmic spiral curve, Archimedes spiral curve, Epitrochoid, Inverse spiral curve, Cycloid, Overshoot parameter setting of the spiral) searching strategies were adopted to improve the diversity of the population and the ability of the algorithm to balance exploration and exploitation. The proposed algorithm can accelerate the convergence speed of ALO and improve its performance. The algorithm is verified by simulation experiments in three parts. Firstly, 28 function optimization problems were adopted to test the optimization performance of the improved ALO. Secondly, it is applied to the lightest design engineering problem of pressure vessels. Finally, the spiral complex path searching patterns are introduced into the muti-objective ALO and 4 typical muti-objective functions are optimized. Simulation results show that the superior performance of the proposed algorithm for exploiting the optimum and it has advantages in terms of exploration. The improved algorithm can better solve function optimization, classical engineering problems with constraints and multi-objective function optimization problems. The improved ALO based on the spiral complex path searching mode has the characteristics of balanced exploration and exploitation, fast convergence speed and high precision.

The grasshopper optimization algorithm (GOA) is a new meta-heuristic algorithm inspired by the behavior of grasshopper groups. Aiming at the shortcomings of poor development ability and low convergence accuracy of GOA, this paper introduces the gravity search operator into the optimization process of GOA to improve the grasshopper's global exploration and avoid falling into local optimum in advance. At the same time, a pigeon search operator-landmark operator is introduced to improve and balance the algorithm's exploration and development capabilities. In order to verify the validity of the improved algorithm, this paper will adopts the gravity search operator and a deterrent landmark operator hybrid grasshoppers algorithm (HGOA) with basic grasshopper algorithm (GOA), particle swarm optimization (PSO) algorithm, sine and cosine algorithm (SCA), moth-flame optimization (MFO) algorithm, salp swarm algorithm (SSA), and bat algorithm (BA) to optimize 28 test functions. And the analysis and comparison of the obtained statistical data results finally show that the proposed improved grasshopper algorithm has better optimization ability.

Whale optimization algorithm (WOA) is a swarm intelligence-based algorithm that simulates whale population predation in the sea. Aiming at the shortcomings of WOA such as low precision and slow convergence speed, an improved whale optimization algorithm based on nonlinear adaptive weight and golden sine operator (NGS-WOA) was proposed. NGS-WOA first introduced a non-linear adaptive weigh so that search agents can adaptively explore the search space, and balance the development and exploration stages. Secondly, the improved golden sine operator is incorporated into the WOA. Due to the special relationship between the sine function and the unit circle, traversing the sine function is equivalent to scanning the unit circle. The search agent performs an efficient search with a sine route so as to improve the convergence speed and global exploration capability of the algorithm. At the same time, the addition of the golden section coefficient allows search agents to exploit with a fixed shrink step. The search agent can develop to areas with excellent results, which improves the optimization accuracy and local exploitation ability of the algorithm. In the simulation experiments, the gold sine algorithm (GoldSA), whale optimization algorithm (WOA), particle swarm optimization (PSO) algorithm, firefly algorithm (FA), fireworks algorithm (FWA), sine cosine algorithm (SCA) and NGS-WOA were selected for comparison experiments. Then, the effectiveness of the proposed improved strategies is verified. Finally, the improved WOA is applied to high-dimensional optimization and engineering optimization problems. The experimental results show that the improved strategy can effectively improve the performance of the algorithm, so that NGS-WOA has the advantages of high global convergence and avoiding falling into local optimal values.

Grey wolf optimizer (GWO) is a new meta-heuristic algorithm. The GWO algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. Three main stages of hunting include: encircling, tracking and attacking. It is easy to fall into local optimum when used to optimize high-dimensional data, and there is imbalance between exploration and exploitation. An improved grey wolf optimizer based on tracking mode and seeking mode is proposed to improve the diversity of the population and the ability of the algorithm to balance exploration and exploitation. The algorithm is verified by simulation experiments in three parts. Firstly, the proposed grey wolf optimizer based on tracking mode (TGWO), the improved grey wolf optimizer based on seeking mode (SGWO), the improved grey wolf optimizer based on tracking and seeking mode (TSGWO), Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), Salp Swarm Algorithm (SSA), Sine Cosine Algorithm (SCA), Ant Lion Optimizer (ALO), Whale Optimization Algorithm (WOA) and Moth-flame Optimization (MFO) are adopted to optimize 21 typical benchmark functions respectively, and the obtained statistical simulation results are compared; Secondly, the improved algorithm proposed in this paper is compared with Binary Grey Wolf Optimizer (BGWO), Hybrid PSOGWO Optimization (PSOGWO) and GWO Algorithm Integrated with Cuckoo Search (GWOCS); Finally, it is applied to the lightest design engineering problem of pressure vessels. Simulation results show that the superior performance of the proposed algorithm for exploiting the optimum and it has advantages in terms of exploration. The improved grey wolf optimizer based on tracking mode and seeking mode can better solve function optimization and classical engineering problems with constraints. It was found the improved grey wolf optimizer based on tracking mode has the high precision and the characteristics of balanced exploration and exploitation.

