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Atmosphere modelling applications are becoming increasingly memory-bound due to the inconsistent development rates between processor speeds and memory bandwidth. In this study, we mitigate memory bottlenecks and reduce the computational load of the Global–Regional Integrated Forecast System (GRIST) dynamical core by adopting a mixed-precision computing strategy. Guided by an application of the iterative development principle, we identify the coded equation terms that are precision insensitive and modify them from double to single precision. The results show that most precision-sensitive terms are predominantly linked to pressure gradient and gravity terms, while most precision-insensitive terms are advective terms. Without using more computing resources, computational time can be saved, and the physical performance of the model is largely kept. In the standard computational test, the reference runtime of the model's dry hydrostatic core, dry nonhydrostatic core, and the tracer transport module is reduced by 24 %, 27 %, and 44 %, respectively. A series of idealized tests, real-world weather and climate modelling tests, was performed to assess the optimized model performance qualitatively and quantitatively. In particular, in the long-term coarse-resolution climate simulation, the precision-induced sensitivity can manifest at the large scale, while in the kilometre-scale weather forecast simulation, the model's sensitivity to the precision level is mainly limited to small-scale features, and the wall-clock time is reduced by 25.5 % from the double- to mixed-precision full-model simulations.

The single-column model, with its advantages of low computational cost and fast execution speed, can assist users in gaining a more intuitive understanding of the impact of parameters on the simulated results of climate models. It plays a crucial role in the study of parameterization schemes, allowing for a more direct exploration of the influence of parameters on climate model simulations. In this paper, we employed various methods to conduct sensitivity analyses on the 11 parameters of the Single Column Atmospheric Model (SCAM). We explored their impact on output variables such as precipitation, temperature, humidity, and cloud cover, among others, across five test cases. To further expedite experimentation, we utilized machine learning methods to train surrogate models for the aforementioned cases. Additionally, three-parameter joint perturbation experiments were conducted based on these surrogate models to validate the combined parameter effects on the results. Subsequently, targeting the sensitive parameter combinations identified from the aforementioned experiments, we further conducted parameter tuning for the corresponding test cases to minimize the discrepancy between the results of SCAM and observational data. Our proposed method not only enhances model performance but also expedites parameter tuning speed, demonstrating good generality at the same time.

Recent years have seen the emergence of local air pollutant monitoring networks that feature close proximity to urban activities, higher requirement for temporal granularity, and improvisions in equipment hardware and installation conditions. These networks are intended for the pertinent monitoring and improvement of urban air quality, but potential technical issues may undermine their ability to serve such purposes. This study utilizes a minute-granularity network in a university campus, and designs and conducts a series of experiments on how it performs under practical scenarios, including response to sudden environment change, reflection of multi-scale influencing factors, usability of different baseline stations, and ability to detect local emission events. Statistical and signal-processing technics are applied for understanding these experiments. The results indicate the source of complexity in such networks, the preferred temporal granularity for capturing different temporal patterns, the necessary reserved time for mobile stations, and the sensor location requirement for monitoring local emission events etc. In practical terms, these results provide a large amount of information on the specific capabilities and limitations of a near-surface, high-granularity monitoring network in the urban environment, and what to consider and to expect as a designer or user of such a network. In scientific terms, it is a strong reminder of the significance of numerous uncertainty issues in similar empirical studies.

