Response of phytoplankton functional group to spring drought in a large subtropical reservoir

Global warming has caused an increase in the frequency and duration of droughts worldwide.Droughts could trigger large changes in physico-chemical conditions and phytoplankton community in waterbodies,resulting in a shift in the phytoplankton community.Spring diatom blooms in reservoirs have been increasingly observed in the past decade in the Taihu Lake basin.The aim of the present study is to elucidate the impacts of droughts on aquatic environment and to determine the driving factors for the succession of the phytoplankton functional groups based on the analysis of data collected during spring from 2009 to 2020 in the Daxi Reservoir.The unimodal relationship between 1-month aggregated precipitation index and phytoplankton species richness indicated the competitive exclusion occurred in extremely drought period.The structural equation modeling indicated that drought-related low water level conditions intensified sediment resuspension,and increased the phosphorus-enriched nonalgal turbidity in the Daxi Reservoir.Concurrently,a steady shift in the Reynolds phytoplankton functional groups from L 0,TD,J,X 2,and A(phytoplankton taxa preferring low turbidity and nutrient conditions)to TB(pennate diatoms being adapt to turbid and nutrient-rich conditions)was observed.The increased TP and non-algal turbidity in addition to the lowered disturbance contribute to the prevalence of Group TB.Considering the difficulties in nutrient control,timely water replenishment is often a feasible method of controlling the dominance of harmful algae for reservoir management.Finally,alternative water sources are in high demand for ensuring ecological safety and water availability when dealing with drought.

Deep brain implantable microelectrode arrays for detection and functional localization of the subthalamic nucleus in rats with Parkinson’s disease

The subthalamic nucleus(STN)is considered the best target for deep brain stimulation treatments of Parkinson’s disease(PD).It is difficult to localize the STN due to its small size and deep location.Multichannel microelectrode arrays(MEAs)can rapidly and precisely locate the STN,which is important for precise stimulation.In this paper,16-channel MEAs modified with multiwalled carbon nanotube/poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate)(MWCNT/PEDOT:PSS)nanocomposites were designed and fabricated,and the accurate and rapid identification of the STN in PD rats was performed using detection sites distributed at different brain depths.These results showed that nuclei in 6-hydroxydopamine hydrobromide(6-OHDA)-lesioned brains discharged more intensely than those in unlesioned brains.In addition,the MEA simultaneously acquired neural signals from both the STN and the upper or lower boundary nuclei of the STN.Moreover,higher values of spike firing rate,spike amplitude,local field potential(LFP)power,and beta oscillations were detected in the STN of the 6-OHDA-lesioned brain,and may therefore be biomarkers of STN localization.Compared with the STNs of unlesioned brains,the power spectral density of spikes and LFPs synchronously decreased in the delta band and increased in the beta band of 6-OHDA-lesioned brains.This may be a cause of sleep and motor disorders associated with PD.Overall,this work describes a new cellular-level localization and detection method and provides a tool for future studies of deep brain nuclei.

Fine quantitative characterization of high-H2S gas reservoirs under the influence of liquid sulfur deposition and adsorption

In order to clarify the influence of liquid sulfur deposition and adsorption to high-H2S gas reservoirs,three types of natural cores with typical carbonate pore structures were selected for high-temperature and high-pressure core displacement experiments.Fine quantitative characterization of the cores in three steady states(original,after sulfur injection,and after gas flooding)was carried out using the nuclear magnetic resonance(NMR)transverse relaxation time spectrum and imaging,X-ray computer tomography(CT)of full-diameter cores,basic physical property testing,and field emission scanning electron microscopy imaging.The loss of pore volume caused by sulfur deposition and adsorption mainly comes from the medium and large pores with sizes bigger than 1000μm.Liquid sulfur has a stronger adsorption and deposition ability in smaller pore spaces,and causes greater damage to reservoirs with poor original pore structures.The pore structure of the three types of carbonate reservoirs shows multiple fractal characteristics.The worse the pore structure,the greater the change of internal pore distribution caused by liquid sulfur deposition and adsorption,and the stronger the heterogeneity.Liquid sulfur deposition and adsorption change the pore size distribution,pore connectivity,and heterogeneity of the rock,which further changes the physical properties of the reservoir.After sulfur injection and gas flooding,the permeability of TypeⅠreservoirs with good physical properties decreased by 16%,and that of TypesⅡandⅢreservoirs with poor physical properties decreased by 90%or more,suggesting an extremely high damage.This indicates that the worse the initial physical properties,the greater the damage of liquid sulfur deposition and adsorption.Liquid sulfur is adsorbed and deposited in different types of pore space in the forms of flocculence,cobweb,or retinitis,causing different changes in the pore structure and physical property of the reservoir.

