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Warming and increased precipitation can affect soil respiration (SR) and plant community variation in grassland ecosystems. Dominant species have important impacts on maintaining grassland landscapes and ecosystem stability, promoting nutrient cycling, and carbon sequestration. However, the mechanisms by which warming and increased precipitation regulate SR through the replacement of dominant plant species remain largely unknown. Here, we conducted an 8-year (2011–2018) manipulative field experiment comprising warming and precipitation addition, as well as a pot experiment simulating different proportions of dominant species (Leymus chinensis and Stipa krylovii), to examine the changes in SR in a temperate steppe in Inner Mongolia, China. Our results indicate that increased precipitation significantly increased SR by 48.9 %, while warming had no significant effect on SR. Warming, increased precipitation, and their interaction did not affect the temperature sensitivity of SR. Warming and increased precipitation were found to increase the biomass of L. chinensis (a rhizomatous grass) and decrease the biomass of S. krylovii (a bunchgrass). SR was significantly positively correlated with community biomass and microbial biomass. Although the proportion of dominant species was not directly significantly correlated with SR, the proportion of dominant species can affect SR by regulating community biomass and subsequently affecting microbial biomass. The pot experiment also showed that community biomass is a key factor affecting SR. This study emphasizes the regulatory role of soil moisture and community biomass in SR. When evaluating the impacts of climate change scenarios on soil respiration and ecosystems, the potential effects of changes in the dominant species should be considered, especially in arid and semiarid regions. • Warming had no significant effect on soil respiration in temperate steppe. • Soil moisture and community biomass are key factors regulating soil respiration in temperate steppe. • Increasing precipitation has a cumulative effect on soil respiration over years. [ABSTRACT FROM AUTHOR]

It has been a very challenging task to develop efficient and robust techniques to solve real-world engineering optimization problems due to the unknown function properties, complex constraints and severely limited computational budget. To address this issue, TARBF algorithm (trust-region based adaptive radial basis function interpolation) for solving expensive constrained black-box optimization problems is proposed in this paper. The approach successfully decomposes the original optimization problem into a sequence of sub-problems approximated by radial basis functions in a series of trust regions. Then, the solution of each sub-problem becomes the starting point for the next iteration. According to the values of objective and constraint functions, an effective online normalization technique is further developed to adaptively improve the model accuracy in the trust region, where the surrogate is updated iteratively. Averagely, TARBF has the ability to robustly solve the 21 G-problems (CEC'2006) and 4 engineering problems within 535.69 and 234.44 function evaluations, respectively. The comparison results with other state-of-the-art metamodel-based algorithms prove that TARBF is a convergent, efficient and accurate paradigm. Moreover, the sophisticated trust region strategy developed in TARBF, which is a major contribution to the field of the efficient constrained optimization, has the capability to facilitate an effective balance of exploration and exploitation for solving constrained black-box optimization problems. • Cubic radial basis function with a polynomial tail has ability to seek the optimum. • Adaptive online normalization strategy improves the quality of metamodels. • 21-G benchmark problems demonstrate the effectiveness of the developed algorithm. • Trust-region based adaptive RBF facilitates both exploration and exploitation. [ABSTRACT FROM AUTHOR]

Benefiting from curved fibre paths, variable-angle-tow (VAT) fibre composites feature a larger design space than traditional straight-fibre reinforced plastics. Herein, an optimisation framework of a full-scale wing-box structure with VAT-fibre composites is presented, aiming at minimised mass and optimised local buckling performance under realistic aeroelastic loading conditions. Local buckling analyses on individual subsections of the wing are performed with refined finite-element models by extracting running loads from an aeroelastic analysis of the entire wing structure. Using this global–local approach, an optimisation is conducted with static failure, aeroelastic, buckling and manufacturing constraints to obtain optimised structural parameters for straight- and VAT-fibre composite wing-box architectures. By optimising wing-skin thicknesses, fibre paths and wing-spar geometry simultaneously via a genetic algorithm, the potential benefit of a VAT design is explored. In addition, the continuous tow shearing (CTS) manufacturing process, which introduces layer thickness variations as tows are steered, is explored. A mass reduction of 12.5% and 13.2% is obtained by using the constant-thickness VAT and variable-thickness CTS designs, respectively, compared to a baseline quasi-isotropic straight-fibre design. The optimised wing-skin thickness distribution also suggests that local buckling is the critical failure mode in specific regions, and therefore needs to be included during aeroelastic optimisation. [ABSTRACT FROM AUTHOR]

The increasing structural flexibility of large aircraft leads to significant aeroelastic effects. More efficient topology optimization techniques are required for the design to further take advantage of aeroelasticity and obtain lightweight structures. This paper proposes a moving boundary meshfree topology optimization that combines the Galerkin method of weighted residuals and non-uniform rational B-splines (NURBS). The solution domain is described by the control points of NURBS and its property is calculated adaptively with an integration subtraction technique. The minimal compliance is searched for using the globally convergent method of moving asymptotes (GCMMA) by designing the locations of control points as subject to volume and flux constraints. The method is first applied to a typical two-dimensional design example with symmetric boundary conditions. The results show that the shape constraints can be conveniently applied, and smoother boundaries are obtained with fewer parameters. Then, a three-dimensional wing structure with asymmetric boundary conditions is optimized. A three-dimensional flight load that combines the high-order-panel and meshfree methods is employed to calculate the elastic loads and update asymmetric external loads during the optimization process. The designed wing satisfies engineering requirements and the presented method can solve the practical topology optimization problems of three-dimensional structures. [ABSTRACT FROM AUTHOR]