In climate models, subgrid parameterizations of convection and clouds are one of the main causes of the biases in precipitation and atmospheric circulation simulations. In recent years, due to the rapid development of data science, machine learning (ML) parameterizations for convection and clouds have been demonstrated to have the potential to perform better than conventional parameterizations. Most previous studies were conducted on aqua-planet and idealized models, and the problems of simulation instability and climate drift still exist. Developing an ML parameterization scheme remains a challenging task in realistically configured models. In this paper, a set of residual deep neural networks (ResDNNs) with a strong nonlinear fitting ability is designed to emulate a super-parameterization (SP) with different outputs in a hybrid ML–physical general circulation model (GCM). It can sustain stable simulations for over 10 years under real-world geographical boundary conditions. We explore the relationship between the accuracy and stability by validating multiple deep neural network (DNN) and ResDNN sets in prognostic runs. In addition, there are significant differences in the prognostic results of the stable ResDNN sets. Therefore, trial and error is used to acquire the optimal ResDNN set for both high skill and long-term stability, which we name the neural network (NN) parameterization. In offline validation, the neural network parameterization can emulate the SP in mid- to high-latitude regions with a high accuracy. However, its prediction skill over tropical ocean areas still needs improvement. In the multi-year prognostic test, the hybrid ML–physical GCM simulates the tropical precipitation well over land and significantly improves the frequency of the precipitation extremes, which are vastly underestimated in the Community Atmospheric Model version 5 (CAM5), with a horizontal resolution of 1.9∘ × 2.5∘. Furthermore, the hybrid ML–physical GCM simulates the robust signal of the Madden–Julian oscillation with a more reasonable propagation speed than CAM5. However, there are still substantial biases with the hybrid ML–physical GCM in the mean states, including the temperature field in the tropopause and at high latitudes and the precipitation over tropical oceanic regions, which are larger than those in CAM5. This study is a pioneer in achieving multi-year stable climate simulations using a hybrid ML–physical GCM under actual land–ocean boundary conditions that become sustained over 30 times faster than the target SP. It demonstrates the emerging potential of using ML parameterizations in climate simulations.

X-ray Earth occultation sounding (XEOS) is an emerging method for measuring the neutral density in the lower thermosphere. In this paper, the X-ray Earth occultation (XEO) of the Crab Nebula is investigated using the Hard X-ray Modulation Telescope (Insight-HXMT). The pointing observation data on the 30 September 2018 recorded by the low-energy X-ray telescope (LE) of Insight-HXMT are selected and analysed. The extinction light curves and spectra during the X-ray Earth occultation process are extracted. A forward model for the XEO light curve is established, and the theoretical observational signal for light curve is predicted. The atmospheric density model is built with a scale factor to the commonly used Mass Spectrometer Incoherent Scatter Radar Extended model (MSIS) density profile within a certain altitude range. A Bayesian data analysis method is developed for the XEO light curve modelling and the atmospheric density retrieval. The posterior probability distribution of the model parameters is derived through the Markov chain–Monte Carlo (MCMC) algorithm with the NRLMSISE-00 model and the NRLMSIS 2.0 model as basis functions, and the respective best-fit density profiles are retrieved. It is found that in the altitude range of 105–200 km, the retrieved density profile is 88.8 % of the density of NRLMSISE-00 and 109.7 % of the density of NRLMSIS 2.0 by fitting the light curve in the energy range of 1.0–2.5 keV based on the XEOS method. In the altitude range of 95–125 km, the retrieved density profile is 81.0 % of the density of NRLMSISE-00 and 92.3 % of the density of NRLMSIS 2.0 by fitting the light curve in the energy range of 2.5–6.0 keV based on the XEOS method. In the altitude range of 85–110 km, the retrieved density profile is 87.7 % of the density of NRLMSISE-00 and 101.4 % of the density of NRLMSIS 2.0 by fitting the light curve in the energy range of 6.0–10.0 keV based on the XEOS method. Goodness-of-fit testing is carried out for the validation of the results. The measurements of density profiles are compared to the NRLMSISE-00 and NRLMSIS 2.0 model simulations and the previous retrieval results with NASA's Rossi X-ray Timing Explorer (RXTE) satellite. For further confirmation, we also compare the measured density profile to the ones by a standard spectrum retrieval method with an iterative inversion technique. Finally, we find that the retrieved density profile from Insight-HXMT based on the NRLMSISE-00 and NRLMSIS 2.0 models is qualitatively consistent with the previous retrieved results from RXTE. The results of light curve fitting and standard energy spectrum fitting are in good agreement. This research provides a method for the evaluation of the density profiles from MSIS model predictions. This study demonstrates that the XEOS from the X-ray astronomical satellite Insight-HXMT can provide an approach for the study of the upper atmosphere. The Insight-HXMT satellite can join the family of the XEOS. The Insight-HXMT satellite with other X-ray astronomical satellites in orbit can form a space observation network for XEOS in the future.