Identification of reservoir types in deep carbonates based on mixedkernel machine learning using geophysical logging data

Identification of reservoir types in deep carbonates has always been a great challenge due to complex logging responses caused by the heterogeneous scale and distribution of storage spaces.Traditional cross-plot analysis and empirical formula methods for identifying reservoir types using geophysical logging data have high uncertainty and low efficiency,which cannot accurately reflect the nonlinear relationship between reservoir types and logging data.Recently,the kernel Fisher discriminant analysis(KFD),a kernel-based machine learning technique,attracts attention in many fields because of its strong nonlinear processing ability.However,the overall performance of KFD model may be limited as a single kernel function cannot simultaneously extrapolate and interpolate well,especially for highly complex data cases.To address this issue,in this study,a mixed kernel Fisher discriminant analysis(MKFD)model was established and applied to identify reservoir types of the deep Sinian carbonates in central Sichuan Basin,China.The MKFD model was trained and tested with 453 datasets from 7 coring wells,utilizing GR,CAL,DEN,AC,CNL and RT logs as input variables.The particle swarm optimization(PSO)was adopted for hyper-parameter optimization of MKFD model.To evaluate the model performance,prediction results of MKFD were compared with those of basic-kernel based KFD,RF and SVM models.Subsequently,the built MKFD model was applied in a blind well test,and a variable importance analysis was conducted.The comparison and blind test results demonstrated that MKFD outperformed traditional KFD,RF and SVM in the identification of reservoir types,which provided higher accuracy and stronger generalization.The MKFD can therefore be a reliable method for identifying reservoir types of deep carbonates.

On the Weyl’s Lemma for Triharmonic Functions

In this paper,by choosing some appropriate test functions,we prove the Weyl’s lemma for triharmonic functions based on the new type of mean value formulas.

Characteristics and influencing mechanisms of production-living-ecological space dynamics in the Three Gorges Reservoir Area(TGRA),Chongqing,China

The Three Gorges Reservoir Area(TGRA)is an important ecological barrier in the Yangtze River Basin,China.Therefore,it is of great importance to understand the spatio-temporal variation and the driving factors of production-living-ecological spaces for sustainable and high-quality development in the TGRA.This study investigated the dynamic variation of production-living-ecological spaces in the TGRA by employing land use data in 2000,2005,2010,2015,and 2018,and detected the influencing factors by using the Geographic detector(GeoDetector).Results implied that the structure and dynamic trajectories of production-living-ecological spaces in the TGRA varied in both horizontal and vertical directions,and the study area was dominated by ecological space.A spatial orientation towards the northeast was detected in the evolution of production-living-ecological spaces during 2000-2018.In terms of quantity,the transition from ecological space(grassland and woodland)to agriculture land accounted for the largest proportion from 2000 to 2018.However,the reverse transition from agriculture land to ecological space has increased since 2000 with the efforts of“Grain for Green”.In terms of temporal scale,there was a fluctuating trend in production space with the continuous expansion of living space,while ecological space showed an inverted U-shaped trend during 2000-2018.The dynamic pattern of production-living-ecological spaces in the TGRA was influenced by both physical and socio-economic variables as basic determinants and dominant driving factors,respectively.Finally,the harmonization and protection of production-living-ecological spaces still require policy-makers’efforts.This work may have potential in advancing our understanding about land use conflicts,and provide a reference for rational layout of spatial functions and the realization of sustainable development in the TGRA.

Evaluation of Generalized Error Function via Fast-Converging Power Series

A generalized form of the error function, Gp(x)=pΓ(1/p)∫0xe−tpdt, which is directly associated with the gamma function, is evaluated for arbitrary real values of p>1and 0x≤+∞by employing a fast-converging power series expansion developed in resolving the so-called Grandi’s paradox. Comparisons with accurate tabulated values for well-known cases such as the error function are presented using the expansions truncated at various orders.