Sensitivity analysis plays an important role in reliability evaluation, structural optimization and structural design, etc. The local sensitivity, i.e., the partial derivative of the quantity of interest in terms of parameters or basic variables, is inadequate when the basic variables are random in nature. Therefore, global sensitivity such as the Sobol' indices based on the decomposition of variance and the moment-independent importance measure, among others, have been extensively studied. However, these indices are usually computationally expensive, and the information provided by them has some limitations for decision making. Specifically, all these indices are positive, and therefore they cannot reveal whether the effects of a basic variable on the quantity of interest are positive or adverse. In the present paper, a novel global sensitivity index is proposed when randomness is involved in structural parameters. Specifically, a functional perspective is firstly advocated, where the probability density function (PDF) of the output quantity of interest is regarded as the output of an operator on the PDF of the source basic random variables. The Fréchet derivative is then naturally taken as a measure for the global sensitivity. In some sense such functional perspective provides a unified perspective on the concepts of global sensitivity and local sensitivity. In the case the change of the PDF of a basic random variable is due to the change of parameters of the PDF of the basic random variable, the computation of the Fréchet-derivative-based global sensitivity index can be implemented with high efficiency by incorporating the probability density evolution method (PDEM) and change of probability measure (COM). The numerical algorithms are elaborated. Several examples are illustrated, demonstrating the effectiveness of the proposed method. • A new functional space perspective to the stochastic mechanics. • A novel global sensitivity index based on the Fréchet derivative. • High efficient numerical algorithm for the computation of the proposed index. • Numerical examples demonstrating the effectiveness and the physical meaning of the index. [ABSTRACT FROM AUTHOR]

Performance evaluation and reliability assessment of real-world structures under earthquakes is of paramount importance. Generally, different mechanical property parameters of a structure are usually not independent, nor completely dependent, but partly dependent or correlated. Therefore, how to reasonably characterize such partial dependency and whether such partial dependency real matters in the stochastic response and reliability of structures under earthquakes are crucial issues. For this purpose, in the present paper, a novel physically-guided data-driven methodology of capturing the correlation configuration of basic random variables and the probability density evolution method are synthesized. The physically-guided data-driven methodology is firstly outlined. In this methodology, the underlying physical mechanism between dependent random variables is firstly involved to establish a random function model, and then the available observed data are adopted to identify the parameters in this model. What is more, physical constraints are also revealed for the initial modulus of elasticity and compressive strength of concrete. The probability density evolution method is then adopted, and the point selection by minimizing the GF-discrepancy is adjusted according to the correlation configuration and physical constraints. A reinforced concrete frame structure subjected to earthquake input is studied. It is found that when the structure is in the strongly nonlinear stage, the correlation configuration has considerable effects on the standard deviation of the stochastic responses, by a factor of nearly 2. In addition, whether the mechanical parameters in different floors are independent or not has great effects on the stochastic responses as well. Problems to be further studied are also outlined. • A random function model for dependent random variables. • A physically-guided data-driven methodology for uncertainty quantification. • Physical constraints for the relation between elastic modulus and strength. • Numerical examples demonstrating the importance of parametric dependence. [ABSTRACT FROM AUTHOR]

• A novel life-cycle reliability evaluation approach by synthesizing PDEM and the change of probability measure. • The new perspective of functionals by incorporating the Frobenius-Perron operator and the Radon-Nikodym operator. • An efficient algorithm by re-using the deterministic analyses. • Three examples showing the effectiveness of the proposed method. Life-cycle time-variant reliability of engineering structures with material deterioration is of paramount importance but still a great challenge. Generally, the computational costs due to embedded structural analyses tracing the life-cycle performance process with monotonically deteriorating material, are prohibitively large. In the present paper, a new method by synthesizing the probability density evolution method (PDEM) and the change of probability measure (COM) is proposed for this purpose. Besides, a functional perspective is provided. The theoretical basis is established by involving the Frobenius-Perron operator, which characterizes the change of random variables, and the Radon-Nikodym operator, which characterizes the change of probability measure of the original source random variables. The material deterioration characterized by time-variant random variables can then be equivalently captured by the time-variant change of probability measure of the basic random variables. Thereby the deterministic analyses at different time instances can be shared in the process of capturing the time-variant probability density of the performance function, and thus the efficiency can be improved considerably. Numerical algorithms are elaborated. Three examples in terms of time-variant reliability evaluation of structures with monotonically deteriorating material and parametric stochastic loading are illustrated, showing the effectiveness of the proposed method. Problems to be further studied are also outlined. [ABSTRACT FROM AUTHOR]

This study introduces a novel satellite image digital surface model (DSM) reconstruction framework grounded in deep learning methodology. The proposed framework effectively utilizes a rational polynomial camera (RPC) model to establish the mapping relationship between image coordinates and geographic coordinates. Given the expansive coverage and abundant ground object data inherent in satellite images, we designed a lightweight deep network model. This model facilitates both coarse and fine estimation of a height map through two distinct stages. Our approach harnesses shallow and deep image information via a feature extraction module, subsequently employing RPC Warping to construct feature volumes for various angles. We employ variance as a similarity metric to achieve image matching and derive the fused cost volume. Following this, we aggregate cost information across different scales and height directions using a regularization module. This process yields the confidence level of the current height plane, which is then regressed to predict the height map. Once the height map from stage 1 is obtained, we gauge the prediction's uncertainty based on the variance in the probability distribution in the height direction. This allows us to adjust the height estimation range according to this uncertainty, thereby enabling precise height value prediction in stage 2. After conducting geometric consistency detection filtering of fine height maps from diverse viewpoints, we generate 3D point clouds through the inverse projection of RPC models. Finally, we resample these 3D point clouds to produce high-precision DSM products. By analyzing the results of our method's height map predictions and comparing them with existing deep learning-based reconstruction methods, we assess the DSM reconstruction performance of our proposed framework. The experimental findings underscore the robustness of our method against discontinuous regions, occlusions, uneven illumination areas in satellite imagery, and weak texture regions during height map generation. Furthermore, the reconstructed digital surface model (DSM) surpasses existing solutions in terms of completeness and root mean square error metrics while concurrently reducing the model parameters by 42.93%. This optimization markedly diminishes memory usage, thereby conserving both software and hardware resources as well as system overhead. Such savings pave the way for a more efficient system design and development process. [ABSTRACT FROM AUTHOR]