The salp swarm algorithm (SSA) is a swarm intelligence optimization algorithm that simulates the chain movement behavior of salp populations in the sea. Aiming at the shortcomings of the SSA, such as low precision, low optimization dimension and slow convergence speed, an improved salp swarm algorithm based on Levy flight and sine cosine operator (LSC-SSA) was proposed. The Levy flight mechanism uses the route of short walks combined with long jumps to search the solution space, which can effectively improve the global exploration capability of the algorithm. Improved sine cosine operator use sine search for global exploration and cosine search for local exploitation. At the same time, an adaptively switching between the two function search methods can achieve a smooth transition between global exploration and local exploitation. In the simulation experiment, salp swarm algorithm (SSA), whale optimization algorithm (WOA), particle swarm algorithm (PSO), sine cosine algorithm (SCA), firefly algorithm (FA) and LSC-SSA were adopted for solving function optimization problems. Then, the feasibility of the improved algorithm for solving high-dimensional large-scale optimization problems and the effectiveness of the improvement strategy are evaluated. Finally, LSC-SSA was applied to train muti-layer perceptron neural network. Simulation results show that the introduction of Levy flight and improved sine cosine operator in LSC-SSA significantly improves optimization accuracy and convergence speed compared with other swarm optimization algorithms. In addition, the improved algorithm can effectively solve high-dimensional large-scale optimization problems. In the application of training muti-layer perceptron NN, the improved algorithm can avoid falling into the local optimal value and obtain the ideal classification accuracy.

Aiming at predicting the key economic and technical indicators (Granularity and Ore content)in the grinding production process, the extreme learning machine (ELM) soft-sensor model with different activation functions on grinding process optimized by improved black hole (BH) algorithm was proposed. Based on the selected auxiliary variables for the soft-sensor model of the grinding, the KPCA method is used to reduce the dimensionality of the high-dimensional data. In order to investigate the influence of different activation functions on the prediction accuracy of the ELM model, seven continuous function (Arctan, Hardim, Morlet, ReLu, Sigma, Sin and Tanh) are used as the activation function of the ELM neural network to establish soft-sensor models respectively. For the shortcomings that ELM model weights and offset values are arbitrarily given so as to result in the low prediction accuracy and low reliability, an improved BH algorithm based on the golden sine operator and the Levy flight operator (GSLBH) was used to optimize the parameters of the ELM neural network. Simulation results show that the model has better generalization and prediction accuracy, and can meet the real-time control requirements of the grinding process.

Clustering as an unsupervised learning method is a process of dividing a data object or observation object into a subset, that is to classify the data through observation learning instead of example learning without the guidance of the prior class label information. Bat algorithm (BA) is a swarm intelligence optimization algorithm inspired by bat's ultrasonic echo localization foraging behavior, but it has the disadvantages of being easily trapped into local minima and not being highly accurate. So an improved bat algorithm was proposed. In the global search, a Gaussian-like convergence factor is added, and five different convergence factors are proposed to improve the global optimization ability of the algorithm. In the local search, the hunting mechanism of the whale optimization algorithm (WOA) and the sine position updating strategy are adopted to improve the local optimization ability of the algorithm. This paper compares the clustering effect of the improved bat algorithm with bat algorithm, flower pollination algorithm (FPA), harmony search (HS) algorithm, whale optimization algorithm and particle swarm optimization (PSO) algorithm on seven real data sets under six different convergence factors. The simulation results show that the clustering effect of the improved bat algorithm is superior to other intelligent optimization algorithms.