Implementation of the Integrated Green’s Function Method for 3D Poisson’s Equation in a Large Aspect Ratio Computational Domain

The solution of Poisson’s Equation plays an important role in many areas, including modeling high-intensity and high-brightness beams in particle accelerators. For the computational domain with a large aspect ratio, the integrated Green’s function method has been adopted to solve the 3D Poisson equation subject to open boundary conditions. In this paper, we report on the efficient implementation of this method, which can save more than a factor of 50 computing time compared with the direct brute force implementation and its improvement under certain extreme conditions.

Existence of Monotone Positive Solution for a Fourth-Order Three-Point BVP with Sign-Changing Green’s Function

This paper is concerned with the following fourth-order three-point boundary value problem , where , we discuss the existence of positive solutions to the above problem by applying to the fixed point theory in cones and iterative technique.

A Set of Almost Automorphic Functions and Applications

For a set S of real numbers, we introduce the concept of S-almost automorphic functions valued in a Banach space. It generalizes in particular the space of Z-almost automorphic functions. Considering the space of S-almost automorphic functions, we give sufficient conditions of the existence and uniqueness of almost automorphic solutions of a differential equation with a piecewise constant argument of generalized type. This is done using the Banach fixed point theorem.

高压管汇材料疲劳性能测试及P-S-N模型曲线的拟合

高压管汇作为压裂设备中的主要易损件之一,其失效危害较大。它的失效原因主要是疲劳、冲蚀、腐蚀或者材料缺陷引起的刺漏和爆裂,其中尤以疲劳失效最不可预估。目前,对于高压管汇材料的疲劳性能研究不够深入,为解决高压管汇材料疲劳寿命的准确描述问题,以某国产高压管汇材料为例,进行了一系列疲劳试验,并基于试验数据,采用多种分布模型和不同S-N模型进行拟合分析,得出综合评价拟合能力最强的P-S-N模型。结果表明,该材料在中长疲劳寿命区,Weibull三参数模型在7级应力水平下综合评价能力最好;在存活率分别为50%、90%、99%、99.9%时,指数S-N模型的拟合系数均大于0.98,拟合能力最好。得出的P-S-N模型曲线可以为高压管汇的疲劳寿命以及安全设计提供依据。

基于量子衍生涡流算法和T⁃S模糊推理模型的储层岩性识别

鉴于梯度下降法易陷入局部极值、普通群智能优化算法易早熟收敛,提出一种基于量子衍生涡流算法(Quantum Vortex Search Algorithm,QVSA)和T⁃S模糊推理模型的岩性识别方法,QVSA具有操作简单、收敛速度快、寻优能力强等优点,有助于T⁃S模糊推理模型获得最优参数配置,从而实现储层岩性的准确识别。首先利用具有全局搜索能力的QVSA优化T⁃S模糊推理模型的各种参数;然后利用主成分分析方法降低获取的地震属性维度;再利用优化的T⁃S模糊推理模型识别储层岩性。实验结果表明,利用反映储层特征的8个地震属性识别储层岩性时,所提方法的识别正确率达到92%,比普通BP网络方法高5.1%,同时查准率、查全率、F1分数等指标也较BP网络方法提升明显。

基于哨兵函数和S变换的风力机叶片材料损伤特性研究

利用声发射检测技术研究玻璃纤维增强环氧树脂复合材料的损伤特性,在此过程中,采用哨兵函数来表征该材料的损伤程度,并通过S变换和模糊C均值(FCM)聚类来分析声发射信号,从而获得材料的损伤特征。三点弯曲实验结束后对试件断口进行扫描电子显微镜(SEM)拍照来验证,可得:通过对SEM照片的分析得到基体开裂、纤维脱粘、分层破坏、纤维断裂4种损伤模式;对整个声发射事件进行哨兵函数分析,观察到试件在弯曲过程中哨兵函数曲线呈明显下降趋势;对依据哨兵函数划分的不同阶段的信号进行VMD降噪处理,然后采用S变换进行时频分析得到不同损伤的特征频率,最后采用FCM聚类进行验证。结果表明:哨兵函数值的突变可作为材料断裂的预警信号,材料损伤类型的识别可依据S变换的频率分布结果进行确定。