• A method for evaluating the performance of train-bridge systems is proposed. • Both random uncertainty and weighted evaluation indexes can be considered. • The performance state of train-bridge systems is classified and defined. • The weightings of evaluation indexes for train-bridge systems are quantified. • The proposed method can more accurately describe the system performance. In this paper, a practical methodology for evaluating the dynamic performance of train-bridge systems is presented, which incorporates both random uncertainty and the weighting of evaluation indexes. Firstly, a train-bridge stochastic model is established by combining an existing train-bridge deterministic model with the Generalized F-discrepancy (GF-discrepancy) minimized point selection strategy and the probability density evolution method (PDEM). The model enables the accurate and efficient quantification of the variability of any dynamic response quantity of interest in the train-bridge system. Building on this foundation, an assessment model based on the multi-layer fuzzy comprehensive evaluation method is developed to determine the possibility of the train-bridge stochastic system in different performance states with consideration of the weighted evaluation indexes. In the assessment model, the main criteria of the train-bridge system are collected, classified, and further structured a logical hierarchical framework, and the weightings of the criteria are solved by analytic hierarchy process method and entropy weighting method. As demonstrated by a numerical example, the proposed method can properly consider system uncertainties, appraisal hierarchy, and the weightings of evaluation indexes, which helps identify critical evaluation indexes and improve evaluation reliability. The proposed approach can provide a useful tool for the performance evaluation of train-bridge systems. [ABSTRACT FROM AUTHOR]

Located in the interior of Eurasia, the Mongolian Plateau (MP) is extremely sensitive to global warming and become a critical area for studying precipitation patterns. Based on the monthly data of 135 meteorological stations during 1976–2017, we analyze the spatiotemporal change in precipitation and discuss its response to atmospheric circulation. The results show that: (1) Precipitation shows increasing trends in spring, autumn, and winter, but a decreasing trend at a rate of 5.3 mm/decade in summer. The annual precipitation also shows an overall slight decreasing trend. (2) The spatial distribution is uneven, the annual precipitation in the northern Great Khingan Mountains is more, but it gradually decreases at the rate of 10–30 mm/decade, showing a trend of "wet gets dry"; while there is less in the southwest Gobi Desert region, but it gradually increases with the rate of 10–20 mm/decade, showing a trend of "dry gets wet". (3) Over decades, the East Asian summer monsoon (EASM) and westerly circulation show a seesaw change in MP. Affected by the weakening of the EASM, the area of arid regions has gradually expanded. The results also demonstrate that the EASM has a higher impact on the annual precipitation change pattern, particularly in the southeastern MP. The conclusion indicated that the variation in the position and orientation between EASM and the westerly circulation may be an explanation for the spatiotemporal precipitation pattern, providing a new viewpoint to the question of circulation mechanisms behind climate change in MP in recent 40 years. [ABSTRACT FROM AUTHOR]

PM 2.5 is a main atmospheric pollutant with various sources and complex chemical compositions, which are influenced by various factors, such as anthropogenic emissions (AE) and meteorological conditions (MC). MC have a significant impacts on variations in atmospheric pollutant; therefore, emission reduction policies and ambient air quality are non-linearly correlated, which hinders the accurate assessment of the effectiveness of control measures. In this study, we conducted online observations of PM 2.5 and its chemical composition in Hohhot, China, from December 1, 2019, to February 29, 2020, to investigate how the chemical compositions of PM 2.5 respond to the variations in AE and MC. Moreover, the random forest (RF) model was used to quantify the contributions of AE and MC to PM 2.5 and its chemical composition during severe hazes and the COVID-19 pandemic lockdown period. During the clean period, MC reduced PM 2.5 concentrations by 124%, while MC incresed PM 2.5 concentrations by 49% during severe pollution episode. Inorganic aerosols (SO 4 2−, NO 3 −, and NH 4 +) showed the strongest response to MC. MC significantly contributed to PM 2.5 (36%), SO 4 2− (32%), NO 3 − (29%), NH 4 + (28%), OC (22%), and SOC (17%) levels during pollution episodes. From the pre-lockdown to lockdown period, AE (MC) contributed 52% (48%), 81% (19%), 48% (52%), 68% (32%), 59% (41%), and 288% (−188%) to the PM 2.5 , SO 4 2−, NO 3 −, NH 4 +, OC, and SOC reductions, respectively. The variations in MC (especially the increase in relative humidity) rapidly generated meteorologically sensitive species (SO 4 2−, NO 3 −, and NH 4 +), which led to severe winter pollution. This study provides a reference for assessing the net benefits of emission reduction measures for PM 2.5 and its chemical compositions. [Display omitted] • Meteorological condition contributed 17%–52% to PM 2.5 during pollution episodes. • Meteorological condition contributed more than 28% to SO 4 2−, NO 3 −, and NH 4 +. • PM 2.5 and NO 2 declined 52% and 82% during COVID-19 lockdown. [ABSTRACT FROM AUTHOR]