The tropical tropopause layer (TTL) is the transition region between the well-mixed convective troposphere and the radiatively controlled stratosphere with air masses showing chemical and dynamical properties of both regions. The representation of the TTL in meteorological reanalysis data sets is important for studying the complex interactions of circulation, convection, trace gases, clouds, and radiation. In this paper, we present the evaluation of climatological and long-term TTL temperature and tropopause characteristics in the reanalysis data sets ERA-Interim, ERA5, JRA-25, JRA-55, MERRA, MERRA-2, NCEP-NCAR (R1), and CFSR. The evaluation has been performed as part of the SPARC (Stratosphere–troposphere Processes and their Role in Climate) Reanalysis Intercomparison Project (S-RIP). The most recent atmospheric reanalysis data sets (ERA-Interim, ERA5, JRA-55, MERRA-2, and CFSR) all provide realistic representations of the major characteristics of the temperature structure within the TTL. There is good agreement between reanalysis estimates of tropical mean temperatures and radio occultation data, with relatively small cold biases for most data sets. Temperatures at the cold point and lapse rate tropopause levels, on the other hand, show warm biases in reanalyses when compared to observations. This tropopause-level warm bias is related to the vertical resolution of the reanalysis data, with the smallest bias found for data sets with the highest vertical resolution around the tropopause. Differences in the cold point temperature maximize over equatorial Africa, related to Kelvin wave activity and associated disturbances in TTL temperatures. Interannual variability in reanalysis temperatures is best constrained in the upper TTL, with larger differences at levels below the cold point. The reanalyses reproduce the temperature responses to major dynamical and radiative signals such as volcanic eruptions and the quasi-biennial oscillation (QBO). Long-term reanalysis trends in temperature in the upper TTL show good agreement with trends derived from adjusted radiosonde data sets indicating significant stratospheric cooling of around −0.5 to −1 K per decade. At 100 hPa and the cold point, most of the reanalyses suggest small but significant cooling trends of −0.3 to −0.6 K per decade that are statistically consistent with trends based on the adjusted radiosonde data sets. Advances of the reanalysis and observational systems over the last decades have led to a clear improvement in the TTL reanalysis products over time. Biases of the temperature profiles and differences in interannual variability clearly decreased in 2006, when densely sampled radio occultation data started being assimilated by the reanalyses. While there is an overall good agreement, different reanalyses offer different advantages in the TTL such as realistic profile and cold point temperature, continuous time series, or a realistic representation of signals of interannual variability. Their use in model simulations and in comparisons with climate model output should be tailored to their specific strengths and weaknesses.

Objective The study was conducted to evaluate the effects of night light regimen on growth performance, antioxidant status and health of Lingnan Yellow broiler chickens from 1 to 21 days of age. Methods A completely randomized factorial design involved 2 photoperiods (constant lighting [CL], 24 L:0 D and intermittent lighting [INL], 17 L:3 D:1 L:3 D)×2 light intensities (10 lx and 30 lx). A total of one thousand six hundred and eighty 1-d-old Lingnan Yellow broiler chicks were randomly divided into 4 treatments with 6 replicates (70 birds per replicate). The experiment lasted for 21 d. Results Photoperiods and light intensities had no effect on average daily gain, feed conversion ratio, and mortality of the broiler chickens (p>0.05). The INL had a significant effect on average daily feed intake (p

Cluster analysis refers to the process of grouping a collection of physical or abstract objects into multiple classes of similar objects. Determining the optimal classification number of a data set is the key to the clustering problem, that is to say whether the data set can be effectively partitioned. Cluster validity study is a process of establishing clustering effectiveness indicators, evaluating clustering quality and determining the optimal number of clusters. A validity function of fuzzy C-means (FCM) clustering algorithm is proposed by adopting the division of intra-class compactness and inter-class separation, whose minimum represents the best clustering. Then, the proposed validity function on FCM clustering algorithm is compared with the known typical validity functions by carrying out simulation experiments to compare the related clustering performance. Three data sets are adopted to carry out FCM clustering, which includes three classical data sets, two artificial data sets and six real data sets in UCI database. Simulation experimental results show that the proposed validity function can effectively partition the data set.

The black hole (BH) algorithm is a new type of natural heuristic algorithm inspired by the movement law of the “black hole” celestial body in the universe. BH algorithm has received extensive attention due to its advantages such as fewer parameters, simple algorithm structure and strong exploitation. For the shortcomings of poor exploaration and premature convergence of BH algorithm, the improved golden sine (G-S) operator is introduced into BH algorithm to greatly improve the exploaration. Then the Levy flight operator with controlled step size has become a better local search operator from the global search operator, so the improved Levy flight operator is introduced to further improve the exploitation of BH algorithm. Ultimately, the golden sine operator and Levy flight operator based black hole (GSLBH) algorithm is able to balance the exploaration with exploitation. GSLBH, BH, Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WAO), Firefly Algorithm (FA), Golden Sine Algorithm (Gold-SA) were adopted to carry out the simulation experiments with 17 benchmark functions, respectively, and the statistical data results are analyzed and compared. Finally, it can be concluded that the proposed improved black hole algorithm has better exploaration, and the convergence speed and accuracy of the algorithm have been further improved.