Concrete-filled steel tube arch bridge is filled with concrete inside the steel tube. The radial constraint of the steel tube limits the expansion of the compression concrete, which makes the concrete in the three-way compression state, thus significantly improving the compressive strength of the concrete. At the same time, it can simplify the construction process and shorten the construction period. Since the rapid development of concretefilled steel tubular tied arch bridge in the 1990s, a large number of such Bridges have suffered from the defects of steel concrete, loose tie rod, and hanger rod rust, etc. Therefore, the reinforcement technology for various diseases has been studied, among which the reinforcement technology for hanger rod replacement is the most complicated and more difficult. As more and more bridges of this type enter the period of reinforcement, it ismore and more urgent to study the reinforcement technology of suspenders. Taking a bridge that has been in service for 23 years as an example, this paper discusses the construction method and construction monitoring of replacing the suspender, so as to guide the construction monitoring of the bridge. Finally, the construction monitoring results of the bridge are given, which can provide reference for the replacement of the suspender of this type of bridge.

Two-way curved arch bridges inherit the fine tradition of masonry structures, making full use of the advantages of prefabricated assembly, it adapts to the situation of no support construction and no large lifting machine and tools, and has the characteristics of convenient construction method and saving material consumption. In appearance, the two-way curved arch bridge has strong national cultural characteristics. The prefabricated components of the two-way curved arch bridge are fragmentary, complicated in bearing and poor in integrity. Most of the two-way curved arch bridges in service have been built for a long time and lack of maintenance and management. Increasing the cross-section reinforcement method is one of the two-way curved arch reinforcement methods. It has a significant effect, convenient construction, good rigidity and stability characteristics after the reinforcement. Through theoretical analysis, combined with a static load test results of the assessment of the bridge reinforcement effect. Through load test, it is found that the deflection of the arch rib after reinforcement is reduced by 9%~19% and the strain of the arch rib is reduced by 12%~22%. Through finite element calculation, the crack width of the reinforced arch rib decreases by 8.3%~14.2%. The results show that the stress and deflection are greatly improved by the method of increasing section.

Fine particulate matter with a diameter of less than 2.5 µm (PM2.5) has been used as an important atmospheric environmental parameter mainly because of its impact on human health. PM2.5 is affected by both natural and anthropogenic factors that usually have strong diurnal variations. Such information helps toward understanding the causes of air pollution, as well as our adaptation to it. Most existing PM2.5 products have been derived from polar-orbiting satellites. This study exploits the use of the next-generation geostationary meteorological satellite Himawari-8/AHI (Advanced Himawari Imager) to document the diurnal variation in PM2.5. Given the huge volume of satellite data, based on the idea of gradient boosting, a highly efficient tree-based Light Gradient Boosting Machine (LightGBM) method by involving the spatiotemporal characteristics of air pollution, namely the space-time LightGBM (STLG) model, is developed. An hourly PM2.5 dataset for China (i.e., ChinaHighPM2.5) at a 5 km spatial resolution is derived based on Himawari-8/AHI aerosol products with additional environmental variables. Hourly PM2.5 estimates (number of data samples = 1 415 188) are well correlated with ground measurements in China (cross-validation coefficient of determination, CV-R2 = 0.85), with a root-mean-square error (RMSE) and mean absolute error (MAE) of 13.62 and 8.49 µg m−3, respectively. Our model captures well the PM2.5 diurnal variations showing that pollution increases gradually in the morning, reaching a peak at about 10:00 LT (GMT+8), then decreases steadily until sunset. The proposed approach outperforms most traditional statistical regression and tree-based machine-learning models with a much lower computational burden in terms of speed and memory, making it most suitable for routine pollution monitoring.