This work evaluates the temperature field, stresses, and peeling loads in thermal barrier coatings (TBCs) with air holes subjected to cyclic thermo-mechanical loads during a complete flight. Analytical formulae for the steady-state temperature field and interfacial peeling loads are developed considering thermal gradients in the thickness and longitudinal directions. In addition, viscoplasticity finite element modelling is conducted for the mechanical behaviors of the TBC system. A unified Chaboche-Lemaitre constitutive model that combines a power flow rule with non-linear anisothermal evolution of isotropic and kinematic hardening is integrated into the numerical framework. The results show tensile peeling stress and shear stress are generated in the vicinity of the hole, which motivates the initiation and propagation of hole-edge cracks. The maximum peeling and shear stresses locate close to the thermally grown oxide/bond coat interface and they increase with increasing hole radius. The interfacial peeling moment and shear force could characterize edge cracking in TBCs. As the hole radius increases, the interfacial peeling moment and shear force increase quickly when the hole radius is less than 1 mm but remain stable when the hole radius is beyond 2 mm. This work not only helps to explain the failure mechanisms of hole-edge delamination and spallation in TBCs but also guides the design of thermal barrier coated hot-section components in gas turbines. [ABSTRACT FROM AUTHOR]

The standardised growth curves (SGCs) can compensate for the drawbacks of the single-aliquot regenerative-dose (SAR) procedure, such as low utilization of time and instrument in the optically stimulated luminescence (OSL) dating. In this study, 12 sedimentary samples of 6 lakes from the Mongolian Plateau were selected and 90–125 μm coarse-grains quartz was extracted to establish SGCs. The results show that the OSL signals are mainly composed of a fast component, which is applicable for SAR procedure. The difference in the shape of the dose response curves (DRCs) limits the application of the standardised growth curve (SGC) for which the growth curves among the aliquots corrected by the test dose are scattered. The equivalent dose (D e) estimated by the global standardised growth curve (gSGC) by the regenerative normalization (re-normalization) method is more consistent with the full SAR procedure, so the gSGC method is suitable for determining the D e values on the regional scale of Mongolian Plateau. For different lacustrine sedimentary samples, the D e values estimated by full SAR procedure and gSGC method are consistent for the doses of up to ~280 Gy. The discrepancy of growth curves may result from the sensibility changes of inter-cycle in SAR measurement and the recuperation, also we do not exclude the influence of feldspar inclusions. [ABSTRACT FROM AUTHOR]

Grazing exerts a significant influence on greenhouse gases (GHGs) fluxes within temperate grasslands. While previous studies have explored the impact of varying grazing intensities on GHGs fluxes in meadow steppe, typical steppe, and desert steppe, there remains a knowledge gap regarding how grazing intensities affect GHGs fluxes across different grassland types and seasonal variations. In this study, we conducted a meta-analysis of Inner Mongolia grasslands involving 38 published reports and aggregating 143, 160, and 118 records for the CO 2 , CH 4 , and N 2 O, respectively. Our results demonstrated that grazing reduced annual mean CO 2 emission, CH 4 uptake, and N 2 O emission by 15.29%, 10.56%, and 24.73%, respectively (P < 0.05). Specifically, during the freeze-thaw period, grazing induced a decrease of 28.06% and 58.41% in CH 4 uptake and N 2 O emissions, respectively, compared to ungrazed treatments (P < 0.05). Further dissecting the impact, we found that grazing reduced CO 2 emissions in desert steppe, typical steppe, and meadow steppe by 20.28%, 15.54%, and 11.79%, respectively. In addition, grazing decreased CH 4 uptake in typical steppe by 15.14% and decreased N 2 O emission in typical steppe, meadow steppe, and sown pasture by 20.25%, 24.18%, 52.91%, respectively. Crucially, the alteration in plant biomass due to grazing-induced changes underlying drivers that govern the responses of GHGs fluxes. These results furnish compelling evidence that grazing, particularly in non-growing season, holds the potential to mitigate grassland GHGs fluxes in temperate grasslands. Our results can deepen the understanding of the grazing effects on GHGs and offer valuable insights for refining grazing management strategies in pastoral regions. [ABSTRACT FROM AUTHOR]

In this study, a lifecycle operational reliability assessment framework for water distribution networks (WDNs) is proposed on the basis of the probability density evolution method (PDEM). The occurrence models of daily accidents are fitted using the maintenance data provided by a local water administration sector. For a given accident, two types of accidents (e.g., leaks and bursts) are distinguished in different occurrence probabilities and simulated in various ways. The pipe deterioration process in the lifecycle is reflected by incorporating the time-dependent pipe roughness model. Considering various randomness in the model, PDEM, a newly proposed and developed method for a stochastic system, is used to evaluate the lifecycle operational reliability of WDNs. The framework is demonstrated using an actual WDN, and the nodal reliabilities in the lifecycle are obtained. Comparisons of the operational reliabilities of all nodes calculated via the PDEM and Monte Carlo simulations prove that PDEM is an accurate and highly efficient method. • Accident and deterioration models is established for WDNs. • The operational reliability of WDNs is evaluated based on the PDEM. • The accuracy and efficiency of PDEM is illustrated by the comparison between MCS. [ABSTRACT FROM AUTHOR]