基于XGBoost算法的v_(P)/v_(S)预测及其在储层检测中的应用

鄂尔多斯盆地碳酸盐岩地层蕴含着丰富的油气资源。在勘探实践中发现,大牛地气田马家沟组断层发育、断距小,类型多样且成因复杂,给勘探、开发带来了较多挑战。为了应对这些挑战,提高储层预测的精度变得至关重要。在分析大牛地气田敏感弹性参数的基础上,建立地震属性与储层纵横波速度比(v_(P)/v_(S))的关系,提出一种基于XGBoost算法的地震多属性v_(P)/v_(S)预测方法。为了进一步提升XGBoost算法的预测精度和泛化能力,采用贝叶斯算法对XGBoost算法的超参数进行优化,从而找到最佳的超参数组合,以确保模型在训练集和测试集上的性能均能得到提升。将XGBoost算法应用于Marmousi 2模型进行横波速度预测,预测值与实际值相关系数超过0.88,而均方误差、平均绝对百分比误差分别低于6.55×10^(-7)和4%,验证了该方法的准确性和可靠性。在鄂尔多斯盆地大牛地气田,应用该方法获得的v_(P)/v_(S)成功识别出含气储层,结果与实际钻井数据一致。理论模型和实际数据应用结果表明,XGBoost作为一种强大的机器学习算法预测精度较高,为直接由叠后地震属性预测v_(P)/v_(S)提供了一种有效的途径。

Continuum Skyrme Hartree-Fock-Bogoliubov theory with Green’s function method for neutron-rich Ca,Ni,Zr,and Sn isotopes

The possible exotic nuclear properties in the neutron-rich Ca,Ni,Zr,and Sn isotopes are examined with the continuum Skyrme Hartree-Fock-Bogoliubov theory in the framework of the Green’s function method.The pairing correlation,the couplings with the continuum,and the blocking effects for the unpaired nucleon in odd-A nuclei are properly treated.The Skyrme interaction SLy4 is adopted for the ph channel and the density-dependentinteraction is adopted for the pp chan-nel,which well reproduce the experimental two-neutron separation energies S_(2n)and one-neutron separation energies Sn.It is found that the criterion S_(n)>0 predicts a neutron drip line with neutron numbers much smaller than those for S_(2n)>0.Owing to the unpaired odd neutron,the neutron pairing energies−E_(pair)in odd-A nuclei are much lower than those in the neighbor-ing even-even nuclei.By investigating the single-particle structures,the possible halo structures in the neutron-rich Ca,Ni,and Sn isotopes are predicted,where sharp increases in the root-mean-square(rms)radii with significant deviations from the traditional rA^(1∕3)rule and diffuse spatial density distributions are observed.Analyzing the contributions of various partial waves to the total neutron densityρlj(r)∕ρ(r)reveals that the orbitals located around the Fermi surface-particularly those with small angular momenta-significantly affect the extended nuclear density and large rms radii.The number of neutrons Nλ(N_(0))occupying above the Fermi surfacen(continuum threshold)is discussed,whose evolution as a function of the mass number A in each isotope is consistent with that of the pairing energy,supporting the key role of the pairing correlation in halo phenomena.

Green's functions of two-dimensional piezoelectric quasicrystal in half-space and bimaterials

In this paper,we obtain Green’s functions of two-dimensional(2D)piezoelectric quasicrystal(PQC)in half-space and bimaterials.Based on the elastic theory of QCs,the Stroh formalism is used to derive the general solutions of displacements and stresses.Then,we obtain the analytical solutions of half-space and bimaterial Green’s functions.Besides,the interfacial Green’s function for bimaterials is also obtained in the analytical form.Before numerical studies,a comparative study is carried out to validate the present solutions.Typical numerical examples are performed to investigate the effects of multi-physics loadings such as the line force,the line dislocation,the line charge,and the phason line force.As a result,the coupling effect among the phonon field,the phason field,and the electric field is prominent,and the butterfly-shaped contours are characteristic in 2D PQCs.In addition,the changes of material parameters cause variations in physical quantities to a certain degree.