BCC-CSM2-HR is a high-resolution version of the Beijing Climate Center (BCC) Climate System Model (T266 in the atmosphere and 1/4∘ latitude × 1/4∘ longitude in the ocean). Its development is on the basis of the medium-resolution version BCC-CSM2-MR (T106 in the atmosphere and 1∘ latitude × 1∘ longitude in the ocean) which is the baseline for BCC participation in the Coupled Model Intercomparison Project Phase 6 (CMIP6). This study documents the high-resolution model, highlights major improvements in the representation of atmospheric dynamical core and physical processes. BCC-CSM2-HR is evaluated for historical climate simulations from 1950 to 2014, performed under CMIP6-prescribed historical forcing, in comparison with its previous medium-resolution version BCC-CSM2-MR. Observed global warming trends of surface air temperature from 1950 to 2014 are well captured by both BCC-CSM2-MR and BCC-CSM2-HR. Present-day basic atmospheric mean states during the period from 1995 to 2014 are then evaluated at global scale, followed by an assessment on climate variabilities in the tropics including the tropical cyclones (TCs), the El Niño–Southern Oscillation (ENSO), the Madden–Julian Oscillation (MJO), and the quasi-biennial oscillation (QBO) in the stratosphere. It is shown that BCC-CSM2-HR represents the global energy balance well and can realistically reproduce the main patterns of atmospheric temperature and wind, precipitation, land surface air temperature, and sea surface temperature (SST). It also improves the spatial patterns of sea ice and associated seasonal variations in both hemispheres. The bias of the double intertropical convergence zone (ITCZ), obvious in BCC-CSM2-MR, almost disappears in BCC-CSM2-HR. TC activity in the tropics is increased with resolution enhanced. The cycle of ENSO, the eastward propagative feature and convection intensity of MJO, and the downward propagation of QBO in BCC-CSM2-HR are all in a better agreement with observations than their counterparts in BCC-CSM2-MR. Some imperfections are, however, noted in BCC-CSM2-HR, such as the excessive cloudiness in the eastern basin of the tropical Pacific with cold SST biases and the insufficient number of tropical cyclones in the North Atlantic.

With semiconductor technology gradually approaching its physical and thermal limits, recent supercomputers have adopted major architectural changes to continue increasing the performance through more power-efficient heterogeneous many-core systems. Examples include Sunway TaihuLight that has four management processing elements (MPEs) and 256 computing processing elements (CPEs) inside one processor and Summit that has two central processing units (CPUs) and six graphics processing units (GPUs) inside one node. Meanwhile, current high-resolution Earth system models that desperately require more computing power generally consist of millions of lines of legacy code developed for traditional homogeneous multicore processors and cannot automatically benefit from the advancement of supercomputer hardware. As a result, refactoring and optimizing the legacy models for new architectures become key challenges along the road of taking advantage of greener and faster supercomputers, providing better support for the global climate research community and contributing to the long-lasting societal task of addressing long-term climate change. This article reports the efforts of a large group in the International Laboratory for High-Resolution Earth System Prediction (iHESP) that was established by the cooperation of Qingdao Pilot National Laboratory for Marine Science and Technology (QNLM), Texas A&M University (TAMU), and the National Center for Atmospheric Research (NCAR), with the goal of enabling highly efficient simulations of the high-resolution (25 km atmosphere and 10 km ocean) Community Earth System Model (CESM-HR) on Sunway TaihuLight. The refactoring and optimizing efforts have improved the simulation speed of CESM-HR from 1 SYPD (simulation years per day) to 3.4 SYPD (with output disabled) and supported several hundred years of pre-industrial control simulations. With further strategies on deeper refactoring and optimizing for remaining computing hotspots, as well as redesigning architecture-oriented algorithms, we expect an equivalent or even better efficiency to be gained on the new platform than traditional homogeneous CPU platforms. The refactoring and optimizing processes detailed in this paper on the Sunway system should have implications for similar efforts on other heterogeneous many-core systems such as GPU-based high-performance computing (HPC) systems.