Carbonaceous aerosols have great adverse impacts on air quality, human health, and climate. However, there is a limited understanding of carbonaceous aerosols in semi-arid areas. The correlation between carbonaceous aerosols and control measures is still unclear owing to the insufficient information regarding meteorological contribution. To reveal the complex relationship between control measures and carbonaceous aerosols, offline and online observations of carbonaceous aerosols were conducted from October 8, 2019 to October 7, 2020 in Hohhot, a semi-arid city. The characteristics and sources of carbonaceous aerosols and impacts of anthropogenic emissions and meteorological conditions were studied. The annual mean concentrations (± standard deviation) of fine particulate matter (PM 2.5), organic carbon (OC), and elemental carbon (EC) were 42.81 (±40.13), 7.57 (±6.43), and 2.25 (±1.39) μg m−3, respectively. The highest PM 2.5 and carbonaceous aerosol concentrations were observed in winter, whereas the lowest was observed in summer. The result indicated that coal combustion for heating had a critical role in air quality degradation in Hohhot. A boost regression tree model was applied to quantify the impacts of anthropogenic emissions and meteorological conditions on carbonaceous aerosols. The results suggested that the anthropogenic contributions of PM 2.5 , OC, and EC during the COVID-19 lockdown period were 53.0, 15.0, and 2.36 μg m−3, respectively, while the meteorological contributions were 5.38, 2.49, and −0.62 μg m−3, respectively. Secondary formation caused by unfavorable meteorological conditions offset the emission reduction during the COVID-19 lockdown period. Coal combustion (46.4% for OC and 35.4% for EC) and vehicular emissions (32.0% for OC and 50.4% for EC) were the predominant contributors of carbonaceous aerosols. The result indicated that Hohhot must regulate coal use and vehicle emissions to reduce carbonaceous aerosol pollution. This study provides new insights and a comprehensive understanding of the complex relationships between control strategies, meteorological conditions, and air quality. [Display omitted] • Carbonaceous aerosol characteristics and sources were investigated in a semi-arid city. • Anthropogenic emission and meteorological condition impacts were quantified. • Secondary organic carbon formation due to meteorological conditions offset emission reduction. • Control strategies and meteorological conditions should be considered simultaneously to improve air quality. [ABSTRACT FROM AUTHOR]

Purpose In real-world cases, it is common to encounter mixed discrete-continuous problems where some or all of the variables may take only discrete values. To solve these non-linear optimization problems, the use of finite element methods is very time-consuming. The purpose of this study is to investigate the efficiency of the proposed hybrid algorithms for the mixed discrete-continuous optimization and compare it with the performance of genetic algorithms (GAs).Design/methodology/approach In this paper, the enhanced multipoint approximation method (MAM) is used to reduce the original nonlinear optimization problem to a sequence of approximations. Then, the sequential quadratic programing technique is applied to find the continuous solution. Following that, the implementation of discrete capability into the MAM is developed to solve the mixed discrete-continuous optimization problems.Findings The efficiency and rate of convergence of the developed hybrid algorithms outperforming GA are examined by six detailed case studies in the ten-bar planar truss problem, and the superiority of the Hooke–Jeeves assisted MAM algorithm over the other two hybrid algorithms and GAs is concluded.Originality/value The authors propose three efficient hybrid algorithms, the rounding-off, the coordinate search and the Hooke–Jeeves search-assisted MAMs, to solve nonlinear mixed discrete-continuous optimization problems. Implementations include the development of new procedures for sampling discrete points, the modification of the trust region adaptation strategy and strategies for solving mix optimization problems. To improve the efficiency and effectiveness of metamodel construction, regressors f defined in this paper can have the form in common with the empirical formulation of the problems in many engineering subjects. [ABSTRACT FROM AUTHOR]

Microorganisms are the basis for sustaining ecosystem function and are extremely sensitive to global warming and altered precipitation. Nevertheless, the seasonal pattern of the microbial response to a coupled effect of warming and increased precipitation is largely unknown. We set up a six-year manipulative experiment to verify changes in microbial response to warming (approximately 2 °C), increased precipitation (20 %), and their interactive effect in a semiarid steppe in Inner Mongolia. We detected changes in microbial species richness, diversity, community structure, and microbial interactions during different periods of the growing season. The results suggest that warming and increased precipitation had a stronger impact on the microbial community in the early growing season than in the mid- and late growing seasons. In the early growing season, warming significantly decreased bacterial diversity, while increased precipitation did not significantly affect the microbial community. The interactive effect of increased precipitation and warming significantly increased bacterial richness but decreased fungal richness and amplified the dissimilarities among bacterial and fungal communities. Compared to the early growing season, less microbial taxa significantly responded to warming and increased precipitation in the mid- and late growing seasons. Warming weakened species interactions, as evidenced by fewer nodes and links and lower complexity and robustness in the network. Increased precipitation strengthened species interactions and eased the warming-induced negative effect under the interactive effect. Our findings emphasize that more attention should be given to the changes in the microbial community in the early growing season of grassland in Inner Mongolia under future climate change scenarios. • Climate change impacts were more obvious in the early growing season. • The interactive effect increased bacterial richness but reduced fungal richness. • Fewer taxa respond to climate change in the mid and late growing seasons. • Species interactions weakened by warming but enhanced by precipitation addition. [ABSTRACT FROM AUTHOR]