Objective This study was conducted with the objectives to examine the impacts of inorganic selenium (Se) and different types and levels of organic selenium on the serum and tissues Se status and antioxidant capacity in broiler breeders. Methods Five hundred and forty 48-wk-old Lingnan Yellow broiler breeders were randomly assigned to 6 dietary treatments, provided same basal diet (0.04 mg/kg of Se) with 0.15 mg/kg, or 0.30 mg/kg of Se from sodium selenite (SS) or from selenium-enriched yeast (SY) or from selenomethionine (SM). The broiler breeders were slaughtered after an 8-wk experiment. Results The results showed that SM was better than SY and SS, 0.30 mg/kg level was better than 0.15 mg/kg level in Se deposition (p

The precise contribution of the two major sinks for anthropogenic CO2 emissions, terrestrial vegetation and the ocean, and their location and year-to-year variability are not well understood. Top-down estimates of the spatiotemporal variations in emissions and uptake of CO2 are expected to benefit from the increasing measurement density brought by recent in situ and remote CO2 observations. We uniquely apply a batch Bayesian synthesis inversion at relatively high resolution to in situ surface observations and bias-corrected GOSAT satellite column CO2 retrievals to deduce the global distributions of natural CO2 fluxes during 2009–2010. The GOSAT inversion is generally better constrained than the in situ inversion, with smaller posterior regional flux uncertainties and correlations, because of greater spatial coverage, except over North America and northern and southern high-latitude oceans. Complementarity of the in situ and GOSAT data enhances uncertainty reductions in a joint inversion; however, remaining coverage gaps, including those associated with spatial and temporal sampling biases in the passive satellite measurements, still limit the ability to accurately resolve fluxes down to the sub-continental or sub-ocean basin scale. The GOSAT inversion produces a shift in the global CO2 sink from the tropics to the north and south relative to the prior, and an increased source in the tropics of ∼  2 Pg C yr−1 relative to the in situ inversion, similar to what is seen in studies using other inversion approaches. This result may be driven by sampling and residual retrieval biases in the GOSAT data, as suggested by significant discrepancies between posterior CO2 distributions and surface in situ and HIPPO mission aircraft data. While the shift in the global sink appears to be a robust feature of the inversions, the partitioning of the sink between land and ocean in the inversions using either in situ or GOSAT data is found to be sensitive to prior uncertainties because of negative correlations in the flux errors. The GOSAT inversion indicates significantly less CO2 uptake in the summer of 2010 than in 2009 across northern regions, consistent with the impact of observed severe heat waves and drought. However, observations from an in situ network in Siberia imply that the GOSAT inversion exaggerates the 2010–2009 difference in uptake in that region, while the prior CASA-GFED model of net ecosystem production and fire emissions reasonably estimates that quantity. The prior, in situ posterior, and GOSAT posterior all indicate greater uptake over North America in spring to early summer of 2010 than in 2009, consistent with wetter conditions. The GOSAT inversion does not show the expected impact on fluxes of a 2010 drought in the Amazon; evaluation of posterior mole fractions against local aircraft profiles suggests that time-varying GOSAT coverage can bias the estimation of interannual flux variability in this region.

Objective This experiment studied the effects of first feed intake time post-hatch on growth performance, nutrient apparent metabolic rate and intestinal digestive enzyme activities in broilers. Methods Two thousand five hundred and twenty LingNan Yellow broilers were randomly allotted to seven treatments with six replicates of 60 each. The only experimental factor was the first feed intake time which was 18, 24, 30, 36, 42, 48, and 54 hours after hatching. The whole experiment lasted for 21 days. Results During the whole period, the 30 h treatment had the best body weight and average daily gain (p

According to the requirement of real-time monitoring of the conversion rate of vinyl chloride in the production process of polyvinyl chloride polymerization and the nonlinearity of the industrial data, the Elman neural network with strong nonlinear performance is chosen to build the soft-sensor model. However, because of the early stage of the Elman neural network to train the connection weights between the layers, the training effect is difficult to guarante with the connection weights. So the whale optimization algorithm (WOA) is adopted to optimize the Elman neural network, to avoid it falling into the local optimum. At the same time, to solve the problem that the position of the search agent is randomly distributed in the initialization process of the WOA algorithm, and to introduce the idea of chaos, a chaos WOA (CWOA) based on the idea of chaos is proposed to improve the diversity of all search agents and egocentricity of agent search by utilizing the chaotic features. At the end of this paper, considering that the input vector dimension is too large, the neural network topology is very large, which will lead to the complexity of the training process. Therefore, the locally linear embedding method is introduced to reduce the dimension of high-dimensional input vectors. The simulation results show that the chaotic whale algorithm can significantly improve the prediction accuracy of economic and technical indexes of PVC polymerization process, which especially has a significant improvement in the prediction effect in the early stage and meets the requirements of real-time control of the production process of the polymerization reactor.