Fine particulate matter with aerodynamic diameters ≤2.5 µm (PM2.5) has adverse effects on human health and the atmospheric environment. The estimation of surface PM2.5 concentrations has made intensive use of satellite-derived aerosol products. However, it has been a great challenge to obtain high-quality and high-resolution PM2.5 data from both ground and satellite observations, which is essential to monitor air pollution over small-scale areas such as metropolitan regions. Here, the space–time extremely randomized trees (STET) model was enhanced by integrating updated spatiotemporal information and additional auxiliary data to improve the spatial resolution and overall accuracy of PM2.5 estimates across China. To this end, the newly released Moderate Resolution Imaging Spectroradiometer Multi-Angle Implementation of Atmospheric Correction AOD product, along with meteorological, topographical and land-use data and pollution emissions, was input to the STET model, and daily 1 km PM2.5 maps for 2018 covering mainland China were produced. The STET model performed well, with a high out-of-sample (out-of-station) cross-validation coefficient of determination (R2) of 0.89 (0.88), a low root-mean-square error of 10.33 (10.93) µg m−3, a small mean absolute error of 6.69 (7.15) µg m−3 and a small mean relative error of 21.28 % (23.69 %). In particular, the model captured well the PM2.5 concentrations at both regional and individual site scales. The North China Plain, the Sichuan Basin and Xinjiang Province always featured high PM2.5 pollution levels, especially in winter. The STET model outperformed most models presented in previous related studies, with a strong predictive power (e.g., monthly R2=0.80), which can be used to estimate historical PM2.5 records. More importantly, this study provides a new approach for obtaining high-resolution and high-quality PM2.5 dataset across mainland China (i.e., ChinaHighPM2.5), important for air pollution studies focused on urban areas.

Uncertain parameters in physical parameterizations of general circulation models (GCMs) greatly impact model performance. In recent years, automatic parameter optimization has been introduced for tuning model performance of GCMs, but most of the optimization methods are unconstrained optimization methods under a given performance indicator. Therefore, the calibrated model may break through essential constraints that models have to keep, such as the radiation balance at the top of the model. The radiation balance is known for its importance in the conservation of model energy. In this study, an automated and efficient parameter optimization with the radiation balance constraint is presented and applied in the Community Atmospheric Model (CAM5) in terms of a synthesized performance metric using normalized mean square error of radiation, precipitation, relative humidity, and temperature. The tuned parameters are from the parameterization schemes of convection and cloud. The radiation constraint is defined as the absolute difference of the net longwave flux at the top of the model (FLNT) and the net solar flux at the top of the model (FSNT) of less than 1 W m−2. Results show that the synthesized performance under the optimal parameters is 6.3 % better than the control run (CNTL) and the radiation imbalance is as low as 0.1 W m−2. The proposed method provides an insight for physics-guided optimization, and it can be easily applied to optimization problems with other prerequisite constraints in GCMs.

Abstract We have developed an optimized protocol for plasma targeted mRNA sequencing in our previous study. Here, we performed plasma targeted mRNA sequencing for 40 colorectal adenoma patients and 39 colonoscopy-proven normal controls in order to find potential circulating mRNA markers for colorectal adenoma. Results showed that GSK3A and RHOA were differential expressed genes identified by a cut-off of fold change >2 and adjusted P value

Traditional trial-and-error tuning of uncertain parameters in global atmospheric general circulation models (GCMs) is time consuming and subjective. This study explores the feasibility of automatic optimization of GCM parameters for fast physics by using short-term hindcasts. An automatic workflow is described and applied to the Community Atmospheric Model (CAM5) to optimize several parameters in its cloud and convective parameterizations. We show that the auto-optimization leads to 10 % reduction of the overall bias in CAM5, which is already a well-calibrated model, based on a predefined metric that includes precipitation, temperature, humidity, and longwave/shortwave cloud forcing. The computational cost of the entire optimization procedure is about equivalent to a single 12-year atmospheric model simulation. The tuning reduces the large underestimation in the CAM5 longwave cloud forcing by decreasing the threshold relative humidity and the sedimentation velocity of ice crystals in the cloud schemes; it reduces the overestimation of precipitation by increasing the adjustment time in the convection scheme. The physical processes behind the tuned model performance for each targeted field are discussed. Limitations of the automatic tuning are described, including the slight deterioration in some targeted fields that reflect the structural errors of the model. It is pointed out that automatic tuning can be a viable supplement to process-oriented model evaluations and improvement.