Cellulolytic bacteria with probiotic functions play a crucial role in promoting the intestinal health in herbivores. In this study, we aimed to correlate the 16S rRNA gene amplicon sequencing and fiber-degrading enzyme activity data from six different herbivore feces samples. By utilizing the separation and screening steps of probiotics, we targeted and screened high-efficiency fiber-degrading bacteria with probiotic functions. The animals included Maiwa Yak (MY), Holstein cow (CC), Tibetan sheep (TS), Southern Sichuan black goat (SG), Sichuan white rex rabbit (CR), and New Zealand white rabbit (ZR). The results showed that the enzymes associated with fiber degradation were higher in goat and sheep feces compared to cattle and rabbit's feces. Correlation analysis revealed that Bacillus and Fibrobacter were positively correlated with five types of fiber-degrading related enzymes. Notably, the relative abundance of Bacillus in the feces of Tibetan sheep was significantly higher than that of other five herbivores. A strain TS5 with good cellulose decomposition ability from the feces of Tibetan sheep by Congored staining, filter paper decomposition test, and enzyme activity determination was isolated. The strain was identified as Bacillus velezensis by biological characteristics, biochemical analysis, and 16S rRNA gene sequencing. To test the probiotic properties of Bacillus velezensis TS5, we evaluated its tolerance to acid and bile salt, production of digestive enzymes, antioxidants, antibacterial activity, and adhesion ability. The results showed that the strain had good tolerance to pH 2.0 and 0.3% bile salts, as well as good potential to produce cellulase, protease, amylase, and lipase. This strain also had good antioxidant capacity and the ability to antagonistic Staphylococcus aureus BJ216, Salmonella SC06, Enterotoxigenic Escherichia coli CVCC196, and Escherichia coli ATCC25922. More importantly, the strain had good self-aggregation and Caco-2 cell adhesion rate. In addition, we tested the safety of Bacillus velezensis TS5 by hemolysis test, antimicrobial susceptibility test, and acute toxicity test in mice. The results showed that the strain had no hemolytic phenotype, did not develop resistance to 19 commonly used antibiotics, had no cytotoxicity to Caco-2, and did not have acute toxic harm to mice. In summary, this study targeted isolated and screened a strain of Bacillus velezensis TS5 with high fiber-degrading ability and probiotic potency. This strain can be used as a potential probiotic for feeding microbial preparations for ruminants., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Memristor is an ideal electronic device used as an artificial nerve synapse due to its unique memory function. This article presents a design of a new Hopfield neural network (HNN) that can generate multiscroll attractors by utilizing a new memristor as a synapse in the HNN. Differing from the others, this memristor is constructed with hyperbolic tangent functions. Taking the memristor as a self-feedback synapse of a neuron in the HNN, the memristive HNN can yield multidouble-scroll attractors, and its parameters can be used to effectively control the number of double scrolls contained in an attractor. Interestingly, the generation of multidouble-scroll attractors is independent of the memductance function but depends only on the internal state equation. Thus, the memductance function can be adjusted to yield various complex dynamical behaviors. Moreover, amplitude control effects and quantitatively controllable multistability are revealed by numerical analysis. The accurate reproduction of some dynamical behaviors by a designed circuit verifies the correctness of the numerical analysis. Finally, based on the proposed memristive HNN, a novel image encryption scheme in the 3-D setting is designed and evaluated, demonstrating its good encryption performances.

Approximately 400 million people work and live in high-altitude areas and suffer from memory dysfunction worldwide. Until now, the role of the intestinal flora in plateau-induced brain damage has rarely been reported. To address this, we investigated the effect of intestinal flora on spatial memory impairment induced by high altitudes based on the microbiome-gut-brain axis theory. C57BL/6 mice were divided into three groups: control, high-altitude (HA), and high-altitude antibiotic treatment (HAA) group. The HA and HAA groups were exposed to a low-pressure oxygen chamber that simulated an altitude of 4000 m above sea level (m. a. s.l.) for 14 days, with the air pressure in the chamber set at 60-65 kPa. The results showed that spatial memory dysfunction induced by the high-altitude environment was aggravated by antibiotic treatment, manifesting as lowered escape latency and hippocampal memory-related proteins (BDNF and PSD-95). 16 S rRNA sequencing showed a remarkable separation of the ileal microbiota among the three groups. Antibiotic treatment exacerbated the reduced richness and diversity of the ileal microbiota in mice in the HA group. Lactobacillaceae were the main target bacteria and were significantly reduced in the HA group, which was exacerbated by antibiotic treatment. Meanwhile, reduced intestinal permeability and ileal immune function in mice exposed high-altitude environment was also aggravated by antibiotic treatment, as indicated by the lowered tight junction proteins and IL-1β and IFN-γ levels. Furthermore, indicator species analysis and Netshift co-analysis revealed that Lactobacillaceae (ASV11) and Corynebacteriaceae (ASV78, ASV25, and ASV47) play important roles in high-altitude exposure-induced memory dysfunction. Interestingly, ASV78 was negatively correlated with IL-1β and IFN-γ levels, indicating that ASV78 may be induced by reduced ileal immune function, which mediates high-altitude environment exposure-induced memory dysfunction. This study provides evidence that the intestinal flora is effective in preventing brain dysfunction caused by exposure to high-altitude environments, suggesting a relationship between the microbiome-gut-brain axis and altitude exposure., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Semen Ziziphi Spinosae protein (SZSP) is a new plant protein resource with good food functional properties and health care function. However, the biological activity of SZSP has not been further studied, which greatly limits the development and utilization of SZSP in the food industry. The aim of this study was to investigate the protective effect of SZSP on immunosuppressed mice and its inhibitory effect on immune-stimulated RAW264.7 cells. The results demonstrated that SZSP remarkably improved the immunomodulatory secretion in serum (interleukin-2, tumor necrosis factor-α [TNF-α], interferon-γ, immunoglobulin-A, immunoglobulin-G, immunoglobulin-M) and primary macrophages (nitric oxide, interleukin-1β, TNF-α) and promoted the NK-cell killing activity of primary splenocytes in CTX-induced immunosuppression mice. Immunohistochemical analysis results indicated that the secretion of CD 4 + and CD 8 + in the spleen and thymus can be regulated by SZSP, leading to inhibition of the damage induced by cyclophosphamide in mice. Meanwhile, in order to clarify the immunomodulatory mechanism of SZSP, we showed that SZSP significantly inhibited the secretion of NO, interleukin-6, and TNF-α and reduced the phosphorylation expression of p-ERK, p-JNK, and p-IκBα in lipopolysaccharide-stimulated RAW264.7 cells. Therefore, the immunomodulatory effect of SZSP may be related to the activation of MAPKs and NF-κB signaling pathways. Based on the above studies, the preliminary purification of SZSP was continued, and S1F2G1 with immunomodulatory activity was obtained. Taken together, SZSP has an immunoregulatory effect in vivo and in vitro and may be a favorable candidate of functional food raw material for regulating immune responses., (© 2023. The Author(s).)