Temporal and spatial distribution of phytoplankton functional groups and role of environment factors in a deep subtropical reservoir

Phytoplankton and environment factors were investigated in 2015 and phytoplankton functional groups were used to understand their temporal and spatial distribution and their driving factors in Wanfeng Reservoir. Seventeen functional groups(B, D, E, F, G, J, Lo, MP, P, S1, T, W1, W2, X1, X2, Xph, Y) were identified based on 34 species. The dominant groups were: J/B/P/D in dry season, X1/J/Xph/G/T in normal season and J in flood season. Phytoplankton abundance ranged from 5.33×10~4 cells/L to 3.65×10~7 cells/L, with the highest value occurring in flood season and lowest in dry season. The vertical profi le of dominant groups showed little differentiation except for P, which dominated surface layers over 20 m as a result of mixing water masses and higher transparency during dry season. However, the surface waters presented higher values of phytoplankton abundance than other layers, possibly because of greater irradiance. The significant explaining variables and their ability to describe the spatial distribution of the phytoplankton community in RDA diff ered seasonally as follows: dry season, NH4-N, NO_3-N, NO_2-N, TN:TP ratio and transparency(SD); normal season, temperature(WT), water depth, TN, NH4-N and NO_3-N; flood season, WT, water depth, NO_3-N and NO_2-N. Furthermore, nitrogen, water temperature, SD and water depth were significant variables explaining the variance of phytoplankton communities when datasets included all samples. The results indicated that water physical conditions and hydrology were important in phytoplankton community dynamics, and nitrogen was more important than phosphorus in modifying phytoplankton communities. Seasonal differences in the relationship between the environment and phytoplankton community should be considered in water quality management.

Neuroprotective effect of liraglutide and memantine in a rat model of Alzheimer’s disease

Objective:To assess the effect of memantine combined with liraglutide on aluminum chloride(AlCl_(3))and D-galactose(D-GAL)-induced neurotoxicity in rats.Methods:Male Wistar rats were divided into 5 groups of 5 animals each:the positive control,the negative control,the memantine-treated group,the liraglutide-treated group,and the combination group treated with memantine and liraglutide.AlCl_(3)and D-GAL were used to induce neurotoxicity.Behavioral tests,brain beta-amyloid protein,and oxidative stress biomarkers were evaluated.Results:The Morris water maze test indicated an enhanced memory in the combination group.Moreover,the combination treatment of liraglutide and memantine resulted in a remarkable reduction in the beta-amyloid protein level in the brain tissue.Neuronal inflammation and oxidative stress biomarkers were significantly reduced,and the levels of antioxidant parameters were enhanced.Conclusions:The combination of liraglutide and memantine exerts neuroprotective effects and enhances memory and cognitive functions in rats with Alzheimer’s disease.

GREEN'S FUNCTION AND THE POINTWISE BEHAVIORS OF THE VLASOV-POISSON-FOKKER-PLANCK SYSTEM

The pointwise space-time behaviors of the Green’s function and the global solution to the Vlasov-Poisson-Fokker-Planck(VPFP)system in three dimensional space are studied in this paper.It is shown that the Green’s function consists of the diffusion waves decaying exponentially in time but algebraically in space,and the singular kinetic waves which become smooth for all(t,x,v)when t>0.Furthermore,we establish the pointwise space-time behaviors of the global solution to the nonlinear VPFP system when the initial data is not necessarily smooth in terms of the Green’s function.

频率矫正广义S变换及在储层含油气性预测中的应用

随着我国油气勘探程度的不断加深,陆相页岩油气等非常规油气逐渐成为我国勘探开发的重点,陆相页岩非均质性强、储层薄且纵横向变化剧烈等典型特征导致含油气性预测精度差,影响后续井位部署和生产等.时频变换方法是储层含油气性预测最有效的工具.为了提高储层含油气性预测的精度,本文提出了一种频率矫正广义S变换(Frequency-Corrected Generalized S-Transform, FCGST),该变换既具有S变换多尺度多分辨率的特性,又可以通过自适应选取窗函数参数以控制窗函数的带宽和主频等,这一特性可以适用于不同工区地震数据的处理与解释.与传统的S变换和广义S变换不同,该变换修正了传统S变换和广义S变换中的尺度因子,改善了S变换和广义S变换等传统方法中时频谱主频向高频方向偏移的问题,有利于精确描述复杂储层含油气性.合成数据算例表明,频率矫正广义S变换提升了地震信号局部时频特征的表征能力;将频率矫正广义S变换应用于鄂尔多斯盆地某工区储层含油气性预测中,实钻结果数据进一步验证了该时频分析方法在储层含油气性解释中的有效性和潜力.