Soil organic carbon (SOC) has a significant effect on carbon emissions and climate change. However, the current SOC prediction accuracy of most models is very low. Most evaluation studies indicate that the prediction error mainly comes from parameter uncertainties, which can be improved by parameter calibration. Data assimilation techniques have been successfully employed for the parameter calibration of SOC models. However, data assimilation algorithms, such as the sampling-based Bayesian Markov chain Monte Carlo (MCMC), generally have high computation costs and are not appropriate for complex global land models. This study proposes a new parameter calibration method based on surrogate optimization techniques to improve the prediction accuracy of SOC. Experiments on three types of soil carbon cycle models, including the Community Land Model with the Carnegie–Ames–Stanford Approach biogeochemistry submodel (CLM-CASA') and two microbial models show that the surrogate-based optimization method is effective and efficient in terms of both accuracy and cost. Compared to predictions using the tuned parameter values through Bayesian MCMC, the root mean squared errors (RMSEs) between the predictions using the calibrated parameter values with surrogate-base optimization and the observations could be reduced by up to 12 % for different SOC models. Meanwhile, the corresponding computational cost is lower than other global optimization algorithms.

Nitrogen and water availability alter canopy structure and physiology, and thus crop growth, yielding large impacts on ecosystem-regulating/production provisions. However, to date, explicitly quantifying such impacts remains challenging partially due to lack of adequate methodology to capture spatial dimensions of ecosystem changes associated with nitrogen and water effects. A data fitting, where close-range remote-sensing measurements of vegetation indices derived from a handheld instrument and an unmanned aerial vehicle (UAV) system are linked to in situ leaf and canopy photosynthetic traits, was applied to capture and interpret inter- and intra-field variations in gross primary productivity (GPP) in lowland rice grown under flooded conditions (paddy rice, PD) subject to three nitrogen application rates and under rainfed conditions (RF) in an East Asian monsoon region of South Korea. Spatial variations (SVs) in both GPP and light use efficiency (LUEcabs) early in the growing season were enlarged by nitrogen addition. The nutritional effects narrowed over time. A shift in planting culture from flooded to rainfed conditions strengthened SVs in GPP and LUEcabs. Intervention of prolonged drought late in the growing season dramatically intensified SVs that were supposed to seasonally decrease. Nevertheless, nitrogen addition effects on SV of LUEcabs at the early growth stage made PD fields exert greater SVs than RF fields. SVs of GPP across PD and RF rice fields were likely related to leaf area index (LAI) development less than to LUEcabs, while numerical analysis suggested that considering strength in LUEcabs and its spatial variation for the same crop type tends to be vital for better evaluation in landscape/regional patterns of ecosystem photosynthetic productivity at critical phenology stages.

The correlation of expression pattern of candidate genes in PPAR signalling pathway with meat quality traits in Longissimus dorsi muscle of pigs was investigated. Meat quality traits were measured and correlated with the candidate genes mRNA expression, which included peroxisome proliferator-activated receptor alpha gene (PPARα), peroxisome proliferator-activated receptor gamma gene (PPARγ), stearoyl-CoA desaturase gene (SCD), lipoprotein lipase gene (LPL), and phosphoenolpyruvate carboxykinase 2 gene (PCK2). Results showed that expressions of SCD and PCK2 were correlated with intramuscular fat content (P < 0.05). PPARα and PPARγ showed negative correlations with water loss and colour score (P < 0.05). SCD was positively correlated with marbling score and negatively correlated with shear force (P < 0.05). LPL was correlated with b* value, shear force, and moisture content (P < 0.05). PCK2 had a positive correlation (P < 0.05) with colour score. The revealed correlations indicate that these genes in PPAR signalling pathway are important for meat quality traits in pigs, and the further evaluation and investigation of these genes would help us better understand and utilize the regulation mechanisms of porcine meat quality.