In this experiment, laying hens were divided into a high productive group (group H) and a low productive group (group L). The purpose of this experiment was to screen and isolate a potential probiotic associated with the laying rate from group H by comparing the results via 16S rDNA high-throughput sequencing. The high-throughput sequencing analysis results showed that there were some differences in the composition of the gut microbiome between groups H and L on the Phylum and Genus levels. Through isolation and identification, we screened 16 lactobacilli strains. Among the 16 strains, S5 showed good acid tolerance, bile salt tolerance, and cholesterol degradation. Therefore, we chose strain S5 (identified as Limosilactobacillus oris, named Limosilactobacillus oris BSLO 1801) as a potential probiotic to promote the productivity of ordinary laying hens. During the animal experiment, 288 Hy-line white hens (30 weeks old) were divided into four groups, with six replications (n = 12) per group. The control group received the basic diet, and the treatment groups received the same basic diet supplemented with 10 7  CFU/kg, 10 8  CFU/kg, and 10 9  CFU/kg of BSLO 1801. The laying hens were acclimated to the environment for 1 week before the initiation of the experiment. Dietary supplementation with 10 7  CFU/kg and 10 9  CFU/kg of BSLO 1801 increased the laying rate significantly, and the potential probiotic improved the egg weight in all treatment groups. Additionally, the cholesterol content of the yolk dropped significantly in the 10 9  CFU/kg group, and the weight of egg yolk was significantly increased in all treatment groups. However, no significant differences in eggshell strength, eggshell thickness, protein height, and Haugh unit were observed among the four groups. These results revealed that lactobacilli spp. are important bacteria of the intestinal microbiome in highly productive laying hens, and BSLO 1801 was isolated as a potential probiotic. Through these animal experiments, we also found that adding BSLO 1801 to the basic diet of laying hens could effectively improve the laying rate, average egg weight, and yolk weight and reduce the cholesterol content in egg yolk., (© 2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Giant pandas are uniquely vulnerable mammals in western China. It is important to develop an animal model to explore the intestinal flora of giant pandas to understand the relationship between digestive diseases and flora. Existing animal models of intestinal flora focus on human flora-associated animals, such as mice, and there is a very limited amount of knowledge regarding giant panda flora-associated animals. To fill this gap, fecal microorganisms from giant pandas were transplanted into pseudosterile and germfree mice using single and multiple gavages. Fecal samples were collected from mice at four time points after transplantation for microbial community analysis. We determined that compared to pseudosterile mice, the characteristics of intestinal flora in pandas were better reproduced in germfree mice. There was no significant difference in microbial diversity between germfree mice and giant panda gut microbes from day 3 to day 21. Germfree mice at the phylum level possessed large amounts of Firmicutes and Proteobacteria , and at the genus level, Escherichia -Shigella , Clostridium sensu stricto 1, and Streptococcus dominated the intestinal flora structure. The microbial community co-occurrence network based on indicator species indicated that germfree mice transplanted with fecal bacteria tended to form a microbial community co-occurrence network similar to that of giant pandas, while pseudosterile mice tended to restore the microbial community co-occurrence network originally present in these mice. Our data are helpful for the study of giant panda flora-associated animals and provide new insights for the in vitro study of giant panda intestinal flora. IMPORTANCE The giant panda is a unique vulnerable mammal in western China, and its main cause of death is digestive system diseases regardless of whether these animals are in the wild or in captivity. The relationship between the intestinal flora and the host exerts a significant impact on the nutrition and health of the giant pandas. However, the protected status of the giant panda has made in vivo , repeatable, and large-sample sampling studies of their intestinal flora difficult. This greatly hinders the research depth of the giant panda intestinal flora from the source. The development and utilization of specific animal models to simulate the structure and characteristics of the intestinal flora provide another means to deal with these research limitations. However, current research examining giant panda flora-associated animals is limited. This study is the first to reveal dynamic changes in the fecal flora of giant pandas in mice after transplantation.

Background: Intestinal microbiota plays an important role in maintaining the microecological balance of the gastrointestinal tract in various animals. Disturbances in the intestinal microbiota may lead to the proliferation of potentially pathogenic bacteria that become the dominant species, leading to intestinal immune disorders, intestinal inflammation, and other intestinal diseases. Numerous studies have been confirmed that high-altitude exposure affects the normal function of the intestine and the composition of the intestinal microbiota. However, it is still necessary to reveal the changes in intestinal microbiota in high-altitude exposure environments, and clarify the relationship between the proliferation of potentially pathogenic bacteria and intestinal injury in this environment. In addition, explored probiotics that may have preventive effects against intestinal diseases., Methods and Results: C57BL/6 mice were randomly divided into three groups, a high-altitude group (HA), control group (C), and high-altitude probiotic group (HAP). The HA and HAP groups were subjected to hypoxia modeling for 14 days in a low-pressure oxygen chamber with daily gavage of 0.2 mL of normal saline (HA) and Lactobacillus johnsonii YH1136 bacterial fluid (HAP), while the control group was fed normally. L. johnsonii YH1136 was isolated from feces of a healthy Tibetan girl in Baingoin county, the Nagqu region of the Tibet Autonomous Region, at an altitude of 5000 meters. Our observations revealed that gavage of YH1136 was effective in improving the damage to the intestinal barrier caused by high-altitude exposure to hypoxic environments and helped to reduce the likelihood of pathogenic bacteria infection through the intestinal barrier. It also positively regulates the intestinal microbiota to the extent of Lactobacillus being the dominant microbiome and reducing the number of pathogenic bacteria. By analyzing the expression profile of ileal microRNAs and correlation analysis with intestinal microbiota, we found that Staphylococcus and Corynebacterium1 cooperated with miR-196a-1-3p and miR-3060-3p, respectively, to play a regulatory role in the process of high-altitude hypoxia-induced intestinal injury., Conclusion: These findings revealed the beneficial effect of L. johnsonii YH1136 in preventing potential endogenous pathogenic bacteria-induced intestinal dysfunction in high-altitude environments. The mechanism may be related to the regulation of intestinal injury from the perspective of the gut microbiota as well as miRNAs., Competing Interests: HW was employed by the Guangzhou Beneco Biotechnology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Wan, Zhang, Jia, Qin, Sun, Xin, Zeng, Jing, Fang, Pan, Zeng, Bai, Wang, Ma and Ni.)