We present the Efficient CH4–CO–OH (ECCOH) chemistry module that allows for the simulation of the methane, carbon monoxide, and hydroxyl radical (CH4–CO–OH) system, within a chemistry climate model, carbon cycle model, or Earth system model. The computational efficiency of the module allows many multi-decadal sensitivity simulations of the CH4–CO–OH system, which primarily determines the global atmospheric oxidizing capacity. This capability is important for capturing the nonlinear feedbacks of the CH4–CO–OH system and understanding the perturbations to methane, CO, and OH, and the concomitant impacts on climate. We implemented the ECCOH chemistry module in the NASA GEOS-5 atmospheric global circulation model (AGCM), performed multiple sensitivity simulations of the CH4–CO–OH system over 2 decades, and evaluated the model output with surface and satellite data sets of methane and CO. The favorable comparison of output from the ECCOH chemistry module (as configured in the GEOS-5 AGCM) with observations demonstrates the fidelity of the module for use in scientific research.

Top–down estimates of the spatiotemporal variations in emissions and uptake of CO2 will benefit from the increasing measurement density brought by recent and future additions to the suite of in situ and remote CO2 measurement platforms. In particular, the planned NASA Active Sensing of CO2 Emissions over Nights, Days, and Seasons (ASCENDS) satellite mission will provide greater coverage in cloudy regions, at high latitudes, and at night than passive satellite systems, as well as high precision and accuracy. In a novel approach to quantifying the ability of satellite column measurements to constrain CO2 fluxes, we use a portable library of footprints (surface influence functions) generated by the Stochastic Time-Inverted Lagrangian Transport (STILT) model in combination with the Weather Research and Forecasting (WRF) model in a regional Bayesian synthesis inversion. The regional Lagrangian particle dispersion model framework is well suited to make use of ASCENDS observations to constrain weekly fluxes in North America at a high resolution, in this case at 1° latitude × 1° longitude. We consider random measurement errors only, modeled as a function of the mission and instrument design specifications along with realistic atmospheric and surface conditions. We find that the ASCENDS observations could potentially reduce flux uncertainties substantially at biome and finer scales. At the grid scale and weekly resolution, the largest uncertainty reductions, on the order of 50%, occur where and when there is good coverage by observations with low measurement errors and the a priori uncertainties are large. Uncertainty reductions are smaller for a 1.57 μm candidate wavelength than for a 2.05 μm wavelength, and are smaller for the higher of the two measurement error levels that we consider (1.0 ppm vs. 0.5 ppm clear-sky error at Railroad Valley, Nevada). Uncertainty reductions at the annual biome scale range from ~40% to ~75% across our four instrument design cases and from ~65% to ~85% for the continent as a whole. Tests suggest that the quantitative results are moderately sensitive to assumptions regarding a priori uncertainties and boundary conditions. The a posteriori flux uncertainties we obtain, ranging from 0.01 to 0.06 Pg C yr−1 across the biomes, would meet requirements for improved understanding of long-term carbon sinks suggested by a previous study.

We have designed a gradient-index (GRIN) metasurface to modulate water surface waves (WSWs). The metasurface is composed of an array of coiling-up-space units with a deep sub-wavelength scale, and can focus/scatter WSWs when the units are arranged elaborately and pierced into water. The modulation of WSWs has been ascribed to the relative effective refractive GRIN of the coiling-up-space units, which can be tuned by changing the parameters such as the plate length of units. This work may have potential application in energy extraction of water wave.

The effect of a laterally applied electric field (≤10 kV/cm) on the photoluminescence of colloidal PbS quantum dots (diameter of 2.7 nm) on glass was studied. The field provoked a blueshift of the emission peak, a reduction of the luminescent intensity, and caused an increase in the full width at half maximum of the emission spectrum. Upon comparison with the photoluminescence of p-type GaAs exhibits the uniqueness of quantum dot based electric emission control with respect to bulk materials.