The seed oil of 138 accessions of 14 Acer truncatum (Aceraceae family) populations native to China were analyzed by pulsed nuclear magnetic resonance spectroscopy and gas chromatography-flame ionization detection. The oil content ranged from 17.81% to 36.56% (mean: 28.57%), which mainly consisted of 14 types of fatty acids. Palmitic (4.69%), stearic (2.30%), oleic (25.19%), linoleic (32.97%), linolenic (2.76%), cis-11-eicosenoic (7.90%), erucic (16.49%), and nervonic (5.76%) fatty acids accounted for 98% of total fat. The nervonic acid content ranged from 3.90% to 7.85% among the accessions. Significant variations in oil content and predominating fatty acids were observed among populations. Hierarchical cluster analysis and principal component analysis detected obvious geographical variation trends among A. truncatum populations which correlate with environmental variations (especially altitude, temperature, and precipitation) and supported the grouping of the populations into three groups according to geographic locations.

Physical parameterizations in general circulation models (GCMs), having various uncertain parameters, greatly impact model performance and model climate sensitivity. Traditional manual and empirical tuning of these parameters is time-consuming and ineffective. In this study, a "three-step" methodology is proposed to automatically and effectively obtain the optimum combination of some key parameters in cloud and convective parameterizations according to a comprehensive objective evaluation metrics. Different from the traditional optimization methods, two extra steps, one determining the model's sensitivity to the parameters and the other choosing the optimum initial value for those sensitive parameters, are introduced before the downhill simplex method. This new method reduces the number of parameters to be tuned and accelerates the convergence of the downhill simplex method. Atmospheric GCM simulation results show that the optimum combination of these parameters determined using this method is able to improve the model's overall performance by 9 %. The proposed methodology and software framework can be easily applied to other GCMs to speed up the model development process, especially regarding unavoidable comprehensive parameter tuning during the model development stage.

A new hetero trinuclear manganese(III)-iron(II) complex, [Mn(salen)(H2O)]2[Fe(CN)6], in which salen is the dianionic form of N,N’-bis(5-methylsalicylidene)-1,2-diaminoethane, has been prepared and characterized by elemental analyses, IR, and single crystal X-ray crystallographic determination. The crystal of the complex is monoclinic: space group P21/c, a = 10.768(2), b = 14.248(3), c = 15.222(3) Å, β = 95.394(2)º, V = 2325.0(8) Å3, Z = 2, R1 = 0.0754, wR2 = 0.2366. The bis-Schiff base ligand coordinates to the Mn atom through phenolate O and imine N atoms. Each Mn atom in the complex is in octahedral coordination, with the equatorial donor atoms come from the Schiff base ligand, and with the axial donor atoms come from a water O atom and a cyanide N atom. The effects of the complex on the antimicrobial activity against Staphylococcus aureus, Escherichia coli, and Candida albicans were studied. DOI: http://dx.doi.org/10.4314/bcse.v29i3.8