Water availability is the main factor affecting the forage productivity of artificial grasslands, particularly in semi-arid regions. Generally, intercropping of gramineous grass and leguminous grass can achieve high productivity. However, how different water availability levels affect the productivity of intercropping system remains unclear. Here, we conducted a 3-year (2015-2017) study by manipulating the water conditions (CK equivalent to the annual precipitation, +50% treatment equivalent to 50% increase over the average precipitation, and -50% treatment equivalent to 50% decrease over the average precipitation) to explore the responses of plant traits, nitrogen use efficiency, and biomass of the monoculture of Medicago sativa (a leguminous grass, M.s ), monoculture of Elymus nutans (a gramineous grass, E.n ), and intercropping of M.s and E.n in a semi-arid region in Inner Mongolia, China. The results showed that the biomass obtained by intercropping of M.s and E.n decreased by 24.4% in -50% treatment compared to the CK treatment, while that of the monoculture of M.s decreased by 34.4% under the -50% treatment compared to the CK treatment. However, there was no significant difference in the biomass between intercropping artificial grassland and monoculture M. sativa under +50% treatment. Compared to monoculture, M.s can obtain more nitrogen by biological nitrogen fixation and decrease the proportion of nitrogen absorbed from soils under intercropping in the same water conditions. Under the intercropping system, the proportions of nitrogen absorbed from soils by M.s were 87.4%, 85.1, and 76.9% in -50%, CK, and +50% treatments, respectively. Under monoculture, these proportions were 91.9, 89.3, and 82.3% in -50%, CK, and +50% treatments, respectively. Plant trait, but not soil nitrogen content, was the main regulator for the productivity responses to water level changes. Our results highlight that intercropping can achieve higher productivity in both dry and wet conditions. Therefore, considering the fluctuating rainfall events in the future, it might be useful to alter the proportions of intercropped forage species in an artificial grassland to obtain optimal productivity by reducing the limitations of nitrogen availability. However, the economic viability of intercropping M. sativa and E. nutans should be evaluated in the future., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Wan, Gu, Yan, Bai, Bao, Yang, Gao, Ganjurjav, Hu, Zhou and Chun.)

Background: Chicken meat is one of the most consumed meats worldwide and poultry production is increasing at an exponential rate. Reducing antibiotic usage has resulted in the recurrence of subclinical necrotic enteritis again and influenced global poultry production. Probiotics are potential antibiotic substitutes that can be used to prevent subclinical necrotic enteriti. However, the precise mechanism of action of probiotics and information on which gut microbes confer this efficacy remain elusive., Methods and Results: The subclinical necrotic enteritis animal model was used to reveal the mechanism underlying the effect of probiotics on intestinal health through RNA sequencing and 16S rDNA amplicon sequencing. Bacillus licheniformis H2 feeding significantly reduced the relative abundance of Clostridium perfringens in the ileum and markedly ameliorated the pathological damage in the ileum and liver. In addition, oral administration of B. licheniformis H2 contributed to the enhancement of the intestinal barrier function and epithelial renewal, reducing energy consumption, and improving enteral nutrition absorption. Probiotic B. licheniformis H2 also ameliorated the inflammatory response and increased the immunity of subclinical necrotic enteritis infected broilers. Finally, B. licheniformis H2 feeding regulated liver gene expression to suppress immune response and promoted growth and metabolism depending on the gut microbiota., Conclusions: These results indicated the mechanism of probiotic action of B. licheniformis H2 in maintaining intestinal health and thus promoting growth and B. licheniformis H2 may serve as an antibiotic substitute to prevent subclinical necrotic enteritis in poultry farming., Competing Interests: Author HY and LL were employed by the Chengdu Slan Biotechnology Co., Ltd. Author HL was employed by the Qingdao Vland Biotech Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Zhao, Zeng, Zeng, Wang, Sun, Xin, Zhou, Yang, Lei, Ling, Khalique, Rajput, Gan, Wan, Yao, Fang, Pan, Shu, Jing, Zhang and Ni.)

As one of the predominant compositions of PM 2.5 (particulate matter with aerodynamic diameter ≤2.5 μm), carbonaceous aerosols not only have adverse effects on air quality, but also can affect climate change. Although there are extensive recent studies on carbonaceous aerosols, comprehensive studies on their socioeconomic influencing factors in a resource-based city are relatively limited. In this study, the spatial-temporal variations of organic carbon (OC), elemental carbon (EC), and secondary organic carbon (SOC) were investigated in January, July, and October in 2015 and April in 2016 in Wuhai and its surrounding areas. The population distribution and industry layout have led to the uneven spatial-temporal distribution of carbonaceous aerosols. The concentrations of carbonaceous aerosols were higher in winter due to the unfavorable meteorology and the increased emissions from heating. The SOC is a significant contributor to OC in the cold season (52.0% for January). Primary carbonaceous aerosols pollution is higher in the industrial sites of resource-based cities, whereas the SOC makes a significant contribution in the residential sites. The results of backward-trajectory and concentration-weighted trajectory analysis suggest that the local emissions and short-range atmospheric transport from nearby areas have a significant impact on PM 2.5 and carbonaceous aerosols. A strong correlation between population density and OC/EC ratio was found, indicating that the megacities with high population density have a higher SOC contribution than the resource-based cities. Resource-based cities are characterized by high level of primary OC emissions, whereas cities with high energy efficiency have a more significant SOC contribution. These results provide a more comprehensive understanding of carbonaceous aerosols in a resource-based city., (Copyright © 2021 Elsevier Ltd. All rights reserved.)