The aim of the present study was to investigate the associations of single nucleotide polymorphisms (SNPs) in candidate genes with fatness traits in the Longissimus dorsi muscle of pigs. The polymorphisms of genes were investigated, which included beta-3-adrenergic receptor gene (ADRB3), heart fatty acid-binding protein gene (FABP3), and hormone-sensitive lipase gene (LIPE) as well as lipoprotein lipase gene (LPL). The intramuscular fat (IMF) content and fatty acid composition contents in Longissimus dorsi muscle samples were measured. Results showed that ADRB3, LIPE, and LPL SNPs were associated with IMF content (P < 0.05). ADRB3 AG heterozygotes exhibited higher IMF content. LIPE A allele was associated with greater IMF content. LPL CT heterozygotes exhibited the lowest IMF content. ADRB3 c.1192G>A had highly significant association with the total monounsaturated fatty acid (MUFA) (P < 0.01) and the total polyunsaturated fatty acid (PUFA) (P < 0.01). LIPE c.442G>A was significantly associated with the contents of C12:0 and C14:0 (P < 0.05). LPL c.624C>T was significantly associated with the percentage of C16:1 (P < 0.05) and the percentage of total saturated fatty acid (SFA) (P < 0.05). The pigs with ADRB3 G allele had more MUFA, and the pigs with LPL T allele had less SFA, implying that the ADRB3 G and LPL T in pigs may be beneficial to human health. In conclusion, the results suggest that these genetic markers are important sources of the variations for the pork selection to obtain favourable meat with higher IMF levels and appropriate fatty acid composition.

Biocompatible and biodegradable carboxymethyl cellulose (CMC) has been modified with 4-hydroxybenzylamine (CMC-Ph) in order to prepare CMC-based microgels through the horseradish peroxidise/hydrogen peroxide enzymatic reaction. CMC-Ph was identified as a blend, and the amount of the grafted 4-hydroxybenzylamine per 100 units of CMC was between 17 and 23 according to the molecular weight of CMC. Through a special designed co-flowing microfluidic device, CMC-Ph microgels were prepared with the radius from 100 to 500 μm via adjusting the flow rates of the disperse phase and the continuous phase, respectively. The chondrocytic cell line ATDC5 was encapsulated in the CMC-Ph microgels. The cell-laden microgels were cultured for up to 40 days, illustrating the biocompatibility of CMC-Ph and the microfluidic approach through the enzymatic crosslinking reaction primarily. CMC-Ph showed a great promise to encapsulate the cells for further fabrication of the injectable scaffolds.

During the period of senescence of desert plant Alhagi sparsifolia Shap. the maximum photochemical quantum yield measured as variable to maximum fluorescence ratio (Fv/Fm) remained relatively high, although the number of active reaction centres per cross section (RCs) decreased significantly. The efficiency of electron acceptors beyond the primary quinone acceptor (QA) decreased. The effect of temperature and irradiance on photosystem activity was maximum after 6 d. Our results suggest that: 1) the down-regulation of photosystem activity was due to the decline of both RCs and electron acceptance between plastoquinone (PQ) and cytochrome (cyt) b6/f; 2) photosystem activity presented negative correlation with daily mean temperature, and 3) reduction of daily sunshine period and increase of temperature at noon can stimulate the speed of senescence.

The "Blue Sky Project" was proposed in 1998 to investigate by how much emissions should be reduced to increase blue sky frequency in Beijing, which hosted the Summer Olympics in 2008. This paper focuses on the temporal variation of visibility and its dependence on meteorological conditions and suspended particles at Beijing using the hourly observed visibility data at Beijing Capital International Airport (BCIA) from 1999 to 2007. It has been found that about 47.8% (24.2%) of the hours in Beijing are "bad" ("good") hours with visibility below 10 km (equal or higher than 20 km) between 1999 and 2007. Due to the high Relative Humidity (RH), summer is the season with the lowest mean visibility in a year. Although PM10 index was reported in a decreasing trend (Chan and Yao, 2008), the increase of RH has resulted in a decreasing trend of visibility over BCIA in the summer from 1999 to 2007. To ensure blue sky ("good" visibility) for Olympics 2008, daily mean PM10 index should have been reduced from 81 to 44. This requires that not only vehicle emissions, but also other emissions should be limited. Observations verify that blue-sky-hour rate increased significantly after mean PM10 index was reduced to 53 during Olympics 2008, however, the visibility of 2009 returned to the mean level from 1999 to 2007 during the period 8−24 August. RH (aerosol) contribute 24% (76%) of the improvement of visibility during August 2008.